diff --git a/Phase 3 - 2020 (Summer)/README.md b/Phase 3 - 2020 (Summer)/README.md
index 0036ceda5..9818b1173 100644
--- a/Phase 3 - 2020 (Summer)/README.md	
+++ b/Phase 3 - 2020 (Summer)/README.md	
@@ -10,7 +10,8 @@
  
 |Week   |Start Date        |Content                                            |End Date         |
 |-------|------------------|---------------------------------------------------|-----------------|
-| 1     | 29 Mar 2019      |**Python** + use of **matplotlib,numpy and pandas**| 4 Apr 2020      |
+| 1     | 29 Mar 2020      |**Python** + use of **matplotlib,numpy and pandas**| 4 Apr 2020      |
+| 2     | 05 Apr 2020      | ML Coursera Week 1 & 2                            | 11 Apr 2020     |
 
 > Will updated as time proceeds.
 
diff --git a/Phase 3 - 2020 (Summer)/WEEK4_exercise3_solutions.ipynb b/Phase 3 - 2020 (Summer)/WEEK4_exercise3_solutions.ipynb
new file mode 100644
index 000000000..258f3e901
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/WEEK4_exercise3_solutions.ipynb	
@@ -0,0 +1,643 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 20x20 Input Images of Digits\n",
+    "input_layer_size  = 400\n",
+    "\n",
+    "# 10 labels, from 1 to 10 (note that we have mapped \"0\" to label 10)\n",
+    "num_labels = 10\n",
+    "\n",
+    "#  training data stored in arrays X, y\n",
+    "data = loadmat(os.path.join('ex3data1.mat'))\n",
+    "X, y = data['X'], data['y'].ravel()\n",
+    "\n",
+    "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+    "# This is an artifact due to the fact that this dataset was used in \n",
+    "# MATLAB where there is no index 0\n",
+    "y[y == 10] = 0\n",
+    "m = y.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def displayData(X, example_width=None, figsize=(10, 10)):\n",
+    "    \"\"\"\n",
+    "    Displays 2D data stored in X in a nice grid.\n",
+    "    \"\"\"\n",
+    "    # Compute rows, cols\n",
+    "    if X.ndim == 2:\n",
+    "        m, n = X.shape\n",
+    "    elif X.ndim == 1:\n",
+    "        n = X.size\n",
+    "        m = 1\n",
+    "        X = X[None]  # Promote to a 2 dimensional array\n",
+    "    else:\n",
+    "        raise IndexError('Input X should be 1 or 2 dimensional.')\n",
+    "\n",
+    "    example_width = example_width or int(np.round(np.sqrt(n)))\n",
+    "    example_height = n / example_width\n",
+    "\n",
+    "    # Compute number of items to display\n",
+    "    display_rows = int(np.floor(np.sqrt(m)))\n",
+    "    display_cols = int(np.ceil(m / display_rows))\n",
+    "\n",
+    "    fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)\n",
+    "    fig.subplots_adjust(wspace=0.025, hspace=0.025)\n",
+    "\n",
+    "    ax_array = [ax_array] if m == 1 else ax_array.ravel()\n",
+    "\n",
+    "    for i, ax in enumerate(ax_array):\n",
+    "        ax.imshow(X[i].reshape(example_width, example_width, order='F'),\n",
+    "                  cmap='Greys', extent=[0, 1, 0, 1])\n",
+    "        ax.axis('off')\n",
+    "\n",
+    "\n",
+    "def sigmoid(z):\n",
+    "    \"\"\"\n",
+    "    Computes the sigmoid of z.\n",
+    "    \"\"\"\n",
+    "    return 1.0 / (1.0 + np.exp(-z))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Randomly select 100 data points to display\n",
+    "rand_indices = np.random.choice(m, 100, replace=False)\n",
+    "sel = X[rand_indices, :]\n",
+    "displayData(sel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test values for the parameters theta\n",
+    "theta_t = np.array([-2, -1, 1, 2], dtype=float)\n",
+    "\n",
+    "# test values for the inputs\n",
+    "X_t = np.concatenate([np.ones((5, 1)), np.arange(1, 16).reshape(5, 3, order='F')/10.0], axis=1)\n",
+    "\n",
+    "# test values for the labels\n",
+    "y_t = np.array([1, 0, 1, 0, 1])\n",
+    "\n",
+    "# test value for the regularization parameter\n",
+    "lambda_t = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1. , 0.1, 0.6, 1.1],\n",
+       "       [1. , 0.2, 0.7, 1.2],\n",
+       "       [1. , 0.3, 0.8, 1.3],\n",
+       "       [1. , 0.4, 0.9, 1.4],\n",
+       "       [1. , 0.5, 1. , 1.5]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_t\n",
+    "#y_t\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def lrCostFunction(theta, X, y, lambda_):\n",
+    "\n",
+    "    #Initialize some useful values\n",
+    "    m = y.size#5000\n",
+    "    \n",
+    "    # convert labels to ints if their type is bool\n",
+    "    if y.dtype == bool:\n",
+    "        y = y.astype(int)\n",
+    "    \n",
+    "    # You need to return the following variables correctly\n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "    \n",
+    "# ====================== YOUR CODE HERE ======================\n",
+    "    h = sigmoid(X.dot(theta.T))\n",
+    "    \n",
+    "    t = theta\n",
+    "    t[0] = 0\n",
+    "    \n",
+    "    J = (1 / m) * np.sum(-y.dot(np.log(h)) - (1 - y).dot(np.log(1 - h))) + (lambda_ / (2 * m)) * np.sum(np.square(t))\n",
+    "    \n",
+    "    grad = (1 / m) * (h - y).dot(X) \n",
+    "    grad = grad + (lambda_ / m) * t\n",
+    "# =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost         : 2.534819\n",
+      "Expected cost: 2.534819\n",
+      "-----------------------\n",
+      "Gradients:\n",
+      " [0.146561, -0.548558, 0.724722, 1.398003]\n",
+      "Expected gradients:\n",
+      " [0.146561, -0.548558, 0.724722, 1.398003]\n"
+     ]
+    }
+   ],
+   "source": [
+    "J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t)\n",
+    "\n",
+    "print('Cost         : {:.6f}'.format(J))\n",
+    "print('Expected cost: 2.534819')\n",
+    "print('-----------------------')\n",
+    "print('Gradients:')\n",
+    "print(' [{:.6f}, {:.6f}, {:.6f}, {:.6f}]'.format(*grad))\n",
+    "print('Expected gradients:')\n",
+    "print(' [0.146561, -0.548558, 0.724722, 1.398003]');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def oneVsAll(X, y, num_labels, lambda_):\n",
+    " # Some useful variables\n",
+    "    m, n = X.shape\n",
+    "    \n",
+    "    # You need to return the following variables correctly \n",
+    "    all_theta = np.zeros((num_labels, n + 1))\n",
+    "\n",
+    "    # Add ones to the X data matrix\n",
+    "    X = np.concatenate([np.ones((m, 1)), X], axis=1)#mXn+1\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    for c in np.arange(num_labels):\n",
+    "        initial_theta = np.zeros(n + 1)\n",
+    "        options = {'maxiter': 50}\n",
+    "        res = optimize.minimize(lrCostFunction, \n",
+    "                                initial_theta, \n",
+    "                                (X, (y == c), lambda_), \n",
+    "                                jac=True, \n",
+    "                                method='CG',\n",
+    "                                options=options) \n",
+    "        \n",
+    "        all_theta[c] = res.x\n",
+    "\n",
+    "    # ============================================================\n",
+    "    return all_theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lambda_ = 0.1\n",
+    "all_theta = oneVsAll(X, y, num_labels, lambda_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predictOneVsAll(all_theta, X):\n",
+    "\n",
+    "    m = X.shape[0];\n",
+    "    num_labels = all_theta.shape[0]\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    p = np.zeros(m)\n",
+    "\n",
+    "    # Add ones to the X data matrix\n",
+    "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    \n",
+    "    p = np.argmax(sigmoid(X.dot(all_theta.T)), axis = 1)\n",
+    "    \n",
+    "    # ============================================================\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Accuracy: 95.18%\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = predictOneVsAll(all_theta, X)\n",
+    "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  training data stored in arrays X, y\n",
+    "data = loadmat(os.path.join('ex3data1.mat'))\n",
+    "X, y = data['X'], data['y'].ravel()\n",
+    "\n",
+    "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+    "# This is an artifact due to the fact that this dataset was used in \n",
+    "# MATLAB where there is no index 0\n",
+    "y[y == 10] = 0\n",
+    "\n",
+    "# get number of examples in dataset\n",
+    "m = y.size\n",
+    "\n",
+    "# randomly permute examples, to be used for visualizing one \n",
+    "# picture at a time\n",
+    "indices = np.random.permutation(m)\n",
+    "\n",
+    "# Randomly select 100 data points to display\n",
+    "rand_indices = np.random.choice(m, 100, replace=False)\n",
+    "sel = X[rand_indices, :]\n",
+    "\n",
+    "displayData(sel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the parameters you will use for this exercise\n",
+    "input_layer_size  = 400  # 20x20 Input Images of Digits\n",
+    "hidden_layer_size = 25   # 25 hidden units\n",
+    "num_labels = 10          # 10 labels, from 0 to 9\n",
+    "\n",
+    "# Load the .mat file, which returns a dictionary \n",
+    "weights = loadmat(os.path.join('ex3weights.mat'))\n",
+    "\n",
+    "# get the model weights from the dictionary\n",
+    "# Theta1 has size 25 x 401\n",
+    "# Theta2 has size 10 x 26\n",
+    "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n",
+    "\n",
+    "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n",
+    "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n",
+    "Theta2 = np.roll(Theta2, 1, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25, 401)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Theta1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(Theta1, Theta2, X):\n",
+    "\n",
+    "    # Make sure the input has two dimensions\n",
+    "    if X.ndim == 1:\n",
+    "        X = X[None]  # promote to 2-dimensions\n",
+    "    \n",
+    "    # useful variables\n",
+    "    m = X.shape[0]\n",
+    "    num_labels = Theta2.shape[0]\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    p = np.zeros(X.shape[0])\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "    \n",
+    "    a2 = sigmoid(X.dot(Theta1.T))\n",
+    "    a2 = np.concatenate([np.ones((a2.shape[0], 1)), a2], axis=1)\n",
+    "    \n",
+    "    p = np.argmax(sigmoid(a2.dot(Theta2.T)), axis = 1)\n",
+    "\n",
+    "\n",
+    "    # =============================================================\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Accuracy: 97.5%\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = predict(Theta1, Theta2, X)\n",
+    "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Network Prediction: 8\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if indices.size > 0:\n",
+    "    i, indices = indices[0], indices[1:]\n",
+    "    displayData(X[i, :], figsize=(4, 4))\n",
+    "    pred = predict(Theta1, Theta2, X[i, :])\n",
+    "    print('Neural Network Prediction: {}'.format(*pred))\n",
+    "else:\n",
+    "    print('No more images to display!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Network Prediction: 6\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAF/0lEQVR4nO3dv2pUaRzG8Uwyx/gvSEDFpLOxibWVWFoYYpELEEECFt6Ago3pcwMWSiwtBUtRm5RegmCVEFJEDYJDzN7AEH8vO9HHmc+n3H04ZHW/+8Lyek7v6OhoCsgz/bd/AGA4cUIocUIocUIocUKo/nF/czAY+F+5cMK6rusN++tOTgglTgglTgglTgglTgglTgglTgglTgglTgglTgh17PU92kxP1/9b1/KH3P2B+Mnk5IRQ4oRQ4oRQ4oRQ4oRQ4oRQ4oRQ4oRQ4oRQ4oRQru/9Rq839MVoQx0cHJS3LVf9Zmdny1vGh5MTQokTQokTQokTQokTQokTQokTQokTQokTQokTQk3k9b2ZmZny9vPnz+Xtw4cPy9v5+fnydmNjo7xdWFgob3/9+lXe8uc5OSGUOCGUOCGUOCGUOCGUOCGUOCGUOCGUOCGUOCHUWF3fq74p7+fPn+Vnvnr1qrzd2toqbweDQXm7trZW3i4uLpa3ZHNyQihxQihxQihxQihxQihxQihxQihxQihxQqixuiFU/eblzs5O+ZmvX78ub7uuK2+Pjo7K2xcvXpS3t27dKm9bvhHKn+d3B0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KN1fW9k9DyDct+v/7LubS0VN7u7u6Wty0/b8t3SluuGzIaTk4IJU4IJU4IJU4IJU4IJU4IJU4IJU4IJU4IJU4I5frebxweHpa38/Pz5W3LG/UuXbpU3rqSNz6cnBBKnBBKnBBKnBBKnBBKnBBKnBBKnBBKnBBKnBBqIq/vtVxba7m+d/HixfL2ypUr5e25c+fK25afl2xOTgglTgglTgglTgglTgglTgglTgglTgglTgglTgg1Vtf3qtfyzpw5U37mwsJCebu/v1/eHhwclLdnz54tbxP0er2RP7Plo8DT0/UzJ/kNhE5OCCVOCCVOCCVOCCVOCCVOCCVOCCVOCCVOCCVOCDWR1/fm5ubKz7x27Vp5+/Lly/J2Y2OjvH327Fl52+/Xf0tbrq61XMlruWr37t270u7t27flZz548KC8vX79ennb8s81Ck5OCCVOCCVOCCVOCCVOCCVOCCVOCCVOCCVOCCVOCDWR1/dOnz5dfuby8nJ5++bNm/L248eP5e3Xr1/L28uXL5e3LdfRWq7vffjwoby9f/9+abezs1N+ZsvbFdfX18vbP/1WPycnhBInhBInhBInhBInhBInhBInhBInhBInhBInhBqr63tVh4eH5e3Kykp5u7u7W94+efKkvH306FF5u7q6Wt5evXq1vG25Pvf48ePy9vv376XdhQsXys+8ceNGeTszM1PeevseMDU1JU6IJU4IJU4IJU4IJU4IJU4IJU4IJU4IJU4I1TvuLWGDweD/v0LsH9fyxrVv376Vty1X3J4/f17ezs7Olrcn9Ta5ruvK2+qHjJ8+fVp+5r1798rblut7o3ij3jBd1w19taGTE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0K5vjdCLdfh9vf3y9uWD+2+f/++vN3b2ytvv3z5Ut5++vSpvL19+3Zpt7m5WX7mqVOnytuTupLXwvU9+MeIE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0JN5Pc5T0rL9xtbvjfZ8o3QO3fulLe93tCLKUO13Dy6e/dueVu9zdPv1/9VTbj1MwpOTgglTgglTgglTgglTgglTgglTgglTgglTgglTgjl+t5f0nLVr0XLlbyW7c2bN8vbtbW18nZ7e7u0a/n1avnmZjInJ4QSJ4QSJ4QSJ4QSJ4QSJ4QSJ4QSJ4QSJ4QSJ4TyfU5KWq76DQaD8vbHjx+l3fnz58vPbPlZE/g+J/xjxAmhxAmhxAmhxAmhxAmhxAmhxAmhxAmhxAmhvH2PkpYP0nZdV95WP557Um8rTObkhFDihFDihFDihFDihFDihFDihFDihFDihFDihFCu7zFyLVf9WraTxskJocQJocQJocQJocQJocQJocQJocQJocQJocQJoY79eC7w9zg5IZQ4IZQ4IZQ4IZQ4IZQ4IdR/G4MIiDd4UC4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if indices.size > 0:\n",
+    "    i, indices = indices[0], indices[1:]\n",
+    "    displayData(X[i, :], figsize=(4, 4))\n",
+    "    pred = predict(Theta1, Theta2, X[i, :])\n",
+    "    print('Neural Network Prediction: {}'.format(*pred))\n",
+    "else:\n",
+    "    print('No more images to display!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Network Prediction: 6\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if indices.size > 0:\n",
+    "    i, indices = indices[0], indices[1:]\n",
+    "    displayData(X[i, :], figsize=(4, 4))\n",
+    "    pred = predict(Theta1, Theta2, X[i, :])\n",
+    "    print('Neural Network Prediction: {}'.format(*pred))\n",
+    "else:\n",
+    "    print('No more images to display!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Network Prediction: 7\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if indices.size > 0:\n",
+    "    i, indices = indices[0], indices[1:]\n",
+    "    displayData(X[i, :], figsize=(4, 4))\n",
+    "    pred = predict(Theta1, Theta2, X[i, :])\n",
+    "    print('Neural Network Prediction: {}'.format(*pred))\n",
+    "else:\n",
+    "    print('No more images to display!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Network Prediction: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if indices.size > 0:\n",
+    "    i, indices = indices[0], indices[1:]\n",
+    "    displayData(X[i, :], figsize=(4, 4))\n",
+    "    pred = predict(Theta1, Theta2, X[i, :])\n",
+    "    print('Neural Network Prediction: {}'.format(*pred))\n",
+    "else:\n",
+    "    print('No more images to display!')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/Answers_textual b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/Answers_textual
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--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/Answers_textual	
@@ -0,0 +1,12 @@
+1.After comparing all the the bifeature graphs , on the basis of observation I have concluded that Feature 1 vs Feature 2 graph depicts 
+the labels in the best possible way as the two labels are nearly divided into 2 regions of concentric circles(1st quadrant only) centered
+at origin with radii 4 and 8 respectively
+2.PCA analysis
+Again feature pair 1,2 comes out to be the best pair as it distinguishes the labels in the best way . This conclusion has been arrived at in 2 ways
+a. In the first code block , the variance ratios have been compared after PCA reductionof feature pairs as well as feature pairs with labels
+-From the analysis it is concluded that features 3 and 8 retain the largest variance ratio when PCA-reduced from 2-d to 1-D
+That means that this feature pair is the most alike and hence redundant i.e. one feature can be expressed in the other's form  
+But when combined with the labels: 1,2 retains the highest variance ratio which means that it ,this pair, can depict the labels in the best possible way
+b.In the second code snippet the 1D reduced graphs of each pairs vs labels has been analysed at feature 1,2 pair comes out be the best pair again ,by observation
+From the analysis its clear from the figures that feature 1 and 2 pair distinguishes the labels most appropriately
+The second best pair seems to be 2,10 which matches with the first analysis made above {91.99 for feature 2,10 pair and 92.11 for 1,2 pair}
diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/PCA_analysis.ipynb b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/PCA_analysis.ipynb
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--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/PCA_analysis.ipynb	
@@ -0,0 +1,757 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 [0.60185502] [0.92110448]\n",
+      "1 3 [0.51331434] [0.70309869]\n",
+      "1 4 [0.53577351] [0.71336418]\n",
+      "1 5 [0.5122996] [0.70152416]\n",
+      "1 6 [0.51136194] [0.70547678]\n",
+      "1 7 [0.52795251] [0.71051211]\n",
+      "1 8 [0.5080902] [0.70212856]\n",
+      "1 9 [0.50551827] [0.70101767]\n",
+      "1 10 [0.70843126] [0.87237759]\n",
+      "2 3 [0.51284582] [0.77299924]\n",
+      "2 4 [0.51364179] [0.78717074]\n",
+      "2 5 [0.51399512] [0.77094376]\n",
+      "2 6 [0.50957615] [0.77586953]\n",
+      "2 7 [0.52942058] [0.77885416]\n",
+      "2 8 [0.51392033] [0.77077898]\n",
+      "2 9 [0.50525977] [0.77142953]\n",
+      "2 10 [0.74562299] [0.91996292]\n",
+      "3 4 [0.52596273] [0.51902525]\n",
+      "3 5 [0.5116986] [0.50604489]\n",
+      "3 6 [0.50144882] [0.52295068]\n",
+      "3 7 [0.51624331] [0.53170865]\n",
+      "3 8 [0.96538812] [0.65931985]\n",
+      "3 9 [0.50718756] [0.50504136]\n",
+      "3 10 [0.50399196] [0.73114927]\n",
+      "4 5 [0.51243867] [0.53387302]\n",
+      "4 6 [0.50208561] [0.51255531]\n",
+      "4 7 [0.51966986] [0.54590229]\n",
+      "4 8 [0.51660687] [0.51821219]\n",
+      "4 9 [0.51070635] [0.51873565]\n",
+      "4 10 [0.51570476] [0.76355034]\n",
+      "5 6 [0.52352081] [0.52367569]\n",
+      "5 7 [0.51257143] [0.52968964]\n",
+      "5 8 [0.51345915] [0.50664275]\n",
+      "5 9 [0.71532175] [0.58950696]\n",
+      "5 10 [0.50233362] [0.75063015]\n",
+      "6 7 [0.50159157] [0.50823628]\n",
+      "6 8 [0.50274626] [0.50422703]\n",
+      "6 9 [0.50441627] [0.51942506]\n",
+      "6 10 [0.50571473] [0.75360267]\n",
+      "7 8 [0.50919461] [0.52930209]\n",
+      "7 9 [0.82504383] [0.6230977]\n",
+      "7 10 [0.51820473] [0.75869471]\n",
+      "8 9 [0.50661449] [0.50365656]\n",
+      "8 10 [0.50141312] [0.75067264]\n",
+      "9 10 [0.5031281] [0.74863745]\n",
+      "[0.96538812]\n",
+      "[3, 8]\n",
+      "[0.92110448]\n",
+      "[1, 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.decomposition import PCA\n",
+    "data=np.loadtxt(fname='data_wk1')\n",
+    "x=data[:,0]\n",
+    "Y=data[:,1:11]\n",
+    "maxk=0\n",
+    "maxl=0\n",
+    "itr=[0,0]\n",
+    "itrl=[0,0]\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "p = StandardScaler().fit_transform(data)\n",
+    "yp=p[:,1:11]\n",
+    "r=p[:,0:1]\n",
+    "for i in range(0,10):\n",
+    "    for j in range(i+1,10):\n",
+    "        x1=yp[:,i:i+1]\n",
+    "        x2=yp[:,j:j+1]\n",
+    "        \n",
+    "        f=np.concatenate((x1,x2),axis=1)\n",
+    "        pca=PCA(n_components=1)\n",
+    "        principalComponents1=pca.fit_transform(f)\n",
+    "        k=pca.explained_variance_ratio_\n",
+    "        g=np.concatenate((r,principalComponents1),axis=1)\n",
+    "        principalComponents1=pca.fit_transform(g)\n",
+    "        l=pca.explained_variance_ratio_\n",
+    "        \n",
+    "        if k>maxk:\n",
+    "            maxk=k\n",
+    "            itr=[i+1,j+1]\n",
+    "            \n",
+    "        if l>maxl:\n",
+    "            maxl=l\n",
+    "            itrl=[i+1,j+1]\n",
+    "        print(str(i+1)+' '+str(j+1)+' '+str(k)+' '+str(l))\n",
+    "\n",
+    "print(maxk)\n",
+    "print(itr)\n",
+    "print(maxl)\n",
+    "print(itrl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " From the above analysis it is concluded that features 3 and 8 retain the largest variance ratio when PCA-reduced from 2-d to 1-D\n",
+      "That means that this feature pair is the most alike and hence redundant i.e. one feature can be expressed in the other's form  \n",
+      "But when combined with the labels: 1,2 retains the highest variance ratio which means that it ,this pair, can depict the labels in the best possible way \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"From the above analysis it is concluded that features 3 and 8 retain the largest variance ratio when PCA-reduced from 2-d to 1-D\")\n",
+    "print(\"That means that this feature pair is the most alike and hence redundant i.e. one feature can be expressed in the other's form  \")\n",
+    "print(\"But when combined with the labels: 1,2 retains the highest variance ratio which means that it ,this pair, can depict the labels in the best possible way \")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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zs7ZpNhE8FxEB7Al8PSK+DkxtXVhmZtYuzfYRPCHp08D+wM6Suskdv2ZmNrY1e0awD/A34EMRcT+wKe7kNTMbF5o6I8g7/+Orpu/GfQRmZuPCoIlA0hNA1FsERERMa0lUZmbWNoMmgohwh7CZ2TjX9C0mJO0k6cA83itpi9aFZWZm7dLsD8qOBA4DPp1nTQJOb1VQZmbWPs2eEewFvBt4CiAi7sW/IzAzGxeaTQR/zz8oC4DK7SbMzGzsazYR/EjSt4GNJf07cBHwndaFZWZm7dLs7wi+IultwOOkR0weEREXtjQyMzNri+HchvomYEPS5aGbWhOOmZm1W7PfGjoYuAZ4L7A3cJWkg1oZmJmZtUezZwSfBF4bEQ8DSJoBXAksalVgZmbWHs12Fq8AnqiafgK4Z/TDMTOzdhvqXkMfy6N/Bq6WdA6pj2BP0qUiMzMb44a6NFT50dgf81BxTmvCMTOzdhvqpnNHtysQMzPrjKY6iyX1kZ5VPAeYXJkfEW9tUVxmZtYmzXYWLwZ+D2wBHA3cCfymRTGZmVkbNZsIZkTEd4FnI+KyiDgI2KGFcZmZWZs0+zuCZ/Pf+yS9E7gX2Kw1IZmZWTs1mwi+IGk68HHgG8A04NCWRWVmZm3T7E3nlubRx4C3AEhyIjAzGweaflRlHR8buoiZma3vRpIINGpRmJlZx4wkEcRgCyUtkvSgpJuHKPc6Sask7T2CWMzMbB0NmggkPSHp8TrDE8DLh6j7NGD3IervBo4FfjGcoM3MbPQMdYuJdX5AfURcLql/iGL/CfwEeN26tmNmZiMzkktDIyJpU2Av4OQmys6TtEzSspUrV7Y+ODOzgnQsEQBfAw6LiFVDFYyIhRExEBEDfX19bQjNzKwcw3lm8WgbAH4oCaAX2EPScxHx0w7GZGZWnI4lgojYojIu6TRgqZOAmVn7tSwRSFoCzAV6Ja0AjgQmAkTEkP0CZmbWHi1LBBGx7zDKHtCqOMzMbHCd7Cw2M7P1gBOBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVzonAzKxwTgRmZoVzIjAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeGcCMzMCudEYGZWOCcCM7PCORGYmRXOicDMrHBOBGZmhXMiMDMrnBOBmVnhnAjMzArnRGBmVjgnAjOzwrUsEUhaJOlBSTc3WL6fpBvzcKWkbVsVi5mZNdbKM4LTgN0HWf4n4M0RsQ3weWBhC2MxM7MGJrSq4oi4XFL/IMuvrJq8CtisVbGYmVlj60sfwYeA8zsdhJlZiVp2RtAsSW8hJYKdBikzD5gHMHPmzDZFZmZWho6eEUjaBjgF2DMiHm5ULiIWRsRARAz09fW1L0AzswJ0LBFImgmcBbw/Im7tVBxmZqVr2aUhSUuAuUCvpBXAkcBEgIg4GTgCmAGcJAnguYgYaFU8ZmZWXyu/NbTvEMsPBg5uVftmZtac9eVbQ2Zm1iFOBGZmhXMiMDMrnBOBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVzonAzKxwTgRmZoVzIjAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeGcCMzMCudEYGZWOCcCM7PCORGYmRXOicDMrHBOBGZmhXMiMDMrnBOBmVnhnAjMzArnRGBmVriWJQJJiyQ9KOnmBssl6QRJt0u6UdJ2rYplLYsXQ38/dHWlv7vuChMmgJT+LliQyi1YsHp+VxdMnZrGBxt6e2HOnKHLefDQaOjuhsmTGy+T1ny/Vs+vN0yYkN7j/fk939ubhsr7f/HitT8TCxakMo3araxX7/O0YEH6W698bdlKHdZZEdGSAdgZ2A64ucHyPYDzAQE7AFc3U+/2228fI3L66RE9PREw+DB79tBlPHgYD8OkSRETJw5/vZ6eiPnzm/s8QWpj0qS16zj99JF9pq0pwLKI+vtVpeWtIakfWBoRr66z7NvApRGxJE//AZgbEfcNVufAwEAsW7Zs3YPq74e77lr39c1ste5uWLVqZHXMmgV33jkq4Vhjkq6NiIF6yzrZR7ApcE/V9Io8by2S5klaJmnZypUrR9bq3XePbH0zW22kSQD8mVwPdDIRqM68uqcnEbEwIgYiYqCvr29krc6cObL1zWy17u6R1+HPZMd1MhGsADavmt4MuLflrR5zDPT0DF1u9uyWh2K2Xpg0CSZOHP56PT0wb15znydIbUyatHYdxxwz/LZtVHUyEZwLfCB/e2gH4LGh+gdGxX77wcKF6bqklP7ussvqI5vubpg/H5YvT38r8yWYMmXo+mfMcBKxkenqgg02aLwM1ny/Vs+vp7s7vccr7/kZM9JQef8vWgSnnrrmZ2L+/FSmUbuzZqXP0Uknrf15mj8//a0tf+qpqa3qsgsXps+kdVTLOoslLQHmAr3AA8CRwESAiDhZkoATgd2Bp4EDI2LIXuARdxabmRVosM7iCa1qNCL2HWJ5AIe0qn0zM2uOf1lsZlY4JwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeFaevfRVpC0EujU7UN7gYc61HY7ePvGNm/f2Nbq7ZsVEXVv1jbmEkEnSVrW6Jd544G3b2zz9o1tndw+XxoyMyucE4GZWeGcCIZnYacDaDFv39jm7RvbOrZ97iMwMyuczwjMzArnRGBmVjgngmGSdJyk30u6UdLZkjbudEyjSdL7JC2X9LykcfFVPUm7S/qDpNsl/Ven4xltkhZJelDSzZ2OZbRJ2lzSJZJuye/Lj3Y6ptEkabKkayTdkLfv6E7E4UQwfBcCr46IbYBbgU93OJ7RdjPwXuDyTgcyGiR1A98E3gHMBvaVNN6eJXoa6Ul/49FzwMcj4h+BHYBDxtn/72/AWyNiW+A1wO750b1t5UQwTBFxQUQ8lyevAjbrZDyjLSJuiYg/dDqOUfR64PaIuCMi/g78ENizwzGNqoi4HHik03G0QkTcFxHX5fEngFuATTsb1eiJ5Mk8OTEPbf8GjxPByBwEnN/pIGxQmwL3VE2vYBztSEoiqR94LXB1ZyMZXZK6JV0PPAhcGBFt376WPbN4LJN0EbBJnUWHR8Q5uczhpNPWxe2MbTQ0s33jiOrM83emxxhJU4CfAIdGxOOdjmc0RcQq4DW5v/FsSa+OiLb29zgR1BERuw62XNIHgX8Gdokx+EOMobZvnFkBbF41vRlwb4disXUgaSIpCSyOiLM6HU+rRMSjki4l9fe0NRH40tAwSdodOAx4d0Q83el4bEi/AbaUtIWkScC/Aud2OCZrkiQB3wVuiYjjOx3PaJPUV/nmoaQNgV2B37c7DieC4TsRmApcKOl6SSd3OqDRJGkvSSuANwA/k/SLTsc0Erlj/z+AX5A6Gn8UEcs7G9XokrQE+DWwtaQVkj7U6ZhG0Y7A+4G35s/b9ZL26HRQo+hlwCWSbiQdtFwYEUvbHYRvMWFmVjifEZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeGcCKwuSZtJOkfSbZL+KOnr+Xv4o91Of+WumZIGJJ0w2m00GceVLajzNEl7j2J9d0rqHa366tR/lKRPtKp+W385Edha8o94zgJ+GhFbAlsBU4BjWtluRCyLiI+0so1B2n5jJ9qVNC5+3V+7HeNlu0rhRGD1vBX4a0ScCi/cC+V/AwdJ6pF0gKSzJP08nzF8ubKipN0k/VrSdZLOzPeIWYOk7fP9138NHFI1f66kpXn8KEn/I+mCfCT8XklflnRTbndiVV2XSbpW0i8kvSzPv1TSsfle77dKelOePyfPuz4/U2LLPP/J/FdKz5y4Obe1T1Vsl0r6sdLzKBbnhImkIyT9Jq+zsDK/kVzPFyVdBnw0/7r0J7mO30jaMZebkbf/t5K+Tb5vUvVZVJ7+hKSj8vgrJV2UX9/rJL0iz/9krvtGVd3zXtLhSs9quAjYukG875J0dY7jIkkvrfofLZR0AfC9/L44U9J5wAWSpki6OMdxk6Q983qfV9VzBSQdI6kjBwCWRYQHD2sMwEeA/64z/7fANsABwB3AdGAycBfpfj69pOcYbJTLHwYcUaeeG4E35/HjgJvz+FxgaR4/CriCdFvebYGngXfkZWcD78nLrgT68vx9gEV5/FLgq3l8D+CiPP4NYL88PgnYMI8/mf/+C+mZE93AS4G7Sb/+nAs8RrpXURfpl7w75XVeXLVt3wfelcdPA/aus/2XAidVTf+gqq6ZpNspAJxQef2Ad5JultcL9Fdes7zsE8BRefxqYK88PhnoAXYjPRhdOfalwM7A9sBNucw04HbgE3XifRGrf3x6cNXrehRwbdVreADp3k4vztMTgGl5vDfXrxz/dXl+F/BHYEan3/clDz59s3pE/Tt0Vs+/OCIeA5D0O2AWsDHp4S//Lx8UTyLtMFdXIE0HNo6Iy/Ks75MeGlPP+RHxrKSbSDvmn+f5N5F2JlsDrybd7oNc5r6q9Ss3KLs2lyfHc7ikzYCzIuK2mjZ3ApZEOgt6IB+1vw54HLgmIlbk7bg+13kF8BZJnyLtUF8MLAfOa7BNFWdUje8KzK46kZgmaSppZ/1egIj4maS/DFZhXmfTiDg7r/PXPH83UjL4bS46BdiSdKuUsyPfM0tSo3swbQackc+2JgF/qlp2bkQ8UzV9YURUno0g4IuSdgaeJ93++6URcaekhyW9lpRsfxsRDw+2bdZaTgRWz3LSkfELJE0jHfX/kXQk+beqxatI7yWRdgT7DlJ3oyRTz98AIuJ5Sc9GPoQk7VQq7S2PiDcMtn5VfETEDyRdTTrC/oWkgyPilzXxDRpPdZ2SJgMnAQMRcU++RDO5iW17qmq8C3hDzQ6VnBjqvVbPseZl3Up7jWIX8KWI+HZN/Yc2qL/WN4DjI+JcSXNJZwIVT9WUrZ7eD+gDts8J/c6qWE8hnUFsAixqIgZrIfcRWD0XAz2SPgAvPO7xq8BpMfgdV68CdpT0yrxej6StqgtExKPAY5J2yrP2G0GcfwD6JL0htzdR0pzBVpD0D8AdEXEC6S6k29QUuRzYR+lhIX2ko/JrBqmysmN7SKk/ZF2+JXQB6cZ4lRhfUxXLfnneO0iXaAAeAF6S+xA2IN0SnUj36V8h6T15nQ0k9ZBuuHdQjg9Jm0p6Sa5/L0kb5rOJdzWIbzrw5zz+wWFs13TgwZwE3kI6a6w4m3S75dfl+KyDnAhsLfnIey/gfZJuIz2b+a/AZ4ZYbyXpKG+J0t0UrwJeVafogcA3lTqLn6mzvNk4/07a8R4r6QbgemCob//sA9ycL+28CvhezfKzSX0YNwC/BD4VEfcPEsOjwHdIl6t+SrqD5HB9BBjIHbm/Az6c5x8N7CzpOtKlnbtzm88CnyP1ByxlzdsWvx/4SH79rwQ2iYgLSP0Qv86X2X4MTI30CMgzSK/bT4BfNYjvKOBMSb8CHhrGdi3O27WMlNBeiDP/7y4h3Q121TDqtBbw3UfNrO0kdQHXAe+r009jbeYzAjNrK0mzSd8guthJYP3gMwIzs8L5jMDMrHBOBGZmhXMiMDMrnBOBmVnhnAjMzAr3/wEzqr7P08qQOwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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FYGZmw2vr0pCk10mansffI+kYSfOrDc3MzMZDu30EXwcel7Qt8HHgNuA7lUVlZmbjpt1E8FREBLA7cGxEHAvMrC4sMzMbL+32ETwi6RPAe4AdJXWTO37NzGxia/eMYC/gb8AHIuJuYBPcyWtmNim0dUaQd/7H1L2+HfcRmJlNCkMmAkmPANFsFhARMauSqMzMbNwMmQgiwh3CZmaTXNu3mJC0g6T98nifpM2qC8vMzMZLuz8oOxw4BPhEnjQVOLWqoMzMbPy0e0awB/AO4DGAiLgT/47AzGxSaDcR/D3/oCwAarebMDOzia/dRPADSd8ANpL0b8CFwDerC8vMzMZLu78j+JKktwAPkx4xeVhEXFBpZGZmNi5GchvqG4ANSZeHbqgmHDMzG2/tfmvoAOAq4F3AnsCvJO1fZWBmZjY+2j0j+Bjwioi4H0DSHOAK4OSqAjMzs/HRbmfxSuCRutePAHeMfThmZjbehrvX0Ifz6J+BKyWdReoj2J10qcjMzCa44S4N1X409sc81JxVTThmZjbehrvp3KfHKxAzM+uMtjqLJfWTnlW8NTCtNj0i3lRRXGZmNk7a7SxeAvwO2Az4NLAC+HVFMZmZ2ThqNxHMiYhvAU9GxKURsT/w6grjMjOzcdLu7wiezH/vkrQbcCcwt5qQzMxsPLWbCD4naTbwEeCrwCzg4MqiMjOzcdPuTefOzaMPAW8EkOREYGY2CbT9qMomPjx8ETMze7YbTSLQmEVhZmYdM5pEEEPNlHSypHsl3ThMuVdKWi1pz1HEYmZm62nIRCDpEUkPNxkeAV44TN2nALsMU383cBTws5EEbWZmY2e4W0ys9wPqI+IySQPDFPtP4EfAK9e3HTMzG53RXBoaFUmbAHsAJ7ZRdqGkZZKWrVq1qvrgzMwK0rFEAHwFOCQiVg9XMCIWR8RgRAz29/ePQ2hmZuUYyTOLx9og8H1JAH3ArpKeiogfdzAmM7PidCwRRMRmtXFJpwDnOgmYmY2/yhKBpKXAAqBP0krgcKAHICKG7RcwM7PxUVkiiIi9R1B236riMDOzoXWys9jMzJ4FnAjMzArnRGBmVjgnAjOzwjkRmJkVzonAzKxwTgRmZoVzIjAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeGcCMzMCudEYGZWOCcCM7PCORGYmRXOicDMrHBOBGZmhXMiMDMrnBOBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVrrJEIOlkSfdKurHF/H0kXZ+HKyRtW1UsZmbWWpVnBKcAuwwx/0/AGyJiG+CzwOIKYzEzsxamVFVxRFwmaWCI+VfUvfwVMLeqWMzMrLVnSx/BB4DzOh2EmVmJKjsjaJekN5ISwQ5DlFkILASYN2/eOEVmZlaGjp4RSNoGOAnYPSLub1UuIhZHxGBEDPb3949fgGZmBehYIpA0DzgDeG9E/KFTcZiZla6yS0OSlgILgD5JK4HDgR6AiDgROAyYA5wgCeCpiBisKh4zM2uuym8N7T3M/AOAA6pq38zM2vNs+daQmZl1iBOBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVzonAzKxwTgRmZoVzIjAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeGcCMzMCudEYGZWOCcCM7PCORGYmRXOicDMrHBOBGZmhXMiMDMrnBOBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVrrJEIOlkSfdKurHFfEk6TtItkq6XtF1VsaxlyRIYGICuLujrS0NXV5q2ZAkceCB0d4OUhhkz0vR6Bx4IU6asKePBw0QcBgbSZ7mvb+3pXV2w005pfuP0DTdc87qvb+1to37bGsjbU7NptbL17TbWZeMrIioZgB2B7YAbW8zfFTgPEPBq4Mp26t1+++1jvZ16akRvbwQ0H6ZMaT69uzstGxGxaFHr5T14KG3o6UnbRrNtq6cnYurUtaf19qZtqKdn3bqmTl2zndmYA5ZFNN+vKs2vhqQB4NyIeFmTed8ALomIpfn174EFEXHXUHUODg7GsmXL1i+ggQG47bb1W3b+fFixIp0JrF69fnWYTUbz56e/7W5b3d2tt6HadmZjTtLVETHYbN6U8Q6mzibAHXWvV+Zp6yQCSQuBhQDz5s1b/xZvv330yzoJmK1tpNvVUNvQaLZRW2+d7CxWk2lNT08iYnFEDEbEYH9///q3OJokUlu2u3v96zCbjObNG9m2NdQ2NJpt1NZbJxPBSmDTutdzgTsrbfHII6G3t/X8KS1OkLq707IACxeOfVxmE1VPT9o2mm1bPT0wdera03p70zbU07NuXVOnrtnObHy16jwYiwEYoHVn8W6s3Vl8VTt1jqqzOCJ1Rs2fHyFFzJmTBilNO/XU1JHV1bWmA2v69HU7sBYtSh3Ine6o8+BhNMP8+emzPGfO2tOliDe/Oc1vnD5t2prXc+asvW3Ub1u17anZtFrZ+nYb67IxRyc6iyUtBRYAfcA9wOFAT04+J0oScDywC/A4sF9EDNsLPKrOYjOzQnWkszgi9h5mfgAHVdW+mZm1x78sNjMrnBOBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVzonAzKxwld59tAqSVgHreQvRcdUH3NfpICrmdZwcvI6Tw3DrOD8imt6sbcIlgolC0rJWv+KbLLyOk4PXcXIYzTr60pCZWeGcCMzMCudEUJ3FnQ5gHHgdJwev4+Sw3uvoPgIzs8L5jMDMrHBOBGZmhXMiqIikoyX9TtL1ks6UtFGnY6qCpHdLWi7paUmT5ut5knaR9HtJt0j6r07HUwVJJ0u6V9KNnY6lKpI2lXSxpJvy5/RDnY5prEmaJukqSdfldfz0SOtwIqjOBcDLImIb4A/AJzocT1VuBN4FXNbpQMaKpG7ga8DbgK2AvSVt1dmoKnEK6QmBk9lTwEci4qWkR+IeNAn/l38D3hQR2wIvB3aR9OqRVOBEUJGIOD8insovfwXM7WQ8VYmImyLi952OY4y9CrglIm6NiL8D3wd273BMYy4iLgMe6HQcVYqIuyLimjz+CHATsElnoxpb+ZHEj+aXPXkY0beAnAjGx/7AeZ0Owtq2CXBH3euVTLKdR4kkDQCvAK7sbCRjT1K3pGuBe4ELImJE61jZM4tLIOlCYOMmsw6NiLNymUNJp6dLxjO2sdTOek4yajLN37OewCTNAH4EHBwRD3c6nrEWEauBl+e+yDMlvSwi2u77cSIYhYjYaaj5kt4P/BPw5pjAP9gYbj0noZXApnWv5wJ3digWGyVJPaQksCQizuh0PFWKiAclXULq+2k7EfjSUEUk7QIcArwjIh7vdDw2Ir8GNpe0maSpwL8AZ3c4JlsPkgR8C7gpIo7pdDxVkNRf+1aipA2BnYDfjaQOJ4LqHA/MBC6QdK2kEzsdUBUk7SFpJfAa4CeSftbpmEYrd/L/B/AzUufiDyJieWejGnuSlgK/BLaUtFLSBzodUwVeB7wXeFPeDq+VtGungxpjLwAulnQ96SDmgog4dyQV+BYTZmaF8xmBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVzonAmpI0V9JZkm6W9EdJx+bv1I91OwO1u19KGpR03Fi30WYcV1RQ5ymS9hzD+lZI6hur+prUf4Skj1ZVvz17ORHYOvKPcM4AfhwRmwNbADOAI6tsNyKWRcQHq2xjiLZf24l2JU2KX/c3rsdkWa9SOBFYM28C/hoR34Zn7mPyv4H9JfVK2lfSGZJ+ms8YvlhbUNLOkn4p6RpJp+d7vKxF0vb53um/BA6qm75A0rl5/AhJ/yPp/Hwk/C5JX5R0Q263p66uSyVdLelnkl6Qp18i6ah8n/Y/SHp9nr51nnZtflbE5nn6o/mvlJ4lcWNua6+62C6R9EOl50wsyQkTSYdJ+nVeZnFteiu5ns9LuhT4UP5l6I9yHb+W9Lpcbk5e/99I+gb5Hkj1Z1H59UclHZHHXyzpwvz+XiPpRXn6x3Ld16vufvWSDlV67sKFwJYt4n27pCtzHBdKen7d/2ixpPOB7+TPxemSzgHOlzRD0kU5jhsk7Z6X+6zqngsg6UhJHTkAsCwiPHhYawA+CPx3k+m/AbYB9gVuBWYD04DbSPfm6SM9l2B6Ln8IcFiTeq4H3pDHjwZuzOMLgHPz+BHA5aRb6m4LPA68Lc87E3hnnncF0J+n7wWcnMcvAb6cx3cFLszjXwX2yeNTgQ3z+KP57z+TniXRDTwfuJ30y80FwEOk+w51kX6Ru0Ne5rl16/Zd4O15/BRgzybrfwlwQt3r79XVNY90OwSA42rvH7Ab6cZ3fcBA7T3L8z4KHJHHrwT2yOPTgF5gZ9KDzZVjPxfYEdgeuCGXmQXcAny0SbzPYc2PTw+oe1+PAK6uew/3Jd2n6bn59RRgVh7vy/Urx39Nnt4F/BGY0+nPfcmDT9+sGdH8bpv10y+KiIcAJP0WmA9sRHqQy//LB8VTSTvMNRVIs4GNIuLSPOm7pAfANHNeRDwp6QbSjvmnefoNpJ3JlsDLSLfxIJe5q2752g3Grs7lyfEcKmkucEZE3NzQ5g7A0khnQffko/ZXAg8DV0XEyrwe1+Y6LwfeKOnjpB3qc4HlwDkt1qnmtLrxnYCt6k4kZkmaSdpZvwsgIn4i6S9DVZiX2SQizszL/DVP35mUDH6Ti84ANifdAuXMyPfCktTqfkpzgdPy2dZU4E91886OiCfqXl8QEbVnHAj4vKQdgadJt/J+fkSskHS/pFeQku1vIuL+odbNquVEYM0sJx0ZP0PSLNJR/x9JR5J/q5u9mvRZEmlHsPcQdbdKMs38DSAinpb0ZORDSNJOpdbe8oh4zVDL18VHRHxP0pWkI+yfSTogIn7eEN+Q8dTXKWkacAIwGBF35Es009pYt8fqxruA1zTsUMmJodl79RRrX9attdcqdgFfiIhvNNR/cIv6G30VOCYizpa0gHQmUPNYQ9n61/sA/cD2OaGvqIv1JNIZxMbAyW3EYBVyH4E1cxHQK+l98MyjG78MnBJD30n1V8DrJL04L9craYv6AhHxIPCQpB3ypH1GEefvgX5Jr8nt9UjaeqgFJP0DcGtEHEe6o+g2DUUuA/ZSetBHP+mo/Kohqqzt2O5T6g9Zn28JnU+6yV0txpfXxbJPnvY20iUagHuA5+U+hA1Itzon0n32V0p6Z15mA0m9pJvn7Z/jQ9Imkp6X699D0ob5bOLtLeKbDfw5j79/BOs1G7g3J4E3ks4aa84k3Sr5lTk+6yAnAltHPvLeA3i3pJtJz1z+K/DJYZZbRTrKW6p0J8RfAS9pUnQ/4GtKncVPNJnfbpx/J+14j5J0HXAtMNy3f/YCbsyXdl4CfKdh/pmkPozrgJ8DH4+Iu4eI4UHgm6TLVT8m3f1xpD4IDOaO3N8C/56nfxrYUdI1pEs7t+c2nwQ+Q+oPOJe1bzn8XuCD+f2/Atg4Is4n9UP8Ml9m+yEwM9IjHE8jvW8/An7RIr4jgNMl/QK4bwTrtSSv1zJSQnsmzvy/u5h0Z9fVI6jTKuC7j5rZuJPUBVwDvLtJP42NM58RmNm4krQV6RtEFzkJPDv4jMDMrHA+IzAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8L9f00ZwIEuU4bmAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAc0UlEQVR4nO3deZRdZZ3u8e9TlYSETGiqFAkksVtAwQsKhcIFIUpERBGxcdE0aoNi1gVa5To3XBka0WWjtCAiphHTSEREQQZbZVBAGkUCMiSCDMoQwhAGIUwKye/+8b4Fh8o5VaeGfU5Vvc9nrb1qnz28+3f2qdrPnmofRQRmZlaujnYXYGZm7eUgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAmibpLZLukPSkpHe3YflbSLpR0mpJh7R6+WOVpAWS7upnfFs/V2s/B8EYJ+kuSc/kP+IHJX1X0rQG00rSYZKWS3pK0gpJZ0vassnFfRH4j4iYFhEXjdy7aNrngIsjYnpEnDKchiRdJemAkSmrqeW9UtLVkh6R9Jfcv0Orlj+AEftc8+/U/JEpq+ll7ifp1ryDsEzSnq1c/njgIBgf9oyIacA2wHbA/2sw3TeBQ3P3MmAz4CLgXU0uZy6wvN4ISR2Sqv59arj8VpM0YZCzPAEcCHST1v3XgAskdY50bUMwZterpDnAfwEfB2YARwBnS5pVQXnjV0S4G8MdcBewoOb18cBFdaZ7LbAG2GYYy1kLPAM8CXQCVwHHAr/Jw+cBG5PC5VHgduDDNW18EfgBcFZu40bg70nBtQq4p/a99Fn+lbn+Z/O8fwdMBk4A7gUeBE4BJufpZwH/ndt9DLgQmJ3HfaVPW18HXpP+HF6yzKuAA3L/QbmGk/J7O7pm+K15GT8DNmliXXYAewMBvLzBNO8BbgBW5/XyhZpxr8nzfghYkd/j52vGrw98L9e0nHQkddcgPtcNgO8C9+f2/w3oyNNvCvwKeAR4OC9nZh53Vp+2Pgks6Lvs3Ob8mt+Js/O8q4ED8vo5HLgzL+MHwMsa1L8jsLLPsMeA7dr9tzmWurYX4G6YH2BNEACb5D/8Y+tM9y/AncNc1gt/wPn1VXn5rwMmAhOA/wG+QdpIb5P/kHfJ038xbyQW5Gm/D/wZ+Hx+fTBwez/Lf2HDnF+fDJxH2sOeQdrwH5vHdeeN7ZQ87lzgR/201UwQPJ9r7Mzt7gP8Edg813808OsB1uFy4DnShvxb/Uz3NuD1eaO4dV6P766tFTi1Zj3/Fdg0j/8qcHleL3OBP/TdGA/wuV5ECtX1gQ2B64CP5HGbAbsCk4BX5M/7q/201UwQ/A3YM7/XKcCnc7uz8/v7DvC9BrVPAH5NOqrtBP6BFJxT2v23OZa6thfgbpgfYNoQPwn8Bbg7/wGv80cAHAVcNcxl1QuCI2tevzpv5KbWDDseOC33fxH4Wc24vYHHeXFv82V5AzetwfJrN8wdpD36uTXj30KDIAF6gFX12sqvmwmCP/UZfwnwzzWvJ+QN8uwB1uNkYH/gg4NY9ycDx9fWCmxYM/56YJ/c/5IjK+CQvhvjRp9r3vg+A6xXM/6DwCUN5t0HuLaf35FmguCXfcbfTt55yK83yeu1o0ENHwWeIgX1U8Duw/k9L7Eb7HlOG53eGxGXDjDNI8CrKlj2vTX9GwEPR8RTNcPuJu3Z9nqwpv8Z0sZ5bc1rgGmkcOvPhsB6wI2Seoe92CNNBU4EdiOd6gCYPkCbA7m3z+u5wDclnVgzbC3p9Nh9jRqJiGeBJZJuk3R9RKxzfj5fSP4ysCVp73s90umT2nYeqHn5NGm9Qfqca2u9u7831cfcvKwHa9ZrB2mHA0kbkk6P7Uhanx2kU1PD0Xe9zgEulLS2ZliQjkBq3zOS3gl8ibQTcAPpGtn5kt4eETcPs65i+GJxOS4D5kl64wi3W/v42pVAV94I95pDPxvFYXiQdEph84jYIHczI2JmHv9Z0hHKmyJiBulUS6O6Ie1JImn9mmEbDjDPvaRTJhvUdFMi4pom38Mk0rWOen4A/Jh0zWEmcBo1QTeAB0h70b3mNDkfpPf0NOnaRe97mhERW+XxXyHtnf+vvF4P6FNXvfX6wjrNF4P7XsjtO88K4O191uvkPsHXa2vg8oi4PiLW5nW/lHT6yprkIChERNwCLCLdUbGLpEmSpkj6J0mfGaFl/Jn0R/glSetJegPpTpklI9F+n2WtIW0cvy6pO98au7Gk3fIk00kbtMfyHSRH9mniQV66EX4gdx+Q1ClpIWnvuD+nAkdIeh2ApA0k7VNvQkk7SNpR0sS83g8HXg5c26Dt6cCjEfGspO2Bfxygllo/BA7P9cwhXR9qSkTcC1wBfFXSjHw32Gsk7VxT11PA45I2IZ3Pr9V3vd4KTJf0DkkTSacoJw5Qxqmk36E5AJJeIek9Daa9FthF0lZ52h7S0cpNzbxfSxwE45ik0ySdXDPoUOBbuXuMdC72PcBP8/RfkHThMBe7L+nOkgeAHwGHR8SvhtlmI58infb4Helaw8V52ZDuJppJOiV2NemOnlpfB/bL9/SfEOlk80dJd6s8TDoP3++efUSck5dzjqQnSBufdzSYfAovrvf7gLcDezTYy4V0UfrLklbnmn7YXy19HEW64+cu0vs+YxDzAnwAmEq6yPwYcA4vHh0dBbyJtL4vIB211PoScExer4dFxGPAx0i3eN5HuuOq0XvudQLwc+Cy/P6vJp3yWUdEXEa6znBenvZs4JiI+GXzb9eUL7aYmVmhfERgZlY4B4GZWeEcBGZmhXMQmJkVbsz9Q1lXV1fMmzev3WWYmY0p11133cMR0V1v3JgLgnnz5rF06dJ2l2FmNqZIavgf5j41ZGZWOAeBmVnhHARmZoVzEJiZFc5BYGZWOAeBmVnhHARmZoVzEJiZFa6yIJC0iaRfSbpF0nJJn6gzjSSdJOkOSTdJ2qaqenodcghIZXcTJrzY39kJCxa8dFh/XWdn+tnVBVOmDH65CxbAtGlDr72jo7XrqqMj1Tx9ev/rY948WLJk9Px+TZ9efz3Pnv3i38KSJelz7B3X1ZXqnzev2vXZ7nUzFrv11kufV2Wq+jJk0vembpP7pwO3AVv0mWYP0hdnCNgeuGagdrfddtsYqoMPjgB37qrpJkxofw3NdBttFHHmmRETJ7a/FneD6848c8ibvwCWNtquVnZEEBH3R8T1uX81cAswu89kewFn5Dp/C2wgqYovWAdg0aKqWjaD559vdwXNWbkSjjgCnnuu3ZXYYB1xRDXttuQagaR5wBtZ96v/ZpO+LLvXCtYNCyQtlLRU0tJVq1YNuY41a4Y8q9m4cs897a7AhqKqz63yIJA0jfS9podFxBN9R9eZJdYZELEoInoioqe7u+7D85rS2TnkWc3GlTlz2l2BDUVVn1ulQSBpIikElkTEuXUmWQFsUvN6Y2BlVfUsXFhVy2bpYvhYsNFGcNxxMHFiuyuxwTruuGrarfKuIQHfAW6JiBMaTHYB8KF899D2wOMRcX9VNZ1yChx8cFWtjx21R0YdHbDrrs0fLXXk35hZs2Dy5MEvd9ddYerUwc1XS/WOISskpZqnTas/vnd9zJ0LixePnt+vadPqr+eNNoL77oP994fvfjd9jr1mzUr1z51bXV2t/vzGi0mT4Mwz0+dWBaWLyRU0LO0E/Bq4GVibBx8OzAGIiFNzWJwM7A48DRwYEf1+2UBPT0/4+wjMzAZH0nUR0VNvXGUHsxFxFfWvAdROE8ChVdVgZmYD838Wm5kVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVrrIgkHS6pIckLWswfqakCyXdKGm5pAOrqsXMzBqr8ohgMbB7P+MPBf4QEVsD84GvSZpUYT1mZlZHZUEQEVcCj/Y3CTBdkoBpedrnq6rHzMzqm9DGZZ8MXACsBKYD+0bE2jbWY2ZWpHZeLH4HcAOwEfAG4GRJM+pNKGmhpKWSlq5ataqVNZqZjXvtDIIDgXMjuQP4M/DaehNGxKKI6ImInu7u7pYWaWY23rUzCO4BdgWQ9Epgc+BPbazHzKxIlV0jkHQW6W6gLkkrgKOAiQARcSpwLLBY0s2AgM9FxMNV1WNmZvVVFgQRsd8A41cCu1W1fDMza47/s9jMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwlUWBJJOl/SQpGX9TDNf0g2Slku6oqpazMyssSqPCBYDuzcaKWkD4BTgPRGxJfD+CmsxM7MGKguCiLgSeLSfSf4JODci7snTP1RVLWZm1lg7rxFsBrxM0uWSrpP0oUYTSlooaamkpatWrWphiWZm4187g2ACsC3wLuAdwBckbVZvwohYFBE9EdHT3d3dyhrNzMa9CW1c9grg4Yh4CnhK0pXA1sBtbazJzKw47TwiOB94i6QJktYH3gzc0sZ6zMyKVNkRgaSzgPlAl6QVwFHARICIODUibpH0c+AmYC1wWkQ0vNXUzMyqUVkQRMR+TUxzPHB8VTWYmdnAmjo1JGlHSVNz/wcknSBpbrWlmZlZKzR7jeBbwNOStgY+C9wNnFFZVWZm1jLNBsHzERHAXsCJEXEiML26sszMrFWavUawWtK/Ah8AdpbUSb7wa2ZmY1uzRwT7An8FPhIRDwCz8UVeM7NxoakjgrzxP6Hm9T34GoGZ2bjQbxBIWg1EvVFARMSMSqoyM7OW6TcIIsIXhM3MxrmmHzEhaSdJB+b+Lkmvrq4sMzNrlWb/oewo4HPAv+ZBk4AzqyrKzMxap9kjgr2B9wBPAUTESvx/BGZm40KzQfC3/A9lAdD7uAkzMxv7mg2CH0r6NrCBpI8ClwL/WV1ZZmbWKs3+H8FXJb0deIL0FZNHRsQllVZmZmYtMZjHUN8MTCGdHrq5mnLMzKzVmr1r6CDgd8D7gH2A30r6cJWFmZlZazR7RPAZ4I0R8QiApFnA1cDpVRVmZmat0ezF4hXA6prXq4F7R74cMzNrtYGeNfTJ3HsfcI2k80nXCPYinSoyM7MxbqBTQ73/NHZn7nqdX005ZmbWagM9dO6YVhViZmbt0dTFYkndpO8q3hKY3Ds8It5WUV1mZtYizV4sXgLcCrwaOAa4C7i2oprMzKyFmg2CWRHxHeC5iLgiIj4MbF9hXWZm1iLN/h/Bc/nn/ZLeBawENq6mJDMza6Vmg+CLkmYCnwK+AcwADqusKjMza5lmHzp3Ue59HHgrgCQHgZnZOND0V1XW8cmBJzEzs9FuOEGgEavCzMzaZjhBEP2NlHS6pIckLRtguu0krZG0zzBqMTOzIeo3CCStlvREnW41sNEAbS8Gdh+g/U7gK8AvBlO0mZmNnIEeMTHkL6iPiCslzRtgso8BPwa2G+pyzMxseIZzamhYJM0G9gZObWLahZKWSlq6atWq6oszMytI24IA+DrwuYhYM9CEEbEoInoioqe7u7sFpZmZlWMw31k80nqAH0gC6AL2kPR8RPykjTWZmRWnbUEQEa/u7Ze0GLjIIWBm1nqVBYGks4D5QJekFcBRwESAiBjwuoCZmbVGZUEQEfsNYtoDqqrDzMz6186LxWZmNgo4CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8JVFgSSTpf0kKRlDcbvL+mm3F0taeuqajEzs8aqPCJYDOzez/g/A7tExFbAscCiCmsxM7MGJlTVcERcKWleP+Ovrnn5W2DjqmoxM7PGRss1go8AP2t3EWZmJarsiKBZkt5KCoKd+plmIbAQYM6cOS2qzMysDG09IpC0FXAasFdEPNJouohYFBE9EdHT3d3dugLNzArQtiCQNAc4F/hgRNzWrjrMzEpX2akhSWcB84EuSSuAo4CJABFxKnAkMAs4RRLA8xHRU1U9ZmZWX5V3De03wPiDgIOqWr6ZmTVntNw1ZGZmbeIgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMrXGVBIOl0SQ9JWtZgvCSdJOkOSTdJ2qaqWuos3N1wumnTYMqU1i2vo2No83V1pW4w83R2vvgeh1rnvHmwYMGLbUkwYcLw1kFX17ptjmS33nqN13VHBxxyCCxZMvj1Odq6of4utbvr7EyfQVWbxIiopmFpZ+BJ4IyIeH2d8XsAHwP2AN4MnBgRbx6o3Z6enli6dOlwChv6vGYl6+iAtWvbXUXZDj4YTjllSLNKui4ieuqNq+yIICKuBB7tZ5K9SCEREfFbYANJr6qqHjMbJodA+y1aVEmz7bxGMBu4t+b1ijxsHZIWSloqaemqVataUpyZ2aizZk0lzbYzCOqdo6l7nioiFkVET0T0dHd3V1yWmdko1dlZSbPtDIIVwCY1rzcGVrapFjMbSIdvMmy7hQsrabadn+wFwIfy3UPbA49HxP2VL7Wii+NFmToVJk9u3fKGeoF/1qzUDUbvxm7q1MEvr7fOuXNh111fuuEc7p7crFnrtjmSJk1KP+utayldpDzjjMGvz9FmrN4s0tExrAvFA5lQSauApLOA+UCXpBXAUcBEgIg4Ffhv0h1DdwBPAwdWVcs6HAZmQ7P//u2uwCpQWRBExH4DjA/g0KqWb2ZmzfFJPzOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMytcZY+hroqkVcDd7a5jFOsCHm53EaOc11H/vH4GNhbX0dyIqPuwtjEXBNY/SUsbPXPcEq+j/nn9DGy8rSOfGjIzK5yDwMyscA6C8aearzAaX7yO+uf1M7BxtY58jcDMrHA+IjAzK5yDwMyscA6CcUbS+yUtl7RW0ri5vW0kSNpd0h8l3SHp8+2uZ7SRdLqkhyQta3cto5WkTST9StIt+e/sE+2uaSQ4CMafZcD7gCvbXchoIqkT+CbwTmALYD9JW7S3qlFnMbB7u4sY5Z4HPhURrwO2Bw4dD79HDoJxJiJuiYg/truOUehNwB0R8aeI+BvwA2CvNtc0qkTElcCj7a5jNIuI+yPi+ty/GrgFmN3eqobPQWClmA3cW/N6BePgD9jaR9I84I3ANe2tZPgq+85iq46kS4EN64w6IiLOb3U9Y4TqDPO90zYkkqYBPwYOi4gn2l3PcDkIxqCIWNDuGsagFcAmNa83Bla2qRYbwyRNJIXAkog4t931jASfGrJSXAtsKunVkiYB/whc0OaabIyRJOA7wC0RcUK76xkpDoJxRtLeklYAOwA/lfSLdtc0GkTE88C/AL8gXeD7YUQsb29Vo4uks4DfAJtLWiHpI+2uaRTaEfgg8DZJN+Ruj3YXNVx+xISZWeF8RGBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgdUlaWNJ50u6XdKdkk7M99+P9HLm9T7tUlKPpJNGehlN1nF1BW0ulrTPCLZ3l6SukWqvTvtHS/p0Ve3b6OUgsHXkf5o5F/hJRGwKbAZMA46rcrkRsTQiPl7lMvpZ9v9ux3IljYv/7u/7PsbL+yqFg8DqeRvwbER8FyAi1gD/F/iwpPUlHSDpXEk/z0cM/947o6TdJP1G0vWSzsnPZHkJSdtKulHSb4BDa4bPl3RR7j9a0n9JujjvCb9P0r9Lujkvd2JNW1dIuk7SLyS9Kg+/XNJXJP1O0m2S3pKHb5mH3SDpJkmb5uFP5p+SdLykZXlZ+9bUdrmkH0m6VdKSHJhIOlLStXmeRb3DG8ntfEnSFcAnJHVL+nFu41pJO+bpZuX3/3tJ3yY/L6n2KCq//rSko3P/ayRdmtfv9ZL+Pg//TG77JknH1Mx7hNJ3NFwKbN6g3j0lXZPruFTSK2s+o0WSLgbOyL8X50i6ELhY0jRJl+U6bpa0V57vWNU8x1/ScZLasgNgWUS4c/eSDvg48B91hv8e2Ao4APgTMBOYDNxNeo5PF+l7EKbm6T8HHFmnnZuAXXL/8cCy3D8fuCj3Hw1cBUwEtgaeBt6Zx50HvDePuxrozsP3BU7P/ZcDX8v9ewCX5v5vAPvn/knAlNz/ZP75D8AlQCfwSuAe4FW5tsdJzyjqIP0H7k55npfXvLfvAXvm/sXAPnXe/+XAKTWvv1/T1hzS4wsATupdf8C7SA/J6wLm9a6zPO7TwNG5/xpg79w/GVgf2I30ZevKtV8E7AxsC9ycp5kB3AF8uk69L+PFfz49qGa9Hg1cV7MODyA90+nl+fUEYEbu78rtK9d/fR7eAdwJzGr3733JnQ/frB5R/8mctcMvi4jHAST9AZgLbED60pf/yTvFk0gbzBcbkGYCG0TEFXnQ90hfFlPPzyLiOUk3kzbMP8/DbyZtTDYHXg9ckpfXCdxfM3/vA8Guy9OT6zlC0sbAuRFxe59l7gScFeko6MG8174d8ATwu4hYkd/HDbnNq4C3SvosaYP6cmA5cGGD99Tr7Jr+BcAWNQcSMyRNJ22s3wcQET+V9Fh/DeZ5ZkfEeXmeZ/Pw3Uhh8Ps86TRgU2A6cF5EPJ2na/TspY2Bs/PR1iTgzzXjLoiIZ2peXxIRvd9pIOBLknYG1pIe+/3KiLhL0iOS3kgK299HxCP9vTerloPA6llO2jN+gaQZpL3+O0l7kn+tGb2G9Lsk0oZgv37abhQy9fwVICLWSnou8i4kaaPSu7zlEbFDf/PX1EdEfF/SNaQ97F9IOigiftmnvn7rqW1T0mTgFKAnIu7Np2gmN/Henqrp7wB26LNBJQdDvXX1PC89rdu7vEa1C/hyRHy7T/uHNWi/r28AJ0TEBZLmk44Eej3VZ9ra1/sD3cC2OdDvqqn1NNIRxIbA6U3UYBXyNQKr5zJgfUkfghe+5vFrwOLevccGfgvsKOk1eb71JW1WO0FE/AV4XNJOedD+w6jzj0C3pB3y8iZK2rK/GST9HfCniDiJ9PTRrfpMciWwr6ROSd2kvfLf9dNk74btYaXrIUO5S+hi0gPxemt8Q00t++dh7ySdogF4EHhFvoawHvBugEjPxV8h6b15nvUkrU960N6Hc31Imi3pFbn9vSVNyUcTezaobyZwX+7/50G8r5nAQzkE3ko6aux1HulrMbfL9VkbOQhsHXnPe2/g/ZJuB24DngUOH2C+VaS9vLMk3UQKhtfWmfRA4JtKF4ufqTO+2Tr/RtrwfkXSjcANwEB3/+wLLMundl4LnNFn/Hmkaxg3Ar8EPhsRD/RTw1+A/ySdrvoJ6XHXg/VxoCdfyP0D8H/y8GOAnSVdTzq1c09e5nPAv5GuB1wE3FrT1geBj+f1fzWwYURcTLoO8Zt8mu1HwPRIX7l4Nmm9/Rj4dYP6jgbOkfRr4OFBvK8l+X0tJQXaC3Xmz+5XpKfArhlEm1YBP33UzFpOUgdwPfD+OtdprMV8RGBmLSVpC9IdRJc5BEYHHxGYmRXORwRmZoVzEJiZFc5BYGZWOAeBmVnhHARmZoX7/yfDMWQPT9TIAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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7I5C0EJgFdEtaBhwDjAWIiNMi4g5JvwRuBV4GTo+Ihl81NTOzcpSWCCLigBbqnAicWFYMZmY2sJYuDUnaVdKGefwjkk6SNLPc0MzMbDi02kfwPeA5SdsDnwfuA84oLSozMxs2rSaClRERwPuBb0fEt4FJ5YVlZmbDpdU+ghWSvgh8BNhdUie549fMzNZvrZ4R7A/8FfhERDwMTMOdvGZmo0JLZwR5539S4fX9uI/AzGxUaJoIJK0Aol4REBExuZSozMxs2DRNBBHhDmEzs1Gu5VtMSNpN0sF5vFvSa8sLy8zMhkurPyg7BjgS+GKeNA44s6ygzMxs+LR6RrAvsA/wLEBEPIh/R2BmNiq0mgj+ln9QFgD9t5swM7P1X6uJ4KeSvg9sJOmfgMuA/ywvLDMzGy6t/o7gG5LeCTxNesTk0RFxaamRmZnZsFib21DfBkwgXR66rZxwzMxsuLX6raFDgeuBDwL7Ab+VdEiZgZmZ2fBo9Yzgc8AbI+JxAElTgWuBeWUFZmZmw6PVzuJlwIrC6xXAA0MfjpmZDbeB7jX06Tz6Z+A6SeeT+gjeT7pUZGZm67mBLg31/2js3jz0O7+ccMzMbLgNdNO544YrEDMza4+WOosl9ZCeVbwtML5/ekS8vaS4zMxsmLTaWbwA+APwWuA4YClwQ0kxmZnZMGo1EUyNiB8AL0bEVRFxCLBziXGZmdkwafV3BC/mvw9Jeg/wIDC9nJDMzGw4tZoIviJpCvAZ4DvAZOCI0qIyM7Nh0+pN5xbl0aeAtwFIciIwMxsFWn5UZR2fHriKmZmNdINJBBqyKMzMrG0GkwiiWaGkeZIelXT7APXeJOklSfsNIhYzM1tHTROBpBWSnq4zrAA2G2DZ84G9Blh+J/B14OK1CdrMzIbOQLeYWOcH1EfE1ZJ6B6j2L8A5wJvWtR0zMxucwVwaGhRJ04B9gdNaqDtb0mJJi5cvX15+cGZmFdK2RAB8CzgyIl4aqGJEzI2Ivojo6+npGYbQzMyqY22eWTzU+oCfSALoBvaWtDIift7GmMzMKqdtiSAiXts/Lmk+sMhJwMxs+JWWCCQtBGYB3ZKWAccAYwEiYsB+ATMzGx6lJYKIOGAt6h5UVhxmZtZcOzuLzcxsBHAiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIjAzqzgnAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIjAzqzgnAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIjAzqzgnAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIjAzqzgnAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4orLRFImifpUUm3Nyg/UNKtebhW0vZlxWJmZo2VeUYwH9irSfmfgLdGxHbA8cDcEmMxM7MGxpS14Ii4WlJvk/JrCy9/C0wvKxYzM2tspPQRfAK4qN1BmJlVUWlnBK2S9DZSItitSZ3ZwGyAGTNmDFNkZmbV0NYzAknbAacD74+IxxvVi4i5EdEXEX09PT3DF6CZWQW0LRFImgGcC3w0Iu5qVxxmZlVX2qUhSQuBWUC3pGXAMcBYgIg4DTgamAqcKglgZUT0lRWPmZnVV+a3hg4YoPxQ4NCy2jczs9aMlG8NmZlZmzgRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVVxpiUDSPEmPSrq9QbkknSzpHkm3StqxrFhWs2ABdHeD5MFDe4YxY+Cww+q/N3t7oaMj/V2woPn7uNW6ZgOJiFIGYHdgR+D2BuV7AxcBAnYGrmtluTvttFOsszPPjBg3LgI8eGj/MGfO6u/Nrq7Vy7u60vR67+NW65plwOKI+vtVpfJySOoFFkXEG+qUfR+4MiIW5td3ArMi4qFmy+zr64vFixevW0C9vXDffes2r9lQ6+yElSvTeKP35syZsHTp6tPWpq5ZJunGiOirV9bOPoJpwAOF18vytDVImi1psaTFy5cvX/cW779/3ec1G2ovvbRqvNF7s970talr1oJ2JgLVmVb39CQi5kZEX0T09fT0rHuLM2as+7xmQ62zc9V4o/dmvelrU9esBe1MBMuAzQuvpwMPltriCSfAuHGlNmHWstmzV42fcAJ0da1e3tWVptdam7pmLWhnIrgA+Fj+9tDOwFMD9Q8M2oEHwrx5MHVqqc2YNdXZCXPmwKmnrpp24IEwd266zi+lv3Pnpum11qauWQtK6yyWtBCYBXQDjwDHAGMBIuI0SQJOAfYCngMOjogBe4EH1VlsZlZRzTqLx5TVaEQcMEB5AIeX1b6ZmbXGvyw2M6s4JwIzs4pzIjAzqzgnAjOzinMiMDOrOCcCM7OKcyIwM6u4Uu8+WgZJy4GBbiHaDTw2DOGMBF7X0cnrOjq1c11nRkTdm7Wtd4mgFZIWN/oF3WjjdR2dvK6j00hdV18aMjOrOCcCM7OKG62JYG67AxhGXtfRyes6Oo3IdR2VfQRmZta60XpGYGZmLXIiMDOruFGbCCSdKOkPkm6VdJ6kjdodU1kkfUjSEkkvSxpxX00bLEl7SbpT0j2SvtDueMokaZ6kRyXd3u5YyiRpc0lXSLojv3c/1e6YyiJpvKTrJd2S1/W4dsdUa9QmAuBS4A0RsR1wF/DFNsdTptuBDwJXtzuQoSapE/gu8G5gG+AASdu0N6pSzSc9tW+0Wwl8JiJeD+wMHD6K/69/Bd4eEdsDOwB75cfzjhijNhFExCURsTK//C0wvZ3xlCki7oiIO9sdR0neDNwTEX+MiL8BPwHe3+aYShMRVwNPtDuOskXEQxFxUx5fAdwBTGtvVOWI5Jn8cmweRtS3dEZtIqhxCHBRu4OwdTINeKDwehmjdIdRVZJ6gTcC17U3kvJI6pR0M/AocGlEjKh1Le2ZxcNB0mXApnWKvhQR5+c6XyKdhi4YztiGWivrOkqpzrQRdTRl607SROAc4IiIeLrd8ZQlIl4Cdsh9ledJekNEjJh+oPU6EUTEHs3KJX0ceC/wjljPfzAx0LqOYsuAzQuvpwMPtikWG0KSxpKSwIKIOLfd8QyHiPiLpCtJ/UAjJhGM2ktDkvYCjgT2iYjn2h2PrbMbgC0lvVbSOOAfgQvaHJMNkiQBPwDuiIiT2h1PmST19H9rUdIEYA/gD+2NanWjNhEApwCTgEsl3SzptHYHVBZJ+0paBuwC/ELSxe2OaajkDv9/Bi4mdSj+NCKWtDeq8khaCPwG2FrSMkmfaHdMJdkV+Cjw9vz5vFnS3u0OqiSvAa6QdCvpwObSiFjU5phW41tMmJlV3Gg+IzAzsxY4EZiZVZwTgZlZxTkRmJlVnBOBmVnFORFYXZKmSzpf0t2S7pX07fw9/qFup7f/TpuS+iSdPNRttBjHtSUsc76k/YZweUsldQ/V8uos/1hJny1r+TZyORHYGvKPfc4Ffh4RWwJbAROBE8psNyIWR8Qny2yjSdv/qx3tSlqvf93fr3Y9Rst6VYUTgdXzduCFiPghvHKflH8FDpHUJekgSedK+mU+Y/j3/hkl7SnpN5JuknR2vpfMaiTtlO/N/hvg8ML0WZIW5fFjJf2XpEvykfAHJf27pNtyu2MLy7pK0o2SLpb0mjz9Sklfz/eBv0vSW/L0bfO0m/OzKrbM05/Jf6X0LIvbc1v7F2K7UtLPlJ5zsSAnTCQdLemGPM/c/umN5OV8VdJVwKfyL0/Pycu4QdKuud7UvP6/k/R98n2XimdR+fVnJR2bx7eQdFnevjdJ+vs8/XN52beqcD98SV9SetbDZcDWDeJ9n6TrchyXSdqk8D+aK+kS4Iz8vjhb0oXAJZImSro8x3GbpPfn+Y5X4fkDkk6Q1JYDAMsiwoOH1Qbgk8B/1Jn+O2A74CDgj8AUYDxwH+l+QN2kZyJsmOsfCRxdZzm3Am/N4ycCt+fxWcCiPH4scA3plr3bA88B785l5wEfyGXXAj15+v7AvDx+JfDNPL43cFke/w5wYB4fB0zI48/kv/9AepZFJ7AJcD/pl6GzgKdI9zrqIP36d7c8z6sK6/Yj4H15fD6wX531vxI4tfD6x4VlzSDddgHg5P7tB7yHdLO9bqC3f5vlss8Cx+bx64B98/h4oAvYk/TQdOXYFwG7AzsBt+U6k4F7gM/WiXdjVv349NDCdj0WuLGwDQ8i3RvqVfn1GGByHu/Oy1eO/6Y8vQO4F5ja7vd9lQefvlk9ov4dPovTL4+IpwAk/R6YCWxEenjMf+eD4nGkHeaqBUhTgI0i4qo86Uekh87Uc1FEvCjpNtKO+Zd5+m2kncnWwBtItxEh13moMH//jcxuzPXJ8XxJ0nTg3Ii4u6bN3YCFkc6CHslH7W8Cngauj4hleT1uzsu8BnibpM+TdqivApYAFzZYp35nFcb3ALYpnEhMljSJtLP+IEBE/ELSk80WmOeZFhHn5XleyNP3JCWD3+WqE4EtSbdgOS/yvbgkNbqH03TgrHy2NQ74U6Hsgoh4vvD60ojof56CgK9K2h14mXT78E0iYqmkxyW9kZRsfxcRjzdbNyuXE4HVs4R0ZPwKSZNJR/33ko4k/1oofon0XhJpR3BAk2U3SjL1/BUgIl6W9GLkQ0jSTqW/vSURsUuz+QvxERE/lnQd6Qj7YkmHRsSvauJrGk9xmZLGA6cCfRHxQL5EM76FdXu2MN4B7FKzQyUnhnrbaiWrX9btb69R7AK+FhHfr1n+EQ2WX+s7wEkRcYGkWaQzgX7P1tQtvj4Q6AF2ygl9aSHW00lnEJsC81qIwUrkPgKr53KgS9LH4JXHRX4TmB/N7+T6W2BXSVvk+bokbVWsEBF/AZ6StFuedOAg4rwT6JG0S25vrKRtm80g6e+AP0bEyaS7mG5XU+VqYH+lB4n0kI7Kr2+yyP4d22NK/SHr8i2hS0g31uuPcYdCLAfmae8mXaIBeAR4de5D2IB0q3Ui3c9/maQP5Hk2kNRFumHfITk+JE2T9Oq8/H0lTchnE+9rEN8U4M95/ONrsV5TgEdzEngb6ayx33mkWzG/KcdnbeREYGvIR977Ah+SdDfpmc8vAEcNMN9y0lHeQqU7Lf4WeF2dqgcD31XqLH6+Tnmrcf6NtOP9uqRbgJuBgb79sz9we7608zrgjJry80h9GLcAvwI+HxEPN4nhL8B/ki5X/Zx0d8m19UmgL3fk/h74P3n6ccDukm4iXdq5P7f5IvBvpP6ARax+S+OPAp/M2/9aYNOIuITUD/GbfJntZ8CkSI+KPIu03c4Bft0gvmOBsyX9GnhsLdZrQV6vxaSE9kqc+X93Belusi+txTKtBL77qJkNO0kdwE3Ah+r009gw8xmBmQ0rSduQvkF0uZPAyOAzAjOzivMZgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcX9fwycjBS6ao1PAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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2wp28ZmaTQkdnBLnxP7Jm+nbcR2BmNim0TASSHgGi0SIgImJuJVGZmVnXtEwEEeEOYTOzSa7jW0xI2k7S3nl8QNKLqgvLzMy6pdMflB0MHAB8Ms+aBiytKigzM+ueTs8IdgN2BR4FiIg78e8IzMwmhU4Twd/yD8oCYPR2E2Zmtu7rNBF8T9JxwAaS/hk4H/iv6sIyM7Nu6fR3BF+U9PfAw6RHTH46Is6rNDIzM+uKsdyG+lpgPdLloWurCcfMzLqt028N7QtcDrwT2B34paR9qgzMzMy6o9Mzgo8Br4yI+wEkzQMuBU6sKjAzM+uOTjuLVwCP1Ew/Atwx8eGYmVm3tbvX0Ifz6B+ByySdSeojeDvpUpGZma3j2l0aGv3R2C15GHVmNeGYmVm3tbvp3KHdCsTMzHqjo85iSYOkZxVvCcwYnR8Rb6ooLjMz65JOO4uXAb8FXgQcCtwK/KqimMzMrIs6TQTzIuLrwBMRcVFE7AO8psK4zMysSzr9HcET+e9dknYB7gQ2riYkMzPrpk4TwWclrQ98BPgqMBfYv7KozMysazq96dzZefQh4I0AkpwIzMwmgY4fVdnAh9sXMTOzZ7vxJAJNWBRmZtYz40kE0WqhpBMl3SvpujblXiXpKUm7jyMWMzNbSy0TgaRHJD3cYHgEeGGbuk8CdmpTfz/wBeAnYwnazMwmTrtbTKz1A+oj4mJJw22K/StwGvCqtd2OmZmNz3guDY2LpI2A3YBjOyi7SNJySctXrlxZfXBmZgXpWSIAvgwcEBFPtSsYEcdHxEhEjAwODnYhNDOzcozlmcUTbQT4riSAAWBnSU9GxA96GJOZWXF6lggi4kWj45JOAs52EjAz677KEoGkk4EFwICkFcDBwFSAiGjbL2BmZt1RWSKIiD3HUHavquIwM7PWetlZbGZmzwJOBGZmhXMiMDMrnBOBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVzonAzKxwTgRmZoVzIjAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeGcCMzMCudEYGZWOCcCM7PCORGYmRXOicDMrHBOBGZmhXMiMDMrnBOBmVnhnAjMzApXWSKQdKKkeyVd12T5uyVdk4dLJW1dVSxmZtZclWcEJwE7tVj+B+ANEbEVcBhwfIWxmJlZE1OqqjgiLpY03GL5pTWTvwQ2rioWMzNr7tnSR/AB4JxeB2FmVqLKzgg6JemNpESwXYsyi4BFAPPnz+9SZGZmZejpGYGkrYATgLdHxP3NykXE8RExEhEjg4OD3QvQzKwAPUsEkuYDpwPvjYjf9SoOM7PSVXZpSNLJwAJgQNIK4GBgKkBEHAt8GpgHHCMJ4MmIGKkqHjMza6zKbw3t2Wb5vsC+VW3fzMw682z51pCZmfWIE4GZWeGcCMzMCudEYGZWOCcCM7PCORGYmRXOicDMrHBOBGZmhXMiMDMrnBOBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVzonAzKxwTgRmZoVzIjAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeGcCMzMCudEYGZWOCcCM7PCORGYmRWuskQg6URJ90q6rslySTpK0s2SrpG0TVWxrGbZMpg9G6TVh9mzoa8PhodTmWbrDgysue7oMGUKLFnSfp3Zs9ec19+/Kq6+vubbaDSMtbwHD42G6dN7H0OzYeHC9vHNmLH6Z3jJks4/GwMDjT/3S5akz3Vt2eHhFE/9/Gb1LFuW1pFWrTMaY6Nyo/uwZMnq083apYkQEZUMwPbANsB1TZbvDJwDCHgNcFkn9W677bax1pYujejvj4DWw8yZqWz9utOmtV8XIhYvHvs6Hjx4mLhhypSxrzNt2uqf+8WL127bU6euqmfp0tSeNCpX2860Kteo/FoAlkc0aa+bLZiIARhukQiOA/asmb4ReEG7OseVCIaGOv9nDg2t/br9/WNfx4MHD70faj/3nRw0tqunXRvQablG8Y1Rq0TQyz6CjYA7aqZX5HlrkLRI0nJJy1euXLn2W7z99rUvO5Z1n3pq7OuYWe/VfmZHP8fjqaddG9BpufryE6yXiUAN5kWjghFxfESMRMTI4ODg2m9x/vy1LzuWdfv7x76OmfVe7Wd29HM8nnratQGdlqsvP8F6mQhWAJvUTG8M3FnpFg8/vLN/7syZqWz9utOmdbadRYvGvo6ZTZwpU8a+zrRpq3/uRz/HYzV16qp6Dj88tSeN1LYzrco1Kj/Rml0zmoiB1n0Eu7B6Z/HlndQ5rj6CiNTZMmvWmtfeZs2KkNI1uGYdMkuXRsyb1/z6XX//qo7iVuvMmrXmvL6+VXFJY7seOdbyHjw0Gp7NX2zYYYf28U2fvvpnePHizj8b8+Y1/twvXrxmX8HQUIqnUR9Co3qWLl3VBzC6TqN2ZrTc6D4sXrz69Dg6iiMiaNFHoLR84kk6GVgADAD3AAcDU3PyOVaSgKOBnYDHgL0jYnm7ekdGRmL58rbFzMyshqQrImKk0bK1OH/qTETs2WZ5APtVtX0zM+uMf1lsZlY4JwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeEq+0FZVSStBG7rdRxjMADc1+sgusj7O7l5f9ddQxHR8GZt61wiWNdIWt7s13yTkfd3cvP+Tk6+NGRmVjgnAjOzwjkRVO/4XgfQZd7fyc37Owm5j8DMrHA+IzAzK5wTgZlZ4ZwIukDSEZJ+K+kaSWdI2qDXMVVJ0rskXS/paUmT9qt3knaSdKOkmyV9otfxVEnSiZLulXRdr2PpBkmbSPqZpBvye/lDvY6pSk4E3XEe8PKI2Ar4HfDJHsdTteuAdwIX9zqQqkjqB74GvAXYAthT0ha9japSJ5GeJliKJ4GPRMTLSI/S3W8y/3+dCLogIs6NiCfz5C+BjXsZT9Ui4oaIuLHXcVTs1cDNEfH7iPgb8F3g7T2OqTIRcTHwQK/j6JaIuCsirszjjwA3ABv1NqrqOBF03z7AOb0OwsZtI+COmukVTOKGomSShoFXApf1NpLqVPbM4tJIOh/YsMGigyLizFzmINIp57JuxlaFTvZ3klODef4u9iQjaTZwGrB/RDzc63iq4kQwQSJiYavlkt4PvBXYISbBjzfa7W8BVgCb1ExvDNzZo1isApKmkpLAsog4vdfxVMmXhrpA0k7AAcCuEfFYr+OxCfErYFNJL5I0DfhH4Ic9jskmiCQBXwduiIgjex1P1ZwIuuNoYA5wnqSrJB3b64CqJGk3SSuA1wI/kvSTXsc00XLn/78APyF1JH4vIq7vbVTVkXQy8Atgc0krJH2g1zFV7HXAe4E35c/sVZJ27nVQVfEtJszMCuczAjOzwjkRmJkVzonAzKxwTgRmZoVzIjAzK5wTgTUkaWNJZ0q6SdItkr6Svy8/0dsZHr2jpaQRSUdN9DY6jOPSCuo8SdLuE1jfrZIGJqq+BvUfIumjVdVvz15OBLaG/GOa04EfRMSmwGbAbODwKrcbEcsj4oNVbqPFtv9XL7YraVL8ur9+PybLfpXCicAaeRPwl4j4BkBEPAX8X2AfSTMl7SXpdEk/zmcM/z66oqQdJf1C0pWSTs33almNpG0lXS3pF8B+NfMXSDo7jx8i6ZuSzs1Hwu+U9O+Srs3bnVpT10WSrpD0E0kvyPMvlPQFSZdL+p2k1+f5W+Z5V+XnQ2ya5/85/1V+fsR1eVt71MR2oaTv52dLLMsJE0mflvSrvM7xo/ObyfV8TtJFwIckDUo6LdfxK0mvy+Xm5f3/taTjyPc3qj2LytMflXRIHn+JpPPz63ulpBfn+R/LdV8j6dCadQ9SeqbC+cDmTeJ9m6TLchznS3p+zf/oeEnnAt/K74tTJZ0FnCtptqQLchzXSnp7Xu8w1dzfX9LhknpyAGBZRHjwsNoAfBD4jwbzfw1sBewF/B5YH5gB3Ea6784A6RkEs3L5A4BPN6jnGuANefwI4Lo8vgA4O48fAlwCTAW2Bh4D3pKXnQG8Iy+7FBjM8/cATszjFwJfyuM7A+fn8a8C787j04D18vif899/ID0/oh94PnA78IIc20Okewr1kX5lu11e57k1+/Zt4G15/CRg9wb7fyFwTM30d2rqmk+6rQHAUaOvH7AL6aZ2A8Dw6GuWl30UOCSPXwbslsdnADOBHUkPYVeO/Wxge2Bb4NpcZi5wM/DRBvE+h1U/Pt235nU9BLii5jXci3QPpufm6SnA3Dw+kOtXjv/KPL8PuAWY1+v3fcmDT9+sEdH4Tpq18y+IiIcAJP0GGAI2ID2k5b/zQfE0UoO5qgJpfWCDiLgoz/o26eEujZwTEU9IupbUMP84z7+W1JhsDrycdOsOcpm7atYfvVHYFbk8OZ6DJG0MnB4RN9Vtczvg5EhnQffko/ZXAQ8Dl0fEirwfV+U6LwHeKOnjpAb1ucD1wFlN9mnUKTXjC4Etak4k5kqaQ2qs3wkQET+S9GCrCvM6G0XEGXmdv+T5O5KSwa9z0dnApqTbnpwR+f5XkprdK2lj4JR8tjUN+EPNsh9GxOM10+dFxOhzCwR8TtL2wNOk23Q/PyJulXS/pFeSku2vI+L+Vvtm1XIisEauJx0ZP0PSXNJR/y2kI8m/1ix+ivReEqkh2LNF3c2STCN/BYiIpyU9EfkQktSojG7v+oh4bav1a+IjIr4j6TLSEfZPJO0bET+ti69lPLV1SpoBHAOMRMQd+RLNjA727dGa8T7gtXUNKjkxNHqtnmT1y7qj22sWu4DPR8RxdfXv36T+el8FjoyIH0paQDoTGPVoXdna6XcDg8C2OaHfWhPrCaQziA2BEzuIwSrkPgJr5AJgpqT3wTOPZfwScFK0vnvqL4HXSXpJXm+mpM1qC0TEn4CHJG2XZ717HHHeCAxKem3e3lRJW7ZaQdLfAb+PiKNIdwvdqq7IxcAekvolDZKOyi9vUeVow3afUn/I2nxL6FzSDexGY3xFTSzvzvPeQrpEA3AP8LzchzCddHtzIt0vf4Wkd+R1pkuaSbox3j45PiRtJOl5uf7dJK2Xzybe1iS+9YE/5vH3j2G/1gfuzUngjaSzxlFnkB59+aocn/WQE4GtIR957wa8S9JNpOcs/wU4sM16K0lHeSdLuoaUGF7aoOjewNeUOosfb7C80zj/Rmp4vyDpauAqoN23f/YArsuXdl4KfKtu+RmkPoyrgZ8CH4+Iu1vE8Cfgv0iXq35Auj31WH0QGMkdub8B/k+efyiwvaQrSZd2bs/bfAL4DKk/4GzgtzV1vRf4YH79LwU2jIhzSf0Qv8iX2b4PzIn0KMZTSK/bacDPm8R3CHCqpJ8D941hv5bl/VpOSmjPxJn/dz8j3bX1qTHUaRXw3UfNrOsk9QFXAu9q0E9jXeYzAjPrKklbkL5BdIGTwLODzwjMzArnMwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyvc/wc0Q5rLp3aqNwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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wL/DdyqIyM7OWaTYRPB8RAewNfD0ivg5Mqi4sMzNrlWavESyX9HngQ8AukjrJF37NzGz91uwRwX7A34CPRsRDwDR8kdfMbExo6oggb/xPqhm+D18jMDMbE4ZMBJKWA1FvEhARMbmSqMzMrGWGTAQR4QvCZmZjXNOPmJC0s6SDcv9USa+pLiwzM2uVZn9QdjRwOPD5PGo88L2qgjIzs9Zp9ohgH2Av4GmAiHgA/47AzGxMaDYRPJt/UBYAA4+bMDOz9V+zieCHkr4FbCLpY8AVwH9VF5aZmbVKs78j+IqkdwFPkV4xeVREXF5pZGZm1hJr8xjq24ANSaeHbqsmHDMza7Vm7xo6BPgN8AFgX+DXkg6uMjAzM2uNZo8IPgO8KSIeA5A0BbgOOKOqwMzMrDWavVi8FFheM7wcuH/0wzEzs1Yb7llDn8y9fwKul3Qh6RrB3qRTRWZmtp4b7tTQwI/G7sndgAurCcfMzFptuIfOHduqQMzMrD2aulgsqYf0ruKtgQkD4yPiHRXFZWZmLdLsxeIFwB3Aa4BjgSXADRXFZGZmLdRsIpgSEd8GnouIqyPiYGDHCuMyM7MWafZ3BM/lvw9Kei/wADC9mpDMzKyVmk0EX5S0MfAp4BvAZOCwyqIyM7OWafahc5fk3ieBtwNIciIwMxsDmn5VZR2fHL6ImZm91I0kEWjUojAzs7YZSSKIoSZKOkPSI5IWDVNuB0krJe07gljMzGwdDZkIJC2X9FSdbjnw6mHqng/sPkz9ncCXgZ+tTdBmZjZ6hnvExDq/oD4irpHUN0yxfwHOA3ZY13bMzGxkRnJqaEQkTQP2AU5rouxsSQslLVy2bFn1wZmZFaRtiQD4GnB4RKwcrmBEzIuI/ojo7+npaUFoZmblWJt3Fo+2fuAHkgCmAntIej4iftzGmMzMitO2RBARrxnolzQfuMRJwMys9SpLBJLOBmYCUyUtBY4GugAiYtjrAmZm1hqVJYKI2H8tyh5YVRxmZja0dl4sNjOzlwAnAjOzwjkRmJkVzonAzKxwTgRmZoVzIjAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeGcCMzMCudEYGZWOCcCM7PCORGYmRXOicDMrHBOBGZmhXMiMDMrnBOBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVzonAzKxwTgRmZoWrLBFIOkPSI5IWNZg+S9KtubtO0rZVxWJmZo1VeUQwH9h9iOl/BN4WEdsAxwHzKozFzMwaGFdVxRFxjaS+IaZfVzP4a2B6VbGYmVljL5VrBB8FLm13EGZmJarsiKBZkt5OSgQ7D1FmNjAbYMaMGS2KzMysDG09IpC0DXA6sHdEPNaoXETMi4j+iOjv6elpXYBmZgVoWyKQNAM4H/hwRPy+XXGYmZWuslNDks4GZgJTJS0Fjga6ACLiNOAoYApwqiSA5yOiv6p4zMysvirvGtp/mOmHAIdU1b6ZmTXnpXLXkJmZtYkTgZlZ4ZwIzMwK50RgZlY4JwIzs8I5EZiZFc6JwMyscE4EZmaFcyIwMyucE4GZWeGcCMzMCudEYGZWOCcCM7PCORGYmRXOicDMrHBOBGZmhXMiMDMrnBOBmVnhnAjMzArnRGBmVjgnAjOzwjkRmJkVzonAzKxwTgRmZoVzIjAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8I5EZiZFa6yRCDpDEmPSFrUYLoknSzpbkm3StquqlhWs2AB9PVBRwdMnZq6wf19falcvbISjBuX/taOG66bODHVM3j8QN0SdHauPl5K7c+du3o7G25Yv67R7lrRhjt3L8Vu8PZg7tz0V1r9e1rbTZy4alpnJ0yYsHp9tXUM7vryNmfwNqp2fJUiopIO2AXYDljUYPoewKWAgB2B65upd/vtt4919r3vRXR3R8DwXVdXxPjxzZV1586du5F23d0Rc+asuY3q7k7brhECFjbarlZ2RBAR1wCPD1Fkb+C7OcZfA5tIelVV8QBw5JGwYkVzZZ97Dp59ttJwzMxetGIFzJu35jZqxYq07apQO68RTAPurxlemsetQdJsSQslLVy2bNm6t3jffes+r5lZ1VaurD++4m1XOxOB6oyLegUjYl5E9EdEf09Pz7q3OGPGus9rZla1zs764yvedrUzESwFNqsZng48UGmLxx8P3d3Nle3qgvHjKw3HzOxF3d0we/aa26ju7rTtqlA7E8FFwAH57qEdgScj4sFKW5w1K52D6+1NV+qnTEnd4P7eXjjzTDjjjDXLwqqsXTtuOBttlOoZbKBuSHcJ1I6H1P6cOau3M3A3QtVa0YbZS9Hg7cGcOekvrP49rbXRRqumdXTABhusXl9tHYP19qZt06mnrr6NGhg/a9boLVsdSheTK6hYOhuYCUwFHgaOBroAIuI0SQJOAXYHVgAHRcTC4ert7++PhQuHLWZmZjUk3RgR/fWmjauq0YjYf5jpARxaVftmZtYc/7LYzKxwTgRmZoVzIjAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8JV9oOyqkhaBtzb7jiyqcCj7Q6ijUpe/pKXHbz86+Py90ZE3Ye1rXeJ4KVE0sJGv9QrQcnLX/Kyg5d/rC2/Tw2ZmRXOicDMrHBOBCMzr90BtFnJy1/ysoOXf0wtv68RmJkVzkcEZmaFcyIwMyucE8EISTpR0h2SbpV0gaRN2h1Tq0j6oKTFkl6QNGZupRuOpN0l3Snpbkmfa3c8rSTpDEmPSFrU7lhaTdJmkn4h6fb8f/+Jdsc0WpwIRu5y4A0RsQ3we+DzbY6nlRYBHwCuaXcgrSKpE/gm8B5gK2B/SVu1N6qWmk96q2CJngc+FRGvB3YEDh0rn70TwQhFxGUR8Xwe/DUwvZ3xtFJE3B4Rd7Y7jhZ7M3B3RPwhIp4FfgDs3eaYWiYirgEeb3cc7RARD0bETbl/OXA7MK29UY0OJ4LRdTBwabuDsEpNA+6vGV7KGNkYWPMk9QFvAq5vbySjo7J3Fo8lkq4ANq0z6ciIuDCXOZJ06LiglbFVrZllL4zqjPM92AWRNBE4DzgsIp5qdzyjwYmgCRGx61DTJX0EeB/wzhhjP8wYbtkLtBTYrGZ4OvBAm2KxFpPURUoCCyLi/HbHM1p8amiEJO0OHA7sFREr2h2PVe4GYHNJr5E0HvhH4KI2x2QtIEnAt4HbI+KkdsczmpwIRu4UYBJwuaSbJZ3W7oBaRdI+kpYCbwF+Iuln7Y6pavnGgH8Gfka6WPjDiFjc3qhaR9LZwK+ALSUtlfTRdsfUQjsBHwbekb/rN0vao91BjQY/YsLMrHA+IjAzK5wTgZlZ4ZwIzMwK50RgZlY4JwIzs8I5EVhdkqZLulDSXZLukfT1fN/8aLfTN/AkS0n9kk4e7TaajOO6CuqcL2nfUaxviaSpo1VfnfqPkfTpquq3ly4nAltD/uHM+cCPI2JzYAtgInB8le1GxMKI+HiVbQzR9v9qR7uSxsSv+wcvx1hZrlI4EVg97wD+GhFnAkTESuD/AgdL6pZ0oKTzJf00HzH8+8CMknaT9CtJN0k6Nz+XZTWStpd0i6RfAYfWjJ8p6ZLcf4yk70i6LO8Jf0DSv0u6LbfbVVPX1ZJulPQzSa/K46+S9GVJv5H0e0lvzeO3zuNuzu+Q2DyP/0v+q/yOiUW5rf1qYrtK0o/y+ycW5ISJpKMk3ZDnmTcwvpFczwmSrgY+IalH0nm5jhsk7ZTLTcnL/1tJ3yI/56j2KCoPf1rSMbn/tZKuyOv3Jkl/n8d/Jtd9q6Rja+Y9UundClcAWzaId09J1+c4rpD0yprPaJ6ky4Dv5v+LcyVdDFwmaaKkK3Mct0naO893nGqe5S/peElt2QGwLCLcuVutAz4O/Eed8b8FtgEOBP4AbAxMAO4lPX9nKundBBvl8ocDR9Wp51bgbbn/RGBR7p8JXJL7jwGuBbqAbYEVwHvytAuA9+dp1wE9efx+wBm5/yrgq7l/D+CK3P8NYFbuHw9smPv/kv/+A+kdE53AK4H7gFfl2J4kPVuog/Tr2p3zPC+vWbazgD1z/3xg3zrLfxVwas3w92vqmkF6hAHAyQPrD3gv6eF2U4G+gXWWp30aOCb3Xw/sk/snAN3AbqSXrSvHfgmwC7A9cFsuMxm4G/h0nXhfxqofnx5Ss16PAW6sWYcHkp7F9PI8PA6YnPun5vqV478pj+8A7gGmtPv/vuTOh29Wj6j/RM3a8VdGxJMAkn4H9AKbkF7W8t95p3g8aYO5qgJpY2CTiLg6jzqL9JKXei6NiOck3UbaMP80j7+NtDHZEngD6fEe5DIP1sw/8FCwG3N5cjxHSpoOnB8Rdw1qc2fg7EhHQQ/nvfYdgKeA30TE0rwcN+c6rwXeLumzpA3qy4HFwMUNlmnAOTX9uwJb1RxITJY0ibSx/gBARPxE0hNDVZjnmRYRF+R5/prH70ZKBr/NRScCm5MejXJB5GdkSWr0zKTpwDn5aGs88MeaaRdFxDM1w5dHxMD7CgScIGkX4AXS47pfGRFLJD0m6U2kZPvbiHhsqGWzajkRWD2LSXvGL5I0mbTXfw9pT/JvNZNXkv6XRNoQ7D9E3Y2STD1/A4iIFyQ9F3kXkrRRGWhvcUS8Zaj5a+IjIr4v6XrSHvbPJB0SET8fFN+Q8dTWKWkCcCrQHxH351M0E5pYtqdr+juAtwzaoJITQ7119Tyrn9YdaK9R7AK+FBHfGlT/YQ3qH+wbwEkRcZGkmaQjgQFPDypbOzwL6AG2zwl9SU2sp5OOIDYFzmgiBquQrxFYPVcC3ZIOgBdfz/hVYH4M/YTVXwM7SXptnq9b0ha1BSLiz8CTknbOo2aNIM47gR5Jb8ntdUnaeqgZJP0d8IeIOJn01NBtBhW5BthPUqekHtJe+W+GqHJgw/ao0vWQdblL6DLSg+wGYnxjTSyz8rj3kE7RADwMvCJfQ9iA9Ah0Ij0bf6mk9+d5NpDUTXpA3sE5PiRNk/SKXP8+kjbMRxN7NohvY+BPuf8ja7FcGwOP5CTwdtJR44ALSK+83CHHZ23kRGBryHve+wAflHQX6V3MfwWOGGa+ZaS9vLMl3UpKDK+rU/Qg4JtKF4ufqTO92TifJW14vyzpFuBmYLi7f/YDFuVTO68Dvjto+gWkaxi3AD8HPhsRDw0Rw5+B/yKdrvox6THVa+vjQH++kPs74P/k8ccCu0i6iXRq577c5nPAF0jXAy4B7qip68PAx/P6vw7YNCIuI12H+FU+zfYjYFKk1y6eQ1pv5wG/bBDfMcC5kn4JPLoWy7UgL9dCUkJ7Mc782f2C9PTWlWtRp1XATx81s5aT1AHcBHywznUaazEfEZhZS0nainQH0ZVOAi8NPiIwMyucjwjMzArnRGBmVjgnAjOzwjkRmJkVzonAzKxw/x/pn/eMWa8X9wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#in order to look at the reduced data and understand which feature pairs depict the data in the best way , the following code segment:\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.decomposition import PCA\n",
+    "data=np.loadtxt(fname='data_wk1')\n",
+    "X=data[:,0]\n",
+    "Y=data[:,1:11]\n",
+    "m = X.shape[0]#999\n",
+    "c= 0\n",
+    "\n",
+    "\n",
+    "for i in range (0,10):\n",
+    "    for j in range (i+1,10):\n",
+    "        x1 = Y[:,i:i+1]\n",
+    "        x2 = Y[:,j:j+1]\n",
+    "        \n",
+    "        x=np.concatenate((x1,x2),axis=1)\n",
+    "        xd = pd.DataFrame.from_records(x)\n",
+    "        xd = StandardScaler().fit_transform(x)\n",
+    "        pca = PCA(n_components = 1)\n",
+    "        principalComponent = pca.fit_transform(xd)\n",
+    "        st1 = 'Principal Component ' + str(c + 1)\n",
+    "        principalDf = pd.DataFrame(data = principalComponent, columns = [st1])\n",
+    "        prin = principalDf.to_numpy()\n",
+    "        for g in range (0,m):\n",
+    "            if X[g] == 1:\n",
+    "                plt.scatter(prin[g],X[g], color='red')\n",
+    "            else:\n",
+    "                plt.scatter(prin[g],X[g],color='blue')\n",
+    "        str6 = 'P.C. from feature ' + str(i+1) + ' and feature ' + str(j+1)\n",
+    "        plt.xlabel('One dimensional reduced array')\n",
+    "        plt.ylabel(\"Labels\")\n",
+    "        plt.title(str6)\n",
+    "        \n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "From the above analysis its clear from the figures that feature 1 and 2 pair distinguishes the labels most appropriately\n",
+      "The second best pair seems to be 2,10 which matches with the first analysis made above [91.99 for feature 2,10 pair and 92.11 for 1,2 pair}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"From the above analysis its clear from the figures that feature 1 and 2 pair distinguishes the labels most appropriately\")\n",
+    "print(\"The second best pair seems to be 2,10 which matches with the first analysis made above [91.99 for feature 2,10 pair and 92.11 for 1,2 pair}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/Plotting.ipynb b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/Plotting.ipynb
new file mode 100644
index 000000000..81e1fe443
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/Plotting.ipynb	
@@ -0,0 +1,507 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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4DACwBxUUFmoc+2kgMRhNWoNxTL7GgDuGBxJDQdXrb/jjIxnMJozBBKtOJ8p4GxvJ+SRNTYMjz0SqScufGIxj8jWGNBOQpVjv4e5H4i5BJ5kwKioCJOdFyAykD4M9qKDDYEBARQXPpNVhMEi63vldr1oFaDQDO6ZrAUOaCQwFVa+/4c5HIkbQhcKgvTFheMtArjcMxqACNqSYtIRAetfHjtF4913Kq3ftzZiuFQxpJjAUVL3+hjsfiRhBLykh398bE8Zgt4H7GzqdFSUlbY6ggtjYwacZuTJpCYH0rquqFF69a2d/4aU1a64r4s9gSDOBoaDq+QOkD5fkcG9sjCZe70zQ2Wr9+fPk8lauTBgmE4WDB8mhx/1pAx/sgQbsoILrCb72dwwlf+GQZgJDQdUbCAh9QCPSjgCI4Z1vJ+j2rUhS61UqGhZLXzKhKxMGc4+WFjIB8MYGLupjqK4eMoRjsIB5H2fPkkmZp+96KPkLhzQTADxXP2UIQ+gDWpG2FN/oXxOwSQ8DQFbrLRYFEhN7odPZJJkwSPfgP899uPIxUMuXgxoihGMwgPQ+2EhJoT1+10PJXzjkmYAM30PoA0q9fMKlTVpIrdfpbNi+XVptJKF7aLVWr5zCrnwMioYG4nXXGuGorgYWL44c9BFVQsxeq7Vi0iQzVq1SQaPxbNxi/sLrLeJMZgLXKQbSNi32AbmySfsijFHoHpMmmb36WIWYS9ve04jMeQm0QDzitRRoYDJRmDMnAFVVgY5j/oyocmffCr2PMWMs2LDh0tVESs/GIeQvPJO9+LqLOJPbS16HYGzyIaWlUJeXI6S0FFFZWaBMJr88v8NgQK9ezzkm1eFuMHRAr+/lHHPXhOOLe5AgxFwS208jpLQUym+/hSUhgfOb2LzZzYFyciIHRXOgwkINqqq4xRwZbae/4e6+dSkwVFcjMicH0bNmITInx639z/gLuzIzYZ44EV2ZmWgrKcHK4rGC2qCnGOh9IGsC1yEG2qnljcPdF2GM/RUKSYqzT8U5rMASAICitha906ej5447XM57sOYwDGRWsbv7VizvgTKZEDBnDqe8NuOkZ57lStsg+Qt9vT6DYR/ITOA6xGBwannjcPdFGGN/hELqdFasXXsJjz0WhdGdxzAWp7ACS5CMPsKlvHwZrZs2ubzXYM1hGMisYnf3rRiz1+QUchrsAHaGEp6fD1VlpccRXL5en8GwD2QmcB1CToLrPxQXh6Kzk8JCrEU23uX9LnWNB2sdH4OhA8eOBXNMQv7KKvZk3woxe0GGcqQCARe5jgJ3tGRfZ10Phn0g+wQGMSiTySObpjc2+esJnq6fGIxGu9y0BCtwDqmc3+iwMFA1NZKeNVjr+Oh0Vuza1YvMzC5MnGhGZmaX30wTvty3Qgzl0o9kkidVS2a0D1+tz2DYB37pJ+BrDIV+AqSEK5taDXNGBtoLChyqq9A8HFEW10gSnLvvw1WYnq/6HDjjzjtjUFtrlwL1qMZKLEU86vGjIhyZ9D8lP0tqbf+BwED3RfDFvqVMJgx36rncEJKMQ13jkIl/8s73Z38ANqTsg/7uJyAzgUEKoWYWAJfAuDOPwRzfzMxDSoiglA+nv5qBPPhgNI4e5ZejmICvUI673XoWuznQYKrjM9i/DanQdnTAsnixg6Fk16xCxdFA7ME0jMIPjvNq1SkI2P9uvwlJrr47V/vgumgqI0Ma2ARQdfas4HmeRPoMhigEBiRCD61Wcr0WKc60/nKOjxxpxdGj/OOpqOIdc/Ws67WOz6BBcjL3G8mJhPFoCKZit0ODq0c8vsh4Hst0kf0yBCnfXTKqUYxCUHQjrIhDBwywwn9au8wEBglIBFD0fDeJmRDhXP/wGRRtV/vNVCRE6OnPPhMNETxueNMhTQnViWE70/rLOU5yDCaHNGBF1xKfP+t6h7810753l4y5KAZwVYMsaAPQP891JbAMhkJ1MhMYJCARQDG4S2CEohCaa62Iysry26YTIvTW5ctBC0jvvcZm0RoxDNjOtP6qEEsKS1yc3YDEPIAVKTokHfHuYCA004Eope0q+megc3oAmQkMGgiZL6zDhkHR1QWl2ew45gmBEYpCiEe9Xzed0DwVDQ2C0vu3zYkw1oozAOcwvf6sEMs348ShraQE2qIiWEymfnfED2bfjlQMVHy8JyY4b9bbVfTPYMjpkUNEBwmECOAe6n48mVGBC9Mf4qSvu0tgSKUU2Nmu/tp0QvOkR4wQDBEsiikgXqPVWkXD9Kw6nZ0Yx8aCamy0M4R+Kp1h1elg3bIFl9asAQBE5uX5LCyVDUaCLi0NQXm5GqWlIcjKihoUJSfcwWCIj5cCb9fbVQmTwZDTI2sCgwQk88U5pOLxlpdhLEvGv/UlXqnKjCq8PvMkmhsViEc9J9vVX5tOyExDL18Oq0ZDlN5RmAQQnLGTJplFpTq/21v90E9AigR9LWgKQhJykukQKJNnPirngANfNBn2VmNxZYIaDI2tZCbgAfqjQifbfHHuYCuOtyRhCVbAiGQAvlGVk1GNbcrHEABuiK0lIQGd2dl2yfXqnDqzsxFaXOzzKqRCZpphyclASwux3ISnWZr+trf6o5+AKwl6MEWBiUGoDtOq2nmIyoLbjJPE8Oljx0C9613op7caC2UyYVxhId4T+I4GQ2MrmQm4if6ULhkC+MSsaJS38GPRvVWVNYWFCCDkWFiSkxGZl8eZU/DOnVBYLI6/fSnRultXyFOHnr/trf7oJ+DKxjwYatFIgUMzffgMmmutXM3UCLcZJ4nhK6qqcObh9VBvLxqQjF6ptGKgG1v5hQm88cYbqKioQEREBNZctZlevnwZ69atw4ULFxATE4NnnnkGYWFh/hiOV/CHdNlfqeRCRDHgzBlQTskobAYADFyHLGfTxpo1lyR/0P62t9IjRvT781xpRdeKrR2wM4ItSYuhri3n/eYu4xQMrKi1R5Y5ur+5qcV7UytoMET+SIFfmMDkyZPxwAMP4PXXX3cc++ijj3DzzTdj5syZ+Oijj/DRRx8hOzvbH8PxCv6QLn1dpIqBEFGUCn93yKo71Ihfzx2B6q4QxzF3TBv+trdaly+Hrby8X5/nSivyZy0aX/gehPakzYVA6EzMbQK2/3rEOzShNw3H3dbivQkrHQyRP1LgFyZw4403orm5mXPsyJEjWL58OQAgIyMDy5cv9ysT8LSFnj+ky/6KZxYiipa0NASXlbm83p8RC5TJhHWPNaC6K51z3B3Thi/srW4RuuRkv9h3xcIc3REgxOYmpdSBL3wPHQYDAg8fhqqujnNcdfIkKJOJuHbEulDx8bAkJHDucw6pWIIVAOyakKeSuZSwUtJ6RbpJKwaqG+CA+QR+/PFHDBtmby4+bNgwtLe3C567Z88e7NmzBwDwpz/9CVqt1qtnV1cDv/gFt4XesWPB2LWrF8nJLi5etQr0sWOcwlR0SgpUq1Z5PS42tFrgav8L2F/TMN451dXAs8+qUFcXhxEjaCxfbhUfv1YL+rPPYF2+HIqGBtAjRoBevhwUAPoXv+DOSaXimIT6Y45sqFQqzr2phQvR0Pk74rltbUHSx8FaSPIqCqO6GldbLfaVVRbbJyqVCsPS04WfV10NirX21uXL4XrDuQetFvjsM/teaGhQXN0XNJKTuTMXm1t3twpz5gwXnffChRSMRq6JyWgMQNFLQdjyj2C3Bqy49VbAiQkE1NdDW1QE65YtvEuohQv5Dvj6erTf+yD2XspARGcD6hHPCazQ6VQIamgjDiGorc2rfU1ay6+/Dsa0UYUoCDqGxCsSaEV1NQKcCt4FHzuG3l27oIqL67fvDrhGHMNTp07F1KlTHX97W0xp8eJIDgMAgKoqBRYvtriWMDUaUO++y5f2NBp43NBUBEISWZ8k1pfqUV5ucy2JaTTAVb8MG85zckQH+WGOAL9IVrTRiASQCwVGRV1BS0v/Oznd3Sdihb4Y6ZVNvGzl5f0Srkp6xc7DEpubWk2hqkpJ/I2Zt9EYDYDvZ2jY/R1+/LjRrciy6NZWwp0A7NmDixUVvGujjUbi+Se/6sBD5p2843p9L3Jz23ClMAohhOuuREXhkhf7mrSW588r8Lfzo7AXe7ASS5GirsXojGjYCp4lfkeRixdzuqABdse2ZfFioKTk+iwgFxERgYsXL2LYsGG4ePEiwsPD/fZsb51n/vLmi6ncvo4CIc3p0oQJng1cAO6ou9a4OKzAEhzCBPyAUY7jySENMBj8k+PoSyfrYHMSis0tIEAh+BsDwX7L5ipEPfYEqM5OxzFXdnchEyvV0kIsaSJ0fpU5kT+exL7qsv3lIxJaSwAw4mqdIjOQGdqFDTrytzmQ/oMByxi+/fbbceDAAQDAgQMH8NOf/tRvz/a388zTJtJihF5o4x08qHbrWf5qcu1uE/EOgwGJemA3pmIOinEv9uHR0FK8v63Bb/Huvtwng81JKDS3uLB2jBhBri7PnrfB0IEUdS3ndyYDnc0AgD5mJwRSprjYtaTza9UpDvs/GzqdzbFfhJrHe6uJCa2lM5qNvYJNjgYyc9gvmsD69etx6tQpdHR04IknnsAjjzyCmTNnYt26ddi3bx+0Wi3y8vL8MRQA5BZ6IxO6sbIzD9GzjvvMKeOt80yI0BuNKjQ3k6W1lhYKLS2UpGeZTBQenRmKmqY+JfnoVza891En8RpvokHclYSZDza2sBDvNL3JcrJ6F+HkDnwZpTUYygOwYTB04OhhBWrq+uz3qTiHVSfng96yBeXlOtF563RW7MxYiZfL7kE94nkZ6M4QY3bMu9bOmMELVSZdS3L4r+xcCWMZ37/CZlz2/TsOjY3v+TSTmrRPSNBV7kNIRV+PC7aGJKaluOPH8gRDtqlMR4cWixdb0NREIS6sHatOZmJM/ZeO373pQsWYPWbvz8XHFzN4v2dmdkky2eTkRKK0lG/FDA21orNTmsQu9qw/ZFnwj4P8+f3ip+ehTozmEHsAXnXCip41C+pyfjy4eeJEtG7fPmgbmbjT+EWKT8DXnc68waUFLxKJuDUrC0dy17ucN7H7XUgIlF1dvGdJaebjTSMgksCVkkLj3XebnXxo/dPJjb1PwsJsOHlShfp6brnxvV138Zgke25CXdXkpjL9hORkOIhjZE4OQlgMALBLqeH5+bi4aZNb92U+jFojsAdkE9fevUHIyYl0KYmQJIyQEBuRAajVNpjNfOuemP265f9qAULziotHzqP8SJLj74qKAKSlWbzyQQw2SVgqfNn4xZKWBuVVU0lPejqnTehAILXjOIqxkXdc0dAgad4kibwzO5uXfW5Tq6Ho7BQM+WTgjc2eFFa9apUKGo1vM6mF/FrO6+UsPKyqeQrJR/laElvLcfbLmUwUCnM0aGtTISrKNb3wFEOWCbAhZK9VHzjgcuMyYF764wcX4v4WI5ZiG64QYxGA9nYlSktDXJprSBu7poYitjfUaGiwqk07IGa/TrCdJx8H19ZrNAags5PsPpLqJB0MhbIGCpTJhPBZjyK4rqbv4HffD9h4GIhVdJV8D0JAQVtJCcLz86E+cABKsxlKsxnBZWVQVVaKO4i9zOtwJsR2Cdr+/75w8pM0n7Zdx7AyYyfmF0RzvmPnsUTmgFgEUUgI4msurumFp5BLSUP4Y1CazTynFMmRyi43G9BiZyh1IKtebDCSiBiYzbR9eys2bLiEkSPJGyA9vUe0ZC0J+dHrkYpznGOpOIcc/Nnl2BlIdZL2l1NOCvzl/BaCMv9VLgMAEFxXA2X+q34dhzOESndbryZxegqrTgc6NJTTAwNw7SBmrr20YQNat2/HpQ0biP2lPXmXvnDyk/xaieYq3FP2Mqe8NGUy8RzAQmstJATl54cLai6+hqwJwP4xBO3axdu0AFddE3L0sk0l9UgAAMEYd2cYjSrk5EQ6nL0xMTaMHClsexZyVhYU2JPt3MkyHvbGsyh7+AEssy132IXzsRwL8DfeuenpPaisVHnlJBULrfU0g9sVBkNVzcaKFsQQjjdUtMDTFCBfZJe6qujqTSCAN9FQrnNj3H+XQt/N4uwziMxZKWkdheYUj3pJpSmkajkmE4UDB/jaPtA/NaBkJgD7x2DOyCCWTmgPi0NOjp04nT+vRG0tnzuzTSVLsCMg8JYAACAASURBVAITcIgY405CZSWFioq+RJPaWuDoUeHN7Wwi0ulUyM3tO88d+2bvhAnQbC/EptxcUO3tsIaHo/L511D7yl2cVomeMhmpMJmoqxmXfevgK0Ltj6qaDEFWtbUhMiqK93HXIx43E65rQLwgExAj8u5WsiXdqxrJyM8PR0VFLIDtSE/vQYGhnVM2whvm6akPyJPcmPUPn8GWpMWiRJzcFvQMxuc9JHkdheZUf1Xrl1KawpWDmzKZsP5hM8xm8jr1Rxj7kI0Ocva4kz6s7oSRmEZ/hi/rx7i4l9URlgkAelRjJZYiKrIXW8OeRO3w8QjVBvIiBkJCbOjqErbISYki6o+oGnciYnwBoSgoqVFUYpg1Kxrl5XypauJEM7Zvb/Xq3oC0qJ8XF1zCH8t+iVH4wXHOOaRi9fR/YdmmSLfv6U4UDele38ffjfsse1DXHMQ5NyHBgg8/bEV6+jBkZVm8eieeRkOJ7YXGRor4Lu/FPuzDFN4zXH0b7kYjkeZ0DqmYit0wIhmZmV14r3GqaBScGBjf0S/q3sF+3Mf7Xa22Yf/+Cx5/i3J0kAuQVOO8zpX4skycAQB8U4kRyVii34SSkjas1VkBdALo5BFXIScvg4Eq/+vLiBgp6M/yx/2dGCgl/2F+QTTmn9yFJ+pfRDzqUY94vBW/DKsLIgDwx+Hqnu6YWkj3erH+CdQhiHduXZ0KhYUalJT4JqveEyev2HPF+mQzcCcL212TFTMnZf6rOHugFVXmREd9IsY0ai30PAqO8R0JmZIzMsxydJCvQVKT2Zvn+Kxol/dQq20AgLVrL6G4OFRUenYmrjk5kThKiBhgMKzqW1AmWpKtd6AqEPoC/Umo+6ssNwMphESnsyLvtWHIzd2E9nYK4eFWFBVdgk7XS77WxT3dMbWQ7iUWtMAQeV+8E0/Kq4g9V6gbGdMnm4HULGxPTFZWnQ7WTa9BaaKwo1CDhCYK6bFdju/dmyg4xndEMiWzTbK+xtBlAhL6wQptyJGoQjJqcAVqlJvvRllZMCorVW7bsF1lGqoa62GbshA5aZ8CI5MEzTJ+76XrY5AyuJNDGrA4uwGAtAxhIWdif5XlZiCFkJhMFPLyIh3+pPZ2JfLyIgX3i6t7ukNoSPcSC1qwE3lVvzNPIYg91/ldJpkOYVXtPF4CltTcE2/zEkjashQNSGivMr6jZBixG1OxFCtRj3go1IF4qWR0v5lkh6xPIHbhQlB9tZodYNsDSU6qVJzDbkxFMozYh3sxBfscv3liwzaZKMyYoeX4FBgwts5izMFcFHMyHBnJP6itDdYffkBAbS3veimZloMFHae6UTDzOBo7IxzZq4l6bq9ZIW2nv7NBxSDF9u3K5+FMFJZkf4eb8x4WvadQdqmU8UnxCbSwooOkMk/KZEJ4fj4CKyoAeJ4QJ/W5rtZeir9M6jr6CmJ7dXN+K9F39OcH/43nNpJzjtyB7BNwgpR+sKJ9UNEXFcDAExu2TmfFpElmIpFgbJ3Mf4XC0IRcy4Otg5EYRr2zBH/vdGLKrF6zYtpOYeG4Aeury5b8gtracIUQHSRm5yZHw4zHjrXbcUPxKkHiJNXUQpJMIwwGfIgfkZ9POyLT0tN7UFDQziG27viGKJMJUb/6FaeHdXBZGQK++w6tH37oFmGV+lxfNA3yd39foQinlZk1UFy5gv/CJ1iKFRzf0TurdQD6r6TKkGUCUvvB6nRWFG1XE6MCnKsWemrDdmXrZDMboTA0ElTff4+ABf+LpViB4x2pHNVTTKq2b1RpeQu+ghBT7jU22wnMww/ztB3GCdjY+B7xWn851hlCotVqiXXphcyKYWE2YaJQPBYbvCROfRpGNOLiimFY0/cOdbBi06aLXt2fDU1hIYcBMFDV1fVruWwSEXcVsjuQEBII9jbe7KgwMBfFUAdakTG5B6sL2nE1baPfMGSZgDv9YJ0ljvawOMw/uQrG+r6qhd7YSxmN49V8JVoPnEWiucqhcTgzm9hYq6Dj0BlUSwtiynbgjziGqdiNciSjoiIAO9Z+i5vz+FL1ibXbkZU3nkOUmLyFzz4LQlpar4MhAJCcSCQl6UiIKR86HYWfzXoUAXV8cxdg13b8WRrcExgMHTh8OBB1ddzP7eRJFeLiyNZYbxmYp3H+JhN1tWtYNEZo2rECS5Ha4bqyrtie9EQj9TRRzVljDIG4f8wXfZLdgdBedS4xY+6hEBpK+yWhccgyAXf7wbIlDpOJQmh+OLQ99hdEUqXdhU5nxWubrKBMSmgKd6DXmIjSyol4pvMlR4s8V2FovYmJUF65wivHOwo/YCWWYi6KYTQGoCN3DQJq+SGIHblrYKzl+0kAoKtLiaNH1Th6FPj6kx9BKyjUml03gJdKjKzLl6P5X/+HEV3VjmPnkIqubiWv5AIb1thYvzkxPSUYOp0VY8f28phAfX0AlEpyhJC3DMyTJLm+d0XB3jUsBsfwx6s+sHKPEqkA94sEepOoJhZe22EwcLTf77KXICvvZr9mk5P2ahC6iHXG/KXJDl0mAM/sgaQNWlmpcvzmrVTBHtMIE4X0Qg0SmswcB5lQVENbSQki8/KINdnZsdQhl8hSW1i7NA3jfA9fajcaA5CfH84zMUgmRsnJyEn+CJnfFTrsoUuwAn/DbwTHwWhu/R0BBHifQdvRQfbcDB9uA0X1+pyBeRLnT3pXP2AUlmIlijFXNAa/w2BAwP/9H88kZElIEIy2EfpePM3ypkwmqA8eJP6mMhp5Jt1V/3oMRmu628/xBqQIpyu1bfgnMnnn+kuTHdJMwBMIbdD8/HBebR1vpQopYWjOzkhXqe0A8EN3PLHI9eXwOMCLUOQ9e4Jw6FAAJkzok27dIUYBP160t+JjganF5IzexESOVNrfCW7elp8QMgPo9Va8/volnzMwT0xkQu+KvXdEE6n+8Q/J0UFiTNUTBubo4SxgPFc0N/N8Shet5Ja2/S2Bs/cqZVLjx1+twcn6mzl5ASMTuvs9HJeBzATchNAGragI5IV59qdUIeSMJGkJzn6F56wv4WfBhxDf3Wd66U4YCU3RQujzennETipsNgUeeSQa48b1+Q7cIUYFMUX4ujaB8zH8Bb/FI4EfIbCnr2WhP5uxMNLq3r38DFtAOsFwFf/u6z3iiYlMSkauq0Qqqf03xJiqJwxMLFiiV6+HLSrK7uBiQShfwlcSuJQETqtOh4h/rMa/8ldjRUUm6jEC2vQReLbA5hd/ACAzAbchtZ8oA19JFVJNTWwtodfYjM9O6GDo7fMrAPayFvdadmMp8vtC0ehlWB0f4VBVz55V4YcfKFy5ogRNc1tZ6lADGsB5jOQ/39rnO6ioCMDatZckE6OkkcDuo31JMkxIrmJYGLpvuRvKy5c9juX2xFRHkladIZVg+MpkJTUz3JPnZWd34rPPgjj1rNhRar7s/yAm7a9ZI33PMBByTFu1Wkephxingv4rsAS7MRXNrIREX/mS3EngtOp0iNy0DGscR8h+ov7CkGUCnpYuJkV6JCRYMHZsL8rKgnnn+0KqIBEjJlonLY1Cbi7FdbLqdDhueNN+TS+ZgH3fm8o1vdQDhYX25CWDoQNZWVHo7qagRjfMCEYoOnATTmIUfnAQhVwUoQzTYQZ/3oBdsisuDiVWbxxbyC3fC63W3ly+IgvFxrncGzUBcNGQxN31k2KqI0mrbLhDMCiTCeMKC/GeF2U93M0Md0fDYLKa2QxAG9yOkttWI96WgK7YdJ+GWopJ+0IVP533DCdvQsAMap40CVadDvlYgT/iGCcRywoVVOhFXJwFKSlWn/qS3O2p7e8oJTaGJBPwtnSxc5I1TdN4/PHLXtfbFwKJGLGjdcrLozhjZ8rRGmv5/Y0B160o2c9LRwXKcTc6oUEj4pCD11GNFCiCAnCWvglmM5kBsO/JtYGSCRn92WcOLUYsJ8CTeHMh08OMGVpMmmQW/OCEpNXwcBumTLnicdgi4FlZDyHCop0xA+ZJk7zSkA4eVPPMmS3d4Xg5Zi2PkfiiTpUrc1UyqlGMQlB0I2ydGgT84Tuo6ur65u20fiQzKJ2S4tBcjnfYq32uxFJO4EE9kjB9XDe2FhyzzynPN7W33ClON9A9L4YkEygs1HDq1ADC9nvnDf+/nRs55aABe6jfuxvRbxEqQsSINHaG4DTV8hvDAPay1zfc0IsvvuDbuMPCbLznPYUNaEYsfsAoGJGMuShGKs7hkxsX4aUXwvHYY+JN7501ISFC9sX9BWheW4Ti4nF4oT0Ft4GfF+BpBrTQ+rW0UKJtPoWk1SlTrrhlw3dXKhSCIGFpaUFIaanbjEWKucvZnOkrhiZmriI9wxnO60fKHlatWgWrxt6JKy7OivKr+9cZV45UImbyZE5TKW9rb0mpKVV3qBFrcjvweeNNaLYMTMY7MESZgNToA9JmbFX/ESD0iWrZfxab8yPR2BHpc3VOih+i9eA5RM96Asrz5xFQWyvo9Jo0yYzOTgXxN9LzaFCcYlaMnT5Wnw7NhF7s2dPiyDCurKQ4DIGkCQkRsh5jI2bP1sJiUeAhJOE2wjmeNqV3tX5CH5wv8g/EwhbdZWpisfiA+4zFlbkL6GPijDCkPniQF4Ej9bkkkweJyEnNiHdeP+eQb61W60i1NRg6eP4OBkkXT0IJcitMTzOdXRWnqzvUiKzZUaiypAvdQs4T6E9IjT4Q6ikKTORdm9RThXvK/o2NVyUNb9U59gej0diQkGDhJRtxnt9yHOqWvmYWQuVoDYYO5OXxG5kAwOXL9g+ETfyWYAX2YBqK0Wen79Xr0XZ1M7NNPa4Kf1EmE5Tnyc3t6xEPi8XOnJjubGz7rTdOSVfVWgHyB+etM9dV2KK7TI1EWHjPFGEszkTYaBT//ENCbKipofDigktY992jokl7rhiaOyYPqRnxYuvHznxmGM62ba147LEojqBCKkXNQGU02nsEe2D2clXXaE1uhygDAOQ8gX4FqXQxScLrNfI39gosQbliIqroFMcxZiNVo++Yp+ocZTKhNX8z5hxYgipWRm58vN2B1djIf2VhaOdtZOdytMMTKTxdcgN0OqtLJsglfvFYHfYvrMBShF9uFI3OEXNEOrQqQrVT5xBWI5Id9tufhNfiJ1O8q//Cng/J9s2euztzAriEVa/nOuldhS1KZWrsGkDj0nZhRdpSRFX8h8hchAgjiQiHhpLnrFTSUKtt6OqyNz3Kw8sIRo3oGF0xNHfyLFxpPID4+vEzn/sYDqO5Nht7of/2E6y0Pc8rRc2Aqqx05DwA7pmI7O9sHBob3yNaBhraw0Sv90fZbgYDXkr6448/xr59+6BQKJCUlIQnn3wSgYGBotf4opR0R4cWixdbRCW8s3fmIaP2fd61X+BuvIUnOOaRZBgdJZ8ZuNvCkCGUC4wr8S6yeb87t7FkMAFfoRx3C97XOa7eZKIwa1Y0L8Lpww9b+80R5dzK70vcBTOCHA46dggrG75oMcmGL8tOHzoUgLlzozkmBva9omfNIrYatGq1aNm5UzIxIY3XXv9JvNw0c70Y86MoGlaruHlwL+7Ffdgv+LuUvI0ZM7ScXtoM0tN7sHMnl5kRS0THx8Ny002SwoSltCsVai3JwBYSAmVXF++4lPLsUvZY3p1n8T4hcGN44EXc/V9qDj3yVQvZQVlKuq2tDZ9++inWrVuHwMBArF27Fl999RUmT57c789OTnbdlL0opgAJtV/z6ns/h5exGb/hHfe2qigjOYp1fiIhFVW8Y72JibDpdIIfDCnCqT/hrOLHox7TsIdjrlKhBxb0EYr+kIZ8Fa9vMlF47LEono2ZLd26CluUgvz8cMEqo28SzA0AHCaMHzTj8Oh361BTJxzBFRRoRWe3OBkQytq2arWSo5Kam8mMhnTcmxLRlMmE1oOtAG7m/cY2+ZG0fACgFQpcmTYNygsXoCa0/ZPix5Gi9Sws0uDI7BpUWUY6zklR1aDkvTYkTJDWSMlXGHBzkM1mQ09PDyiKQk9PD4YNGzbQQ3JAERNFDCtjmyvGac9jRLrWJ1VFGUIp5NR17mUMAClJPVhmfQvsS1xJZoWFGmKEU39GIzgTRGdzVTzq8T94C2/jCZzXjkP0pFGO9cvJcT+fQyyMUcjE406sdmGhRjAqiiE23nSuYsZz4AC5B3VTE8VzhDpL0C/jSdQI5HAwCDe3oNNF9zYhH4070TMxMTbnhF0A9tpJJLhT14t5b6g5j9crf42krhdBYgKMUGYyUWio1CMTX/HOuTJtGi5u2oTInByQer9K8eO46h/BmPbunGDCref+g5auUMSFX8bCIo3fGQAAgB5gfPLJJ3R2djb9m9/8hi4qKiKes3v3bnrRokX0okWLaJqmabPZ7PU/q9Uqfs6ZM/QPiZPoVJylAVrwX0aG/T5nzpjprCwLnZFhpbOyLPSZM+6PyZKVRdMAXQU977kpKTb6zBn+c86ds9LmM2doS1YWbc3IoC1ZWbT5zBnR52RkWEXn0i//zpyhbSkpnAfagoKIi2rNyHCsaUqKjbgOYs86t/sM3RDm9KyUFNF1cfdZQmsI0HRWloUzb3feDfvfgw9apD3Daf8w/yZjr+jeTcVZegce5O01HarpxIB6zjE9quhtmEPvxb102fBfuzUP85kz9KP6g5Ln4c4/9nvbhjkuvx+z2f796FFFn0UqZzANYaw9QtqvLvYQ8y8ri/zeHnzQ4tF+dkmrJP4TwoD6BC5fvow1a9bgmWeeQUhICNauXYsJEybgZz/7meh1vvAJuLKzMTbDauixFCuxB1PRRJCYpNqspUiZbEmOeW6tOgXRGaMFa4l4Yi90ZTPtr6b1zq38FJ2dCC4r453H2F2l2HadYTJRaJj6DDI7+Y1mxOy57j5L6PyQEBv27r0g2A6Rva6d2dkILS4mrrPJRCEjIwY9PfyQRrXahv37+c9w9kFkYxvRtxSLRkzFHqzAEgTjCroRxAsB/izo/+H3V4oIK2VfkzcNx0WbEjm3yqw1gmf+89QXw37G+fNKR+9mtu+C+X7qEY9YbS9yd453PGfWrGiUl6uhRzVHy/849BH8bfpmx1w8bT0p5BNIS7MQqwq4oiHXtU/gxIkTGD58OMLD7dX87rzzTnz//fcumYA/wJhm7A7fuaiGnriJpZh8JNfUZ9lC45ua8Hbsjqsbz7e1RMTi3/uzaT3JfKGqrBQ0l3haDjmn03XrUIBLTM6eJX8KQs8irWFYGI0tW1qRjGpocrgEEgBvXYN37oTCYnH8zV7n/PxwIgMAgIwMs6QG9aQwYXaPbAAoxf/DzTjJCQFuDEvBny4/TXy2Xt+LxdlneHNRHD6K3yaXYceRVE42+tyydUjvtLv9haLV3IFYghvbd8F8twDQNSkTl3R9+46JjjM6JY/N6SzmJd0571cpIaNCfieh0Gx/5QMIYUCZgFarxdmzZ2E2mxEYGIgTJ04gNTV1IIfkgJANe3HiFpzXTXDLoejKUdRfkrcQxJyjmhzfZLdKgav+vGKhrEKaVWMjJezIZNlzpWTLMs8igbSGq1apEHnxByITtaSl8daVzQAA7jqTImnssJcoIcHZB5EMI97Bb/Bf+AQWqDAdZShCroMBnEMqnsE6AMCWxMWYoDsPa2wsnq57FcbD/GgtrdY+57GFK3lzCa6rQU1dM8wYzTkewWLIbMJs1k1Eq267wByFIZbgJjW/xFU7V9J+p0wmhM/i5kooDh9F+4fvSQqXNpnsWgsJA90Bb0CZwOjRozFhwgQsWrQIFEVh5MiRmDp16kAOyQGSUy9RDxSVqGHVSQ/7BMQl2v6UvMVAco6aTBRaD7YSXGr917RerD+vkMaSnd0pqFnFxVmJxKAhJBlKFjGQki3rStNzXsOODi3OPLweGYSubcrOTufLiXC9zgoUF4diwgThPhPK/Fdx9kArqsyJWIIV6IS9dMJxjMOXuAfVSOEEOej1vVCXFKH1qkCjWBgLHOY/edIkuwYilMzVTeiOJYUhuwOxEipMwEYRcjF12DcIuv9naMvN5X1HbAbetvc0EttPO8K8GfQamzkBCetb/sBLlguuq8Hl/Fdh3fQaAGGTLyNwMGYrNvyZDyCEAY8OeuSRR/DII48M9DB48CRMTUiiF5NofVVXRirEGsxnZUVhZUsSkQl4+tF6AyGNRUyzMhg6kFWRiKnGvqiudioSN4/sxIirLQatOp0gMdFqrRgzxuJ26GjdoUZkzVPiL5fJRLy9QwkpcW/MOqen9xDtx4C4+cCq0+H3odtQauYTZLb5YySqMAwXYU1MdJglGSJWV6dASIiNl//AECuh0Ndg8OPql2AF7lKUI5XuC2OuUaWgLXsxx8MmVRsW+pZGogrJqEE86jEOx6H6yUhYt2yBVcCWzjDwyJyXiPkC+yp1KK3oW8NcRStIX/73+1vx7KxoaDQ2fPddACf3hhFMXs1XEgWO4GAr1q695LdqoUIYcCYwmOFOmJqYRG8wUMI2+Dzp1Qa9hdgYCwvHOcpEkKTop2pWATmRfi1xC5A1FjHNqo9xxOIt4+vQVe7DS53PIPmUETjVN9+4OLJ9NrSjCX8JexHRhvmSNTHKZMK6xxpQ1ZkuKPnuN9+FW1UnMNLSRwxplYpjEmKbLgoK2nHggJpY7dWV+cB5fYajkVMzn/ELfIl78KbuHY606pxRnJZmhV5v4bx3ocZFRgKZvBCix5SuPdwwa8sKpBfHYsME8cqyJG2YVMrd2c8BAF2x6ZKIG2kuDSHJeKbzJc5552nye63uSUR5OTmM12gMwGqDGW1fVgPgJ4Z1d9vLd/urWqgQyEYqGW5DTKJnCFNmZhcmTjQjM7PL8eKlVBv0xxgZwsGo1MWYg324F//GdDzctRmlR9NQWhqCKVNi8OCD0cjJiYTJNDAOLSllLzZsuIQy/QL8vfMhDnEIMBqhzH8VnZ0KqNX8GHWjOR6/LPsjfvzVH0GZTJLGoyksRENnBAC75HsOXL8WY3ufbNmDA4mzYZ44EV2ZmWh5/310ZWY6/nZul/n3v7fySjtIMR84r48KvZiBf+Je7MMcFGM3psIKFZZghWPNSNpVZycFvd6CDRsuEQMYmLFfmP4Q5sfvQj2SHOeo1TZMn96NtLRehwYyBfswF8UwIpmjzYjtS2fodFaMHdsXKBGKDvwFv+WactzIxXCeS1dmJn6X/Ckvg13ovToniDqj9ctqJNjI9bKAPg12ICFrAj6CkJ1UZTQiYMH/IrSiBY8jHqXpSzHfEC0qVfXq9ejMznYZiUAqkiUkUbiqZskmHGyzwRwU4wm8jS9hj9hi9zEQLAAmotr3Vy36kQndWNmZh+hZxx33FXonZw+0okykD8IPGIUX65/A2xJNclRjoyPBj51I6JxgCADP67ZwSolcmjBB8L4TWFVa3cludl6feiQhMaYbiy4vR2R3I77EPXbipU+EwdAGwP1ILGctebWJQmFhF2+cOTmRpJwrjjYjlL3ba2wmHu/o6JNdO6HBAmz2qs4Uey4mE4X9U7W8c1y9VyEk2M4To7TiUefQGq/J6KBXX30VTz75JEJDQ309nmsWQhK98nQlYrorEAN7DmNq2RHMP7kLq/8RYdcECL6HzuxsRObliarHYkWyhPIPhKpZKk0mLF54Bsd2DUeVOdFxnFQYjw1SATAx1V5ZX4/ouXM5NVmY36Dlf3hCcPYVxIW1Y9XJTIwp+5JzX0taGvF69hyFUI94ySY5a1wc50N3Dj1kw91IEE/6D5N9KREA1jmOpcdaYTD07RVP+vpKGaeUctzfNicRjCXAt82JTrFG5CgbZr0zp/Dj7d0VOsQywcXeKwkJqHU4nJ2z49PxNRZiPYCBjw4STRZ7/31+8TQA2LVrF6ZMmQK1Wo3Zs2f32+CE4I9kMVcgJf44E26hIlTFmIMdmW8LftxCxa3YyU7uJDcNW7CAmJTFRq9ej3NJP8NLX0x3WRiPDecieUJj754+HYFffgmKECXTlZkJVUmJx+9D6Jm9sbGgL3YgsKfvHZyDvcOUKwluDorxduYOaZrAVcZXa4TjQ9fgRxzFrZw+zJ4mRwk905dhxb4srEe6t5A2c+hQAJ6b3Y5dlvt5tbgWpX+C13ZqOPcRCusljVXb0QHF/fe7LLLHBpNI5oyAABt6e/uYT3x8LxQKBcc3ER/fi5tusuDyZSVMJiWeqX0WeeAn3LH3oJQ1HtBksR07dmD06NG8i61WK9ra2lxW+7xeISTtXlq71p4BelWip2pqiEWo4lHvUAFJH7OU1nTuNMZRHzjgck4BRiNGpjVhk34JZ141qhQssQjbPZ2lGKGxB1ZUEBkA4L0TXOiZAU1NeBprcDsqRFV4FXphATdmfFn8W+gwrObdkxwGaNfmdEVF2LLnGYfGxc5ajQu+hGfWjkSCjqwxulO3qD/Cijlhk21BiIqS3j5Tyr1JAs+hQwFXGwnFEE0t6fpYAH3XCYX1JiaSCSm1fDkogq8hPD8fdGgokYEKaUT33mtGaCiNpibK0YHvwgUlFAoaw4fTPOf5rFnR+HNtLn6JjznMrRnR+HP489AHduC29AuClQD8CVEmkJ+fj82bNyMiIgKzZs2CWm3nkN9++y0WLFiAiIgIvwxysEHIkRVaXMyRHIWKUNUjHmFhNsGPWciM0R5mJyDuJJ5oCgs5bfPEoLx82WGaUhmNUDQ3wxo+ChPaTdCExOC7s6Gg6b6qjwoFjcpKCjmsqCEpteCdYY2N9co5JfbMYxiPIuSJXm9BAJKifkSy0oh4NGBpeikuP74OzxWO5RBlACKZ3zpYt2zBxYoKxztlJ0ehG+jNs0uh1UjmEPzs7E7k5UVK7jHrTVixlMJ6dslTWg8Fb7ro5eZGOhoJOZta7CajNs75QoKPTkcmpIoGcua4+sABwVaSQuargoJ2wSgqiurF669z14BpZ+nM3L5GOv7c/lsAgOW7BPTmj4Wyo8MvSaJCEP32brzxRqxatQo7d+7EokWLkJWVhQki4q5PfgAAIABJREFUjqyhAqlNpDsMBlz697eI7652HGMiCtIg/DFb0tLQnTCSk5xyDqmYf3IV8g4FIC8vUnLiCTPWOsQLVidlYI2NhVWnQ4fB4GgAk4palKAMvw7dgZN0Jud8mlbg1Ck1Tp3qI1qUgKPbkpZGNEnZQkLQYTBIiqEXgljHLVdzZtDcqcEH+2Oh02lhMo0nEvu0NIvLEsGMjyfq4Yd5DXSYyKRHvyvmlHcu+3cgr5yzWFMid5qYc373gQbhy6bo7e1koq5S2dzq+ZxkOoToWYt5hJQeMYJ4vrNQxGagrkqNS22O08dM+pgbE8rqmGddHVR1dX3jYL0LNrOm9HpQhKQ3X8GlAEZRFGbOnIm7774bmzZtQllZGXp6evplMIMdzItRnT1L/N05rNOq0yHnhk/x0NEVPHNEwmUzqA7yx6y8fBm5Yz/BPXUvc6+rT4Yxt5fIAIRUYkZKfhULHWaRH6HBrTiKkegLXWOH1ZGYExMCKYS+D4GcZAeAVyfIGhqKtq1bfVKTqK2kBN/OKEJASxNGohopVzthkSIzSDCblY4PWehD7+wka1/OJjirTgdbUhJItZMbjzSh5iI3Mkmonr9gZI6HYcW+SEx0p0OYGEwmCr29ZHdkXJyVyFBIUnqKqgaraudBfTVLm01IrcuXw1ZezvXTqdVEzZjNQMWc8VLNsM7MRPf9frzc8rhgFzOgz1TVXlDAZdbl5YgqL++3KgKStfCYmBgYDAZ88803OHHihMM0NFRAkqLYEIxNHpmEuUf5TtXYWCusAnXcrbGxON6Y6uhXzIaQ9CSkEjNScq7xz5wCeHpUY13oC7gvzYQA/XBuGCdB0pQiUTMfglCSnSeNQqQ6QK06HTZOegelpSHQoxp7MA2j8IMjMuOF0HUwpd2HUG0gPv9czXHyOY9frDQBCaToDiFC3dAZLvm+SaZDCDjUyqs0KqVPAWndVAJ7V+g4CU1GcjHDZoHjJJhMFB6dFY7ubv46q1Q0iorIBNiZsCaZDmFV7TxeHoiDqSUn8/acUOVa1fffIzInx+WedCeKis1MInM2IqTU9TqrDxxAeH6+X6sIuGWKbWlpgUajwfz5830+kMEOoX6xFwOH43D4fShNW4r5iIYO3M0gFiLXAf7HbAsJQWd2NuKKyZstPNyK9nY+AdN9vx+RORt5m5iRkmMLC/GJcRHym/+A2uHjMVwfixGGdehkMQ7G1pt7JpoXsrcCS/BV8H2o7hbueuYq1M2dDGzAffMFWwVnbLEp6lqMzojGuoIRsOo6AXRiwYJhxJIMzPiFPnRSUx+h5C0SobYkJOCTuunEe4ehA5fRFwmTinNYVTsP2tl1xEqjYgxVaN0UAr4hRTM5Hp8B2wcQevowgEm8cxKbvwV4AZ1kvJqvJHY7CwmyYNu7FzFhApehsBlaZFwc3rw61+hZix0aAOd8llQvpXItAFAtLbwKoiRICXklocNgQODhwxzzDwlKs5nT11hoXr6EJCbQ0tKCoqIi1NTUAAC2bduGQ4cO4dtvv8UTTzzRLwMbbBCywx7tGYsHWt4DyoB/V/JNMkI2RgD4feE4DA/9K9ZR/w/BVnsYo7KrC8P+8Acs/vO/UFFxM2+zrV17iedETMU5vNzyOEJKjcRNzHwIGgBrAQB9UTrMB15TQ6GyMgBdXUrMBV9CS4YRn9z2HF6IeQtGowqVlRQnnro/CmFJNV8wRCK6sRG70sZhadoKNF6Ox47Yt2EwdKDXSUMqKGgXJeYGQweOHlZwCNXIhG4UFLQDgKTkLVL+h6KzE0/XrccneJBX3vlvmI+3gvPQ2B3JCc8Ft9AoZ/5CDFVo3ayR5FIZtuHDiccBvg+gGG+gGiN44y8Y/mcArwneh42WikYAMbzjt4edwoQJ3HwRUUFAolmM68iOxJK123FD8SqoDx7k5c4EGI2ImTwZtEaDnvR0tBcUcL4lT9uTWnU69I4d65IJiN6jn+p3SWICb7/9Nm699VYUFBTgt7+1e7bHjRuHrVu39sugBiOENlw9qx+w0RiAV/OV2Bb6e44artPpeGVlmQ9rB17jFd5S1dXhpo2LUVKylbjZmE3YevAcklqOcyogBhiNOPPweixO2uIyckMo7joCPxLPT7H94JiHWOw3G97Es0txgDoTiRtRjiL1bqzM2MnJzGbD1YecjGp8Rv8RL+IJR87EC71voSj/bRzvSEVcnBVr1rgu/OUshUbPmkVMHGLe3z0BJ6Dsbne9Lq4cwALrBhX5c7fq9YL3cvYB0KCI44/VpzuCOV2983jUg9T+MR4NALhMQEwQkGIWIzuyx6Ok5E3c2phJTKBUms2A2YzgsjKoTp5E2z/+wWMEnrRhVXZIE5J60tNF+2z4GpKYwLlz5/Dcc89BqewzQ4SEhKCLkAh1vUKoaJZz7ZDWA2cRYu5LXCJJ5uwP6y4cIj4vsKLC5WZb2/O/uA3/4R231jajvNbusxGL3BCKu5ZS/pc9NqGQQW+jUaRIeiQikWiuwj1lLyOrcpPg3MXWVlNYiNj6L1GMvgxkNAP3lL3s8NOQ1rW6Gli8OJIT+llcHOr4e6NmBGLAravPmVd4OJTtrpmAK4lQaN08IS7O/pElWIE9mMYZv02thrmz01FrydU7X5peiiNlqTxtYml6KZyZgxBD6zU24/eF44Coz5Brzcf44bU83xYg7sgulhDOHFBf7zNbvJTw6V69Hu0FBQDg0CJVOh2xJLavIIkJREREoLGxkZM0VltbC60bqf7XMuxEjrvhvm1OxLzaVbzEo0RzFedvtvrOEMu9e4O8Ggsj2TyEJNxGOMdZOxGK3BBygJIqiVoSEqDo7ET0rFnEEtSkkMFxAlKcVE1FkgNUgEjEo96jqBVA2FEaz3KOO9/bZKIwZ04Aqqr6Eih37gx2xMEDQHP8KuxOqODVpWfmdWntWl7WuVilUSEIVcZseHwlEuKtgr4EZn+2takQFWXP/XD2jzA1dLbGPYt7Ln4MpdkMJSM1V1YSm+c4m/CiC+Zj18n5eLG+T9NaFv8WIgpWOzxqzFgeP6vH/SiHM/pKPaehFCXQU70oeZ3P8MWieTrWCIcWsyGkebmr5RL3c3w8LDfdBOXly7z3wayXVqsVLIntC0hiAjNmzMArr7yCmTNnwmaz4YsvvkBpaSlmzpzZbwMbLOASub4Nt7boEpAXCXbEV4q6FivMS3j3oJqaBE0v5bgLmfgn75qe9HTieNiSDYlYk7QTJurFWWLXaPhVNAF+cw7lT2+C6uRJTlSFcwlqzvXGAKx/+Az+3r6XeH+SpkKSJ0h29TPZi7GSlcTFSNfOYBghKczSVYE7qrKSOG42c3W+d2GhBlVVCs7vbAYAAF/Wj8Ez0z/B2juWOJLxbMOHw3qVsLPn6/j9qnZADx8OC+s8MVh1OpxYux3Gx9YhorPRHmLctQLIu9o7gCDV8vdnCCoqArB27SWeIxT6RGjTtFCW8ePthZrnODtrI/6xGm9zmNFqQl2sAFTgZezBEV5pc+dSz6ROfaq2NujOF6CcUJkoNpZbt4vkH3CMl6B5USYTomfN4tj4Aw8fRuuHHwq+H096lPgDkpjAfffdh7CwMOzduxfR0dH4z3/+g9mzZ+OOO+7o7/ENOITUyeLiUJ5deWXnSiSX8aUKa2ysoOnlGazDrajgxOxfGZ7gUAmdwZZs2JUNfxJei8vhcUTthGnH6MyE4uN7kZBg4dQ/CUM7bsJJpKKqrzlHaCgCnOo19ZWg5jd0B4DmWiuUIJs2SJpKSQnxVF6FR+c5kKRrNiPklbVwYaLSFBYSy1uQmCv73lLDSk9cTsWlTcKmBedkPQa9FIWO11/nJRIJSaAri8eitPPv3Jsbgfz8cISG0g4mujj7DMYWr8TCg4/D2HI/93SBfZ6d3Qnr46clzdcxL0IOjZCJhf2tsPf4OO15jJoUjadqVsF4lF/7ienUx24DuQrzcJjaj2rrSMd5nAY5V8dBIuqAXVInaV7h+fm8c1V1dQjPz8fFTZuE14Ewb3+3l3WGSyZgs9mwfft2PPTQQ0OC6DvDVQMTtqmh8dBvUbNvN6dxCNNFqXE1+T6tYXr8wrIPz19Z7kgMe0u1DKsRwQk3ZaR454bo7AqKBkMHkBXF0U6YDU9iQvX1AZg+vRt33NFDdDID9uYcYg5aoXDKeIG8AjFNxRVIc/iyfgwev2cX1oxdwmmpyBTnco5YchVxRJprB0Lxm4CtMPb2ER7newutgzOkVIx05QyV4mcR2rfOjWqO7YzCHksFGsEXUACg9eA5JBu6sGEDV0oX6kDnC6em89iZPT5xjBnbN7QCOZGAQHlqZf6rHIEgGUbstU7Gs3Fb0ZByJycIwJn4XvzznxG2caMjRJMUHcRAKIxT6DgblMmEY8/uxP8ceQrhvW34l2IOYq19NMMf7WXZcMkElEolPvvsMzz88MP+GM+ggzvJISuLx6LCQu6iJHSfyEgrTteO4lbprAdezb+A1zbZr3HVFJ0hSDqdFdvXnsCa3A40tochLvwyFq7VIEEXJ0gULl9WYtOmVlCmLkRlLSF+vKTmHoBdunPVtJuBLTwcB8N/TtRUdN/vBzVvs8vUeKE5fHAkDU/ufwNKADsKNUhoopAe20X0N7iKOCI57zToxFv3bsWS0PGC0VAGQweOHQvmmIRUKppjEmIzDiHpz1XfB6lhs0L7zblTWZVlJJZipWAyYFLLcURlLXEQJYYRCzV1Zzs1HSatqCgHA5NC2Fx9c2Kx+o0zWnjmwWQYscyyFNrtfVprwKFDgmXN+5P4UiYTTv5yNf7rwt9hQSC2wYCREPYj+gOSzEEZGRnYvXs37r//ftcnX2dwJzmksZEi1hxPaDJjzRq+bVWv70VUlI1UXQCtB86CMik5H54ztForpk4FcnPbHJLN+LwslDAJNO19RcuE2ikyH5aYvVLMQSslixMArkyZgpBsA5Rzg8COiGVyHKgSI+jt/8H/3vAp6DGjiARcjLAx9mBXTmBXEUdCc40umI8NOuFa9ZFxcdi18VUsfmMEx2xSXBzKyw95ccElLDkwByFmrvTHOIbFbNNCTCxo715Oxitp36rVNmK7ynrE4x38hldeg2HmbKLk3IGOEXh6tbEYX9LHxDkmrdpa4OhRyUTW1TcnFuJ7AvFEDaUB8Y7gU8pkQtRjj/HKvLtDfHvS04mZx0K+PAaawkL894XVsMAeQBAPct5AfyWGkSDaT4DB0qVLce7cOURFRSE6OhoKRZ90UyBgu+5P+LufgNSYeFc1/kn3KSzUEK+Zg2LM1X6CjZPegdGoQkUFv2z3xIlmfP65wjEPsVr+1UjGjANLOA1VpNQyZ8aMmvPIvSAciseAZHNnR76wa+5zkqKuguldQBqbyURh8uQYIiHTaq3YubPFZey+0PjYxMlB3JuaYAsLAwBepUfSfeiUFDS/+64gkXOYUowLkI13eb/3Jibyis45j1FTWEh8x6S5OO+3zk4FMVN6DopRjLmc0tfO78Y8cSJat28X3ePMfm5spLDq/Dxk1PL7kfQmJsKWlOTS9i31m3M+v+bzenxw6QFesMTq6f/Csk12QUjoO2HP0xUokwlRv/oVx1d2ZXgCnh5f5sglIY05etYsxJZ/gh+vlkzchmziXuiePt1R7lql16PFByGiQv0EJDGB/fv3C/42efJkT8fkMXzNBHzlmHG3MYfJRCE/PxyndreiluZ29NqNqahGCqZgHyjKBquVT/jsvYpVjnlEz5oFdTk/nI4pmsV85LXqFERnjHZZy1xoPtvXnsDY4pWC68UmooxG4Yp4MdiHezEF+xzzc5bshUo+MGOTUs2SND6pDK07YSSeGfsJMitW4P4Wvjeb3fjHGQwB3Yt7cR/28363CeQJWLVatOzcKch8nMEmIMz7AYDW/M08QSBFVYM9lskOYi/UCImZl9CecM5kF5ojG64avEiF85j0qOaYZN+KX+bo5AcIfyfseTqDRCOAvlj+9rA4ZJ5chS/rxziuIe3HyJwcjC1djZqr3frYda4YWBISQNM0h8H4Yq08airDYCAIvb/gHE0AAIrDR9H+4XtuL7izmso0n8jLi+RJBuyNuwarUYHbeRLYl7gHAIgMoE89HuYyppqpmuhIUjIDXaGZuKQjhwoy0tz580pexVKjMQDrHjPi753CCXHECAihLFYnsCOHmpooXljr449f5pV8YI+tsFCDNw3HRZm6c8SRUG18kv09uK4G99S9jAC4X86ZMaUIJuMJMAHzpEmctWU0gqC9e4nnO9fLDzx8GDRNI7a+Hnvwb44g8NzjbYgtToe5KcHR2lT77LNQVPWZqtiOXSFTjLPJUmiObAiZX9ztV+D8bMYkO1x5AeHDA6CJDUZhYd99hEyCTFlzZ4hFlLE7/X1Zz9WQSHkqHQYD/vqFAQ9c9QkwZrWXFEswbawRYaOHE4vcDXgBuX379gn+dt999/lsMAMB52iC/8/eu4dHVZ57/59kJoQkBEMSkpDDTBLUoKjVdG+lKEUFtW+r737jhm2UaNH+dm1rNAVqlAqGGCr9RUGjWMW+Fg+xjUUbWyqVg1hKaSi6E0EUIkgyQwghJiHmyJA5vH9M1mQdnrVmTQ5Aa7/XleuCmTVrPWut57nv+7kP3xv8C72n5Ak8681xocghZQwF412XT9xnKNJYA6IsGgly2uiGBgxzqs1Q50oIFoCW0KKildaboHLrKfzoUYJBfc8TJniFz3DNmk7uvTeetjZtoLjVMaBZsOP27GFgurZ5R7B3ZFSIZqaqWg0ppqEXVBUVi4kyayQlpufWUL9veSqj3BDYXJvPqsqXKC5+XiFkBzZtwr10KQOOVj5qTacivhTKMwJCVFRtLaosVt+jCOp5OJx+BXoJA9O9H3NPy6+4s6WSujrjnhdGtOZmgvFmKaY9NhuX/OEnvPNgKd/f8yM6B2IgYhyTr8zE98T9dNpsxNwyH9Fed8BhTPQ3XJhSAjtV2QqdnZ20tLQwbdq0ESuB3t5eXnjhBY4ePUpYWBg//OEPufDCC4P/cJTQUqvNJgA4XtvGSOqhg/Gui/L9dzODdhKoI1fYBlGCnDZ6xQrLYLN5cU61HnWuSFjpBaDVEKV/qhez0Geuqn4FOMU4fISxhRspoiJwz9HR/l2UXo3GrFkuoW86vfUjIlTMknrNO/QK3aR3ZMQXJRJyLROyaS5YqkMQbsxy6i19MORiIlEQW0/pixDRdoLq6mitkM3KYl/x835h3BQBTUCdsTDWqyx+JX0pM2xHCXc6hfEOaR5K1v/OnZEa5R6s8tsoTVlc5R3aczbDYRVKFqHHZuNrT9zCofyr/e/uNPBXGMh/3d8XozVDUN4GH7Wmm+RpDQ2mlEBJSYnms+3bt3NsBIx4EtavX8/ll1/OkiVLcLvduExO4NFCs4lsgmAQ+QtbWhKEx0qWgbpa10EW27iBq/krK1ihUABJtNAqEy3yTkrHj70GxCjOI8+pFlHn6uVtmyl4yoo+TlmftiparVRE1lOY2+0PDNpsAaHwBYnU8nV6iCGVZlpIwRozjldf7eDJJ8Xc+x3vHeAXM35JXdpTCqZPu32A0vgKv9AyQITDwUe3VLBT0K8Bht6REV+UOjummVSW9ZQRfud4/pRzHxmZaASL0pUiZjkNhW5bj6lUpPRFkFxvIiEbavMYUUZPePR4UiafxpOcTNeSJbq7HDM7UKN+BcXF3Xy0+Usa+oY6iUmZTZJLVUKwnhcimOGwCpVi2mh3UTF5FWlNH2o8A88klZrkaQ0Nw27teu211/K9732PO+/UEmGZRV9fHwcOHOC+++7zD8ZqxarDdDhWqM5dztQtH2geeHXucqFyUENk8X65+SOORtYB2sY7KRP0CcIkci45S+MEuvkfhtLOssM+V3RSSg3fCORrzmUm9VMzNh1rJj19AJvNS3Kyh6UFx0lfjKIgTaRU9KwnX1IS7Rs2BGIxGccacRPBclbiJZzYqAHWvdpDaqpXt49yetcBLt6yjqrIT1gxq5ouT2zAN51RjrCQSI2IthO0CSizQfvsJpaUELljB70uK9/jlwEFLUoHpg/K6m6lsu5OYUrkcBko9WCGL18UaFS73tRuC7PuDQlyBdfqGMBWv52f9S4iq84Bdf7dV/1DT9P+eBUTulromZhC7JolpNjSKC8MvgO11W/H4pwinLc2m4c3Xmvhqbt209J7XiCu5sGqcakeO+TC6bSE1ApTL234YMFSVhYOEQauWdOpSQnWu47h7iIzg7l1KgODMnLtycDozZ0AfCbg8XgUf/39/b7Nmzf77rvvPjM/10VDQ4Pv4Ycf9q1du9b34IMP+p5//nlff3+/5ritW7f6HnroId9DDz3k8/l8PpfLNeI/j8fjc7lcvoMHXb5Z6Z/7XmOB7z2u873GAt+s9M99Bw+aO487P9/nA83faoo0H0/lkO/z9Fk+18GDvtmzPaKf+ewcCYxlQ+QdvnuuO+S7+Wa3b/ZVPb47rL/xHcGu+MER7L4kjivOkZHhNT1++d/Bgy5fdrZXca7sbMG5Dh70ufPzfZ7Zs33u/Hyf6+BB08/FO2FC4Pj7bz6keO52jvjA57v5ZrdmHPJnKH8Gb0+4Qzm+gwd93uxs4bXlf6+xQPhV4H4H7/Hwlbf5dsbcoHjeC3jNdx3v+ZIiTwrPcR3vBf7jzs8flflq9rnrHjf42cdJ1yqetfSXn+9WrI38fLfw3uTHqedOfr7bN3u2x7fTfrvwmb894Q7hs9ZbC+p33nlzkGcpu+/tqbdr7hF8vuvZHNL61numh7ceNLVW5M8lP98d+F5vfbjz882vwxD/9GAqRfS2227TfBYfH8+9997L5ZdfPmwF9Pnnn/PII49QVlbGBRdcwPr164mKiiI/X2vZyjFaKaK1tScDDVW++CKcpCQfdrvbVJMICXrpZvJUx2RamMu2QNZPX14eBVQKfdoSMtP6eWf6IqZ278OTkmK4zX+dfAoYqoa88cZ+1q8/aWr8aoSan21U+Tp5zhzDdMN58xKoqdHulhITPRq/8OXUMp1PNXUF27mOtXl/VFjX6jx/6/79Git4LlsDFn0yLVzMp6TEfMmiV+3YUk8Tn59PkwNuYBsv8t/CdMfbE9+lqk1bQCnl3YMyvXMkMFPfYAZm0piltWE23Vl9Tr30UPmakJCX558forWgXjd/i5xNxK/XGKYnS9CbW9exnXv4Fb/Le3HYOzKn08L8+fHCXt/ytGajZ51Fg249TUxlJUcboeSLIpqSLifjwgkUFQWvgQmGEaWIrlX5ziIjI5k40XyvVD0kJCSQkJDABRf4wx0zZszg7bffHvF5zUCeVSPBYhnguefMKwAw12zmYj5V8K9H7tzJ0nUHNZ3DYmI85OR4SIrpYsWePC7e8tfAd16Dns4+lVujp0fsRjGDUNwVwcjYerKmMfETLZeKlOVglm8HYAlrhEU1zaQKMzBEnccO72xnX1uGJug+l23+99MLX6y7lU8/jWB2k4PlvMbnnK+bCbQ8t5qa+usNKTMsbW3E5+fTUVVFA1khpT7KMRpN4sG4011hYRwOh5X2dj+VdE6Om5wcNz094YYGgdn0UDULK/jdS6JqeqlWRq7wj7jS2RQkPVlCsGCxEV+VUYqqJNhFCkC6HwklJRMN4ipaN21vQUEgbpIDVFHNgMWOb8Vm2mJjGSuYUgIbN27knnvu0Xz+8ssvj6jfcFxcHAkJCTQ3N5OamsrHH39Menp68B+OAuRZNRIcjgjy8hKorm43vTi7i4sZv3mzwuJV+1vV2TSWtjYuX3wrG9b8jlWV0zSL8fjcRVzl+qviN3oZH8HYLccSwQRT3ZcXMButEpCyHETBtKzo41w4bRyb/6oMrC+jjGssNWTKiLake8812du4z2nh3jmT6esbUpJqoX1oRzsel78i/tig0NJL6UwoXUgVQ35w+2d/ZmVPkYYyI8LhILzkCfLrq0JKfZTDTIaKWagVvchidTj8RkewYkdRfwzR8zoeneWns1YhOdmjUUwpE7pY8v49ZA1oA/LZvQ2K3+spwuLibvZu7eJIz5CRJg8W662RUFK7RZDOu3t3BNu2ifuG6AWn4woLxW1BV6yA1at1rzlSmDIZd+zYIfz8L3/RdrUKFffccw/PPPMMP/nJT2hsbCQvL2/E5zSD48fDhJ+3tFj5r9leOu9+LNApyQgem42BnBwA+onk99yicDWIyNTA/3KnV65k7dpONmxoZ+3azsAkO6/3uPBa6t1ANzGKQCX4UytHu9evHoIJporJpRxmquI7KcsBCBDe3R5TzXVsZwGVvNf3DZ458h9kpvUrT2pP58Czb1AdczvbuY5KFjCXrWBPN32/NpuHnBx/lslFfMICKoXWpmTJSqRqUiZQJQvYznX8Lup2bovf7O9sBaxd28lvN/ay/sNp2BLFfPrHa9t0rUIRnE4LhYVxzJuXQGFhHF2xU4THjUbfWSPBpjdGp9PCvHkJVFdH09WlFCPS83ov5TZcM2fSl5fH8dfeALvSwFPzAT1fvI+tybfz6+7/4DfxPww8b+ldO8gylZ4snW9TdS+3Rb0dmFtbmYsHK+si72dVYwFxhYWaNW6UFQXGGXTS/TidFu66Kx6vVyxj5ApI/p4/39kuPD7suFgejBYMdwJSkZjH49EUjLW2thI7CluUzMxMfv7zn4/4PKFiSmw3cJ7wu6Onp/D4lmtYX59vyufqycyEujqicHEZ+7iGXWTTwJSw46z0PaKxDCWIJm9Li0V3O+2aPdtPB3DiBLs/jaejM5zv8yIDjKOWXFxEMW3awIh9h4HxBaHTCJo6ZyLLYXrlSsX2HoBmB+/cuIhlV65RxSZScV7xVMBizE32UFxszpKWkJnpoa4O+ohmBSsU76YpMptlLr+1OoPdlLEsQKomZQJZrT7c/WH+DKQ6+Gjzl/wpp5CMTOBHP8I7frww5+i4wBUC4mwbs30TRqvvbLDUYNEYS0omKvpQqOEgi7s1Td90AAAgAElEQVTCX+Ot1f73kwKGfZ3VrsXFNDPX+meOuDMD58wOP0KZN3h6soSsb2ZQsf0Lwkue4HhtG595rmRT//W8euo2RdaSfI0Hy4oyyqCTdguFhXH09orPExk5ZKSp3/NexPTcviliA2C0YKgEpCIxt9utKRg777zzAqmd/4goYzlv83NOIQ7ONpNquhWiPIVM0T82SMhdNHlTUjy62+lwGbd5hQ6R1wWtu7E4I0clECnqnLTvmWpWVvo7e10Wu5Kn0uoUgskbHY2lsZG4wkKWFixjfu2l3OkYSqP0W0sdQ9fR2U1M7fmYteu1sYmRpFg6nRZ6e8OIjPTicGkLtlZShmOLf1clfVfKctZZ7uNA1BX0esbT369c3A19UwIpob6NGzXFcOAX1tU5y0EQ189wat/XEyXhwr4JxcmP83TiA4Ax173R/Yt83cFiMyLXiYjQUI3mZmVdQbC+znJXSBYOtrmvZWnKS7ScjCLd5VcAaoMqmCL02Gx41j9LInB+YSE3Vf9Q8b3anTRcGmu5y8xIqc6e7Qocp9516LkdfStW6J5vNGCoBKQisaqqqqAZO/9oyO7ex01s5veI3U/SttNM03azberk0Ju8xcXd5NemM9cxZEF/GZOC/dVFpNjShtgSGy1ER3s1/u1VTd8lPp8Rk03pdU76oqCM6v4/AFDDxeyI3MbmWQ+R0tuApb4eS28vkXV1UFfH5bW1wriH/PmZKcQRIVR+GZF13RJp57XZv6S0tIsBm4eFTgvv1g/gcEQoLX93GPToj0UKeAoVQHo6HVVVLCQhcG4JovdlcTpp3+EFQR37JyeSseCfW1adFpih3L80n0WCTYJRwZMZmG0YJDIGsnDwujsfi0u7nk5PSqQmZo6G0iLUa4ByRz4SGmu9xk8SoqO9lJYO1QmJmufMZSvrEn/KtRc6AzU9k7Ky4Gz3GJYrAJ/PhzyrNDx8+JkoZxO+KVN4ikXUcgVHyVR8Z6Mx4Mc327RdQtjp08LPvTExeCZN0u0VKxdqOTluyEnh+Z6XApMsxSZuETnL8lesntNK6l8HIyab0uuQ9LX+PYr/H3RN5eLaN2i4+r+YrPrNUNxDfxxmmsmrMRx+GZGv1+UK59NPrQqCP/kCdzq1BHoi6HVRA/DabHhsNmz4hcfT8w/S2uTRvK+JJSX4YmKI3LmTdNdTwEzD6wTLDFK78u7vXWdYASzdt5QdlJAwgN2ur1xzc0/rsrnKYTZJQc8Y0MOHfdO59mSVKUoLCV4d9/XuA/FUFMZRXNxNFg1syimhrDePZqaQmDtFw7Yr2tEEq3qOifHw6qvK8Yl2HQ6yWDfrJS4dxYLCYDClBDo6OnjppZc4cOAAvar+q2+8oeUM/0eAZ8UK0mtuYofjWhbxFLv5Bj5gBjU8zSKycJhuhWiG3vfUjTeyr/j5Ieu1XGlBmMnJFgmyFZ7lwpzsM9mUorfXosvBpDeOIaWXwGU5m/j/Ly1jfIfTVPPtUCkNQH+L3tQUEWjqIwkS6Rzz5iUIG/7IoRf4lyDf0dhsHl7JWEpkk7auRM78KY9FGF1HerbqXdGygk+4dLFyPrZH/gTR7kKaz3LB5qdZHwpSimJDpaUWPt3RTZMrKXBcOKfxMuQmCmUXITIG+tMy2euezgze0Rwvp8MG/fcvfzZrjkQwQ3DtjpMWqqujqdsTxmbfT7i4eRe/YR0A3r9GM/CjHDyZmYbzUi+4PmmSh8hIH6dOhXHvvfHk5p6mtLQLm82jmx23qvE+4gq1tCNjBVNK4MUXXyQyMpJHH32UkpISSktL2bBhA1dcccVYj2/skJVFR1UVzvlP80DTs8zjLV7g+8znLRrIZhfXCEncRJaNKFVSDqnEXC3o6/aE8c70RVTU5gmbfKsntUiQDYfN0gz0OifVCJeRPgeTaBxqpVfDxWzN/jWvv95qKshrhtJALbimxK5DJATlUD9zPf9wVJSHGMspZvVvZbXnxwE/tZogT8gAqmPxylOAs3AoqENEzXcArJ99Rufdj3H7J0oOpYJ3nyK3X3lsuusIot3FZ59ZKRy0gkXPXq8WhKoqtl1cTlndrYExfp8XeJEf0DTxIuLnXBRSHYSa3kTi5286EcE2Dir85PIAvhxqA01dC9SHOHPrPPwumsZjUTzGD6hkV+C78L6+gHvTqDOa3pzs7Q3j5Mmh77ZsiWL/fitvvdVhimqjo6oKEkdCZRkcppTAZ599xi9+8QvGjx9PWFgYmZmZ/PCHP2TZsmXMnTt3TAc4FrA4nViWLCHO4WDyxZfxHd/6wCLaxTd1f6dn2ej5Gr0TJ3Jqzhy6i4tZWT5dYyk0Hovi8WPX6Db5Vk9qkVASBZOOhGfzk8ZVYLC4g6GrtFRTaTtAOMt4THh8de5yrq+vMeXWEQU+jxwJC+pqkxAseCcSXKtSW6lN26oQliLIn7nIUrNaffT3W+gnhlq+xkcxM4nJSudAZzpvxv+AeR0vGHZfM8v8qUgwEKCFJFLaWnl8yzU0qoiH4/q1KYVlLON9rtMYDW1tFjGT6CCMakFiM6GyTjnGb7KLvjn6jXWMIM+bl/Pzy4n6LOlJ/ObiRwMBfDnUBpq6FshMEZuooE2CkQtOb06ePi1o5ykLmEs7sLjCQqJrlVly0vWo0jYvGk2YcuiHh4djsfgfZkxMDF1dXURGRtLR0RHkl+ceJAFhqaoisqaGi7esY7PvBm6a1U5kpJLZMz2ylZsnvs/scbvIn7SJTTn3k0WD5px61t2pOXPoXLsWj82mayk0k6rb5Fs9qYuLu7HblWyK6hz2ShZwvXcb1XU5VFdHk58fj9NpLjinuCebjY633qIvLw/PoCUSgZfn+RETUJLg2e0DLCxNoKOqir68vEBuuLzNYWFhHPNviWFV7g7at34qvKbZIKLoOcgVtEhwXdi8i3emLyIvr4+ZM12kpGiDuECgERAMBQGl32SknFI0jneQxa29vybz879wbVMVa/ddy7VNVVxxcANHGwe7Tqny0CWLV/6cXLNFxMHgU8XbupjA35hBJQtYxBpgqKBNDpGwy8LBEp4QXgf06wGMgqndxcUM2O2Kz82mrVqcTuIKC0mYN0+Yr6+mWr+TSuawncVJr7KwNMHw/UtQ1wIto0xYt2JU2KkZt457UzQn1fJEDvVcH81iwFBhaidw/vnnU1dXx5VXXsnXvvY1nnrqKcaNG8fUqVOD//gcg56A+N1V/82+Pz/PEyXhtH1wjPQvP2Wl66dkuQaPPQ1sgYH6dzVbQjPBTaMydpEPWDSpRZ3LPv10PI4mAZvlIMwEs/UgWWYJ8+YFMp6+yS72cZm/O5Vq2+9BS8+rdv38kHc4ybeE13M6w00xPBplaIC5tNN7bosU0n2HqbiOJEvN4nRy27VejgpcKv39SmEtTxsVuRDMMH9KPDKfFv0WT1NroMZCck8uGHzfIgNCtDs8zFSeoUj4XCSIlLBR9pYRQ63F6WRiSUkgwUCe0hqMbgT010t9vX+MRu9fwpQpyhxteY+Dy5Oa2F5vY1HvzwLPNDOtn0d9L2CkB/TcrKI5qdfTGbQGntFzHmteZVMEcr29vfh8PiZMmMDp06fZuHEj/f39fOc732HSpEljPEQtRkIgp0f45po5k87Vq4MGeEHchzRi927iioqwdHXhmTiRzooKBmbMCPimjzbC/6p/TsN5LlWsSv1/jyZeRsKs8027cbo/7afk7iZauiZwwJVNq0v7PmbOdLFhg7ga0Qz0OlgZ9dOVoG5M/h7XkUUDN7BNofRSOUYzaab6BAdLDzUz3tsvaeUvJ7Xkh99M3Mdv9iZqrrGu904WbcnjdQoM71fCdWxnO3MA+OLGW7k35jXDdFa9vseipAH1vFE/S9D22TVqUiRBIj9T999Wr4nj0Vncl/MnyBSnZopqTAAGUlPpeOst3X7T8vfjdFqYo6L4kJA/aRMvXft/gwZOu7sTuemmMN2ECxFZ4tTmXcTfdReWXm38IFTCPqmqWl1Ul5o6wNvPKvt0y3mD1NeblJsbeB8jwYgazZ9rGIkSMBIQgKlm6K6ZM2nfsCHwf9FC8UZGcvrf/x1rQ0NgMTRg55GYp3DmXE9y4mlW7c/jwuahIFSok8zidJK0YEGgH2wBrwmFlKhheygwYrAEDKuK1WyOr1FAAa8HlJ4UVMzlQ5bwtOF4nU4LJSUT2bEjEpdrSDhERnqZPdsVyLoIxrhpcTpZfPURfu29Q3ON/MTNFG28XCN4d0VeyxRXo0bgRtFHv6DgUM4k+rfI2Vzt+nPgO6kxe2VljKk6B7mwSpnQpZk3n6VezdJLqmnpmciECV7277fS3CyLY3AaN8bFXXLhKFcCMKSgBhytGutZr5m63jrqy8vD0tKia4jJ19XNNydQVydmAt3OnKDrRc4UbJYVV2/sA+npdGzYEHK2jjRnpeK63NzTrLz3AJcvvlWXQVRtCKjfx3AxIiUwMDDAm2++ya5du+ju7uaVV15h7969HD9+nG99S7y1H0uMRAkYCYi4xYuFk1MNT2IirlmzAi/JaNKLIFk8etafWaivK7IKzVjWZiAaKxBU2BbNd/FG05DP206DsJ+ynG8pPrqX2TeFCdkbjci75Pdq9GzjCgs5UV2reVZTOcwfbnySZTFrNNXYesrLzhEe51HFsWoGzEoWaNx1MTEeBbVAKO9Jujerw0FYayveyZMVKYxypWH77M/8sK2MF/kBn5PNfi6hhyEGYLUCBXSFjnpXJ0GttPV22+B3C7ntdlM7S73ryRXsjvTbiNxQIXxu6vswU2AYc8t8zqv9m+ZcX+ZeTe/G3wrvKVSEurMeayVgyt30yiuv0NHRwQMPPMDjjz8OQEZGBq+88spZUQIjgeTHHP+zChy7mzlOKtU5y1lIApeZLFixtLURXV1N2J46Fk1/h//e3c7XQxiDFOzRa3EnyssW0RAnqtxWUmrhTyf9AudFc0xZPmYhGqse62FseTndxcXE5+ezqgk+lAlbB1l8j1/yR25hHANs4lss4mmFqyKn7yOqq69WZKyIMorUUPQHNmgfaGlpEaZhPsajxN77JC1Pan3jyyjj2vAdZHmHsnYk5ZWaOsAll7hxfekldf8Wf5rfoAL4nGxNrQmg4ZYJJXbjsdkCzzeiqQmamhQpjDabLXCeuMJ1RFfv4puDaY/DdTv6xygWF/JYgtNp4dBRG7MRKwFv8xcsTnyNZZF7B9NW/RAFlIuLu9m7qUNRE6Cul/A0tZKfH69QoAEl2dFBXHx8YP2YKTA8E/19z2YQWARTSmDPnj0888wzgRRR8DeV+UfMDgJoIIsFn/6aI22D2QNb4N36AX63ZimXqtP3IiLwJiQQ5nJhOals1BJ1rJFrjj3OATJCUgJGOfyinUrYnjp+4tvMruah39XWRnDQ9YXm91k4eDXmXr7Y8PcQRjQ8GE1mKQCfBQphmxLVSclFr2Ox30hTfS8PfrpaoQCmcpj7WEsNVwcE4/PF+3SpFNSQBJJIkYLfdWU9dAgQp2EOLF7MlJxZmms5yOJJ7yKeZgn9RPI/lit5etpaci9MDpDYJSYmcrJ2CrHlubhOpLHbmcF3m1YF9cVLkPromrFYzfYXUCctZOFgvX3Z4G7NvIvQ6bQEgrJqSEFOabdG0yoO8DZRaOnPP2mfwrotF/Mu2xS8Td7SBzW7YJvNw8bZK3l8yzW69RLNpOJwRFBSMpGYGB80HuW5+juI7vNn8UXjDzr/KOcDUwWGFZNLx7y/73CpUsYKppSA1WrF61WmO3V1dY0Ki+jZQHl5LEeOKNPHHI4IVlZO53mdbIeEefOwCLa4qTRzDy9psjH0YJRC53RacM1/mtlNysUddayRH/AYu2QuBYcjgsa4FKZxVHMeb1KS5rOxgNFklisIubB1XeH3+3YCqXffzdZPtUVR8ubgJ05YiC0vJ911K6JiJzWSk8UxgYi//52wsDBNsFKNCIeDspzl/I/9WU0wtohnAIjCxTWeneReuEK522hoILzkCT6vbacZG/d4/i/NxGiuER7uE9IM2+q307I7nfzFlwe1WM1ak0YZPKHwL5WXxwqZMeXU5UNVs1m8y7fI4/ea4w8M+O3pQH9mF+TF9LFWRyEllC5kfb04WUOe3vmXbeGc8kbyGmVMQdtzoK23BaOK6QDOQH/f4VCljCVMKYEZM2awdu3aQAOZkydP8vLLLzNzZvBFeS7CqOJUz41g1EFMSj17ih/zv8K2MN53KvD9QGoq7ksuIbynx9DvL1lRv2oSbwlF+ct7ey5gGh9oxzqYu212kYdKxibBaDLHlpcLfyNZOxanE+v+/UThVljj6rzt5GQPlpYWYRoteJGXukhptUIrOYQ40tSejxXpfrbP/szjbfdqKnblAtfidMJ/LWDy0SNMBi4Fvk4+zfyH5vwzZ7o4VntSkyn2s95FOItm4GhSFgc5HBHMnx9PRoY38H7ihmNNysJ/RvxLogJVvTUjpy6XH7OIp7iU/RqLWuQa06NikXZy7pwc3Dk5hLe10Xmgjc/7UzkyeC5pl3XK6w8gpyJW8v71o61pF9Xi5NemGzLfjhRGivlsQFcJvPvuuwF//w033MDmzZtZsmQJp0+f5oEHHmDOnDnMmzfvjA10NBEbKy7iMCK7Egk8+aR2kMWt/J57b/iUNTHLhC/XSNhKVlQorfkecq/k6/xdQz3bXVxsmmRtOGRsEowmc3dxMeP27FFY3u60NIVbJqK5mQFS+T23EEuPJpVREuqe8hSyqNH48O+65gAvTH5Ek/mhZyWbhSc5WcGl4/eray1RucCNLS9n3NEjiu8rKOLvXEUrQwLbbh/giSe+ZNKP7lNQLkhujo6uTOGY1BxHItelyJrUy8k3co+IClT18vbtdjEhmmQYmUlTVa87o+SNBrK4QzVfx9MXoITXWz+PTnudPbtzlP0JrI0sLegA2fsJVoMyWjCKWZ1p6CqB3/zmNwEl8PDDD/PKK6+wcOHCgBtIig38o8HptLB/v/a209LcQcmu3Dk5hA/mDzdnXcm3/udZHN6hSR0W5uNr/yeZzv/QvlxRzvCePeN4801/K0vJihIV+vSnZfLCwCPQqjynfKFdlniU82clBIRweaE5krXhkLHJYTSZ1Yln8v9LgjqDZtxEsJyVfE42REaSO92lYLCUFHCWY8itNGC30/FEldCNEAojpTc6WtEaVCRI9XY8BwuWsrIwjpYWC+sOtWvszCwcrGERP06s5MIL3QqBEjE5TEgL4RjQpy0IHBPEdSmHXuzAtHtkEEYUy5LVvqoRPooeqoUJuHwMICqKNIp32Nau1ezS+tv6ApTwepz8mdEn2Oa+Vul6dC8jZV0OvsoYRexIHlj/KkBXCaSkpPDqq6+Snp6O2+3m/fff1yxqgOuvv35MBzjaKC+PVeRQS5g+Xb8jl8gyeaDj+3zuzVYc5/OF8eCDcVxxRZvmXKJOTMeOWSkpmcj69ScDVpTagrKkJ3FxxX/xq/v/m8f4Ae/wbTqJD5xDWmgzL3SxYe1QQZgZkjXQz/jQ+9wsJEtfjojm5kDgUi6o5TGDvm9r0+RC3T4LhXZqqiYmYJSbHez6BwuWcqvMd6/bFQoLs2a5ND19f7J/FS+zVyGsGizZLO7/mfCe1JBcl3rMtBL0dkXG7hHtu9ezkLNoCKyNHOA9ankk5imaw9Jx9CTQSLbmXOnpA9hsXl0rW2/M1sH3Ke3S/DG09djYzX4uDXSAm8tWnrYs4YZ/6yAs1Z8dFLd4sTARwLujRcHbZEQS988K3ZVeVFTEH/7wB3bt2oXH49HtJ/yPpgT0hGNPjz6Nksgy6fSKg+K9vRahFa3XiUn6XG5pSYJdyh2PKf8hk5t3Ucku3YIw9ZY6GMmahOZm8X23fPolCfPmCQvAzCBY4DLU4JiZ7bMiIygjgzCXi/C+vkAFtzc1VaNIGsiinG/R4rOQgodiurGhfXbq668sjAvaFeowU3kh9VGeVFm65eWx7GpO1rpLPFp3SVSUR9PNDPwcR3ffPUlTOKd25entipbnVlNTf73iHqKjvTQ2Wvjudy0UFWnpO0Q8+rGF2o5gv+69lYH0dJp6LJpajOywz6mq+JK0Gfq7Nb0xW+rrsTidikpqmlaxjRuUGWgxX5L26jIibr6Ek7W1iowwNdTEfcH6NPwzQlcJpKam8oMf/ACAxx57jEcffVTv0H8omBWOcogEmh7pG5gnQpPDyBcpv74ZniF5G0W5gBBtvU+dErv1XKd8gYKf4VhHwdLg1Na11Wajo6ho2BaYUU+H8K4u4hYvpqOqSrG4RxIP0esK9YtJPyXdMlh/krucJ0vP05xL+q0Zd8lFF7lpb/cqxpiW5tZUBQfGoXLl6SnbhNKFVDHUSKa+3kJvr4W6ukjq6qCmJt7Uc9BT9uE9PdijXGztV2V/+ZaRXJlL5wzjRkPjN29WuOkALL29AQEtz0SSlOk9/ApLehLTNvwYjy0FGhoMaWBE7K1w9vL1zxZM7fn/WRQABG8fJ4JIoJWxjDf5T1xoCaJECkWvE1Nu7lAnMr0erGrXiWT1NI3LIi2uj0dy3iKBhXgQc82MD3cx++oeVpS7NYtaL7RjZSh4PhzryIylL7euExMT8YygKjJYTwfRPQSLhwR2Fo2NhDc3E376NFgsDFx0EbaGEmoGuYECvyWL/3vtS37+HSRni3YuBOvpK0dmYhfPPec1TUwGSiPEyJVmwz/fCgvjNDvVYHGhQEGWjoVt6ezEGxEhdMG4ToiDt/IxD+Tk+Hn81ecdFNAillGAmTYXG2x+t6hlxQosgjkhVfyH9fYKe2acrXz9s4WxJqg75yBZ3BUViTidblPRf5FA82BlIif5QqUE9BRKaWmXxnpLTR1Q9Bw1e/0sHLxmvZuw025/sFjGblpefplGsJ3yRhK1611eLrmAhaUJisrK8X0RiAKEM1QVn6FaR2c6Dc5MRpD6HoziJkY7C8vOnazie4pKaIDsbJ+pTloiQyQ9spVwVx9OWatTf3Xsk8TZHlUI43nztOyncqRM6PLTMw8GOw8WLGUllbouL7PxIwlmOukBhA8MCD9XC1lRYZ8nMxMESkD6rZkdfdhxbV8FAPeFFwZoW0TsrcHy9YebUn2u4iunBGCwzd8rHkULPdB/uZJAi8y7kwMtCXzO+SyjjC9IJYNGLks5wcnsyw0Vis3m4a23hpd6phao4U6nny5ABsnSPeFYLzzHl95Y/nPL4+TXr6eqqiMQ0LO5fs1xlRLIoJGnWKQcwzCsozOZBmcmI0h9D3qCZMIELwcFRXtyqGknktItlG76BrGx/nPqscqC2PW3qvEHjKur1RTOpfakoeZ/NdpJZKb1s2p/HtEygrn4jXupdW8LxBvULq9QXaTBdl1y9BFFNP2B/6uFrF4Ka+eaNYY7STM7et+UoToMOfRckmYMFTMuxH80JXFOsIh6vV4efvhh4uPjefjhh4MePxICOQlyUiY9dko12+O6Qzcyoc2hXagz0xTsh0YQWj06Def1JpAeQdfp3Fzual2jIGyTsIBK7uFXzGE7eXl9VFJAdHW1JtAcQzcbuZnrGEoE8EZHM5Aj7rM6WhNeRJIV7FmpjzWyTkWMk6IFnZbmxufz8VrzjcLezXpwzZxJ2Pvv09bWRsTu3STedpui1aTPaqXtjTcCikCNUEjFROOWiOCeYhEXb1mnOY+axE5O+ma2x7UEI4I4Nar5D3qZEMh08/vrh96B0X1LRYd6AlpEBS0fb2J3N2E33aRLcDgcBCPRC/VZmsE5QSA31ti0aRNpaWn09/cHP3iUYcRO6XBEcNdd8YFy+b1kUMBftHwzTk8ga8EIwZppiMYi9SGe2r1vSBAaZE+UZj7Bh01pwiblEh3DiRMWLD6/+0QdaO4llkcjy/lj9Dwm0EvYqVO6fVZHElgNBjONR+RQW3XeCRMADCu1jRqB6BUd6UHe/COuqEihAADC3G7iior44u9iTqdQsqWMkggS5u0Tnl9dcS539ajPZ7NZKSrSf4d6809dc3GYqSziqcAORO6vl2CURRZsJ6kXQwtgsI/4aLokg7nO9OJMT5SE81rMDw0NmrO1gzjrSqC9vZ3a2lpuvfVW/vjHP57x65eUTDRkp5TzpYjSAAEimpqIz88PamHoFcHEz59Px4YNQn++1Ie4Er91J22V9bInsr/cK2xS7sEaqG5OTvbg7fWnuKrdGlMsJ1jpWsp5LqW7ST5ebYbGEEbSyUwOo2flzcgQLqSRuJ98Pn9T8A8+8AdI9d61CN7oaLqLi5Ha+Vi6xHEevc8hNNeEkbAwojeRQ+3qkQtUv+WpL3x6CwqI2rhRs9M5+eSTRG3dyuGd7exry9BUCIvcS14d/rFQ3Y9CwsDExFF3SQZznekpifYdh4h2De141AaN02nh9nkTFT2w6/aE8Zs3u8a6z/zZVwIvv/wyBQUFZ20XsGOHtmmFHqQ0wBcjC7nRtUnxnZkMGj2rR1IiJxL2Cr+XL+AIh4OYykrd7In+85JIt7RT6biTbmLYzyXs4prAggz4TUuGfqPI4DBheIgyNOQYTopsQwMsXRoXEGzrHeJAdIA6mZEX9hjuAmWph9l8zoUcJFGHQGxg2jSlMpo4kXCBwPdMnKj5TPG9CYEVbPcl2lE0WrNZ5h7i7NFLXhBRMIuebUxlpXCnE7V1K51r19LntLBM4BJRX1Pij1JDTi9iBnq7Rt/mzRACyaUZ96NeLGJpwUHiCldiP3QvNdykObecNhu08uKJknCFAgC/AfhESQ+/2Wj6FoYH31nEhx9+6PvlL3/p8/l8vv379/tWrVolPG7r1q2+hx56yPfQQw/5fD6fz+VyjfjP4/H48vPdPr8NGNrfHUlbhF94Zs82vKY7P9/wxLfbdwq/WsBrmuvonevtCXf4Dm896HPn5/s8s2f7Dt18vy//5k7f7Kt6fLfbd/rev6zQd7t9p++DmG+GfuODf+78fJ/L5dJ9fvn5bv89Hxwahzs/3+c6eNJltg4AACAASURBVFD4XA4edPmys72Kc7w94Y6QxjKcP7PvfyqHfEewBx2Dx+Pxn3vrVp/XalUc47Vafa6tW4eub/LZmB3zHZEbfJ6kJJ/75pt9rq1bFec+vPWgLz/f7Zs92z/nDx4ceu7S5/fffMjnyshWjjk7Wzguz+zZQee//Nzya5pZD+6bbw7pPeqdx3PHHebPc/Cgz5tt7v4197b1cOC3R7D7pnJIMZTs8UeF80f+vGYn7Re+12uTPh6aVyP808NZ3QnU19fz4YcfUldXx+nTp+nv7+eZZ57hgQceUBw3d+5c5s6dG/j/aARJEhMTcTh8QGhWq90+wCM5b4E2vZhT8fF0trXpWhSWoiLia2p0g5clCU/xN67SUBjLm2hI1+kuKsL7h78zZZA3Hfw+2KKeleT+Ipm1a1cDfj71p50HiM/Pp8lBoILz22TwbyHduR8Ddru/qKutjaIiCzU1WouvqKiDk7X+7CN5nra3pkZouS9dGseRI8o89aKelVwZXaO4PxHcTiftw5wPDkcCRu//Ij4hlzoNh70cx6OzuK9+BeS7WbUKYmPb4OKLiXjjDW120MUXw+D8MPtszI75uCue8NZW+OMf8dbW0vbWW4FzRQGrVysZTw8/8DK37FgWaNjy/7GCcSit1bAjR3AvXaptJhQfL2ioCa7ISE4OvovYWFi9Wvm9+jUlOBzCp+/u6Ajpneqdh2PHTMuKuKVLGXfE3P3L783ptNAy/xFymvy/lbtXpcY9K3tXkrVFO38keQEwxdsETNcck+I9htt94T9vYPiOO+7gjjv8fV4/+eQTNm7cqFEAY4lQinbAz3lSVdVBAgsZqH9X03z7eMFS0oIENDuqqoifP1+T4gmQYfdR9dxQgM7fT3YhWc3aYKHHZqMw50/cWlemYWlMO6GsgpT868t5LRAAFvm8G8kgchxMOT3Uo8CICtsoQKmmEwB9l5nIreQgi8Jpf+I1+1LdtFgYWWHPlNgujBrV9BHNClboKgCnxc43+97DUZcFdbB3r4/XX/fTLQzMmKEbBDbbEEYEvTkrD/zKOZrUkBTQEsdKjpAu+72YgllUHyJiiAWw7t9vKkFCwmg1V9E7j16KqAjD6falR/8uuVddF86kfe0GLE6tvFAH/ZfnVvPBlqmahI7ludXAdabvYzg46zGBswmRf08P8jQvDzY+XrMBx11PcV5vi18A95XB4nQ+yNGmKcoXuMdmo2PDBiFVrp/BUJnxYHE+SZ9esDAzgzvrtLQDGmrewQl+TBZb0KP6/da1/bpU2CLoZWgEW1Ty4ObRo2L+Ip89IyDI9OiFR9KIo4zl7OUnqh4FQ2iJtDPX5e8TcRNbiGaoT8Tx6Cy/ApAFPo8cCTMVFB9Je0HRnBXtFvXOJSmgY6pAsV42lEgge2w2BqZP1ygBI+Ujwmg1V9E7j2/FCtPnGI5CCkb/brYeweJ0kkUDmyO+Q8nA8kBCx6OpL3Be6ZOm72G4OGeUwPTp05k+XbsdGktIlqyoRiCDRq6gjm7OI3nSKYqqvq5I11pZOZ3q3l8rT+iAlt42oW054BjigTaaFCJXkt6iKi7uZu/eKEWXtOzIJlb2riRi941+dsyWFsKP+i17Nd+RmrvGbh9gYWkHnbaRZ1MYLSpRcNNq9eF2D92HOpA4Wtkzckzt3qfIjIrlS8KAzok24udcNJgu6u8TYadBwexaMbnUvwNQwUxQfCQWsHz31b7zMBlt+4TuKvW5pGfyyHv+ftjquaBHwawnkMO7xZXRoVSWj1ZVud55JmVlaX1QOhiOQjKifzeiSJFDbtxMBSq5E29kJK7Zs+kqffKMsJmeM0rgbMFm87B+/UmcTgvPXL+Llv44TS/TgZh0vrApt/Z6mTHNpAophd8/kE6Kc4iZUTQpQs2Nt9k8bNo0wE8f+JL2HYdIdx2hzLWMrC0OfNtf0qTwlbm15HMxMR5ycjzY7e6gecmhFG8ZLSpRaqnbHRaUYng0smcU50vxN6tR1330zfEXaPn76lqFzK69JeJMnwkTxA2L5BipBSztvizOPhLmPayxyAdSUxXnkj+TWwf7YavrQxxksTB1E9WXLCXB1Y4r0p81F7d4sTgdd7RcOSZTOIPNvdFIBfVkZGBpaSHM48GTmEjnmjWGxYm2o4eoYbaQ/n1a1Y9NCXCRazDc5cIXE3PG6Ky/8kpAgs3mYf20lcK0S3nPXsmiOnRI/Oiqc5dz2c7dTOlvDHx2mKn8uH8VXy8J59n1+kJ2OL7irCx4LeZeRQ4yIEzhS0/38E7SQ5S0PkBT0uUk2SNMF6SIFNSXmz+iMOdPkJmhOY+RlaenQG02Lxs2qEkSQkMotQt6+e69BQWD4xmbLlOjaQG3v/kmE0tKGFdbC/irxrtKSxXnkj8TudUaICKMzCZh9gU8WHoeA7ZncXd3Y1VV2qqNkTPZJzdU42g450+YN0+hTK0tLcTdfz8dsgC7ejyrmgjwR6mNBI+8p4OBAhuJa3C08C8lMAiL00n4F18Iv5P37NXLKwe/C+N7955i0fYnuJm3NQFbe+0+QL/yY7gTwmw7Ra/NRuyGZ1kDQK+p30gQKagpfQ3cWlfGnXWVQmtbzzoz4uwpLIwLqWJSvcD0uJNEbhq9fPeYykq6B3sPJLS0UJmSQvdqpZDu7hbHMYz6Usih92xCrRr12GycXC++Zwlqxk3Jar1oYhMvzvndoFAaInsTsW+qjREziiyUnaMRRhJIN3t+9W4K9GMc0niyQMMf9eOqacqmPkEU2GjtqEaCr7wScDotvFzSzrIdC4gQVMl6o6KwNDYSV1jI/b3rhAogMdHDrFkulhYc5PLFt2J1rxTyxKdyHCMlMNwJYbad4kgmlnGHqtAqhUXBzYwMn4ZlNRgFhWiB2WPu5G+DrQblSJmgLd7Su6fIP/+Z8Zs2GXacMlJkw8VY0XCoxypZrXlz+lRJCH6hHb59u/A8amPEyAUzmtb7cI0jdQGinkI1MqJE15AfLy+0dNlm0m5TcogFU2BnckelB3Nmyz8ppEV3zZbHNRV9EsL7+4msqyO6upq2Px8WHiP5aKdXriTC4aCMZUxFeexQupfy+oWFccybl0BhYRxHbrgLn1Wpl+XuCT10FxczMLhbkf9ODqOJZXE6iSssJGHePD8FsdOpOcYMHYHZSmHJzXLjjf0kJnpITPTg8fg0TVIkxaI35vj58zUL7Ge9i8iKUgY9p3KYVfvzNPely8F08qRuxykJxcXdpKW51T9l/34rTmfoFdNg7MoaCYqLu7HblbTO6sC7JLSjq6sJ+/JL4XlCMSKMhF+oGI5x5HRa+Pa3I6iujqamJpLq6mjmzJnM7t1aI87IiBJmR4UwHiMF1rL7GAfnP83BtiRORNnovuTr9OXlnfH2ll/pnYC06PRypNXIOP058A3N562t/qwW6YWr+Xjk6V6SHSKy+u7c8lsu0XFPdOqwT4J4a95bUBC0dy6Yt9hE3Z4OMzXARwShW8H19Vba2owFpsNh1biIAn1tBXUDWTjYEnkzK/oXK7iTspod9Km29iIrzAhyq9Bm8zB9+oCmb3Rz8/C5k0aThkMOM7GNYPTQZq1TaTcx/r33xN/rWO9GrqPhWMvl5bGKrDmAvr5w7rornm3blD3A9eoe1AF2+fFmx6OnMHosscTflk+ue8j4bDxopaP0eVJsaQq3oN0ubvc5WvhKKwFp0ZlljCxjGe9zneb4pCS/8NNrnj6Qnk7Hhg26wToJ5/WKm2CYCRKJtuZGikOCWX+rvNtTP5Fs4UaKqFDkyUtWcBYNQX3BovsXob7eouh6VVsbwQc5PzIUWFnWo5qMHxC7MxQtLj/7DItBSqHa0tOLCxg1YzF6LsNpfWoWwRg39SxW78SJnJozx5Q/30yzma4JWqEY1G8+jEC6nkIV9QA3G2CXH292PHoK49BnVma4ld6HTPcRHEWrOb2hQmEg1tSYb/c5HHyllYC06MwyRmbhYAlPsISnFZ/b7f7z6L1w0fZONElDKdgZLYTib5W6PUXh4jL2cQ27yKaBU0RSw9U0N/spc6vqg+8s9BapHNHRXgWLK/jdI3q1GOB/3u6cHNNtA+XKU4/bXjqv2tILRWib2XENp/XpaEHPYt3RfyX5m17C++cYcv/dQ2lpl64gCrabOMxUnqQMdbNaM4aI2fTgYAWIIFbSZgLs6uPNBKX1FEbETQ8Jj5/Q1cLPxpCdV4SvdEyguLibrOjjgYyJav43/eizivanZfK71B8pPpMvUumF9+Xl4Zo509C/JxIgyyjjMFMVnx2PzhrTIFEo/k157EHa6bzI9xVxgbba48IF/dEtFRQWxgX85XoCND19gJkzXeTl9ZGTI25PqKZFljCQnk5HVRU9994bcmzF6bSwuNefLimHNzKS/htvFL5HM752CWZ85JLbJi+vL/AMxsr6A2UsKKy3l1PJ6YrvG7Fx98CLtLom0XZyHFu2RDFvXoJuzEPPoOggjkoWMJetfNwzVfN9qIFfdSzN6bQE3KtSDKCpKYKwMJ/w96OxswoFksJo37AhwBrQM1FMadEzMWXM3IJ6+MrtBCRroaPDSnx8LBVPHuI3D+6mpfc83mI+/8aHpMZ8iScnB88gkbecN+dJzqO8vI/WQz3YPv8rZW0l2OZ3BtoHmrUQRFafiMqhetpynrWFHhgMdF1yDJDR+hGlkyvIyESzbQ3JvymzavQ44/0ZUFpEtJ2gujo6kO1SUNDL5s3j6etTdnKTC73CwjhRm1mqc5dzfX2N7o4rtrxcN/VT5CIbis8k8y7b/PTRkU1cMDsBb+mDhm6HnBw3vb3+e7jsMrBa3SxeHKfJRjEr6II2ShkliHYmR8PT+ZD/zTf4O5+TzQJeV7xbgGPHrJSXx1Jc3K1JZY3TMSg28Z1Atlxucp/me7OGiMXppL3kZRbsWMYR1xCFXW1tBDk5bo317POFYQkbwOM78zurYIitWELjbR+QKY8JWLOJrVhCSuXYuQVFOCfaS4aK4baX1Gv99rs1HzGtcpVpf+Nw2gfqjUcK1jmd4TQ1aX3k8jaAIohaz4nucyqH2cpc0u1orNqArzqEwiW9Z7kp5/6g7Q1vvLE/UIkrYcIEH+XlHWzdGhUQLAUFvSxeHCds1ReIOwjGrNf+0DVzprANqF7LwNvSd1CxIVKXmsIM9YWk1EJpHylhtPLsRdAbTyULSOUYZSznz1wv/G1u7mna28OE6+jSxcqMrcNMZS5bA70sRDsbPV4o+TyVjrnbsVLRDlVCYqJHmGQwg7+RFXMCZ871IRVHngm07D5Gd9FqJnS10DMxxa8AZqSNSYtK0GcR/UopgWD9QcFcsc7kq64SZqYMpKfrMkcGg6izUGZaP795U98HC2IloHefC6ikkjsNBU+oY1ZnnAQyd3QEgX/M4gUbE+NRxACyoo+z5sl2Xt2aHVLFbjCBq37Hhw5Z2b9/nOb469jOZvs9mg5Q5eWx7NwZGTSzCYbmlhlBJ0fE7t0k3HmnIhtrOP1x9RSJnqLcznU0k8q7fEsobMHvstMzWJ4v3hdQzl0TUlhOGR/3TA367oIZItI7vY73hMpJb06N9pw/U5CvLX+7z7YRK69zkkr6TCOYr02kgTdvHk9OzgCZmUOTeDjtA4MhiwY2+37CY/xgKK3U9wLn8SQeQrP+jHiNYPRK0kWuCw/BXUZ6UAeBG/qm8NsHd/P8tp6QBJ+Ri0v0ji0WcWprKs2KAGWwinERpLkVSkaJxekk/q67NO1DjapkRcaLSCEHgtEGdR/LKGM9C/kL13CUTMX3ycluJk/2IrCBOHHConGH+oPAwalAgrlRJXeamvhOQm7uac3uUs6uOpo0DGeiF3Ao7T5Hiq+UEgiWzSFKW+zrC6euLpK6uqHqzck67QNxuYgrLDS9bZdbaeFHj5Lc3EQlu4YOaEaT224GVqt4cydV94aSbTQcl4S0oPXaDObkuNmyJcrgDENo6T0vJHoAabze+HgGPB58SUm4ZT0Yygu179jj0eZHiASI2bRWOeR+XLPxotjyciy9YloPkTDTqzQWpdJGOBz8z7cr+MO/r+SptDqijjUGvnNgCyjsu3mZR1jJW/wnf+cb9DABL1YsFh+TJ4uVZnKyZ9RcWOrzSL2I1cR34J9TpaX+9fj0/IO0Nnk0JJCjlWE3kqrusXTvjQRfKSUQLAUvWNqiwxFBSclE/i3zJVY0fYcIlMHHcJeL6OpqU+XxZnKqwbwFI59gh2t/TxKdtDJk7UlCTWqKbvacIyn91ytSAoQxgZ6eMM05UmkO6RloXC4WC93PPRcYr9E7/ga7GI9LV4AEmx/B6LDNwojGQCTM9CqN9VJpx588wbotF7M/dTNvzypi0oEPCevsJNztDqT9ptLMjWzjAo4whyEaiebmCLKzPURHezVB/aUFB0eFKkL4HlNTcaelkXXMISC+8wYEcMWGyFHvOyGH3rMuKZlITIxPd3cQ6lqSr2eL3Y6lqGjMFMZXSgnIhVJHx3ji408pXpaZTmM7dkSyxXUjO3iPV/ku6RxjHMo0QTPkVsFyqiWYsWDUE6wPKzGc4hZ+Tw+xAaGWSDvtr72mCLYZWSajQdyll+0i9XGQCsEuvRQadrfS2D+UOicpLk9yrqlrmRmv3juOjvbS3JfKNm7Q5YXX+63EHfWjH1n5xS/ctDoGSG/9iNL4CjLKtRlZwaDnqtFT4KHSmktuwV3NF/LAl9/n173vApBBs6bIbhfXaH7/wQfjFL03YmI8rFkzRJsiR4TDwcSSEnwxMaYtYOF7bG6m/8YbOX3llaSeOMGLyVriO1C63cZ3dNAemcByyti3eOqouG70nrW6H4l6dxDKWtIojJoa4k22Hx0OvlJKAIaEkt/PphROZjqNSS96F99kKg28x3Vcz581x40G86dZC0Y9wSZxkkayicAdSDfdxTU8l/wob82Y4L++CctkrGlu5bQR778PuZP7uYq3ae2fGFBc6XboUD0D3R7OQcZrcTpZ2fsYeyOHeuuC34pds6aTyspkHnK8wwOtJVye1ESEPUkhsPR2ktJiT0xM5NIJ+wYpLRzQBNSFbg2LYhqemBg6Xn3VdM0J+FNpp+/Yo+DFUlN9tPSepzuORks2yzxlms/lwg78sZzKyhi+o0fIt2OHIRmfGnrvMbynh3YTBV2S2627O5GbblJmMdXtCeOd6YuY2r1vWC4ZvWetfibq4q4Bh3jNyJtNSRhr1lQ1vnJKwAjSTqHilo9obItlP5fQw1DzkPFhpzjlG6/4TahVvlJQ6d5Ddm5Cm50xkJ6O12YLiWNePcFe4S7msF3ROcxq9fHGL9pgcNdiqkpzDGluRdvq2i+y+fcbT/GrQHvLXDoE9MShBjs9ycmB3yU7HGzjXaE7YcaMTiAWWCMk2h4uB0+oCzhUmgQ95bSwNIGVJRu5ZsvjGlpzCamCQGsHcWziO1RNWw496ciblkVGejUCDwaDwjrPX4+MT+95jNa8W7HCgsOhtNwbj0Xx+LFrqMSfxhyqghY9a6NnIuGj1gxmC873UWs6F6g+O9M9Bv6lBFSw2Ty8NGsd0dXVNGBXkMB1Jp3POyeUdQDLKGNO9N+Y0tcQ+EzPgpcHlWp5nG18oHE9hLrla2iAT+vt5PG3wGffZBfvcT13Rr1FZ8RkJk70UFHRyYwZMs54ExNtLGlu9bbVH/dMpXN9aG40SagYjVf+uwCvkwv6YvJCaqc5XA6eUBdwKJ2yjJTTwtIE8uvXC3e3WdHHKetbpvlcKu7Ku7CPqmLlef0tN7VB/eRkj/D5eyMjNUoAjJ/HaM07NYGcBHnF+XDcm+pnbfRMJFRMLiWt6UPFej/MVJ5JKuVZ1e/OdI+BfykBAaRJmOWQkcDZ7Xy05nfsXzygXFD2dI6veYPzKlcx4Gjlo9Z0KuJLoVzbbUtu/cqrgy9LPMr5sxKGlS2wYoWFmt6fcSl7FBPsguhm9rzXoOhwJIeZiTZaHbBECJV3R3L/WA8dEv7OcuKE4Xj1hLNoOz4SnK0mIXrKSS60HA4rra1hJCV5sds9LC04TvpiFJa+5C6Sgtrq88pbbkqQjhU9/7DeXiGPk3fCBN17Ga15p6dn1LufUBV0KM8kgMwM5tYp2QCWUUauPRlQvrcz3WPgK1UsJsHidJJYUYHb4dD1C0qCJyDYJ5dCZgYFBb1UVsZoLC4zVX7z5iVQU6PlJpo50zXstop33JHCjh3hikbozaRSnbucZzfq002EWrw0Eoh8+Luap3LnnQmKDJO0NDfTpw/Q3R1umOsuQrBiIL0CsuqY25my7alRyfNOTEzkZG3tGXuuowH1PH8mqZRxF55vWJwkKhI0KgJTt24Ef7aPqHXjaCIvL4U9e5RuGqlyPkum+UajkCzYMzm2u4X82+I54s4MfJZtbaTqjQ7SZqRo1oicCt5qs9E2CtlB/6oYHkQowi+U8m0z1chmjgkVS5YkU1Wlda2YOedw6CLUiNi9m7iiIixdXXgmTgxwKMmvoX7en6VezU1hWxXV0dHRPmJi3HzxhfJZf5CTz+QtvzMcgxkha3E68c65Q+G2kyqZc/OSR4WvR6reHo3nejYhqkIfCSbdfbdwNzDWVbzqtRFDNxu5mev4S+CzM6Wg4woLOVFdq3Avl7GM5LxcuouLDWXSaL2Pf1UMDyKUwF0oTcvNMP+NBVXwihUeamq8wzpnKH5nEdQcSuFdXSTedpuCQ0n0vB9r/gGNKH2ofX1h9PWZz3X3JCbivvBC00LWY7NRmPMnbq0r0wRJ005ofdYjwUif67kGtZV6sGApKyunm66YDe8Wz8WxbqauXhu9xLI89ZdUX7KUiT0tZ1RBW1paFD1GJLhOpJ3xbCA1vnJKIJTAnRmaiWD85XIft5nsklCRlcWon9Ms4oqKhGydcUVFAQ4l0fM+pkMFLYJerrtr1qzQF0hmBnfWaXs/n2lq4X8kiHZy8Rv3UuveFsgyClYxe7biJOK1cR4DtmdNEFmMLgwz185wNpAaZ1UJtLW18dxzz9HZ2UlYWBhz587l29/+9pheM5QJaRS8NMsiqbbIjbJLhltWfqboh9Uww6HUFTtFY8nr8b+I8Pq0R7n+qJY2ejhBsrPZtOUfFSIrNdN9hJUsD6QfB2t4cjabqZ+ttaFGsMw1EXY7M4h0WhhktB8znFUlYLFYuPPOO8nOzqa/v5+HH36Yyy67jPT09OA/HiZCmZBGQkPkKnK7w0hPH8Bm84ZskY+UouFswKPDoeSZOFRbsZwyfsJeRebS9/glG6P+i67+IebOjAwfLpeH1lbllHzvyPl8/OyGkKi+9TAWO7F/BqiND1atgkGuHj0rVZ1hY9TwRJ3tI2UGxS1efE5x6IwljDKeRDLpMFP5btMqyI9n82af9DrGBGdVCUyaNIlJkyYBEBUVRVpaGh0dHWOqBKSXkVhRgdvp1BUqFqeTy8rL2RwPJZ4impIuV/CR67mKbDavbqaPkaU/Vn7B0SKtEjEnRlRUaPsqEEZF8kp+Ny+BlBQPjrZETaOcZZRx/kU+7Pa+gDBetcrKAw942LJF27h9ZeV01o6Sb/RcsQzPFYiMD9/evVhef93f39eAbVSOYC41KU4yHGPnXCVeMwOL06npXdy5erVi/JJMOjj/aTxNrcqiPoc/trF69diN8ZyJCbS2ttLQ0MD5558f/OARwmOz4XnlFdp1Iu7yiZoDVFHNgMVOx3NDEzXUpuDBJv9Y+AVHa3ehz5x4NeHPPMOk++8n3OO/7zB8/J//KWMt36SGLGKi3PTKKpcl5NqVPRxWrEhk927xdByrtnrnIs4ETbEcIuMj7MiRgPEhslIbrdkscw/RSYTiUtMzduLnz6djwwahMfaPtkOWYHE6if/P/yRCls0YtWULEZ98Qvubb2oUwdKMV6hp0qaQHz8uLnobLZwTSuDUqVOsXr2ahQsXEh2tTaHctm0b27ZtA+DnP/85iaPgJLNarbrnsSxZgkUwURMrKvC88grg3zHv3etTVCVmZ/tYtUp83mDntNjtIGjyYR1MERur+zCDJUu05fcORwQVFYlU8peAApBwPp8HfMa9/VYm0KWg38jOOB14Tg0NsGBBhG51J4DNpn+PZtDQ4C+qO348jClTfKxY4SEreHuDkGG1Wkns7sayYgVhx4/jmzIFz4oVmL2Y6Fns3RvFpk0DYzJeAGtHh/Dz8R0d/meemIhv82Y88nv63gq+8ZKNzOPewefpIytr0oiuF9HURNINN+C7+GJ82dmB56Y3hyfn5+PevNnw2RqtjTMBy5IlWATp7NZjx4Rr0G63iEQAaWmM6X2cdSXgdrtZvXo1s2bN4qqrrhIeM3fuXObOnRv4/2jkzMpzbzU+UYcDke3pdjoDu4fYWHj9dYuCCfP8809z8mQXsbFayy0hyDktRUXE1wj65hYV4VHdr3y8PrudkzqFJMGuaRYORwIIzuR0unH7xNeQ+4wvYT9TORLIj/7pRX8lNvZR2tpg6dI4jhzRdvWSYLX6mD+/g5O1nw/LJTC0ixkaZU2NN5DNMpquhsTubsJuukkhtLw1NXSuWeMv/AlyDdGzOHIkjKVL3WPmwoqLj0drdsGp+Hg6pTkSG4vcHxEFrF6t3KFKhwZ7nnrXAwjr6SFszx7YswfvIGtmnM4cDnc4CLvpJsMdQSj59eod2LKCT/xxqBHMC731B+I1WFRkoaZGW5f06KO+f946AZ/PxwsvvEBaWho333zzWRmDaLvpiYkRHivKIJIzYW7ZEkV9vVWYLhcsK8lsqXwoNLO6hF6DVZxmJ7eR68tDcJ/xVI4o8qNdPTMDKXrBOPrd7jBeXwe31A/PJWBU6/F88b5RdTVYVqwQWq3xd92laBKjdw0ztSaaa45QiYncPb7s7JAydwJjaGwkor5e0RFNfa+i64kgxcP05rD8mNGo9pW7O+00EL8xn2hZE/jhzAujsatliaSE4uO9eDwDJCX5sNvd/qr5rEmMYu2eBuLk9jOE+vp6DSXg9AAAFOJJREFU/vKXv7B//34efPBBHnzwQWoHAyhnCiIfpaW3F6/KLSXKIDISMGp0FxczYLcbnlMKnrVv2EDn2rXCCWcUQDZzTZ/VSkRTE5E1NURXVxOfn4/F6dT8Vo7i4m7sdiVvu+QHFl1DTlcs79AVuE/ZAjDTw6Gt9rjpe1bDSLCG8izNIOz4ceHn6i5hetcYbpwpurpa931anE7iCgtJmDePuMJCzbuWjI++vDxcM2fSl5fHwKZNpoWdYgx1dbotMdXXcxsIyMC5T5wQzi/1MSOFeh2vZDmZMgUAw5sX3cXFnErSsgw3WTI4WLA08H9JCVVXR1NXF0lTUwTt7WFnLHPtrO4Epk2bxm9/+9uzOQR9YrFp0/DY7YZWeaiWmzsnh/BBgXA6N5eu0tKQLU6jALI2qGgB2e4i3OkkQtUc1ow1ZZRaKe8pLDUXf5Iy0npS+fqEL1i1fyFZzfrpuGZ6OKSiI1xNCAAjwToawXg5bQYe8wtWdI1Q6xiCZZSZDaqqK5wTExMxa3qaaY6kvlePzcbAZZdhDdJTw5OcHFAa8fPna+audMxIoV7HqRwTHjccJtgfX76Fm7b8lG+wG4AarmKR52lyK5NZO8Pv4guFmWAscNZjAmcbum4auz3oNtOs5SZajNb6euFvg23v9cbbNSFFJ4MHbIP3kTBvHqIO4Yd3ttPntBhaHUaplUbNxS3OJ+kzcHEFejhUJPLZZx7q6y2KhvN2+wDLc6pBSz1jSgAYCVZPubnCQb2MHTVtBoAPkIe4vdHRGutYb+yh1DFYnE4id+4Ujl8SVmeCjsBMcyTRvepRSUiQGwsem42ODRvGrG2keh2H2iPECB+0nc863tZ8nuA4Hfj3cNyAo4mvvBIYSTWjWcvN7GI0Y7npjXc5ZUGtCT0Fsq8tg2X58aaaZYcKMzw6NpuHV17x0NbWJmRjTGAhA/XvKu7ZnZZGWG9v0NiGkWA18+6NGot/XUSbAXiionBfcQWe5GR6CwqIW7zY9PwyU8cgzROLjrUuCauxSjsWNYDXgzc6mt6CAu0Y9YyvxERcs2ZpjZ8xpDVXr+NllHGNtUbhEpLeWagxmNZWcdab/PNQ3YCjja+8EhjJ5DJruZldjKa6fanGa7XZ6CgqYt/iqcJryK0JvcrEZYMKRK4wzlaBjloISuPwxscz4PHgS0rCk5iIdf9+BTOlUeBOT7CaefdGW/U3dWgzwiIiaN+wIfD/0RZeRi4YhQU9ypw9Rg3g5VTRvrAwwgbJicP7+ohbvFjzbvQUsFHwdayI+bTrOJmOgiqSVFXqQMiJBJMne0Wbb5KSvIF/n206k6+8EoCRTS4zlpvZxWhWWcjHm5iYiKetzZQ1IQm9j26pIKLthKbdoKQwzpUCHaHQsVggMVFRgAOhuTlCUXBGW3UztBkw+sJLb560W5N4JGcTC0nAhrmdTigI1gA+lLjTWFr2w4F2HafROUP5zuIKC0N2r2Vmeqir035ut48tsWQo+JcSOAMwuxhHYrmZtSY8NhvrZr0k7GsgKYyzTW0rQW8c4b2iDsDm3ByhKjgj5dq5RECbYbXSWVERdBwjgd48+ZP7BtZtuZh366WeF6MraM00gNeLOxm+mzFsaTKaO9rhuNfMrsuzSWdyVlNEvyoQpeGJhI6ZNFI9SNZEXl4fM2e6yMvr0/XxG6V8wplvdK0HM0FHOcwoy1DTQo2e1cCMGf7eCenpeCdOxGu3K3opjBWCpeXK05QbyKKASq7zbaeAShpkTeZDhamWpGZ3vSbSW0eMhoZRvcZwjLRQ1uXZwr92AmcIZlwCI90im7Umgm0/zxb/u+Z6OuM4nZuLtb5+WG6OUBVcsGc1MGNGoHdCYmIiA2NZ1TMI+Tw58F4HB7rSFW498LurjILawxFCZna0Zne9ZyRzSad4b7jXGK577VwnLfyXEjjHMBbBL9GW2GaznZP872bG0VVaCjAsZTlca+5cW8TSPPmZTsvS5GTPqOefmzFSTFe+n4Hdpm7x3jCvMRwj7R+BAfVfSuCfHMMJ8p4rQbtg4ziT1ty5CiOf8+LFccLfjCT/3OyONugxI9xtmmFb9U2ZMqJrCH8bgpF2riRYBMO/lMA/KaRFcu/OJdzUFvqW+Fzpkzva4zhXFNxowchdFUr+udxitdjtWHRICUcDTqeFx3pXsuz/tXf3MU1dfRzAv22RV3lruynBwcC3jGyTqWxIxiMTmBtxyTAG37eGZZtOMmcWEacbJeiCOjYgovyhAfeYsAUTSUzIeKxmskx85qYoC4yUPQhuqAiIINBKe/v8YegK3AK33Pbe9v4+//FS7u+U3vu795zfOcfnBuYZJ9biT+f10+nmMmu1YFgWZXRWwh9/1y8bHBRFgcVUKAl4INuTJAviGOQVEy6J5cqVWdi5MwT9/QoEBZlRXNyH+PiRqV84Cb73DLDXXTXdyhQuixLOREfHk1V3L13ygdE4Bz9AhwP4HNE+f2HhShWYvN3TOt60u7miolyW8Nnu+hmfiXsDAOI79ySXBEaztVdvL0KUSlHdBfJ1cbA9SficAi81V67Mwvr1auu+0f39cqxfr8b333c7nAj4HqydzHTrz+2W4uYewfaAf/OSrNja3T662ZARSA8YwtGIqWdKBx4+jJ4L+wAsm/Bztm4uVz3Rsr2HcqOR9XfFdu5JKgmMz9b+EE8fHZ8XB9sJTvuRj3hcGbPHrzv3gbvSzp0h1gQwymSSYefOEPz3v/cd+puuXixsWstQ2Bmk1V/qwVnjP4POM0lWbO22NdU4he25+wzWgi0JuGqZBTb23kPGx2dMMhDjuSepeQJ8Lx3MJy7LUk/Fti+4HVFIwXmcxmbcVP/L7hwFZ+roUCArKwTr1qmQlRWCjg732C6yv589Tnvfnw6hFwtjY2+Q9n/GsXt9O/p5BKbeN2KqC7jtuZuP/ZiP1jE/d+UyC2zsvYfGlSunnB8kNGk9CYhkEhQbPi8O4/uC2xGF/ZHl+O67XvS5eJKKK7s/+GDbJTcywj6TNSjI8biFXiyMDVvF1F8+0dhvzJ/wu44mq8n2jZjOBdz23I1CO84jBZ/jAP4Keg7K5OdcuswCm8nKmcV20R9PUklALJOg2PB5cRB6LRJbQq+VzgVbwhq/OLSXlwXFxY7HLfRiYWzYFiU80Ps52v8zcXaxo8mKrd0+PgxWrjQiL69/ys/m+HM3Cu04ja0YSk4XRaWNO1edSSoJiLlGnM+Lw/gB5i1bBnmtRuHC3hNOe7v4Pnrs/dYy+PmZMWuWjJfqIDElaFvjFyXUXHuAH1pGeEtWM223mM/dUWIpq+ZKZrE4cfUmJ+kct4IkF6MVBr69vTCItDqIy0kyfjNttrtZLy/LmAHOyMgRl3XHZNmZ0RoQYIZO122Ngcum4M6ybp0K9fUTy/oSEoyoquphecVYYmgDH0bb4cjn0ZmsdfjTvNP2tP/HTIlyo3khjGZrtVqNPpF9QPhYnoDtbnZ8hYsru2OyswdQW+uLoaGxNQiDgwrRdQmJsb+eKz7nIIhtuQx3vdMWO8klAU83VRXGKFdVo0REmLF48QiuX594hy1kRQwbMfbXc+Fug/BEHCRVIioFk1Vh2HLl3e2zz7rHHbY7LPs7GT7LjIl00JPAJNxhBcDx2O5m2cYEXHl360532GLrAuFCbHMQ+F4eQwrYrjlQq516TEoCdrjLCoDjsVVhbNkyiNOnAwQb4BNrRYynEdOYBnVNcWfvmmOprQUCnfc0J7nqoFFTjbiHZGXB/+zZCd8fShdHXfIoqoAQD77bwPVOmu3C60glGB/tsFcVlp4+5LInLXf7TNm75pg3bMC9wsIZ/33RVgc1NDSgvLwcDMMgOTkZb7/9ttAhARD37GLi+Ry5kxbTE5fYuqbcgb1rjr3NcfgiaBJgGAYnT57E/v37oVKpsHfvXixfvhzz5s2b+sVOJubZxcTzOTrTWixjGmLqmnIX9q459jbH4Yug1UGtra2YO3cu5syZAy8vLyQkJODq1atChmQ1k03fCZkpd7+Tzs4eQGTk2JnVYi0GEAt71xyzVuvU4wr6JNDb2wuVSmX9WqVSQa/XT/g9nU4HnU4HACgoKICah9FyLy+vyf+OWg1LbS3MWi1kd+7AEhYGi1aL0KiJ66kIacp2uAlPaAefbYiMVKC+fuL3IyKc/z7x0Q61GqittUCrNePOHRnCwizQai2IigrlKcqpud1nys41x2vhQqhNJqcdVtCB4fr6ety4cQPbtm0DANTV1aG1tRWZmZmTvs4VA8PugtohHny2ga9BXkd4wv8CoHaMJ8qBYZVKhZ6ef9Zk6enpQWio6+4UCBErMQ3yEs8maBKYP38+7ty5g66uLiiVSly+fBkff/yxkCERIhpiGeQlnk3QJKBQKJCZmYmDBw+CYRi89tpreOaZZ4QMiRBCJEXweQJLly7F0qVLhQ6DEEIkiRaQI4QQCaMkQAghEkZJgBBCJMwtF5AjhBDCD8k+CeTk5AgdAi+oHeLhCW0AqB1i4+x2SDYJEEIIoSRACCGSptBqnbxEnYhFR0cLHQIvqB3i4QltAKgdYuPMdtDAMCGESBh1BxFCiIRREiCEEAkTfO0gIYh1X2Mujh07hmvXriE4OBiFPGxCLYTu7m6Ulpair68PMpkMKSkpSEtLEzoszh4/fozc3FyYTCaYzWbEx8cjIyND6LAcwjAMcnJyoFQq3brEcseOHfD19YVcLodCoUBBQYHQIXE2ODiIsrIy3L59GzKZDNu3b8eiRYt4P47kkoCY9zXmIikpCW+88QZKS0uFDsVhCoUCW7duRXR0NIaHh5GTk4MXX3zR7f4Xs2bNQm5uLnx9fWEymfDFF18gNjbWKSess9XU1CA8PBzDw8NChzJjubm5CAoKEjoMh5WXlyM2NhaffvopTCYTjEajU44jue4gMe9rzEVMTAxmz54tdBgzEhoaaq168PPzQ3h4OHp7ewWOijuZTAZfX18AgNlshtlshkwmEzgq7np6enDt2jUkJycLHYrkDQ0Nobm5GatWrQLwZKvMgIAApxxLck8C093XmLhWV1cX2trasGDBAqFDcQjDMNizZw/u3r2L1atXY+HChUKHxFlFRQW2bNniEU8BAHDw4EEAQGpqKlJSUgSOhpuuri4EBQXh2LFjaG9vR3R0NDQajfVmg0+SexJgq4h1x7s2T2IwGFBYWAiNRgN/f3+hw3GIXC7HkSNHUFZWhj///BMdHR1Ch8TJb7/9huDgYI+pq8/Pz8ehQ4fw2Wefoba2Fk1NTUKHxInZbEZbWxtef/11HD58GD4+PqiurnbKsSSXBGhfY3ExmUwoLCxEYmIiXnnlFaHDmbGAgADExMSgoaFB6FA4aWlpwa+//oodO3agqKgIv//+O0pKSoQOy2FKpRIAEBwcjLi4OLS2tgocETcqlQoqlcr6RBkfH4+2tjanHEtyScB2X2OTyYTLly9j+fLlQoclSRaLBWVlZQgPD8eaNWuEDsdh/f39GBwcBPCkUqixsRHh4eECR8XNpk2bUFZWhtLSUnzyySd4/vnn3Xa/b4PBYO3SMhgMuHnzJiIiIgSOipuQkBCoVCp0dnYCABobG51WMCG5MQFP2de4qKgITU1NGBgYwLZt25CRkWEdRHIXLS0tqKurQ0REBHbv3g0A2Lhxo9ttN/rgwQOUlpaCYRhYLBasWLECy5YtEzosyXr48CG++uorAE+6VV599VXExsYKHBV3mZmZKCkpgclkwtNPP42PPvrIKcehZSMIIUTCJNcdRAgh5B+UBAghRMIoCRBCiIRREiCEEAmjJEAIIRJGSYBIRmdnJ7Kzs/HOO++gpqZG6HAIEQUqESWScfz4cfj5+UGj0czo72i1WiQmJjp1obW8vDx0dHRYa8QzMjIQFxfntOMR6ZLcZDEiXd3d3UhISBA6DJjNZigUikl/R6PRYN68eVAoFNDr9cjPz0dxcTEtcUJ4R0mASEJeXh6amprwxx9/oKKiAocOHYJOp0N9fT1MJhPi4uKg0Wjg7e2NR48e4ejRo9Dr9WAYBosXL8b7778PlUqFyspKNDc3Q6/Xo6KiAklJSXjrrbeQlZWFyspK68Xd9mnhxx9/xIULFzB//nxcunQJq1evxoYNG3Dx4kWcO3cOfX19WLBgAT744AM89dRTAIDIyEhr7DKZDGazmda5Ik5BSYBIQm5u7pgLc0VFBe7du4cjR45AoVCguLgYZ86cwaZNm2CxWJCUlIRdu3aBYRgcP34cJ0+eRHZ2NjZu3IiWlpYx3UFdXV1THl+v1yMhIQEnTpyA2WzGL7/8grNnz2LPnj0ICwtDdXU1iouLceDAAetrCgoK0NjYiJGRESxZssRjVvgk4kIDw0RyLBYLLly4gHfffRezZ8+Gn58f1q5di59//hkAEBgYiPj4ePj4+Fh/1tzcPKNjhoaG4s0334RCoYC3tzd0Oh3S09OtXT7p6em4desW7t+/b31NTk4OTp06hb1792LJkiWQy+l0JfyjJwEiOf39/TAajWP20LVYLGAYBgBgNBpx6tQpNDQ0WFcHHR4eBsMwDl+I1Wr1mK/v37+P8vJyfPvtt2Ni6O3ttXYJAU92lHrppZdQU1ODuXPn0oq3hHeUBIjkBAYGwtvbG19//bV13Xlb586dQ2dnJ7788kuEhITg1q1byM7Otm5INH4TotHdnoxGo3VTnL6+vkljUKvVWLt2LRITE6cVM8MwuHv37rR+lxAu6PmSSI5cLreOCzx8+BDAk21HRzeCMRgM8Pb2hr+/Px49eoSqqqoxrw8ODsa9e/esXwcFBUGpVOKnn34CwzC4ePHimJ+zSU1NRXV1NW7fvg3gyZ6y9fX1AIC///4b169fx+PHj2EymVBXV4empibExMTw9h4QMoqeBIgkbd68GWfOnMG+ffswMDAApVKJ1NRUxMbGIi0tDSUlJXjvvfegVCqxZs0aXL161fratLQ0lJaW4vz580hMTERmZiY+/PBDnDhxApWVlVi1ahUWLVo06fFffvllGAwGFBUVobu7G/7+/njhhRewYsUKWCwWVFVV4ZtvvoFcLkdYWBh27dpFA8PEKWiyGCGESBh1BxFCiIRREiCEEAmjJEAIIRJGSYAQQiSMkgAhhEgYJQFCCJEwSgKEECJhlAQIIUTC/g/XZbn5fb1xhwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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G7xkOwi2MNqS9DN8aMoLhKFTndmchev9ut9vws0Tv5/nnoxkcHGIyu3ZJnHOOk56eaGw2ibKyT8J+d1qF6j5TzGAkkFpZSayCEQDE2u2kVlZybN06v/Pb2/0XBEBHh/h4sNC6P0AuQ1VbpZyciN472dJPX59a4rXbY7nhhnTGj3dHhEhrPX9gIJpt2xK9f58p5g95bVSwWcgIdnA5kznkOdAJzhKPEJGdnSa8X1bW0HyCNftkZ4vfhfKeIwGLw0FqZSWW9nav/0oWmiorU1XzgZEzPwYzzlAwffpJ1RpVHjcK0ftRMgLwmJ5kc1RtLdTWpg/bXjBDS31gaW8XH+/o8D/mcLDq8K28wqVsZh42Gr2/RWpTam36FLpZwRIAnDabx5QVoXtPoIGzEw4Jf2tujqW2Np6amiRKStJxOEJnelrP94VMQE435LXRgr9ktZKKIUZwCrIQUV7eg82mNh3YbE7Ky3u8f+sRThGM3NMoLA4HaaWlZMydS1ppKRaHw/B16SUlJNXUEF9bS1JNjUerPnV9qIKS3nhCGmtjo+44Q8Hy5d3k5qrff26uk+XLuw3fQ34/NhrZzDymUR/wmuHcC2eMZvDoo4+yb98+enp6uOOOO/jOd77DZZddNuLjcGVni49nZan+ljfCrOYhLaKIOuawHWz5IW1KEcrLe6ivj1URiuTEQf70pQfJdefRnzWdnvJyxhQU4Kj/JChHdnl5D+9u/ZTG/iGtYhIH2c4cFo55krf79McmL8zy8p6QHOjl5T1s2xavcIxrI1KalghGpUZ5beTh30cjlxbxvTs6sFpdVFcfpbIylY4OC1lZ/u9Ii3Du2hWPw2Hxe59G7mkE8jpWasOx9fUcra4OGKEWSIsORXvRHQ9o/qYn5VuWLVOZfX3HGQqsVhd/+lN47z8726XSKF/mKt5lesDrhmsvnFEO5GARyeY2sr2WpsOsP3A1Of1DUr7TZvNbcGmlpSTV1Pjdb2f+jcQ/tzaiapw8Nr1F19OTyZVXRqmYhs3mDKhSttS1s+YWO+19o8mllRUsId8G765+gRsWnOcnrfpi+vSTdHUF/1wZdXWxzJ+fQX+/vpJaXNw/LKYFEfERfW/vuf/1X5xo/ZS9nMshJrOEFdgp4IXkmyjue9rv/v3FxYYITmlpGjU1/o5oCO59BgutddxfXExMdbWu/Ttj7lzia2v9jg/MmEHXc88JTV+B5qI3HsD7WyM2KlhJC7kkZyfjPP98TR9T9k03Eb1zp+Y4TxccDgttc+7yrptGbFwu8EX5Ity9YPoMdKBetIXU8wprkhdzWaGDWNs4oaSoZU4qsh6mK8KbtoBGqqjEIrXjIpseynGhHs+yZRaVIwqM2WfzirJZs+OkRzLu6MCVNZ2j5eXkWbNVkqfDEe0Xfgpw5EhUWGGpRUVOXnnlYx5aGk1nfRtp7k/ZfbyQw8fHec8J1fxhBMH6iKKiokilj4t4k4t4kxnRtcwdtZWacyu4qrGWxJYm77lOm82TA2IAIg1QRqTs7CINyKhZVOTcTgugRYeiveiO55Tc6kc028HWPuS8b9uWQ9ymu8guygO0/Wm+2v5Iw2p1cVahHdk6VICd7cyhgpU0jzqL+KKz2bs3htZWNTMdrr1gMgP87bV2Cri+749kOlzMtA1QTg9W1AvYqDkpXOipzUoG1dYmDg81olK6rFYh4bNaXV4CpCXlpaRINDeH9lwZBTRSfaCE2E7PHBuxsTh5DY7Cyxhnix3WaCIt4hPjwyDAwzhiWtTmoInuBhYcW8b8f1bRkLuVmisWkjHQxYn0dF0npS9htpSXU10N116bSWen/7sL1zSgtY4GCwuF57uysrzEQcu5/cLqhZxXX++nVSkZoHINGYGRfVXBSpX07Oe874O2+XVEv/JHXFYrrmXLcNfW6o7zdCHWloXSVVCAnSrm0z/bo1EqrQJWawxlZcMXSGEyA7TttZ2dFmpqkoRRHT3l5cTqbIRIRS8YlVxzcsTWvkg5smUpT5bgc2nje+P/wfXvLA/7ub5zLMDOH/uup99mzMQSDrSIj+XAASwOh+qbaTEOOarrX61TuP2rm6l+KYZjOuYVLcJMdTUzZ6YKzUVa79NoOKrWOhosLMRpswnX8ZhTf2s5t1dWncNj1dUKrTIr7CidQPsqtr6eFrvazCFy3uf0N9Iv75GCAo4qxulOSQEgbcGCiEQWhYNA81UyU49Je/iixUxmQOCoFpGa7rJaVQtMuRGMSvNGYFSNX7bMRW2t209yF6mUoWZM+0rw8/65mT7BEkpKcgelygYTwRUqtJhzT3k5CVu3Et3frz6/r8+P4WoxDmW+hxEJXo/Bl5c/5mcu0vuORsNRtd5xdG+v5jqWoRcVpKVVhgq9fQVwtLqarBsGQKGN6jnvlfc9tm5dRPdmJBBoviMJkxmgb6+VIdrkWhshWDu0HoyaowoKMGSfDaeMge+8RCGWAFOnOoNSZYfb5BaIADgLC4nfvdv/uo4OFeM8P3Ula/J2q/wCB5nEElZ4//ZI8PrbSo/5BWNnDyaOX+sdR58Kr9RblyOd06DHYFxWKz97zkJdidM791byxOcK1k8k92akEGmGGirMPAOGTCDFxf1kZvovcBuN/NJxq+HY5khKuj3l5ThtNtUxLXunrFI+91wX69YdC5qACMesiOuO37VL9ZsoxBLAZguOSAQzx1CgRwAAXBMmCK87lHIeJSXp1NQkUVsbz+PbzuZyaSsfX3E9n07/GjXJ32UO27HjqRdg1LkXiPkZ+Y4QXBy/6B0DxDY3B4y5j2ROQySg3K8zZgyw7eLFtCaqa1xorZ+R0EI/qzA1g1OQN6Cv5Gyjkddj5jChucGrmsbW1/Of1c+xsuocoaklkpJupNXIYAiISKJWYgVLqKNI7cwLgUgo55hw9GhA52uwCEQAtOy2FazwY5yyX2DdhmPkOCxMr0wlr2MgqDjzQHZiowhGYpffcfoNNxDr4/EXScYOh4W777bw4YeZHDkSxejREvn5TsaNc2Oz6c81kBkyEv40tWN6NBbHH+k3sEdGKvADPntVZU1m4ANfNf2XjoUeRqBArN2O/ZY11PT90XtMaWqJ1GaXoaVGKhebzWahrMw/OckXwRAQkUStRAF2tuTexsJza2jvHeVHEEWbvpEC4QaR55iZmennfA2XeGgRAHdKikfTa29nsLCQwcJCont7vcSkbcEo4XUy4ww2UsY7nggx+IXz9rPnpXQaBid4j02MaWLhvKOA/5xdVivu8eMRhX8pJeOWunZump9DY78Fuf6OfInF4mT9em1tJZAZUiRgJGzdirOwENeECSELAUZNLZHemyJYHA66lm7k5p1LaBgYCgY4U8qqaMFkBgIoN3nGXIfKWSVjdJ9a2lTaakfCKeS76YzWLRH5R7SkeS2J2pWZyeCUKbiyshhdXs6vrU5AXRxNtOmj3trNPdJW/tU6JIUF2iCRcPiJCMBgXh4xe/cSq0hc9E02G05beSTsxOdUrWTHYD0VrKSVXE/S4OASsqqmc6zIc2+lwJCa6qa80cps/BPFZMnY4nCw5pY2GvvFmbCBch4C+TFEAkZ0f7/HZ7N797A7c4d7b8rr9W77ShrIV/12uuoyGYXJDDQgS6MxH30k/L1V4DxVmlqG2ymkteluuCGd557TJq7BOCi1JOqBmTMDzk206RNbmriDX/AvqlRj1tsgWvb+9BtuwD1+vCFNQSYA0Usfoq2+kzZy+evgt1jQsZACn/sqzSVGGaev5jJSTRks7e3euHQlBjo8DlWRlH6AVezg36pQTKVknFpZSVtfqe5z9SKmApkhtQQMGZF25srmLrs9Q6GJGtuboZh55PWqFVwhendnijnJZAYCBLKVtyUVsKR/hd/xUCXGUMwgWpuuuTmWkhJ9DcGoeSMclTpQTL4SesRF6z6xzc1e24URabKRAkoOVGPvHCKMf+fLpxoEDc1PaS4xwjhFa0XaswfLU08Ne3hgIPu3UGCggDlsP5Wt24olfxxTq3/mHaulvV0zMECG3joPpE1pjVmJSDlz1SWiPWvMqKkm1Kg7eb36vkO5vPkFHx4mrTTDu8dFz9n9VhR/O+cuVSfDkQg1NaOJBNCylbsyM+kvLqZt8zNgU6uAycku7PYYSkvTgqrkGajyoxb0ciMiVdlQlqj7i4sZmDGD/uJiwyq8kZh8GXrExQjxUEYGaUFEGA8xmQpWqp/n40gMFNkjWitRDQ2MWro04LhlhFo1NFAUlpbAYKeA+VQxm1dZZH1S9T1d2dmsYAmTOCi8NlCAQKDII62oJiXkb+BwWCgtTWPu3Iyg9xUEHzkXiWvl9ap8h3KG9Dye4rzOf6j2uOg5TS2J/HLbxRGrsGoUpmYggJY0OjhlCj3l5UytXMWejA7edY3nodHLea3xC/T1Waivt1BfHxeUoyjUuOd58/rYujVBs8CbUtoOxwEbqrlLpFUcz5vAb6X7UQpNgYiL6D4iBJImtQijkjlphiPqvD+ttRK/c6dfBrNw3GH4RALZv42UCPdlxD3l5eTXl7DdPudU2YeJtJNNNu0UJHdw12obeVZtBh1Im1KOOcZu92R69w2Vx/0w92ss7HucpmtHc+CARVXRVt5XBTQaCkwIp9eIJiO1618rr9cC+1CdoVujNzHZLS5v3t7uX9wQ1OvSK+ycqtw6XDCZgQB60SfKjTsLmHL0LS7qf8Ubaw7BOYpCiXt2OCwsWJCmW+kzK8uFw2Fh49Iuluy8maSBoYiokci41CJUDzOajUv3UVy/glxayS7MxM293sJ7jY2eDmhDm9oCivtEOxx+oZEQODRQizCOy7cwYJ2h6UgMRKy7U3MYK7hv30CMIdu3EWFAz6asx6wDJVOKGLH83fJvuIGqZrUvgj7oryr2Oqe14GuGtDgcpJaqibc8Zi+j7ejgUMp5fPP9NTQJmsaAZ189tDTakwWveGfNbx7hu1HbaWpRN0QqLBS3iBzvqMPiiNdd/1rrZf/+WGFJcRnKdZ/b0cHvsl4gxt6FqFWBpaND8zm+5tSRyIMwmYEAWrZywG/j5vR7bIHzFU5RMF5YLJS4Z5FqqYTN5mTevD5KStJZab+bfPxDYwMRqkg4tUSEqsDRyK8VJS3YBs4D73C0uppGCrj55lhVK0yPNAhWBfEQlZwO5MfQcgb/rHoqXVbtMsaBiHUFK7iHPSqHbDcptJPFpF27AmoHgYSBcDLGfaX0lBQ3AL290fqBAwZDUI0gEDNVrpElpWkqgi5CZ33b0NrBU9TwytZNNKG+zm73MAObzal6d5M4yKrmW0n/LyeD555LdE+PUFvW6rXR3x8dUNBrpIBKqmiXLGTjYmXmAs4WcANXVpZwXU7ioLdxlfLc4SbWJjMQQEuqTVuwQHi+yClq1JkcipNWS4UdNcrN7NknvM1m7PZYQ3VbfBEOAQoEPeJaSRUNDerqq75aVqihgaE2gwlErN/rmeR1yE7kEOeyl1H0Mope6PQ0Y9HTwkJyAgeheeoFC4gkdnmckUrOCsYMqteGVUYubd7/y6Wsm5goPLe3N5rq6qP8quQj2u0nvf06CrBDK6qwYl9tuYBGzo77hLf7pvrdV0/QEzqE89awNXcvU1r/5T3WllRA27yFfusyO6WbVXtvo6BVXcHXbr8M663GcolChckMNCCSarU2yKfJ2aDoChZMFm4oxE1LtZw9+4R348sbK5i6LTKGs3etHnFtl4zZeEP1Y4SSJBaIKGZnu6g95ZDdzDxmUKc6L5AWFkgYiGSP7bq6WMrK0ujutjA66SQbXD9l9sdDjWSUBLGnvJzEPXuIahjSKgMJKeH0S4DAPg6bzUlFYQ1s8/ztW8raF1lZLqxWF1UTlhBtH2pu00Myqajb+Cm/k6zNTPlkJW/jzwz0BD0th3DZzBf52Sff54LjbwESdf3n88hPRvPwnywCs9rD9FdWctgexdX719HYlwP18Eb98PZANpmBQVgcDqL6+nDHxxM9MOA97rTZsK2+i+Kqfl2JU88JGSxxE6mWEydKKgYkb6wlrKCIOs24chEiSYB8oUdcsxEv8JFu8q5EIGKt/BahaGGhOoGDfSd1dbHceGOmt+F6d3cCc3iWOWznd9xOAXYVQXRZrTi3bGFw4UJDQorQHPTmm17bt74AACAASURBVES53eJ5+wgjDoeFvr4o4uPdDAwM+cKSktxMner0lsDI4DacB17WjeUHtUCmbG7TiI12srmIN/3ncOo7ydpMKOVWtPbOe3sTmXR8H9l4nlHMnzmvdS8PL/0L929IU50r04OFpWk01qvLmQ9n4prJDAxAGEseHY179GgGCwvJy/WXOJXE352aSuz776sao4TjxBWZPFatiiE1dYhAzJvXx7Zt8dj7CpjGbn5BBUXx7/KFWRm4l98bkuYRLlF2OCz8om8lS+L3kD/gL3GW08OePYkqU5G8+U5XYo6SWDvtR3j3SD5r05dD5XjvGORv4dyVDYI2BoFMK8E6gUOp/1RWluZlBEOIZgdXcjb7uJKtrOEucpWMq6DAsJAiNAdptKX1FUbq6mK55ZZ0lX0+Pt7NrFkDLF/ejdXqUnz/L5JT+DYrCivIrncK33d+vrq15sEfrGT5S9+hrW80TUzgJ/xKyAy8WdgKbeZc/kMPKUQBXx7zIYurC3XXndbeKezf7ddzYTKHKK5fATwivGY4hTIRTGZgAMJYcrcbyyefkLhtGzEHDqgIe6CkNYhMQ24lA/I0vvD8X442kjdXH6lUJK1m8+YuJhQ5RbdTIVIESIkhW2oWL7PDY2OPb1YxJysutmxxsnDhoErLAobNh2EELquV98of84yhOdZTnmS3egzr1h3D4ijDWRLZjlqRanzf3a1NQE6QxA7mcDk7+EvKw6RpnqmNQJnFMpz5+aq94nBYhD2wBwaiSU6WvIxA/f3H8o7t16x+/Bi1C5x+61S5LhwOCzffPpGGvknec+7jIb7Ibi7lH95jzfETiT31nVzZ2cJ+xO8PBG4Xr7V37v301zDgf36OToJfTmo3CGLVslO6A44jFJhJZwZgNIVeRqACb977DlO4mMhu2d8fTVVVsqHrfUsEFxf3h014lWOSk56+NvA6tydvVmkpBQX4JXlp+TCKizP43vfGhJyUpAVRspMwgsvezMANZd5kMfA0X3GVlASdpGcEUmBapIlRo/S/3bnsZRALFYreDMHASHIggPtUBJGMysrUgLkyWt+/qio54DqtrEz1C0oYJJbv8CwNTOAoaTQwgce+/Jh3XD3l5SxOXuPnj2jszwmYdOa7d6644jiFhYM8ELWEeWymEXXCXc70TM17raDCL/nPE2lUoTuGUGFqBgYQbAq9USlJz3wQTqJYJNTLUCtyamE4EoDa22Nobx9awpHQFrQiqdLT1bZvb9/d5kPeQobNbx5h4bk1dA08RXrWiYiYsiIV2bV27THmzs1EksS9sidziFLW81jvE/gWHTQCo8mBcstJGXoRRLJZUm/tBFqnWtdOooGJNAGQzjGWNN5Ot+Npb7irvfAsYW6AkfUqLod/Hv/iPOoo8pZAabRM5JPbfy6oL3tqjD3veRPXvIUIWUJub14IXygwTM3AAIJJoQdjzEPPfBBqiQoZI92ZygjCGZORTFowXmpAr8yBlhT68cfqreLbd7cRG99o3cgL28ayc2c0NTVJlJSkh62taI1n6VJxeW0tFBU5Wb/+KFFRbuI4ofpNjmvPpdXvexgtleFbuuT4FVcwKBB2YvbuVd1D69smJw+ZCIdj7fja7xNbmlTafZZNnMcTzB7SKoHyQ/5AFTdzqWsHK6vO0bzelZ3tLUT4KrOpYj4F2Iel9wKYzMAQGimgpPBtZmW+x0/TNnAyUb0RfQl7T3k5rmSxSUaub+RrPlBuuvQbbtDtzBUIZ1pnKgg8Jnn+MVdc4Ud0RNdqIZDkJktrcvcyX6KtJUmOGyepxuAbOSQKc4xEjSit8ezcGR80o/n2twd4442P2Zh1LzdTxaW8ys1UeSXVT5Oz1WuksTEooUR2hHc99xyfbNiA84IL/M6JbW1VrWPRt01KcrNp05Dmo7d2AtUvKi/vISVFbV/LpcUvqQvU2n1E9lDTYTYzj1e4lM3Mw0YjAK8wh/lUYaeAjg6LJsMd7g6AvjDNRAGgVvXG8g/Ooz53BjUzFzKqt10YcueyWnEVFmKp99czB6dM8XMaG3E4g3EfQ6ScjpGE3ph855+EOtpKvvaGG9JpbtbOvAa8WbZaCJRDoSVJ2myDrF/f4x1/x6FzmdfxA1rIJY9WDmokPoUb+aE1noGBwJmwIlitLgpevJm5PuutLakA26a7yFZWZF22DEsY/YKje8SEM9jKsFrnQODAAqvVRU2Nk+uuG6pzdDcPqSrVylBK3OHuIYvDwfoDN5FzigEAFFGnapMKcF7KId0MbWXYcYzVytGysmErI2MygwAQEQ9l60MtDNpsxAmYgdEm3SL42lv1EGmbfySgNSYjWapWq4vnnjvqt/l98f77+rVjAvku9CKplLbg786tVJVASEEc4RGuaa68vIctWxJUsfcyunYdxOLoD5o4iHIbosvLybaqExSj2tqE1xsVSoxmMRtZq6JzSkvTDCVHfv3rsGNHp5ewf5jy/zj+/p9JbGnyniOSuANmb+v49FIrK0nqb1RdM5lDqtI1NpuTFVTorn1l2HFmZiauTkEsbYRgMoMACNXxGUyZCaMO5/+rMJql6iutNTRYVA5kgJaWGF2JOZD92YhEWFmZ6ldDp5dRJCcO0nd8aDy+ZrBQAgKsVhezZg2wTVC8bXzne6SXLAkpYkmU2+A7RkmjQY9Rm/Vwt5gMZW9KEhxJnsAHv3qWqVWrQup2ZqTSrNaaPnfMYWacNdQze9SC8BhuJGEygwAI1nml3FCivrrBNOn2RXRvr/GBf4YQTB0cpbQ2d26GHzMAfWJgJIcikKTa1CS+f+FZbmy2fo4eTSA9/YSmGQyGiIdWT2jlOlqTej7fzFujYkCy0zdSncGEiZWJiQxmZRGjIEzBEPPhbjFpdG82NorMSdOorn4sJNOpEU1Wa01PuSSD59YNxQJFqgZUJPC5Zwb+JZN7VPXSH0/N4UjuKv7VOsV7jWbPYI2KmoEkN5EE1c447mI1FiRvca1QFki4zeRHAqFKkKFEmYRrC3Y4LBw4oFUSepB1646dSgAcYiZaxCN66UOe7ms+Nu8XVr/LeQuG1tHZ1LI1dy9Lxyyi45MEdcE1jEmRcq4ETYcp+3gp08YdJtY2RJyFiZXHjxMlSRy/4oqAAo0WhrP9q9HkyGXLLH59CMIp62BEkzW6podbewoGn2tm4HBY/Eom734riq3SPWSdqjA4Fng1cQf3pK1lT8x0MqfncO9yt4dh+FR8DLVRjW/Jg9c+yOdnx1d5HU11FLEl9zZGB7lAItFMPhiEWjJCOf+Eo0c5kZ5uiOiEmikdij9FntuuXfHCJCllKKQvtIhHW32nqg0neIhUT9kjxDar19GU1n+xKf92Yj8JvpeDHASBvZkdfNMTVtkM1A+tB00Cd+IEUnIyXRs26D7jdMAoY29rE+dWiDRII8KTEWneqFY03NpTMPhcMwNRdmJTSyK/4A6qGCo3G3e8m18d/x4AzgM2jrWuJm3BAj8iK2VkCJ9jRHKTJajS0jRqfIpTHWIyC8+t4ddWY+GVMkJlTqEg3OQoef6ZmZkc83GSaW3QkYiasjgcdC3dSPlrt/D/nL+hkQV0Mt3vvMJCl3bDEw3i0aZRaC2lW2xHlsaNw2mxBC1FykEQm31yI2BoPeiZKkfSfh2sJmuEsefkiFO3RTkVRoQno9K8Ua1oOLWnYPC5ZgZGWiH6ItZuJ62szK/bVqzdjtOlQQyCMO9o2aPbe0cRbGZoKF3UQsVwlb0WbdCErVvp2rwZZ1GRkBhEqqid/OxV9p/yv9zCZA7xMlfxroAZ2GzirlqgTTxqCiu85ZiV6B2Vgyg4adBmo2f9eq8UKUeXpS1YgCs7m/fnLWFV1VS/ecvrXK+q6rFHHiFh61ai+/v9fh8p+/VwabI/+IGLl15CVQhPpEEaFZ7OJGk+kvhcMwOjLed8YekWhxG6BZKbKzmZvnnzDI1Hzx4dSohisM6pcIjocFVYFG3Q6P5+0m+5hc4dO/w2oJ6GAgQ1P/nZ32SLV6IOpayxFvG4jQxePuBfaC119d04F9QJJU9ZioytqyP9llu8/YMPk0vJS+k0DA5plfK85XWu19vCZbXStXmz6p7K5waD2Lo60srKsHR34xo1imNr1+IsKgp43XBosg6Hhdtvj6Wvb8gCkJTkZvXqY/5l5oMQns4UaT6S+Fwzg/Jy/5LJE/KOc7/0W/T4gWvUKKIFDMFls9G9eDEZ8+d7JSxLXx9pCxYYkm60inbp2aO1oNd/QbS5wzXzDFcJDM0N2tcnJBJ65RsOHIjRnJ9eYxalcFDAUKPzD2POIv/aswwxTRHxsCI2c2Vb83QlT4vDoVpjALEM4hpUm0NkzUz2rSyx6/e2cBYV0bljR1hJTrF1dWTeeCNRgx5NKbq7m8wbb6TzmWcCMoTh0GRFpuD+/mgefzyFqipJJRiknebInlCZaKTwuWYGVqu4ZPJoHubjpQ/R/nYH7Z8m8Tf3VZTxKwqw47TZOLba32cgb6rUyko/VduodKMlXRcWeghNaWmaIalWpG674+MZmDWL7uXLhZs7XDPPcJS9Bv2wWxGR0HqH9fVxdHaKI0oeK39PaJ4YLCwEwMpQ+YVGbFSwkhZysWb2cU+A7xDI/q1l89aTPEVrLJsjmr24h3wrWdxn/xs/PbKUaeOaibWNE2bPK5OcGus/obLUuDaVVlbmZQQyogYHSSsr4+M3/XsIqOY8DMRYr5yHMpGvvj6W51YvYdowRPYYWQfhMNFI4XPNDGCoZLISjY4CT8jfJ0OE7YnoOyhIbuXYQD5jH4hmQuFMbh//KBve/xptfWnkdB+jYulGxnTafR8BGJNutKTrzExXUFK70LQyMICUnKwp5QUy8wQyIQ2XM7envDwoW7bRonYyOjosmuaJwcJCnDYbqXY7TmJoJk9d474d/l3iFH6H4Yzk0pKg9XpxDzGdVGC1T9NHMcTx+fraopYJVeu4EiLfSltSAXc2rYLSNEPryXedpqaKy5P4ZnTb7bGsqprKYxH2BRhdB+Ew0Ujhc88MRBBJyb3uJP7TMxl64HA71DOWv1hWMOg6tahOwtvbpvBywreEXVmNSDci6To318l778X6JVfpSe2hqNt6Zh6jJqRwS2A4HBbuvtuC3Z5BTmq3p557z3ucnD6d2HfewXL8uPdcLYlNS0MpLBwUZvFmZbk031d0b++QucZuZ/EHizl0XFyMznfewxnJpSVB90SNAoWlKFzNLJT4fE0T6qjAVVZ9Q6xfPWDlrr4HsO8u8GsmJIJonSYmusnKkujoEIeXKtHRYfH2f5Yl+dTKSi9DCCVnx+g6CIeJRgomMxBAr8a6El5GcAqHmMyyE4vYlHS7SpI1qmparS5Wrz7mbVqemOjC5Yqio0P8meSKh6mVlcQcPUqaHJ8fgrqtZ+aRmWMuh7Hh4DiJJNqP86t7x/HwM8Ya5gTC0Ea2ABZgLHu4x1tR86NxX6Vi9AO09aeRM6qXu1enkmf1n6deUTNfn4E8P1el9vtSmk2a5mZArf95wnj1YYzkEknQ7qQkJjz8A4q36/fiDgbBxOfLOLZ2rcrcASDFxHBs7VpDz9QLsQ7EiOR1aqORlVSQSwutx/N4wP0LyBpPR4d+kcOsLO1scZFp2Iim57SLv7fvOgiHiUYKJjMQIFhTgxKt5OKcOhWXzRa0qim3q5Qrc3Z361cYz07pFlb7PLZ6ddBZjXpmnvZ2C7kcJgY3tXxtaK7/bKKlrp28ImPlNPSgVfv9Ml7lSW7h+0c2DplnuqFugdg8I89FRDC05mc0bjwYJ/lwlhnQik4aa7Wy7tuRK05oJD5fJC13PvNM2I7QUKLT2tstQ02HFE7yooE6vjPwMh1Cnd0DWTDQkuS1wskzr72WgZkzhXvc4bDQdsBGMW/4Pc93HYTLRCOBM4YZvPvuu2zYsAG3283s2bO57rrrTttYRFKyUeTSistmC8kUIGytqAG9iofJVVUhxUFrEVEPEXSoGAGAnQksmneYvmkZYTep19r8TUzkm2yhF7WEZNS5rSRWadnZPBZGFmgwTvLhLjMwEqGNy5a5qK11a85Xzx4erp07lOi07GwXPxYk1k3mEIuO388N/NHvmsxMFzNnDgzVhNLS6LTMOJ2dJNXUCLWEyspU6vse4DzeUo2pLamAaJ914CwqiggTDQdnBDNwu9088cQTLFmyhIyMDBYuXMiFF15Ifn7+iDxf5ByVpUi7PYZ9+yycOCEwBUS7cbmHpPdJHOT+3N/SU/5wSOMwap7Kz/dIxZMWvCf83dLREVFiUV7ew50vJqrs0TI6jyfzbm08AFu3JlBY6GTChOAZg3Lzq9R88ljCCi8zUP7m3JWNxaEd+mjUeef5/ufT3v60LlPTM0EpI71WrYLUz1BiklZwQEGBtjYFw+sXCSU6rby8h4E/N4PAZ1yQ0EqSxa0K3bbZ/LVLTY1Ow4wjQzTv9nYLdgqYw/ZTa7aVVnKpmVrBr63+FWGdRUUj5iwW4YxgBgcPHiQ7O5usU6rTjBkzePvtt0eEGehFTMhS5/e+N0bofLxoxknGJfXQWd9GLm1UTK/h09t/xc8r/bNAjcCIeUq5gEeq4qHV6qIgq5d6gdCUyJBTt78/mt2749ltwNnnC3nze+rn+Kj5p5qCAOrfOsFZUqtptzVCrPSc48qChbIJxGq1qrQR0fV79kg89ZQF6ymG7CW2C9RrIlKZ0uFANH6ZqRcWWigrG4q28zUJxWj04IjftQuLwxEW4wslOs1qdTH4tUzY5f/bhz259CsEt+RklzDxTEujE4aTE8O9PMgeppFHKxX2GpQkXt7PdgpUIb/Ftn7gzOo1AoB0BqC2tlZ67LHHvH/v3LlT+sMf/uB33vbt26X77rtPuu+++yRJkqSBgYGw/33rWy7JU+Xc889Gg7SZm6W942ZJgyUl0sD+/dKsWepz5H+zZrlU99q/f0CaONGtOmfiRLe0f7+xsYiuz893S9dcMyjNmuWSSkoG1ffav19yT5yoGpR7/Hhp8JprJNesofFH4j3t335QmhTTqBqblSYpF4fw3YAklZQMBveM/QPSC8k3CW/2tOW70mZuFv42WFIivJ9r1izh+a5Zs7znlJQMCsf+k2s+8n+3Eyf6vU+t6+W5+35TGw3Siyk3SV0XzJJeTLlJstEQ0lqJ1D+t8cv/UlLc0vbtGmstJUXzQtG7GpF/+/dLA+PV4/yISar3LPpGJSVDe+zg9v3SYEmJ/x7a7znuHjdO6iZZ+jqvqmjGG9EzpGPXDJ0vpAcJh6WPrvlJSO/G5XJF5B1p4YzQDCTJ3/4QFeUfyTBnzhzmzJnj/bszzK4/DoeFbdvGef9WOZ+OANXgrq1lauEWdnK23/WHDrm45BLJK9V5sh3jVOc0NESxcOGgoZDL1FR46ilLQGnIO+3UVCxPPeWt9jkQH0/M3r3E/vWv3nPdtbW8u/oFYc0aEXylv75580iuqmJCezsvFU1m+cFbae9PI9bZz77jE2hlvOZ8HI5BOjuN11NKTYUrzmmBt/x/u+wsBx3tFhB88kGHgy7BWkhLTyfJ/3ROpKd7i+HZ7Rl4opfU+GZdBVGdDapjUQ0NDC5cqDIFaF0vz33RT2NpaBgLKNZX7yHYA98GzuFNbyvEYNaKHoIJgdQav4ze3ii+/e0Y3r54DWc3+LyP3l7cSUnCHJCohgai5szBPX78yJZOT03F8uxT7L/hUVzNR2gllyWsULWalOFwDFJff0wRxeZBbe1EqqsfHcpMX7jQ+y4/KS+HsjLumtPGP/ouVdMMN/DXN3D+x6OtplqtPPWUhYeWRtO18yPyBxpYcWIJBX+14/zPX4LOOfGURg+/01lurrj22hnBDDIyMujqGiIaXV1djBkzZtifW1mZyokTQ0xnpUZVxxWFFbxsU9edj4mRaG6ORQ4wqK+PJSNDHH0RTH2eYGP1ldU+pZISYlvViUfNdrhxfg6N/f41a4wkSiW+9JI3wuEsatls+wdHa6ppZMqpssjaYwulFMXguBzh8ajcLCZ9QYIa/9+0zGJGnLjB1qfyDQnUc3RaHA66drrxFEIXry/fVojh1nIKNtnNiGmyvz+aFfXFPM3jfr85p04lxuHAIiBSsc3NyBsknIS7YM1pLquVheOfpLY5Xve+WVku3cx7rcz0o9XV2Asvg3ptmiGbIq1WF5uTf0TSQI3mOWcK9GMXRwiTJk2ira2NI0eOMDg4yBtvvMGFF1447M/1ddhqVXUc1dtOdfVRiov7mTFjgPx8J4ODas3Fbo/lyBFxXLYvUXQ4LJSWpjF3bgalpWk4HKETAIvDQVppKTFXXEH8Ln9jaQUraexXE1h5sftC2ODEJytSXsSyTbe4uJ/p00+SnKyeY6gJTxWs4CCTVMcOMokKVtBTXo7TZlP9phehI0cJ9RcXMzBjBv3FxX4Eqby8B5tNXRrcZnOSPT1TfE8fxiO6fuJEyRummD8wJE1rrS8l4wmnlpPF4SD9hhs0/SQiiMYvQitiJu2y2RiYOTPg9Xpj0IPs06ipSaK2Np6amiRKStID7plATE5en3ohrHo+pyybh4HoVYL1/n8EqweHgzNCM7BYLHz/+9/ngQcewO12c+mllzJ+vLb5IVLwXTB6VR192y36hBwDMG6cG4vFvwqlkiiGWxBOCZEU6IsWjXLcvhKoxeEQMhPhc08tYuU7kaW3cBKeHA4Lz7w1ib/4RF8sYQV5vbm4rF1BR+gEiqrydVSmpHhCUW7/+JesT9pNjqKpuZLxKKXVwsJBCgsH6e2NJivLxapVMaSmesIUlVVOtdaXXDJdi4H6SsZL5r3v6d+rMAMBnrUgWphoEx7l/O32GPbsicHl8pcRM6fn4Dxg09SyfDWwYMagh1BrZomikZKS3Eyd6sRmG1qfupqdDhEvf8Rz/1a7Ns3w/t9AoMeZEExwRjADgOnTpzN9un+d+OGEb9XSJazga5ZaClxD0tzxvAmGk49sNhfr1x8L2Ew9UnX/RZKLL3KSP0VUiMY3cSi9pESo6osQqDdxKJCZpEe7KvAruDY9y2OXVhJ3IxsokP1ceY/UVDd798bQ2hqLjTjqOJ+vRfeRNtqN68vTvUX+RAxdGeXlse16iEABtd4qp3/jauawnWyOeK9rSyqgZmoFxbZ+4fh9n2WjkfSXSkgaHFqjclE9vbXQnaKdGKj8dnV1scyfn+EXgnnvcjdH0WbESiYd7XAImVIoUW6hlkZXMrm4ljZ+1LrYr9Un6Iew6mWmy/ffuHQRl+ysVWmAvtpqIHOlaD299VYc55zjpKcn2ru2M8XKasQQJYm8t58RtLbq9x0wgp6eTG/VUotFouPNZipOLvNKpb/NvZ+H/zRatUkDEQM9zJ2bQW2tvy1zxowBnnsuuOY1GXPnEl/rXx/BHR8P8fG4Ro1i36Lf8O0Hr9Ida1ppKUk1AmM8nixIpakoUPXTUFFamkZNjcjdK363Rr5BoJ7UonsAjKWVN/i6X6ln+TqtsRYX9yt6IHeKn5+by+C55xruKez7rM3MYx5P+Z3nyszUZOYHmcRtuVv81rEWZAZ59GgC6ekngpZSQ+0FLkKgd21kLONuvpmoBjWxVo5FS6s1Og+vwKGjreqdo7f2ZdhsTrZulUhNHT4H8ueeGcgb1+GwMHv2WGE/gfx8J+PHu4Ux4sGaRcJd3EroEXEZTpuN/6x+jpVV52iOVYupuDIzOfr446Q8/jjxO3f69UWIZC9lLSaZmenipZc6/d6tkfeo9X76i4uH6t8I7hHPcT7gLG/Ded/rAjF0ZdSHEUKhB99nvcKlXMbrfuedHJNJ3Cf+hKKBCVzGq9gpCHqNhRO9Eu68ZYg0ozXJi7ms0O4n5YsQaA2M1Dz0oLWefFFS4uKRR8L3M5zR0URnArQaywB+UUOy9BmKWSRSdf8dDgs/6Xucrvh7PCFrLPEjXuBxeE2tWsU6nYWvZdMcmDkTZ1ERUlWVihHI9zUaDWEk1FHL9DZz5oCQyRoxHwRy3GndY4BEKlhJFfOF1wVVoyjMTHCjfq3d8V8lg/0qbeYgk7xhqxB+pFIwiFQGvNLcE2U/zLr9V5PT1wj1QH3gKKVwnbcjUfbD9xuLMvDtFGgWDowUTGZwCkZLQYTb1zfcuv8Oh4WlS0cpmnOMBWZQm3AJ78VdSHK3/yLXyhSVEcimGc6GMhrqGCyTNEKQAznu9CJORH2w5euGq5GPCEvmvc/8bWsY3ddGK3n8lv/HxTG1TBhUmz0eTH+E+vY4P8e7Mr5exKzOBMdlIMiCV1rpQpLqG1W/BRJKRipLPxwo15Ow0N6pDPycnOHN0zCZwSkEU6k0XAnLV6uQQ01FG1LLwemLhhP51Eedx0z8CXTUkSN+x5QIVKjNd0M1YuNnrKH235cgXTCK6dNPsnx5t+FGO6INLDPJtWszcTgGAzLJefP62LYtXrfJeSAmV17e43cPGdm0qf5WXjdcjXx8YXE4OG9BCdP7hsY/O+kNuh5eTf/2TapvReV47LuT/BzvMkTMSmmCsdHIj6nAvaWZ2FkZuJffSzgey1Bq/we8ZwhCSU95OYl79vj5DCJVMDAU+L4bS3k51dUe68TtuxYxudM/F2VN8mLOXrZxWMdlMoNTEEl7FouEy+WvmoXb11cJvQ3ZdPtiShacp7KXPihQH2Wsj/4JM9nh9wz3uHF+x3yhpw4riWojNi7hdRxMgJNAJ2zblsjevTH86U+CRLYgNrDV6uLJJ10Bs5blUt9KIi5qch6IyVmtLjZtOsot30mmzzXkO5jEQR5gMR2JVppjC+gdlU3q6rvJtg6ZaEI1EwYjiYsYaU5/I6O3b/J+K5mwbLB3MD/Z5mkGc2pNiEIplWPYtSuezk6fss8DwDZwHngHaetWT1p4kBiuLm+hSPkuqxXnli0MLlx4RhQMFL6bN99k1Lnn8nRPDzF8JLzuskIHiQWK6gPDAJMZnIJI2ps3r48FC9KG1RygbMjhuyHL/jUPHSMndgAAIABJREFUe58n3FZPfZQ3/9iUPmEYqcsnUStYKInqol234+ic4HdOa6vYfKa1gd0pKaSVloYkOYrCc/v7o6mqSqaoyOf5PkzO4bD49fR95/x5rNh9Pa3kkkur1//y6vFLmX38VegGm07/BKMINsckECP1JSzFvEFRUh2lU/+OZBtvKFQVtLNoXcuWwSOPBD1PLW3w3WvX8vjMJ0LWokIuC15QcMZk+grfTWurX+UAX8TaAgt04cJkBgqIpL3hMgfIEl3XK4uBLwk3ZFvfaO//A5UymJnfwENO/03hzM2NiEosE1X73AxhfSAQm8+EGzg3l9j33yemZSh7U5YcjZgmQo091yLGWwrH+TmLQe03CNdXBMHnmASShLU0h822hZrETzQGrSzaqLY24XFf+Go7q5qgUHBebGcHNTVJISdZGu07cSZDi8HrQWZ4w12gx2QGARBuMpUISoluPNcDXxJuyDxFmQK9UgbpsZ/y0rTFJPzVP9Fn8NxzhR2YQnUa6vlWtKJpfDdwVF8fidu2qc7zliuorg55DHI9IC1btRYxrihcQbXtZRVhPcgklrBCdW64vqJgmZgvI23ExuLkNdjtl5FVGssGgy0VA41BK0JJyhGXoFBClKT2btJ6XqHeL7pNZq5KBhhK3aEzRcoPBVoM3u+8zEwGp0wZUYZnMoMwoVXlU8/8oZTo5HIFog25giW8kTSbxv4c3VIGV4yuI7VHLMVF9/aq/jZqqtAiquXlPbz1VhwtLeqlk5urbT7z3cAZc+cKz7N0dNDYCAsXpnkd5oAqC9NqdWlG8yyZ976urVqLGLf3jlIxrDrHeG5tXuVX6dKor0g5B+W4g+3epWSkh+1RXL1/HY19Od6wyvnJxloqKiEawxJWMCO6lolutZNVWrYM0BYeHA4Lt9yS7heS3difw+LkNfyx73rvMV/m2tFhiWhpltOBUIQqkaYswsDMmSPO9ExmEAB6kmagKp8gdpwpVcUC7GxnDmv5KZdE7SRfGpLu823wzOo2VlWNpsZewWV7XifXNaQhHGQSv+cHPDb9GaR0jUJiPoRBSzpee+27PDHzcXWdGwFRtVqtPP98F0uXjqK+3lOuWy+aSDgmDenoUMp5XPuNWL8y4DKUhEJkvptauUo3ckmPGCsZVrzDAj4VWX19RVrrwuGwcPPN6jnI49aql9PU5IkmExETeVwLS9No9GkQf1ffAxQl1WnWTxJBNIaPk2wcePgZsrf/UmV+GVNQgKP+E02CXVmZKozEAnAUXka/rZiDu7p4r3O8MMw13NIsekxKZbZaFZIfPOCzQ2FkvpqyOyXFz2R6uqKdzAxknSzLQOnoRjKAwT/bUeu641dcgZScrGkPja2rI37e7Rw+PpY2cvkr3+DHuS8w+k8PM2bMGKKuvDJg6rxWtuOlvMqrzMZpszFYWOhnxhHNI1RovdeSwrd5YdtY3Wv1smi1MqkHZszg2COP0LV0I9fuXELDwFAHPa0yInoZ5nrr4keV5+tmRsv3tdtjOHDA4hcaq0VMtL7b9dMPsNkWXKRMXV0st9ySHvDZmZmZlJQMas6nvd2imTmrnK9W2ZAFC9JCLs2iNYfVq4/5BX1MnCjx1FNHIqptRLKSgNEs589FP4MzFYFi5I06g3xtuFpREYHq/TiLinC/+hIFlZVM7uhgRtY/6Sl/2HNNZqYh51qg+v2xdjvRfYKQJME8/H43GFuu5Qhsv1OsESihZ7fXi1xKLykhy25nBy9TwUqa4yeSMesL3LvcLTSPZbS3U5WdTc8j+mY+GfK6aG9/Wnfcsg+qtDTNq1nJ0JOKtb6bZBsfNIOuqkr2k+i1nq3n59AaU1KS26tF6eVkGDGbidZUIwV+fgp5DmVlaTQ3q7WNhoaosJ3/vgg1iEGEM8UPYjIDHQQK7TPsDPIx1YQTFaG5cBr9+/WK7icyE0ziICtYMvQMl7j3lZ4t2khseSCTm+3Aq7xBse789ez2WkwW8B4rwO6JHBqAj7me2ys3q0pDn7cgcHy83row6hcIlpiIfDV5eYMhhTlrPfuVVxL8zFV68xGtpeRkT+6GksFqBWEEyuTWWlM/Lnxbs3RMd3fkiLQegvX/BMJwJOkFC5MZ6CBQaJ+I+PhW+dSy/0VSGrA4HMTefDNxiixLrSQfpaTWtesg4zvf86tr9NqJi/h63n9IbGkKOA8ZAbWoAMwitbKSB/rqeYvzOMRk4TMC5XhoMdm0BQuE53+0s4uagSFVf/62NapsX985eJ+jsy58y6JrjVuLmKRG92pmo/tadJV/B+PM1Hp2d3e0KvQzM1OfYIebiR3oeq011dVyALl7nC9GjXLR3e3PKCKZKAqRLUkyXEl6wcJkBjoIlOQiIj7eaKIRjINOraxUpduDmIj5Eow1jw+QM/9+cvrVIZW3H1/LVeccZ/VXlhieRyAtyojJTXamV7CSVnIZxae4xmRw9KyvGiY0IiarRbyVvgOA0X3iiCyjZr6e8nKsVhdbtji9ZdG1xl1e3sPut6Joakn0HhtPE/tqYzjsyvAeUzprfcuQyIl+5eU9QTkzRYRMCdlkVF0dmGCHG3qtd73WmsofbAIu9juenOxi7VqxzyDSdaMiWZLEaMmW4YbJDHRgxJwjIj7HiopGdJxGSj6Iox+mcW7B3yl5f4VfcbP/9A5wbIPxhRhIizJqcvOacU6h/xJ9p7UR9VpEvJvjJ7JkQJ1HoNfpTvV3gHVRUEBAAmm1uvjbOXfxy5aLvZnPPSTzF5faTCYTZj2zUrBROUpC9sorCUJJWmlW0SLYw23a6E7NEcr/9/FLb/c4GcmJg2za9AlFRU4/Ii13nos0IpWDdKa0xTSZQQCcKc4dPRxKPZ9f8mNayCVPUU5BScS0CIYrfyJ/ExQ3M6JWK4mBOzXVk1msiPBSaVEhmNwCmaaMqtci4r2ybyX2beo8giWsYHbSG4ZCNSOxLib1vEeVosn8pbwiPE/PWZuV5QrJmal0ZIuiYjzfX5s8jIRpo4IV3MMev7LcT/DfKg0yl1buH/NbRuc+jAurH5GWO8+dqThTKquazGCEEWlpyuGw8N3319DEkLmhjiK25N7GaAUR0yIYRvo2a83DlxgM5uVx/IoriO7tpTslmwpW8N6CSaecsws5z4DJLXPtWgYdjsAdo9rbiT582K+9opZ67Uu8b3NYePmAet7Y8nn3vmf58JcPk9LdLixQZxRGvrMvEVBmnCuh5ayVv1NlpTiI3ghD17d9axdAGAnTxns9nn4MvmW5J9Lop0HSCv0RNqsMZ3lv5b1zUh9nVe4RprT+y/v76cg1MJnBCGI4pKmNS7tY0XK3qpLpISaz8Nwafm11es8Lp2+zCCJiENPSwsmvfIXdyzcJTVIvrH7O08hdx+TmevJJuhRinK/2EbN3b8CiXkbUa63ChD9acAH25lMlMUItUNfYaOg7+2pDK1hCbczFNAxO8J5jxFkbjjMzVNt3KKaNYAWh7GwXtYJ+2BfzL+H5kTSrDGd2tP+9x1Kft52/XXEXkzvfJurIEdzp6aRWVo5oVFFQSWf9/f3s378fSZIoLCwkJSVlOMcWEMOddBZpGG3BZ3TTWBwOnJfcrGrGLXe3ypuRq0rcCadvswh6CV7fzdoeckKOb8tIX6IaCI3YWJj/JI7xRUFLc6EmEvlKkOs//QE5r/rXWBIl7fkmHL0/bwmrqqYG7ZQMtQ2rHvT2RqC17PtOFs7bz7QF1wfVF1m0ZidGN7DDfZmwq59WUmQoezySSWVG7339FR9TfeDLmu/otCadPfDAAyxevBgAu93OAw88QHJyMgC9vb0sWrSIgoICvVuYUECWphqxUcFKr42/wl6DrOgrCaDnvB/TvMVNxqxYvwSprqUb+eXAcpWvQK5k+kLW71TPDlYCDMSQ9OycgWzYRpidw2Fh4IZHmdUcHCOYE/M6Dc0TQNCm1Be+BKupKXjbu4hg/YR2RMVBRJKrr/kqD1hXFDyxGY6Cir7w1dIG8/KEZRRE7+TdrTm80o+q2lMgs5Joza7sq6Rgm/+acCclRdSsEsmkMqP37qxvI7bz9EUV6TKDDz/80Pv/qqoqvvGNb3DdddcB8Je//IWqqioqKiqGd4T/h+DKzqYRG5ezQxUJ8cb+2fzREY3V6vKaX1Tnnepv8M6BIUne4bBw884lNDAUHllHEduZw8T4ZqGJwCjBEEnk8du24SosZPDUhu+bN4+ErVuJ7u/3niMTg+xK/aqigUwoMjH532Zjar8zPx+31cpCxy89jEABuz2Wp657lUddPwXg5PTpdC9fTiMFfgQrKcktvP+HH8Zo1g4SOeZbDEYlfZYgLMGRm+v1ESnNfpWl/u+ksT9Ht6+0FgpopIpKLFI7LrLpu30ezgPqCrOu5GSObtpk2JxixBcQ6aQyI/fOxVho83DBsM+gqamJe++91/v31VdfzZ///OdhGdRIQqvC5HCgp7ycxdvaONSnTqpq7M+hstKjfsraQwUr/ZKvlOGClZWpNAyoVc1DTKaClayZVYNT1CzFoPlJ5A+w9PVhqa8nrr6euLfeQpIkFSNwJSdzbPVqb2VTLRu2EcejTGC1Qj2VUKrRjrkZXo3A+1wa+XnHPVhONWFI3LaNhNdfp3HM5dDxa5Syan9/NMnJLr9SDZ2dFs06/CIpbwkrKKJOFQXTHD+R2NPYajFcaDVlcX71q3Rt2KA6riX56vWVFkFLcDi2enXIuTxGfQFaa3jevD7NpECj0Lp3RWEN+JcEGzEhQpcZuFwu9u7dC0BUVBRRUUNZlVFRUTidTq1LPxPQqzA5HAzBZbViLzzLU4LYB7L6KZtfWgQbR3me1oZrjp/o6V/rg2Cc14FqLilNA95r+vpIrqriWFGRrknKiONRnpuIqMpwx8czMGuWqp6TSOJaSYVflE7UyZMUdfyNHexXdYoDKCx0YbMNeFtCKiGK3fd9ZjI92ClgDttZw8+4iDcBiZbUKUw4dc5noQm9L4JxGGtJvtnJn6o68YWa1Z5cVaXtewkg6BjNyTDa+TAUeqG1PzK4zU/rGcmoIl1mMHr0aB577DEAYmNjaWxsZMqUKQAcOnSIrM+w2guehaEsGwCR6WilhyxbrJAZyOqnHGGSZ9cOMwTtDZcx6wu4rP5MOphQQKM1l3yhJAxaJikjMdXy3GSi+iqXMZEm1fnRAwPE1derIi606i5pQdkpTobNNsi6dceYOzfDjxmAv83Y95m/oILfUMogFs7jfbLxvJPszpdxlnzAu6tfUPW1hs9GDf9gYuG1JN+7VtvoryqOWFY7eBjrxqVdLNl5M0mKQAotQScYX4DvGi4tTTPESIxAtD9cnN5ObrrMYP369Zq/ZWRkUFZWFvEBjSQi7SQyIp0ECgOU4+0XLd1I7c5L/Moty+dp3efe5WK7t9bGSnjlFdJKS1VjNdqAwxdG1FkjyWXKudkpoIkJfswAwNLZSVJNjarXglLiGu+o49zmvbrjyVVoDcr3a9RmbLW6eG71f1hzi532vtFcyTaKeZFWcoV9hR8p68HeHBmCMpIIJilQS/LNs2ZzrChyWe2yyWel/W7yCVyOBcLzBUSSXmjRCpfVynvljw1pjpUjpzkGlWfQ2dnJ0aNHmTJlCpkGetWe6Yikk8ioGcZIVI/LaiVtw/085bBQWdmvWRNGeR+rNYayMm3pUmtjRXd3qwiqvCCPVlczaulS4l9/neiTJ1XXDI4bhxQTo5ltrAe9Ug4Oh4W777Zgt6dRWDhIYeEgvb3RWBxZfr4AJZQbXylxWRzxJPzXaGgVl+RuxMaqxOWMinUzapSL1auPed9fMLH751St5I996jBLUegjQFu3OBw70lU1I41gKu1GygwWiAHJJh+tlrAiE1Y4ORmRohd6tEIU2KAsHDicMJRn0NnZydq1a2lqagJg8+bN1NXV8e6773LHHXcM7wh1EG6egcdnMM6vwmQoKrvRHILhQqAYZCMx+8E04elevjyi6qxeHkQB/klcvhiYMYOu557zO25xOBi1dClxb79N9KefEuX2aE7eMFSfBK//z97bh0dV3vn/r2QSQhImxCSQ5xkCSlQULdtaltaNDxT7tba/psKaanBp+93a2rSpUdOiYIhQ6UaBWul26a5FJVRcrNnqSiWAFZEm6tcAViqxQJghT6QhhjzhkMzM74/JmZwz5z5PMxNii+/r8rrk5Mx5uM9935/n90f+7fVy9+UbnvMvr/FI912aAkCOkrzXeK61SHU8Gvnr0UY4ee1W61mMrGm9xi9Sw58tlFLKVtW1pfkc+h7h1mREo1bH5naTtmSJqnJeet5SaoU1CHl5w8yaZSMt7aOILYWImtv86le/4lOf+hTV1dV861vfAmDu3Lk888wzYT/QxwFmGSbNYCLJpmxuN7Z77yXd5dKmPpBpdpP37CG2r8/wWbXeKXZgICxuHj2NUT+wN/bsCfv2YRNsUFouKq/DwYej2S7yjUUrDVXurpFbGfJnt9t9vPdeXJBFtIGbeJvd7GKhQiD4Y2ODwgcCWu296+00VihpMGbknmXNYAXpi9+dMC57CQoh57RRXm6ztB6skOaZsab15pmkqYsSDfQs1XBrMiJlKg2+r0AQQGD9dfo1EkNa4wn8TJzVFg2YEgZHjx7lxz/+MbGxY+yGSUlJDMlSC/9WYYZh0gzGi2zKjOaUVlKCzeUKNqQRNmSRXyclRSgMVOycUXwnt9vGrbemKWiYX3klgcsu8+J0jhgWfEmbglbLSbMuKmljEaWhyu8X+uyhGmEopLReKY9+2OkUpkDmOrIUG0rWlD7WvlfM7PoxigUrTYGiidD3bGiAhoY0SxuPFb96pPxGYy6fgiCH0cyEVi4pSg9m1KWWlRHX00NqWlpUxi2S4j7R+8rhzcwkC+NxHq8YkylhMHXqVDo7OxXmRWtr699F3CBaCId10whmNCczC0p0HTNNeKL5TlVVKSo+/rNnbTQ12WhqmqRZ8BXqj42kS5wcVvy/Im1XhJMZc/HMXkCcw0FPeXlA+MyfH9zMUysq8NntXAQ829+PNyuLmMFBEmUEZRD4filVVfiTk4lzubA1N2OTtSIdr8YnWlr9kiVpbN+uLRDk1sTJk+IOZKJxjdSaVmrqObyQ+SuWlx4huXYNtrvvJr65OVgLk8TENIyRQy9lW1pXlej3mpAwHjEmU8Lgy1/+Mv/2b//GV7/6VXw+H2+88QZ1dXXBauRPEL1NSg5TG72JBSW6TszISLByV+tZrbyTkfYa2u83FKKCL63AXjToo60EEl0uc3kW6ddezOmN28nIyMBrkl/JlyBuKJ+wdy+xHo/wb+NFUaBZu9Iaz8KFGTzzTA/z54+lLbvdNqqqUti7N4Esj2uUXbSNjpgcHvSvCdZvaH7HKFieyoQB/bGeiIYxMCYsT/9lE/kcUnUWHM7LCwopBwEBV1WVQlPTJPr7Y/B4xr9zG5gUBjfccANTpkxhz549pKen8/rrr3PbbbdxzTXXRP2B/pYR7VaWCfv2if8m2+iNFlRnYxvHd/SSxNWcJREHLvJH0yl9Docw6Kq4jol3ihYba2Ghl9mz4YMPvHR1xZCW5gt28oq2f9SK/7erK0ZwBSW0Njwj14DWhq91XMJ4xKK0rCWAwUEbd/5zMn/47yPkzs9SuJSctLCbL4z57P3wOVsj9161A78zX3Ncw7U8tRQPo7GG898wRul6uxK4MkgbU4BLk6yvuTlOWOcC4bfXNIKhMPD5fGzfvp2vfe1rf/eb/8ehKbX0HGklJcJAKSg1J70F1dnYRtptJcwbGcvBduHgJDnk045vyhRSy8oifl8zFsy8eeeor08U/TwIp3OEVavgpptiggGzAwfGryjLrP932jQfophfaqqXyy8f0RUkRtXcELAO5Jt/6L9FGA+KAqN2mIPeJDbc6WLD7nPU1MwNnreGlaqaihne42xxLtdVJMKxpvUUD9totqMeIhm3cFJmJdebk5ag5dROLhtSV/HT618Uvq+WW3L6dD+f+9zZcas7MBQGsbGx7Ny5kyVLlkT95h8nfFyaUoO+NhmqOek1helfUqEQBABO3OznH8nKgfjDhxXUEmbft7Oxjf7ydUzp62AgJZu8lEFmCc6Ta2HV1X0cPhxPW5t4yknazqpVGbhcxjQQ5xMzZng5cEB9/PrrPYbPZKaa21NUhD85ObghxgwOklgvIKkZxXhRFEgFdEv+OQOXVzwHOgenYq+pobPz2eAxK3n+obBqTWspHilVVcQ3N+v+NpJxC7e/QWenTW05AdedbaC/citeh0OlhJ5ybRZe67LL/OO6BsTRnhAUFRWxa9eucXuIjwPsNTW0uqCULVzPHkrZQqsrcPx8Q0ub9GZkCDdrr8PB0VVP8/XMXSzsfJbv1szF7bYxpU/Mgjg5boSRK65QcQxJ2rweJGujqPU5/qHvdYpan8P2fjMnDUjIHA4vzz9/muLiIRYs8LBo0VkWLTrLggUeiouHgouqo0PskpnIoqzKyn6cTiXFh1lTvb+ykmGnU/PvI7kBMj5bZ2dQkPdVV9ORpKSG72MKf2Q+e/NuG1cFZU7tGv7g/SemoM42g0DFtu3UKYVLyWzv6GhAa21MampSECdK8Nls+D77WYaKiyMaN72UWT1kZXmFllOe53hAAIwqoUl1dSQ0NJBUV4ez+VXhtbKzTbeeCQumU0tfeeUVXnzxRdLT0xWEddXV1eP2cOcTJ0/Al0KopRuZz8uuH6H/ua3DyNzU0iY9116rWfEpItxbmziXf+h7XXV+XvwpEhrFWpSRNtdfvk5lbczwu3jDdi35Xv2KZDNuGa0JP2WKONvICNFw/UWSXx7qCvGNNoSKHRjAN2UKce+9p7ACJOvse4W/52sHVivaPbooYIHDw3bHaa3bRQxbZycFuHiZm/kSOxggJfi3WRxlNSvwZs5TuJSs5vlHAqu8WSNXXQWvv06vQfGc0TwJl4qisrIf347WAA196D1PnRJaOj8ZvIfGpPm0DI11xXA6h1m16mMgDG688UZuvPHGcX2QiUbVX8tVlNHHuJiqrh+wXud3VjcbM+am1cCaFuHeM9dW8Y+ndzBDtnkPE0fmWTecFT/fS+84+e5Vmcybd47q6j7VhqdlbSQmwtAXzJOQaWHVKi/79vlV7qT33ovD7bZWAGXG9Wf2+0WSX67lCkktK1O18AxaZzNqWXqgVvWb8cgikUPabP+J/bzLXEXT+dWsIM8JPZWVqrTOx6a8yGpWkjLQqfr+0YzFaa2NkcJCoWttxOk03OTMzJNwqSgcDi/xRema1NQiS6cAF7+/tIzlzi0K5aOg4CLGsymjKWFw3XXXjdsDNDQ0sH37dtra2njkkUeYNUvkfR5/nJx+tbAIqXX61Sh4d2UIJ85gpkLTamBNS2vp89rpeW4brvJ1TOnrJG+4JSAINHCUWdzn+QndHhv19Ym8914cv/2t0ic6kJKNyIMwkJpD70Y9sTkGvc2hoADmzBlWCYP2dutxA6PA9kTHifTSgivXWePQidaGK99spabzvqQkhi+9FK9zHj2y6yoFZCrDPEGozSIa45i3DnDPnJd5t3+WyjI27LAnWxsnXTFUdf2Ak+lXcyVtbMj9EoltJ4LnSgrURQbvbCYBIhJOo5N3/ZjBVw8qlLITcTPpKV1O/qafMk3wm3zn+MYHRDAlDF59VezDgkDaaSTIz8/nvvvu41e/+pXxyeMILWrp6U7t4g/5JAq2snTlkLnEww+3i7VYrY27Z8/7pJb9RMFeGKyWNXAr6WktWfNzyXozsEmnLl4MDWphcJjLOMC8oCtCgmgDtj9+Lydue1s1se2P36s5TnKY2YD7+8WhLKtxA6MajEgrYCOFXlqwFddUNIWagrakp4ePIqzcTamqUo1xYtsJPt/2CJtGqcMly1jEQSV6D4nZs6QkLcAA2wp/ZCrv5eykbtFyoXWiBzO1OpG4CtfUzqFpZPdoNtGo229kNVd+y03v2Uo2c0jhYjubO4PB0lJVpt94M9WZEgb7QvLde3t76ezs5NJLL41YGOTl5RmfdB4QjuSX9zRWtLJshcYSMYGV1sad1/e+ij0UzLmVKiv7OXQoUUW4F/rsWptPGRt5DfF3DN2As+bn0imzNgZSsrA/fi9Z8427koG5DTha7JBGNRgTyScFxu5As66paAs1SRHJyMgw9LXrweZ2k7B3r/BvcupwyTKuxfx7iCzs/e2zueuzW9i42ZpGbbb4LVxXYWenDRcFir4ZAB/1JnKKrCCVhiQo3ij4IU9ULFYJRf/OnWCPdgRzDKaEQVVVlerYq6++Spug49V4Yvfu3ezevRuAn/70p1Ghw4iLiyMjI4OMDNi508+qVYGMluxsP6tW+Sko0DYybU4nNDRotqh8/PEMnn5auYGtXQuHDvkVG/csjvJt/oNSttDmyiG7ZISqnRkUFDBK6axOtZRfOyMD6uv9rFjh03/2tWvxHzpEzHFlADi0E5gcDkecapwzbsmAWwQkhS0t2FatIqajA392Nt5VqwJ+HxnienqE9zny6oc8em8ma9bECMdo5kw/a9eqn0UXgvf1z5xJ3Nq1ZGRkBL9fKOIcDlP3aWmBVatssjH3UlAwNqcMkZGBf+dOvLIx869axUUhY2YErTGd3NMT0Rox/R4asN17r2a9RGgLzJ6eyUzG/Hv09Ii3rp6eyapzDd/DYJ5ECqfTJppmSOHgUEFx3QfvEd+lFoq+hx8mY7M47TQasNTPQI7rrruOb33rWyxdutTw3NWrV9Pbq5aoJSUlfOYznzF9z4ULF7Jw4cLgv63S64ogp7e122HdusBxm9uNfXkNfh0frK28nLSGBtpcYkpYt3uE7m6lF9Vuh61bA26fnj3vk9f3Pt/mP/gmT40JFBf88aaAZeFypQIi8jTltfPzM1i3TjkequGx27Ft3RroUyCjO1jNCvbxedzBxowB5OQMU17eQ3e3sUYuJ8yT4GtooGfbtsCtJT/wyZMWQ7yWAAAgAElEQVTCfOY/n8ll2zYbb73lZ+vWD9m6FZVJbrd7rQXQ7HbiH32U1PJybH19eFNS6H30UYbtdtxNH/Joz885nfB98jzHgxQBvoQEzvX00NfUZDIRYOzbNDT42Lath3nzLjI/N+WTToLFeZ2aloaa9Bg+SkszrdmLfPUXzZsX0RpLl5EnyjGCjf/g24pjaWkf8RHm3yMtLRUEZ6elfUR3t3KvMaTiHl0Xqhid3W75W4hQXm6joUFp3c/iKHP4Ey9SrDo/y6ehZLe1RWXPi4jC2udTpvWdO3eO119/neTkZFM3X7lypanzPi4w64OV/KuZSzzC4LPcrSFfbKlZWfyyshI7NSTV1fH/8YLQsvjZkiM4P/qIBm7SvbYVSJTO0vNM3rOHgj4Xr3EdP2QDb/KPAHwmtZkHf3up6ewdvWKguOZmXZK8o8xiBasBOH48JhiniDSAZnO7Sa2oCFIGx/b1kVpREdJ6chqwgAb+kd3cSIHHRWJ9PXHNzWEnAozKv/OGSAkFbW43KYu/rgi+xrx1APa8EpFbQsv9EoeXp/hmsP+05NLsR/0evoQEYgYHsbndim8RSUBX+KxRpJIJhTzecHrfUfK732U1KwA4zJWKte90DrOysE6YfeTPzlYfjCJMCYOvf/3rqmNpaWncddddUX+gjwMs9Qt2OPjhdhuNJcOaE1NLuPSuX0/rm13Ut6s3e4CuVi9Pchdvh9Q/RIObRJr8UgObAlz8jq8F/z50fTG9DvOLQ68YKJRWQyLJe7dvFu/35akC16dO2SLKjpF+K+p9oNV68jizuIcNTGGQNnLIdbXzQNVTpG5+SHiPaLdMNQOtRIJISRJjqx5VCAIIBHnP3LcKNq0L+1votU+9mGNsyniATdc+OfYeyDrsjVqusR6PUDhH2lsgXIQ7FlK8weYeIq1kRXBMdrGQB5M34C68genOeCor+0lnGcPNr6iEu3/VqvF6LcCkMNgYsgEmJCSQkpKicbY1vPXWW/z617+mr6+Pn/70p8yYMYMHH3wwKtcOF1YDi0YTU0u4/Ln8vyk9+zIfCY3jQJCtABe7WMhK1nAyYy7p114c1qTX2kgGS0tJqK9XUCR/kPM5lg9uomNximkOlj57tjBFTgs+h4OfZO4QdnW6csoxXctML7vKTEc3rdaT9dzEWdm3aNh7HVs1ahv0g9xhe181oZdIUEBLRGmlbW93C7/dybc6mSYYzzM7D9K55TlV0oBoo+zZto2ML39ZyLN13Ww3V4Y2hXc48Ccnq2INImUsktqPcBCNrK1QwZ2ZmcmGymy8jrH1JwnFUOF+UUFBVNxWWjA1a1966SW++c1vqo4/9dRTLFu2LKIHuOaaaz52BHjhUOvqTUxNeonWLlqZKvxbIkNBU1LK9/bMXsDpjfosoyJobSQvrD/IlRUVCkFwLjGFb478J/vrpynOFbWDlDbj0tJB1r+3lqcEKXLMmS0sBvJNmSI09WfO9LOalZqWWTClUCO7ygxzZXbKgLBW4myIUD7uyaOmRtyOUt9NIU46iMTa0XJLPVoVy7bm8Dcot9vG8TN5XC34Wwc5zBSMZ/ZQC413buDc7g26Qlh6Ds+11wrbp2qtp4nO8tKCaG61umD5Eg/u/HTTipMZl9R4uq20YIqbaK9Getjrr6upDv4eIOKTiaS8Xku4hGZUyLGInaqeula4XtxuG2VlqSxenM6SJeouXS5XPP3l61STe9LZPr7T9RPVuRIHiyRY6uqSaGhIoK4uiaVL09nfPpuF7KKWO3iV66nlDu6Z8zJ91dUMCwJWCfv2Me97N/N2YQl3LfpzkKNox45hUvrFVc62U6cMOWKMWEKHnU7ufdyu4hqarFGSreX2kaxBiWtJzq8kfHYBB01aSQk2t3YRoBxabqnupg7NWI0Z1NTYecC3hqMhVINHmcX/jtykSaM+dbBTwcuj51odLC3FGxJf9CYnM1haKrz2eHUNDIXN7Sa1rIz0xYsDOf0G3yJ0bkkp5c+1FgXXQklJGm73xPFoRQJdy0AqNvN6varCs66uLuzjmPM63pC0256eONLSUhUSPdqNakS+U3nQNBQzcs9S41+HPOPTijAy06YRtKklcgSpptKmKNqMh4YCOkVoityCAQ9ex2lGrrhCRbsQe/Ysk5qamEYTTzjfCWqyGRkZjOhsBka+es2NJCMDz7XXCltPZmZ6Geo+y859aoptrUC9yDrS6x0caS2AllsqB/E3TNi7VxV0FUHKgQ/NdV/NA7zSczM2xG6JdnIUglJTm3e5SA2xPgFsg4OkVlQILZhwA+JWKKbjGxtJX7pUQW5nZFGFzi2tlPKJZNiNBLrCQCo2GxkZURWeTZ06le9973vj92TjCPVmqW4yHU0zTRIusVWP0tHUzQd9Odx37hFF0FRCUpKPdT8f4Aw/p6S8n46+KWSnDHDveju5DnMkXWbbNGpRS4gsFmlT1NqMRZB+E9uvH+wO3RT1NoOsGv2CNK3fhi7yULee223jiE4SgBwiYfvSS4mMjATqIkS9g/VcH2Y2MS23lFbmSazHY0rQSEImVJB/hbqgZdrJdLLoCv5NUmTmyQSllhCO7erSbAAf73KRtmQJvvx8hdssHGVML6YSWipgc7tJu/NOFcup1vNI7r04lwtvcnJQsLVpWPYTybAbCXSFgVRstm3bNkpKSs7LA50PaLkanqo6zfrkFePS3KaFAkqat+Hq1t+kh4Zi2bRpCs3NV45lvPRBY4W6olm+ici1UTMbttM5jH39vQxXNCo3TuL4T76lOlfaFLU0VL2WlWaYJhXd23Q2A6OUwnCtOivZKaL5IwkCCSq+KY0xODblSlM8+VrPl84yfHufEhZ3mfGxi8ZzFkf5GfcE/z1MHL+L/Sp235kggyrOPCorx4rEtISwPz0dYWegUcS3tgb/PnnHDjxFRfRVV1tWxqyk+qZUVaksFdHzSBl/qRUVynTXUa6mzC6bYUr53xJi/H6/JV5Uv9+P/CexsabCDuOC9nbtylk9LF6cTkODsveskxbeSFhInmesCtGXlMRwYSHeGTMiFgxlZanCzBkRJk3yce6celyLi8eCmSJNyOkcDm4YonvN4DjOlA9Ju/Gy4EYX39hI2p13KhaHxLPUTg7T82z8cPuliuCx6L7r1/dSW5usZFkczXIRNXUPxVBxcZACwaiwRhKCE5VSuOgvm3i9+0rD3yxY4GH79tPB34cGWIedTkoK3+aFenUuj/xbGyHtttuY/MYbquPSmBpBPp757kbWtv6LKl7150V3sSJ5ve6YB8dIJoTtNTXC4LEetFpB6kG0piHwDf7wh5jgnLK53Uy77jrDTnLBZ8nLE1o2Q8XFwmQGaQ2Ox3w0szbMIKKis56eHp588knef/99BkMW9HPPPRfxw51viLTbNaxUCAKA2KEhEg4cgAMHImaztOJeEQkCUJqfeppQZWU/B3eeUfChz+Iov2YZ/57yE9ydtuB5c2trg5t0kGyPHHJp57+m3U/25RnEVvQHLSWHw6GpQc+fP7Z5iTY/X1ISwwUFxLe0KEx0q8H5iU4pzOcQgX62+pBriFoWS0eFOEXbrKvB5nYT19KiOj6ck2N6TJVN5RNIKwG5LBh2OkmvXsZGh0FXN4E2r1droIVweJW0LFa3O5aWFn+wds5eU2NaEADY+sRNfmynTk1YrcN4wZQw+NWvfkVCQgIPPfQQVVVVVFdXs337dj71qU+N9/ONC4QpjQniBhQSImWz1Gs2bhbyzUUvkOpweHluSycb7mykc3AqObTzbf6Db8TVcrx1RtC0bWqKZ2caFKIm23PSQmzPQyTWj1G5SgLR4XAYbsaigGns0BDe2bPp/a//UmyKg6WlQa3b5nRiKy+fkN7TWgh9l9WsoJH5iuBhXJxf4SoSEgUKNstISfnsNTWqjnUAI1dcEdYYmmUtNZsmqxCCLhexXV34p08nRieWANbTSLX6N7e2xnPzzX62bg2sC63YjR8Q9djzDw8Ljo5lNp1vxWQ8YUoYfPDBB/z7v/87kydPJiYmhhkzZvDd736XFStWKLiC/lYgl+g9PZNJS/uISwbFDSjkiCTP2ajZeMZF58Bmo7tbvMmHbi5Gm0ju/Cw27D4X3HT/xf1IQBDI4HLFU+UtZxt1qsyINaxkhlepcVoRiHoBU2lTtLndpFRVkX777WPaWkMDaaOcRh8XgRD6LvJCwNaEmaQXXcIddxF0kzkccZSXa7sK5LEeu91Hbu6Ion+D0zlMaekgZWWphpkxWuMcOzAQ9vsasZZaLb6Srid/7+zL+1jrK2Z2+37xM1hMI5XW9JIlabSGVpfLKE40uwh+/vPYTp5UKTC2s2dV9Cnj1cUtmk2AwoEpYRAbG4vNFtikkpOT6evrIzExkR4NtsS/BUgSPeCH6+Vk448ZeeMA2UNqk1tCJHnO0mStqkrh9dcm8dG5sU1/Ru5Znn2+T9PXn5en9kOa4WZR9ERYnC4Mdu3p/TQdSQW0DSn9iJE0OQcT9NE6lcLns6eAGYjeRSoExANDycX0zt8YdJMF5pS2IAj1M+fkDLNo0VkGBmLJzAykqVZUpJpqvq43zlZSLa0gnDRZ9XtPoyl3Fzs+fxeFb/+3wnUT7mbrcHjJz/cJ49WS200r0H3m0UcBSFuyRGWxSPQpPocj4jRzLehR1iTX1p4Xq9mUMLj44os5cOAA11xzDVdddRUbNmxg0qRJE9aVLNpwu22UVFwNQ3uCudb9MSnM879N/mjOfTgUDaFwOLxs3vyhZgBUa4MXbQKh/kojbVTLkugeSOYf2YPD1gayUyJtcm6UK25UKWyz4GMebxj5veUC0uZ2Y7v3XtJdLqF2J4r1tLfH89nPDrN5cyDYXFaWqhkPCnVJaI3z4dIVprKUwkE4FcKi9z7RlsiD1/wHv3zt7qjV9BhZzEbZZr78fGH2k8/h4PR269X/IoiE9FwNAatI7hhnq9mUMPj+978fzCBatmwZL730EmfPnuVLX/pS1B9oIjA2UWW51n64LW8vTzse4NiUK/nS4Q2cqB8rSopkYWn5GaUN/tGqWLqbOsihg5WFdaSzDC/6fXn1tFEQWxLT6SSOYVwU4PIWkJTkCxaQ/Yi1PB+zhDN+O7my/rf9lZWmzFmjRWdUKRzbFchrHy/t1gqkdxFpjaC2dmwy6uZQ94kZgjsrJHha47y25lLdBINIxjScCmG9d4pmTY8WxYmWxRyK8a5+1qqHOJQuFqShGXjjaTWbEgZyqupJkyZx6623Rv1BJhJaE/WkYz6nt29nRVkqJ9qU1anjVWlYQEuAa6Z7VEuoB+/+WryFhYyMatZGWoHWBrp9/Z/YcKcrGFRezQq8xAWphC+9dBin04vLZePIkVx+N5QfvOYfk27kufUdODhn2l8czqKT4J8+3VSXt/MFr8NBz/btwvRQPWsnnC5uVoPKonHWmtMuV1zEYxpOhXC0utcZQW4xd7mGyes6yE8yfklK1TArWS3suyxHpHTgRtDKAjzozafI5DXGi6PJlDAYHh7m+eefZ//+/fT39/P0009z6NAhOjo6+OIXvzguD3Y+YTRRzyddsWhDsQ0OYmtqYlJTk2GKq94GOrd2Db8ZVOd8r2ElS6nF6QxYGt//RjxNQ8rc95ahbNbWTmXrYHFYtAqhAmp56Qqu1nG9jDiduumzE5HBoaWFA6SWlTF5zx7h7+SL10ysJxpc/VpzuqsrRhVgtTqm0jicrnqK1U3FtJNDRmEW9+PDgfi+0XgnswFWh8PLLyvfDQjuVlcwVnYfh1jILhooUAjA0OsG/fQabqtIAr1ae8k9A2t4MeFtVZ1TaJU0RJ+jSYIpYfD000/T09PDD37wAx555BEg0Mj+6aef/rsQBkYT9XxpNWDsPjHaePU20Gc1rp1De/B9bW43p/f6QEBq3OUaJuGwmLRQT1sRC6gr2b7+Ba7YtFzRdQ3GNLHOivETwuG6n0K1cDOU2fLFayY3PRr561pzOi1NP8BqFoGK+ifGKurr4Z1mcXzL5nYzt6aGnWlQ5S2ndfrVQe5+s+9kNYNJpFRdzLGg4iOtiaDQMHndSGmstfaSA70X83l2s4aVzExo5ZKidLjrDlX183hlMoFJYfDWW2/x85//PJhaCoHmNn/L2URyGC2+aHdV0oNV6oZQ6PpmNa5ty5s+RgFdVkOe52vAAtV5eV0HNQt29LQVLQG1tvZSNsq6rtlOnSLO4aBnNGPCSAiHq6FF0/1kFAgXLV4zuemR5q9rzemaGjsHDqjPt6rYmLXa5JtnIbCNOoZtTnp+sY0WCkylz4L1DCYtpUpOwnjqlM3ydSMlHNRLMQ/yQ3mgOHmIjfN7FZaofG2MB0wJg7i4OFXry76+vr9p1tJQ6C2+81lpaKZiU2/j1dtAtfyhl277IV6HF7fbxul9p4VFVTMTWqme9rgwPdWXkKCrrZxyiQt3ukaPy7XtjIwMvKO57XpCOBINLZruJ61NpzdmKu9kfpFfpFVDTf6EBL5Fc1prTJeXHiG1bI1CsKoY3mQw6zrV2jxjqx4NcHWZFMhWM5jM0MZnZmoXoWldN9JeC/K9JMZ1kq8fribN00E7uYqOf0EWXo21MR4wJQzmz5/Pxo0bg41sPvzwQ5566ikWLFBrj3+vkC+s8cxwkfulOz4YIvnIn8jyjmkzRmai3gaql+EjactruvMp5fVgUVU7OeTQzgNFb5CfDAi0Sk9Rke4GnN91EAThsbyug8Almr/TE8L2svA1tGjGgLQ2nf/138LSzlroBA5MXOA7FKIxXV56hKsrvqYSrP6dOzV7IJt1nWptnh1N3SrSRj2BbDXLx4g2PrgmaqxdNxrZRoqYhmfs+ebTGEzmmAiyO02iuldeeSUYD+jo6GDnzp3s2bOHc+fOMWnSJG688UbuuOMO4uONqZLHC+ES1clhlfxJjyAumgtdug+u1mDtw5nkLJzP3KNqNwjK9wiHyE0i0nPSwm6+oOhYJhGHAcJsGiNtvP+WMr50oEZhacziKC/P+xH2l57QfA89pC9eTEJDg+q4Z8ECw3zwh7/Ry+frHyGHNoVGZoUcToLN7Wb4ujsUgb+jzAouajnCuf75gNQHOxTekhJOrVsn/I3ZdaB17Z0ZJXyx+1nVcTm5nxxaRH96c09yI07u6eF0QjorWc2fBmYp1oTV64bzHCJojUstd7DCuVm4n0wYUd2zzz4bFAY//vGPefrpp1m2bFnQPSTFDi40nK8MF2HtwyAU1wZ8iXoIx98sacuhjU6GMzK5etuYnzIceuj8GbDrgNLSWM0KMp3zCHfEwtXQbG43Gw5/nUROBI/Np5FlOTuorBS3INV9DoeDNUUvjQqX9iDFs6hXRajlMR70A3Krda79GKtZSUp/B75RDT+2v191Ly3tPaZD3DgHtK02QBEHEGWNDTudPJ3/MKiJVnXTZ0Vzr4UCasrEVrqcVmO4u5uHADht6rqaDW6i1PhKa8znZpycMAtSUxhkZWXxzDPPkJeXx8jICH/4wx8QGRE33HDDuD7gxw3nK830fKazgtLslzc6Kb52SMFWGU6BUH9lJXlNJdS6lgaPDTud9ESQFRFuPri9pobEthOKYxdzjJ/NXM8jNes1XX96rsFl1emUNG8OKglOWthCKTM5Shan6CSL48zijSkPAKlAdJqrh0KurTtp4Rm+wjSZhSeH/F5agtWfnS08LkHUIEgra2xO7Zrg5nm4dAWv/0AtLHNy9JMyQudetBIBrM7paBTJaY35xdem0ztBrkRNYVBeXs6LL77I/v378Xq9mv2OLzRhcL7STM9nOiuMb8ZUtNuIRnJNkUbWgpPb3l7Bcc8YL5R8UzHadEKDgk8cuZmcoTG30UxOsIBGvnpoP/d942Xe7Z/F2pMVFLWGn5UigtxqXcPKoKsvlJp8NSsokN1LszHNqlVh319CMGtM9k5ry1IVxHwSrrhixNImHomVPhGkcAqiPvsm1uZ0KYj6xjNt1Aw0hUFOTg7f+c53AHj44Yd56KGHzttDnS8Y8ciIYJThEq0JZmZz1up0JurPW1ubrBvwHu+MqWhSDkRyTZFGtpI1HPfkKY7JNxUzm46kJaeWLSepSdkXQ8KUUyf4fP0jvMJqCvmD8Jy4CDiZ5NakRDQYSk0O0Mh8drGQnNEMGC3BelFBAXR343bbqKpKoalpEgDz5p2jurpPNTe0rNku13Cg4fzouuDEWgLE6UoMDFhrlBWu9RxNq8zsmtci6nt50T3MGvjTuBHgWYGpbKK/V0FgxCMjgtamWUBLVM1+o805dHJJfXfXr+9VMV7K+/OCtiktijWYzZw6H5pWNO4h0oJbE2YKe1lIm4qVTceoaHAmx9jNFxQ9hRW/b25WNLK38s5ya1IiGhQ1bT/GxaxkDb/KfCF4TEuwut02br01jfb2sflUX5/I4cPxvPjzA1xauzb4bHPta2jgctU1nAdfJqlpLFj6i6SDNLFHFVexavWGaz1HWisgwYpQ0SLqW3HNejZu/ngkFZgSBn+PiGRCiDbNSFIdrdxHgpa2Wl6eqqIbMOrPqwWRe+TgzjM8t6WT3PljGvZ4+L9DoXePFgo0BZZoMw3VgtMHLxH2spA2FSubjlHRYBadzJQFr1XvOTgYnDNWx1VuTa5gNfNp1Gza3jqpwJRLoqbGrhAEEuLaTpK99DaSZJTvG3IP8F7OTva3zw4em8VR1vgeUPw2e6iFDckP8rXB3wSPheOSDNe1GWmtgAQre8j5jgGGgwtWGGhNiDiXS2HSmu3yZDTBoq05a02uvj5zk8vMJBQJnJahbDbc2ciG3eeCzx+ppiUfGy3O9nCKl/SsNflz3e/28U7zsKrHwOBgDIsXp2O3+8jJGaa9PRCYDVIGDKbjc9+veNb+ykoSDx0i5rjaVXSUWXQxTVcYwNic0XrntCVL6Nm+XTVGSmsyh8emvMilTe/yre5SVRptju0UaAgKObTm2RpWqnp/JLadoG7Rcu767BZO7ztKfve7gfgEatfXDYVuip1DEbkkw3VtRouZ1IpQ0WvLuXhx+oQx8spxwQoD0YRowcmDh+6no2n6GG1zU4lCE9PS1kYK1T5QCEww+W8Cwby7ad3hI70onvurfWFNAK3JlZLipa/P2PdqxiTX2gg6B6cqNvpINC3VeGpwtodTvFSLOSEVuqlMmeLj8OF46mWU5bm5I3z983+h5u0vBmoKPEA9DDe/o3hWr8PB8I4djCxfrmjzeCZjBsveW8t32h9mAY26YxKkxNZ45/jWVtJK1PPSXlNDemcntVlZ9K8LaP0pi5er0mi/xX/yk7MV2GvmGQprrXmm1fwoZaCTjZt7SV/8HRK61XUgwXdwTldZpuEoTOGkUUeLmdSKUKms7OfAWzEK9uM4m4/W1vggV9REFyZesMIgdEK04GRhzB6Oe8ca9jQyn12uhWRVVeFPTsbW2UnsyZMqTvt4l4uRwkKGnU7hBDt0/0vc5XqV06RzlkRGmBTcTLTIvYygZSKLYga5tFLBY/ycclwUKExpvZiA9kbQrtjo++zZAlo7Jc+/1iI3a1VoLbwODe321CkbNr95ISXfVMoE2S5tbXH4YtoVxWVaz0pBgXCTfcxt46mqB7hub4PqOhLkm5Key0l+Xz0FJbHthCqbaCPfowAXnlPiBkZyVFb28+ab8SpXUW9iNpxVny99c71nF228ondIqK+3RN1uFtHKbrMiVApoYaf/Ph7mO7STQwszOOGdqThnIhl54QIWBtKEyHj8cUbcbpa7H+F4q7JzmxRoe2bv/9UkaJMQOzAgnGD722dx2xurGEFcqR3uBNDrdCY1yOl57QNyz7UETfWvT3qB6uteYVl1uqmUycrKfg7uPEPL0Fi++SyOBvogZM4DAsLkvvfW8hSHFFXLZ3NnBBvh6Pm9zVoVWguvrnClpr/fS3juAE2LqG+KqWdV/E0mCFOzsqiurgS2MjQ6T3xTAteMHRhQbUpmO6xputAGB3WziT74wMGmslRd94TD4eW3v+1RZRPNuOsehiv+qLivLyGBmMFBbG638Nl9CQl4ioroq6425Qa0Qt1uFZFkt40pUOlkF77N6sKVhhlB9poaMtv3U0sglfR69nCCmarzJjKGcMEKAwhMCO/TT3O6u1uzR3A7OYaCAKDRnc/yik+RlVVL5bqxxVW+JFVTEEiwMgG0NHl5pzOHw0std5J4TrlLZp87yQbu4UPHZsA4T9vh8PLclk423NlIy2AmnWSRQRcPJm/gnlInuaPX2N+eqahabieHN+Y8wEOOVFLLynQ1f7OmtpY2t4x0Xgnx90uWTz/huQO0LKKslAHoM35WCXqC0MxGZLrDmk4Gk1Y2UTmP8273XFx1SYbuidB2rZ2dNtbUzmHF+u1ctmlFkII81uMhsb6euOZmerZts6R9R0rdfr4gShF9M3cjc+YM098ZS1aN2Pcf+n65iKl0JoKTSMIFLQzk0NoAsmM7wSf8UxAn4mbyL61rcbUmAFBfn0BhoZcZGWc403EODISB2Qkg0uR37pzMjBnDDA7GkZ6egdM5QmVlP5lNTcJrTJIdN5PhkDs/i3uegduWZnNiKJsTzOTNQfhjRcC9JaexCNJmAAsGPMBpQ81fpEF2JBXQUbpcpdeLtDkH2kFEL+G5A7RccPeutzNcIXYFihCNFEa9DmuHS1ewtiyV03/ZRD6HVMHac/Pm0bpXnDZbzyI8BPzXZqxTsRV5NTsKC7jco1Q65O9o+j0jpG4Xnj9qlcX19JCalhYVV5NIgWpri1O4FUXCNfT9RMzA40WLbxafCINRiDaAgqQOVsx7Rcih0pt1Me6RHD7oz+U+z08UOdODgzaammw0MY04zune18oEEE3EoaFY/vzngBByuSbR1DSJpqZ4PvCCkb1hJmXS5nazrtxDy9A8xTnSBmLYgNxA8/c6HPxp/XZcd25g6mBngNtnaDVU5JmOpegFEcNxB4RWFP+gq4qr008SX5tp2AVLjsLkGHkAACAASURBVGilMIqsosOlK1hSceXofLgSuDLo/inAxbDTSV91NelVs4RuNEkQSDCyTrWsyNWDxTzLpojfsb+ykkl//CNxOr+zku0TapUlYT7dWS/GpaVAySESrqFKTwEuduQsY/kVdXQOpIwrLb5ZfCIMRlFAC28X3k3nYDft5FA3byXLqtNJ426GS15XaGUf5HyOm2J2caIzUeeKAYwwiRi8+GVbcwxe5sb9mVk3ZCmyiYwKvMxMRAhMxqbMzzKfl1V/6730H4L/b5SnLS2oU62/Ft7n1Ckb69b16l7DKMjmdttYUn41rbKc88BLEHEwLRKqcWHrxCZr9RPRbK4eKtTWlqWqNudjXMwDGZt48tpNwQ3s/mp12qwIcgVAGreenjjS0gIxBa25146Yvyicd4zt1f7WvqQkbCdOkFpWZkrDF1llrS5YvsSDO187ldMoxqWl/IQiVLiKBPrUykqecAwTSp43UfhEGKCcANMI6Fk3NDfQwzbhR1w+uIkT9caCQMJVHKSXi+gllVR6eZo7+aeR/QwlF9PrCCxwM6RbZiciwKO569jqP8jkrrEUwBPkU3r85zzmtqk4dUR52tKC0vNvGl3DTA+F0CI5Cfv2JZjKwRZt+oCpgjk9ROrmGc/m6lqbs3v2dfRuvDL4b1Ha7HvvxSmyg5KTA5QlIJqHgZhCYeGI8H4Z87IZbjbvOtOCvaZGMzbni40ldmiIhAMH4MABUwI51CoLBtJbLw7GBkXuHL3aDl9+PmvsczmQu0GRIiqCsCDR4eDdyl9SU2PnxAkbf10Sy/Tp/qBrd6J7XXwiDDBe9KFaWcfiFEvXn8P71LI0mOJXxcPk0s5KVx12RrXjJepNMdTc1GuZFwq/M58fZtSrqZXbC6ipUfPqi7paSAtK5N9MSPAxOBiDe1Sw6GnwWq4aketBju5uG93dgU1PK8ipJUQLC0dMFczpwcjNE+wb4Romv+sg1dMeJ38GsHYt2O3jQtAnwUpVtPR9pOe96CI/p0758XoDlemDgzYqKlKDQkPkDiosHMHpVAfq76/20cP4UToDxIZ0WTQjkEOtMlEgXdimU6e2g9ZWLqeBnTnvsXxRwL0jEq5arl/RXG1tJeja3bYt0EZYNKeMOs9FA58IA6z7drUWYmqql3PnYhgaGiv6klIxRSl+fzxyI481xlFRoaaQkCA3N+VanssVR3OzjcFBtYYoTcaKillskgV1Q69pZI1IC6oAF7tYyA/ZQD038RFJeDyx1Ncn0twcF3ahjFm3F2gHObU2r8FBceFdaMGcHvTcPOqxK+L/teay68BCZhy6GdvWrYFsNR3On0i65VmlYhB9azmk8dX6JgMDsbqB+vGidPbHxhLjU2dwGMUkQq0yLVoOlTvHRCB7dvt+tnz2Lno3j1n1Zqqg9ZQflyueqqoUmpvjhHMqr6lEt/NcNGCNJvDvFFZ9u5WV/Tidyr6+Tucwv/99N3v2/JXi4iEWLPBQctGOYEBPpJm0DGVTXq72/coRqulJWt5LL3Wze3c3xcVDzJvnwen0MW/eOYqLh4Kbs5H2qJdaCoEFNex0AgGBYGeQj0jSPN8qrLi9QBzktCJQAFI4Yzq4OVhaij9OqS/54+IYLC0Vjp1UlxJz/Dj2mhrN60obc11dEg0NCdTVJVFSkobbbf5dJMVAmmvy7y6CkRUGgfHVmzPS3Nu+/XQw9ThakM81Cb6kJDyf+5zwfKOYhGSVDRUX4ysqIjNPPLah60v0HCKEzqHBwRg++CCOffsSqKpKEX5Lo7na1DRJc07Fu1zYLFKKW8UnlgHWfbtGfnJJe00t+y+S6vQ1Ez0uIaNMI7l7RtQSz0h7NEot9TocHFz/AuvK++nom8IRjz67p1VYcXuB2AWitXnNm3eOP+/tp9UzXXH8AJ/i2JQrR1vM6CO5tpaYEaWvPGZkhOTaWjo7vyT8jdRwXU/gRKtbnhUqBjNCU5rHVsnfosG7JW3ep6ueYnVTMb4RH9+b/CRXf3iC+ORkbIODwXOltWlkXck7nf2w6UMaS5Rurs/lfMCmweWkLO5QPLfctRfrduvWeLjdNhYvTlekltbXJ/Lee3H89rdjwtnmduM4+RcaBL3AjSDNKb3Oc9HAJ8KAyMrTxR2kA5ALGa0grBaXUF5e5H2VjYSWkeXgdtsoqbgSl4YLK/T8SJ/vgw/igjGCUGhtSFqbV3V1H6sq42jdpzzfzQzmvfYLir4h5uSXQ899qEfVAfqa60QwWBpZYdL4yr9JT89k0tI+0nVhRZOxtoUCSpqfgO7WsT7co5/Al5TE8KWX4h0VBC0UWOpyVkALOwqrWD1YTDvZXHbpOTa0LCGx/oTwuYO8Wxo9jyVFsabGLmzU094+Jtyla9zdmsfL7GAAdczR6RymsHBEwYclQZpTRp3nIsUnwmAUVvLR9XztBbQotCQpL32lq44/HrlRQe2gxSUkNRYvoAV7WWQal572aKQFmnEtRFooE8oJVFeXpDpHTzDqCby+EbH+7zlnE2pvodBzH4rGTooP+WfO1M2mOd9d7ED8rZOTvRQWelXZLNI3ycjIoKmpX1f7jlZvABibb1tkXdokxA4N4XU6g9esKbNgXbUE2GszXa5gTcTR/3cZ//rRas3ubxKMFEU9i0sS7vaaGlpd8E2eUggCW8wwl83xc8klI8E1pIwZjM2pcDrPWcWEC4MtW7bwzjvvEBcXR2ZmJnfffTfJyckT8ixmg3paZv6jVbFsaxZrSXaHg9+4Y6mpUdP2no9mOSIYWQ5aEz0jw8vs2SNRL5SprOzn0KFEjh8f678gCUa9e2gJPCNtWK69iaDnPpSPXZdrmLyug1RP/zmZznkMr12LVyfQN54tRrUgSjEF6O/XDhu2tKjTc0O1bzPJF2bdSNJ802JElV/TUsOhVauwyb5hC06++NGLut3f5NBTFPXmmCTcbZ2dwpih1x/PJZcoM/u05lSPrPPceGHChcHcuXO5/fbbsdls1NbWUldXR2lp6Xl/DivNtbUmYndTB/HdYi1Jyi8WCZpoNMtpaYHly1MtZ6foWQ5aE/3aaz3jwqzocHjZsWOY5ctHotJ600xMQs81Y6QVKsfuEuAJegnEb/QW7Xi3GNW7r5Riamaur1plw+VSjk+o9m2UfGHFjSTNN6lLm9Y15eeGQmRdhfrazXZ/M4PKyn7eemuSylWUkzMm3L1ZWaazmULnlM19P9TUkFpRodnrI1qYcGFw1VVXBf9/9uzZNDbqc72PF6wE9bT9xeIAz0lXjCX/JlhLd3W7bdxxRzzHj08yfX0zWF56hIM7s1WuLSsarNXgYkEBURM00qYrquGQYOSaGY/ezdKzid5TPl6+Uesitr8/qq1Ezc71jo6Y0J8Cyg3MKPnCihtJEt4rXIEubXJXkfyaNrebTYPf576E0xz35AUb9mjNzVBfu9bG3BabZ6nKGQLf8fnnT+v2iO6vrCS7vgMG1b/Xm39me31EDf6PEdauXevfu3ev5t937drl/9GPfuT/0Y9+5Pf7/X6PxxPZf0eO+L233+73FhX5i6a/5w+Eg5X/TZ/u9R85ovzdkSMe/8yZPsV5M2f6/H+55fvBA3v5nH8Gx/xT6fEn24aE1y4pGVE9z0hJid9bVOT3Op3qH4B/pKRE9R4lJSPC699yy4i/pGTEX1Tk9ZeUjKjew2hsfDNn+o/j9N/BFv/17PHfPuV//Ed2HbV8DflD+WbO9HuOHNH8jdfrjfy7hvx35IjHn5/vU41PXp7PcEyOHPFYHsOw30EwXlbGzux/RUVe4S2KipTPffvt4vP05u1IScnYMx454vdOny58F29Rke5433bNUf8+59f9A5+VXfPIEf/ILbf4fZMnK651cvJM//dv+Yvmt/EePaoY1zvYInyvO9gyNtaTJ/tHbrklKuPt8Xj8R3Yd9c+c0qHaM/Tm00hJiek9wMp/Wojx+/XyYaKD1atX0yvgHSkpKeEzn/kMAC+88ALHjh3jvvvuIyZGrJGEor1dnKFjBqFSt5QtbEXsnhL5rUWFJpKfv8GVx428Gmhio4MFCzxs335a+DwQyGmXpzYOO51CrWDx4nQaGhJU109I8OHxjPmEzfjfJaSWlZFUV6c6PlRcbFpTDucaohRZs9CzQtxum672JrrW6aqn+PLeFRz35AWPmxnDcN9Ba7zksDL+WtAK1BcXK/3X/f0Z3HRTjDC5wWgOieazHFbfI5LrZWRk8GFTU9Ddd2zKlXzpsJJSYhZHgzVBcmituXBgtjhNQvrixSQ0qLvFeRYs4PT27WE/R06O2DI6L26ilStX6v79tdde45133uGhhx4yLQgiRaj5KqJckCAyoUVmvkSZvPSGAkbO6gsCUJqIInM6ZmSE4bw8fA6HbrqrlttKLgi03kML0WDcjBZrp6l7GfimJU5+6Vx7TQ22CrHrSrrWva41HCdPcZ/x7EYlGq/QLmUShUkkMBvALigg7NiGaD5LsMJdJH2rhH37sOkI2NA5FdpXm/LyoLBIBZ5191FT4w80hvrgNR7pvkvYqzmafRSstuiMJtGhGUx4zODgwYP87ne/o7q6moQEtXY7XghdeBLlwvy4d+gaSVedbzYH3Otw0Bs/TdgSUI7Qxae1cfocDkMtQJSFE2oVSDD9HiYnol4G1vmczClVVaZ802YCmtJGZjboJ0I4hVih46VFYfIbd2zU6k9crji6umJIS/NRU2NXbfbh9BgG7fl8Om46DxbuYBnpOFBSpYeOF6BrDcghn1NmfO3y90ot2xQsDhW+yzgoL3oIjsWJE5xLTGHS2bGOSlIHwfHAhAuDJ598kpGREVavXg3AJZdcwre//e1xv69PkPpXgIvrs97juVZ1laCVHHCtQrLERC+f+pQ4JTOcjVO+EV9+uY+LLz7HwEAsmZleBgdjhAUsZt7D7bbx/cFNnE64jzzP8WDTlFCNzigrxUxld6gwGeV4swSb203C3r3iv4UsZDMBTWkj0yoUdLtjgwR9IrS8fpLqr3bQMVgWzGHPayoxdDeEjpcWhcmjVX/lic2RZR5JbU0l1tjWVjhwIHpN2bXm8+9HvsCm+st5Rdb7W6+HsxlBEDqnrNY+GLUXjVR5scJDJR+LFpws43/4V54Mkk3+h/8hHmOqQpBGCxMuDJ544onzfk+b203ce++pjo/k5nLv43YaK8RtFM3i8cd7ue22DEZGxjT1uDg/tbU9zJ8/LPyNVUoM0UbsdI6RxrndNlUBi5n3UF53GrCAhoTreKloDenVyxSbmVFWimHBTmMbHXduoGywg3ZyWcFqbj40g61b1Rut3oLSoz8OXchmXFfSRqblOmxtjWfx4nSef/608DnvKE7m+GBx8Fgj89nlWkimEdNmyHidfP8K+FB93um9f8Hmjh2Xrl3RcoOJ5vNRZrGC1ar76PVw1oNWT2Wr7klp3FOqqoItPCVESjluJWUdlIJsJWt4net5nevHTmhHyDocDVyQRHX2mhriBcHn4TlzyJ2fZYkATIT584d57rlu8vKGSUnxkZcX+LeWIAAlsZZnwQKGiot1NUkjkjmrRGZ61z3uyWNF8nrVs5gp/JFSM09v3x6kA4eAQM5eehvFg89yA69RylZ28wW8x0+oiO+MiN00XWwJCaqFbMYCk8jKJNdhHm7V+W1tcVRVqWkFamrsHB9Q3kPKYTfjbpCPV/p1s4Xn5Hn0ifDMYjxpMeTz+Z2UImq5g4XsUnQElO5j1P9Yde2MDIaKi/nra6/x4ebNqnkZjpXtdTj4cPNmDv5mH3vzbuOdlCL25t3Gn9Zvj0joGq3TUMjHIhI3ZTiYcMtgIqA1+WIHBgB9P6lZX/D8+cO8+eZfLT2XlZx2Mws5HH+vlQ0iEloFe00NSUMtimMXc4w1rOSXp55UHDe0QDQWv6eoSPVtzFhgcg0959QpPP/Pjqh7qZSZJId2R7Acy+6Gysp+Du3oUWQzSfQEtlPiwiwriOT7mXF9SPP5JxrZS9J9jtnn8gh3K6khcHFu3jzimptV38qqu036nZGGH+DiuhpX67bAgT5wVkTGEWZV4Mrnsl5TqfHABSkMwg1sRpOUS+v6ckFzpHQ5a2rnCBdcOAvZjCCzct1IaBXiNPyzMzmmupfRgtJa/H3V1arfmCUllAtm/1UpYDJTVGv8spLPWHI3SJtteqIbn+c4WXQyi7H4zVDmPOOLGCDc72fV9aHFi+RyxfGNb1zEnw/9jFYmB//2O77C/0z7JldU3wdgiUAyMG5zIW0n5d4qrp7eypTZ+fQuWWI496PhNgsVkna7uhcDiNeTze0mZnAQX0ICsR6P0E05nrQl56XOYLwQbp2BFhOh0aYejdx7K880EDOFL/pfZj//BChzvN1uG19fnKLIlZ6Re5Znnxfnzpt9Z3EsQls7spo7LWHaZz8rpAY+Geekbd+bimuYyYsPCjrZpgFETK0M8I1vXCQMxi9adDaYriohUA0+XZHZVZDUwXNbOky32xR9A3kevNncdzPCX+/7adVLmK1TEN3H5bJx5Ei8ogGUCMmJI+x+VR2T0YPW3N3zX8dx/N+bDOe+Vr2OvB7I6v1zc0fw+/2qTmih60m0Pn0JCTR/5p9ZkbSOzoEUHI44ysu7Iw7ua9UZXJDCAMYWyuSeHj5KSzO1UWgVgZyZ9zm+4awPu2sVaAuaPqYwl3eDvlZpwdncbs7ceh8Pt3+HdnLIoZ1vTPsf/v1Tv6SjP0X1HFYEWbgbvBWk33JLoKdt6PNcdQ29O5TPaVVAQfgCXwS328att6YpFnROzrAm42l/f4ZpfiWRu6Wmxi7cbEsydiqa3eshvrGRtDvvVPUBsPL+WsIgkk1TS5CIoCdc9K7tpIU1rGQmx8iik/T4PqYO96jOl+a+tBcc23eaQ935QXoLq8+h9W6LFp0lOdmvOx/MrM9ICjLlmNCis48j5I0vek0OsJZ76dVmB3VNY5OgqSmeF9Yf5NLataa1Uq04RgoDrGElS0fbV8ppcTPb91PLfmA0J/2vuzlWP03xHNKGqeWWER0PN7fcCrwzZgRyGUOQcNlM4fNoFT9p+a6jSa3scHj57W/NF18VFMAvK98d08prxr6//HntdnX/3KameNLSxK6F0Gb3WrC53aQvXUrs0JDieOj7h9t6U8sVJjGh6sFKZzqrgdLOThtOWsZ6IUjQyNuwnTqlUBquBK4E5tMYDHZbccvotQzdvFlfSJ7PAk0tXLDCIByIfNMdSQXcM/gT5YmuVrKX3qYIkBrFFvR6r+bIAklyWlw5tBp+V1WlkJzs5zuHs1gkuHZMV1fgelHoVmUFWn5+Lc52kYDS812nR3lxWRKQLWL68YPrXwg0C9JhUXW54vF6xbuX2cChvaZGJQgkSO9v1e8vR2VlP2++Ga8QYgCHD8fr1l+AtVanVgOlWVle7hb0QtCCNzNTqDRczDGeTv0Bj1//nCWrOJKA/PmuNhbhgkwtDRei9M/vFf5eYVICrGEl2SGZMpJWpoX+ykp8SWLzWWp7J9dSQiePVhra3r2BVMxvezZylFmqv/umTw9qR0l1dSQ0NJBUV0daSQk2tzqlMlrQSqWlIDCWNreb1LIy0hcvJrWsTPgsegG/iVxctlWrhFbJuvJ+Uy0+p0/3C3tsm9VQ9VI1pfe3mvIoh8Ph5YorRlTH29riDH8v6h+eSTtJDCiOzcg9azlQWlnZz8wEdRxKBCm7SGusrj27i19WvmvJParVG93Me4h6L0da42AVn1gGWAu0dXamk5VVS+W6UY2hLBVCvB1mmnOEwutwcHrLFpWftyOpgLpLV1LsHFJoKaGatVYamkRJ4aKAhexiDSv5FE3M4f3AfZ3OqLpUrEArlbazsY3spbcrLKuYtw5wz5yXebd/Ftn2PlazktON/wr8g+r3p07Z6F8XXnqhFqy4VLR61Xb2mvOVO50j/OIX/WHHbTQFYXJy8P0jrTHQaopj9PtQl1++u5G1rf8CBKxbKf71wJw3SHU8pPit0Tp1OLzEF6VDvfb9vRkZeK69NvhbrbGK9Xgsz/9w3JnB54qg9W608IkwaGkh7dZbFUVo8W++Sc9vf6tgvNQyqUVpc2eSs4Xc5UZa6fD8+XTv3q2YELGVlTzhsAMhpHghk2dV2lvsP3CrIrsolJ/IRQFLqeV6XuVVbmTY6eRw6Qom33UPIk+0JVK6KLmZWlrAdecG5oVYVoltJ/h82yNsohaYxiHuYw5/QiQMMjO9UV1cod/fSQsd9Q9yWaGLeKf6uqH8+RLZ3PsDxveW9yION24jcsH5kpLoeeaZ4HNG2nozkt/L3y198XISWgPPWcvS4DmegQXIvexm07p91fcz3PyOkFpCFEDvr6xk8o4dwgr2cFyKVt2ZoQJhPJUvI1zwwsB2333YQrKS4tvbSamq4sPNmwFtk1ryx6en+/F6h5k+3YfT6cVZeg/DFX8MSyu1MiHk5zoyMni26cMgE6MuP1HGMEPXFnO4dAVLKq5kTXe+UBiYdalEs/5i1Sob/zoo1qzlsZNjXMwV/IlZHNXMw47W4pJ//2CAcvAYNAFN6nf1rlpFzIsvEjs0JCSbkyM3d4Q5c4aDnFLRyNzSEoQQyFqxdXayxj6XA7lKGmcrrqhote40684za73K3z3O5SKmqwtbTg4f5eZq1pOcu+YaJu/bp3oG35Qplt5FC+NJ+xFNXPDCIOatt4THJzU1Bf9fy6TeuzdBoXnbbMP84he9ZDlyg1wn0nVGCgtNP1O4WR6hWokWP1H5tqvpdWxkbVkqLlc8K9DvLGWEaLqZOjpiNNsetofERfqYyi4WspI1tKZcRtqNl41LGqz8+68RBChV71pQwHBhIQkHDggD+xDoI33ttZ5xa3cZKghDBfblNLBr8uusvOgx2m15ZMzL5v5qn24tSeh83Lath0erYulu6iCHDlYW1pHOMrw4TFuKZquFrWTbhL67UcagPzGRYeKIR9Y7JIpb43jSfkQTnwgDE+dY6RewZEka27f3UADENTcHOdgT6+uJa2421JYlkxJXK2tYSQ5tdNRnM+mZe8iab42CwOHw8ssfHeLsveuZ7mnFE5NATrwNZ00S/ZWVdHYGqLrl8YQc2hnOyOTqbeZ7rUYzLS472y8UTnKSMwk5tFOAi1qWMnRj5IV/WpB/f7PxICl1ViuwP3v2yHnVCoVZMx+9z7MffQmA4WYnPWzDizpWpuXiKKCFbc0lY32/62G4+RV6168ntaLClKVouiJ8HBMC3H9N5tvUK9hB/5Nv8avujaZ6RxgJvkhdcucLF7ww8F1zDbb//V/VcX9cHDa3G6/DITSJtfoFtLbGU1KSxtuFd4elLdfU2MHVqsyVHoSOpY3E7vmNJbdLZ2Mbl/2ghBkjx4PHXEcdnD46QlZTCdmFbxNgJh2LJwAUXzvERsfENOFYtcrLTQ15LHSNCafexCxq7NW4usaytiSOHohO1oWeNSb//maatcOYxpvrOr/8MlowIoPTmpt6Lo5aaoJUy8EGPK52Hrr7UaadMj/3zbjzwuUbMoOqv5bzOkVKdlCg+v0P2bx4sa5lY8ZFGi2X2njjgk8t9T72GCO56gUe19kZTK8UMYAWFYkpkyGwWDqbxGapkbbc2WkTuiKyh1osM1X2l69TCAIAJ27cOIl3uVjNyohSGIP3MZkW53bbKCtLZfHidMrKUoOso3JI3bXmFWfyywVPsrH4f8l5dQM/+11KcPy/tuivvLjoMXIW5Bqyu5pBY2M8CxdmaLKiyr9/3byVdCQpU4lF7yppvA8sekOV7jgRG4FeHYsE0dzUc3HYOjuDMZGtlPIaN7CVUv5P11ZacKp+8/6eHs3vbvj8Fll9reDk9KuFxzvPphqmWuu5SCWEyyB8vnHBWwYUFHD6+edJW7JExZUj12YKaKGWGmz+TrxkcfiuFTQ3axcQtZMTVlA2K8sbVmqqCFP6xIHYxNE2bIVNv2VnfhdV3nJap1/NdGd8WD5sr8MRcA2Ul2Pr68ObkkLveiXldVtjJ7cvzaZlSFmpLVoUWpk0iqrerCx6q9dFvBm43TaWLk1XceWEBvjGnslOrPs3DMncGkdKl7OmZoxQUGrQ43U4SN38EFvdNmpqhsaV3sMIRg1cQDw39VwcXrJYyd2qmMhx/0xWskaRHQTwfl8edXVJusVtei6X8cq2yXTGB5IBQiBPWNCybIxcpKEW57p1vR87ISDhE2FAYJL58vNBQJwWWrIu4eqmJravf4GvlV9Ja6taINTNW8kNzQ1CszZ0gqwoPRykrthkz+adyXb4SPCcOoKkpQWWL09VuDk8KdnQpz7XN2oQ2rq7KeyuYxt1DNuc9PwiPE3L5nYHfMSj4xfb10dqRUVQc7O53Wy4s4OWISXTpqWezOPEGFtTY9ckTduzZzJlZanCnHA5pUOoT/3QIb+iQc/5oPcwgsI373IRf+SIokpZy+Wi5+Lop5LWHT4QGMmtCTMVx7Ua28gR6TcOJ73Z5nazZvBhDiWsEFKFK84VBat1XKSRVHlPBGyrVmnU//8NoL8/clM7KSmJoaEhEl5/nfgjR1R/P/fpT5Owfz+TGxsVx21nznDR8F8pevxGdu9O4MyZMdPX6RxmzS9imLx4IbE9PfjS0jj36U9zZsMGWiigpCSNxsbJtLbGcfZIK9994StM+3MDca2tJB97n7z0IXpGUkj2ju3kw04nZzZswD91quoZA0RqKTQ0TKK1NY4jR+LZvTuBm5bPIn7nHlJ9Y8yaLhw4OMEklBWktjNniO3p4aObb7Y8hlMffFA4PtL1pj74IJve/SdOhFRqA6Sl+fjnfx5rGC19D6v3CAdut42f/jRFUxh4PDHBsfzCFzxMnarmdHzwwak0Nk5WHPvwwxh6emK5+WaBRJ9A+KdO5aObb+bs7bdz9qtfVc1NiTvpwQen8utfJ/OHP8Qxf/4QixefpacnlrQ0H5/+9Dk2bDiDw+HFP3Uqrx2cxvvHklX3+ocbJ/PluUd5h3KraQAAHltJREFUtz2TVz2fZxmbFZX6od8dIvvGkiCZ3NhIXGsr8UeOkLB7N54vfIHE7GzhnJJ+k9W0h694X6CbDNISBpg/7QOeHlhMAUor6tynP616juErriBh925sZ86MHRtdqw/UOFRz48wZW9hzQ2ttWIVdo6/sBW0ZuN027r3XhsuVTrZ9E2tzupjdvj/4d0lbSq2oEP7eduqUbtWhF7VZW1OmDMitYaXKrz/5VCv+RYsYSv6MqYKpmhq7gjIZAtrXf+66nBXPbcNVvo4pve0MeRPImWkj8dQQdKsnY7i8PUamsq2zM+JGHZJ/OhioHG2CkhPmM0taW3e3sf9az4IJJ23wfPNAiSByuYRqsg0N0NCQxrZtPWqq8NFn//FdK3inOVVlOdxf7aPXYdzYRo5IstJ0fffbtqnOd7tt/GyJh1Otvw7OpVqWggfOzl1EXALIZYGW5aSXDfW3klIq4YIVBmMT3wbYgGk05e7i5UX3MGvgT4qPapQtY8UNcMqlDNhqxQdiBwY4PVr0ZgSJrVFKRZX6CZ86lUPW/Fyy3lyvON+jQZcbbpqe0fh4s7KEjToKkjqorDSXw3DMPpev8Izi97/jK1z253byBK4cI4iyZPQQuoAlV99f/iJeQlpCLlxXSLi1J1ZgVByl5y5dW3upMCZiJZNGNI9acPLggXtpu6yD7JQB7n3cLuwLYUWQBNd+a1HwWCPzg/0iYgcGLFWva8Uy/lZSSiVcsMJANPFPtCWy4pr1bNys3NijldZmc7txNnfwR8aapZtNVdTDXPsxnuErigyk+TTy2JQXgVTV+dFO0zO63mBpKc6dO9k1tDDIP5Nl66KqoJb8mkmmNOOVrOYY0xTHBkjh7d4U3q6z7ovV0tomTfJx7pxaQMkXsMgXLMe1ecfZNHg/KYs7VJp/OAV65833fOIkW1itUChcFCho00XPPqd2DRs1nl1kOZeWDgoFW+g8ClZvn70YzgJ98PZtJ9j2XKdKIOgpJKGbnGjtS32qa1mKNzMzKsHqv5WUUgkXrDAwMuHk5rDPbsebn0/sKIHcuXnz6Kuutmza22tq+MlgE29xZVDDXcFqPh/zBjP8Y4vsbO4MKgbX8O7idFNa4GpWMi0kFfVijrGalQzzhOr8aJNi6V1PCi7HDg0FC8T8MTHEeP1wGDgc0Ix7168nubaWuJ4eUgXNhjr61c3n5bBa3q+ltV13nUdYtS1fwFpWRUaGl1vnNfPz97/MpPox159c8w/HFXI+6Axsbje/aL6dbMY4oSRe/8xRxcSq9i3a8PUFm3IePXjg3oAgkOH4yAzWlbtY/6Zy89dTSC4KeTa9PtXRZArVcyFL+Di4DCVcsMJAz4QTmcNyxDU3h3VPW2cnBbiCFAoSQ6P38ksZmj0P26lT9E3J4rsH7uaO+uWsIhBMO/j7zzJp60rNCuSUfnEKacpAJ1otNaKdpqd1PZE2GRPSXC/e5eKi0juJOxsQtkkEWEr7nn/WkFhNDiu+WC2trbo6ELTXW8Bam8ns2SOsT17BpJPKGJBc8w+nQO98+J7tNTUKllgIKBQbkh8ku3JD4BlNPruoJeuBt2J49vk+Q8Emn0dtl3WAMsYMQGefmjPIioKjNZem59miVrsgQc+FPN491a3ighUGeiacaAOTI1zeHWkxSRqyhKHZY1QKD3+jl8f++n+YwViByxc/eomu2w/gf227cJJ8HBpjaMGo8lWCJAgkJLadYKDqUbybA5aN6HuFwoov1khr09O4dRUJA+05HBfd+fA9az33DYVuBjVo00H87I9WxSoEAQRcsI9WDdDZb16wZacMCFOjs1IG1Acxr+BoMQp8ePk1tNCHg8jG1Wx8Z6Ko47VwwVYgS5tBSYlXVRVoZgMLNY3NVNeaqdQtblqtEAQSpntaNSuQ+ysr8c9UtouMdmMMM81mRDBT+aqFDlkVt7yKc968cyQnKxdXOL5YSWvbvv00GzeaLwbSa2JiGEz//9s796imrnyPf/PgYVBAkgoKDQWturQPSkuLzLC0PjuM9o62y6EWR+qs0T6s1lYjWMdA1dEiWHGVSu+tF5yxA73Y0TvOsK6Wuny0Q6ftME5tUQYtEpGXQKmIvJKT+0dMyOOc5JyQcND8Pn+RcE7O75y9z/7t/du/hxuRtO4WTeHTJy3yccjtFz1u8BiesrdVsb8/bVVNghTbG/ljECu/YvNdrPwK3sgfdI10p1+a+9L8+T0ICDCV6uzrk+LEiVE2keeuYHu+ZjMYVzS7NSOh1KU1PrsyAEyd4uBBA9rabI0pXF4N1m6Nm0d/ZtmaFZKv3NVSNtzA7oIJcHcSg1qNgfJy6DMzvVIYYyjLWbbZpFEuh0Q/GOfQLR2NIMZxtteECVBZfbZecptnX86ier3lgeNsVdGl0WDUv/4FyfeDpiJ7xSzURMfH9myP0E1n1naKjWUJkgyFRrPf6bVNkbuO8fcT0IR1mnDem6qRiREo/agZeevq0XxjNCLsvImG0i/VagOCgoysySb57MVwPd8pU/S893dG2opeYjQaHSNp7hAaG7kHTr6oVCq02aW3te9kbDnp74vsQcnhG1CrDVjD4Uu9ePEtQRt8Op0MjbPXY0lPCev/by3mzsypUqnwQ1WVVzajQjlcUbnksd8U605LQ9ChQxZFZf95x/UX8cJnLztkKc2d/2dsLXL0huID28saHT0wLNGfqq4urylmvnD1SWepsy3tdltu+c6dqPohVPBz7HzhLTx9YoPN+zIRl/Dn+bkILdrqoMgz0y5i+qHtgvst337J9o4DwLPPKlFZGeDwfVJSH8rKBieIbJu8L+U8xPl82WJX7H/T/Lv2yoytAI+r+xDKhAnsmXR9emVgjf3sJ3PPn0wdtKUFmd+/hcvNtl4NV66NQk6OEfs132D12TewBs027niA8A2+nJwxqOrZgXh87mAqGpgwwbnZh6MIuyc2o4QsZ/nO1joTEy1/L9LJkP5MOV5sfMuSQrhwwlbkZocALuy3XLN/UQuKxMSIWrEK4N50bmuTOeQH4irpqlKpkJPJf6ZrRpmdjvJv0/FW44sWJ4mtEwoRkp1r05pGIyDp7kbIq2uhsAr25Ntv3TWzCIkR4erPCDsOgH+NEjYz2EgodWkNKQNwLfkeRGmpaTmse1YJsPQ7Sf1VhKWmYkHbYEcxu+PVIwb//rccL7xgcmzr6pK6NFU0N8tQjxjMwim8g9cwA38HANSEJmDqx2867SSyrCzIvLQZ5Sr/ivVg/H73q2ioB36LPwxGCtdvQbgLOYIeuBfr+w9CKpUiLq4Xudk3XM7gnZlC7rToT0/jyvvKPKBrNF2cz1Clcs+TyaBWI+TjXPynzSCXa0l3YXu9e/AvFFsCvgD+/dYdM4urGBF7kxXXJu86gxZH4BjZHB/fj+++88O1a4NDa2SknnN/R+xSl9aQMoBrP26uF2ttqxZ+DXYFQ3AZ2/FbLMchtLXJHMpOOrPbmq9Tjxgswf9avl/85C3sxzeWkoVsS2muIuxc2ROF2M+5vEgupmU6vFjL/PyQYWdS+wKJ+NO/M5C1JpSX33lNDb9u6azd+GxUDkdUr1iYPWZQ3wAFbuECpjsc09Iic/oMS0vd92TiGuRcBXyZsZ7dc7WTO55ZzmJE2MxnXKuPuHENiJYNOJjPVq++iVdftTVt2lviR1JsgTWkDOB69sPlhhqnvAo4Jjq1SX1rj7MlNtd1MtMuujS92BdhN+OJ7Ilcy9ntOdMdXqw9A686pDS+jElYc/E1nP3OMX31UMw5ztotL6/T6UblnZZRkguugUWtNuBPe85h/PJfYuOtt1iVQXi4we2+724UrbOAL2vMs3shQWr2ZhazEunokCMszJSyxFmMCFt/c+ZlVVrguKmv1QajsdG2Pzc2Ok/pIWZsgTWkDOB69sPlzeGXE86aB92+Y9vDtcTmus70nO0u/ZENWVlgKtlTZnvCfs4202N7saoQ7/AdAHxpeJT1+kMx5zhrN1ceON7eUxiO2Z+rgWXqoZ1Q3KpjzQtlHtBzctgzWJr6vtwtTyZncLWZ9QTKenYvJEjNGkclorB4+7DBtdJxtvpgqzl++rTjhjQAlyk97M1ibP0HKhW8CSkD8Jv9sEUSsnWUJkUMttyyrdVrj7MlNtt1eG2UxcQMe/ZEthe7D6NYjmT/vqVFNqSAKlft5iz606t7Cl7czLfG1cBi7jf2Ue/hqgGsK42DWm1w8QxN+12erMfAdr37Inuwefpn6LuZ5DC7d7eduJTIlCl6REc7mnec2fT5bvLm5IxhLYULDGY2CDh7lvX/1u8yl5I3Hj9uqprkJUgZwD0/boC9ozSlZQKvR8EuFboFd5bYfDfKhjt7ItuLPWHCACQSic0GmkLBsNYM0Omk6O2VICjIgO5u23oQfJ6Rs3YTs0i5Nzfzba7jYpJg3W+so94HAqPAvH4vDBERkGk0KC01D54ytLZKoVQakZMzxlKxzZ6h7LVwtVmoeitr6hR324lLiVRV+eP99ztw6FAQ73ed7yYv1zUDAhhsSfvOVDqTwzXU+l3mUvKGrCwgL8+lHO5CyuA27s5+7DtKBGDT2UePNkU43rwpdXuJPdQso97Knsj1YgO2uX3S0rrx+uu2Oe/lciMaGvwsxeUUCgZTpw5g8mQ51q3jb7e3L0faBQ2gg1eKlDtTMNYD5H9dbmKx0FvVd/CQCcnVJIEr4M+vocFS1c+vqgooLYVGYyq6ZG6Tqip/h4pt5vsc6l6LkHfN3b7LpUTa2mR4/fVQXvIKbaeICAOiUYe1yEc5fo5GTIAOavxkphxTD+3kTHFj/y5zKXkuJxFPQUFnHgrk8Db2AUGWwK3bHVW+cyfanCwh+UTsehPr6+t0UtZSoaaUIHLe7cEVtKOfMgXNJ2ocCuGEL463UdxmmST1V/HstXdxpH8hWmQToIqPwMZsxiG7JFeAkLl6nXnA+gPSkIYPHeS9tXgxujQah99hAgLQN3Om4Ey4fIKWrPuNVKdzqPNtlisNh3gFTnIFs0VFDaCszPMDLOBe33XlQuoqIFRoQBgANH9xDWG/TLUpVnVFHouOj0oxPXcdAiorHc5hAgLQ/sc/YsAq7oYrmM6QmooWD6wMuILOSBm4UAYj0Q2MraMaY2PR+uGHostmDdez44r8DA5mkJJixLp1bbwUFddLcyn0ETzV+T8OEbB/jd+EMccGU3rLdDoEa7UwnvoCn/TPxDrkWwIGrSPMnV2LbSCNRh0qMM8moto8kIzJyWH9HetjAPDqc2b5/atMXgyuUqurFi2yHGtNX1ISnjSe5BWNy9V2gGnGvmdPJw4dCmI1IbkzwA4FnU6GRYtUvCOCrREade/qHAAu290ch1GsbceW04sQ1Web0sR4/LjTCR9fKALZDUZqVSrWtNDffy9atkM2nD27iAj2FBM3bkhRWjpYatHlLJNjOb21ZzOre6u2dS3MNd/s5fsPHMN0VFsCBs0R5pZyj07s881G28GmHjGYi0/wvmozZk3W2dZ3cJIE0a++HsFaLeQ1NTbPLeDECRimTIH+tjnBXCfC/vk6S60u0+kg4/i/ITwcERyR3jqdFDrdoKnIWTBbfb0fli9X2uwPWZuQhitLp3kSomxuxuFANVZgp039ZcD1noM70c3OzunMy3Mw2ZkxP4NvNPtvr2bC8X+owHb8FrEBDbh/phJM9kaMjYkBvGjF8NmspXxw1nm5MkIKyVroLkPNdigkm6W7OHt2bFk4rTG7DrqCy2Z+TTGJ9fuGcXFO5TMHDJqx9lhxZp9nGyDrEYP3kw+gvazMVMfg9uTBVRZXf5YBQ9bdDf+qKiiOHDFtQt4e7Dhr/rIwJicHsu5uh+8ZhQJdGg1nmzQ0+OGZZwb7r6u2s3cUsG7L4cjSaVaSiiNHEFBZiZkNH+GUfB6irYr28NlzcCe62dk5ZmcTA4d7qKylxcYDqh4xWI5D+EnfKawO+sOwrPhFVwalpaXYsGEDNm7ciO3bt6Ojo0NskSxwdd6B+laHAf/JpBCsevQqsjbKOf2iPQVXp5PqdC5T+Q6HsgKcv/jW6aiDgxnW4/i4eHKlBFclsBcBGhc92C5c8ln7u1vPHp2lHxeSYprtd4RgHvCFDqycfXnqVEuQWmlpByIiHP3wGxv9oNWaKs2Zj4uK4lYI9pjbcjiydLIpyfv0l3EwKtMhVb0z+KSbF3qOQa1GX3Iy67mG8HBcuSJuChXRlcHTTz+N3Nxc7N69G/Hx8Th8+LDYIlng6rznWqMcBvxeYyD+2vwETn/muLkGeLZB2Tqd2UskoLLSZgZpj7MgHk/i6sU3e5TMmdPLehwfF0+u/PobsxmXgzOXfOaAwfsie2yPd5LL31q5JSX1ITXVwDngmH+nZ/58MAG2tveB6Gj0x7MH7Vkja2kRPLByHm/Vj9RqA/R6CetxVVX+NseVlXU4PGP7GhNmzG3pzgArFC6ll6i+KqhuhTt1J/icw/UMvkvbgpoa9s1uTxYxcoboewYKxeDg2dfXB4mEvTOKAZdLZ35YNmsaCgDo5Qi68mSD2sc3sHmJcNlihyuBG1932KG6vbL5gKvhOm6ETb5m6XgcC16GJY9fd/Am4rqW5ZpW7pImpwTnfus/FBU5eIiZn439noHD+bePFeJuPFT3ZHu4Ct3buxBbt+VwZOn05OrDnSRyrs7hegY7c6ayxuIEBRmG7ALOlxHhTVRSUoIzZ85AoVBAq9UiOJi9+HlFRQUqKioAALt27UJ/f/+Qry2Xy6HXs4eoAwDq6iDLyoKkqQnG8eNhyMrCiqxJKC3lHjwDJb3oNQZaPsfGGlFePoCYGM5ThoR8/nxIT592+J6ZORP6EydsvluxQsYqe2qqqdCPR2F5dmwPoa4OyMqSoalJgshIYOtWvdeelTvyCcVln+Ir1/ffQ1JdDcnNwcI/xthYDJSXm+QUKj+P4595Roa//MWxfyxcaMDHH7vuH9ZtOX68EVlZhuFpSysB/FJSbIoL2TyzEcr8+XKcPu2oDJ54gsGZM6a+NOR+dRt/f3/W74dFGWzbtg2dnY4+vampqUhISLB8PnLkCAYGBrB06VJevytWnIErH+afR/wd/jMeHDaffiFucGIWfeHDnRL34QxP3gPb6sGbm4k6nQzPPqt0SMF8+HD7iOgffGAr0uMJl0xvwqdAlreL24yIlYGZ69evY9euXcjjGVghZtCZTidD1kY5Tn+msDENxcqvoPSjDktpvuFAptNh3PPPO5Ra5LJxih2A5gxSBuIzmO0zEGFhvSOqf7jDndAefCZpd32ls6amJoy/nX7566+/5hR0pKFWG/DfHxlw7Yt65K3rYq3ROlwIrYHsycRjxN2HuX+YBh/qJ8OBp7PDuoPoyuDDDz9EU1MTJBIJVCoVVq1aJbZIgohMjMCevw/v4M/KCCi1SBCE+4g9SRNdGWzYsEFsEQiCIHwe0eMMCIIgCPEhZUAQBEGQMiAIgiBIGRAEQRAgZUAQBEGAlAFBEAQBUgYEQRAERlg6CoIgCEIcfH5lkJGRIbYIHoHuY+RwN9wDQPcx0vD2ffi8MiAIgiBIGRAEQRAAZFlZWVliCyE2sbGxYovgEeg+Rg53wz0AdB8jDW/eB20gEwRBEGQmIgiCIEgZEARBEBgB9QzE5Ny5cygqKgLDMJgzZw5+8YtfiC2SYN577z1UVVUhJCSEd7nQkUZbWxsKCgrQ2dkJiUSCuXPnIiUlRWyxBNPf3w+tVgu9Xg+DwYDExETe9bxHGgzDICMjA2FhYXe0a+Yrr7yCwMBASKVSyGQy7Nq1S2yRBNPd3Y3CwkJcvXoVEokEL730EiZPnuzx6/isMmAYBgcOHMCWLVugVCqRmZmJxx57DFFRUWKLJohZs2bhqaeeQkFBgdiiuI1MJsPy5csRGxuLnp4eZGRk4KGHHrrj2sLPzw9arRaBgYHQ6/XYunUr4uLivPLiepvy8nJERkaip6dHbFGGjFarRXBwsNhiuE1RURHi4uLwxhtvQK/Xo6+vzyvX8Vkz0aVLlxAREYHw8HDI5XIkJSXhq6++ElsswUybNg2jR48WW4whMXbsWIuXxKhRoxAZGYmOjg6RpRKORCJBYGAgAMBgMMBgMEAikYgslXDa29tRVVWFOXPmiC2Kz3Pr1i1cuHABs2fPBgDI5XIEBQV55Vo+uzLo6OiAUqm0fFYqlaitrRVRIgIAWltbUVdXh0mTJoktilswDINNmzahubkZCxYswP333y+2SIIpLi5GWlraXbEqAIAdO3YAAObNm4e5c+eKLI0wWltbERwcjPfeew/19fWIjY1Fenq6ZdLhSXx2ZcDmUXsnzuLuJnp7e5GXl4f09HQoFAqxxXELqVSK3bt3o7CwEJcvX4ZOpxNbJEH84x//QEhIyF3jl79t2za8/fbb2Lx5M44fP47q6mqxRRKEwWBAXV0d5s+fj5ycHAQEBODo0aNeuZbPKgOlUon29nbL5/b2dowdO1ZEiXwbvV6PvLw8JCcn44knnhBbnCETFBSEadOm4dy5c2KLIoiamhp8/fXXeOWVV7B37158++232Ldvn9hiuU1YWBgAICQkBAkJCbh06ZLIEglDqVRCqVRaVpiJiYmoq6vzyrV8VhlMnDgRTU1NaG1thV6vx9/+9jc89thjYovlkxiNRhQWFiIyMhILFy4UWxy3uXHjBrq7uwGYPIvOnz+PyMhIkaUSxrJly1BYWIiCggK89tpreOCBB7B27VqxxXKL3t5ei6mrt7cX33zzDdRqtchSCSM0NBRKpRKNjY0AgPPnz3vNscJn9wxkMhlWrlyJHTt2gGEYPPnkk7j33nvFFkswe/fuRXV1Nbq6uvDiiy9i6dKlls2mO4WamhqcOXMGarUaGzduBAA899xziI+PF1kyYfzwww8oKCgAwzAwGo2YMWMGHn30UbHF8ll+/PFH5ObmAjCZW376058iLi5OZKmEs3LlSuzbtw96vR7jxo3Dyy+/7JXrUDoKgiAIwnfNRARBEMQgpAwIgiAIUgYEQRAEKQOCIAgCpAwIgiAIkDIgfJDGxkZoNBr86le/Qnl5udjiEMSIgFxLCZ9j//79GDVqFNLT04f0O1lZWUhOTvZqQrfs7GzodDqLj/nSpUuRkJDgtesRvovPBp0RvktbWxuSkpLEFgMGgwEymczpMenp6YiKioJMJkNtbS22bduG/Px8Sp1CeBxSBoRPkZ2djerqaly8eBHFxcV4++23UVFRgcrKSuj1eiQkJCA9PR3+/v64efMm3n33XdTW1oJhGEyZMgW/+c1voFQqUVJSggsXLqC2thbFxcWYNWsWFi1ahDVr1qCkpMQyyFuvHk6dOoVPP/0UEydOxOnTp7FgwQKkpqbi5MmTOHbsGDo7OzFp0iSsWrUK99xzDwAgOjraIrtEIoHBYKA8WoRXIGVA+BRardZmgC4uLkZLSwt2794NmUyG/Px8HD58GMuWLYPRaMSsWbOwfv16MAyD/fv348CBA9BoNHjuuedQU1NjYyZqbW11ef3a2lokJSXhgw8+gMFgwJdffokjR45g06ZNGD9+PI4ePYr8/Hxs377dcs6uXbtw/vx5DAwM4OGHH75rMooSIwvaQCZ8FqPRiE8//RQrVqzA6NGjMWrUKCxZsgSff/45AGDMmDFITExEQECA5X8XLlwY0jXHjh2Ln/3sZ5DJZPD390dFRQUWL15sMQUtXrwYV65cwfXr1y3nZGRk4ODBg8jMzMTDDz8MqZReW8Lz0MqA8Flu3LiBvr4+mxq/RqMRDMMAAPr6+nDw4EGcO3fOko20p6cHDMO4PSCrVCqbz9evX0dRURF+//vf28jQ0dFhMRUBpgpXjzzyCMrLyxEREUEZdgmPQ8qA8FnGjBkDf39/7Nmzx5L33ppjx46hsbERv/vd7xAaGoorV65Ao9FYCiPZF0MyV5/q6+uzFOfp7Ox0KoNKpcKSJUuQnJzMS2aGYdDc3MzrWIIQAq03CZ9FKpVa9g1+/PFHAKZyqOaCNL29vfD394dCocDNmzdRVlZmc35ISAhaWlosn4ODgxEWFoazZ8+CYRicPHnS5v9szJs3D0ePHsXVq1cBmGreVlZWAgCuXbuGf/7zn+jv74der8eZM2dQXV2NadOmeewZEIQZWhkQPs3zzz+Pw4cP480330RXVxfCwsIwb948xMXFISUlBfv27cOvf/1rhIWFYeHChfjqq68s56akpKCgoACffPIJkpOTsXLlSqxevRoffPABSkpKMHv2bEyePNnp9R9//HH09vZi7969aGtrg0KhwIMPPogZM2bAaDSirKwM77zzDqRSKcaPH4/169fTBjLhFSjojCAIgiAzEUEQBEHKgCAIggApA4IgCAKkDAiCIAiQMiAIgiBAyoAgCIIAKQOCIAgCpAwIgiAIAP8Pmwg4ZqM8MGUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Lz3EHa13J1kxNjapDsTsyks8MPEsa06nPolV/b+wZI/vbyHqREJ+QQIzK8QsJCZzxMrZLWUBOa72fOhUcmqdVQO6SMIHm5mZaWlp46KGHAGhtbeXhhx9m5cqVxMfHX4ohASA0NQFOBjCfCk4wiZt5TZUJOJKTAWhqUt/izc1Gtv6lQURVFYmLFxPW1dV/rLqaM6tXe5i+lJDe2x3ezGHSvCohNDfrjjMlxTeTmtE5d6So25vV3i1QaD1Lid7hwwEoLBzBqVPybXnqVDiFhSMoH8RxK6FmijJyjTcTYOqIDlCJCUnDs1Kwt/UioT0vj4jqatlY7RYL7Xl5hsduFME0BWut99TUgW0Df0mYgNls5o9//KPr7/vvv5+VK1cyYsSISzEcF6QNu4wVnGASAAUUM4sqJnHCdZ77gtL6cMnJl6dPQLDZSLjrLhkDAKdZIX7JEiLq6zWvVdtIVVURLF6cSFdXv1wo2e2lzeAv0c3La6e6OkJmI7VY7EyZ0sPOnZ4SpNE5H0wiofYsMSwMU2+v7LyIQ4cQbDaqq9XnpLp6GO1/HbxxK6FmZ1dDWJ/p1Kj/Yum6OPbffpKanvGuYxNMJygWPa0ERpmdw2ymrazMyYCam3EkJw+IA13PZ+UPI9Ba70VFA8sEhKKioqIBfQKwdu1aXnjhBVpbW6moqCAmJoYMN9vejh07yMzMJDIy0tD92tvbAx5TTEwMXQpCaL/qKiIrKnjqbDYncY7vLKN4lVtJ4jT2EQnE3zKTs2vWuBbUVVfZqaiI5OzZfinUYrGzZs1ZRo7s/3iCzcbIRx8l9tlnifznP7FfdRXiyJFexxRsjHz0USIPHlT9zQSYurs9josjR3L+lltk7w3OTbBgQSJdXXIJ/OxZgc6/vMXth4qxX3UVF6+7jsiKCoSzZ13n2C0Wzq5Z4zEHsrGOFJk/v5u2tjASEnq59tqLrFlzlswpNbzxcjef9fZrjRPCT7KuuI4R6cO9zoE4ciTd8+cT1tZGb0ICF6+91uPd/IXyG6o9qzcpiXBFyGJYezthbW2std0mY6j99xW59xfhHvdqf+ghhj/99ICvq9hnnyVcR0CQcPHaa7lwyy2MfPRRoqqqZL8JZ88S1tbGhVtucY1pRPpwvvblTzn39ick0cqs0cdY85smJn/0d9314m0/iSNHcuGWWzh/221cuOUW3XXmDl/m6tFHR1JVFSU7dvasQFtbGLfccsHQPdyhtd6nTYsOCl2I0/Aum0RRHFg2MwAIRlOZ9vYk8vN7PNQ4wWZjyaJuXqif63HNggVdqjZnSSVsbhZITparhILNxojCQiL37iXMjcDaLRYPqciIPTLQEMHEhQuJrKxU/c2enq6qCTiysmhetcrjeE5OPOXlatZXuIE3eIMbqY+cwIq5f+X7917gytIVQZHM4nNyaC6vZhkraCCNNBoopoCUm6YgxsZekvBJaQ20tUWRkHBB1yyg9Q26Z8/mm8MrVLWcm246z6ZNn8mOqUnb3taVv+snPieHmHKno7YWC+t4gIdYxVg3s437s/XesXX7dq9r3TVOlfWi9t6O2FgcU6bQ06cZ+fvdffEJLFyYSGWlp+A6e3Y327e3+vX8QMekh8vKJ3CpYbMJfRE9/Q7dfjXOzIPbBaqy7B5qWV6eugZiNjtUmYOeHVUZsibYbAhLl5JotWpuzmCECGqZZnpjYjizbp2HT8BusSBqKIta/hDot+mmd9dw/c5fs+joJsrKng5OjH9TExlYKWWx/B32NskY7WCFT3qaBWJ0zQJ65rHleef46KNwGhr6115amp3lyz0N576GQhpdP2p2bqHPrFVvxeUve4UFrGAZEyLruWJuIr3LH3Ldx5GS4gquOEUaY/sYdbIPJh0tJ7DaewudnQjV1Qyrrh607/6fZgrWwpAsG1FSEicL6QRnKFZJiVNdMpsdlJW1sWBBF7Nnd7NgQZdfdj49O2otFr6/714WLkzkgawe2m/4X4SyMiIrK4kpL2f0vHmMuuceWaaj3qY3iva8POwWi+yYIzaW1q1bsc+aRVtZGV0LFtA9ezZdCxbQVlamGZantQmGc45i+m26aTTI5jdQaBHRMIUpy9e58RclJXEygQH615Na5qraN5Bs+2azg5dekq+9l15qc2mpsnudPKk6Hi0HqpH1IzG08vIYKisjKS+PISsrgVoyaCsrIz99i8tfZiWDxZRyXfce7o3dKiO6h+f/kEzTbraRzR6+yjayyQzfw6Hs/nXhb1Zv3UnIZis3sJtstlKLfC79/e42m8Dddzujs3Jy4rHZ9AMN8vLasVjssmN6wuLliiGpCRiJ6NGS7r3BXYraeKyVq1XOOU+kU5o6PQlOA5j5gFfZRSYZODdpWHc30Tt3En70qEuq8TfKxh3enGZ6EpgSao6s4Zzj79zieg+ABpxqaLAiptrz8og+cECWd9AbGenBBMC3uXFd46PJRGs9max1mpK33jdQW3tqUnxvjLopTsuBamT96DG09evN2MYlgoprwP3bCjYbqx+6QI04UXZOTc94VpaOYf2sM1Bb65dWa7MJfPfoBmpJdR2rYpZs77i/k9Fv2a/NCUhhmu7anFYUUFlZm6Yp+D8FQ5IJ+KvGeQsHU5oFDjBOlQlUM9MlTUk4wSSWscLDxOGu3gcrtNEXQq8H5SZIGX6OlR8tYHLDW65zjjORAoqB4KjJzm8wnbakg6RcfI/lY37HOIuIqbOT6J07Pc73dW78MblpracHWgqJqNc21/jyDdSk+LCuLhyxsQidna5jetFCRtaPNwHJyN6JKymhsTNH9bwWq1NyFoqKVBPfvGX1lpTEUdslZ35qe8eRnOzTt9Rjfnl57bpRQP4Ii5cThiQTyMtr58CBaJlJKDKyl85OEzaboMrJjYSDKReSWnhpb2Qkv4teBirrRpKYlZCkGq3QxkPZBazM8a+EQqBQboKIqlzOZR/l0PmJNJEMmHiW/+VcdDLm7KXAWL+f5fkN5lIlzKZsQxsZ1BJ+9GjA4ZP+lBzQCu2bkVinKjX7pZ1oSPGOKVPotlgMOdyNhMZ6I/Ja7+puAhGammQOY3ekt3wAXIGpsVH9Pb3MjRaTct870jv58i31mJ++dvSfzQBgiDIBs9nBjh12HnjAwd69kXR3h9HdHcbOndEcPRquav83shCUC8lKBpnsYmPSI8ybbHNtUnvJdCjHA2oJMtAvqamZcg5lF7Ao9+qgxSoHitjSUmLOnyadYWRQSwp9NaPOgz23KiCHndY3WLvoCOu2RwYlNtyIyURpYhDy8igrwyM6KKIkGao972VUO3F/Tlhdneo5PRaLYY3CSPy8NyJvxATiSEmhmAKqmCXTeCdynOVjfgc8iZjab85xdyAn2wQe1BDEQJtJjUkX6DbPlr2TL+ZTPeb3n5gQ6guGJBMAp68zNlaku1vuG9fi8FoLwb3Wh9pCspLBD6KeY/uqfqKsttEmclzmTJWglNSUppyVOfGXlZQibbxxKgzNXQpTs9XWkqFrbtP6Bi31DhKysmgrKwvYzOXNZKJlYqCsjPXrzX3hfM55DyQpTe05Yni4rO6QP5qOcv3YbAIlOfI590bk1Uwg7qbS1LiNlER+hV3dmR5hvMmWmZwBHEVF9FZWyqKNAKiHqiy7phCjxaQeLJtKq3m7/F29fEv3McfF9TJ2bI8sWzsjppH87EZWlk5VvY9R86b7Wj8Xl8oyijnYPtGr1t4fdhxOQkL8gGn4Q5YJgG8lH7QkhSNHIlwmJLUFClBfH0FWVoJrYavZ0u/t2kTBJ8/Qc7GH+6P+xDVpTURYxvjtlLxUUoq3MglCc7Mqgat/p4U7TLs4eao/Rl6p0Wh9gzQaAq4S6cr1sG7CEruYX3X+zOVodCe2IwoLDZsYAslcVTNlmHp66ElJwTFhQlCyYPVMnL4IEJ73GU1z4nZ22K+jtLffTv9J2nUs7txI48IRWCzJPLR6OyVLujhRL/eP6Qkxvjhj9Ziw2ruPHXOBb0a/Qfv5cCfT6iogPRfyV79MdfXVuiYwLSjX+mjg5xwgk11UkqGptfsadhwIhmyyWFJSEllZPVSXN7OCZaRxigbGUkAxMxckq0o6mZlJdHZ6Elf3JDKbTWDRogTq6yN0z5POLSmJ4+RJgaNHI2SZohaLtjTkDq2ELa3ENiNQSunhK1dy2mAtW281ZroWLABwJR5JyGYr28j2OF85t8qNO5HjrsgQKRHJV6jdNyOmkX9MzWGcRZQV1xs9b55qFJLRJCh36EWuaCVb9UZG8umePTLi7y0CRmtMWmvn9vS9bBmXbziZTHkfC7VUMF/mC+uKiuemEW/zVsvn+s+z2ElMFKmulhdghOAlXGklnWm9+52UegRndC1YwMG8p/2KAnJPsnNHKXeymFJAfa8OxL4OJYv1wb1RyjOOKM4Lh0l39Ntbrw+vpC27DKUD02x2MGWKg+pqTyagDC0dN64XtQx79/PUCI87jJp0jDjqfIGqGeLAAYRt2wxJnZL0q5Yl3RiTQWN2Plc+scTjulMaTnHl3JaVtbF20RFa6h0uE4MksftbSE3N11DblUq+Zatr/gWbjYRFi1QZgD/P9ha5opcLoUwy9DeBUM+8FllfafheyvusYJmMAQDEXDjDfRd+xVt9hA+ca9zhkMfZSwhWwpVaJJzNJrBvn3qJGrXgDKG52e8oIC2/xNf5O1vJpoBimps9nzmYGv6QShaTNkxMeTlhe/eS+K/XZQwAYHxPDVNLV6peb7H0qB5XLti4uF6v5ykJj4VatpLNbm5gK9lYqDX0wYOV2OYau5oZoqbGUPKNzSaQkxPPgtz/4s7Ycv6+5h3KY+/gDW6glDv5ctduvp07gxNx0z2u1YomUc6t2exg3fZIdk/4IaUslplsOrOz/Uo+8rbhXIRWo3ZOb2RkUKOQwGnK6NWopeXu2AwkgVDPvObLvZT3SeOU1/tKGDOmd1ATriTh6/Rp9W+uNsZAqrRqMfNRnCGbbVQwn6uHn/D4fTCzkYeUJmC0EqJWmJoRqdtmEzh0yFO6T0uTn+dOeNTU51lU8cTwVwHvpbWDGavsb0Kammbz+utX09X1f/ITrbBsSjFlltdk3+KxtN/zluk7Mp+A5JgD+UZymM3Yd+ygJz/fpeYb6QCnBW8bztu66Z47N2hRSJH79pG4cCGOlBQufvGLRO3b53GOO1EKJIHQaICCt3sp79OgEQasJmVbLA42bDgzaAlXalqfhPFjz/NL0x9kYb1aznejSWhqfgl3TOIExSzDzpOy48HW8PUwpJiA1oZRQovzG3FKlZTEedSDB7jqqh7Zee6ER0191locAw1JclHWfXlk+L+IR3vxq20utWqYAE0dIzwcpiPz8vhzw8esuctKU+dImWNOlZBnZMjU/PicHL9bCnrbcHrrxm6xcG75ct37KyHYbJohn8Lp0wh99nt7Who9Y8cSfqpfsvaIFvMjgVD6holNTeyYMp1lU4pp6hjBOFsVK+vvlmXeersXeO6LDSceY1azPD/mOBP5dVQRuBXXlOZ4MBOutLS+pCQHz794jvRRz9DlJlxI69s9kmh63AnWHLqD6FMnXddrCRzuwQFRu3cTds6zBtSIjiaU3g/3OTVSlDAQDCkmYKTBh7ewO28LVmuRdXSEyQjoxrhUDkWU8Il9oqb6rLY4BhrteXnUv9PCLQ2bZTHebx36Dn+u+pgZuer256amRMPPSE52qNpqryzJ4f86FU40K4YIeSASsTfm3qvhFLcnJ/uc9+DNtOSOiIYGzt90Exe/+EXN6CJfw1CVPoRpVFJmec1ZI4pIErLAnQcYDUN13xc220i+950d3NfwS9JooIE0fp/2GCueHElpaRctVjvm1g8pTFjNuBIGrNa/WrixltY3Z06383snZaj6ENy13J/wa6I5KTvHW/jzmfXrNZ3EekLn+vVnZGHHA4EhxQRUN0xaGj1XXUVYR0dQwu60FlnK8HMeoWJvRv+b2fbdmuqz1uIYyMb2DrOZ/KvKOdEwWnb85KloVi1pp0yjDEJKSilGoKfSBkLIAy2poWTukn+Dk3VsOPIJak0QTeG+bx8t05JW7aOwjg5aN23SvJ+vYajesmiDkXBnNjt44qWRlJT8wcVUn8hrx2y2c13awf59YAX+HZxUqeYAACAASURBVPxqr3qhr/6YWZRarpbQFrlvHxFVVZpmycFsZuQLhhQTcJjNfLD6ZVYtaae5PY7kuHaWrotj7CxjLQCNEF+tRVbMMo/Nl3a+ljWxj/Kzzl/pdi9TjiGY3YzU0Niu3uGt6Zx6wxahuZm8Ve3s2BHlkXwHTlXbbHbQ0mIiIaHXVY9FOd5Ayg8Hc4O5z/FWiklVSH0Swk+d8jk3QYvRiXFxoMIEjDAxvVpQyhLl4Rq2aYnRBrOulJrG7E9ZDl/hLbvf16JvSu1eS2gTTp8m4a67ZLWcIPhMNtgYUkzAZhPIyr0aqxTDfxaqcj3j8bWyWZXEd8eOKObO7Wb58nOu67VMCxNz1bt5fXWKjZmWZB62/p2ftS7n6kSbbpKY0TomgTSf0dRmNPrBOpKTMZsdzJ3brdoQZebMixw9Gk59fQT19fDvf6szrkPZBWT9NUHWarAy/HrKstt0Kw5J79qbkIDd4UAcMyag5iLuc6wl9bme7WMdIC2N5eLMmUGpfSQbW5/pR3BrUO+IjVUf1yD0KYbAtD2j0DLJ7tsX6Urs9MUHodwPajXBJCgZgOu4FyYbaLOoQDCkmIARAqoVd104ZQdWq3yjaNUbUltkWps/wjKm79w4kpI2e000MhI/7E/suLcUeovFztLVcdhzLZqEavnycxw9Gi6b4wmR9XzvwOPkN+cC/X0J1BjXytKp1PTIE2Rk5YdVoNpdSxBo37DB703kPsdaUp8ENeKpt6G1NBbJuRxMKVGr+UpvTIysx/RgmiT8Mdv5av7UEmJOnxZkmftGodTurWTwfZ7hb9xKOD0cYhrpnOqvk6UCb476QJtFBYIhlSegR0ClBhdJt96qqq4uqC7WvK+Rhil6jUR8gdFSvr7EjisbiezcGY0oitx003lmz+4mK8up3YydlaLadMa9Dn5ZWRvfvulT5ka+zZ2UUtF9PXc0r6eC+ViolT1XYlyS/X337iiPsbmfp4a4khLqrfImI/V9zmR/4T7HBRRznImq56l+v746+THl5a4GQQlZWa6cBcmGrzaHkpTYun27s3R4gARAS+q2T52q+Q0HGr7uA60mN3oNX9SavUjwp7mRMhcnK+l1NnMPp0niaj7iCNN0GYC3fa61X8MKf+vTOP3FkNIEjDpt1ZCqkcwkwVtiVyB1ZNxhtJSvGrRUbjUNqaEhgi99yc6mTa190QlSY43pNDU9rymRmc0Otsb+mJhueRTEJE6wgmWuVHlwMi5vmdPSeVqoOwlfdy9AhrPJyN+tD+NvHzP3OZYqwa6JfZTMjE+IOtNM75gxODTMTUbq5AfL7u4NmlK3D5VHgw1pHyStW0ePzaYZhimtL3/KOEtE+9ZbkzySwizUcu++R0hcqN3GVeue0vPiczYSU24lm62cYJKmybB3xAgu3Hij12do7ddje1sJswkkJXkdXkAYUkzAF6etEmmfi8Fis2sSKyOZfL5sfi2TgtFSvqrP11BJjZiYfHFIay1q92xMiXHpJe+4n6eFwk+XqDboKWx5gNWaV8mhVxraOcfJpOatod3sQGsk0j3C3nhD/fcg2ryN4nKNRnGYzTi2bKHVzfSptb4SEtSz770JXWazgzlzutVrGp0+0dfRzz+zizSvp6zO5Dctk+GFG290hYzG5+Ro2vu19mtNdzovl8RRVmZ4aH5hSDEBrQQMLaetO0aeOMj2Jz+kYOPnXD0IJAQ7k8+bjdCbY8vb5ldKXP6UuQB55yX3+62Im840PIufCeljmG3uljEuLQY0YkQvN97oPUGmbswM1cYt9WNmAOpOOtmYVOY6ascORsydy9PLlxsiDt6K5sHgOV5lz9SRui83aK2vQGoLKYU+taRMfyKTpHlNXtQN9eqOYmm/GbH3t+fl0fr3A4y72N8uVerIN3YQqgEPKSYA6gkYRpLIIhoauLJ0BZs2re8vOzxAae6BhtHpmZ5sNoHvfCeBhob+DTdmTA9paXbZMSVj0+unoJTgPoh+jF0RbzLR/onrmN1iYWrZg2w3y9PftEx0N954wVAER7IlQrVxyxiLtnbhDtW2jSr9nX29hzu8JW8FEhUiXV930qkV1Y2ZQbIlwrUm1aTuwYTR99NaX2PGiAiCXdf8qQWl1nzNJ3UuDUA2Rj+0NIfZzIPbBeq/8wn3NfySFpIIw8Fnw5IxzxtL7/KHcJjNhjLZa8ngHtNOHqXQlVxXQDFWMpiZ3MVAk+lBYQJPPfUU1dXVjBw5klWrVgFQVlbGe++9h8lkYuTIkfzkJz8hISFhMIbjAW/1PSRIi8XXEDOl5F2QfYippSs1N0awGsqrMYzCwhEyYg/Q0hLO9ddf4EtfsmsyNi1i3dIS5lE2u/Z8GoUso5TF9EZG0j13Luc0pOpAa6T4c707YQo/dkzzPKOMV+t7ebMJBxoVIl1fb3Xzi9QD1Ze2u5xyfGrvpzR0a60vi6WHDRvafRa6lHtu1aozTCxJVO3op6alGYlIyqCWXaY7ZNnD50fDueVrXd/PSI2on3Zu5Ej3RJm/DCA21tG3jkfpvmugGBQmMG/ePG6++WY2bNjgOvbNb36TrKwsAHbs2MGLL77Ij370o8EYjgckybm1cDPF1QtoPBdH+sUaWZli8E+lV9o6LdSS8NcsYnr6VT/lxghWQ3k1qNVuB2dznBde8GQyNpvA0qUCVqtzUbr3U5DqwatVQJCKhYV1dyPGxmoSNV+ahATjeiOmG3doJVe5Ewlz3UpW4llzR7IJa0FL40u69Va658zxqhVI1y/rc1C643LogasbpaYwdOsxc3+ELjX/wvbVBcww4CMx6v+KKymR1Q8CiD51EtE9CEBjL7vXiGqN/DnOGgJyTJniGBQmPihMYNq0abS0yEOoYmL6HTbd3d2YTCblZYOKWjLIOvok1tPSh59NFbNcDUuUi8Vo7LLS1rmCZYx3YwDguTF8degFo4zEuXMmFi5MlF3fvxkE6Es3EgSRqVMvMnlyf/SGGmNxdwLbrdrhcxB4FVRfrjdaSVaCqcVz7EoiUclc9pt2UyHeqNqNTAuaGt/p08SUl3vVCqTrjfRi8IaBKEXiTaNVPnP16jOUlsYGbGbV8i+sLJ3K0wYi9AwnZBrQ2I1YGdK7a4DZHse1StcHG5fUJ/D888/zz3/+k5iYGAoLCzXPq6iooKKiAoDf/OY3JAUhZio8PFx2H6e0K980J5hEwZhneO6rzyIWFTEqw5nsVFsLd94ZQU1NP+M6cCCaHTvsZGTIbkFbm3yKtcLJotraQBpTezthV19Nb3s7ps5OGD6csKuvZtSoUR5qtNGx1NZCUZGAKKoz24sXw6isdNavf++9aK65ppd33zXR0iJPJXE4TFitw3jpJTsZGaNYuRIOHBBlz1eWI/6w1cwXgxznpvx+hq9ra1M9ruzfK0FIS/N4jtpaqREnsowVlLIYcfhwxD/+kVEzZ7p+l+a/sdFEaqpIUZGDZIsFVLqHSYiwWklatw7Hli1QW4tQVISpsRExNRVHURFC3/VavRjMZucceZsrX9az2rXK95KuETTeL9xs5mRdOHfeOUbnmeH4awZR7jkJb70Vxe2/+CKpqdspetY5TuUTwsPDNa9va4uSzaPe+7nOS0pCfP11HH3fzvHRxzS1hpPP467SKD/i91RGzaPmQrrrHsOHi5w6FcXSpcmsWGFi3LiBixO9pEzgjjvu4I477qC8vJzXXnuN2267TfW8zMxMMjMzXX8bbd+nB2XLPas1EfCUnE5O+grNq66UHgxAfn48NTVy6bemxkR+fo+HRJqQEA/0az2a4WQJCYT39PBZdbUr1d+F7m6Ev/2N3g8/9JAMjYxFLtF7R12dibo67XM7OvrvHxcH27Y5Jbp9u0S+3FHBOpa4JOLjTGRNYiFPBtkx6UsbR3fEJyTg2bQPelJSVCt77qqZwMasHplUqrVWJBOYqaODnqee4sy0aYD6/FdW9rJ99UPMqKzUlRJ7bDbOqKyJ3spK2lavJr6ykmJrAVXMkpmELBY7S5a0cfq0w+tc+bKe3aH1XpLZRFiyhATF+9ktFtqWLKGgQKSmRi5gGHmmESj3nISWljAkxc59nO5ISkoiIaFH9fqEhAuyap567+dwn++4OFi1CptN4IMbfs0T/NQjr+Xpa5/m96MfxWoVOHIkgo6OMN5918S778K774ps2/ZZwJqZVnvJyyJj+Prrr+edd965ZM8XbDbMdVWqv6mFovnS+k2ZvVhAMSfDJ8jOcTcd6Jkr1LJ+jYxFKxY/KclBUpJ/C0vZ9nH9+jPMmW/iINN5i+td3cQy2YVoGefXMwYCWhmrZ9at8zh+nIkUn/4x3y7/Eb3zbifinp8i2GyGOnK5mwT0zBNtZWWcv+kmzS5ijuRkTdt6bGmpM1RxwUz+PvNhbk/fy3Uzz/rcXc7fVoZ6ZhPQz45ubFTXSFus6iGhSkix92pd5PQyhtXGqYTa9WrBBnrvp4aSkjh+fmGFal7LxpgHWb/+DBaLw6MPR02NyecsZ19wyTSBxsZGUlNTAXjvvfc0udRAQ3IUrqyH9xSZp+PHnleNMvGl9Zun4zKZtuwyxpSuVLVLemt8o4wQ0hqLzRbmKpaltcmncYg0Ginja7rPVIPau+bltZNVnc5ia3+Ug3PzqJtgLgX0wmel3sgX9n5AdzccZzylLGY8NugGdoL96Pvkr36Z6uqrdTtynRueQk5OPE1NAseOObeZhVpWsIw0TtHAWMqtywAIP3pUtYy0JBzE5+aqvovQ3OyKAouDvuQ477kRSvjbytAI89CKUktNFVWvNR99A8GWqp9hayCPxn3PffJJuGo7SS0m50uwgS8JoE1NAp2qeqiz0RK0DmpvYQmDwgTWrl3L4cOHaW9v57777uO2226jurqaxsZGTCYTSUlJlywySJKyMoBdZLKMFTSQRhoNPCb+npE8gQP5gvQ1LNHTcTmWM7PUF463nAVlhJDaWADq6yNcxbK0Nvm40wcppoD9CubnDVrvGmikz2BBb+OGHz3KqG6nzeBmdnv8HmG1cmXpCsrKnqakJM7ZIOXoG/yq82cuE9j5seNZ8NFK3mpQyVZ1Syi68cjbjCi8QlXzs6ene206H6wENH/DdAPpg1tU5OCdV1uo7Up1HZvIcX7V+TPiSmb6FVXlHs7rvudycuJlmcNq45Sc1G1t4SQkxJOX1x70yCqt+XIfy2D2FpZgEkVRnSVfxmho0K/jYwSSnTRx4UIidZxzXQsWqC5ItYSxDGoDSvxJSkpy+QRUCYPFoqpu2mwCixYluOL106jjcfJJ4xRCejIx6/JYlOspuUqRT1IN/7qk6UTNvIKPPgqX5RKkp4tMm3aBjo6wy4aw++sT0IN75ye1vgYSke+ePZvW7dtd17nyDpqbCTebub9tGRt3TpPdeyvZZLPN45mOpCRXqKA73J+hWilVYy24o5+weW9P6E8CpFoopcXiWZpdLfJo5sxR1M7Kovjf33YJXdIcK+dXCa09q3Wdt3EafY9AoZaoCTB2bA8vvtiqOZYJE0S2bWsZMJ/AkMsYVsKb5K2VoKWU7oNVDlZmrrBaOd9wjrruZBqEcZRPWcb3SMSMZ9G2ceN6qa9XkTjrwZ5bxfbVL7OydCrNzQLmT/bw69P3uohaBlZKWUy3eSatm/7qQRBWrgwnLu4z3XEPZLezwYJkiqvFwnyVonQS01RK4O6aRVJSEgfnecpV3voSKOH+DH+KD3oSkxjdBDJ/wnSNaH5aMfevvy4ybjyU/nux7rurwVfNyNs4tXwbixYlMG5cL6lx5yhmGRPbDwZU699sdvDSS20UFo5whVXPnHnRaz8S5/4buL005JmAtzheoyp3MDsmSUTFtYGa+hboTih9y8Fzz7Uxa5bccSWpkVr1Ua4sXcH6vnFIVRCViDhyxOkkN5tlBMEpdWuPdzC6nQ0U3LOHpebvy1B33i1jBZssBV7j/9VUeq2oMKPNZHytPOpP9U1/YDb354s0NQkeXeO0xlFU5GCtnwXu/CmMl0EtpZQgiE04SKGdPJeZV8sOLzVBgtEc4Od9QkBlQLX+zWYHmzbpC1QSBstGM+SZgLtDMHLvXpmDTq/Fo1LqTRyAjklqG6izU2Dx4kR27/5URmAlu26aVV3ilMYh2GyYOjvpDQsjrFdeOC6sq8svpjVYBCdQKL9bfvYRZuTKtTcxPJxTPepqc13SdEObX2ljt1DL6OhzOByRCBfl62sgmsmA8YifQGsXeRMAtMbR2Gjy0HDODU9hGcUczJ2oq036qhmpaunvvOPsLd7ezso6M3ezEqtb0yMlJCGglMVBb4cJ/Wvz5EmBo0cjZBFCBw6IbNsmDJhANeSZADgX1WebNslsu1oLS2vR75+SqpL4rV+XxGoNp6XFxOjRvYwf72DlSmdIsQStDdTVFeZBYCU1sntRsmpVTUdysqGSCf4wrWATHGl+OFnHkk8LmTGmjghLYMRR7bt98Hoqu7uQbX1TTw8pUWfhguc9EudMwmH2ztTcVXqTtY71R/6b1K7+hjpq9ZSCXd/fiIMxGCZMbwKA1jik6CAPrdegNumLZqSqpTc0ENHnW5xLJfvC3mJO7z91GUGDW2a2L/tEb93bbAKFhSN4881I7Hb1iH0pRHSgBKoQE3CDkYWlteiXTSnmScv7ftUlkfruKjm+XjSBWsiY2ewgYt1t9Ga/Stj5867jPWPH0p6XZ6hkgj8RJ8EkONL8YK2ngq87TVt9RdECUcPVvlttV6pLunPHdxwvsG/srR7tNX0pFy7Z2ONz8omplndU81ZPKRgwEvETDBOmNwFAaxxFRXJbx0Bqk+6+nmWswIGJNeTKuoGN662nLPmn/GLqq5w44fAoigjyPBCj+0Rv3av1LdfCQIaIXhbJYv9J0Fr0H3ZMNJQ4otdERZkUkpfXTmy0ev2QlOGeHd8Fm41RDzwgYwAA4sWLzt/dNoN7O8ZaLNRi4buxLzPfuomcnHjd9n1KGEmu0SM47ok/axcdwWqN0K397g+0vluDSt2dC/ZwrrzS7mon6GvylTsGo7G6GtxbIs6d26v6DsEYmzcBQNmaURqHshzFQMbHn4ibzrd4mWkcZhvZfJ1/qLaD/ILjHXbu7GH79jaP9eyeB+JLYx69de+toZI7BjJENKQJ+Ai9RW9Ek9Ba7BKUmbgvf76Q7/zrYToY4TruXJBPAI/Jro0rKSH8lKdPIOLTT4krKcGRkqIa+bKXr2CKjqKuc4yzNn81HNjRxl/nriBx+fc86hUpYSRKRKsap2C1yiSl5r7IJ61oGn+Jp9Z3GxnWDm6ukXMMZwLH+fkb3+b6r/ZybpWxxjJaGOgYfz2o9c6QjSEIYzOicRiJPBqo+HibTeCOQ2s4SbTrmGaklqOfca1efYYlS+I5d05gZMxFnpq0ibTesXQlz/TJLKnHaJtEYwxuwgQxqE2rlAgxAR8RaP17PRMPeC76uY43Ocg2WRJbMQWkdYylVXGtXrax0NzMmVWryH+tiRPn3WrMUEsaDVSev052fk13Or/eeT2bjmYhvv663FmhAr2NLthsCEePqv4W1tIiq9kjFUPTiqbxt/a71neLHjeN0n/dyQROcBUfMYIOZvMO9AA7Ifyjj2h76SVDm76/7HZ/NVZBJZLl/Njx5Hau4KCiautgIxjtJ4OVIBjovtJCSUkcJ09Fy45prS0uXIDaWmyfxZObG+8yCZ07F8WPIov80gb1GG0K+vcaNcrBvHndoRDRyw2BLnqtDF9Q5/iOlBQyqPSwW3clz0QJvZwHR3IytWTwD8d01zEpp+CH/EH1mgbSiLBacRQVQV8zIH8QV1KC0OlZzqA3JgZxzBjcGxIU4yyGpteyzx1GHYpa3w1GkJW1iRXWe5iNZ/2oiIYGQzZytbLbznEAigiYBR+t5K2dk3XHOxjwJ/9ADYGWApfuMRDZ5mqadwHFXM8/GU+d7Lhw/jwUFVHS/Se//BNqwoiaECCt4zy0aYF7Apm3EO1AEWICfiCQRe++2KXooDFjerFY1JNC1KQ1R2wsndnZHvduz8tj2FtvEa6ogS8KAp3Z2ZSUxHHuYn/6vGR31ypFLDnCTI2Nfr2rBC0NxT51Kg6LhWHV/f0hM7Cyi0zy07fw8Ji/80BLITPG1BNhGRNQ7XfQ/m5lZW3E3qreehCMmaD0x9FvJszJiZeVk9Ab72DA1/yDgYQv+8popJm25q1ewM7U2EjTRd/9E9rCCDIhwJ3RmumnBV2fNOKoaaBbiCEtvoOl6+IYa/be9jYYCDGBSwCtxa7G8R1mM2dWryZx8WLCuroAEDo7ic/N9XA8O8xm7DNmEL5zp+weJoeD2NJSmpq+Ljsu2UYl6dvdT+DuCBNTUwkEmipxn0SkZHLpFlhXFonDHIdge5CIvs0eV1LisdmD4VA0mx3Ez1FvPQjaNnJ3QtR6bCNwtddxDIQD1N9Y/0BzBHyBUkpWhkP7Al9CW9U071+HFTC+14YaxNRUUrp9908YFQKUMJsdPJ130Pk+5/vepwPsud7LggQLISYwSAhkw8WWlroYgIQIq5WERYto275ddp+wdnUbqtDc7CEVSbZRSfpW+h2kjmpiUZEPb+oJPduznknCSI/aYDkU2/PyGPbuux6OdXtamqqNXDm2cRxAjQkoxxFsB6i/sf7BKnNiBGpSslYClJF94i201f0e8SkpbF9d4CqZkpzs4OvWk84ACAV6IyNxFBWR95nv/gl/mbtgs5GwaJFHL4uBSEjTQogJDAICbiiuYU6JqK8nIStLdh89R5RSKiqgmDnsw4LNVT+oZ+xY7FdeSVhHfyTEqIwMAjFKerM9a5kkjPSoDZZD0WE20/rii4woLHSZpy7OnClL6NIbm5o2pTaOYIxXWerCHwISzDIn3qAmJaslQBndJ3oRN2r3mFFdzdNu94jISVZlAv8a9Q0eu/cKEhIu+Nzq0h/m7hqrWpNuBj6MWEKICQSIYEgu3qDn8FXeR0/qlvwRaxcdoaXeQRoNhNGfh2BPT/fQLIIFf2zPRuLY/XEoan0zKXPcG2w2gaX77qWJHFmF0V1kUjDmGU5O+ormOAJ1gBrJ+gZPAqI0x2z+RCNkdwAIj1Ep2eg+0RN0jNxDbY+cDJ/AXU2/xdoUhrdie2rwh7l7S94cjDBiCDEBGXw12QQquditLa7GI3p2Um9F7tw3rjep22x2sGVcPpH1nqV4e/sI4eWCE3HT+TU/8Sjn7EhOli1cXx2KgWhlLtPG6f5GPO4VRp/76rP97Ug1EEhggZGsb5ATkNpaZOYYC7WEmT7RvC7YFWGNSMlajBU8GZOeoKPXgMf1joo9UmUbx9318tpB3pz1Sloh5OVRVoZvwohOSLevobqBYMgxAenjhbe1EZ+QYMj+rEUcfJVclDXqrz+8n/Lq/kgRLTuptGjVbIcAYTYbiQsXyhjXwbyn+zdyiXxBXsoEJqNQS/KpYhY70r7HyLw8ry3ItQhZoFqZmmnDvcJooP4Tb/DWeQ48CUhRkSDrA7yCZQwXOzyuc8TGciQ7P+gVYdWkZPdwaG+MVbkua8mgcMoOFnQWk0oDqTOT6F3+kE8NeNw10/yFiVjrPdt7umsq7kS/Ny6OiEOHZP6jiOpqKCtj/XofysZr0IXHkjcwsmzNoAlkQ4oJKAl9DP2E3h/iYDTtvj0vj/p3WrilYbPMZvzChdtl5+kVinKYzbRt3+7BqMTwcCdj6GMOEdXVfLh6O1m5MzQ38qHsAtbsXExj50iX1JVuIWDJI1AJ0v36urow6k95Etv8q8p50qzfP1Yvd8BbtVdv76Bl2pAqjBrxnwQyT1pEzp6e7tTkVGL9lf18tTJmHVOmsKL0St2QW3/G7q1GvhZjnUUVN8ZW8rNsiyu9q//bJrOR5wGwHLVTRhtmHH4lwHnTVIyY4LzRCq0cAjW68Fb4d3iecx59QwYKQ4oJ6Nav8aOOii9SR/5V5ZxokNcZ7cEzSUQvmkCpxobZbKpOwfYlq7DWl8mOSxs5L6+drNyrsXb2J5u9HXMjL6xuDCguOdCeAjabwM+/c5b7GpaSxilyWA94mlWkXqx60AvXK9X5ZkbeIS6uV/V6oxVGA50nLSKnp7Eq+/lqZcz2WCy69vtAxq40gbmHQ2s9s4UUnu9cwNu5/V2+vOWF+JMAp6apREb20tlpwmYTmG7QBBe5b5+HRg5QVRXBXXcl0NnZ/55SDsFvVejCyVPRlJSIg5Y3MqQKyOkRen9MJO15edgtFtkxLamjsX2ExzE1eAsVlNTY1u3b6R03TvWc4efU37O5WVDdRLVdqawsnWpofFrQ25xGsLmwlc0Nt5DNNr7KHmbyb9XzjIRS6hEyvW/m7R1sNoFDh+S/h2Pn6aglbD72FUZ/6UuEz5lDfE4Ogk09Dj3QeZKInLdChe4oKnLICqIVUMzJ8Amyc6Q50JOKjcxPTk48CxcmqhYhdP/97rsF1+/eSqm4P8OX5vat27dzZv16r2YVSVO56abzREU5GWZ3dxg7d0aTlZVA3Undy10QTp8msrKSmPJyErKyEGw2bDZn/w93BuD+Tlp0YSCrhioxpDQBPULvjxrpi9ThbaGD8UJRLr/GsWOqv3eMSAHPIqMkJ2s3+TCy6PRMAYEmQS2oLpaViFALuRw/9rzu/EjzYjl2L5V8zeN3qcif1jfz9g4lJXGy8tLg1Ob+deEL3PfR75wH6utlZkblWghGsphepJV6P18U5phk2rLLGFO60mMO9KJccnPjNcfuTUtQ/l5ZCZWVCZSVtemWUlHOz0AVmjObHcTGily4IDedWa0RFDqWUKaVSagBV6VQSmUNYtzR3CxcksbySgwpJuBv0pIejIY+qi30sWN7uPJKu6uBu5FCUd7sk3aLhbjVS7Hk2lU3spbE6W3RedvkgS7mNEXpigysvMlc8nnclcD2mPh7RvKEqy2gO9zn5ddUs19RKdU9XK+WDEoopUkUSMFBHu2Y8f4OWgS8gkxuYLcsqkXLRjyQK+fwegAAIABJREFUm16vn69nRNJYzszyXLd6Iaz+agnr15/x+rv0zH37Ijl92nOepfkxGorpj+9Cs83kmBnYBYucbqSlOTuTdXQQ/sknCCp+IG+VQqW5HYjCeb5gSDEBd0If1dbGBbfoIOl3bwTd38xfI/HhRgpFaYUIOpKS6J4zx6nSm8dqPsvfRedtEwe6mNOmRsO/5MfG0SAvnNcAXRrON/d5cc+ArkuaTuKcSa7312Nm3t5Biwg2k0IzTi3TPapFzZ80kJter5+vL/X/tEJY/dUSwLsGJD1T7fu4z08gze29+S60vu8YSwRtG7QFxPicHGLKPTUFvUqhsbH94x6Iwnm+YEgxAegn9ElJSZzxMQs20BjzYFRb1PJr9EyeLCOOWs/yd9EZ2cSBLOaYGGNdtbUc9cp5kTKguyfPpnX9dtdxoxKp2jsYMVu496JV8ycFa9OrSbp6/Xy9XWvk+f5qCWBcAzIyP972kb9dyvLy2jlwIJqamv75khiQnoCoZ2FQqxQaE9PLc8/1M6Rg0IVAMChM4KmnnqK6upqRI0eyqk8k2bp1K++//z7h4eEkJyfzk5/8hNjY2MEYjt8YzFR7rY0ajBh/fxadt00caHioVs0jJbTe0+i8GJVI1aAkUJ98bOL0Z8M8zmsgTdOfJNhsTC8p4fkAirZpSbpTpqh3oXOPDvJXSlZ+31WrzsjO96bh+KIBBUoU/fW7mM0Oduywk5/f4xOD1jMlu1cKvVSSvjcMChOYN28eN998Mxs2bHAdmz59Ot/97ncRBIHS0lLKy8vJVimPPJjwZuoZrFaBehtVqz75kex8VrhlHwd7oemF0VVVRZCbGx9QglGvgZKSdouFzuxs4nNyPJL9jDr2A7XJuxOoe+4ZhaJgKwAxKbGq2mGwirZpSbpTpvRgsXj6gtz7+epJyU/nHVRd/0YYhzcJXvm72RzOkiXGw4d9ETCM9rx2f9fO7GxiS0tJaWujNCGB9lW+MWc9TeFSS/reMChMYNq0abQoatxfc801rv9PnjyZqirPhh6DCSMbdLAybUtK4sBaz1aWkcYpGhhLgbWYkpJk1q/vlzrCrVZMLS3YYqewcHE6tV392cfBblQitdy7//5RNDcLiKLJFUb31lvDNEPgAl389uRkeidOxJGc7GQAubmqyX5GHfuD4YizT5+Ow/yZx/FgaZJakm5HR5gqIc7IGOU9Jt9q11z/JSXTVRnHbwvDeHKTcTON++9O/5cxBuCr5uLtG6vt9ei//hVTj1OTigGGvfuus5Bie/uAl9m+1LgsfAJvvPEGs2fP1vy9oqKCiooKAH7zm9+Q5KXnrRGEh4fL7iMsXYqgskGT1q3DsWWL88DKlYgHDmCqqXGdI06YQPjKlUEd07BTdVTwNVnI5CyqKDj1OklJ45xllFeuJOKWWzDV11PISmqR1/y3WiNYty6JLVsCZwLh4eEcPpzE3XdH0NHh2YxDyQAktDWIJC9diqmxETE11dmhTNlhXHpGd7fqcWHqVMSdOwkHEu6+W/UbHfyfJ3n2q89RVJTEqL7qouGgWloiKQlef12kqMhBY6OJ1FSRoiKRjAxvhSg80d2tvn26uyOd66G2FqGoyPX+JpX+zwBRbW0+rR+LRaDSs/QTZnM4M2eOkgqsIs2C+1rXvLb1Q1UGNTori4IzGXybsRRQLKuv07r3OL+5dzQH2yf0zaND6/N6QLn/tOBs1+kpYOitbY9vHNdOMcuY8IuDzt4YHR0e60hiAK7xnTolKwsRfeAA9h07NNfvQMLoXPl9/wG7s0G8/PLLCILAnDlzNM/JzMwkMzPT9ffpIPRaS0pK4rPq6v5sYY2Y+x6bjVbpeXFxCNu2eUqbcXF+l1p2V0tFi4XPlizhxw1rZQwAYBIn+JltCadPO1tBxufnM6yPGZ0iTfXeNlsPp0/rZ9caQXt7Ev/zP+F0dal3Y9JC2sHXEd7pz1zurazUNH3EJyQQozhWi4X8E8uxzROdBfZOdjNF5TlCSyNlZQKVlb28vPoDppau1K/qGufZLVP5+YyYIBIS4sFj1JCQcIHPqp2NQtyJjUPD53UhIcFwkIJgs7GsbTPvRhZQ053uOm6x2FmypE1VunZK3c77L1kiUFnpGX1TmLAaVKKOw6xWZmJlJk5BJJNdLkaQ3n2C8X97gSdZC8Bf/wrPPdfGrFn6ZT2UY9KD1ZqI1K7THe5rW+tbrVqlLvX3RnrWCfIGU00NPfn5l6QLm9G58oa0NHU6cUmZwJ49e3j//fd57LHHMJl8IzABo7bWUElercJTroWX678N3mOBVlaSUFnJVTHqXP/atgpO22zOgndu/gmt9pDBctoWFQmaCS8SwrHLymBM5Di/Oi+v6Khn+lDa9GuxkBm+h5r68dBXGeODmA3sptpVXVJCg8QErfWkLr6dmK7a/mf6YXM3aoLQK4ymZvoROjvpjYmRNQjypVqktF6SrVYqeI1lrKA+cgKJc6/goeW9AUX4jCsBjSRtFyZxghUsYzGlTOQ4P+L33MELrt87O53Zsbt3fxo0M6SRgAS9b6X2HcI0tE5vUGoPwUSwK7f6gkvGBD744AP+8pe/sHz5ciL94MyBQigq8vpRtTZooPVfJGjZiDsFSFQb88VuWgs3UxC7mtZjGxnHAYop0G1oEoyxKkMM1fA4D1HNtR6dyZQVEpdZy1FzAStt+vm2XzsZgBtqu1J5NHYN/9f5bdex40ykgGLAWR0z1Y0BgH82d6MhhnqF0bz1VdbzW2gFKChzIUpZDN3QFbuAM2bj76dmu/dWrlzCNA5xJ6UUU9DXiU5eh6irKyyo/ZL17PuCzUb3orU8W9/s9Jv1mavcv5XWd+iNjJQxAzE83MMkpETEkSMIfUJYMBEseuIvBoUJrF27lsOHD9Pe3s59993HbbfdRnl5OT09PRQXOzfwFVdcwY9+9KPBGA6g3TzdkZREz+TJuhnDWkRi0aIExo3rNczJtRZovSOVJJqJ4YLseB1p3Lq3gJruGJytDK92JSftIpNHY9dwcfxEfnr218xIrCOiJJnczhVYrXJtxlenrbIAWT9EpIbdMzhALutkv9ZiYb4ic/ftIzfyf7Yw19xoETzbwkSXBuAO25Sv0mVZQM1bn/FBi9xOrVUd09foLV9CDNUKo1VXCxyrMzMXT+O7w2LRZUh6AQoDGZ3mMJv5YPXLrFrSTuO54ayw/4I55ys8zpvJB64EvuNMVL1X2+6Pic/5VVCcqe6MtsVqJ73lA5YnrMNc2EnEoUPMdbPbu5urpG+lFczRPXcuYmysixlL0UFRbW10R0YybN8+hPPnZdeEdXUFJRxcueZ/GYQ9GggGhQk8+OCDHse++tWvDsajNVETN50ifuDRsKR7zhyvH1kzvbw+QqrobIiTay3QGibSTDIL+IvseD6Py+zA4ExOeiRpI3+as5F12SLxud8kot7qJKDVUBB5gNeokDn0QJ2gaRHkoiIHlZW9HgkvTzzxGY8/PgKrNUK1MuUyVsgYADil+ZKSLqeUpkPwUlLUM1DHWCI4s349Ee1JFHzNJBvT2dhU6PS8xtforUDCSKUGLtSvpIL3ZL4dI6YfvQiigYxOs9kEZ3XZeud8LuYP7AnPZHxPfxBEf+vRDs4NT+HEvivhvOe90s99TEx5ud89i9VMI65m7NLaVoG7uUr6Vlqhw2ptQ8/MmuX0FZ4+TdKttyJUe/agDITh2mwCmwtbKdxzGzEX61zHC4e9x2vsNbRHBwKX3DF8KWCzCdx54Alq6E/0cW9Y4g1GisEZ4eSd2dlE7tyJ0NlPuRpjMijocmpHV/ORjIjUR04AFXOmbfI8zqy/mvicHNlir8XCsu7lxNDlcY2SoEVUVZFw112ysUibOGNmkmYM+H/9l/N4uXUZNx55W2aOqR+WARc9xystbj2Cl5f3tG6YX0aGsiiaA0v2z7Dnvu01V8BbPkggYaT9DVwyyGQXK1hGGg0I6WOYWvagPO9EZRx60v6ZVas8CFpvZCSmzk6/zBTuz8+tW4m1fq7rNysZzOupYEt6PrPMdaqa8S//YuenPxVxOPrNhRM5TjEFgH+mOC3TyP4pPzFUzjmNBtm38rcmWI/F4uo17Q5/Ga70XmusPyKVOtlvqRfrWMODfFsh9A1WEbkhyQRKSuKoqZNnehptWAJyImGhtm+jn5LZJUGfkws2G/G5uTKiKw4fTmvJang8Has1wkVEJkTWc8XcRBK5AlSSk1zNL9wIiJopRoKSoAk2G4mLF8scliBv6q5mR7bZBH5bGEZr9QnSaODMzOuJj7mCsI4OHMnJJHZONjxe2dw0N/tZPmCsbMOfGH41yyimMXeES6LMwDMgQCmxBlLawd1/YiWDxZQCMNvczXZzf6SWlhbUM0Ut/slJfCSCNqKwkMi9ewnr7iasu5vonTs5869jNG19gZRZ6r0ClFA+v1mlxo2VDB4xb2H7ds8IM5tN4PHHR7gYwAyquZLDsraQ4LvkrGVqbeo8zWiNa9whpI/x0MD96W/tT1VhPUjv9WXU86GuC6sCt1YVg1lEbkgyAatV/bWNNCyBfiKxubCVgr23kt7drzK72yX1OLmaFGzq6GDCrucoK/tyHwFK4+XkP5CX147d7OAhWy/vH1WvDgpy85KaKQYgPd3usUniSko8GIAErU1sswncsXAEJ09FA6OBq9n/r4lObeqlVTjMZp/G6w5J2vIn09I9ekspUX7w+ln+HbmIiM88G/GEFf6WH8dulZkg/LHHavlPlGtBSwvqmTIFu8WiSXwcZjNibKxHhEtqVy1Vd63hYsUaD2ZVWwv5+fJscmWjFG8RZkooifVSVpPNNo/zvEnOStPPyZPqglMDaVyteyfnPDm1rcAlaH81CCUkbevR3a18m3GEaRSUix/Zy4J5XZektMSQYwI2m8DRo+oLzRf1y2x2sDq2gBg3BgD9dskCyyZVTi4tiqjdu1XvK0nB/hR/c5detHIHzGbPUEK9vrXKpu4SSkri+hhAP04wiV823Mcf+kwA0ngLC0dQXe3UvNzr2wRb2lKOT615Tn1XHCNVzj+2t5Xybu2Ma6MhfGr+E/doFsn8otULIqyjwyvx0fpeIzubPEyQNpvAnXdGUFPTr/lWV0fwegKynAu9CDM1KP1iBRQziyoPH4hU5kPN9KbGqGNinOKwUsP+59R7+GpdpWY5Z3+JtB780SDc4a5tfR74PNBOtOq5ji/MvGSlJXSZwN/+9jdm9TlL/n9BSUmcaoZrTEyvz+qX1macnlTnIiDSxq87CesbFvHwZ48Sc7FW9TrwLjnpScfu0kvKngvgWbkAmy2MhQsTjRWli42lXaOpu5ZzvIE0D+3h6NFwV434nTujOXo0vG9+giNtqUGzoiZpXMnHHseVDndlX12jIXxqvgotM5QaJLOPGvGRGNG9xyx8TSXyqIE0DxNkSUmcrCqm9G5SoxT3EN6r+JCpKa18NmGGV2lU6Rez9vlA3H0IyjIf0G96IylJlVF3dYXxuajjvHrhZhlDOV/7Fmd+t5pPN+6kuHoBDaSRdFWKLD/CZhMoybk0sfZqUNP24jhPO9HEuXnU7WlpnFu+fLCH54IuE9i6dSvbtm3jc5/7HPPmzeNLX/rSJYnpDya0iMPUqXafF4wW8Zw0J5EzfQwgISuLeit8nQqKKCIdbQYgTpggk4L96V0gFVNb/s7P2f/ZZJlkFy70qkYwqRWl642Joe255zSfp+UcT6OB3uHDXX8b6Qk7EFmYWuN7gYVkItfC6iMnUNBd7HFuc7OAYLOxdlG3zGkK+o5/NUYdl+O9T62eFuTOiKr5NRXslxFJKV9ipkKbbbaq+7jq4z/HJ73XeTQ5Hy+c53lFhVA1qJbVtqQTWbaO1r5rlYEKIPczae3FJ6KXMemCPGM++tTJ/8feu8dHUd774+9kEkIuG0ISkpDLbhLQgCilOb1wqJwg4OVb9dQoaFriKbbfb/W00WjUtdHEEInSrgJiUavfn0U0tvHgMf6kh0ogVkRMxGMA64UoZLOTewwhJNnAsju73z82MzuX55md2UuCet6vF38wmZ155rl9Ps/n8v5g/LlX8OP2BtiGJt/ZBHzU7jVvAtAsqFmWkZxOCwvPo7Z2NKQCg2FZxBw8SPxbZ/xlGJ6dhyVp3Yg2pU07L5FqGmhMTAy2bduGhQsXYteuXfjVr36FZ555Bp999tlUtS/koG0OJpP+CTBmNsOVJXXEubKyhIXMawK8fZ4Wx+5OTMREcTGce/YIk4EXIHGNjYq6pf5gsFhwce8h7MNqrEM9rsDbyEUHXJx0uPmNjFS39qvmZjiXLqW+w2weQ16s1I4sjgzhEYpyioHAbB5DVpYy+WcvrkHXpKmMS03FRHEx6op2K8LzACAjYRTJJSUY6KYUk9HxDbRTI5eaqqlWsFiY8lp3PdbhbVyBeqzDauwDTNmK02zO4FHi87JHPkflpY0Kv5G3yLl/Rlfe1FdcPIFlyxwoLp5QbLj+8hpoazGHIfsn+tqGlLUcbN1wrC1H/PU3o852G0wiJYtUu5llGdx0UzKammIxNMRgaIhBU1Ms1qxJUdREDhT82hVXG7PChFK8jCvQjEdnWxCzaxvsu/9DUw3kcEP1JBAREYG0tDSsXbsWa9euxeeff44DBw7AYrEgPj4eRUVFuPnmm6eqrSFBqFkkPR4P9f/8IuDt86RYegA4t2qVUOiGJ7EJhnGSf6+QVQrgCjSjE/mKe4WkGp0audHI4W8LyrDxyI2KLGHHuO87p6uGqtHIYdEip6ImcBdyUYnfY4epSth017MM3iI4sDeiGtE2m26nKQnUpCUNeSmAUpjykUepqRwuvtiFwnQOZvMw8mCFocx3eqxNdOC/kSXZ7OfhBGrTnsJtY38mvkurcPPnuFdz/EeBvhYzClKJUWV9Mj+XCVbsx5WY3+09NVwGJb8RyTzW26ssCtTTEwWLxSAi3wsc8rWriNTrBlpLlAEaYoh9UCYTg/JyJmymLV2O4YULF2LhwoX4xS9+gcOHD+PAgQNhaVQ4wWsw27algmVdQXniDRYLonulG0R0b6+wUXMZGbDChE7kAgDuwLPIQheuwLvC/dTiI0Fkh5IWXyg2MjlycoH6I7cqrov9GqEWuryJTF5PgISvviIfdLtSF2sKCZ1X8TEA/U5TEo6XViJ59zFJ8lVnVD6GSytB3iqlWGw4iV/jMUUo8vLlDmEjJoWdXhQXJ5TaFAvrdFMhtfSh3jlBc5qrOf5ng97vbtwPZ/tHit81FlRLhEMdqolEi3zCGOlbaCdTIHSnU/naJUXqqZkT5T6olhagpSU5bDQSqkJAruXymDFjBi6//HJcfvnlIW/QVMBo5LBzJxc0w6a/jfrT0iqU7E5GpysXAGCHAVdhP36P+7E05iguKkqBu/Z+4iYWTHbo8dJKzN17VJK49XDmH3Eo4iZJRE+wschaontCWUNVvsnJ6wmI4Y0CI5eBTFk+H5xRuvhIWi0/BuKaxb3IRFo2g7sbFuj6hrr6RWhz7ReSx3qRiSrXRhTWp2P7UvWokOjWVmw/+G+IEqVDL0Ur1mfugdnsi3UiaaDVE3XoichBlqcLL+AXyIMNTpMJw5TSh3rnhLrT3L/jn9jvIP9uPVIkJzaaeTVzUuEhfYvB4Cb9BAAvMIIPmJSvXVqkHk3oBFoeM1CofvGWLVtC/sJvEvxt1JvqF6DDJaUadiEaW7Mfx65dw3CqbCKBhk960/+XABPNwoZzJj4Dpj/cg79kjsJi8YQsFllrLHUerKiHBV1ngZoPy3Hf/1mI7JEvUTtnG3JyodkxpsdEZrEYiMynfIFvLRCPAW9ac5pMk0JHX7/19zOS5DEeWQPqjJZCIt9ZaR7HfJxE46WVcBr/IFxz2nynRIkJYlKXa4lZgd1FdUipXR+y0ofhcvyTfidvL8OmEykknKnpKF4+QSxA/+mnZMUgK8vlnRfWESRVVhKrq2ll+ZSvXb2n8Kn2o6kKAXlo6NDQEIaHh3HxxReHpTFfB4gjdtwGA5yZmRKTkHijpg1mWprH74QKNFnFtyhFG44dKK738vWEWpOwIg8W1KPfwyADHMwYg1FkZpBHSJ3E/MmFm4b/7s7CviOrkd1WooljRquJjGFZnDp4CiCkFxUUcJo3uVAlDAGB+0bUEvkSx/uF1EaWZdDXbkIx3gdANkF0OLJRFb8F241SJtRw1PNtbp6JsrKkkIdpitvLsHfDWdKqUJSWNJRLvpGHxWJQ+IgAICPDhddeO4U8WBH943VCnQ4rTHioqQ/teYvQbo2ThJarcYPJ582DCe/h0KfKU3hpqR1lhJKwU+1H03T2GRoawrZt29DZ2QnAGzra2tqKo0eP4o477ghLwy5EkGyurqwsnL3qKmLCCm0w29sZITwN8E0oeTpGIFqUFi0iVNzlJFPAsT3DEm3TFyH1smJTOon5WIpWXGnbjwdrXkTSjodV36fFRMaPUc5QHUhCwGRSpwtWPNtoxMfmZ339ZQmsvwL1jfhL5ONhsRjQZn8UV6MRcTin2wQRKGhzfHQ0Eo2NcWGlRNYqpPn53tw8k/ic/HyvYmAoswhVA4WTlH0+8InyN/7MM+K1mwTgL6z0FF5aaqfW5Z6KEqhiqFcKmcTzzz+P7373u9i5cyeiorxyY/Hixfj444/D0qgLFSRzRFRPDzzx8Ti1a5ci3MtsHoPJJI3TjotzU+vxhgJai3A0NsahpSUGjY1xKClJDig87vGaSIUpoMORjceaLhfCWeURUnIMIgOvoBTXH6hCf2sPksrKkLJmjTfLVBYOO2Y2w2kySa7JTWT8GG1EFebhhOTeQBZSqPpLS0glCf4S+Xjw5qa9uBqAPhMEyzIoK0vCmjUpKCtL0vVtpDkuRijnNgl8XgyXng6mv98rEETzRjx+o6Pk7Y7EZUWjXRHj4MEY1b4S96vFYoDZPIZdu05h+/YR1NfHU81o8rlSUsKFtbaAppPAiRMn8Nvf/haRkb5OjIuLwwTlmPpNhd6IHZJTtLOTwZEjyoS7UGlo/rSIUDmdGJbFqQNugEDr1YtMBf0xbVMCvKF+TzjuxqK1exHj9tnI5U5fseY3c3gY5wjRQeLwWLEzNz3VifKGJboXUiiddIGYXki+IVIiHy/878FWXIZPNEc0BVvQRDzHm5tnEjdaf3M7urUVSeXlYEZHwSUmYmTbNtUcFTHU6Mg5o5E4fmLQuKxoSosYQ0MMVq9ORUEBB5PJJTkh+utXfyd28VzxlpcMX0i1ppPArFmz0C/bALu7u79RdBJaQNPKWtkcqkbADyavAeTmhtfe50/jDJXTyWCxSIjzxOCjM5iBAUF7J2nm3nu7sB9XohhvSgQAIMouFYE/ZruamjCyfTusyJNosaOGucK9vDP3bazCC8ufC0iTmq5kNx60RL6TmT+SfHdpqR0mk1NIJDuEy/G76CrclPEuin5op5481IScVvBzfNWqc8S/q83t6NZWpN5yC6K7uxE5Ooro7m7v/1vJbJtyqAULAPTxS0x0K/pkzGyGJ9+bS6OmtIhht3tNu/ITor9+na78GRI0nQSuv/56/P73v8cNN9wAt9uN9957D42NjbjhhhvC3b4phxpVA0krO4F5+Hn3JqBEGcdLepbZzKho6iSWHv3go3EYTz84ZGAMZnBQ91PonXxMfz9R2xRnDYvpj9MtFvyX7QHUDN6Fg2PfRe+ZRADA71GpiPWWvEclL4KkbQ1mbsK+rDbE9nQK1+QmI9K4WJFH9JNcCItV7hsiffcHH0Rj3jwOdnsk7DDi5cL/i9raUTxl5JCaGk0tVB6MkJP3Y1VpJdraluiyZSeVlyvKOka4XEgqL8dXH3zgvw0BZiWvWnVOcirjv8WTmgrX+fOonVWP962rYJ3wKRWRkR643fRSq+ITor9+nWq7vxo0CYGVK1ciISEBzc3NSElJwbvvvotbbrkFP/jBD8LdvimFv6Mlv6EdX/skuO5Bb6w3Xz/ABomJgPYsNDSgoQEhiZsP5BtCNfm4jAzkoYWYiMTHoovpj0e2b4cBwDaWRWTNbfjywCl0OLKRTzgdSN6jkhdB0rYO9V6Me676L2z5QRXRWUjqn+4PBvHTiH2S6I3pctJpAem7e3ujJZmw7e3a4t0DFXKkfrysrQ2vb9mFuvpFmuc2Mzqq67oc/oIFtIyf/FsiARgZBq+93I26+lnCt9jtEWhqIrOA8uA3eX/9Gsr8mWDhd6a43W7s2rULN9544zdu05fDXxw6ry0kjJ7C58iRFJABpNoT7VnJa9ciKScH9RkZGNsceuIoYsKQrQ5d18cjZbk3ZC8Uk48/FeXZfNQU7rg4OBcswISpkFpAnV9scwAsAzARmSAppiGGmIeJBJq29Y/xeRjZQY6qIo3LI713oFNG8SvW6sKxWIOJ0FLLeiW1Xw38Jglbt0DdfCZ+Lkyl9wAUmhOAPr8X1G/Cdh0RbVxiIiIJGz6XmKjp9/7yafxttizLwLH2SRR1+/8WbwJilKqPgd/ktQifYMNzQwW/QiAyMhJ79+7F2rVrp6I90wra0XJmczNm33Yboj75BNG9vQI3uJynRKw90Z4V3d0NnsYzkBqs/jYPanWxIQCNPg032MlnRR5+XfAhhuz9yEQvqgsbhbBQGkgbR5x7XFSuXgrnokWqzwtEiyWNi79wymAWK2m8AO2MlyRoKW8K+Dfp8G1bFN+Bp5kfI5eb9PHYAWfF+6pzM4rCiOrPfCfnw4netg2pt9wiMQl5oqIwsm2bn6/zQkuYKG38eLPan7rJbZZ/i1ig2GwMjh+PliQkijf5C0nT9wdNZ8aioiLs27cPV199dbjbM62gHS0jR0cR26RktBLzlMilPO1ZYuitwaolksNfdTE9kS38oh0ejkJysi/xR9oOb1WxlvaVaMAw8lgr1adCE4w0K2vk+Lhq+wIx1UwVrxJAH6+CAldQEUdEGmcC1NovbtvL2IhcSJ38/NwcM5sV4wmT8bg2AAAgAElEQVQATHs78bk08x2dD+dHwKuvBhwdBARe/IU3q9GIHUnfIhYo/PrQXv70woTmENG33noLb775JlJSUhAR4Vu2tdNYDCHUIB0t/cEU1YOrVp5V8JFrfZaa5iR3vD1ir4PNJp2Y8s1DS3UxLU4/5QbmS/yhRT48XhOJhnYVn4oGwSiGlgI7WrUtgXjOZgMXHy+p7RwOXiWAHiFit5OD8vSwd4q/OyHBjU8/jZZkw/prv7htNA6eKJuNWgdZ3H883HFxVPOdeqjtUk1O4GBACgbo708BQK+K5o+i5euyyfuDJiGwatUqrFq1KtxtmXZI4tCbm4m2SjlsriyiE05+TI1kWa8pSH4fZaMjOd6qYo7hLexXcN+LNw9JdbGDTq8ZSAYtGq7aoqXZpIfa+hA9RPepjJnNmLl3L5UGgYcVJjwUvxU220qkl0WrHqO1LERSX/L+C85kwiyzGX9BaHmVAG22ezH0ljeVl5HUY3oQt42mCUcMDirmbLTNhkiCAADg7U+K+Wg6Q21pwRKLC/agBZcIYbU81xaTnTZZq1j5LeJKgTVflaMrbQnSTepz9EKHJiGwYsWKoF7yzDPPoK2tDbNmzcLmzZsBAC0tLdi1axd6enrw2GOPYd68eUG9I1Tgj5ZJZWWIa2xUvZev5kQ7youPqaSJqKZtkOzn2Y4OCU0uj8sSTiKprEqi5Yxs345ylkFLCb3QuxrUFi29qlif4poVJjx48HbY1qQgIyMJL+YtQOKnbdT3npmdg1WOFljtc4E2AG1SkxfNRKUGUl9GTkyAM5mEovShoNHwfrBVICAzdm1CC4oUtxQWnlc4GEkRK3qqyunVSsVjSNOE3cnJgv9KCzhZNjftfWJMRagtzYm9saAab5kaYLNFC+R++fkevPLKIJEgkMqD1aZexSxkcytM0CQE3n77berfVq5c6ff3K1aswDXXXIOnn35auJaTk4P77rsPzz//vJYmTDlI5hxXVhYO2b8LbsQuDQ+Ff41GLxkZzX6eH9MNiHKqfpT5BbZ+eq0kLp43wRiNRt3mEn7TWWyoQwsuUdzHP4Nki68uaJTwvb+LH+Fa7MH4UKJwIrk1vgDFIAsBLj4eD3z/LVib5kqu80LWbB6jmqhUwxBVYsn1ZsyqLWqGZRG9zkdAtgk/x4dR76Bjkkqc76faWu8JkzYu/sJ8tUA8nozJBKa8XEFpwo8hrwlvjX8IKwtYoeShwWIBjhxRPPt8YSGi2tt1MdxOZ6gtbfwTx/sV62PTpigYDOS5pMaDRVIEg83GnipoEgIHZbUyR0ZG0N/fjwULFmgSApdccgkGBwcl17Kzsyl3XxigbdrbLIvR2BinuJ+k0ZC0Oa0OLJr9/KKiFBTHTwiT9jl7JWKbOiX3iE0wgZpLtmYdwSeZe3Go18cYyy9ami0+BevhbH8L0TYbrDB5BQCkoX732B/FjyMbEeNWZpdyBQX4eIx8IhwYYAKmcFCLJdfzTH+LOrLmcYGADPBmLO93rUBl9k50GZcK/QRAVTsMpqocQBjPlhYkt7RgZMsWxNfXg+nvR1JGBl7fUimK6U/HXPNWnIMV0RYLkioqqCy5fFF0Peyq8jljNEahvHxqNkO18ZevDy9FA/k5/niwSFXMprIuQMDwBIjm5mbPSy+9pPn+gYEBT0VFheJ6TU2N58SJE6q/3bdvn+eBBx7wPPDAAx6Px+NxOBxB/+M4LqDfHT/u8OTnuz2AR/iXn+/2HD8uu/f4cY87P98jvtGdn+9xHD+urU0af88VFUnu4f9xRUWav8lVUkJ8xsh1JZ6SEpdnxQq3p6TEpfxG0r/jxz2ukhLPz9KaSI/0AB7Pu8n/SvyDq8T7PtJvSkpcnqIijvi3oiI/Y6nSl3qeqda248cdnvdnrvA7FlrmD3VM09I8XFGRx1VSojqPaOPpTkhQn0+kfsrJ8biuu07Te6di/QX0T8daVGsX36/r8DJ1HojvD3i+hqmvaAi4jM6KFSvwy1/+ErfeqiwvGGqsXr0aq1evFv5PS4HXA6/E1/8cgwF45RWlE85g4CQaRFJlpWAW4BHR0QFXZSVVm5O0yWAA88orSm3LYID4RUnJyVCeS4BzyckY0fh9KTYbSMasmcMsNj83IGmX30caDMDmzbCuSQEGybe88r1NWNp+TGFqcw4P449D/4Kb4024x/6oYGozmZwoLx+e5F1Rfm1y8jkMDaloVip9mZx8TvMzbbYUgNBTLOtCZSWHG89l4Z8JrxePRWVlEjo6Zkj+3tERgcpKl6Ad0sY0cnAQmDxRu1taFOYh3lT1ULM3l0WOCFnIrXw+EudsVxcc3/seRv4sqkc8jesvIGhcS/7axZSXI7mlBRttSrqUuDg31q4dxtCQj1E1OTkJAc1XGULVV5mZ5BOMJiHgdktTOs+fP493330X8fHxQTfs6whNJpYgagTzkDuWSY7CQCuQSd4TRClLGmiOwPh4DutrUzAMn6nNnZCAqE8+QWxTE2IBFON9LI1twd0xTyMyikF1QSNSsB5mc17AdmVaLLkeW7Wac7O/nyE6WLtj8hEto3wmQWxK0BJeLDcPiU1VNyKHKARIEM/HYOasXkf2VCPQXAL5M3gerNe/+C3Kjt+Nw9w/wYFYTExEoqIiSWLvvxApR0jQJAR++tOfKq4lJyfj9ttvD3mDvimgJp6xLBiW1bVA/DkKg61+FQpBIgdpAcTFufHSS95FwkFUdKOsTGJ3BoC5Zzvx6tlrvf9pApztb0l4l4aHZyI5+VzQ0RZ6cg3UFrXFYkCLLNSwF5l4r+hBPGxMEu7XEiUjH9OoL74AQ9AEGdF4WSwGwNaNl1GNhfgE5xGFGfBl4cpzI4TrIkEfqDIQCkf21wW8MNlQloSDn0q1fLm9/+uSNRzh8VCqyYvw1VdfSf4fExODRI3cHgDw5JNP4rPPPsPY2BhmzZqFm2++GQkJCfjTn/6E0dFRxMfHIzc3Fw899JCm5/X2aqN5VUO4j6OkhcHDV6dWukBobaKFq04UFwet3fDoae3H5vIx9I0mYG7iOO7dZkDW0gzVdvlDa2s0ysuTMDrKIDGRw7ZtI1i6VFmAJGXNGsS0tPh9nvh7p9ScIAItHp/kNDaZnGhoGAbgcwQbDG588kmUhOyNv4+2OdDG3x0Xh6+am8EZjSi7bgyWI9cSGVk9CQkYtliQ+PvfC/NRyMUoWCnEuefBqpizfXF5+E3B34DcHOoGFsj8nOrx0xqqqbVda9akoKVFWReksNCB3btPEX4ROC4Ic9Du3bvxi1/8QnH9xRdfxPr16/3+/u677yZe/yYT0vHaXPLatcSEGz10EaEwLanBW5z+Mti6JzemUaC1wrsx5cEK5t57kWKz6TrmsyyDiookdE8+c3RUeVzmoTWTOJDvDdRMQds0aKZAXuvbti0VLOuSRALJhUNWlgtXXXUW4+ORmrTDMbMZMU1NCk0+cmJCmEflX9VQKbkjxscRu2+fcLroskXgfx3fTsjFADB5j9M2iLfbjV7fzJE84Ag9vFHP/AwkzyNYhCNUk3aiO348GizLXHDavho0FZU5cOAA8fq7774b0sZMJ4IpsUcDZzTCnZND/Jsu30AYbPZi0ELZXqw55S0T2dCAmJYWxDU2CmUjA30mX1SDYVmhlGSE3Q4nRUsRg/9elmXw858zfseKP43FNTbqan+gJSWNRg47d3JCASGjkSP2Q09PFGZ/dhhve65APUqRB6v6dxuN4AoKyN84OY+WpHWpPmNmc7PABVRpelnCkw/4xoY3d9xmasKN9j9LstNpxWb8zU9+rOOvX4u+1fegrXEABw5E6irVKZ4vpNKjaghF4Rw5zOYxxMcrN/qJiciwltMMB1RPAnySGMdxioSxwcFBGAxfr4+lIRSaAk1zDMUGHg6bvRg0Z2Vx20ZVGohAnjkwwBBNZa6sLJy96ipEjo8LjmJ5fPqY2SwaKwZ8pA5trPTG2/OnhnsP3g7bkJQsMdD4blo/DHZziOn2msC02M9dJhNmtCmT7Ph5FG1KByUHD4CXBDGusRHRbW0YSDlGvEfsnNZD86A2P+VjXYz3cRkOC+y7WvpVzecAwO9JLxyUFUYjh4ICDm1tymdMVdW5UEFVCPBJYi6XS5EwNmvWLPzmN78JX8umEMEmdagJEUbHBm61ekMIFYIkBM5fNdBpIMi+F/Ephib81BygpM05qqcH53/wA5zascP7Dt6MI/teS5n2sdJjphBvNP0oI/4ukMWtpW+1CFb5RivnV6osrcISDYSF0TYbcrijAIHOgmUjBVOGHpoHtfmZVFamaJOYfRfw3680YZ5YU6PIXCYJ1HBRVphMLrS1zVBcn44SkcFAVQjU1NQAABoaGlBSUjIlDZoOBKspqAsRbRs4yzJYty5aEkMu1nBDEeJGAy3qJaMgVUIDwUNslqEJP7VIGqbC/+ZM+149Y6XnFCbeaLRQS2v1NZD6wYhOjCMeV6AZWZPV2DL9mAfFGy3Zpn8Zdm15HYvq68DYbIgcHAQzMkKk465NewqtzDLFnO3ujsZP1yTiL6+N6g5vpI0X09/vLWyEOvQg0/e9oj72W8WMIsxntLUpoqZIAjVcoZpflxBQf9DkGBYLAI/HA3FAUWSkJrfCBY1gNQV/G5OWDdxiMaCjQ8qsP1Up5rRQNjfuh7P9I8Up5tPSKmwqS8LBgzEYGpJ+u5aKXGrhsylr1qhuqnrGSo8ZTbzRkGonixe3qnkiNVXyXHnfzu44io/70/H/o1i4pxVL8WbCE0iCOvh5VFmWBGubMjxxU/0CSSUsWtROjsmDhqeHcXPxLHT1z5T8rbMnFo/XjOMPO5whCW88aViMf8VLkr5sxVL8Gt52atk09VKQqxWDCWWo5tclBNQfNIWIDg8P44UXXsDnn38OuyxC4dVXXw1b42gIdYgoy3qdjGI+9qwsF1577ZSmAS0rSyLyCRUXT2jewGkhZ8uWObBrly/kLJyshCTtFgBSt22Di2XBpafj09IqrK24TLWgSWKiG6tW0WP4SZuoJypKUl2KFkarFoqpSoznx4wm3zB57bUrdTFSls+XfItaSGRUQ4NqON+dt0Xj9aY5ius3XvUV/rBDGT5LAm2u/Evqx/iP3ROq9ZTF/frT75zCu0OXEZ/zl2OpiuuBgPa9xpQx/NO/MJrmL+07XAUFxGJPwYROT1fosRouiBDR559/HjExMXj44YdRU1OD2tpa7Nq1C9/97neDbtiFArks1CAbBYTiWKhFww0nK6Fqws/OnRhpa4PBYsGW27+CbUi9otXoqDfyg9Y2LbUWaHZyWigm7fu1mtHkp4Y82LDDVDW5YQbua5Cjb4ycX9M/nghAW3w5ba7kDH2M5JIqXxGfyX4WC3GxEPSaZJRCwEsJHhohQPvevEvjsX07uR/loPkcAOhmMwX0KVIXeiZ0KKBJCHzxxRd45plnMHPmTERERCA3Nxf//u//jqqqKgmnz9cVFotBkrwDAL292k0xwR4LWZaB3R6BmTM9OHfOZxKSCxI138Oz5o+DmqxqkTTYtMmv05QENXOWeHNOWbOGyFtP21T5UMyhIf+bptZFrMf5ruZr8LegQuGkJCkd83ACG1GlEJ6c0Qhu506cImiS1YWN+LBpnsRUMw8nUF3YCJJwCAS07507V6pkiTfmxYaT2IhqJI71ScaMJMz1BkxoUaT6W3swVr4Zyac7MO/cZ4jjfNaPcGRC8+9LGO3DeOJcGLbdi4yl5EI/4YAmIRAZGQmG8dp+4+PjMTo6itjYWAwPD4e1cVOFUISQBVpqjjQpY2LcKCpyKEpW0toZYesKnn9eRbtlNmwQKApoTtOoKDdcLqV/SEsfhisPgnS6mbl3L5wFBeBycxUbRqCnBsCngc7289tQnBqF09D1RzEwFI3MSWdrHrzt0ZqDklK7Hns+WY9Heu9ALzKRiV48nPlHzKp9AqGyatO+d8MGnxAQrwETrHgJ/4o5osQ32lwOREv3FwnY9a4VybeUoNDVQfy93kRPf+hv7ZG+bxTovOVD9L/aMGWCQJMQmD9/Po4cOYIf/OAH+M53voOtW7dixowZF0w1sGAxnVWPSJPS4YhEfLxHcZKgtfOuwRpEdwfOPw+ob8TRfb6KYSSnaV5cHy66PAlNTbGK32vpw3DlQdAqisUcOQIcORKwVqe7QJBos1Ly+HOoLD2ORZY6XZuZ0cjhheXPEX0TWoUnZzRi1n8+gecl3/FESLVc2ik5L2+2QOApXgN1qFZkPpPmcqB8Rf4Uvr7/XYvLKQJAeHcIM9fHyjcrBE6uqwO28s3I+GCL7vcEAk1C4M477xRs5OvXr8fu3btx9uxZXHvttWFt3FRhOkO99JxCaO2cneBEaffLkhC8PNh0TVa1jThm2zbhWh5s2IfVqEadoD1WL2jE6dpnFCUTY2M5vPNODL7znXQUFp5XnGx4hCsPgna64RGMVqf11EDarC5ra8Ozk5sV6e/dHwyi8tJG9I0lEm3WAn+RbQdM8bfiUfs9wilAN4NsmEKP5YLvWZXxFK+BCHAohf+5HGjhHX8KX9yIskSqHHpPqGoCK2GU/L6EUW3+klBAkxAQU0bPmDEDN910U9gaNB2YzlAvracQfuGnpHjAcU6kpblhMnEoLbXjhlu3wwofDUArlmIfViMjIcGbYq9Bw1TbiLkNG+BuaZE4TevhqyNxNvUqGER9aLMx+PzzaJw9y+DsWe89TU2x+OSTKPznf5Kd2OHYjLSEFoaKf4kGf5uV/O9WmPDj3hdxstcXUXN07xm8+nI/spZmKMyH76MYrXFL8bcFZcgxeS4IxyVp0zuz9yjKRER04kha8Rq4DS/CCV/kEz+X02Ubb6DOeX8K30TSXOAM/feurCzdJ1S1OTCeOBcYVf5mPFFfWGww0CQEnE4nXnvtNRw6dAhjY2PYuXMnjh07hr6+PlxzzTXhbuOUIFCbfrDQcgoh+Q0Yxomnnx6BxWKAdUIannoS8/FQ7Bbs/OROCfWCv+MydSPOy8NwQwNSiosRpaJd831YVpaEtjZlCKMeZ7tekCI+SNnacoSKf4kGf5uVPJmqE7noRL7kXuvEXGz9t1Zs3X8eFstihfnQOjEXlaaXL5iShfymJ0kSm+jF5Ueexb1HnkRbWzT27vWAZ50pLbVj9+5YuFwREgEATM7l+K3YapZyHQXqR5IrfAkJ3lopFRVJyMjgcNfGjej8xQfIFZloxhGHKLgwE+fhXLRIt5ClzYGYgweRnVaIzggTcj2+OdoZlQ/Dtnt1vSMYaBICO3fuxPDwMO666y489thjALyF4nfu3PmNEQJ6EMpYffGkpHHkqzmzaOakd13L0N0bLaL/Ct78weXlEYUAO5SAyjIf3YXNRp9WAwNMyHMd6BEfEFgxo2w2MO3tEibOUPIv0eBvsyIlU5HQb58Fg8UCm62B+He1Pg8GgThfecF2JfZLvuvvuGKyrdHYsIHD5s3e6/X18XC5IkiPAgCwBSvBGaX5ScH4kXhlhTRvjh3Lx5NPvY7ue+5BnOM0ziIWRtiQMxkQQcrA9gfaHGCGhjBvqAldyMR7zHLExgLjSZmYePA+/N/6S9D/hHd9bNoEhJOmTdPMOXz4MJ566ikhRBTwFpX5pkQH6UE4YvX5SelNClFqc2p+A5o5qceZgSuxH/uwWrAXA8GZP0iT2QqTl8JAlMFKYlfkkZDgDnn/yYWkCVbU2aoRf30XDMtThI1La+JYKGEvLUXs7t2SRDhPVBTspaUAgGpsxEkok6nkyEQvmIEBDA6SN0va9WAQqPOVy8hANX6tEGy98EW7WK2+9tLmN480kzIvJRR+JJJy1dERgR37LkL9j5MQ1/h35XsDODn6qxSXg17kcL2YuLIYH5u34ecKweTBK6+Ej55akxCIiopSlJgcHR39xrCI6kGwZHOBQM1vQDIn8TiJ+ahGncR+H4z5gzSZH4rf6uWwEcFuZxAb68bZs9KQ0cxMb0ZsKPuPYVncfvBelKEfvcjCVtyJLPTjBfwCWUO92NhYhey2EmHjChf/Eg3x9fUSAWCFCdWuOnSXpCPlimhYv/JfnInPAeDSCzHnrJuUUoG0NLfyYpBQI27zxMdTTwdjZjO697gBB/3Z4gMlbX4DIr4pyolEPp56TplqytXY5tBFrGmuFDcwQBVM4dxfNAmBpUuXYvv27UIBmdOnT+PFF1/EsmXLwtKoqYaeI284aGn9Qc1vYDRy2LXlH7hubTYG3WmK3+7HalhhQh5suiaxeDGlpDA4f342xsZSMLfgQ2wsqMa88X+AS0+HzbaSSGG8cKETqalugWWRjw6qqCAz5ND6T21seE316iEfs+bDeARt+KHw+1YsxT7baqSHMLZbD8T2YImJxAmgCYiPdRF/Z2JY5HMnhByAbBMwbDYj18LhyBHC/abQa4lUW/aBA4h0+HZ4+emAMxqRUhRNJB/kIdZFSPN75gwOq2Z/iM3JFhhr7ApqcdKJRO8pXU25CnXEmlhg0WhHuPT0adlfqELgrbfeEuz9V155Jfbu3Yt7770X58+fx1133YVVq1ZhzZo1YWvYVEHvkXc6cgr8RS8tqq/Dle4b8QpKFb8dQAZWx7yH3UV1SKldr7mqlnwxAXwOwBx8ZPqDsLDSy6KJQsBkIjvaaf2XkOBGWZmURptU7lBM1CbXVKtRB6vMqcqfhl4YeNbvd4cDYhNaNeoUJhL72SjEx3Ow232L3GRyYteWISyq3z65ARVieHIDmspwZirRn0Oq4pN8TffXuvFRu5PKMZWf70sWk8/vjIRRbPqkGBf3HgIo1kvSO/We0kl9mZ/vEfoyXCdH0omai4+HvbQUGfVTv79QhcBf/vIXQQj89re/xc6dO7F+/XrBDMT7Br7u0BtvPF05BbzfQNCMK3yaMdPfT0zi4tHhyEZV/BZsN2o7TpIWkxjihaW3PypLj+PY7mR0uHKFa3lMJ4aOuNH0lW8Db2uLxocFv6ZTWTQ0KDTVHpAJsnqRqdkMJj55jBrmohob8fHYvIAd2GNmM2bu2YNIh4PavoICDiaTQyLgs4wZGFmqnH9TGc5M2qzcMTEKIQDQmTtrahJx4EAMHA6faVCeMczfz2/USWVliOs95Ld98nfq1aJJfblpUxQMhvCGhnNGI0a2bEHKrbcicmICAMDY7UiqqEDVlhy0tS2hCqZwgCoEMjIy8NJLLyE7Oxsulwt///vfiaRqK1euDFvjpgJ6442nM6eAdmpxFRQISVxL0YpBKDU4+UJQM7P4c9SJn6e3PxbV12G/q02SbLaRq8Khry4XiowAXkHTbx8iukz5sZFrqjRKi4z4M5rMYPL+nQPgPhzDauxDC/ICcmBzRiMcRUWIbWqits9kcumy905VODPJJBJhtxOZO0lC1mjksGPHaV9yGyVjWA5/SX60dwZySpf3ZerYGFxllWEhjBObWDd1/QeKJgUAj2ibDQvqN6Gh4dkpFUxUIVBeXo4333wThw4dAsdx1HrCX3chEEi8cTgWIcOyfgu6004troICuGNikOew4UrsJ5qF5AVR1Exgao460vPyYEU9LGA8/eCQgTGYwYFCodDfr0g2A6Aw4wBeDZ5EY8YTtck11Y2oQkvU5dJTRlwf7nnJBM6oHGe5IIyw21WrYAXiwOZr4bpnzMDG88rTWm7W2QuyCIlvw0pBRkY9zJu9gp1hWd3MnXrXi5YkP9I7gz2lMyyL6HXrMKPDlyMQKsI4uYmVo9i5mIEBpWBKTaUKzFCAKgQyMzNxxx13AAAeeeQRPPzww+FrxTQi3PV7tYDflBmbDbwOTpp8NA0pcnxc0Db9FUQB/JvA1CKO5M/T61OhLfBegqmksbAaK9tbiGMzG0pNNT09HQ2lw9hUnybSOiORNSkAxJrYXMMoNn1yH9JFZgd3jDLBDZCWgtTjoJP3TR5saMJVqIr+HXoSLkbq97Nwf637gitCou5gDZ3DlMqnQ1iTrqwsOBctQuT4OPWd4lNphK0Ldw3WYElKF6It2tposFgQIRIAQOgI4+QmVnG4rBjhTl4kQVNRmQsNoS4qMx3x42KoFSkRT77Zt91GLaIxZjYLGw6fqdkdk4+UoosUG03KmjWIaWlRPMexbBlO7doFAJIjfHJyFM6fP4/x8UiFuUdr23mQhMbZrFxc6dmLQ70XC9f4QjF5sBLHRm+hDdLGNg8nFHkUJHQgF53IRS+y8N5VD+LhHfQaYOJ26e2bQOEvuk1vX4WiSJI/pI6NIeLqq6kFb4JZk/6K6dAQf/1azGp7X3FdvC4ChbwQkAlW7MeVErI8WhsviKIyweKZZ55BW1sbZs2ahc2TaYLj4+PYunUrvvrqK8yZMwf33HMPEhISpqI5CkxH/LgYWvwSDMsi+tNPFfc4MzOFBcJraJkDA3g+/fXJ68pqVVpMYOIjqXcSnqa2nVhDluJToYXePYFZsFgmlKUoEZqxITm7SXkUcsfneUQhH53IRycA4KZPD2GU/YsiNJE/YZhMDMrLvYk9wRSf0YpAE7rUEK4wRXE/WXrvw+WE02hkzePgdvwhqDUZCLkcyzLoazehGEohEApK801dFeAwgF5koQobYUMeVmMfdmZXYqmxa1qUTx5TIgRWrFiBa665Bk8//bRw7Y033sBll12GG264AW+88QbeeOMNlJYqbdnfBmjZlA0WC6J6ehT3uC69VBKfHSwfvl7Qasiq1cwltdOI8Do7aRub3AzlKCrCOBLw5YFTyHDYhM2fR2xPJzyizUR+wmhpAVpaktHQMIykMNVJECNQNk01hCMMWt5P50Fmz/zywClEssFlxwYifC0WA9rsj+IyHJZo531xeYgMwjTMC+kiEdX7UrRiNfYBpmzENGzDqWk2B05JlfhLLrlEoeV/+OGHKCoqAgAUFRXhww8/nIqm6AbDskgqK0PKmjVIKitDdGur5P+84y8YjJnNcJpMkmvyTVnNH6AXvDY+UVwMx7JlmCgupmqODMuC+RsJHXEAACAASURBVPnPqd/rpT2QhqV6NeyNutsVTtA2NrG932kyYbS2Fs4df0DkO6/Cnkpxbos2E7XYdC3jGiyCOW2wLIOysiSsWZOCsrIksKxXUJrNYzCZpCfIYMOgtdrEOxzZsFiCYyIIJNijv58RtPN6rMPbuAL1WIeyBX8LSjsnCen5OImd2ZUhKQsbCkzJSYCEM2fOYPZsbx2m2bNnY3SUwKc6if3792P//v0AgN/97ndIFfPQBoioqCjlc6xWMBs2IKKvD565c8H98peIvv12ibNIzgMTe+wYnHv2AHl5CBipqfDs3Qv3I48APT3wzJ0Lz4YNmC16JmMyedVM+XdM2scDeaeXYc07CYgVsaxWRK9bh4iODsFhLf/eUw7yFDrlSEFqKjkbNhQgjp8KHv+1FT/f9wgM473CkZzJycKG7xyGe6xI0eepqcDZpZnAXwnvFvX58DD5+4eHZ2J2YSE8e/eCE80p+bgGCy3zgtRXViuwbl00Ojp8+T7HjsVizx4nCguBvXs92LCBQ19fBObO9WDDBg/y8vzVTaPAasXwodMAFgmXqrARS9Eq0bpPYB6qsBG5wzODW+ObNsFz7Jhk3Xry8xG1aRP1uSYTg5YWwIY8SahyycUcUlMJG7V8r9iwgbgHRFH41ZbP64erUFt/6p3rejFtQkAPVq9eLallHAonidzZIo7QEbB7NyLsUvZCsQAAgIiODrgqK4O3WxsMSN2xQ/pt4vaVlyO5RRkpM1xeDi5M8WNJlZWScDlA+b3JyUkAlE7E5ORzRDK8UEGPs4xhWRj/dwnmjfv6blXc++h78lXELf0tJLr05DNZlsF9bbV4ER9KNqqzWbkYFfW53+83GMCWP+m1hdsYZFRyMJtPh0wD1DIvSH1VWZmEjo4ZkmsdHRGorPTmLBgMEFg+eQQyzfh1lT1YB7EQ4LXuOlQjE73oRaZgKy9MnlDMHV1spgYDmFdeUTqWDQbqR5SXM2hpSVaEl5aXD2NoSDpWpL3C3dJCPE0nJScTZgdwLjkZIxo79BvhGCZh1qxZOH36NGbPno3Tp08jMdE/kVY4QTq2MTIBQAPp6B0IBa8awlV9Sw1aTA1m8xiOHI5AZ4+vtCQt9j3UfaIVpLGdO2HFrPpNxKxcwGu+ONSbrtio3lv0IB42+rwd/mLTw8E6K+/HkS1bEF9fr2tehJujhm9jzMGDYIaGsAkPKEKX3YjAg7Fb0HXWx3lFMjsF4vzW61gW6jZvSwXLulSTHvX4YS6EEHR/mDYh8L3vfQ8HDhzADTfcgAMHDuD73//+dDUFgPYsRRLktsZwRGwAoY1i0rIha7Gt5sGKvZ778AhExco9f8QsPCFJGAtXn2gB09lJvq5iN+c3Sbl54F/aPgbDskKb5RnTRmMUyst9G3yoWWdD1Y/h5MAitTEHvfg7ilCJ3/syxVGF8wv/CZWml1WzzcPh/CbBaOSwcyeHoaFT6t+nww8zHcqbXkyJEHjyySfx2WefYWxsDHfccQduvvlm3HDDDdi6dSvefvttpKamoqKiYiqaQgVtw3PHxQn8HoCXC15sEvoi80eotD+HvjW+erCLp2jSBgqtG4kWLcZgsSC99xDqIeJ66QUmZN86VQtZDoZlEd3eTvybmqOQtknmDH2M5JIqSV8pw2l9vw1G4ybRIodqboWDA0uu/cuRg15FpviEqZAoDCVht1/ejsfQpsjnCHdpUBr0Op71KG+kMQ+jOwDAFAmBu+++m3j9QspCtpeWIqapSVF5Sn7UtpeWCv9nI0347CM3ftN0s+BsLGnLxrEU8uSc2dyMpLIyXZqAP370QEwsWjdkXotJ3bYNLpaFezLCK6miQkJeR4J8gU5FzDwPKUdLBdJlHC2Al7VR7UhO2iR5Xn89m26gGjfNjLQ3GSgg3K+3H0PNgUVSLORowxJswb1C8XiaWUQRdour8SGhQNJ0ZNcC4TPxsCyDm25KRm+vb8w/+CAab7/tmf7KYt90MCyLpIoKiQBwx8VhZMsWOJcuxcjSpZL7R5YuBcOySFj1M9xw1ipcX4pWrLbtw1EuB0WE90SOjiKusVHz8d2fPTlQ04De4yy3cydG2tqo5HUkyBdooDVh9UIrRwtXUCDNTJUJUcFGfP1RDAxFC+YLfhPSuukGqnHTzEg1XDkaQOai1ws1Th+9ygVJsZDjMyzCKyhFy4wi7L3qcRge+hnxmVoS+/gkyUARjH8qXCaemppEiQAAvHW577uPw3PPBfVoVXzrhQDDskheuxbRsnJNkRMTiK+vVwgAXsu8/eC9uHrCKvkbTzb2VFotljGt1EWhRZNkWBZPrnXA1i0VJ2J7cqAmlkA2ZDXyOqfJ5FcrCpf2JF/Mj9jrYLP5voMWj+4ymfwKUaORwwvLn6MWACG1RU4CaDQaA9K4aWak7rQlcDL++zsYBKJc+POp8eGfANBxPgfVCVux2UgWpFoS+8RJknqh9n287cWfkAgHywBfgEmOw4fDS9v/rRYCwmQg1euDUtsTa5llIE/6TPTCY8rB8NNeTWFmczMiCTkQapok366B7j8R/87bkwM1sQSyIaslq2nRisKhPZEWc1XMMbyF/bDBG7NNikfnv1WLENXaV2okgEajUbcTmGZGSjNFC3MrXI5GLf0iN1PWGRbjEijzFT6K+j4+d10shH/y6Oujb2xaEvvOD2mL3CNB7fvQ0DCtQQwknDkTATbILGo1fKuFgL8jrFzbEx9TaRrmmfgMmM1jgqbAk4jJ+XUeTHiPSqvAt4vGP8/bk90UQ6HbDwdTIBuy2ulBq1YUau2JNH7Zjg6B+hmAKkdLVyewES9LOI/yYJMIUa19FWrHt5oZKdxcV/6UC5KZ8kjWVuzN/MRbDWwSTpMJjxX8F15vUlaFyOx8HylrzEQtW80fw+PoYDYuCtP3TVcQQ2HheTQ1xSquOxwRKClJDluG8bdaCKgdYUnanviYStIw++LyYHrpHmSIBmrMbEb3B4P4ce+LkhjpQ5/ehL+wo8RB5dulhRY6UOjdSKYy3lmrvZY2fvkx3dIi5wSOFpZl8LP2p2HFXOFaK5ZiH1YjXe7P0NBXoXZ8T2fxIn/mQpLNvrMnFpVXNeLlH94uEZb3Q1lmMj+qE4/aShEzOZfkWra4Ktn7zR5cwe3DNpQL/pgTmIen0mrxhzB8XxSmNohBjNraUXz6aTR6epTbcjBhxf7wrRYCtMngzM4mHv3Ex1RxxuPi1C7MX56CSLMZGUbpCYEzGlF5aSNO9kq1oc6eWFgsHuKg8u3iq4XxVbjSshnc3bBA2Agix8jCIBA+IX8IpTlHS/F4LUdx2vhdVJSC4nglI6kYFosB1glpLudJzMdD8Vux1TwXJKi1OxyO76mqICaHP4FPs9n3jydiZIeSGFAszHLYVmzq/rkkyoemZbe3R2Gci8bHWIxDuBxW5AuZxYWmdAD6+4ZlGdxpfw6nYu5DtqNDEak0G1MXxCCH0cjhtddO4frrUzE0pOzjcBWb/1YLAVrB55Ft24ibm/yYakMeqkw70NAwjBEVDa1vjJwNTRtUcbv4Klw+rnHfe2iTNeqLL3SHompBKMwQpE1+xuHD3oIhY2OI7OpS+Gj0ZmO6a+/3W0+ZtpGxBSvBGZX2Zn/C6euQGaoFvKBzJyfDyXHwpKXBNfkd/FzSG/YqFmYpayoR0600wcq1bPFpQ56w5z0Nkzl51CA1Y80BsAwtMSuwu6gOKbXrhe8LxVgGGn1kNHJYvtxBrOcQrmLzU8IieqGCL/jsjvN1OF/wmcQOyh9Ti4snsGyZA8XFExI7HY2VUe+i0crySWKpBABmaAhxjY1eR2UIWE5DCZK9NaqnB7FNTYhpadHspAfI/XR0y+v4d8tixRjIoeZ41dpuwZkoagtXUuKXmfVCBS/o4hobEXPkCKK7uxFx6hTRZh8oy6hWLZsmpNMwgN0ziuFYW46y68ZUx1gOkhmrw5GNqvgtisgff+tPzi7MrzOWZXDnbdG4ZYUbv2q8Eb0tvbrXYjhYXNXwra8sFqrqTyRnGV8dCwD1b0LlrP5+RJlMGCov17Vx+MvSDEUVq1ARWAH0qmb+IP8OUpvUxkBuDtJzr1q75VWnQtlXcvhLHKRBa5v0rAVS8XgtbdFa9YtW3Wwd6oVcgROYJ/Dya3Gayqt78Vi2zIFdu7xUEVr6ivYNR7e8jrUVl1Gr14n70d9JQdy/XiqSoaD9QRccgdyFApoTyGkbRFlZkuqCEy/Krq5IdHfTOWJITr48WKWTqaUFyRQ2Qhp4E03KmjVEITBdqfU0aCkiLofWo7genh6x49VmYzA4GImUFA8sFoMw1uKFGtnVRf6eKcpaDQcRnRxaK9wZLBak9PejPiMDY5v1mRzlWegk3xLDsnjOfifuj+jDSU++EF4qjxDi83JutdVrcpqGii+JdircXD4GWzc9yY3vRy1+LzUqklDjWy8EaJvS2+1GNLb5NBH5giMtShJ4uz/JyWcoC10ompZjdqCaZLCQFnh/DpsyByWhhHJ0IROdsZcgLtqJ8cQMGLbcq3C4k6CXp8do9PZBSUkyuruj0d3tTdhpa4vGri3/wJIK6UKV80ZNpc2fmkFck4j4eE9IxtTfHApV/DyfhX6KpLSI3jEHwD+jBVfg73gc96IcTyn4g/jcAS1O02D5kngBOLO5mfz8vntxHRYociL4JDe+H6crBJWGb70QIDmB+uLycI/9Ucl9co2StChJUNMyQhmK5s+ZFawmGagAUb53Dv5rxgGszvhvbE7ZiIs63kbk2bO+NiMK6yIbcPDscuAsgFHAVOEzq5Fq+fIIRNOjba6by8fQIHNgRrhccGZnw200TjkbJE3AvfNODM6f97n2Dh+egddeOxWQIPA3h6Zi8yK9Iwu9eBL3Eu/nN1gt2nwwYbdauJEKuY9QiI+E8pG8IMhEr6QfpysElYZvvRAghT7+pnMTbEeUVYLE2gZtUYrhT8sIZSiavxDOYCiNrValT4MkQEh2TotlseK9jvMM/qv/h/hs9P+i+ew/S8MF4cKv3M/hIJZL2llTk4j29ihiLV++DYFoetRwx1Fywp3baJT4AKYKNAEnFgAA0NMThZqaROzYcVr3O/zNIdrmdeLgKdyxJiUkp0vaO9wJCYDbLWH05ako9GjzesNu/fncSBDMVKhHfkw3Hix6D8O1vtMSad1bYUIl+xjYEPWjHnzrhQBACH0sSwKOKO8Taxu0RZmd7YTR6NakZYQ6rFAthDMYSuMNGxjYbNL75AKEZipA8l6QeS8B68RcCSkYj0xCpnRb2wxF7LS4DfxJJTnZDY5zIi3NA5PJ5XcMaOOYkTgO66g0y3sjqhSJZFMFkoADPACU9As0DhotUJtDNKXl46EctAzFTL47OD8F7R3nrrxSoPpw2gZxdDAbT6XVotCUDrM5PJm0WrR/D8MgglO+e2FiN4pXTcBsjkaS8WGI75CveytMWB31Djq6c4HJ4LgPPojGpZe6MDYWSTz1hhLfeiFA0l7NZsavRknTOvUsALnmdS7ZiAfOV+Pjinkh1wb8mUrUohVoPC9iAUIzFZRzNWhEA7VdYlIwtWs0DAwwRFMXwzjx9NP++482jiUPpGD1Xe+gw5UrXG+JuhwNpcMUwpDwwmjksGXLCP7t35Jht/P9Hl5iMTlISouYGA4IPrNVTTESC6iLgMmM4fAl02lhRnXNnUsMa164KpnaB/J1X8k+5hUAIvT2RguMoqRTbyjxrRYCVO21oQENDVC1HQZqX1Ta1hkYt28HyzJYty5NUvg7lNEfaqYSfw6/uXPJUcTikxHtGL8krRsmxkn1n2TEnwFEuVnyTYVvZ0GBi8irkp7OBWXqoo2jxZKPDpc0RLHDlYtN9WnYvjQ8G48/v0t9fbxIANBRWHg+oOeLwSsFTtsAjg7mYNucWiB3Maq27MKC+k1gBgbwzhdG3D70mMQJCngFc6DJUhdSJS5/zKh8vZGkigrdp3mxQGPXpAgnABr+hzYiTFBNADI/CwBQy6LQa19Uc85aLAaJAABCO/BqQstflNKGDRxaWtyqJyPaMf58ajoKUl04cyYSo6ORcLt932gyOXHPFhP23l6C6KEBSbFxAEhMdGPVqnPCe8Q+AXEbKirIVHxa0+xJ4zhgcxLvDVfqvtrcALwKSXPzTL/Pycx0orZWyVqrJzBArhQUAcjq/m+sPrIPN7YtQUPDszAaOTxXlgQbIZY/I2FUoVTM3LMHjqIijNbW+t3Q5SYplmVgKZv6qDaqzy41FY7lywXhFKzQop3S5fgf2ogwgCbpu2wRYYnJVtNYw134G6ALLVo/xBw8CIZlkVeY6vfUQzrG98bm4cajv8MHgz4NPj6eQ0EBJ9jrs4wZ2LT8BWJi0KpV5yTtpdXyDXW9XIZlYWrvw/soDtkz/UEtBFQu/OTIRQfy0In0VCfK/3OJco5arXCsrcGfugeECng25FGVDJJyRIrJp50ufzdRrvh9pMOB2KYmRLW3S3j7/UGLcAwkYo3/3WLDSWxENRLH+ryb/qZN4Mt4keb0F5k/QuWljei0zcLg2gjMmeNGbm4SzOZnA94byP4eJcI1977VQoAm6WsG71IkfYRCK1fb6MNZ+NsfaP3ADA0huaQEnr17YTRKHbAVFUmShccZjfjHll2w/dtWzLL3e7X6sxthOys1FdjtDEwmh6QftUb10BJoAokKIpkrAAiRIM/Zz6IHu/EurhB+kxfXB7OZzrQiFJVpb0fkV18ReXdobTj15XMALlPcQ3KIiyHJSF1ejBGj1KnLsCyi161DUXeHcE0cwkhSMmhKAe+wP3gwRuC3lysHVaWfYt7PXqe2V8zbrwV6hKMWRU0sVEyw4iX8K+aImIA9x46BeeUVby6DTMs/mXAZrv10KzpFZsnubuDIkeCURHk/JiS4FWyi4aSN+NYKAasVeJDAJujKykKffRbxN8Fq5Wobvdk8hmPHYiUmoXAOvBgkjYdHtM0GbsMGYPNmolYmjmLo6pqDbvuf/b5P3o/B0ibr/T3JBzLzrbfgcbrAuLxmIAOAv+HHeBCP4mMsQSZ6Ub2gEQYjmcCYVFQG3d2Y0dZGTKiStyEHx0ASAjTMihjBdZ6/+q3Xa7BYENHRIbkmDmEkKRk0pYB32A8NMRJ+e7FATyrbhEiHg/h7HicOnkKMFZrq5tIUpw8/nIHTp9Uj1kgQC5U6VEuo4AEgoqNDkvcgNk1VlSWhs0fpl9L6bjXI+1FJGxEepzDwLRUCXidsNDo65kDMJvjX7z+I+R3NyDn9CYAlit8Fq5WTNNa4ODdKS+0wGjns2eNEZaVr6vnj+VT+668nxkJH9PUBIGtl4igGrUhP5xSaOGM2Y/v2wJ1/evwzJHOHOGGNRxzOYSMehmHScz1hKqbGoqhFkpASquT302pH0Bziq6504vn418EMZGEivVB3zYVM9FKVjGCigPw5UwFvSGnl6mhccslsjI1FqppyaIrTmTPkE5k/RU0sVDLRQ7yHlLTFsgwOvqO+XYbLdPs/tBFhAMkJ2+HIRm3nL9HQ+3LYirkYjRyefKAdpXfmw855beATE5GoqEhCQ8MwCgsRMu+/3gxfzmiEY/lyIoGYZ66XX19Lgpw/mExOVJYen9byfVo2Kh4sjFiEzwMuvyn8XbaxyO/na0f8H/x/eC9mJYqKHIKDl+QQv7/WrTD9kEA19WWnUc0XYjOI0zaIfZ8acZ/jUWIUECCda5u6jCgilJnkcQLz8AA2obc7At3dPuFGM6dUlh7H0b1zYZ3w1XiIi3NjYoIsBMSKGmkNiIUKrTqgPFmTZRn8dE0ihk6r519Mhek2HPhWCgF/WaLyYi7pqU6UNxAcbjrBsCz+4/4+2LlLJdd5rUqjmdQvAqWIoMVoezZsAKA9ikGMzEwnLr3UhfHxSOF0s8hSN63cKXpI7JIjRsClpMJVQE540/pM+cZCuj8PNqzHi2h2rEZ8vEcYq2BMZWNmM2KPHZOYhJwmExY03C2pTaFor8gM8mdKFFB6OgeWZXDfTWdwR++9yEQPhjEL3UwOsjkf4d5EZAKOui9Fx+Rpohc5imeRThYMy2JJRQmaJyCsxYz4M2jPXYW2T5U1OmJi3IKiRlsDW7aMCKdxUnVAT36+QthbLAaqGSgN/RhExpSZbsOBaRcCe/bsQXNzMzweD1atWoVrr7027O9UyxLFZHQdX8wFANHhFggMFgv67GXEv4XyKBlo3Dwt3G12Xh4wNKQ5isFf1vR0c6eo+UDEOI8ozPX0AUOQRLbITysMyyLCboc7JoZoDyedIvyZXMTzIZgKY5zRiI7n9qD7thokjPbrIuTjoeZ4f7HmFF7s/VfJRtrJ5eCDjGuxJP80uPR0VNjr8FzTJX7fI18DvMksD/BllduBkjPvoA1Fit8XFTmEuUZbA/X18SKhmoknEt7EoxMVSDr+kfemS5TtpCmN6ejH8/F348Wr/jSlNA+hxrQKAZZl0dzcjMceewxRUVF47LHHUFhYiLlzyeX9QgWaE/beLQY4K0zExI9QMHA6bQN+iseHZjiCCTdVow3QGsXg78QxXeX7AELlrKQkMFYrGLsvY20CMzGEVBjhv8IZycnsjo6GOyUF7sxManQQL3CPr30SXPegIkciVKYFlmWw7vaL0dE9ecwUEfKFwvFe3LZR4VzNRRfaXT/CqV3Pg2UZWGsSERPjhsOhXsNK/s00ZaE27Sm0MssUQkmcH8GvAROsqEM1MtGDXmSh0VYtRLoBAMOOIrHkuM8X9te/Ivkf/5AIe5rSuBr7sfqq8/jeNJQADSWmVQj09PTgoosuQkyMl3dk4cKFOHz4MH7yk5+E/d2XXOLG6OScWbDAibg4D8qfWITFBXuwsaAaieP9giZsRR71aFlfH69JMLAsg752E9Hf4As9nB2SbwtnuKlaFIOaqUIsRDOjnoQl8mPku0XH8Kgo2EtLFfeGMjmIWAyEYTD80kuIr6+XcNJsYH8FDCnTOOWnFaKT2enEuX/+Z7+mLc5oxKltf5BRQYQ2KixUSYi00wiJ5wkA5qKXaJKJiXHj+98/D5stBl1d6pFwNGUhx+RBw9PqJrKMDA4mWLEfV0qE1Krj7yOS/bOwwWthRjWbx3DkcITEJDQPJ/Bw5h8xZn6C2MavFTzTiK6uLs9dd93lGR0d9Zw7d87z4IMPel544QXFffv27fM88MADngceeMDj8Xg8Docj4H/Hjzs8+flujzcX2PsvKkr6//x8t+f4cd9vSkpckr9r/Z34X0mJy2NCh+dLzPN0wORZh5c9V6DZc2PUG57j+054HA6Hh+M4v20vKXF5ioo4T0mJi/ou0jfybdP6DPE/f+1S/Dt+3OMqKfFwRUUeV0mJ5/i+E4r2zMOXng6YJB3qKilRbXtQbXI4PK6SEuUgTr430Hu5H/yAeB9XVBTQXExIcHv27Qt8fsv/FRVxkueb0OF5Ges8H81a4f2W48eDmm8j15H7aeS6Euq6KSlxeU6c4PzPw+PHPe78fMmP3fn5qm0Wt/2NhJ/5HUOuqEjT+B0/7vCUXDfiWZH2D8/P0po8X153p6Z2hOJfIHOd9I+GaT0JZGdn4yc/+Qnq6uowc+ZMmEwmREYqj4yrV6/G6tWrhf8HU76vsjIJHR1SL7/LJYsU6ojA1rtOYEt8FZj+fqz/0oQWKDlSSL+rrHQRNSabLQU25GE19qEO1fgF/oReZKJxcTUMl8RiaGgIqampaGs7TdSCfVqVT2NsaXETj/WnTzOYPz8Ro6Pe7ywsPI/a2lGcPg3FMxobI4RIFJq2radkojhenkft7pvRYZ8nuU9ccYmHi2VRWelSjA+pXwMp45gijuEXwcWyigInTHk5kltalGUQy8vBTd7LsCzmfPIJ8V3nkpMx4qd9pLk4Ph6BZ55x4ZJLQmNiSE5OAuB16ko04zMAGgD3ZCU7K/Ik86601I6KiiTJXPngzUH8reA3yMmFYOJiHirHjCOtiO3pFO47m5WLcw+Vw1bhAgg9zrIu5OREYPNmaf8oustgAPPKK0isqcGMtjYAwPn58zF6+jQ4P0kGBgOw4uJOoE35N/F4JyUnQ+nyVo6fwQBsfg4Akif/LcIQsdGhR6hKll6w5SVXrlyJlStXAgD+/Oc/IyUlJazv01QHAFZUHbgecQ5vRMXVaMF+fCgpFEEDze7Om2hsyMOtqBeuF5smwDMhqvH2a3X2ko7g7e3eYSY9w+GIRFNTLNrbo0JCVkc6XtOS7+RsoVx6eljpM/T4IrRwwhgsFgm/vfDb+HhNdOB6vzUQM5nY/0VKjoq22RBZ8zhK2hskc2Pv3pmKMEzrxFxsPHIj6o/cKgnpHX3tL/AQ+kndLKl964lqbxds9moOejmiTelEISAeb7107tNVnS+cUPfUTAHOnDkDwKvdHz58GD/60Y/C+j4tYY51qEa2Q5plycBFtX+KQbO7m81jMJmkpGRyO6iXtz84bqFA+Im8N3XDsbYcKWvWIKmsDAzL0u9VAcmZR3OGi/uTX3hqGwfLMigrS8KaNSn4+c+9FNJ6MGY2w2kySa6pLXjeSX5k8+soRT2KK76LsrIk4b00xyVXUKAp30GP74YX7o2NcWhpiUFjYxxKSpL99gGfhFhcPIGFieQ6yX1tQ4o5Q4vD5wW3QP8AXz+d2rULI9u3C9+uZc6Lv48fW3Efq5I8+oGW8eaF/URxMRzLloErKaEKmEDH4ELHtJ8ENm/ejLGxMURFReGXv/wlEhLIFZ1CBS1hjvkx3YAo0s8KE67EfokzlwSTyYnSUjuxQL0WagM13n6tG0Yg/ESCmaD7pEBpG2jyFknb3ogqvB+3SpLwk5t1Fg8ueg+O8WUS7ZEWjlhaapeccLRwrJP4gfQyPqrlXCRRThYu2cZDg1oGubztd9qfC5guOy/Pm4SYVJYCKHMB0aejfoNYcDMDA6qasVY6D7U+TunsJLZDSzgxv8FH1jyOvrYh9CETjQXVWI8UGEVlXsQRUXdMaAAAIABJREFUcampqYK5Tw49odeBUmlPB6ZdCDzyyCNT+j5+YpaUzIHNptR2srOduOiSFKDJd60adVQBsHDmCTwRW40cphezjHPwszsfw6HeOcLfxUla/uK91Xj7tZKk+eMnIglAmpkgkOQt0vE62wS8uqUPm+pnSTaDJOPDOCX7PZ3fX1/ug1qNBPk3qS1Ytfc+S/hWUrKRGnJyOHR1MXC7vXNxYiISW+48jf8V8VOJnf1UzH3wUpxIocdMRjN9NBZUS+Y7j/h4ThK1NA8nsBFVwv9PJlzmNylRS44DrY8fr4nErvZ24m+0hhNbkec1dQ1NPr8JeKudHiJrtXp9NSShpvU07q8+x4WGaRcC0wGjkcPevS5cfXUEsTKYG/fD2f6RMIg9FE3pu0kncCj+St9iPQi8iKMS34GecDwSb39cnBudnV5t69kHjmHGI08gZqgf/Uwm3syphtdJ5YOasOA32JqaRBw4ECPEbevhUPEHmi09y5ihuRgLaePQaz/XWhTd34JVe6/8W0cTMrBxxu/x3xU5mkKG5Rsojzt6H0EsOiXXvObJZYp79YT90sZmPVKw+xOnhAMqM9OJP/xhBP9R/hkGuzlkTpbX5OtBu+PiUI2NAZ9OxKD18VBbX1A+F0Cf9u7jFPM568VCTetpXOvck2O6Tg/fSiEAeI/ItKMqB+liSWcZYuWfrQlViO3ulFwTMzTy0Kqtidtks0WhvZ2B3c7gyJEYDB+xYsGbtyCPm/RVuIAl7x3G+pv24In/nKX5CG40ctix47Qkvp9h04nfF2jyllrCmRh6nGx6cx+0ZiXTFmzy2rUY3rULGRnkgjX8e/lv1UvVQdqceJCE8kZUoSVmBToc2cK1QPIJiGPDAhERUlNkREQEMjPd2LYrRiEkufh4DL/0EvqeUFI3AIDNFkU0idJAHVuuj3h9Im+Bps2RYVmcOngKJHZW0pr0l1Oh9TQeSEa8mjKitfZCoPjWCgFA/agqXix3swxaS5yKwV+S0kXcPOUOZD3aGt+msrIkScHwOlT7BMAk5uMk7uh9BBbL85Lv0HIEF9/DsHfDWdIasoL3WqB309RTM4BlGVR0bcIAOKFAPK/BygUbbcFGd3cjuaQElVteR1vbZX7fq9dcRcto9XLrKCkd8mDD7qI6VMVvCTnLrMVikGR9A0BPT9Rk2+lRUrTNu72dkcxd2rjySkBnJ6MghTOZnCg+91fi89tG5uMiP9/Eb6o5Q3UgCQHSmiSdSEywovzvdyH9O61IB/DRwn/CQwVb8I/xedQxCCQjXtUBHipSMQq+1UJAK2jadbSFHIImDn0MNPtTXt6QZrLJRG/Q4ZPTUddV76YpHwMax7ogXLp93DKtWIp9WI1sExSCTY34Ldpmw6L6OjQ0POvXuanXXEXLaF2KVvx2znO4acYhiU/AaTJhzu1Xob6+FIynHxwyMAYzOAQ/RrS2d33pwpc/rEDCaB/GE+fCsO1JZCz1CSiaY1teB5k0rqRwaHnVOcuvy/D9gb9J+ucE5uGptFqQqzr4wG+qehiB5ULNBCveQRFyR3xRVSkH92J71lU49dpr1PWhN+wUmF4+rf8RAhpB0q5Jg302KxfvLXoQy8YdSE/nUFl6HIssdbrsfKTyhjTa215khoQOgjMa8bH5WZ95xhLeGOhA8gG0cKyThMtJzMcPZxzB8gIX7odbEhnij0yOGRjQdLKiacUsGylU4RLDbB5DX9NDmG+XOuTn4ySe/e4zGK2Vxt7bS0sVBc1D5WyktT3r07+jyPOq9z+jQOctH6L/1QZBEJCUo85Or/lSDvm4esOhpdcUVedyc7D6yL7Jk1KvwK90TepZbxizypriN1U9jMByTrE6VCMXyrDaqJ4eVft+IEqV2ukh3Jv0/wiBAOE9yi4GkveinKvBkrRuRJvSMGY242FjEoBTAUcJGCwWPGpvw2FcJmgwVdiIy/EeciFlnfxj5sN4IgQ8M2rmGa0myXDa+NUgdqjd/qUJbYTs7q/Oz8brTcBHssgQfsEmr12L6G6lbU+rX4QWedXdHS2pwsXDaOSwsMBGPEkmjvfjlMx2n1RWphBU3Tagcq0DbE5KUIlLpLZnMv3YypX/v/bOPT6q6lr832TygDwIJIFACHkQJAhX4IfgJ1UxyKsVa22il8IVhSsWLaYq2BtEoAF5WYQgGvDSHxQwUWhRY0XRhkcTUBGBGBR5Q8gACZEwhDwJmcf9Y5jJPM6ZOZPJJPk4+/tXMnPmnHX2OXutvddeey2r4+K15yl9cRU9D2ZZ3YelgUxP78p339lfw/a5OgqHtpRrUlEMT5Y2r6/dF32a1T8+bDVLkupTlkpVaUZg28JOQ05fBJmNus5G6ErXxUw4mj20TkYxeYQRaAHWCjOJPLYRp2pi21rrju5KlIBareLll1WUlkaw/sw17rIZwURThm7QAK72vrs55nnYAlYuCms1v7Cce0aJS9IVH79araKuzscus2RLXGe2htbZ7m4p14QuNhbN9u32yeVcWBcxhx4/1o3SMutUEHJuLiU7Ws33aeMuMO9dudTPvC713bc+fDZoFok137sUXSI1op+W/yIJdaWUEMcClnCZaHpTxsTr0n56E0rXbhyFQzuS6/9XvkDn/ResfiPVp1xxyVgOXuLiVGRkXCc2Vie7rwJaP+Nte7hkTQgj0AKU+rOV+vms8wKpOEof7sJ6BANQ3z+Vquy3iQQiMS13KTcAjkLQ3E3X4E5ai8BAPSkpjTz7bK3LW/KlDK1UhJazeyohgZlJh6isu2KsJzwsj4hF04z5cRSG7iVQQoLmPKX8QtE1pRTV6ej7mFu3nvLHu1i1ga27QGrvyoXLnVl2+X5yWQ+A/8GDaD78UFF0ie2IvnBYDx6t+4h8fslNi+w6/745lg/VPrLPRekGMalwaCljYR3AoKbbqALJ69r2KUulerHUh8yfXuBixFCiVvhbyWP7PlpuQlRlZOB/8CD+ZdaBHtrevT0SNGGaPZjet66zZxuf+/LlygoytxBhBFqAUoWpNErAVoFKVTxq6B3v1ovnzDXlbp4Xd9JamGYDxoRlrlVDkzO0gyMvEomOykp7uWxdE9aKoDtwFwdOjWYbGhLUJYpdeqErVtD7ZhpIGAHJgu42o79zIXfx8I+ruZBvX3ZRZWMw5PauWAYl+JeV0SUzE3bskDxWDrVaRQZvcIlO9ufX9WTFinqHayRK1lAchWjLEbpihWwRe11UlKSx/j7jnduBAv7GGVOR9XvlePASi+bDD60T2A0bRvWiRR4boUv1U8PRo6jee89j1xRG4Dae8GcrnZLaKlDLbKOmBbH/NfyZlYRZLWq6gjPXlONpfLNXUq6d3E1rUVQUYKewFVVDkzG0/UZGsCOj0m7W4Wp4Zy7KXXqqK1cko1H6Bl4iI0N6T4Cl73h+ele7MoZWCknB3hXb8OSAoiKX35gVK0K5VGFvAEy0VhU8VyumyRl8fWAgdVOmSBrrmUmH7PdjlF6i8T/nEtFHLbuGZLpHXWws1zdtUiyju0j1U5/z5z1aelUYATwXs67UzyelQG2zjVKG7AhMiQGT60BNpT8ByqbxjtrJ3bQWcjhTOHVTptB5xw58tFrzZ6YCNUpdE45mMSqD8tA9Xc+eJHDAbi3n1ZQv6Rr7Z/Nxcs/L2WzK2d4V27QOLcVZpt3Tp42bwTwZPSbpgpMx+I0pKQTn5koa68q6K1im27DNkyW3htReRePbI1RUGAHcj1l3NJVVEiWgtHavlEJUasDkOtDeU7H0uh3C6Gxk5qydlLSJnLFIStKSn29fzNtZZwzOzbUyAAA+Wi3BublUJSc7vSeVWs3yi7PRUWHerGVZ4lGHdLv5nT5N1/R0K6NuKuqecP68eS2nKS4OzaJt5tG4o+flSsSU7TvY5/yXLL8y3bwpzsStYcMkayg4wpmhrqxUkZcXpMhd5wg5YyjnuqzKyjLPrE0L1pcC+xLBHSy78CxJEtcwzoyaN4tJ5cmyXUNqz6Lx7VF6VRgB3I9ZdxdTh16zJhK1Wota7culS/YGQUoZKDVgNRkZ3PhXMb3qS8yfnSWRWXVLGaYw14uzdlK6U1nKWICx7oGSHcGWuDNyMimblEvNyiaZbxjLLoiLISOjhhqk9xGoKisJysvDv6iIH7K2syR3EFeuRDB84L9Y0G+OVXlSy5mfo+flyq5osF00DSbicS2Wewq1vXtTvWgRVQ6SokkhJYevrwG93v0ylSYc1c4YLOO6DM7NRbNtG9cyN/NI4XxjCo1GIB++C1rLHorsjOCCYXkcODXafB25TZeDIy9yb/9G2U2IbYWUC9nVpISuIowAnqnJ62oyqARKyOUltIZSqgf2IlW/nK/K+pu/l1MGcop5//5AHn/cMn48lvSkz0n7brHVxptSEuhdIb3YZktrtZOcsXB1oRCkR04lxDFXvQz1447j5+Uii7bEzCVw2xq7PFKB+/c3FyS/jX9pKaVPrSav7n0ADtCPT+K2yY6QHRlSV2aYdu0QG8u1Dz6wcz2WkMATE+SToknhziYwpUhtFjMZla0ODLsuNpb5wVmcb7SuB1ZS34t5wat5vy7N/FlTXBwRi6axjeZ7kcuT1W9kBNuzr8luQnSVlhafkXIh+y1f7rSSmjsII4BreWmU4OomMcuSjCqMHsxdvYuYNf4zhzlKQF4xV1aqzAutpk5PfB+e/M4+bFKpEm/tdjJhMpgRV66Q27MnNauUx0fbjpxKiGOsXwHnL8WbO/u33wYwaFATNTW+1m4HGWWTHHuRa5autNsuvYjHH7czAgBhddbncTRCdmZI3ZlhSrkeV6RLJ0Vb80gxG0eulx2cxMY2p/C+ckXF1avSRWZaOlCS2yxWWqpi6k+O8z7JGVJ10mjq41Lt1t9icZwnyzIsNy5OxYsv2u/wdgVX1xhtsX2OkZGRHi1jKYwArvn4naFSqyV3nzpKJSs1Iu18+QJZ98ynapP76wktdTfY0prtZMLd3Ou2I6e56mVGA2DB5ct+VgnSnBWFkfO/yvlrbctkgnGELDUa9JQhlUN25lHpb3ZnSbW1lCLz8zNY1dV2R265zWLFxQEU6R3nfZIzpD3i/J2uv9nuH3j58v+wu3IEjfnGdjLtE8jKqiI3N7hFZSRdXWNsb4QRuI3tCMxU7s6Vl8Cs0CTSD4C8n9odv7atYj592k8yNt5dd4Pl9VrzRXYWuurStNpgoPxmuPR3FjgqCuNol7CUv7Y8KIH59Yvtjg0J0cuOBuWegSfq18opTFMoqX9pKdcyNzM/OMvqulKKTKv1ISamidhYvZXcLcmDL7VZDLBbdzhHP+bGbGHNtkCrspUtNaRqtYrMzCEcOrSdGzd87a4HxvfjqafCrRLh5ecH8u67GpKTm+yOt8WTdbI9gdcbAakXuIQEl6dzarWKxv9802qR0RZXR5jOIgJslcaqVVWsWBFKXl6Q3bGt4W5wBUvZBoeeYzEL6FJTbqckHBlAJfmMbGcSfTiKVOpgW6SKwjjbqi91fPmUuTA7hrjS5pTQN4J7kVe/gPzSO61+bzkalCpo4o4LQQ7bpGgAIVQzg/8FjO4z4yJr8ztTVORPeLhe8nyxsXq2b2+uB6dkJifVxxKGRZqN4f4CPyqvB9hdy8TF2GR0sc3XbOlgRq1W8dhj4VbFc+SwzYRaV6fiyScj2LPnqtPrtHTtrL2K2Hu1EZB7gaU2mDiazpk68N8uyY/cXR1hOjpepVbjm/kG+sJrpDXGMJ/FHCCBoiJ/srKqHI6SHI3aWquykaVCi6OEd/kN3S3C8iyVhCMDqCSfke1MQmqzlhS2RWGUYnt8T+CjrGJ6Pfm75sirOhhx6CD72C27CckWT7kQYmN1rF/fxKOP+pnz9dfShafZbN7PYFmoxnRdnU56xBsSYm0cnM3kpPpYp507MYwbR8K8eWRnxzJ5yDX2OTDcciGyrrbLihWhigyAHPX1voqeR0tmKq2RwLGleLURkHuBbTeYmHDWgeXSPTfFxDj1cWuTklA1NGDQ6x1uTbfsVN0xFhw0hTWWliaQmxssO0pyWL0IWq2ykaVCk6tfXPzIGtaP3Mj8KXO5S8YAXpnteFqtUqsJ3L/f6jtT6uBXI9ej7j+KkBA9x475WXX+1vbBD8hdTpBF6C0Yy0FK5S+SGw3KuRB8Si/SNX2uW4Z545oG6uvDrD47Rz9eYjXfSKS4AOjRw4DBoLUrNnPsmJ9VamxnrkypPubb2Aiffkr4Dz+g2baNaG4gN3trzWflbBOcCdsCN5Yocem0ZKbibgJHd/BqI2B6gW0zJXbRXZc83lkHlsr50xQX59AASClmP5ni2uA8YZrJ9y81WnFYvej237bftST3jGVnk4vLrqyEvLwgdu68mwfvOcyqpPkk1v6gqHJVVJQOSi4aI6okoiYSKGXjyPVUZRsVi2UpzdasyGVCThH2DbxkjGO/TXzvBpbUzSbicfssn1L3GkcJ2ScfIqio2cC4WkNApVZzZbcBqXxGtsnhrK4dpyUy0sfOCJSVWc9OnLky5doGjO+Xb+YbLBjWnUP5iVazt07UM67TPhZk9aB3rHzhH1dwtgkuMFDPuHEG/vu/NXZrAiaURkO5OlNpz3UErzYCup49m1PyWryAMfU/0bu39SjI0YjE9HLZ5vxRxfRgwLaXnNYOcKUotVyniqaMOEpYpp5LxONqdD17UjdlCsG5ueZRZPmpOjLJMRs7U/idqqICDNLRGoGFhWhLSlzKYmjZ2aRmRyXEMZvVgDF53Bf7IzkVlMnfc8rpndzc4R1Nq1ULF6KSKQRj60qz7JDu+F1VarV0MjEZRXhHSgSpwfVUVKjoGVLN8mOp9M//yvy9pUKXutfVwfPoVWc9w1BSsNwSR0nt5AxAUJCejIwaZs/uKln+sqKiORrKmSvTUeU2gDOF1/B/fx47j03jtbLnzKk2FjOfhJulNM12PIhyhYyMGj7/PJCbN6WUexMff6xh2LBuVFY28e67Gp58MsKu5KUSt2pLcDeBozt4tRGoychgXn455+qs/ceXGnswflAD99xzS9Ho0bIDm3L+BAfrSOqhI26F1uFvXY0MkutUNwilwG8s8ZfOm+PjbXPqqHzi+JL7zH5qc/idWo3vzZuS5/VtbES1cCGsWiX5vRSW7SE1O3qRNfxkk46hpL4Xq5/6htW7b5k7k6NptU+5dBFyXWSkrNKQ8rsePOjPf/yH1m4PgS0qtZrwxx6zSivcOT8f/x9/5Ppbb0kqQv2i/yE71mh8uqanE1T2ldU5LRW61L2OLpUuOuNKHhm5pHadqJc1AgMGNBEbq2Nw6Dne5Td25S9XhnwCdAWMayQ/ZG2n5sVVhFRfobZLT0KzXuYWsaxID4ULy8nuXEx0Q4nktc43xvBR7gDe+XAlm1wMrXaV2Fgd772n4b/+K8KqjkV0dBMffmi9AJ+c3MSePVfJzOxirpeclGTsS0rDml0xFEoTOHqCdjcCn376KXv37sXHx4c+ffowc+ZMAgLkIwVaE11sLKVJd0p2tNpaXzZtumb/hQSWHbi0VMXJk/7U1akoKjIW3HYU5eFqZJDUyOtSYF96ddURX2FdiN42p068odTKT32Ofsz3Wcp7l6Y4vD85hQvyI+tmhRbNypBPWMwCyosq+b6yD/mMlzzXlbowuw4vN6029OoleY7GkSNlO5qU37WszN9qvWDnzk6kpDSyaFG1fYEgm7zyYCw1aEpnELpiBZ00Gm6Gh9t1eCXG3vZe/dOVF52RQy6pXbVPF3YYfiP5m7g4430vZoHVgj4YXY+LWUDT7Sq/arWKSbOHUnrptuO6GqL/2ISPj8mVlEQRe1jNLIZzhD4W23XPksh8FhO3/yxkgL5PH5AIr25J8jQ5BZyc3ERBwVXF7sFTp5pDrvPzO3PqlB87kzKJcjJ7d3X/iyf24CilXY2ARqPh888/Z/Xq1QQEBJCVlcXXX3/NqFGj2kyGqDh/yY7W0lQI6eldKSqy3l7vKMrD1cggqTBF/4wM/t/s2aCgr9imGi43SCtTS+QUrrOwxub77UoTb1OvVjF/UjiNMhvboilT3OF1CxeiP3DApUpgShYGGxt9zZ3d0nA78m2b0hlUZWcTGRlJlcQ6RUvCgFtSsFzqHFJJ7Y7OeYcfXi7nQoP1s42ObnZ5dKmRNv5daq9gGh7JGVZLSkkgjY8tXEvWaUvur8wlfNJ8tElSKeBcT57mTAEr9dfLLdYurktl6+3CPVbXtXh3XXXzQtuFb9sivQTehuj1em7duoVOp+PWrVt06+bpiprWZGTUEBdnHQ7nTkSCqws8JqVen5qKPiWF+tRUpz5Qk8K5tn07VdnZDkMtbbHd3WprFGxpiotDt3Ch5HeOIhqkMI12xo9voJevtVI1pUFW3OETEszt1njvvYrazZU01rb34ah9lchck5FBU1yc1WfOFLrlu6H0HqXO0bRzp905oh8dwta9vowf30BkpI7ISB3jxzdYuUWUGC6lETfQnB59DHt5klxKSTA/d5PCdLWNpHAWAKEUuXsrQ3pQZNku7ZESuqX4GAwyK4JtxM6dO9m6dSsBAQEMGTKEF154we6Y3bt3s3v3bgBef/11bt265fZ1/fz80N52l5SUGBNalZf70KuXgYULdSTYl6dVxNSpKrZts395Jk3SsWWLYyVkKZPLlJTgP2ECPuebXUIGPz8rl9AFv0RGaZvzpvcNucLu2mS7zIv6uDiIj8fQqxe6hQvxu+MOSbnGj/ejsNB+HJGSoic/3/F9lOy7yGupxyivDTUvBMb3VdG0cydKGr8lbVVSAhMm+Nvl0pHD6j5KSvAfOxYfG3eFoU8fmnbtMsvsUK6SElQLF+JTXm5u2xa/aC7Q4vdK6p3q29fqGcm9787oRD3j+RdvMsv8/ulTUtCuX+92G/mNH49vYaHd5/qUFLT5+Y5/a9FWsn351zd4//gwh+2imjoVlURsp27SJHRbtrh2P+7oBQvk3OztagRqa2tZtWoVs2bNIigoiKysLJKTk3nggQcc/q5MwjfrKsZsga2flEnKRRIX16Ro56e7Mpn9oLfdRObooNv/n5wylyW5g8w+x7lTTjJ0dpqdu8F2tCknV3p6V8ndyampjssPysnrSoRFS9vKMlw0JETPjz/624VBmrC9D9noIAVt1Z64I5OzZyT1vkdHW64JGOndW8ugQU3U1vrSR/0Nyy9NtRt81KemtsoCcNf0dILy7CvEKzm/ZVs56ssJlDhsFymXlLNwcSUyuUN0tHRJ0nY1AgcOHKC4uJg//OEPABQWFnLmzBmeeeYZh7/ryEYAWh6X3h4KRIkilpPLHYPnLq3VVsZcMl0oLAy0ihhp6X383IyAEqTed0C2D6jUano88YTVSLqlClIKdxSwbVu5s8fEnUGOI5laSoc0AmfOnOGdd95h+fLlBAQEsHbtWhITE3nooYcc/q6jG4GW0hFlAsdyeXojVktkagmtdR8d8Rl2SJlqatDOneu2gpSjpQq4Q7bVz9kIAPzjH//g66+/RqVSER8fz3PPPYe/v+P8HsIItC0dUa6OKBN0TLmETMrpiHJ52gi0+z6BiRMnMnHixPYWQyAQCLySdg8RFQgEAkH7IYyAQCAQeDHCCAgEAoEXI4yAQCAQeDHCCAgEAoEXI4yAQCAQeDHCCAgEAoEX0+6bxQQCgUDQfnjtTOCVV15pbxHs6IgyQceUqyPKBB1TLiGTcjqiXJ6WyWuNgEAgEAiEERAIBAKvRrVwoUzZKC+gb9++7S2CHR1RJuiYcnVEmaBjyiVkUk5HlMuTMomFYYFAIPBihDtIIBAIvBhhBAQCgcCLafd6Am3Ftm3bOHz4MD4+PoSFhTFz5kzCw8PtjisoKOCjjz4CIC0tjVGjRnlMppycHI4cOYKfnx9RUVHMnDmT4OBgu+Oef/55OnXqhK+vLyqVitdff91jMrkiV3FxMZs2bUKv1zNmzBh++9vfekymAwcOsH37di5fvsyyZctITEyUPK6t20qpXG3ZVrW1taxevZqrV6/SvXt3Zs2aRUhIiN1xv/vd74i9XW0rMjKSOXPmtLoszu67qamJ7Oxszp8/T2hoKC+99BI9evRodTlclaugoICcnByzjvjVr37FmDFjPCrTunXrKCoqIiwsjFWrVtl9bzAY2LRpE9999x2BgYHMnDmzddYKDF5CXV2d+e/PPvvMsH79ertjampqDM8//7yhpqbG6m9PUVxcbNBqtQaDwWDIyckx5OTkSB43c+ZMw40bNzwmR0vk0ul0hvT0dMOVK1cMTU1Nhj/96U+GixcvekymixcvGi5fvmzIzMw0nD17Vva4tm4rJXK1dVvl5OQY8vLyDAaDwZCXlyf7Xk2ZMsVjMhgMyu77iy++MPfFL7/80pCVleVRmZTK9e9//9uwYcMGj8tiyY8//mg4d+6cYfbs2ZLfHzlyxLB06VKDXq83nDp1yjB37txWua7XuIOCgoLMfzc2NuLj42N3THFxMYMHDyYkJISQkBAGDx5McXGxx2QaMmQIKpUKgP79+6PRaDx2LVdQItfZs2fp2bMnUVFR+Pn5ce+993Lo0CGPyRQTEyNbHq89USJXW7fVoUOHSElJASAlJcWj13KEkvs+fPiwebadnJzMsWPHMHg4VqWtn4dSBg4cKDljM3H48GEeeOABfHx86N+/P3V1dVy/ft3t63qNOwhg69at7Nu3j6CgIDIzM+2+12g0REREmP8PDw9vM8W8d+9e7r33Xtnvly5dCsC4ceMYO3Zsm8gE8nLZtlVERARnzpxpM7kc0V5tJUdbt9WNGzfo1q0bAN26daO6ulryuKamJl555RVUKhWPPvoo99xzT6vKoeS+LY9RqVQEBQVRU1NDly5dWlUWV+UCOHjwICdOnKBXr15MnTqVyMhIj8mkBI1GYyVDREQEGo3G/Kxbys/KCCxevJiqqiq7zydNmsSIESOYPHkykydPJi8vjy+++EJRbWNmerPkAAAHPElEQVSpGUNrygTw0UcfoVKpGDlypOw5wsPDuXHjBkuWLCE6OpqBAwe2q1xSo7W2aCsl52iPtnJEW7eVUtatW0d4eDgVFRW89tprxMbG0rNnT7fkskTJfXuibZyh5Jp333039913H/7+/uTn57N27VrJgWNb4qm2+lkZgQULFig67v777+f111+3MwLh4eEcP37c/L9Go3FbgTiTqaCggCNHjvDnP/9Z9oGaFqfCwsIYMWIEZ8+ebXe5IiIiuHbtmvn/a9euuT0iUfr8HNEebeWMtm6rsLAwrl+/Trdu3bh+/brsqNrUVlFRUQwcOJALFy60qhFQct+mYyIiItDpdNTX1zt0ibSVXKGhoea/x44dy3vvvedRmZQQERFBZWWl+f/WeI/Ai0JEy8vLzX8fPnxY0o87dOhQjh49Sm1tLbW1tRw9epShQ4d6TKbi4mL++c9/MmfOHAIDAyWPuXnzJg0NDea/v//+e3NER3vKlZiYSHl5OT/99BNarZavv/6a4cOHe1QuZ7RHWymhrdtq+PDhFBYWAlBYWCg5W6mtraWpqQmA6upqTp06RUxMTKvKoeS+7777bgoKCgD45ptvGDRokMdnAkrksvS1Hz58uNXbpiUMHz6cffv2YTAYOH36NEFBQa1iBLxmx/DKlSspLy/Hx8eHyMhIZsyYQXh4OOfOnWPXrl0899xzgNEHnpeXBxhDRB988EGPyfTHP/4RrVZrHvnccccdzJgxA41Gw/r165k7dy4VFRWsXLkSAJ1Ox/33309aWprHZFIqF0BRURFbtmxBr9fz4IMPelSub7/9lr/97W9UV1cTHBxMfHw88+bNa/e2UiIXtG1b1dTUsHr1aiorK4mMjGT27NmEhIRYveunTp3ir3/9K76+vuj1eh5++GFGjx7d6rJI3fff//53EhMTGT58OLdu3SI7O5uSkhJCQkJ46aWXiIqKanU5XJXr/fff5/Dhw6hUKkJCQnjmmWfo3bu3R2V68803OX78ODU1NYSFhTFx4kS0Wi0A48ePx2AwsHHjRo4ePUpAQAAzZ86UDUl2Ba8xAgKBQCCwx2vcQQKBQCCwRxgBgUAg8GKEERAIBAIvRhgBgUAg8GKEERAIBAIvRhgBgddQVlZGRkYGTz31FDt37mxvcQSCDoEIERV4De+88w6dO3dm2rRpbp1n4cKFjBw50qOphZ9//nmqqqrw9TWO05KSkpg/f77HrifwXn5WaSMEAkdUVlY6TNLXVuh0OnOWVkfMmTOHwYMHt4FEAm9GGAGBV7Bo0SKOHz/OyZMn2bx5M3/5y1/YvXs3Bw4cQKvVMmLECKZNm0ZAQAC1tbVkZ2dz5swZ9Ho9SUlJ/P73vyciIoKtW7dy4sQJzpw5w+bNmxk1ahSPPPII6enpbN261azcLWcLBQUF7Nmzh8TERAoLC/nlL3/JpEmT2Lt3Lzt27KCqqop+/foxY8YMunfv3s4tJfA2xJqAwCvIzMzkzjvv5OmnnyYnJ4f8/HzKy8t54403eOutt9BoNHzwwQeAMVvjqFGjWLduHevWrSMgIICNGzcCMHnyZKvzTJ8+XdH1z5w5Q1RUFBs2bCAtLY1vv/2WvLw8Xn75ZTZs2MCAAQNYs2aN1W/efvttpk+fzpIlS7hw4UKrtodAYEIYAYHXYTAY2LNnD1OnTiUkJITOnTuTlpbGV199BRgzSCYnJxMYGGj+7sSJE25ds1u3bjz00EOoVCoCAgLYvXs3qampxMTEoFKpSE1N5cKFC1y9ehUw5m9au3Yt69atY9CgQSxdupS6ujq3710gsEW4gwReR3V1NY2NjbzyyivmzwwGA3q9HjBWntuyZQvFxcVmxdvQ0IBerzcv1LqKbUGSq1evsmnTJt59910rGTQaDd27d2fAgAHmz1NTUyksLOTEiRPtnqlV8PNDGAGB1xEaGkpAQABZWVnmnPqW7Nixg7KyMpYtW0bXrl25cOECGRkZ5qIetqmOO3XqBBiNh6mMqVTBF0siIyNJS0uTLSRki6fTKwu8F+EOEngdvr6+jBkzhs2bN3Pjxg3AWEDIVE/65s2bBAQEEBQURG1tLdu3b7f6fVhYGBUVFeb/u3TpQnh4OPv370ev17N3716r76UYN24cH3/8MRcvXgSgvr6eAwcOAMYoppMnT6LVarl16xaffPIJ1dXVJCUltVobCAQmxExA4JU88cQTfPDBB8ybN4+amhrCw8MZN24cQ4cOZcKECbz11ltMnz6d8PBwfv3rX1sVIp8wYQJr165l165djBw5kqeffppnn32WDRs2sHXrVkaPHk3//v0dXv+ee+7h5s2bvPnmm1RWVhIUFMRdd93FL37xCxoaGtiwYQMVFRX4+/sTHx/Pq6++alXtSiBoLcRmMYFAIPBihDtIIBAIvBhhBAQCgcCLEUZAIBAIvBhhBAQCgcCLEUZAIBAIvBhhBAQCgcCLEUZAIBAIvBhhBAQCgcCL+T8cLuNrMyayGAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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/fxMTJnSyaJEvYWF9t9IQ0VMCgUBwjsnLC5MpDAC93o9fb82jnCTZ/nKSmEuubbs/8gZ1VtrbVWRnt5CQcPbltUcoDYFAIDjH1NYq51FUSImksp58prOR68lnOqmsR49FEyRRzrV8i45KVpHBBq5nFRnoqKSz06dPk/qsCPOUQCAQnGNGhx1kFs8QRzU1DGIuuTbF0IOl8dwAfiKRSuKoIZe5+NKNEX8SOGQbmUIpqaynrs5186SzhVAaAoFAcBZxzLNoy8ggJD9ftv3irkcIcpj0bwv8gvYTaoqZxFAO2o5NZAMD6XGQlweOZP6JJ6kmjkEnFclQDrKQHD6OeZO+ntaF0hAIBIKzhFKeRdBnn6EyGm3bAUVFqNvaZOcN5SBf/OIJ/lPmz9C2g7JjA6mlOz4es1bLwdBR3Lrpr1Qw2Ha8lBTWk0piQNXJqKm+bU4nfBoCgUBwhpSW+nH11QMYPu5Shuk38hXX2I7ZKwzASWFYiTEd4YbhesVjZq2WhjVrmBuyhIquwbJjBxlKDgu5aLymzyrb2uM1K43XXnuNsrIy+vXrx+LFi52O79q1i7y8PKKjowG4+uqrmTJlyrkWUyAQCGSUlvoxdWoURqMK8OM4oUxkIxu4gev41uPrmGJiLGUIy5SPgWsHelVAIuYFj5268KeB1yiNCRMmcOONN/Lqq6+6HDNy5EieeOKJcyiVQCAQuCcrK+KkwujBiD8zeZ9Kh/BZV3TrdLRkZwPgV1YmM291x8WhamtDM2UKiw5rmckiJ6e5ZvxFmLTdZ/iXeIbXKI2LL76Yo0ePnm8xBAKBoFcMBjWPPKJGr9dQW6s8jTYR4dG1TFFRNBYUYNJqAWgsKLA40uvqMIeG4rdrF0FFRQCMp4RNvluYYCy2KQ6drpvHFvRdVVtHvEZpeML+/ft57LHH6N+/PzNmzGDw4MGK44qLiykuLgbg2WefJSoq6ozu6+vre8bX6Au8US5vlAm8Uy5vlAm8U67zLlNlJepHH8Xnhx843hXIQ81r+N4cA7juWxFBk+3/Zp0OU2sHfg0KL8YpKfRPTu7ZjoqCggIA/GfORF1dLRs+xFhBvu6vzB2Sz8CBEvPnSyQk9Di/+/pZqSRJkvrs6qfI0aNHee655xR9Gu3t7fj4+BAYGEhZWRnvvvsuL7/8skfXrampOSO5oqKiqK+vP6Nr9AXeKJc3ygTeKZc3ygTeKdf5lMmvtBTN9On4nDhh21dOEuP5khqUX1x96bL5NLp1Ov67ZA0P/qUf+TWpDOGwbKw5KAjjyJEYT5qorCsOg0FNxE23MuLYFqfrdyUnU//ZZ4r3PhvPKi7Odb7HBRM9FRwcTGBgIADJycmYTCaam5vPs1QCgeDnisGg5m/3NhFy529lCgMsIbI6DIrnqVRm3r7yZa4eK9GelkZjQQFPLx/CtzXD2MYVTuN9OjrwLysjuLCQyPR01AaDrTaV/7GflO/hYMo3GNRkZkYwZYqGmTPVGAx917nvgjFPNTU10a9fP1QqFeXl5ZjNZsLC+j5lXiAQ/O9RXVrL3TMG8rf2ZwhGOUS2w0XFWUny4Z/aWUxadjdgyd1o2HwAGEA/3L/o+un1hOXlkUc+er0fx1z4Rcwno0jBufhhSQmUlERSUNDYJyG4XqM0XnrpJXbv3k1LSwt/+tOfuOuuuzCejG+ePHkypaWlFBUVoVar8ff356GHHkKlUvVyVYFAIDg1qktrufOuKPSmgcRR7XJckJveFvY9usPy8ojvvAMYSw2Der2/uq6OWkmNjkouolxxjEmns/3fVfHDvLwwli1rcjz1jPEapfHQQw+5PX7jjTdy4403niNpBALB/yIGg5q7ZwxEbxoI4HKSN6FiKPsoYSzg/PJq3wRJXVtLLnMpJYW55JJCqaxMiCM+BgPLT0wmBAPhtDodNwcH28JzwXXuhr3iOptcMD4NgUAg6Gvy8sKobB9o276Pt7iNT6hEJxvXjR/9Ridy65WHCFHJJ/ZE30PMydhr2zbFxpKAnvWkci3fksMCvuWXnEC+OgCQfH3xq6piVP1XJNrVppLde8QIm7McIDZW2QTVV937hNIQCAT/06gNBiIyM9FMmULD1/IVQCdBfMb/I4VSPiKN4/0H0zF5Mi0ffcBLFy+nUH8V/5UuZTr5XM9GppNPsXECl+QvtF2jJTub7rg4EtCTzww+IINrKCEQSzJed3w8nWPH0h0fLys50kKIorz2pimAjIw2fH3lQbC+vhIZGcq+mDPFa8xTAoFAcK5xLDA4mO3AKKdxR4nlrXFv8cuCTk6cPEd98pwE6slnhmz88U1m/nZvEztakhgd1sRLJh+FdYUlE9ya2KeZMgWqqgDoIIASrmYY5Qyxi9LqGDREZpoCyM8Pcc5IN6rIzw8hJeVn7NMQCASCc01YXp6sZEcuc/mIOxR7cXfsqUYz5U/4HD6M38nJ3RX9jh3m0aLbSGU9s3iGQJzHd8fHyzLBzXbRoEF0MpmNHGIwhdxGP1qoIY5vLnmSp7TyiKpz7dMQSkMgEPzPoq6tpRIdOSy09ae4lq8p5ldOYwfX7yCgvsTja1t7XLiKwDJrtTLfhD32MnUSQMnJqrljWzuBBtnYc+3TEEpDIBD87HFsjGTNvD4YNprbeJ+DDLWNDaKFOP+fqOkaYNuXRDm5zD3l+8ZR4zoC62TlWqt8/mWW8raV6JhEsUwmK0qKIDu7hbIyP1nYrU7XfbK3xtlHKA2BQPCzRqkxkl9ZGY0FBeSQy0EGyMZ3EMal3Xu4nnXUEGdrs5qAcq8LAFNIiGKfjBriFMNs7avaWuVTnyz9kcNCRYUREmJSVARarYmCgkby8sKoq1Oj1fqSldU3iX0glIZAIPiZ4+i3gJ7M6yMtHyie0y35Ojm3HTHrdHQPGoQpJoa2jAwiZs+W3aecJFvv73vi1lJ46RzCW2sxxcTIakw5yleNct2n4cNNLhWBVmuyJfJZak/1XTMmoTQEAsHPFoNBTfOmKkYoHPPV64nVKU+uQXS4vW63Toe0bh0Nds5r+5LmzaGxvEAug1rjSI5pJzu7H93aVxy8ERbUtbWy7UEoF1jV6YyK+881QmkIBIKfJdaaTBvdFP3LfrWFdesCaW+Xp6zp0VKjHkScqceJbQ4OtiTWnTQt9U9IALtqsiatlqZly2zbTwGOTmslTLGxsm1r9ri9iSourpu2NhVTpmiIjbWYqc5Fa1clhNIQCAQ/S6w1mWqJUcyuNkdHo9WaWLWqgYwMDR0dPYqjhsFMMX3AF0F3EDhS61S2/GzSkp0t69aXgJ61cfcw59JCalvDCQ01s2uXH0VFQbZzysr8+qwgYW+IjHCBQPCzxJq/UKHgVIaezOqUlG7Gjet0Ol7COGZ1vIhRp6Np2bI+URhgWaE0FhTQnpZG59ixtKel0e+jF3hlZTdr1jQQEiJRXS1/v7cWJDwfiJWGQCC4IKkurWVxVgtHmkMZGN7KI0vDGJTSY+qx5i/0Fr0E0NKi/P5cQxzd+qNkZkZQW6t2Mg25CuU9VRxNW/ac6+S93hBKQyAQXHBUl9aSPjWSCuPJNqnNsGXqIQr+XmtTHD35Cwmksp6F5JAYUMVF4zWYFzwm65B3+LCy0oijho37tBSW9WSIW01DUS2VLkN5z+aq5Fwn7/WGME8JBIILjsVZLVQYh8j2VRiHsGBWl23bmr+QltbOoLFxfJz2JrX/t4Y/hqwibfYVZGZGUFrqR3p6JFVVzpWhkijnLwHLebjtadl+q2lIPX++y1Des0l2dgs6XbdsX18m7/WGWGkIBIILCoNBzZe1lyoe23D0cgyG4zbzkX3+gmOHO4CiogDa2pzNPLqAGj4d/wJzf3ob/bYEp+N1dWpUfkcUZVDX1WEwqMnLC1M0aZ0qjsl7MTEiegqA1157jbKyMvr168fixYudjkuSxMqVK9m2bRsBAQHMmjWLxMTE8yCpQCA4H6gNBnzmPY95cwOLjfH8iddpQ+4MPiEFkpdnVuxYp9ThTklhAAy6UkPEyqcgMwK2OR8PDTUjRQ50PgAcDB3lpJx6i3bqTcnYK7/zjdeYpyZMmMCTTz7p8vi2bduora3l5Zdf5g9/+ANvv/32OZROIBCcVyot/oMBRR8ztnMzGazmUnYqDt20KYDLLovhsstiuPfe/hgMFsXgyqGshNVfkJ3dQlxct9PxXbv8KP/9Qrodelt063TkkOuy/aoS1hVQYWEwJSUBFBYGk54eaZPb2/AapXHxxRcTGhrq8vjWrVu57rrrUKlUDBs2jLa2No4dO3YOJRQIBOcL9fz5VOkhg1VczwYyWEUMtYpjjx1TU19v+VdUFMRvfhNJbWk1iw7PZAPXs4oMdFTaxgcHm2Xn2/sLtFoTl17qnIldXe3L/BVDnEJlGwsKqPwpXFEuvV5ZCbjr8e0Og0FNZmYEU6ZoyMyMOGdKxmvMU73R2NhIVFSUbVuj0dDY2Ej//v2dxhYXF1NcXAzAs88+KzvvdPD19T3ja/QF3iiXN8oE3imXN8oE51+uykp49FE1P/zgA0hcdZXEc4fhNofKr4M5RDS1HCXW9cUAv5oq4mamE9taYduXQimprEedOITly42sWKHmyBEVAwdKzJ8vkZDQM6/U1ipPk5WVPvRPToaCAsAymfYHGhuVxzc0+Ck+V1fjGxsDXX4OlZUwfbofFRU9zZe2bw9i7dpuYmP79vO7YJSGJElO+1Qq54buAKmpqaSmptq26+1S/U8HSwGwM7tGX+CNcnmjTOCdcnmjTHB+5TIY1EyZoqG62vrWrOJf/4Lj6gecKr8eZghXsNWmNHx8zJjNzsaTheTIFAZYel28Fz+HgNVL0WpNOLpRrX++waBm1y55FVwrO3dKTJggOfkgNJoo9Hp/p/GhoUZuvdVEWZnlWHJyFwsWNBMZGQYKTZ8iI09QX6/sx5gzJ4KKCvk9KipUzJljpKDgzOe8uDjloolwASkNjUYjexANDQ2KqwyBQHDhkpcX5pT9DPCD6UrF8bu5xPZ/JYUBuGyClKI9TEMvEUh5eWGYTMrXbWvzoaQkAIAta1u55KoAmo2hHD2q/DK7d68fu3b1TPRFRUHs3OnLK680nXI/jPOZ8HfBKI0xY8bwxRdfcM0113DgwAGCg4OF0hAIfma4mgw7CfJov7+/ma4u+SR/nH6K59o3QTpVeRyp6oym6uuebV9fyalvt8nkrExqavzIzw855ZBa9wl/fTute43SeOmll9i9ezctLS386U9/4q677sJotDigJk+ezBVXXEFZWRkPPvgg/v7+zJo16zxLLBAIzjauJkNPmTDBUkPqx00nUHV1kkIJY9jiNK5bp2NXxlwWuSgPcqbyGI0qgoJMdHT0rnQsjZNOLaTWfbe+vn2Z9hql8dBDD7k9rlKpuO+++86RNAKBoK9wl5OQnd3CDz/4K5qoAKKiTAwbZiQ01MzOnb7U1MgnzQULmtFqTWimTCGgxLmftzk8nBMTJ7IrYy53zh7Vay6F0uSsVptdmqzs8fNT0eG+LQdweuVAzmfCn9coDYFA8PNHKSvbOlknUMnovDx2xdRR3KDjkRNPo0eejT1uXKcsw9vVpOnYo8LKiYkTaVq2jEWZEYphru/Oa2BJyFxbAcLajDkMHx5OW5tFSSQnd/HHP7by6Awjle3KyX1WwsNNNDe7Vy5xcadfDuR8JfwJpdELaoMB9SOPoNHrz6iKpUAgcJ2T8Pw8Hwr29RT/+w3fcaXv90wwFtsUh6Nz2N2k6dijAuSVbZV8FToqmbv5VoI7eyKtIj/bzj5jMfUnZdi3z5e4ODN/X3WEl2b+wJHWMMI5zjauwMCQnmvpulmypInZs+XKKSjITGCghFrdEz11vsqBnC5CabjB1vBdr8f6FeuLKpYCwf8KrhzLlZvq8OuSF/8bYqzgvfg5PKl9D63Wl6ws5zIcrkxd1h4VDfPeJbcsjRriiBoey19rDjEiL5PlBxrYzmBbD2+whObGd1Y4ybCQHGaQD/Qk3S1bFsvKrSEY58xBXVfHwdBR5JBLbWu4bNXjTTWjzhZCabjBVUP6yDvvxDx4sFh5CASnSFiYWXF/ZJdy8b8U7WHWrGk4mTvirDDc1XiqJIH0fa+gr7cc1xVVErkxnWBjBaOAUfQk+elJIG45i4kAACAASURBVDGgCpx7MRHn0LPbFtaakGDrgREBvEI3ju1dvalm1NnCa8qIeCOODd+t+FVVEVBSQnBhoWUlYjCcY8kEggsPg0HNzp3K76k38W/F/aaYGIuJeOZMNFOmEJGZafu99VZ+w/H4QnIYYnRO8lse9SRpae1cNF6jKEMN8kS389XHwlsQKw03uHKm2eOn1zNgwgQ6x4+necECseoQCOywNx8dPuwji3ay52WyuI1/OXXXa8vIcGkirq3tmeR1VLKQHOKopvvrWNSGLNlxcJ3kN2GYgVHLmjAbHqN7339k1oVDvonMNeb23KcP+1iczXLqfYlQGm5QcqYp4dPZSVBREX67dtHw4YdCcQgEKJuPXKHH0l3vvfg5pGgPY4qJoSU726WJOCwvj9hYi59BRyXFTOpROPXQnV7C6OFrKeFi23k1DFK8tzXJz+oH8Zn3PEfK6jlCHKtHPMXw4FgGtXbKfBJnO0CmN1ObNyGUhhusX6KopUsxGgz4GAz4VVW5HO9bXU1YXp7LXr8Cwf8SSuYjt+jiCShYKivt4cpErK6rI3uxJYdioT5HtkIBi2LJHZ7DF7oCmwxzyeVa3xKZicqxV7jFD1Jg84PwjWV1YT9590WAjDtTm7f5RIRPoxdMWi2m996jYc0aGteskdXPr0QnK9VciQ51Xd15lFYg8B48KcEREmIiObmLtLR2xbdqRxOx9Tc3ef8b5OWFsWRJE5dFHVa8dnhrra3d69ixnSSnxdD4d+dS5vYTvSdlyt2tfk6X81lL6lQRK41TwLryiLzzTqqq1ExyKNVcSgqfhr5AxHmUUSA417iyxbsqwREf341Wa/YoBNXeRFyJruc3Vw8UWkw4W5KjoMj5XFNMDFqt5R5W+RbmX8KcjLlckr8QdW0tYXl5MtOSJ5O3u9WPK3rzV7ivJeVdCKXRCwaDmkceUaPXa05+2GpYs4a/3lDDwQ55qeaDDCWH3JOhdz3nXwjOLYHAE9QGA2F5ebaM6b0Zc0iffbmiLV6pBEdwsJkBAzxTGCA3ET9ZfA8H6+W/Ob3ej5zhubyi+49iIp+Sr2D7Z5EUG8tIwDLe3rTU2+RtMKg5cFjLeJxLlDgWQLQ+q8OH4O59r1LZ3lP+3NFf4b6WlHchlIYber5wajhpvbR82GAYHA/7nc+pbQ3HGqt9ITm3BILe8CstJfK3v0Xd1mbbN3Ddj9C+AezKfej1fsybF05IiIRGI2EyddOvn8ShQ2ra2tRs2xbAtm1QVBTA++83kpLi3E7VHquJWD9BsqwwHNhSn0RjQYFFmdXV2ZzoJq2WvExnc1OFcQg5LCSfGZa/66RpqWnZMreTt/X3TNUiitnqFOll7xux+j389HpyWUUl8pIjjv6KCykRUCgNN7iyby69cQu64018S4bTOdr9m4jIXE5LdjZ5eaMvGOeWQOAOtcGAZsYMfNrbZfsHtlfKMqatbN4cQGdnj8vUfKSWYJOZNruch7Y2NTNmaNiw4adeM70XLXJtwtm7149KEtAqBKC4Mjc55l5YTUvuJu9MW70qS6SXJcS3BnV8NCMKHpL5Ruz9HtUoNzRy9FdcKImAQmm4waV983gwK8jke1JkPo0kynmm/o8EF+rxKyujTrNddp41lnzkhsNEZGpENrnggiF83jwnhWHFMWMakCkMgBpTLINwdli3t/uw9NYfWTFuue33oGhS2i7x/PPHKSoKoK1N7XQNVy9iA8OaAefOe44ym0NDbf93NXnbzwd6EmyKcqy2kzVaeSa4vd9jkMLzAe/0V3iCUBpucPVmE0cNCehZTyo5LKSGOOKoIZe5PXZSvZ7Bph+B8YBDLHkzUAj+P/xA9yWX4NPSIkqSCLwWtcFAwObNLo87vrUHBJidlAZAG6FO+wDq6v0ILiy0+RaUVugVFSry80MYPtxEWZnzy5yrKKNcctjOo04vd7nMdfn3uOJUnNX2UV+5zKXU4QXTW/0VniCUhhuU7Jv2X7gE9Da7qBJP91vE1GPL6dd2hCEcIpFDsuO+1dX4VvdkqYpiiAJvJCwvD59OhaJMQDOhzMWSMR0ebmbixBO0takoKnLutBfBMZoUGgTFUUMlOnL0C+n+9THu7riPTI5QwyBZQcG6OjU6ndHWY9seV2/tSS073L7cWfFpbXX/EDg1Z7V91Jf1BfOvIS9iGH4D0To/r/VXeILXKI0ff/yRlStXYjabmThxIrfffrvs+KZNm1i1ahWRkZEA3HjjjUycOLFPZdJqTaxZ8l9enN1ORVUAdSYNURwlh4WKXzxHEiq/JsnFkl4Je4ecQHCusfoRqqt9qakZQHS0hE5nZKVeOZTUiA838W/bpD5x4gmWLWuyRBh90yTrN5FEOe9wD9PUH1JjipXt/wNvMIlijKgpbpokczDbFxS0+hdOJcrIFBtLAiVuX+7As9avjv4OV5V3oSfqy+qcj4mJ4dGMeBbm+1Fba3nOjorDMTLNWy0PKkmSpPMthNlsJisri7lz56LRaJgzZw5ZWVnEx8fbxmzatImDBw/y+9///pSvX1OjbFPsDWsERJUep5yMJMopIpVEF4rDFBIiizLxlM6xY2lYs8ajsZbKnwrhJOcRb5QJvFMub5LJXcmPj0PuJq3tA+dziKecodQwiE8G3MvrV7xGeMsRTLGxHJh0L889ZqS2rZ/t7T5eBz8u+ZhF+SNo+LqcwfU7yGUuOSzkG65hIzc4rcYB8pnOvMRVrHl+KyPyF3H4EMz7KYuq6Mt7fWu3j2KyIvn6ojrZShoskU+ns8I/lc9P6fnaZ5orydmbXK7C+c/G9youTtl5D16y0igvLyc2NpaYk9p+7NixbNmyRaY0zgfWCIgcVskUBlhyMv7a/zU+OHaz03mmqChMWi3qsjK3168lmliOys89WdXzQnjjEPx8cFfy4+G2p0kJLmVge6Vtn1nti9ZUhRZLWZ27G/+OT1HPRHxxWRlL319CSP4y1HV1NIdeyV/IZccLScTGmnhxeSejZs/FT6/HhIpiJikqDIDRUYcpWr4f7X134qfXMxwooJButY7GV91P9o5v/KaYGNoyMgjJz5dt9/XvrbcyIe6yzJUsD+7C+aOizqroTniF0mhsbESj6alIqdFoOHDggNO477//nj179jBw4EBmzpxJlIunU1xcTHFxMQDPPvusy3G94dvYCLgOmav1c/HFSk21ZHUoKI0KhnCIBFqCY7girBzqepSGlJiI76xZRE+fjqrCUh+nEh1z19dRffEoBiYGMn++iYSTIfG+vr6n/bf1Fd4oE3inXOdNpspK1PPnozpyBGngQEzz59PY6Lqis54EUoK2kxBqYBA1/M13AUk138rG+JiMsm0/vZ7INWswFRRQWQk33eRHRYXKdnz79jEUvb2OISvm80DhOwztlNeOsueS1DhUK/+Gj8KkGrV0Kab33nP/90ZFQUEBYJnw+gHccotlu7KSqJtusv3eAIK2b6d7+XLUK1bInpHth3eSU/n8GhuVp9rGxkCioqJsc40jgY2NivewJBzLnf96vR9Ll0axerWqT79XXqE0lCxkKpVKtn3llVdyzTXX4OfnR1FREa+++irz5s1TvF5qaiqpqam27dNdqkVERhJMT8icffnlGgbxzbCH6A7SOS8ps7KorlET+eEPsuJo5SQya+haQkcNJju7hQAqaXdISArLy8O/ooJKdDzMi6zjV5xoDYYfgB/g009VDB/ezZAhltj1sDDvMG9Y8SaTiz3eKNf5kMm+2J4Vc0kJI4avZbNdRVhHDA1hGLgEuITSgJEUc22vPj2jwUBDfT1z5kRQUSF3XldUqEiZNpRx41bwxkWTYafyNay/pwFPPOH2HqdLxJw5+FfIe2yoKipQ3367zLxsLilxMhWdyucXGRkBBDvtDwjopL7+mG2uceREZCRNCvfQ6zWAc8SYwWDEaFT9/M1TGo2GhoaeOOeGhgb695dHWYSF9RQMS01NZfXq1X0uV0t2NsfX/Uhu+1wOM4gV3C9z0v2m8luaXl4iW+pal7YL8yIoMxbbEoBqiGMuufxUo2PDKksykwmt09KzW18nr7HjQHu7jy2jdvt2idWr1RdsFIbg3OPKDOJYEdYdFZ3xsoxqV1idy67ynerr1RQWBjMjREsa3zod746Pt03U0sCBClfwzIHtDpd1pBz8kWcapDInYy9b1sZT1Rkt279rlx8Ggxp1Lz3NHXEf/tu307pXVLlNSkriyJEjHD16FKPRyHfffceYMWNkY44dO2b7/9atW8+Jv8Ok1fLA8M/5lmt5l3udyi8HVR8iJD+fpmXLaFizhqZly2SFz6wJQBPZyAzy0ZNgS0RSwmBQs3GfjhwWKioMRyoqVC6vJRAo4WqSPFJWz5IlTaSltXPVVWbi47tJTu4iKkp5cir3H2mr8PwQi+lSB8qO2094riY4Kw+3Pc2RYLnpp1uno3HNGtvvyTR/vqzCtOM9ThdPGq1ZOd0K1mqDgctn30Fyp7NirK72JS8vzOZ7cVeB157s7BZ0Onn5lXOV++EVKw21Ws3vfvc7nn76acxmM9dffz2DBw/m73//O0lJSYwZM4bPP/+crVu3olarCQ0NZdasWedGuCGDmbEtnw1cr7gcd/VFcvdD+frrAAwG5xVCXl4YZW1PE4vyD9tKCC20YVEWjnZNgcAex6AKc5jyS8aO+sHMnR1BQUEjycm+NvNGZmYEhYXOhpOd6sv4nisB2MQNbNX8msLL5xLeWitbcYNyfoM9ehL4S8InfHD8ZtTNzZjCw2laskQ+YSYkuKwvdSYoNVozBwcrZr97sqpRimgafXJ112zxpjhhTUw0aZ0tD644n7WqvCLktq853ZBbsHwJpk+PZkHFDDJwNom1p6W5jG5ITY1yKnlgxbGxC8CUKRpKSgJkSsGRJMp5i99zL++iJ4H4+G6+//6n0/zrzj7e6DsA75Srr2VSCuM0Rkdjbm7H/0RPMls5SbZcCEtfix6loRSlExxspr3d2UiRltbusnaSdTL9+usA6uudfxPTgj7m/zp+Y9vujouj8aOPbErB/lmd7ehC2/XsoqkiZs/uNfzV8fNzFVa7LnIqw7cVksEqVivUq3P33E6Hvg659QrzlDej1ZpYu7abbyY/SVVAouyYu+WxVmvi/fcbCQ42Kx53bOwCPasTJYURSDu3Uch6Urmer1hIDgDR0XKdX11ay+yrDzBt5BFmX32A6lL3qxbBzxcl/8Xho0FMOvEp+UxnI9eTz3SbwgDlInr2jYzS0toZPly5Kq27hkHWek6ffVbvZFZJCKrh6Y7ZsqZm99Y8R+NjrzldR20woJkyheDCQgJKSgguLEQzZQpqg8GjZ6KE9Q3famLuTkk5JVORFVdhtfN+ygIs5USSKJcdvxDLiXiFecrbSUiAp1ZGoDas5ie7/sGFw3O4Bw1alJeEKSndbNjwE7feGqX4duX4I1Naxieryhgp7XbKQLcWXNPpjD11+/d1csfu16gi2TKoGb79jZ6ia58g8vlZItfjfwTr9yFwwwanYzks5Cuu5yuuVzx3VOhB1DNzbb2v2zIyGJ2fzwd2b/V/zhvNtm2W8fYRhWpDDGrDQ26/Z1qtiSVLmsjKiqC5WU14uIkVx+8HnBNoS74by2oHM274vHmy0jtgKcejSUvDlJBw1vIsTsVUZMWVw78q+nK61ToS9D316qoCEtGMv4jHFphP2aRUW1pNS9ZiQpuP0Bo+kLCljxCbotz/vC/oVWm0t7fz/fffc/jwYTo7O9FoNAwdOpTRo0efC/m8Cqf+wUXwxT5nM5M9Wq2JceM6Fe3C+/f7kpkZYbNFKtkpl7c9zYCij53OrSGOxESJORl7bSaIZ3mTLgJk46rQMeebNNakp4u6Vv8DKJmkwJLvk8NC/oVzMqqVa+L28+Kum1FXH7IFcwZ99pksezpw7Vqe+8UdbBu0Aqm6tqcIJ0AVdKeX9prFPHt2BFVVlt9Qc7MP9/m8wqVsdwr+qDAnkpcnN934u0iY9a2txfekk/981XBz5ceM1vnR+KrFHxNXV8ebMR+fVGzu+4goUVtaTeTUdJKtofzNcGjqFmr/XnDOFIdbpbF3716ee+45wsPDAaitrWX06NFs2LABjUbDo48+ajv2v8DpNn935Qi0hhzaN2ZyLMtsNjxG9z55V7KqgES+Gf8ka1/uZvCchbZjTWg4inM0SAthlmSrO++URaQIfn4omaTchXBbiY/vpvDiOQQVHZLtt1cYAD6dnVz0zQesizNQGxvL0Fp5RGFvoalKv6EKcyKtilkKp9cj+3zVcHNXF+t0Vi5KtGQt7lEYJxlirECftZjY75ec8fU9wa1PY8WKFfzud79j6dKlLF26lD//+c+EhYXxyiuvkJiYyDvvvHNOhPQWTrf5u71dWCmEUcm/YUUpFM9v02qeWhlBQoI8hLLJRXdy6eTH7FdVZUnsOgP7r+DcYzCoycyMYMoUDZmZEVSX1hKRmYlmyhQiMjNln6dSSG1vIdxDBnWwZk0j4S1HPJZpWM23XGNULpfuLjTV1W/ouIvIIsfqtV3JyXQ7vOs6bvcmQ1+h5P/xtEun42dsMCg/p9Bm5c8otPnc+S7drjSOHj3Ktddea9seN24cq1atwsfHh7vuuosHHnigzwX0Js6k+bt1BZF5Swt31OfassqtpZ/dKR53byn2ceaumr3YN5yxfwsTNa68H096XAeuW0fDqlV0p6Qo5h24KoPTz7eViTd02Ozqp5Kz4A53oamufkOdBOGrMmKUeqYkJSfxzj8u4pH1rfxBetOWNPsWv+dd7pX5/M406e90OZ3ue6fSFro1fKClH48D7d1+aKZMsXyGllIRpyW/J7hVGoMGDeKHH37g6quvBiy1n6KjLRmNAQEBiuU/fo5UVsKcORHo9b6EhJhkYbS9RT/Yx20PDGvm8d33cTXf2I4P4jC38i/27w+W+TfAs9BC+zhzpWYvSg1nAjdsoP+99+K7cyd+duHIQYWFdP7iFxx/+WWhPM4j1s/dV69Hvaee/+sYSAVDbS8Yjj2ufdrbifztb6kvLlbMOxgYchwUCi7fcKsPryzrsasrnetYEXY/SdzGZ9QSS0xrE59Ez2Dk0Z6ktd4S7lyZanVU8qC0lA0BN9EaFktU8kBFJ/GzywfztTSAr7lOtt/+eZyNpL9ziWKxSH0VnXfOQTPYIPvthy19hENTt8jKEx1Cy5CO3QSUWH7LXVu3o/7H6j77DbvN09i3bx95eXlERFjMHo2NjWRnZzNy5EgOHTrEP//5T7KysvpEsLPJ2cjTsC+2FhxsZsSIbnQ69wk1Sm8QSZSznlQS0Cvamq35GwlU9loq2RqP7VdaSr+7f4t/Z5vN4VlDHANVR1go/dVljaBu1BwnhCiHVxdjTAwNn3xyWl86b8yHAO+US0kmV45skOdTXM9GNiLvJ2PNGXLMO9iVMZc7Z49yWZbb8f5RS5diNBhsOQuhy5cTsHkzuzoTuYqtnLDzPwSqu/j6lw8yyvxfjxPuDAa1LKJQ1tXyJK6+69Mua+Cr+lFO1xzv/y3rxmSfVtLf6a64lfI0lEqV94Y1P8tKb8+jJ3qqlrYuPxJO7Gawg5Xhp8l30L3yFU/+fEXc5Wn0mtzX2trK/v37kSSJ4cOHExqq3LLRmzkTpeEqI9aThBxX504nn3xmuE32ySeD4MJCp2MdkycjhYSgrq3FV6ejPisLn3nPK0ZYHb90DOF7f3RyZtrzLb+0ta+1x1XSYm944+QM3ilXS0sUc+YYHbKH/6z4uVvJZzozyOf/Ucgn3CE7ptSLxRaOfcjzHhSulNlVNyRg6HA2+/SWYKo0meblhdl+G6vI6DVx1irTI5f9l4L6XzmNTY9ax+LtzsqkN5SUdMegITx8yb/Z0ZLkdvK3f0699ctwh+M84cnzsNJw2TRG1X/lNHZH1HVEbXfugeIpZ1SwMDQ0lORkS9x/fX09NTU1DBs27LSFudA4Xec3gF6v/HitPZVd2Zrr6tSoJWXHVsDmzT2tN0tKiCwp4WBzNAMUxpr3G9wqDIBOAplEsW31Y8XqSKwtrWbfrPd5v+FWan3iiL06lnl5JlEk8QyxrGD9ZNVf164NpDS4gcvdnGf1Tym96Tna8e0nRE97UBgM6pNltzWyCdOk1dLkNwA6nM9pbnb9W3Blr1+ypMlmpoqjWvFcJWd2TnIhW4qSnEywOcmFwKkrDaVos6DqQ1xb/QzLybfJ29vkP29e+GlFVoKzyc7V8wjcsIGIzEzZSqiGOMW/+ghx9FVxdI8ywuvr68nJyeHhhx8mN9fSD7i0tJQ33nijj8TyHk7X+W0wqNm3T/nHZP3hu3Jcx8S4dko69mr20+vp36r8JaMXhQGWL91BhpLDQtn+5tBYakurabvzEXLqsvjCOJkfuy5lz9ct/PjLHB4esY1plzXwl3v9XEZ6COSoDQZb1NNLd+6VmTwBOjt92HlssNtrWF84WhyijZTs+O4a+yhhneALCtSUlARQWBhMenqk7fMND1f+zrvaD67D1PPzQ2yRRt1Ryt91JWe2ZsE9rI27h+nkcz0bmU4+a+PuQbPgHpcyuMNVAUf74BF30Y1geW6bNwcoHvPk5dIx6kodr+zE92luJriwUBYBWZicQzlJsnHlJFGYnNPrfU8Xj5TGm2++yRVXXMF7772Hr6/l7Xn06NHs2LGjzwTzFrKzW0hMlL/XeZL6n5cXplh3KiSwm6diXsUcHs5TMa8yJEbe0N567ZbsbKeqnuYA5S+mr7FTcb/RP1Bxv5VykpiL5SWgxm7V8zVjGbtjBb+fFsRvze/a3up0VLKSe3iBbP7RcjNf1Y/i46IBTB/fRdO9fxOhvG6wvvVby1/UVSlPtHPJdZoErNh/XtZJzRwe7rLMhcuy3y7CUd3lIQEsXdqEr6/8t+DrK7F0qes3aXcrdWuk0eWfZSlWsN2bMccWhjpzphqDQY1Jq6XfRy/wZtrHfD42hzfTPqbfRy+cttPX1ctZjYMVwN3kn5cXRmen8lTqSWQl9ERdrVnTwIg1Dzk9D3vsFf89CzTcE7dWVhbmd/FF3LNA4/L8M8WjMiLl5eU88cQT+Pj0PJjg4GDaFSpB/tyw1p6aM8d4StUkXf1YLjHtYFhdKQDDmktZF3cjcyYXcqi+Hz/VmIk+foilt+rJSS7EZ4m8V0dHfTuar9c5XbNbUr5XkDaS7k4/2dvmN+pxdJl8bf09rDWH+nOMWmL4hFv5C69hrPUDh7yPYNr5HSs5hLwGV0XXYJ4pupaV+9KR1q3r03C/Cw2rPb/h6wYG1y9kEY8zmBqWkck2kmWfAVgqvqaynoXkMJLdDGM/ZnxoQMM9vIOeBFlE3ImJEz0Kx7bH5+BB+t97Lz4tLTLHr6vv7FG9JcIqJaWbv/+9XlYCZOnSJuLizGRmRig6gD1ZqSu1ZN2bMYc7Zl9uU2IlJVBSEnnSTHR2EuVAOWLMXjlbcazeYI/1uTk2acv1W0B29qn7gO2fR+CGDfg0O8fYWhW/VmvihY/6kZf3pm1+emuRb582Z/NIafTr14/a2lqZc6Sqqsrr2mf2FQkJnHLstasfy0Xde2Tbw2q+JffSHG6ue5HDtUEcZgRbGMGWoiTW7rwH88m3KINBzQu37eQdviWcntVJO4F0oVxyOqi6ksb337cpHnNoKJr2GG7Z8gwVnfJ+JB/zGz7mN4rXsbKHS1weqyHO0u95/nxYvNjtdX4O2IfFHqr2Z15XDjXqwQwY2Y+/BT1DUssOmsMG8ujORXxbEwOMQkcoVoPUJezhEvaQQqmsYCBYFMczgfP59MSNhJ2Mle1HCx8wncU8TBYvk4DeZpJyFf2jNCEC+NXV4VdU1LN9suxGbKxycqh230bUhoGYtFpSUuRO795yDNxlSdvjmIu0MDPitH0Ep4KjwmoOjeWenYvQ18j7e1irNxQVBTB8uAmdzmhLh4iNNSlGPN3g+x1q/g8Tp74Ksj6PiMxMxcAIH4MBtcGASat1yg2xOOhP+ZYe41Fp9I0bN/LPf/6T22+/nXfffZf777+fwsJCbr/9dsaNG9d30p0lziR6Ck4v8kbpx5QYUEVxp3ObzGlRXyhGhEwnn7cmr0IKCcHwZRVxTXtkCsMeEyrUCu7RnybfwR9DVlGn70a3byNPtz3MYeK5mbW0cvZKwAw52f08LtrEX/452usc5Wcjeso6OZsO6FHv2UewqU0xbNo+rPpLruMm/s0JQunHMcaziZd4WPYd+ICp3E2BbVun6+Yr7TS0X3/kJEN3fDxmrdYWWgq4Dc1WGwz0S7uLwNrDbv+29rQ0dmS/zt0TzVS293TJs/4tMWnJim/3nkQXWldbKv1hHjw6j8ujD+Oncx8a6xiGamXs2E7WrGlQOOPs0VsZdyuBgRLXXXeCP/6xFdVvs0hrc45WOt0oRCvuQrCNgwbRfcklTivGvi6N7tFK44YbbiA0NNRWc+qrr75i6tSpXHXVVWck2M8ZpeKDC9sWklDk/OE72k+t7GMYDet3Ey9VMaKX+ykpDIADmxso7LT8qL8jjR8YxaX8V1FhREWZCAw024rJeYovXRwi0WK2Ogrfpvceani6Me3nEsfEzEU7HyWmRt59TalEhzWwIJe5zOQ9TmAxURynP5+Sxo9cwSYm2BTH7XzCdPKpCh9J5MSRZGe3EDNb+UXHrNXKwmojMjNdOrutnSQPGBMYhXuloa6rQ6s18fnwTHK33UENccRRY6uu3FmnXAzPnc/CqmQ1tbX8n1qN/57/4NPRAVVAmfvCgmdSfeFMsb65u6pObeXECRVFRUGUfq1m44n9imPOtJyJdSUUeeed+FVVyY75VlfLKv5anyd9bAHqVWmYzWbWrFnDHXfc0adK4scff2TlypWYzWYmTpzI7bffLjve3d3NsmXLqKioICwsjIceesiWne6taLUmXs/eYTMdmMPC6I6Lk2VhHwlOoMKk/LaVxcvES1WKxzyltFMewHmQI+SAVAAAIABJREFUobSgbGcdNszI4sVNTiskJTQcJZqfMKB16v+h1/sxblw0sbFGli5tIiVFXs3Tk7IJaoOB8HnzbFVNu5KTaV6wwPb2fCblTzxRWM4yDmA77zqFJpc7+Hes1BBHDgs5zBDn+yPP6DbiSz4zaJ/Y81bqyh/hFFbrgbPbVVim0nUHD4H8bc69v12V5XA1uceGNrt8Q7birrCgp2atvuToUVXvg4DmDn9e5CHFnulno5yJSavFPHgwVLmfC2wO8oICt+POlF6jp3x8fFi3bh1qdd+FVZrNZlasWMGTTz7Jiy++yLfffkuVwwPauHEjISEhvPLKK9x8882sXu2c/HK+cFVszDFiJqioCJVKRcfkyRxPvobCkGn8sn0DVcf64YtzmWRX8dquaFeHyLa/8xvH4zzvNM6k8LHrqOQZw0yumJ3G2uF/4Y7JP7nsDw3QQDR7uMRlh0GjUUVVlR9Tp0ZRWipXQL1F6agNBiJ/8xuCiopQ19ejrq8nqKgIzZQp+JWWyp5pcGEhx3/zKH+514/MW1oov/JB+l36C2Iuu4z+997rFNFlVQaFhcGKYaXuZHQMTa5Exy4uVfz746hxmYcDPavLcpLI4W9IgH9JCVG33kpEZiZtGRlue2Jbw3d9DxxQvL79ZKUUlmmPOTgY9aFDtvtKiZ43G3PVqzqXHLcKw4qrN3HHMNT0dJPHxf/OFgMGKDdQU0LJWmAcNOislTPxtC7YuSjU6JF5avz48axfv55f/crZ7n42KC8vJzY2lpiTX/SxY8eyZcsW4uN7nLVbt27lzjvvBCAlJYV33nkHSZJQqTx7G+grakurOfLbF8lsO2IrQJheFk9BQaOtN7A9vtXVdF11FfeGFFJY1mMLNuJHCC0M89dTbR7IUaOGGjyvj3/IN5HjL7/ERetX2iJQZpa8czIKSk4gJ0iiXBZKu8k3lSFVFVAFF1NCge4LJmi3U1+vXH3UU4xGFdOmabj55hO2N3pXJg1rMmRYXp5sNWbFt7qaiKws2TK9Eh031byLsaaVYm6WOSKDiorw3bmTxo8+si3ZXXZXmxdOSIhkW330lpgJFtOUkpkvlGZymeuU+2KPD2bymc5cchnIEVRYekJQW4t/WRl+ZWU0nYye89XrUR09ijkykrC8PMV2pPZYJ3nrikpfH8VtgV/w5In5xFHDccIJDjIzNqmG4Mq9qNvaCNi2DbZtw6+sjO6338b42msue3E7rtSWLGkiPz9EFl2YNNuzcHx3b+L2Dl6Lnf7cmi+HDDHZmk31RpxCztWZ1OZzfMZzM+YwSiGowRFTTEyfd9bzOOT2iy++4NNPP0Wj0cgm6gULFpyxEI2NjWg0PXHFGo2GAw5vUPZj1Go1wcHBtLS0nNd+HmqDgYEZ00juOGTbl0Ipqfr15OXF8IEb00GtQphsG2GoLr0I/6M+UAV/4nUGcZjr6SkT0EwoO7mUOqIBFf1otoTPGnNJXh/DsmVX2saG3xoMCiLEhx7nnbGvkkMuta3hPGOYY1EYdvjp9Qw2/QiM7/U5+PpKGI2ulXdXl4+sb4grk8aePZZYfI2L5wagdgg/tPoUVpGBGiMZrKKaOAZZ7fE18iW7K4W1eXOALNY+JERZRvvJocrFSiKWWipJ5OaY79kkTaH6qHO+zAZS2UAqACoFf5SfXk9Ifj4t2dkWM09VlcU8sW0bgevW4aMQ7m6KiqLrZPWGwAce4cg+HWVtT5+MzBrKH4Pfl9VMU+f9GfXO/9jOr0RHjn4hVdMHEnlNPtmLW0igUmYK3JUxl3SHOlZKGdOevBlLKhVtGc5ldPqSUzFtZme38PnnAZw44d7KolQUFMCvpua0+noom28v5+MlaxiRv8gWCelYcNT6stD/lO52Gkge8OWXX7r8dzb47rvvpNdff922vXnzZmnFihWyMQ8//LBUX19v287MzJSam5sVr7d+/Xrp8ccflx5//HFJkiSps7PzjP6ZTCbF/cZbbpEkcPq3iunS+PEmyZiernjcmJ4upacblQ5JoaFm2bYvXdJisqRvAiZIHwXdLemoUDwPJGn8eLmcru6Rnm7sGbd3r2SOjla84Keh6VJQkNnpUHS0WYqLM0n9+pklnc4kvf++5V4BASaXstnfe+/eTik42Pm6IEm33GJ0+dwkkEw6nWx7AhskkKR80qUkDsiGJ3FAqkAnmcaPt32Grp6JJ5+F7XogNdBP2sEl0iqmK34miYlmae/eTmnv3k7plluMUnS0SfL1Vf6bU/hW+W8dP97ts3Aaf9VVkjkxUbbvAEky+ew/e9P48bZxFeicnl/i4E7pYPw42fXuDv2k9++U9XvlIIur38KZ/AZP6Z+CTObERKlz716X51x1lfJ3OjraLKWkmKUBQc3SRq6THezCV/YZnqqcHv1uT/49xvR02/fE+necjWflDo9WGhMmTOhTxaXRaGho6Amja2hooH///opjNBoNJpOJ9vZ2l8UTU1NTSU1NtW2fafiZqxC2mNJSxfFx1BAZeYL6rCwiS0qcwyGzssiinpIS+dtESIiJ1lb5W40RP16Mf541axqJA5Lzwmj72qQY1REZeYL6+p547awstdM9dLpusrIaqa832XwuqqNHFf+O461qOlARGGgiKAjUahg5spuDB9XU1Fiuefy4ipwcFQUF9dx5pw9Tp0a5XXUYDEaOHWuiu3sAoMIHI5fzI7NZjISaJd/Mp/7zLCK/+srJRGUcNIhjS5bITDPWUiyvkukyiunNyI/xNRqpr///7L17eFTluf7/SSYhJCEhJEPOmSEEDYinptZGlHIQsbvWVhS3WIO17W61NRUBjUaDIQZNd1QoFWv1V4tI0PhFxUrL1ghURA2IDWClgkLCTM6QBEgyCWEyk98fk7VmHd41Mzngqbmvy+uSlTVrvev0Ps/7HO67RXhPRtOlYm713qszxB38EJPLqaokAojlFLGc4gIOqHotJlDDuXMTKSpqJyrK43k/84zneEblqRlU67YBnI6NxWSzEWg20dXQoKuwmcQRVrCMhf08SnZ7Ly0tnm8tJjZWvmpRFVh17Sge4leUsVPe1tApzmEpjwtAVBSmDRvkXpaQqiphArXXbqc1gO9zIGWkRquJmPx8RlWr73VQdTW9+fmGq4GUlBgQvBuXX95NeXkIVVVdPF/4NM4d95LSU4MFu9xbA55neDKAcSvHfNvnVip5VNW7A+J7rOuJamk56yW3AdGIbN++3fC/4UBGRgaNjY0cO3aM3t5ePvjgAy655BLVPt/+9rd55513AA/v1dSpU7/0fIYROoLHyhKPWtU9qcRQpPKVmelpElpPDtuYxXpysFKDxeJWScFu3twiTD5qK0v8KYmJuIkkKLtiT582MXNmD/v3N2M2u2WDIUFKYmdnO/m//3OSmuokJEScRDSZ+pg/Pw6n0/PquQnhFDFMo5IcNvBKuydv1vbqq3TPnYvLbMZlNtM9dy6tr7yCMzubtvJyGsKsADzNHXxANrk8iZUa3fnqQtRJXOme3JS6Q+Yuuhp9lz3AqVPBFLoeYjtXUsZCQ4r5SRzhz/ySWyjj7ZgbWLv2hDBhK0oap5uOCkMbUqgh0ASo02qlL0bcnKcMqUklq3a7iSWOFXQFexwvo6S9NsHriy9NC6lBrddqNZxohlssSVt8ouRqGiitChgn+qVvzWJx8dDaGC56ZxnnWrtUBiNQXQ/tmK9uKedjLuR9LpPnAPhiyo0DQUArjZ07d6r+ffLkSZqampg8eTKzZ88e8iBMJhM///nPeeSRR3C73cyaNYu0tDRefvllMjIyuOSSS5g9ezZr1qzht7/9LWPGjOHuu+8e8nmHijNZWYQrOmsBmojnvGnhRPZPGtKHIye2lqjLPJWdnA//7CT38CNVMjebXTwcvJncXKuqRLS8vI3Vq83Y7b1y8jGdGqJy1R6WxWIx7KA1+oiaSNB1KUvcO/5Yf7/3Pdi9+zg33RTLe+/pY/m7d4dy5oz6GNKKoIyFTHRX09UfBz6xdq3wXC6LhfbweJJ7bETh4DJ2cxm7uZSPdONONJ/BZVFXDlksLtal5RNWVwl4YvmfcIFO1yQuro+GusCKEeb0Zym6L53LCYN9tL07sbEhNOx28/7xK2gkiUSaODEqAcvMFNxF98pd3aO3bNERVQL0BQdz5uKLcVmtOHJyiL31VuF5pYlfmuy8MfMEbmUq09gdkOojQDEFfBBxpaoB0F8prNF75g4LC7y6qKaGmPx8v7kIXySNgZYxK5FODVsyCyl2zKOBJENxKBEVSqCl4KIxR9PJNHYxjV1ks4vbkreQl6cuStm1K1RH6aItbz8bCMhoFBYW6rZt376d+vqBlYT6QlZWlkzBLuGmm26S/3/UqFEsWbJk2M43HGgvKtIlo8aHdxB74mOCvvtd+uLj6e0nXlug4NEBcfKwmGWMVxgM8Hix1/2zmBu6X5S37f0wiL9PXcyGnoOcTog17Az21TwF+mSlJOBUxbd0S2PJywmk6WrXrlA++GCUcD+twZCg9GjDdu6UKRKMMGG0fiLShmIyOExR8h8BtRiN3W5iSW0JzbjkhPnbzPH0VZgvJG76JFnzoaCqmGx2qQy5CB1E8lNTGTs/uIrR3x3F6tUnuTz5iC5MojTiS5cmUHV8ojxezw2CeZFdrLF49nFZLPTMmKFzTgCC3G5cVqtMN2Fy6OX5ehhFWswpbr/039xWFIfF4iJXQdFRzSSmsVuo+jghpZuH+v6E0m6kWuHllY2UlI0NmIvNaLLumTEjoEnVZLcTesstqtCSsrpMeX99rSZOPvGEjlbF12pAWgEk2Gy8xDPUYOXB91ex9M7ZJFhDdaqqkpGXnnlUaWlAhsNozBImcYRN5+fjtHjf4127QlWh4Pb2YObPN3PeeU4uuMDEokWms1aeHBCNiAhut5tf/OIXrDXwBr9KOJs0InIs0mYj9OBBYVVLlymSua4tvK+RqNQKOcXNn09YZaXu99uZxZWoQ4GSkBN4XvzezEzhxOKLxkBJUSCiw5CgFJPxJzbT0WEmKyuErq6AIp/C65Guycjg2e0mIr9/HRec0ueUDjCF37JGzkGkprpwp6XJglU1pBuqKaZaUZ1TulZsdf1EdA2cGT2GmWP3MrrZmzvoCY3kCud2PsLb/JpKLdvDruacHi/XmPaafvKTRHbs0N8nLVWGyW5n/MyZwtWGJLxk9O5IcEVG0vbCCzizs1UUHUrOJMlpqB+dQez3JnFvkdtbPaXxngdShSSiwvD1fLUw4l9yR0SovrdAvgOtqqGvcSvPK/o+xozpY926Ftm7H+x1Gl2fElqBre9+d7xP5obkZCevvjr4vpYh04i43er49JkzZ3j33XeJjIw0+MV/DpTEYmH93ctaRLgcbOEaLuRjYchHPlaANM3abaE2m4pOQAlf8VrlkvqBnbdzpEVvMFJT1ZQgInoUpae5fLlpwAZDVLJo1C0sTeQrTmVwAXqjMZVPZRnUvpAQgup6PaWq/YJVv8ncI2zay09dx+ryMNXH7b3WBJ5ufk6+1lPU4FZMPFd+8Ds+alYLk9WRRlHPAypDqL2mpCSxvyat2lQTc0wMwYJnKYVW/OU+TA4HcQsXcnzbNhUxoZJV90JzLc9Of42QkhJaojwToQs9o6xocpQpLEBoTAYbugFjT1zroIXabPRmZuK0Wg1XE1pixEDPKyoU6OwM4tZbY9m6tQWLxUV0YaFPShcjGBFLKqEModntJpqafE/dDQ2e3qO1a42CpYNHQEbj5ptv1m2LjY3l9ttvH/YBfV3hb4kZTacqdAJ6uuWOvDxdDX4T8TqaZoAoTqn+baTQ5y/RKH1EtvlxIFhMSUl49TY1PYqr1FO/X1I2me3bB2YwIungXn5HoqChJETwEUnNeQWIw0aumBh6zzuPYLtdV0kUarNR3dgMCp1Dic56SnstUaVxuslMm3cC/UT67ymBG3ulEV++3EVlpVtIlSGamPtCQlTPWTkZBjLxBHd1EV1YyDOM4Z6wVqp7UmVq9gLrWsrL2zhpcXnYq31U3xjlDaILCwk5dMgwRDpY4r5AiwEAgjs7h2SgjM5rVCjgcHhylU/nfUzYjh3Cffx1aauMqiBioXzOktPkq0JRQlWVOEQ8VARkNNZoHnZYWNiX2lT3VUQgL7Y2qSjRLXvzGxacmZme7tx+OAkhBP0SM5C6sUCrNwCSotpBIBorqtiQaD6kXE4NVha8MY5ql740UUIYXcTRSgMeZbrLeJ/R9JBMA3PZSjj60MvuT2O5b75adlRKxEse8l2sZgs/5DhmeggjmiBSEyyUdOeQKeDqiTvTBP3UHyo663Zgk/88kAjR0S7a2/XGUtQlrDTi6ekYrtqicvUTc1Bvr47lVhqnL2I7JcJ27CC8p4fxwDRgZlglK2ZslvMdgcDIQRpVVYVJY2wC8bT9oSMvj/D9+wlS5DRckZHCHI4rIWFIBgq83dgcLeGpiH0kddUYFgqAJ2IQVVoqDB9KY/IH5Zi1ITRHTo7soC2pLcFWN2NwFzZMCMhobN68mZ///Oe67c8//zy33XbbcI/pa4lAPD1Tajzm0/oeC6VOgGvCBJTcBWk08DZzuIUNVHK5vL0dY3oPd3Q0p6+8Uuhhicj60qmh5JN72M/zukSoqComurBQlfxfxgqqXem6/SSMoZ2/8wNSgxuZ7d6KjXRWUMBs3lHtJ8fUSWYsp9jTfQkN/bF3UTe5jXSW8nv1yU7Cnk2wL+IptlGlK5P9AX+TO7FXsEy3UhnMJLd69Uldf4oo5CYy4qKVDPioONKw3CoRCLGddmJL7almZWQBJy2BX+9APH8YHB+S9J4ePWri+PHxJMf9i/TUPRTF/4E0a5+QSmUgTpKv83pzXplUsY1VkQ+SN+F1/nFwDg0u/bUnJLgMn5dr1GiWOFbwscb58QWtAVGuOJsFDqQRsrLOBLzvQBCQ0dixY4fQaLz77rsjRqMfyiVmyGefEXLwIMEu7wN2Wq1MLr+bc5b0Chvzgmy1xOTmE2Kz6bwoFyG6UIfIi5VgpOZmxC67JbOQ8xrelyuIJFrsW9M/pbT0QR0b7ChN7sZo6R5DG9ewxdsU54ZVkQ9yveNFGkhRGYloTrGXbwlZYcFrWEXspyLUdCXxYOQqXnRcL287TAZ/YJH8byNCyIFOcpKi3X13hjK++d9M7KtWNQK6Q0PpmTVLZukNBIMpD/X1O/CUuIq84UCvV+SBS/CVhB5oL4boPa2rC+VDZvBe3+VsevBjppatwB0bi9PlkqsUjcJQ0ribbU7Sju2jaPxq0iYg3F/LTWYjnesdLzLv3C6efNjBrbe6VDLO343/nDsrl9HZcgSRGPPbwVfxTMV58r9FVZO+oA0FGq14wsPddHd7V7spKb0UFekV/4YDPo2G1Lzncrl0jXzHjh0jakTWUwVfS0zpBRWVrFqpYc3B/yKiyvsRuiMicE6eTLt5gk5JzIjrBnx7W0ZkfcWOebzEM6RjkxO3NViZs+c9qnu8ISfphddOAUYv8jVs0dFFz860M8/aRdlnRdx/IIx6UoW/FaG52aSqm/+/k5dzqtdYTtOeOZsu6zxMzc3sqbeSY3tEVYhgRAipox8PoFIoO9vJjn86GfezIt3EGex0EvLvfwd8nQAHc/JJeks/MfvzpEUrXndYGD0zPCGNwU7qRh747Ew7odZ4eVzanIZozNr7eTAnnxVlU2XnxOEIMnQK6utDKM45zhvd3mojp8lEx1NPGRoMtQGawUd1Kby9dw6pu2+g9/zzVSJGTU1ibe3mZhPZ2U62bm2Rw4mTw2opePf7pLvEHf2NEenc0bVaLQNrS+H5wgd4aK24EVML7QpGVBpttTpVpJEWSwiLFrWetZJbn0ZDaurr7e3VNfiNHTuWO++886wM6psApQFRhoSiotwkJztVXdWrIh8kyaHuZg7u6sJlteJc8ySP202UlnbR3GwicUw7JZ/cRnqD98N0RUbiysz06W2BcWNeA0m6bctYoZOElbz9DZqmRtGLnB7eQHG33rDtO5ZKU5iJmlYrTQPk45Q0GqS6+RzWswFjwrt4a6j8DMZ3mOHqIKRolZUa4mnEjZoWQTvJ+aoUEt3n4A5POE+5ikqhgeK6AlIXLAgoX2K3m1iw5GLo2iaX+p6KTMS6cjGJFt/Nhr4qlUx2uzypS+OrC5tInOMc7rXrCx6UMPTAreqycX9JaJPdTvT8mwmvPypvG/fGfqpcW2WDfm7oEV5jKWH0MJrTMnu09PcPuy+Sx3+YiTTbEhn/gzOkztRrePuiuC9rWKgKs4ZWVZGUuQdfuT1lOLF62kNCg3FiVDxh11zOnUdLYC86GdiZOyox2TfI98WXU6JdOaZj423mkJ+6jlpLtioPlp39xTAC+/xqpaa+8vJyFixYcNYG8U2GaKmdktLL3LnddHYGk5DgYrbNBoJqXaV4vPLDNNkfp6u0lNFtbZyOjfVbIy+9kJbaEioFrLXmrCSch9RlinVhExHkpmluNtH+RBGhBw7IZb7Si6wMbT347QpSa0GZUjgaMpGf1pVgqxMt5H1DpNEgMlbK/ZX5GGXSOfizw6w5+F8kurwThuSNa0NIRpVC26/bwF0uTz4lK+sMRUXtWCwuXImJupp+KzV8zIUk2iqIvfFG2jZu9Gk4vBNdurfazgHzyrpYk+1fH9soESwZlNbC57l2R4HHKegBKuCfh3yrLfpjAvB3bgnBhY+pDAZAuqtariy0UsNbzllM0CgNKvm9egnW9UwcPeHJZWnDP8aOkj6kGmqzUZy5jH9anwxI/Gl0W6Pw2NWjp5C0Zg3kxrBi7690ebPUHg/rwcd5T/NYYTCtO9yk9lzfH9KsVDklopVjqpX+8vCzK3trhIBcPaXB6OvrQ9kPGBw8sBLL/zSIPJ36+hAuvfQMa9d6HnpoboLQaBiFDaQP02w2+yRD03rJJfyUPSHvUN07Qd7HanVyy+3w22e2MM9RTBINJGWZieMc0EcySEhw4bJYaH3lFaJKSxm9bRvB7e2q0BZAj3uayuvcZU/zGAyME+ZahIe7mTLFS+Wt1WhQGqsjTKSJRMbH9JA6yypMOErGNyZ3OREH1CG14J4eakinoPRCWd61mGUk7tqmG1cNVu5pvp+WfirBiopwDhwI5ZVXWjHl5fFgRSNHHF6DofI06+qI7V9xiGQ5TXY7t+9cSi5NOg9bO0EHCnXxQwwOVlHdE67aR1mMIcJwya82VbUI/Hhvjm4Fy3QGA9Td/uGcFjoKoL8Oo3Eb5QQzOv/lsw9Jgt1u4mh3MpcIjtEZ7Vkd5OV14N5SJ3S+Qt7ZiXvLTczrL3ve0U8Y8jZzSNfI9Q5XCfFwISCj0dbWxnPPPcenn36KQ1Pm9vLLL5+VgX1TEIiHJvImelNSCHI4iJs/f1CSpqD3ktOxsbV3pmppm5PjYMmSGGy28TzDSwBYD3lipP885DT0uJRNjaJuVm3pY/78uAGvMMaNc/HUUyflD1aU5NUZq/Om0bpGXF0kQVTpUoO13/uWcjjj2c897KVMp03oWVGpw0T19SE8VhjM+shSGoLy5e2+KrS0spySkb+6xfvMlB72YAjrRCvdsDAxmaQvo2Qkv1qQc4CY3JKApXeNpGclz9+XWmUyDaTEnyb55HHqzhif45jNy78kGrevnKArIcGwok2J0tIoqnpXcAm7Vc/3SNBEolYvxW438VhhMP/TJ877jjrRwjR2MA3vM1ZysCmLE4ZaQjzcCGiZ8OyzzxISEsJDDz3E6NGj+d///V8uueQSfvnLX57t8X3tEYiHpmXD7Z47l76+PsIrKnRMnQOBaHJMx8Y6ywNs3NjKmjWe5JkoOV5WFumTIVdCR16eT1lSf/fBFxoavBKwRufSIqCaeIHxEeVwPB/xw7p9jaRTW3d8TsSmTaR2HpK3DaRCSxQKkzzswepji1a6SrEpJXwZJRFj8msr93HBkhuFjLJGEEnPKhmVfalVmlLjeeWvp0id6dt5shzaLo9BOe7Ls05xU+oO/p51H4lzM/k8/rvksJ5ZbCOH9RwePYUQm83D4+XnW2tqMsm9QmXcwnZmUcYtLDpvC2eSLdw8P5rXKsbz6zN/8Cm1C95n7Ll+j/Ecbvbf4URAK43PPvuMP/7xj4wePZqgoCAmTJjAr3/9awoKClS6FSPQw8hD004ASm8iJjdXpyUxmP6BQMo2fa2EAvG4tMvnEIuFtkWLdN5mXl4H+948SU23N5acxlG+xV5Ojkvn36YLhKXIO3eGYbd7xqIqa7bZMB06pCpNDrROX7SyM8rhfMzF8v+7o6PZGf1ffFIn1gVP7fEkRZW5Fl8VWtqPz6jW/0Jz7aD1sY2eb1iYW2U8AjFK2vchJrdkwLQZtxXFcdsnW7ij4WGSaaCBZJ4c/xCZ30okpbOHT4Nvxfbeu4TRQyJenZe+pCQmb7wbl8XFLbcH8+Z7biFdTQaHecSxmOjCTJklWT3uc4An2W83cfP8aI7iDdPtOp3N21VzoKqVBysasWVOIcEaKgxPSU6QTZl3Auad20VpKRytD5erpo4xnmBcHMPMJVQRgn6lJ4XLkmkYln6Ts4mAjEZwcDAmk+fli4yMpL29nfDwcNra2s7q4L4J8MfVJMJgeP9FEE2O2hdyOGLVSoNnNptxCfIsFouLv3/7fh55b66cLJd6Gbq/M5dbIjcJBYpaWjzhlddW7vNIXfaHQU489RT2hlE8saiDxvYxJEV3snRlFCkW/41nojhxnEOcw1HStZy+8krC8lYz9obRODQh8bRRjRSf8YQ8lLmWv0X+N1f2fSAsndXKchoZ+UnT4+gQ0N4riRW1DZvS+3Vh1BF+w6Oeck9FjmTGjB4iI/sCfidFGMx7arG4ePzVsZSWPiufe3VeBxaLhyPp5M9e40q204tJrhzrYAwXTR7HKIvFw1C8JEY2GFP4hG4iSKSJDLz9Me4dTfJqQVSZVFoaxdF6dV7nCJNYzCoPTb5jkieaUYY5AAAgAElEQVTPWCXurcjL62D//nCqq70NnZLhXbIkRp/LAtyY2MdFXIJeeLyBZCaG1fHAjPdoKxoYI8EXjYCMxqRJk9i7dy+XXnopF110EatWrWLUqFFkZPhedn0T4esDNUIgHrsSg23s0u0fQBIt0JXQcGCi6zBlrNdtD+7sJK/IR9OerY6khTcRoZh463Yf47+D3vZ++O2wa4nvCiAltHHie+1udh/opb5e/Uns5VvUYCXVSj+1uYtXX22jsDDaw+3jcvHdsL38/vSvST+jzh+VsZCuufPoyHuRrgASmUZG3pGTY1j2W0M6N9wQqyrh3r07lFdfbSOdGlYduJlwjsp/k7QZiorGDrmOf7Dvqa/vobhqnpzkVnrwPznwNo+hD7f9D3/mu3zI5agZfoN7eoRcWGEVFbS98AJNTdcIz7+Ly2hGfV3a5LrJbufC0lI+Nrfx0ZlEXhr7P9x86s9cHFfLqMIxWGry+A1rdbmsSRzhPabpzlkXNpH3ZjzAhqJQYiwPDaDn+8tBQNToDoeDvr4+xowZw5kzZ9i8eTPd3d1cc801OlnWryKGixrdHy34cCFQiuXhkHUERcfsELzOQMZklDSXaKubdtVT/ZNVxPY0qrzi9eSQwwbVb4x6NLR08/K4Ojro9SPi87OfjaOiIlz325tSd7B6Y5huf9FzUsKIFlu6321to4mNPa2636Km0KjSUsP7Ns+xQTjmuXO72RR5i/B3x+dej3Ptk7rtEgJ9r4ZKeS7CzRe18m6LPlU+M/4TSv46nmuvNavCmCE4eYu5OkoaAJfZrOPCAk/j7G+u2KPq1JYQTxPH0BtDia4+ECLJGqzYsTCDnbrjHGAKU/lUHl/P9OnDXg11tuVeA1ppKCnQR40axQ033DCkASnR2dnJqlWrOH78OOPHj2fx4sVC7e+bbroJS/+NNZvN3HfffcM2hkBh1FHtq1RxMPiiy+wGuhIaLHxWif3whyQeOkRWj5fdU6oqESWTjahLRBVARiI+2smto0OcIK61ZAtr4o3kcn1NBnrHI0IV/hBVyvgKA1V9JmYyraoahekc8e+iO5sYjgp/7XvaGRzF4cMhhFx9H53RSUStXkpidmDKhxLMWYnCMOGYqRYWLAjX5b16CfWZPBchuKuLYpbxprUcmy1Uzj1kcJgeRnEb6wxFyETPXMswnY6NCPRkigAWPCGzoRrXLxMBGQ2n08krr7zC+++/T0dHB+vWrWP//v00Njby/e9/f0gDeP3117ngggu47rrreP3113n99dfJydF7kKNGjeKxxx4b0rmGCqOkos02uPp5X/gqldkNJiQngnaScY8ZQ8gnnwipLcBbVXIqMgntNxioVrXdbuL3N/bQXPf/ySp96diECduB5neMJvPec881fHaDcTx8hoE+E/7J/+8CQCD0KdJ72rSrntibFvDd3n7D3A5Hb9pD08vlAzIc9xa5qTrQrco3TEjphshIw+9MRJPvjogAA7kA8BjO8vI2ni9spWDHtXIRA8A7QbOY2fcP2XAow7X+JBAkJNCCkxBC8Y7hTHg0o6ZMossPc8NXHQGV3K5bt47a2lruuusugoI8iZ+0tDQqDD72gWDPnj3M6OfFmTFjBnv27BnyMc8WjCaVgwdDsduH33B8FSB5xps2RVBZGcamTREsWBA76OuVJpnWjRvpi4zUVYlpcaG5FusLi3Wltg8l/8kzmSigzcVIY3+5bgbvMJsN5HAVW6nBcyxlwtZuN+FwBOl6GHzldwYzKQfaWa2Er7JmIybTrKwzAZdDm+x2YnJziZs/n5jcXKipkcMwgZbTdix6ggm91aptE3qr6Vj0hM9zaY9nsbh46ZV2VWnvS6+009FhLAagLX19zzSd4K4uTCeNV89SP8bKyAKVwQCY0Gdj76jv8qb5Zm6f+282rvwXF5b+mrgf/pCQffsMj6mFZDBcZjNd8+ZxYvtbtG7eLDftfV0R0Erjww8/5A9/+INccgseEabhqJ46deqUnBcZN24c7e1iZkan08n999+PyWTixz/+MZdeeqlwv7OJ/JyDvP36uXT2qcNnXV3BwxKiGoiE5heFsxmSC8RrmzQ9jpPZKbpw3di8PF6indJS4wqg5wtbWWFbqqocUjZQSZO7UQPcjBk9Mj2ICEaJ6wM5BZTkxghXZoOpVvMVriwqaufAgVBVAl9iOA0kzCmM0e/fT/SkSQMqpx3TLqbUGNPufcaB8niJwqVGKocSpNLX9IhGtnVd5nNfpeE0egfHnTnO1S3lXHlgJ72/htHHjJsOtTkN7bm+rmEoIwRkNEJCQnSSr+3t7QGz3BYXF3NSYPUHwmf1xz/+kdjYWJqbm3n44YexWCwkGnh6W7duZevWrQD87ne/8yiRDQEhISGYzWYSNi7l/L7fsktQAdHWNnpo56mpIfSWW1RCM+H79+PcssVDnORjXD4OyfLlJhobg0hK6mP5cpf4UDU1mJYvJ6ixkb6kJFzLl8vnbGsLUbN09k++bW1W4bn9jUkJk9UKPnSt+yZOJKSkxHM8s1nuoA4BxvX/522qlrZ6r6ng3WtJxXs/pRxJA8mqYy9datKFPnp6gomNHUVWlo9CD7OZvrfewqW4d9W/WMGC2yeqSjH37w9nyxYn6elQUgL79/ep/j5xYh8lJX6eZYeZu8M20hgaRFJYH8vHuUjvvy3btrlZvtyleM5u0tPHyWPU3jclTEuXYtLG6KurGd3ZKRzH6LY24Tirx6V4hKw0aB+TzAX9+4vOFWqzYV69Gte6dYbXDrBiRRAffqi+b0qMjXLxg9hKVrT+RqehAuCOj4cpU+hLSqJv+XLG9b/f/t7BkPp6dnMZBbygCm/Kx7Va6f3znzE995znHeifE4M6OnTn+qIwkG9wUMcPZKfs7GzWrFkja2ecOHGC559/nmnT9JOnCMuWLTP829ixYzlx4gTjxo3jxIkThoqAsbGxACQkJHDeeedx9OhRQ6MxZ84cVdPhUCsJpGqEOJuNDKqFRiM29jQtLYP3vGPy81WJWvB8vL35+YbxcV9VEl7v2TsZVla6dZVekven/JjdlZWydzQ57CR/5keqePGP+Cv/63qVlhZ9k5u/yg1lfiQp6g+UJNcS2lDHMlbgIog7Tc9w0cQTNLaPofDEMhou6yYpbCdFSU8ZaiCIEJOfT+pp9f2UciR/S/0lxzZswBUVBS0t2GxxgD48ZLf30tLiJ2UcFQVPeEMw+bkxuomtujqI/Pxe1qw5SVQUbNigr56KinIZKqzW72ripoVJ1HR52YiVz1IzBMCnWqsKcTab4MrB7XYLt5+OjRXynUWsXEzNf+9Wsb4eJoP83iJ+X3UCi8VleK5eu51WPwNOSzOzYcMJbrwxlro6fVn2D9xv8KLtesEv+8d9+eXq76j/fKZFi4itrPQpntbDaN5hNoCXH6rfcDhTUmg97zz9A1BiGCocB4KzXT1lmNN488035f+/6qqriI+PZ+nSpXR1dXHXXXcxbtw45s+fP6SBAVxyySXs6NfW3bFjB9/5znd0+3R2duJ0evhk2tvbOXToEKmpgeswDBdciYkUU0AGh1Xb0yMah9zXMBwNfcp4cc+Ni8CmVnCTwkpKGLG4RpWWAlAs4E6KppOif14/YFoTbX7ktYrxXN32/7gxdBMbyKGcW8hxrWPfkXH8oHk95Sd+wLsnLublpiu5Zm8pzZuqAqZTMbqfE8PquHvjZAD5Xllq3hPuOxiup0ByFlL4paLCY0h8Nnra7ay61aYyGCB+loOBUV7mTFaWKh9Sg5WfRL7GVba15ObG6HJaidkpFF72NxWlxhzeZvexc+RxDjUxb7G42LixDavVqdqeHtHII47Fhr/z1WEthfCOz72eY8Hxwn2UjLhSeHOgY/8mwXCl8dJLL8mVUffffz/r1q3jtttuk8NSUm5jqLjuuutYtWoV27dvx2w2s2TJEgCOHDnC22+/zR133EF9fT3PPvsswcHBuN1urrvuui/FaHTk5ZFatYC3bV4a8MTIU/z3Y+dQWjpxSNVFw1HpoowXz6CSrXwkk91J0CZc/Rmr6A5xrDqk20F0YSF9kZGqHIyIuVWCKD9y9HQyl/Iuf+A3VJNBJB084/6VjsVUzkXYFgZEp2J0P8+ZEYebGg377y/4SEO1bSR16w/D0WGvzG0F19bS6BCHbgbLequEKC/TN3Ei7UVFgMepqLUF8V8H11DjSJK7pN96azTr17eSne2dwA+7JrJe0ZCnHWcgDAX+IGJYKDl6J+l79SuFk0FjcV51Je6ie32uTl0WC7+OXE+Vu1nXxa3kxZIgGZG+iRO/0nQfZwuGRiMxMZEXXniB1NRUent7+cc//oGoD3D27NlDGkBUVBQPPfSQbntGRobccZ6ZmckTvpZ/XxAkryShtJSnbH9i37FUHo1+hFvuSVfx4AxU0tFuN/FbxzO0ht1Dao+XCmEgH5QvsjtlZ6128vJnrHzJh4bt2KGSDw2tqqLvrbc8IRsNTHY7rTtbQcBxepx4prGLaeyii9EqSVYltjKHWWwjcaeTRf18VEYwmqDcRfcK2X+1eiAPTH2PGIv+vfSHoXbYi5LFgZYXDwTeMGEcF2ZuoThzGdGdTR5OrJIST+gOOLlmDfm5MdRUqSleurqCycmJZfv2loCT/EaJefCs+kxNTbRHJbGMYj7uyDB0wPQcWCBg5uAffTNZH7meNRb/YWMlAaFEX9JAsoqaXkJY8BmOz7mesX94VL5P/0kw7AhvaGjgjTfe4Pjx4xw4cIApU6YIDyAJNX2VMVwd4RJE1TZaGHUmayE61sSwOjbPWEFc0W0+PSTluOLmzydMkNDbziyuxCPVK+peV05SKjW3Gedwb5GbdGowz5mjIgb0BdeCBTQ/oS+zjF2wgPm2VbzBPN1vfsQm/oo3Hu1Pkc/oWrSQPPYQm42Q1lacsbG4JkwgxGbT6Zxr0TNtGq0bfVOsGyHQDntR7Pnkzx7m0YorvIp//RTeWtGh9IhGXtwWrO8kD6Dyzh+zgXZc8+fHUVkpprWfO7ebtWtPBHRcEUTv32Ey+ITzcRAl/z4ra5xhnN5kt+P+3o0kOdXh2KNYWJpVwZOb/U/subkxQu6zyEi1JnhEhFteYQ0XI8Nw40vrCE9OTuaOO+4A4OGHHxauBv5TIQqzaBFo6EB0LFePk+P/PknGkiUBl94arQhMqfFMs/QYTl7+1dwg+IUXiFu4kOAub7e2OyxMtcqQENSoD2dJnr1RQFO7/UGKdYp8ITjpRXGfbHX03JhPXJrdZ+NZR14esQsWEGyzEWazwd69uBQMB0YYSqx6sB32druJW3YUUK3QTZcSr8qVUGLkKRa/YJXJGQcqSat956zUsMK2jMhra4mZHucp8VJ40L5o7auqvB3pgyHnlN4NrdqhBCl3U15u3GTqsliojbuIpCa10ZiAnT9+OhfsL/v9foxWiErt7aFS7HxTEFD11IjBUMMo2alEoKED7bFkdsy6I9D/DfiaACQYhWMml9/NRj+ykC6LhYLIlQoBIg+8/RjZHN+2TRVWCHI4hJ3cfUl6vXEpb9KEeCJu1myPvyKdNyIep7hqHg2uBI52jsfu9HYVD+QeicJ2JocDd0SEyggq8WVRU5eWRumegZTLWZuaz3OWpxW9Fl4nwaiYwXzttUI6E+mds1LDKhZzNW8RwWloATZ5+jRMG7wa1vk5B9myaQo96DmuRrlOy/8/GOYA6d3wqC8aqfGFUFODbhWjDANPTj4BgvRcQrcdZwDa7L4MXnYAErv/SQjIaIxADX+CQoHEsKUP7PPP1Y/Al9Kbr+TvUPmq/FX9aGlNTHa7jkHUFRlJUHU1Mbm5qnNLqyAte6h8bsV2p9VK72PLibFYkIJcubkx2Pt596zUsJ3ZTFQwt4LxPTJK9DsnT8Zltcp0JuBh29XeN1HYp4b0YaFV0d0Hg2dQFzbRp664YTFDSwsRmzbpDGpioktI3S0hqLoa8+zZuKZModdqZZzDwXQWsxU9ZVBm50eY7GHUkO5zUjeC9G4YcYkBHDsWxPLlwbpeGmWTaahVLJkMnnfDlFfEreGvySsjpa67hC+Kg+3rjhGjMQiIlrKRkS4yM11Yrb1+JxFfOZGBKL1pEShflWgiTEyMEe5rtGJSGSmbjdCDBz15jw8/JAK15y+tghJtTRxlou5Y8ePO0DNlmqGhk+43tjq2cpXOYMjXJbhHRmG7d+zn8oz1OfKeMH5WorBP3e5j3KykZGfghQ9GMHJG4macg8viFP4NfBcrgN6g5uV10FjxIJMceoMhwd4dz7KqxdRXJZMc3IRRP/Zu57dpLbyH0siVQuaAa681M316j+E3Ib0bKTbjvGN8vJvGRrFBlUS6TIKVthI7doZToVgpKXXd/9PDTQNFQNxTI9AjM7MXs9mF2exi7txutm5tYfPmFr9192CcEzGbXZhSxSGc4aoHN+IUKsg5oKt/97dikoyUy2rVhXqUvR6SgbGmiie+lJkWWjduNOTkkUIH61LzhZ6xPB7BPRLxLx0mg9tbHvXLoyUK+zzccIdOvGe4eiby8jqEz+DeIrGmt4RAZHCVBtVicTE707iZTcovbCCHd5jNi+6f8D7fE+7bxRiKq+YZrpJaWkw+77P0bjww9z1Sg+sERwCr1UVSUh9WalhPDtuYxXpysFIji3TVkE5beTlOg1L8J1iq21ZfHzIsz+0/DSNGY4CQVgkVFR6a5pYWE4cODWzBZrOJ97dYXEzeeHdAJHODhVH8e3JZSUCa4CIE0pjosli4e+PkgA2TyW5n3M9+RsJFF5Fw0UVcVHgr2eONKV17U1KE98hlsbBv5Wv8xLqT74T8k2m8xwy8DKa+JnzRdS3hCXnCUmI4eiZEOtyBPAOlxrzLoE9Ga1BDrcZOiCi/cBp9ZZGEBpL8hmx93WeXxULM2odYvTGMyEj1caT3Y8UvDvNOyBxy2MBs3iGHDWzlKqzUqI7tmjBBtypyYqKVWOG5h+O5/adhJDw1QAwHgd+xY+I6omPHgs66loavCX6wMd1AGhOlHE5srBuXy0l8fJ9hKM9ktxN7ww0qBtzwigrc4fpErAQjLTG73cSCJRf4rHYzmjhE15XFPrLYJ/NYScZnKD0TSgz6GfSv+kQhNXdYGEEOBya7XX6PRIUTEozyC8G4EJGLmLOSuFcQstXC3wSdne1k69YWYTI6YelyTL1iWpiFlGH7rJfFcxqpc6zAQq2KIyoUF8t4RNWvJGG4ntt/EkaMxgAxGGprLcaPd1PXvxJXkgGediRisi86q1oawyUlq4RoAmqMSKcxJ59ExDkck8nJU0+J49xRpaVCyvTg7m5ckZHCnpHQhgaCCx/j15HrVQnqQMqjjSYOXxOrcsIatDxuTQ0xftQEB1qRJDkd0YWFcvNlcE8P4RUVhBw6JOeZRNomoQcOEFJfb9hMePl0J1Uf9eHo9k4bE1K6ubfIrao+2rkzTCeWBIFN0EZG83R1I6JC6eT+sR46GEKVy9sDpOWImkC17rcpKb1nRdb4m44RozFADAdNxIQJLvbuRV/BcgKcCyqHhUrZqNlrOKgctHBZLPxr5UZst65irKPJ00nbVQxLUuWJZCCrM1+U6a7MTLDbhTKen+9oZZOiZLWqKpS4ON+U2j71MvonVvO11wrPNyW6jnlXdg2qeioQNUGRsQ2oIslioS8yUtdHo02IiyriokpLKTryCpUHrqDaNUH+28SwOv4UvgJH2a8oKZss7FuQJnyjJj+j+xyIYdzbnMwVgt82kNzfgCcuVS5jIQC9hPBjNvEPZtEVMpbZs0/7pL0fgTFGjMYAMVSaCOUxVtgGV14rdzq3tRETGxuQRoJyQjob4a8VZVPZ5HhRvdGGPBmI0NxsEhs3H9VAQceOGf6tukedBLXZQnG5xMl3s9nls6pHgstioWf6dKHW9pQrYwddohlVWqqiwQf9sx9KKHQwBJiSEUkzm9lQdYLHCo/TuuNzD7VNTwHpFTach97k6SH0PGhhZBilpjrJkLjHFpPIhzpeqJUxy8mc6KKqSv+OSRxRh8ngGW7nda5nO7NYc+3fRkprh4ARozFADKbr1egYkdfWehqqNPD1YWsNgra8FXwz10oVSsMd/vJlGIxWZ4lj2oXG7eTKlYTu3q0LUfUBoXXiCpu6sIkU9BTrtsfHuzGZnAOittDibKzOApnUhxIKHQ5G2fWRvyaiR20sA3FqpN+vWXPS6xQsEYfgjAzjwoVxKj63MWMmCnmhsmYlEOQ4RRXjdWMIxk0Zt1BAMVfwPgCnIhNHQlJDxIjRGCBMdjsXlpby0hDV9SwWl4eyQe/AEmy3q5KWSvgzCDA8NOsDha+wndHqrJhlwmuJLCuj7dVXGXvvvYz64AOC+wXAROUDLrOZnunTWeFYga1CL3Zjtbp46qmTrF5txm7vDdjIa1dAJ1euJLKsbMCrM6PQS3tUkmCag/Yx3sl+KKHQ4TB0gb5H0jXabCEcb3CTePooGSE2Hp68jvSadwmp9/YeaR0cI8OoNBgAnZ1B9IWnsbDbm8y2Wp0U5OxjzG8Xs5/ndRVf25jDNuaQxlGKKaAxIh3rC4tJHAlJDQkjRmMAGCjHjz8YJVpD6+qINaA+CKi89Swku3Xn00yqBTn5VFVdLAzbGa3OMpZ8bHgtLosF9/jxssEwQu+553JyzRpus5t485B+RSGdf906l39BJcW1GT1nuRt8if/EtK+cxPMUcw/7deGWxylGIu0ZaCjUyNDV2oIoPHYXtXEXk1Aa6tdo1tRAfn4MrZ8/Qxr7dWp12qo47TXWMpk9TGbBe09xjqZZVevg+CvVVSKzez/Hxk3itGmM3NE9ubSEiIb3ZW6uI0ykiURM9HKEc0nBzvaY60mYleVpYrWk+D/RCHxixGgMAIF4+QOBlF+IvfFGXdjF6LiBGISzEU5RQjSpXlBVxWsrN/LYxiyhRy+qivF3LYFoiEv7DkfYUILRc24tfJ4Fh54MODHtKyfR1BEjDLekdCYDrQO+JiNDt2/la9y45AJsdaEenq4q32O2203cckso1dWj8NDYX6CqRNK+R76q06IQG7fDO1u5Y34ciYkucnIcenaF8F5VhZaEKXzK4hOrWEiZ3BslvSPp2OSkN3jYnX/Fs2xJvo2oV//IyW+QRveXjRGjMQCcjbCPy2LBnZYGgli96LhKg6CiMnecw712t8z66S/ZPRAqbS18NQiuKy8P2KP3Z9z80WM4rVYO5uSzIjdmWHmgjJ5zcdU8bC2BJ6b95XkqSdf1DmQlqDvrA+3bMHomTyzq8BiMAMdcWhqlk6s9wiQeMD/Dc9OfMSQ/FKEBsVf/cUsalS0eqnVl0ttmM3G8oY9xLZ9TjZVOvNLPGRymmAJq+mlopGsoM3hHRoXBGzMeZ2zR48PW4zQCD0aMxgBwtsI+Azmuisr83QKqT2upzNtkw2G0+hlqmG24jKfIuB3IKaCkdDJNTSYujFrBmvAKQrr1fRnO1FT+tXIj1y+52KfnL8Xb29pCiI2NCcioGD2PBoOmN6PEtL88z/794aoJetD9Hhg/k8b2McLtrTsPY7J36Z63kRGwnzuTk2v0Alq+wksFFDOHt0nEW/GmVcKz2UIpK4ukIOdAf8l2Iw2kyPtcwL+YSI0cItvHxfJvm5tNdDwhdjzOKX/U049iOLoRDBZfutGorKxk48aN1NfX8+ijj8pqfVrs27ePtWvX4na7ufLKK7nuuuu+4JEOPuzjrw59oMeVqcxPG1GZ+/ZMjbzS2BtvxJ2W5nfl4cvIDfSFkoyb3W6isDCaHT8Jo6fHkwSt5Dx+GTqVb/Oh7ndui4UVZVN9lqTq4+0RAfU5GD0Pc2Yi6NngDRPTvnISFouLLVuc5Of36kJPg6EYN3omSdGd0K7fntbyMbELCnSOwkCT73l5Hez9MEjHxwUeyvsj47IY+x03wZ2dvPOZhdtbHtUp4QXZaklaeBNZXV5qlmx2cQ2bSaJRlVP5Fns91CGkk5AQ2Kp6BMOLL91opKWlcc899/Dss88a7uN2u3nuuecoKCggLi6O/Px8Lrnkki9cJ7yGdAoztzDPUUwSDSRlmf3qDwfSoDWYF38g5ZjaUJTp6FHhb0Pr6uQwma+Vhy8jN85wxMbwxfr7qfMcodFwJST4vQeD7XMweh734uafBsl2EfzlJNLT0Y1DdC/2v3GMLZNzSTt3lOq9UBqXC6NWsCplL+H1R+XfOa1Wlq6MYtcS9ZilUI8obzbQFZDF4uLvUxfzaP0VchI6kSYyqCaLj1j5nRd4cq2nV+aZ3BhsAnW8u44VktSl5vKaxBGZ+mM/F8s5lQnYWcEyCqxr5TGdTQaFEejxpRuNQCb+w4cPk5iYSEJ/uGbatGns2bPnCzEadruJpUtNfPaZmUOHTDgcCTzDSwBYDzkppw1fi2CjiauwMFqWyYSBv/iBeoSiUFSXyb9yXajNxsEbf09+2jqVtysZIHdsLE6Xi774eHr7DcZgvTtfydQCipkZVklqj7cRTjJQiaW+78GQ+hwEz8PCwJPtA+WSEt2LatcEig8soOzAQlUVl9K4VHIenyS/xaa5+bLWd0deHimWRMrL21h97T6aW0JJ7peQlTx3bUjR1wrICBkdH1PGM7rt25nFq53RSIl9o5XXxXG1spiWEhJFiLa7+0Jz7bBQ0Y9gcPjSjUYgaGtrIy4uTv53XFwcn3/+ueH+W7duZevWrQD87ne/w2zA/OkPNTX0V5IEgYCozWYLZfVqM+vWGb+8bW3iW/zuu6Pp6DCTrm8tCGhczlM9jA7u4bTbq908cWIfJSUhqus1LV2KSROKinA56Awaw5i+Tp/ncdUdo7LOc/z9+8PZ8kw1595+i6qTuW/UKHjxRcb1X0hISMiA77fRPQKwkU7pVW+xaswyghob6UtKom/5csalp1NSAvv396m8YuU9sFpNCGTTsVgGPkYJZjOUl0v/CoFBra36fy24V0b3QsqnhNpsmEiMfioAACAASURBVFev5m7KdKJE7zecy13f+3+s2+xSjcxshrI5z2PyDtw7BotFN4bExJABXaPJakV0oxtIVt1rsxneequP5ctdNNacJrlpL8UJa4iqO+rzmrX/P3VOMq6swd/34cJg3vUvAmd7XF+I0SguLubkSb23tWDBAr7zne/4/b2IwTQoyEhxGubMmcOcOXPkfw9WZD0/P6a/9NAYW7dCVdUJQ68nNjYGBLTSp08HkZ/fO2A6A7vdxM3zozla76WZDqeLOaPfZdlj8URFJaK83DibTWDu4OO+83GmWsm21BJstws7rZUfanV1EIU/q6O8Tk19EVRdTW9+vuyVD0bU3ugegccT/cmDUTRbnlD/oaWFqCjYsMGk8/yjoly0tMCiRSYqK/UcSIsWtdHSMjQv1V/eIZDqNNG9MroXyQoSwV67HVtfLyJHxm7vFVavmRYtIrayUhdSbFu0CJdmDAN9hqZFi4j8xy7GNB+Vtx0mgz8lP8Tji1pU9zrmhJ2/tBUSts9DpohNcED0CXPp+o3G/GVgMO/6F4HhGFdysrGS4hdiNJYtWzak38fFxdHa6v0QWltbGTfu7HsagWiBSyIwRsvlvLwOtmwZLSd3lRgMl39paZQu6dhNBNGnW5ha9iwnswPr66gmg6ctz7FxY6swhKX9aAGa+itxpFLfepJJoYFltk0EKmUjmmxFYYuwMDczZvT4JZXzFf5R5hTa2kYTG3t6WEpy/eWpRPdz9JYt9MyYQXtRkc8wXl5eB/veOkVNl1drfQztHGYiOaynmAISEhJINAiJBqS0OAwJY+VzjIoaR4t7G4t5SO45KeE+1nQs5qLCXvmaTXY70fNvVuVdtOgmjArmsojVcsJ8YnA1RZf8ja6UeSNJ7q8AvhbhqYyMDBobGzl27BixsbF88MEH3HXXXWf9vL7KCZWU5g22FDbfeysPjv+TzrO0WFzMmNFDRYW+umQwXP5GhqyBZGHJ68GcfGLf2M8El3eFIBmErARxIn6XPY2f1pXoqlwSozupafeouikpGz44eCUv2oP9Tsa+JtuB5AoG0mMiGRWP9zU0kjppohRRfysT7KLqNBE9udF4X17fxKpbd1HjSOATzqeTaHYzjd1MozLkCspz2shLHjhx5nAljMWFCxN1PSfPddzMrIqFhB44QOsrrxBc+JhPgwEQTg8X8jFX8D4TqSGZBh6Y8x5pm1/4Snr1/4kI6jNSr/mC8OGHH/KXv/yF9vZ2IiMjmTBhAg8++CBtbW0888wz5OfnA1BVVcW6detwu93MmjWL66+/PuBzNAi0GQKB3W5i/vw46uvVtjUYJ1uZwyzelbc5CSGUXvnfnyVfTv75m2jsiCYqys0nn4TQ0DB40jwJubkxbBJUoNxCGc/Oe001KUgfN7Y6Xecx1lSfXcHaSSE9opH/9/g+Vt57mpccXt0CyXheZK4lY3ocISUltESJ1x1GY583ryvgMJ3Ii3darV5uKANDMtQlu68KL+85XJxzTi/Wz9/h0ZbbVdQbSnTNm8fJNWt8jslkt7Poxh5erpuh+5t0vyQjFgiTrHZ1l06NoeH1d6+MnqMWs9jOdq6Ur/nIzlYuaHnXz6/UcFqttJWXMy4r6ytnNP5Tw1NfutH4IjBYowHws5+N060SrNSwgVu4HEGWFeggkk84nyNMooBibKSTktLL1KlOOjuDh0Rx4c1peMeUwWG2JN/G2FfV3a9GH3dqqpONG30brPpdTay61UaTY6xccZNqhVlRH/L+J/HyfVDpgQB9Y8Zw5pxzcE2YoJu458+Po7IyTHeuadN62LgxsC7ymNxcIU25VpxJmmwCnQiN4Gt14QsZHOZfnE84Pbq/ncnKotdqZXRbG6cF1PYShuN+iYzdhJRuKpyzOOfYbnlbb0oKra+8gqs/Me7rXhmNS4sfsYm/4nHuDmVdz23/up9qZxoAl1HJKhYbGlZ3aCg9s2bJoa2v4gT9VRwTfENyGl9ndHSocxGiiVKLKBxcxm4uY7dXFrQ+nR9O/YyVCQUe765UHFbxF3qxWFy89Eo7jxV20ravmUR3PcuyNgnpEoxCWRaL26/Bmlq2ghcdmsnZBqmpnwIeo7ECvR5IUGcnYXv3wt69ul4PX2XCgTa0GXaja9T8hsIJJiGQ1YURjjCJKrKEjoXp0CFGVVUBXmr7f63cyIqyqarrHw7BL1EJ79H6cIrIpQyv0Qiprye6sJATa9f6PWagJINSqUoNVq769GlqnfHy3/7KPKr4Fm9yNT3mFKZM6SX0008Bj1H1l/sZwZeHEaPhB9oPRDRR+oIkC1pAMQU7riVC0W+gnVQDpfewWFw8udaF2XwuLS2xwAXCtOhQJh2jybko/g/sMk3DZgslWcNgqoV24jaq08/JcfhtgJSFp3yUWuuuYYhU8IFIxZrNnvGJViF/iCngsu7rVQp67ogIoYGz3bpKJWIlcTINVfDLZvNdwquEZMj8IS+vgw8/HKUL22qxi8uYxTZqTJOo7Y7X/b2WCcwI38Pmzd20jfRcfG2gL+kZgQp5eR1MnOiN4PmbKEVIpoEVLFM1qIF3UpXgi0VXC7vdxE9/6sm55ObGsGtXKLm5MfK/7XYTeXkdWK1q5TrRpGO3m3S/Naq6SrP2UV7exk2pO3ALi3nVUE7cUjXTvHldTJvWw7x5XZSXt/WT1Yk7t8FrTCM2bRLKrrojxPH1gXKCmex2YnJziZs/n5jcXDha63P/CSnd7Mi6kzlsFf7dOWsmx995h6558+iZNo2uefNwZmYK9x3rUBtpiZNJdL8CDWva7SYOHRI/o2hOBXQMIwQS1R5NN7/gL/zF9VPWk4OVGt0+limjR5r0vmYYWWn4gbJD9sTmfxLWq49RSyWoP+V5rmKb7u8NJJPBYeHxnbZj5PaztFo/v51HqdLFeUWiNx7P3IRUq795czi9vd7eFX9VSZLn7rQ103jISpXjEblaqqoqlI0rC7jYgCrEYnGxLi2fsDpxTkcJ7cQtKpE1CqPZbCHk5sZw+86lnNsCy1gvl/kWU4DF7KBn+nQcOTnELFkyNMEhwSrvqYh9VLFNV0VmNrv4XlYbJZ/M49yK93mUN9mjqSiTjLOkyy6FHIOPHxeeX+T5NzebdPfLZLcTlesNXx7IKaCkbLIwrFdaGoXDIb63e/kWNVhV79qZrCyf98hXfkdVTUgKf+JXPM/PVatyOVSruJ9Way8j+HphxGgEAIkjaJxjBeEV6omyBm8J6ntcrst31JPMR2TxY14XHnv7IQubqjyeciVXs4etMs+OBO3EKwqbKA0GeDzVG2+MJS3NTWKiiyeeOKkK9SgnyHl8wAV8KH/QNlsoJWWTedpHXb8/2nIIfOI2CqMdOmSiqmoUP6FHV+a7i2z+brmPqDVPAvjsQbDbTfzudjvX7PJUkCUKOMNEq7ykrhpWRT7I9YqwkVT1dmHpr4lo8EiIpmOTRYBqzRcSN32SyjhrjVFfSAhBvd7JsjEinYIuvVStP0qYGqws2BxLda93pbXvrVO8vL6JlOxEn31GdiaoqDmcycm0FxUZ7+8jvyPK8/2IvxKNmnFACtVKpbnJyYNn9R3Bl4cRozEAtBcVEbZzJ8Hd3fI2j1qYZzKzkS4L60zlE77FflJo4PcsFR6vITydxY5HVNu0PDuiiTeQpkOAurpQWaZDWnkArJgXxJ6mXYC3ikX7QTc3m3zW9QtJC5OTCc7KoretTdg8ZpTsFuU6IiLcOBwmrNSwkf/WSXkeYRKFx+5iZf+/jcZqt5u454ZTPN/wA++kVgHdB6pof+Ulbz7JIIczO9POPGuXfqWm2V8SAeo5dxqtazbK20XGKKi3F2dqKqaMDE7HxtKYkw9LUlXd0cowonTfWne2ktayQuaOuptVVPdOUB27piuJVbfuYtXWMyQmxgivSUKt+UJ6zp1m2OinLMr4vNYCdSWgWXWBOM+nNRjydYXUY45xycp7I6Gprx9GjMYA4LJYaC0rI27hQoK7PGI59Zqwgq1fWGcKB/g35+uOUc0EjpJOA8msDFuOrVv/Efr7mAcikSmPq58k8cCBUOqbvCsXqYplBzNVVBX+kuVGHcbjsrJoFeQdfDX2AWRm9uJweFJsl0w5wU3/ehhL17+YwFF+wXPCMdTFXwzotTaUKC2N4o6GpbpJLbz+KH2KJL3RyinUGi/sIQlUA8XIGLktFvoqKjjZ0kIiGIYR1ffNq6T3F26jgquFx25yjCWqtJS8vKd1xliJuOmTVAZONW7NqmYGlWzlI114CWDiAApDvn3tOPavOXta9SM4+xgxGgOEMzub49u2yZNlQnUfCOYFOxYOk6GarP7B97iWv+HoJ92IMaCC8PUxg7gKKSSkTxei0qKqapSwyqe2P1Txfd4EBiEG5CcparebuPHGWOoECnKFhdEcOhQiX4uVGp6unMOEXm/RQAriPpt4q/9S2KYmk2HxgjJXNFBNk0D31xoXKf9V+9lFJP/UxKJFnryFMnfhz8M/wiR+wP9x2oCvK5kGTM3NpFPDlsxC7jt1M1vbs1Xklv6esWiFpF2NApjHnSGj/SiiV/kMIYxSNLwOp+TwCL48jDT3BQBfzTJ2u4mZM8cLuaUujD7MfR2FJPY1so8L+SO/YTkPy8nCFWFFdMRZBtUpbrebWL3aLOtx//KqfxPx6OOMaW/kfucKtnZPF1yHy7BBbVrwByReHEefNS2gxkOjzuy+t95SdYT763XQjmk9OeSwQbWPMm8kIT2ikRe3+acuyc2N4fpNv9IdE8BlNtMzfbq8mpMn6wC5mQLZX3mfRNehfd5GPGAiD1+EcLo4wHkkzs0k5NAhVf7jwchV2DNnE28N9fmMzWYzfTNnEiZgrt3OLK5kO+CpHnur7yrO7c/taPFPLsYeOYXZmXZCrfFD4o36KjbSfRXHBCMd4cOCs2k0QNw1Dp7Oa8m7FiULD5PBw1dspnO8NWDtAtG4RAnSOSHvqOLdVquTzMxe4TgBrp97XBbLkeCr4c6wM3vBApqf8DLS+qOc0BqNbcxiNu/o9pM89AaSSYw8xeIXrKRk60NE2jHn5DhY+dsT6pyGBtru8eGGZFx+sfN2ylv0ISUljYrRfS3jFh23kwg/ZhMbrYvpzcwkvEIvM9g9dy59kZGGzaOSM3Lb1hyubtFTqe9IvYkHLOtISHCxwrGE8yr0OhrKfcM2rh6WvMVXcYL+Ko4JRjrCvxYoKmpXhVjAM0nHxrrlRLQoWTiJI9xy8GEuePkJr9e6xD8JnxbaUEI6Nrb2ziQ/dR21lmzZGAGenIamKSs52cm9RW7VNr9Mrgax+qDGRlV4pfXzZ/DE4vVITnbidqtDag2kCPdNTXXxnOVp2aOHM0Tl5qomP60wkTTmlU/CH9a+yTW7lnFpx3bG9RxTHXs4usdF0Hb396RNAMG3rGQ7NrqvyQYhOiUmhtXx6Iy3aCsqJ2bJEuE+YTt2qJoNlc2jylLuSh5lK3tU76zTamVy+d1stHgoTOLmf2w4Fmlf10ii+xuHEaMxDDCS9SwtjWLvXs8+RnH1BFdDwJ3gEuTu6LY2YmJjhfKt6dhYZ3mA1o3q3Mgrr7RSWBjNnj2j6OoKIjy8j/PP71Ud19TUxJLaEmwasjwlk6tRIrgvKkp1LWnsR2Q0EhN7CQoKoqlJ/QreRwlXhFSqchrKlYDJbie6sFA4+RVmbsFmUyei5Sa5V8fS0vIEEfPnQ6XaaIA6vzEYjW7d8QbQ96EsOjC6r6bUeKZZerDbg3W5IfCsajdsDCXG8hAuH8dR3jNQG0xlKbeyEvBCcy2TpsfpHBmjczhTU8/qym0EXy5GjMYwQdS0pkxYG3nQn3WmcOrG3zOjTt8JbiS3qpyMIvB2RGt1Lh4Y8x7aokuLxUVRUTsLFsRy4kQoPT1QURHOZwfgrb57SOiPTzcbJOklr9goERzcP3YJxRSwi2xdHN8oVBacmkjb6nLiy0p0uQLRRKy8X/McxbIUr2jMIJ7oarCSb38U+/w4ISOxltIkEAyk70OZkHbk5BC+ebPcx1GDlYKgR7DFXEVCgoulS9tZsiRGt6rVjk/0fNxhYTqjAV6DKZVyaxv1but9jj/mRemu3+gdGDEY32yMGI2zCOUK5H8rish27NLlNPKcj/CXup8Lf6+UW5UmrgtFWg1dXRwePYXvn35DNTkf3fdtNv3st0R3NKpCXkYkdg9zB2V4jMZF7OMX/EWeOCS23gQDDQ5pch9///2q4yob3+R8xEorix6fanDP3CRmp+jEpEA8ESuRZBDC8YzZ86prJzo5/1M3QahTDeoVVqBaHoH0fVgsIdx74z4ml5bIxwtyOFQG4yq2cqRvEnwCfOJp3vvzhN/y59SbqYu/2DCpLXo+QQ6HMM8hlQknJrqEubfsk7u47YYtPP7qWNV5hlvYaQRfD4wYjbMMaQWSm5vEnE1v63QtbKQbrkLO4wDryfHsZ0untDSKlwwmo2Xhj3PktMKbp4bnj13L+ApFHuWNCqqfLKPZNkt4DCWVxWPcp9IHyWYXtyVvIS9vrLxN1FDXl5SEFlLjGwAO6CqbR2KiOKnrqz/EaCKWkJRlxnrIaUDwN04es3Kiy7c/6jEYftDcbBpQGDGQvg9zRwdBV9+oXg2M8soLKxtHJdR0JfH8v6+gnJk4TVbanvIkq5XUItLEXUM6pZTR1GciERf5tx/kYkVFFajLYPPyOmiseJBJDn3u7Y6GhyktfVa3mh4uYacRfH0wYjS+IOTldbCgKpWFNv1kWUAx2ezSJcoTOUYOG/gRf+UTzqdj5wTcWd5yVmU46t9d31L9VpR4j3A5mHDnQpLMfwVm68ZxgPPIYT1PcwdRmqa5SRxh0/n5OC1P+rxO1/LluDVa1FqYmpvJe2IQynM+qEucVivuonspx78CoHKis8+PM1xhKJE4pt0noaR24gykj8O0fDmm/lJcOax4pkHu+NY2jkqQjHuozUZ0YaGqtBY8hmzfytdYsOQCTVHABWxc+RpTy1YIVwYWi4spmTYQkN0m0zAoeeIRfPPwpRuNyspKNm7cSH19PY8++igZGRnC/e68805Gjx5NcHAwJpOJ3/3ud1/wSIcGZahKS/gmJR3XpeZzxeltdLV0s4yH2c/FpNDAr/gTz3IH9S3JrNt5kuXjTxJ6vFld868JVRsl3sf0dVJwMo99lOu82GMksoEc7mQNlym0FiREdzbhV/onPV325MPeeQf7iWhVnqWYAhLHjDEsHvCVNzCK0yu1ty0Ya4aLEEh3fQaHKfnkNkISu4V/1xJKSiGsvrg4nC4X7vh4XP0GQ7kiCWpsFPZu7CKbt5lj2NSorKQaVVWlY/4Ntdl4YlEHNkEz5ROLOli90TiEFGpNEBqNBpIHJU88gm8evnSjkZaWxj333MOzzz7rd9/CwkKio6O/gFGdHUihKmHDmzWVsPLVHP7Nr7mmpVQ1iWzgJ8gs9t2eSeW8xFqONKknfSWMQl4AaX21cp5hK3NoRu3BH2GS0GgESjUuefKnbrqfa997SDchbu56mLGoiwcCqVg6GzF0UXe9haN8i72041UtTG+w4QxOFV+v4r4Imx5NJk4+9ZRunH1JSSzjf4S8WstYISwiyOAwxRTI/25vD+oPvKnR2D5GONZjdS5iFywwTFZ35OUR8tF+RtWqNeX/lPwQj4+QC46Ar4CeRmpqqs9Gkm8ijHQlLBYXhccX6SaR/7+9cw+Lskwb+A+G81GGUY6CSILpakbiZ6Z5Qmur3Q03ixLXyl1r1VbTFqMkMDRdNQ+l9mmaFbbSWtJh8ypFw6zwgIh+eUoUUOSgOArDUZiZ749xhjm8A2NyKp/fdXldzDvPvO/9PjM+9/vcR/OvqaAukANNgyXPrVCoGTasge+Hz+Y60lnY+YpoQp3L2MJk7uSExfvzSaXYubfJsV9SAuL1U5MkF8TXT00yOaZXohkZbmRnO5OR4cbYsd3Zv99Sfr1CurJtG9fWrPlFCsO4b8bApX9n24r/Iza2lvuiKnnSPYMsRvEZE9jDWLYw2VBxWNOjB42hoSbnKnbuzZyahZw/r9s53kxPFHVKisU86ykh0BBE8Ecy8OYqf+BzkwrI+YSTdf1eyc8HeEkXDAykxKo8oJtfdu3g8vgJHFPczzeKOJaP/8LCCS64fen0ncbNsGiRriLsuHHjiImJsTouMzOTzExdY5wlS5agUChu6boODg63fA5zFApINyTcOqB31JYFettkY7e3l64zFRMDH3xgx/k/f0gCS3iYHQRSQgjn8aSGajsPFGlvol39Evz3v5ImkCLCWDruG1Z6JGFXWoo2IABtSgo+Yc35BQUFkJIio7TUjoAALSkpasLCTOeq1MqTeZl9kMl8zp0ru9EbpJnaWnumTFGQk9NIWJj5GW4eg1wFBThOmoTdueYn6cFHj5K+Y4euBn5BPxwewKTirOEcERGoU1KofCmFE5llnK0PYn5DKkU7w9iVr2XHjkb8lUrJ67solRa/IQd/fwLG+cN/m4/pw13vdjqOxs6ZXu4q3F26UVniwzEG8gPDKaC3IZACYADHTfu09+7NgvXBHHpOy7lzzb+TEAqpxp3R7MZth5oXTyi4/36J+/T3x/vLrXgDfQHpsImOpT3+D94qXVEmaH+5OqSMSGpqKteuWdqZ4+LiiI6OBiAlJYXJkydb9WkolUrkcjmVlZUsXLiQZ555hn79+tl0/fYuI9KWtFZ2Q88j3fZyoimSc9XNpiV9vD7ApFGNnGtoXrTDyWcXMSh+p0D1zRcGM0pxEa3WQzJHyrym/0xUlI9hrl54xpHtO7tbfP6B4Vdw6+5qMEUVFTmQm+tkMQ5MS2zcCvrv0FqZjtrYWIMz21pdLb1Jx9p3FBtbyxbiWz0/NJfr+PlnNadPywxl4M3DXRtDQ3lC/g0ZR6Q7/oFO0axXvMLQkAvkXQpmdfcF0Ksn8fE1/GfWCS4Vq/GkkiPczQV6GT7n4KDl448rGDrUtHxMVyyPIWSynd9EGZGkpKRbPodcLgfA29ub6Oho8vPzbVYavyakbOzmhJPPW9emADBXtop9ruNw9ZCxoWcqd8/5likXFnOuwTSbW28n39BnO9DsH/BbupSviuaRfOkfVuP+zXMTXqhZL9medeJEOeHhMuTybiQkqPjnAg25x+sovNicxBfUo56fCry5+H3zT8/e3rSEiTHWInZ+ada2tbDdxqLmLPHWfCfW+pmUl8tQvdl61JRU50U3Nw3rnF/hjqumEW+ORUXMUieTgWUdKD1FhLEi6j1dKZtiR91O9QgcOOBIH0UEjcVnKeAuE4UBusZds2Z148AB6W6CAoEUvwrzVH19PVqtFldXV+rr6zl27BiPPfZYZ4vVLuj9HcnJXuzd62xSPdfFvoEHNDtYyYsGu/Z2dSxbqicxuXoL08ue4T328i3ST6XFzr1NFi+9f8ATbjQzsuxNIfXUfcX5JcByB9Hc9MmNb75xITKykYj+GiL611FdbY+fn5qaGjt27jT92Wk01l1rHh4annnGx7ATiYq6znPPVVtkRduatW0tbHfP6RACzssMn28p/8BaxJWfn9omZ71UcmVtrT093aR3xIN6FBOoaTTJUjdG3wfe/JwlJY6UlAQAlrkzeqqqRBit4ObodKVx8OBB3nvvPaqqqliyZAm9evXi1VdfRalUsn79ehITE6msrGT58uUAqNVqhg8fzqBBgzpZ8vYjJETN5s1XDU/T+pDUxYXxRB6xNH3oQzDPcgcPs4NqpCPMfEf2QR3SaHG8pad2KcducMM5YFiL91Bba8+RI7psdmNz12OP+bZ6/3qCgpo4csSBy5ebF8OdO13Zt8+ZujpTRWOctd0SNfHxOH32JQ7a5sRFNXaE1JyieuIsLq5OsNpzW4/UbtA4x6S1hDdrO5USAiVLOzqG9uB3iiZJpREcrJvbOXNa7tJnDS8v4dwW3BydrjSGDBnCkCFDLI7L5XISExMB8PPzY9myZR0tWqdjXs+q20zgiOU440xuawojNNSyki38smq2qcwn23mUic+kJYwX9NbyIhQKNRERTUa7Esv6VOYKQ48tyWfuW7aYKAzjxDqv4kpyJ3anWNPsr5Daweh3g8uS7XE4eIgX6pZzd9Vp6hIGMt/1TY6pwls0mVmbg4yoJMaczpY0banmSN9zfb19i+dsCQcHLatX37q/SHB70elKQ2A7Uslt+YQbomisoVDoFrkwCizKTSxdOlDSP9FSNdswivhy5ELmu6+gvFxmtfKqMfoFvTWfzYgRDQZFeTO7Emi9RS2Ag9HcSSXWYaZXre1gwigg/ac/43jthkmpAdz3lfASZ4hhF9mESSqc8+dl1NTY4eysMTE9hoY28vQCX5SkY5+8jNLcCkoJJCMyiafxtaoUKip0TcCGDLmOo6OGxsbWo+gduY6/v4ZVa1UWTnCBoDU6PU9DYDt6e3ltbCyVUffxucdTJh3dPKiS/NyIEQ2EUYA8Lg63jAycs7Nxy8hAHhcHhRckP6Nf5I/Hz+cp9+2MZjfxpFFAKI2hoXR/bjxbiGePdjS5/eK4L/DnFmXXL+j6p/Tx4+twdjZdoc3LiLT09OzmJv1Z4xyMbjNnIjt/3jBGdv48stOnDa+lajtJIbWD8Vy6FEeJqDx9S1RoVjh69Lu6nTtdDQrD2VnD+PF1BuVSQBjRp9O5q2IvD1ZsZf3OfsTFyfnbuBNsd3+K3YwmjXgCaf7eGhrs2bfPBQcbHgGDnS/xw6c/sf+wUigMwS9C7DR+ZRjby/uqFEQlNhFU3oC/RxXP1a7i74f+bmI20i+m1pLO5jXMJcM4UeAGfn5q3SI3ZwBFNVGG45/aT+RxxXH+9x9/xvViIaBzie8KyuXF8V+RV9mXn37SUlPTvNCaKwNrPhtzc05Cgoqi78uouCzjPM1JdWGyQhJfruX1DX2oqpLh5aVmxYprBsUoVVAQhULnnK5pdvZbq+0kNRd69JFkLrt3Wx0fQya7GU0JwjCsAAAAGC5JREFUQaT/nAQ3esK/n3yFhUVzTSsHN4Th7q413LeUk5yiYga99DgBtQWGQ/9hIiX0NBlWV2ePu7vaZO5dXTW4uGiRyXRBBAsWaAkKsV7DSyBoDaE0fsWEhWFmNpnBR+dlLF1aa7EQWws1/d2VfbjIrlOvbs6TcHDQEh9fI7mA1WucGXd4Ba4Umhx3vVjIiiHzcfgyndzcqy0qA/3C61tWxhZ/f1RvSpcCCQlR8/Xd/6R65+Ebu4LelOFPD3UZaQs0FKt1slVV2TNnTjcORS6zno2dnm4xB9ZqOxljvIORav4khT/l+KOrR3XviWzK9qcTFKhm/t5JBNOcVDiU/cSwi/LyZuUl5SRfSJKJwgBQ4WkxDiAyUk1oaMMvah8sENiCUBq/MaSaQYH1UNOaRie+4gGTnhlNTXZs2eJuNcrHWjFEfeE+azKAdAhvS10KvVSldKeIVOYzjkwK6U0hvTHvEVVU5EhZTYVEIHCzXOZzIFXbSUYjnt3s6devCQ8PnQls7gwXQk+XsqjmNGHmlSFbIVx7jr2z3sQz+jpuDedM3ruDs6xkNml+zY2jpExyUvNtTeGFhja1STKkQGAN4dO4TVAlJKBxdrY43oMKxpBFPB+RyThC0T3RlpfLrPoUrBVDVPv5QUGBVZ8C3FxtJmhe6G3xP5RYMTfpCwqqEhJMakeFUcS7TMWdZtOZGkcedMjkXY9Z/HxcF+L7Y643W2tiGUcmBYRanF8l8+ZH+2GU0UPy+h5VZVZ3er/na96tmGCYr/nxxw15F3oq3S3zLFKZT4C96TlbKy0vELQFQmncJqhDQmgYObLFMcZOXL1pw3wBA11Bw3xMy700hoZSEx+P40MPWTjbTZzRZWUUEEo8aSbOdfPy4nr0C70t/oeMqCSLgoLG2djGgQQNw4ZxefwEkgLfpcbI1BNOPm9UPMcbO4ebZLJDc1a9ObI/jqFs22d8KxsrKVe1l7/VnZ4L1/Hd941hvgbMmcj2FXkmxSxDP3zR4r6CQ+HzbSWSRS8FgvZEmKduI6oWLLBo2GNOICWGJ1Z9pNPC2EJ2lw2gHl3+QhFhTOVdPvefhmvvHoasZ8+lS00KAYJlk6KzngP5Ix9alEv/wmO5RT9zMCp3MrGhxUKOxiGr+mzssx4DSCKV0jlehIbKmDVLRohZ4t3yGz6gK/vy6VlxzOYGSHoaQ0M5Hj9fl1ynXkQ0B01qRxXahdLtlUmo7u6Oy44drfpDHIuK6LtlMWtMkgODJLPMg0L8WTNUmKIEHYtQGrcRxiUunPfts2jeAyAL7mF4YtVHN6lqHRjPN9iBSY8J195BXNm2rfmzVkwwxruIJFI5a+Z5OMsdvH5sIh889phk3211SAizt8nYH2faytXNTUPfvo2EhjY7fNXolIJ50mJ2NmRnyyUT9dasuYbvY8/jXJFtON5aAyTj5k+Ll/a9cR1dMy19S98qPLlHm0O/f72AMj2dhpEjJXt0tzRfxnMg2qoKugJCadxm6Bcfa5Vc+6bPRn1DYTQvuvcA9xgq5errXjWercfvrrsAuB4VhcZTOqLHuElRqUo6Y/1SmR3OZbpFW9+u1Lycx810+pOK/Gqp1Ii5+egdnmcGayjDH9DiTRVVMh8G9K2lNiLWagHDIsKYjK6l72j2sIexUKTz5diy0zOfL4GgqyGUxm1Ka4X1pBZdvU1/C5PR2tvjaPRE7LpzJ01+fmiDg7ErbrYjqd3dcSgqotvMmagSEvD3l66RZNzCtLgInpgcQEFtczmPIwft+Kr/i2xVHbPYjUjVzmqpEq0U5tn2ntRYdi9UQ2N1KMoEXaSX/rpnzkj/NzK+J4eiIsOcyydOxLFY2tamdne/6WZXAkFHIpTGbUxLJg9ri26x1500egVLLnoO5eWoH3mEhuhoZEVFOJ46haymBlluLk65uVz7Og/HO7bjQiT1NDuZzVuYJrGQgtoAQ0OiQC5ScjGIDRejWMV6oDlMt4AwydpZkZHN9aWMsVZqxLCgP/qoiTI0x7GoCMWYMTTgjLreg5e0wQznf1jBXC4aJduZ35Ps9Glk58/rrrNtG/I//9kio1zj6oryww9vqX2tQNDeiOgpgSTWwm3lY+9E07On5HsAdioVxxLeYf+lPtjX1pq8F1hXwB/+b7lBYbhSy5/IMDF5gS5TW9+QKJ6PDCHB/+RNwxi9g92aGQqwiPySCkk9f17GzJndeOwxX5KTvbC/Ku1YNo74mlK3gfI6b3pqixnGfuawmh08yCS2MJo9TGKLxT3JamoMYcXqkBCUn36K+pFHUCsUqBUK6saP5/KePTQOHWp1bgWCroDYaQgkaan8t3qp9TIU5zwHEhcn571i6ad1Y5NNHW5owVBlNuiGgz3AvZKpNUkmUUhg6ZyWlZdTppXeEVVX25v4QEJCHJg1y7J4oPEuZTpvIJNI3lPhblHY8FMmMJ5vWHWjt8lATrCOv+NFc2/uWlw4QT8Gk2uQV486JAT1p592yc5vAkFLCKUhkEQfbivleFYlJOB44ICFeaUpKIgkUikqcrSaAKgPWdWbnoK5QDE9mU8qWYxhr/N4evdzJDjnLWilEbHGwwN/d+sNkYwz03UtMNUmXQjPXAiB4sVwo+CjtUz384RYJBbW48YXxHKcAYZdxU/8jnOEE0iJoYd3bwp0znCEg1vw20AoDYFVrJYkuWFe8UpOxilX9xR9PSqKqgULKH1ZF0E1n1SGst9kt6Av4y7VC1tfh6moIYziQ1BMkEmvC/0uxNjkA7od0aUDxTxf8rqhEOD/Br5GQoK3hdzmEWMhlDCQY5ThTwOu1hWdXZBVBWYcHHCOcEPklJ7h/ABYtnwVCH6tdLrSSEtL4/Dhwzg4OODn58f06dNxd3e3GJeXl8fmzZvRaDSMHTuWRx99tBOkFehRh4RwdfNmi+MBAbrVtcgsZ0H/5F1EGGnEW5ie9Nno+kV3HotxQGNS3XY/Qw1P9XU4c3hXDenZO0i//jrBRgUUH1X/wM8l/2Hm0v6GiKrFi6GnUQkTqV4aUoquMTSUL+qfBeu+cUoIpJQA3uE5k+O9OUtS/3SLEF2B4NdMpyuNgQMH8tRTTyGTydiyZQsZGRnEx8ebjNFoNGzatIn58+fj6+tLYmIigwcPJjjYts5xgo4jJUVNdraGoiJHQ86CPgkvUqEhkjru3H8BqdYfxv4O87LfYPpU70oDw7U/MFB11MSPAOBRXkhh/Eoy6v5tOHb0qJbcbsZJhpa1rIoI448uX7P3/nl4VZcZwpD/UuLEjicKOdfUS/KeewTLcFi9ho0r3yX1UDmlaj/8FNeZu9YHz6EbEDnbgt8Sna407rqRHAYQERHB/v37Lcbk5+fj7++P3w2b8LBhwzh06JBQGl2QsDBaTcLrNtMXLFudWy042NIYc4VhuEadaXb6uXN25AX3RF99y1qZENd+ITRufpsrRseCQiD94zIWTC9j96VB1GtdDO+FhjYyO70vjSFqvD8eyvJW70Ag+HXT6UrDmD179jBs2DCL40qlEl/f5tafvr6+nDlzxup5MjMzyczMBGDJkiUoFIpbksvBweGWz9EedEW5HBwciIryIT3dcATwMR20eDHao0dN6lQVu/Tm23tTcDugpbbWzur5A23ogQHSCuidwEXc73QIu3PnrJYJiYiQSc6p4hEF2x+BggLdbqq01I6AAC0pKVrCwnwkztQ6XfH7g64pl5DJdtpbrg5RGqmpqVy7ZrlJj4uLIzo6GoDt27cjk8kYMWKExTit1tILaWdnfWGJiYkhJibG8PpWwxp1kTddLzSyK8plk0yensg++sgkG90xIYHUEDce3l/B5Mm+1NZaphCZJ8zpqcLDZMdhrW/69aAALq3+CPvkZTz67X/Z1RjDJZrDh4OCmpg16woVFdYrxXp6wptvmh77pV9BV/z+oGvKJWSynbaQKzDQ+q6/Q5RGUlJSi+9nZWVx+PBhXnvtNUll4Ovry5UrzQaDK1eu4OPzy57uBF0Da9noQ4c2snv35RtJezIuXbLHr1s9d1zLZYH3MkLPXYK65vH5hPM07/E8GwwO97e7v0aTU0+MI2h799bqckxCQlBvfpvg8zIGJXuRm6tTELpWqFWitLhA0Aqdbp7Ky8vj888/Z8GCBThLNAkCCA8Pp7S0lEuXLiGXy/nxxx/5xz/+0cGSCjoK6VDfPsAGLt9ou6o59BNHaiN52/UlPAb0J831Xaqr7fHzU7M6QQVcMfGrLF7sgKenaXXbzZuvduRtCQS/CTpdaWzatImmpiZSU3XmhD59+jBt2jSUSiXr168nMTERmUzGs88+y6JFi9BoNIwePZqeLZSyEPx2MQ717Q2sBKDhxj9TjBWPbsveERIKBL9tOl1pvP3225LH5XI5iYmJhtdRUVFERUV1lFgCgUAgkEAULBQIBAKBzQilIRAIBAKbEUpDIBAIBDYjlIZAIBAIbEYoDYFAIBDYjFAaAoFAILAZoTQEAoFAYDN2WqnCTgKBQCAQSCB2Gjbw8ssvd7YIknRFubqiTNA15eqKMkHXlEvIZDvtLZdQGgKBQCCwGaE0BAKBQGAzspSUlJTOFuLXQO/evTtbBEm6olxdUSbomnJ1RZmga8olZLKd9pRLOMIFAoFAYDPCPCUQCAQCmxFKQyAQCAQ20+n9NLoi6enp5OTkYGdnh7e3N9OnT0cul1uMy8rKYvv27QBMmDCBUaNGtatcaWlpHD58GAcHB/z8/Jg+fTru7u4W42bMmIGLiwv29vbIZDKWLFnS6TLl5eWxefNmNBoNY8eO5dFHH203mQCys7PZtm0bFy9e5I033iA8PFxyXEfOla0ydfRcVVdXs3LlSi5fvkz37t158cUX8fDwsBj3xBNPEBISAuiaWs2bN6/NZWnt3hsbG1mzZg3nzp3D09OT2bNn06NHjzaX42ZkysrKIi0tzbBGPPjgg4wdO7ZdZVq3bh25ubl4e3vzpnnTekCr1bJ582aOHDmCs7Mz06dPbzs/h1ZgQU1NjeHvr776Srt+/XqLMSqVSjtjxgytSqUy+bs9ycvL0zY1NWm1Wq02LS1Nm5aWJjlu+vTp2srKynaV5WZkUqvV2pkzZ2rLysq0jY2N2pdeekl74cKFdpXrwoUL2osXL2qTk5O1+fn5Vsd15FzZIlNnzFVaWpo2IyNDq9VqtRkZGVZ/V/Hx8e0qhy33/vXXXxv+P37//ffaFStWdLpM3377rXbjxo3tKoc5x48f1549e1Y7Z84cyfcPHz6sXbRokVaj0WhPnz6tTUxMbLNrC/OUBG5uboa/GxoasLOzsxiTl5fHwIED8fDwwMPDg4EDB5KXl9euct11113IZDIAIiIiUCqV7Xo9W7BFpvz8fPz9/fHz88PBwYFhw4Zx6NChdpUrODiYwMDAdr3GzWKLTJ0xV4cOHWLkyJEAjBw5st2vZw1b7j0nJ8ewox86dCg//fQT2naM5emM78MW+vXrJ7kb1JOTk8P999+PnZ0dERER1NTUcPXq1Ta5tjBPWWHr1q189913uLm5kZycbPG+UqnE19fX8Foul3foIr5nzx6GDRtm9f1FixYBMG7cOGJiYjpVJvO58vX15cyZMx0iky10xlxZozPmqrKyEh8fHwB8fHyoqqqSHNfY2MjLL7+MTCbjT3/6E0OGDGlTOWy5d+MxMpkMNzc3VCoVXl5ebSrLzcgEcODAAU6ePElAQABTpkxBoVC0izy2olQqTWTw9fVFqVQavudb4bZVGqmpqVy7ds3ieFxcHNHR0Tz55JM8+eSTZGRk8PXXX/P444+3ek6pHUlbywWwfft2ZDIZI0aMsHoOuVxOZWUlCxcuJDAwkH79+nWaTFJPgh01V7aco6PnqiU6Y65sZd26dcjlcsrLy3n99dcJCQnB39//lmXTY8u9t9f83IpM99xzD/fddx+Ojo7s3LmTtWvXSj5odiTtOU+3rdJISkqyadzw4cNZsmSJhdKQy+WcOHHC8FqpVN7SYmOrXFlZWRw+fJjXXnvN6o9A75Dz9vYmOjqa/Pz8W5LtVmXy9fXlypUrhtdXrlxpkyceW7/DlujouWqNzpgrb29vrl69io+PD1evXrX61K6fKz8/P/r160dhYWGbKg1b7l0/xtfXF7VaTW1tbYtmmo6QydPT0/B3TEwMH330UbvJYyu+vr5UVFQYXrfV7whEyK0kpaWlhr9zcnIk7dCDBg3i6NGjVFdXU11dzdGjRxk0aFC7ypWXl8fnn3/OvHnzcHZ2lhxTX19PXV2d4e9jx44ZIl46S6bw8HBKS0u5dOkSTU1N/PjjjwwePLjdZLKVjp4rW+iMuRo8eDB79+4FYO/evZI7ourqahobGwGoqqri9OnTBAcHt6kcttz7PffcQ1ZWFgD79++nf//+7brTsEUmY19BTk5Om8/LL2Hw4MF89913aLVafv75Z9zc3NpMaYiMcAmWL19OaWkpdnZ2KBQKpk2bhlwu5+zZs+zatYvnn38e0NnwMzIyAF3I7ejRo9tVrhdeeIGmpibDk1WfPn2YNm0aSqWS9evXk5iYSHl5OcuXLwdArVYzfPhwJkyY0KkyAeTm5vLBBx+g0WgYPXp0u8oEcPDgQd577z2qqqpwd3enV69evPrqq506V7bIBB0/VyqVipUrV1JRUYFCoWDOnDl4eHiY/N5Pnz7Nhg0bsLe3R6PR8PDDDzNmzJg2l0Xq3j/++GPCw8MZPHgw169fZ82aNRQUFODh4cHs2bPx8/NrczluRqZ///vf5OTkIJPJ8PDw4K9//StBQUHtKtOqVas4ceIEKpUKb29vHn/8cZqamgAYP348Wq2WTZs2cfToUZycnJg+fbrVEO+bRSgNgUAgENiMME8JBAKBwGaE0hAIBAKBzQilIRAIBAKbEUpDIBAIBDYjlIZAIBAIbEYoDYHACiUlJSQkJPCXv/yFHTt2dLY4AkGXQITcCgRWeOedd3B1deXpp5++pfOkpKQwYsSIdi2XPWPGDK5du4a9ve45MDIykvnz57fb9QS3L7dtGRGBoDUqKipaLArZUajVakMl4ZaYN28eAwcO7ACJBLczQmkIBBIsWLCAEydOcOrUKd5//33+9a9/kZmZSXZ2Nk1NTURHR/P000/j5OREdXU1a9as4cyZM2g0GiIjI/nb3/6Gr68vW7du5eTJk5w5c4b333+fUaNG8Yc//IGZM2eydetWgzIw3o1kZWWxe/duwsPD2bt3Lw888ABxcXHs2bOHL7/8kmvXrnHHHXcwbdo0unfv3skzJbjdED4NgUCC5ORk7rzzTp599lnS0tLYuXMnpaWlLFu2jLfeegulUsknn3wC6CqKjho1inXr1rFu3TqcnJzYtGkTAE8++aTJeaZOnWrT9c+cOYOfnx8bN25kwoQJHDx4kIyMDObOncvGjRvp27cvq1evNvnM22+/zdSpU1m4cCGFhYVtOh8CgR6hNASCVtBqtezevZspU6bg4eGBq6srEyZM4IcffgB0VU6HDh2Ks7Oz4b2TJ0/e0jV9fHz4/e9/j0wmw8nJiczMTGJjYwkODkYmkxEbG0thYSGXL18GdDXA1q5dy7p16+jfvz+LFi2ipqbmlu9dIDBHmKcEglaoqqqioaGBl19+2XBMq9Wi0WgAXXfHDz74gLy8PMNCXVdXh0ajMTimbxbzJj6XL19m8+bNfPjhhyYyKJVKunfvTt++fQ3HY2Nj2bt3LydPnuwS1YQFvy2E0hAIWsHT0xMnJydWrFhh6ClhzJdffklJSQlvvPEG3bp1o7CwkISEBEMjHPPS3S4uLoBO2ehbC0s1SDJGoVAwYcIEq423zGnPcuGC2xthnhIIWsHe3p6xY8fy/vvvU1lZCeiabul7wtfX1+Pk5ISbmxvV1dVs27bN5PPe3t6Ul5cbXnt5eSGXy9m3bx8ajYY9e/aYvC/FuHHj+Oyzz7hw4QIAtbW1ZGdnA7oor1OnTtHU1MT169f54osvqKqqIjIyss3mQCDQI3YaAoENTJo0iU8++YRXX30VlUqFXC5n3LhxDBo0iIceeoi33nqLqVOnIpfLeeSRRzh06JDhsw899BBr165l165djBgxgmeffZbnnnuOjRs3snXrVsaMGUNERESL1x8yZAj19fWsWrWKiooK3NzcGDBgAPfeey91dXVs3LiR8vJyHB0d6dWrF6+88opJRzmBoK0QyX0CgUAgsBlhnhIIBAKBzQilIRAIBAKbEUpDIBAIBDYjlIZAIBAIbEYoDYFAIBDYjFAaAoFAILAZoTQEAoFAYDNCaQgEAoHAZv4fEgWg8umvMiQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "data=np.loadtxt(fname='data_wk1')#extracting data from the data_wk1 file created in the notebook home(with the first non-data row removed)\n",
+    "                                 #into a 2d numpy.ndarray data\n",
+    "X=data[:,0]#labels 1-D array\n",
+    "Y=data[:,1:11]#features 2-D array\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib import style\n",
+    "style.use('ggplot')\n",
+    "for m in range(1,11):\n",
+    "    for n in range(m+1,11):\n",
+    "        y1=data[:,m]\n",
+    "        y2=data[:,n]\n",
+    "        for i in np.arange(999):\n",
+    "            if X[i]==1.0 :\n",
+    "                plt.scatter(y1[i], y2[i], color='red')#, align='center')\n",
+    "            elif X[i]==2.0 :\n",
+    "                plt.scatter(y1[i], y2[i], color='blue')#, align='center')\n",
+    "        plt.title('Feature'+str(m)+' vs Feature'+str(n))\n",
+    "        plt.ylabel('feature'+str(n))\n",
+    "        plt.xlabel('feature'+str(m))\n",
+    "        plt.show()      \n",
+    "               "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/plot_code b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/plot_code
new file mode 100644
index 000000000..c9acb1613
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/plot_code	
@@ -0,0 +1,24 @@
+#this code has been written in Jupyter Notebook
+import numpy as np
+import pandas as pd
+data=np.loadtxt(fname='data_wk1')#extracting data from the data_wk1 file created in the notebook home(with the first non-data row removed)
+                                 #into a 2d numpy.ndarray data
+X=data[:,0]#labels 1-D array
+Y=data[:,1:11]#features 2-D array
+from matplotlib import pyplot as plt
+from matplotlib import style
+style.use('ggplot')
+for m in range(1,11):
+    for n in range(m+1,11):
+        y1=data[:,m]
+        y2=data[:,n]
+        for i in np.arange(999):
+            if X[i]==1.0 :
+                plt.scatter(y1[i], y2[i], color='red')#, align='center')
+            elif X[i]==2.0 :
+                plt.scatter(y1[i], y2[i], color='blue')#, align='center')
+        plt.title('Feature'+str(m)+' vs Feature'+str(n))
+        plt.ylabel('feature'+str(n))
+        plt.xlabel('feature'+str(m))
+        plt.show()      
+        
diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/README.md b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/README.md
index d5d44caf3..7971b316b 100644
--- a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/README.md	
+++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/README.md	
@@ -45,4 +45,4 @@ Follow this [GitHub Tutorial](https://towardsdatascience.com/getting-started-wit
 
 3. After **4 Apr 2020 EOD** no request will be entertained.
 
-4. For any serious doubt regarding code or data set create an issue in this very repository or comment on the FB post ***(of particular week)***with your doubts.
+4. For any serious doubt regarding code or data set create an issue in this very repository or comment on the FB post (***of particular week***)with your doubts.
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Data/ex1data1.txt b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Data/ex1data1.txt
new file mode 100644
index 000000000..0f88ccb61
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Data/ex1data1.txt	
@@ -0,0 +1,97 @@
+6.1101,17.592
+5.5277,9.1302
+8.5186,13.662
+7.0032,11.854
+5.8598,6.8233
+8.3829,11.886
+7.4764,4.3483
+8.5781,12
+6.4862,6.5987
+5.0546,3.8166
+5.7107,3.2522
+14.164,15.505
+5.734,3.1551
+8.4084,7.2258
+5.6407,0.71618
+5.3794,3.5129
+6.3654,5.3048
+5.1301,0.56077
+6.4296,3.6518
+7.0708,5.3893
+6.1891,3.1386
+20.27,21.767
+5.4901,4.263
+6.3261,5.1875
+5.5649,3.0825
+18.945,22.638
+12.828,13.501
+10.957,7.0467
+13.176,14.692
+22.203,24.147
+5.2524,-1.22
+6.5894,5.9966
+9.2482,12.134
+5.8918,1.8495
+8.2111,6.5426
+7.9334,4.5623
+8.0959,4.1164
+5.6063,3.3928
+12.836,10.117
+6.3534,5.4974
+5.4069,0.55657
+6.8825,3.9115
+11.708,5.3854
+5.7737,2.4406
+7.8247,6.7318
+7.0931,1.0463
+5.0702,5.1337
+5.8014,1.844
+11.7,8.0043
+5.5416,1.0179
+7.5402,6.7504
+5.3077,1.8396
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diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Data/ex1data2.txt b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Data/ex1data2.txt
new file mode 100644
index 000000000..79e9a807e
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Data/ex1data2.txt	
@@ -0,0 +1,47 @@
+2104,3,399900
+1600,3,329900
+2400,3,369000
+1416,2,232000
+3000,4,539900
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+1000,1,169900
+2040,4,314900
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+2238,3,329900
+2567,4,314000
+1200,3,299000
+852,2,179900
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+1203,3,239500
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Figures/cost_function.png b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Figures/cost_function.png
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diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/exercise1.ipynb b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/exercise1.ipynb
new file mode 100644
index 000000000..dc594f146
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/exercise1.ipynb	
@@ -0,0 +1,1302 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Programming Exercise 1: Linear Regression\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "In this exercise, you will implement linear regression and get to see it work on data. Before starting on this programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n",
+    "\n",
+    "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n",
+    "\n",
+    "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, and [`matplotlib`](https://matplotlib.org/) for plotting.\n",
+    "\n",
+    "You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "from mpl_toolkits.mplot3d import Axes3D  # needed to plot 3-D surfaces\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils \n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading\n",
+    "\n",
+    "After completing each part of the assignment, be sure to submit your solutions to the grader.\n",
+    "\n",
+    "For this programming exercise, you are only required to complete the first part of the exercise to implement linear regression with one variable. The second part of the exercise, which is optional, covers linear regression with multiple variables. The following is a breakdown of how each part of this exercise is scored.\n",
+    "\n",
+    "**Required Exercises**\n",
+    "\n",
+    "| Section | Part                                           |Submitted Function                     | Points \n",
+    "|---------|:-                                             |:-                                     | :-:    \n",
+    "| 1       | [Warm up exercise](#section1)                  | [`warmUpExercise`](#warmUpExercise)    |  10    \n",
+    "| 2       | [Compute cost for one variable](#section2)     | [`computeCost`](#computeCost)         |  20    \n",
+    "| 3       | [Gradient descent for one variable](#section3) | [`gradientDescent`](#gradientDescent) |  20    \n",
+    "| 4       | [Feature normalization](#section4)                   | [`featureNormalize`](#featureNormalize) | 10      |\n",
+    "| 5       | [Compute cost for multiple variables](#section5)     | [`computeCostMulti`](#computeCostMulti) | 20      |\n",
+    "| 6       | [Gradient descent for multiple variables](#section5) | [`gradientDescentMulti`](#gradientDescentMulti) |10      |\n",
+    "| 7       | [Normal Equations](#section7)                        | [`normalEqn`](#normalEqn)        | 10      |\n",
+    "|         | Total Points                                   |                                       | 100    \n",
+    "\n",
+    "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once. They must also be re-executed everytime the submitted function is updated.\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "## Debugging\n",
+    "\n",
+    "Here are some things to keep in mind throughout this exercise:\n",
+    "\n",
+    "- Python array indices start from zero, not one (contrary to OCTAVE/MATLAB). \n",
+    "\n",
+    "- There is an important distinction between python arrays (called `list` or `tuple`) and `numpy` arrays. You should use `numpy` arrays in all your computations. Vector/matrix operations work only with `numpy` arrays. Python lists do not support vector operations (you need to use for loops).\n",
+    "\n",
+    "- If you are seeing many errors at runtime, inspect your matrix operations to make sure that you are adding and multiplying matrices of compatible dimensions. Printing the dimensions of `numpy` arrays using the `shape` property will help you debug.\n",
+    "\n",
+    "- By default, `numpy` interprets math operators to be element-wise operators. If you want to do matrix multiplication, you need to use the `dot` function in `numpy`. For, example if `A` and `B` are two `numpy` matrices, then the matrix operation AB is `np.dot(A, B)`. Note that for 2-dimensional matrices or vectors (1-dimensional), this is also equivalent to `A@B` (requires python >= 3.5)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section1\"></a>\n",
+    "## 1 Simple python and `numpy` function\n",
+    "\n",
+    "The first part of this assignment gives you practice with python and `numpy` syntax and the homework submission process. In the next cell, you will find the outline of a `python` function. Modify it to return a 5 x 5 identity matrix by filling in the following code:\n",
+    "\n",
+    "```python\n",
+    "A = np.eye(5)\n",
+    "```\n",
+    "<a id=\"warmUpExercise\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def warmUpExercise():\n",
+    "    \"\"\"\n",
+    "    Example function in Python which computes the identity matrix.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    A : array_like\n",
+    "        The 5x5 identity matrix.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Return the 5x5 identity matrix.\n",
+    "    \"\"\"    \n",
+    "    # ======== YOUR CODE HERE ======\n",
+    "    A = []   # modify this line\n",
+    "    \n",
+    "    # ==============================\n",
+    "    return A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The previous cell only defines the function `warmUpExercise`. We can now run it by executing the following cell to see its output. You should see output similar to the following:\n",
+    "\n",
+    "```python\n",
+    "array([[ 1.,  0.,  0.,  0.,  0.],\n",
+    "       [ 0.,  1.,  0.,  0.,  0.],\n",
+    "       [ 0.,  0.,  1.,  0.,  0.],\n",
+    "       [ 0.,  0.,  0.,  1.,  0.],\n",
+    "       [ 0.,  0.,  0.,  0.,  1.]])\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "warmUpExercise()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 Submitting solutions\n",
+    "\n",
+    "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the grader object, and then sending your function to Coursera for grading. \n",
+    "\n",
+    "The grader will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "Execute the next cell to grade your solution to the first part of this exercise.\n",
+    "\n",
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# appends the implemented function in part 1 to the grader object\n",
+    "grader[1] = warmUpExercise\n",
+    "\n",
+    "# send the added functions to coursera grader for getting a grade on this part\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 Linear regression with one variable\n",
+    "\n",
+    "Now you will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and you have data for profits and populations from the cities. You would like to use this data to help you select which city to expand to next. \n",
+    "\n",
+    "The file `Data/ex1data1.txt` contains the dataset for our linear regression problem. The first column is the population of a city (in 10,000s) and the second column is the profit of a food truck in that city (in $10,000s). A negative value for profit indicates a loss. \n",
+    "\n",
+    "We provide you with the code needed to load this data. The dataset is loaded from the data file into the variables `x` and `y`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read comma separated data\n",
+    "data = np.loadtxt(os.path.join('Data', 'ex1data1.txt'), delimiter=',')\n",
+    "X, y = data[:, 0], data[:, 1]\n",
+    "\n",
+    "m = y.size  # number of training examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1 Plotting the Data\n",
+    "\n",
+    "Before starting on any task, it is often useful to understand the data by visualizing it. For this dataset, you can use a scatter plot to visualize the data, since it has only two properties to plot (profit and population). Many other problems that you will encounter in real life are multi-dimensional and cannot be plotted on a 2-d plot. There are many plotting libraries in python (see this [blog post](https://blog.modeanalytics.com/python-data-visualization-libraries/) for a good summary of the most popular ones). \n",
+    "\n",
+    "In this course, we will be exclusively using `matplotlib` to do all our plotting. `matplotlib` is one of the most popular scientific plotting libraries in python and has extensive tools and functions to make beautiful plots. `pyplot` is a module within `matplotlib` which provides a simplified interface to `matplotlib`'s most common plotting tasks, mimicking MATLAB's plotting interface.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "You might have noticed that we have imported the `pyplot` module at the beginning of this exercise using the command `from matplotlib import pyplot`. This is rather uncommon, and if you look at python code elsewhere or in the `matplotlib` tutorials, you will see that the module is named `plt`. This is used by module renaming by using the import command `import matplotlib.pyplot as plt`. We will not using the short name of `pyplot` module in this class exercises, but you should be aware of this deviation from norm.\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "In the following part, your first job is to complete the `plotData` function below. Modify the function and fill in the following code:\n",
+    "\n",
+    "```python\n",
+    "    pyplot.plot(x, y, 'ro', ms=10, mec='k')\n",
+    "    pyplot.ylabel('Profit in $10,000')\n",
+    "    pyplot.xlabel('Population of City in 10,000s')\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotData(x, y):\n",
+    "    \"\"\"\n",
+    "    Plots the data points x and y into a new figure. Plots the data \n",
+    "    points and gives the figure axes labels of population and profit.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    x : array_like\n",
+    "        Data point values for x-axis.\n",
+    "\n",
+    "    y : array_like\n",
+    "        Data point values for y-axis. Note x and y should have the same size.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Plot the training data into a figure using the \"figure\" and \"plot\"\n",
+    "    functions. Set the axes labels using the \"xlabel\" and \"ylabel\" functions.\n",
+    "    Assume the population and revenue data have been passed in as the x\n",
+    "    and y arguments of this function.    \n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    You can use the 'ro' option with plot to have the markers\n",
+    "    appear as red circles. Furthermore, you can make the markers larger by\n",
+    "    using plot(..., 'ro', ms=10), where `ms` refers to marker size. You \n",
+    "    can also set the marker edge color using the `mec` property.\n",
+    "    \"\"\"\n",
+    "    fig = pyplot.figure()  # open a new figure\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE ======================= \n",
+    "    \n",
+    "\n",
+    "    # =============================================================\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now run the defined function with the loaded data to visualize the data. The end result should look like the following figure:\n",
+    "\n",
+    "![](Figures/dataset1.png)\n",
+    "\n",
+    "Execute the next cell to visualize the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotData(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To quickly learn more about the `matplotlib` plot function and what arguments you can provide to it, you can type `?pyplot.plot` in a cell within the jupyter notebook. This opens a separate page showing the documentation for the requested function. You can also search online for plotting documentation. \n",
+    "\n",
+    "To set the markers to red circles, we used the option `'or'` within the `plot` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "?pyplot.plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "### 2.2 Gradient Descent\n",
+    "\n",
+    "In this part, you will fit the linear regression parameters $\\theta$ to our dataset using gradient descent.\n",
+    "\n",
+    "#### 2.2.1 Update Equations\n",
+    "\n",
+    "The objective of linear regression is to minimize the cost function\n",
+    "\n",
+    "$$ J(\\theta) = \\frac{1}{2m} \\sum_{i=1}^m \\left( h_{\\theta}(x^{(i)}) - y^{(i)}\\right)^2$$\n",
+    "\n",
+    "where the hypothesis $h_\\theta(x)$ is given by the linear model\n",
+    "$$ h_\\theta(x) = \\theta^Tx = \\theta_0 + \\theta_1 x_1$$\n",
+    "\n",
+    "Recall that the parameters of your model are the $\\theta_j$ values. These are\n",
+    "the values you will adjust to minimize cost $J(\\theta)$. One way to do this is to\n",
+    "use the batch gradient descent algorithm. In batch gradient descent, each\n",
+    "iteration performs the update\n",
+    "\n",
+    "$$ \\theta_j = \\theta_j - \\alpha \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta(x^{(i)}) - y^{(i)}\\right)x_j^{(i)} \\qquad \\text{simultaneously update } \\theta_j \\text{ for all } j$$\n",
+    "\n",
+    "With each step of gradient descent, your parameters $\\theta_j$ come closer to the optimal values that will achieve the lowest cost J($\\theta$).\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note:** We store each example as a row in the the $X$ matrix in Python `numpy`. To take into account the intercept term ($\\theta_0$), we add an additional first column to $X$ and set it to all ones. This allows us to treat $\\theta_0$ as simply another 'feature'.\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "#### 2.2.2 Implementation\n",
+    "\n",
+    "We have already set up the data for linear regression. In the following cell, we add another dimension to our data to accommodate the $\\theta_0$ intercept term. Do NOT execute this cell more than once."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add a column of ones to X. The numpy function stack joins arrays along a given axis. \n",
+    "# The first axis (axis=0) refers to rows (training examples) \n",
+    "# and second axis (axis=1) refers to columns (features).\n",
+    "X = np.stack([np.ones(m), X], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "#### 2.2.3 Computing the cost $J(\\theta)$\n",
+    "\n",
+    "As you perform gradient descent to learn minimize the cost function $J(\\theta)$, it is helpful to monitor the convergence by computing the cost. In this section, you will implement a function to calculate $J(\\theta)$ so you can check the convergence of your gradient descent implementation. \n",
+    "\n",
+    "Your next task is to complete the code for the function `computeCost` which computes $J(\\theta)$. As you are doing this, remember that the variables $X$ and $y$ are not scalar values. $X$ is a matrix whose rows represent the examples from the training set and $y$ is a vector whose each elemennt represent the value at a given row of $X$.\n",
+    "<a id=\"computeCost\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeCost(X, y, theta):\n",
+    "    \"\"\"\n",
+    "    Compute cost for linear regression. Computes the cost of using theta as the\n",
+    "    parameter for linear regression to fit the data points in X and y.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n+1), where m is the number of examples,\n",
+    "        and n is the number of features. We assume a vector of one's already \n",
+    "        appended to the features so we have n+1 columns.\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The values of the function at each data point. This is a vector of\n",
+    "        shape (m, ).\n",
+    "    \n",
+    "    theta : array_like\n",
+    "        The parameters for the regression function. This is a vector of \n",
+    "        shape (n+1, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The value of the regression cost function.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost of a particular choice of theta. \n",
+    "    You should set J to the cost.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "    \n",
+    "    # You need to return the following variables correctly\n",
+    "    J = 0\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE =====================\n",
+    "\n",
+    "    \n",
+    "    # ===========================================================\n",
+    "    return J"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the function, the next step will run `computeCost` two times using two different initializations of $\\theta$. You will see the cost printed to the screen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "J = computeCost(X, y, theta=np.array([0.0, 0.0]))\n",
+    "print('With theta = [0, 0] \\nCost computed = %.2f' % J)\n",
+    "print('Expected cost value (approximately) 32.07\\n')\n",
+    "\n",
+    "# further testing of the cost function\n",
+    "J = computeCost(X, y, theta=np.array([-1, 2]))\n",
+    "print('With theta = [-1, 2]\\nCost computed = %.2f' % J)\n",
+    "print('Expected cost value (approximately) 54.24')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions by executing the following cell.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[2] = computeCost\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section3\"></a>\n",
+    "#### 2.2.4 Gradient descent\n",
+    "\n",
+    "Next, you will complete a function which implements gradient descent.\n",
+    "The loop structure has been written for you, and you only need to supply the updates to $\\theta$ within each iteration. \n",
+    "\n",
+    "As you program, make sure you understand what you are trying to optimize and what is being updated. Keep in mind that the cost $J(\\theta)$ is parameterized by the vector $\\theta$, not $X$ and $y$. That is, we minimize the value of $J(\\theta)$ by changing the values of the vector $\\theta$, not by changing $X$ or $y$. [Refer to the equations in this notebook](#section2) and to the video lectures if you are uncertain. A good way to verify that gradient descent is working correctly is to look at the value of $J(\\theta)$ and check that it is decreasing with each step. \n",
+    "\n",
+    "The starter code for the function `gradientDescent` calls `computeCost` on every iteration and saves the cost to a `python` list. Assuming you have implemented gradient descent and `computeCost` correctly, your value of $J(\\theta)$ should never increase, and should converge to a steady value by the end of the algorithm.\n",
+    "\n",
+    "<div class=\"alert alert-box alert-warning\">\n",
+    "**Vectors and matrices in `numpy`** - Important implementation notes\n",
+    "\n",
+    "A vector in `numpy` is a one dimensional array, for example `np.array([1, 2, 3])` is a vector. A matrix in `numpy` is a two dimensional array, for example `np.array([[1, 2, 3], [4, 5, 6]])`. However, the following is still considered a matrix `np.array([[1, 2, 3]])` since it has two dimensions, even if it has a shape of 1x3 (which looks like a vector).\n",
+    "\n",
+    "Given the above, the function `np.dot` which we will use for all matrix/vector multiplication has the following properties:\n",
+    "- It always performs inner products on vectors. If `x=np.array([1, 2, 3])`, then `np.dot(x, x)` is a scalar.\n",
+    "- For matrix-vector multiplication, so if $X$ is a $m\\times n$ matrix and $y$ is a vector of length $m$, then the operation `np.dot(y, X)` considers $y$ as a $1 \\times m$ vector. On the other hand, if $y$ is a vector of length $n$, then the operation `np.dot(X, y)` considers $y$ as a $n \\times 1$ vector.\n",
+    "- A vector can be promoted to a matrix using `y[None]` or `[y[np.newaxis]`. That is, if `y = np.array([1, 2, 3])` is a vector of size 3, then `y[None, :]` is a matrix of shape $1 \\times 3$. We can use `y[:, None]` to obtain a shape of $3 \\times 1$.\n",
+    "<div>\n",
+    "<a id=\"gradientDescent\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gradientDescent(X, y, theta, alpha, num_iters):\n",
+    "    \"\"\"\n",
+    "    Performs gradient descent to learn `theta`. Updates theta by taking `num_iters`\n",
+    "    gradient steps with learning rate `alpha`.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n+1).\n",
+    "    \n",
+    "    y : arra_like\n",
+    "        Value at given features. A vector of shape (m, ).\n",
+    "    \n",
+    "    theta : array_like\n",
+    "        Initial values for the linear regression parameters. \n",
+    "        A vector of shape (n+1, ).\n",
+    "    \n",
+    "    alpha : float\n",
+    "        The learning rate.\n",
+    "    \n",
+    "    num_iters : int\n",
+    "        The number of iterations for gradient descent. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    theta : array_like\n",
+    "        The learned linear regression parameters. A vector of shape (n+1, ).\n",
+    "    \n",
+    "    J_history : list\n",
+    "        A python list for the values of the cost function after each iteration.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Peform a single gradient step on the parameter vector theta.\n",
+    "\n",
+    "    While debugging, it can be useful to print out the values of \n",
+    "    the cost function (computeCost) and gradient here.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.shape[0]  # number of training examples\n",
+    "    \n",
+    "    # make a copy of theta, to avoid changing the original array, since numpy arrays\n",
+    "    # are passed by reference to functions\n",
+    "    theta = theta.copy()\n",
+    "    \n",
+    "    J_history = [] # Use a python list to save cost in every iteration\n",
+    "    \n",
+    "    for i in range(num_iters):\n",
+    "        # ==================== YOUR CODE HERE =================================\n",
+    "        \n",
+    "\n",
+    "        # =====================================================================\n",
+    "        \n",
+    "        # save the cost J in every iteration\n",
+    "        J_history.append(computeCost(X, y, theta))\n",
+    "    \n",
+    "    return theta, J_history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After you are finished call the implemented `gradientDescent` function and print the computed $\\theta$. We initialize the $\\theta$ parameters to 0 and the learning rate $\\alpha$ to 0.01. Execute the following cell to check your code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize fitting parameters\n",
+    "theta = np.zeros(2)\n",
+    "\n",
+    "# some gradient descent settings\n",
+    "iterations = 1500\n",
+    "alpha = 0.01\n",
+    "\n",
+    "theta, J_history = gradientDescent(X ,y, theta, alpha, iterations)\n",
+    "print('Theta found by gradient descent: {:.4f}, {:.4f}'.format(*theta))\n",
+    "print('Expected theta values (approximately): [-3.6303, 1.1664]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use your final parameters to plot the linear fit. The results should look like the following figure.\n",
+    "\n",
+    "![](Figures/regression_result.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the linear fit\n",
+    "plotData(X[:, 1], y)\n",
+    "pyplot.plot(X[:, 1], np.dot(X, theta), '-')\n",
+    "pyplot.legend(['Training data', 'Linear regression']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your final values for $\\theta$ will also be used to make predictions on profits in areas of 35,000 and 70,000 people.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\">\n",
+    "Note the way that the following lines use matrix multiplication, rather than explicit summation or looping, to calculate the predictions. This is an example of code vectorization in `numpy`.\n",
+    "</div>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\">\n",
+    "Note that the first argument to the `numpy` function `dot` is a python list. `numpy` can internally converts **valid** python lists to numpy arrays when explicitly provided as arguments to `numpy` functions.\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predict values for population sizes of 35,000 and 70,000\n",
+    "predict1 = np.dot([1, 3.5], theta)\n",
+    "print('For population = 35,000, we predict a profit of {:.2f}\\n'.format(predict1*10000))\n",
+    "\n",
+    "predict2 = np.dot([1, 7], theta)\n",
+    "print('For population = 70,000, we predict a profit of {:.2f}\\n'.format(predict2*10000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions by executing the next cell.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[3] = gradientDescent\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 Visualizing $J(\\theta)$\n",
+    "\n",
+    "To understand the cost function $J(\\theta)$ better, you will now plot the cost over a 2-dimensional grid of $\\theta_0$ and $\\theta_1$ values. You will not need to code anything new for this part, but you should understand how the code you have written already is creating these images.\n",
+    "\n",
+    "In the next cell, the code is set up to calculate $J(\\theta)$ over a grid of values using the `computeCost` function that you wrote. After executing the following cell, you will have a 2-D array of $J(\\theta)$ values. Then, those values are used to produce surface and contour plots of $J(\\theta)$ using the matplotlib `plot_surface` and `contourf` functions. The plots should look something like the following:\n",
+    "\n",
+    "![](Figures/cost_function.png)\n",
+    "\n",
+    "The purpose of these graphs is to show you how $J(\\theta)$ varies with changes in $\\theta_0$ and $\\theta_1$. The cost function $J(\\theta)$ is bowl-shaped and has a global minimum. (This is easier to see in the contour plot than in the 3D surface plot). This minimum is the optimal point for $\\theta_0$ and $\\theta_1$, and each step of gradient descent moves closer to this point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# grid over which we will calculate J\n",
+    "theta0_vals = np.linspace(-10, 10, 100)\n",
+    "theta1_vals = np.linspace(-1, 4, 100)\n",
+    "\n",
+    "# initialize J_vals to a matrix of 0's\n",
+    "J_vals = np.zeros((theta0_vals.shape[0], theta1_vals.shape[0]))\n",
+    "\n",
+    "# Fill out J_vals\n",
+    "for i, theta0 in enumerate(theta0_vals):\n",
+    "    for j, theta1 in enumerate(theta1_vals):\n",
+    "        J_vals[i, j] = computeCost(X, y, [theta0, theta1])\n",
+    "        \n",
+    "# Because of the way meshgrids work in the surf command, we need to\n",
+    "# transpose J_vals before calling surf, or else the axes will be flipped\n",
+    "J_vals = J_vals.T\n",
+    "\n",
+    "# surface plot\n",
+    "fig = pyplot.figure(figsize=(12, 5))\n",
+    "ax = fig.add_subplot(121, projection='3d')\n",
+    "ax.plot_surface(theta0_vals, theta1_vals, J_vals, cmap='viridis')\n",
+    "pyplot.xlabel('theta0')\n",
+    "pyplot.ylabel('theta1')\n",
+    "pyplot.title('Surface')\n",
+    "\n",
+    "# contour plot\n",
+    "# Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100\n",
+    "ax = pyplot.subplot(122)\n",
+    "pyplot.contour(theta0_vals, theta1_vals, J_vals, linewidths=2, cmap='viridis', levels=np.logspace(-2, 3, 20))\n",
+    "pyplot.xlabel('theta0')\n",
+    "pyplot.ylabel('theta1')\n",
+    "pyplot.plot(theta[0], theta[1], 'ro', ms=10, lw=2)\n",
+    "pyplot.title('Contour, showing minimum')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional Exercises\n",
+    "\n",
+    "If you have successfully completed the material above, congratulations! You now understand linear regression and should able to start using it on your own datasets.\n",
+    "\n",
+    "For the rest of this programming exercise, we have included the following optional exercises. These exercises will help you gain a deeper understanding of the material, and if you are able to do so, we encourage you to complete them as well. You can still submit your solutions to these exercises to check if your answers are correct.\n",
+    "\n",
+    "## 3 Linear regression with multiple variables\n",
+    "\n",
+    "In this part, you will implement linear regression with multiple variables to predict the prices of houses. Suppose you are selling your house and you want to know what a good market price would be. One way to do this is to first collect information on recent houses sold and make a model of housing prices.\n",
+    "\n",
+    "The file `Data/ex1data2.txt` contains a training set of housing prices in Portland, Oregon. The first column is the size of the house (in square feet), the second column is the number of bedrooms, and the third column is the price\n",
+    "of the house. \n",
+    "\n",
+    "<a id=\"section4\"></a>\n",
+    "### 3.1 Feature Normalization\n",
+    "\n",
+    "We start by loading and displaying some values from this dataset. By looking at the values, note that house sizes are about 1000 times the number of bedrooms. When features differ by orders of magnitude, first performing feature scaling can make gradient descent converge much more quickly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "data = np.loadtxt(os.path.join('Data', 'ex1data2.txt'), delimiter=',')\n",
+    "X = data[:, :2]\n",
+    "y = data[:, 2]\n",
+    "m = y.size\n",
+    "\n",
+    "# print out some data points\n",
+    "print('{:>8s}{:>8s}{:>10s}'.format('X[:,0]', 'X[:, 1]', 'y'))\n",
+    "print('-'*26)\n",
+    "for i in range(10):\n",
+    "    print('{:8.0f}{:8.0f}{:10.0f}'.format(X[i, 0], X[i, 1], y[i]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your task here is to complete the code in `featureNormalize` function:\n",
+    "- Subtract the mean value of each feature from the dataset.\n",
+    "- After subtracting the mean, additionally scale (divide) the feature values by their respective “standard deviations.”\n",
+    "\n",
+    "The standard deviation is a way of measuring how much variation there is in the range of values of a particular feature (most data points will lie within ±2 standard deviations of the mean); this is an alternative to taking the range of values (max-min). In `numpy`, you can use the `std` function to compute the standard deviation. \n",
+    "\n",
+    "For example, the quantity `X[:, 0]` contains all the values of $x_1$ (house sizes) in the training set, so `np.std(X[:, 0])` computes the standard deviation of the house sizes.\n",
+    "At the time that the function `featureNormalize` is called, the extra column of 1’s corresponding to $x_0 = 1$ has not yet been added to $X$. \n",
+    "\n",
+    "You will do this for all the features and your code should work with datasets of all sizes (any number of features / examples). Note that each column of the matrix $X$ corresponds to one feature.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note:** When normalizing the features, it is important\n",
+    "to store the values used for normalization - the mean value and the standard deviation used for the computations. After learning the parameters\n",
+    "from the model, we often want to predict the prices of houses we have not\n",
+    "seen before. Given a new x value (living room area and number of bedrooms), we must first normalize x using the mean and standard deviation that we had previously computed from the training set.\n",
+    "</div>\n",
+    "<a id=\"featureNormalize\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def  featureNormalize(X):\n",
+    "    \"\"\"\n",
+    "    Normalizes the features in X. returns a normalized version of X where\n",
+    "    the mean value of each feature is 0 and the standard deviation\n",
+    "    is 1. This is often a good preprocessing step to do when working with\n",
+    "    learning algorithms.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    X_norm : array_like\n",
+    "        The normalized dataset of shape (m x n).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    First, for each feature dimension, compute the mean of the feature\n",
+    "    and subtract it from the dataset, storing the mean value in mu. \n",
+    "    Next, compute the  standard deviation of each feature and divide\n",
+    "    each feature by it's standard deviation, storing the standard deviation \n",
+    "    in sigma. \n",
+    "    \n",
+    "    Note that X is a matrix where each column is a feature and each row is\n",
+    "    an example. You needto perform the normalization separately for each feature. \n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    You might find the 'np.mean' and 'np.std' functions useful.\n",
+    "    \"\"\"\n",
+    "    # You need to set these values correctly\n",
+    "    X_norm = X.copy()\n",
+    "    mu = np.zeros(X.shape[1])\n",
+    "    sigma = np.zeros(X.shape[1])\n",
+    "\n",
+    "    # =========================== YOUR CODE HERE =====================\n",
+    "\n",
+    "    \n",
+    "    # ================================================================\n",
+    "    return X_norm, mu, sigma"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Execute the next cell to run the implemented `featureNormalize` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# call featureNormalize on the loaded data\n",
+    "X_norm, mu, sigma = featureNormalize(X)\n",
+    "\n",
+    "print('Computed mean:', mu)\n",
+    "print('Computed standard deviation:', sigma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should not submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[4] = featureNormalize\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the `featureNormalize` function is tested, we now add the intercept term to `X_norm`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add intercept term to X\n",
+    "X = np.concatenate([np.ones((m, 1)), X_norm], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section5\"></a>\n",
+    "### 3.2 Gradient Descent\n",
+    "\n",
+    "Previously, you implemented gradient descent on a univariate regression problem. The only difference now is that there is one more feature in the matrix $X$. The hypothesis function and the batch gradient descent update\n",
+    "rule remain unchanged. \n",
+    "\n",
+    "You should complete the code for the functions `computeCostMulti` and `gradientDescentMulti` to implement the cost function and gradient descent for linear regression with multiple variables. If your code in the previous part (single variable) already supports multiple variables, you can use it here too.\n",
+    "Make sure your code supports any number of features and is well-vectorized.\n",
+    "You can use the `shape` property of `numpy` arrays to find out how many features are present in the dataset.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note:** In the multivariate case, the cost function can\n",
+    "also be written in the following vectorized form:\n",
+    "\n",
+    "$$ J(\\theta) = \\frac{1}{2m}(X\\theta - \\vec{y})^T(X\\theta - \\vec{y}) $$\n",
+    "\n",
+    "where \n",
+    "\n",
+    "$$ X = \\begin{pmatrix}\n",
+    "          - (x^{(1)})^T - \\\\\n",
+    "          - (x^{(2)})^T - \\\\\n",
+    "          \\vdots \\\\\n",
+    "          - (x^{(m)})^T - \\\\ \\\\\n",
+    "        \\end{pmatrix} \\qquad \\mathbf{y} = \\begin{bmatrix} y^{(1)} \\\\ y^{(2)} \\\\ \\vdots \\\\ y^{(m)} \\\\\\end{bmatrix}$$\n",
+    "\n",
+    "the vectorized version is efficient when you are working with numerical computing tools like `numpy`. If you are an expert with matrix operations, you can prove to yourself that the two forms are equivalent.\n",
+    "</div>\n",
+    "\n",
+    "<a id=\"computeCostMulti\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeCostMulti(X, y, theta):\n",
+    "    \"\"\"\n",
+    "    Compute cost for linear regression with multiple variables.\n",
+    "    Computes the cost of using theta as the parameter for linear regression to fit the data points in X and y.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n+1).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        A vector of shape (m, ) for the values at a given data point.\n",
+    "    \n",
+    "    theta : array_like\n",
+    "        The linear regression parameters. A vector of shape (n+1, )\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The value of the cost function. \n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost of a particular choice of theta. You should set J to the cost.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.shape[0] # number of training examples\n",
+    "    \n",
+    "    # You need to return the following variable correctly\n",
+    "    J = 0\n",
+    "    \n",
+    "    # ======================= YOUR CODE HERE ===========================\n",
+    "\n",
+    "    \n",
+    "    # ==================================================================\n",
+    "    return J\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[5] = computeCostMulti\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"gradientDescentMulti\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gradientDescentMulti(X, y, theta, alpha, num_iters):\n",
+    "    \"\"\"\n",
+    "    Performs gradient descent to learn theta.\n",
+    "    Updates theta by taking num_iters gradient steps with learning rate alpha.\n",
+    "        \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n+1).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        A vector of shape (m, ) for the values at a given data point.\n",
+    "    \n",
+    "    theta : array_like\n",
+    "        The linear regression parameters. A vector of shape (n+1, )\n",
+    "    \n",
+    "    alpha : float\n",
+    "        The learning rate for gradient descent. \n",
+    "    \n",
+    "    num_iters : int\n",
+    "        The number of iterations to run gradient descent. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    theta : array_like\n",
+    "        The learned linear regression parameters. A vector of shape (n+1, ).\n",
+    "    \n",
+    "    J_history : list\n",
+    "        A python list for the values of the cost function after each iteration.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Peform a single gradient step on the parameter vector theta.\n",
+    "\n",
+    "    While debugging, it can be useful to print out the values of \n",
+    "    the cost function (computeCost) and gradient here.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.shape[0] # number of training examples\n",
+    "    \n",
+    "    # make a copy of theta, which will be updated by gradient descent\n",
+    "    theta = theta.copy()\n",
+    "    \n",
+    "    J_history = []\n",
+    "    \n",
+    "    for i in range(num_iters):\n",
+    "        # ======================= YOUR CODE HERE ==========================\n",
+    "\n",
+    "        \n",
+    "        # =================================================================\n",
+    "        \n",
+    "        # save the cost J in every iteration\n",
+    "        J_history.append(computeCostMulti(X, y, theta))\n",
+    "    \n",
+    "    return theta, J_history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[6] = gradientDescentMulti\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2.1 Optional (ungraded) exercise: Selecting learning rates\n",
+    "\n",
+    "In this part of the exercise, you will get to try out different learning rates for the dataset and find a learning rate that converges quickly. You can change the learning rate by modifying the following code and changing the part of the code that sets the learning rate.\n",
+    "\n",
+    "Use your implementation of `gradientDescentMulti` function and run gradient descent for about 50 iterations at the chosen learning rate. The function should also return the history of $J(\\theta)$ values in a vector $J$.\n",
+    "\n",
+    "After the last iteration, plot the J values against the number of the iterations.\n",
+    "\n",
+    "If you picked a learning rate within a good range, your plot look similar as the following Figure. \n",
+    "\n",
+    "![](Figures/learning_rate.png)\n",
+    "\n",
+    "If your graph looks very different, especially if your value of $J(\\theta)$ increases or even blows up, adjust your learning rate and try again. We recommend trying values of the learning rate $\\alpha$ on a log-scale, at multiplicative steps of about 3 times the previous value (i.e., 0.3, 0.1, 0.03, 0.01 and so on). You may also want to adjust the number of iterations you are running if that will help you see the overall trend in the curve.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note:** If your learning rate is too large, $J(\\theta)$ can diverge and ‘blow up’, resulting in values which are too large for computer calculations. In these situations, `numpy` will tend to return\n",
+    "NaNs. NaN stands for ‘not a number’ and is often caused by undefined operations that involve −∞ and +∞.\n",
+    "</div>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**MATPLOTLIB tip:** To compare how different learning learning rates affect convergence, it is helpful to plot $J$ for several learning rates on the same figure. This can be done by making `alpha` a python list, and looping across the values within this list, and calling the plot function in every iteration of the loop. It is also useful to have a legend to distinguish the different lines within the plot. Search online for `pyplot.legend` for help on showing legends in `matplotlib`.\n",
+    "</div>\n",
+    "\n",
+    "Notice the changes in the convergence curves as the learning rate changes. With a small learning rate, you should find that gradient descent takes a very long time to converge to the optimal value. Conversely, with a large learning rate, gradient descent might not converge or might even diverge!\n",
+    "Using the best learning rate that you found, run the script\n",
+    "to run gradient descent until convergence to find the final values of $\\theta$. Next,\n",
+    "use this value of $\\theta$ to predict the price of a house with 1650 square feet and\n",
+    "3 bedrooms. You will use value later to check your implementation of the normal equations. Don’t forget to normalize your features when you make this prediction!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Instructions\n",
+    "------------\n",
+    "We have provided you with the following starter code that runs\n",
+    "gradient descent with a particular learning rate (alpha). \n",
+    "\n",
+    "Your task is to first make sure that your functions - `computeCost`\n",
+    "and `gradientDescent` already work with  this starter code and\n",
+    "support multiple variables.\n",
+    "\n",
+    "After that, try running gradient descent with different values of\n",
+    "alpha and see which one gives you the best result.\n",
+    "\n",
+    "Finally, you should complete the code at the end to predict the price\n",
+    "of a 1650 sq-ft, 3 br house.\n",
+    "\n",
+    "Hint\n",
+    "----\n",
+    "At prediction, make sure you do the same feature normalization.\n",
+    "\"\"\"\n",
+    "# Choose some alpha value - change this\n",
+    "alpha = 0.1\n",
+    "num_iters = 400\n",
+    "\n",
+    "# init theta and run gradient descent\n",
+    "theta = np.zeros(3)\n",
+    "theta, J_history = gradientDescentMulti(X, y, theta, alpha, num_iters)\n",
+    "\n",
+    "# Plot the convergence graph\n",
+    "pyplot.plot(np.arange(len(J_history)), J_history, lw=2)\n",
+    "pyplot.xlabel('Number of iterations')\n",
+    "pyplot.ylabel('Cost J')\n",
+    "\n",
+    "# Display the gradient descent's result\n",
+    "print('theta computed from gradient descent: {:s}'.format(str(theta)))\n",
+    "\n",
+    "# Estimate the price of a 1650 sq-ft, 3 br house\n",
+    "# ======================= YOUR CODE HERE ===========================\n",
+    "# Recall that the first column of X is all-ones. \n",
+    "# Thus, it does not need to be normalized.\n",
+    "\n",
+    "price = 0   # You should change this\n",
+    "\n",
+    "# ===================================================================\n",
+    "\n",
+    "print('Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): ${:.0f}'.format(price))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You do not need to submit any solutions for this optional (ungraded) part.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section7\"></a>\n",
+    "### 3.3 Normal Equations\n",
+    "\n",
+    "In the lecture videos, you learned that the closed-form solution to linear regression is\n",
+    "\n",
+    "$$ \\theta = \\left( X^T X\\right)^{-1} X^T\\vec{y}$$\n",
+    "\n",
+    "Using this formula does not require any feature scaling, and you will get an exact solution in one calculation: there is no “loop until convergence” like in gradient descent. \n",
+    "\n",
+    "First, we will reload the data to ensure that the variables have not been modified. Remember that while you do not need to scale your features, we still need to add a column of 1’s to the $X$ matrix to have an intercept term ($\\theta_0$). The code in the next cell will add the column of 1’s to X for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "data = np.loadtxt(os.path.join('Data', 'ex1data2.txt'), delimiter=',')\n",
+    "X = data[:, :2]\n",
+    "y = data[:, 2]\n",
+    "m = y.size\n",
+    "X = np.concatenate([np.ones((m, 1)), X], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Complete the code for the function `normalEqn` below to use the formula above to calculate $\\theta$. \n",
+    "\n",
+    "<a id=\"normalEqn\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalEqn(X, y):\n",
+    "    \"\"\"\n",
+    "    Computes the closed-form solution to linear regression using the normal equations.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n+1).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The value at each data point. A vector of shape (m, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    theta : array_like\n",
+    "        Estimated linear regression parameters. A vector of shape (n+1, ).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Complete the code to compute the closed form solution to linear\n",
+    "    regression and put the result in theta.\n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    Look up the function `np.linalg.pinv` for computing matrix inverse.\n",
+    "    \"\"\"\n",
+    "    theta = np.zeros(X.shape[1])\n",
+    "    \n",
+    "    # ===================== YOUR CODE HERE ============================\n",
+    "\n",
+    "    \n",
+    "    # =================================================================\n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[7] = normalEqn\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optional (ungraded) exercise: Now, once you have found $\\theta$ using this\n",
+    "method, use it to make a price prediction for a 1650-square-foot house with\n",
+    "3 bedrooms. You should find that gives the same predicted price as the value\n",
+    "you obtained using the model fit with gradient descent (in Section 3.2.1)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate the parameters from the normal equation\n",
+    "theta = normalEqn(X, y);\n",
+    "\n",
+    "# Display normal equation's result\n",
+    "print('Theta computed from the normal equations: {:s}'.format(str(theta)));\n",
+    "\n",
+    "# Estimate the price of a 1650 sq-ft, 3 br house\n",
+    "# ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "price = 0 # You should change this\n",
+    "\n",
+    "# ============================================================\n",
+    "\n",
+    "print('Predicted price of a 1650 sq-ft, 3 br house (using normal equations): ${:.0f}'.format(price))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/utils.py b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/utils.py
new file mode 100644
index 000000000..d0c909d57
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/utils.py	
@@ -0,0 +1,48 @@
+import numpy as np
+import sys
+sys.path.append('..')
+
+from submission import SubmissionBase
+
+
+class Grader(SubmissionBase):
+    X1 = np.column_stack((np.ones(20), np.exp(1) + np.exp(2) * np.linspace(0.1, 2, 20)))
+    Y1 = X1[:, 1] + np.sin(X1[:, 0]) + np.cos(X1[:, 1])
+    X2 = np.column_stack((X1, X1[:, 1]**0.5, X1[:, 1]**0.25))
+    Y2 = np.power(Y1, 0.5) + Y1
+
+    def __init__(self):
+        part_names = ['Warm up exercise',
+                      'Computing Cost (for one variable)',
+                      'Gradient Descent (for one variable)',
+                      'Feature Normalization',
+                      'Computing Cost (for multiple variables)',
+                      'Gradient Descent (for multiple variables)',
+                      'Normal Equations']
+        super().__init__('linear-regression', part_names)
+
+    def __iter__(self):
+        for part_id in range(1, 8):
+            try:
+                func = self.functions[part_id]
+
+                # Each part has different expected arguments/different function
+                if part_id == 1:
+                    res = func()
+                elif part_id == 2:
+                    res = func(self.X1, self.Y1, np.array([0.5, -0.5]))
+                elif part_id == 3:
+                    res = func(self.X1, self.Y1, np.array([0.5, -0.5]), 0.01, 10)
+                elif part_id == 4:
+                    res = func(self.X2[:, 1:4])
+                elif part_id == 5:
+                    res = func(self.X2, self.Y2, np.array([0.1, 0.2, 0.3, 0.4]))
+                elif part_id == 6:
+                    res = func(self.X2, self.Y2, np.array([-0.1, -0.2, -0.3, -0.4]), 0.01, 10)
+                elif part_id == 7:
+                    res = func(self.X2, self.Y2)
+                else:
+                    raise KeyError
+                yield part_id, res
+            except KeyError:
+                yield part_id, 0
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/README.md b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/README.md
new file mode 100644
index 000000000..2982c4167
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/README.md	
@@ -0,0 +1,25 @@
+## Week 2 (Apr 5 - Apr 11)
+
+1. Enroll to the **Coursera Machine Learning** [course](https://www.coursera.org/learn/machine-learning).
+2. Apply for the financial aid if you wish to get certificate.
+3. Complete All Video Lectures and Quizes of week1 and week 2 of the [Coursera Machine Learning](https://www.coursera.org/learn/machine-learning).
+4. If you are following [YouTube Playlist](https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) complete till ***Lecture-2.9***.
+5. Complete the assignments in python either from scratch using numpy or directly using [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html).
+6. For linear regression in sklearn you can take help from [here](https://jakevdp.github.io/PythonDataScienceHandbook/05.06-linear-regression.html).
+7. Add your jupyter notebook files and create pull request.
+8. Install Anaconda for python3.6/3.7 , [installation docs](https://docs.anaconda.com/anaconda/install/) choose you OS and install accordingly. <ins>**Add Anaconda to your path variables**</ins>
+9. [Jupyter Notebook Tutorial](https://www.dataquest.io/blog/jupyter-notebook-tutorial/), Open Jupyter either form Anaconda Navigator or from the terminal.
+10. For any serious doubt regarding code or data set create an issue in this very repository or comment on the FB post (***of particular week***)with your doubts.
+11. **Each part of the assignment has equal weightage - total 100 points.**
+12. After **Apr 11 EOD** no request will be entertained for assignment submission.
+13. Complete assignment in octave (for the sake of your certification, it is optional and does not consist of any point).
+14. You submit the assignments to Coursera directly from the jupyter notebook (\* ***Conditions Apply***).
+
+### Additional References
+> **Some articles maybe in an advance level for some people, So if you don't understand don't worry**
+- https://elitedatascience.com/learn-machine-learning
+- https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d
+- https://machinelearningmastery.com/gradient-descent-for-machine-learning/
+- https://towardsdatascience.com/understanding-the-mathematics-behind-gradient-descent-dde5dc9be06e
+- https://towardsdatascience.com/machine-learning-fundamentals-via-linear-regression-41a5d11f5220
+- https://towardsdatascience.com/coding-deep-learning-for-beginners-linear-regression-part-2-cost-function-49545303d29f
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ex1.pdf b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ex1.pdf
new file mode 100644
index 000000000..c4c41c707
Binary files /dev/null and b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ex1.pdf differ
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/submission.py b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/submission.py
new file mode 100644
index 000000000..10113e47d
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/submission.py	
@@ -0,0 +1,105 @@
+from urllib.parse import urlencode
+from urllib.request import urlopen
+import pickle
+import json
+from collections import OrderedDict
+import numpy as np
+import os
+
+
+class SubmissionBase:
+
+    submit_url = 'https://www-origin.coursera.org/api/' \
+                 'onDemandProgrammingImmediateFormSubmissions.v1'
+    save_file = 'token.pkl'
+
+    def __init__(self, assignment_slug, part_names):
+        self.assignment_slug = assignment_slug
+        self.part_names = part_names
+        self.login = None
+        self.token = None
+        self.functions = OrderedDict()
+        self.args = dict()
+
+    def grade(self):
+        print('\nSubmitting Solutions | Programming Exercise %s\n' % self.assignment_slug)
+        self.login_prompt()
+
+        # Evaluate the different parts of exercise
+        parts = OrderedDict()
+        for part_id, result in self:
+            parts[str(part_id)] = {'output': sprintf('%0.5f ', result)}
+        result, response = self.request(parts)
+        response = json.loads(response.decode("utf-8"))
+
+        # if an error was returned, print it and stop
+        if 'errorMessage' in response:
+            print(response['errorMessage'])
+            return
+
+        # Print the grading table
+        print('%43s | %9s | %-s' % ('Part Name', 'Score', 'Feedback'))
+        print('%43s | %9s | %-s' % ('---------', '-----', '--------'))
+        for part in parts:
+            part_feedback = response['partFeedbacks'][part]
+            part_evaluation = response['partEvaluations'][part]
+            score = '%d / %3d' % (part_evaluation['score'], part_evaluation['maxScore'])
+            print('%43s | %9s | %-s' % (self.part_names[int(part) - 1], score, part_feedback))
+        evaluation = response['evaluation']
+        total_score = '%d / %d' % (evaluation['score'], evaluation['maxScore'])
+        print('                                  --------------------------------')
+        print('%43s | %9s | %-s\n' % (' ', total_score, ' '))
+
+    def login_prompt(self):
+        if os.path.isfile(self.save_file):
+            with open(self.save_file, 'rb') as f:
+                login, token = pickle.load(f)
+            reenter = input('Use token from last successful submission (%s)? (Y/n): ' % login)
+
+            if reenter == '' or reenter[0] == 'Y' or reenter[0] == 'y':
+                self.login, self.token = login, token
+                return
+            else:
+                os.remove(self.save_file)
+
+        self.login = input('Login (email address): ')
+        self.token = input('Token: ')
+
+        # Save the entered credentials
+        if not os.path.isfile(self.save_file):
+            with open(self.save_file, 'wb') as f:
+                pickle.dump((self.login, self.token), f)
+
+    def request(self, parts):
+        params = {
+            'assignmentSlug': self.assignment_slug,
+            'secret': self.token,
+            'parts': parts,
+            'submitterEmail': self.login}
+
+        params = urlencode({'jsonBody': json.dumps(params)}).encode("utf-8")
+        f = urlopen(self.submit_url, params)
+        try:
+            return 0, f.read()
+        finally:
+            f.close()
+
+    def __iter__(self):
+        for part_id in self.functions:
+            yield part_id
+
+    def __setitem__(self, key, value):
+        self.functions[key] = value
+
+
+def sprintf(fmt, arg):
+    """ Emulates (part of) Octave sprintf function. """
+    if isinstance(arg, tuple):
+        # for multiple return values, only use the first one
+        arg = arg[0]
+
+    if isinstance(arg, (np.ndarray, list)):
+        # concatenates all elements, column by column
+        return ' '.join(fmt % e for e in np.asarray(arg).ravel('F'))
+    else:
+        return fmt % arg
diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Data/ex2data1.txt b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Data/ex2data1.txt
new file mode 100644
index 000000000..3a5f95245
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Data/ex2data1.txt	
@@ -0,0 +1,100 @@
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diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Data/ex2data2.txt b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Data/ex2data2.txt
new file mode 100644
index 000000000..a88899234
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Data/ex2data2.txt	
@@ -0,0 +1,118 @@
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diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Figures/decision_boundary1.png b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Figures/decision_boundary1.png
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diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Figures/decision_boundary4.png b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Figures/decision_boundary4.png
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diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/exercise2.ipynb b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/exercise2.ipynb
new file mode 100644
index 000000000..39983d90f
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/exercise2.ipynb	
@@ -0,0 +1,965 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Programming Exercise 2: Logistic Regression\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "In this exercise, you will implement logistic regression and apply it to two different datasets. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n",
+    "\n",
+    "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n",
+    "\n",
+    "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, and [`matplotlib`](https://matplotlib.org/) for plotting. In this assignment, we will also use [`scipy`](https://docs.scipy.org/doc/scipy/reference/), which contains scientific and numerical computation functions and tools. \n",
+    "\n",
+    "You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading\n",
+    "\n",
+    "\n",
+    "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n",
+    "\n",
+    "\n",
+    "| Section | Part                                 | Submission function   | Points \n",
+    "| :-      |:-                                    | :-                    | :-:\n",
+    "| 1       | [Sigmoid Function](#section1)                   | [`sigmoid`](#sigmoid) | 5      \n",
+    "| 2       | [Compute cost for logistic regression](#section2) | [`costFunction`](#costFunction) | 30     \n",
+    "| 3       | [Gradient for logistic regression](#section2)     | [`costFunction`](#costFunction) | 30     \n",
+    "| 4       | [Predict Function](#section4)                     | [`predict`](#predict) | 5      \n",
+    "| 5       | [Compute cost for regularized LR](#section5)      | [`costFunctionReg`](#costFunctionReg) | 15     \n",
+    "| 6       | [Gradient for regularized LR](#section5)          | [`costFunctionReg`](#costFunctionReg) | 15     \n",
+    "|         | Total Points                         | | 100    \n",
+    "\n",
+    "\n",
+    "\n",
+    "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once. They must also be re-executed everytime the submitted function is updated.\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Logistic Regression\n",
+    "\n",
+    "In this part of the exercise, you will build a logistic regression model to predict whether a student gets admitted into a university. Suppose that you are the administrator of a university department and\n",
+    "you want to determine each applicant’s chance of admission based on their results on two exams. You have historical data from previous applicants that you can use as a training set for logistic regression. For each training example, you have the applicant’s scores on two exams and the admissions\n",
+    "decision. Your task is to build a classification model that estimates an applicant’s probability of admission based the scores from those two exams. \n",
+    "\n",
+    "The following cell will load the data and corresponding labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "# The first two columns contains the exam scores and the third column\n",
+    "# contains the label.\n",
+    "data = np.loadtxt(os.path.join('Data', 'ex2data1.txt'), delimiter=',')\n",
+    "X, y = data[:, 0:2], data[:, 2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 Visualizing the data\n",
+    "\n",
+    "Before starting to implement any learning algorithm, it is always good to visualize the data if possible. We  display the data on a 2-dimensional plot by calling the function `plotData`. You will now complete the code in `plotData` so that it displays a figure where the axes are the two exam scores, and the positive and negative examples are shown with different markers.\n",
+    "\n",
+    "To help you get more familiar with plotting, we have left `plotData` empty so you can try to implement it yourself. However, this is an optional (ungraded) exercise. We also provide our implementation below so you can\n",
+    "copy it or refer to it. If you choose to copy our example, make sure you learn\n",
+    "what each of its commands is doing by consulting the `matplotlib` and `numpy` documentation.\n",
+    "\n",
+    "```python\n",
+    "# Find Indices of Positive and Negative Examples\n",
+    "pos = y == 1\n",
+    "neg = y == 0\n",
+    "\n",
+    "# Plot Examples\n",
+    "pyplot.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10)\n",
+    "pyplot.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotData(X, y):\n",
+    "    \"\"\"\n",
+    "    Plots the data points X and y into a new figure. Plots the data \n",
+    "    points with * for the positive examples and o for the negative examples.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        An Mx2 matrix representing the dataset. \n",
+    "    \n",
+    "    y : array_like\n",
+    "        Label values for the dataset. A vector of size (M, ).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Plot the positive and negative examples on a 2D plot, using the\n",
+    "    option 'k*' for the positive examples and 'ko' for the negative examples.    \n",
+    "    \"\"\"\n",
+    "    # Create New Figure\n",
+    "    fig = pyplot.figure()\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    \n",
+    "    # ============================================================"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we call the implemented function to display the loaded data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotData(X, y)\n",
+    "# add axes labels\n",
+    "pyplot.xlabel('Exam 1 score')\n",
+    "pyplot.ylabel('Exam 2 score')\n",
+    "pyplot.legend(['Admitted', 'Not admitted'])\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section1\"></a>\n",
+    "### 1.2 Implementation\n",
+    "\n",
+    "#### 1.2.1 Warmup exercise: sigmoid function\n",
+    "\n",
+    "Before you start with the actual cost function, recall that the logistic regression hypothesis is defined as:\n",
+    "\n",
+    "$$ h_\\theta(x) = g(\\theta^T x)$$\n",
+    "\n",
+    "where function $g$ is the sigmoid function. The sigmoid function is defined as: \n",
+    "\n",
+    "$$g(z) = \\frac{1}{1+e^{-z}}$$.\n",
+    "\n",
+    "Your first step is to implement this function `sigmoid` so it can be\n",
+    "called by the rest of your program. When you are finished, try testing a few\n",
+    "values by calling `sigmoid(x)` in a new cell. For large positive values of `x`, the sigmoid should be close to 1, while for large negative values, the sigmoid should be close to 0. Evaluating `sigmoid(0)` should give you exactly 0.5. Your code should also work with vectors and matrices. **For a matrix, your function should perform the sigmoid function on every element.**\n",
+    "<a id=\"sigmoid\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sigmoid(z):\n",
+    "    \"\"\"\n",
+    "    Compute sigmoid function given the input z.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    z : array_like\n",
+    "        The input to the sigmoid function. This can be a 1-D vector \n",
+    "        or a 2-D matrix. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    g : array_like\n",
+    "        The computed sigmoid function. g has the same shape as z, since\n",
+    "        the sigmoid is computed element-wise on z.\n",
+    "        \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the sigmoid of each value of z (z can be a matrix, vector or scalar).\n",
+    "    \"\"\"\n",
+    "    # convert input to a numpy array\n",
+    "    z = np.array(z)\n",
+    "    \n",
+    "    # You need to return the following variables correctly \n",
+    "    g = np.zeros(z.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    # =============================================================\n",
+    "    return g"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell evaluates the sigmoid function at `z=0`. You should get a value of 0.5. You can also try different values for `z` to experiment with the sigmoid function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test the implementation of sigmoid function here\n",
+    "z = 0\n",
+    "g = sigmoid(z)\n",
+    "\n",
+    "print('g(', z, ') = ', g)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the submission object, and then sending your function to Coursera for grading. \n",
+    "\n",
+    "The submission script will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "Execute the following cell to grade your solution to the first part of this exercise.\n",
+    "\n",
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# appends the implemented function in part 1 to the grader object\n",
+    "grader[1] = sigmoid\n",
+    "\n",
+    "# send the added functions to coursera grader for getting a grade on this part\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "#### 1.2.2 Cost function and gradient\n",
+    "\n",
+    "Now you will implement the cost function and gradient for logistic regression. Before proceeding we add the intercept term to X. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the data matrix appropriately, and add ones for the intercept term\n",
+    "m, n = X.shape\n",
+    "\n",
+    "# Add intercept term to X\n",
+    "X = np.concatenate([np.ones((m, 1)), X], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, complete the code for the function `costFunction` to return the cost and gradient. Recall that the cost function in logistic regression is\n",
+    "\n",
+    "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m} \\left[ -y^{(i)} \\log\\left(h_\\theta\\left( x^{(i)} \\right) \\right) - \\left( 1 - y^{(i)}\\right) \\log \\left( 1 - h_\\theta\\left( x^{(i)} \\right) \\right) \\right]$$\n",
+    "\n",
+    "and the gradient of the cost is a vector of the same length as $\\theta$ where the $j^{th}$\n",
+    "element (for $j = 0, 1, \\cdots , n$) is defined as follows:\n",
+    "\n",
+    "$$ \\frac{\\partial J(\\theta)}{\\partial \\theta_j} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} $$\n",
+    "\n",
+    "Note that while this gradient looks identical to the linear regression gradient, the formula is actually different because linear and logistic regression have different definitions of $h_\\theta(x)$.\n",
+    "<a id=\"costFunction\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def costFunction(theta, X, y):\n",
+    "    \"\"\"\n",
+    "    Compute cost and gradient for logistic regression. \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        The parameters for logistic regression. This a vector\n",
+    "        of shape (n+1, ).\n",
+    "    \n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n+1) where m is the total number\n",
+    "        of data points and n is the number of features. We assume the \n",
+    "        intercept has already been added to the input.\n",
+    "    \n",
+    "    y : arra_like\n",
+    "        Labels for the input. This is a vector of shape (m, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        A vector of shape (n+1, ) which is the gradient of the cost\n",
+    "        function with respect to theta, at the current values of theta.\n",
+    "        \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost of a particular choice of theta. You should set J to \n",
+    "    the cost. Compute the partial derivatives and set grad to the partial\n",
+    "    derivatives of the cost w.r.t. each parameter in theta.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you are done call your `costFunction` using two test cases for  $\\theta$ by executing the next cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize fitting parameters\n",
+    "initial_theta = np.zeros(n+1)\n",
+    "\n",
+    "cost, grad = costFunction(initial_theta, X, y)\n",
+    "\n",
+    "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx): 0.693\\n')\n",
+    "\n",
+    "print('Gradient at initial theta (zeros):')\n",
+    "print('\\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))\n",
+    "print('Expected gradients (approx):\\n\\t[-0.1000, -12.0092, -11.2628]\\n')\n",
+    "\n",
+    "# Compute and display cost and gradient with non-zero theta\n",
+    "test_theta = np.array([-24, 0.2, 0.2])\n",
+    "cost, grad = costFunction(test_theta, X, y)\n",
+    "\n",
+    "print('Cost at test theta: {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx): 0.218\\n')\n",
+    "\n",
+    "print('Gradient at test theta:')\n",
+    "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad))\n",
+    "print('Expected gradients (approx):\\n\\t[0.043, 2.566, 2.647]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[2] = costFunction\n",
+    "grader[3] = costFunction\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.2.3 Learning parameters using `scipy.optimize`\n",
+    "\n",
+    "In the previous assignment, you found the optimal parameters of a linear regression model by implementing gradient descent. You wrote a cost function and calculated its gradient, then took a gradient descent step accordingly. This time, instead of taking gradient descent steps, you will use the [`scipy.optimize` module](https://docs.scipy.org/doc/scipy/reference/optimize.html). SciPy is a numerical computing library for `python`. It provides an optimization module for root finding and minimization. As of `scipy 1.0`, the function `scipy.optimize.minimize` is the method to use for optimization problems(both constrained and unconstrained).\n",
+    "\n",
+    "For logistic regression, you want to optimize the cost function $J(\\theta)$ with parameters $\\theta$.\n",
+    "Concretely, you are going to use `optimize.minimize` to find the best parameters $\\theta$ for the logistic regression cost function, given a fixed dataset (of X and y values). You will pass to `optimize.minimize` the following inputs:\n",
+    "- `costFunction`: A cost function that, when given the training set and a particular $\\theta$, computes the logistic regression cost and gradient with respect to $\\theta$ for the dataset (X, y). It is important to note that we only pass the name of the function without the parenthesis. This indicates that we are only providing a reference to this function, and not evaluating the result from this function.\n",
+    "- `initial_theta`: The initial values of the parameters we are trying to optimize.\n",
+    "- `(X, y)`: These are additional arguments to the cost function.\n",
+    "- `jac`: Indication if the cost function returns the Jacobian (gradient) along with cost value. (True)\n",
+    "- `method`: Optimization method/algorithm to use\n",
+    "- `options`: Additional options which might be specific to the specific optimization method. In the following, we only tell the algorithm the maximum number of iterations before it terminates.\n",
+    "\n",
+    "If you have completed the `costFunction` correctly, `optimize.minimize` will converge on the right optimization parameters and return the final values of the cost and $\\theta$ in a class object. Notice that by using `optimize.minimize`, you did not have to write any loops yourself, or set a learning rate like you did for gradient descent. This is all done by `optimize.minimize`: you only needed to provide a function calculating the cost and the gradient.\n",
+    "\n",
+    "In the following, we already have code written to call `optimize.minimize` with the correct arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set options for optimize.minimize\n",
+    "options= {'maxiter': 400}\n",
+    "\n",
+    "# see documention for scipy's optimize.minimize  for description about\n",
+    "# the different parameters\n",
+    "# The function returns an object `OptimizeResult`\n",
+    "# We use truncated Newton algorithm for optimization which is \n",
+    "# equivalent to MATLAB's fminunc\n",
+    "# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy\n",
+    "res = optimize.minimize(costFunction,\n",
+    "                        initial_theta,\n",
+    "                        (X, y),\n",
+    "                        jac=True,\n",
+    "                        method='TNC',\n",
+    "                        options=options)\n",
+    "\n",
+    "# the fun property of `OptimizeResult` object returns\n",
+    "# the value of costFunction at optimized theta\n",
+    "cost = res.fun\n",
+    "\n",
+    "# the optimized theta is in the x property\n",
+    "theta = res.x\n",
+    "\n",
+    "# Print theta to screen\n",
+    "print('Cost at theta found by optimize.minimize: {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx): 0.203\\n');\n",
+    "\n",
+    "print('theta:')\n",
+    "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*theta))\n",
+    "print('Expected theta (approx):\\n\\t[-25.161, 0.206, 0.201]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once `optimize.minimize` completes, we want to use the final value for $\\theta$ to visualize the decision boundary on the training data as shown in the figure below. \n",
+    "\n",
+    "![](Figures/decision_boundary1.png)\n",
+    "\n",
+    "To do so, we have written a function `plotDecisionBoundary` for plotting the decision boundary on top of training data. You do not need to write any code for plotting the decision boundary, but we also encourage you to look at the code in `plotDecisionBoundary` to see how to plot such a boundary using the $\\theta$ values. You can find this function in the `utils.py` file which comes with this assignment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot Boundary\n",
+    "utils.plotDecisionBoundary(plotData, theta, X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section4\"></a>\n",
+    "#### 1.2.4 Evaluating logistic regression\n",
+    "\n",
+    "After learning the parameters, you can use the model to predict whether a particular student will be admitted. For a student with an Exam 1 score of 45 and an Exam 2 score of 85, you should expect to see an admission\n",
+    "probability of 0.776. Another way to evaluate the quality of the parameters we have found is to see how well the learned model predicts on our training set. In this part, your task is to complete the code in function `predict`. The predict function will produce “1” or “0” predictions given a dataset and a learned parameter vector $\\theta$. \n",
+    "<a id=\"predict\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(theta, X):\n",
+    "    \"\"\"\n",
+    "    Predict whether the label is 0 or 1 using learned logistic regression.\n",
+    "    Computes the predictions for X using a threshold at 0.5 \n",
+    "    (i.e., if sigmoid(theta.T*x) >= 0.5, predict 1)\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        Parameters for logistic regression. A vecotor of shape (n+1, ).\n",
+    "    \n",
+    "    X : array_like\n",
+    "        The data to use for computing predictions. The rows is the number \n",
+    "        of points to compute predictions, and columns is the number of\n",
+    "        features.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    p : array_like\n",
+    "        Predictions and 0 or 1 for each row in X. \n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Complete the following code to make predictions using your learned \n",
+    "    logistic regression parameters.You should set p to a vector of 0's and 1's    \n",
+    "    \"\"\"\n",
+    "    m = X.shape[0] # Number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly\n",
+    "    p = np.zeros(m)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    # ============================================================\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After you have completed the code in `predict`, we proceed to report the training accuracy of your classifier by computing the percentage of examples it got correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  Predict probability for a student with score 45 on exam 1 \n",
+    "#  and score 85 on exam 2 \n",
+    "prob = sigmoid(np.dot([1, 45, 85], theta))\n",
+    "print('For a student with scores 45 and 85,'\n",
+    "      'we predict an admission probability of {:.3f}'.format(prob))\n",
+    "print('Expected value: 0.775 +/- 0.002\\n')\n",
+    "\n",
+    "# Compute accuracy on our training set\n",
+    "p = predict(theta, X)\n",
+    "print('Train Accuracy: {:.2f} %'.format(np.mean(p == y) * 100))\n",
+    "print('Expected accuracy (approx): 89.00 %')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[4] = predict\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 Regularized logistic regression\n",
+    "\n",
+    "In this part of the exercise, you will implement regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance (QA). During QA, each microchip goes through various tests to ensure it is functioning correctly.\n",
+    "Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests. From these two tests, you would like to determine whether the microchips should be accepted or rejected. To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model.\n",
+    "\n",
+    "First, we load the data from a CSV file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load Data\n",
+    "# The first two columns contains the X values and the third column\n",
+    "# contains the label (y).\n",
+    "data = np.loadtxt(os.path.join('Data', 'ex2data2.txt'), delimiter=',')\n",
+    "X = data[:, :2]\n",
+    "y = data[:, 2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1 Visualize the data\n",
+    "\n",
+    "Similar to the previous parts of this exercise, `plotData` is used to generate a figure, where the axes are the two test scores, and the positive (y = 1, accepted) and negative (y = 0, rejected) examples are shown with\n",
+    "different markers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotData(X, y)\n",
+    "# Labels and Legend\n",
+    "pyplot.xlabel('Microchip Test 1')\n",
+    "pyplot.ylabel('Microchip Test 2')\n",
+    "\n",
+    "# Specified in plot order\n",
+    "pyplot.legend(['y = 1', 'y = 0'], loc='upper right')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above figure shows that our dataset cannot be separated into positive and negative examples by a straight-line through the plot. Therefore, a straight-forward application of logistic regression will not perform well on this dataset since logistic regression will only be able to find a linear decision boundary.\n",
+    "\n",
+    "### 2.2 Feature mapping\n",
+    "\n",
+    "One way to fit the data better is to create more features from each data point. In the function `mapFeature` defined in the file `utils.py`, we will map the features into all polynomial terms of $x_1$ and $x_2$ up to the sixth power.\n",
+    "\n",
+    "$$ \\text{mapFeature}(x) = \\begin{bmatrix} 1 & x_1 & x_2 & x_1^2 & x_1 x_2 & x_2^2 & x_1^3 & \\dots & x_1 x_2^5 & x_2^6 \\end{bmatrix}^T $$\n",
+    "\n",
+    "As a result of this mapping, our vector of two features (the scores on two QA tests) has been transformed into a 28-dimensional vector. A logistic regression classifier trained on this higher-dimension feature vector will have a more complex decision boundary and will appear nonlinear when drawn in our 2-dimensional plot.\n",
+    "While the feature mapping allows us to build a more expressive classifier, it also more susceptible to overfitting. In the next parts of the exercise, you will implement regularized logistic regression to fit the data and also see for yourself how regularization can help combat the overfitting problem.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note that mapFeature also adds a column of ones for us, so the intercept\n",
+    "# term is handled\n",
+    "X = utils.mapFeature(X[:, 0], X[:, 1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section5\"></a>\n",
+    "### 2.3 Cost function and gradient\n",
+    "\n",
+    "Now you will implement code to compute the cost function and gradient for regularized logistic regression. Complete the code for the function `costFunctionReg` below to return the cost and gradient.\n",
+    "\n",
+    "Recall that the regularized cost function in logistic regression is\n",
+    "\n",
+    "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^m \\left[ -y^{(i)}\\log \\left( h_\\theta \\left(x^{(i)} \\right) \\right) - \\left( 1 - y^{(i)} \\right) \\log \\left( 1 - h_\\theta \\left( x^{(i)} \\right) \\right) \\right] + \\frac{\\lambda}{2m} \\sum_{j=1}^n \\theta_j^2 $$\n",
+    "\n",
+    "Note that you should not regularize the parameters $\\theta_0$. The gradient of the cost function is a vector where the $j^{th}$ element is defined as follows:\n",
+    "\n",
+    "$$ \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left(x^{(i)}\\right) - y^{(i)} \\right) x_j^{(i)} \\qquad \\text{for } j =0 $$\n",
+    "\n",
+    "$$ \\frac{\\partial J(\\theta)}{\\partial \\theta_j} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left(x^{(i)}\\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m}\\theta_j \\qquad \\text{for } j \\ge 1 $$\n",
+    "<a id=\"costFunctionReg\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def costFunctionReg(theta, X, y, lambda_):\n",
+    "    \"\"\"\n",
+    "    Compute cost and gradient for logistic regression with regularization.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        Logistic regression parameters. A vector with shape (n, ). n is \n",
+    "        the number of features including any intercept. If we have mapped\n",
+    "        our initial features into polynomial features, then n is the total \n",
+    "        number of polynomial features. \n",
+    "    \n",
+    "    X : array_like\n",
+    "        The data set with shape (m x n). m is the number of examples, and\n",
+    "        n is the number of features (after feature mapping).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The data labels. A vector with shape (m, ).\n",
+    "    \n",
+    "    lambda_ : float\n",
+    "        The regularization parameter. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the regularized cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        A vector of shape (n, ) which is the gradient of the cost\n",
+    "        function with respect to theta, at the current values of theta.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost `J` of a particular choice of theta.\n",
+    "    Compute the partial derivatives and set `grad` to the partial\n",
+    "    derivatives of the cost w.r.t. each parameter in theta.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ===================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you are done with the `costFunctionReg`, we call it below using the initial value of $\\theta$ (initialized to all zeros), and also another test case where $\\theta$ is all ones."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize fitting parameters\n",
+    "initial_theta = np.zeros(X.shape[1])\n",
+    "\n",
+    "# Set regularization parameter lambda to 1\n",
+    "# DO NOT use `lambda` as a variable name in python\n",
+    "# because it is a python keyword\n",
+    "lambda_ = 1\n",
+    "\n",
+    "# Compute and display initial cost and gradient for regularized logistic\n",
+    "# regression\n",
+    "cost, grad = costFunctionReg(initial_theta, X, y, lambda_)\n",
+    "\n",
+    "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx)       : 0.693\\n')\n",
+    "\n",
+    "print('Gradient at initial theta (zeros) - first five values only:')\n",
+    "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n",
+    "print('Expected gradients (approx) - first five values only:')\n",
+    "print('\\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\\n')\n",
+    "\n",
+    "\n",
+    "# Compute and display cost and gradient\n",
+    "# with all-ones theta and lambda = 10\n",
+    "test_theta = np.ones(X.shape[1])\n",
+    "cost, grad = costFunctionReg(test_theta, X, y, 10)\n",
+    "\n",
+    "print('------------------------------\\n')\n",
+    "print('Cost at test theta    : {:.2f}'.format(cost))\n",
+    "print('Expected cost (approx): 3.16\\n')\n",
+    "\n",
+    "print('Gradient at initial theta (zeros) - first five values only:')\n",
+    "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n",
+    "print('Expected gradients (approx) - first five values only:')\n",
+    "print('\\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[5] = costFunctionReg\n",
+    "grader[6] = costFunctionReg\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.3.1 Learning parameters using `scipy.optimize.minimize`\n",
+    "\n",
+    "Similar to the previous parts, you will use `optimize.minimize` to learn the optimal parameters $\\theta$. If you have completed the cost and gradient for regularized logistic regression (`costFunctionReg`) correctly, you should be able to step through the next part of to learn the parameters $\\theta$ using `optimize.minimize`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 Plotting the decision boundary\n",
+    "\n",
+    "To help you visualize the model learned by this classifier, we have provided the function `plotDecisionBoundary` which plots the (non-linear) decision boundary that separates the positive and negative examples. In `plotDecisionBoundary`, we plot the non-linear decision boundary by computing the classifier’s predictions on an evenly spaced grid and then and draw a contour plot where the predictions change from y = 0 to y = 1. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5 Optional (ungraded) exercises\n",
+    "\n",
+    "In this part of the exercise, you will get to try out different regularization parameters for the dataset to understand how regularization prevents overfitting.\n",
+    "\n",
+    "Notice the changes in the decision boundary as you vary $\\lambda$. With a small\n",
+    "$\\lambda$, you should find that the classifier gets almost every training example correct, but draws a very complicated boundary, thus overfitting the data. See the following figures for the decision boundaries you should get for different values of $\\lambda$. \n",
+    "\n",
+    "<table>\n",
+    "    <tr>\n",
+    "        <td style=\"text-align:center\">\n",
+    "            No regularization (overfitting)<img src=\"Figures/decision_boundary3.png\">\n",
+    "        </td>        \n",
+    "        <td style=\"text-align:center\">\n",
+    "            Decision boundary with regularization\n",
+    "            <img src=\"Figures/decision_boundary2.png\">\n",
+    "        </td>\n",
+    "        <td style=\"text-align:center\">\n",
+    "            Decision boundary with too much regularization\n",
+    "            <img src=\"Figures/decision_boundary4.png\">\n",
+    "        </td>        \n",
+    "    <tr>\n",
+    "</table>\n",
+    "\n",
+    "This is not a good decision boundary: for example, it predicts that a point at $x = (−0.25, 1.5)$ is accepted $(y = 1)$, which seems to be an incorrect decision given the training set.\n",
+    "With a larger $\\lambda$, you should see a plot that shows an simpler decision boundary which still separates the positives and negatives fairly well. However, if $\\lambda$ is set to too high a value, you will not get a good fit and the decision boundary will not follow the data so well, thus underfitting the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize fitting parameters\n",
+    "initial_theta = np.zeros(X.shape[1])\n",
+    "\n",
+    "# Set regularization parameter lambda to 1 (you should vary this)\n",
+    "lambda_ = 1\n",
+    "\n",
+    "# set options for optimize.minimize\n",
+    "options= {'maxiter': 100}\n",
+    "\n",
+    "res = optimize.minimize(costFunctionReg,\n",
+    "                        initial_theta,\n",
+    "                        (X, y, lambda_),\n",
+    "                        jac=True,\n",
+    "                        method='TNC',\n",
+    "                        options=options)\n",
+    "\n",
+    "# the fun property of OptimizeResult object returns\n",
+    "# the value of costFunction at optimized theta\n",
+    "cost = res.fun\n",
+    "\n",
+    "# the optimized theta is in the x property of the result\n",
+    "theta = res.x\n",
+    "\n",
+    "utils.plotDecisionBoundary(plotData, theta, X, y)\n",
+    "pyplot.xlabel('Microchip Test 1')\n",
+    "pyplot.ylabel('Microchip Test 2')\n",
+    "pyplot.legend(['y = 1', 'y = 0'])\n",
+    "pyplot.grid(False)\n",
+    "pyplot.title('lambda = %0.2f' % lambda_)\n",
+    "\n",
+    "# Compute accuracy on our training set\n",
+    "p = predict(theta, X)\n",
+    "\n",
+    "print('Train Accuracy: %.1f %%' % (np.mean(p == y) * 100))\n",
+    "print('Expected accuracy (with lambda = 1): 83.1 % (approx)\\n')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You do not need to submit any solutions for these optional (ungraded) exercises.*"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/utils.py b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/utils.py
new file mode 100644
index 000000000..7c52dbe41
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/utils.py	
@@ -0,0 +1,147 @@
+import sys
+import numpy as np
+from matplotlib import pyplot
+
+sys.path.append('..')
+from submission import SubmissionBase
+
+
+def mapFeature(X1, X2, degree=6):
+    """
+    Maps the two input features to quadratic features used in the regularization exercise.
+
+    Returns a new feature array with more features, comprising of
+    X1, X2, X1.^2, X2.^2, X1*X2, X1*X2.^2, etc..
+
+    Parameters
+    ----------
+    X1 : array_like
+        A vector of shape (m, 1), containing one feature for all examples.
+
+    X2 : array_like
+        A vector of shape (m, 1), containing a second feature for all examples.
+        Inputs X1, X2 must be the same size.
+
+    degree: int, optional
+        The polynomial degree.
+
+    Returns
+    -------
+    : array_like
+        A matrix of of m rows, and columns depend on the degree of polynomial.
+    """
+    if X1.ndim > 0:
+        out = [np.ones(X1.shape[0])]
+    else:
+        out = [np.ones(1)]
+
+    for i in range(1, degree + 1):
+        for j in range(i + 1):
+            out.append((X1 ** (i - j)) * (X2 ** j))
+
+    if X1.ndim > 0:
+        return np.stack(out, axis=1)
+    else:
+        return np.array(out)
+
+
+def plotDecisionBoundary(plotData, theta, X, y):
+    """
+    Plots the data points X and y into a new figure with the decision boundary defined by theta.
+    Plots the data points with * for the positive examples and o for  the negative examples.
+
+    Parameters
+    ----------
+    plotData : func
+        A function reference for plotting the X, y data.
+
+    theta : array_like
+        Parameters for logistic regression. A vector of shape (n+1, ).
+
+    X : array_like
+        The input dataset. X is assumed to be  a either:
+            1) Mx3 matrix, where the first column is an all ones column for the intercept.
+            2) MxN, N>3 matrix, where the first column is all ones.
+
+    y : array_like
+        Vector of data labels of shape (m, ).
+    """
+    # make sure theta is a numpy array
+    theta = np.array(theta)
+
+    # Plot Data (remember first column in X is the intercept)
+    plotData(X[:, 1:3], y)
+
+    if X.shape[1] <= 3:
+        # Only need 2 points to define a line, so choose two endpoints
+        plot_x = np.array([np.min(X[:, 1]) - 2, np.max(X[:, 1]) + 2])
+
+        # Calculate the decision boundary line
+        plot_y = (-1. / theta[2]) * (theta[1] * plot_x + theta[0])
+
+        # Plot, and adjust axes for better viewing
+        pyplot.plot(plot_x, plot_y)
+
+        # Legend, specific for the exercise
+        pyplot.legend(['Admitted', 'Not admitted', 'Decision Boundary'])
+        pyplot.xlim([30, 100])
+        pyplot.ylim([30, 100])
+    else:
+        # Here is the grid range
+        u = np.linspace(-1, 1.5, 50)
+        v = np.linspace(-1, 1.5, 50)
+
+        z = np.zeros((u.size, v.size))
+        # Evaluate z = theta*x over the grid
+        for i, ui in enumerate(u):
+            for j, vj in enumerate(v):
+                z[i, j] = np.dot(mapFeature(ui, vj), theta)
+
+        z = z.T  # important to transpose z before calling contour
+        # print(z)
+
+        # Plot z = 0
+        pyplot.contour(u, v, z, levels=[0], linewidths=2, colors='g')
+        pyplot.contourf(u, v, z, levels=[np.min(z), 0, np.max(z)], cmap='Greens', alpha=0.4)
+
+
+class Grader(SubmissionBase):
+    X = np.stack([np.ones(20),
+                  np.exp(1) * np.sin(np.arange(1, 21)),
+                  np.exp(0.5) * np.cos(np.arange(1, 21))], axis=1)
+
+    y = (np.sin(X[:, 0] + X[:, 1]) > 0).astype(float)
+
+    def __init__(self):
+        part_names = ['Sigmoid Function',
+                      'Logistic Regression Cost',
+                      'Logistic Regression Gradient',
+                      'Predict',
+                      'Regularized Logistic Regression Cost',
+                      'Regularized Logistic Regression Gradient']
+        super().__init__('logistic-regression', part_names)
+
+    def __iter__(self):
+        for part_id in range(1, 7):
+            try:
+                func = self.functions[part_id]
+
+                # Each part has different expected arguments/different function
+                if part_id == 1:
+                    res = func(self.X)
+                elif part_id == 2:
+                    res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y)
+                elif part_id == 3:
+                    J, grad = func(np.array([0.25, 0.5, -0.5]), self.X, self.y)
+                    res = grad
+                elif part_id == 4:
+                    res = func(np.array([0.25, 0.5, -0.5]), self.X)
+                elif part_id == 5:
+                    res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y, 0.1)
+                elif part_id == 6:
+                    res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y, 0.1)[1]
+                else:
+                    raise KeyError
+                yield part_id, res
+            except KeyError:
+                yield part_id, 0
diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/README.md b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/README.md
new file mode 100644
index 000000000..2e93309bb
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/README.md	
@@ -0,0 +1,8 @@
+## To be done for next week
+
+1. Complete All Video Lectures and Quizes of week3 of the [Coursera Course](https://www.coursera.org/learn/machine-learning/home/week/3).
+2. For logistic regression you can take help from [here](https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac).
+3. Add your python(if you are doing by your own) or jupyter notebook files and create pull request.
+4. Each part of the assignment has equal weightage - total 100 points.
+5. Complete assignment in octave(for the sake of your certification, it is optional and does not consist of any point).
+6. To understand the difference between l1 and l2 regularization you can check [this](https://www.youtube.com/watch?v=sO4ZirJh9ds) video.
diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/submission.py b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/submission.py
new file mode 100644
index 000000000..10113e47d
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/submission.py	
@@ -0,0 +1,105 @@
+from urllib.parse import urlencode
+from urllib.request import urlopen
+import pickle
+import json
+from collections import OrderedDict
+import numpy as np
+import os
+
+
+class SubmissionBase:
+
+    submit_url = 'https://www-origin.coursera.org/api/' \
+                 'onDemandProgrammingImmediateFormSubmissions.v1'
+    save_file = 'token.pkl'
+
+    def __init__(self, assignment_slug, part_names):
+        self.assignment_slug = assignment_slug
+        self.part_names = part_names
+        self.login = None
+        self.token = None
+        self.functions = OrderedDict()
+        self.args = dict()
+
+    def grade(self):
+        print('\nSubmitting Solutions | Programming Exercise %s\n' % self.assignment_slug)
+        self.login_prompt()
+
+        # Evaluate the different parts of exercise
+        parts = OrderedDict()
+        for part_id, result in self:
+            parts[str(part_id)] = {'output': sprintf('%0.5f ', result)}
+        result, response = self.request(parts)
+        response = json.loads(response.decode("utf-8"))
+
+        # if an error was returned, print it and stop
+        if 'errorMessage' in response:
+            print(response['errorMessage'])
+            return
+
+        # Print the grading table
+        print('%43s | %9s | %-s' % ('Part Name', 'Score', 'Feedback'))
+        print('%43s | %9s | %-s' % ('---------', '-----', '--------'))
+        for part in parts:
+            part_feedback = response['partFeedbacks'][part]
+            part_evaluation = response['partEvaluations'][part]
+            score = '%d / %3d' % (part_evaluation['score'], part_evaluation['maxScore'])
+            print('%43s | %9s | %-s' % (self.part_names[int(part) - 1], score, part_feedback))
+        evaluation = response['evaluation']
+        total_score = '%d / %d' % (evaluation['score'], evaluation['maxScore'])
+        print('                                  --------------------------------')
+        print('%43s | %9s | %-s\n' % (' ', total_score, ' '))
+
+    def login_prompt(self):
+        if os.path.isfile(self.save_file):
+            with open(self.save_file, 'rb') as f:
+                login, token = pickle.load(f)
+            reenter = input('Use token from last successful submission (%s)? (Y/n): ' % login)
+
+            if reenter == '' or reenter[0] == 'Y' or reenter[0] == 'y':
+                self.login, self.token = login, token
+                return
+            else:
+                os.remove(self.save_file)
+
+        self.login = input('Login (email address): ')
+        self.token = input('Token: ')
+
+        # Save the entered credentials
+        if not os.path.isfile(self.save_file):
+            with open(self.save_file, 'wb') as f:
+                pickle.dump((self.login, self.token), f)
+
+    def request(self, parts):
+        params = {
+            'assignmentSlug': self.assignment_slug,
+            'secret': self.token,
+            'parts': parts,
+            'submitterEmail': self.login}
+
+        params = urlencode({'jsonBody': json.dumps(params)}).encode("utf-8")
+        f = urlopen(self.submit_url, params)
+        try:
+            return 0, f.read()
+        finally:
+            f.close()
+
+    def __iter__(self):
+        for part_id in self.functions:
+            yield part_id
+
+    def __setitem__(self, key, value):
+        self.functions[key] = value
+
+
+def sprintf(fmt, arg):
+    """ Emulates (part of) Octave sprintf function. """
+    if isinstance(arg, tuple):
+        # for multiple return values, only use the first one
+        arg = arg[0]
+
+    if isinstance(arg, (np.ndarray, list)):
+        # concatenates all elements, column by column
+        return ' '.join(fmt % e for e in np.asarray(arg).ravel('F'))
+    else:
+        return fmt % arg
diff --git a/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/Data/ex3data1.mat b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/Data/ex3data1.mat
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diff --git a/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/Figures/neuralnetwork.png b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/Figures/neuralnetwork.png
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diff --git a/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/exercise3.ipynb b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/exercise3.ipynb
new file mode 100644
index 000000000..e37be91fd
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/exercise3.ipynb	
@@ -0,0 +1,923 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Programming Exercise 3\n",
+    "# Multi-class Classification and Neural Networks\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "\n",
+    "In this exercise, you will implement one-vs-all logistic regression and neural networks to recognize handwritten digits. Before starting the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics. \n",
+    "\n",
+    "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n",
+    "\n",
+    "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading\n",
+    "\n",
+    "\n",
+    "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n",
+    "\n",
+    "\n",
+    "| Section | Part                                 | Submission function                   |  Points \n",
+    "| :-      |:-                                    | :-                                    |  :-:    \n",
+    "| 1       | [Regularized Logistic Regression](#section1)     | [`lrCostFunction`](#lrCostFunction)   | 30     \n",
+    "| 2       | [One-vs-all classifier training](#section2)       | [`oneVsAll`](#oneVsAll)               | 20     \n",
+    "| 3       | [One-vs-all classifier prediction](#section3)     | [`predictOneVsAll`](#predictOneVsAll) | 20     \n",
+    "| 4       | [Neural Network Prediction Function](#section4)   | [`predict`](#predict)           | 30\n",
+    "|         | Total Points                         |                                 | 100    \n",
+    "\n",
+    "\n",
+    "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once. They must also be re-executed everytime the submitted function is updated.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Multi-class Classification\n",
+    "\n",
+    "For this exercise, you will use logistic regression and neural networks to recognize handwritten digits (from 0 to 9). Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes)\n",
+    "on mail envelopes to recognizing amounts written on bank checks. This exercise will show you how the methods you have learned can be used for this classification task.\n",
+    "\n",
+    "In the first part of the exercise, you will extend your previous implementation of logistic regression and apply it to one-vs-all classification.\n",
+    "\n",
+    "### 1.1 Dataset\n",
+    "\n",
+    "You are given a data set in `ex3data1.mat` that contains 5000 training examples of handwritten digits (This is a subset of the [MNIST](http://yann.lecun.com/exdb/mnist) handwritten digit dataset). The `.mat` format means that that the data has been saved in a native Octave/MATLAB matrix format, instead of a text (ASCII) format like a csv-file. We use the `.mat` format here because this is the dataset provided in the MATLAB version of this assignment. Fortunately, python provides mechanisms to load MATLAB native format using the `loadmat` function within the `scipy.io` module. This function returns a python dictionary with keys containing the variable names within the `.mat` file. \n",
+    "\n",
+    "There are 5000 training examples in `ex3data1.mat`, where each training example is a 20 pixel by 20 pixel grayscale image of the digit. Each pixel is represented by a floating point number indicating the grayscale intensity at that location. The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. Each of these training examples becomes a single row in our data matrix `X`. This gives us a 5000 by 400 matrix `X` where every row is a training example for a handwritten digit image.\n",
+    "\n",
+    "$$ X = \\begin{bmatrix} - \\: (x^{(1)})^T \\: - \\\\ -\\: (x^{(2)})^T \\:- \\\\ \\vdots \\\\ - \\: (x^{(m)})^T \\:-  \\end{bmatrix} $$\n",
+    "\n",
+    "The second part of the training set is a 5000-dimensional vector `y` that contains labels for the training set. \n",
+    "We start the exercise by first loading the dataset. Execute the cell below, you do not need to write any code here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 20x20 Input Images of Digits\n",
+    "input_layer_size  = 400\n",
+    "\n",
+    "# 10 labels, from 1 to 10 (note that we have mapped \"0\" to label 10)\n",
+    "num_labels = 10\n",
+    "\n",
+    "#  training data stored in arrays X, y\n",
+    "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n",
+    "X, y = data['X'], data['y'].ravel()\n",
+    "\n",
+    "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+    "# This is an artifact due to the fact that this dataset was used in \n",
+    "# MATLAB where there is no index 0\n",
+    "y[y == 10] = 0\n",
+    "\n",
+    "m = y.size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 Visualizing the data\n",
+    "\n",
+    "You will begin by visualizing a subset of the training set. In the following cell, the code randomly selects selects 100 rows from `X` and passes those rows to the `displayData` function. This function maps each row to a 20 pixel by 20 pixel grayscale image and displays the images together. We have provided the `displayData` function in the file `utils.py`. You are encouraged to examine the code to see how it works. Run the following cell to visualize the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select 100 data points to display\n",
+    "rand_indices = np.random.choice(m, 100, replace=False)\n",
+    "sel = X[rand_indices, :]\n",
+    "\n",
+    "utils.displayData(sel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### 1.3 Vectorizing Logistic Regression\n",
+    "\n",
+    "You will be using multiple one-vs-all logistic regression models to build a multi-class classifier. Since there are 10 classes, you will need to train 10 separate logistic regression classifiers. To make this training efficient, it is important to ensure that your code is well vectorized. In this section, you will implement a vectorized version of logistic regression that does not employ any `for` loops. You can use your code in the previous exercise as a starting point for this exercise. \n",
+    "\n",
+    "To test your vectorized logistic regression, we will use custom data as defined in the following cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test values for the parameters theta\n",
+    "theta_t = np.array([-2, -1, 1, 2], dtype=float)\n",
+    "\n",
+    "# test values for the inputs\n",
+    "X_t = np.concatenate([np.ones((5, 1)), np.arange(1, 16).reshape(5, 3, order='F')/10.0], axis=1)\n",
+    "\n",
+    "# test values for the labels\n",
+    "y_t = np.array([1, 0, 1, 0, 1])\n",
+    "\n",
+    "# test value for the regularization parameter\n",
+    "lambda_t = 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section1\"></a>\n",
+    "#### 1.3.1 Vectorizing the cost function \n",
+    "\n",
+    "We will begin by writing a vectorized version of the cost function. Recall that in (unregularized) logistic regression, the cost function is\n",
+    "\n",
+    "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^m \\left[ -y^{(i)} \\log \\left( h_\\theta\\left( x^{(i)} \\right) \\right) - \\left(1 - y^{(i)} \\right) \\log \\left(1 - h_\\theta \\left( x^{(i)} \\right) \\right) \\right] $$\n",
+    "\n",
+    "To compute each element in the summation, we have to compute $h_\\theta(x^{(i)})$ for every example $i$, where $h_\\theta(x^{(i)}) = g(\\theta^T x^{(i)})$ and $g(z) = \\frac{1}{1+e^{-z}}$ is the sigmoid function. It turns out that we can compute this quickly for all our examples by using matrix multiplication. Let us define $X$ and $\\theta$ as\n",
+    "\n",
+    "$$ X = \\begin{bmatrix} - \\left( x^{(1)} \\right)^T - \\\\ - \\left( x^{(2)} \\right)^T - \\\\ \\vdots \\\\ - \\left( x^{(m)} \\right)^T - \\end{bmatrix} \\qquad \\text{and} \\qquad \\theta = \\begin{bmatrix} \\theta_0 \\\\ \\theta_1 \\\\ \\vdots \\\\ \\theta_n \\end{bmatrix} $$\n",
+    "\n",
+    "Then, by computing the matrix product $X\\theta$, we have: \n",
+    "\n",
+    "$$ X\\theta = \\begin{bmatrix} - \\left( x^{(1)} \\right)^T\\theta - \\\\ - \\left( x^{(2)} \\right)^T\\theta - \\\\ \\vdots \\\\ - \\left( x^{(m)} \\right)^T\\theta - \\end{bmatrix} = \\begin{bmatrix} - \\theta^T x^{(1)}  - \\\\ - \\theta^T x^{(2)} - \\\\ \\vdots \\\\ - \\theta^T x^{(m)}  - \\end{bmatrix} $$\n",
+    "\n",
+    "In the last equality, we used the fact that $a^Tb = b^Ta$ if $a$ and $b$ are vectors. This allows us to compute the products $\\theta^T x^{(i)}$ for all our examples $i$ in one line of code.\n",
+    "\n",
+    "#### 1.3.2 Vectorizing the gradient\n",
+    "\n",
+    "Recall that the gradient of the (unregularized) logistic regression cost is a vector where the $j^{th}$ element is defined as\n",
+    "\n",
+    "$$ \\frac{\\partial J }{\\partial \\theta_j} = \\frac{1}{m} \\sum_{i=1}^m \\left( \\left( h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_j^{(i)} \\right) $$\n",
+    "\n",
+    "To vectorize this operation over the dataset, we start by writing out all the partial derivatives explicitly for all $\\theta_j$,\n",
+    "\n",
+    "$$\n",
+    "\\begin{align*}\n",
+    "\\begin{bmatrix} \n",
+    "\\frac{\\partial J}{\\partial \\theta_0} \\\\\n",
+    "\\frac{\\partial J}{\\partial \\theta_1} \\\\\n",
+    "\\frac{\\partial J}{\\partial \\theta_2} \\\\\n",
+    "\\vdots \\\\\n",
+    "\\frac{\\partial J}{\\partial \\theta_n}\n",
+    "\\end{bmatrix} = &\n",
+    "\\frac{1}{m} \\begin{bmatrix}\n",
+    "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_0^{(i)}\\right) \\\\\n",
+    "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_1^{(i)}\\right) \\\\\n",
+    "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_2^{(i)}\\right) \\\\\n",
+    "\\vdots \\\\\n",
+    "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_n^{(i)}\\right) \\\\\n",
+    "\\end{bmatrix} \\\\\n",
+    "= & \\frac{1}{m} \\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x^{(i)}\\right) \\\\\n",
+    "= & \\frac{1}{m} X^T \\left( h_\\theta(x) - y\\right)\n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "$$  h_\\theta(x) - y = \n",
+    "\\begin{bmatrix}\n",
+    "h_\\theta\\left(x^{(1)}\\right) - y^{(1)} \\\\\n",
+    "h_\\theta\\left(x^{(2)}\\right) - y^{(2)} \\\\\n",
+    "\\vdots \\\\\n",
+    "h_\\theta\\left(x^{(m)}\\right) - y^{(m)} \n",
+    "\\end{bmatrix} $$\n",
+    "\n",
+    "Note that $x^{(i)}$ is a vector, while $h_\\theta\\left(x^{(i)}\\right) - y^{(i)}$  is a scalar (single number).\n",
+    "To understand the last step of the derivation, let $\\beta_i = (h_\\theta\\left(x^{(m)}\\right) - y^{(m)})$ and\n",
+    "observe that:\n",
+    "\n",
+    "$$ \\sum_i \\beta_ix^{(i)} = \\begin{bmatrix} \n",
+    "| & | & & | \\\\\n",
+    "x^{(1)} & x^{(2)} & \\cdots & x^{(m)} \\\\\n",
+    "| & | & & | \n",
+    "\\end{bmatrix}\n",
+    "\\begin{bmatrix}\n",
+    "\\beta_1 \\\\\n",
+    "\\beta_2 \\\\\n",
+    "\\vdots \\\\\n",
+    "\\beta_m\n",
+    "\\end{bmatrix} = x^T \\beta\n",
+    "$$\n",
+    "\n",
+    "where the values $\\beta_i = \\left( h_\\theta(x^{(i)} - y^{(i)} \\right)$.\n",
+    "\n",
+    "The expression above allows us to compute all the partial derivatives\n",
+    "without any loops. If you are comfortable with linear algebra, we encourage you to work through the matrix multiplications above to convince yourself that the vectorized version does the same computations. \n",
+    "\n",
+    "Your job is to write the unregularized cost function `lrCostFunction` which returns both the cost function $J(\\theta)$ and its gradient $\\frac{\\partial J}{\\partial \\theta}$. Your implementation should use the strategy we presented above to calculate $\\theta^T x^{(i)}$. You should also use a vectorized approach for the rest of the cost function. A fully vectorized version of `lrCostFunction` should not contain any loops.\n",
+    "\n",
+    "<div class=\"alert alert-box alert-warning\">\n",
+    "**Debugging Tip:** Vectorizing code can sometimes be tricky. One common strategy for debugging is to print out the sizes of the matrices you are working with using the `shape` property of `numpy` arrays. For example, given a data matrix $X$ of size $100 \\times 20$ (100 examples, 20 features) and $\\theta$, a vector with size $20$, you can observe that `np.dot(X, theta)` is a valid multiplication operation, while `np.dot(theta, X)` is not. Furthermore, if you have a non-vectorized version of your code, you can compare the output of your vectorized code and non-vectorized code to make sure that they produce the same outputs.\n",
+    "</div>\n",
+    "<a id=\"lrCostFunction\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def lrCostFunction(theta, X, y, lambda_):\n",
+    "    \"\"\"\n",
+    "    Computes the cost of using theta as the parameter for regularized\n",
+    "    logistic regression and the gradient of the cost w.r.t. to the parameters.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        Logistic regression parameters. A vector with shape (n, ). n is \n",
+    "        the number of features including any intercept.  \n",
+    "    \n",
+    "    X : array_like\n",
+    "        The data set with shape (m x n). m is the number of examples, and\n",
+    "        n is the number of features (including intercept).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The data labels. A vector with shape (m, ).\n",
+    "    \n",
+    "    lambda_ : float\n",
+    "        The regularization parameter. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the regularized cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        A vector of shape (n, ) which is the gradient of the cost\n",
+    "        function with respect to theta, at the current values of theta.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost of a particular choice of theta. You should set J to the cost.\n",
+    "    Compute the partial derivatives and set grad to the partial\n",
+    "    derivatives of the cost w.r.t. each parameter in theta\n",
+    "    \n",
+    "    Hint 1\n",
+    "    ------\n",
+    "    The computation of the cost function and gradients can be efficiently\n",
+    "    vectorized. For example, consider the computation\n",
+    "    \n",
+    "        sigmoid(X * theta)\n",
+    "    \n",
+    "    Each row of the resulting matrix will contain the value of the prediction\n",
+    "    for that example. You can make use of this to vectorize the cost function\n",
+    "    and gradient computations. \n",
+    "    \n",
+    "    Hint 2\n",
+    "    ------\n",
+    "    When computing the gradient of the regularized cost function, there are\n",
+    "    many possible vectorized solutions, but one solution looks like:\n",
+    "    \n",
+    "        grad = (unregularized gradient for logistic regression)\n",
+    "        temp = theta \n",
+    "        temp[0] = 0   # because we don't add anything for j = 0\n",
+    "        grad = grad + YOUR_CODE_HERE (using the temp variable)\n",
+    "    \n",
+    "    Hint 3\n",
+    "    ------\n",
+    "    We have provided the implementatation of the sigmoid function within \n",
+    "    the file `utils.py`. At the start of the notebook, we imported this file\n",
+    "    as a module. Thus to access the sigmoid function within that file, you can\n",
+    "    do the following: `utils.sigmoid(z)`.\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    #Initialize some useful values\n",
+    "    m = y.size\n",
+    "    \n",
+    "    # convert labels to ints if their type is bool\n",
+    "    if y.dtype == bool:\n",
+    "        y = y.astype(int)\n",
+    "    \n",
+    "    # You need to return the following variables correctly\n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "\n",
+    "        \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3.3 Vectorizing regularized logistic regression\n",
+    "\n",
+    "After you have implemented vectorization for logistic regression, you will now\n",
+    "add regularization to the cost function. Recall that for regularized logistic\n",
+    "regression, the cost function is defined as\n",
+    "\n",
+    "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^m \\left[ -y^{(i)} \\log \\left(h_\\theta\\left(x^{(i)} \\right)\\right) - \\left( 1 - y^{(i)} \\right) \\log\\left(1 - h_\\theta \\left(x^{(i)} \\right) \\right) \\right] + \\frac{\\lambda}{2m} \\sum_{j=1}^n \\theta_j^2 $$\n",
+    "\n",
+    "Note that you should not be regularizing $\\theta_0$ which is used for the bias term.\n",
+    "Correspondingly, the partial derivative of regularized logistic regression cost for $\\theta_j$ is defined as\n",
+    "\n",
+    "$$\n",
+    "\\begin{align*}\n",
+    "& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)}  & \\text{for } j = 0 \\\\\n",
+    "& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m} \\theta_j & \\text{for } j  \\ge 1\n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
+    "Now modify your code in lrCostFunction in the [**previous cell**](#lrCostFunction) to account for regularization. Once again, you should not put any loops into your code.\n",
+    "\n",
+    "<div class=\"alert alert-box alert-warning\">\n",
+    "**python/numpy Tip:** When implementing the vectorization for regularized logistic regression, you might often want to only sum and update certain elements of $\\theta$. In `numpy`, you can index into the matrices to access and update only certain elements. For example, A[:, 3:5]\n",
+    "= B[:, 1:3] will replaces the columns with index 3 to 5 of A with the columns with index 1 to 3 from B. To select columns (or rows) until the end of the matrix, you can leave the right hand side of the colon blank. For example, A[:, 2:] will only return elements from the $3^{rd}$ to last columns of $A$. If you leave the left hand size of the colon blank, you will select elements from the beginning of the matrix. For example, A[:, :2] selects the first two columns, and is equivalent to A[:, 0:2]. In addition, you can use negative indices to index arrays from the end. Thus, A[:, :-1] selects all columns of A except the last column, and A[:, -5:] selects the $5^{th}$ column from the end to the last column. Thus, you could use this together with the sum and power ($^{**}$) operations to compute the sum of only the elements you are interested in (e.g., `np.sum(z[1:]**2)`). In the starter code, `lrCostFunction`, we have also provided hints on yet another possible method computing the regularized gradient.\n",
+    "</div>\n",
+    "\n",
+    "Once you finished your implementation, you can call the function `lrCostFunction` to test your solution using the following cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t)\n",
+    "\n",
+    "print('Cost         : {:.6f}'.format(J))\n",
+    "print('Expected cost: 2.534819')\n",
+    "print('-----------------------')\n",
+    "print('Gradients:')\n",
+    "print(' [{:.6f}, {:.6f}, {:.6f}, {:.6f}]'.format(*grad))\n",
+    "print('Expected gradients:')\n",
+    "print(' [0.146561, -0.548558, 0.724722, 1.398003]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the submission object, and then sending your function to Coursera for grading. \n",
+    "\n",
+    "The submission script will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "*Execute the following cell to grade your solution to the first part of this exercise.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# appends the implemented function in part 1 to the grader object\n",
+    "grader[1] = lrCostFunction\n",
+    "\n",
+    "# send the added functions to coursera grader for getting a grade on this part\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "### 1.4 One-vs-all Classification\n",
+    "\n",
+    "In this part of the exercise, you will implement one-vs-all classification by training multiple regularized logistic regression classifiers, one for each of the $K$ classes in our dataset. In the handwritten digits dataset, $K = 10$, but your code should work for any value of $K$. \n",
+    "\n",
+    "You should now complete the code for the function `oneVsAll` below, to train one classifier for each class. In particular, your code should return all the classifier parameters in a matrix $\\theta \\in \\mathbb{R}^{K \\times (N +1)}$, where each row of $\\theta$ corresponds to the learned logistic regression parameters for one class. You can do this with a “for”-loop from $0$ to $K-1$, training each classifier independently.\n",
+    "\n",
+    "Note that the `y` argument to this function is a vector of labels from 0 to 9. When training the classifier for class $k \\in \\{0, ..., K-1\\}$, you will want a K-dimensional vector of labels $y$, where $y_j \\in 0, 1$ indicates whether the $j^{th}$ training instance belongs to class $k$ $(y_j = 1)$, or if it belongs to a different\n",
+    "class $(y_j = 0)$. You may find logical arrays helpful for this task. \n",
+    "\n",
+    "Furthermore, you will be using scipy's `optimize.minimize` for this exercise. \n",
+    "<a id=\"oneVsAll\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def oneVsAll(X, y, num_labels, lambda_):\n",
+    "    \"\"\"\n",
+    "    Trains num_labels logistic regression classifiers and returns\n",
+    "    each of these classifiers in a matrix all_theta, where the i-th\n",
+    "    row of all_theta corresponds to the classifier for label i.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n). m is the number of \n",
+    "        data points, and n is the number of features. Note that we \n",
+    "        do not assume that the intercept term (or bias) is in X, however\n",
+    "        we provide the code below to add the bias term to X. \n",
+    "    \n",
+    "    y : array_like\n",
+    "        The data labels. A vector of shape (m, ).\n",
+    "    \n",
+    "    num_labels : int\n",
+    "        Number of possible labels.\n",
+    "    \n",
+    "    lambda_ : float\n",
+    "        The logistic regularization parameter.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    all_theta : array_like\n",
+    "        The trained parameters for logistic regression for each class.\n",
+    "        This is a matrix of shape (K x n+1) where K is number of classes\n",
+    "        (ie. `numlabels`) and n is number of features without the bias.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    You should complete the following code to train `num_labels`\n",
+    "    logistic regression classifiers with regularization parameter `lambda_`. \n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    You can use y == c to obtain a vector of 1's and 0's that tell you\n",
+    "    whether the ground truth is true/false for this class.\n",
+    "    \n",
+    "    Note\n",
+    "    ----\n",
+    "    For this assignment, we recommend using `scipy.optimize.minimize(method='CG')`\n",
+    "    to optimize the cost function. It is okay to use a for-loop \n",
+    "    (`for c in range(num_labels):`) to loop over the different classes.\n",
+    "    \n",
+    "    Example Code\n",
+    "    ------------\n",
+    "    \n",
+    "        # Set Initial theta\n",
+    "        initial_theta = np.zeros(n + 1)\n",
+    "      \n",
+    "        # Set options for minimize\n",
+    "        options = {'maxiter': 50}\n",
+    "    \n",
+    "        # Run minimize to obtain the optimal theta. This function will \n",
+    "        # return a class object where theta is in `res.x` and cost in `res.fun`\n",
+    "        res = optimize.minimize(lrCostFunction, \n",
+    "                                initial_theta, \n",
+    "                                (X, (y == c), lambda_), \n",
+    "                                jac=True, \n",
+    "                                method='TNC',\n",
+    "                                options=options) \n",
+    "    \"\"\"\n",
+    "    # Some useful variables\n",
+    "    m, n = X.shape\n",
+    "    \n",
+    "    # You need to return the following variables correctly \n",
+    "    all_theta = np.zeros((num_labels, n + 1))\n",
+    "\n",
+    "    # Add ones to the X data matrix\n",
+    "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "   \n",
+    "\n",
+    "\n",
+    "    # ============================================================\n",
+    "    return all_theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After you have completed the code for `oneVsAll`, the following cell will use your implementation to train a multi-class classifier. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lambda_ = 0.1\n",
+    "all_theta = oneVsAll(X, y, num_labels, lambda_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[2] = oneVsAll\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section3\"></a>\n",
+    "#### 1.4.1 One-vs-all Prediction\n",
+    "\n",
+    "After training your one-vs-all classifier, you can now use it to predict the digit contained in a given image. For each input, you should compute the “probability” that it belongs to each class using the trained logistic regression classifiers. Your one-vs-all prediction function will pick the class for which the corresponding logistic regression classifier outputs the highest probability and return the class label (0, 1, ..., K-1) as the prediction for the input example. You should now complete the code in the function `predictOneVsAll` to use the one-vs-all classifier for making predictions. \n",
+    "<a id=\"predictOneVsAll\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predictOneVsAll(all_theta, X):\n",
+    "    \"\"\"\n",
+    "    Return a vector of predictions for each example in the matrix X. \n",
+    "    Note that X contains the examples in rows. all_theta is a matrix where\n",
+    "    the i-th row is a trained logistic regression theta vector for the \n",
+    "    i-th class. You should set p to a vector of values from 0..K-1 \n",
+    "    (e.g., p = [0, 2, 0, 1] predicts classes 0, 2, 0, 1 for 4 examples) .\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    all_theta : array_like\n",
+    "        The trained parameters for logistic regression for each class.\n",
+    "        This is a matrix of shape (K x n+1) where K is number of classes\n",
+    "        and n is number of features without the bias.\n",
+    "    \n",
+    "    X : array_like\n",
+    "        Data points to predict their labels. This is a matrix of shape \n",
+    "        (m x n) where m is number of data points to predict, and n is number \n",
+    "        of features without the bias term. Note we add the bias term for X in \n",
+    "        this function. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    p : array_like\n",
+    "        The predictions for each data point in X. This is a vector of shape (m, ).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Complete the following code to make predictions using your learned logistic\n",
+    "    regression parameters (one-vs-all). You should set p to a vector of predictions\n",
+    "    (from 0 to num_labels-1).\n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    This code can be done all vectorized using the numpy argmax function.\n",
+    "    In particular, the argmax function returns the index of the max element,\n",
+    "    for more information see '?np.argmax' or search online. If your examples\n",
+    "    are in rows, then, you can use np.argmax(A, axis=1) to obtain the index \n",
+    "    of the max for each row.\n",
+    "    \"\"\"\n",
+    "    m = X.shape[0];\n",
+    "    num_labels = all_theta.shape[0]\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    p = np.zeros(m)\n",
+    "\n",
+    "    # Add ones to the X data matrix\n",
+    "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "\n",
+    "    \n",
+    "    # ============================================================\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you are done, call your `predictOneVsAll` function using the learned value of $\\theta$. You should see that the training set accuracy is about 95.1% (i.e., it classifies 95.1% of the examples in the training set correctly)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = predictOneVsAll(all_theta, X)\n",
+    "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[3] = predictOneVsAll\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 Neural Networks\n",
+    "\n",
+    "In the previous part of this exercise, you implemented multi-class logistic regression to recognize handwritten digits. However, logistic regression cannot form more complex hypotheses as it is only a linear classifier (You could add more features - such as polynomial features - to logistic regression, but that can be very expensive to train).\n",
+    "\n",
+    "In this part of the exercise, you will implement a neural network to recognize handwritten digits using the same training set as before. The neural network will be able to represent complex models that form non-linear hypotheses. For this week, you will be using parameters from a neural network that we have already trained. Your goal is to implement the feedforward propagation algorithm to use our weights for prediction. In next week’s exercise, you will write the backpropagation algorithm for learning the neural network parameters. \n",
+    "\n",
+    "We start by first reloading and visualizing the dataset which contains the MNIST handwritten digits (this is the same as we did in the first part of this exercise, we reload it here to ensure the variables have not been modified). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  training data stored in arrays X, y\n",
+    "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n",
+    "X, y = data['X'], data['y'].ravel()\n",
+    "\n",
+    "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+    "# This is an artifact due to the fact that this dataset was used in \n",
+    "# MATLAB where there is no index 0\n",
+    "y[y == 10] = 0\n",
+    "\n",
+    "# get number of examples in dataset\n",
+    "m = y.size\n",
+    "\n",
+    "# randomly permute examples, to be used for visualizing one \n",
+    "# picture at a time\n",
+    "indices = np.random.permutation(m)\n",
+    "\n",
+    "# Randomly select 100 data points to display\n",
+    "rand_indices = np.random.choice(m, 100, replace=False)\n",
+    "sel = X[rand_indices, :]\n",
+    "\n",
+    "utils.displayData(sel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 2.1 Model representation \n",
+    "\n",
+    "Our neural network is shown in the following figure.\n",
+    "\n",
+    "![Neural network](Figures/neuralnetwork.png)\n",
+    "\n",
+    "It has 3 layers: an input layer, a hidden layer and an output layer. Recall that our inputs are pixel values of digit images. Since the images are of size 20×20, this gives us 400 input layer units (excluding the extra bias unit which always outputs +1). As before, the training data will be loaded into the variables X and y. \n",
+    "\n",
+    "You have been provided with a set of network parameters ($\\Theta^{(1)}$, $\\Theta^{(2)}$) already trained by us. These are stored in `ex3weights.mat`. The following cell loads those parameters into  `Theta1` and `Theta2`. The parameters have dimensions that are sized for a neural network with 25 units in the second layer and 10 output units (corresponding to the 10 digit classes)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the parameters you will use for this exercise\n",
+    "input_layer_size  = 400  # 20x20 Input Images of Digits\n",
+    "hidden_layer_size = 25   # 25 hidden units\n",
+    "num_labels = 10          # 10 labels, from 0 to 9\n",
+    "\n",
+    "# Load the .mat file, which returns a dictionary \n",
+    "weights = loadmat(os.path.join('Data', 'ex3weights.mat'))\n",
+    "\n",
+    "# get the model weights from the dictionary\n",
+    "# Theta1 has size 25 x 401\n",
+    "# Theta2 has size 10 x 26\n",
+    "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n",
+    "\n",
+    "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n",
+    "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n",
+    "Theta2 = np.roll(Theta2, 1, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section4\"></a>\n",
+    "### 2.2 Feedforward Propagation and Prediction\n",
+    "\n",
+    "Now you will implement feedforward propagation for the neural network. You will need to complete the code in the function `predict` to return the neural network’s prediction. You should implement the feedforward computation that computes $h_\\theta(x^{(i)})$ for every example $i$ and returns the associated predictions. Similar to the one-vs-all classification strategy, the prediction from the neural network will be the label that has the largest output $\\left( h_\\theta(x) \\right)_k$.\n",
+    "\n",
+    "<div class=\"alert alert-box alert-warning\">\n",
+    "**Implementation Note:** The matrix $X$ contains the examples in rows. When you complete the code in the function `predict`, you will need to add the column of 1’s to the matrix. The matrices `Theta1` and `Theta2` contain the parameters for each unit in rows. Specifically, the first row of `Theta1` corresponds to the first hidden unit in the second layer. In `numpy`, when you compute $z^{(2)} = \\theta^{(1)}a^{(1)}$, be sure that you index (and if necessary, transpose) $X$ correctly so that you get $a^{(l)}$ as a 1-D vector.\n",
+    "</div>\n",
+    "<a id=\"predict\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(Theta1, Theta2, X):\n",
+    "    \"\"\"\n",
+    "    Predict the label of an input given a trained neural network.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    Theta1 : array_like\n",
+    "        Weights for the first layer in the neural network.\n",
+    "        It has shape (2nd hidden layer size x input size)\n",
+    "    \n",
+    "    Theta2: array_like\n",
+    "        Weights for the second layer in the neural network. \n",
+    "        It has shape (output layer size x 2nd hidden layer size)\n",
+    "    \n",
+    "    X : array_like\n",
+    "        The image inputs having shape (number of examples x image dimensions).\n",
+    "    \n",
+    "    Return \n",
+    "    ------\n",
+    "    p : array_like\n",
+    "        Predictions vector containing the predicted label for each example.\n",
+    "        It has a length equal to the number of examples.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Complete the following code to make predictions using your learned neural\n",
+    "    network. You should set p to a vector containing labels \n",
+    "    between 0 to (num_labels-1).\n",
+    "     \n",
+    "    Hint\n",
+    "    ----\n",
+    "    This code can be done all vectorized using the numpy argmax function.\n",
+    "    In particular, the argmax function returns the index of the  max element,\n",
+    "    for more information see '?np.argmax' or search online. If your examples\n",
+    "    are in rows, then, you can use np.argmax(A, axis=1) to obtain the index\n",
+    "    of the max for each row.\n",
+    "    \n",
+    "    Note\n",
+    "    ----\n",
+    "    Remember, we have supplied the `sigmoid` function in the `utils.py` file. \n",
+    "    You can use this function by calling `utils.sigmoid(z)`, where you can \n",
+    "    replace `z` by the required input variable to sigmoid.\n",
+    "    \"\"\"\n",
+    "    # Make sure the input has two dimensions\n",
+    "    if X.ndim == 1:\n",
+    "        X = X[None]  # promote to 2-dimensions\n",
+    "    \n",
+    "    # useful variables\n",
+    "    m = X.shape[0]\n",
+    "    num_labels = Theta2.shape[0]\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    p = np.zeros(X.shape[0])\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # =============================================================\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you are done, call your predict function using the loaded set of parameters for `Theta1` and `Theta2`. You should see that the accuracy is about 97.5%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = predict(Theta1, Theta2, X)\n",
+    "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After that, we will display images from the training set one at a time, while at the same time printing out the predicted label for the displayed image. \n",
+    "\n",
+    "Run the following cell to display a single image the the neural network's prediction. You can run the cell multiple time to see predictions for different images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if indices.size > 0:\n",
+    "    i, indices = indices[0], indices[1:]\n",
+    "    utils.displayData(X[i, :], figsize=(4, 4))\n",
+    "    pred = predict(Theta1, Theta2, X[i, :])\n",
+    "    print('Neural Network Prediction: {}'.format(*pred))\n",
+    "else:\n",
+    "    print('No more images to display!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[4] = predict\n",
+    "grader.grade()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/utils.py b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/utils.py
new file mode 100644
index 000000000..633a5636d
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/utils.py	
@@ -0,0 +1,104 @@
+import sys
+import numpy as np
+from matplotlib import pyplot
+
+sys.path.append('..')
+from submission import SubmissionBase
+
+
+def displayData(X, example_width=None, figsize=(10, 10)):
+    """
+    Displays 2D data stored in X in a nice grid.
+    """
+    # Compute rows, cols
+    if X.ndim == 2:
+        m, n = X.shape
+    elif X.ndim == 1:
+        n = X.size
+        m = 1
+        X = X[None]  # Promote to a 2 dimensional array
+    else:
+        raise IndexError('Input X should be 1 or 2 dimensional.')
+
+    example_width = example_width or int(np.round(np.sqrt(n)))
+    example_height = n / example_width
+
+    # Compute number of items to display
+    display_rows = int(np.floor(np.sqrt(m)))
+    display_cols = int(np.ceil(m / display_rows))
+
+    fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)
+    fig.subplots_adjust(wspace=0.025, hspace=0.025)
+
+    ax_array = [ax_array] if m == 1 else ax_array.ravel()
+
+    for i, ax in enumerate(ax_array):
+        ax.imshow(X[i].reshape(example_width, example_width, order='F'),
+                  cmap='Greys', extent=[0, 1, 0, 1])
+        ax.axis('off')
+
+
+def sigmoid(z):
+    """
+    Computes the sigmoid of z.
+    """
+    return 1.0 / (1.0 + np.exp(-z))
+
+
+class Grader(SubmissionBase):
+    # Random Test Cases
+    X = np.stack([np.ones(20),
+                  np.exp(1) * np.sin(np.arange(1, 21)),
+                  np.exp(0.5) * np.cos(np.arange(1, 21))], axis=1)
+
+    y = (np.sin(X[:, 0] + X[:, 1]) > 0).astype(float)
+
+    Xm = np.array([[-1, -1],
+                   [-1, -2],
+                   [-2, -1],
+                   [-2, -2],
+                   [1, 1],
+                   [1, 2],
+                   [2, 1],
+                   [2, 2],
+                   [-1, 1],
+                   [-1, 2],
+                   [-2, 1],
+                   [-2, 2],
+                   [1, -1],
+                   [1, -2],
+                   [-2, -1],
+                   [-2, -2]])
+    ym = np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3])
+
+    t1 = np.sin(np.reshape(np.arange(1, 25, 2), (4, 3), order='F'))
+    t2 = np.cos(np.reshape(np.arange(1, 41, 2), (4, 5), order='F'))
+
+    def __init__(self):
+        part_names = ['Regularized Logistic Regression',
+                      'One-vs-All Classifier Training',
+                      'One-vs-All Classifier Prediction',
+                      'Neural Network Prediction Function']
+
+        super().__init__('multi-class-classification-and-neural-networks', part_names)
+
+    def __iter__(self):
+        for part_id in range(1, 5):
+            try:
+                func = self.functions[part_id]
+
+                # Each part has different expected arguments/different function
+                if part_id == 1:
+                    res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y, 0.1)
+                    res = np.hstack(res).tolist()
+                elif part_id == 2:
+                    res = func(self.Xm, self.ym, 4, 0.1)
+                elif part_id == 3:
+                    res = func(self.t1, self.Xm) + 1
+                elif part_id == 4:
+                    res = func(self.t1, self.t2, self.Xm) + 1
+                else:
+                    raise KeyError
+                yield part_id, res
+            except KeyError:
+                yield part_id, 0
diff --git a/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/README.md b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/README.md
new file mode 100644
index 000000000..d96ad5fc7
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/README.md	
@@ -0,0 +1,7 @@
+## To be done for next week
+
+1. Complete All Video Lectures and Quizes of week4 of the [Coursera Course](https://www.coursera.org/learn/machine-learning).
+2. For python implementation of neural network you can take help from [here](https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6).
+3. Add your python(if you are doing by your own) or jupyter notebook files and create pull request.
+4. Each part of the assignment has equal weightage - total 100 points.
+5. Complete assignment in octave(for the sake of your certification, it is optional and does not consist of any point).
diff --git a/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/submission.py b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/submission.py
new file mode 100644
index 000000000..10113e47d
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/submission.py	
@@ -0,0 +1,105 @@
+from urllib.parse import urlencode
+from urllib.request import urlopen
+import pickle
+import json
+from collections import OrderedDict
+import numpy as np
+import os
+
+
+class SubmissionBase:
+
+    submit_url = 'https://www-origin.coursera.org/api/' \
+                 'onDemandProgrammingImmediateFormSubmissions.v1'
+    save_file = 'token.pkl'
+
+    def __init__(self, assignment_slug, part_names):
+        self.assignment_slug = assignment_slug
+        self.part_names = part_names
+        self.login = None
+        self.token = None
+        self.functions = OrderedDict()
+        self.args = dict()
+
+    def grade(self):
+        print('\nSubmitting Solutions | Programming Exercise %s\n' % self.assignment_slug)
+        self.login_prompt()
+
+        # Evaluate the different parts of exercise
+        parts = OrderedDict()
+        for part_id, result in self:
+            parts[str(part_id)] = {'output': sprintf('%0.5f ', result)}
+        result, response = self.request(parts)
+        response = json.loads(response.decode("utf-8"))
+
+        # if an error was returned, print it and stop
+        if 'errorMessage' in response:
+            print(response['errorMessage'])
+            return
+
+        # Print the grading table
+        print('%43s | %9s | %-s' % ('Part Name', 'Score', 'Feedback'))
+        print('%43s | %9s | %-s' % ('---------', '-----', '--------'))
+        for part in parts:
+            part_feedback = response['partFeedbacks'][part]
+            part_evaluation = response['partEvaluations'][part]
+            score = '%d / %3d' % (part_evaluation['score'], part_evaluation['maxScore'])
+            print('%43s | %9s | %-s' % (self.part_names[int(part) - 1], score, part_feedback))
+        evaluation = response['evaluation']
+        total_score = '%d / %d' % (evaluation['score'], evaluation['maxScore'])
+        print('                                  --------------------------------')
+        print('%43s | %9s | %-s\n' % (' ', total_score, ' '))
+
+    def login_prompt(self):
+        if os.path.isfile(self.save_file):
+            with open(self.save_file, 'rb') as f:
+                login, token = pickle.load(f)
+            reenter = input('Use token from last successful submission (%s)? (Y/n): ' % login)
+
+            if reenter == '' or reenter[0] == 'Y' or reenter[0] == 'y':
+                self.login, self.token = login, token
+                return
+            else:
+                os.remove(self.save_file)
+
+        self.login = input('Login (email address): ')
+        self.token = input('Token: ')
+
+        # Save the entered credentials
+        if not os.path.isfile(self.save_file):
+            with open(self.save_file, 'wb') as f:
+                pickle.dump((self.login, self.token), f)
+
+    def request(self, parts):
+        params = {
+            'assignmentSlug': self.assignment_slug,
+            'secret': self.token,
+            'parts': parts,
+            'submitterEmail': self.login}
+
+        params = urlencode({'jsonBody': json.dumps(params)}).encode("utf-8")
+        f = urlopen(self.submit_url, params)
+        try:
+            return 0, f.read()
+        finally:
+            f.close()
+
+    def __iter__(self):
+        for part_id in self.functions:
+            yield part_id
+
+    def __setitem__(self, key, value):
+        self.functions[key] = value
+
+
+def sprintf(fmt, arg):
+    """ Emulates (part of) Octave sprintf function. """
+    if isinstance(arg, tuple):
+        # for multiple return values, only use the first one
+        arg = arg[0]
+
+    if isinstance(arg, (np.ndarray, list)):
+        # concatenates all elements, column by column
+        return ' '.join(fmt % e for e in np.asarray(arg).ravel('F'))
+    else:
+        return fmt % arg
diff --git a/Phase 3 - 2020 (Summer)/Week2exercise1sol.ipynb b/Phase 3 - 2020 (Summer)/Week2exercise1sol.ipynb
new file mode 100644
index 000000000..ab1774404
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week2exercise1sol.ipynb	
@@ -0,0 +1,701 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#warmup_exercise\n",
+    "def warmupexercise():\n",
+    "    A=np.eye(5)\n",
+    "    return A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 1., 0., 0.],\n",
+       "       [0., 0., 0., 1., 0.],\n",
+       "       [0., 0., 0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "warmupexercise()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#2 Linear regression with one variable\n",
+    "#2.1 Plotting Data\n",
+    "#data=np.loadtxt(fname='Ex1data1.txt')\n",
+    "data = np.loadtxt(os.path.join('Ex1data1'), delimiter=',')\n",
+    "X, y = data[:, 0], data[:, 1]\n",
+    "m = y.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotData(x, y):\n",
+    "\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE ======================= \n",
+    "    \n",
+    "    fig = plt.figure()\n",
+    "\n",
+    "    plt.plot(x, y, 'ro', ms=10, mec='k')\n",
+    "    plt.ylabel('Profit in $10,000')\n",
+    "    plt.xlabel('Population of City in 10,000s')\n",
+    "    # ============================================================="
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotData(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#2.2 Gradient Descent\n",
+    "X = np.stack([np.ones(m), X], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Computing Cost\n",
+    "def computeCost(X, y, theta):\n",
+    "\n",
+    "    \n",
+    "    # initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "    \n",
+    "    # You need to return the following variables correctly\n",
+    "    J = 0\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE =====================\n",
+    "    h = np.dot(X, theta)\n",
+    "    \n",
+    "    J = (1/(2 * m)) * np.sum(np.square(np.dot(X, theta) - y))\n",
+    "    \n",
+    "    # ===========================================================\n",
+    "    return J"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With theta = [0, 0] \n",
+      "Cost computed = 32.07\n",
+      "Expected cost value (approximately) 32.07\n",
+      "\n",
+      "With theta = [-1, 2]\n",
+      "Cost computed = 54.24\n",
+      "Expected cost value (approximately) 54.24\n"
+     ]
+    }
+   ],
+   "source": [
+    "J = computeCost(X, y, theta=np.array([0.0, 0.0]))\n",
+    "print('With theta = [0, 0] \\nCost computed = %.2f' % J)\n",
+    "print('Expected cost value (approximately) 32.07\\n')\n",
+    "\n",
+    "# further testing of the cost function\n",
+    "J = computeCost(X, y, theta=np.array([-1, 2]))\n",
+    "print('With theta = [-1, 2]\\nCost computed = %.2f' % J)\n",
+    "print('Expected cost value (approximately) 54.24')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Gradient Descent\n",
+    "def gradientDescent(X, y, theta, alpha, num_iters):\n",
+    "\n",
+    "    # Initialize some useful values\n",
+    "    m = y.shape[0]  # number of training examples\n",
+    "    \n",
+    "    # make a copy of theta, to avoid changing the original array, since numpy arrays\n",
+    "    # are passed by reference to functions\n",
+    "    theta = theta.copy()\n",
+    "    \n",
+    "    J_history = [] # Use a python list to save cost in every iteration\n",
+    "    \n",
+    "    for i in range(num_iters):\n",
+    "        # ==================== YOUR CODE HERE =================================\n",
+    "        theta = theta - (alpha / m) * (np.dot(X, theta) - y).dot(X)\n",
+    "        # =====================================================================\n",
+    "        \n",
+    "        # save the cost J in every iteration\n",
+    "        J_history.append(computeCost(X, y, theta))\n",
+    "    \n",
+    "    return theta, J_history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Theta found by gradient descent: -3.6303, 1.1664\n",
+      "Expected theta values (approximately): [-3.6303, 1.1664]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize fitting parameters\n",
+    "theta = np.zeros(2)\n",
+    "\n",
+    "# some gradient descent settings\n",
+    "iterations = 1500\n",
+    "alpha = 0.01\n",
+    "\n",
+    "theta, J_history = gradientDescent(X ,y, theta, alpha, iterations)\n",
+    "print('Theta found by gradient descent: {:.4f}, {:.4f}'.format(*theta))\n",
+    "print('Expected theta values (approximately): [-3.6303, 1.1664]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotData(X[:, 1], y)\n",
+    "plt.plot(X[:, 1], np.dot(X, theta), '-')\n",
+    "plt.legend(['Training data', 'Linear regression']);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For population = 35,000, we predict a profit of 4519.77\n",
+      "\n",
+      "For population = 70,000, we predict a profit of 45342.45\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict values for population sizes of 35,000 and 70,000\n",
+    "predict1 = np.dot([1, 3.5], theta)\n",
+    "print('For population = 35,000, we predict a profit of {:.2f}\\n'.format(predict1*10000))\n",
+    "\n",
+    "predict2 = np.dot([1, 7], theta)\n",
+    "print('For population = 70,000, we predict a profit of {:.2f}\\n'.format(predict2*10000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Visualising J(theta)\n",
+    "# grid over which we will calculate J\n",
+    "theta0_vals = np.linspace(-10, 10, 100)\n",
+    "theta1_vals = np.linspace(-1, 4, 100)\n",
+    "\n",
+    "# initialize J_vals to a matrix of 0's\n",
+    "J_vals = np.zeros((theta0_vals.shape[0], theta1_vals.shape[0]))\n",
+    "\n",
+    "# Fill out J_vals\n",
+    "for i, theta0 in enumerate(theta0_vals):\n",
+    "    for j, theta1 in enumerate(theta1_vals):\n",
+    "        J_vals[i, j] = computeCost(X, y, [theta0, theta1])\n",
+    "        \n",
+    "# Because of the way meshgrids work in the surf command, we need to\n",
+    "# transpose J_vals before calling surf, or else the axes will be flipped\n",
+    "J_vals = J_vals.T\n",
+    "\n",
+    "# surface plot\n",
+    "fig = plt.figure(figsize=(12, 5))\n",
+    "ax = fig.add_subplot(121, projection='3d')\n",
+    "ax.plot_surface(theta0_vals, theta1_vals, J_vals, cmap='viridis')\n",
+    "plt.xlabel('theta0')\n",
+    "plt.ylabel('theta1')\n",
+    "plt.title('Surface')\n",
+    "\n",
+    "# contour plot\n",
+    "# Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100\n",
+    "ax = plt.subplot(122)\n",
+    "plt.contour(theta0_vals, theta1_vals, J_vals, linewidths=2, cmap='viridis', levels=np.logspace(-2, 3, 20))\n",
+    "plt.xlabel('theta0')\n",
+    "plt.ylabel('theta1')\n",
+    "plt.plot(theta[0], theta[1], 'ro', ms=10, lw=2)\n",
+    "plt.title('Contour, showing minimum')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  X[:,0] X[:, 1]         y\n",
+      "--------------------------\n",
+      "    2104       3    399900\n",
+      "    1600       3    329900\n",
+      "    2400       3    369000\n",
+      "    1416       2    232000\n",
+      "    3000       4    539900\n",
+      "    1985       4    299900\n",
+      "    1534       3    314900\n",
+      "    1427       3    198999\n",
+      "    1380       3    212000\n",
+      "    1494       3    242500\n"
+     ]
+    }
+   ],
+   "source": [
+    "#3 Linear Regression in multiple variables\n",
+    "#3.1 Feature Normalization\n",
+    "# Load data\n",
+    "data2 = np.loadtxt(os.path.join('ex1data2'), delimiter=',')\n",
+    "X = data2[:, :2]\n",
+    "y = data2[:, 2]\n",
+    "m = y.size\n",
+    "\n",
+    "# print out some data points\n",
+    "print('{:>8s}{:>8s}{:>10s}'.format('X[:,0]', 'X[:, 1]', 'y'))\n",
+    "print('-'*26)\n",
+    "for i in range(10):\n",
+    "    print('{:8.0f}{:8.0f}{:10.0f}'.format(X[i, 0], X[i, 1], y[i]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def  featureNormalize(X):\n",
+    "\n",
+    "    # You need to set these values correctly\n",
+    "    X_norm = X.copy()\n",
+    "    mu = np.zeros(X.shape[1])\n",
+    "    sigma = np.zeros(X.shape[1])\n",
+    "\n",
+    "    # =========================== YOUR CODE HERE =====================\n",
+    "    mu = np.mean(X, axis = 0)\n",
+    "    sigma = np.std(X, axis = 0)\n",
+    "    X_norm = (X - mu) / sigma\n",
+    "    \n",
+    "    # ================================================================\n",
+    "    return X_norm, mu, sigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computed mean: [2000.68085106    3.17021277]\n",
+      "Computed standard deviation: [7.86202619e+02 7.52842809e-01]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# call featureNormalize on the loaded data\n",
+    "X_norm, mu, sigma = featureNormalize(X)\n",
+    "\n",
+    "print('Computed mean:', mu)\n",
+    "print('Computed standard deviation:', sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = np.concatenate([np.ones((m, 1)), X_norm], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#3.2 Gradient Descent\n",
+    "\n",
+    "def computeCostMulti(X, y, theta):\n",
+    "\n",
+    "    # Initialize some useful values\n",
+    "    m = y.shape[0] # number of training examples\n",
+    "    \n",
+    "    # You need to return the following variable correctly\n",
+    "    J = 0\n",
+    "    \n",
+    "    # ======================= YOUR CODE HERE ===========================\n",
+    "    h = np.dot(X, theta)\n",
+    "    \n",
+    "    J = (1/(2 * m)) * np.sum(np.square(np.dot(X, theta) - y))\n",
+    "    # ==================================================================\n",
+    "    return J"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gradientDescentMulti(X, y, theta, alpha, num_iters):\n",
+    "\n",
+    "    # Initialize some useful values\n",
+    "    m = y.shape[0] # number of training examples\n",
+    "    \n",
+    "    # make a copy of theta, which will be updated by gradient descent\n",
+    "    theta = theta.copy()\n",
+    "    \n",
+    "    J_history = []\n",
+    "    \n",
+    "    for i in range(num_iters):\n",
+    "        # ======================= YOUR CODE HERE ==========================\n",
+    "        theta = theta - (alpha / m) * (np.dot(X, theta) - y).dot(X)\n",
+    "\n",
+    "        \n",
+    "        # =================================================================\n",
+    "        \n",
+    "        # save the cost J in every iteration\n",
+    "        J_history.append(computeCostMulti(X, y, theta))\n",
+    "    \n",
+    "    return theta, J_history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta computed from gradient descent: [340412.65957447 109447.79558639  -6578.3539709 ]\n",
+      "Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): $293081\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "alpha = 0.1\n",
+    "num_iters = 400\n",
+    "\n",
+    "# init theta and run gradient descent\n",
+    "theta = np.zeros(3)\n",
+    "theta, J_history = gradientDescentMulti(X, y, theta, alpha, num_iters)\n",
+    "\n",
+    "# Plot the convergence graph\n",
+    "plt.plot(np.arange(len(J_history)), J_history, lw=2)\n",
+    "plt.xlabel('Number of iterations')\n",
+    "plt.ylabel('Cost J')\n",
+    "\n",
+    "# Display the gradient descent's result\n",
+    "print('theta computed from gradient descent: {:s}'.format(str(theta)))\n",
+    "\n",
+    "# Estimate the price of a 1650 sq-ft, 3 br house\n",
+    "# ======================= YOUR CODE HERE ===========================\n",
+    "# Recall that the first column of X is all-ones. \n",
+    "# Thus, it does not need to be normalized.\n",
+    "\n",
+    "X_array = [1, 1650, 3]\n",
+    "X_array[1:3] = (X_array[1:3] - mu) / sigma\n",
+    "price = np.dot(X_array, theta)   # You should change this\n",
+    "\n",
+    "# ===================================================================\n",
+    "\n",
+    "print('Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): ${:.0f}'.format(price))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.4460438603276164, -0.22609336757768828]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_array = [1, 1650, 3]\n",
+    "X_array[1:3] = (X_array[1:3] - mu) / sigma\n",
+    "X_array[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta computed from gradient descent: [340412.65957447 109447.79646964  -6578.35485416]\n",
+      "Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): $293081\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Choose some alpha value - change this\n",
+    "alpha = 0.1\n",
+    "num_iters = 1000\n",
+    "\n",
+    "# init theta and run gradient descent\n",
+    "theta = np.zeros(3)\n",
+    "theta, J_history = gradientDescentMulti(X, y, theta, alpha, num_iters)\n",
+    "\n",
+    "# Plot the convergence graph\n",
+    "plt.plot(np.arange(len(J_history)), J_history, lw=2)\n",
+    "plt.xlabel('Number of iterations')\n",
+    "plt.ylabel('Cost J')\n",
+    "\n",
+    "# Display the gradient descent's result\n",
+    "print('theta computed from gradient descent: {:s}'.format(str(theta)))\n",
+    "\n",
+    "# Estimate the price of a 1650 sq-ft, 3 br house\n",
+    "# ======================= YOUR CODE HERE ===========================\n",
+    "# Recall that the first column of X is all-ones. \n",
+    "# Thus, it does not need to be normalized.\n",
+    "\n",
+    "X_array = [1, 1650, 3]\n",
+    "X_array[1:3] = (X_array[1:3] - mu) / sigma\n",
+    "price = np.dot(X_array, theta)   # You should change this\n",
+    "\n",
+    "# ===================================================================\n",
+    "\n",
+    "print('Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): ${:.0f}'.format(price))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#3.3 Normal Equations\n",
+    "data3 = np.loadtxt(os.path.join('ex1data2'), delimiter=',')\n",
+    "X = data3[:, :2]\n",
+    "y = data3[:, 2]\n",
+    "m = y.size\n",
+    "X = np.concatenate([np.ones((m, 1)), X], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalEqn(X, y):\n",
+    "    \n",
+    "    theta = np.zeros(X.shape[1])\n",
+    "    \n",
+    "    # ===================== YOUR CODE HERE ============================\n",
+    "    theta = np.dot(np.dot(np.linalg.inv(np.dot(X.T,X)),X.T),y)\n",
+    "    \n",
+    "    # =================================================================\n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Theta computed from the normal equations: [ 340412.65957447  109447.79646964   -6578.35485416]\n",
+      "Predicted price of a 1650 sq-ft, 3 br house (using normal equations): $293081\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Calculate the parameters from the normal equation\n",
+    "theta = normalEqn(X, y);\n",
+    "\n",
+    "# Display normal equation's result\n",
+    "print('Theta computed from the normal equations: {:s}'.format(str(theta)));\n",
+    "\n",
+    "\n",
+    "# Estimate the price of a 1650 sq-ft, 3 br house\n",
+    "# ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "\n",
+    "price = np.dot(X_array, theta) \n",
+    "\n",
+    "# ============================================================\n",
+    "\n",
+    "print('Predicted price of a 1650 sq-ft, 3 br house (using normal equations): ${:.0f}'.format(price))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week3Exercise2Solutions.ipynb b/Phase 3 - 2020 (Summer)/Week3Exercise2Solutions.ipynb
new file mode 100644
index 000000000..bd4a41f5e
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week3Exercise2Solutions.ipynb	
@@ -0,0 +1,608 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.loadtxt(os.path.join('ex3data1.txt'), delimiter=',')\n",
+    "X, y = data[:, 0:2], data[:, 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotData(X, y):\n",
+    "    \n",
+    "    # Create New Figure\n",
+    "    fig = pyplot.figure()\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    # Find Indices of Positive and Negative Examples\n",
+    "    pos = y == 1\n",
+    "    neg = y == 0\n",
+    "\n",
+    "    # Plot Examples\n",
+    "    pyplot.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10)\n",
+    "    pyplot.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1)\n",
+    "    \n",
+    "    # ============================================================"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotData(X, y)\n",
+    "# add axes labels\n",
+    "pyplot.xlabel('Exam 1 score')\n",
+    "pyplot.ylabel('Exam 2 score')\n",
+    "pyplot.legend(['Admitted', 'Not admitted'])\n",
+    "pass\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sigmoid(z):\n",
+    "    \n",
+    "    # convert input to a numpy array\n",
+    "    z = np.array(z)\n",
+    "    \n",
+    "    # You need to return the following variables correctly \n",
+    "    g = np.zeros(z.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    g = 1 / (1 + np.exp(-z))\n",
+    "\n",
+    "    # =============================================================\n",
+    "    return g"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "g( 0 ) =  0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test the implementation of sigmoid function here\n",
+    "z = 0\n",
+    "g = sigmoid(z)\n",
+    "\n",
+    "print('g(', z, ') = ', g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the data matrix appropriately, and add ones for the intercept term\n",
+    "m, n = X.shape\n",
+    "\n",
+    "# Add intercept term to X\n",
+    "X = np.concatenate([np.ones((m, 1)), X], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def costFunction(theta, X, y):\n",
+    "    \n",
+    "    # Initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    h = sigmoid(X.dot(theta.T))\n",
+    "    \n",
+    "    J = (1 / m) * np.sum(-y.dot(np.log(h)) - (1 - y).dot(np.log(1 - h)))\n",
+    "    grad = (1 / m) * (h - y).dot(X)\n",
+    "    \n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at initial theta (zeros): 0.693\n",
+      "Expected cost (approx): 0.693\n",
+      "\n",
+      "Gradient at initial theta (zeros):\n",
+      "\t[-0.1000, -12.0092, -11.2628]\n",
+      "Expected gradients (approx):\n",
+      "\t[-0.1000, -12.0092, -11.2628]\n",
+      "\n",
+      "Cost at test theta: 0.218\n",
+      "Expected cost (approx): 0.218\n",
+      "\n",
+      "Gradient at test theta:\n",
+      "\t[0.043, 2.566, 2.647]\n",
+      "Expected gradients (approx):\n",
+      "\t[0.043, 2.566, 2.647]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Initialize fitting parameters\n",
+    "initial_theta = np.zeros(n+1)\n",
+    "\n",
+    "cost, grad = costFunction(initial_theta, X, y)\n",
+    "\n",
+    "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx): 0.693\\n')\n",
+    "\n",
+    "print('Gradient at initial theta (zeros):')\n",
+    "print('\\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))\n",
+    "print('Expected gradients (approx):\\n\\t[-0.1000, -12.0092, -11.2628]\\n')\n",
+    "\n",
+    "# Compute and display cost and gradient with non-zero theta\n",
+    "test_theta = np.array([-24, 0.2, 0.2])\n",
+    "cost, grad = costFunction(test_theta, X, y)\n",
+    "\n",
+    "print('Cost at test theta: {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx): 0.218\\n')\n",
+    "\n",
+    "print('Gradient at test theta:')\n",
+    "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad))\n",
+    "print('Expected gradients (approx):\\n\\t[0.043, 2.566, 2.647]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at theta found by optimize.minimize: 0.203\n",
+      "Expected cost (approx): 0.203\n",
+      "\n",
+      "theta:\n",
+      "\t[-25.161, 0.206, 0.201]\n",
+      "Expected theta (approx):\n",
+      "\t[-25.161, 0.206, 0.201]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set options for optimize.minimize\n",
+    "options= {'maxiter': 400}\n",
+    "\n",
+    "# see documention for scipy's optimize.minimize  for description about\n",
+    "# the different parameters\n",
+    "# The function returns an object `OptimizeResult`\n",
+    "# We use truncated Newton algorithm for optimization which is \n",
+    "# equivalent to MATLAB's fminunc\n",
+    "# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy\n",
+    "res = optimize.minimize(costFunction,\n",
+    "                        initial_theta,\n",
+    "                        (X, y),\n",
+    "                        jac=True,\n",
+    "                        method='TNC',\n",
+    "                        options=options)\n",
+    "\n",
+    "# the fun property of `OptimizeResult` object returns\n",
+    "# the value of costFunction at optimized theta\n",
+    "cost = res.fun\n",
+    "\n",
+    "# the optimized theta is in the x property\n",
+    "theta = res.x\n",
+    "\n",
+    "# Print theta to screen\n",
+    "print('Cost at theta found by optimize.minimize: {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx): 0.203\\n');\n",
+    "\n",
+    "print('theta:')\n",
+    "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*theta))\n",
+    "print('Expected theta (approx):\\n\\t[-25.161, 0.206, 0.201]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotDecisionBoundary(plotData, theta, X, y):\n",
+    "    \n",
+    "    # make sure theta is a numpy array\n",
+    "    theta = np.array(theta)\n",
+    "\n",
+    "    # Plot Data (remember first column in X is the intercept)\n",
+    "    plotData(X[:, 1:3], y)\n",
+    "\n",
+    "    if X.shape[1] <= 3:\n",
+    "        # Only need 2 points to define a line, so choose two endpoints\n",
+    "        plot_x = np.array([np.min(X[:, 1]) - 2, np.max(X[:, 1]) + 2])\n",
+    "\n",
+    "        # Calculate the decision boundary line\n",
+    "        plot_y = (-1. / theta[2]) * (theta[1] * plot_x + theta[0])\n",
+    "\n",
+    "        # Plot, and adjust axes for better viewing\n",
+    "        pyplot.plot(plot_x, plot_y)\n",
+    "\n",
+    "        # Legend, specific for the exercise\n",
+    "        pyplot.legend(['Admitted', 'Not admitted', 'Decision Boundary'])\n",
+    "        pyplot.xlim([30, 100])\n",
+    "        pyplot.ylim([30, 100])\n",
+    "    else:\n",
+    "        # Here is the grid range\n",
+    "        u = np.linspace(-1, 1.5, 50)\n",
+    "        v = np.linspace(-1, 1.5, 50)\n",
+    "\n",
+    "        z = np.zeros((u.size, v.size))\n",
+    "        # Evaluate z = theta*x over the grid\n",
+    "        for i, ui in enumerate(u):\n",
+    "            for j, vj in enumerate(v):\n",
+    "                z[i, j] = np.dot(mapFeature(ui, vj), theta)\n",
+    "\n",
+    "        z = z.T  # important to transpose z before calling contour\n",
+    "        # print(z)\n",
+    "\n",
+    "        # Plot z = 0\n",
+    "        pyplot.contour(u, v, z, levels=[0], linewidths=2, colors='g')\n",
+    "        pyplot.contourf(u, v, z, levels=[np.min(z), 0, np.max(z)], cmap='Greens', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Boundary\n",
+    "plotDecisionBoundary(plotData, theta, X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(theta, X):\n",
+    "    \n",
+    "    m = X.shape[0] # Number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly\n",
+    "    p = np.zeros(m)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    p = np.round(sigmoid(X.dot(theta.T)))\n",
+    "    \n",
+    "    \n",
+    "    # ============================================================\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For a student with scores 45 and 85,we predict an admission probability of 0.776\n",
+      "Expected value: 0.775 +/- 0.002\n",
+      "\n",
+      "Train Accuracy: 89.00 %\n",
+      "Expected accuracy (approx): 89.00 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Predict probability for a student with score 45 on exam 1 \n",
+    "#  and score 85 on exam 2 \n",
+    "prob = sigmoid(np.dot([1, 45, 85], theta))\n",
+    "print('For a student with scores 45 and 85,'\n",
+    "      'we predict an admission probability of {:.3f}'.format(prob))\n",
+    "print('Expected value: 0.775 +/- 0.002\\n')\n",
+    "\n",
+    "# Compute accuracy on our training set\n",
+    "p = predict(theta, X)\n",
+    "print('Train Accuracy: {:.2f} %'.format(np.mean(p == y) * 100))\n",
+    "print('Expected accuracy (approx): 89.00 %')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#2.Regularized logistic regression\n",
+    "# Load Data\n",
+    "# The first two columns contains the X values and the third column\n",
+    "# contains the label (y).\n",
+    "data = np.loadtxt(os.path.join('ex3data2.txt'), delimiter=',')\n",
+    "X = data[:, :2]\n",
+    "y = data[:, 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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62GFYM9OnT9URI+q1ra37b7Gtzfk/TZ8+NbBzVyPksUAiVSBht6gVSNA/+iDN+qiGDLzcmL3erIO8afYka5yUh2qwCi+M4Tu/hsL8kGP69Kna3NxPUynR5uZ+RU+uTAL5FIilMgmRoB1/QZr1UQ0Z9DT5sK6ujtra2m7r8s0pCTrjbtC5tPzEz0mdUQzfxaHCpoUSWy6sUAl65nCpES5eInCinHNQ6Mb8rW99i9ra2gNV40Qk75ySMDLuBplLy2/8UnhRpcL3MjM+qT7HpGAKJESCfoovdV6AF2dy1HMO8t2YH3vsMXbu3MkRRxwBwBFHHFFwTknQVkIQc1yCxA+FF1Yq/GIJ2kKwUGJTIKES9FN8qWa9lwicqIcMsm/MDQ0N7NixgzfeeIP9+/d3e+rdv38/q1evzjtsEqSVkIQZ8Zn4pfDiOHwXtIVgRbYwJ3qYxMXxF7QzOQjGjh2rd955p+7bt09VVVeuXKmHHXZYSU7gIDPuxp1sp+9RR9Xq6NHn6vbt21VVde/evXrHHXfo2LFjiz72ggULtHfv3ppKpfTwww/XVCqlvXv3DnxuST6CjhyMKjIxCrAorOgViKo/KbHLnQCXtJDTfJQa9ZStjMq5aSaJoMPI46aYg44crKZQYlMgMVEgfuBHnH2SQk4L4VcdkGog6BteWIrZa+hs0BZCXEYUwiCfAjEfSALxY9ZwHMesSyFJUU9eCDInVU9O31mzbinrvEGmwk9TjGM8rj7HSsIUSAIIKs6+Em6+SYt66okgk0L25PT9y192xyYZZT6KcYyHETnoR5GtJGMKJAEEFWdfCTffpEU99YSfOamy6SmMvK4umPP6STGhs2YhhECuca1KbUn2gQThs6gGZ3KhgIMgs/F6JcyIuEI+kLPPRlOpeEfiqVoW3qjAfCDJJgifRRhj1lFTaEgoDjVEvFiXhx9+OF1dXWX7RfIN6UyeDC++CPv3Bz97vFwsC2+8MAUSAn6lU6gEn0XYFBoSCnK4yCteZnFfe+21PPPMM2UruuwhnfPPF665Bn7zG+jqOvS8Ucwe74kklvGtaHKZJZXaohjC8jP2Pm5x9nHE65BQoW1RDNsUCkf2kt221OG4pIVBV1PobJzAhrCiwc90CpXmMA4CL0NChbZFNWyTaV2mhxS7urr4h3/4B09Rd6UOxyXNqjXHeMzIpVUqtUVhgVRLuoM4OKTTFAo4qKur07q6uthNoMy0Lm+77TYVkaIyBZRag8OsWsMLmAUSDdWScC0ODuk0hQIOli5dytKlS2M3gTLTurzuuutYsWIFhx12WM6+DQ0NnHzyybS2tpY9N8is2vwEmQq+UsirQETkVBH5lYhsFJEfiUhjxrZf59uvGERktIi8KSLrReTGHNsnisgWEXnJbVdmbLtMRNa57TI/5AmCIKJG4vjDjoNDOpNCQzNxHLbJjogbNWoUP//5z/MqujvuuMOXuUFxjMQLcja+V6xYlDcKWSD/BdwCnAFsAn4lIuln6fq8e3lERGqAe4DPA6cAE0TklBxdF6vqYLfNc/c9BpgGnAWcCUwTkT7lyhQEfkeNxOWH7efs+CBuGIUmSSZlAmUhRRfXGhx+EAdr1opFeaOQAjlSVZer6lZVvQX4F2CFiJyBMw5bLmcC61V1g6ruBhYBYz3uez6wUlXfV9UPgJXAaB9k8h2/0ymU+8P262bt5+z4IG4YhYZmkjJs05Oiq5R8ZtnEwZq1YlEeyeUYcXwmvAwclbVuMLAO2JpvP68NuAiYl/H+K8APs/pMBN5xZVkK9HfX/yswJaPfvwP/muc8VwHtQPsJJ5zgq2PJK36kcE9TrlPej0y+afyaHV+qA7jS8ZIpIG41OEohjvVpbMZ7dyg2nbt7Qz87x/oBwP359vPagItzKJAfZPXpC9S5y98A2tzl63IokO/0dM4kpzJJU+4P2++bdSnzCOJ4w0gqlRBFFYf6NNlRhNUSPemVfAok7xCWqi5Q1UOc5ar6e1X9Wr79imAz0D/jfTPwdta5tqnqLvftXGCo130rlWKd8kFl8k1TikM6qOSQ1UhShuMKEQd/TvYwqs1490gurRJGA3oBG4CBQC2wFjg1q89xGcsXAs+5y8cAG4E+btsIHNPTOSvBAim2KFDQT3elPgFXSkErwz+inBWfbZnbjPfuEMeKhMAXgN8BbwGT3XUzgAvc5f8EXnOVy2rg5Ix9LwfWu+1rXs5XCQqklB92kDfrcjL6Ji2NhhEsYfpzvA6jnnjiQF98l0mnZAUCfMbLuiS0SlAgqqU55eN4s64EB7DhH2H6c+Lgd0kS+RSIl5noP8qx7h4P+xkBUUoVtDhOnkvKfIxs4jDRrRIJ058TB79LRZBLqzgKhzOBa4A/At/OaFOAl/PtF+dWKRZIKcQxWiepBa38DIU2oiWOlnkcoQQL5AigH46zuymj7cYJwTUSRByjdeKYRsMLcZjoZvhDHC3zRJFLq2Q24BMZywIc0dM+cW3VbIEYpWPzViqXOFrmcYQyfCDfFZGjRKQBJyJqo4hM8l+VGYY3wvZB2LyVyiWOlnmiyKVVMhvwovv6ZWA2zpwN84EYkRGFD8LmrRhBkI6obG7up6mUaHNzv1iGClOGBVIrIr1wEh0+pE7iw/1+KzLD8EoUPohKTVxoREdcMmuXgxcFMg8nnXsf4EkROQGI/yerIKo9bDTodCxeMYer4SeVkDK+RwWiqnep6vGqep5rymwGRgQvmpEmDvURoiQuPoikzlsx4kklpIzvUYGISJOI/G8RWe6uOhnHH2KERJLCRoOwluIy6cscroafVEK5ay9DWPOBJzmY/XYd8J2gBIoTUZWOjcuQTSkEZS3FwQeR1HkrRjwJotx12HhRIMeq6k9xHeequgfYF6hUMSBKB1dchmxKIUhryXwQRiVRCSnjvSiQD90a5M5MQqekbWegUsWAUhxcflkscRmy8UKY1pL5IIxKwu9y15GQK7Y3swHDgGeADpyhrPXA4J72i2MrZh5IsRXJMtOsz52LrlqFzp1bXv2AJOTpCTOraVJzZxlGPvwsdx0kFDsPREQ+4yqYdqAVaMFJrniKqr4UhDKLE8U6uIIIyUvCkE2Y1pL5IKqXuIaylzvqUEpm7ThRaAjrQBp3Vd2tqmtV9SV1JhJWPMU6uIIIyUvKkE0cHNxGZRPHUPZKmAhYLl58IFVJsQ6uIELykhQ2mgRryUgucQxlr4SJgOUimn2HTG8Q6QCeyrejql5Q9slFRgN3AzXAPFW9JWv7JOBKYC+wBbhcVf/gbtsHvOJ23eRFnmHDhml7e7sn2dJPF42NbzF+fBcDBzqWx6JFjoNrzZrnupmZ/fs3MW3aVgYNOvRY69bBzJlNbNr0nqdzJ5HW1laeeuopTjvtNG699VZuuOEG1q5dS0tLC21tbVGLZySMUaNG8cQTTxx4X1tby+7duw+8phk5ciSrVq2KQsSq+s+LyAuqOix7fSELZAtwZ4FWrkA1OJUNPw+cAkwQkVOyur0IDFPVTwNLgdsytv1VVQe7rWxllk3v3r1Zs+Y5WlquZ+bMJkaPTjFzZhMtLdcfojygMkLyyiFJ1pIRf5IQyl4JEwHLpZAF8ltVPT2wE4ucDXxXVc93398EoKr/maf/EOCHqvr37vsdqlqUp6kYC6RYirVYksL27duZOHEi8+fPp7GxMWpxjCpi9erVjBkzhp07dx6yLQ6h7GaBFLZAfh+cOAB8DKdcbprN7rp8XAE8lvG+XkTaReQ5ERkXhIDFUKzFEjalRrHE0XlpVAdxD86o9lEHKKBAVPWLAZ9bcqzLaQ6JyKU481EyvVInuBrxy8BsETkxz75XuYqmfcuWLeXKXJA4h+SVqgji6Lw0qoc4B2dUxETAMokyCmszB/NrATQDb2d3EpFRwGTgAlXdlV6vqm+7rxuANcCQXCdR1TmqOkxVhzU1NfknfcLwqgiSnIfLqDziHMoe91GHUMg1uzCMBvQCNgADcaocrgVOzeozBHgLOClrfR+gzl3uh5Pg8ZSezllNFQlLreMd5sxyw+gJyz4QD8gzE93rzf6LwCyc6KsLvezj8bhfAH7nKonJ7roZONYGwCrgXeAlty1z138WJ4R3rft6hZfzVZMCKUcRxL18a0dHh44bN047OjoilcMwqoWSFQjOjPQVwNfc9kvgnp72i2OrJgWiWp4iiHMerihqohvxxh4qgiWfAvHiA2kBzlfV+1X1ftdqGO5hPyNiyoliibPz0hz7RjYWLRgNXhTIm8AJGe/7Ay8HI47hN6Uqgjg5L82xb/SEPVREgxcF0hd4Q0TWiMga4HWgSUSWiciyQKUzyqZURRCnmeVJmJVshIs9VMSDvDPRD3QQaSm0XVWf9FWiAAlyJnpcGTduHOeccw7XXnstqVSKffv2MXv2bJ5++ulEpUGP+6xkI1wK/R7S2O/CP/LNRO9RgVQS1ahAKonly5dz8cUX09XVdWBdfX09S5YsYcyYMRFKZkSBPVSER9GpTETkV+5rp4j8JaN1ishfghTWMHIRZ8e+ET5xT3VSDRRKZfI/3NcjVfWojHakqlqKVSN04uTYN+KBPVTkp9xqiV7wlMpERGpE5HgROSHdfJPAMDwSJ8e+EQ/soSI3YVVL7FGBiMg/48wGXwk86rblvpzdMIrAaqIb2VTqQ0W51kNY1RK9RGGtB85S1cRXRzEnumEYcSezttCECQdrCy1c6L22kN+1SkqpB5Lmj0BxRSQMwzCMkvDDegirWmKhKKxJbk3yDcAaEbkpvc5dbxiGYfjM3Lk/YsKELiSrYpIIjB/fxbx59/Z4jOOP78vGjbm3bdzobPeDQhbIkW7bhOP/qM1Yd6QvZzcSS6kVDg3DKIwf1kNY1RILhfFOL9R8OXsVEkZoXRiUm7zOFJBh5MYP6yGsaoleorBWisjRGe/7iMjjvpy9yggrtC4Myk1eZ9lTDSM3flgPYVVL9OJEb1LVA7NyVPUD4Fhfzl5l+B1aF+ZTvN/J6yx7anVglmbx+GU99O7dm6lTp7Np03vs3buPTZveY+rU6b6W2vWiQPZlThwUkY/jFBgyisQP51gmYT7Fl5sR17KnVidmaXojc2i7sfEo/vznPwNnMn16v1jXWveiQCYDvxKRBSKyAHgKuClYsSoTv0PrwnyKb21tZfny5d2USCY9Ja+zlOzViVmaPZNraPu7390G/Dcf/ehH6OjYHoj14Ac9KhBV/SVwOrDYbUNV1RcfiIiMFpE3RWS9iNyYY3udiCx2t/9GRAZkbLvJXf+miJzvhzxBU65zLOqn+HKS15WrgMCGQ5JA1L/RJBLWrPEg8JQLC/gsThnb4cBn/DixiNQA9wCfB04BJojIKVndrgA+UNVBwF3Are6+pwDjgVOB0cCP3OPFmnKdY3F4ii8neV252VNtOCT+xOE3mjT8HtoOEy9RWLcA1+BUInwduEZE/tOHc58JrFfVDaq6G1gEjM3qMxZ4wF1eCowUEXHXL1LVXaq6EVjvHi/WlOsc8+MpvlzKTV5XjgKy4ZD4E8VvNOmh8WHNGg8EVS3YcOqfpzLe1wAv97Sfh+NeBMzLeP8V4IdZfV4FmjPevwX0A34IXJqx/sfARXnOcxXQDrSfcMIJGjWdnZ06ffpU7d+/SWtqUtq/f5NOnz5VOzs7PR/jkUce0fr6esUJZlBA6+vr9ZFHHglQcoexY8fqnXfeqfv27VNV1b179+odd9yhY8eO9bT/8OHDNZVK6ZAhQ3TFihU6ZMgQTaVS2traekjfkSNHdvuMtbW13V7TbeTIkb5+RqN8wvqNdnZ26tChp+qIEfU6dy66ahU6dy46YkS9Dh16alH/q6hobu6nc+eiq1cf2ubMQfv3b4paRAXaNcf91esQ1tEZy41F6KdCSI512dFd+fp42ddZqTpHVYep6rCmpqYiRfQfP0LroqyBUG5G3GKyp9pwSHIJ6zeaZP9BmrBmjQdCLq2i3Z/gJwB/AObjDCdtBMb3tJ+H454NPJ7x/ibgpqw+jwNnu8u9gK04yqNb38x+hdrQoUP9V80RUMxTfFxJW2LNzf00lRJtbu6X0xJra2vThoaGbk+y6dbQ0KCrV6+O5gMYBQnrN5qEp/eeyLSi5sxBV650ZI+TFUUpFojrb/gVjp22LbEAABs0SURBVOP85247W1UX+aC7ngdOEpGBIlKL4xRfltVnGXCZu3wR0OZ+mGXAeDdKayBwEvDfPsgUWzLHeZ98cg19+hzO2LFjOPvssxNXA6GYGflWtjSZhFWnI9H+A5ewZo0HgZd6IC+o6tBATi7yBWA2jl/lPlX9nojMwNF2y0SkHlgADAHex7F8Nrj7TgYuB/YC16rqYz2dL6n1QPyoDxAnZsyYxpNP3saUKd0jT1SdgIKWluuZOvVgurWf/OQnXH311ezcuZO6ujp27dpFQ0MD9957L5deemkEn8CIC37XvTByU049kOdE5IwAZEJV/5+q/o2qnqiq33PXTVXVZe5yl6perKqDVPXMtPJwt33P3e9vvSiPqPAjQqQSxnkzKTZs0cqWGvmIq/8g6ZFhXvFigbwO/A2OH+RDHB+EquqngxfPX8K2QPyyHCrtKaumJsWKFUpNjpk7e/fC6NEp9u7dd2DduHHjOOecc7j22mtJpVLs27eP2bNn8/TTT1s52yon8z82fvzB/9iiRdFZ55U2YgD5LRAvCuTjudar6h98ki00wlYgxQ7V5KPYG27cqTSFaETLjh07mDXrdubNu5e3397G8cf35corr2bSpOsiuVH79b+PE+UMYR0HvK+qf3CVxvvAR/0WsBLxa4ZpWNXFwiKuww5GMgkj62wxJHlmebF4USD3ApkDdx+664we8CtCpNJuuGEVuzGMKKiEyDCveFEgohnjXKq6H2dOhtEDflkOQd9ww3b4JTls0Yg/USfdrLQRg0J4USAbROTbInKY264BNvS4l+Gb5RDkDTeqKolxG3YwKoeok25W2ohBIbw40Y8Fvg+MwJkB/ATOvIvEeTmjjMKKS4RINpXo8IsT27dvZ+LEicyfP5/GRr+yABmFaG1tZc2aNbS2ttLW1hb6+ZPwvy+WfE70stKRJK1FkcrEj+SJQVIJqSDizIMPPqiALliwIGpRKpY4Jt2M+/++WMiTyiSvBSIi16vqbSLyA3IkKlTVb/ul3cIiqTPRg6TSQoTLxW+LIeqn4TgRlDW2evVqxowZw86dO/P28ZpG3izG3JQSxvuG+9oOvJCjGRVAFA6/qJ2chSh3/Nwq8uUnKN+EnzVIovafJI28CkRVH3FfH8jVwhPRCJIoHH5x/pOWW7TKUtDnJ8iCYH4l3bSiZcWRV4GIyLJCLUwhjeCIYk5GMX/SoK0Vvy2GOFSNjAthW2Ol1CAxi7FMcjlGXL/IFuC3wHXAOUBLZsu3X5xbnOuBeK2PEeS5g3L4lePkDNoJXajeSLqVUnckyqqRcSGoa5uPUmqQhC1jlP/zciCPE72QAqkBRuMUkXoRuBk4NV//JLS4KpBKKMtZiHL+pMOHD1cg0GJZQRStWrBggfbu3VtTqZQefvjhmkqltHfv3rGIxuro6NBx48ZpR0dH4OcKsyBYqeWWw5Ixyf/zohVIt05QB0x0rZJ/9rJPHFtcFcj06VN1xIh6bWvrHkbb1ub8uKZPnxq1iGXj9U8aVUim3xZDnKtGhh1anARrLAwZk/w/z6dAeqpIWCciXwR+AnwLZ0LhzwvtYxRPNSRf8+rkjMoJ7XcN77Aq8pVC2I7isOqjQ+k+szBkrMT/eSEn+gPAs8DpwHRVPUNVZ6rqn0KTrkqoluRrXv6kUTmh/S5a9dBDDzFp0iRSKecvVlNTw3e+851I6pdE7SgOsyBYqRF+YchYif/zQhbIV3AKSV0DPCsif3Fbp4j8pZyTisgxIrJSRNa5r31y9BksIr8WkddE5GUR+VLGtvkislFEXnLb4HLk8ZtikxMmLflaqckXvf5Jo6iDHmeLoVyiDi0O89qWal2FIWPS/ueeyDWuFXQDbgNudJdvBG7N0edvgJPc5eOBd4Cj3ffzgYuKPW8YPpBSHGVJGRvt6OjQMWPG6JAhnyzJEViMk9MvJ3SYDuM4E6YzO0zimMYkH0n5n+eCcpzofjfgTeA4d/k44E0P+6zNUCixVSCl/Egylc6cOejKlU4eqrhFZzz44IOaSqEtLYcF/ifwywltuagOkgRndrGEHYZbDkn5n+cinwLxks49CD6iqu8AuK/HFuosImcCtcBbGau/5w5t3SUidQX2vUpE2kWkfcuWLX7IXpBSHGVJqY9x3333UVsLl166J3BHoF9DCjaz+CBhOrPDIkkTN5PyPy+GHtO5l3xgkVXkLn07GXhAVY/O6PuBqh7iB3G3HQesAS5T1ecy1v0ZR6nMAd5S1Rk9yRRGMsVKSk44atQonnjiiQPva2tr2bNnNytXEtvPl0vm3bt3H3hNM3LkSFatWhWFiJHR2trKU089xWmnncatt97KDTfcwNq1a2lpaUl8osfly5dz8cUX09XVdWBdfX09S5YsYcyYMRFKVhmUUxO9JFR1lKp+Kkd7GHjXVQJpZZCztoiIHAU8CkxJKw/32O+4ltUu4H7gzKA+R7EkwVHm1Qmey/laV0esP1/UDuM4U8mBApVoXSWBqIawlgGXucuXAQ9ndxCRWuAXwIOquiRrW1r5CDAOeDVQaYsg7tXIiqlAmGt4YPduuO8+Yvv5kjSkETZxCi32mzBDhY2DRKVAbgHOFZF1wLnue0RkmIjMc/tcgpODa2KOcN3/IyKvAK8A/XDSrMSCKJITFsOsWbfT2PgWU6Z0MWiQMxQ1aBBMmdJFY+NbzJp1e7f+2SG1+/fDiy/C5MnE8vNBNGHARrRUsnUVa3J51iu1hZXKJM7VyEqpQJgdUisiWl9/mB577JGx+3xp4pyLyjCSBjGLwqpoevfuzdSp09m06T327t3Hpk3vMXXq9FhEWZQyGzZ7eGDw4MHs3r2PU08dFrvPl8aGNAwjeEyBVBmlOPmTODyQRJkNI2kEFsYbR6wmOsyYMY0nn7yNKVO6z1VRdfwYLS3XM3Xq9OgENAwjdoQexmvEk7g7+cul1DxdhuEH1fb7MwukCtmxYwezZt3OvHn38vbb2zj++L5ceeXVTJp0Xaz8GMWSDlFubHyLCRO6GDjQGZZbuNBRjkmd7Wskg0r+/ZkFYhwgzk7+cugpRPk//uPmQOqrB1233UgGxYbIVwKmQIyKoec8ZD8qqVZET5Rag8JvTJFFSyUWjOoJUyCGJ5IwtttTiPLWrZ2A/4kV45KwMS6KrFqpxIJRPWEKJGFEcSMvJv1JlPQUolzn5mwutxJf1BX+8hEXRZYmCQ8dfpKEPHh+YwokQUR1I0/K2G6hPGT33+/k8YLyEyvGJWFjXBUZJOehw0/ingcvCEyBJIiobuRJGdvNF6I8ebKTv2v//u79S02sGJeEjXFRZLlIykOHn1R6iHwuLIw3QfTv38S0aVsZNOjQbevWwcyZTWzalDMzflkkqcZJ9xDlrRx99OHs3r2Xzs7d1Nc7Vsj+/f7UiohDDYrVq1czZswYdu7ceci2KDMPR/VbjZpKDZG3MN4KIConXZLGdtMhyq+/voHBg0/htNP2M2vWblatgh/8AM46C+rrnVTm5daKiEMNirhmHq5GhzJUboh8PkyBJIiobuRJHNvNN4Tyve/B6acLXV0flu1sjkvCxjgosmyS9NBhlI4pkAQR1Y08iWO7hfw2EycqffocUXZixbgkbIyLIsskiQ8dRvGYDyRBZKZKGD/+YKqERYuCT5WQtLHdJPltymXcuHGcc845XHvttaRSKfbt28fs2bN5+umnI6s2GOVv1fCffD4QUyAJI2k38qioVidunLDfauUQKwUiIscAi4EBwO+BS1T1gxz99uGUrQXYpKoXuOsHAouAY4DfAl9R1d3Z+2dTCQrE8EYS09Zv376diRMnMn/+fBobG6MWxzAOELcorBuBJ1T1JOAJ930u/qqqg912Qcb6W4G73P0/AK4IVlwjaSTRb1PtqUgsl1fyiEqBjAUecJcfAMZ53VFEBBgBLC1lf6M66N27N2vWPEdLy/XMnNnE6NEpZs5soqXl+tiOv8ctFYlfeFUMQSrQIJVTtaVs6UauQulBN6Aj6/0HefrtBdqB54Bx7rp+wPqMPv2BV72cd+jQoaXWlDcM3xk5cqQCB1ptbW2313QbOXJk1KKWxYMPPqiALliwoGC/4cOHK6Ctra2RyVAsnZ2dOnToqTpiRL3OnYuuWoXOnYuOGFGvQ4eeqp2dnb6eLyqAds1xTw3MAhGRVSLyao42tojDnKDOuNuXgdkiciIgOfrldeSIyFUi0i4i7Vu2bCnyUxhGcMQ5FYmf5LOswszlFZR1V40pW7qRS6sE3YA3gePc5eOANz3sMx+4CEeBbAV6uevPBh73cl6zQIy40dbWpg0NDd0sjnRraGjQ1atXRy1iNzo6OnTcuHHa0dGRt49Xy+r000/P+9nLvQZhWXfNzf107lx09epD25w5aP/+TWUdPy4QtgXSA8uAy9zly4CHszuISB8RqXOX+wF/D7zufpjVOMok7/6GkQTimookH178FF4tqzvvvDOwpJRBWHe5fB3vvLOVj340d/9KTtmSJioFcgtwroisA8513yMiw0Rkntvnk0C7iKzFURi3qOrr7rYbgEkish7oC/w4VOkNw0fimIokH16GgorJVhyUAvU7Y3K+9PRnnQXXXAN//euh+1RDypZIFIiqblPVkap6kvv6vru+XVWvdJefVdW/U9XT3NcfZ+y/QVXPVNVBqnqxqu6K4nMYlUcUETWZqUgWLVrERz96LHv37uCrX/1K6BE92Z//qKNqqakp3k9RjGIISoH6qZzy+TpuvhmOOw6WLOnev1pStlguLMNwiaoIUjqn1po1a5gx4984+eQOfvADWLmSvOcPIiw11+efNWvPgQzGUNxQUE+KIa2svvnNy/nwwx00NAgXX/xFPvWpT/mWy8sv5VQot9pll8HPfkZi5hv5iSkQw3CJKqLmoYceYtKkScyefafn8wcxZ6JQBuMhQyCV425RaCioUJLHTGU1a9YeVq6Eu+7ax+bNP+Oww/Yxc+ZMX5JS+pVosqf09Dt2SGLmG/mJKRDDcIm68mIx5w8iLLXQ+b/2tYM15dP0NBRUKFtxT8p6795dviSC9Ctjck/p6T/2sX5VUwMkE0umaBguUWfw7en8553HgfTotbW17N69+8BrmpEjR7Jq1arAzi+Soq6ujl27dtHQ0MC9997LpZdeWvS5kpbsMom51fwkbrmwDCN2RF0EqafzZ/qCg5h02NP56+rwreZI0ioWJjG3WhiYAjFiSRTRUFEXQerp/OPHfyWQORNezv/AAymGDz/Xt+JZUSvrYklibrUwsCEsI3ZkFiOaMOFgMaKFC4MtRhR1ESQv51+zZg0XX3wxXV1dB/arr69nyZIljBkzJvDz+/X5q31IKGnYEJaRGKKKhor6KdPL+YOcdBjm57chocrALBAjdiTNwRomra2tPPXUU5x22mnceuut3HDDDaxdu5aWlhba2tqiFq8orGJhcohVRcKoMAWSDKKOhoozcax/blQ+pkAwBZIUzAIxjHhhPhAjMUQdDWUYhjdMgRixwxyshpEMTIEYsSPqaCjDMLxhPhDDMAyjIOYDMQzDMHzFFIhhGIkgivQ2RmFMgRhGCNjNrzyiKvZlFCYSBSIix4jIShFZ5772ydGnVUReymhdIjLO3TZfRDZmbBsc/qcwDG/Yza98okpvYxQmKgvkRuAJVT0JeMJ93w1VXa2qg1V1MDAC2AmsyOhyXXq7qr4UitSGUQJxuPkl3QKKutiXkZuoFMhY4AF3+QFgXA/9LwIeU9WdgUplGAEQ9c2vEiygpNUPqRaiUiAfUdV3ANzXY3voPx5YmLXueyLysojcJSJ1uXYCEJGrRKRdRNq3bNlSntSGUQJR3/ziYAGVS9Lqh1QLgSkQEVklIq/maGOLPM5xwN8Bj2esvgk4GTgDOAa4Id/+qjpHVYep6rCmpqYSPolhlEfUN7+oLSA/sPQ28SQwBaKqo1T1Uznaw8C7rmJIK4hCmfEuAX6hqnsyjv2OOuwC7gfODOpzGEa5RH3zi9oC8gNLbxNPohrCWgZc5i5fBjxcoO8EsoavMpSP4PhPXg1ARsPwhahvflFbQH5g6W3iSSSpTESkL/B/gROATcDFqvq+iAwDvqGqV7r9BgDPAP1VdX/G/m1AEyDAS+4+PXoCLZWJERVRFk+y8rFGuVg9EEyBGNVJ1LXejeRjubAMo0qx4R8jKMwCMQzDMApiFohhGIbhK6ZADMMwjJIwBWIYhmGUhCkQwzAMoySqyokuIluAP4R4yn7A1hDPVywmX3mYfOVh8pVHmPJ9XFUPyQVVVQokbESkPVfkQlww+crD5CsPk6884iCfDWEZhmEYJWEKxDAMwygJUyDBMidqAXrA5CsPk688TL7yiFw+84EYhmEYJWEWiGEYhlESpkAMwzCMkjAFUiYicoyIrBSRde5rnxx9WkXkpYzWJSLj3G3zRWRjxrbBYcvn9tuXIcOyjPUDReQ37v6LRaQ2bPlEZLCI/FpEXhORl0XkSxnbArl+IjJaRN4UkfUicmOO7XXu9VjvXp8BGdtucte/KSLn+yFPkbJNEpHX3Wv1hIh8PGNbzu85AhknisiWDFmuzNh2mft7WCcil2XvG5J8d2XI9jsR6cjYFug1FJH7ROQ9EclZKE8cvu/K/rKInJ6xLfBr1w1VtVZGA24DbnSXbwRu7aH/McD7QIP7fj5wUdTyATvyrP+/wHh3+b+Aq8OWD/gb4CR3+XjgHeDooK4fUAO8BXwCqAXWAqdk9fkm8F/u8nhgsbt8itu/DhjoHqcmZNlaM35fV6dlK/Q9R3D9JgI/zLHvMcAG97WPu9wnbPmy+v8zcF9Y1xA4BzgdeDXP9i8Aj+EU1PsM8Juwrl12MwukfMYCD7jLD+CU2C3ERcBjqrozUKkOUqx8BxARAUYAS0vZ3yM9yqeqv1PVde7y28B7OBUpg+JMYL2qblDV3cAiV85MMuVeCox0r9dYYJGq7lLVjcB693ihyaaqqzN+X88BzT6e3xcZC3A+sFJV31fVD4CVwOiI5TukrHaQqOpTOA+Z+RgLPKgOzwFHi1PmO4xr1w1TIOXzEVV9B8B9PbaH/uM59Mf4PdcUvUtE6iKSr15E2kXkufTwGtAX6FDVve77zcDHIpIPABE5E+ep8a2M1X5fv48Bf8x4n+tzH+jjXp/tONfLy75By5bJFThPq2lyfc9+41XG/+V+b0tFpH+R+4YhH+7w30CgLWN1GNewEPnkD+PadaNXkAevFERkFfDRHJsmF3mc44C/Ax7PWH0T8Gecm+Ic4AZgRgTynaCqb4vIJ4A2EXkF+EuOfkXHfft8/RYAl6nqfnd12dcv16lyrMv+3Pn6eNm3HDwfX0QuBYYBLRmrD/meVfWtXPsHLOMjwEJV3SUi38Cx5kZ43DcM+dKMB5aq6r6MdWFcw0JE9ds7BFMgHlDVUfm2ici7InKcqr7j3uDeK3CoS4BfqOqejGO/4y7uEpH7gX+NQj53aAhV3SAia4AhwM9wzONe7lN2M/B2FPKJyFHAo8AU12xPH7vs65eDzUD/jPe5Pne6z2YR6QU04gw7eNk3aNkQkVE4CrpFVXel1+f5nv2++fUoo6puy3g7F7g1Y9/hWfuuCVu+DMYD38pcEdI1LEQ++cO4dt2wIazyWQakox0uAx4u0PeQsVT3ppn2N4wDckZeBCmfiPRJD/2ISD/g74HX1fHMrcbx2+TdPwT5aoFf4Iz7LsnaFsT1ex44SZwItFqcm0h2tE2m3BcBbe71WgaMFydKayBwEvDfPsjkWTYRGQL8b+ACVX0vY33O79lH2YqR8biMtxcAb7jLjwPnubL2Ac6ju8UeinyujH+L44z+dca6sK5hIZYBX3WjsT4DbHcfpMK4dt0J0kNfDQ1n3PsJYJ37eoy7fhgwL6PfAOBPQCpr/zbgFZwb30+A3mHLB3zWlWGt+3pFxv6fwLkBrgeWAHURyHcpsAd4KaMNDvL64US6/A7nyXKyu24Gzk0ZoN69Huvd6/OJjH0nu/u9CXw+gN9cT7KtAt7NuFbLevqeI5DxP4HXXFlWAydn7Hu5e13XA1+LQj73/XeBW7L2C/wa4jxkvuP+5jfj+LG+AXzD3S7APa7srwDDwrx2mc1SmRiGYRglYUNYhmEYRkmYAjEMwzBKwhSIYRiGURKmQAzDMIySMAViGIZhlIQpEKMqEBEVkQUZ73uJkw12ufv+AsmRldXH839XRHJOchSRZ4s4zi/cLLDrRWS7HMwK+9ki5RnhziHIte1UcbIf7xKRa4s5rlFd2Ex0o1r4EPiUiByuqn8FzsWZlwOAqi4jx2SyXLiTFkUPplMpC1X1fPNX1QtdGYYD/6qqY0o87QhgK06yxWy24mSgvSjHNsM4gFkgRjXxGPA/3eVuWQHEqU/xQ3f5I+6T/lq3fVZEBojIGyLyI+C3QH8RmSAir4jIqyJya8axRovIb919n8g4/ykiskZENojItzP673Bfh4vIU+65XxeR/xIRz/9RETlDRJ4UkRdE5DER+Yi7/l/c460VkZ+IyInAlcB1uawXVX1XVduBvTlOYxgHMAvEqCYWAVPdYatPA/cBn8vR7/vAk6p6oYjUAL1xUlr8Lc7s3m+KyPE4+ZuGAh8AK8TJzPoMTm6nc1R1o4gck3Hck3FqdRwJvCki92pGXjSXM3FqivwB+CXwRQ6m08+Lm17jbpyZ1FtF5B+BmcBVwPXAx1V1t4gcraodIjIP2Kqqs3s6tmHkwxSIUTWo6sviVA6cAPy/Al1HAF9199kHbHdzC/1BDyZyPANYo6pbAETk/+AUAtoHPKVOLRBUNbOuw6PqJDbcJSLvAR/BSVWRyX+r6gb3mAuB/4EHBQJ8EjgVWOWMsFGTcezXgJ+IyMPAQx6OZRieMAViVBvLgDtwspb2LXLfDzOWc6XOTq/Plx9oV8byPnL//7L39ZprSICXVTWXRXU+Tkr3scAUEfmUx2MaRkHMB2JUG/cBM1T1lQJ9nsApBYuI1IiTSj6b3wAtItLPHeaaADyJk7m1xc3ES9YQlhfOdLPEpoAvAb/yuN/rwMfEKbiFiNS60VQ1QLOqtgHX4VRybAA6cYbSDKNkTIEYVYWqblbVu3vodg3QKk5RrRdwhoayj/MOTjGr1TiZWX+rqg+7Q1pXAT8XkbXA4iJF/DVwC0524Y04aex7xB0auwiY5Z73ReAsHCvnpyLyMo7z/1ZV7cRJm3+JiLyY7UQXkWYR2Qx8G/iuiGwWkYYiP4dRBVg2XsOICT6E5hpGqJgFYhiGYZSEWSCGYRhGSZgFYhiGYZSEKRDDMAyjJEyBGIZhGCVhCsQwDMMoCVMghmEYRkn8f2A5NCLw2QTLAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotData(X, y)\n",
+    "# Labels and Legend\n",
+    "pyplot.xlabel('Microchip Test 1')\n",
+    "pyplot.ylabel('Microchip Test 2')\n",
+    "\n",
+    "# Specified in plot order\n",
+    "pyplot.legend(['y = 1', 'y = 0'], loc='upper right')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mapFeature(X1, X2, degree=6):\n",
+    "    \n",
+    "    if X1.ndim > 0:\n",
+    "        out = [np.ones(X1.shape[0])]\n",
+    "    else:\n",
+    "        out = [np.ones(1)]\n",
+    "\n",
+    "    for i in range(1, degree + 1):\n",
+    "        for j in range(i + 1):\n",
+    "            out.append((X1 ** (i - j)) * (X2 ** j))\n",
+    "\n",
+    "    if X1.ndim > 0:\n",
+    "        return np.stack(out, axis=1)\n",
+    "    else:\n",
+    "        return np.array(out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note that mapFeature also adds a column of ones for us, so the intercept\n",
+    "# term is handled\n",
+    "X = mapFeature(X[:, 0], X[:, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(118, 28)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def costFunctionReg(theta, X, y, lambda_):\n",
+    "   \n",
+    "    # Initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ===================== YOUR CODE HERE ======================\n",
+    "    h = sigmoid(X.dot(theta.T))\n",
+    "    \n",
+    "    t = theta\n",
+    "    t[0] = 0\n",
+    "    \n",
+    "    J = (1 / m) * np.sum(-y.dot(np.log(h)) - (1 - y).dot(np.log(1 - h))) + (lambda_ / (2 * m)) * np.sum(np.square(t))\n",
+    "    \n",
+    "    grad = (1 / m) * (h - y).dot(X) \n",
+    "    grad = grad + (lambda_ / m) * t\n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at initial theta (zeros): 0.693\n",
+      "Expected cost (approx)       : 0.693\n",
+      "\n",
+      "Gradient at initial theta (zeros) - first five values only:\n",
+      "\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n",
+      "Expected gradients (approx) - first five values only:\n",
+      "\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n",
+      "\n",
+      "------------------------------\n",
+      "\n",
+      "Cost at test theta    : 3.16\n",
+      "Expected cost (approx): 3.16\n",
+      "\n",
+      "Gradient at initial theta (zeros) - first five values only:\n",
+      "\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]\n",
+      "Expected gradients (approx) - first five values only:\n",
+      "\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize fitting parameters\n",
+    "initial_theta = np.zeros(X.shape[1])\n",
+    "\n",
+    "# Set regularization parameter lambda to 1\n",
+    "# DO NOT use `lambda` as a variable name in python\n",
+    "# because it is a python keyword\n",
+    "lambda_ = 1\n",
+    "\n",
+    "# Compute and display initial cost and gradient for regularized logistic\n",
+    "# regression\n",
+    "cost, grad = costFunctionReg(initial_theta, X, y, lambda_)\n",
+    "\n",
+    "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx)       : 0.693\\n')\n",
+    "\n",
+    "print('Gradient at initial theta (zeros) - first five values only:')\n",
+    "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n",
+    "print('Expected gradients (approx) - first five values only:')\n",
+    "print('\\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\\n')\n",
+    "\n",
+    "\n",
+    "# Compute and display cost and gradient\n",
+    "# with all-ones theta and lambda = 10\n",
+    "test_theta = np.ones(X.shape[1])\n",
+    "cost, grad = costFunctionReg(test_theta, X, y, 10)\n",
+    "\n",
+    "print('------------------------------\\n')\n",
+    "print('Cost at test theta    : {:.2f}'.format(cost))\n",
+    "print('Expected cost (approx): 3.16\\n')\n",
+    "\n",
+    "print('Gradient at initial theta (zeros) - first five values only:')\n",
+    "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n",
+    "print('Expected gradients (approx) - first five values only:')\n",
+    "print('\\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/exercise4.ipynb b/Phase 3 - 2020 (Summer)/exercise4.ipynb
new file mode 100644
index 000000000..f8b0539c6
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/exercise4.ipynb	
@@ -0,0 +1,1361 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Programming Exercise 4:  Neural Networks Learning\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "In this exercise, you will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n",
+    "\n",
+    "\n",
+    "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n",
+    "\n",
+    "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading\n",
+    "\n",
+    "\n",
+    "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n",
+    "\n",
+    "\n",
+    "| Section | Part                                             | Submission function | Points \n",
+    "| :-      |:-                                                | :-                  | :-:    \n",
+    "| 1       | [Feedforward and Cost Function](#section1)                    | [`nnCostFunction`](#nnCostFunction)   | 30     \n",
+    "| 2       | [Regularized Cost Function](#section2)                        | [`nnCostFunction`](#nnCostFunction)   | 15     \n",
+    "| 3       | [Sigmoid Gradient](#section3)                                 | [`sigmoidGradient`](#sigmoidGradient) | 5      \n",
+    "| 4       | [Neural Net Gradient Function (Backpropagation)](#section4)   | [`nnCostFunction`](#nnCostFunction)   | 40     \n",
+    "| 5       | [Regularized Gradient](#section5)                             | [`nnCostFunction`](#nnCostFunction)   |10     \n",
+    "|         | Total Points                                     |    | 100    \n",
+    "\n",
+    "\n",
+    "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neural Networks\n",
+    "\n",
+    "In the previous exercise, you implemented feedforward propagation for neural networks and used it to predict handwritten digits with the weights we provided. In this exercise, you will implement the backpropagation algorithm to learn the parameters for the neural network.\n",
+    "\n",
+    "We start the exercise by first loading the dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  training data stored in arrays X, y\n",
+    "data = loadmat(os.path.join('ex4data1.mat'))\n",
+    "X, y = data['X'], data['y'].ravel()\n",
+    "\n",
+    "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+    "# This is an artifact due to the fact that this dataset was used in \n",
+    "# MATLAB where there is no index 0\n",
+    "y[y == 10] = 0\n",
+    "\n",
+    "# Number of training examples\n",
+    "m = y.size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 Visualizing the data\n",
+    "\n",
+    "You will begin by visualizing a subset of the training set, using the function `displayData`, which is the same function we used in Exercise 3. It is provided in the `utils.py` file for this assignment as well. The dataset is also the same one you used in the previous exercise.\n",
+    "\n",
+    "There are 5000 training examples in `ex4data1.mat`, where each training example is a 20 pixel by 20 pixel grayscale image of the digit. Each pixel is represented by a floating point number indicating the grayscale intensity at that location. The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. Each\n",
+    "of these training examples becomes a single row in our data matrix $X$. This gives us a 5000 by 400 matrix $X$ where every row is a training example for a handwritten digit image.\n",
+    "\n",
+    "$$ X = \\begin{bmatrix} - \\left(x^{(1)} \\right)^T - \\\\\n",
+    "- \\left(x^{(2)} \\right)^T - \\\\\n",
+    "\\vdots \\\\\n",
+    "- \\left(x^{(m)} \\right)^T - \\\\\n",
+    "\\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "The second part of the training set is a 5000-dimensional vector `y` that contains labels for the training set. \n",
+    "The following cell randomly selects 100 images from the dataset and plots them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Randomly select 100 data points to display\n",
+    "rand_indices = np.random.choice(m, 100, replace=False)\n",
+    "sel = X[rand_indices, :]\n",
+    "\n",
+    "utils.displayData(sel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 Model representation\n",
+    "\n",
+    "Our neural network is shown in the following figure.\n",
+    "\n",
+    "![](Figures/neural_network.png)\n",
+    "\n",
+    "It has 3 layers - an input layer, a hidden layer and an output layer. Recall that our inputs are pixel values\n",
+    "of digit images. Since the images are of size $20 \\times 20$, this gives us 400 input layer units (not counting the extra bias unit which always outputs +1). The training data was loaded into the variables `X` and `y` above.\n",
+    "\n",
+    "You have been provided with a set of network parameters ($\\Theta^{(1)}, \\Theta^{(2)}$) already trained by us. These are stored in `ex4weights.mat` and will be loaded in the next cell of this notebook into `Theta1` and `Theta2`. The parameters have dimensions that are sized for a neural network with 25 units in the second layer and 10 output units (corresponding to the 10 digit classes)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the parameters you will use for this exercise\n",
+    "input_layer_size  = 400  # 20x20 Input Images of Digits\n",
+    "hidden_layer_size = 25   # 25 hidden units\n",
+    "num_labels = 10          # 10 labels, from 0 to 9\n",
+    "\n",
+    "# Load the weights into variables Theta1 and Theta2\n",
+    "weights = loadmat(os.path.join('ex4weights.mat'))\n",
+    "\n",
+    "# Theta1 has size 25 x 401\n",
+    "# Theta2 has size 10 x 26\n",
+    "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n",
+    "\n",
+    "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n",
+    "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n",
+    "Theta2 = np.roll(Theta2, 1, axis=0)\n",
+    "\n",
+    "# Unroll parameters \n",
+    "nn_params = np.concatenate([Theta1.ravel(), Theta2.ravel()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section1\"></a>\n",
+    "### 1.3 Feedforward and cost function\n",
+    "\n",
+    "Now you will implement the cost function and gradient for the neural network. First, complete the code for the function `nnCostFunction` in the next cell to return the cost.\n",
+    "\n",
+    "Recall that the cost function for the neural network (without regularization) is:\n",
+    "\n",
+    "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m}\\sum_{k=1}^{K} \\left[ - y_k^{(i)} \\log \\left( \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) - \\left( 1 - y_k^{(i)} \\right) \\log \\left( 1 - \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) \\right]$$\n",
+    "\n",
+    "where $h_\\theta \\left( x^{(i)} \\right)$ is computed as shown in the neural network figure above, and K = 10 is the total number of possible labels. Note that $h_\\theta(x^{(i)})_k = a_k^{(3)}$ is the activation (output\n",
+    "value) of the $k^{th}$ output unit. Also, recall that whereas the original labels (in the variable y) were 0, 1, ..., 9, for the purpose of training a neural network, we need to encode the labels as vectors containing only values 0 or 1, so that\n",
+    "\n",
+    "$$ y = \n",
+    "\\begin{bmatrix} 1 \\\\ 0 \\\\ 0 \\\\\\vdots \\\\ 0 \\end{bmatrix}, \\quad\n",
+    "\\begin{bmatrix} 0 \\\\ 1 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{bmatrix}, \\quad \\cdots  \\quad \\text{or} \\qquad\n",
+    "\\begin{bmatrix} 0 \\\\ 0 \\\\ 0 \\\\ \\vdots \\\\ 1 \\end{bmatrix}.\n",
+    "$$\n",
+    "\n",
+    "For example, if $x^{(i)}$ is an image of the digit 5, then the corresponding $y^{(i)}$ (that you should use with the cost function) should be a 10-dimensional vector with $y_5 = 1$, and the other elements equal to 0.\n",
+    "\n",
+    "You should implement the feedforward computation that computes $h_\\theta(x^{(i)})$ for every example $i$ and sum the cost over all examples. **Your code should also work for a dataset of any size, with any number of labels** (you can assume that there are always at least $K \\ge 3$ labels).\n",
+    "\n",
+    "<div class=\"alert alert-box alert-warning\">\n",
+    "**Implementation Note:** The matrix $X$ contains the examples in rows (i.e., X[i,:] is the i-th training example $x^{(i)}$, expressed as a $n \\times 1$ vector.) When you complete the code in `nnCostFunction`, you will need to add the column of 1’s to the X matrix. The parameters for each unit in the neural network is represented in Theta1 and Theta2 as one row. Specifically, the first row of Theta1 corresponds to the first hidden unit in the second layer. You can use a for-loop over the examples to compute the cost.\n",
+    "</div>\n",
+    "<a id=\"nnCostFunction\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nnCostFunction(nn_params,\n",
+    "                   input_layer_size,\n",
+    "                   hidden_layer_size,\n",
+    "                   num_labels,\n",
+    "                   X, y, lambda_=0.0):\n",
+    "    \"\"\"\n",
+    "    Implements the neural network cost function and gradient for a two layer neural \n",
+    "    network which performs classification. \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    nn_params : array_like\n",
+    "        The parameters for the neural network which are \"unrolled\" into \n",
+    "        a vector. This needs to be converted back into the weight matrices Theta1\n",
+    "        and Theta2.\n",
+    "    \n",
+    "    input_layer_size : int\n",
+    "        Number of features for the input layer. \n",
+    "    \n",
+    "    hidden_layer_size : int\n",
+    "        Number of hidden units in the second layer.\n",
+    "    \n",
+    "    num_labels : int\n",
+    "        Total number of labels, or equivalently number of units in output layer. \n",
+    "    \n",
+    "    X : array_like\n",
+    "        Input dataset. A matrix of shape (m x input_layer_size).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        Dataset labels. A vector of shape (m,).\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        Regularization parameter.\n",
+    " \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the cost function at the current weight values.\n",
+    "    \n",
+    "    grad : array_like\n",
+    "        An \"unrolled\" vector of the partial derivatives of the concatenatation of\n",
+    "        neural network weights Theta1 and Theta2.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    You should complete the code by working through the following parts.\n",
+    "    \n",
+    "    - Part 1: Feedforward the neural network and return the cost in the \n",
+    "              variable J. After implementing Part 1, you can verify that your\n",
+    "              cost function computation is correct by verifying the cost\n",
+    "              computed in the following cell.\n",
+    "    \n",
+    "    - Part 2: Implement the backpropagation algorithm to compute the gradients\n",
+    "              Theta1_grad and Theta2_grad. You should return the partial derivatives of\n",
+    "              the cost function with respect to Theta1 and Theta2 in Theta1_grad and\n",
+    "              Theta2_grad, respectively. After implementing Part 2, you can check\n",
+    "              that your implementation is correct by running checkNNGradients provided\n",
+    "              in the utils.py module.\n",
+    "    \n",
+    "              Note: The vector y passed into the function is a vector of labels\n",
+    "                    containing values from 0..K-1. You need to map this vector into a \n",
+    "                    binary vector of 1's and 0's to be used with the neural network\n",
+    "                    cost function.\n",
+    "     \n",
+    "              Hint: We recommend implementing backpropagation using a for-loop\n",
+    "                    over the training examples if you are implementing it for the \n",
+    "                    first time.\n",
+    "    \n",
+    "    - Part 3: Implement regularization with the cost function and gradients.\n",
+    "    \n",
+    "              Hint: You can implement this around the code for\n",
+    "                    backpropagation. That is, you can compute the gradients for\n",
+    "                    the regularization separately and then add them to Theta1_grad\n",
+    "                    and Theta2_grad from Part 2.\n",
+    "    \n",
+    "    Note \n",
+    "    ----\n",
+    "    We have provided an implementation for the sigmoid function in the file \n",
+    "    `utils.py` accompanying this assignment.\n",
+    "    \"\"\"\n",
+    "    # Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices\n",
+    "    # for our 2 layer neural network\n",
+    "    Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n",
+    "                        (hidden_layer_size, (input_layer_size + 1)))\n",
+    "\n",
+    "    Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n",
+    "                        (num_labels, (hidden_layer_size + 1)))\n",
+    "\n",
+    "    # Setup some useful variables\n",
+    "    m = y.size\n",
+    "         \n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    Theta1_grad = np.zeros(Theta1.shape)\n",
+    "    Theta2_grad = np.zeros(Theta2.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    " \n",
+    "    a1 = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "    \n",
+    "    a2 = sigmoid(a1.dot(Theta1.T))\n",
+    "    a2 = np.concatenate([np.ones((a2.shape[0], 1)), a2], axis=1)\n",
+    "    \n",
+    "    a3 = sigmoid(a2.dot(Theta2.T))\n",
+    "    \n",
+    "    y_matrix = y.reshape(-1)\n",
+    "    y_matrix = np.eye(num_labels)[y_matrix]\n",
+    "    \n",
+    "    temp1 = Theta1\n",
+    "    temp2 = Theta2\n",
+    "    \n",
+    "    # Add regularization term\n",
+    "    \n",
+    "    reg_term = (lambda_ / (2 * m)) * (np.sum(np.square(temp1[:, 1:])) + np.sum(np.square(temp2[:, 1:])))\n",
+    "    \n",
+    "    J = (-1 / m) * np.sum((np.log(a3) * y_matrix) + np.log(1 - a3) * (1 - y_matrix)) + reg_term\n",
+    "    \n",
+    "    # Backpropogation\n",
+    "    \n",
+    "    delta_3 = a3 - y_matrix\n",
+    "    delta_2 = delta_3.dot(Theta2)[:, 1:] * sigmoidGradient(a1.dot(Theta1.T))\n",
+    "\n",
+    "    Delta1 = delta_2.T.dot(a1)\n",
+    "    Delta2 = delta_3.T.dot(a2)\n",
+    "    \n",
+    "    # Add regularization to gradient\n",
+    "\n",
+    "    Theta1_grad = (1 / m) * Delta1\n",
+    "    Theta1_grad[:, 1:] = Theta1_grad[:, 1:] + (lambda_ / m) * Theta1[:, 1:]\n",
+    "    \n",
+    "    Theta2_grad = (1 / m) * Delta2\n",
+    "    Theta2_grad[:, 1:] = Theta2_grad[:, 1:] + (lambda_ / m) * Theta2[:, 1:]\n",
+    "    # ===================== Alterntate solutions =====================\n",
+    "    # my_final_matrix = np.zeros(a3.shape)\n",
+    "    # for c in np.arange(num_labels):\n",
+    "    #    my_final_matrix[:, c] = (np.log(a3[:, c]) * (y == c)) + (np.log(1 - a3[:, c]) * (1 - (y == c)))\n",
+    "    #J = (-1 / m) * np.sum(my_final_matrix)\n",
+    "    # ================================================================\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    # ================================================================\n",
+    "    # Unroll gradients\n",
+    "    # grad = np.concatenate([Theta1_grad.ravel(order=order), Theta2_grad.ravel(order=order)])\n",
+    "    grad = np.concatenate([Theta1_grad.ravel(), Theta2_grad.ravel()])\n",
+    "\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-box alert-warning\">\n",
+    "Use the following links to go back to the different parts of this exercise that require to modify the function `nnCostFunction`.<br>\n",
+    "\n",
+    "Back to:\n",
+    "- [Feedforward and cost function](#section1)\n",
+    "- [Regularized cost](#section2)\n",
+    "- [Neural Network Gradient (Backpropagation)](#section4)\n",
+    "- [Regularized Gradient](#section5)\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you are done, call your `nnCostFunction` using the loaded set of parameters for `Theta1` and `Theta2`. You should see that the cost is about 0.287629."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at parameters (loaded from ex4weights): 0.287629 \n",
+      "The cost should be about                   : 0.287629.\n"
+     ]
+    }
+   ],
+   "source": [
+    "lambda_ = 0\n",
+    "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n",
+    "                   num_labels, X, y, lambda_)\n",
+    "print('Cost at parameters (loaded from ex4weights): %.6f ' % J)\n",
+    "print('The cost should be about                   : 0.287629.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader = utils.Grader()\n",
+    "grader[1] = nnCostFunction\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "### 1.4 Regularized cost function\n",
+    "\n",
+    "The cost function for neural networks with regularization is given by:\n",
+    "\n",
+    "\n",
+    "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m}\\sum_{k=1}^{K} \\left[ - y_k^{(i)} \\log \\left( \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) - \\left( 1 - y_k^{(i)} \\right) \\log \\left( 1 - \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) \\right] + \\frac{\\lambda}{2 m} \\left[ \\sum_{j=1}^{25} \\sum_{k=1}^{400} \\left( \\Theta_{j,k}^{(1)} \\right)^2 + \\sum_{j=1}^{10} \\sum_{k=1}^{25} \\left( \\Theta_{j,k}^{(2)} \\right)^2 \\right] $$\n",
+    "\n",
+    "You can assume that the neural network will only have 3 layers - an input layer, a hidden layer and an output layer. However, your code should work for any number of input units, hidden units and outputs units. While we\n",
+    "have explicitly listed the indices above for $\\Theta^{(1)}$ and $\\Theta^{(2)}$ for clarity, do note that your code should in general work with $\\Theta^{(1)}$ and $\\Theta^{(2)}$ of any size. Note that you should not be regularizing the terms that correspond to the bias. For the matrices `Theta1` and `Theta2`, this corresponds to the first column of each matrix. You should now add regularization to your cost function. Notice that you can first compute the unregularized cost function $J$ using your existing `nnCostFunction` and then later add the cost for the regularization terms.\n",
+    "\n",
+    "[Click here to go back to `nnCostFunction` for editing.](#nnCostFunction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you are done, the next cell will call your `nnCostFunction` using the loaded set of parameters for `Theta1` and `Theta2`, and $\\lambda = 1$. You should see that the cost is about 0.383770."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at parameters (loaded from ex4weights): 0.383770\n",
+      "This value should be about                 : 0.383770.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Weight regularization parameter (we set this to 1 here).\n",
+    "lambda_ = 1\n",
+    "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n",
+    "                      num_labels, X, y, lambda_)\n",
+    "\n",
+    "print('Cost at parameters (loaded from ex4weights): %.6f' % J)\n",
+    "print('This value should be about                 : 0.383770.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[2] = nnCostFunction\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 Backpropagation\n",
+    "\n",
+    "In this part of the exercise, you will implement the backpropagation algorithm to compute the gradient for the neural network cost function. You will need to update the function `nnCostFunction` so that it returns an appropriate value for `grad`. Once you have computed the gradient, you will be able to train the neural network by minimizing the cost function $J(\\theta)$ using an advanced optimizer such as `scipy`'s `optimize.minimize`.\n",
+    "You will first implement the backpropagation algorithm to compute the gradients for the parameters for the (unregularized) neural network. After you have verified that your gradient computation for the unregularized case is correct, you will implement the gradient for the regularized neural network."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section3\"></a>\n",
+    "### 2.1 Sigmoid Gradient\n",
+    "\n",
+    "To help you get started with this part of the exercise, you will first implement\n",
+    "the sigmoid gradient function. The gradient for the sigmoid function can be\n",
+    "computed as\n",
+    "\n",
+    "$$ g'(z) = \\frac{d}{dz} g(z) = g(z)\\left(1-g(z)\\right) $$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "$$ \\text{sigmoid}(z) = g(z) = \\frac{1}{1 + e^{-z}} $$\n",
+    "\n",
+    "Now complete the implementation of `sigmoidGradient` in the next cell.\n",
+    "<a id=\"sigmoidGradient\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sigmoidGradient(z):\n",
+    "    \"\"\"\n",
+    "    Computes the gradient of the sigmoid function evaluated at z. \n",
+    "    This should work regardless if z is a matrix or a vector. \n",
+    "    In particular, if z is a vector or matrix, you should return\n",
+    "    the gradient for each element.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    z : array_like\n",
+    "        A vector or matrix as input to the sigmoid function. \n",
+    "    \n",
+    "    Returns\n",
+    "    --------\n",
+    "    g : array_like\n",
+    "        Gradient of the sigmoid function. Has the same shape as z. \n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the gradient of the sigmoid function evaluated at\n",
+    "    each value of z (z can be a matrix, vector or scalar).\n",
+    "    \n",
+    "    Note\n",
+    "    ----\n",
+    "    We have provided an implementation of the sigmoid function \n",
+    "    in `utils.py` file accompanying this assignment.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    g = np.zeros(z.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    g =sigmoid(z) * (1 - utils.sigmoid(z))\n",
+    "\n",
+    "    # =============================================================\n",
+    "    return g"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are done, the following cell call `sigmoidGradient` on a given vector `z`. Try testing a few values by calling `sigmoidGradient(z)`. For large values (both positive and negative) of z, the gradient should be close to 0. When $z = 0$, the gradient should be exactly 0.25. Your code should also work with vectors and matrices. For a matrix, your function should perform the sigmoid gradient function on every element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\n",
+      "  \n",
+      "[0.19661193 0.23500371 0.25       0.23500371 0.19661193]\n"
+     ]
+    }
+   ],
+   "source": [
+    "z = np.array([-1, -0.5, 0, 0.5, 1])\n",
+    "g = sigmoidGradient(z)\n",
+    "print('Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\\n  ')\n",
+    "print(g)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise neural-network-learning\n",
+      "\n",
+      "Login (email address): gitanjit6@gmail.com\n"
+     ]
+    }
+   ],
+   "source": [
+    "grader[3] = sigmoidGradient\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 Random Initialization\n",
+    "\n",
+    "When training neural networks, it is important to randomly initialize the parameters for symmetry breaking. One effective strategy for random initialization is to randomly select values for $\\Theta^{(l)}$ uniformly in the range $[-\\epsilon_{init}, \\epsilon_{init}]$. You should use $\\epsilon_{init} = 0.12$. This range of values ensures that the parameters are kept small and makes the learning more efficient.\n",
+    "\n",
+    "<div class=\"alert alert-box alert-warning\">\n",
+    "One effective strategy for choosing $\\epsilon_{init}$ is to base it on the number of units in the network. A good choice of $\\epsilon_{init}$ is $\\epsilon_{init} = \\frac{\\sqrt{6}}{\\sqrt{L_{in} + L_{out}}}$ where $L_{in} = s_l$ and $L_{out} = s_{l+1}$ are the number of units in the layers adjacent to $\\Theta^{l}$.\n",
+    "</div>\n",
+    "\n",
+    "Your job is to complete the function `randInitializeWeights` to initialize the weights for $\\Theta$. Modify the function by filling in the following code:\n",
+    "\n",
+    "```python\n",
+    "# Randomly initialize the weights to small values\n",
+    "W = np.random.rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init\n",
+    "```\n",
+    "Note that we give the function an argument for $\\epsilon$ with default value `epsilon_init = 0.12`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def randInitializeWeights(L_in, L_out, epsilon_init=0.12):\n",
+    "    \"\"\"\n",
+    "    Randomly initialize the weights of a layer in a neural network.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    L_in : int\n",
+    "        Number of incomming connections.\n",
+    "    \n",
+    "    L_out : int\n",
+    "        Number of outgoing connections. \n",
+    "    \n",
+    "    epsilon_init : float, optional\n",
+    "        Range of values which the weight can take from a uniform \n",
+    "        distribution.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    W : array_like\n",
+    "        The weight initialiatized to random values.  Note that W should\n",
+    "        be set to a matrix of size(L_out, 1 + L_in) as\n",
+    "        the first column of W handles the \"bias\" terms.\n",
+    "        \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Initialize W randomly so that we break the symmetry while training\n",
+    "    the neural network. Note that the first column of W corresponds \n",
+    "    to the parameters for the bias unit.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    W = np.zeros((L_out, 1 + L_in))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    W = np.random.rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init\n",
+    "\n",
+    "\n",
+    "    # ============================================================\n",
+    "    return W"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You do not need to submit any code for this part of the exercise.*\n",
+    "\n",
+    "Execute the following cell to initialize the weights for the 2 layers in the neural network using the `randInitializeWeights` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing Neural Network Parameters ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Initializing Neural Network Parameters ...')\n",
+    "\n",
+    "initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size)\n",
+    "initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels)\n",
+    "\n",
+    "# Unroll parameters\n",
+    "initial_nn_params = np.concatenate([initial_Theta1.ravel(), initial_Theta2.ravel()], axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section4\"></a>\n",
+    "### 2.4 Backpropagation\n",
+    "\n",
+    "![](Figures/ex4-backpropagation.png)\n",
+    "\n",
+    "Now, you will implement the backpropagation algorithm. Recall that the intuition behind the backpropagation algorithm is as follows. Given a training example $(x^{(t)}, y^{(t)})$, we will first run a “forward pass” to compute all the activations throughout the network, including the output value of the hypothesis $h_\\theta(x)$. Then, for each node $j$ in layer $l$, we would like to compute an “error term” $\\delta_j^{(l)}$ that measures how much that node was “responsible” for any errors in our output.\n",
+    "\n",
+    "For an output node, we can directly measure the difference between the network’s activation and the true target value, and use that to define $\\delta_j^{(3)}$ (since layer 3 is the output layer). For the hidden units, you will compute $\\delta_j^{(l)}$ based on a weighted average of the error terms of the nodes in layer $(l+1)$. In detail, here is the backpropagation algorithm (also depicted in the figure above). You should implement steps 1 to 4 in a loop that processes one example at a time. Concretely, you should implement a for-loop `for t in range(m)` and place steps 1-4 below inside the for-loop, with the $t^{th}$ iteration performing the calculation on the $t^{th}$ training example $(x^{(t)}, y^{(t)})$. Step 5 will divide the accumulated gradients by $m$ to obtain the gradients for the neural network cost function.\n",
+    "\n",
+    "1. Set the input layer’s values $(a^{(1)})$ to the $t^{th }$training example $x^{(t)}$. Perform a feedforward pass, computing the activations $(z^{(2)}, a^{(2)}, z^{(3)}, a^{(3)})$ for layers 2 and 3. Note that you need to add a `+1` term to ensure that the vectors of activations for layers $a^{(1)}$ and $a^{(2)}$ also include the bias unit. In `numpy`, if a 1 is a column matrix, adding one corresponds to `a_1 = np.concatenate([np.ones((m, 1)), a_1], axis=1)`.\n",
+    "\n",
+    "1. For each output unit $k$ in layer 3 (the output layer), set \n",
+    "$$\\delta_k^{(3)} = \\left(a_k^{(3)} - y_k \\right)$$\n",
+    "where $y_k \\in \\{0, 1\\}$ indicates whether the current training example belongs to class $k$ $(y_k = 1)$, or if it belongs to a different class $(y_k = 0)$. You may find logical arrays helpful for this task (explained in the previous programming exercise).\n",
+    "\n",
+    "1. For the hidden layer $l = 2$, set \n",
+    "$$ \\delta^{(2)} = \\left( \\Theta^{(2)} \\right)^T \\delta^{(3)} * g'\\left(z^{(2)} \\right)$$\n",
+    "Note that the symbol $*$ performs element wise multiplication in `numpy`.\n",
+    "\n",
+    "1. Accumulate the gradient from this example using the following formula. Note that you should skip or remove $\\delta_0^{(2)}$. In `numpy`, removing $\\delta_0^{(2)}$ corresponds to `delta_2 = delta_2[1:]`.\n",
+    "\n",
+    "1. Obtain the (unregularized) gradient for the neural network cost function by dividing the accumulated gradients by $\\frac{1}{m}$:\n",
+    "$$ \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)}$$\n",
+    "\n",
+    "<div class=\"alert alert-box alert-warning\">\n",
+    "**Python/Numpy tip**: You should implement the backpropagation algorithm only after you have successfully completed the feedforward and cost functions. While implementing the backpropagation alogrithm, it is often useful to use the `shape` function to print out the shapes of the variables you are working with if you run into dimension mismatch errors.\n",
+    "</div>\n",
+    "\n",
+    "[Click here to go back and update the function `nnCostFunction` with the backpropagation algorithm](#nnCostFunction)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After you have implemented the backpropagation algorithm, we will proceed to run gradient checking on your implementation. The gradient check will allow you to increase your confidence that your code is\n",
+    "computing the gradients correctly.\n",
+    "\n",
+    "### 2.4  Gradient checking \n",
+    "\n",
+    "In your neural network, you are minimizing the cost function $J(\\Theta)$. To perform gradient checking on your parameters, you can imagine “unrolling” the parameters $\\Theta^{(1)}$, $\\Theta^{(2)}$ into a long vector $\\theta$. By doing so, you can think of the cost function being $J(\\Theta)$ instead and use the following gradient checking procedure.\n",
+    "\n",
+    "Suppose you have a function $f_i(\\theta)$ that purportedly computes $\\frac{\\partial}{\\partial \\theta_i} J(\\theta)$; you’d like to check if $f_i$ is outputting correct derivative values.\n",
+    "\n",
+    "$$\n",
+    "\\text{Let } \\theta^{(i+)} = \\theta + \\begin{bmatrix} 0 \\\\ 0 \\\\ \\vdots \\\\ \\epsilon \\\\ \\vdots \\\\ 0 \\end{bmatrix}\n",
+    "\\quad \\text{and} \\quad \\theta^{(i-)} = \\theta - \\begin{bmatrix} 0 \\\\ 0 \\\\ \\vdots \\\\ \\epsilon \\\\ \\vdots \\\\ 0 \\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "So, $\\theta^{(i+)}$ is the same as $\\theta$, except its $i^{th}$ element has been incremented by $\\epsilon$. Similarly, $\\theta^{(i−)}$ is the corresponding vector with the $i^{th}$ element decreased by $\\epsilon$. You can now numerically verify $f_i(\\theta)$’s correctness by checking, for each $i$, that:\n",
+    "\n",
+    "$$ f_i\\left( \\theta \\right) \\approx \\frac{J\\left( \\theta^{(i+)}\\right) - J\\left( \\theta^{(i-)} \\right)}{2\\epsilon} $$\n",
+    "\n",
+    "The degree to which these two values should approximate each other will depend on the details of $J$. But assuming $\\epsilon = 10^{-4}$, you’ll usually find that the left- and right-hand sides of the above will agree to at least 4 significant digits (and often many more).\n",
+    "\n",
+    "We have implemented the function to compute the numerical gradient for you in `computeNumericalGradient` (within the file `utils.py`). While you are not required to modify the file, we highly encourage you to take a look at the code to understand how it works.\n",
+    "\n",
+    "In the next cell we will run the provided function `checkNNGradients` which will create a small neural network and dataset that will be used for checking your gradients. If your backpropagation implementation is correct,\n",
+    "you should see a relative difference that is less than 1e-9.\n",
+    "\n",
+    "<div class=\"alert alert-box alert-success\">\n",
+    "**Practical Tip**: When performing gradient checking, it is much more efficient to use a small neural network with a relatively small number of input units and hidden units, thus having a relatively small number\n",
+    "of parameters. Each dimension of $\\theta$ requires two evaluations of the cost function and this can be expensive. In the function `checkNNGradients`, our code creates a small random model and dataset which is used with `computeNumericalGradient` for gradient checking. Furthermore, after you are confident that your gradient computations are correct, you should turn off gradient checking before running your learning algorithm.\n",
+    "</div>\n",
+    "\n",
+    "<div class=\"alert alert-box alert-success\">\n",
+    "**Practical Tip:** Gradient checking works for any function where you are computing the cost and the gradient. Concretely, you can use the same `computeNumericalGradient` function to check if your gradient implementations for the other exercises are correct too (e.g., logistic regression’s cost function).\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-9.27825235e-03 -9.27825236e-03]\n",
+      " [-3.04978709e-06 -3.04978914e-06]\n",
+      " [-1.75060084e-04 -1.75060082e-04]\n",
+      " [-9.62660640e-05 -9.62660620e-05]\n",
+      " [ 8.89911959e-03  8.89911960e-03]\n",
+      " [ 1.42869450e-05  1.42869443e-05]\n",
+      " [ 2.33146358e-04  2.33146357e-04]\n",
+      " [ 1.17982666e-04  1.17982666e-04]\n",
+      " [-8.36010761e-03 -8.36010762e-03]\n",
+      " [-2.59383093e-05 -2.59383100e-05]\n",
+      " [-2.87468729e-04 -2.87468729e-04]\n",
+      " [-1.37149709e-04 -1.37149706e-04]\n",
+      " [ 7.62813550e-03  7.62813551e-03]\n",
+      " [ 3.69883257e-05  3.69883234e-05]\n",
+      " [ 3.35320351e-04  3.35320347e-04]\n",
+      " [ 1.53247082e-04  1.53247082e-04]\n",
+      " [-6.74798369e-03 -6.74798370e-03]\n",
+      " [-4.68759764e-05 -4.68759769e-05]\n",
+      " [-3.76215583e-04 -3.76215587e-04]\n",
+      " [-1.66560294e-04 -1.66560294e-04]\n",
+      " [ 3.14544970e-01  3.14544970e-01]\n",
+      " [ 1.64090819e-01  1.64090819e-01]\n",
+      " [ 1.64567932e-01  1.64567932e-01]\n",
+      " [ 1.58339334e-01  1.58339334e-01]\n",
+      " [ 1.51127527e-01  1.51127527e-01]\n",
+      " [ 1.49568335e-01  1.49568335e-01]\n",
+      " [ 1.11056588e-01  1.11056588e-01]\n",
+      " [ 5.75736494e-02  5.75736493e-02]\n",
+      " [ 5.77867378e-02  5.77867378e-02]\n",
+      " [ 5.59235296e-02  5.59235296e-02]\n",
+      " [ 5.36967009e-02  5.36967009e-02]\n",
+      " [ 5.31542052e-02  5.31542052e-02]\n",
+      " [ 9.74006970e-02  9.74006970e-02]\n",
+      " [ 5.04575855e-02  5.04575855e-02]\n",
+      " [ 5.07530173e-02  5.07530173e-02]\n",
+      " [ 4.91620841e-02  4.91620841e-02]\n",
+      " [ 4.71456249e-02  4.71456249e-02]\n",
+      " [ 4.65597186e-02  4.65597186e-02]]\n",
+      "The above two columns you get should be very similar.\n",
+      "(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "\n",
+      "If your backpropagation implementation is correct, then \n",
+      "the relative difference will be small (less than 1e-9). \n",
+      "Relative Difference: 2.41486e-11\n"
+     ]
+    }
+   ],
+   "source": [
+    "checkNNGradients(nnCostFunction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Once your cost function passes the gradient check for the (unregularized) neural network cost function, you should submit the neural network gradient function (backpropagation).*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[4] = nnCostFunction\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section5\"></a>\n",
+    "### 2.5 Regularized Neural Network\n",
+    "\n",
+    "After you have successfully implemented the backpropagation algorithm, you will add regularization to the gradient. To account for regularization, it turns out that you can add this as an additional term *after* computing the gradients using backpropagation.\n",
+    "\n",
+    "Specifically, after you have computed $\\Delta_{ij}^{(l)}$ using backpropagation, you should add regularization using\n",
+    "\n",
+    "$$ \\begin{align} \n",
+    "& \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)} & \\qquad \\text{for } j = 0 \\\\\n",
+    "& \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)} + \\frac{\\lambda}{m} \\Theta_{ij}^{(l)} & \\qquad \\text{for } j \\ge 1\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "Note that you should *not* be regularizing the first column of $\\Theta^{(l)}$ which is used for the bias term. Furthermore, in the parameters $\\Theta_{ij}^{(l)}$, $i$ is indexed starting from 1, and $j$ is indexed starting from 0. Thus, \n",
+    "\n",
+    "$$\n",
+    "\\Theta^{(l)} = \\begin{bmatrix}\n",
+    "\\Theta_{1,0}^{(i)} & \\Theta_{1,1}^{(l)} & \\cdots \\\\\n",
+    "\\Theta_{2,0}^{(i)} & \\Theta_{2,1}^{(l)} & \\cdots \\\\\n",
+    "\\vdots &  ~ & \\ddots\n",
+    "\\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "[Now modify your code that computes grad in `nnCostFunction` to account for regularization.](#nnCostFunction)\n",
+    "\n",
+    "After you are done, the following cell runs gradient checking on your implementation. If your code is correct, you should expect to see a relative difference that is less than 1e-9."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-9.27825235e-03 -9.27825236e-03]\n",
+      " [-1.67679797e-02 -1.67679797e-02]\n",
+      " [-6.01744725e-02 -6.01744725e-02]\n",
+      " [-1.73704651e-02 -1.73704651e-02]\n",
+      " [ 8.89911959e-03  8.89911960e-03]\n",
+      " [ 3.94334829e-02  3.94334829e-02]\n",
+      " [-3.19612287e-02 -3.19612287e-02]\n",
+      " [-5.75658668e-02 -5.75658668e-02]\n",
+      " [-8.36010761e-03 -8.36010762e-03]\n",
+      " [ 5.93355565e-02  5.93355565e-02]\n",
+      " [ 2.49225535e-02  2.49225535e-02]\n",
+      " [-4.51963845e-02 -4.51963845e-02]\n",
+      " [ 7.62813550e-03  7.62813551e-03]\n",
+      " [ 2.47640974e-02  2.47640974e-02]\n",
+      " [ 5.97717617e-02  5.97717617e-02]\n",
+      " [ 9.14587966e-03  9.14587966e-03]\n",
+      " [-6.74798369e-03 -6.74798370e-03]\n",
+      " [-3.26881426e-02 -3.26881426e-02]\n",
+      " [ 3.86410548e-02  3.86410548e-02]\n",
+      " [ 5.46101547e-02  5.46101547e-02]\n",
+      " [ 3.14544970e-01  3.14544970e-01]\n",
+      " [ 1.18682669e-01  1.18682669e-01]\n",
+      " [ 2.03987128e-01  2.03987128e-01]\n",
+      " [ 1.25698067e-01  1.25698067e-01]\n",
+      " [ 1.76337550e-01  1.76337550e-01]\n",
+      " [ 1.32294136e-01  1.32294136e-01]\n",
+      " [ 1.11056588e-01  1.11056588e-01]\n",
+      " [ 3.81928689e-05  3.81928696e-05]\n",
+      " [ 1.17148233e-01  1.17148233e-01]\n",
+      " [-4.07588279e-03 -4.07588279e-03]\n",
+      " [ 1.13133142e-01  1.13133142e-01]\n",
+      " [-4.52964427e-03 -4.52964427e-03]\n",
+      " [ 9.74006970e-02  9.74006970e-02]\n",
+      " [ 3.36926556e-02  3.36926556e-02]\n",
+      " [ 7.54801264e-02  7.54801264e-02]\n",
+      " [ 1.69677090e-02  1.69677090e-02]\n",
+      " [ 8.61628953e-02  8.61628953e-02]\n",
+      " [ 1.50048382e-03  1.50048382e-03]]\n",
+      "The above two columns you get should be very similar.\n",
+      "(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "\n",
+      "If your backpropagation implementation is correct, then \n",
+      "the relative difference will be small (less than 1e-9). \n",
+      "Relative Difference: 2.30858e-11\n",
+      "\n",
+      "\n",
+      "Cost at (fixed) debugging parameters (w/ lambda = 3.000000): 0.576051 \n",
+      "(for lambda = 3, this value should be about 0.576051)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Check gradients by running checkNNGradients\n",
+    "lambda_ = 3\n",
+    "utils.checkNNGradients(nnCostFunction, lambda_)\n",
+    "\n",
+    "# Also output the costFunction debugging values\n",
+    "debug_J, _  = nnCostFunction(nn_params, input_layer_size,\n",
+    "                          hidden_layer_size, num_labels, X, y, lambda_)\n",
+    "\n",
+    "print('\\n\\nCost at (fixed) debugging parameters (w/ lambda = %f): %f ' % (lambda_, debug_J))\n",
+    "print('(for lambda = 3, this value should be about 0.576051)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[5] = nnCostFunction\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6 Learning parameters using `scipy.optimize.minimize`\n",
+    "\n",
+    "After you have successfully implemented the neural network cost function\n",
+    "and gradient computation, the next step we will use `scipy`'s minimization to learn a good set parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  After you have completed the assignment, change the maxiter to a larger\n",
+    "#  value to see how more training helps.\n",
+    "options= {'maxiter': 100}\n",
+    "\n",
+    "#  You should also try different values of lambda\n",
+    "lambda_ = 1\n",
+    "\n",
+    "# Create \"short hand\" for the cost function to be minimized\n",
+    "costFunction = lambda p: nnCostFunction(p, input_layer_size,\n",
+    "                                        hidden_layer_size,\n",
+    "                                        num_labels, X, y, lambda_)\n",
+    "\n",
+    "# Now, costFunction is a function that takes in only one argument\n",
+    "# (the neural network parameters)\n",
+    "res = optimize.minimize(costFunction,\n",
+    "                        initial_nn_params,\n",
+    "                        jac=True,\n",
+    "                        method='TNC',\n",
+    "                        options=options)\n",
+    "\n",
+    "# get the solution of the optimization\n",
+    "nn_params = res.x\n",
+    "        \n",
+    "# Obtain Theta1 and Theta2 back from nn_params\n",
+    "Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n",
+    "                    (hidden_layer_size, (input_layer_size + 1)))\n",
+    "\n",
+    "Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n",
+    "                    (num_labels, (hidden_layer_size + 1)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the training completes, we will proceed to report the training accuracy of your classifier by computing the percentage of examples it got correct. If your implementation is correct, you should see a reported\n",
+    "training accuracy of about 95.3% (this may vary by about 1% due to the random initialization). It is possible to get higher training accuracies by training the neural network for more iterations. We encourage you to try\n",
+    "training the neural network for more iterations (e.g., set `maxiter` to 400) and also vary the regularization parameter $\\lambda$. With the right learning settings, it is possible to get the neural network to perfectly fit the training set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Accuracy: 96.100000\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = predict(Theta1, Theta2, X)\n",
+    "print('Training Set Accuracy: %f' % (np.mean(pred == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3 Visualizing the Hidden Layer\n",
+    "\n",
+    "One way to understand what your neural network is learning is to visualize what the representations captured by the hidden units. Informally, given a particular hidden unit, one way to visualize what it computes is to find an input $x$ that will cause it to activate (that is, to have an activation value \n",
+    "($a_i^{(l)}$) close to 1). For the neural network you trained, notice that the $i^{th}$ row of $\\Theta^{(1)}$ is a 401-dimensional vector that represents the parameter for the $i^{th}$ hidden unit. If we discard the bias term, we get a 400 dimensional vector that represents the weights from each input pixel to the hidden unit.\n",
+    "\n",
+    "Thus, one way to visualize the “representation” captured by the hidden unit is to reshape this 400 dimensional vector into a 20 × 20 image and display it (It turns out that this is equivalent to finding the input that gives the highest activation for the hidden unit, given a “norm” constraint on the input (i.e., $||x||_2 \\le 1$)). \n",
+    "\n",
+    "The next cell does this by using the `displayData` function and it will show you an image with 25 units,\n",
+    "each corresponding to one hidden unit in the network. In your trained network, you should find that the hidden units corresponds roughly to detectors that look for strokes and other patterns in the input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 25 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "displayData(Theta1[:, 1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 Optional (ungraded) exercise\n",
+    "\n",
+    "In this part of the exercise, you will get to try out different learning settings for the neural network to see how the performance of the neural network varies with the regularization parameter $\\lambda$ and number of training steps (the `maxiter` option when using `scipy.optimize.minimize`). Neural networks are very powerful models that can form highly complex decision boundaries. Without regularization, it is possible for a neural network to “overfit” a training set so that it obtains close to 100% accuracy on the training set but does not as well on new examples that it has not seen before. You can set the regularization $\\lambda$ to a smaller value and the `maxiter` parameter to a higher number of iterations to see this for youself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#utils\n",
+    "def displayData(X, example_width=None, figsize=(10, 10)):\n",
+    "    \"\"\"\n",
+    "    Displays 2D data stored in X in a nice grid.\n",
+    "    \"\"\"\n",
+    "    # Compute rows, cols\n",
+    "    if X.ndim == 2:\n",
+    "        m, n = X.shape\n",
+    "    elif X.ndim == 1:\n",
+    "        n = X.size\n",
+    "        m = 1\n",
+    "        X = X[None]  # Promote to a 2 dimensional array\n",
+    "    else:\n",
+    "        raise IndexError('Input X should be 1 or 2 dimensional.')\n",
+    "\n",
+    "    example_width = example_width or int(np.round(np.sqrt(n)))\n",
+    "    example_height = n / example_width\n",
+    "\n",
+    "    # Compute number of items to display\n",
+    "    display_rows = int(np.floor(np.sqrt(m)))\n",
+    "    display_cols = int(np.ceil(m / display_rows))\n",
+    "\n",
+    "    fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)\n",
+    "    fig.subplots_adjust(wspace=0.025, hspace=0.025)\n",
+    "\n",
+    "    ax_array = [ax_array] if m == 1 else ax_array.ravel()\n",
+    "\n",
+    "    for i, ax in enumerate(ax_array):\n",
+    "        # Display Image\n",
+    "        h = ax.imshow(X[i].reshape(example_width, example_width, order='F'),\n",
+    "                      cmap='Greys', extent=[0, 1, 0, 1])\n",
+    "        ax.axis('off')\n",
+    "\n",
+    "\n",
+    "def predict(Theta1, Theta2, X):\n",
+    "    \"\"\"\n",
+    "    Predict the label of an input given a trained neural network\n",
+    "    Outputs the predicted label of X given the trained weights of a neural\n",
+    "    network(Theta1, Theta2)\n",
+    "    \"\"\"\n",
+    "    # Useful values\n",
+    "    m = X.shape[0]\n",
+    "    num_labels = Theta2.shape[0]\n",
+    "\n",
+    "    # You need to return the following variables correctly\n",
+    "    p = np.zeros(m)\n",
+    "    h1 = sigmoid(np.dot(np.concatenate([np.ones((m, 1)), X], axis=1), Theta1.T))\n",
+    "    h2 = sigmoid(np.dot(np.concatenate([np.ones((m, 1)), h1], axis=1), Theta2.T))\n",
+    "    p = np.argmax(h2, axis=1)\n",
+    "    return p\n",
+    "\n",
+    "\n",
+    "def debugInitializeWeights(fan_out, fan_in):\n",
+    "    \"\"\"\n",
+    "    Initialize the weights of a layer with fan_in incoming connections and fan_out outgoings\n",
+    "    connections using a fixed strategy. This will help you later in debugging.\n",
+    "\n",
+    "    Note that W should be set a matrix of size (1+fan_in, fan_out) as the first row of W handles\n",
+    "    the \"bias\" terms.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    fan_out : int\n",
+    "        The number of outgoing connections.\n",
+    "\n",
+    "    fan_in : int\n",
+    "        The number of incoming connections.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    W : array_like (1+fan_in, fan_out)\n",
+    "        The initialized weights array given the dimensions.\n",
+    "    \"\"\"\n",
+    "    # Initialize W using \"sin\". This ensures that W is always of the same values and will be\n",
+    "    # useful for debugging\n",
+    "    W = np.sin(np.arange(1, 1 + (1+fan_in)*fan_out))/10.0\n",
+    "    W = W.reshape(fan_out, 1+fan_in, order='F')\n",
+    "    return W\n",
+    "\n",
+    "\n",
+    "def computeNumericalGradient(J, theta, e=1e-4):\n",
+    "    \"\"\"\n",
+    "    Computes the gradient using \"finite differences\" and gives us a numerical estimate of the\n",
+    "    gradient.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    J : func\n",
+    "        The cost function which will be used to estimate its numerical gradient.\n",
+    "\n",
+    "    theta : array_like\n",
+    "        The one dimensional unrolled network parameters. The numerical gradient is computed at\n",
+    "         those given parameters.\n",
+    "\n",
+    "    e : float (optional)\n",
+    "        The value to use for epsilon for computing the finite difference.\n",
+    "\n",
+    "    Notes\n",
+    "    -----\n",
+    "    The following code implements numerical gradient checking, and\n",
+    "    returns the numerical gradient. It sets `numgrad[i]` to (a numerical\n",
+    "    approximation of) the partial derivative of J with respect to the\n",
+    "    i-th input argument, evaluated at theta. (i.e., `numgrad[i]` should\n",
+    "    be the (approximately) the partial derivative of J with respect\n",
+    "    to theta[i].)\n",
+    "    \"\"\"\n",
+    "    numgrad = np.zeros(theta.shape)\n",
+    "    perturb = np.diag(e * np.ones(theta.shape))\n",
+    "    for i in range(theta.size):\n",
+    "        loss1, _ = J(theta - perturb[:, i])\n",
+    "        loss2, _ = J(theta + perturb[:, i])\n",
+    "        numgrad[i] = (loss2 - loss1)/(2*e)\n",
+    "    return numgrad\n",
+    "\n",
+    "\n",
+    "def checkNNGradients(nnCostFunction, lambda_=0):\n",
+    "    \"\"\"\n",
+    "    Creates a small neural network to check the backpropagation gradients. It will output the\n",
+    "    analytical gradients produced by your backprop code and the numerical gradients\n",
+    "    (computed using computeNumericalGradient). These two gradient computations should result in\n",
+    "    very similar values.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    nnCostFunction : func\n",
+    "        A reference to the cost function implemented by the student.\n",
+    "\n",
+    "    lambda_ : float (optional)\n",
+    "        The regularization parameter value.\n",
+    "    \"\"\"\n",
+    "    input_layer_size = 3\n",
+    "    hidden_layer_size = 5\n",
+    "    num_labels = 3\n",
+    "    m = 5\n",
+    "\n",
+    "    # We generate some 'random' test data\n",
+    "    Theta1 = debugInitializeWeights(hidden_layer_size, input_layer_size)\n",
+    "    Theta2 = debugInitializeWeights(num_labels, hidden_layer_size)\n",
+    "\n",
+    "    # Reusing debugInitializeWeights to generate X\n",
+    "    X = debugInitializeWeights(m, input_layer_size - 1)\n",
+    "    y = np.arange(1, 1+m) % num_labels\n",
+    "    # print(y)\n",
+    "    # Unroll parameters\n",
+    "    nn_params = np.concatenate([Theta1.ravel(), Theta2.ravel()])\n",
+    "\n",
+    "    # short hand for cost function\n",
+    "    costFunc = lambda p: nnCostFunction(p, input_layer_size, hidden_layer_size,\n",
+    "                                        num_labels, X, y, lambda_)\n",
+    "    cost, grad = costFunc(nn_params)\n",
+    "    numgrad = computeNumericalGradient(costFunc, nn_params)\n",
+    "\n",
+    "    # Visually examine the two gradient computations.The two columns you get should be very similar.\n",
+    "    print(np.stack([numgrad, grad], axis=1))\n",
+    "    print('The above two columns you get should be very similar.')\n",
+    "    print('(Left-Your Numerical Gradient, Right-Analytical Gradient)\\n')\n",
+    "\n",
+    "    # Evaluate the norm of the difference between two the solutions. If you have a correct\n",
+    "    # implementation, and assuming you used e = 0.0001 in computeNumericalGradient, then diff\n",
+    "    # should be less than 1e-9.\n",
+    "    diff = np.linalg.norm(numgrad - grad)/np.linalg.norm(numgrad + grad)\n",
+    "\n",
+    "    print('If your backpropagation implementation is correct, then \\n'\n",
+    "          'the relative difference will be small (less than 1e-9). \\n'\n",
+    "          'Relative Difference: %g' % diff)\n",
+    "\n",
+    "\n",
+    "def sigmoid(z):\n",
+    "    \"\"\"\n",
+    "    Computes the sigmoid of z.\n",
+    "    \"\"\"\n",
+    "    return 1.0 / (1.0 + np.exp(-z))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/exercise5.ipynb b/Phase 3 - 2020 (Summer)/exercise5.ipynb
new file mode 100644
index 000000000..ccaf2be0f
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/exercise5.ipynb	
@@ -0,0 +1,1279 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Programming Exercise 5:\n",
+    "# Regularized Linear Regression and Bias vs Variance\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n",
+    "\n",
+    "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n",
+    "\n",
+    "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading\n",
+    "\n",
+    "\n",
+    "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n",
+    "\n",
+    "\n",
+    "| Section | Part                                             | Submitted Function                | Points |\n",
+    "| :-      |:-                                                |:-                                 | :-:    |\n",
+    "| 1       | [Regularized Linear Regression Cost Function](#section1)      | [`linearRegCostFunction`](#linearRegCostFunction) |  25    |\n",
+    "| 2       | [Regularized Linear Regression Gradient](#section2)           | [`linearRegCostFunction`](#linearRegCostFunction) |25      |\n",
+    "| 3       | [Learning Curve](#section3)                                   | [`learningCurve`](#func2)         | 20     |\n",
+    "| 4       | [Polynomial Feature Mapping](#section4)                       | [`polyFeatures`](#polyFeatures)          | 10     |\n",
+    "| 5       | [Cross Validation Curve](#section5)                           | [`validationCurve`](#validationCurve)       | 20     |\n",
+    "|         | Total Points                                     |                                   |100     |\n",
+    "\n",
+    "\n",
+    "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section1\"></a>\n",
+    "## 1 Regularized Linear Regression\n",
+    "\n",
+    "In the first half of the exercise, you will implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, you will go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s.\n",
+    "variance. \n",
+    "\n",
+    "### 1.1 Visualizing the dataset\n",
+    "\n",
+    "We will begin by visualizing the dataset containing historical records on the change in the water level, $x$, and the amount of water flowing out of the dam, $y$. This dataset is divided into three parts:\n",
+    "\n",
+    "- A **training** set that your model will learn on: `X`, `y`\n",
+    "- A **cross validation** set for determining the regularization parameter: `Xval`, `yval`\n",
+    "- A **test** set for evaluating performance. These are “unseen” examples which your model did not see during training: `Xtest`, `ytest`\n",
+    "\n",
+    "Run the next cell to plot the training data. In the following parts, you will implement linear regression and use that to fit a straight line to the data and plot learning curves. Following that, you will implement polynomial regression to find a better fit to the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OsrT4d2PLO3V3AP4qIp4BNucSlZmZ5SZLi///ArdLup7kUYqvAc6StAPwwxxjMzOzHIzZ4o+Ii4BXAt9Op1dFRH9E/C4iPlRtO0lfkfS4pDtHLfuEpF9Iuj2d3liPP8LMJp9SqcTSxYuZXSwyraOD2cUiSxcvplQqNTu0tpd1OGcHyVO3fg3sLek1Gbb5KnB4heXLI2L/dLo64/HNrIUMDAwwr7ub6f39rB4eZnMEq4eHmd7fz7zubgYGBpodYlvLMpzzbOAfgbuAP6WLA7ih1nYRcYOkudsYn5m1mFKpxDELF7Jq0yYOHrW8CzirXOaIcpkFCxeydmiIrq6uZoXZ1rK0+N8KvCQi3hQRR6TTgm045omShtKuoBdUW0lSn6RBSYMbNvgRv2at4rxzzuGEcnmLpD/awcDx5TLnL1/eyLBsFEVE7RWkAeAdE3nkYtrivyoi9kvnZwNPkPxi+CSwa0S8d6z99PT0xODg4HgPb2ZNMLtYZPXwMLXa8iVgfrHIo0891aiw2pKkdRHRs/XyLKN6NpGM6rmWUcM3I2LcFTsj4rFRAV0IXDXefZjZ5PbExo3sOcY6c9L1rDmyJP5V6bTNJO0aEY+ks28jeY6vmU0hM2fM4MExWvwPpetZc2Spx3/xRHYs6evAIcBMSQ8DHwcOkbQ/SVfPA8D7JrJvM5u8jjr6aC7q7+escrnqOv2FAkctWtTAqGy0qn38kq6MiHdKuoMkUW8hIrrzDm6E+/jNWkepVGJed/dzRvWMWAMs6Oz0qJ4GmEgf/8npv2/OJyQzm4q6urpYsXIlCxYu5PhymePLZeaQdO/0Fwr0FwqsWLnSSb+Jqg7nHNUX/zpgu4h4cPTUmPDMrBX19vaydmiIzX19zC8Wmd7Rwfxikc19fawdGqK3t7fZIba1LMM5/xl4FbAnsA64EbgxIm7PP7yEu3rMzMavWldPllo9H4uIw4D9gJuAD5F8AZiZWQvKUrLhfwPzgRnAbcAHSVr9ZmbWgrKM43878DTwPeC/gLUR8YdcozIzs9xk6eo5kOQC749Jn7ol6aa8AzMzs3xk6erZD3g18FqgB/g57uoxM2tZWbp6ziYpwfwF4JaIqH47npmZTXpZSja8SdJ2wIuBl0i6x8nfzKx1ZenqeS2wgqS2joA9JB0bETUfxGJmZpNTlq6ec4E3RMQ9AJJeDHwdOCjPwMzMLB9ZnsBVGEn6ABHxP0Ahv5DMzCxPWVr8g5IuAi5J59+N79w1M2tZWRL/B4AlwEkkffw3AF/MMygzM8tPllE9m0n6+c/NPxwzM8tb1cRf7QEsIxr5IBYzM6ufWi3+dwC/b1QgZmbWGLUS/2URcaCkSyLCD8c0M5siaiX+7SQdC7xS0tu3fjMivllrx5K+QvLYxscjYr902c7AFcBckhvC3hkRv5lY6GZmNhG1xvG/H5gH7AQcsdWU5Tm8XwUO32rZGcC1EbEPcG06b2ZmDVS1xR8RNwE3SRqMiIvGu+OIuEHS3K0WvwU4JH19MXA9cPp4921mZhOXpR7/uJN+DbNHHuKe/rtLtRUl9UkalDS4YcOGOoZgZtbespRsaIqIuCAieiKiZ9asWc0Ox8xsyqiZ+JXYo47He0zSrum+dwUer+O+zcwsg5qJPyIC+HYdj7cKODZ9fSzwnTru28zMMsjS1bNW0svHu2NJXwfWkDy85WFJxwGfAV4v6V6S5/d+Zrz7NTOzbZOlSNuhwPslPQD8jqRQW4xVsiEijqzy1uvGFaGZmdVVlsTfm3sUZmbWMFmGcz4I7AEclr7elGU7MzObnMZM4JI+TnKT1bJ0UQH4Wp5BmVn9lEolli5ezOxikWkdHcwuFlm6eDGlUqnZoVmTZGm5vw1YQNK/T0T8Etgxz6DMrD4GBgaY193N9P5+Vg8PszmC1cPDTO/vZ153NwMDA80O0ZogS+L/YzqsMwAk7ZBvSGZWD6VSiWMWLmTVpk2cVS7TRXJRrws4q1xm1aZNHLNwoVv+bShL4r9S0r8BO0k6Afgh0J9vWGa2rc475xxOKJc5uMr7BwPHl8ucv3x5I8OySUBJY36MlaTXA28gGcr5nxFxTd6BjdbT0xODg4ONPKRZy5tdLLJ6eJiuGuuUgPnFIo8+9VSjwrIGkrQuInq2Xj7mcE5JZ0fE6cA1FZaZ2ST1xMaN7DnGOnPS9ay9ZOnqeX2FZR7bbzbJzZwxgwfHWOehdD1rL1UTv6QPpA9cf4mkoVHT/cBQ40I0s4k46uijuahQqLlOf6HAUYv8ZNV2U7WPX9JfAC8APs2WT8oajohfNyC2P3Mfv9n4lUol5nV3s2rTpooXeNcACzo7WTs0RFdXrSsB1qqq9fFXbfFHxFMR8UBEHJnesft7kiGdMyTNyTFWM6uDrq4uVqxcyYLOTpYVCpSAMskF3WWFAgs6O1mxcqWTfhvKcufuEWk1zfuB/yJ5SLrv+jBrAb29vawdGmJzXx/zi0Wmd3Qwv1hkc18fa4eG6O315bp2NOZwTkk/AQ4DfhgRB0g6FDgyIvoaESC4q8fMbCLG3dUzSjkifgV0SOqIiOuA/eseoZmZNUSWssxPSpoB3ABcKulx4Ol8wzIzs7xkafG/heTC7lLg+yTXho7IMygzM8tP1Ra/pFOA/wZui4hn0sUXNyQqMzPLTa2unt2BzwMvlTQErCb5IljT6HH8ZmZWP1UTf0R8EEDSdkAP8ErgvcCFkp6MiH0netD0+b3DwDPA05WuOpuZWT6yXNydDhSBv0inXwJ31OHYh0bEE3XYj5mZjUOtPv4LgJeRtMxvJunqOTciftOg2MzMLAe1RvXMAZ4PPAr8AngYeLJOxw3gB5LWSap4I5ikPkmDkgY3bNhQp8OamVnNO3cliaTV/8p02g/4NckF3o9P+KDSX0XELyXtQlLn/58i4oZq6/vOXTOz8ZvQnbuRuBO4mqQ+z3+TPLLz5G0JJn1gOxHxOPAt4BXbsr96KJVKLF28mNnFItM6OphdLLJ08WI/j9Qazp9Fy1utevwnSbpc0s9J7tp9M3AP8HZg54keUNIOknYceU3ySMc7J7q/ehgYGGBedzfT+/tZPTzM5ghWDw8zvb+fed3dDAy4Jp01hj+L1gi16vGfSzp2PyIeqdsBpReRtPIhubh8WUR8qtY2eXb1uGa5TRb+LFq9TaQe/6kRsbKeST/d730R8bfp9LKxkn7ezjvnHE4olyv+jwZwMHB8ucz5y5c3MixrQ/4sWqOMWZZ5MsizxT+7WGT18DC12k8lYH6xyKNPPZVLDGbgz6LV37aUZW4547k49sTGjew5xv7mpOuZ5cmfRWuUKZf4x3txbOaMGTw4xj4fStdrFI/qaE+T8bNoU9OUSvylUoljFi5k1aZNnFUu00Vy9bgLOKtcZtWmTRyzcOEWCfSoo4/mokKh5n77CwWOWrQo19hHeFRH+5psn0WbwiJi0k8HHXRQZHHKBz4QywqFCKg6nVEoxNIlS/68zfr162NmZ2esrrL+aoiZnZ2xfv36TDFsi8kUizWe//tbvQGDUSGnTqkW/2Vf+xrHlcs11zm+XOaySy7583xXVxcrVq5kQWcnywoFSkCZ5CLaskKBBZ2drFi5siHD5zyqY0uN6PKaTN1qk+mzaFNcpW+DyTZlbfF3SFGu0doPiD9CTOvoeM6269evj6VLlsTsYjGmdXTE7GIxli5Z0tDW1S477hjrx4h/PcTsYrFhMTXL1VdfHTM7O2NZoRDrIcrp376sUIiZnZ1x9dVXt8QxJmIyfBZtaqBKi39KDeds9eFw0zo62BxRs1Z2GZje0cHTzzxTY63W1ogbmXyzlLWDthjO2eoXxzyqI9GILi93q1lbq/QzYLJNWbt6Wv3i2EQuTk9FjejycreatQPa4eJuq18cO/G007iwUGBNlffXkPxiWbJ0aeZ9TqaLl1k14kYm3yxl7WxKJX6A3t5e1g4Nsbmvj/nFItM7OphfLLK5r4+1Q0P09vY2O8Sq6v3F1ar3BDSiy8vdatbWKv0MmGxT1q6eqaIeozpaudurEV1e7lazdkCVrp6mJ/UsU7sl/npo5cTWiC+tVv5iNMuqWuKfcl09lpjIzWyTRSOu1bT69SCzbeHEP0W1+sXLRlyraeXrQWbbYkrdwGXPavWb2cxs27XFDVz2rFa/mc3M8tOUxC/pcEn3SFov6YxmxDDV5XFPgJlNDQ1P/JKmAecDvcC+wJGS9m10HFOdL16aWTXNaPG/AlgfyUPX/whcDrylCXFMeb54aWaVNPzirqSFwOERcXw6vwj4u4g4cav1+oA+gDlz5hz04INj3WdpZmajTaaLu6qw7DnfPhFxQUT0RETPrFmzGhCWmVl7aEbifxjYY9T87sAvmxCHmVlbakbivwXYR9JekrYD3gWsakIcZmZtqSk3cEl6I/A5YBrwlYj41Bjrb4AxiylOJjOBJ5odxCTlc1Odz011PjfV1To3e0bEc/rKW+LO3VYjabDSBRXzuanF56Y6n5vqJnJufOeumVmbceI3M2szTvz5uKDZAUxiPjfV+dxU53NT3bjPjfv4zczajFv8ZmZtxonfzKzNOPHnQNIHJYWkmem8JH0hLUM9JOnAZsfYaJI+K+ln6d//LUk7jXpvWXpu7pH0D82Ms1lcqvxZkvaQdJ2kuyXdJenkdPnOkq6RdG/67wuaHWuzSJom6TZJV6Xze0m6OT03V6Q3x1blxF9nkvYAXg88NGpxL7BPOvUBX2pCaM12DbBfRHQD/wMsA0hLcr8LeBlwOPDFtHR323Cp8ud4GjgtIv4amAcsSc/HGcC1EbEPcG06365OBu4eNX82sDw9N78Bjqu1sRN//S0HPsyWhefeAqxIH3y/FthJ0q5Nia5JIuIHEfF0OruWpEYTJOfm8ojYHBH3A+tJSne3E5cqHyUiHomIW9PXwyQJbjeSc3JxutrFwFubE2FzSdodeBPQn84LOAxYma4y5rlx4q8jSQuAX0TET7Z6azfg56PmH06Xtav3AgPpa58bn4OqJM0FDgBuBmZHxCOQfDkAuzQvsqb6HEnj8k/p/F8CT45qWI35+XlefrFNTZJ+CLywwlsfBT4CvKHSZhWWTblxtLXOTUR8J13noyQ/5S8d2azC+lPu3IzB56ACSTOAbwCnRMRvk4Zte5P0ZuDxiFgn6ZCRxRVWrfn5ceIfp4j4+0rLJf0NsBfwk/QDujtwq6RX0CalqKudmxGSjgXeDLwunr2BpC3OzRh8DrYiqUCS9C+NiG+mix+TtGtEPJJ2lT7evAibZj6wIC10uT1QJPkFsJOk56Wt/jE/P+7qqZOIuCMidomIuRExl+R/5gMj4lGSstPHpKN75gFPjfxkbReSDgdOBxZExKZRb60C3iXp+ZL2IrkA/uNmxNhELlU+StpnfRFwd0ScO+qtVcCx6etjge80OrZmi4hlEbF7mmPeBfwoIt4NXAcsTFcb89y4xd8YVwNvJLlwuQl4T3PDaYrzgOcD16S/iNZGxPsj4i5JVwI/JekCWhIRzzQxzoaLiKclnQj8J8+WKr+ryWE103xgEXCHpNvTZR8BPgNcKek4klFz72hSfJPR6cDlkv4FuI3ki7Mql2wwM2sz7uoxM2szTvxmZm3Gid/MrM048ZuZtRknfjOzNuPEb3Uh6YWSLpdUkvRTSVdLerGkQ0YqCDabpH+WVPMmszodZydJi+uwn+sl1fUB47X2KWmlpBfV2HY7STdI8jDwFufEb9ssveHmW8D1EdEVEfuSjLue3dzIthQRH4uIHzbgUDsB40r86c19Tfv/UdLLgGkRcV+1ddICctcC/9iwwCwXTvxWD4cC5Yj48siCiLg9Im5MZ2ekrcmfSbo0/aJA0sck3SLpTkkXjFp+vaSzJf1Y0v9IenW6vFPSlWlN/yvS+uM96XtvkLRG0q2S/iOt87IFSV+VtDB9/YCkM9P175D00grrXy2pO319m6SPpa8/Kel4STMkXTtqHyMVNT8DdEm6XdJn020+lP6tQ5LOTJfNVVJz/ovArWxZtmHrWJ7z90nqTW9+G1nnEEnfzXo+tvJu0rs9Je2ppK77TEkdkm6UNFKD6tvputbCnPitHvYD1tV4/wDgFJJa8y8iuTMT4LyIeHlE7AdMJ6njM+J5ERZvVdcAAANkSURBVPGKdLuPp8sWA79Ja/p/EjgIQMkDb/438PcRcSAwCJyaIe4n0vW/BHywwvs3AK+WVCS5q3gk7lcBNwJ/AN6W7uNQ4Jz0y+sMoBQR+0fEh9KkuQ9J+eX9gYMkvSbd10tISnYfEBEPVgqyxt93DTBP0g7pqv8IXDHB8zGf9L9hGsfZwJeB04CfRsQP0vXuBF4+xr5sknNfnTXCjyPiYYD0Fvy5wE3AoZI+DHQCOwN3Ad9NtxkpzLUuXR+ShPt5gIi4U9JQunweyZfKf6c/GrYD1mSIa/Qx3l7h/RuBk4D7ge8Br5fUCcyNiHuUFBI7K03ifyIphVupe+sN6XRbOj+D5IvgIeDB9BkNtVT8+9JSD98HjpC0kqRG+4eB11Zaf4xj7ApsGJmJiH5J7wDeT/JlNbL8GUl/lLRjWivfWpATv9XDXTxbIKqSzaNePwM8T9L2wBeBnoj4uaRPkFQb3HqbZ3j2c1qtLq+AayLiyHHGXekYo90C9AD3kbSuZwIn8Oyvm3cDs4CDIqIs6YGt/obR8X06Iv5ti4VJrfnfZYiz1t93BbAE+DVwS0QMp786xns+fj869vQLbuRhOTOA0Un++SS/dqxFuavH6uFHwPMlnTCyQNLLJb22xjYjSeaJtP+51hfHiJuAd6b73xf4m3T5WmC+pL3T9zolvXicf8NzpBczf54ecy3JL4APpv8C/AVJbfSypEOBPdPlw8COo3b1n8B7R/rZJe0maTwPEan1910PHEjyhXRFhvWruRvYe9T82STPTPgYcOHIQkl/CWyIiPI44rdJxonftllaW/9tJF0hJUl3AZ+gRk3wiHiSJKHcQXLB8JYMh/oiMCvt4jkdGCIpcb0B+F/A19P31gLPuVg7QTcCj6WlpG8kaQWPJP5LgR5JgySt/58BRMSvSLpZ7pT02bR//DJgjaQ7SB6RtyMZ1fr70kqmV5E8r/eqsdav4XvAIQDpF/bLgbMj4lLgj5JGKsoeSlJt1lqYq3Nay1DyUPJCRPxBUhfJ0MIXpy1z2waSppPUdJ9fqyy2pG8CyyLinoYFZ3XnPn5rJZ3AdelFVQEfcNKvj4j4vaSPk1ygfqjSOkoeEvNtJ/3W5xa/mVmbcR+/mVmbceI3M2szTvxmZm3Gid/MrM048ZuZtZn/D9cM/apxPVORAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load from ex5data1.mat, where all variables will be store in a dictionary\n",
+    "data = loadmat(os.path.join('ex5data1.mat'))\n",
+    "\n",
+    "# Extract train, test, validation data from dictionary\n",
+    "# and also convert y's form 2-D matrix (MATLAB format) to a numpy vector\n",
+    "X, y = data['X'], data['y'][:, 0]\n",
+    "Xtest, ytest = data['Xtest'], data['ytest'][:, 0]\n",
+    "Xval, yval = data['Xval'], data['yval'][:, 0]\n",
+    "\n",
+    "# m = Number of examples\n",
+    "m = y.size\n",
+    "\n",
+    "# Plot training data\n",
+    "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1)\n",
+    "pyplot.xlabel('Change in water level (x)')\n",
+    "pyplot.ylabel('Water flowing out of the dam (y)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 Regularized linear regression cost function\n",
+    "\n",
+    "Recall that regularized linear regression has the following cost function:\n",
+    "\n",
+    "$$ J(\\theta) = \\frac{1}{2m} \\left( \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right)^2 \\right) + \\frac{\\lambda}{2m} \\left( \\sum_{j=1}^n \\theta_j^2 \\right)$$\n",
+    "\n",
+    "where $\\lambda$ is a regularization parameter which controls the degree of regularization (thus, help preventing overfitting). The regularization term puts a penalty on the overall cost J. As the magnitudes of the model parameters $\\theta_j$ increase, the penalty increases as well. Note that you should not regularize\n",
+    "the $\\theta_0$ term.\n",
+    "\n",
+    "You should now complete the code in the function `linearRegCostFunction` in the next cell. Your task is to calculate the regularized linear regression cost function. If possible, try to vectorize your code and avoid writing loops.\n",
+    "<a id=\"linearRegCostFunction\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def linearRegCostFunction(X, y, theta, lambda_=0.0):\n",
+    "    \"\"\"\n",
+    "    Compute cost and gradient for regularized linear regression \n",
+    "    with multiple variables. Computes the cost of using theta as\n",
+    "    the parameter for linear regression to fit the data points in X and y. \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset. Matrix with shape (m x n + 1) where m is the \n",
+    "        total number of examples, and n is the number of features \n",
+    "        before adding the bias term.\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The functions values at each datapoint. A vector of\n",
+    "        shape (m, ).\n",
+    "    \n",
+    "    theta : array_like\n",
+    "        The parameters for linear regression. A vector of shape (n+1,).\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        The regularization parameter.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        The value of the cost function gradient w.r.t theta. \n",
+    "        A vector of shape (n+1, ).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost and gradient of regularized linear regression for\n",
+    "    a particular choice of theta.\n",
+    "    You should set J to the cost and grad to the gradient.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.size # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    h = X.dot(theta)\n",
+    "    J = (1 / (2 * m)) * np.sum(np.square(h - y)) + (lambda_ / (2 * m)) * np.sum(np.square(theta[1:]))\n",
+    "    grad = (1 / m) * (h - y).dot(X)\n",
+    "    \n",
+    "    grad[1:] = grad[1:] + (lambda_ / m) * theta[1:]\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # ============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are finished, the next cell will run your cost function using `theta` initialized at `[1, 1]`. You should expect to see an output of 303.993."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at theta = [1, 1]:\t   303.993192 \n",
+      "This value should be about 303.993192)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "theta = np.array([1, 1])\n",
+    "J, _ = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n",
+    "\n",
+    "print('Cost at theta = [1, 1]:\\t   %f ' % J)\n",
+    "print('This value should be about 303.993192)\\n' % J)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the submission object, and then sending your function to Coursera for grading. \n",
+    "\n",
+    "The submission script will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "*Execute the following cell to grade your solution to the first part of this exercise.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "### 1.3 Regularized linear regression gradient\n",
+    "\n",
+    "Correspondingly, the partial derivative of the cost function for regularized linear regression is defined as:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left(x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} & \\qquad \\text{for } j = 0 \\\\\n",
+    "& \\frac{\\partial J(\\theta)}{\\partial \\theta_j} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m} \\theta_j & \\qquad \\text{for } j \\ge 1\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "In the function [`linearRegCostFunction`](#linearRegCostFunction) above, add code to calculate the gradient, returning it in the variable `grad`. <font color='red'><b>Do not forget to re-execute the cell containing this function to update the function's definition.</b></font>\n",
+    "\n",
+    "\n",
+    "When you are finished, use the next cell to  run your gradient function using theta initialized at `[1, 1]`. You should expect to see a gradient of `[-15.30, 598.250]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient at theta = [1, 1]:  [-15.303016, 598.250744] \n",
+      " (this value should be about [-15.303016, 598.250744])\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "theta = np.array([1, 1])\n",
+    "J, grad = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n",
+    "\n",
+    "print('Gradient at theta = [1, 1]:  [{:.6f}, {:.6f}] '.format(*grad))\n",
+    "print(' (this value should be about [-15.303016, 598.250744])\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[2] = linearRegCostFunction\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fitting linear regression\n",
+    "\n",
+    "Once your cost function and gradient are working correctly, the next cell will run the code in `trainLinearReg` (found in the module `utils.py`) to compute the optimal values of $\\theta$. This training function uses `scipy`'s optimization module to minimize the cost function.\n",
+    "\n",
+    "In this part, we set regularization parameter $\\lambda$ to zero. Because our current implementation of linear regression is trying to fit a 2-dimensional $\\theta$, regularization will not be incredibly helpful for a $\\theta$ of such low dimension. In the later parts of the exercise, you will be using polynomial regression with regularization.\n",
+    "\n",
+    "Finally, the code in the next cell should also plot the best fit line, which should look like the figure below. \n",
+    "\n",
+    "![](Figures/linear_fit.png)\n",
+    "\n",
+    "The best fit line tells us that the model is not a good fit to the data because the data has a non-linear pattern. While visualizing the best fit as shown is one possible way to debug your learning algorithm, it is not always easy to visualize the data and model. In the next section, you will implement a function to generate learning curves that can help you debug your learning algorithm even if it is not easy to visualize the\n",
+    "data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# add a columns of ones for the y-intercept\n",
+    "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "theta = trainLinearReg(linearRegCostFunction, X_aug, y, lambda_=0)\n",
+    "\n",
+    "#  Plot fit over the data\n",
+    "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1.5)\n",
+    "pyplot.xlabel('Change in water level (x)')\n",
+    "pyplot.ylabel('Water flowing out of the dam (y)')\n",
+    "pyplot.plot(X, np.dot(X_aug, theta), '--', lw=2);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section3\"></a>\n",
+    "## 2 Bias-variance\n",
+    "\n",
+    "An important concept in machine learning is the bias-variance tradeoff. Models with high bias are not complex enough for the data and tend to underfit, while models with high variance overfit to the training data.\n",
+    "\n",
+    "In this part of the exercise, you will plot training and test errors on a learning curve to diagnose bias-variance problems.\n",
+    "\n",
+    "### 2.1 Learning Curves\n",
+    "\n",
+    "You will now implement code to generate the learning curves that will be useful in debugging learning algorithms. Recall that a learning curve plots training and cross validation error as a function of training set size. Your job is to fill in the function `learningCurve` in the next cell, so that it returns a vector of errors for the training set and cross validation set.\n",
+    "\n",
+    "To plot the learning curve, we need a training and cross validation set error for different training set sizes. To obtain different training set sizes, you should use different subsets of the original training set `X`. Specifically, for a training set size of $i$, you should use the first $i$ examples (i.e., `X[:i, :]`\n",
+    "and `y[:i]`).\n",
+    "\n",
+    "You can use the `trainLinearReg` function (by calling `utils.trainLinearReg(...)`) to find the $\\theta$ parameters. Note that the `lambda_` is passed as a parameter to the `learningCurve` function.\n",
+    "After learning the $\\theta$ parameters, you should compute the error on the training and cross validation sets. Recall that the training error for a dataset is defined as\n",
+    "\n",
+    "$$ J_{\\text{train}} = \\frac{1}{2m} \\left[ \\sum_{i=1}^m \\left(h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right)^2 \\right] $$\n",
+    "\n",
+    "In particular, note that the training error does not include the regularization term. One way to compute the training error is to use your existing cost function and set $\\lambda$ to 0 only when using it to compute the training error and cross validation error. When you are computing the training set error, make sure you compute it on the training subset (i.e., `X[:n,:]` and `y[:n]`) instead of the entire training set. However, for the cross validation error, you should compute it over the entire cross validation set. You should store\n",
+    "the computed errors in the vectors error train and error val.\n",
+    "\n",
+    "<a id=\"func2\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def learningCurve(X, y, Xval, yval, lambda_=0):\n",
+    "    \"\"\"\n",
+    "    Generates the train and cross validation set errors needed to plot a learning curve\n",
+    "    returns the train and cross validation set errors for a learning curve. \n",
+    "    \n",
+    "    In this function, you will compute the train and test errors for\n",
+    "    dataset sizes from 1 up to m. In practice, when working with larger\n",
+    "    datasets, you might want to do this in larger intervals.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The training dataset. Matrix with shape (m x n + 1) where m is the \n",
+    "        total number of examples, and n is the number of features \n",
+    "        before adding the bias term.\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The functions values at each training datapoint. A vector of\n",
+    "        shape (m, ).\n",
+    "    \n",
+    "    Xval : array_like\n",
+    "        The validation dataset. Matrix with shape (m_val x n + 1) where m is the \n",
+    "        total number of examples, and n is the number of features \n",
+    "        before adding the bias term.\n",
+    "    \n",
+    "    yval : array_like\n",
+    "        The functions values at each validation datapoint. A vector of\n",
+    "        shape (m_val, ).\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        The regularization parameter.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    error_train : array_like\n",
+    "        A vector of shape m. error_train[i] contains the training error for\n",
+    "        i examples.\n",
+    "    error_val : array_like\n",
+    "        A vecotr of shape m. error_val[i] contains the validation error for\n",
+    "        i training examples.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to return training errors in error_train and the\n",
+    "    cross validation errors in error_val. i.e., error_train[i] and \n",
+    "    error_val[i] should give you the errors obtained after training on i examples.\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    - You should evaluate the training error on the first i training\n",
+    "      examples (i.e., X[:i, :] and y[:i]).\n",
+    "    \n",
+    "      For the cross-validation error, you should instead evaluate on\n",
+    "      the _entire_ cross validation set (Xval and yval).\n",
+    "    \n",
+    "    - If you are using your cost function (linearRegCostFunction) to compute\n",
+    "      the training and cross validation error, you should call the function with\n",
+    "      the lambda argument set to 0. Do note that you will still need to use\n",
+    "      lambda when running the training to obtain the theta parameters.\n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    You can loop over the examples with the following:\n",
+    "     \n",
+    "           for i in range(1, m+1):\n",
+    "               # Compute train/cross validation errors using training examples \n",
+    "               # X[:i, :] and y[:i], storing the result in \n",
+    "               # error_train[i-1] and error_val[i-1]\n",
+    "               ....  \n",
+    "    \"\"\"\n",
+    "    # Number of training examples\n",
+    "    m = y.size\n",
+    "\n",
+    "    # You need to return these values correctly\n",
+    "    error_train = np.zeros(m)\n",
+    "    error_val   = np.zeros(m)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    for i in range(1, m + 1):\n",
+    "        t = trainLinearReg(linearRegCostFunction, X[:i], y[:i], lambda_ = lambda_)\n",
+    "        error_train[i - 1], _ = linearRegCostFunction(X[:i], y[:i], t, lambda_ = 0)\n",
+    "        error_val[i - 1], _ = linearRegCostFunction(Xval, yval, t, lambda_ = 0)         \n",
+    "\n",
+    "        \n",
+    "    # =============================================================\n",
+    "    return error_train, error_val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are finished implementing the function `learningCurve`, executing the next cell prints the learning curves and produce a plot similar to the figure below. \n",
+    "\n",
+    "![](Figures/learning_curve.png)\n",
+    "\n",
+    "In the learning curve figure, you can observe that both the train error and cross validation error are high when the number of training examples is increased. This reflects a high bias problem in the model - the linear regression model is too simple and is unable to fit our dataset well. In the next section, you will implement polynomial regression to fit a better model for this dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Training Examples\tTrain Error\tCross Validation Error\n",
+      "  \t1\t\t0.000000\t205.121096\n",
+      "  \t2\t\t0.000000\t110.302641\n",
+      "  \t3\t\t3.286595\t45.010231\n",
+      "  \t4\t\t2.842678\t48.368910\n",
+      "  \t5\t\t13.154049\t35.865165\n",
+      "  \t6\t\t19.443963\t33.829962\n",
+      "  \t7\t\t20.098522\t31.970986\n",
+      "  \t8\t\t18.172859\t30.862446\n",
+      "  \t9\t\t22.609405\t31.135998\n",
+      "  \t10\t\t23.261462\t28.936207\n",
+      "  \t11\t\t24.317250\t29.551432\n",
+      "  \t12\t\t22.373906\t29.433818\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "Xval_aug = np.concatenate([np.ones((yval.size, 1)), Xval], axis=1)\n",
+    "error_train, error_val = learningCurve(X_aug, y, Xval_aug, yval, lambda_=0)\n",
+    "\n",
+    "pyplot.plot(np.arange(1, m+1), error_train, np.arange(1, m+1), error_val, lw=2)\n",
+    "pyplot.title('Learning curve for linear regression')\n",
+    "pyplot.legend(['Train', 'Cross Validation'])\n",
+    "pyplot.xlabel('Number of training examples')\n",
+    "pyplot.ylabel('Error')\n",
+    "pyplot.axis([0, 13, 0, 150])\n",
+    "\n",
+    "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+    "for i in range(m):\n",
+    "    print('  \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[3] = learningCurve\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section4\"></a>\n",
+    "\n",
+    "## 3 Polynomial regression\n",
+    "\n",
+    "The problem with our linear model was that it was too simple for the data\n",
+    "and resulted in underfitting (high bias). In this part of the exercise, you will address this problem by adding more features. For polynomial regression, our hypothesis has the form:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "h_\\theta(x)  &= \\theta_0 + \\theta_1 \\times (\\text{waterLevel}) + \\theta_2 \\times (\\text{waterLevel})^2 + \\cdots + \\theta_p \\times (\\text{waterLevel})^p \\\\\n",
+    "& = \\theta_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\cdots + \\theta_p x_p\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "Notice that by defining $x_1 = (\\text{waterLevel})$, $x_2 = (\\text{waterLevel})^2$ , $\\cdots$, $x_p =\n",
+    "(\\text{waterLevel})^p$, we obtain a linear regression model where the features are the various powers of the original value (waterLevel).\n",
+    "\n",
+    "Now, you will add more features using the higher powers of the existing feature $x$ in the dataset. Your task in this part is to complete the code in the function `polyFeatures` in the next cell. The function should map the original training set $X$ of size $m \\times 1$ into its higher powers. Specifically, when a training set $X$ of size $m \\times 1$ is passed into the function, the function should return a $m \\times p$ matrix `X_poly`, where column 1 holds the original values of X, column 2 holds the values of $X^2$, column 3 holds the values of $X^3$, and so on. Note that you don’t have to account for the zero-eth power in this function.\n",
+    "\n",
+    "<a id=\"polyFeatures\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def polyFeatures(X, p):\n",
+    "    \"\"\"\n",
+    "    Maps X (1D vector) into the p-th power.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        A data vector of size m, where m is the number of examples.\n",
+    "    \n",
+    "    p : int\n",
+    "        The polynomial power to map the features. \n",
+    "    \n",
+    "    Returns \n",
+    "    -------\n",
+    "    X_poly : array_like\n",
+    "        A matrix of shape (m x p) where p is the polynomial \n",
+    "        power and m is the number of examples. That is:\n",
+    "    \n",
+    "        X_poly[i, :] = [X[i], X[i]**2, X[i]**3 ...  X[i]**p]\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Given a vector X, return a matrix X_poly where the p-th column of\n",
+    "    X contains the values of X to the p-th power.\n",
+    "    \"\"\"\n",
+    "    # You need to return the following variables correctly.\n",
+    "    X_poly = np.zeros((X.shape[0], p))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    for i in range(p):\n",
+    "        X_poly[:, i] = X[:, 0] ** (i + 1)\n",
+    "\n",
+    "\n",
+    "    # ============================================================\n",
+    "    return X_poly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you have a function that will map features to a higher dimension. The next cell will apply it to the training set, the test set, and the cross validation set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized Training Example 1:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.        , -0.36214078, -0.75508669,  0.18222588, -0.70618991,\n",
+       "        0.30661792, -0.59087767,  0.3445158 , -0.50848117])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = 8\n",
+    "\n",
+    "# Map X onto Polynomial Features and Normalize\n",
+    "X_poly = polyFeatures(X, p)\n",
+    "X_poly, mu, sigma = featureNormalize(X_poly)\n",
+    "X_poly = np.concatenate([np.ones((m, 1)), X_poly], axis=1)\n",
+    "\n",
+    "# Map X_poly_test and normalize (using mu and sigma)\n",
+    "X_poly_test = polyFeatures(Xtest, p)\n",
+    "X_poly_test -= mu\n",
+    "X_poly_test /= sigma\n",
+    "X_poly_test = np.concatenate([np.ones((ytest.size, 1)), X_poly_test], axis=1)\n",
+    "\n",
+    "# Map X_poly_val and normalize (using mu and sigma)\n",
+    "X_poly_val = polyFeatures(Xval, p)\n",
+    "X_poly_val -= mu\n",
+    "X_poly_val /= sigma\n",
+    "X_poly_val = np.concatenate([np.ones((yval.size, 1)), X_poly_val], axis=1)\n",
+    "\n",
+    "print('Normalized Training Example 1:')\n",
+    "X_poly[0, :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[4] = polyFeatures\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Learning Polynomial Regression\n",
+    "\n",
+    "After you have completed the function `polyFeatures`, we will proceed to train polynomial regression using your linear regression cost function.\n",
+    "\n",
+    "Keep in mind that even though we have polynomial terms in our feature vector, we are still solving a linear regression optimization problem. The polynomial terms have simply turned into features that we can use for linear regression. We are using the same cost function and gradient that you wrote for the earlier part of this exercise.\n",
+    "\n",
+    "For this part of the exercise, you will be using a polynomial of degree 8. It turns out that if we run the training directly on the projected data, will not work well as the features would be badly scaled (e.g., an example with $x = 40$ will now have a feature $x_8 = 40^8 = 6.5 \\times 10^{12}$). Therefore, you will\n",
+    "need to use feature normalization.\n",
+    "\n",
+    "Before learning the parameters $\\theta$ for the polynomial regression, we first call `featureNormalize` and normalize the features of the training set, storing the mu, sigma parameters separately. We have already implemented this function for you (in `utils.py` module) and it is the same function from the first exercise.\n",
+    "\n",
+    "After learning the parameters $\\theta$, you should see two plots generated for polynomial regression with $\\lambda = 0$, which should be similar to the ones here:\n",
+    "\n",
+    "<table>\n",
+    "    <tr>\n",
+    "        <td><img src=\"Figures/polynomial_regression.png\"></td>\n",
+    "        <td><img src=\"Figures/polynomial_learning_curve.png\"></td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "\n",
+    "You should see that the polynomial fit is able to follow the datapoints very well, thus, obtaining a low training error. The figure on the right shows that the training error essentially stays zero for all numbers of training samples. However, the polynomial fit is very complex and even drops off at the extremes. This is an indicator that the polynomial regression model is overfitting the training data and will not generalize well.\n",
+    "\n",
+    "To better understand the problems with the unregularized ($\\lambda = 0$) model, you can see that the learning curve  shows the same effect where the training error is low, but the cross validation error is high. There is a gap between the training and cross validation errors, indicating a high variance problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial Regression (lambda = 100.000000)\n",
+      "\n",
+      "# Training Examples\tTrain Error\tCross Validation Error\n",
+      "  \t1\t\t0.000000\t138.846777\n",
+      "  \t2\t\t0.114107\t144.125230\n",
+      "  \t3\t\t106.956580\t70.863286\n",
+      "  \t4\t\t121.740879\t78.372963\n",
+      "  \t5\t\t102.949459\t63.845046\n",
+      "  \t6\t\t97.169857\t59.532632\n",
+      "  \t7\t\t83.326539\t59.585493\n",
+      "  \t8\t\t76.491825\t58.699842\n",
+      "  \t9\t\t71.297176\t59.564455\n",
+      "  \t10\t\t64.350636\t59.731344\n",
+      "  \t11\t\t58.997943\t60.409869\n",
+      "  \t12\t\t57.977080\t57.842195\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lambda_ = 100\n",
+    "theta =trainLinearReg(linearRegCostFunction, X_poly, y,\n",
+    "                             lambda_=lambda_, maxiter=55)\n",
+    "\n",
+    "# Plot training data and fit\n",
+    "pyplot.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n",
+    "\n",
+    "plotFit(polyFeatures, np.min(X), np.max(X), mu, sigma, theta, p)\n",
+    "\n",
+    "pyplot.xlabel('Change in water level (x)')\n",
+    "pyplot.ylabel('Water flowing out of the dam (y)')\n",
+    "pyplot.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n",
+    "pyplot.ylim([-20, 50])\n",
+    "\n",
+    "pyplot.figure()\n",
+    "error_train, error_val = learningCurve(X_poly, y, X_poly_val, yval, lambda_)\n",
+    "pyplot.plot(np.arange(1, 1+m), error_train, np.arange(1, 1+m), error_val)\n",
+    "\n",
+    "pyplot.title('Polynomial Regression Learning Curve (lambda = %f)' % lambda_)\n",
+    "pyplot.xlabel('Number of training examples')\n",
+    "pyplot.ylabel('Error')\n",
+    "pyplot.axis([0, 13, 0, 100])\n",
+    "pyplot.legend(['Train', 'Cross Validation'])\n",
+    "\n",
+    "print('Polynomial Regression (lambda = %f)\\n' % lambda_)\n",
+    "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+    "for i in range(m):\n",
+    "    print('  \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One way to combat the overfitting (high-variance) problem is to add regularization to the model. In the next section, you will get to  try different $\\lambda$ parameters to see how regularization can lead to a better model.\n",
+    "\n",
+    "### 3.2 Optional (ungraded) exercise: Adjusting the regularization parameter\n",
+    "\n",
+    "In this section, you will get to observe how the regularization parameter affects the bias-variance of regularized polynomial regression. You should now modify the the lambda parameter and try $\\lambda = 1, 100$. For each of these values, the script should generate a polynomial fit to the data and also a learning curve.\n",
+    "\n",
+    "For $\\lambda = 1$, the generated plots should look like the the figure below. You should see a polynomial fit that follows the data trend well (left) and a learning curve (right) showing that both the cross validation and training error converge to a relatively low value. This shows the $\\lambda = 1$ regularized polynomial regression model does not have the high-bias or high-variance problems. In effect, it achieves a good trade-off between bias and variance.\n",
+    "\n",
+    "<table>\n",
+    "    <tr>\n",
+    "        <td><img src=\"Figures/polynomial_regression_reg_1.png\"></td>\n",
+    "        <td><img src=\"Figures/polynomial_learning_curve_reg_1.png\"></td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "\n",
+    "For $\\lambda = 100$, you should see a polynomial fit (figure below) that does not follow the data well. In this case, there is too much regularization and the model is unable to fit the training data.\n",
+    "\n",
+    "![](Figures/polynomial_regression_reg_100.png)\n",
+    "\n",
+    "*You do not need to submit any solutions for this optional (ungraded) exercise.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section5\"></a>\n",
+    "### 3.3 Selecting $\\lambda$ using a cross validation set\n",
+    "\n",
+    "From the previous parts of the exercise, you observed that the value of $\\lambda$ can significantly affect the results of regularized polynomial regression on the training and cross validation set. In particular, a model without regularization ($\\lambda = 0$) fits the training set well, but does not generalize. Conversely, a model with too much regularization ($\\lambda = 100$) does not fit the training set and testing set well. A good choice of $\\lambda$ (e.g., $\\lambda = 1$) can provide a good fit to the data.\n",
+    "\n",
+    "In this section, you will implement an automated method to select the $\\lambda$ parameter. Concretely, you will use a cross validation set to evaluate how good each $\\lambda$ value is. After selecting the best $\\lambda$ value using the cross validation set, we can then evaluate the model on the test set to estimate\n",
+    "how well the model will perform on actual unseen data. \n",
+    "\n",
+    "Your task is to complete the code in the function `validationCurve`. Specifically, you should should use the `utils.trainLinearReg` function to train the model using different values of $\\lambda$ and compute the training error and cross validation error. You should try $\\lambda$ in the following range: {0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10}.\n",
+    "<a id=\"validationCurve\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def validationCurve(X, y, Xval, yval):\n",
+    "    \"\"\"\n",
+    "    Generate the train and validation errors needed to plot a validation\n",
+    "    curve that we can use to select lambda_.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The training dataset. Matrix with shape (m x n) where m is the \n",
+    "        total number of training examples, and n is the number of features \n",
+    "        including any polynomial features.\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The functions values at each training datapoint. A vector of\n",
+    "        shape (m, ).\n",
+    "    \n",
+    "    Xval : array_like\n",
+    "        The validation dataset. Matrix with shape (m_val x n) where m is the \n",
+    "        total number of validation examples, and n is the number of features \n",
+    "        including any polynomial features.\n",
+    "    \n",
+    "    yval : array_like\n",
+    "        The functions values at each validation datapoint. A vector of\n",
+    "        shape (m_val, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    lambda_vec : list\n",
+    "        The values of the regularization parameters which were used in \n",
+    "        cross validation.\n",
+    "    \n",
+    "    error_train : list\n",
+    "        The training error computed at each value for the regularization\n",
+    "        parameter.\n",
+    "    \n",
+    "    error_val : list\n",
+    "        The validation error computed at each value for the regularization\n",
+    "        parameter.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to return training errors in `error_train` and\n",
+    "    the validation errors in `error_val`. The vector `lambda_vec` contains\n",
+    "    the different lambda parameters to use for each calculation of the\n",
+    "    errors, i.e, `error_train[i]`, and `error_val[i]` should give you the\n",
+    "    errors obtained after training with `lambda_ = lambda_vec[i]`.\n",
+    "\n",
+    "    Note\n",
+    "    ----\n",
+    "    You can loop over lambda_vec with the following:\n",
+    "    \n",
+    "          for i in range(len(lambda_vec))\n",
+    "              lambda = lambda_vec[i]\n",
+    "              # Compute train / val errors when training linear \n",
+    "              # regression with regularization parameter lambda_\n",
+    "              # You should store the result in error_train[i]\n",
+    "              # and error_val[i]\n",
+    "              ....\n",
+    "    \"\"\"\n",
+    "    # Selected values of lambda (you should not change this)\n",
+    "    lambda_vec = [0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10]\n",
+    "\n",
+    "    # You need to return these variables correctly.\n",
+    "    error_train = np.zeros(len(lambda_vec))\n",
+    "    error_val = np.zeros(len(lambda_vec))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    for i in range(len(lambda_vec)):\n",
+    "        l = lambda_vec[i]\n",
+    "        t = trainLinearReg(linearRegCostFunction, X, y, lambda_ = l)\n",
+    "        error_train[i], _ = linearRegCostFunction(X, y, t, lambda_ = 0)\n",
+    "        error_val[i], _ = linearRegCostFunction(Xval, yval, t, lambda_ = 0)\n",
+    "\n",
+    "\n",
+    "    # ============================================================\n",
+    "    return lambda_vec, error_train, error_val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After you have completed the code, the next cell will run your function and plot a cross validation curve of error v.s. $\\lambda$ that allows you select which $\\lambda$ parameter to use. You should see a plot similar to the figure below. \n",
+    "\n",
+    "![](Figures/cross_validation.png)\n",
+    "\n",
+    "In this figure, we can see that the best value of $\\lambda$ is around 3. Due to randomness\n",
+    "in the training and validation splits of the dataset, the cross validation error can sometimes be lower than the training error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lambda\t\tTrain Error\tValidation Error\n",
+      " 0.000000\t0.029686\t43.849641\n",
+      " 0.001000\t0.112684\t9.872207\n",
+      " 0.003000\t0.170937\t16.304746\n",
+      " 0.010000\t0.221505\t16.943800\n",
+      " 0.030000\t0.281840\t12.829510\n",
+      " 0.100000\t0.459324\t7.586857\n",
+      " 0.300000\t0.921763\t4.636826\n",
+      " 1.000000\t2.076201\t4.260600\n",
+      " 3.000000\t4.901371\t3.822930\n",
+      " 10.000000\t16.092273\t9.945554\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lambda_vec, error_train, error_val = validationCurve(X_poly, y, X_poly_val, yval)\n",
+    "\n",
+    "pyplot.plot(lambda_vec, error_train, '-o', lambda_vec, error_val, '-o', lw=2)\n",
+    "pyplot.legend(['Train', 'Cross Validation'])\n",
+    "pyplot.xlabel('lambda')\n",
+    "pyplot.ylabel('Error')\n",
+    "\n",
+    "print('lambda\\t\\tTrain Error\\tValidation Error')\n",
+    "for i in range(len(lambda_vec)):\n",
+    "    print(' %f\\t%f\\t%f' % (lambda_vec[i], error_train[i], error_val[i]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[5] = validationCurve\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.4  Optional (ungraded) exercise: Computing test set error\n",
+    "\n",
+    "In the previous part of the exercise, you implemented code to compute the cross validation error for various values of the regularization parameter $\\lambda$. However, to get a better indication of the model’s performance in the real world, it is important to evaluate the “final” model on a test set that was not used in any part of training (that is, it was neither used to select the $\\lambda$ parameters, nor to learn the model parameters $\\theta$). For this optional (ungraded) exercise, you should compute the test error using the best value of $\\lambda$ you found. In our cross validation, we obtained a test error of 3.8599 for $\\lambda = 3$.\n",
+    "\n",
+    "*You do not need to submit any solutions for this optional (ungraded) exercise.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial Regression (lambda = 3.000000)\n",
+      "\n",
+      "# Training Examples\tTrain Error\tCross Validation Error\n",
+      "  \t1\t\t0.000000\t138.846777\n",
+      "  \t2\t\t0.080704\t143.808317\n",
+      "  \t3\t\t15.062851\t6.660747\n",
+      "  \t4\t\t9.168031\t4.322521\n",
+      "  \t5\t\t7.265773\t4.511018\n",
+      "  \t6\t\t5.748444\t4.861816\n",
+      "  \t7\t\t5.782694\t4.230118\n",
+      "  \t8\t\t4.879505\t4.360617\n",
+      "  \t9\t\t4.641373\t4.792851\n",
+      "  \t10\t\t4.232232\t4.684220\n",
+      "  \t11\t\t3.820540\t4.807688\n",
+      "  \t12\t\t4.901371\t3.822930\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lambda_ = 3\n",
+    "theta =trainLinearReg(linearRegCostFunction, X_poly, y,\n",
+    "                             lambda_=lambda_, maxiter=55)\n",
+    "\n",
+    "# Plot training data and fit\n",
+    "pyplot.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n",
+    "\n",
+    "plotFit(polyFeatures, np.min(X), np.max(X), mu, sigma, theta, p)\n",
+    "\n",
+    "pyplot.xlabel('Change in water level (x)')\n",
+    "pyplot.ylabel('Water flowing out of the dam (y)')\n",
+    "pyplot.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n",
+    "pyplot.ylim([-20, 50])\n",
+    "\n",
+    "pyplot.figure()\n",
+    "error_train, error_val = learningCurve(X_poly, y, X_poly_val, yval, lambda_)\n",
+    "pyplot.plot(np.arange(1, 1+m), error_train, np.arange(1, 1+m), error_val)\n",
+    "\n",
+    "pyplot.title('Polynomial Regression Learning Curve (lambda = %f)' % lambda_)\n",
+    "pyplot.xlabel('Number of training examples')\n",
+    "pyplot.ylabel('Error')\n",
+    "pyplot.axis([0, 13, 0, 100])\n",
+    "pyplot.legend(['Train', 'Cross Validation'])\n",
+    "\n",
+    "print('Polynomial Regression (lambda = %f)\\n' % lambda_)\n",
+    "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+    "for i in range(m):\n",
+    "    print('  \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5 Optional (ungraded) exercise: Plotting learning curves with randomly selected examples\n",
+    "\n",
+    "In practice, especially for small training sets, when you plot learning curves to debug your algorithms, it is often helpful to average across multiple sets of randomly selected examples to determine the training error and cross validation error.\n",
+    "\n",
+    "Concretely, to determine the training error and cross validation error for $i$ examples, you should first randomly select $i$ examples from the training set and $i$ examples from the cross validation set. You will then learn the parameters $\\theta$ using the randomly chosen training set and evaluate the parameters $\\theta$ on the randomly chosen training set and cross validation set. The above steps should then be repeated multiple times (say 50) and the averaged error should be used to determine the training error and cross validation error for $i$ examples.\n",
+    "\n",
+    "For this optional (ungraded) exercise, you should implement the above strategy for computing the learning curves. For reference, the figure below  shows the learning curve we obtained for polynomial regression with $\\lambda = 0.01$. Your figure may differ slightly due to the random selection of examples.\n",
+    "\n",
+    "![](Figures/learning_curve_random.png)\n",
+    "\n",
+    "*You do not need to submit any solutions for this optional (ungraded) exercise.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def trainLinearReg(linearRegCostFunction, X, y, lambda_=0.0, maxiter=200):\n",
+    "    \"\"\"\n",
+    "    Trains linear regression using scipy's optimize.minimize.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset with shape (m x n+1). The bias term is assumed to be concatenated.\n",
+    "    y : array_like\n",
+    "        Function values at each datapoint. A vector of shape (m,).\n",
+    "    lambda_ : float, optional\n",
+    "        The regularization parameter.\n",
+    "    maxiter : int, optional\n",
+    "        Maximum number of iteration for the optimization algorithm.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    theta : array_like\n",
+    "        The parameters for linear regression. This is a vector of shape (n+1,).\n",
+    "    \"\"\"\n",
+    "    # Initialize Theta\n",
+    "    initial_theta = np.zeros(X.shape[1])\n",
+    "\n",
+    "    # Create \"short hand\" for the cost function to be minimized\n",
+    "    costFunction = lambda t: linearRegCostFunction(X, y, t, lambda_)\n",
+    "\n",
+    "    # Now, costFunction is a function that takes in only one argument\n",
+    "    options = {'maxiter': maxiter}\n",
+    "\n",
+    "    # Minimize using scipy\n",
+    "    res = optimize.minimize(costFunction, initial_theta, jac=True, method='TNC', options=options)\n",
+    "    return res.x\n",
+    "\n",
+    "\n",
+    "def featureNormalize(X):\n",
+    "    \"\"\"\n",
+    "    Normalizes the features in X returns a normalized version of X where the mean value of each\n",
+    "    feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when\n",
+    "    working with learning algorithms.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        An dataset which is a (m x n) matrix, where m is the number of examples,\n",
+    "        and n is the number of dimensions for each example.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    X_norm : array_like\n",
+    "        The normalized input dataset.\n",
+    "    mu : array_like\n",
+    "        A vector of size n corresponding to the mean for each dimension across all examples.\n",
+    "    sigma : array_like\n",
+    "        A vector of size n corresponding to the standard deviations for each dimension across\n",
+    "        all examples.\n",
+    "    \"\"\"\n",
+    "    mu = np.mean(X, axis=0)\n",
+    "    X_norm = X - mu\n",
+    "\n",
+    "    sigma = np.std(X_norm, axis=0, ddof=1)\n",
+    "    X_norm /= sigma\n",
+    "    return X_norm, mu, sigma\n",
+    "\n",
+    "\n",
+    "def plotFit(polyFeatures, min_x, max_x, mu, sigma, theta, p):\n",
+    "    \"\"\"\n",
+    "    Plots a learned polynomial regression fit over an existing figure.\n",
+    "    Also works with linear regression.\n",
+    "    Plots the learned polynomial fit with power p and feature normalization (mu, sigma).\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    polyFeatures : func\n",
+    "        A function which generators polynomial features from a single feature.\n",
+    "    min_x : float\n",
+    "        The minimum value for the feature.\n",
+    "    max_x : float\n",
+    "        The maximum value for the feature.\n",
+    "    mu : float\n",
+    "        The mean feature value over the training dataset.\n",
+    "    sigma : float\n",
+    "        The feature standard deviation of the training dataset.\n",
+    "    theta : array_like\n",
+    "        The parameters for the trained polynomial linear regression.\n",
+    "    p : int\n",
+    "        The polynomial order.\n",
+    "    \"\"\"\n",
+    "    # We plot a range slightly bigger than the min and max values to get\n",
+    "    # an idea of how the fit will vary outside the range of the data points\n",
+    "    x = np.arange(min_x - 15, max_x + 25, 0.05).reshape(-1, 1)\n",
+    "\n",
+    "    # Map the X values\n",
+    "    X_poly = polyFeatures(x, p)\n",
+    "    X_poly -= mu\n",
+    "    X_poly /= sigma\n",
+    "\n",
+    "    # Add ones\n",
+    "    X_poly = np.concatenate([np.ones((x.shape[0], 1)), X_poly], axis=1)\n",
+    "\n",
+    "    # Plot\n",
+    "    pyplot.plot(x, np.dot(X_poly, theta), '--', lw=2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/exercise6.ipynb b/Phase 3 - 2020 (Summer)/exercise6.ipynb
new file mode 100644
index 000000000..68dc598db
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/exercise6.ipynb	
@@ -0,0 +1,1875 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Programming Exercise 6:\n",
+    "# Support Vector Machines\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "In this exercise, you will be using support vector machines (SVMs) to build a spam classifier. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n",
+    "\n",
+    "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n",
+    "\n",
+    "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Import regular expressions to process emails\n",
+    "import re\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline\n",
+    "from stemming.porter2 import stem\n",
+    "import nltk, nltk.stem.porter\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading\n",
+    "\n",
+    "\n",
+    "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n",
+    "\n",
+    "\n",
+    "| Section | Part                                             | Submitted Function                | Points |\n",
+    "| :-      |:-                                                |:-                                 | :-:    |\n",
+    "| 1       | [Gaussian Kernel](#section1)                     | [`gaussianKernel`](#gaussianKernel)        |  25    |\n",
+    "| 2       | [Parameters (C, $\\sigma$) for Dataset 3](#section2)| [`dataset3Params`](#dataset3Params)      |  25    |\n",
+    "| 3       | [Email Preprocessing](#section3)                 | [`processEmail`](#processEmail)          |  25    |\n",
+    "| 4       | [Email Feature Extraction](#section4)            | [`emailFeatures`](#emailFeatures)         |  25    |\n",
+    "|         | Total Points                                     |                                   |100     |\n",
+    "\n",
+    "\n",
+    "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Support Vector Machines\n",
+    "\n",
+    "In the first half of this exercise, you will be using support vector machines (SVMs) with various example 2D datasets. Experimenting with these datasets will help you gain an intuition of how SVMs work and how to use a Gaussian kernel with SVMs. In the next half of the exercise, you will be using support\n",
+    "vector machines to build a spam classifier."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 Example Dataset 1\n",
+    "\n",
+    "We will begin by with a 2D example dataset which can be separated by a linear boundary. The following cell plots the training data, which should look like this:\n",
+    "\n",
+    "![Dataset 1 training data](Figures/dataset1.png)\n",
+    "\n",
+    "In this dataset, the positions of the positive examples (indicated with `x`) and the negative examples (indicated with `o`) suggest a natural separation indicated by the gap. However, notice that there is an outlier positive example `x` on the far left at about (0.1, 4.1). As part of this exercise, you will also see how this outlier affects the SVM decision boundary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load from ex6data1\n",
+    "# You will have X, y as keys in the dict data\n",
+    "data = loadmat(os.path.join( 'ex6data1.mat'))\n",
+    "X, y = data['X'], data['y'][:, 0]\n",
+    "\n",
+    "# Plot training data\n",
+    "plotData(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this part of the exercise, you will try using different values of the $C$ parameter with SVMs. Informally, the $C$ parameter is a positive value that controls the penalty for misclassified training examples. A large $C$ parameter tells the SVM to try to classify all the examples correctly. $C$ plays a role similar to $1/\\lambda$, where $\\lambda$ is the regularization parameter that we were using previously for logistic regression.\n",
+    "\n",
+    "\n",
+    "The following cell will run the SVM training (with $C=1$) using SVM software that we have included with the starter code (function `svmTrain` within the `utils` module of this exercise). When $C=1$, you should find that the SVM puts the decision boundary in the gap between the two datasets and *misclassifies* the data point on the far left, as shown in the figure (left) below.\n",
+    "\n",
+    "<table style=\"text-align:center\">\n",
+    "    <tr>\n",
+    "        <th colspan=\"2\" style=\"text-align:center\">SVM Decision boundary for example dataset 1 </th>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td style=\"text-align:center\">C=1<img src=\"Figures/svm_c1.png\"/></td>\n",
+    "        <td style=\"text-align:center\">C=100<img src=\"Figures/svm_c100.png\"/></td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "In order to minimize the dependency of this assignment on external libraries, we have included this implementation of an SVM learning algorithm in utils.svmTrain. However, this particular implementation is not very efficient (it was originally chosen to maximize compatibility between Octave/MATLAB for the first version of this assignment set). If you are training an SVM on a real problem, especially if you need to scale to a larger dataset, we strongly recommend instead using a highly optimized SVM toolbox such as [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/). The python machine learning library [scikit-learn](http://scikit-learn.org/stable/index.html) provides wrappers for the LIBSVM library.\n",
+    "</div>\n",
+    "<br/>\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note:** Most SVM software packages (including the function `utils.svmTrain`) automatically add the extra feature $x_0$ = 1 for you and automatically take care of learning the intercept term $\\theta_0$. So when passing your training data to the SVM software, there is no need to add this extra feature $x_0 = 1$ yourself. In particular, in python your code should be working with training examples $x \\in \\mathcal{R}^n$ (rather than $x \\in \\mathcal{R}^{n+1}$); for example, in the first example dataset $x \\in \\mathcal{R}^2$.\n",
+    "</div>\n",
+    "\n",
+    "Your task is to try different values of $C$ on this dataset. Specifically, you should change the value of $C$ in the next cell to $C = 100$ and run the SVM training again. When $C = 100$, you should find that the SVM now classifies every single example correctly, but has a decision boundary that does not\n",
+    "appear to be a natural fit for the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# You should try to change the C value below and see how the decision\n",
+    "# boundary varies (e.g., try C = 1000)\n",
+    "C = 1\n",
+    "\n",
+    "model = svmTrain(X, y, C, linearKernel, 1e-3, 20)\n",
+    "visualizeBoundaryLinear(X, y, model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section1\"></a>\n",
+    "### 1.2 SVM with Gaussian Kernels\n",
+    "\n",
+    "In this part of the exercise, you will be using SVMs to do non-linear classification. In particular, you will be using SVMs with Gaussian kernels on datasets that are not linearly separable.\n",
+    "\n",
+    "#### 1.2.1 Gaussian Kernel\n",
+    "\n",
+    "To find non-linear decision boundaries with the SVM, we need to first implement a Gaussian kernel. You can think of the Gaussian kernel as a similarity function that measures the “distance” between a pair of examples,\n",
+    "($x^{(i)}$, $x^{(j)}$). The Gaussian kernel is also parameterized by a bandwidth parameter, $\\sigma$, which determines how fast the similarity metric decreases (to 0) as the examples are further apart.\n",
+    "You should now complete the code in `gaussianKernel` to compute the Gaussian kernel between two examples, ($x^{(i)}$, $x^{(j)}$). The Gaussian kernel function is defined as:\n",
+    "\n",
+    "$$ K_{\\text{gaussian}} \\left( x^{(i)}, x^{(j)} \\right) = \\exp \\left( - \\frac{\\left\\lvert\\left\\lvert x^{(i)} - x^{(j)}\\right\\lvert\\right\\lvert^2}{2\\sigma^2} \\right) = \\exp \\left( -\\frac{\\sum_{k=1}^n \\left( x_k^{(i)} - x_k^{(j)}\\right)^2}{2\\sigma^2} \\right)$$\n",
+    "<a id=\"gaussianKernel\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussianKernel(x1, x2, sigma):\n",
+    "    \"\"\"\n",
+    "    Computes the radial basis function\n",
+    "    Returns a radial basis function kernel between x1 and x2.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    x1 :  numpy ndarray\n",
+    "        A vector of size (n, ), representing the first datapoint.\n",
+    "    \n",
+    "    x2 : numpy ndarray\n",
+    "        A vector of size (n, ), representing the second datapoint.\n",
+    "    \n",
+    "    sigma : float\n",
+    "        The bandwidth parameter for the Gaussian kernel.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    sim : float\n",
+    "        The computed RBF between the two provided data points.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to return the similarity between `x1` and `x2`\n",
+    "    computed using a Gaussian kernel with bandwidth `sigma`.\n",
+    "    \"\"\"\n",
+    "    sim = 0\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    sim = np.exp(-np.sum((x1 - x2) ** 2) / (2 * (sigma ** 2)))\n",
+    "\n",
+    "\n",
+    "    # =============================================================\n",
+    "    return sim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the function `gaussianKernel` the following cell will test your kernel function on two provided examples and you should expect to see a value of 0.324652."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = 2.00:\n",
+      "\t0.324652\n",
+      "(for sigma = 2, this value should be about 0.324652)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "x1 = np.array([1, 2, 1])\n",
+    "x2 = np.array([0, 4, -1])\n",
+    "sigma = 2\n",
+    "\n",
+    "sim = gaussianKernel(x1, x2, sigma)\n",
+    "\n",
+    "print('Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = %0.2f:'\n",
+    "      '\\n\\t%f\\n(for sigma = 2, this value should be about 0.324652)\\n' % (sigma, sim))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2.2 Example Dataset 2\n",
+    "\n",
+    "The next part in this notebook will load and plot dataset 2, as shown in the figure below. \n",
+    "\n",
+    "![Dataset 2](Figures/dataset2.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load from ex6data2\n",
+    "# You will have X, y as keys in the dict data\n",
+    "data = loadmat(os.path.join('ex6data2.mat'))\n",
+    "X, y = data['X'], data['y'][:, 0]\n",
+    "\n",
+    "# Plot training data\n",
+    "plotData(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the figure, you can obserse that there is no linear decision boundary that separates the positive and negative examples for this dataset. However, by using the Gaussian kernel with the SVM, you will be able to learn a non-linear decision boundary that can perform reasonably well for the dataset. If you have correctly implemented the Gaussian kernel function, the following cell will proceed to train the SVM with the Gaussian kernel on this dataset.\n",
+    "\n",
+    "You should get a decision boundary as shown in the figure below, as computed by the SVM with a Gaussian kernel. The decision boundary is able to separate most of the positive and negative examples correctly and follows the contours of the dataset well.\n",
+    "\n",
+    "![Dataset 2 decision boundary](Figures/svm_dataset2.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SVM Parameters\n",
+    "C = 1\n",
+    "sigma = 0.1\n",
+    "\n",
+    "model= svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n",
+    "visualizeBoundary(X, y, model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "#### 1.2.3 Example Dataset 3\n",
+    "\n",
+    "In this part of the exercise, you will gain more practical skills on how to use a SVM with a Gaussian kernel. The next cell will load and display a third dataset, which should look like the figure below.\n",
+    "\n",
+    "![Dataset 3](Figures/dataset3.png)\n",
+    "\n",
+    "You will be using the SVM with the Gaussian kernel with this dataset. In the provided dataset, `ex6data3.mat`, you are given the variables `X`, `y`, `Xval`, `yval`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load from ex6data3\n",
+    "# You will have X, y, Xval, yval as keys in the dict data\n",
+    "data = loadmat(os.path.join('ex6data3.mat'))\n",
+    "X, y, Xval, yval = data['X'], data['y'][:, 0], data['Xval'], data['yval'][:, 0]\n",
+    "\n",
+    "# Plot training data\n",
+    "plotData(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your task is to use the cross validation set `Xval`, `yval` to determine the best $C$ and $\\sigma$ parameter to use. You should write any additional code necessary to help you search over the parameters $C$ and $\\sigma$. For both $C$ and $\\sigma$, we suggest trying values in multiplicative steps (e.g., 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30).\n",
+    "Note that you should try all possible pairs of values for $C$ and $\\sigma$ (e.g., $C = 0.3$ and $\\sigma = 0.1$). For example, if you try each of the 8 values listed above for $C$ and for $\\sigma^2$, you would end up training and evaluating (on the cross validation set) a total of $8^2 = 64$ different models. After you have determined the best $C$ and $\\sigma$ parameters to use, you should modify the code in `dataset3Params`, filling in the best parameters you found. For our best parameters, the SVM returned a decision boundary shown in the figure below. \n",
+    "\n",
+    "![](Figures/svm_dataset3_best.png)\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Tip:** When implementing cross validation to select the best $C$ and $\\sigma$ parameter to use, you need to evaluate the error on the cross validation set. Recall that for classification, the error is defined as the fraction of the cross validation examples that were classified incorrectly. In `numpy`, you can compute this error using `np.mean(predictions != yval)`, where `predictions` is a vector containing all the predictions from the SVM, and `yval` are the true labels from the cross validation set. You can use the `utils.svmPredict` function to generate the predictions for the cross validation set.\n",
+    "</div>\n",
+    "<a id=\"dataset3Params\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dataset3Params(X, y, Xval, yval):\n",
+    "    \"\"\"\n",
+    "    Returns your choice of C and sigma for Part 3 of the exercise \n",
+    "    where you select the optimal (C, sigma) learning parameters to use for SVM\n",
+    "    with RBF kernel.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        (m x n) matrix of training data where m is number of training examples, and \n",
+    "        n is the number of features.\n",
+    "    \n",
+    "    y : array_like\n",
+    "        (m, ) vector of labels for ther training data.\n",
+    "    \n",
+    "    Xval : array_like\n",
+    "        (mv x n) matrix of validation data where mv is the number of validation examples\n",
+    "        and n is the number of features\n",
+    "    \n",
+    "    yval : array_like\n",
+    "        (mv, ) vector of labels for the validation data.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    C, sigma : float, float\n",
+    "        The best performing values for the regularization parameter C and \n",
+    "        RBF parameter sigma.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to return the optimal C and sigma learning \n",
+    "    parameters found using the cross validation set.\n",
+    "    You can use `svmPredict` to predict the labels on the cross\n",
+    "    validation set. For example, \n",
+    "    \n",
+    "        predictions = svmPredict(model, Xval)\n",
+    "\n",
+    "    will return the predictions on the cross validation set.\n",
+    "    \n",
+    "    Note\n",
+    "    ----\n",
+    "    You can compute the prediction error using \n",
+    "    \n",
+    "        np.mean(predictions != yval)\n",
+    "    \"\"\"\n",
+    "    # You need to return the following variables correctly.\n",
+    "    C = 1\n",
+    "    sigma = 0.3\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    \n",
+    "    C_array = np.array([0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30])\n",
+    "    sigma_array = np.array([0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30])\n",
+    "\n",
+    "    my_array = np.zeros([C_array.size, sigma_array.size])\n",
+    "    err_array = np.zeros([C_array.size, sigma_array.size])\n",
+    "    \n",
+    "    for i in np.arange(C_array.size):\n",
+    "        for j in np.arange(sigma_array.size):\n",
+    "            model= svmTrain(X, y, C_array[i], gaussianKernel, args=(sigma_array[j],))\n",
+    "            predictions = svmPredict(model, Xval)\n",
+    "            pred_error = np.mean(predictions != yval)\n",
+    "            err_array[i, j] = pred_error\n",
+    "        \n",
+    "    ind = np.unravel_index(np.argmin(err_array, axis = None), err_array.shape)\n",
+    "    C = C_array[ind[0]]\n",
+    "    sigma = sigma_array[ind[1]]\n",
+    "    # ============================================================\n",
+    "    return C, sigma"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The provided code in the next cell trains the SVM classifier using the training set $(X, y)$ using parameters loaded from `dataset3Params`. Note that this might take a few minutes to execute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0 0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Try different SVM Parameters here\n",
+    "C, sigma = dataset3Params(X, y, Xval, yval)\n",
+    "\n",
+    "# Train the SVM\n",
+    "# model = utils.svmTrain(X, y, C, lambda x1, x2: gaussianKernel(x1, x2, sigma))\n",
+    "model = svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n",
+    "visualizeBoundary(X, y, model)\n",
+    "print(C, sigma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One you have computed the values `C` and `sigma` in the cell above, we will submit those values for grading.\n",
+    "\n",
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[2] = lambda : (C, sigma)\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section3\"></a>\n",
+    "## 2 Spam Classification\n",
+    "\n",
+    "Many email services today provide spam filters that are able to classify emails into spam and non-spam email with high accuracy. In this part of the exercise, you will use SVMs to build your own spam filter.\n",
+    "\n",
+    "You will be training a classifier to classify whether a given email, $x$, is spam ($y = 1$) or non-spam ($y = 0$). In particular, you need to convert each email into a feature vector $x \\in \\mathbb{R}^n$ . The following parts of the exercise will walk you through how such a feature vector can be constructed from an email.\n",
+    "\n",
+    "The dataset included for this exercise is based on a a subset of the [SpamAssassin Public Corpus](http://spamassassin.apache.org/old/publiccorpus/). For the purpose of this exercise, you will only be using the body of the email (excluding the email headers)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1 Preprocessing Emails\n",
+    "\n",
+    "Before starting on a machine learning task, it is usually insightful to take a look at examples from the dataset. The figure below shows a sample email that contains a URL, an email address (at the end), numbers, and dollar\n",
+    "amounts.\n",
+    "\n",
+    "<img src=\"Figures/email.png\" width=\"700px\" />\n",
+    "\n",
+    "While many emails would contain similar types of entities (e.g., numbers, other URLs, or other email addresses), the specific entities (e.g., the specific URL or specific dollar amount) will be different in almost every\n",
+    "email. Therefore, one method often employed in processing emails is to “normalize” these values, so that all URLs are treated the same, all numbers are treated the same, etc. For example, we could replace each URL in the\n",
+    "email with the unique string “httpaddr” to indicate that a URL was present.\n",
+    "\n",
+    "This has the effect of letting the spam classifier make a classification decision based on whether any URL was present, rather than whether a specific URL was present. This typically improves the performance of a spam classifier, since spammers often randomize the URLs, and thus the odds of seeing any particular URL again in a new piece of spam is very small. \n",
+    "\n",
+    "In the function `processEmail` below, we have implemented the following email preprocessing and normalization steps:\n",
+    "\n",
+    "- **Lower-casing**: The entire email is converted into lower case, so that captialization is ignored (e.g., IndIcaTE is treated the same as Indicate).\n",
+    "\n",
+    "- **Stripping HTML**: All HTML tags are removed from the emails. Many emails often come with HTML formatting; we remove all the HTML tags, so that only the content remains.\n",
+    "\n",
+    "- **Normalizing URLs**: All URLs are replaced with the text “httpaddr”.\n",
+    "\n",
+    "- **Normalizing Email Addresses**:  All email addresses are replaced with the text “emailaddr”.\n",
+    "\n",
+    "- **Normalizing Numbers**: All numbers are replaced with the text “number”.\n",
+    "\n",
+    "- **Normalizing Dollars**: All dollar signs ($) are replaced with the text “dollar”.\n",
+    "\n",
+    "- **Word Stemming**: Words are reduced to their stemmed form. For example, “discount”, “discounts”, “discounted” and “discounting” are all replaced with “discount”. Sometimes, the Stemmer actually strips off additional characters from the end, so “include”, “includes”, “included”, and “including” are all replaced with “includ”.\n",
+    "\n",
+    "- **Removal of non-words**: Non-words and punctuation have been removed. All white spaces (tabs, newlines, spaces) have all been trimmed to a single space character.\n",
+    "\n",
+    "The result of these preprocessing steps is shown in the figure below. \n",
+    "\n",
+    "<img src=\"Figures/email_cleaned.png\" alt=\"email cleaned\" style=\"width: 600px;\"/>\n",
+    "\n",
+    "While preprocessing has left word fragments and non-words, this form turns out to be much easier to work with for performing feature extraction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.1.1 Vocabulary List\n",
+    "\n",
+    "After preprocessing the emails, we have a list of words for each email. The next step is to choose which words we would like to use in our classifier and which we would want to leave out.\n",
+    "\n",
+    "For this exercise, we have chosen only the most frequently occuring words as our set of words considered (the vocabulary list). Since words that occur rarely in the training set are only in a few emails, they might cause the\n",
+    "model to overfit our training set. The complete vocabulary list is in the file `vocab.txt` (inside the `Data` directory for this exercise) and also shown in the figure below.\n",
+    "\n",
+    "<img src=\"Figures/vocab.png\" alt=\"Vocab\" width=\"150px\" />\n",
+    "\n",
+    "Our vocabulary list was selected by choosing all words which occur at least a 100 times in the spam corpus,\n",
+    "resulting in a list of 1899 words. In practice, a vocabulary list with about 10,000 to 50,000 words is often used.\n",
+    "Given the vocabulary list, we can now map each word in the preprocessed emails into a list of word indices that contains the index of the word in the vocabulary dictionary. The figure below shows the mapping for the sample email. Specifically, in the sample email, the word “anyone” was first normalized to “anyon” and then mapped onto the index 86 in the vocabulary list.\n",
+    "\n",
+    "<img src=\"Figures/word_indices.png\" alt=\"word indices\" width=\"200px\" />\n",
+    "\n",
+    "Your task now is to complete the code in the function `processEmail` to perform this mapping. In the code, you are given a string `word` which is a single word from the processed email. You should look up the word in the vocabulary list `vocabList`. If the word exists in the list, you should add the index of the word into the `word_indices` variable. If the word does not exist, and is therefore not in the vocabulary, you can skip the word.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**python tip**: In python, you can find the index of the first occurence of an item in `list` using the  `index` attribute. In the provided code for `processEmail`, `vocabList` is a python list containing the words in the vocabulary. To find the index of a word, we can use `vocabList.index(word)` which would return a number indicating the index of the word within the list. If the word does not exist in the list, a `ValueError` exception is raised. In python, we can use the `try/except` statement to catch exceptions which we do not want to stop the program from running. You can think of the `try/except` statement to be the same as an `if/else` statement, but it asks for forgiveness rather than permission.\n",
+    "\n",
+    "An example would be:\n",
+    "<br>\n",
+    "\n",
+    "```\n",
+    "try:\n",
+    "    do stuff here\n",
+    "except ValueError:\n",
+    "    pass\n",
+    "    # do nothing (forgive me) if a ValueError exception occured within the try statement\n",
+    "```\n",
+    "</div>\n",
+    "<a id=\"processEmail\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def processEmail(email_contents, verbose=True):\n",
+    "    \"\"\"\n",
+    "    Preprocesses the body of an email and returns a list of indices \n",
+    "    of the words contained in the email.    \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    email_contents : str\n",
+    "        A string containing one email. \n",
+    "    \n",
+    "    verbose : bool\n",
+    "        If True, print the resulting email after processing.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    word_indices : list\n",
+    "        A list of integers containing the index of each word in the \n",
+    "        email which is also present in the vocabulary.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to add the index of word to word_indices \n",
+    "    if it is in the vocabulary. At this point of the code, you have \n",
+    "    a stemmed word from the email in the variable word.\n",
+    "    You should look up word in the vocabulary list (vocabList). \n",
+    "    If a match exists, you should add the index of the word to the word_indices\n",
+    "    list. Concretely, if word = 'action', then you should\n",
+    "    look up the vocabulary list to find where in vocabList\n",
+    "    'action' appears. For example, if vocabList[18] =\n",
+    "    'action', then, you should add 18 to the word_indices \n",
+    "    vector (e.g., word_indices.append(18)).\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    - vocabList[idx] returns a the word with index idx in the vocabulary list.\n",
+    "    \n",
+    "    - vocabList.index(word) return index of word `word` in the vocabulary list.\n",
+    "      (A ValueError exception is raised if the word does not exist.)\n",
+    "    \"\"\"\n",
+    "    # Load Vocabulary\n",
+    "    vocabList = getVocabList()\n",
+    "\n",
+    "    # Init return value\n",
+    "    word_indices = []\n",
+    "\n",
+    "    # ========================== Preprocess Email ===========================\n",
+    "    # Find the Headers ( \\n\\n and remove )\n",
+    "    # Uncomment the following lines if you are working with raw emails with the\n",
+    "    # full headers\n",
+    "    # hdrstart = email_contents.find(chr(10) + chr(10))\n",
+    "    # email_contents = email_contents[hdrstart:]\n",
+    "\n",
+    "    # Lower case\n",
+    "    email_contents = email_contents.lower()\n",
+    "    \n",
+    "    # Strip all HTML\n",
+    "    # Looks for any expression that starts with < and ends with > and replace\n",
+    "    # and does not have any < or > in the tag it with a space\n",
+    "    email_contents =re.compile('<[^<>]+>').sub(' ', email_contents)\n",
+    "\n",
+    "    # Handle Numbers\n",
+    "    # Look for one or more characters between 0-9\n",
+    "    email_contents = re.compile('[0-9]+').sub(' number ', email_contents)\n",
+    "\n",
+    "    # Handle URLS\n",
+    "    # Look for strings starting with http:// or https://\n",
+    "    email_contents = re.compile('(http|https)://[^\\s]*').sub(' httpaddr ', email_contents)\n",
+    "\n",
+    "    # Handle Email Addresses\n",
+    "    # Look for strings with @ in the middle\n",
+    "    email_contents = re.compile('[^\\s]+@[^\\s]+').sub(' emailaddr ', email_contents)\n",
+    "    \n",
+    "    # Handle $ sign\n",
+    "    email_contents = re.compile('[$]+').sub(' dollar ', email_contents)\n",
+    "    \n",
+    "    # get rid of any punctuation\n",
+    "    email_contents = re.split('[ @$/#.-:&*+=\\[\\]?!(){},''\">_<;%\\n\\r]', email_contents)\n",
+    "\n",
+    "    # remove any empty word string\n",
+    "    email_contents = [word for word in email_contents if len(word) > 0]\n",
+    "    \n",
+    "    # Stem the email contents word by word\n",
+    "    stemmer = PorterStemmer()\n",
+    "    processed_email = []\n",
+    "    for word in email_contents:\n",
+    "        # Remove any remaining non alphanumeric characters in word\n",
+    "        word = re.compile('[^a-zA-Z0-9]').sub('', word).strip()\n",
+    "        word = stemmer.stem(word)\n",
+    "        processed_email.append(word)\n",
+    "\n",
+    "        if len(word) < 1:\n",
+    "            continue\n",
+    "\n",
+    "        # Look up the word in the dictionary and add to word_indices if found\n",
+    "        # ====================== YOUR CODE HERE ======================\n",
+    "        if(word in vocabList):\n",
+    "                 a=vocabList.index(word)\n",
+    "                 word_indices.append(a)\n",
+    "        \n",
+    "\n",
+    "        # =============================================================\n",
+    "\n",
+    "    if verbose:\n",
+    "        print('----------------')\n",
+    "        print('Processed email:')\n",
+    "        print('----------------')\n",
+    "        print(' '.join(processed_email))\n",
+    "    return word_indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have implemented `processEmail`, the following cell will run your code on the email sample and you should see an output of the processed email and the indices list mapping."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------\n",
+      "Processed email:\n",
+      "----------------\n",
+      "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n",
+      "-------------\n",
+      "Word Indices:\n",
+      "-------------\n",
+      "[85, 915, 793, 1076, 882, 369, 1698, 789, 1821, 1830, 882, 430, 1170, 793, 1001, 1894, 591, 1675, 237, 161, 88, 687, 944, 1662, 1119, 1061, 1698, 374, 1161, 476, 1119, 1892, 1509, 798, 1181, 1236, 511, 1119, 809, 1894, 1439, 1546, 180, 1698, 1757, 1895, 687, 1675, 991, 960, 1476, 70, 529, 1698, 530]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  To use an SVM to classify emails into Spam v.s. Non-Spam, you first need\n",
+    "#  to convert each email into a vector of features. In this part, you will\n",
+    "#  implement the preprocessing steps for each email. You should\n",
+    "#  complete the code in processEmail.m to produce a word indices vector\n",
+    "#  for a given email.\n",
+    "\n",
+    "# Extract Features\n",
+    "#with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n",
+    "    #file_contents = fid.read()\n",
+    "    \n",
+    "file_contents = open( 'emailSample1.txt', 'r' ).read()\n",
+    "\n",
+    "word_indices  = processEmail(file_contents)\n",
+    "\n",
+    "#Print Stats\n",
+    "print('-------------')\n",
+    "print('Word Indices:')\n",
+    "print('-------------')\n",
+    "print(word_indices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[3] = processEmail\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section4\"></a>\n",
+    "### 2.2 Extracting Features from Emails\n",
+    "\n",
+    "You will now implement the feature extraction that converts each email into a vector in $\\mathbb{R}^n$. For this exercise, you will be using n = # words in vocabulary list. Specifically, the feature $x_i \\in \\{0, 1\\}$ for an email corresponds to whether the $i^{th}$ word in the dictionary occurs in the email. That is, $x_i = 1$ if the $i^{th}$ word is in the email and $x_i = 0$ if the $i^{th}$ word is not present in the email.\n",
+    "\n",
+    "Thus, for a typical email, this feature would look like:\n",
+    "\n",
+    "$$ x = \\begin{bmatrix} \n",
+    "0 & \\dots & 1 & 0 & \\dots & 1 & 0 & \\dots & 0 \n",
+    "\\end{bmatrix}^T \\in \\mathbb{R}^n\n",
+    "$$\n",
+    "\n",
+    "You should now complete the code in the function `emailFeatures` to generate a feature vector for an email, given the `word_indices`.\n",
+    "<a id=\"emailFeatures\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def emailFeatures(word_indices):\n",
+    "    \"\"\"\n",
+    "    Takes in a word_indices vector and produces a feature vector from the word indices. \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    word_indices : list\n",
+    "        A list of word indices from the vocabulary list.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    x : list \n",
+    "        The computed feature vector.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to return a feature vector for the\n",
+    "    given email (word_indices). To help make it easier to  process \n",
+    "    the emails, we have have already pre-processed each email and converted\n",
+    "    each word in the email into an index in a fixed dictionary (of 1899 words).\n",
+    "    The variable `word_indices` contains the list of indices of the words \n",
+    "    which occur in one email.\n",
+    "    \n",
+    "    Concretely, if an email has the text:\n",
+    "\n",
+    "        The quick brown fox jumped over the lazy dog.\n",
+    "\n",
+    "    Then, the word_indices vector for this text might look  like:\n",
+    "               \n",
+    "        60  100   33   44   10     53  60  58   5\n",
+    "\n",
+    "    where, we have mapped each word onto a number, for example:\n",
+    "\n",
+    "        the   -- 60\n",
+    "        quick -- 100\n",
+    "        ...\n",
+    "\n",
+    "    Note\n",
+    "    ----\n",
+    "    The above numbers are just an example and are not the actual mappings.\n",
+    "\n",
+    "    Your task is take one such `word_indices` vector and construct\n",
+    "    a binary feature vector that indicates whether a particular\n",
+    "    word occurs in the email. That is, x[i] = 1 when word i\n",
+    "    is present in the email. Concretely, if the word 'the' (say,\n",
+    "    index 60) appears in the email, then x[60] = 1. The feature\n",
+    "    vector should look like:\n",
+    "        x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..]\n",
+    "    \"\"\"\n",
+    "    # Total number of words in the dictionary\n",
+    "    n = 1899\n",
+    "\n",
+    "    # You need to return the following variables correctly.\n",
+    "    x = np.zeros(n)\n",
+    "\n",
+    "    # ===================== YOUR CODE HERE ======================\n",
+    "    for i in range(n):\n",
+    "        if i in word_indices:\n",
+    "            x[i]=1\n",
+    "    \n",
+    "    \n",
+    "    # ===========================================================\n",
+    "    \n",
+    "    return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have implemented `emailFeatures`, the next cell will run your code on the email sample. You should see that the feature vector had length 1899 and 45 non-zero entries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------\n",
+      "Processed email:\n",
+      "----------------\n",
+      "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n",
+      "\n",
+      "Length of feature vector: 1899\n",
+      "Number of non-zero entries: 45\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract Features\n",
+    "#with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n",
+    "    #file_contents = fid.read()\n",
+    "file_contents = open( 'emailSample1.txt', 'r' ).read()\n",
+    "word_indices  = processEmail(file_contents)\n",
+    "features      = emailFeatures(word_indices)\n",
+    "\n",
+    "# Print Stats\n",
+    "print('\\nLength of feature vector: %d' % len(features))\n",
+    "print('Number of non-zero entries: %d' % sum(features > 0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[4] = emailFeatures\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 Training SVM for Spam Classification\n",
+    "\n",
+    "In the following section we will load a preprocessed training dataset that will be used to train a SVM classifier. The file `spamTrain.mat` (within the `Data` folder for this exercise) contains 4000 training examples of spam and non-spam email, while `spamTest.mat` contains 1000 test examples. Each\n",
+    "original email was processed using the `processEmail` and `emailFeatures` functions and converted into a vector $x^{(i)} \\in \\mathbb{R}^{1899}$.\n",
+    "\n",
+    "After loading the dataset, the next cell proceed to train a linear SVM to classify between spam ($y = 1$) and non-spam ($y = 0$) emails. Once the training completes, you should see that the classifier gets a training accuracy of about 99.8% and a test accuracy of about 98.5%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Linear SVM (Spam Classification)\n",
+      "This may take 1 to 2 minutes ...\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the Spam Email dataset\n",
+    "# You will have X, y in your environment\n",
+    "data = loadmat(os.path.join('spamTrain.mat'))\n",
+    "X, y= data['X'].astype(float), data['y'][:, 0]\n",
+    "\n",
+    "print('Training Linear SVM (Spam Classification)')\n",
+    "print('This may take 1 to 2 minutes ...\\n')\n",
+    "\n",
+    "C = 0.1\n",
+    "model = svmTrain(X, y, C, linearKernel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy: 99.88\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compute the training accuracy\n",
+    "p =svmPredict(model, X)\n",
+    "\n",
+    "print('Training Accuracy: %.2f' % (np.mean(p == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Execute the following cell to load the test set and compute the test accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluating the trained Linear SVM on a test set ...\n",
+      "Test Accuracy: 98.70\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the test dataset\n",
+    "# You will have Xtest, ytest in your environment\n",
+    "data = loadmat(os.path.join('spamTest.mat'))\n",
+    "Xtest, ytest = data['Xtest'].astype(float), data['ytest'][:, 0]\n",
+    "\n",
+    "print('Evaluating the trained Linear SVM on a test set ...')\n",
+    "p = svmPredict(model, Xtest)\n",
+    "\n",
+    "print('Test Accuracy: %.2f' % (np.mean(p == ytest) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 Top Predictors for Spam\n",
+    "\n",
+    "To better understand how the spam classifier works, we can inspect the parameters to see which words the classifier thinks are the most predictive of spam. The next cell finds the parameters with the largest positive values in the classifier and displays the corresponding words similar to the ones shown in the figure below.\n",
+    "\n",
+    "<div style=\"border-style: solid; border-width: 1px; margin: 10px 10px 10px 10px; padding: 10px 10px 10px 10px\">\n",
+    "our  click  remov guarante visit basenumb dollar pleas price will nbsp most lo ga hour\n",
+    "</div>\n",
+    "\n",
+    "Thus, if an email contains words such as “guarantee”, “remove”, “dollar”, and “price” (the top predictors shown in the figure), it is likely to be classified as spam.\n",
+    "\n",
+    "Since the model we are training is a linear SVM, we can inspect the weights learned by the model to understand better how it is determining whether an email is spam or not. The following code finds the words with the highest weights in the classifier. Informally, the classifier 'thinks' that these words are the most likely indicators of spam."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top predictors of spam:\n",
+      "word            weight         \n",
+      "----            ------\n",
+      "our             0.50\n",
+      "click           0.47\n",
+      "remov           0.42\n",
+      "guarante        0.39\n",
+      "visit           0.37\n",
+      "basenumb        0.35\n",
+      "dollar          0.33\n",
+      "price           0.27\n",
+      "will            0.27\n",
+      "pleas           0.26\n",
+      "nbsp            0.26\n",
+      "most            0.26\n",
+      "lo              0.25\n",
+      "hour            0.24\n",
+      "ga              0.24\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sort the weights and obtin the vocabulary list\n",
+    "# NOTE some words have the same weights, \n",
+    "# so their order might be different than in the text above\n",
+    "idx = np.argsort(model['w'])\n",
+    "top_idx = idx[-15:][::-1]\n",
+    "vocabList = getVocabList()\n",
+    "\n",
+    "print('Top predictors of spam:')\n",
+    "print('%-15s %-15s' % ('word', 'weight'))\n",
+    "print('----' + ' '*12 + '------')\n",
+    "for word, w in zip(np.array(vocabList)[top_idx], model['w'][top_idx]):\n",
+    "    print('%-15s %0.2f' % (word, w))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5 Optional (ungraded) exercise: Try your own emails\n",
+    "\n",
+    "Now that you have trained a spam classifier, you can start trying it out on your own emails. In the starter code, we have included two email examples (`emailSample1.txt` and `emailSample2.txt`) and two spam examples (`spamSample1.txt` and `spamSample2.txt`). The next cell runs the spam classifier over the first spam example and classifies it using the learned SVM. You should now try the other examples we have provided and see if the classifier gets them right. You can also try your own emails by replacing the examples (plain text files) with your own emails.\n",
+    "\n",
+    "*You do not need to submit any solutions for this optional (ungraded) exercise.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Processed emailSample1.txt\n",
+      "Spam Classification: not spam\n"
+     ]
+    }
+   ],
+   "source": [
+    "#utils\n",
+    "filename = os.path.join('emailSample1.txt')\n",
+    "\n",
+    "with open(filename) as fid:\n",
+    "    file_contents = fid.read()\n",
+    "\n",
+    "word_indices = processEmail(file_contents, verbose=False)\n",
+    "x = emailFeatures(word_indices)\n",
+    "p = svmPredict(model, x)\n",
+    "\n",
+    "print('\\nProcessed %s\\nSpam Classification: %s' % (filename, 'spam' if p else 'not spam'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6 Optional (ungraded) exercise: Build your own dataset\n",
+    "\n",
+    "In this exercise, we provided a preprocessed training set and test set. These datasets were created using the same functions (`processEmail` and `emailFeatures`) that you now have completed. For this optional (ungraded) exercise, you will build your own dataset using the original emails from the SpamAssassin Public Corpus.\n",
+    "\n",
+    "Your task in this optional (ungraded) exercise is to download the original\n",
+    "files from the public corpus and extract them. After extracting them, you should run the `processEmail` and `emailFeatures` functions on each email to extract a feature vector from each email. This will allow you to build a dataset `X`, `y` of examples. You should then randomly divide up the dataset into a training set, a cross validation set and a test set.\n",
+    "\n",
+    "While you are building your own dataset, we also encourage you to try building your own vocabulary list (by selecting the high frequency words that occur in the dataset) and adding any additional features that you think\n",
+    "might be useful. Finally, we also suggest trying to use highly optimized SVM toolboxes such as [`LIBSVM`](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) or [`scikit-learn`](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm).\n",
+    "\n",
+    "*You do not need to submit any solutions for this optional (ungraded) exercise.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotData(X, y, grid=False):\n",
+    "    \"\"\"\n",
+    "    Plots the data points X and y into a new figure. Uses `+` for positive examples, and `o` for\n",
+    "    negative examples. `X` is assumed to be a Mx2 matrix\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : numpy ndarray\n",
+    "        X is assumed to be a Mx2 matrix.\n",
+    "    y : numpy ndarray\n",
+    "        The data labels.\n",
+    "    grid : bool (Optional)\n",
+    "        Specify whether or not to show the grid in the plot. It is False by default.\n",
+    "    Notes\n",
+    "    -----\n",
+    "    This was slightly modified such that it expects y=1 or y=0.\n",
+    "    \"\"\"\n",
+    "    # Find Indices of Positive and Negative Examples\n",
+    "    pos = y == 1\n",
+    "    neg = y == 0\n",
+    "\n",
+    "    # Plot Examples\n",
+    "    pyplot.plot(X[pos, 0], X[pos, 1], 'X', mew=1, ms=10, mec='k')\n",
+    "    pyplot.plot(X[neg, 0], X[neg, 1], 'o', mew=1, mfc='y', ms=10, mec='k')\n",
+    "    pyplot.grid(grid)\n",
+    "\n",
+    "\n",
+    "def svmTrain(X, Y, C, kernelFunction, tol=1e-3, max_passes=5, args=()):\n",
+    "    \"\"\"\n",
+    "    Trains an SVM classifier using a  simplified version of the SMO algorithm.\n",
+    "    Parameters\n",
+    "    ---------\n",
+    "    X : numpy ndarray\n",
+    "        (m x n) Matrix of training examples. Each row is a training example, and the\n",
+    "        jth column holds the jth feature.\n",
+    "    Y : numpy ndarray\n",
+    "        (m, ) A vector (1-D numpy array) containing 1 for positive examples and 0 for negative examples.\n",
+    "    C : float\n",
+    "        The standard SVM regularization parameter.\n",
+    "    kernelFunction : func\n",
+    "        A function handle which computes the kernel. The function should accept two vectors as\n",
+    "        inputs, and returns a scalar as output.\n",
+    "    tol : float, optional\n",
+    "        Tolerance value used for determining equality of floating point numbers.\n",
+    "    max_passes : int, optional\n",
+    "        Controls the number of iterations over the dataset (without changes to alpha)\n",
+    "        before the algorithm quits.\n",
+    "    args : tuple\n",
+    "        Extra arguments required for the kernel function, such as the sigma parameter for a\n",
+    "        Gaussian kernel.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    model :\n",
+    "        The trained SVM model.\n",
+    "    Notes\n",
+    "    -----\n",
+    "    This is a simplified version of the SMO algorithm for training SVMs. In practice, if\n",
+    "    you want to train an SVM classifier, we recommend using an optimized package such as:\n",
+    "    - LIBSVM   (http://www.csie.ntu.edu.tw/~cjlin/libsvm/)\n",
+    "    - SVMLight (http://svmlight.joachims.org/)\n",
+    "    - scikit-learn (http://scikit-learn.org/stable/modules/svm.html) which contains python wrappers\n",
+    "    for the LIBSVM library.\n",
+    "    \"\"\"\n",
+    "    # make sure data is signed int\n",
+    "    Y = Y.astype(int)\n",
+    "    # Dataset size parameters\n",
+    "    m, n = X.shape\n",
+    "\n",
+    "    passes = 0\n",
+    "    E = np.zeros(m)\n",
+    "    alphas = np.zeros(m)\n",
+    "    b = 0\n",
+    "\n",
+    "    # Map 0 to -1\n",
+    "    Y[Y == 0] = -1\n",
+    "\n",
+    "    # Pre-compute the Kernel Matrix since our dataset is small\n",
+    "    # (in practice, optimized SVM packages that handle large datasets\n",
+    "    # gracefully will **not** do this)\n",
+    "\n",
+    "    # We have implemented the optimized vectorized version of the Kernels here so\n",
+    "    # that the SVM training will run faster\n",
+    "    if kernelFunction.__name__ == 'linearKernel':\n",
+    "        # Vectorized computation for the linear kernel\n",
+    "        # This is equivalent to computing the kernel on every pair of examples\n",
+    "        K = np.dot(X, X.T)\n",
+    "    elif kernelFunction.__name__ == 'gaussianKernel':\n",
+    "        # vectorized RBF Kernel\n",
+    "        # This is equivalent to computing the kernel on every pair of examples\n",
+    "        X2 = np.sum(X**2, axis=1)\n",
+    "        K = X2 + X2[:, None] - 2 * np.dot(X, X.T)\n",
+    "\n",
+    "        if len(args) > 0:\n",
+    "            K /= 2*args[0]**2\n",
+    "\n",
+    "        K = np.exp(-K)\n",
+    "    else:\n",
+    "        K = np.zeros((m, m))\n",
+    "        for i in range(m):\n",
+    "            for j in range(i, m):\n",
+    "                K[i, j] = kernelFunction(X[i, :], X[j, :])\n",
+    "                K[j, i] = K[i, j]\n",
+    "\n",
+    "    while passes < max_passes:\n",
+    "        num_changed_alphas = 0\n",
+    "        for i in range(m):\n",
+    "            E[i] = b + np.sum(alphas * Y * K[:, i]) - Y[i]\n",
+    "\n",
+    "            if (Y[i]*E[i] < -tol and alphas[i] < C) or (Y[i]*E[i] > tol and alphas[i] > 0):\n",
+    "                # select the alpha_j randomly\n",
+    "                j = np.random.choice(list(range(i)) + list(range(i+1, m)), size=1)[0]\n",
+    "\n",
+    "                E[j] = b + np.sum(alphas * Y * K[:, j]) - Y[j]\n",
+    "\n",
+    "                alpha_i_old = alphas[i]\n",
+    "                alpha_j_old = alphas[j]\n",
+    "\n",
+    "                if Y[i] == Y[j]:\n",
+    "                    L = max(0, alphas[j] + alphas[i] - C)\n",
+    "                    H = min(C, alphas[j] + alphas[i])\n",
+    "                else:\n",
+    "                    L = max(0, alphas[j] - alphas[i])\n",
+    "                    H = min(C, C + alphas[j] - alphas[i])\n",
+    "\n",
+    "                if L == H:\n",
+    "                    continue\n",
+    "\n",
+    "                eta = 2 * K[i, j] - K[i, i] - K[j, j]\n",
+    "\n",
+    "                # objective function positive definite, there will be a minimum along the direction\n",
+    "                # of linear equality constrain, and eta will be greater than zero\n",
+    "                # we are actually computing -eta here (so we skip of eta >= 0)\n",
+    "                if eta >= 0:\n",
+    "                    continue\n",
+    "\n",
+    "                alphas[j] -= Y[j] * (E[i] - E[j])/eta\n",
+    "                alphas[j] = max(L, min(H, alphas[j]))\n",
+    "\n",
+    "                if abs(alphas[j] - alpha_j_old) < tol:\n",
+    "                    alphas[j] = alpha_j_old\n",
+    "                    continue\n",
+    "                alphas[i] += Y[i]*Y[j]*(alpha_j_old - alphas[j])\n",
+    "\n",
+    "                b1 = b - E[i] - Y[i]*(alphas[i] - alpha_i_old) * K[i, j] \\\n",
+    "                     - Y[j] * (alphas[j] - alpha_j_old) * K[i, j]\n",
+    "\n",
+    "                b2 = b - E[j] - Y[i]*(alphas[i] - alpha_i_old) * K[i, j] \\\n",
+    "                     - Y[j] * (alphas[j] - alpha_j_old) * K[j, j]\n",
+    "\n",
+    "                if 0 < alphas[i] < C:\n",
+    "                    b = b1\n",
+    "                elif 0 < alphas[j] < C:\n",
+    "                    b = b2\n",
+    "                else:\n",
+    "                    b = (b1 + b2)/2\n",
+    "\n",
+    "                num_changed_alphas += 1\n",
+    "        if num_changed_alphas == 0:\n",
+    "            passes += 1\n",
+    "        else:\n",
+    "            passes = 0\n",
+    "\n",
+    "    idx = alphas > 0\n",
+    "    model = {'X': X[idx, :],\n",
+    "             'y': Y[idx],\n",
+    "             'kernelFunction': kernelFunction,\n",
+    "             'b': b,\n",
+    "             'args': args,\n",
+    "             'alphas': alphas[idx],\n",
+    "             'w': np.dot(alphas * Y, X)}\n",
+    "    return model\n",
+    "\n",
+    "\n",
+    "def svmPredict(model, X):\n",
+    "    \"\"\"\n",
+    "    Returns a vector of predictions using a trained SVM model.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    model : dict\n",
+    "        The parameters of the trained svm model, as returned by the function svmTrain\n",
+    "    X : array_like\n",
+    "        A (m x n) matrix where each example is a row.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    pred : array_like\n",
+    "        A (m,) sized vector of predictions {0, 1} values.\n",
+    "    \"\"\"\n",
+    "    # check if we are getting a vector. If so, then assume we only need to do predictions\n",
+    "    # for a single example\n",
+    "    if X.ndim == 1:\n",
+    "        X = X[np.newaxis, :]\n",
+    "\n",
+    "    m = X.shape[0]\n",
+    "    p = np.zeros(m)\n",
+    "    pred = np.zeros(m)\n",
+    "\n",
+    "    if model['kernelFunction'].__name__ == 'linearKernel':\n",
+    "        # we can use the weights and bias directly if working with the linear kernel\n",
+    "        p = np.dot(X, model['w']) + model['b']\n",
+    "    elif model['kernelFunction'].__name__ == 'gaussianKernel':\n",
+    "        # vectorized RBF Kernel\n",
+    "        # This is equivalent to computing the kernel on every pair of examples\n",
+    "        X1 = np.sum(X**2, 1)\n",
+    "        X2 = np.sum(model['X']**2, 1)\n",
+    "        K = X2 + X1[:, None] - 2 * np.dot(X, model['X'].T)\n",
+    "\n",
+    "        if len(model['args']) > 0:\n",
+    "            K /= 2*model['args'][0]**2\n",
+    "\n",
+    "        K = np.exp(-K)\n",
+    "        p = np.dot(K, model['alphas']*model['y']) + model['b']\n",
+    "    else:\n",
+    "        # other non-linear kernel\n",
+    "        for i in range(m):\n",
+    "            predictions = 0\n",
+    "            for j in range(model['X'].shape[0]):\n",
+    "                predictions += model['alphas'][j] * model['y'][j] \\\n",
+    "                               * model['kernelFunction'](X[i, :], model['X'][j, :])\n",
+    "            p[i] = predictions\n",
+    "\n",
+    "    pred[p >= 0] = 1\n",
+    "    return pred\n",
+    "\n",
+    "\n",
+    "def linearKernel(x1, x2):\n",
+    "    \"\"\"\n",
+    "    Returns a linear kernel between x1 and x2.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    x1 : numpy ndarray\n",
+    "        A 1-D vector.\n",
+    "    x2 : numpy ndarray\n",
+    "        A 1-D vector of same size as x1.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    : float\n",
+    "        The scalar amplitude.\n",
+    "    \"\"\"\n",
+    "    return np.dot(x1, x2)\n",
+    "\n",
+    "\n",
+    "def visualizeBoundaryLinear(X, y, model):\n",
+    "    \"\"\"\n",
+    "    Plots a linear decision boundary learned by the SVM.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        (m x 2) The training data with two features (to plot in a 2-D plane).\n",
+    "    y : array_like\n",
+    "        (m, ) The data labels.\n",
+    "    model : dict\n",
+    "        Dictionary of model variables learned by SVM.\n",
+    "    \"\"\"\n",
+    "    w, b = model['w'], model['b']\n",
+    "    xp = np.linspace(min(X[:, 0]), max(X[:, 0]), 100)\n",
+    "    yp = -(w[0] * xp + b)/w[1]\n",
+    "\n",
+    "    plotData(X, y)\n",
+    "    pyplot.plot(xp, yp, '-b')\n",
+    "\n",
+    "\n",
+    "def visualizeBoundary(X, y, model):\n",
+    "    \"\"\"\n",
+    "    Plots a non-linear decision boundary learned by the SVM and overlays the data on it.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        (m x 2) The training data with two features (to plot in a 2-D plane).\n",
+    "    y : array_like\n",
+    "        (m, ) The data labels.\n",
+    "    model : dict\n",
+    "        Dictionary of model variables learned by SVM.\n",
+    "    \"\"\"\n",
+    "    plotData(X, y)\n",
+    "\n",
+    "    # make classification predictions over a grid of values\n",
+    "    x1plot = np.linspace(min(X[:, 0]), max(X[:, 0]), 100)\n",
+    "    x2plot = np.linspace(min(X[:, 1]), max(X[:, 1]), 100)\n",
+    "    X1, X2 = np.meshgrid(x1plot, x2plot)\n",
+    "\n",
+    "    vals = np.zeros(X1.shape)\n",
+    "    for i in range(X1.shape[1]):\n",
+    "        this_X = np.stack((X1[:, i], X2[:, i]), axis=1)\n",
+    "        vals[:, i] = svmPredict(model, this_X)\n",
+    "\n",
+    "    pyplot.contour(X1, X2, vals, colors='y', linewidths=2)\n",
+    "    pyplot.pcolormesh(X1, X2, vals, cmap='YlGnBu', alpha=0.25, edgecolors='None', lw=0)\n",
+    "    pyplot.grid(False)\n",
+    "\n",
+    "\n",
+    "def getVocabList():\n",
+    "    \"\"\"\n",
+    "    Reads the fixed vocabulary list in vocab.txt and returns a cell array of the words\n",
+    "    %   vocabList = GETVOCABLIST() reads the fixed vocabulary list in vocab.txt\n",
+    "    %   and returns a cell array of the words in vocabList.\n",
+    "    :return:\n",
+    "    \"\"\"\n",
+    "    vocabList = np.genfromtxt(('vocab.txt'), dtype=object)\n",
+    "    return list(vocabList[:, 1].astype(str))\n",
+    "\n",
+    "\n",
+    "class PorterStemmer:\n",
+    "    \"\"\"\n",
+    "    Porter Stemming Algorithm\n",
+    "    This is the Porter stemming algorithm, ported to Python from the\n",
+    "    version coded up in ANSI C by the author. It may be be regarded\n",
+    "    as canonical, in that it follows the algorithm presented in\n",
+    "    Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,\n",
+    "    no. 3, pp 130-137,\n",
+    "    only differing from it at the points maked --DEPARTURE-- below.\n",
+    "    See also http://www.tartarus.org/~martin/PorterStemmer\n",
+    "    The algorithm as described in the paper could be exactly replicated\n",
+    "    by adjusting the points of DEPARTURE, but this is barely necessary,\n",
+    "    because (a) the points of DEPARTURE are definitely improvements, and\n",
+    "    (b) no encoding of the Porter stemmer I have seen is anything like\n",
+    "    as exact as this version, even with the points of DEPARTURE!\n",
+    "    Vivake Gupta (v@nano.com)\n",
+    "    Release 1: January 2001\n",
+    "    Further adjustments by Santiago Bruno (bananabruno@gmail.com)\n",
+    "    to allow word input not restricted to one word per line, leading\n",
+    "    to:\n",
+    "    release 2: July 2008\n",
+    "    \"\"\"\n",
+    "    def __init__(self):\n",
+    "        \"\"\"\n",
+    "        The main part of the stemming algorithm starts here.\n",
+    "        b is a buffer holding a word to be stemmed. The letters are in b[k0],\n",
+    "        b[k0+1] ... ending at b[k]. In fact k0 = 0 in this demo program. k is\n",
+    "        readjusted downwards as the stemming progresses. Zero termination is\n",
+    "        not in fact used in the algorithm.\n",
+    "        Note that only lower case sequences are stemmed. Forcing to lower case\n",
+    "        should be done before stem(...) is called.\n",
+    "        \"\"\"\n",
+    "        self.b = \"\"  # buffer for word to be stemmed\n",
+    "        self.k = 0\n",
+    "        self.k0 = 0\n",
+    "        self.j = 0   # j is a general offset into the string\n",
+    "\n",
+    "    def cons(self, i):\n",
+    "        \"\"\"cons(i) is TRUE <=> b[i] is a consonant.\"\"\"\n",
+    "        if self.b[i] in 'aeiou':\n",
+    "            return 0\n",
+    "        if self.b[i] == 'y':\n",
+    "            if i == self.k0:\n",
+    "                return 1\n",
+    "            else:\n",
+    "                return not self.cons(i - 1)\n",
+    "        return 1\n",
+    "\n",
+    "    def m(self):\n",
+    "        \"\"\"\n",
+    "        m() measures the number of consonant sequences between k0 and j.\n",
+    "        if c is a consonant sequence and v a vowel sequence, and <..>\n",
+    "        indicates arbitrary presence,\n",
+    "           <c><v>       gives 0\n",
+    "           <c>vc<v>     gives 1\n",
+    "           <c>vcvc<v>   gives 2\n",
+    "           <c>vcvcvc<v> gives 3\n",
+    "           ....\n",
+    "        \"\"\"\n",
+    "        n = 0\n",
+    "        i = self.k0\n",
+    "        while 1:\n",
+    "            if i > self.j:\n",
+    "                return n\n",
+    "            if not self.cons(i):\n",
+    "                break\n",
+    "            i = i + 1\n",
+    "        i = i + 1\n",
+    "        while 1:\n",
+    "            while 1:\n",
+    "                if i > self.j:\n",
+    "                    return n\n",
+    "                if self.cons(i):\n",
+    "                    break\n",
+    "                i = i + 1\n",
+    "            i = i + 1\n",
+    "            n = n + 1\n",
+    "            while 1:\n",
+    "                if i > self.j:\n",
+    "                    return n\n",
+    "                if not self.cons(i):\n",
+    "                    break\n",
+    "                i = i + 1\n",
+    "            i = i + 1\n",
+    "\n",
+    "    def vowelinstem(self):\n",
+    "        \"\"\"vowelinstem() is TRUE <=> k0,...j contains a vowel\"\"\"\n",
+    "        for i in range(self.k0, self.j + 1):\n",
+    "            if not self.cons(i):\n",
+    "                return 1\n",
+    "        return 0\n",
+    "\n",
+    "    def doublec(self, j):\n",
+    "        \"\"\" doublec(j) is TRUE <=> j,(j-1) contain a double consonant. \"\"\"\n",
+    "        if j < (self.k0 + 1):\n",
+    "            return 0\n",
+    "        if self.b[j] != self.b[j-1]:\n",
+    "            return 0\n",
+    "        return self.cons(j)\n",
+    "\n",
+    "    def cvc(self, i):\n",
+    "        \"\"\"\n",
+    "        cvc(i) is TRUE <=> i-2,i-1,i has the form consonant - vowel - consonant\n",
+    "        and also if the second c is not w,x or y. this is used when trying to\n",
+    "        restore an e at the end of a short  e.g.\n",
+    "           cav(e), lov(e), hop(e), crim(e), but\n",
+    "           snow, box, tray.\n",
+    "        \"\"\"\n",
+    "        if i < (self.k0 + 2) or not self.cons(i) or self.cons(i-1) or not self.cons(i-2):\n",
+    "            return 0\n",
+    "        ch = self.b[i]\n",
+    "        if ch in 'wxy':\n",
+    "            return 0\n",
+    "        return 1\n",
+    "\n",
+    "    def ends(self, s):\n",
+    "        \"\"\"ends(s) is TRUE <=> k0,...k ends with the string s.\"\"\"\n",
+    "        length = len(s)\n",
+    "        if s[length - 1] != self.b[self.k]: # tiny speed-up\n",
+    "            return 0\n",
+    "        if length > (self.k - self.k0 + 1):\n",
+    "            return 0\n",
+    "        if self.b[self.k-length+1:self.k+1] != s:\n",
+    "            return 0\n",
+    "        self.j = self.k - length\n",
+    "        return 1\n",
+    "\n",
+    "    def setto(self, s):\n",
+    "        \"\"\"setto(s) sets (j+1),...k to the characters in the string s, readjusting k.\"\"\"\n",
+    "        length = len(s)\n",
+    "        self.b = self.b[:self.j+1] + s + self.b[self.j+length+1:]\n",
+    "        self.k = self.j + length\n",
+    "\n",
+    "    def r(self, s):\n",
+    "        \"\"\"r(s) is used further down.\"\"\"\n",
+    "        if self.m() > 0:\n",
+    "            self.setto(s)\n",
+    "\n",
+    "    def step1ab(self):\n",
+    "        \"\"\"step1ab() gets rid of plurals and -ed or -ing. e.g.\n",
+    "           caresses  ->  caress\n",
+    "           ponies    ->  poni\n",
+    "           ties      ->  ti\n",
+    "           caress    ->  caress\n",
+    "           cats      ->  cat\n",
+    "           feed      ->  feed\n",
+    "           agreed    ->  agree\n",
+    "           disabled  ->  disable\n",
+    "           matting   ->  mat\n",
+    "           mating    ->  mate\n",
+    "           meeting   ->  meet\n",
+    "           milling   ->  mill\n",
+    "           messing   ->  mess\n",
+    "           meetings  ->  meet\n",
+    "        \"\"\"\n",
+    "        if self.b[self.k] == 's':\n",
+    "            if self.ends(\"sses\"):\n",
+    "                self.k = self.k - 2\n",
+    "            elif self.ends(\"ies\"):\n",
+    "                self.setto(\"i\")\n",
+    "            elif self.b[self.k - 1] != 's':\n",
+    "                self.k = self.k - 1\n",
+    "        if self.ends(\"eed\"):\n",
+    "            if self.m() > 0:\n",
+    "                self.k = self.k - 1\n",
+    "        elif (self.ends(\"ed\") or self.ends(\"ing\")) and self.vowelinstem():\n",
+    "            self.k = self.j\n",
+    "            if self.ends(\"at\"):\n",
+    "                self.setto(\"ate\")\n",
+    "            elif self.ends(\"bl\"):\n",
+    "                self.setto(\"ble\")\n",
+    "            elif self.ends(\"iz\"):\n",
+    "                self.setto(\"ize\")\n",
+    "            elif self.doublec(self.k):\n",
+    "                self.k = self.k - 1\n",
+    "                ch = self.b[self.k]\n",
+    "                if ch in 'lsz':\n",
+    "                    self.k += 1\n",
+    "            elif self.m() == 1 and self.cvc(self.k):\n",
+    "                self.setto(\"e\")\n",
+    "\n",
+    "    def step1c(self):\n",
+    "        \"\"\"step1c() turns terminal y to i when there is another vowel in the stem.\"\"\"\n",
+    "        if self.ends(\"y\") and self.vowelinstem():\n",
+    "            self.b = self.b[:self.k] + 'i' + self.b[self.k+1:]\n",
+    "\n",
+    "    def step2(self):\n",
+    "        \"\"\"step2() maps double suffices to single ones.\n",
+    "        so -ization ( = -ize plus -ation) maps to -ize etc. note that the\n",
+    "        string before the suffix must give m() > 0.\n",
+    "        \"\"\"\n",
+    "        if self.b[self.k - 1] == 'a':\n",
+    "            if self.ends(\"ational\"):   self.r(\"ate\")\n",
+    "            elif self.ends(\"tional\"):  self.r(\"tion\")\n",
+    "        elif self.b[self.k - 1] == 'c':\n",
+    "            if self.ends(\"enci\"):      self.r(\"ence\")\n",
+    "            elif self.ends(\"anci\"):    self.r(\"ance\")\n",
+    "        elif self.b[self.k - 1] == 'e':\n",
+    "            if self.ends(\"izer\"):      self.r(\"ize\")\n",
+    "        elif self.b[self.k - 1] == 'l':\n",
+    "            if self.ends(\"bli\"):       self.r(\"ble\") # --DEPARTURE--\n",
+    "            # To match the published algorithm, replace this phrase with\n",
+    "            #   if self.ends(\"abli\"):      self.r(\"able\")\n",
+    "            elif self.ends(\"alli\"):    self.r(\"al\")\n",
+    "            elif self.ends(\"entli\"):   self.r(\"ent\")\n",
+    "            elif self.ends(\"eli\"):     self.r(\"e\")\n",
+    "            elif self.ends(\"ousli\"):   self.r(\"ous\")\n",
+    "        elif self.b[self.k - 1] == 'o':\n",
+    "            if self.ends(\"ization\"):   self.r(\"ize\")\n",
+    "            elif self.ends(\"ation\"):   self.r(\"ate\")\n",
+    "            elif self.ends(\"ator\"):    self.r(\"ate\")\n",
+    "        elif self.b[self.k - 1] == 's':\n",
+    "            if self.ends(\"alism\"):     self.r(\"al\")\n",
+    "            elif self.ends(\"iveness\"): self.r(\"ive\")\n",
+    "            elif self.ends(\"fulness\"): self.r(\"ful\")\n",
+    "            elif self.ends(\"ousness\"): self.r(\"ous\")\n",
+    "        elif self.b[self.k - 1] == 't':\n",
+    "            if self.ends(\"aliti\"):     self.r(\"al\")\n",
+    "            elif self.ends(\"iviti\"):   self.r(\"ive\")\n",
+    "            elif self.ends(\"biliti\"):  self.r(\"ble\")\n",
+    "        elif self.b[self.k - 1] == 'g': # --DEPARTURE--\n",
+    "            if self.ends(\"logi\"):      self.r(\"log\")\n",
+    "        # To match the published algorithm, delete this phrase\n",
+    "\n",
+    "    def step3(self):\n",
+    "        \"\"\"step3() dels with -ic-, -full, -ness etc. similar strategy to step2.\"\"\"\n",
+    "        if self.b[self.k] == 'e':\n",
+    "            if self.ends(\"icate\"):     self.r(\"ic\")\n",
+    "            elif self.ends(\"ative\"):   self.r(\"\")\n",
+    "            elif self.ends(\"alize\"):   self.r(\"al\")\n",
+    "        elif self.b[self.k] == 'i':\n",
+    "            if self.ends(\"iciti\"):     self.r(\"ic\")\n",
+    "        elif self.b[self.k] == 'l':\n",
+    "            if self.ends(\"ical\"):      self.r(\"ic\")\n",
+    "            elif self.ends(\"ful\"):     self.r(\"\")\n",
+    "        elif self.b[self.k] == 's':\n",
+    "            if self.ends(\"ness\"):      self.r(\"\")\n",
+    "\n",
+    "    def step4(self):\n",
+    "        \"\"\"step4() takes off -ant, -ence etc., in context <c>vcvc<v>.\"\"\"\n",
+    "        if self.b[self.k - 1] == 'a':\n",
+    "            if self.ends(\"al\"): pass\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 'c':\n",
+    "            if self.ends(\"ance\"): pass\n",
+    "            elif self.ends(\"ence\"): pass\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 'e':\n",
+    "            if self.ends(\"er\"): pass\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 'i':\n",
+    "            if self.ends(\"ic\"): pass\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 'l':\n",
+    "            if self.ends(\"able\"): pass\n",
+    "            elif self.ends(\"ible\"): pass\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 'n':\n",
+    "            if self.ends(\"ant\"): pass\n",
+    "            elif self.ends(\"ement\"): pass\n",
+    "            elif self.ends(\"ment\"): pass\n",
+    "            elif self.ends(\"ent\"): pass\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 'o':\n",
+    "            if self.ends(\"ion\") and (self.b[self.j] == 's' or self.b[self.j] == 't'): pass\n",
+    "            elif self.ends(\"ou\"): pass\n",
+    "            # takes care of -ous\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 's':\n",
+    "            if self.ends(\"ism\"): pass\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 't':\n",
+    "            if self.ends(\"ate\"): pass\n",
+    "            elif self.ends(\"iti\"): pass\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 'u':\n",
+    "            if self.ends(\"ous\"): pass\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 'v':\n",
+    "            if self.ends(\"ive\"): pass\n",
+    "            else: return\n",
+    "        elif self.b[self.k - 1] == 'z':\n",
+    "            if self.ends(\"ize\"): pass\n",
+    "            else: return\n",
+    "        else:\n",
+    "            return\n",
+    "        if self.m() > 1:\n",
+    "            self.k = self.j\n",
+    "\n",
+    "    def step5(self):\n",
+    "        \"\"\"step5() removes a final -e if m() > 1, and changes -ll to -l if\n",
+    "        m() > 1.\n",
+    "        \"\"\"\n",
+    "        self.j = self.k\n",
+    "        if self.b[self.k] == 'e':\n",
+    "            a = self.m()\n",
+    "            if a > 1 or (a == 1 and not self.cvc(self.k-1)):\n",
+    "                self.k = self.k - 1\n",
+    "        if self.b[self.k] == 'l' and self.doublec(self.k) and self.m() > 1:\n",
+    "            self.k = self.k -1\n",
+    "\n",
+    "    def stem(self, p, i=0, j=None):\n",
+    "        \"\"\"In stem(p,i,j), p is a char pointer, and the string to be stemmed\n",
+    "        is from p[i] to p[j] inclusive. Typically i is zero and j is the\n",
+    "        offset to the last character of a string, (p[j+1] == '\\0'). The\n",
+    "        stemmer adjusts the characters p[i] ... p[j] and returns the new\n",
+    "        end-point of the string, k. Stemming never increases word length, so\n",
+    "        i <= k <= j. To turn the stemmer into a module, declare 'stem' as\n",
+    "        extern, and delete the remainder of this file.\n",
+    "        \"\"\"\n",
+    "        # copy the parameters into statics\n",
+    "        self.b = p\n",
+    "        self.k = j or len(p) - 1\n",
+    "        self.k0 = i\n",
+    "        if self.k <= self.k0 + 1:\n",
+    "            return self.b  # --DEPARTURE--\n",
+    "\n",
+    "        # With this line, strings of length 1 or 2 don't go through the\n",
+    "        # stemming process, although no mention is made of this in the\n",
+    "        # published algorithm. Remove the line to match the published\n",
+    "        # algorithm.\n",
+    "\n",
+    "        self.step1ab()\n",
+    "        self.step1c()\n",
+    "        self.step2()\n",
+    "        self.step3()\n",
+    "        self.step4()\n",
+    "        self.step5()\n",
+    "        return self.b[self.k0:self.k+1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/exercise7.ipynb b/Phase 3 - 2020 (Summer)/exercise7.ipynb
new file mode 100644
index 000000000..e23daee37
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/exercise7.ipynb	
@@ -0,0 +1,6734 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Programming Exercise 7:\n",
+    "# K-means Clustering and Principal Component Analysis\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n",
+    "\n",
+    "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n",
+    "\n",
+    "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Import regular expressions to process emails\n",
+    "import re\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "import matplotlib as mpl\n",
+    "from matplotlib import animation\n",
+    "\n",
+    "from IPython.display import HTML, display, clear_output\n",
+    "\n",
+    "try:\n",
+    "    pyplot.rcParams[\"animation.html\"] = \"jshtml\"\n",
+    "except ValueError:\n",
+    "    pyplot.rcParams[\"animation.html\"] = \"html5\"\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.animation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading\n",
+    "\n",
+    "\n",
+    "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n",
+    "\n",
+    "\n",
+    "| Section | Part                                             | Submitted Function                | Points |\n",
+    "| :-      |:-                                                |:-                                 | :-:    |\n",
+    "| 1       | [Find Closest Centroids](#section1)              | [`findClosestCentroids`](#findClosestCentroids)  |  30    |\n",
+    "| 2       | [Computed Centroid Means](#section2)             | [`computeCentroids`](#computeCentroids)      |  30    |\n",
+    "| 3       | [PCA](#section3)                                 | [`pca`](#pca)                   |  20    |\n",
+    "| 4       | [Project Data](#section4)                        | [`projectData`](#projectData)           |  10    |\n",
+    "| 5       | [Recover Data](#section5)                        | [`recoverData`](#recoverData)           |  10    |\n",
+    "|         | Total Points                                     |                                   |100     |\n",
+    "\n",
+    "\n",
+    "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 K-means Clustering\n",
+    "\n",
+    "In this exercise, you will implement K-means algorithm and use it for image compression. You will first start on an example 2D dataset that will help you gain an intuition of how the K-means algorithm works. After\n",
+    "that, you wil use the K-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common in that image.\n",
+    "\n",
+    "### 1.1 Implementing K-means\n",
+    "\n",
+    "The K-means algorithm is a method to automatically cluster similar data examples together. Concretely, you are given a training set $\\{x^{(1)} , \\cdots, x^{(m)}\\}$ (where $x^{(i)} \\in \\mathbb{R}^n$), and want to group the data into a few cohesive “clusters”. The intuition behind K-means is an iterative procedure that starts by guessing the initial centroids, and then refines this guess by repeatedly assigning examples to their closest centroids and then recomputing the centroids based on the assignments.\n",
+    "\n",
+    "The K-means algorithm is as follows:\n",
+    "\n",
+    "```python\n",
+    "centroids = kMeansInitCentroids(X, K)\n",
+    "for i in range(iterations):\n",
+    "    # Cluster assignment step: Assign each data point to the\n",
+    "    # closest centroid. idx[i] corresponds to cˆ(i), the index\n",
+    "    # of the centroid assigned to example i\n",
+    "    idx = findClosestCentroids(X, centroids)\n",
+    "    \n",
+    "    # Move centroid step: Compute means based on centroid\n",
+    "    # assignments\n",
+    "    centroids = computeMeans(X, idx, K)\n",
+    "```\n",
+    "\n",
+    "The inner-loop of the algorithm repeatedly carries out two steps: (1) Assigning each training example $x^{(i)}$ to its closest centroid, and (2) Recomputing the mean of each centroid using the points assigned to it. The K-means algorithm will always converge to some final set of means for the centroids. Note that the converged solution may not always be ideal and depends on the initial setting of the centroids. Therefore, in practice the K-means algorithm is usually run a few times with different random initializations. One way to choose between these different solutions from different random initializations is to choose the one with the lowest cost function value (distortion). You will implement the two phases of the K-means algorithm separately\n",
+    "in the next sections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section1\"></a>\n",
+    "#### 1.1.1 Finding closest centroids\n",
+    "\n",
+    "In the “cluster assignment” phase of the K-means algorithm, the algorithm assigns every training example $x^{(i)}$ to its closest centroid, given the current positions of centroids. Specifically, for every example $i$ we set\n",
+    "\n",
+    "$$c^{(i)} := j \\quad \\text{that minimizes} \\quad \\lvert\\rvert x^{(i)} - \\mu_j  \\lvert\\rvert^2, $$\n",
+    "\n",
+    "where $c^{(i)}$ is the index of the centroid that is closest to $x^{(i)}$, and $\\mu_j$ is the position (value) of the $j^{th}$ centroid. Note that $c^{(i)}$ corresponds to `idx[i]` in the starter code.\n",
+    "\n",
+    "Your task is to complete the code in the function `findClosestCentroids`. This function takes the data matrix `X` and the locations of all centroids inside `centroids` and should output a one-dimensional array `idx` that holds the index (a value in $\\{1, ..., K\\}$, where $K$ is total number of centroids) of the closest centroid to every training example.\n",
+    "\n",
+    "You can implement this using a loop over every training example and every centroid.\n",
+    "<a id=\"findClosestCentroids\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def findClosestCentroids(X, centroids):\n",
+    "    \"\"\"\n",
+    "    Computes the centroid memberships for every example.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of size (m, n) where each row is a single example. \n",
+    "        That is, we have m examples each of n dimensions.\n",
+    "        \n",
+    "    centroids : array_like\n",
+    "        The k-means centroids of size (K, n). K is the number\n",
+    "        of clusters, and n is the the data dimension.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    idx : array_like\n",
+    "        A vector of size (m, ) which holds the centroids assignment for each\n",
+    "        example (row) in the dataset X.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Go over every example, find its closest centroid, and store\n",
+    "    the index inside `idx` at the appropriate location.\n",
+    "    Concretely, idx[i] should contain the index of the centroid\n",
+    "    closest to example i. Hence, it should be a value in the \n",
+    "    range 0..K-1\n",
+    "\n",
+    "    Note\n",
+    "    ----\n",
+    "    You can use a for-loop over the examples to compute this.\n",
+    "    \"\"\"\n",
+    "    # Set K\n",
+    "    K = centroids.shape[0]\n",
+    "\n",
+    "    # You need to return the following variables correctly.\n",
+    "    idx = np.zeros(X.shape[0], dtype=int)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    for i in range(idx.size):\n",
+    "        \n",
+    "        J = np.sqrt(np.sum(np.square(X[i] - centroids), axis = 1))\n",
+    "            \n",
+    "        idx[i] = np.argmin(J)\n",
+    "    \n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return idx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `findClosestCentroids`, the following cell will run your code and you should see the output `[0 2 1]` corresponding to the centroid assignments for the first 3 examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closest centroids for the first 3 examples:\n",
+      "[0 2 1]\n",
+      "(the closest centroids should be 0, 2, 1 respectively)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load an example dataset that we will be using\n",
+    "data = loadmat(os.path.join('ex7data2.mat'))\n",
+    "X = data['X']\n",
+    "\n",
+    "# Select an initial set of centroids\n",
+    "K = 3   # 3 Centroids\n",
+    "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n",
+    "\n",
+    "# Find the closest centroids for the examples using the initial_centroids\n",
+    "idx = findClosestCentroids(X, initial_centroids)\n",
+    "\n",
+    "print('Closest centroids for the first 3 examples:')\n",
+    "print(idx[:3])\n",
+    "print('(the closest centroids should be 0, 2, 1 respectively)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "### 1.1.2 Computing centroid means\n",
+    "\n",
+    "Given assignments of every point to a centroid, the second phase of the algorithm recomputes, for each centroid, the mean of the points that were assigned to it. Specifically, for every centroid $k$ we set\n",
+    "\n",
+    "$$ \\mu_k := \\frac{1}{\\left| C_k\\right|} \\sum_{i \\in C_k} x^{(i)}$$\n",
+    "\n",
+    "where $C_k$ is the set of examples that are assigned to centroid $k$. Concretely, if two examples say $x^{(3)}$ and $x^{(5)}$ are assigned to centroid $k = 2$, then you should update $\\mu_2 = \\frac{1}{2} \\left( x^{(3)} + x^{(5)} \\right)$.\n",
+    "\n",
+    "You should now complete the code in the function `computeCentroids`. You can implement this function using a loop over the centroids. You can also use a loop over the examples; but if you can use a vectorized implementation that does not use such a loop, your code may run faster.\n",
+    "<a id=\"computeCentroids\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeCentroids(X, idx, K):\n",
+    "    \"\"\"\n",
+    "    Returns the new centroids by computing the means of the data points\n",
+    "    assigned to each centroid.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The datset where each row is a single data point. That is, it \n",
+    "        is a matrix of size (m, n) where there are m datapoints each\n",
+    "        having n dimensions. \n",
+    "    \n",
+    "    idx : array_like \n",
+    "        A vector (size m) of centroid assignments (i.e. each entry in range [0 ... K-1])\n",
+    "        for each example.\n",
+    "    \n",
+    "    K : int\n",
+    "        Number of clusters\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    centroids : array_like\n",
+    "        A matrix of size (K, n) where each row is the mean of the data \n",
+    "        points assigned to it.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Go over every centroid and compute mean of all points that\n",
+    "    belong to it. Concretely, the row vector centroids[i, :]\n",
+    "    should contain the mean of the data points assigned to\n",
+    "    cluster i.\n",
+    "\n",
+    "    Note:\n",
+    "    -----\n",
+    "    You can use a for-loop over the centroids to compute this.\n",
+    "    \"\"\"\n",
+    "    # Useful variables\n",
+    "    m, n = X.shape\n",
+    "    # You need to return the following variables correctly.\n",
+    "    centroids = np.zeros((K, n))\n",
+    "\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    for i in range(K):\n",
+    "        centroids[i] = np.mean(X[idx == i], axis = 0)\n",
+    "    \n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return centroids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `computeCentroids`, the following cell will run your code and output the centroids after the first step of K-means."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Centroids computed after initial finding of closest centroids:\n",
+      "[[2.42830111 3.15792418]\n",
+      " [5.81350331 2.63365645]\n",
+      " [7.11938687 3.6166844 ]]\n",
+      "\n",
+      "The centroids should be\n",
+      "   [ 2.428301 3.157924 ]\n",
+      "   [ 5.813503 2.633656 ]\n",
+      "   [ 7.119387 3.616684 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compute means based on the closest centroids found in the previous part.\n",
+    "centroids = computeCentroids(X, idx, K)\n",
+    "\n",
+    "print('Centroids computed after initial finding of closest centroids:')\n",
+    "print(centroids)\n",
+    "print('\\nThe centroids should be')\n",
+    "print('   [ 2.428301 3.157924 ]')\n",
+    "print('   [ 5.813503 2.633656 ]')\n",
+    "print('   [ 7.119387 3.616684 ]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "%matplotlib notebook\n",
+    "from matplotlib import pyplot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 K-means on example dataset \n",
+    "\n",
+    "After you have completed the two functions (`findClosestCentroids` and `computeCentroids`), you have all the necessary pieces to run the K-means algorithm. The next cell  will run the K-means algorithm on a toy 2D dataset to help you understand how K-means works. Your functions are called from inside the `runKmeans` function (in this assignment's `utils.py` module). We encourage you to take a look at the function to understand how it works. Notice that the code calls the two functions you implemented in a loop.\n",
+    "\n",
+    "When you run the next step, the K-means code will produce an animation that steps you through the progress of the algorithm at each iteration. At the end, your figure should look as the one displayed below.\n",
+    "\n",
+    "![](Figures/kmeans_result.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\"\n",
+       "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/\n",
+       "css/font-awesome.min.css\">\n",
+       "<script language=\"javascript\">\n",
+       "  function isInternetExplorer() {\n",
+       "    ua = navigator.userAgent;\n",
+       "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
+       "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
+       "  }\n",
+       "\n",
+       "  /* Define the Animation class */\n",
+       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
+       "    this.img_id = img_id;\n",
+       "    this.slider_id = slider_id;\n",
+       "    this.loop_select_id = loop_select_id;\n",
+       "    this.interval = interval;\n",
+       "    this.current_frame = 0;\n",
+       "    this.direction = 0;\n",
+       "    this.timer = null;\n",
+       "    this.frames = new Array(frames.length);\n",
+       "\n",
+       "    for (var i=0; i<frames.length; i++)\n",
+       "    {\n",
+       "     this.frames[i] = new Image();\n",
+       "     this.frames[i].src = frames[i];\n",
+       "    }\n",
+       "    var slider = document.getElementById(this.slider_id);\n",
+       "    slider.max = this.frames.length - 1;\n",
+       "    if (isInternetExplorer()) {\n",
+       "        // switch from oninput to onchange because IE <= 11 does not conform\n",
+       "        // with W3C specification. It ignores oninput and onchange behaves\n",
+       "        // like oninput. In contrast, Mircosoft Edge behaves correctly.\n",
+       "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
+       "        slider.setAttribute('oninput', null);\n",
+       "    }\n",
+       "    this.set_frame(this.current_frame);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.get_loop_state = function(){\n",
+       "    var button_group = document[this.loop_select_id].state;\n",
+       "    for (var i = 0; i < button_group.length; i++) {\n",
+       "        var button = button_group[i];\n",
+       "        if (button.checked) {\n",
+       "            return button.value;\n",
+       "        }\n",
+       "    }\n",
+       "    return undefined;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.set_frame = function(frame){\n",
+       "    this.current_frame = frame;\n",
+       "    document.getElementById(this.img_id).src =\n",
+       "            this.frames[this.current_frame].src;\n",
+       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.next_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.previous_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.first_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(0);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.last_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(this.frames.length - 1);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.slower = function()\n",
+       "  {\n",
+       "    this.interval /= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.faster = function()\n",
+       "  {\n",
+       "    this.interval *= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_forward = function()\n",
+       "  {\n",
+       "    this.current_frame += 1;\n",
+       "    if(this.current_frame < this.frames.length){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.first_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.last_frame();\n",
+       "        this.reverse_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.last_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_reverse = function()\n",
+       "  {\n",
+       "    this.current_frame -= 1;\n",
+       "    if(this.current_frame >= 0){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.last_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.first_frame();\n",
+       "        this.play_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.first_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.pause_animation = function()\n",
+       "  {\n",
+       "    this.direction = 0;\n",
+       "    if (this.timer){\n",
+       "      clearInterval(this.timer);\n",
+       "      this.timer = null;\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.play_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = 1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_forward();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.reverse_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = -1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_reverse();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "</script>\n",
+       "\n",
+       "<style>\n",
+       ".animation {\n",
+       "    display: inline-block;\n",
+       "    text-align: center;\n",
+       "}\n",
+       "input[type=range].anim-slider {\n",
+       "    width: 374px;\n",
+       "    margin-left: auto;\n",
+       "    margin-right: auto;\n",
+       "}\n",
+       ".anim-buttons {\n",
+       "    margin: 8px 0px;\n",
+       "}\n",
+       ".anim-buttons button {\n",
+       "    padding: 0;\n",
+       "    width: 36px;\n",
+       "}\n",
+       ".anim-state label {\n",
+       "    margin-right: 8px;\n",
+       "}\n",
+       ".anim-state input {\n",
+       "    margin: 0;\n",
+       "    vertical-align: middle;\n",
+       "}\n",
+       "</style>\n",
+       "\n",
+       "<div class=\"animation\">\n",
+       "  <img id=\"_anim_imgc4929d808ab14548981138d94df1e088\">\n",
+       "  <div class=\"anim-controls\">\n",
+       "    <input id=\"_anim_sliderc4929d808ab14548981138d94df1e088\" type=\"range\" class=\"anim-slider\"\n",
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+       "          </button>\n",
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+       "    </div>\n",
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+       "  </div>\n",
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+       "\n",
+       "\n",
+       "<script language=\"javascript\">\n",
+       "  /* Instantiate the Animation class. */\n",
+       "  /* The IDs given should match those used in the template above. */\n",
+       "  (function() {\n",
+       "    var img_id = \"_anim_imgc4929d808ab14548981138d94df1e088\";\n",
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+       "AM8++6w0JRUevfLKKyxcuBCz2cyAAQN44403/D0kv8vJyWHevHlkZ2cTGhpKVlYW+fn5/h5WwJBK\\\n",
+       "HEIIIXRJlhCFEELokgQwIYQQuiQBTAghhC5JABNCCKFLEsCEEELokgQwIYQQuiQBTAghhC5JABNC\\\n",
+       "CKFLEsCEEELokgQwIYQQuiQBTAghhC5JABNCCKFLEsCEEELokgQwIYQQuiQBTAghhC5JABNCCKFL\\\n",
+       "EsCEEELokgQwIYQQuiQBTAghhC5JABNCCKFL/w8DJcbaiy5AdwAAAABJRU5ErkJggg==\\\n",
+       "\"\n",
+       "\n",
+       "\n",
+       "    /* set a timeout to make sure all the above elements are created before\n",
+       "       the object is initialized. */\n",
+       "    setTimeout(function() {\n",
+       "        animc4929d808ab14548981138d94df1e088 = new Animation(frames, img_id, slider_id, 500.0,\n",
+       "                                 loop_select_id);\n",
+       "    }, 0);\n",
+       "  })()\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<matplotlib.animation.FuncAnimation at 0x180949dc648>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load an example dataset\n",
+    "data = loadmat(os.path.join('ex7data2.mat'))\n",
+    "\n",
+    "# Settings for running K-Means\n",
+    "K = 3\n",
+    "max_iters = 10\n",
+    "\n",
+    "# For consistency, here we set centroids to specific values\n",
+    "# but in practice you want to generate them automatically, such as by\n",
+    "# settings them to be random examples (as can be seen in\n",
+    "# kMeansInitCentroids).\n",
+    "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n",
+    "\n",
+    "\n",
+    "# Run K-Means algorithm. The 'true' at the end tells our function to plot\n",
+    "# the progress of K-Means\n",
+    "centroids, idx, anim = runkMeans(X, initial_centroids,\n",
+    "                                       findClosestCentroids, computeCentroids, max_iters, True)\n",
+    "anim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3 Random initialization \n",
+    "\n",
+    "The initial assignments of centroids for the example dataset in the previous cell were designed so that you will see the same figure as that shown in the cell above. In practice, a\n",
+    "good strategy for initializing the centroids is to select random examples from the training set.\n",
+    "\n",
+    "In this part of the exercise, you should complete the function `kMeansInitCentroids` with the following code:\n",
+    "\n",
+    "```python\n",
+    "# Initialize the centroids to be random examples\n",
+    "\n",
+    "# Randomly reorder the indices of examples\n",
+    "randidx = np.random.permutation(X.shape[0])\n",
+    "# Take the first K examples as centroids\n",
+    "centroids = X[randidx[:K], :]\n",
+    "```\n",
+    "\n",
+    "The code above first randomly permutes the indices of the examples (using `permute` within the `numpy.random` module). Then, it selects the first $K$ examples based on the random permutation of the indices. This allows the examples to be selected at random without the risk of selecting the same example twice.\n",
+    "\n",
+    "*You do not need to make any submission for this part of the exercise*\n",
+    "<a id=\"kMeansInitCentroids\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def kMeansInitCentroids(X, K):\n",
+    "    \"\"\"\n",
+    "    This function initializes K centroids that are to be used in K-means on the dataset x.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like \n",
+    "        The dataset of size (m x n).\n",
+    "    \n",
+    "    K : int\n",
+    "        The number of clusters.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    centroids : array_like\n",
+    "        Centroids of the clusters. This is a matrix of size (K x n).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    You should set centroids to randomly chosen examples from the dataset X.\n",
+    "    \"\"\"\n",
+    "    m, n = X.shape\n",
+    "    \n",
+    "    # You should return this values correctly\n",
+    "    centroids = np.zeros((K, n))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    randidx = np.random.permutation(X.shape[0])\n",
+    "    centroids = X[randidx[:K], :]\n",
+    "\n",
+    "\n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return centroids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.4 Image compression with K-means\n",
+    "\n",
+    "In this exercise, you will apply K-means to image compression. We will use the image below as an example (property of Frank Wouters with permission to this class).\n",
+    "\n",
+    "![](Data/bird_small.png)\n",
+    "\n",
+    "In a straightforward 24-bit color representation of an image, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often referred to as the RGB encoding. Our image contains thousands of colors, and in this part of the exercise, you will reduce the number of colors to 16 colors.\n",
+    "\n",
+    "By making this reduction, it is possible to represent (compress) the photo in an efficient way. Specifically, you only need to store the RGB values of the 16 selected colors, and for each pixel in the image you now need to only store the index of the color at that location (where only 4 bits are necessary to represent 16 possibilities).\n",
+    "\n",
+    "In this exercise, you will use the K-means algorithm to select the 16 colors that will be used to represent the compressed image. Concretely, you will treat every pixel in the original image as a data example and use the K-means algorithm to find the 16 colors that best group (cluster) the pixels in the 3-dimensional RGB space. Once you have computed the cluster centroids on the image, you will then use the 16 colors to replace the pixels in the original image.\n",
+    "\n",
+    "#### 1.4.1 K-means on pixels\n",
+    "\n",
+    "In python, images can be read in as follows:\n",
+    "\n",
+    "```python\n",
+    "# Load 128x128 color image (bird_small.png)\n",
+    "img = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n",
+    "\n",
+    "# We have already imported matplotlib as mpl at the beginning of this notebook.\n",
+    "```\n",
+    "This creates a three-dimensional matrix `A` whose first two indices identify a pixel position and whose last index represents red, green, or blue. For example, A[50, 33, 2] gives the blue intensity of the pixel at row 51 and column 34.\n",
+    "\n",
+    "The code in the following cell first loads the image, and then reshapes it to create an m x 3 matrix of pixel colors (where m = 16384 = 128 x 128), and calls your K-means function on it.\n",
+    "\n",
+    "After finding the top K = 16 colors to represent the image, you can now assign each pixel position to its closest centroid using the `findClosestCentroids` function. This allows you to represent the original image using the centroid assignments of each pixel. Notice that you have significantly reduced the number of bits that are required to describe the image. The original image required 24 bits for each one of the 128 x 128 pixel locations, resulting in total size of 128 x 128 x 24 = 393,216 bits. The new representation requires some overhead storage in form of a dictionary of 16 colors, each of which require 24 bits, but the image itself then only requires 4 bits per pixel location. The final number of bits used is therefore 16 x 24 + 128 x 128 x 4 = 65,920 bits, which corresponds to compressing the original image by about a factor of 6.\n",
+    "\n",
+    "Finally, you can view the effects of the compression by reconstructing the image based only on the centroid assignments. Specifically, you can replace each pixel location with the mean of the centroid assigned to it. The figure below shows the reconstruction we obtained. \n",
+    "\n",
+    "![](Figures/bird_compression.png)\n",
+    "\n",
+    "Even though the resulting image retains most of the characteristics of the original, we also see some compression artifacts.\n",
+    "\n",
+    "Run the following cell to compute the centroids and the centroid allocation of each pixel in the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ======= Experiment with these parameters ================\n",
+    "# You should try different values for those parameters\n",
+    "K = 16\n",
+    "max_iters = 10\n",
+    "\n",
+    "# Load an image of a bird\n",
+    "# Change the file name and path to experiment with your own images\n",
+    "A = mpl.image.imread(os.path.join('bird_small.png'))\n",
+    "# ==========================================================\n",
+    "\n",
+    "# Divide by 255 so that all values are in the range 0 - 1\n",
+    "A /= 255\n",
+    "\n",
+    "# Reshape the image into an Nx3 matrix where N = number of pixels.\n",
+    "# Each row will contain the Red, Green and Blue pixel values\n",
+    "# This gives us our dataset matrix X that we will use K-Means on.\n",
+    "X = A.reshape(-1, 3)\n",
+    "\n",
+    "# When using K-Means, it is important to randomly initialize centroids\n",
+    "# You should complete the code in kMeansInitCentroids above before proceeding\n",
+    "initial_centroids = kMeansInitCentroids(X, K)\n",
+    "\n",
+    "# Run K-Means\n",
+    "centroids, idx = runkMeans(X, initial_centroids,\n",
+    "                                 findClosestCentroids,\n",
+    "                                 computeCentroids,\n",
+    "                                 max_iters)\n",
+    "\n",
+    "# We can now recover the image from the indices (idx) by mapping each pixel\n",
+    "# (specified by its index in idx) to the centroid value\n",
+    "# Reshape the recovered image into proper dimensions\n",
+    "X_recovered = centroids[idx, :].reshape(A.shape)\n",
+    "\n",
+    "# Display the original image, rescale back by 255\n",
+    "fig, ax = pyplot.subplots(1, 2, figsize=(8, 4))\n",
+    "ax[0].imshow(A*255)\n",
+    "ax[0].set_title('Original')\n",
+    "ax[0].grid(False)\n",
+    "\n",
+    "# Display compressed image, rescale back by 255\n",
+    "ax[1].imshow(X_recovered*255)\n",
+    "ax[1].set_title('Compressed, with %d colors' % K)\n",
+    "ax[1].grid(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You do not need to make any submissions for this part of the exercise.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.5 Optional (ungraded) exercise: Use your own image\n",
+    "\n",
+    "In this exercise, modify the code we have supplied in the previous cell to run on one of your own images. Note that if your image is very large, then K-means can take a long time to run. Therefore, we recommend that you resize your images to\n",
+    "manageable sizes before running the code. You can also try to vary $K$ to see the effects on the compression."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 Principal Component Analysis\n",
+    "\n",
+    "In this exercise, you will use principal component analysis (PCA) to perform dimensionality reduction. You will first experiment with an example 2D dataset to get intuition on how PCA works, and then use it on a bigger dataset of 5000 face image dataset.\n",
+    "\n",
+    "### 2.1 Example Dataset\n",
+    "\n",
+    "To help you understand how PCA works, you will first start with a 2D dataset which has one direction of large variation and one of smaller variation. The cell below will plot the training data, also shown in here:\n",
+    "\n",
+    "In this part of the exercise, you will visualize what happens when you use PCA to reduce the data from 2D to 1D. In practice, you might want to reduce data from 256 to 50 dimensions, say; but using lower dimensional data in this example allows us to visualize the algorithms better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Y8rvGW0kdsEaJeMYZoxHrkaNbkOZ7cs+tde6u1Z/TaZ15Votkmn8ITTuc7PDRTD5wOqWkDlRUIg4N/YXXRr/jtSDt7EqSREsdwjRVu5qftLnQBweP9wULTnCz8E8EcVdSB6l1Il7nUPahoU+2bEHitJy16bDxWK1qKyFMU3Ui6yW1SVJSByhOIg4NXeJnnvmalr+E7ezJnUS/MY8tddE0S+qhDGfT+lpt7nR703sOHvw4Tzyxmt27n2p6X/w9uXdRO0lyOfAEpdIEpdJE2ydJXnTReiYmJqlWr428J287gxaJik8ykuQKqbgFHgsWLE6kwKTbQ+clXUrqjCRZaRV3YcaHPvQBdu9+ikOHDrJ791Ns2XJ9R2c96xzpsCmpM5LkCqksWs40Dp2XhER1tru5NFDWWtKVVlkXeEhvoQUd4YnzCHvDDZ/juuu2xDoXSy2nvCgq27u51FLHFzV3enhJZHJb90px0KSlttrryRodHfWZmcIejpm6SqXCypWr2LdvG413DLmXcnkdO3fep8GoPmVmOzzisEo9fgcolC2AJJ+U1AEqwjnZkh0ldYDyfk62ZEtJHaAQtgCS/FJSB6h1YUoFs/ewf/9zxdh8XhIVO6nNbNDMfmZmd6UZkLSqELsbeC3ub6Ba3cH8TQSzOu1CwtJOS3058Fhagchh0YUpPwDeDdwFfJ7CbD4viYqV1GZ2GvB2YCLdcGROowqxUul8zMbQVJc0E7el/hLwCeCFqBvMbMzMZsxsZteuXYkEVzTzD2WL0xdesWIFW7Zc/+LKqoULj8P9kqbvUa1uYOvWL6u17mMtk9rM1gJPu/uOZve5+1Z3H3X30aVLlyYWYFE021A/bl847lRXtbpH/et+FlU/OndROznsSeBx4ClgH3Bzs69R7feRklqR1d62ReFubyvdo5tVWu6+2d1Pc/eXARcCP3D3i9L6kCmipMo+46zBrg17rI/9PaV4NE/dA52UfTbqf+/e/QxDQ1tpthlCLak3Nfye0h/aSmp3/5G7r00rmKJq3heuAOPA63n22acZGVnG2rXnc+aZrzmm/33bbafywgtVjjvu7cCVzF+DDZuBdcBN1Ka6QKWk/UktdQ9El33eDayidqrlduB59uzZzre//XKee86oVt/A0XPRBw58BzMYGpqg9jh/9ImY8zdDUClpP1JS90DjvnAFuBjYBlzL/OSFL1ArMLm4ft9853Do0CWcfvorKJU2AAepjV9ez+EWukbb9PYnJXUPNC773AI0HzyDDcCxA13V6gYef/xX2qZXGlJS90Djss9bgOaDZ7WkbjTQtZx9+57peJvedotgJGei5rq6uTRP3dj8/cjAYp1HBYNNj7Rp93youOdKS9jQWVrh6eb8q04PQg/hAHhJRrOk1uN3RtorJJmv876y9j7rE1HZ3s2llrq1eGdKn+Bwjx8+S/ljPjQ00vEjsk6rLA7UUoen2Wb+tUKSdwBvpNZSDwOrGBzcyne/e0fHG/Nr77P+oKTO0NFrps2GgZWY/Qj4Zv36d0qlj1Muw5133sp5553X8ftp77P+oKTO2Pw10y+8cJDZ2Z1s3HgOIyPrEz86J8lD+SRcOnQ+MHNJvmXL9Yl+30qlwu7dz1CtbqX2aN/45I/aINx9ib639JZa6j4wt0HDbbedCtxAbeHHVehc6WJSUhdcpVLhggsuZt++bVSr1wIfoLbw43lqC0Fq/fh3vetpnY5ZEErqgms8N72C2gKQp4CDlEof4cQTT1ALXRBK6hzppGZb53L1HyV1TnS6caHmpvuPkprwVy0d2y+Ov4m/5qb7T98ndRJb96atm5ptzU33oaj60W6uvNR+52XVUjc123n5GaU9qPa7sbysWuqmX9ysxlxz08XU10mdl5HhbvvFjc7lSrL8VMJitZY8WaOjoz4zM5P4903awMAg7gdoXi1bZWBgmEOHDvYqrGNs3DjOxMRwfZCssVJpM2NjBxIvL5UwmdkOdx9t9Fpft9R5GRlufl41aJNBma+vkzovI8PqF0s7+jqp89QCql8scfV1nxpq89QXXHAx1eoGqtUNwHLgCUqlCUqlCaamblLCSHC66lOb2UIze8DMHjazR83smuRDzI5aQCmali21mRmwyN33mlkJ+ClwubtHrqTPU0stkkfNWuqWO5/Uq1f21v9Zql/JP7OLSCJiDZSZ2aCZPQQ8DXzf3e9PN6xjhb7oQiQUsZLa3Q+5+1nAacBrzezMo+8xszEzmzGzmV27diUaZB4WXYiEou3RbzO7Gvi9u38h6p4k+9SVSoWVK1exb982ojbLK5fXsXPnfZqnlb7R7ej3UjM7sf73YeDNwC+SDTFaXhZdiIQizuP3qcAPzWwn8B/U+tR3pRvWYXlZdCESijij3zuBV/Ugloa0HY9Ie4IvE83LoguRUASf1HlZdCESiuCTOk+LLkRCEHxSa9mhSHuCT2rQoguRdvT90kuRPNJ2RiJ9REktUjBKapGCUVKLFIySWqRglNQiBaOkFikYJbVIwSipRQpGSS1SMEpqkYJRUosUjJJapGCU1CIFo6QWKRgltUjBKKlFCkZJLVIwSmqRglFSixSMklqkYJTUIgUT5yjbPzSzH5rZY2b2qJld3ovARKQzLU+9BA4CV7r7g2a2GNhhZt939/9MOTYR6UDLltrd/8fdH6z/fQ/wGPDStAMTkc7EaalfZGYvo3ZW9f0NXhsDxur/3Gtmv+w2OGAJENLB04qnudDigfBiSiqeyEPbYx+7Y2bHAz8G/trdb08gqDjvORN1tEgWFE9zocUD4cXUi3hijX6bWQn4FvD1XiW0iHQmzui3AZPAY+5+ffohiUg34rTUq4H3AueZ2UP1620pxzVna4/eJy7F01xo8UB4MaUeTypH2YpIdlRRJlIwSmqRggkyqc3sy2b2tJn9PIBYgiuTNbOFZvaAmT1cj+marGMCMLNBM/uZmd0VQCyPm9kj9TGgmQDiOdHMpszsF/XfpXNSe68Q+9Rmdi6wF7jJ3c/MOJZTgVPnl8kCf5ZlmWx9RmKRu++tTzf+FLjc3e/LKqZ6XB8FRoERd1+bcSyPA6PuHkThiZl9FfiJu0+Y2QKg7O7PpPFeQbbU7v5vwP9lHQeEWSbrNXvr/yzVr0w/nc3sNODtwESWcYTIzEaAc6lNDePuz6eV0BBoUoeqWZlsr9UfdR8Cnga+7+5Zx/Ql4BPACxnHMceB75nZjnoJc5ZeDuwCvlLvnkyY2aK03kxJHVO9TPZbwBXu/mzW8bj7IXc/CzgNeK2ZZdZNMbO1wNPuviOrGBpY7e6vBtYAm+pduqwMAa8G/tHdXwX8HrgqrTdTUscQcpls/THuR8BbMwxjNbCu3o+9lVqh0s0ZxoO7/7b+59PAHcBrMwznSeDJeU9TU9SSPBVK6hZCLJM1s6VmdmL978PAm4FfZBWPu29299Pc/WXAhcAP3P2irOIxs0X1QU3qj7l/AmQ2k+LuTwH/bWavqP+nNwGpDbS2tfSyV8zsG8AfA0vM7EnganefzCicuTLZR+p9WIBPuft0RvEAnAp81cwGqX0wf9PdM59GCsgy4I7a5zFDwC3u/p1sQ+Iy4Ov1ke9fAe9P642CnNISkc7p8VukYJTUIgWjpBYpGCW1SMEoqUUKRkktUjBKapGC+X8HRMm0p9LX3wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the dataset into the variable X \n",
+    "data = loadmat(os.path.join( 'ex7data1.mat'))\n",
+    "X = data['X']\n",
+    "\n",
+    "#  Visualize the example dataset\n",
+    "pyplot.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=1)\n",
+    "pyplot.axis([0.5, 6.5, 2, 8])\n",
+    "pyplot.gca().set_aspect('equal')\n",
+    "pyplot.grid(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section3\"></a>\n",
+    "### 2.2 Implementing PCA\n",
+    "\n",
+    "In this part of the exercise, you will implement PCA. PCA consists of two computational steps: \n",
+    "\n",
+    "1. Compute the covariance matrix of the data.\n",
+    "2. Use SVD (in python we use numpy's implementation `np.linalg.svd`) to compute the eigenvectors $U_1$, $U_2$, $\\dots$, $U_n$. These will correspond to the principal components of variation in the data.\n",
+    "\n",
+    "First, you should compute the covariance matrix of the data, which is given by:\n",
+    "\n",
+    "$$ \\Sigma = \\frac{1}{m} X^T X$$\n",
+    "\n",
+    "where $X$ is the data matrix with examples in rows, and $m$ is the number of examples. Note that $\\Sigma$ is a $n \\times n$ matrix and not the summation operator. \n",
+    "\n",
+    "After computing the covariance matrix, you can run SVD on it to compute the principal components. In python and `numpy` (or `scipy`), you can run SVD with the following command: `U, S, V = np.linalg.svd(Sigma)`, where `U` will contain the principal components and `S` will contain a diagonal matrix. Note that the `scipy` library also has a similar function to compute SVD `scipy.linalg.svd`. The functions in the two libraries use the same C-based library (LAPACK) for the SVD computation, but the `scipy` version provides more options and arguments to control SVD computation. In this exercise, we will stick with the `numpy` implementation of SVD.\n",
+    "\n",
+    "Complete the code in the following cell to implemente PCA.\n",
+    "<a id=\"pca\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pca(X):\n",
+    "    \"\"\"\n",
+    "    Run principal component analysis.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset to be used for computing PCA. It has dimensions (m x n)\n",
+    "        where m is the number of examples (observations) and n is \n",
+    "        the number of features.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    U : array_like\n",
+    "        The eigenvectors, representing the computed principal components\n",
+    "        of X. U has dimensions (n x n) where each column is a single \n",
+    "        principal component.\n",
+    "    \n",
+    "    S : array_like\n",
+    "        A vector of size n, contaning the singular values for each\n",
+    "        principal component. Note this is the diagonal of the matrix we \n",
+    "        mentioned in class.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    You should first compute the covariance matrix. Then, you\n",
+    "    should use the \"svd\" function to compute the eigenvectors\n",
+    "    and eigenvalues of the covariance matrix. \n",
+    "\n",
+    "    Notes\n",
+    "    -----\n",
+    "    When computing the covariance matrix, remember to divide by m (the\n",
+    "    number of examples).\n",
+    "    \"\"\"\n",
+    "    # Useful values\n",
+    "    m, n = X.shape\n",
+    "\n",
+    "    # You need to return the following variables correctly.\n",
+    "    U = np.zeros(n)\n",
+    "    S = np.zeros(n)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    Sigma = (1 / m) * (X.T.dot(X))\n",
+    "    U, S, V = np.linalg.svd(Sigma)\n",
+    "    \n",
+    "    \n",
+    "    # ============================================================\n",
+    "    return U, S"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before using PCA, it is important to first normalize the data by subtracting the mean value of each feature from the dataset, and scaling each dimension so that they are in the same range.\n",
+    "\n",
+    "In the next cell,  this normalization will be performed for you using the `utils.featureNormalize` function.\n",
+    "After normalizing the data, you can run PCA to compute the principal components. Your task is to complete the code in the function `pca` to compute the principal components of the dataset. \n",
+    "\n",
+    "Once you have completed the function `pca`, the following cell will run PCA on the example dataset and plot the corresponding principal components found similar to the figure below. \n",
+    "\n",
+    "![](Figures/pca_components.png)\n",
+    "\n",
+    "\n",
+    "The following cell will also output the top principal component (eigenvector) found, and you should expect to see an output of about `[-0.707 -0.707]`. (It is possible that `numpy` may instead output the negative of this, since $U_1$ and $-U_1$ are equally valid choices for the first principal component.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top eigenvector: U[:, 0] = [-0.707107 -0.707107]\n",
+      " (you should expect to see [-0.707107 -0.707107])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  Before running PCA, it is important to first normalize X\n",
+    "X_norm, mu, sigma = featureNormalize(X)\n",
+    "\n",
+    "#  Run PCA\n",
+    "U, S = pca(X_norm)\n",
+    "\n",
+    "#  Draw the eigenvectors centered at mean of data. These lines show the\n",
+    "#  directions of maximum variations in the dataset.\n",
+    "fig, ax = pyplot.subplots()\n",
+    "ax.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=0.25)\n",
+    "\n",
+    "for i in range(2):\n",
+    "    ax.arrow(mu[0], mu[1], 1.5 * S[i]*U[0, i], 1.5 * S[i]*U[1, i],\n",
+    "             head_width=0.25, head_length=0.2, fc='k', ec='k', lw=2, zorder=1000)\n",
+    "\n",
+    "ax.axis([0.5, 6.5, 2, 8])\n",
+    "ax.set_aspect('equal')\n",
+    "ax.grid(False)\n",
+    "\n",
+    "print('Top eigenvector: U[:, 0] = [{:.6f} {:.6f}]'.format(U[0, 0], U[1, 0]))\n",
+    "print(' (you should expect to see [-0.707107 -0.707107])')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[3] = pca\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 Dimensionality Reduction with PCA\n",
+    "\n",
+    "After computing the principal components, you can use them to reduce the feature dimension of your dataset by projecting each example onto a lower dimensional space, $x^{(i)} \\rightarrow z^{(i)}$ (e.g., projecting the data from 2D to 1D). In this part of the exercise, you will use the eigenvectors returned by PCA and\n",
+    "project the example dataset into a 1-dimensional space. In practice, if you were using a learning algorithm such as linear regression or perhaps neural networks, you could now use the projected data instead of the original data. By using the projected data, you can train your model faster as there are less dimensions in the input.\n",
+    "\n",
+    "<a id=\"section4\"></a>\n",
+    "\n",
+    "#### 2.3.1 Projecting the data onto the principal components\n",
+    "\n",
+    "You should now complete the code in the function `projectData`. Specifically, you are given a dataset `X`, the principal components `U`, and the desired number of dimensions to reduce to `K`. You should project each example in `X` onto the top `K` components in `U`. Note that the top `K` components in `U` are given by\n",
+    "the first `K` columns of `U`, that is `Ureduce = U[:, :K]`.\n",
+    "<a id=\"projectData\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def projectData(X, U, K):\n",
+    "    \"\"\"\n",
+    "    Computes the reduced data representation when projecting only \n",
+    "    on to the top K eigenvectors.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n). The dataset is assumed to be \n",
+    "        normalized.\n",
+    "    \n",
+    "    U : array_like\n",
+    "        The computed eigenvectors using PCA. This is a matrix of \n",
+    "        shape (n x n). Each column in the matrix represents a single\n",
+    "        eigenvector (or a single principal component).\n",
+    "    \n",
+    "    K : int\n",
+    "        Number of dimensions to project onto. Must be smaller than n.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    Z : array_like\n",
+    "        The projects of the dataset onto the top K eigenvectors. \n",
+    "        This will be a matrix of shape (m x k).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the projection of the data using only the top K \n",
+    "    eigenvectors in U (first K columns). \n",
+    "    For the i-th example X[i,:], the projection on to the k-th \n",
+    "    eigenvector is given as follows:\n",
+    "    \n",
+    "        x = X[i, :]\n",
+    "        projection_k = np.dot(x,  U[:, k])\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # You need to return the following variables correctly.\n",
+    "    Z = np.zeros((X.shape[0], K))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    Z = np.dot(X,  U[:, :K])\n",
+    "\n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return Z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `projectData`, the following cell will project the first example onto the first dimension and you should see a value of about 1.481 (or possibly -1.481, if you got $-U_1$ instead of $U_1$)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Projection of the first example: 1.481274\n",
+      "(this value should be about    : 1.481274)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Project the data onto K = 1 dimension\n",
+    "K = 1\n",
+    "Z = projectData(X_norm, U, K)\n",
+    "print('Projection of the first example: {:.6f}'.format(Z[0, 0]))\n",
+    "print('(this value should be about    : 1.481274)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[4] = projectData\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section5\"></a>\n",
+    "#### 2.3.2 Reconstructing an approximation of the data\n",
+    "\n",
+    "After projecting the data onto the lower dimensional space, you can approximately recover the data by projecting them back onto the original high dimensional space. Your task is to complete the function `recoverData` to project each example in `Z` back onto the original space and return the recovered approximation in `Xrec`.\n",
+    "<a id=\"recoverData\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recoverData(Z, U, K):\n",
+    "    \"\"\"\n",
+    "    Recovers an approximation of the original data when using the \n",
+    "    projected data.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    Z : array_like\n",
+    "        The reduced data after applying PCA. This is a matrix\n",
+    "        of shape (m x K).\n",
+    "    \n",
+    "    U : array_like\n",
+    "        The eigenvectors (principal components) computed by PCA.\n",
+    "        This is a matrix of shape (n x n) where each column represents\n",
+    "        a single eigenvector.\n",
+    "    \n",
+    "    K : int\n",
+    "        The number of principal components retained\n",
+    "        (should be less than n).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    X_rec : array_like\n",
+    "        The recovered data after transformation back to the original \n",
+    "        dataset space. This is a matrix of shape (m x n), where m is \n",
+    "        the number of examples and n is the dimensions (number of\n",
+    "        features) of original datatset.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the approximation of the data by projecting back\n",
+    "    onto the original space using the top K eigenvectors in U.\n",
+    "    For the i-th example Z[i,:], the (approximate)\n",
+    "    recovered data for dimension j is given as follows:\n",
+    "\n",
+    "        v = Z[i, :]\n",
+    "        recovered_j = np.dot(v, U[j, :K])\n",
+    "\n",
+    "    Notice that U[j, :K] is a vector of size K.\n",
+    "    \"\"\"\n",
+    "    # You need to return the following variables correctly.\n",
+    "    X_rec = np.zeros((Z.shape[0], U.shape[0]))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    \n",
+    "    X_rec = Z.dot(U[:, :K].T)\n",
+    "\n",
+    "    # =============================================================\n",
+    "    return X_rec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `recoverData`, the following cell will recover an approximation of the first example and you should see a value of about `[-1.047 -1.047]`. The code will then plot the data in this reduced dimension space. This will show you what the data looks like when using only the corresponding eigenvectors to reconstruct it. An example of what you should get for PCA projection is shown in this figure: \n",
+    "\n",
+    "![](Figures/pca_reconstruction.png)\n",
+    "\n",
+    "In the figure above, the original data points are indicated with the blue circles, while the projected data points are indicated with the red circles. The projection effectively only retains the information in the direction given by $U_1$. The dotted lines show the distance from the data points in original space to the projected space. Those dotted lines represent the error measure due to PCA projection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Approximation of the first example: [-1.047419 -1.047419]\n",
+      "       (this value should be about  [-1.047419 -1.047419])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_rec  = recoverData(Z, U, K)\n",
+    "print('Approximation of the first example: [{:.6f} {:.6f}]'.format(X_rec[0, 0], X_rec[0, 1]))\n",
+    "print('       (this value should be about  [-1.047419 -1.047419])')\n",
+    "\n",
+    "#  Plot the normalized dataset (returned from featureNormalize)\n",
+    "fig, ax = pyplot.subplots(figsize=(5, 5))\n",
+    "ax.plot(X_norm[:, 0], X_norm[:, 1], 'bo', ms=8, mec='b', mew=0.5)\n",
+    "ax.set_aspect('equal')\n",
+    "ax.grid(False)\n",
+    "pyplot.axis([-3, 2.75, -3, 2.75])\n",
+    "\n",
+    "# Draw lines connecting the projected points to the original points\n",
+    "ax.plot(X_rec[:, 0], X_rec[:, 1], 'ro', mec='r', mew=2, mfc='none')\n",
+    "for xnorm, xrec in zip(X_norm, X_rec):\n",
+    "    ax.plot([xnorm[0], xrec[0]], [xnorm[1], xrec[1]], '--k', lw=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[5] = recoverData\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 Face Image Dataset\n",
+    "\n",
+    "In this part of the exercise, you will run PCA on face images to see how it can be used in practice for dimension reduction. The dataset `ex7faces.mat` contains a dataset `X` of face images, each $32 \\times 32$ in grayscale. This dataset was based on a [cropped version](http://conradsanderson.id.au/lfwcrop/) of the [labeled faces in the wild](http://vis-www.cs.umass.edu/lfw/) dataset. Each row of `X` corresponds to one face image (a row vector of length 1024). \n",
+    "\n",
+    "The next cell will load and visualize the first 100 of these face images similar to what is shown in this figure:\n",
+    "\n",
+    "![Faces](Figures/faces.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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W1taEN/f392V9gYNQBa3ldDqNZDIpezebzSTswO9rL53f75f9K5fLSKVSRrjB6/Xi4sWLAObnwOfzGTkGzWZTzjrDFuQL63nUZ8RKOicgm80actnn8xnnvtVqyfzS6bQRV+aZJQUCAUynU/FG2Ww2wzOg19lKR16W1WpVTO5Op4MzZ87IZ4xbcbNZhsAXh0IhdDodcSu8//77uHXrlgRpHQ6HxIh6vZ5RZlKv11Gr1YQJfD4fSqWSkVARj8eFkeh6Aeabws3V8S/OQScJMI7AS7tSqaBarcpv+v0+wuGwHI5MJmPE0/L5vMyHgXG6nV988UW8+uqrOHXqFAAglUohEokYLmDS3t6eHF4mwPDQ87+6HKJSqQhD8qIlUzBuxwtwOBwKY3DOPBiNRsO4aGezGTwej+wpyzD4bh7QRCIh82CMjuPkWLim5XIZ7XZb3kGXK5nS7XYbMQV9EFqtllwCwIFrn+9aWlqCx+MxXHG6jIMXNolrxsPClPetrS1Mp1MR/vl8Hp1OxziQOq7xhS98AV6vV5SsTqcjrurd3V20220ZQygUQiQSEeFFtxYF0mAwQD6fl4tbu+cLhYKsS7vdxvb2toyZAkLv7Wg0kjnq0ipgzn9er1fWhAkvwFyA9Pt9fPDBBwDm528ymQgv53I5nDhxQuafSqVw48YN/PSnPwUAXL58GV/72tcAHChPwDwR7Otf/zru378v+9NoNCTWVCgU0O12JdGqUCjg8uXL4n6LRCKSKMf5cY9++ctf4o033pB1Z6yKbteVlRWcOXMGjz32GID52dXufrrgT5w4IWUWOzs7CIVCePzxxwHM+a1UKhl8EwgE5O9Wq2WcY4/HI2U43Gu+r9/vSx4HcBCW0TFkv98va85SJfKDzn1IpVJ49913AczlkA41zWYz5HI52dtWq4WrV68a8qFWq4kB8N577yESicgF6HK55DzVajV8+OGHIof39vZQKpVEDoRCIXg8HhkzeZKXJMvhSDouqC9Pq6vZ7XZjOBxKbPSll14yDLdkMoler2fEvClPSqUSarWalOhFo1HJ+wDmit3a2tqhMNKDFF8rPTJmyUXv9Xo4d+6c+OCr1SrK5TLeeecdAHPmu3TpkhGbGQwGYjEkEgm8/PLLwmiZTEYya59++mnEYjGDCZvNpizi6uqqXMYclw7unz9/XsbcbrcNgaGTLRjX0dl5WsvnxUlmsNvtqNVqIhT1YjImqWuA/H6/HI6lpSV89rOflfkzg5C/19rgzZs3hXnX19dFswfmF1q73RbrkBccx/zee+8hGAwaMcBUKiVz1BaCzWZDpVIRQd1oNFAoFGTPwuEwUqmUWDdOpxO1Wk3Wk4I7EAjIBdbr9bC/vy/va7fbyOfzMr9Op4NAICDzYTIQ97JSqWBzc1PW3OPxiHBtNpvGxdPv9/HKK6/I4fzEJz5hJI0x9k3LVGe9ATBibJFIRPaGXg6u8WAwEN4A5oJNW1ahUAjnz5+XOV65cgXXrl0DcOCd0PV+eiw6VsbP9cHXGYS/+MUv5Pu0BKmE1Go1VCoVeX4qlUIqlZI56hgw99rv94tyt7GxIe+6cuUKcrmcrAdj21TgmICkhVuv1xMlVWds6ph4OByGy+WS/bx27RqKxaKMLRAIYDKZCO9HIhHcuXNH/maeA/nX4/Hg6tWrAOZW62QykQxmt9uNYDAoZ9Xv9+ONN94QSzKZTGI6nYoSSUGeyWQkWSaTyaBQKIgC4vF4cP78eSMzPZ1Oyx60Wq1DiYhacHe7XeEpKuKUaZlMBteuXZPxDgYD1Ot1Gdfjjz+OdDote6KVqFwuJ0oCrWKds5DNZuViuXHjhpFb4XA4sLu7K96AYrEotdtc852dHXnmrVu3cOPGDQAHBgHflc1mEY1G5SKmJ4i8z7E/jHScUGd+06vDMTOhaW1tDcD8DFWrVVnneDwu79zd3UWhUJC/k8mkkQdQKBTQbDblHESjUcMTeZRluYhZLmhBC1rQghb0CDrSskwkEkYmot1uF/OVyDfUXP1+v6QaA3NXjnZ7NJtNbGxs4Pd+7/cAzLWDu3fvAphrILlcTly28Xgcx48fN9Lmc7mcaN+0/qgRaHfbtWvXRCNh7JPaOV2C2tIEDlydH374IcbjsWij2WwWjUZDNMl+vy8uIrvdjl6vJ5qj3+9Hu90W7eipp57CyZMn5bd2u92IoWj3zdmzZ8XdvbOzY6Skt9ttFAoFcVnXajWjbKHZbKLb7Yo23Gw2YbfbJbs2Go3K/JrNJra3tyVeOJvNDtWl1ut1oxxIu4poyXPuXNO9vT3R5PL5PK5fvy7jjcfj+PznPy+WFd1l1KivX7+O5eVl0Wzz+Tx+/etfA5hrzZlMRtxCjF8yHpFKpUQjJ5/0ej0Zv3ZD6po1u92OSCQiz2G8ndZtLBaTzGiSdgPF43GJUfP3r776quyrzpjsdDpS28Y5jEYjw0rX5QbaUvF4PGL90Q1MD0I2m0Wz2RRNmBY23XWj0QjNZtNA0Eqn07h8+TKAeXYoPTK7u7toNBpiYZG3aMGn02nJjOa6a22cFqieP+fWbreF/5555hlUq1W88cYbAOau0s9+9rN47bXXZF2XlpYMfksmk7IvOtv3c5/7HPL5PL7xjW8AAN5880385Cc/wcc//nEAwG/91m/h3r17RsyQaDXA/HwCcx7n2mezWQSDQWMPtOuaSDc6xq/LtKyIX/RQ8D25XE4suvPnz8PhcOD1118HcFDvyf1+5513cObMGQkBaRnn9/vF1UkPnHazdjodkUuTyQSPPfaYzLvRaKDVakm26NLS0iELUJfyBINBmW88HhfeAOZW2/Hjx+XZ6XQavV5Pvq9jllwv0oNilho5KRKJ4NOf/jSAOR9qD0C320U+n5d1Z+wUmHt9nnjiCXHHB4NBw1vInAldHqNzQKx5LZqOvCyTyaS4Wrxer1HrSGHDOCYBB7ggXDQeVq/XiwsXLuCTn/ykDIqxgX6/j0AgcMiFyw0lI/DQsZ6T7jadoryysiJj2N3dRalUMvz+2mUUjUaRy+Xk+9ls1qiFpCtOF6Pz/9fX13H69Gm899578jxdN8VDpuHKNHiCvixTqZQwbLValUsbmAuut99+W1xZ9XodnU4Hzz77rIyp1+vJmIfDIabTqbhSmYwCzJWBra0tWa9jx46h0WjIxcbUb7pd0um0oWzoBBleuIVCQdKxucanTp2SiyiZTOLChQuHFB0dr85kMkZKvwZY+MxnPiMXC+vQyFOhUAjT6VSUIyZccZ114J7jBmC4/oB56nm/3zdqRbvdruF+y2azEttiajo/Zx0mAKnD45hZokHeZZ0XL1OWZ3GNtbttY2ND9s5ap9zr9YySHMZcKXAYj9cuTybMAXMljG6tr371q/iP//gPEW6EPNOXnk7OCwQC8Hq9ojhyrvx//h2PxxGJRERwlctl3L17VwTSysoKEomEXFx+vx/Hjx8X3ggEAobSfP36dRnj6uoqotGo8PLFixcRCATkHMViMTz++ONGspCOR1F5i8fjwtdut9vIhSBUGsnpdCIajRpJjNpd3+l0jGQ2JvFwTXUOxY0bNxAOhyWxS/M191fLG62cBINBUegHg4EB2UkZxD1YXl4WmEhgrnQFAgGJfdfrdTz77LOi8EynU7mgr1y5ApvNJmc5HA4jGAwacW+32y08x4QyrWw/iHTNuKaHXabBYBCrq6uyt/l83gDviEaj8hkTmLju0WgUy8vLxt7rxECCStBQ07xspSMvS13jR2HCCTCZggszHo+NmjfGGLmJo9EIFy5ckISXyWSC559/HgDw4x//GC6XSzR34qxSUAQCAUPwcSGpHWhtIJFIyHOYCUsLgd/T6Bc6zsh6Op00YbfbDc1LJ6qcPXvWiCXu7e0ZGZaRSEQENxNPeMlwc4D55lEQJZNJXLx4UZItGo0GXnjhBTmA5XIZw+FQEhfcbje63a5orI1GA6VSSSwMp9NpZD2mUimxOpvNJlKplGiz58+fh9/vl5osCmPuA595+/ZtGT/RP8h8Fy9exMrKihEf1OvpdrvFUwDMYzc6MSyTyQhffPTRR0ayErNs9SHs9XoGGglwcEnqw6cTRZgZqy87K3gE60E5phMnTkicg3tKPgqFQnIRlUolsYT5rHg8btTqassyHA4jkUjI78m7wNwSu337tsxd7xWxT3XCiM1mMxQ7n88nn2cyGfj9fhHsen9eeOEFlEolyd7mGnOdWd+qM2nv378v/KEtS113yDo97g+fxzgba041QEc+nxfFidmmXGcd869Wq0aWZ7lcxvLysryDgCQci8fjkUxyAIac0gk84XDYACzQcc7BYGBceORNHaMkzjXHwGdZa7iZXU1jg8oN5+p0Og1e10p+u9024rytVkv4icqKnoOub/f7/YjH43JZLi8v49SpU6Jcaw9LPp+X3AzubTAYNPIwPB6PAQzR7/dlbNoK1WStDLAm2bhcLlQqFUEGcjgcSKVS8jlrnPmeVColF/p0OsXW1pYkkd27d8+o5V1ZWUE2m5U9a7VaglkMmEAKh8b90E8WtKAFLWhBC1oQgEdYlq1Wy3D7TCYTA5ZLayyERtO4glpLaTQaSKfTErux2WxGXC2fzxv+ee2S0LVifLeuZ7KWCPB7uVwOyWRSNMF2u23EXgibx+9TG9X1droURVsnbrcb6+vrorm/++67h+ohtdbJLgO0OrQbtt/vG/Wn/X5ftPULFy4YME90v+naJr/fL5rl3bt3EYvFRHuvVCqiRe7t7WFjYwPnzp2T+dTrdcl+c7vd8Hq94uIl8gzHTBdYuVwW11ogEDDKeoibyn3v9Xro9/tG3EPjip45c8bAo+31egaO5WAwMOpr9/b2jJrUer0uVi7hyahFajelhnobDofw+XxipTCbjvzocDjgcrlkTxKJBCKRiFFnCcBA6eGYvV4vPvjgA3Gb644OfLbGpzx27BhyuZzwpP6ujuVubm4KFilJn09arjqrUYdNaJVxDpFIRN6VSCTwwgsv4JVXXpF19vl8su7JZFKsPY5xOp3KehFpi2urSwT8fr/R6USfL5Yh8AzZ7XYj/kREJp3dznfSAuC6BQIBZDIZkRt0FfNvZpDzrFMOaUhPKwayFYMZOMgR4Pd1LFyXZZG0GzUYDBoZ1hqvOBgMIhqNyn7RhatDU6RarSbxcpbXkOx2OwKBgIHwpUMMiURC4tnA4axbXX5z9uxZ8fIBc37X3XasUI6DwUD2AXi4S9NaH01kJq4/5Q7HkUqlpLyJxP3lnLS7W4+BWbP02LBSgGOz2+1GxvX/unSEFyQw39ydnR2jho0bBhwk+HBz6c+n0HA6nUilUkYciALjYx/7GK5cuSKMwxojHUvQ2JVMXafrkYlCnDzHMB6P4fP5ZOFKpdIhIHXrRQscuBvZhkwnCeg05HA4LId3e3vbKKGwggswUUYDY5PomuM6ElAamDOCTlfnnPjcQCBgCLLNzU3DVZJOp+XSeO2119Dv9yX2xoJv7e93Op0GRCETk4CDOA8vBmC+j8ePH5fxc548DIyv0NUWCoVw69YtcWWGw2EDsJ/P5NxqtZpc0tlsVpKQAEjJBV2YjE1oYHySjhMCB5i7nKd28/BzDSTucrmM5LZOpyOx0l6vJ0JxbW0N8Xhc1mhrawudTkeezdgjDy9bxpEntUCYTqciBFZWVlCr1URQBQIBuN1ucSdqBQqAJLJp967GHdZnG5hD/vHyv3PnjuFC9Hg8h0DCv/rVrwoPkl+A+QXO72meBub8o2PobMVkBf3QwCcaw3YwGAgPsm2YduHxcgYOYoRcn06nA7fbLc/+8MMP5XdWrFoNDMC15JrpBg+UBxpiUMNpAgeu50gkYpR8scREr6kuuyGOrbUVH3DgHgXmdaLBYFAU2uFwKM0UOCeNxUy+5fkj8L0GKqE8WVlZwdbWlsj58XiMRqNhYEfrdoHRaBSZTEbkiQ5taNfrw6DudOkIAEnoIXY0z4bD4TAUI33B8TPKfBo8GuqyVCrJHeH1euF2uw3X+sPoyMtyOBwaiRClUkkEJ5NluJAazYd/7+7uitXy8Y9/3MDQpIYNAE8++ST6/b74qC9evIhgMGhsis/nk02gZckEH33xUNvh+FutlsyBQAlaKGrNiNFPoUQAACAASURBVAeHmxCPxw20h263a4AsayEXDocPHXyv12tclhqkWQtFgq4DcyF48uRJ48KOxWJSf1Wr1dDr9YzsLx3TvXjxotEphH0MgTkzx+NxETi8GPluao4aUF0rKfoAcs0ZXOd4qbGTL6xAxoyLaUtKF8hrMADW6WkNMpFIHDpUOh6oM5KtmLpa4Oj4FZM8dGyX+LDAXBs9ceKEgRykQbFjsZjMn31ZKYxOnjxp1LbSeicvc31Zs6m1cV1zyXeTfxqNhoFy4na7jd6ke3t7mM1mMudOp2PE3uv1uvAqgd01wIZuINDr9STWDszPle5Zar2kdXKa9pokk0m4XC5jHVk0DhwgJWlsXw1G7nA4hBdYy0o+0kKbz6JVxTF7vV75PRMX9dh1hxCS/n+CPmhgd/ZP5J4AB/yoO5LwYtWZvvqsUZZQnvBy0MlupFqtJrFsr9eLpaUlscKI4EPeJZg+ZeWdO3fQ6XQkdySdThtWmq54OH78uFEXe+7cOXg8HgMAgRjhwPwceTyeByZecv80WessrdjLur5bn2WdjQzMz73uDOVyuYx4O2CC9uvckXq9bvDkUbSIWS5oQQta0IIW9Ag60rIMBoNilTCrlNmfly9fxoULF0TT3dvbM9Km7969ixs3boi76dy5c0gmk4brixoLS1D47JdffhmZTEYynNLpNMbjsWFu7+/vi1aotXEdlxgMBqhUKkbdj3YVP8gC0a1xKpUKtra2DMtFa49+v18sqf39fbTbbQOpJRwOi5ZNS5wakM4QZbozMNdOdbwImMcI+f1AIIBYLGZY8Z1OR7Q4h8NhWEFEbgGAZ599FtVqVeZr7dPI7Ekds9na2pI57+7u4itf+Qomk4mRqu52u8UVd+zYMaNHIbU8jSRTr9cNhI1oNCp76PF4jO4yur1XPB6H3+830H6YwQwclJZQ89V1ktraZvyYfzM7lXvTarXw0Ucfifb+/PPPG+nntGI4B41Jyw4IXNNsNotMJmPwmM6oZCkJ91enxGu3K2NF3BuWPOl2ZBr1pFqtYnd3V8IEzM7WXUd0trO20gAY0GvE8tWuKm21atIeE55nXfupLRd2LuE+WdGPuFdck0ajYWTFE5aP8wMOLMXRaGS4TIEDTGDgwFLT/8ZzqMM4AAzPkm575vV6BU0MOIgT67IE/Sy2g+JvtTxjRiafRc8Fx6l5WcPsXb16FXfu3BG3I7OrrfKDGe73799HMpkUzw8xvnUYhHywvLyMaDQqOQ/7+/sSZgAO2r5xfSqVCnZ2dg7Vr3NtNAKU5l3OX7uzG42G8BJLxLgGRBXTWbj8LBwOY3V1VTygtOjJN1xP7VrW99b/unRED2IymRhp1pubm2g0GkYdk25ztLW1ZaSRD4dDI7g/Go2MFP9kMilF0oFAADs7OxLbCIVCyGazBvSXxuq0JstofzSTWEi6LoitiHTikFWwE+qMz2YNWKFQgM1mEzABYjVyLHRT6Z6b1ubXep31Jul+hzyYZEC6PiiIyuUyyuWyMPDx48eRzWZFSEwmExGQJ0+eRK1Ww1tvvQVgnlzDhBzuA3DgStWCAzAh+jTphr2EkeNe1et15PN5Gc9HH32EarUqNX4ej8doMdZsNiX+wlgh94+QcxrKT4MmVKtVSQriOpK025XJBLo+Tvf4297exltvvSVzYP2tdkfzN1wn7eYpFAoiKM6dO4elpSVDedBjGw6HaDQaDwQl0IKeuL26H6PmVfYa1PBqgUBAkm9OnDiByWQie5vP5435jMdj4RPi71IYJRIJ5HI5ca8x6UO3OdLEufGc6v0JhULG9/W61+t1SbjgWuRyOQOrWScL6bpm68XNshQdp9b//93vflfWlUTXn3bd6v8SgEA33NbvpgJhlSd6vCTixurm6ePx2Cie14qf5gXKUr5TKxCDwQDlctmIdVYqFdn3Wq2G1dXVQ23PNPi7xmjVeSe5XA6tVsuo6daQfyxb0zXXD6OjQNT7/T5qtZoRQ9Y5AnSbcv0J4wnMwSny+byU5Jw8edJw9bNEh+8PhUKiWD2Kjrws9SVDxtegA+wGAhzUVWo/dTqdlo4CTFyh0NeYrKwNYsLOYDAwLgzGCq2MpwUOqd1uS2wmHA5LHSA/07FSChxuPnt26gSSwWAgNY/37983isk1bmy324Xf7zcA0HW8l/0FH0RaWyWaC+ceCAQMLYw9DLnZZFhdt6hrw2KxmCg88Xgc586dk/6Cr7/+OjY2NoTpGO/lAWaHDM5Jx5J0V5TpdGokXrARMQABfad1WigUkM1m5SDcuXMHpVLJsKwY13Q6nUgmk7IfNpvNUJr4HW0Raixc63rrrEeXy2XE8xqNhnSwuHPnDiqVCj71qU8BOEA50XFZbZkyQYjz9/v9+Kd/+icAcyXK6/UaHhq73X4oWU0nSJG0cKcHQPMJzwkw569OpyP82Gq18OSTT8rzrl27JooVcJD4QJpOp0YHHZ/PJ2eVHVp0XFlb6pq0Z4cCXyfQsHMFAIOngQPFibzBTFgdO9XKhlbeKZ909r1uBk3Lk7Lgpz/9KZ599lkDqIRxQi1PgAOrjudQNwHQTQL4HX02SexTy8+obGge0jKNVrH2QJBo0QEHl6V+ju7C0e/3sbe3J0mQZ8+exfPPP2/Ubc9mMyMXg/Pr9XpIp9Pyrp2dHXg8nkPIPNxP1nRqbN2HkbUPrlYkiNNM+cl4Ls8Y//1BSnGlUjEsxU6nY2SbAwceKOAguVIrZA+jRcxyQQta0IIWtKBH0JGW5TvvvCM1dxpFBYBhtgPzG3l/f18sCLZaou94PB6jWq0a2WvWtlPM/rt//z76/b7Rtmh/f1/eTc3iQWnVGq+VVqPWPl0u16GYiCZdtsEMSVo27G5O6vf7EhMinBa1H7fbjUqlYrhXdQ2a9b1a+ybGJP/WZQvU1rQrKBaLiea8ubkp5Ql8ru6+rvFF79y5g263K3N6kL+ebl9NPp/P0Oq0x4FWp/YCtFotyXRmnOnFF1+U8a+ursqe6a4hw+EQly5dkvGxnyDnRguNmiU1Rt2l4kFEq1JnGOp6U8ZotMas23CxBEnHMDVcnc7u3d/fx+3bt+VdPp9PUJe4ftrK01bzYDAw6vCcTqcR9tAxrV6vh1qtJmeo3+8L5itwENvi848fP26UDPE3wDw2vby8LN4Kt9uNdDpt1KZZrSKSFbJMp/wDc4uJnopAICAlEsBBViPf02q14Pf75fez2Uzma62BpMdFu1Db7bZRIw0cZKz+7d/+Lb7//e+LJcK906hljBNry1O3sGOcULdbc7lcMkaWxvBZVo+IdqMzvmmtT9alQaRoNGrwj45nEtFKd7HRmcwXLlww2nBxfzTSEfeDiF/MnL1//77hPWMdLOel60/1f0nW/bKS5ieG/YCDNoTavZvP50VmNJtN8QrV63WsrKyIV+TGjRtG5rfONuaY+v2+zMnqVdB05GX55ptvCkMwNZkLycbJvIjq9brhenS5XGi1Wviv//ovAPN6nkwmYwSHybi1Wg1bW1sijOgmI4Mw1kf3HMEPrDBn/C5923Tjaf++jsMxzZrEIndejn6/H+vr65LooPElW60W0um0uDC73a7RdJnQTxq8m9/hu0m6norFyjpJiS2kAEizVd30dzKZCLO8/fbbKJVKUksZDofltzdv3kQqlZLL0uv1IplMGmnT1lowXdiu3Su67pOJGsBc0Ho8Htnn4XCI7e1tOZy5XM4I9rPVE/dMu6pisRgCgYBgCOfzeWxtbRlt0rRLhYrFw4Q4+cDlchmt3FwuF3Z3d8V1PBqNjIbGb7/9trjogTnva6VLw9e1223cuHHDiLfcu3fPSKpik2Cuj3abWwWqdlFq7GXGZikUKpUK9vb2jFjM9va2MeZjx44ZgNo63d7r9YpwyuVyGI/HRjkL8wo4Lq43YCqrGriD7fA4Ju1W5ue6VCsUChkKAYn8qRuXT6dmY2gKPV2L53K5jN6t+qL7i7/4CwBzBZLxPF1uBRxuDNzpdAy8VybIcf61Ws0AInE4HIYb1el0Gi5DHb9l3aVOCNKADVrh0HWK/X4fxWLRCJFovNpGoyFtu4B5IiaTEbmmnU5H1sUKJakTaWq1GmKxmPzNEJCWebr066iaxQeRLtnrdDpS3rO/vy/KHzBX8vP5vFGvy99ubGwYeNbFYtFo+8Zm3brsSStZixZdC1rQgha0oAX9v6AjLcvJZCLmKbV3XRSsXTG1Ws3Qipntql1X2WxW3JZa669UKiiXy4bZrVOHreDTgFlkrq20lZUVSflnBwiOkRBnGlpNA2qzmbOGtPN6vUbWm87gJToGMNfQKpWKAa2nx0aL0Zq6zs/473QVa9BvnaHWaDSM1j+hUAiNRkMyXN98803s7e1JqvipU6cEVHhzcxNPP/200dx5eXnZAJXWpRpMmKF2q61iq9Wmk2d0un+320WxWJR3bmxsGHBUHKtut0aov2w2ixMnThjd4+/duydWajweP+Qd0C2XdLKMFYhCJ380Gg3s7+/LmJPJJM6cOWMUsGtkFhY2c6+1N6LZbBqdamw2G5rNpriWNZAAx6gtRq3Za+uV7koraDf3ZH9/H/V6XSwEgtnTarIi9mgLkOEFghKcOXMGW1tb4q0oFouSvcn14zM4R1Kz2ZRzTFQdjYgzGAzkDFkTfliGoDvQeDweI4zC81apVIxMaJ4h7aYmCAlwYNVyDkze0utiDY0AJjA8MN9r3Y1Fl4/VajVjjbWssUJ08lxpS1lDI1JWWDNy9bgAiCtet4BzOp0if2h1cg+uXbsmmdP8/unTpw2vAddqZ2cHw+HQAH3Z3983MvqHw6G4oYlmxPWyehI0WQEZNIVCIbjdbmlfxnVnJcLm5iZGo5GBNESkqvX1dUmK5LP29/cl8ZQdmo6CtXsYHXlZ2mw2ya5bW1vDaDSSw8z2LLq1FDuPAHPBZrfbxR3XaDRQLBZFIGlXo9vtNmJRwEHncACCjKPR7KvVqmyK9tlr6CJm7WmhoF08hATj5tOVrOHjtCtVowPRbaUFqNXNqpmFqCfW2C9wIDD5PQ0XR9LumtFoJOsaDoeNLgRf+MIXYLfbce3aNQDzw8H1iMViGI/HwnQrKytGeRDT7bnunU7HQKrRbnFrjNLK8FqILy0tSZcU9kXlXhOzVPdt5MUaDAYNZJ1AIIC1tTVRwCgouJYUKA8qz9FraP18OBwafTzPnj0rrc+AOa/yEgQgpR4UxMy8JEWjUTmMzObUPKkzsBkaeJDLSmduk6+5TvV6Hd1uV8bA+lgqFolEwogvMfau3YLcz3A4jPX1dRE40WgUGxsb8q5qtWpg8lLRIenMxnq9bmQe6nVhRq92d2o0KHa8uXLlCgDgueeek6x7rvP169cBzOPKn/vc5ySuyvOnu/lY22sBB/HF119/Hd/4xjeM1li5XA5Op1P4X/eqBebKWavVkndcu3YNDofD6Fkbi8VkD3TpSCgUMmowOR8Nnad5gOdOx0tJOsbNqgKtXLO+nO9dWlqSPbp+/Tpu3bplGAR7e3sy5tFoJMpot9vFxYsXZY1zudwhNCqd2c091Vixmh7lluUcfT4f1tfX8bOf/Uyeo/fW4/FgeXlZlDvdEtDn8xmZ2oFA4FBmtw5jccy6JOlhdORlOZvNJG7BILDGb7Xb7Uawu1KpCIONx2MBAQBgJHzo5wNzIa41mlqthp2dHYljra6uIhQKGYdQ1yyyDgiYgyFo4HGv1ytCr91uGwWqnJeOnwEw0rvb7bYwh7ZuCUasywmKxaIIB8ZUdJNfHRs8ivmtNaha4+x0OggGgxJ3dLvdWF1dlb54xNJkM+LXX39dUr0vXLhgwFFRc9eFz2wOyzlqIGVdkqHbGlmTahi7IPG9fEYoFJJ1YeKKrqUkT9lsNqOx9XA4NOD68vm8oaBxvR4US9P/zs+4V/yehlOsVqsGjrHX6xWlgj05dcKLLunQqfyACaZuLWwnsMKDrGEmA3Hu7XZbLIhms2lYFMPhEOl0Wvaaa6zry/x+v/Cy5mtagDq2GI1GpVbtzTffRLPZFGF1FDRYPp+XJDcmApF44WvlJxwOy1qVy2XEYjFJtLp+/TpisZhxaVO+ZDIZ2O12EeyERtTxQmtMs9lsyrspSwgbCBzwHmVWqVTC/v6+wavxeFzeSWWSzzp+/DhyuZyxPvri0IALfr/fsMpouet48MPiZ9po0d4Gvk/Xv584cUIuEGBe96uTrrrdLra3t6W0xOPxGPN5/PHHDSNlMpnI2SZ4igbGAA7kOhPl/p+QPi9utxunT5+WWmxdRw7MFYvl5WVR2hKJhKw5E/c0mL/uQUqlSo9VK/9HWZyLmOWCFrSgBS1oQY+gIy1LWg3AXJvRHR0ikYjh72fXBo3Uks/nxRdOtBwNZUaNrVarod/vi9ZIFAia/3QjUPNtt9sIBoMyNsZW9DiBufbNDDYARparniO1NKYsa0SRarVqlHHoomFr9h2fof/Waeg6e1G7AQaDgeGW00TNR3dwWF5eljnF43HEYjGj2SsAfOITnwAAXLp0Sb5LVw+13f39fSNGS7e0Tl/X2jnJCkiurbbBYGC4oE+cOCFlQ8DcmmAcD5hbC9oFtbe3Jy4kZo7qlHE9FsZvtTbe7/cPrSH3g+/QHgFgvmcaQKJQKKBSqUgGYSAQMGJ8BPTWGXXW+ItGUAIOshnpjiMf6Jg594Ck4dJoCesuGtSSuVa5XE5cpaPRyLDumfXHd0WjUaMxsXYVk8915xqN3kRPwoPS/zc3NyUvgcDuGnhbW852ux29Xs9oaD2dTo0uQdVq1dg3yhNmK3OOLEXT7m9desPnkS8pW3Q2bLfbRSKRMEIcN2/elM/pYtXg6LFYTL5PoHjdtvBhZ4eZtvqsWT0h2gunvWHaE8GcDP7NriBcQ8ZKuVfpdBqXL182mmbr0qbjx4+LSzMejyMcDhsdhJaWloQXOp2OAVvKkBbXmOEeKz2stIRjnEwmSCQSUrLCMJUGe9cyj0htpNlsJjKcIQ6eVXrFdO6I9gT9r+HudButVquFUChklCzoQHI8HkcgEDAw965fvy4uP7pkdSBZwxVpX/loNMLKyoqUq0QiEaOmjL0GiaxDtw0AA3+R6Boafmo8HhuxUR1XmEwmckA5Z+1q9vv9RmxRu0vpwtHv0uj/xDZkDFh3lKhUKvIcxlV1UpIuNWBdk/5+rVaTGCWfz7ir0+k04jg+n8/oFBMMBo06Np0Kz3R2DVEIPLjdjr5wOU7AxK0l3b17Vw4Sn61LcrjGPp8Px44dk4Qfh8NhtPNiIF+7aR9W//fqq69KrWelUsEzzzwj68DYOvlod3cXsVhM1qVSqRiCl/vDvdXwWUQU0vB3/HfO02azybMZ++f3tOtKY+gSNo7fYyIWP0+n0+Jq5Hs1j1nrEt1ut+Eu1PWt3AuucyQSEUUKgAibB8WGr1y5Iud2bW3NKD0ajUbw+/0iQ8rlspFAR6LAotuPny8tLRnJdgx1ABCl70EdP7heW1tbchHohDmemWq1iqWlJTlbJ0+exPXr1+VyoNub8kN3HAEOkKA0Pq02NnRZFON9/L3f7zfyODgXyiad+DWbzYxYpw6PTSYTRCIR+T5Rj3TvVr3mwWDQ6Oe4trYm+wfMz5BO0NLykKV8usZYK+NMtATMy9+qCFg7jrAmlXt1/fp1wYPlHHW9Lsuq+GzddSQUChlKJZVcjeDDMfD3D6MjL8uNjQ3RuO/evStZSsBc2IfDYdG62CZKAwbYbDbJxGTfQl6Iuth4NBrB4XDI4oRCIYTDYaPJr94EQqpdvXoVgKmN6xgkhZCuVdJWjNPpNBqy8kKjEIxEIkin08Ico9FILlK73S6NRPksDUfGcemWSsVi0YAQJGl4KhKz5Bjr1PGk7e1tmVOhUMD169flwDPexAOtYQXJINT8H3/8caRSKSMpRF/MGuBB/15DghEPUxdi61ZDe3t7KBQKIrhLpRJu3rwpz2XSA9dRJwu4XC5Eo1GJXSwtLSGVShkt17gXHJ/OiNSM/zd/8zcyhlwuh89//vOGUjAcDkVwnT9/Hqurq3Lh1+t17O3tyQVPDFoNXqHfxbZjwFzg+/1+2Y94PG4Um1Pg8ywwTs/36ucQhJ5j1oKLiVs6g1zX5nU6HQPPVjdBZ7ISL89YLGbUGC4vLx+Knz5MqHzzm9804Ad1Da61lo+ZwfpS04pgOp1GPp8XQagL4OnV0fXJjO8DB8oF/65UKtje3hYFSWfJ8uwwwU9joyaTSZFZy8vLBhwcrXsN7K8B5nUCVq/XM2ANKeBpIZ87d04aB3B+uiWZvlw0FCj3Tye0BINBA/dX9zGlsqbBHVj7zDlyvXO5HILBoNQ3soWfhjjVCpg1VvmomOXDLijKECrJ0WgUo9EIzzzzjKyNTnYLBALCW1evXkWpVJJ1dzgccp/wXcw8Bg6A1K35Cw+iRcxyQQta0IIWtKBH0JGWJbtUAHOtS2uXuvUTcABSzn/r9/tIp9NiHfb7fYTDYaMjPLWmUChktJVhnaH2M2vNajKZ4O7du6I50yLld6mpBINB0RaBA63LCgKuNRu/3y/jiMViSCaTBlKLrhHTaDfAXNPS2bJas6dL9kGIHBolg5laujmwdjO0Wi0MBgP84he/ADDPdrXZbLIGKysrWFpaEvd5uVyWbGG6utkl/u2338bHPvYxfPrTnwYwt9yCwaDsOTPttEUJzGPMR6VYWyHDdnd3xQvADFuddaazQ3WzaWC+17ToSqUS1tbWBJ3IqpHS9W2FYuS68bmMxdDt2O12pfQJmLuiqIEDcz7QKEter9fQmnXzWN2yiWvGDGB+bo35TiYTsW60S1LzCN2u2m2t33Pv3j1UKhWj6S9jagDkfFnLhPgsWlzAnGeazaZRK3rmzBnDstToQprW1taE36y11z6fD+Px+JB7VGflstYXgLgptYtfd6eZTCZGBwwtf0ajkQE3ubOzg8uXL0uGL3nabrcLH+zt7aHRaBhdhkKhkHgyzp07h5WVFflts9k0UL1ms5lRH+5wOGTNAoGAAWPJXABmtYdCIeEVABJn5LO1Bae7jrCzEddlNpsZuRSUpdo9rXMGms0mPB4PvvSlLwGY5zhwPcLhMJLJJN58800AwFtvvYVSqSTvjkQih5pjAwexSp2l/n9DbCbBdY/H43C5XCKnVlZW0O12DS8L99nhcOCdd94xujK53W6xSlmhoGPFumvS/9oN63K5ZFH5wNOnTwOYL1yv15PFqtVq8Hg8svmM+VBo85LQQVmd7KOTGRh0taZHc0E6nY7RUsVa96XrKv1+vywqBRzT662BZpYAcIxkML7XGvy1FsQDB64Iporr+Cf7tAFmuyv2wQMOutRzvdjNhe+OxWIIh8My59OnT4uLl+urXXChUEj2hH0LKQwcDgdOnTplFB1zXpyDfhZjFEtLS0a5joav4zrquOHKyorRFSUSiRhxR+0uY5IV10wDYzChgHxhre/kxfOg4nJrzI4AFFyjaDRquNB1HAcw6xIBGL0hXS6X0cdUj5G1dBxnqVQyXH1cX92vVM+H/EL3snZltlotUWwqlQpKpZLwbjKZRDwel9g1XYY6bKD5nvFhPvvGjRvybHbAoKJoXXd9+eVyOTlvt27dQjQalTGUSiWkUikjzlcoFAz3tq4ZtAKRdDodo0ZzMplImUomk0EqlRJ5VK1WZfwc46lTp0R+6aQ3fu+jjz7C6dOnDcxcp9Np9IZ94oknhJfZFUWvhZZLuja10Wig0+nIOW21WkbPXvKuVfkm6bwDHWriXnA+xWJRWlxxjL1eT3h9Op0aStUXv/hFJBIJUZZ0zXaxWESlUjEU+UKhIDwWDoelbR6pVCpJrFKfJ75bE8/XgxJ+ZrOZrHs0GkW9Xsevf/1rAMArr7wiyZfA/HLWXYx2dnZEZkWjUVy+fFmS9azlcN1u14hvW2tDNR15WeoY3WQywbvvvovf/u3fBjDPetvb25NF7na7yGQyMggmpnAhGcDWGpFOzCC6B0mjl1AT0lmRZ86ckQJlnchgs9mMWsdAIGCAChcKBdkkCmotqMgAwAEajMYQ1UyqmYHgBZwD41y8YEqlEsrlstFIlaSzjJmgQguAWJkccywWQzAYlAPLy1I3/dXaerfbNbKKtfBivJLrRWuYMZpyuWxk8FKonT9/3uhtqJOCuO4aG/Wxxx4z+ppqi9wqeHXCDpO6+N10Oo1AIGCg0uj/Ei+V39dCXGfA2e12ZLNZvPHGGwDmF18ikTAusHq9blzgvV7PaL+mUYx0YbbGTiYfWMEpRqORKGxaoQJgtD/S3gk2sSWx/lS3vFtaWhLhm8lkDCxSJgPpZAzdmk3HVZlpyHyDSqWC3d1dQ3l8mAZ+5coV+Z1uQABAACS0xZpKpYQ/iaRE3mm1Wmg2m7J+2WxW/r/dbiMSiUgyCvdMN2qezWaCZHX27FkDpEFb1eSXDz74AKdPn5YL2Iq2dOXKFTidTqNHqE6eobWtZQTPVqvVMgDNWSeoEwZ1Mg1jvRwvzyQwT0DjmlmzqUulEra3t2UNg8EgPvzwQ7lsaZR8/etfBzDPmi+VSiI/tJV19+5dpNNp2cdXXnkF5XJZlO+VlZVDHr9bt24Zme8knXip/02T9kZp79rKygrq9bpgxf7rv/4ryuWyzHtnZ8fAi85ms7h48SKAwwAjzLew4hXrXskPo0XMckELWtCCFrSgR9CRlqWOmcxmM9y+fVugqL785S/jH//xH8VdaG1aTNcULRAiiOgWOjq+oLPvPB6PkRXHTDJq0W63G88//7y0edKaOXCgoRBNn9q6y+VCo9EQy4T1ZLokhUgpJD1OjfpC1xrHRO2Lv61UKigUCqLZ0j1CF4eO92rXCy1LvoeuR2pCVqQSAEZsY2VlRRrN8nnUqtgCScd0rbWju7u7Ej9j7RfrHjm3paUlcRFpqC/+V8fDADOWW6/Xce/ePXFH0b2pYwYaDSeZTIoWPZ1OaJUGDAAAIABJREFUjexDuuW0JaldPfr/9Xi4D3TNsCWVhryydoCwxputLkKdTafRV4jrS75nJiOfxWbVdK+ywwq/q5FHdBYf+VTXzy0tLYnWT3c8SWeqc47kwUAgYOQM0PWorR66YgEccttrikQieP755wEATz/9NN566y1ZC2K16pZYLA/hGBOJhPy9vr5uNHSu1WrimbHZbIjFYoY1olt4TadT3L9/X7wouVzO4EtaPbPZTHi5Uqng6tWrAvunM0OBOQ9tbm7K+HZ2dhAMBmXd+B2ue6lUkrNDS2h9fR3A3PIn2pUevzWbnhallnG6vEp7cYA5/zFrHJi7IYfDoXiybty4gVOnTuGFF14AALF2uW66frFareLChQtyVtfW1owxZTIZow47n89LOR9gxt+1BfygWDe/w8+1azQcDiMajUo9fTwex/r6ungw7Ha7QN+dP38eGxsbIjM6nQ6q1apRSx8KhQx5wHAdYNa/W+nIy/LmzZuGi3M0GuHf//3fAQBf+tKX8IlPfAIvv/wygINkGsZuGAfh351O51DZBj+zgoWz0JWLW61WjZjR448/jgsXLkjRKi9wwHS9UbjzsozH40ZrMI6Jm10qlQwsSgIl8zDoZIXpdN42i+4LBtW52OxOzthet9tFMpmUILUWtI1GQza71WodAkrQLXcY09FwVbokh24sDZenSyQ8Ho8cJF7EupBe4/cyxkXlguufSqXE/Z7P5w0sWxZl6/rCWq0mAolCmxc0BZxWsvgsK0Qgu7BrnFUdM+Il96BgveYLtpZjr9Yf/vCHcLlcUlLD1klaWQwGgzIWxsF1idLDiElHuqTD7XYbCptOk2eZDOdHPuHe8Z10BfMyIAygxpLlZcTPtfDSPBYIBIyYLZUqrmMgEDAgDa2gD1r4nT17VgRRp9PB+vo63nnnHQCQ+jfdf5TAEpyj5keee2tIAjhwfVuxprlHrVYLPp8PX/nKV2QOBMgHIEqFrsu12+24ffs23nvvPQCQUjB+9+7du0Z+QLfbRSgUkneS/yhfGKcE5gpjJpORc0MMVV1mopWbdrstoCFcGxJdzvz/ZrMpY2Cpku5JORgM5PPLly/j85//vFwm9XrdkEUaDCCTycDv94uSdezYMQNisdlsGq7+d955x+ghaq2f1aEZrj1Ju2S5t9wXln7wjITDYSwvL4uc1vvJ0izeWxr8g+8JBAKyfk6n02iV9qB8B9KRl6UVVcLpdIrm8MMf/hDf/va3xXLc3NxEpVIRrZOZs7qxsEaq0bU/FOw6RqQzKonww0U+deoUEokELl26BAD453/+Zxmj1rRYh6aFWTKZlIWjlakzDL1erwEIoGM5fr9fxtDtdlGpVGTDWM/JvzWOJTBntNOnTwuyjt4UnXkImJnCDN5zTMxO1YxoRVMpl8siYFizxPmk02mj7lIjbrB+kEQG0ggsAIwek1evXkWz2TR8/g6HQ5QGj8dj1PfRamFsTfc/BcyuIfRW6P6a2hPAOeqLViMQ6UQSa4ZqpVIRpSGbzWJ7e1ssTY5RH2CtVDmdTuNy8Xg8RvxdIxixtk57VHTtKzt+PPXUU8Z6cH90IbYWmNxXnVGus6aZJakFobU+lmNoNpuw2+1y8VJZ1XyjLU19Tq3k9/uNAnmd1V4oFPD444/L34wDayVZI2jt7OwYiS2sbQbmwlE3TWBHEr670+ngzJkzIn8Ivm21ijXw92QyQa1WkwSVs2fPIplMyvlfXl5Gp9MRQUxvFy8iJruQL3W9cTqdNhpo6yQ64KCaQIMslMvlQ3jVwJxH+A4C/Oteszqzm51AaJF/61vfwrFjx4w6X70HvV5PrND9/X1BywEgDQ/0HIrFouSOlMtlhEKhQ4AcVqJic1SdpZ4z+9bqXAu73W4YLxrlSSt2BDwgX7AO1pqxzL+1gmylRcxyQQta0IIWtKBH0JGWZSwWM9pkaavtX/7lX/CFL3wBX/ziFwEAP/rRj9BsNiXelclkRLsFIBaCtZ0ScNDmiaTTrQFIZiK1RLZwOn/+PAAYLbWAA80kkUjgN3/zN0VbePfddzGdTg3kEmvGWqvVEi2E2rWOoenuEHQL8p26fyBjnRzrysoKjh07hrNnzwIwS0f29vYMl8Pe3p6BTqHn1Ov14Pf7xWqiu03XWXk8Hlk7baUGAgHEYjHjtxq3ld0KtKXW7/cNa5NEtzHTuknkEe4n48/cU1ponJfGsOR/qXFaNUx23dAoN9pysfYs1JalJkLw8d1PP/00KpWKaNT0IGhtU2et9vt9BAIBY460CNvtNtxut2j+jHVa+5pqt9CJEyfEBaz5wgo1qKHj3G639GcFDjRrDUmo93I4HKJarcrnhJgE5tZNu90WDwJ/a61js/YsJWkL/Nlnn5W9rlaryOVy4oUoFot47733jG4t2h1MPtb4rclkUtbS6/XKWWdoRCPkaNf5hQsX8M1vflPcjdZaYS0zdG3jYDAQK+zevXuIxWJG1w0dM+/1egasIM885ZTf7xdZQ5euhlcbDodyTnlmdCZ6q9WSNdfr/cILL+A///M/Zcy0SoGDlm8cI8M4nMPGxobRcYZoPjpbnd6x7e1tw8MQi8WMd7VaLXz00UfCs16v16h5fFhs8mFkzYbVcqher+PGjRvyHu2J0+V+hBHUGf4aTs8q0yjTdSu7h5HtQaDTC1rQgha0oAUt6IAWbtgFLWhBC1rQgh5BR7phv/Od78xozq6srODSpUtG1hVhwwAIALfOZNSuG91ii8QkkM3NTWxtbRmB5eFwKO6cRCJhNAi+ceOG0f4rGo3iBz/4gQ0A/uAP/mDGIP5kMsH+/r4E7LUbje93Op2S2HHq1CmjeStdAXR99Xo9I6tTw04xYUkndgwGg4e2VHK73fjRj35kA4Cf//znM7qL9vb2UK1WpQD3pz/9Kb72ta9JYtVbb72F733ve5KC7vF44HA4xPXRaDSMkpWf/OQnki1MNxLnb7PZEA6HxSWVzWYFQQiYwx3a7XZZt9u3b+P999+3feYzn5npIL4GtGbCA12c2WwWsVhM3NGPPfYYzp07Z8Cr6UxoTR988AF+9rOfCbADMxGZ7k43pc5+1W3iJpMJisWiDQD+7M/+bMauIuxeQddTLBZDKpWSfScws4Zq1MlpbrfbQJh6UFsf8gE7kmg3WigUEr546623sL29bbiV/viP/9gGAH/0R380Y9F9NptFJBIxQMUjkYjxbpYVARCkJrrj6N7Tri2djFer1WSdmehFd/f169exublpFHB7PB5J/JhOp/i7v/s7GwAUi8WZLtLX2aHMnuS6M5NSu8g0SAizS3VTeN2Au1arGaDeen0IDq9LRXRhfLfbxbe+9S3bD37wg5nOctZJfMzM1m337Ha7ZNezLEMnoFm701jbbtH9R4QvnsV2u41erycu4HfffRflctlopP3jH//YBgA/+clPZlwjn8+H5eXlQ80FHubS7HQ6BgRopVKBzWYTGTAajeT8MANchzV0gws2Z2eSJxF/tGv5T//0T20AcOzYsRn55eLFizh//rw8hx2b+Dm7IemsWp2UQwQjzb8kHc4habg/VhLosFan0xFIv3/4h3/Aq6+++kD/8cKyXNCCFrSgBS3oEXSkZRkIBAwYL522zxRlHazXgeJMJoNEImGkHQNmoJoWy+7uLmq1mmg7Xq8X6XRaAs35fF4arwJzmKl8Pi9aptYsIpGIBP+ppfJ3jz32GNxutzz3/v372N7elmLXYrGIlZUVSbZg3Zqu9dMJPQ6Hw9BgqHnxc11CUCqVDLgrHSv+7//+b6OUotfriWWWSqXw2muviRVjt9ths9kkqL69vW0AbLN58ubmpqydNUWb66XxWYGDkh1qcLdv30YkEpFgOde7WCwa8x6Px6Lhr6ysYHV1VbRvNv1m0oPT6US/3zdKW3SdpoYfI94w14KWE5OLiJ2q8Xd1oooO1lsTtRKJhKG1s/6K66IhB3u9HrrdrtE2ze12C+/H43ED5k5DJDJhiZ+zKF+v5c7OjgG0rokJY7SwaKHzM657NptFMpk0+ifqZs+RSMRIzGEdNL+7vb2NX/7yl7Lve3t7so61Wk36JgIQMG3d+ICky4YIiEFLw+l0SkNv4CDJTyexEXgCOGgkrq1hbe0DB94p1lxqr9LOzo6cfb/fb2C16r67fB8tWxJLDjRYhS5XoGVo7VlKq0WDU/DfyasEWNCwlISOBOZ8oGUc5wHAmGc8HkcymZT3sO+jbnrA8hIAUqeswVV0LSUBKIADHuGc2bKQY2Fza8o8tjujdaznrpsNcJ01mIMGRyEAh8YT1z07+T293tr6123fNGALf0P4Sn4/HA7jk5/8JAAYvTytdORlGQqFxPXZ6/Vw7949OfTxeBzBYFCENgUeF4SZr2R03cEcmG8SLw+a7zork24ULpbuZr+0tGRkK+rDShOe7+B3+e79/X0DYNnquikUCkbGLnupcaG1y0VnzrLuh+9mFiiZmplkGh2I9OKLLxruNTY15phu374tzB2NRvHiiy8KIzqdTqTTaTn8p06dwv7+vtHJQRfc6l6LFOIUZvfv3zdAwdPptIGgwjHRBchnJBIJyfI9e/YslpaWZF20yweYC8BKpSLryOxczkcXFDcaDQPw2+l0YmVlRRBWCA5AzNr9/X3pMgDAUE50Jna32xXXKnAAVs9DH4lE4Pf7hR/5fY336vV65bIFYHSJ0cDWxPjUaFTEEQbmSlOtVjPmT9K8yO7xPMys7aRSsra2hqeeekoEGbOOeTlqcHryAoVRs9nEtWvXJNuQrjkqjefOnYPP55OxbW1tYXd3V8amXeq6NpuCS/cd1Zi0dHHyTHzwwQdotVryvFAoZKBV6cznRCJhZDVSkdeZqna7XfblxRdfhM/nk441lFO9Xs9QXDVIAZVtriEBFRguCofDOHnypKx5PB7HZDIxZILGst3e3pbuO5VKBW6322iUze8AB4os10bzGnAgT+PxuOG6bjab2N3dFbf77u4u7t+/L3vN2mqNfx2LxYx15Py4ppxPKBTCqVOn5PwFAgEjQ9nj8RgdUDSgjb7smJWta4qj0ajwWyQSMe4Onk+9LxqBR+N/U6Zppcfr9R6qDdYyxe12C08+/fTTeBgdeVlq66HRaODYsWOCepLJZNBqtaTdU7FYNFppdbtdaf/CxdIXz+bmJt5//30Ac+vH7XbLBt+5cwfb29syoY2NDdFKubCpVAp3794FYAoY3SV8MpkgGo3KhX737l1jAzUAAXBg9eo4AhFpSGQ6aoZasx2Px4YfnlYVMI//JZNJWQ9r6yLdFqhUKhmxz2q1agjTV155RVrOfPnLX5aO9ADwq1/96lDTVTIIkZI0wIEua2CXdY4xHo8jEAgYjXL577T2YrEYTpw4IU1sY7EYJpOJjKFSqaDZbMoYhsMhEomExIkTiQTi8biMv9FoiMAoFAqCqkRyu90iEClUdUcWzbM6Rt5sNuU5BAbnXuVyOUEA4Rqn02kDpFx3saDFxjnk83kRGLykyDPRaBRLS0vCd2wLx3Gurq7igw8+MN5FKpfLBih3t9uVtQHmPERBffPmTVQqFZw7dw7AQQxPg2iMRiMZR7VaFX4vFovodrtykXDdKMzC4TCazaZ4K5aWlsSC1uvPPeHvCehPmcAWXYyF3rt3D7dv35acAh2HA+ZIXY899piclXa7bcQ7tbXl8XhQKBSMxg2BQEDWK5vNolQqGWUiwAGwOADxJuj4rlYSWq0W6vW6Abixu7srl8cTTzxxqPUc31OpVPDhhx/K3L1eL9bX18VTQJhH8sX9+/fh9XrFWGFckHMn9Xo91Go14Z9isYh8Pi9zYNcbjnk8HqPdbssZ0tYdYFqW9Xpd4BiB+UXUbDblWblcDuFw2PAcuFwu8XawjJBE3qPix0u50WjAZrNJmc/q6ipOnDghiqDVS0LgEZ4VXfrB+ZFX2UWFBkAwGDQU8GQyieXlZcN7+DBaxCwXtKAFLWhBC3oEHWlZAgda49LSkqE53bx5E/fv3xdXVbvdFm0OgBTYUoPpdrtYWVmRG127XUOhEHZ2dvD6668DmMfhdKYpC32plRG6ihqpdvN5PB7DoiCQLscAmNBIbOvEf9fWZrvdNuDgWAAMzLUuwoLxM91GiVBktJg+/PBDNJtNKdDW2rjT6RQtq1arHYI105nE1H7Z/NnpdOK5554Tq+fVV19FtVo12p3RhaNhuICDxsQadrDb7coes92ZFT5uZWVFnsmYJrPJdnZ24Ha7Za9SqRTS6bRo3+PxGMVi0WigS/cvx0g8XXom6KoqFou4ffu2AXG2u7sLa8YoLSIdH280GuIOrFar6Pf7RvujaDQq1vCJEyewvr4un08mEzSbTdHyZ7OZERf2+/3CM/Sg6F581WpV1pSQZrSeHnvsMbTbbfz85z+X9SG1222JZa+trSEcDouVxhi+zjQtl8tG2zGC6pM8Ho9YHDs7O/Iuv9+PJ598UsZImDPy8pUrVwwoxEgkYsRONWnINrrLeO4YhiBW7GuvvYadnR3DhZ9Op4X/9/b2UC6XZX+j0ajwDXuQkqcbjQb29vbEGuGe0PuwvLwsXjCOk2tCPqlUKhgMBuIOJKwa+aDZbKLZbMqZtvZPZcxRywTN5xogJJVKGedeu/mBA3B7WmP6PPt8PmPM+rf7+/u4d++eWNTWBvd+v9/Iyiekom5wz5yOZrNpFPz3ej3BrCXpMM94PBbQc8DMy9BZxYST5Fq4XC6Uy2UJA9y8eRMnT56U3IRTp04hmUwaGN26QYS2JO/evWtAo4bDYSM8ppt5c8waz/YoeuRlScHqdrvRbDalhOGdd97B7du3jc1+8sknZRPX1tYOxct0YHo4HBqp+C6XS1x7PKg6BXs0GslhrVQqCIfD4pa1dkfR/mufzyfAwKurq9jc3BTG2d3dNdL26eLTvc50KjlRNbjIGhiefe20cAIOLiomhXDM2v27v78vc2NyiI6T6gOplQiO6bXXXhNXc7VaNfAuvV6vUZahOzUAZnCcQNc6oM95atKCkgkqfObu7i729/dFaJ0/fx7BYNDAl9VIRxR2+rLUCTp0/QBz9+/q6qpcCIVCAXa7XS6Aer2OTCZzCG8WmPMMhSD3gG4iCnG+t1qtolAoyLqlUikMBgOjwwqbeQMwgODj8Ti63a7RE1HHFrPZrIEs5HK58PTTT8t+UwniXvF3y8vLyGaz8tyNjQ04HA4RMKVSyXAVE+2Iz41EIrh8+bKc1+vXr8t36X7luPx+P5544gnB/Pz7v/97bG1tyTmi25qXmD5/lUrFABoHDpKUvF4vGo2GrDNLHnih6SQsjrndbss+zWYz2TuOl+613d1dJBIJUS5GoxHK5bLErVutFhKJhPApFWiHw2F0Mzpx4oQoOuzlqkMtwAFqEEMa5OFWq2WUIHW7XYmn37x5E51OR3hwdXXVaICwt7dnlCexeTflrXbvBoNBw9Wt12x3dxe3b9+WPeB6a1d4NBqVC73b7SIWi8mYi8WihLe4JtqFy3dw/1ZXV8UdHg6HkUgkJCRhbTDACz8QCCASiUj5G5P+yJv5fB6bm5uiNG9vb+Ps2bPSuDuTycDtdst53N7ellK5SqWCY8eOSTji+PHjyGazRr9LKxH1R8/xQfTImKUGyL579y5ee+01AMDPf/5zlMtl2fx+v49Lly6JVri5uYmzZ8/Kjc1WT2Q4bhowPwRPPPEENjY2ABy0beJ3i8WiEXPq9/tIJBJy8bJ+DjjopMFFSKfTRlKNy+USBgyFQlhfXxcBRGtYZ2Zms1mj6z3HRPgxXuhsVaMToDSsVCgUwtLSkgg/rSnqC77ZbBoxLWpJur7T+X/Ye7Pfxs/zevxQIiXupCiJpCRKs0ia8WyescfL2E5sJ0GzuChaBEFaFGhuelG0l70u2rui6F/Q+wIBepNeJE0DNGgSN87EduzxeBbPaFbNaKEoUdw3SRS/F/ydh+f9zAb8rvkCQcwR+Vne9VnOc47fbxOyWCyi2Wyaxzo3N4df/epX5iX1ej2bCMlk0kFE+nw+B9XJA5/XDgaD2N3dfYI+TkFB5XIZ7XbbPMfZ2VncuXPH5gXJlxV0pehXLnaOWa1We6JvaIBQ5o00h5OTkwiHww4SkHRzT2tKBL++vm4LjIcd59P6+jr8fr8pgJw6dQq9Xs+sVz4j+2B7e9vZYDUywDpYbs7JZNLxEFl3yANLvZ9AIGAbEcEutPq5UXz22WcAgN///vdGlM9+b7fbdlC98cYb6HQ6Ng7379+3zWlkZASpVMrm5unTpx2gzuuvv47l5eUnjCbOBwVb/Mu//Iutt+XlZRw5csSpU+52u/aZpOSaz/3qq69sndN71ro+NWRZbwcAFy9edLzhbreLy5cv21qv1+t4++23rd/Zfwro4WHKw5m13gqu2dnZcRRkVLGCogxcP71ez+aIz+dDNBq1d/vJT36Czc1Nmzd7e3s4duyYHQisROD+q1iLbrfrIE17vZ79/eHDh8jn87Zm/H6/o9i0v7/vYCmi0SheeeUVe0fd41jbqHNVhaKJgldDUYE53nyoAn/y+bwjqjE2NmbO0sLCAhYXF/Hhhx8CAL788kuMjIzY32dnZ52+rFQq9t+JRAKlUskiXXfu3HEM6GAwiHg8bu8bi8WeQMs+qw1zlsM2bMM2bMM2bC9oz/UsQ6GQE4/e3d01VFY4HMb8/LxZOAsLC6hUKnZiU/PQW2+m5NOaQ2JtJdC3dDVMubW1hdu3b5tVTdkjWsJku+G1lKS63W6bFVkul50SAoZZGVYZHR1FJpNxaiUpxwS40jhAP2SqROCfffaZ3Ys1pyqVE4lEzEpWd1+Zf/g9JYpWsmkNkwJ972NhYQF/+qd/CqAf3qlWq9ZXmh9sNptmxfEZRkdHzUOIRqOYmJiw0Fa9Xkez2TSPjJ6Fen6Li4t44403nHF96623nDDRzs6O9THrU7XOaWxszCFrV8vd5/OZdzQ+Po5EImG6dmfOnDHNQz6XPpt6eEpCTsSxCr1OT0/bfdfX17G/v29o17NnzyKbzZp3+J//+Z/I5/OOziL7tNlsGiE/n/m1115zyiUoBcdn7na7zhxkK5fLdo+VlRWsra3ZOx09ehTxeNxKEX7/+99jenoa77zzDoB+GI8IWI4tMND9m5iYsL5imIreye3bt1EoFOw3H3zwATqdjiMUPD4+btdS8nf17Le2tlAul228Tp486aAYyRLE+Xd4eIjr169bKHV6etpSKcBAs5NjqyU4gUDAQf9Sg5NeIusF6U1rXl5rW+v1uu1x7XYbsVjMQqkbGxsW1QIG9X9EcbLsSecd+5AlHoz43L5920GAttttRKNRR8+0WCza7xmuBVzxgkqlgq+++sr2wEKhYMh7oD8fI5GII8mVy+Us8sPQt64Fhtv5DMqKoyFLEpzTi2Vf8vu6h6t02e7uLvL5PD755BMAcPZIoB/mP3PmjJ0lGxsb2N3dtT2FpWucN5ubmxY6fvToEe7du+fgWILBoMNwFIvFLE9+6dIlK416UXvhYakafqFQyEpHOJAsGZifn0coFLKJ52WeHxkZcTQB9/b27LuVSsWBdZO9Xg+9bDZrE4QhGIY71HVuNBpPiDTrYtjY2LCDZH9/3wHpJJNJTExM2LOHQiEcO3bMQiWVSsUpYGZBK9BfFC+99JIdLLlcDrOzs/bMCnwA3ILdXC5nz6iLAsATkGYvEUIkEsHi4iJOnDgBYFALxfDTyMiILQSqwuuYqEoCVdI12a/F6EptqEQGfr/fJrb2DwCrq1Odz729PXt/5nJVXYEHByHvWsycSqVscRYKBUecmyAKbvoaNlStR6BfysNwLv+uz6hlGMeOHUM6ncZf/MVfAOjPzz/84Q82l+fm5qwPZ2ZmkM1mrQ9XV1ctj8L+IGAI6G8UmvfykhJwo+Z808MuGo2apiu1FrkpsNaT3+d9+cwMtQL9eZFIJGw9hcNhTE5O2m8DgYBTm9ztdpHNZu2ddaN9//337d3W19dx48YNC3cfPXoUx44dswNjcXHRoPwA8Morr+Bb3/qWA5IjQAPoh0nVaCQADRgQSfD9Go0GxsfHHUFvNTpp+KjRzjpK9vnNmzcRCoUs7Pjo0SMsLCxYzp4UbTw8y+Wyo7ykhfjHjh1DPB63/eH111/H7u6ufR4bG3PIPFiH+bRN/PTp03YYECzFNbq4uIhYLGbr6+DgwGgsOXa9Xs8+z87OYmpqysmxc/x4UOrhODs7+8Ta59y4du0awuGwzXUt8FdiDh6OfLeFhQUkk0lzPGg8cr9hiofzIh6PW00r0Fe64Tz4/PPPrQ95XxJ2AP39Jp1O27UZZuezPEuDE3jBYfm0Yn/eNBQKOfmkeDzuWNiUo9FYuXoQ3W7XqZvUBReLxeDz+RwLlErnAAyRphYem4JXaFFwsHu9HtLptFnC5XLZUKxA3+oOBoN2jUwm42x8k5OTjthzpVIxa5v34sadzWZtYICBlaUeBxsJHtivtVrNnkHzl7yP5kmSySROnjxpCz4YDOLUqVPmcfh8Pnu/UqnksCyRWUdRacFg0A6uRqPh5IE0R8X7j42NOZJAlCnSPKPWahJBqPNEOUw1+uCtPfRKbkUiEaeWjQczv6/i27oIksmks3Emk0mEQiGz6huNBlZXV3HhwgW7rs/ns03yRz/6Ed566y1jvNnf3zfk3tGjRx0Qz6lTpxx0Mi15Pg/JETj+9DSAvgHKzTSdTuP48eO2BqLRqAN22djYwObmpkM0ADzJ/MLGGlr+++zsrB1+HCfOdYKU2M8UI6chqMapooZv3rxpOSOgb2zOzs7a5jcyMoJ0Om0G4qNHj5DL5WytpNNph0FLJa6Yr9S5q4w5lGmilzo3N4fHjx/bnsFrKoEEZQU5dxOJBA4PD2090IviPDl58qQDXmN+kPM2FovZuNbrdSdXTUObazOZTDqcwQcHB44koCL8NeKXzWad9e8Vaj88PHTHeozXAAAgAElEQVRwC4xc8f2PHDmCd99919lLyWjWbDafiAIpuGhsbAzZbNZBgmueUufFyZMn7ZlZL8sD+vjx4zh69KjNk16vZ5KQAHDr1i3cvHnT3jEcDjtMX6lUysaEa4tjSIwAP0ejUYexiHNKGYCe1YY5y2EbtmEbtmEbthe0F5aO0OpKJBKYmJhwLBwtceh0Ok6IkOE7Wk5UptDaNVohhALTIlNLAOhbR8pmcv/+fXQ6HbPC1cLW+puRkREHsTo6OopwOGwWDEV61Qqj5cXnUrSl8mkuLCxgZ2fH8hv1et2pX6rX65iamjLLnnVFRMepBZZMJu0ZEomE0avxfcjuwX4lOwu/f+zYMYf+LpFI4MyZM/aOtKJoqTJMRCtKLUVadQCM6ov9Tu8oHA5bP9CzpCfDcCfDipwTyrzi9f4VnadWXr1eR6PRcOq1qBTDvuFzsinFm0ZG1FKlJ08PiKFQzcElk0mbX+Q95nPE43GcOnXK3vnzzz83FGupVML09LRT1xUKhWzO8d81JK+hLuZJgb6XqqEnhl75eWZmxmGkicfjNka3b9927lEoFJyQJpGcfD9NKSjrjz4r7818J2v5tJ9v3bplc3V5eRlzc3NPeIrsi8PDQwSDQYv0PHr0CO122+Yr87teFiOgPxeV/o5/p7fB3LiGtdPpNFZXV+2/eR32YafTQSgUsnxWNpu16ArQj070ej3z7uPxuMNlS8Ue9QY57yuVCiqVioUm4/G4E9ViOZLup/p7XTNa69nr9RCLxWyuRqNRHBwcWGiUz6R10XNzczaWuVwO09PTNi+SyaTlLLnGGYZm3bmWoShXLMuAuMdrNOfcuXN23YODA6Om5Ptsbm466TD1ysklTe+XIXetlWVUpN1uY3Nz0yJ+29vbmJiYsDk3OjrqMH0pp7P+/9Pacw/LZrPpuKcqbcKDQ2uzNGRCZXA+FMNE3GC0voqk1sxtFAoFnD171tlYmLMB+ptjsVh0aNnYFFZNSLUOmuY1ms0mDg4OnA2I0mN8Rs2z6uF/eHjoSFHVajXLAfK7vV7PNmSWFygtGlsymbR385IQ8D20TU1N2YZ0/PhxxGIxJ9zd6/WcCcznn5iYwNTUlOU3q9UqSqWSswEpWT4XM/uP1+HY8tnGxsae4IfkQuv1ek4f8735/Vwuh7m5OdtECoWCzYPDw0M8fPjQ7k+wB/vDe2jyoOUGqSEVShMBg5IGBSDoRry9ve2UDNCQU+LxWq1modeZmRlbnDwAeOhpfgcY1JWqhJLSj+kzZTIZh/icoX+2qakp20DGx8eRz+ft4OF1tHC7VCo5ggZcPyw057UWFhac8KRKPPEzC9QBN2d569Yt/OVf/iWAfv5sZWXFSm7UEAAGBz7zirOzs9ja2rK5OzMzY4INHEMFxmkonaTneliqAZRIJHDlyhUH8MNrsI9IKq5gKK9kWDAYtDnMgnY1DL25cS2ZKpfLdt/5+XlEo9EnJP2UvP/w8ND2It0DSqWShchZ6qYHuIb15+fnHRwG02Ecz3g8bsTlQH8v5XhUq1UkEglLR6gAAN9XifFjsRgajcYTdbYAHHrIo0ePwufz2bgWi0UHO8GyGU15xeNxJ++oqYzR0VFLkcTjcSd9wfWohAYKDqKB5eVQflp77mHJzYUXUSIBMiHQGqXFoYelalDeuXMHnU7HsXC4mWnOCujnmnZ3d538Qq/XM8+TzBYccOYl2Dl8cSJfvTVJ2jTPSJ1C/a5a58pQRBYYvn86nXYIxomE5eIlWxAHWO8zOTnpDK4mw/Xf2Ve8BzDIcSmvba/Xs3EKh8MG9imVSjhx4oTVs2azWYehh4AaVSRQVQ8+k3pZVPDgWHFj1UXu8/kccNbk5KTlrWjZEhQQi8XsfjSKdLNKp9MOT+7BwYHTR8pDq2OtoIdiseg8M4uiVc/x/PnzNlfHx8dRrVbt9yQH58G1v79vmzL1GTUiwxpcoD8nW62W9UetVjOPBXBzU2TFAmC6gmrodTodp2CcnJl8RpK4AzAPgJumRjZu375tyj76XdWKDAQCdu18Pu9wdSooTY1L9rnyCGt96t7eHkqlkmP4nT171slLMroADDhEgUEhuSpR6Frl2mMNNjdxNbjZ38psRVUKAFZgz34gME1zv15hAo2icAwAGBaCfeXlpea6VaEGn8/n5KjZisWiecgENPJ33IP5u6mpKcNBADDjhXObhPQKJFJdyYODA1ubRKYrV643uthoNMwgUcNGD6SDgwPHWSLuQAFnuo/3ej0kk0nrV2p0aj28kg5ofxATobliL6CQeV1+flYb5iyHbdiGbdiGbdhe0F7oWSrXq+bz0um0U5MIuGwxSn/Gz8ePH7c6J2UQIQehMndorRqRtmzM/9FaoOUDuPytzGl46y6VRV8tZFp/qlqiFpGyvNDK5L2om0Yri7B2DWFqWFr5CfXflaqP/6+qJEDfqyD6izJAtPqZY+Q7qPxVt9vFvXv3zKLL5XKOVRaNRk0thk2p3DSsqRGEZDLpeLvxeNz+Pj4+jkajYd7E8vIyzpw5Y2O7ubnpIPB2d3edsNiRI0fs3drtNnw+n1m+hN5rXkdLYzT/oJyd9O6UtmxnZwdXrlyxd56cnHRKZdQT4Dzn3zc3N515oN4I0a7KS6zW69jYmCMppBGWnZ0d81ipDct+Zi0y+2p8fBwTExNODp1sLUB/vU5OTtrYqw5hp9PB7du3zbNmvaKWjug70CPkPNHQ3MmTJ+3dJyYmkM1mnVyT5uUODg4c9hWy37Cf9/f3sbe3Z97Z+Pi4E65mvpz9zHQNn7HRaNhn6oF6c8adTsf5jXIGj4yMIBqNWpSEe5LmGX0+nxNh4HU4tiozpR5Pp9NBtVp1wqMcF/aFlsZoKqlQKNje2Wg0nHWZyWQwPT1tz8z0A9fXtWvXMDk56WjE6t7a6XTst3Nzc/jiiy9w/fp1AH0WqHg8bmHWUqlkUQZgkPbSKJheV+khlcWL6Gpdn4riB9y0FWkGFZHPNj4+btSsvFer1bL9iB4px0X3Sv7+We25h6XWMnKzZ0dOT09jenracY273a4zccbGxhxCaWBQexONRq2e6fPPP3d0Jdl4LeZIdKHE43EHOMCm5MWEEKsklYZlmQdVsngAjoGgoWc9WLjRaE6I9wT6A6hSTXxuTiAtPibknO+qk585FA17Hj9+3L7DSccJSm03TVzzt6FQCIVCwWqzUqmUM1G8IWs+M5uCQ7TUw1v7qWQMBILwM4Wrv/zySwCwPDbzJNvb2xZi2tjYwOnTpx0ygVqt5oCdvOLAnU7H4e9l09KHkZERHBwcONJjDx48sBAZiRuUcL7dbjt5DlJ28ZmVjF4p1ChhxMWrBy7vreUJ2t8aWiRsn3OdG4zqxBYKBSdfpGPp9/uxsLBgKQslLCCtnIbq2u225WTn5+cdPl8KWCuRBNvZs2edAvA33njDyQcpqKXT6diGC/TDi4uLi064X4kHtIyKYDRv/SC/u7+/j1arZes0l8uh1WrZGCqoR9cb003so52dHVuz5Gtlvz1+/NhEH/hMfr/f1obf73eANzoPCCz0ko+odmen07F8r4LYNL/OcVaDsVgsWvg5n89je3vb9tpwOIwPPvjA5lGj0UChULDQqaaWotEout2u0c7t7+/jyJEjDmmIjkGj0UC73X6CHxvo71McG4bI+a6a2+SYKNFArVZDOBx2DAqtGdYyQzoLipFQOlH+t4Zw6Qi+qD33G1ons7e35wBcyCKv+SHGrPl3tXrm5uYca/bIkSPmEd6/f9/pvGq16rAFAXAmYbPZfEJ5m40WJp9JNxwuKD5TIBCAz+ezzXlrawtLS0s2kbhJak0Ok9TBYNBJpFONnANPFJpyWUajUbz11lsAYIg7/rfXUtSkeSKRcMgJVEmF/cn7MO+ogs78b5KBcxLOz8/jpZdecjZo9hswWAxsqj6grB16OHDyKV9rqVSyPqc1TS7bmZkZ9Ho9W9ytVsu8Hwp3c6EfHBxga2vLQbseHBzYYqVqim5WbN7FqihH5rNoCMZiMezt7VlekUYPxyAUClmeEwA+/fRTG/cjR44gEAjY+LB2lePEaIPm3hSUpmNRqVRsLtLQ07ycWsHkulWGmkQiYX1BMIqCuXTDVS9gb28Pjx49cqIViisgIOlp9a9LS0t2rVKpZGANHQ8lOIjH43ZIb2xsmKYqW71edyJBmlvTmlyiUvldepbsZ7JneXNS3rmvm/D+/r6jghOPxx1PcnV11cS8gYGBzIONY8trFYtFA60sLCxgfX3dvG6KOKi2bLFYtLmv69CLAO52u7ZXE0hEBGutVkOhULD1lc1msbKygl//+tcA+saq5vPVCMpms6b9CvRZogqFgkXXfD4fRkdH7dnII6tRLbZ2u+28m3cN6Hza2NjAysqKkVd0Oh2cP3/e5o8efvw7r00GJiWHIWqZ/aW4AF7Pi2V5WhvmLIdt2IZt2IZt2F7QnutZUi4KGJzmGr5gDgnoW3LFYtEps1BuS4Y71Qrhd5PJJPb3980ap1q3F3lLi4WIR1qgGs/X0CeRTpoTAQaw8Uwmg6mpKQu//eEPf8CDBw9MAYLsDhpmYViEnoqGRxTVCAxUPXjv8+fPGz2ZWrRjY2NWg0RaQA0NKaqRXjetWebe1BOtVqtWfrGysmKWMQBH+mxjYwPHjx93SjA07OUNWfB7iqClxag1eertkYNVlU0ajYb1Sy6XM6+J1+bf1tbWsLu76zC4sJ6V46hclJyTGobXPmarVqum6g70Q1WVSsWe2UtNODk5aeFjXtebn2Zeh0haLRVRxiXOT/WWRkdHHUpCNq235e/4PYZ76Uky36jW/MjIiP2efJmcv+qx89/0d71ez9630+kgnU7btZvNpqOoo7iFTCZjnmWr1XLmKkueGFGq1WrY2dmxECCjIvRsWE+nXM4cU/Xe+MzUz+Tf1bNkjlfXMq/pDRuqd7q3t2f7EhHRfF6gPzeYD/zkk09QLpdtjDKZjBPe/M1vfmPRi4WFBezv79vfU6kU2u22vRcpPTUMz6b/zffxcjHTg63Vatja2nJCnv/3f/+Ha9euAejjUs6dO2fRs83NTZsXmUwGCwsL5kkWCgVEIhEnpaKsSgyxsz/VA2aFA6+rOsF+v9+eGwA+/vhjtNttS4vdv3/fSkCAAae11r+yJRIJ/O53v8NPf/pTAMDLL7+Mb33rW9bvpOpUbIhWPei697bnHpaa8/D5fGi1WjYRarUa/H6/hVCYE1LQgHKp+v1+J0wSDAZtgefzeTQaDVsk1WrVNl2gP5EUNEKoMwdFn5OgHGBwWGpuRWtumFvjdRYXF7Gzs2M6fiRh4CDv7OxYeOPYsWOWhwAGxfIKBtIaRfJUKgydrVar2SbJCaG5Le13AiIYvqnVavjwww9t41pYWEC73bawy9ramhksvV4PkUjEQsHz8/NoNptOTaTmfdh/3hChLmKGtpWMQTcYb/kN+Xg1AT83N2ckCtQYBfoLmZsG7394ePjE+2g4XoVwFeKvC5elG/w3zl32GQFHPACPHDmCK1eu4Mc//rE983vvvYfvfOc7AIALFy6YEPkXX3xhfQIMtDA557g4NcekISkNLzWbTQtZFgoFHBwc2DtNTk4iGAxaqqLT6Thh53q9jp2dHRuXnZ0d5PN5p96MB+3Ozo5TdxmNRp0SkEKhgHa7bSFh5sg5L5RSj2T8QP+w+/LLL53w2+Hhoa2Z+/fv4+OPP7b5sby8jFQqZeuVZSXsS13nFNDWmloCdABYmZaWoeg1tDRL85ys1+TfGo2G9SsBN3y+mZkZBINB02VkLo5z/+rVq5biKRaLSCQSePXVV+3+0WjUKbPgoQHAgE1Py2V3u12b89Vq1chW+LdarWbPsL29jXa7bemjWCyGer3u1GEDA23XfD5v/RGJRDA9PW3fefDgAYrFovWd18igHJ0CoNjy+byRvQeDQczOzjq/bzab9gyfffaZkU7wHSm/Bwx4gTXErRgSv99v6/HmzZu4d+8evva1rwHop7y4djgOmvN8HinBMAw7bMM2bMM2bMP2gvZCCBCtKpL30r0PBAKOcDAVLWh17O7uolwum2XMk5sWQCQSMXf/8ePHhp4F+lYhGX+APtqViVreW59NQ2xq1SsVFdAPA8VisSdQjqpy0W63LRzQbDYduLrf77fQQDgcRqvVcgRIAVf6Ry2nRCKB+fl5J0nNphYxC9rVwlFrlh69wruTyaTD4HPr1i2zaFVsNxqN4ty5c3j77betH70ACi2VoffgLQfSUhV6P3w+sqzQslVPn9egoDWfScFaY2Nj1v+7u7tO6I0qDOw7r7wVy1QUmMJWqVQc4MH+/r79jh4I+6lQKOCXv/ylAQyICqV1ms1mHSBYrVbDK6+8AqDvyX/xxRfmrRPwwPGkeo+yDinAR70nDb9TdJipB7IiKdm5FnJrWRMAp5wFGCDK+f4ayue4KpCj1WpZOD+VSsHv9zteKpu3vIgC4kA/7N9sNu3zlStXEAqFTP0lk8k4rGDdbhc7OzsO4l5LsXq9ns0jgmm4HhXpzKZpJX5PU0NMo3jDsOqFbG9v22fOJ/bDm2++ia9//es21p999hk++ugjAH2v+f333zcvrVwuO+hekotrVEfBMvoetVrNIktMlWhKoVar2b5L2k3uWyxD4zza2dlBoVBwwvssK5mbm3OeYWJiAtvb27YfTE1NIRAI2Dtx31LZNLZ6vW4RI4IA+TumnTi2pVIJ6+vrFukhAIjXO3HihKPUo8QlVLb5sz/7MwDAf/3Xf+G3v/2tRQTX19dx7tw5B6WsQDEt3fK2F4ZhuehnZmaQSCQs7NNqtRxKLEK1+VB37txxEHWUwtGJRhj74uKig5orFov4xS9+YW752toaXn31VRvgeDzu6BF6Fdyf1Zhr4TOwpkthxT6fzz7n83mn3kdDRDxItKRD9RTD4bBT1pBOp3HkyBGHnouNfQkM8kVsPp/ParKA/gE/PT3tlFNkMhnnQEskEhYaqlQqhuokLZ1yfyr6T/Or+o7eEIWqeRBtzHmgyDReUxltgP4mqmo1lUrF0HmqDxiJRFCv1x1Gn6eFTJR+jHR//D5bPp+3PpqdnXU4Q7nA+UzNZhPr6+vOhvO9733Pctms5WILh8N2oF64cAEfffSRhXBTqRQymYxDG6nzhv/GzwqjVwWcUqnk5G3INUojzWvYsMxJac/C4bBtMCMjIxaaSyQSDgsP65H5LDRkOI944D9NIk/nb6/XQyqVsk2vUCggk8mYoVcoFJBOp61fSfvIcc3lclhdXTWjWkukGPrnwUZGMPZzIBBwysiIwPSGjsfHx21uci/wStopB3Gj0bDnJ1pcy7S01vPUqVO2fzYaDTx8+NDWMeuFFd2rjF/KwMXxZWs2mw7XqRrtzAMzXB+JRDAzM2PvSweH84TzSmtj+X408rRcTnEoNGwUvazrT+eySp/RYOL8Iwf3zZs3AQwwKewrlvAQxU9ZSFW94Ry6ePEi5ubm8Fd/9VcA+gf6v/3bv1lqjVq/RONTrks5zZ/VXgjw4aaQSqWwsLDwRJG9yjk9ePDAHoqgFXY8Yd9aAuHNG9CSf/z4sVOszLIMJQ+o1Wp2MLNGT6/JZ9SiU43lAwOqNV6XnINaW9psNp0NSD0C9cJY7qFejUK6T548iZmZmacOxt7enpMX0Xf15iyZC9Ukux5oPNw5ocLhsPVrsVi06wGDmlQtiA8Gg07piHp2fDfNWzAioDnVaDRqhhDzuno4eAkBmLDn7/nujUbDIU4nuEDlzEqlkgNI0lpf5uQ4PsolmUwmnfyWl3wiGo1aec8bb7xhIDT2KY0YXo+H75kzZ9BsNo3gYHNzE+fPn3e8r1ar5cwDBThpUwknetFaxK5RAe91o9GoI5k0Pz+PQCBgxu7h4aEZJYlEwiFhZ5mD9o8aO36/HxMTE453xqblElw7PBCCwSAWFhZsY2a9LvsuHo+j3W47ebzV1VXzSJaWlswb1nEH+gcV1y/HUGtrachynl26dMmeV0n+FZzGGmXO3d3dXeOL5vtVq1WbZ4VCwfGAdF2wRpiRgGq1ipmZGcMPUHOS70QAjFfPlk3HQ0uCKpUKarWavTcBcJz73Gu99JoKplQgH5+dLRKJ2CHGfC7Hj5SnfAeNbGikg5qz3IvZN7/73e8ADEBXapA2m00TK3j8+DFyuRw++OADAMB3v/tdOwNo3PB9/viP/xgrKyv4j//4DwB9wKMCwY4fP274E+D5h+UwZzlswzZswzZsw/aC9lzPUtUvSCVGj2Fra8tR8tbCc2AQ7yf8/M6dOw5V2fT0tFkugUAA1WrVQgcPHjxwLN9AIICNjQ0nTFir1SynSVJgAA5RgoY22QKBgFlhm5ubmJmZMeut0+k4KFy/349wOOxIfLGVy2UHJk7GF/2OMnS88cYbDhWbNvXe6B0p/R3VUYBBeEbDv1q6wdAvrcGbN2/aGOzv7zsUafSONQRDZhh9B0WdAXAKsf1+vwPRJ/KO710ul7GxsWEhE5K8a57U5/PZ9Sg5xPtQfYF9ocTcZBjRHEmtVjNPREktlEaNOVMlwN7f37c+9Pv9OHLkiIVqSDjPd2Afa0kN3ycajRrikX3M8DHfT8OBXhSntnA47HgQSkrgpSDsdrvw+XwOK496llx3Gn6mlzYyMuKombAfeQ+mL5RlKhaLPZV0WsOo1WoV9XrdkbxT5Gyn08HGxoa9Qzwex9WrV/Gzn/0MQN9Tm5qaMg9/d3fXfk+k+bNyTQzZaolTs9m0vPPZs2ftu+zj8fFxJ2JAsWeNSIyPj5swwdbWFgqFgo0tUy0sjUkmk+ZNkSGJIel///d/d2QHk8mklc7wXq1W66mUbopUZs6ec5PPrjJn9XrdPCbu15x/jBIo4YbOKfW0NfLCpuVMkUjEYUBSNLpX4KJUKjmi8kqokUqlHFYrRnL47F//+tfxwQcf2BgywgT013yj0XCY1ZLJpN1rd3fXmMH4zKoU8/87DKuNoSLl2Gs2mw41Ui6Xs8V49+5d3L1719E2o/I4O0gVPHTiHzt2DIuLi86hpzWcxWLRNNAAlxuW9VT8bwDOYTc5OWmxcrI5sNMJBSfsuFAoOPmYw8NDq1+8d++ewb+BAQ+k1gL5/X5cvHgRQF/PjYwX3ra6umqyWQyReEExarQonL3T6WBra8uhoKpWq7b5MfQKDMoJNMy0t7dnKiQXL150eGoJmOAC5P/3ej2bnAQy6cFRr9dtbBhyVVUN5WnlYcnFq/lAgl8Y9uGBwMXIPtYyFAV5aO70yJEjjtpFs9m0eTMxMYF79+5Z+E/zVuwn1sHxHXl/AA4N3f7+Pubm5mxTvn//PkZHR53NXMeWhyf/TcucNITJujIFAjEvx7ENBAJOLk43LvYRgSHAoDaT65Tph3w+7+hhTkxMOBqd1KVkH3lDwXwHHrBch7lcDuVy2eo6P/roI9y4ccP6vVgsOqVMb7/9Ni5duuQA8ji2fr/fAU6xnlrrDrXuuVarIZPJ4Jvf/CaA/kE3OzvrUDWGw2GEw2En1M1wNsfjyy+/tP1hdHQUr776qpUQTU5OIhqNOnR/qtG7tbVl1HGHh4cOVy8VPnSdb2xsPBUsozgDMjHp4adArnq9bqFZ3scrZabMRqVSyTHkyMzGZ9JyFvabYke0dMsLrtJ9OBKJGLBrcnISv/3tbx2mMRr2eh/V0F1bWzMQI/c1oB+Grtfrlt/c29tzypmAvvF78uRJAP30RCgUsjn2PNq75x6WusAJMuHECQaDDoAiFos5RAJLS0vIZDJ2DdZE8XDVHAnJzfWQazQatuHu7u7i4GAgt7Ozs4OdnR17QfUgtHFjVlJyryRXNBo1zyubzeKll16yPGupVHKKs1dXV+1aBOtokTTproD+Jjg5OYlvf/vb9v5ejkK2q1evGkCE6Dy13rgxAAPuRy7YjY0NB1TAjV65alW7L5VKOWLOmUzGJk4ymXQsSeYztU6Oz85+qNfrjoYkvRkFMnW7XVtQPFy5oAnM0Pyfbojepvy89OT5PYKZNKfCtri4aPOEhz0jEiS90I1Kvbr19XV0u12LdMzOzqLb7ToGAZGiDx8+RDKZNG+IQraKJFbPknVxXqFlb+t2+2LpnLskSlci8VKpZM9BTmHe99q1azh37pyNS6lUcorCX3nlFZtD169fx82bN22DmZycxOHhoUWV+Fktf7adnR0Ha6BRkvHxcVQqFQN6tFotnDt3zsYzFAphamrKCDrOnj3rHMq63rz0ZERoa/2vEpS0221cunTJjJqf/OQn+OEPf+gYLtzEVYMSGKBdS6USrly5YgcPa/a4XjY3NxEIBIz/mpJWQH/v+OlPf4pf/vKXAPoGyIULFxwZuHK5bONTqVTw4MEDp6aTLRKJOJEcrUmkE8L1X6/Xsbq6arlSrk/ObeZ5FWHO96nX6w5uJZlMGhiH46Vrt9FoGNCTn9mYDwb6+/uRI0eMoCWRSOBnP/uZYVAY5eE+RQ5r7jmkGXz33XcB9JHGCnBcWVmx91taWsI777xj9/70009RKpXMoGE9qxLuP6sNc5bDNmzDNmzDNmwvaC+U6FLvr1qtmmdIInG6zlQuUASiwoopdaPEwRpO01wh/11ps1qtll373r17uHbtmlkT9LIAVwKp1+s5rDpeOiYyA7HdvHkTd+/eNauM5NlqvRN1ReV5rcHUnFEgEMDZs2fNw9AwhD4b0Gd9YUhieXnZ8SwZFlXZMGVKAvoWHEPa9LxoHSotW7lcdpC1o6OjjvIIw4Eapm02m04oHehb3Lzf3t6eU44TiUScukmGvjXfp1ZiuVy28CrHj39j6M+bM1FGoUQi4Vy7Wq3a+2lObWpqyp6/XC5b7gLoW8iLi4v2/UqlglQqZd4+xXQpWD07O+vUwhJNC/RDmysrK7YuYrGYMy/oBWg5Qq1Wc8LobNVq1UHRalg2EAgYTR/QD2FeuXLFPIgLFy7g0umQI94AACAASURBVKVLFmKfnp7GO++8Y97i7du3zQOnAgzX0fr6OqampgyOn0qlHMpCZVHytjt37jiRiLGxMXsHepkMf3e7XUxPT+Pll1+2fuec5Ps2m02bnzqXWa/LfmSeStl4NCR/8eJFnD9/3r5Pz1K9DMpEcX9IJpMWxmPb2dmx8PXm5iYuX75sY8IoDN8vlUrZnsXaR6ZbfvCDH+DkyZNOiFfXwePHj7G6uvpUTycWi9leonJmAKy/VXlnamrKxqBQKGB3d9f2nnw+76wTKvIA/bkaCoWc0L16mvx/lafj7wC3dIu/5TNpuUs4HEYikbD7hMNhlMtl80wpvPDSSy8BAL7xjW8glUpZ+mxqasqRZTx+/Lh5rblcDtFo1N4pFovh9u3bTj28Kj15n1nbcw9LJrCBfohSY7sEaXBxsuSCi35vb88J1TB/wodW4Axr9TjgtVrNeWgWE/Pfbt68ie3tbetMLnY+h9Y9qaYcwzI6aDxA+H2Fm1OdnospHA7bBqOcscCggFyh+0tLS84gKPiIgCmgvygpm5VMJm2zAAbcoZqTIJAA6G8Sa2tr9vdkMolkMml9owQBnU4H9XrdUemgNBAwOKg0rKIbuVLacbKR+IEbDA8HGkm8Hv8eDoed2jLOC95zcnLSKUJXPU6GK7UPWX7Az2qUePXwOI7UuGOeg+ElzaPu7u5av6RSKcv9AP0NU+kXGc4H+qUjyWTSasZGRkYc8BvLdXSTV41HDT3zPgAcAwjoh67UMEin0zh37pzB6wuFAr744gsn/PvRRx/Z59XVVbtnPp/H2tqapRsSiQSWl5ctt7+/v+/U0pXLZedQp7EC9HO0upmqUawyZkAf8KNkCRMTEw7138HBAYLBoI0jpdPYNxsbGzbPWZrAfmXujGN08eJFJJNJy5devnwZQD+0yk16YmLCOTgY7uQ9qQnKXBlp2Nj43JxXum55HYLTWGushqrWtl69ehX1et3mjfLRKl8zAXla0gcM5n46nXYAd8ViEbVazfaxra0tx/AJBoM2B/1+P7LZrK0bGoW6p+sBDwxCqIBbOqJnATAg7wD6a14dABr8PPBjsRjOnDljhgYdIo6Dck1zL6FRSPAg94SxsTGjMeUYKdmFAt287bmHJeVOgIFcEg+eaDRq3iXQR7BmMhnHUtLvsyO1uF5J1anrBwwSvJpI3tvbs2fZ2Nhwaoe8g6L344HJZ3oaCbjmuFibyd8rwETztJw4SpDt9VonJibsoFLgDNCfaDwU33vvPfzP//wPgL4MzrvvvuscGMrQoflGYJAD5H02NzexsbHheIHczAiS4sLx8pGStJr9yWQ5PyuhNN8llUohmUw6rCcKIKAclvJJam43Fos5BowS5vOafH4vRy2fXYED0Wj0ibpQYGD4AAMuWNVezWaztliTySRarZYTYVAPm7/hfTVnx3HWjbPRaDgGgsoVNZtNh5RCPcnNzU0DgGhtItDfDOPxuEMAvbS0ZIcDdQw/+eQT+0xgDpsiJMPhsOXzyMyiHj1zy0Cfb1QjQV6SCo6lcvpyfHS8xsbGHKISbqjKk5xIJBwGHZUYI9qW99J712o1HB4e4hvf+AaAPmgwFApZf/BA29vbs9wzRb4V7KGgqnQ6jQ8++MA8nFKphNu3b9vvedCrvqrWI6vEYa1Ww97eno0XkbBKbq8kBdo2NjYsr0sDXQUtDg4ObJzJ3sM1f/bsWXS7XTvkyfijnN3PAhPxAOPfWRfKvYqMYDxcdb0o8PLgoC8X99lnnwGA5aV1HkSjUUeH+OOPPzbDqFAoOHrGsVjM3pfoXq2kUKOFYB9d251Ox+awGj/eNsxZDtuwDduwDduwvaA917N86623LKzD/1c4eSKRcNgeHj9+7KgAKP0Uc4ca8qQloXUuwAD2TWt6bGwMpVLJ4MBk3ad1qNa4t/ZLhW0ZslCvR603/reiZzX8q3yizWbT8fi8Xk88HjcuSd5bUWeHh4fGsrO0tGSe5ZUrVxCPxy0ez/IJvgOp0jQ3Q/oxoO+NFAoFBw2rz5RKpRzvXyW7mIuilUXVD1qIijpWSLyGbuiZKgxca6ToWWhphsK61fNgiJb3Yk5Ky28UvaeIPn1GAI63x3CbUoTF43FH5HtiYsJhGdK8MZ+FVmi73bZ5T65bzgvmR1WiTMtf+E5eD5J/45wPh8OYmJiwtIc3klGpVDAyMmI59YmJCVMTAfrW+8TEhIUc1drmXNHUh6ZFmAbhPKA8F8dQrXEVlfaK9FKRQj3NmZkZh3ZPa1YBl1uY0n1A37tVWTWig/ndZrOJ5eVlhyHH5/MZs5KiZPne+XweiUTCCeHpWPr9fpw7d85QweFwGFtbWybR9bvf/Q4PHjywtbqwsGDe+t7eHhYWFmyO8Vk12tFut02Zo1wuO162pnw03cMcpXphwCDUHY1GnZQX9y2+PxmMFKmqnuP+/r5T66l7GBHISnkKwN6ZYX3gyX15f3/f9m/WPPM+rFXm32/evImdnR1LVd29exehUMj6MpFIWFrqxIkTyGQyth41DcfP3jrVWq1mz/osxiTgBYflX//1X+PnP/85gP4mfnh4aIS8lG5RMuW9vT0H9k+ydX5W4nUvAa9uej6f7wkKp+3tbRtQwvy11k8b78GcnOofhsNhJ7nrhaArEQMPJQ3bqvuu4AVOFB4KLKUhiMRL36T3zeVyuHDhAgDgww8/xOXLl23Sv/TSSw49lc/nc0JFQH8iaq3YsWPHHJJ5DdHogc9wpJINlEolO+BZy8oNW40GTeY3Gg2HLEFJEgiS4ve50JUCLR6POxyYnNzlctnR4mOIVes+a7WabaBeCjgeQEB/k1BQhOYZd3d3LSwGDAxB5VFNJBK2KXBT1xAb+5T5FA05lctlJ8zq5f9NJpP2rHpQhEIhG4t79+7h6NGjzprgpsS+UJqvRCKBqampJ1IKCn5SKjg1OCmRpymGSqVi661arTrjoIfl3t6eI55AkA8wKABXY3Rtbc14jKemphxhg729PadGUIlLHj58iHK57Bgdegik02l8//vft5BpOBzG48eP8Zvf/AbaNjc3HYCOhnbHx8cdQAupGXnP/f19p277xIkTuHPnju1rygU9OjqK06dPW+jx0aNHDiCp2WyiWCxaHTeN4KeRmGjOjfNQQY1qRHnnW7lcxvb2tvUjQ6laJ820E0kYFNgVCAQcUhPVlex2u0gkEk5drDYNT2vjXNQ86/Lysu2J2WwWa2trNkcZWuWamZ2dtcPy6NGjSKVSzne9wEriaQCYxivH7HkSXc89LEOhEP78z/8cQL/O5/Lly05SNRaL2Qk+MTGBcDjsCOKqKkA4HHY8IK/CQa1Wcw4iJc798ssvHQ0276aocX0deFqFHFyCFLTYVScWO1IT3Ipk1NxaIBBwNhiCWHgtigVrrZYeXGpp+f1+fO973wPQt5oePXpkxOK9Xg9Hjx51BlE9OZLZay5Z35HeMZ+x2+1a/3BjVwANAQBAfyO5c+eObV68ZyaTMe+T/8++JSCHnmUkEkG73bbJyGsoH6gCmtRSjcfjKBQKNn7cDDnenCPMGfV6PWxtbTloRjZvLltztxsbG9je3nbm8szMjKP4oGxVrC/Tw4bX50LktZaWlkyBBxhY9nzHZDKJyclJGy/1hhWoxXWn9Xb1et36nWLqnG+xWMwBS3HMlZxac01qffPfuHZ5UGoOWEne9YCfmppyDlUlMqHBrPXUhULBvL2LFy9id3fXxnt3d9fy5kDfm6RnTaUiZdvqdrvW79/5znfw9ttvOwbTxx9/bJ4b+0M1PsfGxlAoFGyfyWQyTxDeM48J9MExivifmppCOBx2ivrZR4lEAtFo1GHQoQED9Mf91q1b5uFw/1TDnU3z+kB/PqpxHI1GLf+uKksAzFngdYk3UMQ/G71O9SyV6IGGuwIqKT4PPJmz1OfQHCbg8mMzh3j8+HEAMC+TY02gqRK8q3HgJX7pdrv2XtTF5L51/fp1+P1+WwtXr17Fs9owZzlswzZswzZsw/aC9lzP8uHDh4a6+qM/+iPkcjmja6IMjHLDhsNhsyL5/8rruba2Zl5EMpl03Pf9/X3zRra2tnDr1i2zUMlTSUv3aaEJbeo5krsUGKjJa50hFSaAQTmMlpooStdLwaTXAuB4H+12G5ubmw5HoZa1qDXu9/stl3Tu3DlsbGzYPT/++GMAsDwJc5b8u1LN8d1Zt8rvK7cr8xDAAK5Oy52QcjIl3b5921FqYL+fOXPGLPRSqeS859jYmEO3Nz09jdnZWUcxRinFyL2ppS5kNWF4k78lHy8t17W1NZRKJQeNGYvFzDPQMKUit5mDZL3c9evXMT09bR4CkZ/sc4b9ab1qzpfvrCjpvb0967NQKOR4YdSB9NIkessqgAFyD+gjAHVejI+Pm5fK72oYkrRxiqIOh8NmYWvIltEAb16JTfVLgQFlnpaMsC0sLDj0g6rCwb7XtMCDBw/w1Vdf2d8ikYjjmVarVRunUqlk16Z3o9qXwWDQtFrfe+89K/nh92/evPmErFur1bLIBL0d3p9oY0WLj42NOSVDih5n6JseVTAYtOhGNpt10jJeVqxSqYS7d+/a+Klsm7d56SUVac/5pjn0aDRqfc60Bxu9Lq7XRqPh7E1epK2m0hg51HIdzUXq3gg8GU3TyI2GpLe2trC6umprJJFIIJvNOrWSGl1TT5kSi15FGnrsW1tb2N3dtbKVWq2G+fl5K/XSPKu3+Z41IMM2bMM2bMM2bMPWb8/1LL/zne/0fvCDHwDo635pDqRerzveHlGMalHTygEGBOG0UPW3zAkpc4TWuRGcoQTZzWbTrhUIBPCjH/3IBwCXL1/uKfpVAT2Mv9PCUeYGAFYj5c0TqNWiBbnKtEMwAxtZKtSaUg+q0+ngwoULPgCYmZnpkXGCennMTZFkgNZ8MplEIpEwi1XBMwAs/q7esXo1mmsjSToLngluoPVer9dRq9UcgNM//dM/+f71X/+1x+vTS1Pvlblg9oOCpBqNBtrttpNjUSCDAgY4PpqbiMfjBjKjpiLzWI1GA+Fw2EFkv/baa77/77s9BWaoJ0WWIUVfT0xMOEAwBTp42VPIYsRnUPJ5zgH18DRPzHXDe/n9fjx48MAHAHfu3OkRabm9vY1Wq2WRmXg8jkQi4dSXAQNPi9y2iqIE4DwH/8b8j85zza9r7oyfGXXhO09OTvoA4B//8R97imYmOwvQ3xN03WvOH3i+Sj2bRma8Iula+8polSpgeNlZ/vmf/9mXyWR6RBAvLi5idnbW1hrHikQCExMTTg6QqHzNtamwue4lwWAQjUbDPLhKpYJqtepEO1qtlkVJ8vk8/vu//9vm+vj4OH72s5/5AODv//7vbY+Lx+NoNBq2H6TTaRPRBmBRDkYoqBijmAcFqKknySgb+5DalcpcpbqnnU4HN27cwO9//3vr88uXL/sA4M033+yxxvXdd9/F3/7t3zr1709D/mpESyN+mjfmcyrXsjaC7bjWCRLjXBgfH0cmk7E5WSqV8Hd/93dPDV0+97CcnZ21xciJx42IhNxK06ULqtPpOOFAIlzZlOmeoUN+bjabTriQFGwKb/b7/fbCSkelABDK+GjTMhUuOEWWeWXJ9LM3Ka3hUB6kCnrQw5ToQQ6mJru73a4tkkgkYlI3vOf4+LiDHo3FYrYp8tl4PS09YNODTRuh/AwrMRyiUHCSYQODsJtSghFxrCxBqnbBg4FjQoNKlTu0T3WDIaOQbj5EVAIDVimGvXZ2drC2tmafNQzrFdb1hhy9RAq6kRNFzX4iSIr9orRzlUrFGWclTQdg/a2giUgk8sQ89T6T0ioC/fC2grcokK7vxTIWbSpFpIehGqs0DtjP3kOOaESvKgR/qyQROp401rwAOg09KgBNgTLPatq3XrIKvfazImjxeNw5SFSiamxsDOl02llT29vbDsBHKwRGRkYwPT3tqOTw2tvb26hWq7aOiaimoUdie2X82dvbM5IC3gMYkCcAg7SGlpZ1u11HzUSR+FybavApSxYPdQC2rhk6TqVSmJqactDMgHu4jY2NOSkVtvfff99CnQROaTpMx5j7rjJB6TriWaNpPA1rqyFHQ5YOgXccstksOp2OpX6eN9eGAJ9hG7ZhG7ZhG7YXtOd6lqwfAvouNYtS+blSqTjuvCa/x8fHnRAZrVQVVtXkvFqgGrYDnqTJovXAkJRafsrzx0arPxgMIhKJPEGP5rWMaV2obBTgeoN8Jq/upIbX1NMEBp4r4EK0WYsH9D2GVqtllg4LdlXaTMthvOFwJuF533q97njkatmz5IHvRytV+0+tWFrMs7Oz1hcbGxu4fv26AX4I9dd6W0LMgX4YeXp62pknGiZRT4ESYSpsOzExYZDyixcv4sSJE2a9kzeXfMZ8XgD4m7/5G3tGkogzdMVyGY2SaMmDl3CfIVnOD68kmhe8xUgIf6vgjXg8jrm5OVsnCppRsn16ZfQgDg/7uqZ8Rxbpe8dNScK95U4qIK4AO9KlKeCJ6QF+X71Wr5eupSE6thpl4u+UTo4RCK9slnqY6l3ofVloznuUSiXbozguBwcH9sxayqRSY1o/2+l0cP36dZsXTA2xD8vlMiKRiAHiTp8+jVAoZPva9va2Q6Om4xcIBBCLxWydh8NhI5YABqTyBBtp9CyRSNgzs8SH78lwNPuQ9HVamkQPmu+vslv7+/s2B6lNSs+S64rrgnsF7z0/P49EImH31rmcTqfN261Wq/j000/tbwzV8x0jkYiFhwFYlEPHXt9R64ebzSZ2dnZMqo6ScVpbzn7jMx49etSh43xWe+5hSTQgb1IoFMyVXllZwcOHD53iVurRAf3JGA6HbVKSzeJpaC9OKq3BVL5C6g1yw/KyqXhr6NiKxaLDIZtMJtHpdJwQhE5+1kkqO87+/r5TO6kD5OVt1INa8yf6nE+rDdUDiahib+6J3y8Wi7h7964TUu12uw7BgzJ6kDwd6C8U3dhrtZqj38eNQsNZeh9+7+DgwNTir169iq+++spBpGquiMXtfJ92u+2EOrzoXt0QmRPhYiUnKZ8pkUggl8tZ4flLL72E119/3fK9PIAA4B/+4R8Mdfmb3/wGnU7HWJJ++ctf4he/+IXdt9lsOnVfRBHzuZnz4Tt6a9M0f601rey7Xq/nbFaKyNbaNDW2qEPIZ9rY2EC5XLaNijWNXlJ8blihUMjRGdXaupGRESN44GetlysWi5iZmbF1wE2cKRklyfCGQoloBQYbuxKV8H/8rIQO/H/NZSnvrH4X6K8dHi4UOdfcuOa2eE0KvmvjobCysuJgJ5jP5zVCoRBSqZQhKP1+P/L5vJEsKFKbtYM08Fm8z89aw8v/DwQCTyj+AAPRc/ZxPp83BCq5WpWJRgk5gP465jMGg0GUy2Xbt8fGxpy6cyruAH0Gr62tLeeZ0um0kwefmpoyI0v3hAsXLlhaZH19Hevr67ZuIpEIZmdnjSh9cXERExMTTirDiy0BButO0a63bt3CvXv3bM+gcgr7lYhrNWiIbdFrP60997BU8djDw0NcvXrVNslAIIB0Om2Dcv/+fTx69MiIBGq1mh1yAPDOO+/gjTfecOjxOAmvX7+OK1eumHVAxQB2FlUQuCnOz88jk8k4Fh5bIBCwxUnlCGVrmJmZsWdiOQs7iH/zHkTqeSpAR4mSATzhZXrZaNSK1vxtJBKxmHq73cbRo0ftd81mE3fu3HEOOC0on52dxcjIiDFYZLPZJxTK6QHl83l8+umnuHbtGoCBqgWNomAw6EjyLCwsWIkCMDgs//CHP1hJC2WkSN1Hg0JLGNS4UaAM+0wp+ZTxxQtzj8fjT3hxjx8/ts2NRd0Epak1HolEzAN99dVXMT09jVdffdXG9KuvvrKx5eHIcWceTkFMgUDA+bs2NYoYqVDjYXx83Mk1Z7NZvP766wCA//u//7PrqEfA+5PRh1EBbkwkf1DPqlgs2lpIp9MOEIteO99f1VxY9K5ej5eGTlmavBRxWg6hiipebAIPP47T5OSkwwpGeTwF2nlBgDwcS6WSs5GTdYkbPZlcNHrFZ2cfkpSAniJlorQMS4vryXbDfrx9+zYuXLjgAOh4YGxsbGBtbc02eOac2W/JZBLBYNBykwTOKbiGTaUSK5UKdnZ27BlHR0eRz+cdb12l3ngIsd9mZmYcgQs11EldSG/swYMHWF9fd4gu1tfXbf/gns9DUddfLBYzGcKPP/7Y2Rv5Dgo0fOWVV2zsKDSg6+zg4MAx5nguraysoNPp2DMkEglkMhmnPJDMTECfeEWlJp9HdzfMWQ7bsA3bsA3bsL2gPdezVG2ydrvtwIRjsZgDv6dFRCufXppar9Vq1aw4iicDA8i1hihVKHp8fBz1et2s7mAwiIWFBSNM0JCs5scSiQT29vbMBWeIjJp2+XwexWLRrExqt9Eq4TspJ6oiKjUHSQuY3202mw6RPJuGFtjGx8fNY2Boiyi4crmMfD7vWF2Hh4dmWV68eBG5XM7hhpyZmXEsJIYjHz16hDt37pjlTD5ejilLMzQXNzMz41DZAcBnn31mVh1LLGiZ0YNWL1upqdgH6qWoViJLS/i90dFRe9fx8XGHws5bjtRut/HFF18YmTgpBNkH7FPOCY0KaK6CSG5v6YRKR01OTpo13+v1nNC+om2JaFXP0sv5qfl5ncte71W1SbvdrkP+zvdSObODgwMLT8XjcSsr4HOwPxi5UFSnolIZElTRAEUz1ut1iyx4hQpUD5LNWwKmAtbT09MOAYcSWFCbEOh7kjs7O/Z+JMJgvxOtz3l6eHjoFOtz/im5PEkSlOhhf3/fnicejyOfz9vzh0IhR0y91Wo5ZV/lctkibRsbG+h2B2LUjx8/Rjwedwr6+V7AwNNUL5xNJd6Im1B5q1gsZr8jb7OGLmu1mhE97O/vO9JtSlxx//59PHz40PaeUqkEv99v73fixAmH3pD35P6pBBvZbBanT58G0I8ALS8v299brRa2t7ftOnfu3MHo6KhFEpPJpFPF0Ov1UKvVbK6XSiXzUtPptFOqxdI7LQvTa8ViMezu7jp7zrPacw/LRqPh5Lu2trZsU3/8+DHW19dtoh07dgzr6+u2mDmBGJNnWFVr4DSurETVDEFoLpECoUB/Eq6srJgSChXd2RmaD9re3rbDodVqYXR01MiMu90utra27PfNZhONRsNR11DBWYVcE46sQA1tDHNpeUs0GrX+0RwCmU4AmBoGn3lnZwe9Xs8JI6jG5JUrV/D48WOcOnUKQH+CzszM2PUfPXpkqgi3bt1Cu922iZPL5XDixAkHTKQGUqvVwurqqh0+Gpbj2NRqNdy6dcveiyLXPKwDgQBKpZKzGJPJpB003Bw5uR8+fGiLhmrq3uS8LsKxsTE7AEZHR1GpVIxlamFhAa+88gqA/pxh0n9kZAQTExM2f8rlsinbsM8VbMGQGccgl8shl8s5ICbOTda5KoiFpPvsD6/xsLKyYpu+/rt+j3ldVYRX/uT19XXs7+9bWE/xAhynubk52+harZbNi9XVVYcZ6dq1a06aYG5uDsvLy/bbVCqFY8eO2drnOuYYKGDsaU03PWBwUHQ6Hezs7DjlLTw0ALcsjMxRylGsId96vY7p6WmnpEJDmbq38Hk5/lwf6+vrZnADsNw8DwXmHDXFoHP/xIkT+OlPf2rPoyAVAgR573K5jHA47AgUqMKH5t+99aihUAi5XM7+u9fr2Zqp1WpYW1tzCPT1oCkUClhcXLSxjMfj1g93797F/v6+XTufz2NlZcW4fOfm5vDmm2/i61//uvXx2NiYGb9qMH/11Ve2B6RSKcRiMRubXq+H2dlZM5p4trAdP34c09PTjkGgINBWq+UYE1rvH4lEHM3UXq/npPiYy2d7FosV8ILDUnNjqtrNdvr0abMWjh8/7uQ9otGokdYC/RN8cXERZ86cAdBfoJRcobWmC/T06dPGwP/48WOMj4+bxeJliFcVcS0KPjw8tLwH0Pd2M5mMbUzXrl3D4uIiLl68aO+by+XMOw4EAshkMg5Yhn/zSrpQwojPRtorzamoN6H/3Wg0bODD4bADDJifn3eEiZvNpmOUkHKPwKu5uTkkk0kH8co8DknmNSeoXh69IhUaPjg4sN8r/ZQKRI+OjhpggIT4WpOolhsNDm7qs7Ozpi4A9Dc99m2tVnNQnhTcVpo5enkc31QqZX3p3cSV+KBcLjsIVKLo+E40rNjH8XjcNg3m/9iP8XjcrsW6NW/xvBqCzJtoH7Mx9wy4yjz37t1zak5v376NL7/80g5Ljs/y8jKA/nq9f/++zQNa19zIlAhhamoKwWDQ3qFerzubxtramvUJ73P69Gm89tprNobaz2wU+eX8YSSDh3Kr1cLU1JSTR6QRAPS9EQWHsf6T/apKRX6/H8vLy3a49Ho9R0KQG6iSdQBwIlqUxeLaKhQKqFar9nlrawuhUMj2vEKhgHw+bw4BBYs5LzKZjEOcrgeez+dz0LK5XA61Ws3mGNehUs1pH3N9xeNxh9bwzp07WF9ft7nPfZVjdObMGaTTaZs3RNZrXpn9cfr0aYyMjNjec3BwYNSQQH+ufvnllxbhe/XVVzE6Omqk//q+1WrVHKXXXnsN+XzePtMgUQUnxb80Gg28/PLLjlGvANF2u237LA9srdGMx+OOs6G55Hq9Dp/P9wTQ6mltmLMctmEbtmEbtmF7QXthzlItkhMnTpi312637aQH+lbZiRMnzApptVpOTH5hYQFnzpyxUI7CilutloNoPXPmDN58800LJRQKBQSDQcuxjI2NYWpq6onQJwBHMofirfQ86vU6QqEQ3nvvPQB9a+7o0aNmlW1tbTkSSHot9getJZKfM7xRrVaxvb3t5MWOHz9ucGiyHTG8oCTDh4eHDktNtVo1y2hiYgKlUgmXL1+2+37rW9/CyZMnAfTly4rFopFB+3w+JJNJp5ZUWYZCoZBZq9VqUalULwAAIABJREFUFZFIxCy6druNe/fuWdh1bm7OKbWhx0bkJdAPvysqmCK2/Duh9loy5PP57B6vvfYaXnvtNbv2vXv3rMSDlGCcB91u16HyY/hTw/mvvPIKFhYW7F5shPmzKQE0/533IUmz0gTGYjG7biqVQrVadfT3GHEgilY9S/WIFKbO76dSKatp1nItn8/niDXXajUbi4ODAywtLeHll18G0PdiJicnzTKu1WooFAr2jouLi4jH4/Ycfr/f7nXp0iUAA0+8Xq8jl8vZ3Kd3pnWonU7HIkMrKysWnVFKyF6v54hEE92saG3Vg3z48KFDnK/9wD5QrdpEIuGUyiwuLjpoey1DAgbCv/w90I/k0PMlK5iGY6enpy3Fce/ePcTjcfMsiTrltajHSQ/8xIkT9tu7d+9idHTUPm9vb+Pzzz93omnxeNy87qmpKas3BNxcWjqdtr0vGo2i2+0aOv3evXuYm5uzZ7h//z7OnTuHP/mTP7HfLy4u2v6yvr7+RImb7qtK0fn66687++HCwgJGRkZs72ZJFOeg7unxeNyiT9PT0/j1r39tY1Wv1y16AQx0dvnOu7u7JoHIsdNyKy1vIVMX+5HRDa5xnhs8u7SUit9/VnvuYRkIBOwmS0tLOHXqlE3s+/fvO1yHzWYT3e5AycDv92N+ft5e8P3333fizppTSafTDjVZOBzGw4cPbUNloptu+dbWFprNphPD1mfWhaB/I1ECB/P1119Hr9ezjW5+ft4B7RCcwcWqWo8MdzGUcPbsWdy4cQM//vGPAQA3btzA8vKyHZazs7NIJpMWPuEmCAxCd7yegimKxaLlCNhXp0+fxg9/+EMA/YmnGmxcZFr7xAWXTCYxPz/vaCseOXLEJk69Xn9is0okEs6Yso/1+alHCgwOSw3tMjwF9Bf3kSNH8M477wDo11/lcjmb3PPz8/Y81KZU2jUNbfF5eO9SqYRyuWwGni5sv9/vLGIty4jH4wgGg2YohUIhp8A/kUhgZmbGxpzGAQ/4mZkZSwXs7Ow4IV+GWbWUREtnIpEIMpmMzXUFZikpAY0tHlg0PHnw+P1+HBwcWF62WCwiHA7bO7GeUIk92D/Ly8vI5/POQUsqN6BvEOncpTGh+XttfL9oNPqE4szo6KgDGtQwK+uwGaKnKLyGMhVkxbXCZ9B8JwkIlBZO82RKO8kQJ8FJNB6OHj2K9fV1m1/nzp1zqOMuXbrkGGTMm6rG69e+9jUAA8Af91OC9Lh/HB4eolAo2Hiy5lupJ9nOnj1r86ter6PT6eDNN98EAHzzm99EOBw245ki4VyvzONqKFzDmBsbG04N5+zsrI0XS9TY56w35m/5jKq0wub3+y28ns/nMTU1ZY7FJ598gkePHtmai8VieO+99+w5Wq2WU1LEciwe6slk0u7Vbrfx4MEDw9Zsbm7if//3f22MM5kMFhYWbF+emprC9PT0U40Sb3vuYRkMBm2yEZzDDYMPwhei4KjW5KXTaYvBHz16FH6/3xa7iu2ePn0axWLRSVyrUns6nUaj0XDqAQ8PD21T5XXYdEExl8jv6QbKQefkJwctD3jNEwL9QfPW63CT7PV6OHv2rFmdyWQSr732mg1aNBp1ZGbUu1CmlXQ67cgadTodBxk8OzuLTqeDX/ziFwAG3oDWPmk/NptN6x+Sz3NiLC8v49KlS/aem5ubmJ6edkS4lSOUzcttCgwiBaurqw6idX9/35GaymQymJmZsbHz+/3Y3d21xcvcE/v78ePHdi1uZuo1j42N2fuxxpR9rAdPKBSy9yQPp9bLxeNx6xcKgnNuZzIZ5HI5hw3HSw5PNChz5MrHe3h4aIs1HA47Hh7rWvWAYFOPlIA3fYZSqWRW/87ODqrVqhlCgUAAuVzO+plGjx7inGPZbNYhC6CxqsC4ubk5O9CZ/1NWJm2cG3wvL0JXeTu9gtIKxmC/Kb+rAqmURYi/UaFpn89n6zUWiyEUCtm1OMdZLw4M1jm9lNnZWWSzWdvEGZnS/Fav17P3IRmHAtHo+dPA5/tWKhWcPn3a+nxmZgbxeNzAh2Qb01pDNjUuR0ZGkMvlHIzA/v6+U/es7xuPx1Eul53aUWDgBZKViOPTaDScaz9N4oufR0ZGsLu7a581/769vW2sPZTh44F+eHjo4DKy2ayR13MslRyeaFcVL6dBFY1G0Wq1HJAR93Wgbyxks1l7Jxqvyoz1rDbMWQ7bsA3bsA3bsL2gPdezVJ5QUo3x9F9aWnJCeuTW9LLh04NiaJAu++7uriNQmslkzDshqpRWT7VaRbvddiwPb+0Mm8rNMEdBC4RipVq31ev1HHTt8yyLdrttFsnIyIhT0kEv4Pvf/769w/7+vl2bXjo/q6WYTCYtXJbJZExBgv1GlCcwEKRV6SYNVTFfpqEw9VooUQP0PaJ79+45NHb0SDj+ag1rmIX3o1QUre9EIoF8Pv9EvkIt/0Qi4Vi0hHPzGfiuy8vLCIVCTjmSlxpPw8TdbhehUMjmg1eYWy1gcmQCfQtaS4SIXuW8OXr0qBNOZB2mhnFpRScSCUd1hGOhYTSl2yKNnaYntGkIvdPp2Fhtbm5iY2PDcuhce/SSjh07hrm5OeuDSCTiyG4BcPJ3+gzLy8tYWlqyWrydnR2HQoz8vszhqhegjWhl9ju9X7ZQKISJiQnnOfT5iMx8mmIIPW2VcQoGgzbX6UV76e68OSkN5y0uLuLx48eWM19bW8Pk5KR555lMBqlUyqJJu7u7ztwnEpN75sHBgYUHw+EwGo2GeTwTExMO00w2m0W1WrXvh0IhlEolm+saEdM5HgqFHHpJ1seqcg/XBTCo0VTGn0aj4axX/pYqRIxqsU5UazxV8YRlQ3w24iqAPksb9+XFxUUkEgmro1xYWMDBwYHNVWWBAvpRgafVrHMNRaNR26M6nY5TstRsNnHp0iX7LqNiut4Ut/E81ZHnHpY+n8/ZcLSGKJvNPkGSrMTO5XLZmZg8GLlRa+KfOUuGMFdXV/HgwQOb6HNzczhx4oS9cDweRyqVsgNHQznlctlemGTFvM7W1paFUth0sA8PD52C31gs5oRZ2u22E3LSHAQBI1oS4PP5bENhjoz30jwP4+YAjANX8wI+n8+ZDHt7ew5kXsMQuVzOCWWdOHHCxoSUb9yAWVDNazNUzHB3NBpFuVy2zYGbwuTkpD1fPB63jYD3mJubc4AqgHtwdTodJ7fk8/lsMwiHw/Z8qVQKgUDAQlFKDg305+DMzIxdK5/PO0aVF9DDjahWqzkcrMztcdxbrZZTWnHs2DFkMhnnEG632442q3JLemnVlP6Oc8TL/cvP3gJ+vlsikcDa2pr1AfPPbCzw53NMT087n0njyPWqRiPDWvxbPB5HJpMxwnpy0CpPrdY/PquR8pF7BkNknCuHh4dOfpzlIN46QgWHaTmPl3Bdc4Asg/Lyy+r39Xfss6mpKfzqV78C0N/gk8mkhTQpxcb3fuutt4z/FujPGxUxaLfbtt5DoRCq1aodxJyLDHWzvpr3SiaT2N7efmrNqoY+I5GIw1fLPufYkjSdfdVut7Gzs2N9TONO67x5TxrXHK/p6WnnMOJBrOAYHTsCethYNphKpdDtdu1dK5UKKpWKs76ocwsMcCg6hnqYqtFEPm82HrKaYolGo87603s9b04/97BUAm9uCMriofprwKBQGujXRmazWcdLUWSV3++3/97a2sKRI0fMylpcXESj0XBAEdoBwWAQBwcHDgKUjYl03oOMDUB/glJEF4DDdA+4fJfAIP+hXqAy8MTjcQccwOcC8ASQgP+m+VG2Y8eO2QIj6ov9trCwgG63a5Yfa+A0t9VoNOxgOHLkCHZ3dx0yaj5Ls9l0WFyYKFeUmdYk8UBWEW429gmFVPmZ6E/ev91uO4YBD0Blb6rX6+aZ6bwgmT4NifHxceNlBfpG1vHjx41UIhAI4MMPP3TQjWx+v9/Jk2petVAoOO9QLBbR7XZt/MmVqsooWvem2pD0ZrxKOMpvqnWWjH4okEebcpgqyTU9Zc0faZ0zD2y+Mz0ufSa+L3OQypREUVwAxlZEg4wAJ9Wf1et6lXZUz1LXAL+nfK/tdtv6gBu9AqL4/vTeVX2DnL78nld7VvOlytHM5yCI5Bvf+AaAfs5yY2PDUS5ZWloyME0qlcLKyoo9f6VSQSaTsf1nbW3NiQidP3/e3v369etWk8x26tQpM1AoGs6Ik1YeVCoVuy61bTX6Q0Y0YKBFyvnK+mI1PBOJhK25vb09J9fP67HPdV6THEC5gDUqpo7F+fPnHe/N6+UrmUyn03E8VLJtaRRBydWVwzwYDDqGLMGGOgdVIJ45SyVaf1Yb5iyHbdiGbdiGbdhe0J7rWXop52jZA26dJNC3SNbW1oz9/ezZszh+/LjDytBsNs0C0DzH+vo6Hj16ZNZvJBJBNpt1ZH+azaYT4uX/AFe3EHDRqxpuCgaDyGazDmxcLRwiLVU5Xdnxx8bGzBIkfJ4Wyvr6uqOYUa1WHQsvHA6j1+tZjkm9VeYp+a7K3J/L5ayWCuh7kYquXF9fd6xo6repXpuGwYCB9cRyAJUFUq+QsH6vfJDC44loVA9a5Z8YIiLCdWZmBpVKxUocRkdHUSwWrT9GR0fNmh4dHXXo78g0wzlUrVZx48YNsyIvXryIb3/725bP5f8DcKIgfCeVYmO4m++kNY7JZNJh2uGcUCkj7Zt6ve4gssfHxx1GEa/3qHqkz7Js1bsCBrlp9STVwyXikc9FD0HDUap5St1DvbbOe0p+sWmoWaMzqt3JFIYiepVzlmosHF+V4GLfKSpZw3BEutOTPDw8xPb2tkWbstks0um0U++qbEIqtacSXOq9nz171mTcgL53v7S0ZO+7vr6Oa9euOfRv4XDY+pxrke9+cHBgijhvvvmm1Ujz2lppUCqVnFyj9nGlUrHfTUxMOOPCPVvn2P7+vq1Xolu1n0dGRp7APAB9b1jnMr095atVaj0ysSkSnG1qasqZw41Gw/o5FAo5HN5Ud9Gm3iE/c17oWg4EAg5rm1ZV8O/RaNTxnr3lXM9qzz0sQ6GQE5/2Sj/RHQb6E0fr/YLBILa3t53CbtXXGx0dtZzL3t4eHj58aDRn1JTU+LECdzjpecDw3/mcCjHWxsNQgRxjY2P2Xo1Gwymq5sLmc4yPj1tIhsXHDI/E43Hs7u46BfxaqkKxXr6/UvQx7wgM8oYawtBQESWz7ty5Y3+Px+PWB81m08knaTgxkUgYtRvQ39hZzP20e7VaLSdUxH9X6jNucMpp2Ww2rU8ZuuFCYF6bBxxDYzzIMpmM5TtZ5H39+nUA/QMglUpZn9ZqNdPzA/oSQhcuXLB8kIIi9Pk5RhpW1TAkQ55aR1gulx39ynq97ixW5WTV0BXgbkbRaNQplqcYgZINPK9580NaT6v5GpWp4+fJyUl7ZuVpJkWgEmZPT0/bHHxWiMrbj8/6b809qexUo9FAoVAwog6KTHtpIVVXVI0S1XVkOQ9r98h5SqOcUlVKcAH0x1Zz1WoERaNRTE1NmfFGgWb26Y0bN3Djxg2bZ+fPn0en07H30ZruXq+H1dVVu/b8/Dy++93vOkTq1WrVqOIajYbzLBpu39jYcPaZZDJpfc3aU6Wva7fbDsagUCiYscqcnRoVPKgIfOGeR6CeigYcHh46xk673bZ5pQcQ5f84prpn82zQvCM5sPlMKnPHtIZXvIB/05IhhorZmEr0pj30/Z/VXpizVNFePaV5E9YRXrt2DeVy2Q7An//856hWqwZwISBIWRpoubPOjuxAwWAQ8/PzDlpRdQybzab9j53LplYCN2LNYTov72GA6fV6DrFyKBRywBkUyQX6G8rS0pKj6cf3AmAF0IroLRaL9v7sN6BP8MB7sr+UOMDv99tmXKlUcPPmTQMGkMmEfZPL5QwhB/QXFg/OfD6PZrPp5DtWVlZs8X/ta1/D7Oys8cySOUOBLHxH9bK0j+r1uiM2S5SwqiooqpjPRmIIEiMA/cNSc14U0GUfc/HSW7h//z4qlYp5AwrwUU+IaDs9oLrdrs2bqakpHB4emvVO1KmKPHc6HWfecfzj8bgpv/AZuPEBA29dD2oFRujC9gop8/v87D2Qld9WNQzZvCTlmvuNx+P2WyJfSe4wPT2NUCj0TBV5VRbpdDoOmlL/v9fr2XPyeb744gun9pi6k8DgQFQUs+YHy+WyvQ8NEP72wYMHjtHm9UQ1z6b5dvVw6BXzM8eJa+3atWsIBoNG1k99VQW7qWA9mWoA4N133zUGHL7r5uamwwA2Ojpq4DYFzOl7U/Cbz8jIlDoE3e5AHD6fz2Nzc9PemVypCtQkipX7H69Fb1fFMBTgw8PyaQxainSmYcd5wMiLerfdbtfGiuLiKgL+LJ1TXk/vrcaAirADMENVIzTPasOc5bAN27AN27AN2wvacz1LzSHQQ6A1QG+PtVgbGxt4+eWXzar47W9/iytXrph25NLSEkZGRsx7XF1dNehwOp02qDvQR9JqGLLb7aJSqThhWEUjauhH49FUk/e66xoCW19fN2vv4ODAkQpj7opWh/J6MnTA5yCqUfOsSgdYrVaxs7Nj91Y6NcLK+UwaKiVClZ8fPnyIcrlsVtfDhw8tTAgAL730Ek6dOmWW09ramoXXut2uo0yRy+UwMjJiNILNZhOvvvqqXVstc8ANVWjOp9Pp2NgQfUbLl/1Ni3lzcxO9Xs/Qr0ePHsX+/r6Fcu7fv29zJJPJOHI7pCtknzOvptyhxWLRQtSktHpRU1YYYIC+pudWrVaN3hEYlBgpL6my1hw/ftzm7+TkpINW5POr5a8sNd4wpqpkKAqXVHja78Vi8YnwPucFQ8Feqj2+fyAQsGhHt9t15K9yuRwmJycdFLWGvoBBadje3p71J8PrnEP04OnV3Llzx9kHOp2Ogz5tNpsIhUJ2Xy2r2d7edrhvmRNjBKZarWJ9fd1hYQoEAk6emv3C/2b0SnmO+Vx8x2g0apGX1dVVxOP/j70v7Y3zPK8+Qw5n4eycGXK4itRmLZblWE6c+I2TtA4QJG3SIB8LtEWL9i/0W4EC/QdFf0CAth8CtAWKAEXaGk2dxXbiVZYsURJFiTs55OwLh8MZzvthcK4590NKKtqvcwGGSXHmee79vpZznStu6SDFYtHxGGneeSqVQiwWs5CBz+fDK6+8Yt61TqeDQqFgVitTkPh9pZdk/iMwsNZpgaqHBBjsWa6Dra0tHBwc2Dk9OTmJZrNp+y8QCDiWtrJ1sYQaxzAcDjveFo6nl1GNz+X8cd/yzGOqnIZ/Dg8PbR0w7U6zIzi+3vEYHR09RWuo7/buI70L+KxnyXMvy3q9bi9lrIEPbTabKBQKNvmzs7O4efOmXTRvvfUWrly5Ypcly0rx0M9ms7YZk8kkvvSlLzkXwtOnTy32xM5xcJgfyLZ43UNaJ02h7HRdcbD39/fRbDatHcvLyxgbG7MY3vT0tBPTJPco0F8UhULBXFX0q6tLQoE41WrViKXZB8qXv/xli8uxDBUXPA9SJQs/OTmxNo2NjTmH7fLy8il+V24M779vb28jHo/bOG9sbDgbiWtAQSHaN/ZblQLGXxUUMDo66lDo8WDg3GkseH9/3+Z5Y2MD2WzWNh8vCP69WCzC7/c7hz4wAHjpxaFKE8UbJ1RSZQUqkJ5OwSTHx8fm5p2dnXXiNCxACwwoEvnsWq2GdrvtgCC0XV4QDYX4AP6b5qkC/fVFYm+gfxGura3Z8+iao1uw0WjYOBEcROXiwoULODg4MKWDHMKcIxIYnJXuopcjx0sLpLfbbdtvjx49wsTEhI3j8fExdnd3TQFnCS+OrVIUkvBc8xC1oHcikUCxWLQYIAnrFUTC8dN6td1u1y4OrlnlDb1//75hM27cuIFkMon/+I//ANBXBPXM1NxAXpxc9++//z4CgYCDF9jd3XVclQo20gIP6qLk5agXiMZ8CYjjXlhZWbGULGCQl849FAgEnHJ6AEyZ9qaVnIUNGR0ddbhlKQqgo4LL93jJzJkLqu55Kqh8h7cQOoUYC63vqfnFFK8CR4VIjTCvDN2wQxnKUIYylKG8QF5Yoou3tjfZuNlsolwu27/Nzc05wfC5uTmHoX9+fh71et0x8amZzMzMGPIR6GtYWki51+s5gAoN2gM45Xbg3zVoDcBxLwJ9V+j4+LiBko6Pjx23bLVahc/nM023VCqZZqUVH/gudSVQE9I0FrVOVBtXJCYLQWuwe29vzzRDb3rH1NSUg4ar1+vI5/MGCFDoO0vfKAmBVraYnJzE48ePbc7T6bRTGJbjTNcPx4xADM6JjgOBJJyraDRqZXSAvvUXjUbNnagUgqQIVFALyR+AvhZYqVScZOZAIGDPUlf380QTq9kn9aIw5Yhz32w2jYYQgEOyTkAE1znXhbc0lK4dfbcXMq+IUiUSoEeBz6nVakYAD8DGgaCdmZkZS9gG+p4eWk6JRALnz5+3dCVWGyKSNJ/PY2dnx/rYbDYRjUbtXV5rWF1bCgBpNptoNBrmJSFdoYYyRkZGDGF+fHwMn89nAC72kd/V84aAD3XVRqNR+y4RpJwztRYVHBKJRGz9cGw55uVyGcvLy+ZNunjxIlqtlo1buVx2XHztdtvGKJfL4ebNm/aslZUV3L9/30nLUgpFpkpw7JQ6TslhTk5OUKvVnFAFq7nwOVp2DOifMZ9//rn1sV6v2/mjpOz1eh2tVsuQs4uLi055Pm9aBr1IZ1HGaYEH0kVqIQK16BqNBorFoq0/0tlpOI19Y585HqVSCdvb2w5BPXAaZa7Vd7Saz/+6RBdph9hZRZtVq1XU63XbQMFgELVazTZCoVCwxQ4MSs5o/pnGfBThxRgeDzvGqrzpCmdtVuUYZB6aukI6nY4tHEKfGee4desWzp8/b+/54osvcHh46FS14KKamZnB2NiYDXIsFnNid7VaDY1Gw6lAwH8HTrMOcbE0m00nHqSoTP7OWpocK93w58+fdxae8h7SRaYVTVjih+Ol5aY4tzxUFdXIhcqyREpXpzRm5CNlG4hg5bgx/UPp/bxweXVZs14kpVKpOMhadQOqqEuT7nlFwGnahSLtgP5FlclkrF2FQgGBQMBJrVA+UHVFdTodBw3rLdnFPaXsJNpOvSw1VUtRmRSNmQcCAczOzlrY4NGjRw5KcmRkxLg5r127hqWlJVvnfBfdo0zTYh/K5TKi0eip1JKz2q9jeXh46OQeZrNZh8qR36ey1G63Ua/XHUVM516ZV/h5TQPhd7QPDDmom1zZrkZGRqzfnCdVrpPJpJWXi8fjWF9ft/h7MBjE7u6uxS39fr+5tufn5xGPxw05u729jd3dXUszmZmZQSwWs74Wi0V0u4MyatpPzX9vNptOXjWp3ziXTE1SXm0N60QiEYyPj1s7dnd3bS2S/o8MWel0GiMjI8668yKyOS9eyefzTqWiqakpC9Hx/OLaqVQqDlUg96qmPen5rzHSUCiEvb09u+B7vZ5TgotKp+5XPT+f54Z97mXZarWcgrgjIyMOzZnSrpVKJbz33nsWw2RyqBLYnpyc2CSfO3fONAeCE1ST4yHDDnvJgpW6Si1eLY9EiL5eDvl83qEQ04OLpWC4QNlmPi8WizmBZE1opkWrbdL4aDqdRqFQsINDF386nTbrlXFFVULU8gTgFB4mPF0vZT1UtFgyrUoFm6gScnR0hKmpKVukqgny88CAExKAYxXwfZx//jtp9fh3apbAIDVGN7dy+/Lf+C4S2FMIOgD6sUOl9vIewgo11wsKGFxinDuVTqfjkDdQCKpIJpNO2Sl9V6fTQaVSceI7miPGf1MgnbZZIe9aQ5KeFqXx0k3PEnkcg/X1dad82czMjFlImUzGckuBQUyIc5nNZlEoFGxcae2dFbPScWVes8Ys1QvB+Jp6LHS/8fJS4BWFVrQqIWq90LPDNavngrZZL0tgUBqLf1MSiW63i6tXr9peZN7n4uKijRv5l/l9/sxC1bRCL126hNu3b9vF6uVSJjCF+AFVGHVMWOyAQoIQjjk5q5XeLRAI2NzzYiLNXqVScQwC7xhrHWJvQQHOoSpkFJLqAwMFnvNeLBYdgo1qtYpisWjGEin7tI/ErQDuBef1Lq2trcHn81mfCPJSgJTeLTqWXhnGLIcylKEMZShDeYE817JUhhAADoKpVqvZDQ/0If8ffPCB/T2TyWBqaso0AsYH+bwnT56YZbmwsIBer2cWAqHFvO2VzgiAaTPeRG3+rNadEo7TGlZ/vt/vd5glFMGqvnC2g5orq3Xou6nF8d3qKgiFQqhWq2a1EaUH9DU4plYUCgVDxPKZSk9VKBTw+PFj097I1sO/E2qvUHMicElyTGv46OjI3gf0LYhIJOKgEw8PD0+V6GL/OEYa2+a4ULONRCKIRCIOIQD/nf9XC0rdMXS/6BxoCSTOH3/3xlDUsvQiS70ITo2jEnWpHoXR0VGngKymDa2vr9t3uWa0YDH/ne1XxiZ+1mvN6lgBpxP2veOiMSygr20Hg0GzZEKhkOPp0OoysVjM8Rawso3O4cjIiIUvwuGw4w717k1veSjta7vddmKQyhTU7fbp3XSs1GrXKkiaUsP/K+kJyfDVSqXbF3AJE3TstW/RaBR+v99JYQgEAnZO0YJT71mz2TQkpp5RkUjEiTkvLi7i0aNHtqaY9kKEJ8MNynJG8abK6ZmmaFUdF433jY+Pm5XHta3ED2wTi1srDaSGGHhuqrdNLUivNagxSrpigT7qXUMZrExD6sJut+uc4ySxoSu50+nYucqKOco2pqh4L/KWa53ixQyoPPeybDQap0oH6YFycnJiOUZ37tzB48ePLQ6SSCSc1BLGP5UNh66B2dlZnD9/3slhpMkPDBYaO8UcR724KAyMA/0Fx5gev0dGFfZF2e1DoZAT96CrR9usg6oAC16MCkEmCIHt0rJQOoGcYKAfM9jb27M45fT0tMVsORZHR0f45S8rJzN1AAAgAElEQVR/CaAPynnzzTftPXT9KMBEqef0MKrX69ja2rINNjU15dTc7HQ6yOfzdlhorpLGm3kIAv2FzDkH+iCIyclJh/ZL6zLyUOAGVRos5jIqKCoWizkHJDC4VLyxj2ddll5hNRxv3iQPoFqthq2tLbss6KbUahR8l9/vd9ylTMtgf9PpNDKZjB1O3twxFV1vrKDDuYxGo04uKBVI7gXm4nI8WQNROUapsO3t7RmYis9W9x7jR5rvqHtZ2+69LDleQJ/NSNcm2bO4XjKZDNLptOPSr9frtl41/YdAKraZ5aMYB2OVGW8pPG/qzdjYmMNQxQsSGJS/4uWYSqWQz+eNczgUCjksXZVKxeGpzmQyNv7Ly8uYmpqyM6BUKiGdTtt319fXnfldXFzEd77zHVM0NJ9Ry9ABcPhzuXY5LmS9UlCc0t9RAVA3ra4DVW5Yuk4vLp1nPk/ni6Ljyv3ANjx8+BCFQsFxjSsrD9PJODaFQsFhCctms87ln0qlnBJcOzs7BtrkWaQxSsZAgf8D3Z1SEjE3RmNNBwcHdoBMT0/j/PnzeOuttwDANgU1JR4smhzLIOzt27fx+PFjXL16FcAgWV4POz309RBjhymqybJUi9a4K5fL9l5aAaqh62LgYj4r+Zycg/xspVJx6LgAlwaMljj7wE3Ndmisb3R01DbowcEBZmdnbbLHxsZw6dIla0cmk3EuS+WTBPoHGzcwUarsTy6Xs6A9MADU8Fm0nLWgN+BamASD8AAk4IdjzjJabBMXI99VLBadEjuKjKtWq1hfX7dDvVwuIx6PG1pSeSo5J2pNeK1J7yWr1onGtLhOOG47OztOWaRkMgm/3+9YEJx3Igj1HQrgIlLRSw7utUQ5VhqzTKVSePjwobVZabxYh5WXw7vvvov9/X0DlBBtzkP36OjI5mBvb88pa8QYLftLpLvmzuqe8cYCNYdUlVq/34/Z2VlHu//kk0+M6vDKlSt4/fXXTXnz+/2o1WoOOlPrc7bbbVM6d3Z28Omnn1obL126hEQiYWsnFoshmUzaecX267zTC6KxaVXE/X4/CoWCU3BbyVKAgaeF7+B4JxIJxGIxp6Sfxuk+/fRTbG9vm7X/5S9/GalUytrr9Z6pUOmnkCAF6F88qowSC8K/p9Npp/C5lr4i8lwzIlqtluPdAAb7iTSW3pqsbJNybIdCIetrLpfD9va245XjOch2dLtd24+dTgftdtsBDVIJJIiNbYjFYlY6DBh42RQN+yxkuleGMcuhDGUoQxnKUF4gz7UsvYjUWCzm5CzG43GDRk9MTCAWixmVWjKZxOjoqFlx1C5pWSrz/f3793FwcGCIUL/fj1wu51iIWpiZLjJqS3wmMIh7AIMYJbV4+q/p615fX8fTp0/t7+l0GpOTk46rq91umwZEtCUwsGZVC9YYRa/Xc6xMuhKoRa+srNjfNF5Ei4Hj9fTpU0xNTVnsaX19HePj42YxHB8fY3Nz00m9YEyAojmPx8fHDjJMLQG6sPluxgzULesVdVkB/firoqbL5TIePXrkIE2VwqzdbjtuE7rCgL57pdfrGZMMybPZN7qfqIF6GZtU1KJmn7U0m8YugsGg0akBfRfmK6+8YpYW3a7KHsM2ExGtbq5Wq2VrlFR3miqise+z2g7017wiJvkOteq8KNxms2lW+De/+U2LTQKD6jZAf/89ffrUrLQ7d+5gc3PT5vaHP/whotGog7AkyhlwkZrewgXqou92u0ilUra+QqEQvvvd7+LDDz+0NqvVHY/Hkclk7PtaEYdMP2SX+fzzz9Fut/GVr3wFQN8a9vl8ts6IXNaxAlz0JseT50e9XneYmmjJci5LpRIqlYqz93SdqecJgLN21eXK32nhA8C3vvWtU+XyKFpQnYhhdYUrUT+9CZpp0Gg0bDxIUq6MQLTY+B6dX12rZEzSNRiNRs8MKRSLRWsTS69xbmZnZ23+2CeON/szMTHhVCVJpVI2ltFo1PYfvVTKGKa5oURja36ueqP+125YpU5qtVoIBAJ2Yc3MzODixYu2kFhNgu7Fubk5J7E5GAyi0+kYVFrdM3StMHZBsIW6PDUQT8g8TWpNuNXP0S2nboP5+XkHqs68QADmu+chOjo6imazafHBarVql9bExASy2SxeffVVALBYi1LyqRt7Z2cHzWbT3GhaDzQYDDrpDgpMYoyE5AKxWMzZdJFIxKG/YlxKAUJclMxx0woFXhdaMBh0yjsFAoFTMQgFDNCNQYj87Ows0um0xXVYCYR/j8ViDpim2+06yg4Tu4G+a58lvyhzc3OnDj5ubuYhnpXn5Y2z6mVJ+LhWOQiHwwavz+VyNu5AP0dO25lKpSzdiLFFjSHFYjEn30/pCenCflbqCKXT6WBmZsbmslwuO+QPrJbDNv/BH/wB7t27ZxfRw4cPkclkHBc1D5hPP/0UKysrtqZ8Ph/eeust/N7v/Z7NmR6KBFVpcjolEok41IfevEtNNQiFQpienjaX/S9/+Uv89Kc/tT2WzWbR6XRMaWu32wZcY41GrlXWg2T/2TeuFboivQe593cF+JEEge3jntV1Ua/XnRxjnoNso6ZfKSiq3W7D7/ef4prmxfTRRx/h1q1bZyb4c70CgzJgXsAT92ez2XTinSyvx/W3vr6OUqlkrmVV/DY3N3H37l1bu6+//jrefvttB0TkTSNLJBLO+UrJZDKnQIvs+6VLl/CLX/zC1o0X4MnqL0rooTgABeN5453KGwvAURwovCd0/M6SF5ISaCKoHjAzMzM4f/68g5RqNBp24d29e9dhW2GepvJwKiIyHA4bofLOzo7jV6evnN+ldsNB0JhBtVq1JGESTWsMT5Ff8/PzWFxcdFBdapmwZiHbcXBwYAsvHo9bHUAOsi5sL/sHa+kREKUTlkwmzWq4d++ek5A7Pj6OBw8emFKSSqUcDZsFtdUDoMAijfWST1fj0GqdMH+Ll8Lx8TH8fr9DKM7x175qvcper188+8aNGwD6izORSNghNzo6ilqt5uSbar5gOp22TfP48WNHs5+enjZLCcAp4JFa9oB7EGpyOxPl1WJR5hXOiRZD3t3dtbV9fHzsEGwcHR3Zgc6DgM9i/UpV2JRD2Cvafr/f7/weDAZNOfvZz37mFHMmkIgHezweRzqdNuvx0aNHBh4D+nuM85pMJnH16lWbs1u3buHrX/+6PXt/fx+VSuVU7IrfV+Xk2rVr+OCDD2ycVCFj/J/zQiSwWr8PHjyweD0LF1DRUgXa7/fj3Llzp5iidF1qaTuWjvOiHTVmyYtSrRK1UpiHzFg1OUx5nly+fNkUC6B/Ef3zP/+z9V0twOnpaZw7d87ezb3FS+udd97Byy+/bBgQPeMUqUyUqTe/WccpFArZRf/xxx8jn8/b3NFbw+crcUIgEMCTJ0+cYhDsI9+hLGXsF88C3U+jo6MOzzjnhO9cWlqyeacixj7QS8nnqnLMsVWADvN7OW5aEMK7Jukh0bj6s2QYsxzKUIYylKEM5QXyXMuyVqs5/nxljun1eg7VWCKRcCpA5PN5JwWCmoXyflK8+TnHx8dOVQS/34/Dw0On6gMtWcDlAN3b2zNGjXA4bFyrbHMgEDC3SaPRQKlUMi2Eblh1/1arVdOo1a2aSqUcyDZdYsoqEYvFnGK8T58+tTit19127do1AH1t9N69e6ZpLi0tYW1tDR9//DGAvtavldD5fY2ZaT6natIs0aTuSHWVedktvKwxCrdWl53yf/r9fqf46vj4uEOzxpgWP8+q5Zq7RT7HfD6PRqPhxJfUAtTvAQPO1rMKuf7rv/7rKVeVVpNhfij7p+PGtaeoSWU2IecpMKi6oFqvjjm5eL30eF73GX/Wuep2u4YJ+M///E/HvXRycuJQBXKMua6uXbuG8fFxa5fSzs3MzODChQtm/dNlyTVI74qmSTHHle+ifOMb37AYs1bRYF+Vos6b8xaJRPDKK684yM1er2ft0rQw8k6zD5VKxfFQMIVF2YP0+zq3XpYk/ZuGCbrdLhYWFpw2tNttfPLJJwD6qH6lotN0JA21AH0X59OnT51UE3XfN5tNvP/++/jWt74FwLXeucf1O+o10fQdphgRD7K7u4vDw0MLj33lK19BKpUy9/7MzIxZqSsrKw4GZGFhAUdHR06+LZGpQD888bd/+7f43d/9XQB9ty29BhpHrdVqVg6NkkqlnPispiZ5Ufk8x85i6mIsWNMDNf0FcO8eusO5tp9nWb4wZsmXMDbGgWs2m6hWq/Z7LBbD1NSUbQBuNpr/fI66n3Thqn+frhp1LVYqFSd/6/Dw0AZbffK1Ws0WNyH+XuFimJycdNyMrKenB7c3/YCHEcviKCl5uVx2oOmhUMhiuNVqFXfv3j0VxGb/dHEnk0kDIZHPkkCGlZUVLC4unqL4YztYY06T4bXSu7oq6Y7Q2ICSENC1xY1JRUB9/CMjIw6YSBUP/q4uO9a7ZP/pbuGBpeCksbExU2z4N423dDodZ355mZ4Vs/zHf/xHe0c8HsfMzIy5z6LRKFKplKMUKfyc36H7KRwOO5elgs84xtyQjPlqDEldf8w7VeWBoocex51tvnHjBt59912LyVFBYpsZD+O+Gx0dRTabtXgg8y7ZRo1BNhoNJ22FSiZ/p6vSm3sN9LmJ9TDyEhYAg/3HdB5VbkZHR62NvIw0pYXjpHmEwCDNQcNGmrpBGkkvdZ7GEqvVKtrttu2Xk5M+STkV/ng87qRhdLtdh9Izn8+fUmDYzkql4qRlMEWN800wj4KbZmdnzyTe158bjYYTs9QyVvxsq9Wyy/FHP/qRE4rh3udZu7Ky4oRIvvOd7zhnvKYfEdPA766uruInP/kJfvzjHwPoK2jvvvsugL4rX7mlgQEoMJVKYXJyEp9++imAfoiAJC5AP6S3ublp6431j5WeUcdV+0+3rFKeNptNJ03OS4ryLHlhzFLjFGSoYOcjkYgtbPrjFW2XTCbNyqvVak7h3mazac9ibhoXGmsoquWoAd16vY5Go2GDqRpbOBw2ay6ZTDpt8qIgSVKgmiy5PTngyrepFTqIvNRJUd/47Owstra2LJZ1+/ZtPH369Mzgd7vdxmeffQagH7PUy3J1dRVXr1612NPu7i6SyaRNNg8x1WA1b+pZBMwU3XgjIyMOmpGsGlQ+VDvjIUVLXYkPvEnLapE3m81TjDV64SrAyks+wdgEv0vknZIWaFxIF/6DBw9szKPRKCqVioPkjkQi1gdy6CrqVuea1jDX1fHxsVNsFnBjRuRtBVzGGWCgTJx1wSsYhpcK+/b1r38dn332mY3r2NiYWdb8PRAIWJ8LhQLK5bLFf7PZrFPZRlHjyrRF0WfxMj1L8fNW6dEYM/cMx4JoUd1TWo2i2+3XSqVSrIo2lT6OIz0aevF2Oh1Hkdb8Qx1jKri9Xs/pC70siv7s9Xo2bsVi0QG3eZmutFYucQRKqsDxomiFH64fVRYpqth4uUxJlMKzlBeFos+bzaY9n0n6mgfNttEjxH3faDSQSCSci5jVp4A+iIw4EQB47733rF26RwiI43OePHmCDz74wNDnR0dHDs6k0+kgFArZPG1sbODChQt2lhG1zDZqrjnPBDWQSD4DwJRezUt9lgxjlkMZylCGMpShvECea1mqxVar1RyrhDXDlKNU3UC0BBUtq8wmmtfGagTUIMvlslkgQF87mJiYMA2PrhFqPKo9vvbaa2b6T01NOW5WMkdQ6IJRd5pyQzIep0gqTXlQtGKhUMDm5qajCT5+/BjLy8sA+q4E1YzVffjRRx/hH/7hH+w5MzMzppHv7OygXC6bG6VarWJvb8+xIJLJpJPH6LWCNCajlGHshxdZp8wcyv+rPJbqGlOeUo6nlxWJ4s1n0jxPjrGXgUcZYdRVxViN5m+xTfo9wPWSsJQarfe7d+8ik8mYS/O1115zaNeYT6YQeZaJA9zq8ETXeflRNd6naDz+rB4cnRsve5RWzHnttdfwX//1XwD60PxqtXoKOar7kWEGAA4FWiKRQCqVciq/eFmllP+XFUe8MWAA+NWvfmVpQ8CgniDbpCW7vC4yxqnUdUqEOr+v46T0faFQyPFo0KrUUA5DJzrOgUDArL94PO6kELF/tMrIwcz9lEqlHLQsKd24X8LhsD2bffXGptkfxuS8bndajhqXq1arNne0fJWbuV6vO2Xrjo6OnHNPY+ysB6sIV0WzKjsXXc4aBy6Xy+ZJuH///ql0Icrly5ed9fPhhx/io48+AgB88sknqNfrjhdMz4VMJoOxsTF88cUXAAbWo+aDcl8w64KW5fj4OPb39+2umZubc+4EwE1xel7qiO95nJlDGcpQhjKUoQzlBZbl//t//69HjXltbQ1TU1P4oz/6IwCwum7qvy6VShYv3N7eRjAYNDTe5cuXTUOnUBNk3Iao1u3tbeTzedPYTk5OkEgkHIQna1MCfQTpX//1X/sA4O///u971KrIrq/oO8abgAHpLz/PgDvbSC5XZZdRgmzVTsiY4q0OoGCUWq1mfez1evjud7/rA4BYLNZ7++23AQB/+Zd/ifn5eaf+o9ak9NZUBPoWlzIrqaalyhAJG9Sq1TbyZ7WkC4WCxQparRb+5E/+xPd3f/d3PWUu0newigu18YmJCaTTaSdWe3R0ZONArVWBDhpvqNfrDgihWCwa4ILJ/fzuxMQEfvrTn5o1/1d/9Vf40Y9+5AOAb37zm71vf/vbAPqoPvWCRKNRhzgAgGOVjY+POyhbavJcF4r0o/eBWnS9XketVnPYpjSHjGPCMc/n8/iLv/gLHwAEAoGekpVrndPZ2VmMj49bvCiTyeDg4MDm4saNG1haWnKYggKBgMW6w+GwrSWS62uOpsaM2D6ug9u3b+PHP/6xgc58Ph8KhYIPANLpdE89AQDwta99DQDw53/+57h27ZpD4KBgMY671s1US8aLbFckJiv+KPm7xu7IJcpxrlQq+OM//mPfP/3TP/W81pzGTBUdzjn0fl6t1EQi4cSyKWR2Um8HkeJ8JhPqgb41u7q6infeeQdA//z4t3/7Nx8A3Lx5s0csCPlslbdYq4qMj48jl8s5tVfVMxQIBJDP5x1yBArXAc+HYrGIdrtt+49WOM9p7lXOQbFYxN/8zd/4AODixYs9zvv169cxOztrOb8EPnFtnzt3DufOnbO8+0QigXA47FjLWoNSRS1OPpv3BdtMvmYAdiYrwf0f/uEfnjaNMYxZDmUoQxnKUIbyQnmuZanxvF6v52gozD9S5ChL+wB9KijygAIDphlq414IdrPZtMoad+7cwdbWlkMJpnk2iUTCgWwrgkmtHCLtvDEzr+ar8Tev1UWNkM9TC0LjffxdLTqFvXuhzWqVAnAg/Zrnx8/RIvDGNbx8qN54ncYzaJ14Xe+q7SpyjH2mhaUsIl7RdaDxSqYQKcuO0r0xvuplawL645/P5405Z3l5Gffu3bO4G8eJ8dxr164hHA477EYUjTGyL0QqdzodTExMWO3ARCLhcJ/y+946m7oW9OdqtWra9/7+PnZ3d43msVqtIhKJWO7gxMSEk1Om4kUMRyIR866QFlER4iMjI1YiL5vNGl0bAMdaB+BYYd710G63EY1Gba/Ts8F1USwWkclkjI3Ki37WdgcCAbN+33vvPezu7uLChQs2zhrv5XvYxrNipbRiGJujdVKpVFCv183bxJxv9m1qagpXrlyx8WIbyazFZyhK/+TkBJFIxKw/7j3uSUXKcmwPDw9P0cEBA1wG1xDRxWxfqVTC2tqaPY8sZDw3NB9TUcDkWeV7aG1zfxEnokxHSvkJ9NfgWelY7XYbDx8+tMothUIBIyODepfEOGgtSbXs9CxVTwYZsRhfv3jxIg4PD80rubq6irt379q+fvXVV3H9+vVTeAdN9dLzTxHXnCNFuit2xVv9x5stoPLcy9JbfzKVSjlQ9FKpZBdVNBrF5OSkLSwuBn6fyeU6SZpGQsoxYFCEWJNUu92uU9h1b2/PSQqnqAuMLgL+TlcTB4QgJHVDnnXYe6nK9G90TdGc1/FSoYvWmzoA9CeMQAjv5HGDaXFWHTvWY1Pgkbr19G9sv6Zm6OFE6Dy/y/lS8I537jg/mkfInDZ+TgFYbBvHkZRsOh48GE5OTrCxsWEQ9Pfffx8rKyvW/ng8jkQiYc9vt9sOBZoeMBwnzlulUjGCCNI68pBLp9O4fPmyHVi80DgO3W7XSaVQ4FexWMTDhw8tFWhtbc2hJwyFQshms3ZAN5tNJJPJM93sepiOjo46tVVLpRJisZi5j46Pj7GwsGB/Z7oVf2cqjOY48rvNZhNPnz415QHopw2QgjGbzSKbzRonaiqVMsJ8fv9Z0uv17IJbXV3FnTt3TDHMZDKYmJiw587OziIcDjs5qprX1+12DUzy+PFjrKys4PPPPwcwqHHI/sZiMSwuLlrKFS9Tda0DfXIGJblXXtG9vT1sbGzYvOdyOUxPTzu1ITVPlOkRWv6La5DKlVJpalimWCyi2+3i9ddfBzBYB9wnVL6A/r7wpsSwzeyPpnuNjY3ZXN64cQMvv/yyuTxzuRxGRkac8lea0vcv//IvVpjeW+CCeazcN9PT00gmk07+LkXDGEB/H5BecXZ21jG8OAdcN48ePXJSuXgWqptb01d2dnacdBcFf1Eh4zMmJycdBeB/zQ2r1h+LqVKTW15etjp/HLjZ2Vm88sorAPrkuN6kftVANT+uXC47l1oqlUIqlXLIAXTgDw8Psb+/b2gqrQ2pxYdHR0cdy5DIL/V9z8zMOMTNqhXSOvTmbgH9w2p1ddW0rmq1imazaRtpYmLCQQOTQ1FZYihao5FVRKghl0olRKNR07q4WZUDUvPJvDGgYDB4Kv+LbaIV5uUX9RIxaNwLGOQ/6hhxg9GqoiVVLpext7fn5Dz6/X7TGm/cuIF2u+3k4xFBeHx8jAcPHuDevXsA+odXIBAwxeLixYsOl6zXM6AFtr1egMPDQ9swFy9eRKfTsUNheXkZW1tbVl/12rVrmJ2ddeJLekgqy8vW1hYKhYK9K5fLYWJiwuJ0rGGoCoyuBW8tQD1AyCil86IacT6ft3HPZrPIZDKmvXur3Gue8v7+PlZXV+0CZ+4u99X8/Dyy2axdPNls1iH8eFZuGvN/aaEuLCzg/v37+PnPf26fOXfuHF566SUAfQtiamrK4QBVJi+do9u3b+P+/ft2iRwdHTk8s4uLi8hkMnYpNhoNPHnyxKwgzbnlvOolx7kLBoO4e/cugD7rjhZHj8ViiEajjpWSSCQchZlr8+DgACcnJ7ZO9vb2jASBfc3lcqZ8l0olzMzMWNuUpcybON/pdOx82NracjINyFCjmQeNRsPWNpGjWtiBzyqXy/D7/TZ/mUwGV65ccdaol5CDHkTAXcv1et2xqlOplEPmvrm5ia2tLQCDPaJeyFqtZsqcKn1sg/LXNptNG8dms+kwWxH1zXWzsrLioGfPwoRQhjHLoQxlKEMZylBeIM+1LNWdl0wmMTk5adrmb37zG2SzWdOYl5eX0Ww2LWZSqVTMUgH6Wgfpq4C+BcTbv1wuO9op85k0N+3w8NChUEun06Yd0TUD9Blw+I5AIIBsNmua38jICPb3902DAfomPrXvl19+GVeuXDGLg8hNdb3SdXfv3j0Ui8VTGio1QbqR+fdYLIZut+sg7SjXr183S6tUKuHu3bv4zW9+A6DvbopEIg7Homp709PTWFhYsO+TzokatCLfDg8PLa7Cz6qby1vpnNYX+0/XhrIisVYj57LRaKBcLpvVwjgTrZbj42OroQf03UuRSMTJC6V1tL29jd/85jfGNdrpdPDGG29Y5Q1SYrF/xWIRY2NjNlbKOOQt+RYIBAxBODU1hUQiYV4RuoCorW5sbKDVapkra2JiwqkgX6vVzJuxtbUFv99v7jQy5ShiUCkGSdvIOTmLOpA/1+t1x3OhNInMOWUf+R5lalFMAfN1gX7NVK1EwXaw/8zzpbdjcXERwWDQ1pi6u7VNHB+O22uvvYZ4PG5j8cknn6BYLNraOTw8xNWrV+3zU1NTCAaDNo9bW1s2zt1uF5OTk2ZheLlh6epX5PvExITNufIcUw4ODvDkyRPb4yy3Rcvp8PDQ/uPn1dJcWlpCr9cz61EZs0qlkuOJY11JdRufnJzY2VSv15FOpx0kPsUbOtne3rY9HAwGce7cOfMCdDod5PN5p2ya4kOSySSCwaBTxo/zvLu7i4mJCXvWwsICXn31VZv3ZrOJ7e1ts0p5Fqi1TMnn845HLxwO2/pbXV3F5uamzTMru/A9QD+cwbnM5XKOB1CtbuZkKseucgKTDYglD4vFouOhoJfxLHnhZcmFcenSJeRyORsAJuNykhYXFzEzM2OLke44QpwJ39X4EgeL7ksOTqFQwMbGhkPCHgqF7EJgXIMDoEQAY2NjzueUOCGVSllCOQeOJaKA/gXYaDTsoCOQiAN/cHDguHxzuZxTp7FQKJirYHNz81QSeTwed0BKlHPnzjnpKuFw2AAgT548wcbGhiV6a9wD6MdeFhcX8eabbwKAET0rF69CuZVU+fHjx6hUKk4cVTlQmaDMeeGmUSWKcWE9aKempizeR8WH72SpKLZpd3cXk5OTNpcaU97Y2MC9e/fsXTdu3MCFCxfw29/+FkD/8PTS0o2MjNia08tGqRZJc0hFrlqtWjwGgCkIPNR3d3extbXl0J6pu61ardoFX6vVnAuBiqASrWsogBB4rTF6ltBdrsQLZ5F+KzcqMHAtUnHkYdBut+2S2tvbc+oekviB435wcOC4mlnmjuvCm4SufVD6s83NTdy+fdtCF0x5YgpKpVLBwcEBmEb10ksvOSThe3t7Dk2cpkHF43GMjY3ZmXH//n2sra3ZuEajUUxNTRkRvV707NedO3fw5MkTB7Pw9OlTh1IvHo87vyspAZVnTV3jxbuxseEoO5FIxKH2m5mZwfT0tCmGTC+icqOXg45xsdHcJd0AACAASURBVFhEq9Wyy+7atWu4fPmylRHrdDp4+PAhHjx4AKC/TgqFgkOiPzMzY+OxvLxsZ5xy2/Jdn332mZ3x8XjcqSnKeqle3lmgvy+8Bd55Vn744YeYn5+3VJH19XXHSLl27Rp6vZ4pEvPz85idnbVx1zS+drvt7DfGd1XxXV5eNqPu0qVLuHTpkqOEPUteyA3LhXPr1i0kEgk75BcWFhzNnYAHdpAbz4v+VCtA/eTAoIjzysoKbt++bZuI+UsLCwsABoF2LwoPGJCjA3AC6EB/MpeWlmxDsY187sjICLa2tmyxkJGfsre35+QPan7jxMQEisWiITXj8Theeukl09Lq9brDjsHLEIAT9+BlxfcsLi7C7/ebJkj+SEXwsjoA0I/7XLp0yeGs5Wd3dnZQKpVsYezt7TkEx7FYzAl2p1Ipq1mn8wMM4jrMQ1VfP1G3nDtam8DgYuK4sr+0lGdnZ514XiwWs030ta99DePj4xarJmiHh9f09DSazaZdCHqBqzXHHEpuqO3tbTx48MDadOPGDYfPld/jXF6+fNnh4wXgxN8VwLS1tYWtrS270JkvzAsmk8lY3Ua2TUVjlt3uoIarWvnAoCIH90QoFEKn03HIpjXuqIpus9m0ShAA7KLUZ+fzeQPiZLNZzM3NOTFcSjqdtsPUW49ze3sbH3zwgR16Fy9eRCKRMKU5Go2i0Wg4CiY9UsAg5xgYVKfR2NytW7dw+/ZtAMCvf/1rpFIpx/v04MEDGzeeY8pbzDOGFw0rIXHdM4eYv7OQBMchm806NVcV3MQ4nOZ8U6kH+t6lmZkZy0tnJRD2Xc8xNRRoaHBuZmZmUK/XDWDWarWsPjDQXzealXB0dGRE+kBfMeLll0qlUC6Xbb4IouKampmZceLi4+PjDljNW1FF2agymYydIfPz8/jOd75j64AKC89ltoXrgoXPKUdHRw5Kend3185wKr2cM54Jd+7cAQC8+eabhkng958lw5jlUIYylKEMZSgvkBeW6OLtPjc358SzfD7fqRpkWqGaZYd44zMNhRpOo9EwbYZVMqgN9Ho9y9kB+prU0tISHj58CKDvPrx586ZjEVJSqZS9kxUoqIVcvXoVU1NTZqWVy2Vcu3bNtG22hS61RqOBbDbr1Dqjiy+ZTKJQKJglsLCwgF6vZ9ZyKpXCuXPnTDP0+Xz2Gf6u79RcpcuXLzsMHMlk0hCgDx48QLVadaoIaL22QqGAmzdvmqY5OjpqWmmpVMLx8bHjmlL3G+ec76KbiJaC1htVLVEtfLp5NE3jV7/6lVnck5OTyGQyFjNg/hTbFIlELN/xlVdeQSgUsnlmatIPfvADm7/R0VGzWumOYsyZ8wi47muuY64Dut9pcW9sbODWrVumMR8fH1uJOgBm4WkckhrrhQsX4Pf77d3pdNryEQHgjTfeQCQSMVdyr9fDxYsX7V1ey5Jrm+5I9pWxQV37qm1705yItqQFX6lUzCJibis/f3h4iHK57HDDKjKefMFcF/qeq1evWh5lKBRyXI+0rriHfud3fgcff/yxWXmXL19GLBbD+fPnAcDSgtRCYZuPjo4QiUScsn6JRMIqybRaLZw7dw63bt0C0HftaT1euoIvXbpkaFxWOlIWoEAgYJYVy37xDGOFHe6X+fl5pNNpsyyVIzmVSln6CufH7/fbvp+ensbk5KSdRd6zSy3LZDJpY1Yul53Y3ieffIK1tTUHCe73+x33u5dHVzlo1aUbDAaxurpqZ8v4+LjtY47pzMyMfX5qagrRaNTWhcbfFb9BXAHX/Pb2NnZ3d83VvLS0hJmZGZuXo6MjZw0AbknHSqXiZG2cnJzYGnz55Zdx//59/OxnPwMw8LDQPX779m1kMhnzJpxV5cXG8Zl/QX+xc2GzKDIHmW4oBay0222HILjZbDoABDX39cDtdrtIJBIWd2s0Gjh//rx1eG1tzeKLAAxGzAuBkwP0Ly1eboeHh6hWq06OWyAQsIO61WphaWnJ2liv1xGPx50LXesnAgNSZbqG6Rbb2dlBMpnEV7/6VXt3s9k0V8v09LRTQFf9+ap0+Hw+x5UzMzODzz//3BbpxYsXrVYmMFho/D7jxjwUFeQxMTFhicScgwsXLthC29nZcZLR6eJR1zNFffuBQMDg5pubm07tufn5eXz3u9+1Q4J5WFxX8/PzTm5su92297355puIx+M2Vr1eD4VCwdZgJBJxavWFQiHEYjHbdKoETExMOKXHtED1uXPnEIlE7LKMRqN46aWXbDOXSiVn7TLvV+Mk3GRLS0uWQgDAYi98VyaTgd/vN9cy0N9bdMt7Y5YK5PASTOiFyNqfXKuBQACTk5N2kKfTaUcpm5qawltvvQUAlh6hhOWaKxsKhTA/P2+HF9OXvLnBQP9w4v4aGRnBwcGBk9f7/e9/39r80ksvIRqNOtSOiUTC9j0vG+4xn89n48TaihQqE2xTLpdzLpXJyUknV5Zzp+9vtVpOma/x8XHDSwD9i0nPoVwu54QNWENUlXy2JxKJoFqt2iHNepZc62wnlW2S2+v8UhRHwIITvPxv377tkDNkMhksLi7aZcCC3JrXm0wmnbFk/+PxOJaWlkx5TSaTdv6wT+122+Ks29vbyOVyDoEKhcA+9kUvpVdffRUrKyu2v27cuIFsNmvjmkwmkUqlzJVKoKiSxVDJCAQCGBkZsQuef2MfRkZGcOHCBVMeWEtT+QGeJc+9LMfGxuzA8LL189+U01MRrN5Cn966i1qkl2hXDmAmk0E2mzX/fT6fx8bGhg1AvV43VJO3TcFg0Dl42u22HeSHh4fY29uzgeEAaxykVCo5C9N7WWoO1eTkpLVpf3/fSawNBAJIJpPWFk0cBlxtnMwY7MvJyYm1YXp6GpVKxYLhjAtzrBjTUyCHN4Gelz9jG9w4h4eHGB8ft7kqFAq24YF+THN6etrAOmy/5vfxd87F4uIipqamHLTu1NSUk4cIDFBn/J6i1Tg2kUgEiUTCDpCJiQmHYYV1+DSHUVmnNP6gcVxypHJuyEvJC8zLYcxEc0VsK4euFvWtVCrI5XKmyLVaLSvIzTGv1Wp2iKZSKUe5UdHqJYzXaRw2Ho87YAudh0wmY4c536M5tYFAwKyuVCqF69ev20HOC46XYCaTwdWrV3Hz5k37rpI6KPhkYWHB3sl4MhWlmZkZzM/P23z6fD688sorTtzx3LlzduZonAuAU9yZSFFlalHADDlTlf82EonYvuCe9yKmFRxCi0UBKyy2DPQvtPHxcZvb8fFxpxCzel2oXFMJLhQKaDQa9t18Pu/E7kdGRpBMJk9VJwEGlj7Q3xP1et1wGPV63bFQr1+/jpdeeskusEwmYwXYgUGlFK1lyrVJxU6rr7zxxht2fjI+SM8PeWj5Lm0zyQGAgbLJuWC+tHoaM5mMrater+cgh+PxuONx4Ljzs7Ozs5aPe//+fYyOjuJLX/qStWl8fNzW2Pnz57GwsOB4SJ8lw5jlUIYylKEMZSgvkOdalqOjo6cgyxpro8kLnOYkpWtNOUA1dUS5Xskaw7/RZKeFs7CwgGw2a1oj4d7URNSFoLlVoVDoTD5NdXlGo1HHvdtqtZy/KyOO0o+Rg5GafSKRcKoi0ELjs70pHypqkVIzpTug1Wohk8ngjTfesL4zT5Bt0uoLnBe12rWeZzKZdOi6qtWqg9jTOoWM8VLb1XHm3HANUJvkexUpqZpsLpfD5OSkWZakOlS6PG8cju/KZrOONUF3tGqD3W7XNE66uDg/alUlk0lzBW9sbBicneOkMaJwOIyDgwP7N02h4f81h7PVapm1S7c254c5p/w8rVqdb4qmiqTTaac/gUAAsVjMQVdzbIFBxQiOXSwWc/au5qNOTEzgypUrZp3s7e051HB8Ls+CRqOBQCBg46AuNU09Yi41LTVawrTodb8A/f2aSCQcdirlOdVUClYM4roDBjnGnBONCfI88abojI2NOfF3pT2MRCJODJPIZb6TrmCdS93jXu+ZVmEi8w3XEOdKeWmValPHSXmJo9Gok1Xw1a9+FefPn7cxnp+fRzweN+9aLpfD5uam4z3TMMjo6Kjt97m5OSdG6/f7sbOz43hYyOrDMda9q2NBalEAFtJgG+LxuHPPkHJU8TFHR0f27Egk4pxrWimKyHLOkTfW6ZVIJOLkfT+vZOUL6e7UdNbYCzlDlUBbYxfVatVx5RDurJylfBbp25RWTRcK3W3qT1bqL6+blG2iu0LprPTy8/v9CAaDTloDixmzTypaAJjlrDhh4+PjTjkh9kNp4Z5FCzY2NmYX1uzsrLOQSd9EtwET4hU6rnlTvV7PKdGjrnKSGWtycCqVso1FyjDO2eTkpBXE9Y6zurM0N5LE15qy0G63nQNG28dNoEn6/G4oFDLuRgBOUr2OMduSTCadlAgdx3g87qxPjcVqjIp90iK/7XbbIaDQfcC/82CgC1fHSkn/o9Go49qjS1jdiRQWOGbfzp8/by7Oc+fOIZlM2uGfSqVOgROCwaDNHdMgOJbBYNDGiXEruudJDagFrJWrk3SBXEd6ibM0FsdJ04gikYglwQMDAJmOlfIgk/Rf8xY17MMYI9A/tKPRqH2WyinfzXXn3dN6XvCQ5mei0ahzKHsLEfAdnPtGo2GxSD5byxC2220H86GkCqQ9VEJ+TefxFk/gPI6Pj2NmZsZcoV6SAY2fckzz+bxDROI9a5V7eW5uzgGyKaELDQINTRGYpO9mO3nOkH5PidXHx8ed/clzDBjQ6Gkuto6zcjOTGEbzWfW5pD/l56PRqLNPvFSCKs+9LIGBdsCLRX3wWplhZGTEiY8xT0sX+tHRkWOZ8edUKmVJxXwna0ny91AoZJ8nWEjjfDoYagl6J0+J0qmxaN1MfdbY2Jjzfd0Y5Ab1Ep97E7SVGZ+aK3+3SZC4AJ+th1wsFnNikOyL9lEvH9XKvJaKWtaJRMJhYqEnwKspax/4f/6smjm/owT6BGlwfHgZaPxQ2XAUxELWFa4pzpNWKPHmOjImCLggqlgs5gBy2u32Ka8JD2nmlrLN/FkPPo0nMubOZ4+NjdlB4EWlsv0c83A4DJ/P58TJVBQgp/Gk8+fPY2pqyrG0dJwZU9W5Vq5mrV/ZbrdRr9dtbSrohW1QQnzmoLJtelmWSiVTYomA131LixAYEHBwLWlcCxgo1EqsrvzGum+oZHHddzodJ/ZPNCb7oAw+7Hen03H2O888VQQ1d5vgLa5Xvp/P88ZbNzY2HHYfVaJ45mktT5/P5xQVoGg/k8kkcrmcrZ+xsTFUKhVb2wTzcG3XajXHc8X2KrkD33l8fOzEUcvlspOlwH5o/PTk5MSpN0tRohVefmyDErOzTepFIB5ESTCOjo7svWpp80xQr6UK94R+vtPp2HoY5lkOZShDGcpQhvJ/kBfS3XnLU/E29/v9jtZMmiNFKo6Ojpr7cHp6Gvv7+6Z1kKMQ6N/2yvzQ6XQc09hrOjOOoS4LSqPRcGKUXm1bLT+fz2fWCv+uGjNzRbVWmrpV1bLRFA1g4MpSar1nxSzV9OfPXno0jYlRA9Z+qOtrbGzMnsMxAPpak0Ljvdr48fHxKfebtlld6GohMsUGGHDXUpNNJBI4Pj62cabbTanV1FLVOA9/ZxvK5TJqtZpZbZxbZSc5PDw02Ly2PR6Pmwup0Wg4+aa0uHRt0FPCfmv8nha4asSKmPRykqp7nh4JzifdmxwfRWd6K8AEAgFrM+NvWuH++PjY8utYTYFx2enpaSemptZFp9PB8vIyPv74YwD9/NYrV66YW5duWHVD898AN0VA+xIKhWwPcRw1xqdWJ9+TyWTM4iWbj7KBca659xQtrPvTi6XQ/FT998PDQyf8o0LLWF3kSu/GPEu10hQv0Gq1nHq07XbbUsBisRjK5bKTR+utoerz+azdiiztdrv2PZY5Y5uKxaLDh81x4ed5vun4hEIhZw55ZpVKJSe2z/cov7dWQCJewMt8xXZwrbLOr6KI+TztK8+USqWCkZERh3Xp8PDwmeeprm2eN9y7tCK1jRrrfl6M84VuWApdlN4FxQaTI5RC95LGn3Sza9ynXC6j1+s5SdLK4crEeW+xT3ZQadhYN5IDo3yZbKv34vHWgtTLRGMq6l5k6S6t9agXGD/rjfucxT24ubnpxIOUHo4xCHUtAwOXJMub8T3z8/PI5XLWRy0A3Ov1nMuGB766nfT/7CfHUxPA1Y2h4ANSyRH4dPnyZSfth25S/s70EI0bKVhoc3PTxiIWizmbJBaLOfNXqVQcnlaN4WlZMc6ruugU2MR4pcan1eXuBXKossLLgWMcDAatliswSCFim+v1OqrVqrn2dA/ppRuNRp24K8sSKShuZWUFH3zwAYD+oflnf/ZnlvZDV6tyqyoY7cKFC/jJT34CoJ/Y/rWvfc3AKOl02nFh0mWoa4BSKpVs/AOBgFOujFSHGsrR9cUYra5HhkKAQWyV/X306BFWVlasP9ls1gGqaD1eEqBz3FXx1rCD7nEtPcW51dJmLDyglITaXqVMJOZCLz+tFcu28rzkM3Q9UFSBZHFnzpX3e+pmBvqpW6VSyfYY94yCJdnv7e1tJJNJi5OTlpPGCNcxL7V6ve6Mp1f0wlYDgYqPKnLe0MTo6Ki9l/1RlzrXheY/AwOFjf3z5jEzXMH2qOHlledelqpVUfvXxcjLhn9XjYcDyQEgSIIToUTWZJwnko/AAK/1xHfxouQlqXEezV1iGxRIpM/hovOCdigkudaDkG1iHEsPUF0knHw+m4jVsxC8H3/8MX74wx/a96jB8vdwOOzMAwmn+Tt5JIE+QEgtGd3MIyMjVmQWgKEhlTidY805Y3wBgAMiUkVG4xiMVeu8a7yr0Whgf3/fLsvR0VEHqXl8fOxwcWohZf4bq46QzFzbx/qZfBdFwRO8oNhfjX1QdD7ZXwWGhcNhh5+X/eMhpTUZW62WjRfJOziWXoVML2FdX6zMwnFbX1934n2bm5u4f/++Ee5fuHABY2Njxoq1srJi+4ptVnDawsKCKTj379/Hu+++a4nui4uLjkLjRYmrBbGysmLPHR0dRbFYNK1+YWHBITZhAr/GNP1+v1PBptvtOoA0xmwJ+GB1ni+++AKvvvqqc5mqcsC9TNHx9uIodO0qeQqZstg+esA0P5yFAzhOymQVjUbt90Kh4BRWYP8VpXt8fOycAxTGkIG+sqwX2NzcnHN2sr6kKvzMYwQGZDNa75HParVa2N3dtZxhL98wPU9ayEAVfQWr6efo+fIaP/p7p9Oxc71WqzlgNqLPz6rgdHBwYFkO7I+CAvkuBfR0u93/EcBnGLMcylCGMpShDOUF8kJuWHW1eM1nZTHxppZQK1N3k1IekQ4OgGmtykGo2hG1LI116PdpZQF9TUqZHdQFoc9i+9l2tlljKj6fz3EzHh4eOi5ddeUpEw6frVorx85rgQB97ZBaFBGd7Bu1MFpJW1tbePLkiT2X1R/UXdftdh3XtNbz1KrotVoN6XTatPVoNIper+fkopEHlH3m/9Wloq5u/hvn4MGDB049y/39fRSLRdP8Dg8PnZqVo6OjVlcyHA7j448/xi9+8QsAfQ8Cy64BfctY63FS6+c60vif1owkR6fy76rQAuN7ut2uxU0AGLsItXvWUgQGJYJU+1Y3OsdLWWs0xKBasrr5yYSjFkOn07F53t3dRblcNitgaWkJ//7v/44vvvgCQH+Nzc3NOTgBjlulUsFrr71mNHPXr1/HkydPzCqtVCpIp9Nm4TG+RlEL4sGDB059Wa2tevPmTavbyLHRElytVgtbW1u238rlMsrlspNPp5ynvV7PKee2vLxs7ki6eLV2q7pYKerS5M9qfesZ1mq1nPqb6XQaCwsLTtkvei04t1wX1WoVkUjE4n9ffPHFKaYtfbfP50OpVLKx9Lq6iQXhecczkHSPPF/oXVPuV6aEAf2zodFoOLR76qnb3NzE+++/D6Bf9WdyctLxMGlI4fj42ImzK7JUvSSMoyrtpt4t3jaXy2V0u13L6x0dHUW1WnUQwOoK5jywv5lMxkmlUW8Gx11xKc+S516W6rbwBsx5EejA6OIA+oedxoXUzVWv1534JeAGV8+K01F42HDREtBB4b/rYmSbFajAmJ26WYCBWy6dTmNyctJiMMzDpHgBBd6BPssNy0nUvuolWq1W4fP5HKJkdcfs7OwgHo87FG8///nPzbXVarUwPz/v8NtyPFhjU/Pjtra2jFD8woULSKfTzrgrYOMssIFXCIzhQmdSN8fwyZMnCAQCeO211wD0D9qf//znuHfvHoD+BajFdTc3N63vdJGxHbu7u45LmJel1kGlaIiAcQ0FGKiix/xihfF3u12Lj5ELl+vu6dOnDkXb/v6+jRlp6DRlSA8Guqk1zn6W1Ot1FItFZ422Wi2b2+PjYywsLFhJqtnZWTx69MjaUSqVzG0IwOl/IpHA7Oyscb8SbEc37Pb2tnPpeanjtM31et1ZJ5pfvLm5ienpaYdQu91u29pgagLPEBYV5zhvb2/bWiVpuiqJWn6O61jLkGlbNWygomEahiH4Gcb3mNNIkgyWEux03Pq4h4eHNj9jY2NWAB7orxMStQP9tXp8fOzk+ubzeRs7bwqEXgZ6ydZqNUcR0pqsQN/FXqvVHMq6YrFoSsfq6qq58kn6wcszEong4sWLppB5hYXeKXp/qFJEUeVb44zEUXA/Hh8f21oE+vt1Z2fH3pVOp+3ZzFvmOm82mw4JCO8Vr8v3rJCCV4Zu2KEMZShDGcpQXiAvBPh4WUu8TBJaGkohyqSFouYUi8VQq9Ucl4rC6dvttoPkoonP9yjK6ejoCLVazVwPXho2akIkUfBqNJr0rknPBBdQMyQKV5PZ1bJWujGWhvG6dDT9hYg8wHVdqWUxMjJyKp1Fq7lHo1GrksE2+nw+c1VubGzgtddesz7V63VzcTYaDXzpS18ygEw6nYbf77cq6iQfVjeUkjTTwlP3M4t+6xgTVcjPlstl01ZLpRLeeustS1AOBoP40z/9U4PU5/N5I+heWVlxkpFPTk7w8ssvm0vl3r17qNVqDnQ/n8+bJaJVUpRSkMhOziuJHbxJ+TpXT548MTDJxsYGUqmU/f3jjz82q4zkAQqA0vHhnlK3rVrqag2rN+b4+Ni8DBw3dUFnMhkrEQX0LdoLFy7Y5+/evYu1tTXHNU1A3fXr13H+/HmndJvSS25ubhrFHfvQ7XbNOlFRFDHHk78/fPgQ586dM68I0aLcx9vb26jX67Z/CfaiVQQMLMMnT56g1WrhK1/5CoA+SYN6chS5DQwAed5xbrVaDuOXtteb/lWr1Wxcgf66uXv3rhUSLpVKWFhYMPq3Tqdj4318fIyJiQmnQo6CjpQohVKpVOzv6vbmmcB+ar81G4Cf1UyF3d1d7Ozs2GcKhQImJiZsHa2vr9t4x+NxXLx4Ed/85jftWcvLyzYHExMTToEIntFK2UdRWkKek5wPWnfeih9cP0x74vlzcHBghOkAnHOYWRfKyqVodbKF6d3Ctuv/z5L/8WXJxit6q1wu2wFbq9UcntWTkxPH/A2FQo6ZfhbrC0UvHYpeNI1Gw0GEKhOLViqo1+tO3TYvHZ3XNRCNRrGxsWHfX15extbW1qnUCv6cSqUc9g8VL8qq2Wwa8tHb/5OTE+eCL5fLTkqA3++3TUbkqKKSb968aRfInTt38P7779tBpvyYS0tLmJqacuZQ4fabm5vY3t52/k72DGCglJx10eti1AOo0WhgeXnZDpQ33ngD2WzWQQVHo1GL5ei7O50OLly44Gw0VllgewuFgvVvdnYWrVbLXNBayYPlg4ABq4mXelFDBnpRkdqNlQtYPZ6be2lpyS7mo6MjRKNR+9tZa1nHS129+n/+rDyp3j2j6Rte1qGTkxNEo1GrpMJnUfEZGxuzQ//y5cuIx+P2boZP1CXY6XRsHjg2qlRR9MygK43ra3V1FXt7e3YmML+Ve/TWrVsoFouW/kJWIV7qyswSiUQwMzNjfUilUgiHww6DGF2bbKOXTYjz5R17HWeNnbXbbczOzjqMQtls1kqS+Xw+5PN5i/UqPWM6ncbMzIy9g8hSb56puge1GpGeY2qU8DLn3gyFQjg8PHQOfR03b7rO+vo68vm8w5t848YNALCapSxvlcvlsLq6auGI8+fPWwUQwDVa2AeKnjOdTsdJq2FoQl2xum+03B0w4FPmfvayG+le4cXJd9VqNccNyzxLfl7PH6+8EOCjlHNKH8WNQM2p0+lgdnbW2awaMygUCk7CaqvVcqw0ciUC/UNOIdu0BDQOV6/XLdbhnSStWacaMWMtmv7hpau6fPmyk5tFcAMAh6c0FAo59GFcwPzdS1rABc3fdfH3ej2nUDGBNhxnTXvwHsbBYBBzc3N2aFy8eBGff/65jVUul7OL1ktYQPASL9ZUKoV8Pm9zenR0hFKp5MSYgAFlFN/PnFMdB77/7t27eP/99y2W9uqrr56p6fEdoVDIDuXr1687dSf39vaQz+dNwyyVSpifn7fSUazLyPiGph6tra2ZIpfJZE5pkBojoUeAm3N2dtbWJNBfJ6rBR6NRJz1HL3SmtzyLvIJylmbrheVrSa6RkRHs7+87xQUUnMF8QI4VU4aUWIHgkXK5jHv37lkq0fj4OA4ODkwZJZ0i54XxNqWao2jfCNTg2BQKBaytrdl65DrkYZjL5TA2NuYQ92vO6uHhocWGmf/HNnH+tM6j12rXVDY9hzQlgR4ifl5ju4FAAKlUypmrpaUlU0gYQ6ZnRM+0VCrlHNqsxap56N64ZKPRsHNCFXVaSxSCvyihUMjhHqbhwrGemZmxz7NPHIPp6Wmz2E5OThww0PHxMRYXF03hYixbY7zaFvVizMzM2DrpdrsOzy+VBP7da2lyTVDxUG+ed1457l68ghKXaIqOlyTES4GpMoxZDmUoQxnKUIbyAnmuZan0dYwrUovwlrzZ3t5GrBCMQAAAIABJREFULBbDxYsXAZxO5CbbBV16mlRKCilqImNjY6aR8bvehP5KpWJas6JhNa3B7/dbfBMYoB6pkdBkp9BapDCOpfE5RSOqxeAV9l2T0VUDUsuSrD3AINFZLbdHjx4ZVRvRrGw3C9BSaI3x85FIxDT5cDhsCfUcq2q1appzuVx2GFf4PnUFAgOaL+2rMhmNjY3Z++/cuYO5uTlDED548MCpCkD6PfaB3gCOiyaB7+zsYHl52TT3aDSK733vexZTaTQaWFlZOdNtfnBwYBrxwsLCqSRvLahLdhwtfTQxMWGxnGAw6BS0JuE0MEjUVjJpLTtFS4vz62WN8bo0tdICy24BAwucc7e/v49SqWSo4nK57CCuvWhtRVACboUT7hFq2ZOTk5ienjarll4SWtrq2fHiHJRBptVqYXV11ZDQnU4H0WjUrMVGo4H19XUHi+C1hrXEH2OIfJeiOtkHjWdzrjjngIsyJcuT1/PCtZLL5U6xAqkb9+SkX+WEljERsBx/jYvHYjHHcuQ+0KyD4+Nj67uX8ER/bjabFoY5OemX9vKmuPEcz+VySKfTtqfC4TDm5+fNIk8mk86zSBzB/obDYfNAEPmsrstms3kmaj6XyxkugZ4pLbKh1l8kEnHOTp7peiaqBwAYhDN0H/NdmorXbDYdliWSZJxV4MIrz70slY2BLkUN5o6OjtoG2tnZwdrami3e2dlZTE5OOhvSmzvDw2hzcxPr6+tO5zVm5M3nIdycB5IeAnpARKNRZ6EwT1IPdvaDA6eu0mg0ilgsdgoCDZymziN9mBcOrTlaXteC9pUXGBlwNH6UTCZNIajX69jZ2bG+93o9pNNpm/xoNIqrV68azdne3p65uVKpFHq9nr27UqmgUqk40H1+jnOsuaXsi5ZnOsutqP38/ve/j1deecVcM++88w4ePHhgrqs33njDyZ2MRqPmBioUCmi1Wk4FAXVXv/XWW7h165ZB2R89emR9AGAlpzhO3IxnwcO1D1QSFXCgccZSqeS4eHd3dx2mHe0/63PqulE3ES9XBTtQNLZCIJrO8+XLl+3yvHfvngG52PerV69aXIfuRL3wuS6SySRqtZq5ZR89eoRYLGbfzWQymJmZcThPgQFDku45L1+yxqZ6vR7y+bzNYTqdtmpEQF/h3t7etnXGih9sR61Ws3eOjIwgn887yrvP57OQSTqddgCJXipHrwuYc+PlKe71enaRTExMOKEW5mErVaPyIieTSbssmTqmrnwNCSndItDf55quo6LAwXa77Rg1HE8NmZRKJZuDUCiETCZj54PmzwJ9II6WSOt0Oqa88AzmGLKclzedTlmTKKwbyb4dHh6aUkH+aBo1BwcHKBaLTpqPxl0zmQwSiYQDzuF+4tmlAC7lJyZHre73YDBoLmBvSE/lhZclB+rw8BDj4+NOAmq3O6jXFo/HUSgUjJpsfX0dc3NzzkHNhHugb03S+iBIiNYkrSXNjdIYZ6VSMX85301RbZvt0xJPCvJhAr5qvsy95IB742vK8amXmjeB33t5erlkvYAgLgTGSfS5iUTCYn5A31qjBdFsNjE+Pu4s0FQqZYd1u922DTc1NeUUWmXeKL+bSqUwPT1tB/Dq6qqjHXLcSA7A8dbNxo3KiyqZTCIajRpa9OWXX8a9e/dMe71+/bpDok/NGOivv0KhgE8++cT69r3vfc/GbmlpyUlyX1tbQ6FQsDWpYxYIBBx0q4IkeOFrbigtQqB/KOTzebtkR0dHUalU7JDXd3L9cNwIeOLm9JYj4oFL0fFWgBjgWsqdTgfpdNrW/sWLF9FsNp16gxrLJtiF2r2X41OBXUTHqnI0Ojpqigj7SGX3LP5SwLWAKIVCwQAizJMkIGZrawvdbtfWCi8+tuvg4MBJti8Wi6Y0slwWz6u5uTmjfgTc0n0AHIuO48pDmf1i3iTXKos7sF/ke9VDWxHCemaxGIJSMGpdWrV2gL7ienJycmbMUvOyW62Wc/4x95ttpGdGgZZa7DgSiTjfVyIBrg/N6dTPkmSG32EskGtW+6rI32Kx6FBAAv1zhJfl2toafvvb39oeoQKq3ihyJfO7St04MjIo5M6MDC00HYvFnLHWmO4wz3IoQxnKUIYylP+DvLBE187ODoAB2k4piTQeQR8yb+x2u42nT5+aNkfrh1pIPp83rbDRaGB2dtZ84YzZqbVE3zPQd/lub287ZOEUojOBvibEgqRsU6PRcD6v6SOMOVAz0rQJbQcwKD6rMRFl/6CrTcm21eLwkgyzL7FYzNj7+T0lOp6bm0M0GjVtt91uO2wqTFFRWLbGhvf29uz3UCiExcVFc3PRVcI+022kjCR8B11viUTCKRasEHQVfjcajeL69etOGaCtrS08fPgQQN8lrxYA49NAn9A7EonYZ9lnWlN3797F3t6exc1v3brltIFz02w2HQshGAw66TuMybEvLBXF54XDYTx69Mhcmn6/3767srKCmZkZ85JowQB+11u5x0t/R1H2Gy8dV7VaxcjIiK0DEqdzTxUKBYe0fGxszCHV17h4q9VCOp12PBDpdNrmeG9vD5VKxfZuNBpFp9Mx5hpvzNIbC1XmpMPDQ8u5LZfL8Pv9Nn+dTgcLCwuONejz+RzUu8Yvg8GgU3lIq7vk83lEIhGnCLIi39XyVzeq7jWiYzU+Hw6H7fd2u418Pm/xP4YNuBfU4p6cnMSFCxcc5K0+l25YzjMJwRV/QVGydu+68KLu1ZvHfm9vb58qkUerjbFgoO8Vmp+fN3cpketcJyS691YTOSvuV6lUbJwYRuMeoSubcxWJRMzFy77recIzSfE0WrVJY73nzp3DwsKC7ZNYLObkQTMu/T+xLJ97WY6NjdmBkM/nMTc35wARCAEG+n5kjeVkMhmk02k7mHmIs1FqGjN+ohym3g60Wi2HXqxardr3NcB8cnKCjz76yAb5rbfeskVZq9WcOIDf78f+/r7zHoU78zNab00JDTT+Nzk5iWQyeSpPhxNOdy5/VxcFMHBxJpNJ7O3tORD3brfrJOan02m74Ji3xj6Gw2EnjujNeyPhA9B3uzKOCfQ39+7urnMJqFuGVGOav8r30/2km47tpSuS48bYMOeEcVaOIy9rb7XzWq2GX/3qV9aeeDyORqNhsbovvvgC3W4Xb7/9NoC+m0+FG+Hg4MA5QMgtyTYdHR1ZWSBgUE2ehwbTkdiuQqHgUAJq/vHCwgIWFhYcoIYekkD/QOPFpAcsAVhA/7DJZrNO7qfP53NSScLhsK2TarWK9fV1u9CYgsPndbsDPl8e1Iz9Kgcsx0dTZXw+H3Z3d8196r381aWuwkORruC1tTUHUBMMBrG2tmb8r4zbnUUmQCWR/Q8Gg44ySjIN7YMqs1zXGivk99VFS5c7MKj4Q4WESi3XjZeYJBwO2zrjeiB3rdftyj2rpdpUqfeOsQJWjo+PHXCWltmissw9RZ5VCmkd2YdkMulQ592/f99+v3DhAq5cuWLP5v7k/7mOuYaVs1vP2UqlgpWVFcvfLJfLTq59KpXClStXLIbebrcRi8XswkulUohEInZWNxoNJ6yRTCbN8GIlGI6ld+8x7KWx5GfJC+tZcuHeu3cPFy5csJd589RyuRwmJiacoH8gEHByYzRPU/3x2WwWuVzOsVr1AGHOGPPn1tbW0Gq17NmKBl1fX8e7774LoK/VX7582QbO5/M5FgNLX+kGUgAQL3G2k/lmwOkyYoFAwCmszH7oOB0dHdlCUkJo+tHZl0QiYQcT868UVadIRb/ffyoJWUsTMT+Pz2J9TP5NA+veWDAXMDcaGWwqlYoDvlIQRyKRcILngUDglOaqsV6icRmYz2azNp+1Wg3lctkO1/v37+Pk5MTiobFYDLu7u9au1dVVvP766/j93/99GysKkZn6XD1QFC26t7eHZrNpBzFjnDzwAoEA9vb2HKuP85rL5RCNRp1LbmdnxyGr93JRaqxblRNgoFRtbGxgamrK4qhkAVKlSnPe6PngwZ7P5+1gZZs1rjMzM+OgHtXK4brlHJZKJdy5c8fWhTf+o/F2b0x2ZGTExubx48eYnZ21ednb28Nnn31m8XgAuHHjhqNEs3/FYtHJgSQBAJXIhYUFx6ry+/0O6lj3i16o3hw95Wv1+Xw4ODiw/UPkKPciS24p6Ih9XVtbc8jEia3wehJ4LtAQUXwFRYlXuH/4HMZRaaSQ5YnW+cHBASYnJ/GDH/wAQP8yIR81MKgDys9+9tlnphR9/vnnyOfzOH/+PID+Ws/lcs78MGsCcPMsNeZfr9fx4MEDI6Bn+S22ORqN4saNG3Y50rvJeYjH4845oYYHz0p+NxQKOR4F9SJynBW/4C2qoDKMWQ5lKEMZylCG8gJ5oWVJjebx48fY3Nx0KLC81HHhcNgsBGoZyo2oxZMnJibMrcXUAeXBZLUKoK897ezsmEspn8/j5OTE/P/qrnjnnXdMm65Wq/joo4/wjW98w9qoJXSi0SiCwaDjIlOO2mAwiMnJSdO01GpkSoXyygIDlyEh9GqBtdtte/d7771nz1KEWa/Xw/T0tLljaJ1rOSm/329aGtN71FrWcmFeFiGt5kL3jJeBg/1l6gsRzpq7SauCY8D+h0IhiwFyLrW8mhau5ZiXy2Vz/arGz/QidcvNzc1Z2kk4HMbdu3dx//59G6tvf/vblnqioilALEDNPuzv72NqasrG8OjoyDwFwKCSg1af39nZsb2QSCROxaeI5CSsXfmFAZc6UCu7qJasbkNy5pIWkNyw3F8szURhIWUK50HTprimmPeqVqd6XOi6Zx/W1tbw8OFDJ+5HUR5c5i0+K4b5+PFj3Lx5086RtbU1K+XHz77yyisOAxX7dHBw4MxhJBJBJpOxtdvpdAxNrWPJM8XLSkXRSkljY2MIBAJmzbNQss6PssEoypSipbE6nY7DUkPaTmCwj/l3tR69Y5xMJg2xS6tJzzQVLyduNBp1vAikm+T3vK7h119/3UrmAf0zVa1WZRdjfjH3soY69Geg70Wgp+bcuXO2VoD+uonFYrbPE4mEEwsmgxb3ZywWc0Jy6h2kqCWpfL/07HhR6mfJcy9LhSBvbW3h448/tsswGAxafIwv0YR37yZhjqbC973BVD6LABIeHMViEXfv3jVgR7vddtwfGlB+8OCBtbnZbOLDDz+0QDLh8PSlx+Nx53AmIEDBMZVKxUklUW5KxkH4WY1hsj+cYG5ecqR++umn9jklkwb6rhEqEvv7+xgfH7eFwUNcSRuq1aqzsZQ0QIkkeNAoJSFjPfpdza399NNPrSYiv7ewsGBxQsYvdVy81F0EVgH9yz4SiTj0dupGOj4+dtzvGq8lWTj73mg0sLq6av3K5XKYm5tzcswoCuNvNpt4/PixueyYa8bnTE5OOoe6N82nXC6jXq9brpoSXyiknX3XS4tgLo05VatVO9T1UNEcvmq16vDRTk1NYWxszMbVm2xNTmAFgilnr8ZOSXbA/cQyU1wHVBZ4CN6+fRu1Ws2JhVO8F6O3vJ66Zff29rC1tWXu7pdffhlvv/22vbdQKFjZOLaZe5PcoEofFwgEbDwKhYKVnwIGpAD8u+a96pjoxcE8a84nL1JVOLznnIYYFOPANCBN+Tg8PLS1XqvVsLm5aXHjnZ0d56LTcdQ0DMbXqbgVi0WMj49b2KZcLiOXy9m53Wq1kM1mHf5kb71HHTONA1OhUtCixtUZtlF3KyUWi9k+IsCG5CL5fN7BAGQyGfh8PutDJBLB0dGRQ8ihwCwdH294jKloz5oHzeXk2D5LnntZam7a0dER7ty5Y7GXYDCI6elpx7LSy4MNV75Nfo+NVD+yl1hAK23cu3cPq6ur9q5wOIxEImGb23tR6MKqVCr49a9/DWDAgqLgEgXk8MD0suF7GeqBwabwxhK8OZtalPTRo0f47//+bwCuBRUOh+2wpWVLbTqfzzuAGlrh/L7GO/n9Xq/ngJK0/VpTk2xBSm5PMgWgv9FWVlacPFOgf1BR69ve3nbGMRKJIBqNOqQFylAUi8WQTqdPMf5oMWj2hwAPbr5MJoO5uTlbY0+ePEG1WrW+RqNRW5/eMa5Wqw7A5+DgwA7p8fFxixXze6oIEqSkBOCBQMAUmnq97lTS8FpwXn7TdntQi7VarTpFAbzAL+1LqVRyikqrhUGrRw8U5fWcn593kuJVCIDguPt8PjSbTQPUVatV7O3t4e7duwD6Cpw3GZ+ilgnFe6hpLHRtbc3GbmpqCtls1oBZ0WgUjUbDeSaVChJqcF94GYloYXBt7+zsnCqEwPbouaL7icqznnFqsfN8U1LzZ12cbKPOn1rKJKtXAvCtra1TMWygD2Rjm7g/tB6uckeTWF0rxrCtQN+a//zzzx2mNe4hKumcH55vmvtZr9dPxQHPAsmQTU2FsdBPP/0UwWDQlCJejJppEQ6Hrc/kYlav3fNylbvdrqO8q+LM9fi8S5IyjFkOZShDGcpQhvICeaEbllqTz+dDrVaz2odLS0sIh/8/e28W2+h5nQE/JCXuOylR1Dojzb54MrZnxoljO3YSNEjiIk7SBkkKpBe9LooCvWmBAkWBor3JRS+KAi0CJCjSoijQBgWS2HXs1Etsx/bYM+OZ0SxaRhIlUtx3UqTI/4L/c3jeT/JM8d/+PEAQa0h+37u/Z3nOczxGGSTN32dFh5KVRruJdHqDZp0vFAoolUryrpWVFUxPT4vmwtqZGvlHsZa96fV6AkH+4IMPcOnSJdGUaFnp6iZac9TVJCjaYtBxBY1043u1+/HBgwd49dVXRVunFgUMLBPGBVdWVhCPx8USmZiYwM7OjhFj0TlItGJomdlsNgOR5nK5DDYfbamxkouuql4oFOT7dDdaxwCA4QbOZDLiiiEzjHZFAjBSiDTHKUVz8jKO43a7sb+/b7CNsIQZMLAWNALU4/HgxIkTh7phnU6nPLfVamF8fFzy/eim0rSHc3Nz8nexWDQqo3Bc2WYrV26j0TA8KkRZAsNKNjpFQJeus6YuUZgnyLliHFdr89qzw/Zpr0Oj0ZDvHxbT1BZfNpuVtZrNZrG+vi6ISlpx+myg6Jzhw6qraG/T/v4+VldXxZ09NTWFQqFg8Jbu7w9LNzEswjYy5Yd/a2sEGFqmAAxrHoC4Ox0Oh4F9IMMNMESi6xJeVrcrYHqcrP/9aeXKeFbqEI/P55NzgbE0zoEOWa2urkoKCtOyrOkr2nLSlV+IEdCu1kwmI+0MBoMHkKQaeavTxJi1oFHUzP8FcIDNSJc64++BQVoNK84Ag7XMClFsY7/fN3APep413R3XppUKz+oa159rt/bD8ixt1pjCSEYykpGMZCQjMeWhluVf//Vf92k5ut1uA+nG5Hgd59F5T1ZLi8wO/FxrP6xsQUuAeW/UsphfRu2dGhitl1qtBr/fbwOAH/3oR3L7U2vUQAYrgS6JdYFhjpK2GHSeFbkh+U4rcbr2i5PAmNp4u902fPZLS0u4dOmSDQC+973v9ZkLOjc3ZxAFMH9MIy9DoZBhodtsNqPKva72okFMrVYL77//Pn784x8DGMQKNP9vv99HOBwWbs6zZ88axNHdbhf/8A//YPubv/mbvib01rmboVAIgUDAQBDr9lvBP2y/Ji3QZATMH2T7G42GkCPcv38fhUJBvBuJRMKIhe/v7+Ob3/ymDQBeeOGFPq3hRCJhAHp6vR6i0ajBuqMt3UQiAZfLZVhWbB/7YEX7aXCaJn/v9XooFotiHbfbbeRyOfk7lUrh5s2bNgD40z/90z5zShmP1fm4GiVNq8UKtLLmQ+v9q60PjVJuNpsHWFG0ZUZCc+Y9b21t4Sc/+YkNAH71q1/1+Vxa5Pqdmu+WABaOHcFQOk6n663WajUDya3ze9lvTZyuATb8b/5ts9nwj//4j7a///u/l72XTCYNLwGZZDjPjGlyLXPtatSwngMdk2w0GkY8Op/Po1AoGDmmOneXpAsaP/DDH/7QBgB/9md/JudyIBBAIBAQzxL3HttM0nAd99Vk6JrBjOPE9U1LkcCufD6PXC4n1j3j7zynx8fHjZzhVqsl68Jutx+wyrTV7XK5BEPwxBNP4Ny5c4LbiEQicp4CA8tzcXFRilRbc4R9Pp/0YWNjAxsbG4LkZw1i5m6/++67WFlZMTwC77777kFXGv4PqSNWKitKMBjE7OysHIokW+ZGaDQaxvd5IXISdbkdmtia7FejneiK4+ektdKJuBRd8cG6SDQRAjBM8NfBfp3gbzXJ9eXYbreNS0mTcvNdwEEosg7E6zbrKgI6YB8IBKSEGcdmbGxM0G2saK9djjqVw+FwyMJ488038eMf/xjXr18HMLgUFhcXhUmDifgc53v37mF6evoACbhOAaLQZUKXI+fOyvBP15M+QK1sJvqi1WOTTqdx7do1IVZPp9OSagMM6PA0sk9feLqkj3Y5sk1WuLn+nKWY9Pc5N8AA8MTvl0olZDIZSaT+6KOPsLOzI+suGo0arn+WU9JlkSi1Wk0OpvHxcePiYRhCp1lo0n9eeNqFaE31sgIxNGBCE9rrUkl8t9PplDYzpQsATp48KW3gpUpQDokEdGkmgrgASIUguqJDoRCazaYBuNMoaroJAQjtmlaUDiNy57+xrxsbGzLv1gLNdPXyfHE6nUb6idPpNCrKMI3CymIFDBnMuI6azSZcLpf0VaeMAEOGMK4jrgPddr7TqiBq2lFe2Po8oKIBDPetZkbiWZNOp7G7u2ugxCORiEFTmsvlDIIVDZA8zA2vRQOjxsbGJC3q7NmzOH78uLRtd3cX+/v7gl6fnp4WkhvAdP1zLKmg3r9/H5lMRtrMy96aymVVug5t70N7M5KRjGQkIxnJSP7vpATkTKS24Pf70Ww2JW+QrjHtRnC5XAYMW3NZ6hqGLF2l3Rd8JzDQZHd2dsTla7PZUK/XxU0UjUYN8mOtUWqrpdPpSKmcTxOdIkBNklqIzWYzINlai6JLhn/T0ub3mbengS4UzU3YarVw7949SbRnrTr2b2ZmBqdPn8ZnP/tZAAMtTNc5pAtbQ8PJlXvr1i1MT09L8d1Tp05hampKLIhyuWxwO9ISoPuIGjjLjfHfNAigVqsZBBMcU21JasuX1GrsPy12SiqVEvqze/fuYW1tTdqRTCalzBowpHTjOrKCjHTa0t7e3gHgkM4zBGCABrh2AEiOr+ZlpatqdXUVN2/elL8zmYxB/EAtXNcM1Mnseu3u7OzIv8fjcQOIQHIJ7Y5nEj0Ao3QU50kDTvScMLWHbi5aeMylXV1dRSAQMPrrcDjEu6HBarOzs4YnQM+l3W438nrpsmObGKqgFcVwBK0EbcHTMtMeFZ07yj5owuzD8vIKhYJYNCzJxblh7riVvk97h7TLs1AoGKXAdGimWq0eIGnRZBXj4+NGqbp2u410Oi1AHFpGFO0y1yEe5lFaOVA5blY+V3o52Kf79+/LvNfrdcTjcSncznQqXZqt0+nI2X/r1i1Uq1XZJ1ayCuuZoGV8fFza3Gq1kEqljHCYPktZlk+HETQpTLlclv3XbrcRjUbFCxKLxQwQGTAsev8oeehlWalU5FDngcJBvXfvHnK5nLgJiJq05ijyAJqYmEAwGBSXn3bPkbNUEzVrgmUOFDeG1+sVvlhgcEhqdBtFJ5ZrsS4kHZPQf9N/b2WcMQZQuTd6vZ4oC8Vi0UCJ0t2m3R0UPfGbm5t4/fXXJR7E8eE43rlzB++9954wAH3hC1/A888/L4hCxjnYjq2tLVlkly5dwpe+9CU53OhK4+bO5XJYXV2VGGYgEEA+nz8Q96pWq/JMxjx0XFdffo1Gw6jj2e8PijBzs5bLZSMPs1wuyxrrdDr46KOP8Nvf/hbAYPPOzs7iwoULAAZuOr3QmU/Fd1vRybpuIcMCAAR5p9dFMBg0cuA02wrXJuNEGxsb+MlPfgIA+NnPfoZCoSD8mcePH8fTTz8tz3rw4AF2dnZkfkgAoC9uCl2pbL/Ow+O/UegO1Cwo2h1ut9sNNKzmBC4Wi/jkk0+wvLwMYIB+3d7elkMwnU5jfHxc3GDnz5/H+fPnRRHmvwODC4cKQblcRrVaNWLm/X5fxjGbzSKTyRjFlnUuKF32jOfqcapUKtjc3JQ5JCexZmqp1WoGG5cO9ei1oRH8GsnNy1KHVnQ4iDVrNbJZ51aTCYrv1/FPKjM8t8hYxkuvXq8b+0THFTWalcqnrsaiOXIXFhYwPT1t9Fejs6kMM1Rz69YtGUMy6/A84Bmm2cVcLhc03iKdTh+6/zSCVbuJOTahUEjuhsnJSdy4cUPGMRQKIRqNGoQULMTNZ1P29/exu7t7oMg57yVy2bL/u7u7ePnll/Hmm2/iUfLI4s/afw9AGkFWfF2Ak+ASYKBB7+7uymVarVYRCoWM5HRqqk6n04AcZ7NZKQdDOX78uCycWCyGTqcjGqe20rQF0G63jcoDgUDAKOMDmHEtwCysar0kNOUZx4P939/fx9bWlowPWXV0W3RgXVdo7/f7oulQq3v66aelfXNzc9BADx2j6HQ6WFtbk/fMzc1hfHzcYM/gOMdiMXg8HoP9QzMHkeGFfXM4HAapOxcvDxFgeCiz/blc7gC91uTkpGxmlnlje1lKiu/c2dmRg2FjYwM3btyQZ7PQNA8KVoBn+0igbJ1TjhMPXSaeW2M/2urr9XqijSaTSYHYA0O6Px4KBBoBgyLMwWBQoP0TExMIBAIydqwOQtGKDcec4vV6pQ2cb33Y9/v9AyAcfu7z+eD1eo3ULV0EWKdm3b9/H6+99powZPFSpvIwMzODQCAg83Djxg0BWQCm8lkoFGQPdDodI36bz+eFcAQYrBWmgQFDhYbADio1PLyvXbsma4uJ5byMIpGI0PYBwyojunSdTkPS9G66YoUGuLAKkS5ppz1TBD1RWWu1WnC5XDKfuVzOAANpRQgY7HtNkKLTVJgOwjNRe0nS6bS0/+TJk5iampJnFwoF5HI5ufyqGv+XAAAgAElEQVRKpRLa7bZcnvV63aC87Ha7KBaLcg5o70Qul8ODBw8kfYWWMds0NTWF6elpY29rMngteq/Rm8JL9uLFizh16pSU1pubm8Pm5qaQFmxtbeGZZ54xPFvNZtPYG1rhX19fNzAfrVZL9iqVZF3C69KlS1KQnGv1MBnFLEcykpGMZCQjeYT8n2OWpVJJysEAELovahnka9Wumbt374r1VyqVjKKkOq7FBFxqNFtbWwYlUbFYxM2bN8UaSyaTRukeXaBZo1lJ3aZdXrro7d7eHjwej7glmY6i42caUZvJZES77vUGhayp2VcqFayurorWPDU1hdOnT4sFRgJ39l+7IegyAgaWyNe//nWxJL1eL2ZnZ8VFwe/run21Ws3gu52amjIoqXTB2a2tLbEorl+/jkwmI/Nw7NgxLC4uisZWq9WM1BO6X3SC8NjYGO7duydxRSb867gvIficu7m5OdF0aaVyDgKBgHDRXrt2zXDl1+t1fPLJJ0aCtc/nk/YyoVqnT1AIa+dcMXYBQIrhan7Wbrcrmu43vvENzMzMGDG+Xq+Hd999F8BAm7948SIA4Pnnn8fq6iquXbsGALh79y4ePHggcbHPfe5zmJubkzVps9mQz+elbRrZrUk+WOxWlz9yuVwCic9kMpiYmBC3HseC2jvjlSToWFlZwSuvvAIAWF5eNgq1k86Q40rSBrZld3cX5XJZ3LTaDVssFmWdZDIZrK6uGqQXtCb5HK3JE19AT4jf7zdcr91u1yCz12kaAIzYPgsKaItch3bYpomJCVmb+/v74v4EIMTmnBtrehzdkjrJXlvz2WzW8OIwjUo/i+cD459csyyLppG6FI/HI+ldRI1qsolAIGCQN6ytrUk7YrGYnHvA0Fumz3X2/86dO3jw4IGEhFiuj/2bmprC2bNncerUKQADy3JsbEzOQH0uT01NGQXs5+fnBTtx8uRJqTvJsfjCF74gnotCoYBoNCqfM17NPuuUHKbgcA2RC1zXUc7lcnJPBQIBxONxKexOatTD5JFuWApZRjToIZVKyWTa7XY4nU7p0N7eHtxut7hUgMHi4SSWSiWB/NtsNmxtbRmMKZVKxfDTBwIBWVi7u7v49a9/bfi4KTqXk4tNE2Kzojrbo030UCiE6elpcXnQDcZLWlf7zmazePDggeGWttlsciieOHECx48fNwAI3W5XlAer643jlkgkjHdOTEwI6z6/2+8Pq6xHIhHD1bW5uSnk5Jw3DSq6c+eOjOsvf/lLqQEJDA7YlZUVcSFOTk4iGo0eSDHQKQnZbBY3b96UA5BVR/j+QqFguIRJQE9l4Mtf/jIuXLggXLMLCwtSjcBa5cHr9eLtt9+WsXv++edx4sQJWRdOp9PgbNWubi3dbhfpdFrcjru7u3A4HHL4EyzDy9Tj8WBhYcFIH5mbm5O/fT6fXKzXrl1DOp2Ww+yjjz5CLpcz1hhjMMCwOC0vMX3B61h2u93GrVu3JO2H8Uy6yLrdrrHfjh49iqeffloqRjClhgeY3tvJZNIgXZ+ZmUG5XJYY5sbGBoLBoNQf5GHHZ2i3e6lUkjYVCgUkEglR5JaXl3Hnzh3ZQ3a7HRMTE7I+6KrUcb1AICDnxO7urqE81Go1UbK63S62t7dlXfr9foRCIaNYu1bmeVmcPHlS+t3pdIQ3mGPc7XYNsnNdtYiczTouefr0aTk/0um08SxtAIyNjRnk9VbSfrvdjlAoZLgeKdFoVPrd6/WQy+UkfWdjY8OIFbrdbsTjcTmngGHqBTAEVWmsBdtcrVbh9XpF4eJa5hrb2dkRhRoY7E+Px2PwY1P+7u/+TvYjMSoajKbBkpFIBE6nU96jK1BRdOxU70vW46Vy0Ww2kc/npb8ejweVSkXOYZ/PB7vdLuuC59Bh8tDLUm9WxkR0lQOi2yjZbFYm97DFEYvFjIXFTRCNRpHL5fDhhx8CGOYOssM+nw+Tk5My2KyMcZh/2W63y+ZrNpvwer3i36d2zINra2sLa2trclAx3sL3RqNRo9yTBhltb28LyTnbPDU1Jd/P5/NG0NjhcCAajcoC0OOmiRDcbreRz9lqtfDOO+9IlZLd3V14vV5cunQJwABsEY/HjarhExMT0s5yuSwHMzVdXo4vvvgiXn75ZXzpS18CMLgcNQJTU3+xTxxjPYbValX6NTMzI9orxymVSslmJK3hO++8A2BwqJ05c0YuRZ2cfObMGckvBAZ5lCzPAwwASywECww2q6b+02OsgVp2ux3r6+tygPj9fsRiMeMy0CjbeDxuIFpLpRLGxsZkwzkcDgNwMTMzIwpHu902KmtEo1Eh6GYbWYCXv9fCv3d2dvDLX/7SuFQ1ATTp+Pg5q9Xw8rh48SIikYis7aWlJfltJpPB3t6eJHk/9dRT+PGPfyzx8+npaYOcn9RkPKC1QkPUPDBQ/G7fvi0WbCqVQqlUkj5Fo1GZOz5Hx+eDwaCRk5vNZg16SV3thsn5FP6tY/vaSmUbtcXDvczPms0mdnZ2ZEx3d3cN7xBjmJTFxUV88YtflOfduXNHKBXb7faBQvMA5KKemJiA3+83cg81FZ8Wt9tt5PW+/fbb4uXodDqYmJiQc2hqagrVatXAYUxMTBjeomw2KwoM1zZ/G4lE5DLa3NyEw+EwysSRbg8YekJ0hgDla1/72gGCc463jhvzb5/PZ6COdWUijiPPPF3ku1QqGR4EUpLy+YuLi2g0Gvi3f/s3AJAcaO4LTd9qlVHMciQjGclIRjKSR8hDLUvtwhwfH0er1RKkE/MIqUW4XC6J/QDD3Ct+nkgkEIlEROsiSpC/9fv9Rq6Mdm+wULSOg9lsNqPUFMUKSdYxSY/HYzD/UKulhkIKO2r5dCnQUgkGg+JiolZOzZ0IVcbbUqkUUqmUaKl+vx9PPfWU5EdqjdrlcokrgxoO+/bGG28YcPtf//rXSCaTUtD6xo0bePzxx+U9RBvqvCv+dyKRgNfrFa32xRdfxJe+9CUj9ysSiRjWZLlcNpiUgKH1y9/ouKHH4zHcwplMRiw39k+ztqTTaUG1cu7pXueYsH1erxdf/OIXZZ0wBsR3MwygY1AUHf/rdrsol8viOr1w4QKi0ahYmjdu3ECtVhOXzLFjx3Ds2DEpr1YoFAwml3q9LvGVyclJrK2tSczy4sWL+MpXviJtrtfrSKVSMn7sGy1Nnf+l616mUil0Oh2cO3cOwMDaY3kvYIh65PNKpRI+/vhjaRe9MhwDn88n48zPzp49C2BoIbHNx44dw/7+vmjdTHugNq9dZJFIRJCYDx48wObmJpaWlgAAp0+fRiaTMWoJjo+PGyWgotGouLDJ8EMrykoMr1lcfD4fksmk7AOmsel0NG1pH1ZGKh6PIxgMSp3Fu3fvolQqGeeUzmnsdDpGHuazzz6Ly5cvyxxevHjxAMpb513rcoJEy3MsuGe0u/QwefDgAd58803p9+zsLFZWVsTr8eyzz2JhYcGo85nJZGQui8Ui0um0fO52uyUNjaksnK+vfOUrB/LjgeH60R4uwLQsdR6lPscBSBk2zbzGHG9gWPBe10LWY8m0KGBI5adjtI899piEH3K5nOGRYI47z9vDikZIfz71Ewy5UoFBXCORSMhCZoxHx6rGx8flsOehqAPHbrdbDs14PG4slEAgIJt1fX0dGxsbskiz2eyBmoE6Idsa1yLIgTEGJuUvLy8bFR+Yh8YYGfP4Tp8+Le9xuVziYozFYuJ6Iqydh1wwGMSzzz4rG+0zn/kMOp2O4dLRHI4ayOFwOGRcpqamUC6XhexhZ2cHTz31FC5fvgxgsEHPnDkjB3mz2ZREXM6DHpPx8XG5/E+dOoWjR4/KoiM/Kn9bLpeNXK9KpYJqtXqAkT8SiciBwMuVCsv7779vxK5LpRIajQZeeOEFAIOYpI49pdNpIV5gezkWLDSrXVGM5QDDmKbmqbVSH1J6vZ4R5I/FYgYw5Re/+IV8f21tTQ5fYJjeoeOwfCbngPN17NgxI9YUj8cxMzNjwPHr9bocZpwbzTlM8fl8xiFw/PhxGYu1tTVEo1H8yZ/8CYCBy+/f//3fZWyWlpYQj8eN8dAVZoDhGgyHw+j3+xKrevfddzE+Pi5zRhIKTZGm030IMuL88Azo9/tYWFgQpaVUKhm1O1OpFPL5vOyhcDiMWCwmGATSY2pFikomUyt0jiaT5NkOnaajFXV+n+PO3/j9fsNl98wzzyAQCBiX487OjqQZMCTC8+LKlStGoeHp6Wl88YtflN8uLy+Lss1+ado1nVpGgBY/15clL1ZgUNuyXC7jO9/5DoCBArG+vi576MSJE4jFYoayppPy6/W6EfaJRqMyf+FwGOVyWeZnaWkJdrvdWPe9Xs/ABnzaZal/x5Q6PpeGD9cFQ3664L2mcmRKEO+H/f19A/SniWnK5bKACoGhckA3NVPZtEL+afLQy9Lv94sG4vP5jJy2er0Oh8Nh5BlqH/v4+DiSyaTB/xqLxSR2o2M+jDMxVsG4BmNIr776KpaXl8VXzliHNW+Jg8ULCRgcxoy97O7uGkAFgit48WxubgrogM/SC0kX1z127BhOnz5tHNSzs7MSD3S5XKhUKkZMKRAIGAwjFK1dB4NBg5yY2iUvn+9+97tGjM/r9Uole2CwkHiY8z3aMiTABBge9rTMAPOCYVFq/hsXlCaf54VLq2xubg7z8/MyhmfPnkUoFJIYHjDUfgHIgafjETrRXMegaWXo3F+d6M0DUiemUzQqmAAbrpNr166h1WrhmWeeATBQdEKhkIwLY5YalNXr9WT9afKO1dVVJJNJg9xdJ1Qz/q4vz0KhYFipFA2cCQQC8Pl8RtHol156SS608+fPGwWsw+EwKpWKQULOdgODdaKLHjscDplfIjM1aYgmPCDfKNeovoCTyaSsje3tbcm3BoaKE9vCuCLnhcWfqexSkdCIVrY5FAodIEHRnL8sFafRpTpvUSPmKW63G9PT01J8mnFutqNSqSASicjBy2ISGuGqSQmKxaK074knnsD58+dFqcrlcsbF6vP5EAqFRDEPBAKoVqsC6NLWO8GT/N3i4qKxbl544QXZb4uLi2KxAwNLVJf+2t3dNYggNBgmFAoZuAWOB8eQQByui1wuZ3Bea9FxSZLp63NVGzy8KNlmh8OBfD5v7D96CdkuTT7h9XqNc0wDwWZmZgwmq3q9jlKpZKCoP01GMcuRjGQkIxnJSB4hj8yz1ByCwNBMpWlMC4I5YNSYWUJII54YM6PwM1ql1AJPnjyJQqEgt//Zs2eRzWbl2dvb28jn82KJWlMEdC7nwsKCga7TJXZ6vR4qlYrEWNbX1w3rsdFoCNUZMIzTAhAYuqZQa7VaYmFZWVCi0ShCoZBRuYFCpBwwsCbIRAMMLAAyzgADCzYQCBg8uVtbW2IlkSWFGlK5XJZxY0yLbhYWg+a7PR4PpqamxB3SaDSMAtfs6/7+vryP9HX8OxqNGqWTjh07ZuSyMdeW4zgxMWFQ1ukSXnSj0Uqj20pbGjquzjiQjqXxt7ryQjKZRDAYlPV36tQp+Hw+cc0kEgnDou31enjvvfck7pFMJg0kqq400e12Ua/XZS2vr6+j0+nI+BAVrVliNEJUWzraRTg5OWnE7I4dO4bp6WmxPAKBAD7/+c/LnqHlq8eGYwgcrIbjcDjEqiEtnXZtWatx6PWr43/BYPBAJSKu1UQigUKhYMTr+/2+fB6JRODxeGS/0t2p6eg4Piyvp2OAuiA1wyQ6Pkq3vh5n7X1gXE2vcx1L49rWecu1Wk3WbqVSMarbFAoFaU+pVMKxY8dkjLnvOL8ul8vgnq7X64YrUmMxyuWyePSee+45cQOzjfRCcMw8Ho+EWvg+ndWgU8GYa8pnaa+TXvNsM9vDZ+nKUla6O702tWXJ+D+/n0gk4HQ65RzO5/NIpVLGPGjuY+2lI+eubjeLmQMQNz/bXiwWDYpMK2ublkdyw2pIuyYZBwYbVPMo6orVTBqlu42HNHk9vV4vPvnkExlkTRNFtwHz4LxeL5aWluTwottI87JKh9Ri5rM4eX6/3zhc+T5Oyvnz54Xiis/SA6knpdvtot/vG4egXhwcA2sag15sepzZlnK5bNR/5IXB33Oy2cfNzU1jgqvVqkGt5fF4DP7eyclJ47Ipl8syrhwfnUemY8OaI5MH3NTUlEHEzBiq3jAaFMFYA90itVoNe3t7cuCwhBUw2GDkCdbt024/a6qFjn9+Go1cMBjE0tKSECkkk0mcOHHiALUcZXNz0wDeMBld95HtmJychN1uP1Cvki6lI0eOGLU0+ZzD4u/6QPB4PJifnxf32t7enhEb5aF3WKkv63OBIbH6YZ8dVqZIV5cnWfhhBBsaXEY6NysBB9vMPcM94vF4DLc6eYd1qTBdZEGHSJi3qEXX393b25O1BgyVVT3XvEw1J7CmzON61MQGrMGpv69juzzgCSLRQD6Xy2WQFjCeCAzLX2luaUq9Xpe5mJubQzQaNejc9Nww9YYXWrfbRTAYlPPD6/WiUCgYbkjuG51qAwzvAE3ez3MQGBLla6CmnnfNS6zPFY45zywqzNzH29vbaLfbosz6fD4jRKFDCqQgJKhscXFRCkQAA7ezJqYhkcKnUZxqeeRlyYOWLCxcrHa7XSw1/g0MtUyHw4G1tTU5BM+dOwe/3y+T1O12DWTaxMSEPHt1dRWRSERYFZaXl1EqlcRKCAaDBhGBntBarSaaEQEf+pCvVCoyIPV6Hc1m08i90rEbovVksMbG5NDT1gDHR19i1kGnFacrO1DIYcs2kDsRGNZM5EKyci/2ej1MTU0ZIAtdwSUajQrH4sbGBiYnJ2Wzx+NxTE1NycFONiD2y+v1GvmJ+sLnd2gFczNys2u2DV6gbI8mza9UKgaIQzM5aZSbfr91A/NZjAFxbHXsVlt3fI/2lng8HlEAms2mUdWmUqlgYmLCUOb0hmu32/KuUCiEEydOyDrgs/jbbDZ7gMmJqG22haIJoFmEgJdhqVQy4kCsmsK/d3d30e/3D1T24aFgvWh0XFwTTVM0qpp7RCtAlEKhIOQOLG7MPhCAw/fQ2ue7aJlwzbNmp7YGtfKuOT6j0Si63a5Brt3v943YpK4HStFISM3gxLnQVVKYQ8hzi/Euzh0tefYnHo/LszKZDOx2u6xBjejmO0ulknimms0mUqmUAAY1G47b7ZY1T+IGbSnrGB6T8DkOvJR00n4ulzPi1xSrl4S/18qztkqdTqex1x8m+tKlRc+xYWUXjgWBYroAhEYh53I56c/Ozg5yuZwYaUtLS1haWpI1mc/n0el0jCIK1mo1n9rmR/ZqJCMZyUhGMpL/n8tDLctSqWS4PiORiGjf1IA1b6LW+BuNBra2tuT2Z34O0WDFYlH893S90XLkO/mOUCiEnZ0dw2I8evSoaAPa+tDV5VmBRFci0Jy0wWDQqDZPViJrnUPtRtI1DbXlybZoF6fdbjfGp9PpGP59ysLCgsQJw+EwgsGgtFG7qYCBhqprKU5PTyOfz4vmybJb5GvsdDoyZ5ubm/D7/eLOaLVamJmZMWJVOveLY6DdLMDAYmB/WX2GCMZoNIpUKiVztb+/j3A4LHMbj8cNtwmr0WsNW1vTpVJJLOl+v4+pqSmj1I+2HmhVam5PSrPZlHEsFApG3HV2dhYej0d+R4uBvye8nOsyEokgGo3K3Ha7XbEsmZ70rW99C8AAfXfnzh3xVpAFS7uktFWrUY1LS0uS1kQrilZLIpEwNOJOp2PMG6n/dA6qzWaT9bu3t3coJSLHVbNNcT3SmuG46HqCFG3ZkwJSIz6tTC29Xk/Grt1uG+xPROVqb5R2EeuqSOPj41JCC4CkGujqGsDQnck1ROuJz9Brk1YT28NYPL1Dd+7cQafTkTF2u91SuYT9YaoZ07C494jY1KWydnd3BaVaq9WwsbEhf+v8P4fDITgLorZ1m8mJzXlmHI/v1QhkcuhyXDQyn+XKrHVRtadOe8G4rq3hKMD0tNFVrfmlrWhZzcoTiUTg8/kMi5/VlwATnX/06FHY7XbxiG5tbWFubk4QzE6nU0qpsT869n1Y2ykPvSxbrZbBBasXvl4UwGAj6BJddDfRlF5bWzNAFmNjY5IKsrCwAKfTKQuA6RKc8Egkgng8bhTofFgpJs2nybYCw4WhE321SR4KhVCpVIyLTy8OTe/ndDoN+r9gMGi4YDTZOTB0KVH0Z+fOncPPf/5z+Xe73W4cmjpe5HQ6EQqFxCXRbDaxtbVl1H7Th6gGrrCGId/daDQEeAAM6w/qeI6ugcjfVatVudCKxaLhqgoEAgaUvdvtHsgv1bUUWcZIEwbopOVqtSqbka5mnR/JIr/872q1KoqHhrFrFx4h/gwh0H3GNtNNSgUAGJRO06Akut2BwbrgOxOJBGq1mlAdfutb38KlS5eM2L0mbTjs8KKQfo7/rgtbc7x0rFjXLSyVSoabkvFBTbDPQ59zrnMPU6mUtPn48eOYmpoywHu6YLVeqzoNoV6vG2A0xrY17SCVamBAWnD69GkB9hSLReMC1MANgkS0QqQpCnlW6ViUpkHj/1cqFVmbBLRRmJqky6u1Wi1p3/7+Pk6cOCHrj7Ry7H+pVJLxfuKJJ7C3tyfnYSKREB5ltl3vi2w2a4QRtOiQgC6eTdF9YAxT89tq4Bj3Nt9brVblv8PhsOSeAkNSCK0068IVBGDpvFQ9lpqUwIrx0O5dYhz4XGIaSDt44sQJJBIJgwaRSuXk5CTm5+cN0o9Wq2WQWTgcDpkzKhfaOPk0eehlqXn+NIiDn+kcGx2wZSM7nY50cGdnx0C4Tk9PGxMODC3KWCwmaD92wOFwCIquVCrJBgNMK00zTtjtdgOZSRQm39fr9YxqCvl83uB7bTab8Pl8B+Ic7K8+2EnQoK0VzWvZarUMkgadfH7u3Dn8x3/8h3xP5wqWy2Wp6AIcTLxnPT1amqurqxInY5/+93//F8Dg8NXWidPpRLFYlEXNWJW23IjK4zyw77SQCXhgv6LRqIE47nQ6hpLFS4sXXzwex/j4uGxQTQLP+nnaw6D5QQOBgFSs4dg1Gg15llbmdP4bk6IpmgMZGGibOi5+8+ZNsbbYjrm5ObnMHjx4YFizDodDxnRnZwexWEyqza+vryMYDMq+IIJS5zBSAoGA9J1ACk2oHwqF5GCkRk/UH/PleEiEQiGj+ou+HHmp8O9kMolYLCZsVG+//TZOnDghfdBE5/pvPovCy4ljwYNLW+S5XE7Wwmc/+1kjt/DevXtotVoG77GO7Wsmqb29PfR6PQNDofc6P9drm+3VSGR9xnHP6rWRz+dF0YlEIoYCQ+WZZ5zX65U143K5sLOzYyCMNelAs9mUSiocK+4d67hGo1GDrEIDnwg20yh9XaCa8XcdZ9X8y/o9t27dQi6Xk/n1er2Ym5uTeeeY6ouXoB89xgAMogB63XTOrz5L9fcACJsPDYR2u21UySHZBTAkadcYGh2rtvJd08NiBX4dJqOY5UhGMpKRjGQkj5CHWpZW/kQNxS6Xy0ZOnNW/3+12EQ6HhS2HeTPUAPL5vDDrbG1toVwui0uF2gs1e+bO6XhEo9GQZ2ltkOhLYEiXRc2n3+8bMcler4ft7W2xCmhlsU/avaT7CAy0nkAgIG3c3t6WOpzAAI04PT1tVGHRCEttWbrdbrEASqUS9vb2ZBw7nY6BHCPHJftEl4rWUO/evWtUe6EbrNPpYHd312AJ2dvbE3cPGZh0zUDtVuG4akuSLEUaAt9ut8XCpvtLr4v19XWZO7pT6PJrNBriMmq328KpCQxyCy9cuCCfs6oMhW5+HaOmaCox9keXaguHw/J9n8+HRCIh7utr164JgxXXgYa293o9Wbu0GHRc5969e/KscDiMXC4nFlChUJDq9dY2VyoVwz2t1x/Tb3Qe3927d4250xo04zIaecr+V6tVVCoVGUuHw4FIJCL1Bm/duoX33ntPXM2Li4sH5pRCFy0wWNe6KgVjo1xH9Xod29vbMq4ffvghPv74Y6Oeol5rmUxGPEp+vx/T09NibTA1hMKaqppBS8d1OY46hGAtLUUMg7bAU6mUjJPb7cbPfvYzsYSOHTuGWCwm1j3rQfKZbrcbn/vc5wAMazJy7Eg7p5HpY2NjRo40ZXJyUs5O4PBKNfx+uVzGxsaGuNx5rvLcYi6n9rSwf7u7u+h2u5L3ms1mkc1m5W+Px2OglYmS13y8FGsOrKavY4UVnQvMbAv2Z3x83GB/0+Uiw+GwoGHz+TzS6bScP4lEQtC1nG+N7qX7m23Rd4lVHhmz1IFjnVPDGBwXGIvT8nOa9zwkjhw5gnw+LxuB+TDAYGEkEgk5xFl7Ty/SZrMpByxp93TsjaI7zv/Wh6QO0N+4ccM4JDwej+TJ8W8dJ/H7/fJO0onxWeRQZP9XVlawtrYm8TWXy4VGo2EcFJRbt27JhVatVpHP5w23gdPpPACm0JyR/X5fNujs7CxCoZC8l5c6MIwP8rLJ5/MIhUJGcWwr1J9xXQBG7EaDorTrs9FoGAccMISUA4NDfW1tTT5/7733sLm5KZ9PTU0ZFIput1sWN122DNZbXd9072hXI+Uw9woP162tLezt7QkXLOPhmlBDxzIIUmOcZHd3V+aTz+BGZtqITtfR9fN4WR6Wm9ZsNuUQIJmDdn3qC4JgEAKp9vb2sLOzI/Ny4sQJo1hBsViUQrc3b95EIpHA5z//eXm2dpceOXIEe3t7UgZqZ2cH586dO9R1rHP4mAZFZaxarRq1CQmi4nw2Gg289dZb4t4uFAool8uiSPR6PTmo6bbju4LBoMF5ytiaxljofNjDCNWZcsX1z4OU3+V4kB/b5/Phgw8+kNKCn3zyCcLhsOyNdDotl5/dbkc4HJb3R6NRw63OItN8B8eObdE5w9PT0wIc4m/0Oby3tyeKTTqdxu7urjw3mUzCbrcbcWOdz6tzErneuP98Ph/W19dFAeBZyfmk0pE82zkAACAASURBVK7DOhRrXFWDr3jeasVGC2O72rWq87G14sc1zjPB6XQa40NXsXZb6zCR9d1aRm7YkYxkJCMZyUgeIQ+1LNvttlhd1iRaBnK1xdNut0WzZQBXkzEnEgnRBB0Oh1hD9XrdYBQhGIG3PF0+2hVBTYz/TdFgDe0i4HsqlYq4cjqdDiYnJ0W7Y5V3tpklgHTCKrWflZUVbG1tSX/oEtJuQZZVAgagJR1I10ix/f19cTHQ6uI4M/itkYqaoo7VMHQiNxOVAZPyjbB+Wui93oCUXDPmED3HOdMFvCmtVssA8HQ6HQNJShABMCRyoHZKVzTf2Wq1UCgURJvd2dkRF9HRo0dx/vx5QU0vLi4aSc/BYNAgenC73YYnQa8LLdak6d3dXYNwgkwyXJ9+v9/wKhQKBbEmgMHck0Cfha41MUIgEDCQjdFo9ADS0UrDxt9q0IOmRyOYiWCTjY0NLCwsiIeiUqng+vXrstafe+45zMzMyPNZfQEYpLdoD8Pq6ir6/b7hLbDZbJKOVCgUsLGxIV4jaykmWsOaNQUYeqM0w5LX65W1wuLwmhXso48+kj2jrVam/miCA5KPA5Bwik7l0mxQ3F+NRsOgbmR6GTAECVH29vYwNTUlXhubzYaXXnpJ3NVvv/02bty4YaRNcd0T0c6+sDA11wE/5/t8Ph/q9fqhjF+64gvPBn3OVatVITPQpO78vtvtNtCv9+/fN9y6HH+ivrWXKBAIGKlD3DMcH80idVjb2VcrgYQGCnGdaDetzr7ge7RFys9I+q9pEHWGBN3Bh7GS8d2fJo+sZ0nh4tI+f7fbLZNRrVYRDAYNlxhgIuY0IhEYwuTZQW2qV6tVeQbRTLr8jo59aBSuHnQuIH5eLpcNF+fMzIygyQBIuSqdctDpdAQqrumf7HY7YrGY1H5jLpo+9EKhkPxdKpUQDoeNmCdle3tbLojr169LfTdguHD0ZHKxAQNXquZ/Zf1OfUgQVez1epHNZo3qEnqO6abWLE3AECHHdywtLQkqkRc7544uEY6hvqiBYW6crt4yNTUlc+l2u+WwDYVCiMfjcgGwlI5WhkinBwxTDThW1svyYfEIxqeAwTrQObFkomG7yCbCSyIajUqlinA4jPX1dcPdrlOOSqUSJicn5UDZ2dkxch6tlVI01Z/+jIwvjPWSPk2XIQuHw8Le9MorryCRSBg5u6y2E4lEkM/nhf6P9HxUuO7cuYN+vy+XJUt/6QOJwtxX/rvX6zVwDbqKR6vVwvnz5yX8YrPZcPToUfmcJf3oUtSsLT6fT/IaKUyb4ny2222DG1fn8vF3u7u7Bs8ycPDc0/8eCoWMedClBY8ePYpf/OIXeO211wCYLEmzs7M4c+aM8LjynLLW8OXlSnSsdQ8CZlyY+0mj7vP5vLjnWamFc7m9vY0HDx4Y3Knb29uyH2dmZgy3q8PhkDVFTADPbRo5Oi1jfHzc4KWmWOnpNDpWh/bYVx1OY7xZX457e3tGzrBuo76XaNRp7u3DOGv1558mj7ws9eWob3MN9mGjdK4aJ4mNuH37tgHWINclACngq3MHtTAmZqXU4yLWvnF9cTLOo2u5NZtNWSjaQmObJicnRbPiIGsfPjUpJqZzozkcDqOeHjDkHQSGKQV8tj648/m8AKE8Hg/S6bQBR9c5R81mU2I/wOBgq1arRnFlDTDRYCFy2fLZXIC0oNrttgF0Yds1VJz9YuqDJn8GBmum3W7LZiVUm5eFNfhPoAr7FwwGZYyz2axQ7gFD3lEr6EFbX/qy1ApJo9GQA4FQcb05rRy43W5X5p0kBvrQnJubk7kul8uyZicnJw36O8LeaYXx+5oYP5vNHkoRlkgkjAR3jRloNBq4f/++4bnY3t42CqI3m02DINrhcMie29/fF4ud/abFZy2ZlMvl0O/3xaJoNpuGFauBNRsbG/LvJCLhmrdaEKFQCNPT0wdyGzV9ocZFaKWMfdCiwXu0Pii0LDmH7HMul5M8YwrXBS9XfpepNxrYxTg5P3/22WeNuDD32smTJ40Ef9YWtVIOarIP7dXRfZmampI2Z7NZ5HI5WU/lchmpVMqgqHS73WIN+3w+pNNpWZ/j4+M4fvy4KNSBQODAOzU+QadZMC1P70dSQbItFB0X1JYfcLBMnxUIpi9OPZ9cdxrgyPZpHIo2tKykCbTo/y+W5ShmOZKRjGQkIxnJI+ShlqXWLrVrBRhWT9fadqvVEm3c6XRif39f4hWMDfAG397eNlIWAoGAoOAWFhbQarVE+2BVdCtVljbDKdr61RUHgIEGwmoofK5OfyFaTn+uS3bZ7cMq8PyN1rK024RatWat0O/WWhcAse68Xq8UyQVgWOfAQENLp9Pye5ZBoqW6urqKmzdvGukiTPom7RVdvtPT0/D7/dJGWt1a67OyErGN1PqISNRVM6yWgiYWYDK9tUrCYZUuqFFqpJpO7WEivU6apibJtlOsbn6tyfI92l3I8eDfoVBILKRgMIheryefE63N/mgXVyaTMUqmJZNJVCoVg+pRuws1SrrVask7+/2+kRZVq9WwtbUl63FtbQ3Ly8vSR7fbfaDKxRNPPGGUc+I40ZLUcVQWpeZvZ2dnDe07m80eKCIODNh+6OaPx+Not9uyfmilca4XFxcNa5lzoQvGB4NBAw2rUZwadc0KHvqcsq5jbVFoon/utVAoJOcaMNzDOqVBkw4wPsv3NBoNBAIBfO1rXwMwpNwDBl6RbDYr+1bTt/FZfAdw0PthTcOg94XnKN9TLBYFmc/naZfl3NwcFhYWDJpAK6Ugz+xyuWx4Tfr9PjY2NoRp7bHHHsPk5KSsA5ZotFJ/cu70POuzkOT7fG8+nzfSpACzxB4AI31OW7dcF/wsGAweiOlq61IzLgEH0wW1PPSy1DETBlF1DqN26REGzYVXLBYlTgcMLkBeCAAMbj+Wzvn444+l87Ozs2Jma+YYih5I7QbS3xkfHzcGmeW6dPxVL0gdCNbP0nEDxoC63S5cLpdBbdVqtYxSWtPT0+LeYE7cYZXEx8bG5IDh4ub3OE46JtFqtWTTkdP017/+tTzrxRdflFhhLpeTjbCzs4N79+7hzp070qbLly8LwMLlckk7gIP5W3znM888I+XT7t69a6T5UFnRipY+EAnjZn+azabkUQGDmB/dgxMTE8ZC5gWnGV102sXCwoJcDnre9O85RpoDV7vS2EZdMo5xRsb7SqUSstmsEf8jSxLfy/6xrZojWFOoBQIBNJtNo3YmRadi0eXHPthsNqk8AgzSj5aXl8XdOzMzg2AwaKRusR4j28i4c7FYNLg4V1ZWcOPGDTzzzDMAgK9//evY39+XVBO3243NzU2ZY10BqNvtGi52PRbAsLwZAGGwsTJEsY+MmWlAEP+bYSCdE0lwG8d5fHzcAKJx3LRwDfE7OjamS4kBZmyM79ja2jLqvxKcCAz2C4E13BP68gSGnNLEbXAflctlI0ykD/t+vy/v4Pjq2sAEJgKD/a/d1wSvWSvucJ2ur6/LpbW3t2dUijp69KhxXp4+fRrxeFzaUiwWjXdrZaBQKMiZxPPAimPQ2IRyuWy4gem2Zv/1WOpYL0GYXJOxWAwOh0OeRVY6HaPULl+rEaPlkSW6dLwBMKmurJdLsVg00F6PP/64XBZE23FS7t+/b+REkYcQGGhLOimVybv6INE5U9YFrDllNegIGFKoAYODbHt727DSNOKQdFqMB2rhAmQf9vf3cevWLblMz507hxdffFGC/16vF2NjY4delsDwYPf7/Uin0wZIyeqvJ3iBsra2JqjIsbExXL9+XfpMgmm24fTp09J/AiK4CElizcs0lUohEokYoAlgsOGYG0dt/rDEaL4fGCo0lUrFQCWS4JwHdzqdFmReMBjE1taWEKnTwtMIbaIigWECsrZaKczD45ju7e0Z+aV6DqzS6/Wwu7sryNKVlRXs7e3JhtRWAjVmHqD7+/t4/PHH5aAABpeERhRq6i891xoXwFqe/F44HMbs7KwAfHQcFBjMncPhEAvE4XAgnU4bVjg/m5ubMxQY5kez9uzs7Cxu374tCMtTp06h1Wrh2rVrACBoUAD4zne+gz/+4z8GMDhMdXyKlyH3NQF3lFAoZJCNZLNZ44wpFotGPM0aX9Lct8yr5CHK/lkp1WjxAYP1b+W81qWjHA4H8vm8jMP169dx69YtuVz4Xb7DGt9jMQpgoGDMzc3J2UKFledJLpczyAW0aEJ8Fnvg3FMR4xim02ncuXNHvEsTExNyJgEwiCqAgZLFNk5PTyMYDMr5urKyglarhaeffhrAgD5Tr0nSSHKfcZyAAWbl8uXL8j0WUGAbJiYmZH1EIhEUi0Ujl5xEBhxXApmAYfFoYHDO5HI5mZN2u43Tp08bhAbAcJ8x3qmLKHyajGKWIxnJSEYykpE8Qh5qWWpGeaLJdLV0p9Mp2jjdaRoZtbq6KloGydHPnTsHYKDNauSepinb3d2VlAhgYHnq2AHbcRj5r9bGqelQe7PbBxXsaf1dvXoVb7/9tlhJL7zwAhYWFkRjmZiYgMvlEuvY5XIZblbNuNHtdvG3f/u30paLFy/i2LFjopXSOtYE5IdJNBrF+Pi44RrSaFi6GMhk8uabb2J8fFz6mM/nUalURDucn583Kk9sbGyIFst0A2r68/Pz2Nvbk+97PB5cvXpVtF2OfyKRwO/93u8BGFgw//3f/y2WFEsPaTJ70g5yXRQKBYMsvVgsCiNJPp8X7c/j8Rio4Vgshueee076yhQGapVkJjkspWhqaspw2RWLRUMbZ1v4/3pcZmZmcPXqVbGk6vU6jhw5Ii7Oy5cvyzMymQw+/vhj6Q8L1Wp0sqbbymQyRi4v/x8YxDc5rg6HQ0j12aZQKCR9IpUare6rV68aTElzc3MIhUKybrxer3zX7/dLfizHNRQKSVvq9TrK5bL0iQwptMg6nQ5eeuklAMCrr74qSM2FhQUjFsxyeZyvzc1No/QeACM1SFcUYTt02pCe336/D7fbbVDRhcNhYx3qc4L/3e12xSszOTkJl8tluL69Xq8Rmy0UCtIun8+HK1euiNcgmUxibGzMsJY515lMBg8ePBCvzfb2NjqdjpGXzkLMAIxYL2CylDGnFBgWh+e5Ozk5aaRTkXKQz41EIvjqV78q5wPdqPQ2LSwsiFXW6XTwwQcfSAWdjY0NPPnkk+JJIFsO+6sRxgCE6QcA3nrrLUmvYlu5vpj3yD7SW8T+073OsWLOMdmyGo2G3CXlchmRSMQIN1QqFbEs3W63EfvmeaER2J8mD70sSWUGDDar5lkFzHJCTOjVNEKZTEYSnycnJ1GpVIzKIrwcWQqKg05eS+2q0jmKHFwebvqAqVarRhktkgGwvXa7XWDU09PTiEQi0qbx8XFcuXLFqL6uKz9omje3241cLicHyK1bt5BIJCQX7dKlS5ienpbxcLlcKJVKRtUEir44CRrgQRSNRtFsNkXp4EXJ5/j9fjidThnn48ePY3Z2Vt579+5dw93IcmfA4NJrt9uGazgWi8kFPzU1hfn5efzLv/wLAOD999+XceVCjcfjuHfvHt544w0Ag02uXVk8LDnXU1NTEvMDBoubKSvAQRfl/Py8pDtcuXIFp06dkvny+/3w+XzSfofDYVQg0GOsK6HQjcNDmu/WnLHJZFJyaCuVilGxIx6PY2lpSTZgLpczKAHHxsZw5swZAINL6tixYwaHa6FQkHcywfwwN2wsFpM2tlotbG5uGnH8tbU1+Xt1dRWTk5P46le/KmNz8uRJGYN4PG6EJHRZtFqtZuQg8nNN86bPghs3biCZTMqhai2TxcP1d3/3dxGNRg9QN9I9Rx5g9pmhCl2OTpOR6DQl5m/qkn8aAMKwzafFQzWYi/3Y3t5GKBQy8m3dbrfMO/lcqfAzjYRjTHAcL2KPx2PsdybI873T09OiuNZqNaytrcn3aZzwWbyAOC48o4LBoEGnSBCiVpIajYaMeaVSQaFQEMq+paUl+P1+CXU0m01x2b733nu4evWqrLELFy7g/PnzRvxPn4ljY2Oo1+sSRtExyzfeeANPPfUUgEFutQYuEpCjjSddMYY5xdwLmq6PY8k5i0Qi8Hq9BihM5/o6nU4jTs31x7WtlRKrPDJmqZN4OYG6gxoF2el0jHp74XBYDvmtrS3jkLQSV1tJB8bGxg4gxHTw3pofSanX62IRUFMj8pBMLPTZz87O4siRIwJWWV1dxU9/+lO5CI4dO2ZYpp1ORyaw3W7jwYMHRo1E1v3juzQCTKNS2WdKv983rOb5+XmjeKlGBHLc+PnMzAx2d3flELx48aKRvE1Nk+Pk9/tFU3Y6nQYIYXl5GSdPnhRt1WazYXp6WgoZc1y0h8HlcmFxcVEARtVq1UAhEjTBuZ2bm4PH45H542XPi2xubk4OkP39fQQCAdGCnU4nVldXZbPG43G4XC6Z37GxMWxubspG0mxKc3NzMs+8wHnIUyniocx8To1+PXPmjLx3c3PTiJPosj8kKNDeDT3XVHw0YrBWqxnsJJRutyvzynVGL0cmk0E2mzX4azOZjPRJXyLA8PDVSG9rDqdGPTLmBww8PSsrK4YXiXm2gHmQA5Bx/p//+R98+9vfNpLPdTEBv9+PpaUlWW8cb44rrV0NfqP0ej255Nk/zXzFuq2a5FtfxFyP9XpdziibzYZwOCxKULPZNAjAARjYCpvNhlqtJv1liT/tjaNUKhWUSiVB/C8tLRmX+927d7GysmLE23VuKy8zYLBG+Nvp6Wk8ePBA/qZlxfUYCAQwOzuLr3zlKwAGZc9+85vfiHft5MmTCIVCcklrJYlEFNxH0WhUagIDQ2VUW++FQkEMCD1uy8vLckawWLWurarJArj29JnH9gDD858XHgGiwOBc1ecTyR40mKhYLMqck69bg90+TUYxy5GMZCQjGclIHiEPtSyr1arB5aq1LDLDaKSbRqwyRYBaY7vdRrFYPAClBobuGWoQExMT8Pv9hhWnYeHMzaO2QMsCMK2/UCiEWq0m2tnk5KRRLd3lcuHIkSNiOVoh9GtrawfyOdkGMpMwbkVTXru5NBMKq4noPlNIxUXxer1CRXbz5k2Ew2EjT3Fzc9OILzGGBgwQiNrS1ujeeDxuVIHI5/NSBgkYuDC63a5Y3pwXjs8f/uEfAjjIpKJZj1Kp1AEOTl20d2JiAslkUtyUa2traDQa4gqrVqviIiI6lyjUra0tNBoN+VwzwfBdgUDA0FgpMzMzRqUPAIZmypxA4CB3LKvP6EK+sVhMxjyRSMg7fT4f2u22QdGmXZx05+rqO8AwXqqt8u3tbSOskUgkRIsnb6iOsVSrVRmro0ePwu12G25cbVnp/FSm7tACDgQCRgWd27dv46233pL3TE9PG5y82t0NDN1vr7zyCp599lkj3UOvdaYl8G9Ng8c+tlotI3dS0wjqiiDM7+WzXC6XgSlwOp0GypVnRz6fN/hTQ6EQLl26JHOzu7srcxKJRIyC7kS4cp3VajWk02mxhLvdroxxp9NBMpmUmDHbxnVy//59ZDIZo5JLo9E4NG1O92NychLlclmeY7PZkEql5FxyOp2IRCKG+7dQKBhpGvl8Xj73+/0S9mB4R9OS6pJmTFvRKXYbGxsyfxoB3ul0pJj4c889h2QyaYS/vF6vkVM8NjYm9wHXrc6b19SitVpNzg+Ns9G/5RrkWcQ5Ypoe9w0Ljx8mD70sn3zySXz00UcyEBrAY00m5r+xER6PR/hjgYELr9lsGptVd0i7P+hS0WVVmH8IDAkPeDHx0OJzeTAzkZwDSW5MTVel3Q7Mm9RuIw1O0cK8N52vo6HprBHHZ+XzecN9ovMBrcLkZn7v9u3b4t4NhUJYWVkxXFWFQsEgudYlvWw2m4xTp9NBpVIREvBCoWDUCW21Wrh9+7YE5s+fP49oNCrzQjes5o9l7inHnJXlucGcTqdRQmd/f19ciWyfVga2trbkYGbMiO2Px+NSggwYElNol6bf75dNqIEjHo9HxpxxFvaBNT517E1D9q0pUnQN8992dnZkY9MlxN8HAgEjCZogMe4TEuEfVqKLaTbAsJwVx3lzcxOZTMZwnebzeeNSm5ubkwuI3LccZ52IzjHhZ/z/5eVlAIPY1fr6uuzlyclJxOPxA3nIFLb51q1bePnll+XyKRQKGBsb1lclnaYuo6VjU6yfqOtmWsF8HLdAIHCA0F3nJ2sQFQDjoOVFQTAWD86JiQnUajWJgYfDYcOlzj3IsAFTbnR+rk4Bs+YNj4+PGylTOrm+2+2iXq/LvtbuQY3XcDqdWFpaMji66/W6rEfyEnMPcWx1iIL95ueMb46Pj2N6etqg5CNgCBgaCJqsIp1Oy+eanEMDMldWVpBMJg0e3P39fSN0oN20zI3XRDC6oIJ2d+sxB4Z4GK6DRqOBXC5npKXcu3dP7jmmYh0mD70s//mf/xl/9Ed/BAB49913Dd+vHkAOFBt3WIcDgQBCodCBGChFIyibzabB80gkmw702+12WaR6UprNpsRFQ6EQotGoHBjb29tYWloyUH46T4jWLttF7U/n5GgGEZ1DZrfbRSMHhsVn+e50Oo1WqyVWGjVMtl9XNmm1WsaC63Q6hqbIMWAfgOEkZ7NZNJtNyQEkVywAfPDBB0JODgyUDI0SLBaLWF9fx3/9138BGByKly9fxte//nX5vu47MNDE/uIv/kI2o0bWAUO2DF5Ed+7cQb1eFwQyNUw+b3Z21rDkPR6PXNYLCwtIJpPydzgcRjgcNrTvXq8n60jH0sLhsCggrKaiK7VoYFCtVsPi4qJxuLI+KzDYkN1uV8ZYe0Wi0ahB3gEMCTz43fHxcYOsoFQqiTKkx05zXNbrdeRyOTlciSjmeiOpOsEV+/v7+NrXvmYoJjo2rMm4ue41HqHZbOKTTz4BMEDW6rFgjJzjoUkJtPep0+ng1VdfxWOPPSZ9pbcAGBxyGgVP1i6tWOmLgUozMDh8ybDDMdZAuU6nI7FX/VuuFbZBI2wfe+wxfP/738dPf/pTABBEND/n5awt8Ha7LevxyJEjxjg1m02jdqxW0Ox2O4rFosQiy+Wy4W0jWpvnBGOdFI4xKwAtLS0BGMQ+8/m8xA2PHj2KXq9nXJYej8fwKmhlh+T1wLDyiZ4Pa9HvSqVi5CjqvHStWOzv7+P27dvyO4fDgd/5nd+RuWs0GgbJgObk5TrRlyk5XSlcBwREUmEmsQfnMJvNGiCeVCqFN99881BQklVGMcuRjGQkIxnJSB4hD7Us5+fnJWfq7bffNnzwgFmdwOl0GmwQzHOiRuP3+4UhAxhyElI0mwddM9pU1i4KcsnSnaNdZpr5YWtrC9FoVDTfTCaDYDAomg81LF1xW9NzuVyuAxRj7D/br2MM4XDYqLigrVxWltB0ZJQLFy6IlUzuVFoqP/zhD3Hq1Ck8++yz8pwTJ04YJXrW19cF3ba3t4f19XXhbzx16hRef/11AAPN95vf/KZYeZubm0bNv16vhxMnTogb9q233sLy8rK4437wgx/gD/7gD4wx+ad/+idcvXoV3/3udwEMNPpqtSqfBwIBBINBcYGyIgnHOB6PG/RvuvQT48KcP7puOMas6sI5YXqDhsFTFhYWxJrd3NzE7u6uMc+5XE5iNQ6HA7u7u+J2pnWhUZlMpeLn2mWkGX2sOZ90DWqOV+0CZQoQn6Vja3r/OZ1OtFoto+ZktVqV/XX//n389re/FZ7SxcVFw8LQXh+GE7QHpVKpGLmfvV7PCHfs7OwY83CYOBwOrK+v47e//S2AwVpk6AMY7CdWxeC41mo1+XtmZgaxWEw8BdZSdVbRliTjo3pta3e3DlOwPUtLS3jqqacEtbm+vo4jR44YuANd49fn8xmVRGi9akQr13IoFDIYeWq1GlKplJwPxGToEn+sVAIMQyBW4XlFL0kymUS9Xpf9z5xZWlPhcNjgv7XyYWt6O/LtanSvThVh/i3X8tbWloH+JoqdwrX60Ucfod/vC9bk8ccfF+sSGOZC6pQjwKQftTItadGUmMw71+tG54a+9957uH//vuG1/DSxPax+10hGMpKRjGQkI3mEZZlMJvuMpbjdbkxOTooFxJwb7YPXeU6xWMwgHqA/m6JJjmmpaIYVTRZMtKVG1urcmna7jZdeeskGAGfPnu3znadPn8bzzz9vaL6lUukAAlHX2ASG+TzWPCKNxqNVzc86nY4RByK5r37m3t6eWHXb29v4y7/8SxsA/Od//mdf5z7qPEmifjXXqib+pj9fa11kCwLMun5EImqEpdYq+d/aktbaeS6Xwze+8Q3bhx9+2Nc5UTrpl/+tE7t18rjNZkOr1ZKYAoP7mmhdx0isVQ/y+bxYjo1GAz6fz/Be6Lh4o9HA7//+79sA4Hvf+56sC9YZpGbrdruN/Eafz4d4PC6aMdce54Q5ezo+pSvXaBIGrnHOO9Ha1GzfeustfPjhhwb59DvvvGMDgL/6q7/q08on8OXb3/42AOBb3/qWEJoDw1iMdV1owJmVa1THv638vtpyI5KUv202mwfAa9/+9rdtAPDnf/7nfVY2YUK/jpNq1CM9N9bi0TpGyDw4YIiOBQZsMlevXhULiqxC/G25XEa1WjVyjKvVqoFC//nPf2771a9+JS/P5XIS8+OYt1oto27q5OSkxIFPnjyJ48ePG1V7NOCFrDzAsK4px5wxXw1GoceMUigUDDDi9773PRsA/OhHP+rTO+Z2u43fEehkrQ6lEcWaDN3KuW3NYdcoaYLx6BWzouL5b1xz/y9AUvYfvSYke+dd0ul0cPv2bYMFTGMXXC4X4vG41Pw9ceIE4vG4kTPLeWXsXWdp6DOP5AeaAIF1jtm2H/zgB4eal6OY5UhGMpKRjGQkj5CHWpYaiQYMYxsADC0JGGokOs7YbDYNvzV9/hRqTblcDltbW4IcoxVLi4EsOkSGTU5OHoi5UHS7iMTUfLWRSETek0qljDgH252IwAAAIABJREFUtRIrYpdtzuVyosm6XC6Uy2X5G4ARR41Go5iZmZF4Gy1CapIa9RgIBEQzstlsaDQakguYy+UEfQkM4jgzMzMyNpwjWhTU9rSFoVGO/A5FzzGRjPwtP9NsOwCMdBNa03pdWOej2+0atGN6TdGSoAXgdrsPtI8a5+7urlElAjDTWNgeat3a8kulUhL3oeWuY47ValVitVwfOmWGSFxgiN7j74mupdRqNYH6N5tNowwV+Us1+rdarQoaT6c/8PsApBwXKfii0SjW19cNFJ8uV8aUJ84Z8551iT2dmmWtN6g9DowxW+f1sCot2jLkecBxIkuX5gXWSEXrWgZgpOE0m03Zb1tbW8Zet9ls6Ha7ggrf3NxEoVAQ6+TkyZOIxWIGxRowzJ3ku5g7CQzp93TFnEajIW1g7UauC3rauLdzuZxY/vSWcE3qFCdggA9hPFGPrzXlg3OnrT+v1yvWH70AOiYJDHOSdZoO58DKmau9CkSKA4N4eqPRwJUrVwAM0MP6fNHPAAYeOZ0mpL0vmvt1eXkZm5ubstbJ18t10m634Xa7BTtx5coVXLhwwag+pL0+Oi7udDqNvU4vnUZgAzCs1E+Th16WHEw+JJ/PGyTBGoJM15Om19KHhNfrFZg8MHAx6LqEzWZTOsCB1BRgxWJR3p1MJnHkyBFJgdCbWBNxE1bNhZZOp5FKpfDOO+8AGAB+4vG4gGnOnDmDxx57zKh/2W63ZXNcu3ZNDlKmQzDdQLs7gYEr7+jRo3jyyScBDHLeAoGA4aKguFwumVzycHJsMpmMAQS4efMmTpw4gcXFRQCDTRYMBg3iYA0y0JeflfKM80rp9/uo1WrSR7p02WadF6XnnWPFv+12uxxwrAHIv3Vb+U6d6N3tdg3auWKxaHBCalLybreLYrEoB5DH48He3p5sAJ1L1mg05N+5VtkffSADg0OuXC4b4KxqtSoXTSKRMDa/Jm8vFApYXV2VXD0WGOfaPnr0KObn52Xtlstlw21Oom32l/snk8ngwoULkmLyy1/+Eq+//rqkCPAy43Pp6uT3yQurASRc99YLipykOsQyNjYm40n3IfusFQWXyyWXAC9CricWBdBr32azGYCQaDQqbbcCj3TpKZJn69zrzc1Ng9IQgJwZ1WoV8/PzRmkuYLBmSB159+5dI9eWCi77vbe3Z9SYZE4hn5lIJIy/9RhaCRiYI6z3kVZQSCWqOU51uz6NjIHpMToPul6vi2HC/HhrmIeiqSn39/eRSqWkzvC9e/eEVhEYrOVwOCxrV5/DwJDkgf1lX0h6z3WSy+UwOTkpYRGHwwG32y3rhDnGNCDu3r0Lr9dr1KzkODLPlIouFXGuKc6pNqi0Efj/uZ4l4x4cuEwmY7BXaM2dfmH6zv1+v1FVnGwbOu9Qx9JsNpt0KJFISB4VO8iB4CREIhGZCO3nZzyTvxsbGxMC8I8//tjgcx0fH0e73TaK+uoK69wcWtNim7iY2QZywXKzlctlrKysyGFF9hw+W5MS6AuA2jY/P3XqFObn52WhrK2t4eOPP8bVq1cBDOpmPvnkkxJH8fl8Ru03K3GC1gKZ28qNtLOzg/X1dbmcyN5DzZmWfafTMWKkOoZKUmdrPqAmftD1RckSxEM8n89LX5m7ysTsXq+HdDptzBc5N9lXzfykkdt2u92IV1UqFTkEVlZWUCwWDeSkPqTpYdCW+fz8vIHS08pNPp83qiCQXB4YXLzr6+tCxs0C4azIoJPPJyYmxNotl8uYmZnB/fv3AQD/+q//ahCpk/hBV58IBoOCAD5y5AgWFhbkYtKsJSQD13U99eE8NTUlRQcASP1R9lkfktVqVfaXRgkDw9qsmszc4XAYVV90HIzeIx3PZdwrlUqh0+lIfzSSV/d3fX0dwEDB1ixNfKbD4RBr9NatW8b6jEajouRzXPR+abVa6Ha7BimG7o8mzGARCc0oFIlEjPZbY2uHWe5ss1UB5jnd7/eNOUilUsjlckZ91cnJSSNXeWxszMid5FpNpVJ47bXXRCELh8NYWloycsT1RQvAqCeby+UEQa2VLT5fo1+BIafw3t4eHA6HeIj8fj/i8bhRrUbnjwPDernFYhE7OzuGB6JYLBq1W+fn5+XZtKStFvdhMopZjmQkIxnJSEbyCHmoZcncSWCobdKKi8ViiEajYlnt7+8bNfHIq0ht/OTJk8KuAMCo+kCXCrUBVjigS4nsKXw3a8zpmClFu/RYVYN8f9evX4fL5ZJ8OtY943N8Ph/K5bJoaazRSe2o3++LmU5fOMXv9xulYTKZDG7cuCEunqWlJcOa1LFcu91ulCWKxWIGl6PD4RAk2JUrV5DNZvHee+8BGPj7y+WycK2ePXsWoVDIcMNoyi9dJiibzeLOnTtirWxvbyMQCEh/aZ0cRitFzbxarcr/+Axd/YLVO4igCwQC2Nvbk2dubGxga2tLxnJubk40V2q9Gtmn0bnkN6VbqFQqGVqvNc+S/drY2MCDBw/ESqnVagbbCHNxtWtY04AFg0Ekk8kDrmhgWNmFFIubm5sGApvfp2dgbm4OFy9eFOv9woUL8r1Lly6J9TszM4MzZ86Ilh+LxRAIBMQSzeVyyGazhpsSgLTD7/djYWFB1metVhOGnnw+b1jHe3t7BksTOU25xi5fvmyUttPjrL0ii4uLBuKzXC5L3I/jquPVuoIP+xgOh8Uaa7Vasq7cbjcCgYC47i5cuIDf/OY3B8rxsc2snkGPh2Yg07mqpODj37puJkvc0SJniEDXi3W5XEbtRLY9m80a3gu/349IJHIg51t7JAAY4QyKzu+21oJcWVnBzZs3ZU+n02mUy2XDWxONRoV7+rHHHsPS0pKBHtVu5kajIXs3HA7D4XAIWxCpHLXs7+8b5zpFW8p0I+uwW7VaFUuR4RXuv1qthlwuZ3hFNjc3ZW41BV+j0cDKyorBJ01UMzDIdz137px4/Hw+HxYWFmTOrJgBLY+MWbJDpN7SsbFCoSCXAYnUdfyi0WjI5ydPnkQymRQXRalUMhZCOByWjV+r1bCwsCCXpc1mw9TUlECNeYlzo2g3qY518iClC4L1EHnpaoolYBh815tTJ4wnk0kDMKFh10wZ4HdPnTqF6elpOZC2trakpBRglhXTFwIwdHkDA1dIoVCQ52YyGZTLZTmQnn76aaRSKVE0/H4/ZmdnjbI2VlAVXXvXr1/HzZs3ZU5Z2JXj7PV6DbcZRbszWe6G3ykUCgYBONNnNKG0juER2MVad4zFAsPDVF+OdLUCQyILXrR+v9+A6+sD5vvf/76M6SeffGIcXKdOnUIikZBxmZ2dxUsvvSRr+bXXXjM2fj6fN+jy9vf3Zbx1AVtgcElPTU3JxUyKPm7m999/H6VSSfrPsQcGxXM5bnTHU/x+PwqFgnFR6xQj1gjlIRCLxYxUL40nYCoPDzkdxgAGF/HKyoq4yW7duoUXX3xRDvpPq4fK3+q0GdL28T2agm9qagpOp1PeT0WNY6sJROhe5kW3sbGBWCwmGIFSqWTw0MZiMeMi13R3bA8T2PlMXnA8Z06cOCGYAv62XC7LwcuLS6evaO7XdDota7PX66Fer0vfSa+oP9dE8Fagpd5P6+vrsnZXV1dRrVZlnhcWFgyKun6/b7jc6fbW6WRsfy6XM8p/PXjwAHa7XQA+BERZSRn4Lo1vicfjcmHT3c79Mzs7a/COV6tVtFotGXe73Y719XXZjxMTE2IIATBS0a5du4Z33nlH7hIaHzp1bX19Xdzzdrsd8/PzeOKJJwCYZ5tVHnpZMhDLCSIABBgsJB23mJqawuOPPy6H5traGnw+n3SQvKjcYNlsVjpADY6av9vtxtLSkkyoz+fD4uKiBHTJJaoRTxRrLmcqlRIt6nOf+5wUKAUOVk5hfTq+l2TZ7DPBKsDgUqhUKga5NHMI2aZQKCRVxWlt8fv6kNOgGxL9svZlPp/H9va2HDjcDKwx+ZnPfAaFQsFYoH6/30AKa1TjnTt3BFV269YtrKysyKIMhUK4du2a9PfKlSuYnZ2Vy4ht1zyNwGAj6Hp78/Pzckhr1CUw2Ahra2tyKV6+fFkI7/l962XHzcjDiH8TXcnvR6NR+P1+A4FMOXnyJG7cuAFgcMGPj4+Lhjw1NYVwOCzW/szMDC5duiQXXqlUQi6XM3JqOW8ADHRdNBpFIpGQMZyZmUG5XBai5mPHjuHKlSvSlldeeUUUDMDk0/zNb34j8zgxMYFgMCgX08rKCra3tw8AXvQ60ofizMyMUUFeMyMRHKTxCE6nUy69iYkJg9/32rVrCAQC+PKXvwzA3H+9Xk+sGvKfaiBQNpsVxWFsbAzJZFL2ZzQaNeJQBATqSiJcJz6fz6jCQoCgrr3IeDjb0mg05G/Ol1YEvV6vwetbrVaRSqXkTDl+/PiBfFR6nwAIgxfHPJPJyG95ifBiXl9fx7Vr13Dr1i0AA8tY5xbSa2ItVs0xZr+3t7cNJOnZs2dFyeWYl0olOadtNpvBqcv6uPy9vixqtRqWl5eF23dubg6pVOoA8l7Hf3u9nqxRfRZ7vV7jLuj1ejKX4XAY1WpVlBCijLkfvV6v4dULhUIIhUKC00gmk8IS9cYbb2B9fd1YR71eTzxOxHRwHlZXV3Ht2jVpG3OED5NRzHIkIxnJSEYykkfIQy1LxsuAYdksap+dTgfxeFysklgshmPHjhnIMZvNJm5Yj8cDt9strlXGM4BhpQx+RpcRNRai6qh1z8zMGFacdsO2223RxsnyTwuiXq8b3IbtdtvQaAhd1ygsXVqm0+mIdr2xsYFer2eU59Ew6larhVQqJRrO9PS0ASW35qjSSmGbKIFAACdPnjTcCrVaTbSy69evo9lsGkz/ViYWjSK7d++e4YIIhUIyh5lMBltbWwaDUbPZlJgFrUWdG0f2G/7NZ5GrNpPJIJ1Oy1w//vjjSCaTMnfkjdU5WBQrv6e1gjr7p3N7NSKbYwIAL7/8ssxdMplEMBiUMW+1Wtjf3zdy1e7fvy/vmZ+fl3JNwMClptuwv78vFo12r+t+sAZnIpGAw+EQN+LFixfhcrnElWctM0VPxv/D3pv9xn2e1+NnhrNxNs5KznARKYmyZHmTbdlx4thB4sBIg6RIizZF0aB3/ReK3BS972Uvi962DVAgKFIUCdxmb5q4/kZ2Y8vWLlPcOfs+5AyHv4vpeea8H9JS8bueBwhiamY+n3d/n+U852H8lm5Fuo61NBrLtfGdhULBtPNsNotkMmlrn/uRwmo3HFNvPmwkEjHELseca9IbUmCclPVraakRMc75euqpp/Dcc885cTwtoce9oNYw9xvrXHLcGC7wWn3artFo5Ljm2XZaKGRpYr/29/edkk1EZXLv1ut1PHr0yMaRsWyuq0ql4nie1MtCFC4rcQSDQczOzjpsOclk0onrUZSn+PDwEBcuXLDzYDQaodFoWH7nnTt3cPfuXQeFv7KyYvuU56yyYHEM8/k81tfXzf3/7LPP4u233zZXN3Esio+o1+s2fopx0Nqc/HeN+Xe7XYc3l3nswPjcrtVqztmZzWbtXJqdnbU1GI/HUSgUnLVdr9cNt9LtdlEsFq1PRJgzLKKhDq889rLUjUtyWw4qXQ48mHK5nJFoA+OLp9/v2yRGo1HMzs6a6+fixYtOEdgHDx7YJgPGi0vLTCmVnLf2n16Wmh4xHA6RyWTsgGg2mzg5ObELnCAG/r5Wq9miBiZ1DLUmp7e0Et2l9+7dwxtvvOFcNDoepPrS5HuKz+dz4keRSMQuJubw8ZC8ePGiAwQ4OjrCxYsXTdFQkmQ+m/1jnTuCSHjx6EH/4osv2hyzBqD3AtjZ2bEDpt/vo1KpOErDwcGBbTh1bQLAa6+9hoWFBVuckUjkFEG/Aoo0VsiEd51fzSljzVAeOOrSfPDggSku8/PzTlFhjjnHMJPJOMn0mUzG2eydTsdJxlYQVSwWQz6ft2fT3cMQQzqddvIhv/rVr1qhcMDNs3z66aetPuXs7KyRavB5SuDAeeI8JBIJZLNZIzGgAkphoQNgvHdJ1UY5OjqyfVIulx237fr6OpLJpB1uClbTNU4CiS9/+csAgG9+85v413/9V3vvysqKpVCwjV5lqNfrOTgBzilj8UrYr7E5AKcKyGvMU4nDCZ5jaSdeJKurq3j++eft/XQz8uLZ3NxEOBy2cSkUCshms6aU6XeLxSIWFhZsPgaDgeOmvXz5Mq5fv+7kGOve86aaab3bmZkZywl+9OgRHj58aJd8t9vF5uYmvv3tbwMY791er2d9jMfjznmh5+fXv/51c1sC43Wyurpqf1M55VpnaiA/V6Vf81cJNOT3CODUkF+n0zF3PQsqcN0QRMW51DPv9ddfd3ALHGfuw1QqhZ2dHQPYLS8vOwqCNxVG5bGXZTwetx8zhsFGsKiwMkRoUu3x8TH6/b5zCCqHqOY8cSHQf18ul+2y5bO99S0VeKJxB2UQYTIqL7RPP/0UH3/8sW2oL3zhC1haWjLtPRgMOlbO8fExwuGwk9PERRaNRh1GfoIVKJubmxY3oXwW65BayayuokwewWDQqfCgIAgys7CNBDFo0q0uwnA4bJVkYrGYUwQ4Go06lvfMzIxjqWqiN+NM5JJVblsFeZRKJbzwwgt2uTL2pmhZb5FYrUyjsTSCUrRCgFqWSg7Avynkg+UYKpq6Vqs5eZgKdqAEAgGbIypZmpepDD3KApNOpx1LnHl4ekBpjEkPGMavAdiccf0kk0mr9whMrChVfMLhsF2eyWQS0Wj0zDzpZDJpFhrnIBKJOEpjo9GwOatUKigUCqeS0Nk/zufq6qrD1fvBBx9gbm7OKWQeDAadNnq9Bsr4o5cdiUw49zxbNEdaEb30Hmj8GxifcVybxEJwLPP5PPx+v7Wf9Sm1CgcRu8Ak/sd3Km8xL3nObyAQwPr6urUjn8+jUCg4+dFnXe6AS2IyGAyws7ODmzdvAhifofv7+87cXrp0ydq8srLiYARY5UfPda5dVuphDLrX65nST9HLhXtTQZ4Unpf6G52DYrFo4+bl5GV1HVqsPp8P+XzeFAaN4RK3QA/L9va2WZv8fHl52VDm9XrdFNgnyTRmOZWpTGUqU5nKE+SxlmUsFnO0AeUgZPxNuRbVRQa4mj1ZJZS1QWmknnrqKYsxHRwcOG4hWk5aI1A5DZU3MZ1Om8aSSqWsmjnbQOQVMMmbostzbm4OS0tLjiWZyWQM3bW7u2v9e/rpp9FqtczC8/l8Tu4QUWmKJFWaJa8/X/MqadECsHp5ain2ej17biwWc1J2EokEYrGYkyvKdynEnf0/OTlxLHhWimCbtbq7UtspErlardoccF7VZZJIJBwPQ7PZtLWRSCROxW+1Cgr/jf+vaywWiznuUyIR2Z9Op+NQaLGNjD3RGqYFoQw1WmeRaRVcB7QOtboELbZer4dwOGzvnZ+fRy6XszVHDwzngJaouu4oH3zwgbWZ65rzmkqlHHSoF7Z/dHTkIIMZFlDLm3PIdBbOGeOgmuak6T7kQOacenlL2ffl5WVEIhGH1nJxcdHBBASDQRv3ubk5h1+awr+VepEeJEX/0nNCUQtHWW/4N5+tHK2FQsH6z7nSihTKbkT3uTdMwXHSmDldlMprnM1mzarmfytrmY6FjrHG8Rnr4xi+9NJLtuY4Bru7u7Z2+/2+E9sl4pbrRtHm0WgU169fd9Jvzp8/b3eAly+YlVX4Lt3XXH9sPwBnHNVLybXOdbOxsWEeFmC8/tTdv7Gx4XDvaqUb1hHVOZ2bm7P0l5s3b2I0Glmes3q5vPJEujsFrBwdHdnFQsCPwoi9PI7kO9SBYyfUnz8YDAzIAoxdTpq7xeReLjS6RpQ7VgeSB8gzzzzjxEnD4bCT/uL3+3FwcGBumOXlZSwuLpq5TyIETm6r1bL/vnjxonP5Mwamfnh1l4RCoVNxSgpdRGyTAn5SqZTzOx4QyjF7fHxs7h5uUC1DxkWVz+cxGo2cPLFEImEHDA9E3QBeBQiYuNjZL80bPDw8xPLy8qnLXcsuaZxYSZD5t7pVGTemaIwkn8/bhQ6MD/VkMmkbRw/BXC5nG4YuVH1OLpdzyOk5L8Ck9BLbpTEOjpumuTDWy2eRNo3joXSEdNGqG5dycHBg64KXs3IIa1zVS5TNIgbcC0yu51qIRqNOYepQKGQHBpVAJRBh2TJgvI+Oj49P5SyynTxcI5EIFhcXHZ5ikglQTk5OnJigKmfslxL96wFMNy3fq+MAuK5m78FOuXXrlp0rpLrjZcC1q6krXq5bb9pFMpl0gIk6X7FYzPbenTt30Gw2zb3ONaUhAC1Xp/FkzW0kyQpTHgj+0ctRlbNoNOoUeKACoPtVCUfS6TQ+//nP22/D4bB9t9PpWPodMHGfank+nUfvJanzykLgwET55PprNpuGKwDG7uH5+XnHbet1x6uRcu7cObt4GVPVUpPtdtsp6P1Z8tjLUjcBNX69DNR3zdiVsrj3+317ebFYRCaTscuWmiAw3ozlctmenU6nndp05CLVQ1MZIbwkw+QSXVlZQTAYdCzWhw8fOtXiFxYWbJLOnz+PbDZrKCy2jwOvF+dwOHQ0RxKp62JXTZeXgsYWVdSa1aRjJvQrm4xqt8wT9ZJcayxBuTYXFhZs452cnDgxCLbRe/B6x1kPO84vv8MYKJ/J2qQa21Urk6T5enl4RcdMD0hyx2qVCyKeOVaUaDRqG4YoX2qRBJ9pu1hLERgfCjqmoVDISeTm2AGww1XfrRYNCaUVnODdCxQFv7DNvBQ6nY7DNUpwChWCxcVFrKysOFyjGkvVajqdTseJj2WzWdPI2Q6Nt5MoX0EU3v6xbwoKZEzRezB7maGUj1otKtYrBcbW797enhMLzmQyTiUVr5KnljXn8tatW45SoNY6Sf+9lyWVsNFo5JxpvBzVA8F9QKCSxpRJnACML7Vqteogg9XLosaDlzGKlUaAMRGCFnVg5RJeNASBKaE7CVWAibLH8aIhw/6oh4/C+GCz2XTAa1qRiTFkvlMVN75TvQaVSsWwJqy6RKOGiGSus7m5OesfOcWphNy+fRsPHjywNcf1p/nLSuquCrZXpjHLqUxlKlOZylSeIE/Ms9T/Pjo6cm5ezW+hC1I5C70ao+ZYNZtN0yyee+45fPzxx8a9CYxjB6qVqUWhbiu+m3Lt2jXLq9ScKGCc23n16lXThA4PD08hqdrttlkUilIF3CostByVvUItJMYnqVWHQiEnNqDamVbloKuKGjTZfZSuSftP+i2OwdzcHE5OThx0m9bn1PE6OjpCOp12LAFgorkyXcgrsVjMsYTm5uasfcPh0LGIjo6OHO7YXq+Ho6MjyzMkG5DWo9PxViua1HdqhfMZ2j/GNxRZWiqVTENmlRNaEHRx6tyynWxzqVSythG1yXFUVCZj92xbv993NHcvoxF/Q1Er9I/+6I8MiZhIJBxLa39/31mr1WoVlUrF+nTp0iVks1knRkZ0MTC2XGhZs3q8otPVhcg2e60Vxll1j6jWznJQalFo3rK3dmkgEHDcYKwCpGho7ify4tIS29/fd3ImvQhub3UJ/rsyTyWTSSfmzfdo3vbBwYHNCVM4lFpSx1HPSoYelKaSaQ3sm9YjpadOa2lS+v2+s/80JEUMAPtAPIR6prT/c3NzDkpa0eij0QitVsvOIlIDcjyIl1Ak9mAwsM+1go6XISocDltfGVJgHzudDra2thxaRHoO2EeWtgMmsW/gNGZgYWEBy8vLZqUSe6MI5kAgYOPxOMvysZel0i15YyveGmZeOD3dDnz5o0ePcOfOHcdtpP/N8jfAOO0iFAo55LbqDmGnteAw5dKlSwZkobuMGyibzWJ3d9faXq/XUalU7Lk7OztOWTGNxwJuWkq1WjWQBdujOX90jWoysaaa6GU/HA4d95geqF53ks/nQ6vVcnJFeRF728g51FQYddG0Wi2njBDdI3pBasBfc0T1wB8Oh6cOOV5UjCewTcFg0AjagUmsSXO3vBe0bnDGPHU8KFQ6uOb0stzZ2THqQbpq1X2r40IiB32vxti50ZXGTV1vXlIIVQYY89ELQHN51X1+8eJFx33W7XYdEgKtp8qLheN49+5dJ7cNgHPARCIRoxxMJBK4ffu25bW1Wi2Ho5axPCpAPMQ558oBqsQbjOd5ifi19mk4HHaUuUgkYs/jYcs9pO5rJuwrn/ODBw+c2BwwUUo1bs55YV+UQk8VQQJsOAe9Xg+PHj0yhWVxcdEBmM3MzBgNJjChBNU+sF2sb8l2HBwcWL1Pto/pMYAL8BkMBk4anWIcqNBrbBqYKDS8aDWuqGXR9FnER2iOYrlctjbTTc05IJ2glxif7dT51z3sTQMqlUoolUqmSDB8oMqs5tiqsVSv19Htdh0wWjqdttxygiHVWFE8wv/v4s+xWMw6fBYyURGpPAQUWXl4eOgw2P+///f/nJwdskxEo1G8/vrrVuPv4cOHuHfvnuXyESml1b/Vn68Xz8bGhgGFtEIBMJ4wZTnZ29tDs9m02OP9+/ctPgWMY6ePHj2yxZDL5Wxj3L17F7u7u5ZknEgkHHACD1StJclkXP5N0QuOi10tTdXGiSSllcQDSdGYjE/xefoeXohsY6fTcRhItG2sDsJncZy0fbyg9ECcnZ21MfQGzgkg4jphzqECkjR/8azYripoCtgC4Kw5jakTJQyM11c6nTarizE5tZxarZZjlS0vL9t66vV6TpxY4+lEMbI6SCAQMCYoHVtlkVKgjiodGxsbTnxYD3zWxaRlSRIC9uF3v/sd4vG4zRnXlVpS5MsMh8Oo1+vW31qtZrFUjqNevKw4wnHWg0+L5xL0R+HaYF+JXFREqyajBwKBU9V5OOZ+v9/qkHI8FbmqVjRFK+7wcCTRP9unDGD0FnF93bt3D6VSyfb80tISAoGAXSZe1iM9P7e2tuD3+20+iL7WeK5aluT61YooFLUUeUF5gV7sQzgcdpRCjRAFAAAgAElEQVR0AjW5f/mZF3wDTGL5mhOt/W2324jH4w7oU+fTG9tUXIMim2dmZhxPzu7uLvb3963NBwcHiEQipsAxBsux3d/fd9inDg4OjG2KigXHh2uIv2U9XP6tCpVXpjHLqUxlKlOZylSeII+1LI+Pj03rIv8nb17v36wmr+Z8t9u1G5vWA90HJycn9tnly5extLRkvvGFhQWHw/TcuXOOheO1NrzakGpnwWDQLMlarWauV2BMgaYpKSxzo+b+J598YtrypUuXjBfx8PAQ+/v7pg0tLi6e4mXVWCQAh45LNRi1oGi1KOJNafMIV/c+l39Tm1NmJZ0TdWczXqslmTQ3lLX9+LeiZtWNqHPCHE+tFq/15KjZalUEbZNC0fv9vuWFsb26DuhKZfvoTuL3NQ3jlVdesXkvlUqYm5uzeSWyW7X3UqlkJanYF6U6pCUHwKlIEolEHKarSqWChYUFx/3ujSGpBs5YC+eSewIYr2e2cWdnB+122yxPrj2uv8PDQzQaDYf1xO/3W597vZ7x987PzyOVSpmVU6vVUC6XHXTi0dGReYUKhcIpRiPKxsaGw16kbWLKF+NFy8vLaDQa1o5Go+GsazItcX0qejQQGFcP4pzSSuPcc++rZ0cRvPz3xcVFxwXnLdk3GAzsnLp7964T66aFxbAP+Xq5d9fW1pzSUaVSyeHO1upAjH9q++v1+pk1Q5W1itYs+0OOWY6TWpXst+JFaOGq941t6HQ66Pf7tua2trYcfARDFVpnOBQKnRnL1lxxppvpudRut82jcHBwYLzVwNjjpykpZEfjfqxWq06lIdbwZJu8HgblByiVStjd3bU+nsXtTHnsZXn79m1rsNbuAyYLSwPomhfE4D4bQVcjGz4/P4+vfvWrAMYJ/nQJ8l2aF/fo0SNLQOeA6MWjbljG9ICxj93n8zkHRLVaNVfUzZs3ndjVhx9+6MTTMpkMisWiHYrVahV3794FMCl5xIt2MBhgYWHB2tLtdh0+R+adUnRT6oFJV5BeaN48RXU7Hh0dORRc+XwesVjMcfspCKnZbDq5W5ozSdHYgsYjNU7C9jAVRKkHyRnJuVJyeAJpNAma7i6OhfbNy9XoBfgwlUbHkv3RBPVUKoX33nsPwPgQUK7XUql0qjTUb3/7WwMFsC4q5dKlSxgMBuYW6nQ6BpOPRqNWwxIYX5b37t0zl2c+nz+V8+flNqaoIsMLmPspm806ha41mR0YH6JaDJpco1R40um0fZfpQ5pTrYekXqJsr8brdV0fHBw4HLt6kDP9hnPfaDSwt7dnnNCj0QjpdNpcr6wvyed3u12bW4LvlAdY42s8kL3pPdwLPEuWl5eNj7dUKp06LOki5BjX63X84Ac/ADB2F66trVksnHNFJT+Xy5lyTSWO6ygajSKTyTjgJ2ByKTJXkntVzwvdXzxXdZ+QbIS/82Il6vW6zenOzo7Ri/J5el7qHrp16xZOTk4sPEYyBAV5Hh0dmXHidcmrGzafz9tcVioVVCoVGxu/32+ct8DYmKJxAgD//d//7YRyjo6OLCZ54cIFUxSBCd+wpisRdMhxBiZr+KzUNcrUDTuVqUxlKlOZyhPksZbl1taWmdR0k6oFoIw9ZLChVkH3Gi1Totf4/Vgs5kDXu92uaTu9Xg/5fN60Km8xUQaHtZQPpd1uO+6RXq9n2sNwOES1WrXix7u7u3jhhRecwHG327XvHx8fo1AoWABbwTFMsOa7t7e3MRgMTENLJBJOQjYAx4Whbdbiz0wNoZZPTUjdM6SlA8ZWjVqHjUbDKdmjY761tYUHDx44oCwlimclFU1bASYUUEpSrUCUXq/nkEt7GX6UnL7ZbBpQAJiAhRS0QyHCWskhFPFJ0RQSTTVQOTg4cCwYZWOiBaeAAoKA2A5FvPb7fdRqNfu82+2aFRoIBPDWW2/hm9/8pvX/nXfeMRcm38/x85LFe6njlAFGPQDJZNJB8TGRW9NbFhcXrY3sn5YyUlBRIBBwaMo6nY5p52TFombv8/kc1KiKus1pTSnxvfav1Wrh4ODALLJsNmusWcAk5MA2K7qUiEf2j54tBf2VSiXbB3TRcg2oxcT2NhqNM6kLOVcvvPAC4vG4eZOISCaYi/349a9/bWPBMX366aedAsiNRgO7u7tmZRP4pCkfClZTi0fdsESHKyuOJvh3u10HadpoNJxzyufzYXt72zwjune73a4Dktvc3MT8/LyNIYnkNbRGNCqfRfHuSw3lsJSbAg+170Tdso8cN55TSiO4vLzssCx5C8iT9pL3FNHbZ6XIeeWJdHeantFut53LgosZgOV8adyHtFjAeEMuLy/bYudm52Cx1BMwQTCpq0HrmRF5e1bVkVQqZR1nfIyLuN/vY39/3xZGKpXCwsKCLZxcLmewZR1ovnd2dtbhwmWFDWC8kW/evGmusWKxiMFg4HB7KlpPL0tdlIzL6QUGuChkb7xSmYJ6vR5u3brlML9Q4SmVSpiZmTnFJ6opMj6fz+aF31VEJv+dY6zUW/zbW21dc9GojHxWGopWeQDcTabriWPhdWlqSpFeqr/97W+dupd6WWqOFzDejIq8DIVCuHz5suPSrNfrdoivr6/jlVdeATBeI2tra051nfX1dTtAh8MhLl68aAqaFw2sa1njzczf49gOh0M0Gg3LTd7c3MS9e/csdnP9+nUnXuNlNTo8PHRi0Kr8UCnU2LHyF/OC9yotwIRJiP/tnS+ldmTetV5oqgxFo1GLNQJumSq6/1SJPD4+tgu9UqkgHo8bDZxSXp7VZmCilCsnrrImRSIRnD9/3ua2Vqs5rtJ+v49CoYA/+IM/ADBeu9yHqVQKxWLRzpb9/X1Uq1Xnsgdwyu3KfaZKhp4jTE3ScVZKOioRPANqtZrDvsbv6t5QNhtdF3ST8zwgA5jmsuvvvWkYul7a7baNBRV+jbOq0sQzgvnzV69eRTKZtDGoVqu2rufm5rC7u2vjRcVc+3dwcOCcKd51+lny2MtSLR5qM974g2py5CHkwOikRCIRhEIhp7STWobaoXq9jmQyaRdRNBpFpVKxiaOmdFbBYI05chJ08vf3981yvHLlCpLJpFO/MZVKOZyXCkFnGgAw0Zr5GetMcgEwZksJhUIOlF4XTr1ed+KBvV7PsbxarZYpEv1+H/l83r7Pg1Zryh0eHjpJuGqRM5cMmFx6PIBZXFllbm7OSTKnkqR5TK1Wy5l3jQk0m01kMhnLdQ2Hw5bPStHSSl6eSp1f5kKqtq0xWdLqKZiIwrxU9kOVFZIG0IOwvLzstKnValnCOjA+qC9cuGBruVAomJZLy0Qp0S5cuGDejhs3bqDf71v6wcLCAqLR6KlkebaTz+FlpXHChYUFAwT5/X7s7Ozg9ddfBzCuHRmJRCzOeOPGDYRCIePw3dvbMwBTtVrF2tqaXSzcl8qR6uUmBSZKpLZZST6oQGvdWcU58EDke5j/pnnBwOmyY5SZmRnDEGxsbGBzc9PAQsFgEK+99pqtu9nZWTSbzVMxS8bU2V4FAZFbl3ue1h8LBBMcpJZmNpu1dyonMIvK81mcW4JaqDxxn3svbk3wV/pIxuxVmZ6ZmXHKzWl+rs/nQy6Xc/a5pnFojmYymYTf77e5T6fTOHfunF3wjUbDyT+uVCqOEuVNHdH0QwVaEiyqZ5iXjERpBZnSooYEx6vdbju0rF7sTLfbxdHRkc0L46z/F8tyGrOcylSmMpWpTOUJ8ljLUtlbWHhX3UWHh4eOv1q1DGrY6pfe2toyd1yz2TTNYnZ21kEj1ut1FAoFvPzyywAm0PaziooCbuoIC7ACY01H3bV09VK7KxaLTqrE3t6e43ahG9VrIQKTsmFajieZTJpme//+fUM+AmPrJBaLOWxAFLL787lKOkxfPcf91q1b+MlPfmIIvMuXLztE66weoRB1TUtJJBLWZhZUpYuKpOaqfWez2VMWhLaPcRlNiWk0GqZVEv7PZ5C4mHEPwtjVutVkZWBiYdDTwXfR/eR1VXuLzPK/qU0ynKBuPS21tLi4iEQiYe9vtVoYDAb2+cLCAvL5vBP75WfU7JVaMJlMmpt2NBrho48+clKwtJCtimrX7Xb7FM1jOBw2t//i4iIWFxed/Xf+/Hmzfq9evYpisWhEIJubm2Zl/tM//RN+9KMfWUHcpaUlnD9/3lCtdOfqvJCODXAty3A4bH1vNpuOe5vsVOwTK5SwT2Ry0bQnPod/c921222Heq5SqVg8DhinbczPz9vflUoFtVrN3q3WLvtFZLm+T9N8mLrCuQ6Hw0ilUk5h51gsZuuKJQKBCUUk99pgMMDdu3fNKqU1pB4XL60lRRHFZPjSNAxdf36/H/V63UmVUaQp50WZ19h/euUY3ysWiygUCk4x7uPjY/Mi0cV+FrOO7mui/XUP6N3S6XSQy+WcMn+lUsnOlFQqhdnZWSdlRdm4lBlJ28N2kKSec6qu/8dZmI+9LFXYGb14FLDCjaD0Tdvb2xbnOTw8RCqVsg4vLy/j0qVL1lkAzubTKvGpVAq5XM6paKLuNm/VA5Ya6vV62N7edlxc4XDYaL6SySR2dnbsAGk2mw71GOOH6qJQ5oxut2uDzImlW4UM/Mp5SvooPpuiAWdC+jVXMBwOOzGXVquFd955x77/ta99zeah0WjY4c7f8wBNJpO4f/8+vve979mcLS4umqtncXHROQSpOKiLFRgrPhwjpijw8pufn8e5c+dsnDi33IAsA6YUYRrH0lqcBDt502h0PpXTVS9OwM2ZUuYfAhMIN9/c3ES5XDZXFxlDVLS0FNuh7dQ2aAyZACoezs888wxOTk7sXUzP0cONolzKZ1X4UCUjkUjg7bffdnIS//M//9O+WygUnLy9brdrLtxXX30V6XTa1jDdylqVpVqtOrRw6sbzpmdQeFCpcqN9nJkZl+TiGVGpVNBqtWxfs06qchbzGfV63XE3MpVLeZ5PTk5sXdLdqkoNME6dUIVfFfKdnR1Uq1VLA6ICpXSYWq3F5/M59VmVz5p5gvyMvNrcF5VKxUn54HvoPvSmR2kb/P5JyTcC4jhO/X7fodOs1+uo1+tOaEXPUk1PIjsQ3cqrq6tOKg3dt4oJ0bQ3BTeqa5TgTz0bCBDle0ulkikhqVTKyXclXaXWsVWGHmUwYyqZt+oIlRS6ks+iTvXKE2OWFG9wnpen8joq8XilUjENARgfxMVi0TmYeWnNz887qM1AIIBqtWpaIzccB4+8gGrpUEhyDYw39aeffuogTbWMz+7uLj788EMD/Fy5cgXPP/88XnzxRQCTRFqlgeNg7u/v46OPPsIvfvELAOPcHwBGEH7+/HnnsGZulAbDKcPh0IAJjCtqwrcCMILBIN544w076D755BMMh0P8/u//vvUhEomYhhoMBi24/8EHH+CHP/yhzeGVK1eczU5tj7999913cenSJYvRcDH/+Mc/NkVncXERpVLJNoKXlJzxLtUqFdlG0I6WPlPlxKsIqUbqBTcQDKTgIYoe6ETXMSY+GAxQqVRw8+ZNAOMDRWOYmp8ITIrVaiK3HpD6/3yW5hdrabdisehQ7WmcZzAYOIcYi54D47Wt6EkFJOk8KACmVCrZXiFVGTDOG33hhRec/aUHKjDeY0pQomOq6zyTyTgHTjAYdABlHE9gfJgeHh7a/iMqk22mgqNIa445LTqePyTrV7AeMAHGkETBSwv38OHDUxeHknKXSiXnYtLcTXoUtD8KOiL1JD8bDof2XfInKxdsMBg8BfTinOr6VYAcLyx+TzliOeaJRMLGYXd3F41Gw34fCoWcPMxAIGCXXzqdxrPPPusoxop2poXGy5UYjrMUPzUUqPCq1zIejztn4+bmpuWrnpycIJFI2NlaKBSQTCYNh/Dpp5+aMUE6TY4zuXC1Zmqj0bCLl1gAJYP/LJnGLKcylalMZSpTeYI8MXWEMhqNjP4IgJWy0UoMWhB3dXUVq6ur9jmrySsjP2Mma2trWFlZMY2b+WV0p+7s7DgVJIi8Vb80RdMUHj165LjfwuEwut2uY0HcuXMHn/vc5wAAX//613Hu3DlDpjWbTYf4WStRLC4uOikOgUAAuVwOa2tr1geSGAMT1BVFtS61WNvtNpLJpEM5x+9wHBOJhCEXd3d38fHHH1usdGVlBfPz82ZN+v1+yyutVqvI5/O4du0agHGc9fj42CwKpo387Gc/AzBGGH7+85835CblF7/4hcW38vk8UqmUkeC3Wi3TsCmKqqbWqvFIRbQqypmibD/qMgFcxh56HJRCTMeba5FuRI4pC2IzDaNWq+HKlSvW72vXrqFQKNicJRIJ7O3t2XotFAqmuRPZR031008/xY0bN4wl5o033sD6+rrjvg+FQuaGV8tSY1jesAcZaxR1W6vVHMYszdMMh8OWx8hnK8pY6SfpSvNaiEqwrxSFahHk83nrCy19JSpXr0M4HEYkEjE339zcHNLptBPP1v2bSqWwtLRknyWTSRvXdrttMTW2SWNmLJDA/rNv//M//2P7galhtJzo7lQS+Var5aTjrK6umpeFaHieW6lUyqHS0/Hc399HpVJxkOoaYvC6T/XsAFwmNWCy1onBYN9Z4YN7Ym1tzcmt5rnOdaO5oaFQCN1u91QqDc9itTj5t98/Kf6t+1TxLcPhELVazUFV63o7OTlxYt/tdhs7OzvmfUulUshms45HjGvu0qVLDpVev9/H7Oys4wKuVqtOLr22k+vnLHkiwEfhvt6yKBqP4PcUAKNAELptNbajEHNNnCWIQ10FnU7HCX5rW7xVR8g4n0gkEAwGbSGQJEHBF3oI/Pa3v8UPfvADcxsVCgUsLCxYnzKZjAPYGQ6HdmC+9dZbyGQy1iZWPfe6CLlZNWZJijtgHC8jFRZ/FwqF7BA9OTlxfPTRaBRPP/20gaV2d3fx7rvv2thq5Qm6lzlesVjMKWczGo1w8+ZN/OhHPwIwToe5c+eO0RJqLMLrWuMzq9Wqo2AwtUXHTeeOLhjGA1mPE5i4MrUigo4pc041rqcHjLphuWk4Zpr2lMvl0O128ZWvfMW+G41GzbWTy+XwzDPPmGuPVF1sVzabddydtVrNPrt+/ToWFxdtE37xi1+Ez+ezA4lxKqWNpBSLRXNFtdttJzWLFw0PDNKecR0oRRv7TGIQzqHm8ulhzgtD67oqcQLjf2flLHLdApMkfy1P5qVti0Qi9ptUKoVUKmXutp2dHSdlIhqNOgfk3t6erbOrV68inU47YDbm4XJOvfFIYHxecC0zPMDfBAIBNBoNO6TJDauUe0tLS3jmmWcAjM+pVqtlIZLl5WV7NkEoXjpDTbzXGCV5tLW6D8Xn8zlALk0dUSAWv6vzxBCCgppUcdeztN1uIxAIGIiMIR22ke5gjeV/VqhJU4h4rlPRY5xR1+q5c+dO1QllnPHTTz/FgwcPTHG6ePGiE1pjSBCY5GhqWmKz2bQ4OZUozpkCTb3yfwb48Obn4mV+lFcT8pKdc3PGYjGHILxYLFp8IR6PIxqNOtYV4PrtNUZJloqzar39y7/8iy2OTqeDUqnkXODFYtH815cvX8a1a9dMcwwGg/jiF79oC9wbh1KtOJfLORysZKbh39Fo9Mw8o7PYZSKRiGlGR0dH6HQ61kbWnFSy8EgkYgur0+lgNBqZds4cQG6ASCTixFwUUdjpdBxwyscff4x//Md/dGIJyr6jBzkvXiIQFTyjMRUeONxgjF1wbBOJhMMn6UWSatycICllhNEYJvt7Vm3IVqvleDkUmLC6uorhcGjr8e2338Z7771n64oMLdycjMFyHNXjwDxT/rZarWI0GuHVV18FMF43Dx48cFhOFHGp8v7771tfOM60FDudjgFkODYsNQZMCNu1nmOtVrO9wRxCALb31CqlAss51AOX/86LWxGvGo9lm3nQM5+V80dLWXmnNb6kaF7OG+eMQA3lIB4MJsWSyZbDtjK/jgfuRx99BGB81tCjkM/nHc8GlSCeD/SCcJ3QuqGCks/nsbKyYvvn4cOHdvl42bJIqqD1XA8PD20fkM9U9w1FCy/QQ8C1zkvJC7pSa1jz5QH38uW48ruJRMKpxamYBqKkNW+WbFeAC0ZTJHIkEnGsbMai19fX7XedTsc8Xuwf18JTTz3loPhXVlZsjm7fvu3kihKRzHEslUqOZXl0dIRUKoU333wTAJzCBV6ZxiynMpWpTGUqU3mCPLFEl5f/UWMoivzq9/sWt7SHi0bDKgC8uefn5x3f/szMjMUqGJOkhuctiUOt9yxG/nv37uEf/uEfAIw1o+XlZUPdsuQRTW1+rgg0v9/vxEeV5ouaFjC2lLXkGM19RXxpZRT+3lsEmP2lK2pra8spvBsMBk/lUaoFn0wmnYrqLAatrhUdn2QyaW2s1+vmigWA73//+7hz5w6uXr1qn5+V2/ro0SNzTdFVSI8D2UHIIER3peamMfdSx1GtFW/MyBuf0fQbjc94Y5WqjVcqFSdOqF4SoiBpdeRyOVy9etWsFMbnGPs9ODiwuArHgP2bn59HNBq1NdTr9cz1xfFQpO3JyYmDmtR1obykSm/G5zCGBEzQsLR6iKxU9Lq6yLz5kMpcw5irusMVyen3+52UInWVzszMmMv54ODAiQEynsyxYuFrb5qOxlWVYUr5pOld0vNHY5L0RNGC6XQ6aLfbVnmG6y8QCJjHgGXb2B8Whub5o94aYOxmXVlZcdZVoVCwddNsNk9xQet5mE6nHZYkdW2Hw+Ez0bEAHFT0WaKeG3pANG/bS4+neZte5Ggmk7H+B4NBlMtlh8tXsQDhcBgPHjzA97//fQCTkofesZidnbWcVABWqozrkfmdmk6msevFxUWcO3fOQYpz3BYWFhCLxWwN5vN5tFota0uj0UC9Xre7Y2ZmBi+//DJee+21zxxPymMvS4IEKHTjAePDx3soq/l7FkEtXYgcPP6WCeP8LnM2NR9LNwYriCsJMeX4+NjiPFwoBN3Qfaa5TkqcQPJobhZelHyvwsSTySQKhYKlVfBS0MC6ls7xUrPpYr19+7ZNFivWczM3Gg3HvUagEMeGxAjqhtG8PT1wGUdm/+j+4HjdvXvXcc/RhcrxUXLm3/3udwBggCgCjLrdrtHFAeOD5cKFCxYTYIyY7WfsTen7KIeHh878MO9SabBUAfFS9ekYV6tVa1On00E4HDbljIcUD7RyuYylpSXbjMlk0iEOZ44Yn6dur2azicFgYDEx/p6i6VZso+bjaR/eeOMNUzoYC+Va9rpv6VLXtCltM7+vsRzNawPgKHLajk6ng0AgYN+LRqOIRCL2twJgSqWSAV6SySRqtZpTW9Abq9c8y2g06uAkuFeVQEDPG81f5Tjq/lKe4263i1/96lfmMv76178OAHjppZfMBffhhx863LZbW1sGOgJgbl7NVWXRh7M+H41GzkUbiUTssicxAOedZ5yWw9O8YVVINOzEeKXOnYYq6Fbl+aGXsc4D14mmbkUiEczNzTmKHstpARO3Nd/96NEj/OAHPzAXt8rCwoIp2MS6cK5Yx5OfM6aoioTiOoLBIFZWVmwsG42Gc9kPh0Ob9/v376NSqTh1TBn/B8ax7jfffNOhIfwseSLAR+NH3g2kcSBFx/H7ajFwc/I7vOGBycVrjfrfZ2m9ynK5bAn/9H/zWZobo5uNOUM6kFqQmew+6v+fmZkx65GxQm8CMDBe7Mlk0gGXKOrKG7vlQaWIQsp///d/26V75coVbG5u2oabnZ1FpVIx7VcD88AkBqgaqJd0WS0ttUIZg2C+kt/vdzRlJnqzj8qhywt2Y2MDL774oiksBNgwdpXNZrGwsOB8ruNDvly9/Dl/vAw0B0zBQrQuKFw3ioSjcA2xTYoYbjabTsxcAVCUarVqBwwTs3UOuQYJsuFnZEA6C8ADwJ6jsS1KMpl0Dp579+7Ze/x+v3MZElGt4xoOh525V6VK8+OYA6wk6zq23LfaxlAoZNq68sbW63WHD3pvb8/6xEORn2sRBmA8395Edq/CrQhKJQhge5UzVWOYd+/exb179ywfmd6Tb33rW3ZIb21tOWum1+shHo/bZcm+s7+9Xg8PHz60y4MFtNkHLQbP2CL7Tl5ZEqLonuWYHh8f27MZywTg5GfOzc0hkUg454yC4LyWJf9bzyeNSatVSmyEztP+/r5T2DyVSlk+/K9//WvLNADcBP8333wT//Ef/wFgvN9CoZCNNeOT7Guj0cDx8bHD6qWGBmPh9BgqNywNHM7p8fExQqGQzQmJ3unZ+cY3vuEUZ1fPjlemMcupTGUqU5nKVJ4gj7Us1WXk1eSGw6FDC0Y2EdVc+T3gNMOIpp202234fD57F7UZft5sNlEul80S8FYOICoKgJPrCYw1XVoUrNyueXvqlmVc1Us5pnyS1MhoRdI9om5SYMJopOOluX1q7pfLZfzkJz8BAPz5n/85rly5Ys+lu4J9Z3zXS89EDbrT6TiUUworZ3uUnms4HNqzmX/I8eF8vfvuuwDGVG3MN6RW+N5772FhYcFcIp1Ox0Hr+nw+tFotJwar8S7SgLFN6lqkhqysJFrOilow54Qu3rNc3TpG+/v7DmXb9va2s4YY66M2T3o/9okoTK4bzfmilay1MKntAzjlWSDF2lk8q81m05Ca0WjUSZXgfvJa9DoWWqmeFoaOjcY7NdxAZLtadIoSzefzDprYG5fn7/L5PNrttsV6Z2ZmHL5UxqnUolWmLkW68vsa+9bQDNeUul0PDg7w/vvvAwB+/vOf4/Lly8bRy/2az+fxX//1XwAm/Lvqbg6Hw+ZKTSaTToUfom0VT6BeFcbP+FksFrN1kclk0Gq1bG3XajUHi8DnM/atrnxdT+r5AyZ7WNM39Ayjte715HjRsfyutwYvKUGBCaUpK7/QGuS71Fp++umnbT+99957TrpUpVLBaDRy2Ip0H3OdackuRW9z3WjbaWnyXtG1GolETnkY1Hv4WeJ7nNk5lalMZSpTmalzwuMAACAASURBVMpUnmBZfve73z3hTcs6YdRYyKeppLqaNEw0l2osmjd37tw506JqtRoGg4FpDmSV5+dzc3NYWlqypHDWS6TF2Gg08K1vfcsHAN/73vdO+LtEImHoRP6OuV7AhNXlrBwyYEKYrQn2mq+keZMkVdDcT2WXIbcnWYt2dnbw93//9z4A+OY3v3lCkEAwGHRABfF43GE3omVMHzyrIKgFn8vlbJxJOkxRzfn4+NhhGSLRgmr6BwcHTt25v/qrv/Jdv379hBo6id8VZKPAC1pgahFoDIU5YxqHVG35rNiZglbUQiewgajWf//3f8f777/vA4BPPvnkRPPFNJ7C2JKSIMTjcQdhrGuZsXwFDSiwxgtiUeYrxhZpMRClSeu+XC7jL/7iL3z/+6wTPndlZQXf+c53HHKK3d1dh1P4woULRg6fyWSMF5Oidfx03omA5JyVSiUcHh7aGmQ+MdtMbk2ui3q9jr/+67/2AcDf/u3fnjBudXx8jK985SvG7qTWAt/Lyh3A2DoMhULO2tC15K2uofFFAkBovezv76PZbDp5prrO/H4/vvOd7/hefvnlE60tqt4jxgOVyEH/5hngjZuqda/rXgFHLAKgyHRWTmH7f/nLX5rV9tprr+Gdd97xAcDq6uoJCyEvLi4im83a2cl1yjGbm5s7haXQ844eBAXkKTOPeuKIQVHkulp4Pp/PQQ/Pzs7iT/7kT3wA8Dd/8zcnnOd8Po9sNuswh2kbFeTHNir/7VnrRD0mvHvYZj2XOV8as9Vc4FKphC996UtuA/5XHntZKmUUB1FZ+1OplLkLmejJRtOVqo1Sd43P57PBIniHFwlh1VrqRt1CZGDxumsAFyRAomVFLapbkqwefI63QgTBP4rOYx+I4FTwibqB6A6h+R8MBuHz+WzC1U2i8Oz9/X1sb287rt/r168bCUAqlUIkEnEQrl7WCU38pptT26iMPeo2oWiJpFAo5CgbwNjtTbQuDwEvSErn2ftv6gZSMgMAp9xBehjx+Qox13Zx0fP7Oi7q3jo8PHTIGAheoeum1+thc3PTDi6CwggKyOfzTrk19lHbqBtf+0tlRMdci2VzvQATejH+Tt1vZHHiwVepVPDpp5/aZfDMM8841SeYfqRhAK5BHrA8MMLhMPL5vIMebrVahnje29tz+qiKh5Lmk4iEFzpRi+pqjsViDgBofn7expn7V9eEN62IwjHlbxmqoCvQe1ErUI/C8dL1pAhjL3KbFzTnTN3anD9d50qgMRwOHQAZK6JolREtCuHd45pGpYc/0yy0ConXDasXDc91pbTTFCGvYtdsNp2Efi9oUcFqmuDPDAcdH70UvUxCOlehUMghoWHoRS9IZftRZYGhHK4hnnlco1SiFDj2WTIF+ExlKlOZylSm8gR5rGWpWh2pjXi7073GG515WOqmVOCGNxANuCV7stmsQ1G0vLxsWiLNbmplrBVJK1fz1lRbITBG83sCgUlNSq9rmQVFtegoA+LAWFNS7sbhcGhun83NTdRqNSctQ60Paltn8SZSG2Sb2u22U+Pzxo0bZnVns1kUi0WjhnrmmWeQTqfNwldLme3wamxqaXa7XSdhWkulDYdDR/PmuOVyOYccW+eSfVWCaNWoA4HAKUvMS2b8WO1OACTM2+X3NWcNcLVxTUEgR6/S1bGmHvuif1erVWfd+nw+tNttG2d6Ovhs7RtTYxQ0ocAUumW1JiAlGAyaq1O9BRxHdWURek+t/9NPP0U+nz9VQ1LTqqj5z83NOa4oYGw90p3NupL8myETJfegsLA2x0UBItlsFtls1skN7fV6toe2t7dx69Yt4yK9cuUK0um0431QsImuOabCKChwb2/PyXf15oVzXL2pRkr3pm5LL9kFyRuUtERTbLQoOtNteFbFYjFEIhEb01Kp5NTHJXnKWelx2g+2kevAGxahtaz7lG0BxmeenhG6NlmqjeEulhXTNeT1sKiHQs86TVfhPCoXseaY0lrWIu5MAdH5VjAp3dAEXWreKPlfAZzybJBDmH3yEtarPLGepbogO52ONZAuCbo8vTEEdsZbJ0zZIdTtpTlIc3NztpiAicvTWyXBS8wLjA8fLsBarYbZ2VmnAjcT3fk7RY6ReYWum1QqhUAg4CSsss2Mh/Bdt2/fRrVadS7FUChkBxLH6izmDbpcKIlEwimMXS6X7b07Ozv45JNPcP/+fRvnl19+2d7rrebOai/sLy9EYOKa87qhKdFo1Iqjsv/AZEOy7YxfA2M3subQ+v1jthe+o1gsYnV11SlmzXljG5U8WllZeOHxu14OTMBVBjQ/jp+xHxqD05gLn7u4uOiQ0Ssn5sHBAVKp1KlDgu9Q1qFAIGDrDoDx5GoivxYRUKWBubcAsL6+jng87hRKVh7daDRqlxMwjj/rIUJX1lkxv3a7jcFgYPm2BwcHRjjN9jGOC4wP+l6vZ+OsfLC6lqlUcb3Nzc05ys3S0hIajYZhEVqtlpPHVy6XTynF7C/jUMr9ure3Z7nYrKTB8aTrkc9S9LGy6iSTScdl3ul0nEol3gIOuj69+eGDwcBxr2s+Ks8SzicxH1qRJRwOG+czST34HiUKiUQipxDD7EMwGDyloOtly4uV43ZwcGBkHaVSyWk3w0jcywwfcQ8rnzC/T9G8SRLqq3KazWZtv/Ls5J7Y2Niw84Xjqnt7ZmbG1q7P50OlUnEQykoS0uv1sLq6amuWBPbK2vZZ8tjL8uTkxLFwGOsBTvuYCZzhgdLpdJxkUDLY6OFGa4gHhyZYe6sgaLIy/00JfClqtXirUKTTaQeivLCwYKWPgImPnu9tNpvI5/M28EoITm2b311ZWcHa2pptzna7jb29PWeztttt06J149y5c8feEY1GnUt2fn4eV69etT5tbm5id3fXFs9vfvMbzMzMGGkBmVX4fL4XGG/QeDxuG5LBe03FUA2w1Wrhzp07Nj9aBogbkFaTehRU2+ShQCaamzdvIpVKWULx+vo6CoWCEwdSTV+trm63i36/b2uMB4NCyjVO91nFn7lONP1GlRim13i9Ico+pUWKFXBFEnW1cqvVqq31crmMRqPhkJQHg0F7v1qPjUYDX/rSlwAAr7zyCsLhsFMBY39/3ymXdPHiRWP48YJymPqlpCBsE+NBPLxarZZzGRYKBSe2yAtBFQCKKoRUeim3b9/GnTt37Ptk+aIlub6+jvX1dQecoftchcAoTRXxVq7QajYkWue7uab1fOCY0YtzcHDgxIW5P9ieSCSCYrFoHjBeyGp5cq1ubGzg7t27p5idrly5AmAcB9e4Od/Htaz/7vf7rf2lUsnxkpycnDh0kkxz8hbs1jOuWq3aZbO7u2v/zSLRXNupVAq5XM7OTwI6+W564ihqxGjsl+1hG5SsneOm4CiuDxKhMB1EPQ7qQdEzYzQaYWdnx87dK1euYGFhwaGb1GLYygbnlWnMcipTmcpUpjKVJ8gT3bBap29mZsbhkWTqBTAho1a+yXa7bdoDk5Gp4fR6PfOFM5aoGqryIjLBWqH/ym2p2kwikXDiZYqOJZE7TfjhcIitrS2HXk21I2rbGi+gpl6r1RAKhcycpyXC79IdoIVstTyWPrNWq1ni9urqKtbX181N6fP5kE6nTRO6cuUKtra28OGHHwIYuz1//OMfGyn91atXMT8/77iDqWW1Wq1TMaONjQ2jLSuVSk4qBt2p6uLhM70ucLrScrmc40pkbFct8oODA3NfUztUtwotnG63i0qlYn3d2NhwkKQkjSdp8sLCAubn521OtC/Hx8dOzERp9LgWudY3Nzdx48YNswpyuZzF0Tl3tFgAOKWWNjY2cOfOHYcSbXd316mB6I2La6xKLcvNzU2zLLPZrBPXoduO1mGlUnGSrzketDD8fj+KxaK1Q9MqEokE5ubmHHcqMAmZMASilmS9Xne4mila8DiXy8Hv9+M3v/kNAOB73/sednZ28O1vfxvA2I1XLpdt7d+8eRNvvPGGFTZnrU+1ILTmrXpQotGoU+eV1pbG4uip4N/sO9fBwcEBWq2WWXGdTgf1et2sEv473czFYhGNRsP2ajKZdFygjUbDPCq0btSrtbKyYvsWmBBaABNvB+dP5yaZTJr1e//+fWxubjqu7nw+b4XLL1y44HjH/H6/oVr5nk6nY56qfr9vazudTiOVStnZo6mBOoYUpgaehSxlQQFggpVQUpqNjQ0HOawW/cnJuAA3+9jr9bC9vW1jMjc358Qo1YNH4ghNWymXy3b3eIt2nFU4gvLEepbsEDeIpk6QDxUYL85er2cXHtkp1F+u/m71jTMfkYO5vLx8CkSitfmYi8cB0ENR3XisCsLPq9UqHj58aAut2+0iEAgYEXIsFkM8HrcFyhwiThqLnwJjN87S0pJDeE4oNTBxEXIzsBKHxg8pV69etUvY7/djf3/faiiWSiVb4MC4xp/GIfr9Pmq1ml0+R0dHWFhYsLFcXV21Z29vb6NcLtuBUSqVnPga388FQ/cu20w2GWUiotubv+Em14XP6g0AzDWvl4I3fsG+KjgLGB8wmr7EGBbH8vDwEOVy2T73sqFoHFc5gdW1BEzYVbih1tbWTlVqKBQKuHz5MgA4F+f9+/dx8+ZN48hMJpNYXl429+i5c+eQzWbtfc1mE1tbW3ZY6VpWNyqBJlxv58+fx/r6usWaut0uotGorU/G/rj+otEonn/+eTvgDg4O7EAheEfTDTRGyYOdykK5XLZ0Eq+oS5mXGw/mYDDouIpZAUfraN65c8cOxUuXLp0ituacsc18VzKZxHA4tPFoNpsolUq2j5iaocotx5vnEN24XCeJRALFYtHGlOlxeqE9fPjQ1uji4iKKxaLtzYODAztr1I0KjPfiw4cPHXBdPB63i+n8+fOIxWL2txfT4FVWNY+3XC5bv7e3t/HMM884BSA0z5hpFppORiEBO/vLkBX/phtdXcCMtXrlzp07+NznPgdgEjbjPt/d3cVgMLC529rawvHxse2vlZUV7Ozs2IVYLBYRDodtTLWNNOg4HiwqQdfyzs4Oms2m/XZmZsb4q/msz5InXpbcQIPBALVazTqUTqexvb1tiyGXy1nnKEqcPjc3h2KxaJOuidqNRsMY7YEx+/97771n/vyXXnoJ+XzeAd7Mzc05uUMqesF3Oh3cuXMHwLjYczgcthIy9Xod6+vreP311wGMN8PCwoKTu6bancY8KpUKstmsaY71eh2j0Qj//M//DGB8WAUCAUvIXllZcZLo9SBfWVlxFv2jR49MqVhaWkKtVsOvfvUrAMCPf/xj5PN5KyY8Go2Qz+ftwP7ggw9wdHTkzMXHH38MYFyMVrXMQCCA8+fP20JZWFhANpt14iy1Ws0OE0Vmemn9OAdMrNZKIUzuBmAACaW20stXE4hbrRYGg4FZrYlEwgFBcK69YAZVJCj9ft9BXytYJBwOI5FIOEWKv/jFL9ohSQAF+8Skam4sRdpSC2YbvvCFL2B5ednW/fz8PCKRiF00zPWlssMLmqK0h1pBhrnHBIAcHR2h3W6bAknrXzd/LBazQ1Ittr29PQecQWAexyOdTmN9fR3PPfccgDEC+6WXXrLLVmOKrFDCcUsmk7h+/TqAsRKiZe38fj9WV1dtftXaV9F/4/6j4se1W6vVcPv2bfve5cuXkU6nrTrOT3/6U+eie/nll/Hd737X6SdBUlznBwcHDtrVm0dJ5Z+HfyKRQC6XswvO7/eb1X18fIx6vW70asfHx3jnnXesvS+88AK63a4d6olEAhcuXLA1+LOf/Qx/+Zd/CWB84XE/FYtFLC4ummfm8PAQGxsbdi7dunULfr/flARWANL9qMUmFPhFY0G9Y2qF0SOoxoZSPyqWZDQaWZuuXbvmADybzSY2Nzdt/lRBBmCFGLhHzioxxv75/X6Uy2WbQ2Yw6BnGghrAeM3dvn3bOac/S6Yxy6lMZSpTmcpUniCPtSz1Bk+lUggGg6bZMqZIbXtxcRGXL1+2+Fc8HjcoNjD2uS8uLpqmy1JGAHDjxg0nDeXRo0col8tOykYulwMpnvL5vGNhqKtGrZRKpWIk2MDYgsjlcqYhVyoVrK2tmSa0u7uLpaUle169XkcikTDtvdfrOSjWQCBg6LZ0Oo2nnnoKP/3pTwGMkX9Kpffhhx/iwoULDjxan8N3MA5MbW1lZQVHR0d46623AIy1rlKp5DC7XLhwwawk+v6pKT311FNWQueTTz5BrVazlIRAIIBKpeK4TdSlQbceNWvNqVUJBoOmqZKBSNF10WjUxpxucfavUCicIl6nNk26Nq65RCKBWCxmli/HkH8zZ1bp0VS8+aaa7sH8M2CCHOV7WbdQYf3qGleXMstk0SsSiURw69Yta8vs7Czy+bz1d29vD5ubm+ah0fgfMEEA1mo1B13KdajuxEAgYBozkZ383pUrV1AsFs1SVcR1IBBAuVx2Yk2kYwPGXqNQKGSE4++//z4uX75sub4K66/VamYpzs7OIpPJ2NooFAqnYpDaRqJJvdRt6kHyFgyglEolbG5umuW2traGwWBg62xpaQn1et3ioywCrbngR0dH+PDDD82ySaVSjnswEongy1/+slk4Ozs7hogFxiGPq1ev2l5W1yk9AdeuXbO+N5tN+86VK1ecPNlkMolsNntqPQDjs0YRqel02mHLyWQyho6/desW7ty544QBEomEvVc9HBxX9q/RaDho5W63i9u3b9sZRuYfPV+9JfUozz33nDFAxeNxzMzMmAXOPjL2WygUcHx87BQJWFpasnVz8eJFbG5uGhXg5cuXnYLimmry9ttvY39/39ZBJpNx1k4oFMKtW7fs+96sC5XHXpY8NHQgOMj9fh/z8/PWwdnZWZTLZWcS9fNisYhcLufQmnEStra20G63raHPP/884vE4zp8/D2B84Cv/IV1gnAxdUMFg0J6ztLSEQqFgbWIR3z/90z+1ga3Vavb7S5cuoVAoOND2XC5ni2EwGFj/mfiqf29sbOALX/iCjYemqdy7dw+tVssWiMa5ut2uHbyrq6tOvtxwOESxWLRD4OjoCA8ePLCYBKHhvGAKhQIWFhZMsdCL+Ny5c5ibm7M23b9/3wHE/N3f/R1+97vfWdtWV1exsrJyilZQ8/v4Ny+o9fV1jEYjZ9GdnJyYu4zcrcq7yooMHBe691iRgi7o0WiECxcu2KVdLpedw/XcuXMWdwbcosQK8NF2UzSVhOPKceJFyvem0+lTfLY80P3+cZFftikej9vBBQA//OEP8fOf/9xASYFAAJubm3Yg64U+MzNjl1uj0cDs7KwD/df9yaogmiqic3T9+nWsrq7aoZLJZGwNJRIJLCwsOAW6NR+SpAtc60z4p7KiB7UCOUilyO8Vi0WHTjIUCjlxfuIe9Fmq1Hi5VRVotbS0hMXFRZuH7e1tJ0cynU5jOBzapaHrT+O47Xbb5uvpp5/GycmJXazLy8t49dVXTUFeXV1FIBCwcSkUClhbW3NceRo60jDO8fExrl+/7oSQVldXnWLRWiBZXd0KZKpWq6bsAONzWekw4/G4k2rG/FnNV9Xca+Vx5rO4Vs+fP2+GCtuh3LBUer37jHPPcNAvf/lLXLlyxZQqrj1NR/P7/U5er6bHEURIF3un03HIBJQre2ZmBteuXbMzhaEw/s35VmDYZ8ljL0s9PEi87OW45A1drVbNZw2MFwfRVACMbYS/JxAFGE/op59+apcWkbMcvF6vh1AoZIPLKuNnxSw1kbdYLDqxUX5XUVmBQMAhLVfEXDabRSaTMc2z0Wg4hVGbzaZZaQTKsI0XLlzAwcGBbSxe7sqpqm1S/z01eWC8YPW9bK/Goh4+fGi/T6fTWFtbs0OE5PHA+CLLZrO2IYmU5cZ59tlnncONeX3e5HPlevXmZiaTSaysrNgzvDEAn8+HQqFg40QOXSVKYPuIVNSxuXz5slltd+/eRa/Xs/km4EpzWyla0Z2obS83rCIlT05O7PeM8agFonmI6oHhb3k4Xbt2DXNzc3ZokpWGa/327dvY2dmxza6sQ1oir1qtYnZ21saNhYGVaGFmZsbGgshYVXyU6YnKLOe12Ww6h54mujPGw7XL2DF/r2sxHo/bQU00qsYo2TbgdCI+wVp6YSqnq5as0n3M/mQyGetfp9PB3NycoYmLxSI++eQTsxrZplgs5iCMk8mkE4uORqP42te+Zv3s9Xp2XqTTaQwGA/t+KpWy/wHjy4XrgIoO/97b27NyUQDMAlfuaBY2YDu1r1yLBB3xOVxXigiPRCK2x4kw51qnR4L7mYUc+AyN1RMLoXzDWmqRfMOaj0sZDAZm/BAYyjYSFKWXJTCx8rgm+DyeC5obyTYSBKdI24sXLzoc5SRBYFs0P/dxAJ9pzHIqU5nKVKYylSfIE2OWimpUDsHZ2VmHqcTn8yGXyzkcmt4ySJFIxNGcaPG9+eabSKVSFp+h5k8Ng2VfqLHxGRrroKj5nkgkLO+G3x8MBk4lAtWkqMFQ8yXMWhkq+N1Wq+WY7Oq64LM1D5NWtRaapqhV02q1kMvlHK5DtdjJQsR52dnZwd27d+29pKBSyi0KmXf4LBbspoXw3HPP4c/+7M8cvtvt7W3T1r28mvw3LXsGwOEkbTQaDvNROBx2LEuiYfn9UCjk5HnNzMzgy1/+so1tPp8364IoU6Wu4jwDrnsQcGMoas2SXcrLv6kWD4BTLjF1cyr7Tb/ftzXHWCB/WygUHI5larZ0z6kFoS7IarWKDz74wFDOa2trOHfunEPZp+8plUpIJBJ46qmnbE60/FI2m3VKY0UiEUMMMp6plqVq651OB7FYzAnBUNQN5037ACblzjh+DCMAExcZ96dSyfFvjj1ddd6C3F5rhL+nC5HP5jgospTr0FvCinMQDAYd1y8tK+WO1ooemUzGSR3LZrP22fb2NnZ2dhzkqFqWwPhc4LOV7k4Zszgm9CjQ8tMQVbVadaw9ZUUigxfblUqlHEvb5/M5CGqW0+KztJA7rT/Nx6YwtQmYhGr4OSvc6N2hc8m7Qhm3jo+PzatHKkeOTTabdXJf/X6/xSy9BeV5P6hn67PkiTFLTfznpgEmLgiVk5MTp0K1/j/h+poyoPygxWLRgc/7/X7bzKurq1hcXHTyH/XiUTesmutMVFbKvX6/b20k1RWfw/qc/Ht+fv5UnigPeQJYKCQg1tI5SrdFUZIHbbMeXHwXMFlwevAouOT+/ftoNpv2/UQiYUm/HCuFSSu0X2m6gIkLTQnTB4OBtc17+QATvlklbtYLmUTNCs/Xw9UbW1N6rWw2axcZ+6JuHx5cWjNQCZo1JqkXPZ+n65ek7MDEDcQLT11twORi9CZCA2PlQIFv3W4XrVbLuah5yAKTvFnladXvKYk39wefu7q6anGcw8NDVCoV4wxuNBp48cUXjWOYigTXiZbgarVaWFpasj7W63VT0jg2wGSfLS4uYn5+3uEEpeiYk7ZSeYQ1vq3jx3GtVqs2FoyBnTWPjDEr5aUqtozbsb/9ft9izRxPPk/r6AJw3PhaZpDrQAkllC8bGIcKVKlXkpLhcOi4vs+dO2dKE1OblPZQa4oqT7CeBYyPM67INDQlHmFsWH+vJOTsK+CWTGPb1WCamZlxQGXKz8sx0Zg1RSn3ms0m/H6/fY+8sRrL1ng86e3YVuZhEvOiZDiDwcDSFIEx4DCdTjvrQs98YHwOevmvz5Inxiy1Zpjzw//VwPSGbrVaDimx+qVTqZRTYJbJonyWVlBg8WKNBczOztrG6Pf7DgpLD1u/328D12g0cHJyYocptRkuZkWU8rkaRyC7g6IeNeGduW/AhIRAtSOvVqwE2pqkrIF8CjWhbDbrsIrs7u46ifveZGcyJ3Es9XJhPiEXyvLysrWL46B8o91u11E2uHFHo5FjHej/M5bM/misWUW9DoC7vvTyDoVCdqmzSoWuEyoHFK/iRFGLRa03tjmRSDigByXlJt+matTeOov8bSwWw/LysgEuMpkMotGoXSisTKOJ+ysrK7ZviNYEJvPF/1ZGmsFggP39fWvj0dERSqWS5RQzRqm8piT/AOAULiiXyygUCsacUyqV0Gg0rE98L8Ex+XwevV7P1jLHld/Vg0fnw8vpyQtEFUhFy+pFxT54455at1UtZ4Ky9FlaccL7/8Akn1bjzzrH0WgUqVTKiXdp7jSr17BN9ExRGo2GY8HF43EnoV9rRbbbbfT7fbMYVdSaZa6nxg2VCUh5Uznmem5RuVVFUcExWiSclxLXEOuFcgzj8bhDSuAlJ1AlNJfLOQqixiz13cAk95rK4u7ursM/HY/HnXOZdVGBCTpfiUzYZ47laDQyz06n03GseJVpzHIqU5nKVKYylSfIE7lhtRq9sttTg1SGH7WQ6DemFk+OUM2no8ajzDDAxHxXBBPjZvx3pStTzUnderSavJaEWn+dTsfRKvL5vFlQ7XYbx8fHplkpCwzLeSnLibr6vHUsqdVrLTiK0r2pVgtM4kf8fiAQsDqc/J6ykBCCrRo3Nf9Go3GqbJWm2tDVTolEIqeo2dgnda0pMo15rlrjz8u4o9oo451eXki2TTXjYDCIVCrllFRTa0vH2TvGqhGr14LP9fl81r/9/X3cuXPH2pjJZBx3opdmT2PIo9EIuVzOrK5Wq4Xz58/buJPFRq22wWBgVIK6NhOJhP2OqER1n9VqNYdreWtry6z48+fPO3X9SC2nVW/Yn0ajga2tLUMZX7t2zXFtEfXOZzWbTYfvVi34UCjk1PZknIt90D4Sl6AoZPXGqNXFPqso1RrPH65Vv99vlh8wQSx7Q0calonH407JKb/fb1y+wHi9JZNJm/dOp2OlwICxBaRehGw263gjSqWS4w7VPEyyYKk7X8sFqpWmZxrR3xwjctNyTI+OjtBqtezz+fl551zXeDHHQ+N/HFvOg+5nhkgUI6IeP50v7x7RPcjKINzXyWTSMh6ASWUUjg3HlH/n83nHS0kvKjTqzQAAIABJREFUAoV7g3Om2QRkMNI6xZ8lj70s1Z0GnCamBiaDzQOfm5uAAMZQKpUKCoWC00FCiTc2NnDv3j0b9NnZWXNrOo2VpGlv0Va6KwaDgQM91osoEonYBgZOT8L8/LzFeIAxeEYXnvJY0uXDi5ZE1HqYHR0dnSoWzTHUTauJ2XT9khqKMGv2nYtfXRLdbtdclcViEdFo1A4dJd8ulUqo1WqWg0nieOVWVbc1f8e27O/v46233sJgMHDWgpLP072uB6PmJA6HQxsLAM53KXphq1LEw9Gbx6WKgxfKrqLudr2UmHdFpe3WrVvodruW2xoKhdBsNu09mUzGidOyn/x/vbAfPHiAa9eu2SEeDAadUnfD4RDlctkuS1UKlQOUJPzquuJY8u9yuWz7gKAqHrZUYujm04T9ZDLpUFkyLsd1z7grL9pKpeIoxpqapSWs6IbVeJKX3JxtA+CsCx1XzWHVS0BjT+Sh1bQnxrCBCc7BGydtt9t2EXlzBBlX428YT+ce57hwLgkeUYWX80fQG+eDa03JwjUdjlgDXqh6DitGgbSf2odut2vnEt3+WsZO00EYc1QjRmOSXuWCfLAccwVEhcNhLCwsnJn/ruuAY6SgGl3b+/v7qFarTq427wRgco7x+14cgxfIx7lkn+iyZx8UKHZWjqg96zM/gRuzZHxBO6yTSz86B/dnP/sZAFjgOZvNol6v46OPPrJGE9nH/CUObjwet4K7wATpp/ExDcoql2Cv17NB5UHLDcZnEvW3u7vroB6Hw6GjrddqNUP58tla0T4YDNolt7Oz4zAc0eLSS0IvS9W6SOwNwJBrajFo3JB1NMmUROuKm4rf5UFYq9VMcw6FQoZOBcZgAIKggEmgm5fj9va2HcIAnMR5L2uSXlj6OdeQHgKsU8g2qSjSj3Ooh5XXu6GoaVpAmkNG0cOdQCG97CqVio1puVzG1atXDTxTKpVw//59G7elpSVcuXLFYlcaix+NRiiXy3Y4sZanKmFahaXVamFvb8/GWON/VIz475lMxg4IJqOz75VKxXIvOVfRaNT2Hy8xrl+fz2drKp/PG9cqMKnBqRWEWIcTGK99jUeqNj4ajWz8Saqv6GHN6eNFqBdXp9NxDjKuAa8wP1itM+9FrOeX1zrVyjaaI+xFRPNZbA+5f4Hx3iwUCs6FpmdAPB639y4sLCCfz9tv79y5g52dHfstzx5vHJhjfvPmTfzxH/8xgPGeplFChYHPOTo6cuo3Mg6sCrACzHQvAuPzRL0eZ+03ZbJi3VCOH+ty6hh759CrHIdCIWc9kiSBfez3+8ZcBoz3jCrozGpgG4fDoT1rbm4OjUbD8V6Ssxg4DeZ7nExjllOZylSmMpWpPEEea1kqbJ8mObVA1pOkFhKJRHB4eGjupF/96ldot9vGhXjlyhUcHBzggw8+ADCmf3vhhRcAAN/4xjcwOztrWhVdTNRQaVorY4rmpt28eRO/93u/B8BNySD8XfPD6vW6WUjHx8dIJBJOHcP9/X0nllOr1azPzWbT0IYAHHqxcDhsKS8AzF2j+Y5q9ajWRe0HgDHmaCk0luECYFYxP2c8UyuJXLx40dxx9+7dM0uRlIUamysWi2YFEZWm1nGr1XKqgABunVOuAbUW9HPNaeLn6oIhPF9deup24/v4t+aQ8Xl8F6m3OJbeMmCK7FYrfzgc4v79+0b/ViwWkc/n7fuJRAJXr161sS+Xy7h79679Xt3iGxsbuHXrllNKSr0TXLtahqzVajnVKSjqdtTqF2xzJBJxtPj5+XljjLpx4wai0SheeuklAGPkcLvdNg3b5/MZ6q/f78Pn89k8kxVGEc/8H//Wta0WPK1JPtcbJ1SvA60aft7pdDAcDp0qHnwfn6dpJ61WC5ubmwDGZ4SmV3hr3XL/sY9ab1T3u4YlwuGwcf2yb6VSyal+oXFJPp/ni7oHiR7mb1nflvM+MzNj5x4wWQfqrte+8d9pVbIPpVLJCRlwv6i1rmkZ9ATwTKjX607/NVbKXHueD7FY7FQpRa4djicR1rpPNbTA/6d1yTadnJw4LutqtWpzR85azuHs7KxzxlerVadkITBJCyLSXTEWzNt8kvyf6e44WOqfJx0UMHGlsnGsEcjFsL29jVQqZeTL58+fd/L3EomELfx2u+243wA40GIOFBelynA4tEuXG4+HLhcyB50HGBdSLBZzgBxM2yCnoQIzSKqu/IU+n8/hIOz3+85BrmAbJZ8uFovOpdrr9Rz6KsZGdF7UJaZx2f39fWSzWadMGf+bBxnntdFoIJlM2rgybUSftbu7axe1uirUraVpQN44t5fMnBcp102tVnPyb8vlsm0C1spU15i67QA4SlWv17MC1wAc16e2aTQaIRAIWL/u3buHcrlsbp+1tTUnPk1gEdfr/Pw8tre38ctf/hIAnMK1GxsbuH//vo2h3+9HpVIxIvn19XV0Oh0by2azaaXkgNN8tWy3Jupz/FneCxgfBko2PTc3h4ODAwuHJJNJx5WaTCatTQRmKK+s1gtkeEUPN90LKroWzqp/6H2OHsa7u7vY3t62S48pPVyvu7u7BtTgJUblnG48jpcX7+DNg9Y9wfOAYD9VovSZVBKU+OHf/u3fbM9nMhkUCgUnXY5jevv2bXz00Ud20fzhH/4hotGoKWCqtGv7zspt9ua/M+QDTGpK6ufhcNih9FSFgH3iGDBMwt/6fD5H8atUKqbE8PznfPOCUxcwhfFqYKIEa0yctHsUBZQ1Gg2MRiN778HBATY2Npxi5Gz/7Owstra2bJ0wH5N7l/zDvCNCoZADBHvcpTl1w05lKlOZylSm8gR5omWpIAivJqnBev69uroKYGyVaaAVGGuzRBhq8Vwm51Kz2NraQr/fd5BMXmvl+PjYXLrUIgDXVQOcdsUNh0PTMlgmh330VtcIBoPI5/OmIZGEGJhQfhGFyv6r1qTgImrZ/D01fGCS5A2MXWBqPZEsm9aV1zXUaDScwr03btxArVYzgE+73TY3rJIPAGMrrlQqGZAlkUigUqmY1dPr9dBsNg1ApakX6pJTa9L7/16KPLb75s2bAMYeh2w2a1Z+o9EwDwPLsvEzWqXKksSix2zv4eGh0eO98sor9l7vGhoOhzYunU4Hly5dMkaQeDzuENBTW/dWReAc3r1719w+BL+oFs0qHcAYHKSgqZ2dHWxtbZ1Jd6dIUHoIFIymFnYmk0E8Hrf9Fw6HsbW1hU8++QTA2ApisV5gvBe4b1544QWHZH00GjnF1Ykw57j3ej3HlachBRW6GdXlp6A3WoPK3NLpdMxaZD+4/t599128//77AMZr9dlnn3WQ7eVy2SyDubk5S8AHJow4CuwBJm5xPkOJDLiudA1r6ahKpYKHDx8am9fJyQmSyaSzn9STlEql8MYbbwAYe9aazaa1l2k+6obVtqjFQ2Qzx/jk5MRJt1MXZrfbdVDDdMvSSlOqOMAl8icFpLqCt7a2bK2z2gdDPvSunWWlMa2G79S+ekkJ9E4BxutGPQXz8/Po9/s2L61Wy8JjXhL2dDrtVI5iOpB6ahSs9jjL8rGXZb1ePxVv0g7oINMVyAHRkkscAHXbxuNxhwoqGAzaxdPv91Gv1z8z95BVH4heZJwGgOPSs07+7wFxfHxsdf6ACe2S+tx189Bnz74QRclnnpyc2Iaiy1drsPGyYZu1jpqXsoruFvI0appDIDCpU8jPtWqHwrUbjYYTfzs6OrLDJhqNIhKJmAsimUyi1WrZgZLNZjEajZz4RavVsnHnOCgijgeg/v04IWUiL/d4PA6fz+e4erUCQDKZtL+9bjzm6Cn3bzKZNKor73spVEZ4SK+srCCTyThIZ0WaxmIxpFIpJ1UpHA7jwoULAMYHDJl3Njc3USqVnJyy0Whk7jiuCV6eDx8+dCq76NpV6D5TnLhOGKZQ+knNAdTqMhS94I+Ojpwaf1rWic/3xpGVI1Tj5CpEzfN3WiaLaFitOqKl0K5evYpcLmcH2dbWFm7evGmc0fv7+7ZuYrEYer2euTwzmYxToYbv0BilhkE414pEZ6qXnheK9GXKg+aSv/HGG/Ysonk519Vq1VzKL7/8MvL5vKGTeRZyzKvVKhKJhOPy1VQXlXQ67cSLvRcP46EcB9aBBSb1VBmv9iphSr3YarXg9/vtXGauLl3H29vb6PV6NqYs53dWStHR0ZFdhsoPzD6c5dJXVjAvgn5tbc2ha+QdQOpQtpkpJ4p/ULpQ5owrn8BnyRMvS2oaTBvRQ1EvTAZwlU+TzwDG0OlMJmOW1eHhoS2kZDLp+KypUakfXq00xiU1DYWiE8H/Vmosjauy1A03KzV5b3xLL2vl6tT8yE6n41gvzBvypivwXbRq2H5O5tzcnAOBp+bn5YblJiL3KumdgLHFqBYei66yfap0aNmgo6MjxONx2+ws26RJ2cD40Dgr5YPvOyuXTuNWgUDASncFAgGnBNn6+rqjRPEy5d9KZ8icVK6LwWBgFpa+m+/3giU4ZkyKZjFZktlz7Y9GI6cIAPvINudyOQOrZbNZ3L9/36y2ZrOJQCBg+4A0eizI/eDBA4xGI+ddFFrO7GulUnEUOyWtDgQCiMVizmVarVbtQszlcggEAo5nQ61f5WL20i8ScMFxZ44zx1GVaHo+OM4KconFYg7XK/elcu0uLy/bfmN7lHRfOZB1PzL3lcI4mF6OVLI5x8B4/6giqgQLtKqUwELHiSW9NFWG/eaccC1mMhkHyMYYnlqE2v6zCBi0DXq4K6it0Wg4v/XmQsbjcaPa49jrRaVcrzzDOD6JRALLy8u2xjY2NlCtVm1+q9UqLl++7ORhUjqdjsMLruQjbK8XCKZWtXo5GXNVPlvlDWfBCBW9p9SaJvZGsSWfJdOY5VSmMpWpTGUqT5DHWpa7u7uOr1i1TWpJiiQjATMw0ZyoTRBFpYnG1HpfffVV5PN508gI5dW0FaX6ooZMbV01P59vUk09lUo57jRqYdSUaLHRB0/rQ+mUmG7B7ytxucZZQ6GQw6jCPnoTt5WU+KxxZaUEajozMzOOdqfxS2BslWcyGYdw2YsEpAXBVBgdVx0fJssrGi4SiTguDT7vcbFs7R8tWX6faSb6Gy0Kru55/la1Pa/r/+joyGHPyWazTpycotojYyB8z9HRERKJ/4+9N3mONLvKxp/UkFLOqVRKqXku1dRd1TW43W3cNnYYCBsCYyAMG+MNjmDHBnYEfwUbWLCBMIGJMA7CODwQmOZz264e3N1V1dWqUg0qlZSacp6VUiq/RfKcfO4rddX3I37LPJtuVU73vffce8/wnOdE7HPr6+uoVqtOpw6fz2f6pp0xADgIYm8bI3bw0ND/3t6ehY2YuqCOaS6bITZ+78HBgYXxGFqirhcKBYeGkHpE8vNgMOjA91XoNXr321lIZormHr3v0RIv9SCGh4fh9/udfJuy3PCz3M/T09NOAwVFZhLdqrqh46vVak7apNlsolQqmaepyF8llPAWzzN6wfcq0wzL5xTFSbQp0G1hBXTpMLleHIvuc82petmDVO83NjbMcyLOgOeSl4ye/6a52t3dXXv+QqFwqrm8kvdXq1VLkbRaLac5+/j4uNNVicQezNnqerz99tv47Gc/a9/DnCzlLKpQrgubViiln7Yv0/2mLds4b6qnjBTwsww163d9kjz3snz06JHTRktDL1o/BHQWUBPzzLtpOI6bhRNBkMfDhw9x48YNC83xgtDaM82RDA0NYX9//5QbD3T5C3UcGhYYGhqyfNHa2hrGxsYs7zE7O4uZmRmjg+OYmTPRcEc6ncbdu3dtTOTCVTZ75Q/1hiVV+ZUBh6Ahwq+1Bo3PkEgkbMzkb+Xv8n28CAYGBpxQuoYtWe9IBdOQGcfMsA1/G+gc8Mqq482reGvpuGZcH+3+wDpMKqnmW0g/pmEuDXV7azwDgQBmZmbOpKxSgArzfboxpqenbd1XV1dx+/ZtAxrNzMzgwoULdlEdHh468PR8Pm8gj3Q6jXq9bht7eHgYly5dsr6SZHXhoclD8awSAQW5AR2QDueCfLUMq25sbGB/f9/0JhKJ4Nq1awb4IQhEmV+UJUb5hXVtgG4JAYUXhhqRFM0p60XE19hTFuiGK/VMUCAVL5uzOuh49YvzoikYfgfnUt+noWG9UJWKkL+n2IlAIGBzzK4wisNQUZYZ0n4q249e5mqUc+50/HqmZbNZh2KuWq3a73B+OWcnJycoFAqGYahWqw44i89MvYjH406PzXA47NSkhkIh08lwOOyc6UdHRyiVSo5BR9nZ2TFdVcOfz6rf4/P5nHWgIaGlJnqGEIDHecrlcmbY8rLUmulUKmWhfdKqemkQz5LnXpbpdNoulvn5+VPenRYg00r09svTw350dNQuxFAoZJdrpVLBhx9+aO9NJBKnGkszTwR0Ev0bGxtnFuwqZywtZCpOMBhEtVq1XOng4CDS6bQt4vb2NiYmJnD79m0AnQNUCQPUs9zf38fRUZcAm3V6ulkrlYrjUenB4W2tpZeUotdI1cT3a64M6Nao6iXszf/y/YlEAn6/3zxp9l2kJc81VOQYNwTHAsC5DNhcVQ9Tb5F6oVCwMTAnwvecRQvI74rFYta0mN+lG5D5Br4/Ho8jlUqdicT1Gm5evlpFMl+8eBHRaNSMuUajgQ8++MCp3x0bG3OI/HlQ1mo1bG1t2fNeunQJ169ft0vL2/+QHMOai6R4DzWlW+RBTlTt/v4+dnd3DXzBC11zw3Nzc/a3egR9fX2oVCp2GNGD8+Z1vFb3J3Hwch7piekZcXR0ZPn6g4MDq6kGOrqqyEVSx3HN6dlwjF7Ri5rAIRrzvLi9l89ZvMTKI+pFX6tOEf3K51tfX8fu7q5DzM45ImEJL6VUKuVwSdOr5nhJJHCWcR0KhRyUre4ZEhsotkT7q5IX1et5qeHDMQ4NDWF8fNy+i14YhXWpXB8SeSiHN6VSqdi5Q7S+kt4rSDAUCllvU6Dj/W5ubuL+/fsAOoah6kIkEnGch52dHQN/ElRJY/TKlStOT1K/3+8YNc/zLHs5y570pCc96UlPXiDP9SwbjYbVaUWjUSfMwHAab+R4PO54dWyzoqUj2kCXITOgYx1p3ieTyZwKCaqVfevWLTx8+NCsIW+e4SymfIp6Wqurq0ilUlZawVIQtfY2NjZsHCylALoQbc7J0tISQqGQE0Ii2TO/W2nstLsEkbRAl9ZM85LKuPHkyRO8++67DmR+ZGTEPBfCt+kFkQKOonlWwvo578fHx1ZeAnTW39vxBOjkmN955x0bL1tWAd2wLNeGTEfqlanekI2EFjXDQkAHbUdvA+jkfdTSVaQy0AmFKwm9ipdxRvNfZG3SqMnIyIhRNbILDD3rcrlssHmgo7/qrV+4cMEpGVCEYDabxbNnz2x9CW3XHLCulbc2lL8zOjrqhMCJyqT+bW9vY3Nz0yIfJycnCIfDlk9aXV21/9c8nIrXi9OSCvW4NG/Irhd8n/f7hoeHrWyhXC7j9u3bFqJkXbaXDo36qCHZWCzmhHlJicn5SCaTWFxcPOVJeqMO6lkyrK/enDfnpY27T05OsLa2Zvln0szxO2u1mnldoVAI0WjUdHNrawu7u7sWluVzc+7IgKbRD4qS7TP/p2U/tVrtVNhc88ZaYjQ0NOSki3SOI5GIU7PJSCJ1MJPJoF6v2xnIc8obIgfg1BYfHBxgdHTUfsfb0oteNcccCAQwMTFhZ+b29rZRI/J3qYPDw8MIh8NWPkYPlLp+8+ZNTE9P2xoSVct5P2sfUp57WQLdjh4ff/wxXn75ZefBvLyfQ0NDtukKhYJ1DgdgA6Iya0jN5/M59TmkN9IHOj4+tos3nU6jVCo5oT2Ktp9ptVqnFEsvdAJCGHZg2IUKzPIQbTnD+WD3Dy44ofle/k5vGIUHLhUH6FwKfLZarYZKpWIKyIObFwZBCAwjcOw8cO7du3cqlEQlZJmMbsDx8XEntKL9Lr3gJH7uN37jN2zdb9265Sg2L0q+NxgMOiE/wOUPJYz7LCq9crlsugB0i+O9/Jlc/0KhgP/6r/+y3wuFQhZ2VH3hZ/Tw15rGwcFBhyOXa0FdSqVSDpDl6dOnlt8MBAIYHR21zfrSSy9Ziy+gk59Pp9MGmgqFQhgeHnZKb1RUV4EuN/DExIRTAsBOPZyTqakph6eTtInMXaXTabvQU6mUc/FEo1EHNKdcpJwvra9TnVf9YgcRNQQjkYjlhoPBIHK5nM0N+WjVCNZ8HAAnVKx6Rh5atv177bXXkEqlnDIirdXTnDvPIQ1X6rPqxaK5Xfb15PPPzc1hZWXlTLDd/Pw8otGopbWY/lEjUS9izaNx/JRKpeLksvUMY+2xUloC3bD72NiYQzzg9/udjk6Hh4d2KbFXJ+eclywNsGw2i2g0antXy7b4TJTDw0PT3b29PWccvMw1/zwwMOCAdcbGxuzCu3r1Kra3t01vWq2WndnKBc7njkajTthVx0Zj/azerF55IZE6FeHx48dIJBLmDWr8n6JADQ5Ab2zNXRFsA3StYo11n5ycOKCViYkJAxux751uaAoZLTiJ6qEGg0EnYc+LU63MkZERWyRuLrW6dNKr1ao9Az0FRfQqKQGT+bRCta3Y7u6ujSkYDCKTydj3MB+pfTQVVTw9PY1z587ZM2xsbDj5XvUYarUaDg4O7KKm9cr3LCwsIJFIOBf5wMCA/TYBLcFgEF/60pfs+3/2s5/ZZqReaJ2o5jSJ1OMGrFarDjcj2T2AzsVNJih+lvW+/LtSqThMHlo31t/fjz/8wz8E0GWdoSgvKfVcuWAJMqMeDA4O2iGYz+cdjz0cDhsPLXNGHBPr/QgSe/vttx0yeG7WF5E5eHNa+/v76O/vd/rwaT43Go1ibm7O0QOinfnM9PD4LHppEUULdI0S7Q2pBf7efJoy4ihIiQcvdSUWi+Hy5ct22O7t7WFkZMQ50BQHUavVLBdFRKd6itPT08ba9MorryCZTJ5CyFIUlKNIUm/+XGtko9Gok3MeGhrC4uKiU4ecSCTMCJmbm3Mu4s3NTRs/wTLUyd3dXceBoA5SvIhb77pqvSb5pYGOF6b17yTN0EYGAwMDDsCMFw8L/5WMIpfLWVSL3v0n5a41shAKhWye8/k8Dg4O7HcCgYBTU9zX1+cY2CcnJ06N7dLSEpaWlhw+WwX9adswGsGKoq7X687dVSqVnEjcJ0kvZ9mTnvSkJz3pyQvkuZ6lWmWsUaQ1qXFvvtfLHK+NeVkTxRtdGVG8jAz8PoaQBgYGsLS05Fiy6nnqOBmqA2CdxtVC01AxG0V7PQz1frVzCCHO+l3Kg6hhadL/qaVCFC/gWrka2mHNIS2hWCzmhFVGRkZwfHxsSLFHjx7h4ODAalZTqRTOnTvnhJJp0T179gyPHz8+xcjD508mk47XB3QsM6KFiUY7OTkxS/TLX/4yjo6O8NZbbwHoWMSxWMzmiXOqlp3mgarVqpN/UP3xoiHJDat1s81m0yxdRgo+qYOAIku1C4I3dEx0q3oFGi4kxZ5SHzLctbOzg7GxMQvhssPBnTt3AHTC7xrmpGd5Fiel1j7y+akn2WwWoVDI8XKIWuV7NdJDyjNdB50nrZ9jqF89BbX8icr1hjQ5Di0F0dpFv9/v5Dr7+vowOTlp3U9IxcioS7FYRLvddrwejon4B3oUyWQSV65cse+amZlxunqw1ZyWXPC/ZyGR9fc43nK57ETbRkZGEIlEbL+Q8Yp6srW1ZXNaKBSwv7/veE+k7AO6kSfqMs8Ab/SO49S6yWw2a2H/8fFxB7k8ODiIUqlkLF7hcBirq6tG1cj0EdMCU1NTFlUYHh7GkydPDL1MPAXPD3ZkYdmUorUBnMKcMMxPNjZG2QKBAJLJpJOqUkwBKT6pV0xvcW40Z0nRvLl+F+v3lVlNKQr/19ywKgytcQJKpZLT3sm76Zkr03BcMBh0uGP5WSqshnmYu+N7S6WSA29WjkYtwq9Wq/a9DAXTJWdhq/Yy05AYFYzPyGQ3/9Z6HpbOKIm3Al1Y06j5F1U8zfOokcCO91QsHuJUBtZ2UWg0cIzPnj3Ds2fPHBAMFYFk2sor6/P5LLRMuLYWYddqNbvgmcvQOsuRkRH87u/+rq3dm2++iZOTE9u8GpLlvHlLAIaGhky5taaPhozOz8nJiRlNx8fHDgcqwRm6MShKfQacBqUQsAZ0wVicQ0LtqW+NRgPlctnJZTNn2d/fj3g8bnNYrVaRTqeN3o7gBS+FH8VbM+wV6lMul0M8HrdLlxcjx1ipVBz98/v9DiVcq9U6RaSglxyfE+iGQ7XOTUEkeslwPQFYKZUazDS6Oe/BYNBymIlEAgcHB05pCUOj/G7qFWksuRcmJycxNTVlutDX1+kFSkOPxqWWTPDZeCaxtZ0SA3jDskNDQ6fKTTT1ov0u79696xzKWvPMXJ/Ojc/nO2XAnlUGtbu765BcaGnE5OSkgZ0oDx8+tOcPBAKYmpqyut/Z2Vmn9KxareJXv/oVgC43uJLvJxIJOz94H2ijCRoUwGld1tpv5S3OZDIIh8MOBkX5XNkAQI1mJa3xAv28hBl6fhDop7iHcrlsZ8P/L5eldjvgf9vttsP2oGz43IBeZgbvhuQANbdYqVScep1Go4GNjQ2zfvg7vAQUCba5uWm5pXA4bDyy/HtwcNBRJLXC+P+62GxODLgXHA9UBQporR6tLD7T7u4u1tfX7cLReSEnJtDJRW1tbdkhODU15aBL2+22gTn4O0qsQItLASUEFfT39yOZTDoFy9o4dm9vz0GjBgIBPHr0yBDR3BTKPxkOhzE6OoqvfvWrtnYffPCB/QY9e/X+yVcJdA9XbnavJ6UGCJlI+DzkqlSPUEEc+l2VSsX0hd0g1KpXDlrmGfkMzB9xTcihq2vCfUAGIR5O2WwWGxsb9vfw8LCDbn5eEbRXlJmlWq1iZ2fHyb0pSxGbEWgEgwYu4B4gfL+SaSsNEZD3AAAgAElEQVQ7Dr0rNTbUa/XyliqL0MDAwCmkMy+joaEhp+YvEolgenraDvJSqeQgpzUiwbNFO5Zo5woaR7wsW62WdTcCusZSLpezvTQ2NoZWq+V0MfLWigNd47xUKlkzBp1jGtCHh4dOblDPCz6DGjta8O+9kFUymYyDaK9UKgbwm5ubw8LCgl3K4XDYMZoI0OHfk5OTSCQSjnfM/ZNMJrGysmLzf3Bw4Ogu2YLU8NXcv+r2/Py88zmt387lckin04bo5wWsTbWV2YmMPTrviq6n4wZ0meS0Zlj7ybZaLaeHqe4Xr/Rylj3pSU960pOevED+P3uWGhZgqBHotu3xWszeFl+0YBizBzpWbb1eN6tweHjYYbjh7a8dBjY3N508HyWXyznIsIGBASf3qV5af3+/WXwU8oYCXRi8cqV6Reu3GNMHunWYzGXdu3cP6XT6TGu8Wq3ab87MzKBUKplFnEqlTvHTAm45Ry6Xs98lt6pay1pHybwRx65lG4Sv0xOv1+v4+OOPLaykVq8yCgHdDhFf//rX0Wq1LEe3uLjolDScxQSjCFgNkXhLRGq1Gh4+fIj/+I//ANDxRM6dO2coaXpe6vFRhoeH7TkYyeBccl64Bl4qMlqq1CtGPTivAwMDFpoaGBhweojm83nk83knpOvNjXn5RSleffOGnhTVmEgkEAwGnY4dyvRCT5pzo+U+RDBrvl1DWRqi5utErAMu4lrDZ8zn6Z5nLl/nQmsA+TmgUx6j+SXtCkKMgFLn6TjZpUUZfBia1TEzH8bxaQeLXC7ntOzi7+o8eD0RxThw7igDAwO2HmytpzR1Gor0pi+8HqmX8YrlHHfv3sX4+Lg9A8erc0w6OaCzx9VT1QgK8RJ8RkaElN+aqH+OWXO6muoIh8MO+5SGZev1OtLptJOi80btNMXAM/sspiBGBjVnq3iEVqvlRM+KxaKDxWE65SzxPa8Isyc96UlPetKTnrzAs/zKV77SZo7tc5/7HBYXFy03xkJYtcYVWapNXfm614PT/CYtRYoW09dqNeTzefPSqtUqnj17hlu3bgHoxNm///3v+wDgr//6r9u0fJi3odcTiUTQarXMC1hdXUUgEDDPM5PJ4P79++bVMU5OYuB4PG7PPzQ0hGw2a0iw4+NjTExM4NVXXwXQQc797d/+rfGLDg8Po9lsWs5odHQU3/ve93wAUKlU2v/yL/8CAPjxj3+Ma9eu2fcQfKIW9VmE0koG7ff7zbNUEJY2UQa6TVVpZbHzBtd8Z2cHT548wXvvvQegg7z92c9+5rt27VpbQRiXLl3Cb//2bwPo5EwSiYQ9ZzweRzKZdEgWFLBEUIN6zgoa0xyy19P0IrSLxaJx6QIdK/tv/uZvfACwuLjYZt5wcHAQr7/+utVgLi8vY3R01CxqckVqHmd7e9tQmsxFqRdAIVJPPRjVZebg1KvVmsVisYhvf/vbPgCIxWJmyTI3xDEygqBgNd1zPp8PkUjELGyiCTVPq/1JtQl4tVp1Ot0w8sN9wnwugTmzs7P47ne/6wOAixcvtnkGBAIBRCIRxwMIBAKmC96+g8w3KVmEFySiCF6tJyTTDMfIvBS9IuZKFURYKBR8U1NTbWWO8c7x3NwcXnrpJQCdusOpqSlDnnvZwui1UAqFguliNptFpVKx3DU5ZPnb1WrVAfgoNyrQOU/fe+89HwD83u/9Xpt7YW9vD5lMxvStv78ff/RHf2SI1na7jWfPntl3zc7O4rXXXsOVK1cAdM4lXVuNqJCkQXO2+jpBYtTdRqNheVyg45lTl7///e+3NbdJ3eCYvZEVrSlmVyBl0BobG7O/Y7GY0580mUzanvQiyjmXfP/9+/fxr//6rwZirFQqeOedd84kiH3uZXlwcHCKfFrZ/Dk5nKhQKGQXSyAQsC4CFG/Y1vsQXtg7J5CbRENQip71UoIp3ZKG/Ehszsvg9u3bKJfLpkhE3ilDvcL8E4mE02S0WCxayLLdbmN1ddUBMAUCASfJrRtfWTROTk6MeWRmZgb9/f1O+IjF+RSGs/i7PNwAF4TEudT5UKg8CRe0DZB28YjH45icnLTf5gWphzIPaa5VJBJBIBBwaA01TMKwmibk9b9qRBEAoTqo60Pd4vMxlMiLmqApzgu/19u5hcXV3PSFQsEJobMkQNk9lH7Ry9REoAPQBSdQvGF/Xp5nseHEYjE7fAmO08tPQ5oDAwPO5RMMBs8sO1BEoV60zWbTDhB2ddB0hdIeAp1QGedPG48rWpSf0TNAwRcMpXnTIl7xsgcBXWS6MlVpswIvSQgJs/l56oiGYRnqU2J3NaIKhQIqlYqVPITDYefyZ0tAPdeoI6FQCJVKxSFcUKQwLy1+lixS1Av9TgU2Ad2WhQDwO7/zO/it3/otO4d5qZCe8u2338aPfvQje8abN29iaGjIYa+iHrB4X+fUe24r6EzD+GeJ7lvVJ1Yt8HsbjYYTZlWEMtANufPs1defPXuG7e1tYzTihaxrquAiIsqpJ2T8Okt6AJ+e9KQnPelJT14gz/UsK5WKhSyZ+OUNvbm56Xgl/f39yGazVmbQarUQj8ctZDE5OYmxsTGzrLyeotdaUQBGoVDA7u6uWfrZbNaK2QGXz4/1SvwNn8/nJJbHxsbM6mC4kt9bKBQwODhoXh89Q1qp8XjcrFDC0lnGEIvFHCLkw8NDjI6OOnWmykeq0tfXZ+UuBBRpkj0SiZh1R0+EHgdD1AzDMATo7QVHUe+DSXaW5NDS1RZd9Ji55kAnhMy1YTNpLQVR3tBcLodkMmkeOT1PTd4fHx+bt7+/v2/joyfFOVQrHOh6MRrm08iC13unHoyOjmJxcdF+Z2dnBzs7O1a4ncvlkMlkzFplqzA+A0EDCpxSL5NzwL+13liJEPj8LCfhM1FCoZADy1eQA7lQvfzJ6iVpFIGlTt6aV86Njo21sArSYys2vk5icACmP5wbAu78fr/zbPzbC+ZSsgjvuLR9knoQXvCTlzyf5Pgci9Jr6rNq2J91lZq2ILEC0K0vpbC1Gs+Tc+fOIRaLOWeZF/CnZRXetlQaKfBGpry1uJwzeoXU9ZWVFQQCAQfgMjExgc985jP2Oz/60Y/wwx/+0Obl/PnzTpRB11nnmr+rjaI1YnN8fGz1r5wfitZLE0TFMTYaDWcfs1G2grD29/dtT5HnmWAcDW+vra05lIqTk5PO+XPp0iUAXfDR5ubmqSbWnyTPvSyHh4dtI0SjUcf1HRgYQCqVssswFAohn89bQeutW7ewt7dnE7S8vIwrV67gwoULADrurjZv1nBIvV7HwcGBxZHX1tawu7vrkBiwABhw0VBa28RxameGyclJC3kGg0EUCgUrGOcz8xDgRtYNx0tge3sbW1tbFobu7+/HrVu3LI/KfCkXgfmQs+qn+vr6bDF5UHGeBgcHkclkTDGIrtQ6IRUi3fjvuVzOQjTKrgHA2IouXrwIAHj55ZetewrQudiGhobMmKDhNDg4aAYEc1I8BDY3N1GtVk15yaKkfJnsjMJ5a7Vaxpl57949m2PmJPViCYfDppOJRMIhZOZG1vwNRdmV5ubmsLS0ZBf03bt3sb6+jg8//NDWVo2kVCqFubk5C22NjIw4DFYkKeB8N5tN2yfemk0ewFxvGllcLyXYCAaDDtJb0eQMW2nOWpldiKDmQUZkuF6MeuF4D0GG7Ckkswa6taKcd+oF0CWG5xi8KQPteM8LQZ9ZeT4Z6lbjV4v4S6WS6VmpVHJI18kyxRBxKpVyUiKKp9DDvt1u25xPTExgdnbWIU3QOSZqnX8fHBxYPo5rxDz33t6e5fw4x/F43OE0VdYkhuu1lyZFUbjMNfM3C4UCNjY2LEReLpcRDAZtrc6fP4+joyOrJU2n04hEIs7aKnL7LKNQjRvVEV54Z3HFktMa6O4Bnkf8Pu7Ho6MjFAoFOxM2NzcdVjM2EeD5/+DBg1MYAOrFzs4OYrGYrXc2m8WNGzfsGd9++23MzMzgD/7gDwAAP/nJT/BJ8tzLcmRkxJje2ZRV4eTb29vWxaBer2Nvb88hyz2LEksPMi1EV8q3ra0tvP/++/jggw8AdA6cWCxmHs7MzIwdSl7x0tUB3QLsRqOBTCZj/14ul/Hw4UOjxCI7vXZf0LZiQLfrw8bGhpPTLRQKKBQK9kzeOD7LDXiQ6GZttVr2G8lkEq1Wyy6EtbU1vPnmm3jw4AGADqF9Pp93vCeycAAwCi0q3ubmpo25WCw6sX8+L9fwF7/4BS5cuIDXX38dAPBrv/ZriMVittF4WQBdKDoPe14WtI65mSuVikUCgM6BUiqVjG4L6K4/10jLLrTNUTgcdqITwWAQu7u7tmm8pQh6wKihwqJtesqcfxpR8XjcmFEo+Xze5u7w8NDxWsrlMu7duwegowd6WbKpMsc8Pj6OZDJp85XP542KTucV6JZeALAOIupZsfsH51BzT4Tb83VS8KlBqk3Ni8WizUexWDxFB+jz+UynFhYWHONB13JkZMShLdNOHiQ64OvBYBDRaNR0fXx8HMFg0Mnz8dkoPE/Y8YO6zefmZ8bGxjAxMWERm0Qi4bAWUafoTVLYDgroAADPnTtneaxkMolAIOCAcvb3903/GFFRb15LdaLRqO37s/Acu7u7Ft2gMcTzQ8E+Wt5FL4v6dXh4iPX1dXM05ufnkUwmrTkCGZO0k06hULAxe7syqSHIMiD18Or1uo2Rzar1+Sney16jiYVCAY8ePbIxl0olZDIZx4hR425iYsJhMtPmCbys9TzRaN6dO3dQLBYNQEkiCjYVuHr1Kj5JejnLnvSkJz3pSU9eIM/1LEdHRw1RGAwGrRAa6PTw0zwiG59q6yW1jICOF6INcxVZW6lUzO1+99138e677zq0ZoODg0bplM1mMTw8bBaeegDkDVTRUpDj42Osra0BgBEb0IImuo0hQ3op9GC9eS1axkDHQllcXDRPPBwOI5PJ4J/+6Z8AdMIwDCMCbjyfYwM6OYednR0Lk/zzP/8z3nrrLfO2iOilJVkoFFCv120cbEOmfQw5z/F4HJVKxSx3wuQ1Z7m2tuZYw2+88YbB0ElH5ff7zbJlSYLmKWq1mlmyJCDnGkxOTiISiTjo3qOjI7Oc8/m8oQ0PDg5QKBTMoozFYkgmk/Z6PB5HJBKxuRwfHzciacDN/xGtDXQ8Iy2NoAfAMaVSKSNYBrqhVApp9/i75XLZdJXfpznjhYUFy2sRMUlrl96I5nQp5XLZwu+5XA6hUMg8EyJ/lf6OuR2gi+zUvGssFnP0ht4/yw84z6TOU8Rzs9m0kOJHH32ESCRi5QcacVBE6sTEBFqtlnmk2WzWoSOkp6e5Sm07xjCtctLyeRhZUt5nJTphOkMp+vx+v80zoyWak2RolHq+tLTkEHxzr/E3Q6EQpqenbbwHBweIRCK21n19feaVklrSW2qnUatsNms6yP3D8bIEDehEtxSjEI/HTT/ffvttJ3RPz51h1uvXr2NyctLhX9YQp7dgX6NR+/v7TiSuXC47YVqmk87KWSqPNmkZWXb3q1/9Co8ePTJ9pDer6RZtGh6JRFCv1+37Z2Zm7LvK5bJFcwDYWcQzbHp62mnJNTMzg3feecf2Gc/Ns+S5l+X8/LxN8tDQkIU+gE4z6CdPnpjiT09P4+LFi/ajTMgqYS67RHBiNedzcHBgYdfbt2/j8PDQwmKsZ+JvP3nyBMPDww6hNkUJr73w+nQ6fSp0GwgE7JJOJpPY3Nw0BU+lUqc6oVCZCZzhpmP4lgtO3kseOATTaG5Av5cKtrS0hFKphLfffhsA8H/+z/9Bo9Gwz8ViMVQqFQdAUi6XbZ6npqbg8/lMGfb29uyiKhQKDjcnu5dTCRmK5AZ++vQpZmZmbB2YHNfcV7PZRDwed3hjNzc37fJrNpvOZbmzs4Nms2m/yQOVF+CDBw9sPbRbBXC6mffu7q4DQltcXHS+Wy8eJdSPRCJOyOvOnTvIZDL23cw78dDTC5hrp7k2XVeOk799fHyMcDjsgI2UJYVE/Vwv1YtareZA3mOxmOkfyez5TDxANcUAuI2r9TJRsAp7glJXmS/TsgzNu7Jf6ln9ZLXXKsPr3jIhNW6VnapYLCIcDtt6co65N1jSwjlWYBiZWBQQFQqFjHj9/PnzTlNmJVTnc0ajUQtbco6VJ5bnlRrXavTxsqeRpq8pyJCiZRckleff+/v7Ts9V7h+gY1DysotGoxgZGbHzIJfL4dOf/rSlrDY2NvDTn/7UmeNXX33VQuflctkh9udzA53zgqF0/s35Bjoh96GhIRszGZL4rLxUgY4OaZ51f38f6+vrADrlHmoIacqDEo/HbY9ks1kEAgEnPcO1icfj8Pl85sTVajXnTnj69Clu3rxpejI2NobV1VVLQWj+3SvPvSwvXrxoC07PUnNtR0dHtii///u/jy9+8Yv4zne+A6AD1Dg5OTHr4NOf/jQSiYT9rQnf4+NjJ985OztrHQuAjsV2//59e6BKpXKqgwJFO3BocTHQQavpQb+zs4N8Pm8TtLKy4hQ60/LjxhoZGbFkf61Ww4MHD+zAZUcRKuXU1BTm5uaceP/k5KTTbkiFG5ANqAk6IsiDinVwcIBms2mHMw8MAhleffVVjI+P22X1n//5nzaGdruN0dFRG8P29jaWl5dtDZl71IbWlUrFLgVemnpo+f1+RCIR2yCPHj1ygETe3N/h4SEePXpklvOlS5ccS1CbOTMvSD0plUpIp9NOmzcvcbPWPKqu6sUCdC5aRioajQZWVlbswr5//z7m5+ftctjb28PMzIx9fmRkBJOTk47Hro2Um82mzUGtVsPu7q7VubXbbUxNTdnYEokExsfHTYf1UGTtJP8/EonY75TLZadAfHp62sllA91OOEC34462DlPyAK4l0Nl/Q0NDjrU+NTVlvxUMBnHjxg3bN5qParVath7ZbBbb29s2ryQSUPJzL0FDNBp1yCcU4UvaS6BzYddqNdsHNDK5dwmmoeHFCBJ1X8fMOUmlUtamDugc9mroUU85/vHxcZw/f948a6LreXAHAgHTAxJb8Lt03fjfRCJhus76aeqFeu/Hx8eOV3X+/HmLAJLUn3rDLkJf+cpXAHQuh2q16lxwiq5Xg39vbw+FQsGpe7548aLpDXPtnEvmIrlGamjkcjmbl3w+j2fPnjkRBTalBjpOWjAYtN8hElu7w8zPzzsYAhq2+Xzeye1ns1n4fD6bn1wu5zRViMViWFpaMueEwMqzpJez7ElPetKTnvTkBfJczzIcDlt4j8hJWj4jIyOOy5rJZPDRRx+ZNcr6vZWVFQAdpojZ2VkHjUbLgXDkmzdvAuhYhU+ePDFr6tOf/jT29/ctTMuwpLZwoSiNEhG8aglp+Uc2m0Uul7NwASnM6P0ylKoeqvZyU49oYGAAP/vZz8wjDIfDmJ+fd3ISY2Njp1Cy/C7mvP7xH/8Rn/vc5+zZp6encefOHbOqJicnUavVcPnyZQAdD6lerzulCLOzs/Y7jNUD3bpFeicvvfQSEomEhapmZ2edUHk8Hker1bLwBz+nbZNYt0lrtFQqYWBgwL5zYGAAuVzO8c5JO8g1CgQCFqbUekYSetNqHBkZQS6Xc1hNtFUPadm0Ma7qBb+HedpPfepTADqlJMFg0HLZq6urRicHdML3oVDIyaFMTk7aPDGfyOcNhUIO/dalS5csfHV8fIxLly5ZDvq9997D9PS0eRjq8UxPT5tFTGYgejWpVMrQipS+vj57ZtbAqfeoJTzj4+M2jyy3os4Eg0Endx8MBjE9PW3rPz4+7oQgNTecy+WcVIyWF4yNjTm9DJvNJg4ODhzPUsNxh4eHTtsqRVfS43zjjTdsTX7+859bdGp6ehrJZBLvv/++6cLu7q49E/ft8PCwPcfs7KxzpjHape3olKQ8Eok4jEwcp1I5aiutra0t2+dE0vKZlpaWcOXKFQuPhkIhx0vlfqIwknT9+nVcvXrV9Mfv92Nvb8+8OzYa4DkcCoUcusmBgQEH4aveOXsK0wtWGkvOneo600PcFxrJKRQKDuNWIBCwudjb28Pw8LBTkx8MBp2m7vwM55dMPEAnQsh5JH0poxMrKytOmVMgEEChUHAYw0ZGRvDKK6/Yb32SPPeyVEgyY9PaZHRxcdE2RqFQwLvvvmuD+NSnPgW/32+8iisrK9bwFOhuZqATXhkbG7PJvH37tkOlt7W1hXA4bBdIOBzGjRs3nEQ0ZWhoyBRjcXHRehEC3YJ5Tno0GkUqlbLQKnNVSimml52ChwKBAK5everQailVEvkWufFnZ2edvJWK5uKePHmCL37xi06O8pVXXrFDs1KpYHNz0+nNuLS0ZM/MriJU2Lm5OTMe/H4/Njc3bb4ILtEuEJFIxKlN84JkeKjyuWmcUMlefvllB4gyOzuLfD7vXOY+n89AFJxfbvZisWiKz1IlpTibmppyLmrNJfJCZG5Kxz4wMGDrc+7cOSQSCZtDltfwu+bm5pwQGHvx8fvi8bhDJ6flBwMDAxgZGXEuz8HBQXzta18D0OVspRGzsLCAQqHg9GGkTE1N2R5gHaV25tE6vHq9junpaVt3hvu4DjzYeNhNTEw4TYszmYzTyNzv99vlOD09jZWVFefwC4VCZ5Z23L9/34zPl156CRcuXDAD8uTkxHh5gc5h/IMf/MDW3ttrlhR//LdUKmVhMpJ8aNj6woULzqE5Oztrl+mzZ8+wsbHhGIJ8H39/aWkJY2NjDq5CS6dYvK6laUquQhyGXv4cO88aLf5fWVkxQ9Tn8xmACujo3Pj4uO0PvSyHhoZw/fp1AMAbb7yBVCrlkBbMzMyYrq+urlp3EM6x4jaYi6ZDQBwD/18btTNdwNfT6TTa7badJyQlOKuB8tLSkl3ChUIBmUzGdJulLJxH7mtNoyhtZyQSwdTUlM2NEhaQs1xpSBVQGIvFsLy87GBR6vW6GVHPk+delt5GvJq8pqXKQ5ykwdy8R0dHTm1kLBZDtVp1ELDaAmloaMgm5/Lly4hGo7YRtra2EI/H7fZfWFjAtWvXbFN6i18VRKOE15q/AbqWkDKVaJ0R8yva5kbzLZFIxHKDy8vL+JM/+ROL07P+7/79+wA6XpHWqeqhoJy7oVDIWG+AzuVzdHRkCskaOCpHKBTChQsXTnHyUvkV0Tw7O4uHDx9aDonWGi8J5ic5P2RD4poT1KF5pKGhIUMFAp0DPh6POzVU8XjcviMUCiEej+P8+fMAunyTmovjJiK7ioIglNeROWVe7goGAdzatOnpaauh4mVKb6/VaqFUKlnOhDVgvAySyaSjB4lEAoFAwIwMZUVqNBo2J3weRc7qPACdC0W5ZNXwU2QhEXz0HIlCphAsol4QG28DHX3Vov7+/n47IFjIzvkg+bsiCtPptLPGIyMjDvKd4sUAsFkB10MPxWKxiOXlZcdbUaYXoiYVvEEjo7+/H+l02nTl8PAQgUDAqVctFot2SF6/fh2vvvqqGY7UgVAoZP+/sLCAiYmJU/k8RXvq2gaDQRweHprR5eXQVRDj+Pi405aK+kXDnKQE/JtYCe5FRkWAzsVz48YNAJ097QVaNRoNx8nRvLDP50O5XDZ9Yx25NsWm0cS2dJwz7g+O5ejoCHfv3jUiluHhYYyPj9t4dF21/pmkM+fOnQPQRdkryb1yPNMg4ToEAgEcHR2Z8TY+Pm66GwwGHS7t/f19p1Wbz+dDIpFwwGpAl4XqLIY1Si9n2ZOe9KQnPenJC+S5nqW2nFEeQ6BjNYXDYQcmPTU15SBUlZGfsHYNiWo9nMKXGf6jt1Qul62FDV8PhUJObRBFWwJ5WXMU5gx0LB9tTks6J1rfXhYNDcWxPRKt7XK57NSILS8vO23FSCOloS+Ksl7E43EUi0Ur09jd3bXGvRzj6OioY73Pzc3ZHNBT5PcNDAxYCDQajTqQ/8PDQ6dhLhtYUxj2U07HSCTiMJOw5lPbFvX19TneBmnz+J3JZNK8GtaIcYwvvfSSA1UvFos2JtJ2MSxGdDUtUFrQfL96XsvLy7h27RqAjgehHgJzz4pEZHs2oKs3tM6TyaSzF8gsBHRy9972dNoQl22YdB8MDg6eWX+r+fZQKGQWtY5Rf0NRkgxbKTdxsVg0L0hzgaurq5iYmHD0gF0xuGbRaNQ8sHg87uwT9eZffvllx2vb39+332EzYaKQDw8PkUqlHNpLb0svbQLs9/udXHgwGHRq/lRXuGb0GMjvy781h85zZnJyEtPT06YHZETSelyNtBwfHzvIb+IwdN64XsPDw1hcXLTfymazDrJWKdk4p1q3riHwGzduWMTO20ycDGbe7iYUep3UE7bH4jOyXhfoetLcX5lMBlNTU/jyl78MoLM//+7v/g7//d//bWPRFl16LmtkZmhoCBcvXrR5ZkpD51Lno1ar2VkOwFJ2POPm5ubss8yNMtpEWjzqxeDgoJNX5R5RitNPkhdeltonUUOUAJyLiH0HNV6t7Xi46N74Pt+nIJGRkRHMzMw43cCVHBzohHDOSsZ6oef6GR5MWjDuhU3r66pQFO0uD3Rr+ZiTVLqxdrttJR97e3sO96POk5I3f+pTnzIKKgDWq46HHttmMRTCOkUqDkFMVDTl3CXoimNgXk5pzZQSjAaAcuuS11Xb7ejlHI1Gndo5HuIaAtdQKudbyzK00Fw7tdNYUcCVXij/06PQKW6mzM/PWzg6Eong5OTE8jpbW1vIZDLOZclcI9Dta6oHG9uH8XUtBVFOV4adtQ2V6iQp4Ch6WYZCIcf4TCQSpxoOeAm2de0ULMQwFg+y0dFRJ5+mheykxdM0STAYtHXhgaPhSspnPvMZp+8giQiALkiJujs7O2skGvwe0vRxLjSfphc8AYa8fKhn/CwJQ3gI8ns0hwt0Lkhe7sw1q2GnPSb5DFwvHsBeo0rDyHruRaNRO8STyaRjmFKPOafkvlUdpFy9etXWgmT6/E0+v+IutNEBQX5qvHk5hhLUXNQAACAASURBVFVXlZbv8uXLSCaTBuLc3d1FJpOxMOrExATy+byFa7V0RHPP58+fd4j6y+UyyuWyk7NUo8nv9yOXy9kZQ6AYZWhoyIwo1nkrj/Pc3Jxzl3Buga4T9/8iz70s9aCiNaxdDYj848RqxwdeBnpTHx0d2SLp4nMxvByNelCol0vCZO/Fxf/n+8hLqWTF+rvcnHrhqyVLRn1+Rsfv7WowMjLi5Nf4Pcwt/OQnP/nEy1Ll5Zdfxt27d40nke/jM/AA4abjYaO1ctpzrtVqOZdJX1+fxfOZx6KwqwOft1KpOBEA7QaiVqEimwOBgJMnJpCLhzj/XT0k1REF9JANhePhuup4yJHK1725NorO2eHhIfb29pxCcSWbDgQCyGaztqGYk6T3S/5dfUblGqV3TVFvgwAQr9Xt7UoCdA0Pfk4vcALXdJ0zmYzlaXmAqv7o9ynrEo01zWdqxxM1SDjPShiul7JevOwBqt1i2GCcz6Q8wlwLfj4ejzvfpwjIYDCIYrHokF8oGxDHpLl9Ikw5FqBr3ABd1iON0ujZQqQ4vXPqB4V1rdrLlfN2cnJi7DIcTywWc/ASSlZB1qSziFfGxsYcxK0aVWosAN39pb0i2eUDwKmLQg0uoGOQUs95gdEYOjo6wvXr1y33WCgU8OzZM6cumqI1+kNDQ86Fzdc5pnA4jEgkYnthe3sbhULBjNuzeuLyOWZmZnD37l1jPLp06ZKRJwAdnXz8+LGBlI6PjzE1NeV0mvkk6eUse9KTnvSkJz15gTzXsyyVSk7tlba7OssyUSuff2vHeKLsgG5YiP+vbCREtmlrJg1fedvWeFv8aNjQOyb1urSXno5JGWG0fESpsRja4JjC4fApiqZWq2UhirGxMcca0tAbLSW+b2JiwuD2DCUznERvS1lDisWik4tRT1NpAlnPSWub9Y5ejlSuET0eb8uds1o7af+8WCxmlhzRd7RGubbaq05DKplMxsI4Y2NjGB0dPaVryhgCwGFJqdVqNrc6x6urq+ZRF4tF5PN5g+rncjkkEgl84QtfsHl66623LB/GkgalflQOyaGhIYfr9eTkxPEQvHOm+Redd+/71CJmbkn7XmpOjL+rHWa87FNeT5VjbDQaODk5Mb2nR6glRzrf9FS8vTmBbggX6JYaeNtO6Tx4UZKtVss8EJY8aBkOx3BwcOAgWQ8PD5HP551cOdGcAKwuW9MkzHtqJ5ZKpeLoLkuxgE5Eq1wuW4qkXq+j3W6bvubzefj9ftubyWTSnkVRwJzDYDB4KpWkc6p68UmRqLO6ORGtDXRLQbj/uFe1DOro6MjmjRFCfjaRSDg5Wi2HYQcTPle1WsX58+cdGkUVb1SOn6PXr5EMpTK8d+8eJicnbf9Wq1UH/wF0Uy6RSASvvfYavve97wHodFK6dOmSYSSi0SjC4bDtk1KpZJ4s4Na/e+W5l6WGtEhcS+U7OTlxDlaGMPQy0Bo+hnSVJk03kbYTKpfLTgiEF6X2qiMdFCebonlTjk/zrkpizXFxjKyNpPJHIhFMTEw4YBk9qJW6q16vO9DvoaEhJ7meTCadeiANQXDcfFZtwKphcKBzcI+OjjrlHolEwkKEPOg45lAoZPPBPBML4qkkelCrArLGyhuq0VA1P6dAEj2AOP/Uo2aziWAweMowojGQzWaN1rBQKKDVatmzMbyreXAFWVDHNMRL+cxnPmOHXDqdxt7enkOQPTg4aKQX/A3N3ehFwzIpDY1zjKRh09CVNrCmkaW6rBRxXu5Qzecpl+hZ4TP2OgW6oAnNmSlOQHuS9vf3OwQb7ElKA2xubs6p+WOu38uVCrjlAj6fz+lfyZzlWZ+jJBIJ2yNra2tO7082SOY8j46OOlSN3tIu5r4AWP209nnlM3E8pClUo2FwcNApkalUKqZv6XQajx8/tuL6Wq3mtBzTHOTc3BwuXbpkwLxCoYBGo2EXAOtmtTmEnq9q+CnPMYnMdf+1Wi0b487ODra3t52SIj0Tg8Gg8UsDcM60RqPhNGLg2cn19HIIc+2ogwx1Am5JFM8ZrVnXVA73A4FgR0dHWF5etnUiwbumLtRY7+/vNx26e/euY6SxbZvW+7Nv7ovk/zlnSetaLdNareYkkrUecHd319hcOCF6aEajUbPUfT4f4vG4KR2LtFUB1IpuNBqoVqsOkwtFc2zkDVXCdn2dPIba8UInjo2LNWdGRaAFx41cKpUcqxToFukCHVDIzs7Omc1c1chgNwIeZLyEeWgyz8Ea1L29PRweHtqmo5HCcR0eHlpDU7/fj2vXrlluLpfLoVgs2t/cRDwUCR7wWlt62PNzqgf0VIBujR7zF7FYzEFRc165JlozxU1OVhN2odeuFbo56SXT+PFGOfi+nZ0d7O7uOjV30WjU8hyXL1/GuXPnnBwK+SuBDnBBDxG1TOfm5rCxseEUy4fDYeeyVIOE0RfNq1Oy2awDglOwxdDQkGMwbm1t2eUCdAg5fD6fjYMISS9JAUVZoNh9hhd+qVTC3NzcqUvgrN6sAJxDTBHJ9CT1suzv77eD2ufzoVKpmNHCBgEcZywWMz356KOPkMvl8NWvfhVAB/Si0SeSW3BdTk5OjEwe6BiZ7J/Juc/n8yiVSg4AUFG/bKpO/Wo0GigWi05eUQ9d7gU+2+rqqs1VpVJBsVg8NT/ah5FofK4PhQ3ega6+qDes/WMzmYxzwf30pz/F48ePzci4cuUKTk5OLJKlz59KpRwjih1YdH29/S8HBwdPgY2AbpQAwKmLiehcvr9Wq+Hg4MB4t4FOJIGfI5E/dZ35YADGZUtdDQQC2NjYMD1gNEFrR4+Pj88cs1d6Ocue9KQnPelJT14gz/UslQOU/1Uos8/ns9u+3W47IcyDgwMcHh46/R41P6FsQESs8jcqlYoTKmW+lN5VrVZDPp8/M46vvdm8IQqGWdTKL5VK5tH29/cb/BnohIGy2ayNM5PJ4OOPPwbQsViuXbtmzDPsM0mGEaVoA7r99dQDo+hz9PX1YWJiwkIY/f39KBaLZlHX63VkMhknRHj37l3H86xWq06IjSUTg4ODGBsbszUZGRnBs2fPnLCsFxWp1q0ioSlkCNH8odYx5XI5h8ZraGgIhULBYQzJ5/MOOk1DjI8fP3ao+5QN6OjoCJVKxax3opeVrYTi8/msA8Xa2hpqtZqFxH7913/dwjNAp6wiHA7bM925cweVSsUp51Ge1mw2a2Ex1qVxTtVqBU6HoIiQ1s4OlN3dXQeBrPVzRH2TjenRo0dOhIHhbs3nq8fv9dAVNT07O4u1tTXzNugBKrvMwMCAg+ykjjWbTed5+/v7nTIH/hv/1n1QKBRw7949/OhHP7K5mZ+fN7SlRqp+8YtfoFwum4fAujzNb2pf1WAw6JTCnZyc4MKFCxgcHDQvjF6Jnj2KGmbdIed0fHzcaPe4dkQSc0x81lgshpmZGYeVRlmjms0mhoeHnXQFgDPDsMR4qOg5rc/U19eHeDxue5D18pyHtbU1rK+vW1SlUCiYDq2urlpEDOhEKzTVRIYjPqO364+mFLSPKd+jCHrtdlKtVpHL5ewcbjab+PGPf2zjYGtEzqVGQIvFIi5cuGA0h/F4HLdu3bLvSiQSTqlXJBJBrVb7xCiJynMvS31gNnfWMgotlWDjWeXo48EIdEOcPBRisZiFP1utFh48eGAPxHIBhZyzQBjo1uUoFJmicHouii4C38NJ1lqmkZERB8jRarWQy+Xs/QsLC6bEjx49wr179yxUUCqVMDk5aQcwcxBUdm9hrTcUoReEQuT12TimZrNpisL2UAwRHhwcIJvNmmJNTEzgc5/7HICOwcK6KqBzEChB9scff4xoNOqQgOuG9/Y3pLTbbWctgW5yn4AiHqDr6+tW78kxPH361CmVocGxuLiISCRiOcxarYZUKuXUQ2o5DoE1eiDpnHJMLAjn+/r7+7G+vm6H4EcffeTUEf7bv/0bHjx4YCABQt8pBwcH9t0kP1d6wuHhYQt1c/142JEsXnPW+r0aXuvv73Iz9/X1IZfLmaHHOWC4+8mTJ9YHlt9bqVSsxpENdLlW6XTauXhu3rxpIdx0Oo1UKmXzRdAX92u73TYqwUajYfOu5wPQ7UvrzctSMpkMarWaAWTYK5VSqVRMN7761a86YUxy51IPSVrC38rlcna463xp70PmsZWEg5cY0C030ebUenmy0YTS4alzoPPB84x6w1ppzR2SCpJ/U46OjhwjQ/s1MgeuRpYaQp///OcNuMS1zeVyphc7Ozt2dqyurqJareLu3bsAOoQhbK7A3/ICtrRPqoaOWdYFdM5dBZi1Wi2Uy2WHO/bZs2c2xuXlZcuj8/mLxaLNXTwex8svvwwAeP3117GysmLncDKZxN7enu2TRCJh7e6Abm2oluJ8kvTCsD3pSU960pOevECe61kqiweTz7Q2aa1oa6bDw0Ojf5ucnITf73fCRExqAx3L7sGDB/b/2WzWLDJavdpGhvRPAMwKoUek1o031Et4t4oidNmuCegi5mhdzMzMON0Z2DGCz6f0WrFYDH6/3yxDlhPwGdg25qzSEW8Ytr+/37qI7O/vWxIa6HhX2tKq3W471G1bW1vWdofCUJSyzgAdr2d7e9vWbHZ2Ful02kH4apkHn1UBAwQjKRG4MsUUCgUkEgkL7WSzWTx9+tQ84XK57ITu4vG4w9AzOjpqv7u9vW1ADY5DC5Lp8SkohqJk9URicy329vYcWrbd3V34fD6LMJRKJdy9e9d0n0AjLZrma5OTk3jllVcc71e7yzNiola2hra0lIDIZv67sqDU63Xs7+/b2hChqu3nMpmMrVMikcDs7KzT/oy/tba2hmfPnuE3f/M3AXTZcdiN57333kM2m3W66hwfH1tob3t7G3/8x38MAA4tJdHL3uiINhNQoFB/fz/i8Ti++MUvAujoAtMOnDt2MVpaWsLx8bGFiufm5jA2NuZQ6XFvAN09ptSWXB8vQFDZbFS/mHri8xGEqAxDSl7vTQloZIllZxxPqVRyqgd45nGNvGUSGhrWlBYBVN50GYUhWI5jeXkZwWDQAD83b940LzORSODNN9+0z1YqFae9HkP5Wr6kZWLqWRLdyzEODQ05UUot2WMJGNMic3NzmJ2dxZUrVwB09oWGsNvtto2fnV20Bdvq6qrpaiaTcUqqWF6o3/VJ8tzLUtnomWfhYrLPmx4ES0tLxpLCzy8uLgLouMO1Ws3yRrw8+UDqOkejUdTrdQvh0t3nIjEEe1bXc6VDA7rMHhSGQ4HOYZxKpSycS/QeFY/foyhcKl4kEkGpVHJq8TSUQNg5n4G9/ZRJg8KwBNAtk+Fvz8/PO3lEhux4SLDuiYrm9/sdlGM4HP7Eg6JUKqHRaODJkycAOjyzOs9EAupFzTF4aQ0VmcY+eEBnc4bDYRvTxYsXMTY25uT/QqGQ0VWxOwvQzfnwomVYnDpJCjplkdLDTw+Jp0+fOmun8qtf/QonJyemTxsbG85nz507Z+2KADekxHljiJbIYD4Pw6WcD25Sb3mT90IBuvVkfF9/f79dyjTU+DqNV80Fa6kEQ638/f39fccomp+ft3nms/Gy3N/fRy6Xcy5m5RNV1GKtVrP5r9VqTkcWhiGVLUVLk1566SUUi0WnBlAZdZLJpF3Y09PTCAQC1te1v7/fWZfHjx875xMNMs2X8nPc/8y/KTWa5iwBN9fLz9BAYVhW26gpr7SiNlnHqmVtrVbLxsdyCk2DUTT/x7OM6+4t9SL6Ws8bLUFi/k47eHA+8vk8Zmdn7dKiw6T17l6KxWq1apeljlnPiGKxeKrMUEPkh4eHTos4jpP6GQgErLsI4HZZ4bi47jx/+Azlchm1Ws05AxSh/b+us1R+PloG/DLmLJRmaGxs7NTGoIfD5DU3wvT0tAPX1UHyQqNCkN5OPUI2CAVcz0wtO4JqeMjzMtPcjNIu0Xv1UkcpByjHcHJy4hQaN5tNFItFuwRoFTLftr+/77S/0pxlvV53IP0Kpkgmk05+t7+/3wHlMI/Fg551obpO3KD8bf734OAAhULBPntwcGDWPIXlGZxPPpuCfvSQZs5C2yqp5zk4OIi5uTm7XJT4mt+nc0GaNj7r5uam0xqLHhXXROvmVAqFghMh0HVNp9OOB8F6Ol7CCwsLTrNa7ZXHsSvXbSKRcFoq6SFJAIla53pQeEtyKO12G3t7e44hoDVuzGnqQakHP9ClxAM6a3nx4kUAHYMsmUzaGnB+6VlfuHDBaWyeSqUwPDxsBoBGdpSIu9lsYnR01OaRlGdat6feciAQwMLCgv1Nnlb1PL1Ni/W7lUM5k8k4TX6pK6rbABwC/Wg0iuHhYVsbXtzaa3R/f9/xgFgvyc/HYjHH0+QhPT8/j7GxMZsznmk6fqC7D8LhMAYHB89sQ8i15L+zxAtwy0o4p14AmV5U3vpdBbYNDAxgYWHB9gvpBBUTwvI8ANa4m8aDtsjTSAyfTc98L22lGvzM0WrrwL6+Pod/WT1pBdDV63WnKQeJZ5Rukv+u/z1LejnLnvSkJz3pSU9eIM/1LJUWiTeu3uCKjHr69Cn29vYsDDs+Pu4UnzM/QU9TKcK8lE2NRsOhraPVQauw3W47LWvUstXQB39LLSOg69UR9aX5o0aj4SDP+vr6zPNST1tZ+/k8JycnTmiZ9HL8bXZrANx4Pkse+H3qMUQiESdsWa/XkU6nHWalg4MDm49KpYJcLueEkPleIve0MFtLL9gCifPGnLXX+1PPl3SA/D2yJCmDjc45SzYYYmG4XZmdlPIrGAw6xBjarJuhKvXG+DnAtRJLpZKFZhgh4VqSMF49K0VSspWU1xrV8BxzyCTvUL1IJBJOqQjXmWPVULeK5lmZ02F+HegyLPF7VPe1aS/fC8Dy04uLi7hw4QKATp5VO8UQscvvWlpaQqVSsfKCdDqNyclJWyftMq9zxHnQnHI4HHby6UqfxoJxbVysnh7TBkAXkUvvIhAIIBAI2JovLS3h/v37jsemRf8a8tP3aMsrRpIYTsxkMlhfX3eYadQ7ajabKJVKDgKZczYxMYELFy6Yp6nPCHSRs166O84VaTP5mpe9RsPL2mnoyZMnuHXrloXv+/r6MDMzY/svFos5rGZaokcWMj7j8fGxQ4xPogHuT0axuM+8rD383FndopiHBDrh30Qi4ZTPKOMbI0g6h9QL7mV+lvgDpkWmpqac0jZ6vHxG71mi8kKADyedD6v5PgWSlEol3Llzx1xvhhY1R6Mbw9sRQVlqCGzgA5C/0Nu54iyXWS9OHor8t4GBAZTLZcvR1Wo155lIraYHuZeyT7saaLjx+vXriMVizncpCILP+EkdCyjMW+hhOjc3ZyEwAp+46XO5nIU6gW6LMu2EruUCExMTdrF6yw9Y0qA1tbqBtc2WzsnR0ZEd4qOjow7rUaVSwe7urrXrqVariEajTp5YKeq8pQakWgM6Btjs7KxTnqSUbppfB9yDTPM2DLFq/a2WvwwPD+NrX/ua5Uhu3bplvTU5RgU28Lk4xuHhYcf40S4t1Cc1NvSZvWAvzSuGw2GnVEsPTeb19aBXw2N4eBizs7PWJ3VkZMTpOpLL5ZxD7+joyDGUV1ZW7Bnz+byzT3TPNRoNJwyp+SDNr3qfD+jql9Yaavi8r6/P9o+XypIhVzXS4vG4rSkNOG/+XikemVfkBTw8PGz5WqBLxUjDdXp6GhMTE/a8yWTSSQso69OdO3ewublpAKXz58873S6Yf9cuMmpQE/AH4JS+MATNeRkcHDSg07//+7/j8ePHeOWVVwB00mWxWMzKQUgbx0s8lUqdMtzUYFNOVp7Z6gDs7e3ZfClPr84ze/vq+a/lUzT0tHZbdYUXNPfz8fGx5c29nMi8ozhnly9fxsDAgIMt0ZpPxbt45bmXpSLmmM9Ta1sbg05PT2NqaspQRcFgENPT05b3CAQCVk8DwKltqVar2NzcxFtvvQWgkzu7evUqzp8/D6Cj+GyqC3RySPF43C4uPRQVoEIidLW22fMQ6Fy6Cj7x+/2YmZkxhebhwc8XCgWn5u/BgwcOn+jnP/95s9yDwaDlTfj34eGhHbiqSJqUJ3hGD5TZ2VlbxGq1ark8ALh27RqmpqbsIGIuyUuPBXST7LpB9UDd3993DmvS/WkeheLNW+h7Tk5O7Lk3NzexsbFhZA4DA52G2bR0G42Gk2ur1Wo2RycnJ7h37555LnNzczh//rz9PTIy4nDDegkNdA76+vqcPosL/9MAGuha9wqSIngB6HIPU3e3tracg1l7XRLFp0AbFfUCdC7P8iwBmBFCY5GGjRJTcAyjo6NOfl3RlLys+UyxWMw5fBSRvLu7i1/+8pemNxcuXMDVq1cNrEejVxG/KmqdK2DOm0PlZ9WgVvAKCe8VnKJRor6+PkNBKrCQor+l+TKgewFUKhWnZRgARy9CoZDT2LxWqxlegFEPvSw0N6e9dWkAa/2x5jQBt5aSxji/m54Rv0svVY2GHR8fo1gs4qc//SkA4P3338elS5fwZ3/2ZwA6Rn1fXx9++ctfAgBu376N999/H/fv3wfQ2a8kmBgeHrb9qeNTwKPmLEulkkPa7nUIlBBDOZ75ef4OGwgoOJIk90AHvLW1tWWve3OQ5M8GOo5WIpGwNTw+PsbExITNa7VadYgonndZ9nKWPelJT3rSk568QF7oWVK8DBSAG345OTlBJBJx6ILa7baTF5ydnXUQTLS6aNUw77O4uIhr1645eTgNeTK+782lAa6XyfHTUg0Gg7h8+bKNodFoIBQKOTmUqakps1Lr9Tqi0aiTpyWc/ujoCAsLC/YMY2NjmJmZsZBnrVbD3bt37fVYLObkM1SUrJn/r+G1iYkJ87Lfe+89hwQ7Fouhr6/PCctqZwT1GPx+v5FBc96UOovv004XWsOltGWax1Bk7/j4uAP3j0ajWFxcNL2o1WoOVVW5XMbR0ZGDeNTxKRrx8PAQmUzGkG3MMXF9qtUqDg8P7Xm0rIlIRgDGqMMQGUNnHHO9XsedO3eMZo25U3oBd+7ccTo/AF2vhE2jOUaGnLzk4Zw/RhG87d0AOETgh4eH2NzcdDyira0te/3y5ctIpVI2V5xH6vbExASmp6dtHEtLS45nODg4aLqbz+fx6NEjS1dUq1Xs7OwYepaobq6penDsYkHR/BHzjDxDmBvmMzFNQY8knU4b2xLQRaDzvfV6HR9++CGATmTr8uXL5okzyuBl0PHWOa+trdlYeb5xLYhs1abgWsrEz+h6KbtOIBBwmqR761wVic9SH+oFW38xnLq+vm5ngDfn5z3zdnd3bV62trawvLxs9e4TExOIx+PGuPTaa6/h6dOn9v6PP/7Y2LRYmsSzxluWwpSQesvpdNoiMBo904gK0apataD/DYfDODw8tCjlw4cP8eTJE4uqeM9RxZWQdYzNFxYWFtBut+18TKfTTlSFjGZcMy1X8coLuWFVdLKYC1RQwFkHryr37u6uhd/0gC2XywiHw0bLxm7jfADCszmZ+XzeCSd6qaC0rMHLY6r8jA8ePMD+/r4tQjAYRDKZtM9Ho1GnJ50WOTMURSVMJpMYHx83xTo4OMDa2ppTRK799jTcyfAT0O10wE3Ny+rGjRsAOoZFqVQ6lQv25jq05ZMmxiuVyimKMKVa0zVl6zTOl4JWdF739/ctNzs1NYVQKGSHxMzMDMLhsF0eg4ODDviE+U/NGWiOmfOuz6p1nwMDAzanDFspbRbl/v37NicrKysWruJzKS0iQVM8JIeHh41qD+jQgL311lsOPF1bHuVyORvjzMyMY4xpPouitXt6GB0dHVn+JxaLWX0Z0DEsk8mkwwl8dHRk+nf+/HlEo1GrS1xeXsbMzIz9figUcnKI3mf4xje+4ehFqVRywq1aLqR9CxUoRbCMlgR4axQJ5Qc6YdfHjx9b/ol7XmtNlcMzFArZ/Ozt7eGDDz6w3Nz8/LyBDAFYmYmGEIHOZcn3cE4UjKUt+mg0qV7EYjGbc4KIuIbsywh0zpJ4PG6vFQoFJyWk88q52dzcNJ7cvb09fOtb37I5ptAg4BydnJxgdnbWwtPDw8P47Gc/a2v3+PFjVKtVuyyi0Sj29/cNtHT+/Hkb0/r6Ovx+vz0v8/zK/aoAqFwuZwYr4KZBdNzMseqFNTQ0ZL9DHmemvBqNBqampizFxfyzXuJaJzo6Oup0GWk0Grh37x6AzmWpJCkEyXFd6AydJc+9LFkwDrg5Dj6Ql2tRDwDmJDkBBGrwwNFan2azeaoly8TEhG1CHvBKWhAIBE7FujmxurkUoMJYOa2Q1dVVxONxA89ks1ncvXvXvnd5edl6HQKdRaHyV6tVfPzxx3Z4zc/PIxKJ2AHMPne8aDY2NrC7u2sH+ycVv7Jdk6Lijo+PHbJ3RZbpRckxjoyM2IWoraToqelm1t6ZPMz04B4eHj5FhO0FjhwfHxvZRDgcxvz8vMOXqTkFWt4KytGDt91uO823WZsHdAA9wWDQvovE19zcLErn31rnVS6X7QCZmZlxiuxpIetlWSwWzbAbGRlxcqm8dLiZFWhClieOmahRZYzhZyheggcKPWmgk3OMRCLO5Ug+TD47IzRAx3C4evWqecPEHPC3lGS80Wg48zE2NuagL5UUn+OlAaGv8zXNUQJdw9nLi1ytVpHP582DffDgAfx+v/324uKiE9nx+/12MIdCIQdl3Gg0cPfuXZuDYrGI69ev2/OTX9rbECKTyZj3FggEMDo66oCvIpGIjZ81iUq8XiqVzJvlWaf4A0W7atQtHA47SHSg2+8T6Ojgxx9/bNEPbUZALAbn3su0FgqFjAWJnNX8ba6VGgtvv/22nb2jo6O2l9kggIbu3NycY9wcHR05jSiy2azTeEH1GujqPi9LdXICgYDNY7VatXEDHT5bNQh4wfFZ2MQCgLV0UwchkUjY3r937x7S6bTpAVHUPKefR6Tey1n2pCc96UlPevICea5nWbuYcwAAIABJREFUube353QaAOCEHNTDALp5AQBWDkCLaG9vz+nOrjBu1uYw1s2cIS24ZrPpxPuJLNXaPIrmRDT3B8AJK/F7EomEofwYK6flS2ubf7PuiK/du3fP3HY2t6Y3sra25oyRNEu0przz5i1zoOXj9/tx//59fPvb3wYACyF468U0DKNzoHk1oiO5BqxT1FyCtmUj7NrLW6p1XPw8Lf6HDx9iYGDAIgjM29AaLRQKDtet5icBl4GJVGPMpZ3lHWldF79Py5dUvJ0/tDGvWsQAHNouvod6kEql8PWvf91yKpwToGNd1+t102F22fAy61Dq9bqDHvWymqh3q22MvFET5oE17L+5uWmYgYcPHyKVSjldfzTcHo1GTyFFOU6G5/V1LeXSlIoy8rBshGvrzbXt7u5ifX3d1vP8+fOYmZkxD0NpDIGOx0tvg+UtDMtOT0/jypUrNpbd3V2HzYnfoVEWoLMvmUrhHGvJTC6Xs/PF2/KM3LyKitauNxMTE5Y7I5+yprFarZbTSqu/v9/+3tzcxIMHD2xfKUpTo3Isd9HzpNlsWshyYmICT58+dTwvRSCTRo5/KwNPvV7H8vIyXnvtNQAdvVcWJDIYMQLIqJiXKYyvUbR+ns9ABDrQxagwhcAUg3LQNhoN2/fKyqaRKaDjKQ8ODtp87OzsYHNz02GJSqVSFmX5h3/4B3zzm9/EWfLcy3J0dNQOhFqthsnJSSeX5k1uA91F5aGnEF1vSyEFsQwMDFgcORaLWY4P6ChxNBq1g4MJXo3vU3jhAt1DkRPJcIX2mNTu8aR646FweHiI3d1dh4hc+Q0PDw+dtjHNZtNCug8fPkQgELDXK5XKKU5RireuUsMXDx8+xPe+9z288847AIAvfOELmJmZsRAgw5hci3K57OS2NETj9/udmiOug5IyaG6KdGOaT+W8quj85/N5PH782EnWx+Nx+9vbPou/zXn11kgxdAx0w4X8TKPRQCaTsc3KWi7qnB4gJFzgczx79szRZS074eVPfWMfUNWb1dVVGxe5LjlnOlfMh3rLRbzguLO4KTWPenBw4JRHkLRDa0u9e4it7oBOGDcajdrcJhIJO/i1Nph/6/jYrozvb7VaqFQqptsaImRrLH6PhhapezRCBgcHceXKFbtQIpGIGXCcZzWO1Jih3nNvh8NhB+y1sLCAg4MD+61gMOgQofC/2mpsZ2fHWt5RlJSDRBYKUmw0Ghb2J1GLhvS4HgTTKe/sWSVD3Nesy+RlqeFBFulzTMqHzEuXc+bz+XDu3DkzbM6dO4ehoSHTbdJwUs9CoRC+8IUvAOiSkGt4tFKp2BnLNlna3krBahpirlartlY08tTgbrfbto+3trYQDAZtHpnb5us8e/X7td3c0NCQQyRTr9cthTc/P4+dnR0D+7FXMtfw5z//+f/usmSsGuhYak+fPj2FlFRRUmuCPDiRRClSObSnH4tCldCYiwl0ckTqbTFmfRafZrVadbwpL4rWy1RCa5B/9/X12cQxH6ceBsdYKpUcD65arWJ/f99qRcvlMpaXl51+l8pKpBtfm/r29/fj6OjIPvfjH/8Yt27dMg8hn88jHA6bojUaDQf4MDY25uQd1aDhvHMDEpHG56Xia1NWBQCd1XPRW8vabDaxsbFherK4uIixsTE7NJLJpHMg6nMD3QOSz6Z5u52dHafTCz1LPZzb7bYDRKLMzMxg4X96IX744YfY3d11gFvKENLf349arebkVbV2K5vNIhqNml689957Dr+o5rn6+/sdPk0viIrzpxc1Rf8/n88bCQPQBVfoM56cnFgNYDabxdjYmNWXRSIRJ4qg/QDpDSoAQ5lcSqWSU7/MnC71hp4Zv0sJJrS2knuRhy+BXgoIUk5QRjl4OBMUA3T0ZHx83DHCIpGIzXM4HEa9Xrf5YMcML6tXX1+fXfrb29uIxWJ2SDNapB0shoaGzOgKh8PI5/NOZ5hEImHn5uTkpMNr/EksTZzTXC5nqNQ7d+6gUCiYjirXcD6ftzF4oxZ8Nq5Nf38/vvSlL5kRX61WMTIy4nQWWVhYMABMqVSys4RRG2XMKhQKFrmhB+iNjHC+1MNTbAWNBGVD0zrKg4MDx9sFuuA+oKPbajB4CUL4nUAnAqhdSNg8g3nZZDKJ0dFRfP7znwcAfOlLXzq1RpRezrInPelJT3rSkxfIC7uOaKx/f3/f0Fns1UaLmrBrWkLhcNixsJvNppMP0xg0UZi0RLLZLMrlsllo7Pig7P6BQODMcJt6r7RyaWXRMtcQRrlcNmuW9YO0rDKZjFOm0Ww2zapi/SOt5Hw+j3fffddqlNjPk16ccprytyjeNkaPHz82Bo533nnHKblhWJzvJ3JM6/a8pQpaP6ilFfR0lXFDkWG0hp/Hl0g90PxDX1+fhZNY10mvjmFd9aS0VEa9sFqt5nQ5z2QyKJfL5mkQUeelyKMOao7s0qVLjiekdHeVSuWUB61hov7+fgfZzdZrZDq5ffv2qRysroeuO3P1iihUz9JbUsK/G42GwwHMfLjuJ5VGo2E8wUBHP58+feqEu7l36dFpTrJarZqeaH0i56tarZrHz/UB3CiPlxaQOUY+K88MDaFpGoDevdKacW8yn0iPoVKpIJ/PO2uv+cdSqXTKw+D7FOG+vb1t60yWMHo8RGNzf6VSKSfMzDCwUk/qWaO0dKrnQMeLW1tbw507d2xOBwYGbC+qZ7m/v29/t1otBzvC71V0+fvvv29zXC6Xsbe35+SRBwYGDHuxubmJ999/3/5/YmLC2qDl83ns7+87uULtO6yIXMBlr9KyQpYDaYnO8fGxfW+5XMbIyIhTm6z6zXNc94pGw4iU5nwwXwzAWn9pzWaj0bCaam/7PhXf81qS9KQnPelJT3rSkxd4lt/97nfbmoMEuta63+93vBRvMp8MO2pdqKXv7dunHQGq1arj0RB8odZgu912iNf//M//3AcAN2/ebGseY3h42NCuc3NzaLVa9j0TExMOknNnZwf37t2zZPD09LRDMry0tOR0Pvnwww+xvr5uY1hZWTFvuFAoIJ1OW86IHIWcn0qlgm984xs+APja177W5hhpbWtuUwEX7AOnxdlaVEuLWq0uze1qTo+5Ui+JMN9fKpWsoSvQ8ez+4i/+wre3t9dWRK1XvIXnlUrFQQEXCgWz7MrlshV7A3ByyuSe9IJfNEepHK6VSgX7+/uOBfuXf/mXPgB4/fXX20rwrf30OGfq3bPjBMfI7u1Ax2PS3JRGYPj8ytySyWTM0yZaVkEQukf+pxm3DwAuXrzYZj7wxo0b+NM//VOzmJVUnWun+T8WqyuJhnq06s1rv0aOn0hW/vvg4KA9E1HTBPY0m01cvXrVBwDf+c532pqPZuE+59nn8znE5Trv/H/tqKEev+bGSSCuACcl62YuWAvka7WarcPf//3f4wc/+IHvm9/8Zpv5OgB44403jHuYgBUFwLXbbTsLg8EgIpGI5bLZuNxbDwh0UeNKsK971O/3o1qtWsOBN998E48ePbLzIxqN4q/+6q98APDDH/6wrWuj+sN15b9FIhEjeOccM+rAeSH+guPgeml3DgBOgwn+lq4JAZ1KuP+tb33LBwBTU1Nt5lnHx8fx2muvOTlINrHgXLFZOdDpaHV0dGRk8levXsXKyoqdy1evXjXdrFQqp7oYhUIhA/RwryuYDejmV8vlMl5//fUzi+B7Ocue9KQnPelJT14gz/UslRmeHUjUkiuVSg4SDHCZ5ZmnBNxcDNDliQQ6t7nmNjRfwvf6/X67/Rnz1t9S0frN4+Njy/P19/djYmLCkGREe9LjINsNX0+lUsZdymdmznZtbQ3FYtEsv/HxcczMzJgFF41GMTo66pQ9lMtl86DUK1hZWXHaZjWbTfO8iP5UhpjBwUHzflOpFMLhsH0v6eK0vszLkatWpubT2IFFrdZPQpppjaJaavw3XQttk8ZSFa5lsVh0vH39Lv4211fz0UC3Dpdz4UVgKxpPvXWK1nsRWQvA2g7xGavVqnF5cp60t6l6lmwLx7yX3+9HsVi0cRcKBafcpVarOTRwim6dnJw01B4RuXydc0PdZQcgRRBqpMfv96PZbJ5i1+F6qRfEfa6etCLM6Wmydo1WO8epLEVat0zWFS0zU0+Snqd6mt69rePW17zt5cgapZ6Rz+ezXB9z6IrMJjKeOfwnT56gVCqdQrBrnpj9SoFOLm1ubs4iWalUyilVOat8SCN19C6Brk5p9QBF14Lj1n7Dg4ODTru54+NjJ3e9vr7u9HNcWFhw0LHa8UirBcgJ7BU9VxQNrXnWq1evOqVKOzs7NjcPHjzA4eGhRWpSqRSmpqaMyenmzZtOdQTr2ak34+PjNk/5fB7pdNqiHszvsjUdPX9FwlcqFVtTxb945bmXJSeAD6ibutlsolqtOqTB2oKLkGuCKsj9p7WVWgKgYVcWt2qzUD2gWfOlZN6UVqvltNqJx+P2+sOHD/H48WMb42c/+1kHcDA1NYV79+7ZIs/OzuLhw4dOiy5VpGQyae5/Pp/Hs2fPbBFYJ6TkADxIAbfIWBv6sgWQ8m3W63UrHWHIhBtmcXERs7OzNmaGnr1AEaBbvqOw6kwm4xg0IyMjVveWTCYdA0nDtFq7edbBy7XyHjCFQgH7+/tOIXO1WnVKXfhehoj43mw269CytVotjI6OOpssGAzaeLztsSi8wPVgzeVyphflctkh7Y7FYkj+X/be5DfS87oaPywWWfPEYhWL1ZzZ86R5siRbgZHISezEQGwvguyyN5BFdgGyzD8RBPEmCRLHMGAHDpxEgW3Jn2WpZXW31OyB8zwVay5WFcnib1HfuTzP21QL+H7begBBTbLqfZ/xPnc499zhYQNb8FJm3mMymXwKaKMhBwVceNMjCALjOVHFJJFI2NoQPKIuceBs75ycnDikBN7akRyLprBonprmPXtDKNFo1DlXmkYCuDmAKkw7nQ7q9brjrq1Wq06dWiVDyGaz5spkPxRoxWdyHMz3BLrnT92JpCskaGRwcBBXrlyxd1EQa5pVNptFOBy2eaESqjKr3W7b5U/QFRXbo6MjjIyM4M033wTQFfKa7qbFuUm6rgUE/H6/KVkkSFFgClupVLK9SLepgqj4DKB7Bki9yXkqlUp2oZPgnBcZi0FzfAqOAeAUf/YWqteUKK6frhvnvNFoYHJy0r63vr6OYrFoaxmJRBxDLB6PI5VK2T4iWFLXjf8ulUooFosmLx8+fIiHDx9aofN33nkHt2/fNtc5jSWVt1/UvrTqiMYz6vW6w8fXaDScvKVWq2U+d+ZtUQgkk0mrdQh0N7OSipORAzjT7FX70VxEaqheJggOntbejRs3kMvl7D3c2NSEWTBWBdnY2JhxVV64cAHDw8O2oZvNpkNA3Gw2bbyFQgFbW1tOvG10dNQqBfBi9cZ+de7Yp+PjY4cpaWRkxJCXOzs72N3ddSwzkgVz7hRdy7Vjn1hzDoAl53Kew+Gws/Hq9TqGh4ft8CkJhF4OemF6NTRailptvlgsOu/0xlH52Vqths8//9z4PmmFcWys38ic1LW1NUxOTjrFrr1zwPlWdN729jYODg6cmoe5XM4u4VQqZSg79lljO5qjSXQn14cClnsol8sZ1yxwVo+Q50Rzepkzy3nc29tzYisArC5hu93G9evXjUj8woULiEajTnFkKjJ8r1a80CLnwWDQYoJA9+zqJUbPhe6r8+a5Wq06BPaTk5OG3mRjTJv/3t7eNsFGxhgleNBLYXd319DnpVLJIX9grVp+fnV11XiL+XcADvr1ypUrzkWTz+cdJYGWnrJRFYtFO0/7+/s4PDw0K4iFldkUZ0H5x5+p2Gu+rp4LbQ8ePLB19iqrlKOc893dXXQ6HYeFaHh42LwCXGv18imaNxAI2LMajcZT7D+dTsfuAJ51ZfpiU8T0Sy+95GRKjI+PmxwFugqgnhFefFr4W8lVtBBBsVh0OKHT6TSOjo7w4x//GED3vHz729+2PZZOp5HJZJ5ilzqv9WKWvdZrvdZrvdZrX9KeaVlqjl6r1cLm5qZpgdVqFbVazbEg6JoFYJXU1a8cDocdejhaeIzJKecg40QAjG9R3TPK4q+aKkvhAF3NplAoOPmbzK0EgPfffx83b940bfP4+BgTExNm2TQaDQdJpu7Fk5MTlEols3oODw9NuwW6WubBwYG5MC5duvQUB6wtgliGZDJSyjciG7kmGrtrNpuONaIVPNgU+VYoFCyG22w2EQ6HDf03MjKCeDzuoNJY/R04c7epO571CJVNQ62S3d1dbG5uWt3AnZ0dRKNR09aHhoaQzWYdxhui4O7du4fPPvvM9iDL9ND1GI/HcXJyYrlpc3Nz2N3dtWoc6u5USjBaB3TR7e/vw+fzmbZNpB1ddurRAGDl47zoXH5GvSLMheSzvGXH+vr6LAYKwLwiQPfM8Pd9fX3Y3Ny0tW42m4jH42bVLCwsYG5uDp9//jkA4M0333Qo+WKxmJPbvLe3h8ePH9v4q9WqWejRaBTpdNrmb3R0FOPj4zY/mUzGie1tb2+b617PyOLiIo6Pj3H79m0AZ5Yb9y5DNdyPe3t7DtqU7kGtJ0tvQLlcxurqqlOZSFmWstms0agB3XDF1taWU4YM6FoWjF/yu+pNU8rBvr4+RCIR+1w0GkU0GrWYeblcdsqkVatVR2b5/X7r7+PHjzE3N2cWF1H3zGlMJpOYmpqyPaZhlY8//tjWdWJiwuFG5d7WvOVms+mEj7jmOkb2kww/bOqN0Odw/bxlG9XKVY/fCy+8YH2mt4p9Xl5ednL4j46OkM1mnVrIygKWTCYxMDBg3rbLly87+ezFYtFQz+SYpdt6c3MT//7v/27nLJ1OY3p62pC28Xgcr732Gs5rXwrw4eZaXFzEwsKC48JUyjq6DznpwWAQ6XTa4jpKxQXAIRHe3NzEwcGBuS/Io6rgIpI9A7CgOol2tRirJqlXq1Wsra2ZWV6v151FKpfLmJ+ft8OdTqcxNDRkRW4Zm9NSTrpxFhcXTQi++eab+Oyzz8yvPjAw4Lilk8kk8vm8A6Nn88LI6/W6XRhMtubcMLbBw95sNo3yDuge4Hg8fi6QiL58riEPtsZVNSWBaRted5uX61TnnIeV77h37x7u37/v0P6l02mDocdiMUxMTNgh1rjTp59+imaziTfeeAPAWUySc0j3NYXV4uIiVldXbexMNPY2lhniWo6NjWFiYsIO0PDwMHw+n10GXhJuvp+/1/gz+0ShSGJqDTG0Wi0npqgpENzjgFsijzEuKgr7+/u4e/euw0taq9VMKWk2m1hbW7MivxMTE2g2m7h3756tCy/L+fl5q/XKPoyMjNi7jo6O8OTJEwMbMSbOvR4MBo1PVBXsaDSKa9euWcJ7JBJBq9UyIXhwcOCk8ACuG75cLuP09NTOmALDVOnmz8Q6cB0SiYRdfKlUynkPZcBLL73kzLE+m0UUNHVlb2/P9le1WsX6+rrNE1N3lJKQ/aPbnwrazs6OUwCA7lTlYY7H404BZbZarWa0eNwTlLPBYNDBezBOrGEalRdMf9H9yH3vLUTgLW9G4JcaK14gJ9vQ0JB9jmWxeHlevnwZb7zxho3p8ePH2N7edogT+vv77dxFIhHU63UnZUVLbqlyXq/XEQ6HzSC6deuW0TUC3Xvr8ePHFnobGBjAd77znXPH8MzLMplMmtBeWlrCRx99ZFrg8fExAoGALcL+/r79jgM4PT01K296ehqTk5MmwAje0A5rLkwikbDJICiAwol5Quf5mVWb2d7exuPHj20DUutnn7lBlXxdY1VkldAcQc2TPDk5sc9S+PJgs8I6A+mXLl3CyMjIuXmJKmBKpRLW19dN4yQymJrgwMAArly5Ytbg+vr6U3VF+/v7ndiHXibr6+uO1VOv1+1ArqysIJlMOgjfeDxuP2tsQi9LtaSCwaDDP1uv19Hf3+8gVhXxOjAwgHg87ljDivbT3yuiDwA+/PBDlEolpxgv2Xa4JmxahZ6E2lSy8vk88vm8M4eNRsOJO2prtVpOkWrV5Dk2fpeFy1WzVwQovQpeFhQA+OlPf+r8rDHYX/3qV7h3754ht8lsxBj5wcEB7t+/b4ofzyff87vf/c7WiKhHWljT09O4ffu2KQEsKPDee+/Zs27dumXPpuUIuFb2+Pg4RkdHTTB/9tlnePjwoeUmr6+v47nnnsNXvvIVAN39poAR1glVMJk3Jk4l8pNPPsHnn39ufbp16xYGBgbM4h0fH0ckEnFi5UBXxikCmXgJ4OziUOL3Uqlk56BcLhuSH+juMQUess9Ad9+fnJxYf4+OjnDhwgVTjkZGRhwl0u/3O8w7Gv/LZrMmK2u1GtLptFmDs7OzDrCQlyPXut1uO3IzFos51rGCkMjXqoqJFp5IJBIOQBI4Y+TifOl7KCt5NygH73e/+138/u//PoCu3B4YGLDPV6tVPHnyxM755OQkarWazdXm5qbNK2Oo/NvOzg6SyaQpgslk0uEPHx4edgpRe7m0tfVilr3Wa73Wa73Wa1/SnmlZBgIB0zhu3ryJQqFg5uzVq1cRi8XsRt/Y2MD6+rq5hSqVigP/9fl8uHnzJl555RUAXYQTTV/GLxVtSCQW0NVKUqmUWQJ0h2icj415bcAZj+o777xjz11dXbX3dDodNJtNx91E9hI2ukMAOBpLJpPBjRs3nBjQrVu3TBtKJpNO1QFWTaeW7XVxaGw3GAzae2iV0CKgtU6XBPP/1GWhrCPq5qIFQa2q3W5jbW3N5jGfz+P4+NjWpVqtYnp6+qn6gnwvx0V+U6BrDaysrJj1sLe3h4ODA7MGGYfisxi/VAuKFs3U1BQ2NzdNsz09PUUikbA5TqfTWFpasr9Tw6bVrbEX4MxFRDeQ1+1KDwMr2CsTlKZF0ZI+L6WBpaLUSvXWMVR2HMZY6DnQmNB3vvMdfPbZZwC6cdSlpSUn7PH222/b2vzHf/wHBgYGbN8kk0ns7e2Z2/XKlSt48uSJzW0kErFz4o01/du//Rv+4R/+wazUyclJhMNhc6d+4xvfQCqVMgYVRZDSuuJc9PX1mfv2wYMHiEQiFlOuVqvIZrMWp2s0Gk5FnUaj4Zxt4OycMJapKOO1tTULzczMzODx48fmjbhw4QIikYhTLoz9VXyAVjM6Pj52Qhp0deoaaNWSbDbroDSVJzYWizn1bPkuzT1U2dPX14eDgwMnbY3t9u3b9pz9/X1sbGyYHN7a2sKlS5fsDGj4Cuh6ZdSSopdE2auUDUdz3uv1OoaGhpy8VC1ZxsY1URnHylK6hpxHljLTOLJapXQja5yWawN05T89cfPz84aSB87c35zXRCLh8GHTQ8jv0xN4XnvmZTk2NmaLMjIygldffdUh3W61WiaQ6L7joWcMgQLpq1/9Kq5cueIUXaWLhPEFdd35fD7bSIwzahxOD7cKrUAg4ExUKBSyi4MkzfxuLBZzKMK4YbiIjD94a3QCXQGi1E4k5qa7RIPmgJvPxTHrv/lZ1iGkANrY2HDK2fAi5OFhnhrnhvlySpbOcTFupQQH165ds/FGIhGMjo5avI2uO29ZI29R19PTU5vDu3fv4re//a1djmtra7hz547N28zMDIaGhkzQplIpB46vyf+3bt3C6OioQ1iQy+Vsrghi4foODw9jcHDQyTnVxj4mEgknP/Pk5AR7e3u2/5j7ybVlHVDNgdOi4BrXAs7qpPLfdEVzDFqrb2NjwyGL1z3zjW98wy6/y5cv4/DwEL/+9a8BAO+++67Fcjn2Vqtl7qa9vT0EAgFTFCcnJ/HOO+9YKabd3V0Dpw0NDeGb3/wm3nrrLQDdi+Thw4f27nA4jHa7beNPJBIYHx+3d3kLmSsVo+YKXrhwAel02il0MDEx4RCrJ5NJ+/78/DwODg4cIcuLLB6Po1qtmtJ4/fp1jIyM2GWZSqXw2muvmSBnjIx7XePOmm6iNTEZDlLCCMUL8LLTAs31et0hYfCSxlMeBgIBy/sGurLq4sWLti8KhQLW1tbsnGjqRSKRcGpubm5u2nNqtRqWl5dNibpx44aBL/nZR48emRzXwtc6T0DXlb+2tmbrfPv2bWQyGSdWHwqFzIhhAXUNa7FpCgrBV1SiYrEYxsbGrA9HR0fY2dmxvrFkF++LSqViLnp+Xi//SCRiIYVqtYr/+q//svj85cuXbY8A3Qt9c3PTqdn5Re2Zl2U+n7cO379/HzMzM2YZ/uY3v8HDhw9tEYkEpVCfmZnB5OQkLl++DKCr+fX19ZkQV40kk8kgHA6bdrSzs+PEFre3t52cTiZ28/B6mTwYxwmHwzg4ODDtMplM4oUXXnCsw6GhIadCNzcAm1pmikolnyl/ZpK7oh1VaLJv58XTNPFXGTyArsKyvr5uFxhjknwvFQllGVINlQKAz/L5fJabVqvVEAqFbKMQwUwLwu/3O1Yu+67vJ+iJ6LOf/exnODo6snVnQW3Oy9jYGGZmZsxq4QFR61eLyc7OzjrWEHBmFbAYuQJ1mP8KwAFQaAWPSCTiXErVatWpatPf363xSUBas9lEIpEwr8rU1BQGBwdNKNTrdVs/xnEUOVwqlWx9qJBQAPX39zvWsSb4A2fJ88lkEjMzM07C+JUrV+w8RSIR7O3t2cV1cnKCixcvOjViBwYGTFDMzs4aq8nBwYGjzf/Zn/0ZTk5OHKuI3we65y+Xy5kSo3tZ55mXCy+AUqmESCRi63jlyhUn3448xVpVRkE9gUDAAUplMhmnVubs7Kw9e3Bw0FFIeY69CGZFqLOeLc9eMpnE0dGRY4WwyDvnNBAI2IXGPmqVIjYqz5q7e3JyYkI8k8mg1Wo5ifostgy4NUMVjc4cSs4xFQqeIcoHyoPp6WlEo1FDTb///vtYXl62Szwajdp42u02Jicn8fLLLwOAVabR+WIFKP6s6GFtZPfhO5h3D3QVgydPnjhMQPv7+7bWN27cwOzsrJ2Rubly/gkAAAAgAElEQVQ5pFIp2497e3umRLFaCZ/NGDbj7T/72c8wMDBgd0c4HHYUtGdVHenFLHut13qt13qt176kPdOy/PDDD+02r1Qq+OCDD8xvPzMzg0KhYDcxrRne2Pl8HqlUysn/UQRrPB43zT+dTiOZTDrIsVarZVpzs9l0UGZ0hVCDVbRiIBAwf300GjWuRH5vaGjI4dPs6+uzfjCnUZkh1LJkrUY2n8/nsNp4K96r24ha5nm5ed7KCpoTmEgkEA6HTbul9apuaM27ZPyJ7yF3K9C1yPL5vPV5Z2cH+/v75tIlOphaai6Xs7XhOvA5+u/Dw0NLI3jttdcwMzNjGn6j0cDzzz9v7zg5OcGFCxfMwuE8sY/VatVJFVAaKm8tSPaV70qlUo5lqRaP5oJyDbW+pVqhkUgEtVrNYeVhDipwxsyiKQb8LC0wznEqlUKtVjOvSalUwvLyskNvNzo6ei6yW1OxvJVhvLm0TPtRV7PyArNSCM/v17/+dbMmFhcXHcq0VCrlwPGBruWj1kt/f/+5FGHqXfHWlKQsoCVCjmetG1qr1Zy8YG/VD+XYjUaj5kUiu5N6VLxUedrU6tSzozFMIrfpWaFFp9/1hlqUG1flAXNtOYdra2vI5XL27L6+Pictqlqt4vT01LxAalneuXPHXOjEIChrzfj4uJMjnE6nnZi6sjcdHBxgb2/PcmgTiYR5mrysa8fHx04cUtnPOEbNy9awk/JV87zTfcuYtaYOXrp0ycGwFAoFJ/0sl8s5HkGuodaq5BheeeUVc8tubGzg4cOH9i7yxnJdmNZ0XvtSujulxFpfX8eHH34IoOvGGRsbswH7/X6EQiHncGuAG3DzpPRQ+f1+C+7zuyxuC8Bxe3CySQytv2fjhstkMs7fwuEwAoGAHV7GVFTQeQ+Y97Dp87yxGhX63nJEXjJzBbTwkPF7p6enjqAmwIlzqLmfBFHp54Gzy5jrwnlVF8XIyIjj7iZQiJdjLBYzIma+GzgjfeZ7gsEgXn/9dQBnYBBNnk8mkxbL7nQ6mJmZMaG9v7/vxLX6+/vx6quv2mc/+eQTxz1DlyC/S1cy0HW7MnUF6AoCxvUIqNB142VHYBrTmkiFxTkvFouWvAx04327u7smoNRlOTg4iGazae8Ih8OOq5/lvjgGEqBz3fQCUvc846hcO1KrUaCQd5l9YrK5XkTaEomEnYPnnnvO2Sd0P2qZI84DcBZO4FnWuLI2cocqqYO3YK8CeEiFpzJHS6VFIhFH+WRhX66V8t1q8WP+/7yzqwQavLy1qftQMQva2F9v0r4WOCZWgkKZhCncNxMTE7h69arNMfmEGQrQS2pubs6+d+vWLSSTSdsHQ0NDlqbC/lerVXPLEtBDJePmzZuOoqgy7OjoyDFwksmkM190ySrYSPOk1Q2tHNPeeWPKHUMOR0dHT3FC37171y7BbDbrkFtoLPratWtOjvTU1BRqtZpDPHHx4kUnpNdqtcz17wWUaXvmZUnWGT6EpLRA1+pIJpOmZZHAmou0u7sLv99vGosXAaWTygVloHxwcNCJ9zEOp0g25Z31MuHzwmYOFQ+yl4eUwkXzKL0avvKgKu9lf3+/Wab6XfaZk+61GDk/+iyvlakbkhepbmCdC1bx0MvGW2NONynnlt+NRqMO6EAt0UAg4Bw8b908jkv5eEmmzGc0Gg00m00nOZwXJedXOTO1wsW1a9ec/Ftq5nx3o9FwSMiZuH8eZ6k2oqB5QLyI41arhWw2a/vR7/c7lWA4ZiWQVpL5arVq44/FYojH404yvSJtSViu6/dFfVatn+/hGEigwTHwklJPh1blIA8yAENdnlfhgn3UuYzFYhgdHTUBrnF5VfTII6oXExU27ZP+zPXg//XC4V5n0yoq3EN6LvTdXHP9LufpPFYttpOTE4dtyQsu9AL3tNIL9yMAJ4YIdOO1y8vL9nsaGvwuySxUQWGbnJy0cUciEasJC5yB2nQtdQ4rlQr6+vrsc+S+Vp5uZU5LpVJOoYe+vj6nysrg4KApaJTpyvrGpiTrBEYp61etVrPP7+zs2F4BukCvzc1N26+5XM4MIj5PlaiLFy86WBolXgG6+1fjrkqWwxjnea0Xs+y1Xuu1Xuu1XvuS9kzLkhytAMw6UGSaIpqoqdMXvLa2htHRUXPT0lzXslzUJFh6iFBiuq40d1DjeES6nediokbG3yvPKt1A6jZR1wlh7+qu0RpzylRDa0LdrpFIxN5FzYXvooXF73+R1UMrWl2fmgdHtzH/Tkv0i0pmAXBiSGp5UWtWDVBdUXTLcn60rBZ/x7iO1yWusaWTkxPL143H48YqonPMZ9frdbMU6/U6IpGIPWt7exvZbNa0ynA47Lj2C4UCyuXyU3FiwNXyGWPkXiRikGtXqVQwNTVl1iJ5ftVi1TwvIpQ5fm/1Cw1PMMVIrS2fz+esn/ZZ4z8aupicnESj0TCtnuhQrsvm5qbjoSiXy05eoiItG40Grly5YvNKz4V6DzRlgn1kDNI7z1pGS88SqRO9aTZKlVitVu28cb61dJOmCGhZP+auKpuOxkM11AGc5UyyAo3OsZaV845BEbUco55pdX2rG53xTs7ZxMQERkZGnHCJ1u/c29vDysqKnRN1D09MTDi0owAs9tnf34+9vT1zM2reJHCW5kS3bbPZxOHhoZ1PlR9DQ0MIh8PO+BOJhD2PoQH2hfLNa00DcDAmnFv1HBI1zhYIBAxhXygUjA8W6KLeJycn7fmbm5sW011eXsbo6KihjslCpF4i4CykcHh4iFwuZwjg/2c37M7OjlNSZnh42DF/K5WKbU4GihmQbrVauHbtmi0K/d/nwYpbrRb6+vrsb5ubmw5htjfple4ZdTGxMQkeOOMF9G52Nm+NTi6GFlINh8NOPo9C1ff39510BE3hoBKhLhq9mFTAKDCBF6XGNLX/mr7BpnUyWbDbWxeR86ElbCqVipMYTTeW5lWqQNL3UeiyJI/Sa9VqNbuIDg8PHTdyf38/tre3bR5DoRASiYR9fnd318BCa2trqFQq5spfWFjAysqK0atp+SS+WzmHde5VoWg0Gg6oikKYlx9rGvLgUDBreTrlzNW4FoUl+6UhAfZR9xFTlbwkFeyzXvytVsvGRGAHLzAC7AiCaLfbTnk5xqq5F3TvVqtV3Lt3z1xQLCiuF5HmJ3OsWlqLTfcL96WGBAA4e5Wf866X/qzgOCW/0MuQ/fPWcfU+U8uw6eeAMy5k5XbVC6/T6TiXPc+zl69WlQx1yXoBL0pgwLFxHywtLaFYLJ4LltEUmU6ng2Qy6dQ1rdVqT5Gha9qGyk4C1SgTarWaE1rSsBUVZ6XOUyo9ptZ4143fVYW/Xq87oSWNbfv9fodgHzjLhwS6BB2VSsXklJ4LXrg00jKZDPL5vL2LbnPKUSrbXLsvqoELfMllubq66lwURLhyIhqNhiPkVldXjf1lenraidWQJFhjU9yUZEPhwd/d3cXGxoYdOuYoaY6ZXhZ6AVYqFVt4LpyOgRPE9+rkMO6jF02tVrNgscZRG40GEomEaX6Mfar1oX3ku85bDI3z0AJXAaLkB4eHh1arj+/pdDoOUtEr2Kn5KTkD0EVBkhACgKEpeRAPDw9xeHhoAldJodkfXlB6WajlSy5JbuxKpYLHjx+bJtzpdFAuly1usLa2ZtUHHj58iHq9bsH5druNJ0+e2NjT6bSD/D06OnJi33pYVQFpNpsIBoMOMbMX2KEWLjVmPo+MPzzMul94cJWEgBY9n9VoNCzWyLXk3tf9oRaY8gcD3f2nSO9Op4P5+XlD83F81JgHBweNLYj90sTt9957D/Pz8wCA119/HTMzM7anqAxonUItgtxqtRymKuW5VWWMMURVnHTuKCQ5BjL4nBfPZcyWe5V5zsrRq+eK3/F6PlS409PiRZZrbFpjlNxTWsWHcTzOk45NPQVcX42HAmcKCA0VL1AJgIMAJ5hMPQiNRsORlYlEwp6jvK+ck3w+b2eclVP4N2VR4thVAVCZxuIXKrfZFMzFS5h9IKkJ9wXRrcrBW6vVTIm+c+cOyuWy7eXHjx9bbngwGMTp6al5OElSwsuTcX3FPWiu9rMuy17Mstd6rdd6rdd67UvaMy3LTz75xDQYpi+ou61er5tWtbW1hdXVVdPGWVOMvvD+/n4cHBw49SDZfD4fGo2G/Y5aMNMN6Brhu0mxpohRNnIlAl2NOZlMOig7tTSVtxboajTb29v2cyQSQaVSMctSKfji8bjFo4CuJlsul02brFQqT+WCaokvRd2qa4euN0012N/fN0suFAo58TXGz9Q9xTkCum4JtbwVQdlqtXB0dGSWeCwWQzqdNouBLmgv3R3Xn3NItybfr/RT0WgUoVDI1mpubg7FYtGg68vLy1hfX7d9MT8/b6wmn3/+OZrNpu2xTCaD4eFh0yiZ56ruQc3PVavC655TC4f5uF5rQz0QPp/PXF2M3SvPL+eGsWvNvxsYGHiKf1hdkD6fz6xcLxKVTdOFuDaK9tza2sLKyoqtFfex5mfu7+8bLqBcLjv8wrlczt69sLCAgYEBs0r7+7vlkTiHR0dHFoPn+HVuuX+86FXOucZ3vWdXmVwCgYDtSaCLpFYEJS0Ojk+9DOo6Zet0Ovj0009tjH/7t3/r8Bp7Y5R0P+uzWJ+U/SuXyzanPJuM6TElTvvD+aD7U7ludS97361nr1gsmiVPvICGcRga4XO43vw7nwF044GK8GUKEj+r9Jis+sJn8Awod7OeG23RaNTuErqnldtbq70Ui0UcHx9b3LGvrw/ZbNb2497eHj7++GPrJ2sJA11vUyKReCpXlz8zxqyu4vPuhPPaMy/LtbU1c9WomQx0Ba0Ksu3tbZRKJRMSjx49QjgcxvPPPw8ABvGni0w7yIXTXMF4PG4dL5VKjjuGUGFuID0Q6tsuFosYHh52cg/pJuREaWoEqfFownvTXvRy5MZW/36n07FFK5fLDk8tL3w9HGwa56Ew5QW2v7+PwcFBu1zoztAYxO7urtFXkWhc8/Y43nq9jkqlYvM+PDzsxDfpDqWrfWhoyHHpUGhpjI4uOXVNkSaPc+bz+ayu5+7uLvL5vM3Te++9h7m5OSOSuH//vsW9C4UCTk7OyhpdvHgR169fNyFfLped2AXdvXy2lzpOAWG6DxiD1ThjtVp1aNiYKsSml5rP5zPhlclknBhkp9NBNps1QEkoFILf77dnsWaj5hyf12cKIZ3nUqlkgmpjYwP5fN4E0vz8vKN4RKNRRzHa3d2188ycaQV5LCws2GcvX778FFhFayR6UzLU1cdyehyLJu0fHx9b/AroyoytrS3bf3Rpq5DVNVMwCVNwNKVCz5vP1yXK/+1vfwugq7Sx8ez19fWh3W47FxoAB2cRDAadgs7VatVJZFdyAKVR01qwQHdv1mo1xz2qeIFQKPSU25atWCw6sVfmVgLd/cQi95yntbU1h/+ZfMV8VrVaNWVuZGTEUWD0XHBOlVBD4+r8HZs3r1IVN2JYgDMyCs4z15XyZnx83EkpKxaLqFQqtk8KhYIZSIlEAqOjo079X71rvClWDHOdVyLP23pu2F7rtV7rtV7rtS9pz7QsFfna6XQc9pSTkxMH4EMtieZ8NBpFOp12qoxoIWY1w30+n0MnxrJRitZjmgCfTWj4eY1axvr6OuLxuPWZJXq0woUSjUcikae0Di0Vpm5Vv9/vWCckVVaUqKJCWVFcmXrYFBBCTUfBJKqhUjPn55lmw+B3qVRyXBgMYHO83ooF9XrdNMlwOIytrS3TjlkOTF2sbKqN+3w+0xLJxsF2enqKSqVipaIGBwcxNjZm3oRwOIxr165ZgH5jY8P2QaVSwfr6ujF73Lx5E/l83tH+qtWqAxojyT7gWu+KcmSf1fWt7jjOs1aX8Cauq4WhZPPpdNpBdrOiDPcQXWYKt1cLWK1Xb2UaojM5Vp5Bfi+RSNjPFy9eNCAS0NXG7927Zy7427dvm7cil8thdHTUYd8Kh8PmrWg0Grh586ZDNqApWmpZlkolh3hfwwC06NVaVvBaMplEq9Uyq+DJkydOsWHgbN8VCgXs7u4aIT9d/Rw/WabU+0RPBPvCpuuublh6TNSLMDw87CAvlRFrbm4Od+7cMc9IJBIxEvJr166hVCpZ5Yx0Ou2U4OIas1+UL+cRJqh8oMtVixfHYjEH4Xt4eGjjBrpnTs/84eGhgeroSuacqcxSIBHn0LueXkQymyJwSUigITmVgfF43EIlnBctS5hMJp21ZqoX0PXs6J6hPFf3PT0awFkKo1qeX9SeeVlGo1F7aCaTcXzBRL1xo1Moc6JefPFFXL161V5OjtGFhQWbeLqmYrGYE+fJ5XI4ODiwDVEqlVAoFMwtqtBtTiZbMBg0QbS2tmamvE6UXvhakoX5dhRYvCi99e7YKJzZB81fisViqNfrJpy4ofUwaL/0mUxD4ecajYa5PliqTAWQ5mttbGygWCw6iDT2P5fL2X98r6aSHB4eIpPJGPtFoVCwyiycW6B7wXoZgvgOporoRbW+vm5r8sorrzg5tO+++67FbwDgt7/9rQmURCKBxcVFuzyHhoZMGWA7PT11Lt5wOGz7SudY1927fyhQVCj09/c7AsjLlaqHX9OTFhcXsbGxYYcvm83aYQe6B3thYcFQ49xvX3RIFdWo9G5eJhnGUXlemSLAz1+8eBGzs7OOO45Cnn3QFIjh4WHbuwyp8GLievHzun+Z5wbAaMSUEcrrzlXEZyqVcqoJhUIhJ62B3M78bD6ft89SWWWjC1OR8LVa7SmOZkW7nidPdM5ZYYNrRSQqFb1oNIpCoWDu7NXVVVNiycmqbDhkquHchMNhm9NarXauCxboptbxfMViMQeh2+l0EA6HHYUxlUqZArK3t2eIUeCMLpNjWl1dNVpH5pJ746F6TjSEwPxiDVuxqUKiFzLHQ/nAMYVCIWdMDO8A3bVkhgQA22/AWX4t5T4rWPG7VIxVPiqC+1lsTs+8LPf3921xSWenE6U0Q0dHR4jH47h58yaAruba19dnQlDphfT7bIQd812BQMDiodVqFaVS6ak6hecNjP5woCukL126ZM/x+XwOvZb3guPFSCHCBdPF18R89hs4Awfx50wmg2Aw6OTuqdalwlFzsCiEefCPj7vFlHlZTk5OYmhoyCE/6O/vtwNK8mVqisBZrHFychLJZNIEDC9m9olCXnlba7WaUdmxj5oWw8KxnDMKcS3htbKyYms3Pj7uxJImJiYcUA4LM7O/nFvgLJFeBZ4qPM1mE6lUyvqripDOMZUmLzkDm5fOjilDmpum+Wh9fX2mud+7dw+rq6um2bKGKten0+lgb2/PKQPlBSBon/lOKgWqbTPVgn8/PT11SDJUaJJ7k99XcAxjkPy5Uqlgf3/fUQQXFhZsXpmHqgQAbIeHh3Y2eLHoWVGvAIUYn8MUAs4VLSTNy+R5orWulqOXdlAbE+i9fdbcTVpyGm/X/cbve8kaeFaj0Simp6edIvXcgz6fD9PT046iWiqVbP2y2SzS6bTjIfqipqlWmqzPPg4MDNh5DAaDyGQyJg86nY4DlKIniTJhd3fX/kbjhI0XjSpHfr/fiY/yczrHABxglNf63NzcxN27d20uRkZG7MIEzi5Pjol4EFUWlcBGPZyBQACHh4fOhe/lPNc1f1brxSx7rdd6rdd6rde+pD3TsiRLCABDaKrmGgwGze8cDofx0ksv4eLFiwC6WiXJjIGu1nXhwoWnEIrAmfnurWSh7lKlQxoZGXGg/qrB0J8NdN2w9XrdYWJhnJJ94liAroui1WrZz4Tna6kwus+YMqEJuZFIBM8995w9WyHc1HjOQ/CqiwFw4yknJyeoVquWMP7ZZ58hHo+bxc/KIBwLY210oakbqVwuY3Fx0Sztvr4+h3F/e3sbnU7HYk60ML2uK2W3oYWjFrTGeXZ3dx1iAcae1WJSt+ro6Kg9i3EduuGIrNTKHKph1ut1J3le51gRcZxv7l26x5VIQJ/jLfdE6jelXmM8ZXJyEsPDw45mD7iWgroiDw4OjIWF66efU7e9UvTxzGgytRf1B5xR0qXT6aeQ30zNarfb2N/fN+tjbW0NgUDAvEQstafECkrpp43kI5xHrytUifqJ1PYS9Gvsiqk4XActAqBWHtMaFMmqMoLWuxft6B2HIrljsRgODw8NyX14eIhEIuGEWjqdjq01sRS0wJ9//nlbz8nJSczMzDzFfsSzx/AC48REtJ7nPRsYGDC36tbWluMFUQpDfnZpacmZ40ajYRYs0/n491QqZeMdHBzEyMiIQ0qiliW9GSoflbTAuz+UGWlnZ8fkEOeQP5OqkZ6lTCaDnZ0du1vILuUlzNG/qYzQCjqKKOe7tX2R6xv4kstS432E16qQ07I+BAhw0vf29hxXTa1Ww8nJiQkVpX6jq0NzENWFyUWi0Gy1Wg7YSAWMgnAKhQKWlpZs0snlqtyjhULBFolxV41dqJBQWHg4HLb8PKB7yF544QW89tpr9qxqtfpU/UHNUWJTdhlCtXVBp6enTcm4f/8+VldX7fszMzNOiR2W9GILBoN2MDY2NuDznZU84kXGz4+NjWF6etrWlFB97RvXi3NEVxvn3Au0YPUZ1q+ky5UHrF6vO7Rf4+Pj9p7V1VXMz8/b+vj9flQqFYupnpycWJwSOHObaaqMNlW+1PVar9cdQEE4HDYaOuCsOobC/09OTqyfhN8D3VhgIpGwvcsanFptp1Ao2EVFkAj3qDa9DOnuUmDH0dGRk0M8Pz9vgu7kpMtDSpcmAUkcowpINo7nxo0bmJiYsDHE43ETRgAM5s+/ayuVSpZKMTw87KR88ULnfovFYmg0GrZ/CYLjhUKwlipmms+qjftUQyY+n8+Zr2Kx+BQns16eZAHS9K5MJmP9+/jjj7G8vGzjHh8ftxJrXCPlYp2dnbV4Wl9fHw4PD53UllgsZvugv78f5XIZv/rVrwDAUpfOi6NqCs3m5qYTSjk8PMT29rat5fDwMNrttuWCPnnyxKFjZBiK53NyctIqS62vr1uOLd+rTFKku+NZ9sY0vUo/9zJd4ko/mU6nbU1LpRKKxeJTaUGUY0zV4hhDoZBTs1eVSs6NykRVKvv7+40vF3haZmj7UjQsb+hAIOAsEg+vFnteWloyQXZ6empFcgEYma1qJZqAq8FgAmnUz+z3+52Cwprf49UOtGbmo0ePrD5iMplEJpOxDbu3t4dqtYo7d+7Yd0OhkL0nEomg3W7bGG/evOkks4ZCIRPUiUQCly5dsr8Hg0GnjxRMirRjOz09deJHwFlwvL+/H7lczmJ+kUjEQRWPj49jbGzMEYJDQ0OWaxqPx02AMk7Hg9NoNDAzM2MHhTlb3FhMmtZYMhs3lbf8jcZaga41OzQ0ZNr31taWsw+Oj4+xs7NjpMlzc3N28ZTLZWxtbTkcrGoJ0wrghTA1NYV2u23CTRGA3pilF/2qOW6K9NX/c86pvCmXMAEK1GI5XhbvVsATiSYAOMhjnTfAFYqMTWuZI/Xs8JJSEmytTUqBp8quxua8FqAmpzPOzTXXUnQ6X1wzzgUVMc1XLJVKpoQzb49z8eDBA6cIPEEvvIxYFJ5roVYqx8Rn0zuiaPWtrS1bQ14W3jJSuk9OT08RjUatvilRqIoHCAQCVjpqbGwMyWTS3rGxseEoqo1GA6+88gqArgJMZQ/onr3Hjx+bLNIcbm/TfVuv17Gzs2NryTPyu9/9DkB3H4RCIQNWPn78GJlMxgoux2IxTE9PG6iu0+mYPFhfX0ehULDx8A5Q7ILXS6TKhsoFL+JUL9nh4WEcHR2Z7PBiJChLaeQ8evQIDx48cIBV6rnJ5/Mmw4if4DngOuodwbgl8HSeszPvX/iXXuu1Xuu1Xuu1XgPwJZbl5OSkaVGxWMxyf4Az5Bhv8OPjYywvL+Ojjz4C0NVG33jjDYut5XI5h8iY/n/gaXLfZrOJe/fu4e7duwC6mu9zzz1n2jq1SrU8bUD/t1Av0NVEt7a2LMdvaGgI7XbbrDQy8tBS2dzcRDAYdOKBnU7H0VZpsTUaDaRSKdPIxsbGnLwuIrLU9aduNG9VEWrIx8fHTo4p+8k+HB8fIxAIOCjjcrlsmnQmk0EymTT3TzQatXjh0dERdnd3TROORqNIJpNm1TSbTYtj6rzyZ1pw0WjU5qzRaBjNHMfS399vGvXh4SFmZ2etv/fu3bOqHUDXytrZ2THNcHd316mskslkHBdTqVRyKpqEQiGLrSaTSezu7ppFqW5ztSC4fzQNw0u0rns9EAg4cHMicNX9xBgSrSRlG0kmkw5a2e/3OznMpVLJrDHtp9IYkpCc8860Ja5dMBjErVu3zFKhhs+5oTtO0bNKO8d1BLp7WdMYSHiuDCucJ/2Z4+Y7SMtGi4FMLRwD9x5jo/fu3XPoJenup4zRcnuDg4NmmfJn9Rp5UwRKpRLW1tae8hoo4xBjrFpGUD/7yiuvIJ/Pm1x69OgR1tfXnQoWb731lkMTyHXd3NzE559/7ngK4vG4IU79fj9+8YtfOHgCdWlq0ypDp6en2N/fN2/Y0NAQUqmUearW1tZwfHxsc/jHf/zHGBkZMesxEokgFArZexTjkU6nnRgk2aY0s0G9DHTvnkd3p2hYolM171IJ9zl2PX9KCcnG87qxseHI1RdeeMEsZ8Zz9a7QuCRzlf9/p46kUikTYgRtUKhzYihw6vW6weKBrtB7/vnnTWiMjo7i0qVL9v1isWiTQ0g2F3hrawt37twxwaOctGyasKsbSgPujFl88sknALpu1Gw2+5TbkInDd+7cQaVScWJT7XbbBOHGxoZdSi+++CJmZmacC18BPITxaxyWieXePvP7bFrvcWBgALVazUl6V+AU3QicK75Pq4KwDQwMYGRkxALlFJ5KtFAoFJzLksQEAMztQbc40BUoXjIKHmAARsVFN9CDBw/QarVsPMPDw8jn8w5wwxuL1kBxFSQAACAASURBVMouKysrBptnvIZUefF43Mkx1UOr/Lte9yqVOL6nVqs5YJFms+m45zqdDiqViu31crls50Qp19iYnM85JpwfOANo8YLXWLbGURmn4UXSbDYRiURMSFarVad6BOuI8mJi6ToF32haSSQSsT4lk0mUy2V7l1baAc64cNk3veBbrZaFYkZGRpDNZq1PdKfyAuH54Hmdnp42ikOOPxQKOdywXq5TBWHF43H7O3MHueY7OztP8dICrkLijckxtUXTdbLZLH7v934PAPDqq6+iUqnYvBwcHKDRaBgpgd/vN3fo6ekpvve975kQ15Q8oOvy/OUvf+mM/VmCW8fRbDYdejdNv4lEIgiHw6bUU2nimGhAcO+ru5TP04pGym9NfIUqACoD1Yg5Pj42+U6DgMos+6ju25OTs1JgR0dHiEajDvBvenraqQrE0MvJyQk2Nzdx+fJlAF0jTeuEVqtVK8vFvrDOMQAH/+Ftz7wsdWKq1aqTg0e0GQd4fHyMiYkJfPe73wXQ5aYsFApOjtz8/Lxt7v39fdsoHIgiuUKhkIFlcrncU0nh+m8v8wo3Ul9fH/L5vC3KT37yE5yenprQpz+buU/PPfccNjY27CCenp5iZGTENvbo6KhZZYlEAoODg08xaSjiS/vC+JPmeLJVq1Un4E60IdAVpsfHx3YYGItSK0gFOeM4nGcNWFOz15zGgYEB25QLCwsWbGc7OTmxg8Zk5X/6p3+ytRkbG8Ph4aFtZAJh2J+RkRHU63XzOLRaLbzxxhtWk5ICk+N79OiRCVsiBJVvt9Vq2RyPjo7i6OjIIdxnDiXgXpZa9oxKnpYRU6Jy/l33NrVdoLtPFRhWr9dtnUk4TmVgZGTE0YgpmLxxMY3TsnnRkATAAN1DrWhP5jeq16RWqzlKlALSNG/NWySBn9V4aKVScVCOGuPUs6jcqjzPFECsXcrzvru768TB+W/2mQhkPY9URAgEUsIDwMUG+P1+++7//M//oNFomIXFPUVQD8etoCqebWV60v9T+eQ8sj/8vHKOZrNZvPLKK6agEODCPfa///u/ePDggc0F0b1sKuO8MX/GDvk5cqnyOfl83iloQTIFjlHJwyORiKOQ1Wo12wckKOC7NW7NeSLBB7/PprUpyQ3NPc9iC5wrzbAA4CjSfE8qlbK/69zQQlX+3oGBAUcJVa8d14jvplJ/XuvFLHut13qt13qt176kPdOyrFarjvnabDZNMwsGg07csNPpFhF96623AJyV3aKVcnh46OTUfRHfJtvExIRZgD5ft9Cusv/wnfp/wHUREjLM+N3a2ho++OAD+/vo6KihCgGYFan5dgMDAzZmhWizeoS6cACXMktzeoi0O68qvJdqTcfEnD5q8bVaDcFg0NGytNI33RvqwlS+zOHhYbN6SHFGt1itVkMymbR1aTQayGazePPNN23MQNeypFb4ve99z3FvMb6leZe7u7vWh+eeew6vvvqq9Xd3dxe1Ws2qY6ysrDgxEi1YzKLN1ChjsRiq1apZokTWnhf/49/5ew0hMK6hkHhvqSz9+9bWFra2tpyi4LToyJzDWNTU1BTGx8dtvVdXV7G1teVousrV6Y2/azX5TqdjViqLnGtYRK09sp5Q+2dJOY3XcM0ikYiVeuK7tMrDzs4OGo2GzTvRiQq/Z1MmrlKp5FgihULBOGyBM35onr/j42OEw2GzgmgxaWiB+5r5tZqbDbgyod1um8v+o48+QrPZtPgo4/jKQMM+qYtW5RTPtBfZzp9p7eref+eddwB0XdvxeNxBdh8dHeHjjz8GAPz85z93cqK9XgX998LCgsOr2mw27QwQPayWdqlUcmTY8fGxnV9S7mluMj1NzDGlVRiJRDA0NGTyg/KP669sRzovQLfcHr1JzFHm3RAIBBzqVHIg80wxHU2tZe/z+TeVX0B3D2ma1NHRESqVilNWTNNSKIfOa19Kd0eBsLCwgGg0asFrJhircNcK3plMBvF43KFSq1QqzsXGAZRKJYfzMxQKIZ/PO4mk+XzeXHWMLSiMnI39As42GA+jctMCwFtvvWUlrc77PDkzuTgKMGAfuMk0YZ//VzeEz+d7qiah/o0XQl9fn80h0BUQSlvGeIMeKnWHHB0dOSV6VDgGAgHnwqe7l4I9nU6j2WzaZRMKhfAHf/AHFhPks8rlMt577z0AXaHz/PPPm+uFrmgehMHBQaysrNg+mZqawuDgoG1W1lnkWgYCAROOjDtpDJAuG6B7QMLhsONqrFQqTo1DNj1AFHjqwqM7i2NQNyT3JYXI+vq6lY0DznLmAFg9Q/aROZaaU6xxMr7vvGRo7TPd8xzT+Pg41tfXneRzBbTw31peie5Tzp2SLCiBeLlcdgoXUAHlfBEUwaZnmnPJ+VIgX61Wc+bNG1ph2oXS23kJuHXt9ULxxk+Z5sF3dTodbG9v20XMvFYvx64X/6D1XHkZqnueObdAV7lsNBq2T3w+lyOZCgvXc3FxEf/6r/8KoMsprOkiisnw9kt5eamccM8vLS05wKeTkxNTkrUP/Hy5XHZi20q6XigU8OTJEzvb5KWmksSzyLNM3IrG+tl+8Ytf2D4YHBx0CjR4xxQOh5HJZBxFkNSkXGP9bqfTccB2mk9MdzfHy7PIfVev17G7u2ukL0yzO68987I8Pj42rWJtbQ2xWMw0Mib4awFdxjOB7kVbKBQc33ihUHAOh6IWaTEBZ1oXG5kxFCSh6Dxla9DnN5tNrK2tGYKVWgUnZGtrC9/85jft4JDhRHkFyX3KMSv3pCba6u84d+wr0F0kLXCtix2NRu2y3NvbQ7vdtjhpp9Otkcm5ZZK3CvrzgADK3coDG41GnY1Edn7VDIl4A7qAnqtXrz7FZ+v3+21Of/jDHyIQCDjoOmVxOTk5weLioj3D5/NhZ2fHAfH4/X4bHwtuA11ARzAYtGcVCgWUy2UbO0mkqVXv7+8bsAWAk0ivaE/2RQvkkqhc509zYrUaRq1WQyAQcGoP8tnkS+b6V6tVBAIBs9JmZmacogG0gKjEPXnyxPqglqLGU4EuUCMcDpsmfHBw4FTQIRpWY+iKhtUkbcbSNVbXaDQc3mMlktB+8V1srVbLUfYUqX10dPRULUat2egl2O50OsbKxD57EZOKT1DLplarOfl1iUQCBwcHdvYvXbpkY1elQYWy17JkH7mWus78+ejoyCGKZ395gXHPVatV/OhHP8IvfvELmxtldqLX7jxSArWy6PHgejx48ACpVMpBsisDWDabRTQatSwH8qwqIp7t9PQUV69exYsvvmhjf/jwocnbhYUFjI2NOZyvCsxRvMTy8rLt1Xg8bsQyAEwmKSe31k/m+vNCJ/BNvXG6L1h8gvtAK0nx89zLKysrWFlZMRnCd5zXejHLXuu1Xuu1Xuu1L2nPtCxVaz8+Pka5XMZnn30GoKsdaI4etT51R7ZaLdP0Go2GU+mAvwPOINpa88/nO6sgMDg4iOPjY9NYmWai1b7ZFC1Zr9cddCG1Eb73888/Ry6XM6vo+vXr5/JdKnRcf6euK8Zw1LJUDZwVBtS1wKbMI5VKBRsbGw4Df61Ws7F7c42Y26n+fNJuAW6lc6YLcF7p4tKqCtVq1d5xcnKC//zP/8TXv/51AF3XOsfNtVpaWsJHH33kfEerkJTLZRSLRUPSMtbN8ZyenmJwcNBYUsbHx209uedo0cXjcezv75vmynFRQ93c3ES9XndyTnWe2GgNcG3q9bpRsQFnrhpFKGvtVp07zivjeZFIBFNTU+bajsfjGBkZMY9MLBZzXLz7+/s4PDy0/mluqIYUuNcUAZhIJAwi/+jRI+zt7TlhEQ0hsKzaee5TWicas61Wq7YHt7e3USwWHdqzL2paDqmvr88ZD/e+cv0SiQzA8v0UdTs4OOi4bRVFrH1ptVrm5uXY6HkAupZ4p9Mxj4i6irkXGUvTKhqa78dz5q1zqnLByz2rTE4DAwNmufz0pz/FT37yE4cV6bx2ntdIXfbsk+aTLi0tWTm1UqmETz/91EGI697NZDIYHR21MaVSKTs/ExMTmJiYcFi5XnzxRbsTNjc3sbW15VSBoncKcC3LaDRq4Z2VlRUnf5PuXXWZn1fdhHNLT40XtaxN6SV9Pp8TyqlUKsYYtra25vBNK+uXt/U9izi213qt13qt13qt177Esvz7v//7U8ZQqKWqnzkQCDjABuBM02P+nHLwKUpSLThFyAJnFdT5XcYBVFt4/fXX8ZWvfAUAiNTrA4C33377lBbF+Pg4hoeHLbidSqUQjUYdYAYTsjlGr9YGuAhXjb0pSIKxF+XXVB7TYrGI5eVlQ7/du3cP77//fh8A/PVf//WpkpS3Wi3nnZlMxrQ9VhzhGFlQV4EOCihh5Qaga+Vp5Ym5uTlsbm7as27fvo2hoSGzen784x/jhz/8oZPD9cEHH/T93d/9nfWXHgauvyLx2H+N6XH+qA0ShHIeIEk1S+AMNKXVQ0iWoc9UwubvfOc7fQAwPz9/6kW7cn9545Ucq651u912YuylUsm+n8vlbB8cHBzA7/ebdVwoFJwYM4FE1Lq5VkQcv/nmm/jDP/zDPgD4q7/6q1PGXX0+H4aGhswCILsL+8h9oDluGqdhbFrBbxxPsVi0eDCfnUgkbB2Xl5exsbHhJIF7GYDu37/fBwA/+clPbAHJVETrin3g3mGf1epR8AWb5vVp/rDmdB4cHKBYLDrzylqGQNeCUKaoVquFH/3oR30/+MEPTt9++23rn+YsE6PgzfPj/isWi44Xh+dfSfQ5p48fP8bi4qJZMKy7ymeRL5j7Zn5+Hqurq9bfra0t7Ozs9AHA3/zN39j5y2azTvw8n887lULYd807pIzg2hGsyL9rXqUi/onJoEwjapp9fPjwITY2NswTtLq6in/8x3/sA4Dvf//7pxpfVq9Hf3+/Q3bOYurKEay5kcwN1RqwygKnxCAEMynQTcFru7u7CAQCjhfqL//yL89lg3jmZfngwQOnyLC6CugK5eISoMM0DdLbsTG4rVUSuGB+vx+lUsncM3qBAU+jFwcHB3H//n1zNWj1A6Y/AGeIMg2Ge0E5fD774WX38RJwa4KyfpY0Zmx0GSkAKplMOogwfQ9bJBJBPB53XEHqZlEkK38OBAIOkvP09NRhbqGAJU3ep59+CqALG9eDVSqVHKaMW7du4Z//+Z/twCsQQufOSyEFnF+JXpuXBlAve68rW9/FMfKzWn6Hz9U0FjYFBPBd3H/7+/sOYYRWuQDOqNX0HYpsVko2RTXrXHjd7nrhe8+Wd474HSWpJgJQ50RpBzXlhPPMfgNnrlagq0TV63UnHYmuafZRUaPnlcZj0xJ4VCh1vwwMDNj8anUb/l/nhnuHP2uJLmWw4nc1kZ/Kqp4bb1oIAFy9etX64S0YQIpPdfd5098ikYj1j5cAf2ZIBeheHHNzcwYw8vv9uHTpkoHQwuEwYrGYE4JQxVAbU76A7uWgZ9bLHsWLUPcBx8L5U9e3EtG0Wi1HTpHkgpchQzZU5BOJhCkxgBsG6e/vN0WGKUKqjCqKmgqOXpa6D7xpVpq+wpKKlFncA0rLSgQwcHbh8yycR9Vnc/mFf+m1Xuu1Xuu1Xus1AF9iWSrxOWnMlDd0b2/Pyj21220MDw/jhRdesJ9Z8xE4I6vWHB5qXT6fD4uLi6Z1sSYgqeVoaakmWqlU8Mtf/hIA8O677zqaOcEyiUTCIZv2EoTTKvMmdrN5C4VyHGwKM1ftjO8gnR5wVvqLLmFqY+yHun40r5LvURBOq9WyeYxGo07uIS0EamF7e3vW51arhcePH1s60MWLFzE1NeUkBzcaDdOuxsfHMTMzgw8//ND6xv5qHpe+j99XK01zxhQUw7F5LVXNVVXSaL/fj2QyaXuKWjw9Egr+8Ta1WJg/Rjh9tVp1wDOpVMoS9dnHcDhsSdJ0GfFdWpIrEok4KQwsRcR9wjw89WaEw2Ebg4LVNPGfmreST3Bc/L9aIkzkVleVfq9er5v7rNFo4ODgwLTrVqv1FLG8EmFz/c/jAG23207q1Xklj9SSpEUInLnkvS7x8wA0jUYDhULBIT1XEFahUEC1WrW+KFkC3wXACUsQuMZ9cHR0hO3tbZtz9lvTFFiuDTgrfK0E34uLiwC6aRZbW1tOmEZJ2CORiFOKjy5kr6eA/1beVE21Y74mU8/S6TQGBwedGras0wmcAZBoqe7s7DjkHeVy2fKYSYKvbmnS6wGwUnxeMhDOreZfNptNx+On5de4bxTwQ5Anv69eBJUZ9JhorquWsqMVyvlMpVION+6zLMtnXpb1et0mipUYOCASaHNxm80mAoGAoc3IYqJCk5MPuJUWmH+l+XwrKyv2rGw2a5XegbNLkIim+fl5Y4fIZDJOVQ32le/ReCDZLvjc0dFRZDIZx7zXpHhdIEXVAl2BoRuLOZV6ORwdHdllqS4Kza+i8ODiMQaiB1SJyhmH5PO8/I2np6eGXnvw4AGKxaLND9CNnXJ8V65cQSaTMeEVjUZx+/ZtfPDBB7YufIcmtytBBMeuRA6aHM7+cVNSuLK/zWbThHipVHLcgeQmvnbtGgAYAbsKbb3INWesXq+bYvfRRx9hZWXFUUA0jtjpdBAKhZyivplMxjhyqZyoq5OHk4xCmvtJXmX2iTE/4IyAg9iATqeDP//zP7e54R7b3t52XLlUIri/iPxkn5gfzMuDbiz2c3l52ZSF/f19RzGma07zDRW9yLjPeU1RxOc1FfqsCerN41VXsyLqNbd6d3cXa2trNs/1eh0DAwMOWpx8zBwD+XMBOK5idUn29fWZwrK8vGyFrIGzy1Sr7wBwwgaaj7q0tGSFlJeXl1Gr1Zw5DAaDtre59xjGIie0Ms1o4+9JIkBli3Uz2TSPGuiuTz6fx3PPPQfg7GLSteecrq6u4uHDhyangbPCB/yuxrpZsYnnRmtI9vX12drE43GH/5oXo3LqKqEGY5DqEu7v77fc7E6nY33c2dlxuKrpftUCFqqgAd294GUHOq8987JcWVmxDrMArAJ6NL6kPmLgrASQ0jIx8RY4q0YPdDdZLpdzAr56qKhF0iKKxWK4cOGCCeB79+7ZZZlKpayPfr/f0iG0D1qRQ6vNx+NxXL9+3YRiNpuFz+ez56kG3d/fLbLKC/vBgwdYXl62jUpgAzcMYzcUMnpZAq4Q2d7etsuQVRg4NxTi/D7LNnGex8bGMDk56fj1uflp4bDP+/v7iMfjDpvFzZs3nb5dv37dhAOtFAbguXYaG/LGCPx+v3MRMRXAG2uigNnc3DRC6cXFRSddY2trC/l83ubi/v37DmgimUzi7bfftv5TUAFdC5tCMJVKYXR01PZPoVBAsVg0T8ejR4+cy5SkCZzHVCqFGzduWD9KpRLm5uasz/8XjGFzrulMjM9SMFQqFScWxwsMcOOvpVLJoXwkZZmSyOdyORO2w8PDCAaDDghpf3/fxrC2tmYCplQqOYoFY/fck6SE1BiTxk+9zUtkrxeCVoXx7pVYLObQT1KpovJULBaddJZCoWB9Ghsbw+joqAnQw8NDrKysOMo+WX2AM+tQ6euotNHDdffuXYf0nqWuuN+YvqBx0sPDQ5NTy8vL9iwC97ifaaXRAgwGgyiXy3aZZjIZhEKhpzxb7IfiMlikmXPM5wNd5e3k5MTmeHh4GIFAwM4CzxMv/oODA9sX/D/ToC5cuIBr167Z+To+Psb29rad1/n5eSwvLxulIL/HfigZihI6cA05VlZSYatUKg4NohYU4Pe5rgcHBw7rEr0N+g5V9qgsqKL8Ra0Xs+y1Xuu1Xuu1XvuS9kzLcmRkxEE6BYNBs9IY49D4RCKRME2JLh916Sl9E3Bm8o6OjlpCK3Dm9qEmMjMzg06nY5Rgu7u7KJfLZtYrRRHdwuzD8fGxaU3ZbNYp7QJ0tVXGFZ48eYIPPvjAXC+vvvoqksmkaTzKudjpdHBwcGAWxdramrktga5FNjo66nBVzs/Pm8akZbDojgK6GuHu7q5TQohuas5NIBAwzZIxCdbQOzg4wPHxsVOTj9rtysqKUwpnY2MDmUzG3CrJZBKNRsM0+0gkgvHxcVy/fh0A8Jvf/AbAWboDcBYrYiNFIOcwnU7j9u3bxr9La0hLeCUSCdO47969a27fra0t0+b52XfeeQeE+t+5cwe//vWvbeyEvfNdOzs7+Pa3v2395LhmZ2et/BDQtTKOjo5sTaanpxEKhaxPtD7UJddqtZyyZEtLSwC6RBd04wJnRZqVWs/rHlSC869+9av2jpOTE4eqcG9vz4n5V6tVx2uyt7dnlmM+n0coFLLzWyqVsLi4aO46dZeSX1g5TpWGrq+vz0FyA0+XwmMbGBhwOJvb7bZp7Yx3aerF0dGRhUHoueIZ5nrQa7W2tmbPCgQCmJ2dtc/So8R5ZXiC1hELDntdxGrB0MNBrwC5sTWGrnR3TJNS+kXGw4HuWVTe1IODA3tWNpuF3+83L8/w8DAGBgZMnrD2LkMHamEmk0mTQ3Nzcw6SeXp6GteuXbO14f7g/5n+QZlw5coVo5/j+qmsyWQyzt8ePXpkfSZ5DK2x09NTHBwcmDWvlqU3Ns3C7UD3bqF3h+/VeC1r1ipVqHomlYC+3W5jeXnZ6PzoGWToKZvNPiX/2+227W2vx0/bMy/L8fFxe3Cj0cCjR4+sE+y4DrharVqw1+/3o1KpmKuLG0zrW2rVh52dHdsYdNvxEJLpniwwQ0NDWF9ft77oZRkKhZz43/DwsB3GeDyOVCplQi4UCuHChQvmdmU9S4XMs1go4AIWarUaKpWKCeaxsTFEo1HHDaaxuEgkgtHRURO4yoFZrVadHFP1x5P1g59nfE3nUUEJoVAIOzs7Nu/7+/t2MEhIr1ybU1NTJlDHx8cxOztrY6I7hO4Pbs7l5WVz+9DNQYF2fHyM4eFhh1/2pZdess26tLSEtbU1UwZ4sfOgLC0tOSAXdU8XCgV8+umnJkTJh8rLvlqt4uOPP7bDq4qTEkDzolDS6p2dHbt0Nzc3UavVbJ4ymQxisZgdKBa05l5QvmHm9lJIk8hZXXlUKIDuXr9w4YJxlerY1fWZTCYRCoVs/7TbbeRyOTt/m5ubaDab5gLkOnBtGTLRWJdC+ZXYem9v7ymydwVRKOsO4KYS6SXKueY85nI5pNNp63OxWLS4NMekAJJ4PO4I42q16gj5SqXixNNOT09tH9Ltp0WNldtZv6MAP82fLZfLWF1dNaWp0+k4XNGJRAKxWMz2XyKRcFJyisWirScvXfJQj46OYm9vz+QiAUg8V3t7e5icnLTLU5mQDg8P7fc7OzuIRqN2vm7evIkrV67YHC8tLeHx48cOk1l/f789r1wuOyT53pCIFjknGw7ZqEZGRtBut20+GGLhnOs7vaBDBTXyb5RhNESIWWEuLuV4LpdDJBIxGakgq4sXLyIYDBoPLWPF3MusScz5GRkZwSuvvIJbt27Zu7+oPfOy1IE/fPgQCwsLNqksWqtoz0Ag4BT01PweCijNbeNGYaUIRc5qHtvS0hIWFhbs0rp+/bpT1YOaDAfLxWbOIg9vvV7H3t6eY+0BZ4CCCxcu4OLFi/Y8bymnUCjkEDUrAXihUMBnn32Gzz//HEBXOCeTSbO0o9Eo2u32U/lvgJu/eXp6ikgkYmPb2dkxoQJ0D41WOxkYGEA8HndyTScnJ23x1dJqNpuIRCKm8b388ssIBoMOaUMikbBnNZtNVKtVWxfO08LCgm1GAr80+ZnxGLa5uTkTUmtraygUCnbxJJNJHB0d2eFfX193LB5Fp21vb+P999/HD37wAwBd6ymdTpsioRVL+DPbkydPnDlvNpumIR8fHyMUCtmcb29vOyjdk5NuMWSuZSaTeaoUkdKWaQyQcUOleAsGg07B4OPjYxN2GqMl0TjHmkgkTLErlUoOYfZHH32E7e1tpxqFAkouXrzoXDb6HqKv1WLyVo9Q9Ctj/V6EM+dcUeuHh4e2v7LZLBKJhD13f3/fwRQwV1SJPqhsAF3FTAuD12o13Lhxw8bzySef4NGjR/YzlWvO89DQkFO8nmNh/yuVigPSW1hYMCWEjbFEXSPui6tXr2J0dNTOxuLiop1bKl9cr/X1dSwuLtpZHBwcRCKRMGE9Pz+PF1980S5XvSy3trZMsSMBOZ+7sLCAe/fuOUomzzHQlYEsngx0Ly5a3UD3gucl1W63DbcBdGP5/f39lvEwOTmJ+/fvm5ETiUQwMjJi7/71r3+N73//+wC6Z4p7juhyrVSUSqUcDIES+dNIUepAjTuq9ymdTqNWq+Gll14CANy4ccNRQH0+H2ZmZmwNp6ambI7Zzy9qvZhlr/Var/Var/Xal7RnWpatVss0mIWFBVy+fNkhOx8aGjLNKJPJYGtry2H5YHoJf/bm2qhLJJPJmCafSCTw8OFDe9bm5iYCgYChro6PjzE+Pm7akebWqeslFAqhVqvZz2tra5ibmzO3AgvIsk9TU1N49dVXTRNmPzX9xcvmQy3szp07WFxcdIjj2+22EV2/9tpryOVyNn/q4lILipRl1HAODg4QiUTsubu7u47riOOn+/fKlSu4evWquYba7bY9i3UuNY1hcHDQns04nFIWKl0Zx/6b3/zGPjM9PY1cLufAxNWqLJfL+Jd/+Rf893//N4Cu5Z9MJvHuu+8C6GqCXlg5XfmMXVBrvHbtGoaHh80FPTs7i3w+77jNWq2WxR1UY8xmszbO/f19J/41OTmJqakp094fP37ssKIw5sjnsiQQLfa9vT1Dsbbbbezt7dnevHjxIh48eGDWCsm3FVE5PT1tngBNwWGRZaDriaBGDcDmhfsgk8mYJcR+xGIxe242m8XExIRZlrFYzNxkyWQSo6Oj5rJttVrY3d01dHAymcTk5KSdM1Lfcc964fZeejTut+3tbdy7d88skcXFRWxvbzt50fl83ixCzj/XV3ND4/E4crmcua+bzSbm5ubMq46ejgAAIABJREFUyguFQujv77fxkiqP50JTCjTOube3ZxiGra0tK2sHwGQJx0fPFc/4xMQEms0mvva1rwHoWmkswUX3JQsG1Ot1vPLKKzbnOzs72Nvbsz3ZaDRQq9UML6B1eJkCxnH6fD6Hsevu3bsmZ/7iL/4Cf/Inf2JeFK4pZcLPf/5zNBoNs7T+z//5P/bsl19+GbOzs3YuZmdnnf+z/JrS3yUSCVsTL4ObylXd251OB9Vq1cJq8/PzhoDlPGvstFwuP5U7yXOQy+Ucrw+Rt9yjkUjECmADXTesepXOI2Vne+ZlWalU7JAHg0EnRlKtVpFKpUwoMCeIi8+4I4Uo6bo0ZYCTnM/nHfOXnJI8RMPDw0in0/jZz34GAPjkk08QiUTs2Zrgr7ln/IzGWmKxmOO2LRaLNpHJZBLlctkuB35H3Uya+7O1teXklU5NTdlm5DMpYHZ3d62IMvup/6a7rVgsYnt72zYOXRRUSnw+31PpMIODg7YOU1NTltMKwNx2/G6z2TQFiK4NuqYmJiaQyWScOo6Dg4N2efEAFotFu4guXLhglVGALsBlfX3dUaKKxaJzWVy7ds02Nw8F1zoej5twGh0ddYgh+vv7kcvlnGC8cgaTrIGHTAEGY2NjNufZbBZDQ0O2Ntvb21hYWDBBe+vWLcf9S1ozrVk5NDRkY1CXUKVScWqGbm1twefz2V5lDi0vEL/fj29961v4xje+AQC2x/keb91Pzk2r1cL+/r7NO12w3GM3btxAMpm0yzUajWJkZMQueCop/FutVrOLha46KmCXLl3C4OCgCWy6aVVYsWkfGee8f/8+gK4gX19fNzdltVq1fGxtWvxb82ZbrZYJ3KGhIYyNjTln97vf/a7JkXa7jVKpZApBsVg0dzu/D8AJFW1tbWFtbc0uc3WVA2cAH1XWbt68aTE8Xlw8c1/72tdsbH19fcjn83bWOC7u3Vwu51TYOTg4gM/nM8CgXpbeWKCSloRCIczMzNhnWBGH1KC3bt1CLBbDT3/6UwDA7373O3Q6HQsDKK5iZGTEwTCk02knJsv4O5+dy+Vw9epVR3HWfqrbVUE1nU4Hd+/etX3i8/kwOTlp8iafz2NgYMDi0wT38Pyl02lnrfQiHRwcRLVadYqkJxIJpxJTtVq1ef9/JiVotVq2qWgVKTLR7/ebldZsNjEyMmID3NracpKkOQlszFMEuodzamrKNmGxWMTMzIwdOg6Gi3Z6empILADO5aYxK6L8KDBmZ2edMjkUWkpOXa1WHbYVRVpxTjipGidkMVpqxWTr0WR8Zd7QRdHkZOaPcZ6TyaQJZ6C7MQqFggkF8vHyQovFYg5qNxKJmCZYqVQQCoXw8ssvA+hqgp1Ox0EoFwoFWzMWrNbyWUDXEuOcLC8vY3l52WEt8fl8JiQmJycdRhuiUmkhUMAoEQQtuNnZWfj9fgfMNDQ05MTEaKly/TKZjK2nIo6Vf5ZALK5NrVbDzs6O9XF4eNhhrGFcivuICDvOm6KTafFpea9IJGLCqFQqod1u2+fJSsT9QCUJ6F72VL6oBLLPIyMj2NnZsUM+MDCAsbExE9yjo6MOLy8BQpwDzUVjrIl7s7+/H6FQyEENbm9vO8A5zRlWC8JL6r+1tWVxxKOjI4cQo1wuY3p62n7X39+PfD7vKARauFdZX7LZrLElAV0BOjk5afO6u7sLn8/ncKbS8uGYuT5qsRweHpqgZckvruXVq1dx48YN27uvvfYaLl265MgflhXjmvDMpNNp+P1+u8QqlYqTTRAOh5HP541wg8oI54IxOKB7xjR+TmAS5+Wll16ytb127ZoTn45EIg6CPZvNYnJy0qzjg4MD3Lt3D0A3f31iYsLm9OLFi2g0Gqb4agk0ALh8+TJGRkZMGVeMgCLok8kkwuGwrd2DBw9w9+5dG9MLL7zgYE3S6TTK5bID9NL8V7Usj4+PkUql7DyFQiGk02k7RyxuwXOkucP8/he1Xsyy13qt13qt13rtS9qXFn+mNUfNmhqZF84bDAYd2iFFuPHz2rTSAt0xfBZRmZoOoVyBRI3REtA0DNVyqZWqhqzmO2HtykajzP+Mt6g/m++kFkxLkuw4ig7WmKxC7/mz9onupXg8jtXVVetzX1+fQ9MFdLVBWnscD60o8u9SW9fKHeVyGQ8fPjRtN5fLOVox0ziUJ1HRsK+//jqArluOVki5XMbp6alTQDscDpsrKhgMol6vm+a2u7trfLKcJ82p1VQfcgRT4/SW0iIqmPuCfdJYB5s3fq7IZGrn1HS93JXM/VNOWuWypHUIdOOhal2w4oi3ogWtL7qU7t69a+9mS6VSpqV79xOtVd37SkPIsnaKZNazoTmarJrC+Fg0GnX2a7FYRKVSsXPBeeYYdS+rG5acu7rftJzZycnJU3mOijhXjmE+W2N6avV40z4YmuDnmROpzDfAGV0jm6bM/NEf/RHy+bx9JxaLOZ6nmZkZc6vqM5WtSXM01fPm9/sdysRMJoNgMGjnvFarOTmr9Biwcd1zuRzi8biNkxzaWhC90WiYfDg9PUWtVjPPFNHoytdLWcRzSkuSlVEYguEeUnagVqvlMIbpvHKfzM/PO+saiUTwp3/6p3bX0BvIzzMFh9Yy+6s0pIpc19JtgUDASVciHZ6WfVM2qmfVd37mZUn+QqB76bEWGpvyplIAKQ+rBsfpptScJo1FaWyKA1Yi60gk4lQsLxQKtrG8ydJsfA6fSx5K7yWs3KOnp6c2hlqt5pSAUroqJpszVkiOSf6dz1RapfOoq/gsjmF0dBQzMzNObUbNPUsmk06gnNR4mhis4AulD+vr68PDhw8tJjE2NoYLFy447mRvPcHDw0MTioyfJJNJ+87Y2Ji5kAA4AAsADi8s+6Nx5WAw+FQOlQIu/H6/w/OohOgEWVHoe5UKjTUrkItlhDhOphVwnOSmZJ9isZgzhkQi4RA7k2MYOCuDxnUnJzAPYTQadUpFDQ4O4kc/+pGBKFijleulpbw0oR/onk/uP8bouP/D4bCjlChVHH9WJUP3Js8izwHrRGoIQpPClZRC5zwWi5lSB5wpdurOZmyOc6dnTAU4x8Tv8qLVAgSaL8h4mresmMY/+Ux1z0ejUYsff+tb30IgEDCSgqWlJfj9ftvrly9fdnhV2+22gz/Q1B7KO+6TTCZjNX+Briwtl8tmbGxsbDj5g3pGiS3gv6enp23ONjY2HHnCi5N/Z51glYn1et0UxU6nYxiQarWK5eVlu6gPDg6cnM3+/n4MDw/budnc3MTGxoYRdKiRks/nbSynp6eOHCN/LfvIVDS+xxsuokzT/aGFC1QBo/zmXk+n0zg4OHCAc8TTAC5Y1NueeVkmEgnbbESyctNRI1PB2mw27WVe1o9AIOAksGrcighM1Q60okAwGHSqjpBdRDV7nRy9dFlImpOqBygSiaC/v98O+/HxsW0ajlFjrlqXkIeTfVKUMN+tRUnZN86ftxagHpp0Ou3U/ItGo049tv39fTtULGqrTEmVSsXGrJp8vV53ctW4sShwvReZKkLAWQzw0qVLzmWn32GsS2N4hULBYmu8hPh55utxnlVLJtmBt/6hxrCazaaTg6agFr0cYrGYU3VGASpcp/+vvSvrbeu6uovURFEUSVEiJVl2ZMlTmjhxEnRAWtdo0aIt0ABFi+axDwH60Nf+mv6DvhdFgxQNiqZxE3RwYNdDbEt2LcmiZJLizEuRlKjvgVib6xwp8vcD7gaKmhF575nPHtZe27/UtSj42NiYM9eqGCm/6dzcHLa2tmwfMPbC31JhVCL6Bw8e2LN+8YtfWBt0k1erVaTTaWdTs6IChRYvMERV67oi+IHf5eFFhUhrpSpnKi8ltpmHmV8bkvOpngFd84xb6SGXyWTsOSRK4Lww5sq14ZNpK8l6Op0+5iViezh+Whxa1xR/w6ofjPGzog0PbSqFRHtSkdVi8n7xeOVovXPnjlnvTJ7nuiiXyw4xS6FQcDhNdS3reLNwMZ8zOjqoDaz1YjW3PJfLIZ/P23pjXWJFNiv6N5/Pm3eC6GuOKZmDuNYZY/bR8xw7BcQpZqDRaKBcLjteOeXs5mWp3gCChAA4pOxEYKuSdHBw4BCl00PAv+/v79scE7B5koQxy1BCCSWUUEJ5iZxqWbKcEjDQYPxK4xpTODo6cvj81PIABpqfsuOrpnBwcGAQZ4pfIVwtQgqtF7XSlI3C1/RoWSpbTqVScSDNqq0CwwoM7AvbRYYTflbuTxW/Sry6bCidTsc0UMY5lDWo0Wg4rPjUQoGBG1ZTF1iyh2OTSCTMchwfH8elS5fwpz/9yZ7lxw851gCsNJhfg/LChQv2fFqCHEMyOfEZykXK8WDlc4p6LJSdSFHJHH/VbPk7arLsq18JBxhouWwTK+Lw8/7+vuOaisfjToxvfHz8GBw+CAKbA7rMgIF7jfmefBf3jopade1223FFUu7cueNYjp1Ox+FjVeuY40Itf25uDvF43NpBphe6yTVFgDEetV4UhUqLzx+fk3LSFKXOtcU+TU9Po9vtHuP0ZB+4pzQnbnx83D4fHh6aR6VSqRgCFhjiGHSdMV7Oce33+xZ35X5h/iXbp5gGxu85ZrlcDgsLC/aMf//737h06ZITWtIwBkNXACwf+NatW/a3xcVFW1e0jrRWpJZyUytNywxWKhUUi0VLGbp8+TIqlYqds0SnM7ZarVaRz+et/7FYDMlk0rxGzWbT3KjsE/d2Op1GLpezc4JsaZpdQOvSb3OlUnHGSa1jxoEVY6BhuZMsSx0rZX3iWvWtb65V3gE+LoPeNu7pk+TUyzIejzswaN3wR0dHjr+fXK9KLffw4UPHpF9bW7OEX4IqgGHRV24q/8Lhf9OizbrIdVLU9ckcJI19jo+PO4cASaOB4ebwcxg1J0fdsq1Wy9xvzMdUCjC2U591EkS52Ww6h6DmlgVBYEVl+bsgCBywheZ+cSx4aObzeeuvur2BwUWmIANNy+C7T3K3xWIxmytSV/Ez404KAGIf+AwWEmd7tX9axi0Wi6Hb7doBGQQBksmkQ/WnlIu84DgWCjDQNrDupa4zDRHwWeqqozJIUbBIrVZzavolEgknPqOlthh31LSpIAicUm6US5cuWYL77du3sbu7a4cc17mmPRAeDwwOfk1v4TxxvWazWXtns9m0dnB8mDcNDN2y6j5Vsn4fIKOKnV/7Ug85KsD8/YsXL5zc7OnpaeeQ1JBKpVLB2tqaXQKzs7PIZrM27o1Gw1FC2A7uAc6XHtLENGhsTIFViUQC7XYbf/7zn+2Z2WzW+vv8+XM8e/bMKa9HgBZT5Xgo37x5E++++67NX6PRQK1Wc/axTzlIIZkIMFg/a2trzoXG+ebnkZERW1f9fh+pVMqUWIa/eG5o6I3KtBYU0BQqAjqVF/rg4OAY0IljSSEVI8c1Go06BS729/cdw+IkZV6VKO1vs9lEpVJxsDMXL150jA/l+Oaz+C4dZ19eiobVxvjWHfMdAdihxUGp1Wr45JNP7OJ59dVX8fe//x23b98GMKjowdyhXq93jFNS8+L6/T6CIHC0dW2fX4uPQk1TfdutVssZGE3oZVIwF/Tk5CRyuZzTZ+V2VZDR4uIiMpnMsUtP8wv1sNVnKmoOGIJeKJofR8YdjZcpWKPb7WJ2dtY0pGg06miN/X7fkKpffvmlU3SblQF03lWJ0Tw7LeKtG4EasVodU1NT1n6yCFGOjo4MBcn+KEhDrepisYj9/X0nAVnHkRcx15y2gShAvsMHZvhFiRVtXa1WnTkgSleT75Vgf2pqypQFfS6frSAErk+OOQ9zAHj//fetr7u7u3j8+LETmyF3KjAkqP/Wt74FYAhc0bxKJUPg+FG2t7etPwSIcb5ZvNrfWz5oBhgoKGwz46b++uJcE/nK+QqCwMjK2Qe9XAGXTKBWqxkXM3OLfeuE40XliOOhhAoKwtH1F4lEHMUwCALcuXMHn3/+OYABWnZtbc3aQPINKrLNZtMUmHq97rDQNBoNpFIpA0DRU8FLTOOIgIvSJHc2pdVqmRFCvADBM+fOnbOKT8BgHeXzeQfUpOTpegmdPXvWzl62Sc8mVpTR+Of4+Liz5ijKH624D35WhYoWuiq9h4eH9n0aHZp3yXe2223HS3L//n2Uy2WLMzMPnXvbR7/6Hk2VMGYZSiihhBJKKC+RUy3LXq9nWga1Qo0n0Z0FDGHu1DLm5+eNeQEYWG3pdNpyabSGIbUAvossILz16QZS151qhApd7/f7jmbDHE62QWva9Xo9BEFgLESsNqEoLG2XUjaxTYz77Ozs4NVXXzXNl6kFalkyFYXjpeIjBjVNxve/+5UaCoWCaaSFQsHhoDw8PLS/HRwMSnSxUsP+/j42NjYsvqEuZb5b6cC06oSfC6cWj8afmY+rUHVllqHGyTlhTIXjr6jUyclJFAoFm69cLod2u23tOjo6csqXqWWpCDm2Vy1oTS3xrbDp6WmrCgEMrJsgCByUJKH8dOWzDel02plrjqemK6kVq2g8LTfHHDZFo7fbbWP8+fLLL5HNZq2NxWIRpVLJqcii5ZZ2d3fNAqL1zjbMzs6aFQ8M0bBaM1Crgaj1rHFDwM1bJAOYekG0diutRe6xvb099Pt9c2sqUp35uWwD81s5DxMTE6eigTXuzb3EkI1yI09NTVl7SqUSbt68adRzX3zxBTY3N63OK883PrtWq1n8b39/H9Vq1d5/7do1PHjwwNbkzMyMUzbNPxPVek+lUjY3zB/WMyKdTttcTU9PIx6P2xlJ9DtxDAy1sR3MF+dzZmZmbE2qZ4ljx1AWx++rLMu9vT3bx8w64Fgw7KS4BT23NjY2EI/HHQ+DptPpOPHf7F8mk8H29ratA3rVNE5ONqWXyamXpeYtsUMcCE0+Bgbxhv39fXP/dbtdfOc73zEXIGHXpFWq1WrGRcnizgomYrxKO0bR/CrA3USah0dznguQ7gv2IQgCbG9v20BFo1GHrJq+c00kVxewxmwfPXqEUqlkk5HL5RzlotPpoFqt2ibQZHK2je3XQ5G5WBpHUdcH0xD4mXEbHkBjY2O26Aj6UA7UiYkJm4dyuYylpSVz4QRB4Lg9lYBdN6eCMuLxuONS4cWhAfVSqeQE3OPxuB2IDx8+tOK50WgUi4uLdrkTTMS+vXjxwiEloEv9pMvSL/x7kvuF/avX6wiCwPoxMjJiSe3AQDHa2dlxlCm+k+WTdM2qy8gHeDHfk+uCVGN+OxcXF5HL5Uw5I/8lL2zmIrPUGQndOddXrlwxAnlgULJMCwsrkINgET47Go06cS8qBFz7PmaAbVZ3HUUPNZbo4rhubm7iwYMHNk+ZTMZy/YCBq5gHczqddtKEWFhec27VDaupEewTMDg7/IL03LMcY87t48ePcevWLSe+rhdgt9vFzs6OzT3LX7F9Cjw5PDxEOp02wM/ly5et9NlJopel1hMlUE3/vr+/b+9l+EddzSyjBwzXMudA46rb29sOdeaZM2ecPHVgoHTpWaUpgHpGVCoVW0+k/lPqRjUK6PrlWtczCDge6+Y46FhxDldXV51iEaS11HWgoTpV9I7NwVf+JZRQQgkllFBCAfASyxIY3th0nfmuLLp1qGnTDcQANmnXiDrS5Gz+e3Z21qnyrrRQAByLFhgG7qlZ+O4ZTaZXC4Fwd2oPz58/d8gOer0eisWioznl83nrA8kSgIGmpG0ipJxlxCqVisNuQZcvXV9k6ufYqOtCtXNg6PJmHxqNxjEAEJ/LBHNNFFbNcHp62gEL0V0BwLF8+fcgCI5BwUnFBQwRhD6zk58wrNbf9va2WUDb29tIJpP2/Xq97lQToFsNGKw5rVBRrVbRbrcd74cG908jRdaUGY6v78nQJP2NjQ1D5T569Ai7u7vmRVhdXTXLmGXa+Ftq/gqCUCtW3dkAcO/ePaedXAeTk5M4d+6ctYEIaE2zUIu91WqhXq874CgAtn43NjaccYpEIraG6H6nFUf3qQKaNNFbx1mtSE0hAYbsUGwzy4hp6sbs7KwD5Gg0Gk4alM6NrjO6s/25UwSvWjMK2lGLme5dtvfg4MDGZX19HTs7O7YG33zzTVy6dMk8IaVSCUEQmOcjEomYJc+QFt/b6/WQzWbNxfnw4UOnvJR62vhZRcGNSk7BM0+pDJX5qN1u4969e+ZNisViaLfbVgmm1+vhiy++ADCodLK4uOiwBY2OjjqoaXW7+gxveoYp4Qvp7NgHrieuxyAIsLm5aRYukcw6Blru0R8fErkAw5Ae1xip85gR0O/3HST4aWfGS9Gw7LDP1tPv962kDTA4QFlRHhi4zJQ3kRRivIi0LiHjkdzINLO5WGhWa6oJK2IAbpxH8+kODw9Rr9cd91u1WnViFNls1mHV0c3B7yvrkLLFKNyZCDSOx87Ojh0GFM0n1EtJ+TD9i5KbnGPR6XQcLtVIJIK5uTnrY6VScVhTgKFbLZPJOEhMv9pGJBJxGFSIjNN0IGCgCHFM/O9o3h8wdH9yXfBy49w/evTI8myBYZ1CYFh6R5FuExMTppCR8kyrXOjm9der/ltdXrws+XliYsJyCfmevb096//8/LwxOAGDi4iuwkKh4LjyCFPXeC/z0dgW3VuKmNY0qGg0itdff93KGBHGz8tzZ2cHtVrN5mV2dtZxj5ITk66wZrPpPFtTJlhth/1lPImH5uHhodFXcg3o2PqIW64vnx96enraKO84Vq+99podZKOjo05NUmCYt9tqtVAoFJw6r1plhNVsOA+8fNgGXh7lctl+Q/eclghrtVp2aD99+hTNZtP208LCAubn5w0f8PjxYzx48MDGY25uzqjjeI4og1Kz2bQyaPl83ikdyHZwDfplArm/+f+qHGg8ncoalefR0VHMzs7aZUk3rPLB8vzj3qaywpg4Q22tVsu5mI6OjsytCbhGzNtvv2353eQY1zq0mr7SbrdRLBatT6lUyjn/G42Gk+fbbredeCgAx6jJZDJG2VcqlXDv3j3bN8DpCFiVlwJ81MeuGjEPWoVgx2IxR2skATMwjPfppGrMU+NapIFSCiclRKBVwAHRzgZBYHEaxhRVa2CqAgCjXFKOT7W8SGDAdh4dHdlBnk6nnVSafr+PZDJp/dvc3EStVnOeraTuLBbM5/KdBEpprEVzTH0IPy9karuEx1NhUF8/y2nxECS0nf1jvToNfutBqDE9toFQf53X0dFRW7yawwQMS0lRmJZApWJ2dtYOmFQq5eREMVarFysBQ8CQgtA/SIDhAc//7o8xwUR8r6/Va1Fsxge1pBwtNx6wPASZOqEE5/puTW0AXPJ3BSUdHR0hm80aKfmzZ8+cGpvdbhe1Ws3WZzabRS6Xs/dub2/jlVdesQP5008/dTR9zSdUXlhgWMrOVzg0v5Wih5i/fughUiBfpVKxg6vf7zuK38HBgUOTpmkNpMPkWm2329jc3LT+M76mvMh63mgtRR7uzFvlumm326hWq3axMOVC6SFTqZQpEXNzc8hkMvj0008BDC5wvodcunqWFItFxzOgfMXRaNS57P3UEQopPTV+rEBLEk5w75JqjikttKqUTIb7k4q4pl2odc738rylp4NrRxW/X/3qV6bolUolzMzM2PlN2lCecaVSySF0pzLJNpbLZXQ6HWuHpp4QJMZ459zcHFZWVpw8y5mZGUu1oYXKd59W/DmMWYYSSiihhBLKS+RUy9L3P9M9Aww0u06nYy6MSCSCRqPhJJJqeSu6bXiDs3QKMNDy9EYnmkvJb/1kWI0NaPyv0WiY9jY5OWmuAv4uEomYFUP6LLWktOgtq0nQDaHUXYwNqJanCcwsjKpxTY0dUKvyRS1ZzoFaT75lou49HUt1e1Lz3drawu3bty2Jenx8HGfOnLE29Xo9ZDIZx22mjPwa91KaQ8Yt+Ux1nZLuTS2pyclJpyRZIpGwMWaRYmDowqRWzYoU1FhJHM51yio3mvBP0TJETOhnm0h/p2xLSuTsl+xiTJLfVyuLRBRqhY+NjTlIPz9tRcmodV10u10n3h6Px63yS6FQQL/fd8atXC4bwnVjYwPpdNqsgpmZGYfA/utf/7pZmXw/99Pe3p7R47H/3DsUTbHSvetXfen3+44rUWNNTD3gc+kZUGSiuqx1TjV0wHFV0n0ShPgECHqmAK7FxhAI11ez2USxWDQrpdVqYWZmxqE+LJfLePjwIQDgs88+Q7FYtDBPqVRyUoQikYiNKS1JnpecXx0bn3TjpLEn3Zy6IXVt9Xo9tFoth+1M0yRyuZwTPjs8PDRLcWJiAisrK+a1qlarTpiGdwLH3A9v6Fq4ceMGfv7znwMAfve736HZbDpnpZJcBEGAXC5nZ8r6+jrK5bKTEVCv153wmBZu2N3dNev/ypUriEajtq8YQtKQg6a+neaSPfWyVD8xARBqrmsOWaVSwdbWlkOjpCWGOIFKv0Xf+Pz8vFU+BwaXiroDuYj92CPdiT/60Y/sv/usOgCcUjCtVst87vfu3bMcHmA4aXwPWYnoSlGXInP6lNGn3++bC2NmZgZLS0tOzJKUV8Aw/qdjwjHXiip0w/I98Xgc9Xrd3CiFQgGRSMTKBsViMcTjcYf5hQulXC5jenoa3/zmNwEAb731luM2Yj1Adbdp3JrsS767SF3DR0dHDmUeRSsVdDodG9NUKoVcLucE5HkQNptNNJtNh56wXq/bu7PZLLLZrMMNq/FBX/TQ9WHnvEA55spOReVN88J6vZ7FhXSz0XWqrn91JXNu9dDXta5tbLVajuIUiUQMVLS8vIzHjx9b35eXl51Y+zvvvINut2trvd1uY3t7+9hFBAziVvV63cmbZBUPtkMVP5/2UdNkNGbpA784vlz7fqrE/v4+/ve//xkGgTmXylbFMWe1EA0HaLys1+s5pdOo/PisX8Vi0ca0Xq87jF7VahWlUsmpc7qysmJ9qlQq+Pjjj/GPf/zD+sOzDBgqjnyWppXwstD84/ky+BbqAAARqElEQVT5eafSi46dXpqao0hcBj+nUiknZ5H5w5oq4+8/zacnyAkYpivxt8x7pXCcdC2o69UH/f3whz8EAPzlL3/Bs2fPnJJ4AJzwUDabdUCPW1tbprQAg/NVFVDGJM+ePYvV1VVzx0ciEafsFtcT+8j0OM6xhh98OfWy1EORGgsHiPXyaG08evTISRRljqbGzJRIQMt5ra2tIQgCXLt2zX4LuJtLUZ0AnPgMtW1gcDnwwr506RLi8bhTS3B8fNwmpVqtYnd31y6P73//+1ZbDRhsBiatAnAIwhmzpHz44Yd48OCBxZQuXbpkQBFgSGvGyfCJxFUUqcnAt14AlUrF0Gs7Ozvo9/sGLJqbm0MikXBqylFTvH79OpaWlhwLPRqN2sK5e/euY7Xy32wzLVLNv52cnHQ2I4P3fu6janZax69UKuHw8NA2r5beqVarzkFMcJNaNMqryn5wflXD1svQ566kQuLHFX0riTI2NoZqtWrrQhHFRI0rpWKtVjNFsFarOSAzEk/rZUsh7y8wWLsKkLt27RrW1tZsfDKZjFOqaXV1Fclk0g6ker2O3d1dS6h/8eKFrYOpqSmnbBgR5crFrLm1PhpWx6lWqzngLj6b4+3jILQINcEXnL8bN27gzTffdBRwrqMnT57go48+MvR5r9fD/Pw83nvvPQADy3lubs6U1Wq16owz2856rBwj3yrr9Yb1bMm3y/7evXsXn3/+uY35hQsXMD8/bzH3u3fv2gFfKpUs9gkMyVNUEWcsHBievQrCorBcGzC0stiHZDLp4DuIYfB5fBUPQmAcMFiv9EStrKw4yPparXbMOlf+Ye5N33rnbwmMunbtGh4+fOggtw8ODkwRIsiIbZ2ensa5c+dsXVQqFSeHlmhtYFAkW4kkqJBxzbF2pxpevd6wrONpaNgwZhlKKKGEEkooL5FTLctWq+UUk6VmDwy0BkWyRaNRQ6BRFPnG32psh88mK8ujR4+cZ6mo9koU3C9/+UsAQzcrMHCrKEo2Fos5RWsnJiYsjlOpVJzyLNvb21hdXTXN6tvf/jaWl5dNI1QteWxsDM+ePcMf//hH++2NGzdw48YNALDcQaXK03iNaoqq+dElojSC+v+MA9Pde/78eSfexrZT08zlcmZZjo2NOW4GViznsxYXFy0WxnEeHR3F5uamjS3bzjFlKoeSIytxM6tQ0M1RLpcdjwWrC/D7c3Nzx6pO8Nk+7Zo/jnSznRR3UHcy+67rQl3f1EY1DWZkZMRcXclkEp1O51gqBJ/lo0ZLpZLFB8vlskMtx3WhZeMoQRDY/lJXLjBIB7p69Spu3rxp46BsRnRBss1nzpzBtWvXrE+FQsH2Sa1Ww/r6uq1zxuPVJaxz7rs01e3+3//+F9/4xjcADAsCaFUPXwqFAj777DMAg5JX58+fx49//GMAwM9+9jOsrKwYy83m5qaFHwj/57pPpVK4cuWKnQWRSMTxZDG/zkcf83sAjqUfMARDqyWdTptXARhQDK6vr9vfb9265TASVatVJyapa5ntYqhoYWHBmMvYXsANW1GUnJ4IfO5rxvL4HFaL8sutqfUXBIHN4dLSkp0d3PeURqOBTqfjFDJQxh6WFFOvCkU9WG+//Tb+8Ic/OG7UTqdzLKyhlJhjY2N2J9B7xXacP38eKysrAAZrTlNumHurnw8PDx1awWazeSzf+SQ59bJUaDYXPgeZ4Bb6++nS00llmRwOFuNpfDYHdWFhwVnYDLIrRymfBwwOnMXFRfzgBz8A4KZTBEFgLsorV65gaWnJ/tbpdDA+Pm5tuHz5Mp4/f27ff/jwoVWYAAYLVtNj1L24u7uLDz/80ODQy8vL+O53v2ublXRb6nZW2iklPtDF77sw/FxCpr7Q9cc0G27YWCzmxCzq9brNAes9MqaSTCYxPT1tFyw5UDU/ql6vW6K8phroRtAUmsnJSacUGS8vfr9SqaDT6dgFvbKygkwmYwuf+XGAm5eq48+DQJPQtX0UXYsnJXjr3zWWXSwWkUgkLN7FhGiN+4yOjtpcK1iB46c8swqKoCtQ17r/maLVZVKpFGZnZ+3v4+Pj+NrXvoZPPvnExlVjdpubm04IotFooNvt2kHfarUccIxy8NZqNScfl2Ws/INbuXMpa2treOedd+x7yqfMklccx2KxiEKhYM/pdDp4+vQp/vnPf9q4JhIJuyC19uz09DTee+89R5lPJBL2uV6vGyEGMLi4tKILD8etrS2HeEUVNwJH6D6ky1MxD48fPz5WnUkpQdU40HmmwqEYCJ+CUVPK/Oo6XF908SudH1PA2E8FCjGnmK7V9fV1bG9vO+l0HOPnz58jGo2asp1MJjE1NWWfWV+V5wl5dnkRaU56rVYzl/Mrr7yClZUVJ2VI9znXHc/IiYkJzMzMOHmXCwsLBhAlfR7HWase0VhSEKeubZ7LJ6Wb+XLqZRkEgRMcVcAOtW+td6mBfyao6m8VtQQMY0sa22OHNQjLXElqW7u7u3j//fctZvns2TNcvHgRwGBRERG4vr5uCa0AjMRZYxCvv/66LdJyuYy9vT3r071797C2tuYsfo4HFwXjrG+88YaTz8NLTgEVepgztgnAUUJoaek7dXInJyextLRkmiQXrNYeVGDD9PS0XUS0MrnYmd+kF5vGkqPRqMO2w3cqkToL0fIQoPdAyeo1347KCzdONBpFqVSyg0zBTNRaeRjPzMw4uaxKUg+4NQrZFor+m8/lWm6323jx4oXlXpHhg+PGA03zxxKJhFlmW1tbjpbMUnD8brfbdQ5C1ipkW/3DllIul+175XLZWcv9fh/z8/NmmZDcQVG4jUbDxpUxf8bQNF+13+87VpCSjLO9qvCQaEDjXpRarWaHIJGWHDfGQtXyXl5extWrVwEA7777LtbW1pzE/VgsZn/v9/tO8QX1dE1NTTmFm5mHTSWGdVV9y+769eu23pvNpmMpJRIJZ96SySTu3btn/80vlXX27FmcPXvW+r+3t+ewRLFsHzCsJ6rlyDTGR5yCkrpTSM4BDHEgimZlbiUwWLN6WfZ6gwLh3NP5fN76DcC5OGdmZhykOstb8VlETfPs7na7qNfrzhlJUaa0Cxcu4Cc/+Ql+//vf21z5YK2Dg2ERCvJzK3o2kUgcMzg4jj4puwLoyG7E7zMmrQQlXyVhzDKUUEIJJZRQXiKnWpZaVoWMEj4bi289qmh8iVofv59Op48h0/R3wFDDJX8fY4vxeBw3btwwbUHNfWBoYdy9exfnzp0zc91nlu/1elhYWDAt5P79+6hUKg6CV6nINGdzbGwM2WzW/OgXLlxwCgQzDqbWSKfTMSvJh4Kzz8oswnFXK4huEVrVjONpLDmTyZibVn376XQauVzOsf61gHC5XMazZ88cBpMnT54YslZTKTS+d3AwLH5M17PGTNLptLXBj902Gg0nh1GrliSTSaRSKesLU0OUtaXb7TpWrbZN16Yiiukh4ZjSRcdxeeONNwzRyPlResV6vW6uSmBgATEmSXenpkzpnuGYq/Wrf1fZ2tqyNk9NTWFhYcHJD4zFYuYiZCoI21gsFjE/P29jF41GHS+DujSfP3+OFy9eOOkuQRA4MV11GWpoBnDTcA4ODgxxu7y8bHMIwGJl3ENkSlLLRekno9GoE/qgSxuAcRbz3alUCpOTk9YneqEUF+HHrIHBWcH20CJSN6zuebI4sb2/+c1v8PHHH1vc+LXXXrPqLpwDjeeqV42ofMUBJBIJ22NElvrYBcBl5WLJPo3rawhL0eOcq0QiYefW2bNnHczBwsKCrSlmD3A/kYKO361Wq1ZpBRjsk2q16hS/1vFmm+bn53H9+nUbNxagV9yGVjJi/rp6QhTBCwwtS65hRcSrR4ExWw3faJrYaVVHTr0sM5mMww/J/wHD2m++S8LPF+Ni9cv5RKNR28h+Yn2v18PExIR1nOVaGCv44IMPsLq6agFo393GNj158gR37941wIHPW0riA148o6OjRobMNmrtRT3IWd+Q/vpUKuVcasw34zgwXqr5TBSNrZDLUd0uIyMjTn7WyMiIXbqM92mSP6m1+GyNgyqBPd1vShStear7+/uW1sPfc5zUHe8DmcjdqPOsnzUdhbEz5VJVsutEIuEACtQ9w0tMqaqolPBdui40Janf79tmbrVaDkk7uV/10NbyT5wj/n5jY8MOWu4XjYHoZcK4vu8OPAnI8Z///McUvampKScXmSkBjP2SZo7zwlQi9onE6wreYj7j9vY28vm8s5c1X47pAJo/6XP2UpLJpLmnd3d3HcWNLmFNYte1TV5n7aMm0fuUhBpHZtyO3+Xa4Hxwnfku/JmZGSeuyBAO30/CCv49FovZGM7OzuK3v/0tPvjgA3umpquwKALHYmtry+KvvCR9Xl2dfwXL6IWXTCZt7XHM2E8aOPx+qVRyXLykEVUlIhaLWZtTqZTtHxZWoPHR6/WwuLjoKMJBEJgRwzxoLZNGoQIGDJSiixcvWsreX//6V1QqFVsHiUTCUSKJM9ExmJiYsHNMjY1yueysX7ru2Sf2xQ+t/H/k1Mvye9/7Hj766CMAQ4COxpMYr+LLNSeJbBUK0lELSC02Po9CYAAtxlqthq2tLatr+NOf/hTlctk0GtW0dHF3Oh3cunXLyUlaWFg4dhFx887MzBzjptQgu6JheahroromZJPMWBcMkWm+lMtlu3S52DW4rWPFy0Evm6WlJScxX+cpEonYAiEPJH/bbDYdHsX19XWHQLtarToACAoPKvaz2Wza5js8PES1WrX5nJ6eRrfbdQAwytnJPC0lZtc8PWW/YUxO47WatE40nn8RcX40LqFWyuHhocOuxLgjv08UIA+4er2OQqFgyhpzRdl+Fa4ftTRVOdD8Tn6fcufOHdPy5+bmkMvlnOLp/X7flKT5+XmUSiVb69Vq1VF8iAtQq1xrz+r/M+asHhhlkeJhrMotRQnoNzc3HTSpDwChUqyi3ifG45QdSosraHxxdHTUiZEdHR2hVqs5yovyzLJvV69etbX8+PFjtFotm0NWWuFzGRvkXJMrlUhMFuvmOtEajnfu3MHOzo5TPKLX6zn7Ro0RAiZPilkqoOfg4ACxWMzxkszOzlqfGo2God51DNmHRqOBzc1Nh2FLFXn1BCwsLDggqZGRETQaDed80Rxi3X+quNBSfOuttwAAf/vb31Aul50YOPOKORaNRsPJ3c7n8/aeqakpm88XL144MUkCHNXKTSQS9i4SzfjMUCdJGLMMJZRQQgkllJfIqZbl9evXrVbbkydP0O12zaWiaD7geFURIt9Uc2TOHeDm4NAq86se8Lu7u7tIJBL49a9/bc8mc4cvBwcHTmyqXC5besfs7Czy+bz56xlXVP/++Pi4aevRaNRpZzKZdCDYCjPnmLDNjGlpPqJPQ0XZ3Nx06jZWq1UnDqfWip+r1mg0kM/nTeOLxWJYWFiw56kWWavVnLpwtMpYXqpSqTiuvNu3bztUWsrhSc2MiEtFqkUiERs3vl9TJxTZRveeWgjq9tIxowtWuSc1lkieSmrrPvuP/lstUo4r+0frXanHVEve2dlBPp93KAu1zRqT5WeteMK2A0ML6aTUEa0eUSwWnbXLOBPXw8WLF3H//n3bn2QGUveTQvTHxsYc5KyOqx9DpTeAFiLn5CQEoXIrFwoFrK2tGWKXVppaivouWqVqqWrdQ3W5k+tXGcZarZazNtRKpBdIPVsA8K9//ctc2axZStc3zy9dB7FYzCl3NTIyYvmpGxsb2NvbM4+YpquwZJ+y0qgHSJl72FeeGb5MTk46bdKzk6ktXCdMV+N76RXQfNNoNGpWmk93GY/HHYYsRW9zDSjjFll+fFEPAcNbpKjzc9J9q7pWq+Hp06cWLiJjGJ+3urpq63p3dxf9ft9y6cfHx1EoFGy8MpmMU+Eqm806++ir8AMAEDkNKhtKKKGEEkoooYRu2FBCCSWUUEJ5qYSXZSihhBJKKKG8RMLLMpRQQgkllFBeIuFlGUoooYQSSigvkfCyDCWUUEIJJZSXSHhZhhJKKKGEEspL5P8AQdMNc3+TgIgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x576 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  Load Face dataset\n",
+    "data = loadmat(os.path.join('ex7faces.mat'))\n",
+    "X = data['X']\n",
+    "\n",
+    "#  Display the first 100 faces in the dataset\n",
+    "displayData(X[:100, :], figsize=(8, 8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.4.1 PCA on Faces\n",
+    "\n",
+    "To run PCA on the face dataset, we first normalize the dataset by subtracting the mean of each feature from the data matrix `X`.  After running PCA, you will obtain the principal components of the dataset. Notice that each principal component in `U` (each column) is a vector of length $n$ (where for the face dataset, $n = 1024$). It turns out that we can visualize these principal components by reshaping each of them into a $32 \\times 32$ matrix that corresponds to the pixels in the original dataset. \n",
+    "\n",
+    "The following cell will first normalize the dataset for you and then run your PCA code. Then, the first 36 principal components (conveniently called eigenfaces) that describe the largest variations are displayed. If you want, you can also change the code to display more principal components to see how they capture more and more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 36 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  normalize X by subtracting the mean value from each feature\n",
+    "X_norm, mu, sigma = featureNormalize(X)\n",
+    "\n",
+    "#  Run PCA\n",
+    "U, S = pca(X_norm)\n",
+    "\n",
+    "#  Visualize the top 36 eigenvectors found\n",
+    "displayData(U[:, :36].T, figsize=(8, 8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.4.2 Dimensionality Reduction\n",
+    "\n",
+    "Now that you have computed the principal components for the face dataset, you can use it to reduce the dimension of the face dataset. This allows you to use your learning algorithm with a smaller input size (e.g., 100 dimensions) instead of the original 1024 dimensions. This can help speed up your learning algorithm.\n",
+    "\n",
+    "The next cell will project the face dataset onto only the first 100 principal components. Concretely, each face image is now described by a vector $z^{(i)} \\in \\mathbb{R}^{100}$. To understand what is lost in the dimension reduction, you can recover the data using only the projected dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The projected data Z has a shape of:  (5000, 100)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Project images to the eigen space using the top k eigenvectors \n",
+    "#  If you are applying a machine learning algorithm \n",
+    "K = 100\n",
+    "Z = projectData(X_norm, U, K)\n",
+    "\n",
+    "print('The projected data Z has a shape of: ', Z.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next cell, an approximate recovery of the data is performed and the original and projected face images\n",
+    "are displayed similar to what is shown here:\n",
+    "\n",
+    "<table>\n",
+    "    <tr>\n",
+    "        <td><img src=\"Figures/faces_original.png\" width=\"300\"></td>\n",
+    "        <td><img src=\"Figures/faces_reconstructed.png\" width=\"300\"></td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "\n",
+    "From the reconstruction, you can observe that the general structure and appearance of the face are kept while the fine details are lost. This is a remarkable reduction (more than 10x) in the dataset size that can help speed up your learning algorithm significantly. For example, if you were training a neural network to perform person recognition (given a face image, predict the identity of the person), you can use the dimension reduced input of only a 100 dimensions instead of the original pixels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  Project images to the eigen space using the top K eigen vectors and \n",
+    "#  visualize only using those K dimensions\n",
+    "#  Compare to the original input, which is also displayed\n",
+    "K = 100\n",
+    "X_rec  = recoverData(Z, U, K)\n",
+    "\n",
+    "# Display normalized data\n",
+    "displayData(X_norm[:100, :], figsize=(6, 6))\n",
+    "pyplot.gcf().suptitle('Original faces')\n",
+    "\n",
+    "# Display reconstructed data from only k eigenfaces\n",
+    "displayData(X_rec[:100, :], figsize=(6, 6))\n",
+    "pyplot.gcf().suptitle('Recovered faces')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5 Optional (ungraded) exercise: PCA for visualization\n",
+    "\n",
+    "In the earlier K-means image compression exercise, you used the K-means algorithm in the 3-dimensional RGB space. We reduced each pixel of the RGB image to be represented by 16 clusters. In the next cell, we have provided code to visualize the final pixel assignments in this 3D space. Each data point is colored according to the cluster it has been assigned to. You can drag your mouse on the figure to rotate and inspect this data in 3 dimensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support. ' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option);\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"432\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# this allows to have interactive plot to rotate the 3-D plot\n",
+    "# The double identical statement is on purpose\n",
+    "# see: https://stackoverflow.com/questions/43545050/using-matplotlib-notebook-after-matplotlib-inline-in-jupyter-notebook-doesnt\n",
+    "%matplotlib notebook\n",
+    "%matplotlib notebook\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "\n",
+    "A = mpl.image.imread(os.path.join('bird_small.png'))\n",
+    "A /= 255\n",
+    "X = A.reshape(-1, 3)\n",
+    "\n",
+    "# perform the K-means clustering again here\n",
+    "K = 16\n",
+    "max_iters = 10\n",
+    "initial_centroids = kMeansInitCentroids(X, K)\n",
+    "centroids, idx = runkMeans(X, initial_centroids,\n",
+    "                                 findClosestCentroids,\n",
+    "                                 computeCentroids, max_iters)\n",
+    "\n",
+    "#  Sample 1000 random indexes (since working with all the data is\n",
+    "#  too expensive. If you have a fast computer, you may increase this.\n",
+    "sel = np.random.choice(X.shape[0], size=1000)\n",
+    "\n",
+    "fig = pyplot.figure(figsize=(6, 6))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "ax.scatter(X[sel, 0], X[sel, 1], X[sel, 2], cmap='rainbow', c=idx[sel], s=8**2)\n",
+    "ax.set_title('Pixel dataset plotted in 3D.\\nColor shows centroid memberships')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It turns out that visualizing datasets in 3 dimensions or greater can be cumbersome. Therefore, it is often desirable to only display the data in 2D even at the cost of losing some information. In practice, PCA is often used to reduce the dimensionality of data for visualization purposes. \n",
+    "\n",
+    "In the next cell,we will apply your implementation of PCA to the 3-dimensional data to reduce it to 2 dimensions and visualize the result in a 2D scatter plot. The PCA projection can be thought of as a rotation that selects the view that maximizes the spread of the data, which often corresponds to the “best” view."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Subtract the mean to use PCA\n",
+    "X_norm, mu, sigma = featureNormalize(X)\n",
+    "\n",
+    "# PCA and project the data to 2D\n",
+    "U, S = pca(X_norm)\n",
+    "Z = projectData(X_norm, U, 2)\n",
+    "\n",
+    "# Reset matplotlib to non-interactive\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig = pyplot.figure(figsize=(6, 6))\n",
+    "ax = fig.add_subplot(111)\n",
+    "\n",
+    "ax.scatter(Z[sel, 0], Z[sel, 1], cmap='rainbow', c=idx[sel], s=64)\n",
+    "ax.set_title('Pixel dataset plotted in 2D, using PCA for dimensionality reduction')\n",
+    "ax.grid(False)\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#utils\n",
+    "def displayData(X, example_width=None, figsize=(10, 10)):\n",
+    "    \"\"\"\n",
+    "    Displays 2D data in a nice grid.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The input data of size (m x n) where m is the number of examples and n is the number of\n",
+    "        features.\n",
+    "    example_width : int, optional\n",
+    "        THe width of each 2-D image in pixels. If not provided, the image is assumed to be square,\n",
+    "        and the width is the floor of the square root of total number of pixels.\n",
+    "    figsize : tuple, optional\n",
+    "        A 2-element tuple indicating the width and height of figure in inches.\n",
+    "    \"\"\"\n",
+    "    # Compute rows, cols\n",
+    "    if X.ndim == 2:\n",
+    "        m, n = X.shape\n",
+    "    elif X.ndim == 1:\n",
+    "        n = X.size\n",
+    "        m = 1\n",
+    "        X = X[None]  # Promote to a 2 dimensional array\n",
+    "    else:\n",
+    "        raise IndexError('Input X should be 1 or 2 dimensional.')\n",
+    "\n",
+    "    example_width = example_width or int(np.round(np.sqrt(n)))\n",
+    "    example_height = int(n / example_width)\n",
+    "\n",
+    "    # Compute number of items to display\n",
+    "    display_rows = int(np.floor(np.sqrt(m)))\n",
+    "    display_cols = int(np.ceil(m / display_rows))\n",
+    "\n",
+    "    fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)\n",
+    "    fig.subplots_adjust(wspace=0.025, hspace=0.025)\n",
+    "\n",
+    "    ax_array = [ax_array] if m == 1 else ax_array.ravel()\n",
+    "\n",
+    "    for i, ax in enumerate(ax_array):\n",
+    "        ax.imshow(X[i].reshape(example_height, example_width, order='F'), cmap='gray')\n",
+    "        ax.axis('off')\n",
+    "\n",
+    "\n",
+    "def featureNormalize(X):\n",
+    "    \"\"\"\n",
+    "    Normalizes the features in X returns a normalized version of X where the mean value of each\n",
+    "    feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when\n",
+    "    working with learning algorithms.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        An dataset which is a (m x n) matrix, where m is the number of examples,\n",
+    "        and n is the number of dimensions for each example.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    X_norm : array_like\n",
+    "        The normalized input dataset.\n",
+    "    mu : array_like\n",
+    "        A vector of size n corresponding to the mean for each dimension across all examples.\n",
+    "    sigma : array_like\n",
+    "        A vector of size n corresponding to the standard deviations for each dimension across\n",
+    "        all examples.\n",
+    "    \"\"\"\n",
+    "    mu = np.mean(X, axis=0)\n",
+    "    X_norm = X - mu\n",
+    "\n",
+    "    sigma = np.std(X_norm, axis=0, ddof=1)\n",
+    "    X_norm /= sigma\n",
+    "    return X_norm, mu, sigma\n",
+    "\n",
+    "\n",
+    "def plotProgresskMeans(i, X, centroid_history, idx_history):\n",
+    "    \"\"\"\n",
+    "    A helper function that displays the progress of k-Means as it is running. It is intended for use\n",
+    "    only with 2D data. It plots data points with colors assigned to each centroid. With the\n",
+    "    previous centroids, it also plots a line between the previous locations and current locations\n",
+    "    of the centroids.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    i : int\n",
+    "        Current iteration number of k-means. Used for matplotlib animation function.\n",
+    "    X : array_like\n",
+    "        The dataset, which is a matrix (m x n). Note since the plot only supports 2D data, n should\n",
+    "        be equal to 2.\n",
+    "    centroid_history : list\n",
+    "        A list of computed centroids for all iteration.\n",
+    "    idx_history : list\n",
+    "        A list of computed assigned indices for all iterations.\n",
+    "    \"\"\"\n",
+    "    K = centroid_history[0].shape[0]\n",
+    "    pyplot.gcf().clf()\n",
+    "    cmap = pyplot.cm.rainbow\n",
+    "    norm = mpl.colors.Normalize(vmin=0, vmax=2)\n",
+    "\n",
+    "    for k in range(K):\n",
+    "        current = np.stack([c[k, :] for c in centroid_history[:i+1]], axis=0)\n",
+    "        pyplot.plot(current[:, 0], current[:, 1],\n",
+    "                    '-Xk',\n",
+    "                    mec='k',\n",
+    "                    lw=2,\n",
+    "                    ms=10,\n",
+    "                    mfc=cmap(norm(k)),\n",
+    "                    mew=2)\n",
+    "\n",
+    "        pyplot.scatter(X[:, 0], X[:, 1],\n",
+    "                       c=idx_history[i],\n",
+    "                       cmap=cmap,\n",
+    "                       marker='o',\n",
+    "                       s=8**2,\n",
+    "                       linewidths=1,)\n",
+    "    pyplot.grid(False)\n",
+    "    pyplot.title('Iteration number %d' % (i+1))\n",
+    "\n",
+    "\n",
+    "def runkMeans(X, centroids, findClosestCentroids, computeCentroids,\n",
+    "              max_iters=10, plot_progress=False):\n",
+    "    \"\"\"\n",
+    "    Runs the K-means algorithm.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The data set of size (m, n). Each row of X is a single example of n dimensions. The\n",
+    "        data set is a total of m examples.\n",
+    "    centroids : array_like\n",
+    "        Initial centroid location for each clusters. This is a matrix of size (K, n). K is the total\n",
+    "        number of clusters and n is the dimensions of each data point.\n",
+    "    findClosestCentroids : func\n",
+    "        A function (implemented by student) reference which computes the cluster assignment for\n",
+    "        each example.\n",
+    "    computeCentroids : func\n",
+    "        A function(implemented by student) reference which computes the centroid of each cluster.\n",
+    "    max_iters : int, optional\n",
+    "        Specifies the total number of interactions of K-Means to execute.\n",
+    "    plot_progress : bool, optional\n",
+    "        A flag that indicates if the function should also plot its progress as the learning happens.\n",
+    "        This is set to false by default.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    centroids : array_like\n",
+    "        A (K x n) matrix of the computed (updated) centroids.\n",
+    "    idx : array_like\n",
+    "        A vector of size (m,) for cluster assignment for each example in the dataset. Each entry\n",
+    "        in idx is within the range [0 ... K-1].\n",
+    "    anim : FuncAnimation, optional\n",
+    "        A matplotlib animation object which can be used to embed a video within the jupyter\n",
+    "        notebook. This is only returned if `plot_progress` is `True`.\n",
+    "    \"\"\"\n",
+    "    K = centroids.shape[0]\n",
+    "    idx = None\n",
+    "    idx_history = []\n",
+    "    centroid_history = []\n",
+    "\n",
+    "    for i in range(max_iters):\n",
+    "        idx = findClosestCentroids(X, centroids)\n",
+    "\n",
+    "        if plot_progress:\n",
+    "            idx_history.append(idx)\n",
+    "            centroid_history.append(centroids)\n",
+    "\n",
+    "        centroids = computeCentroids(X, idx, K)\n",
+    "\n",
+    "    if plot_progress:\n",
+    "        fig = pyplot.figure()\n",
+    "        anim = matplotlib.animation.FuncAnimation(fig, plotProgresskMeans,\n",
+    "                             frames=max_iters,\n",
+    "                             interval=500,\n",
+    "                             repeat_delay=2,\n",
+    "                             fargs=(X, centroid_history, idx_history))\n",
+    "        return centroids, idx, anim\n",
+    "\n",
+    "    return centroids, idx"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/exercise8.ipynb b/Phase 3 - 2020 (Summer)/exercise8.ipynb
new file mode 100644
index 000000000..50272b50e
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/exercise8.ipynb	
@@ -0,0 +1,1498 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Programming Exercise 8:\n",
+    "# Anomaly Detection and Recommender Systems\n",
+    "\n",
+    "\n",
+    "## Introduction \n",
+    "\n",
+    "In this exercise, you will implement the anomaly detection algorithm and\n",
+    "apply it to detect failing servers on a network. In the second part, you will\n",
+    "use collaborative filtering to build a recommender system for movies. Before\n",
+    "starting on the programming exercise, we strongly recommend watching the\n",
+    "video lectures and completing the review questions for the associated topics.\n",
+    "\n",
+    "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n",
+    "\n",
+    "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading\n",
+    "\n",
+    "\n",
+    "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n",
+    "\n",
+    "\n",
+    "| Section | Part                                             | Submitted Function                | Points |\n",
+    "| :-      |:-                                                |:-                                 | :-:    |\n",
+    "| 1       | [Estimate Gaussian Parameters](#section1)        | [`estimateGaussian`](#estimateGaussian)      |  15    |\n",
+    "| 2       | [Select Threshold](#section2)                    | [`selectThreshold`](#selectThreshold)       |  15    |\n",
+    "| 3       | [Collaborative Filtering Cost](#section3)        | [`cofiCostFunc`](#cofiCostFunc)          |  20    |\n",
+    "| 4       | [Collaborative Filtering Gradient](#section4)    | [`cofiCostFunc`](#cofiCostFunc)          |  30    |\n",
+    "| 5       | [Regularized Cost](#section5)                    | [`cofiCostFunc`](#cofiCostFunc)          |  10    |\n",
+    "| 6       | [Gradient with regularization](#section6)        | [`cofiCostFunc`](#cofiCostFunc)          |  10    |\n",
+    "|         | Total Points                                     |                                   |100     |\n",
+    "\n",
+    "\n",
+    "\n",
+    "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Anomaly Detection \n",
+    "\n",
+    "In this exercise, you will implement an anomaly detection algorithm to detect anomalous behavior in server computers. The features measure the throughput (mb/s) and latency (ms) of response of each server. While your servers were operating, you collected $m = 307$ examples of how they were behaving, and thus have an unlabeled dataset $\\{x^{(1)}, \\dots, x^{(m)}\\}$. You suspect that the vast majority of these examples are “normal” (non-anomalous) examples of the servers operating normally, but there might also be some examples of servers acting anomalously within this dataset.\n",
+    "\n",
+    "You will use a Gaussian model to detect anomalous examples in your dataset. You will first start on a 2D dataset that will allow you to visualize what the algorithm is doing. On that dataset you will fit a Gaussian distribution and then find values that have very low probability and hence can be considered anomalies. After that, you will apply the anomaly detection algorithm to a larger dataset with many dimensions.\n",
+    "\n",
+    "We start this exercise by using a small dataset that is easy to visualize. Our example case consists of 2 network server statistics across several machines: the latency and throughput of each machine. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  The following command loads the dataset.\n",
+    "data = loadmat(os.path.join('ex8data1.mat'))\n",
+    "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n",
+    "\n",
+    "#  Visualize the example dataset\n",
+    "pyplot.plot(X[:, 0], X[:, 1], 'bx', mew=2, mec='k', ms=6)\n",
+    "pyplot.axis([0, 30, 0, 30])\n",
+    "pyplot.xlabel('Latency (ms)')\n",
+    "pyplot.ylabel('Throughput (mb/s)')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 Gaussian distribution\n",
+    "\n",
+    "To perform anomaly detection, you will first need to fit a model to the data's distribution. Given a training set $\\{x^{(1)}, \\dots, x^{(m)} \\}$ (where $x^{(i)} \\in \\mathbb{R}^n$ ), you want to estimate the Gaussian distribution for each of the features $x_i$ . For each feature $i = 1 \\dots n$, you need to find parameters $\\mu_i$ and $\\sigma_i^2$  that fit the data in the $i^{th}$ dimension $\\{ x_i^{(1)}, \\dots, x_i^{(m)} \\}$ (the $i^{th}$ dimension of each example).\n",
+    "\n",
+    "The Gaussian distribution is given by\n",
+    "\n",
+    "$$ p\\left( x; \\mu, \\sigma^2 \\right) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}} e^{-\\frac{\\left(x-\\mu\\right)^2}{2\\sigma^2}},$$\n",
+    "where $\\mu$ is the mean and $\\sigma^2$ is the variance.\n",
+    "\n",
+    "<a id=\"section1\"></a>\n",
+    "### 1.2 Estimating parameters for a Gaussian \n",
+    "\n",
+    "You can estimate the parameters $\\left( \\mu_i, \\sigma_i^2 \\right)$, of the $i^{th}$ feature by using the following equations. To estimate the mean, you will use: \n",
+    "\n",
+    "$$ \\mu_i = \\frac{1}{m} \\sum_{j=1}^m x_i^{(j)},$$\n",
+    "\n",
+    "and for the variance you will use:\n",
+    "\n",
+    "$$ \\sigma_i^2 = \\frac{1}{m} \\sum_{j=1}^m \\left( x_i^{(j)} - \\mu_i \\right)^2.$$\n",
+    "\n",
+    "Your task is to complete the code in the function `estimateGaussian`. This function takes as input the data matrix `X` and should output an n-dimension vector `mu` that holds the mean for each of the $n$ features and another n-dimension vector `sigma2` that holds the variances of each of the features. You can implement this\n",
+    "using a for-loop over every feature and every training example (though a vectorized implementation might be more efficient; feel free to use a vectorized implementation if you prefer). \n",
+    "<a id=\"estimateGaussian\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def estimateGaussian(X):\n",
+    "    \"\"\"\n",
+    "    This function estimates the parameters of a Gaussian distribution\n",
+    "    using a provided dataset.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n) with each n-dimensional \n",
+    "        data point in one row, and each total of m data points.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    mu : array_like \n",
+    "        A vector of shape (n,) containing the means of each dimension.\n",
+    "    \n",
+    "    sigma2 : array_like\n",
+    "        A vector of shape (n,) containing the computed\n",
+    "        variances of each dimension.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the mean of the data and the variances\n",
+    "    In particular, mu[i] should contain the mean of\n",
+    "    the data for the i-th feature and sigma2[i]\n",
+    "    should contain variance of the i-th feature.\n",
+    "    \"\"\"\n",
+    "    # Useful variables\n",
+    "    m, n = X.shape\n",
+    "\n",
+    "    # You should return these values correctly\n",
+    "    mu = np.zeros(n)\n",
+    "    sigma2 = np.zeros(n)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    mu = (1 / m) * np.sum(X, axis = 0)\n",
+    "    sigma2 = (1 / m) * np.sum((X - mu) ** 2, axis = 0)\n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return mu, sigma2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `estimateGaussian`, the next cell will visualize the contours of the fitted Gaussian distribution. You should get a plot similar to the figure below.\n",
+    "\n",
+    "![](Figures/gaussian_fit.png)\n",
+    "\n",
+    "From your plot, you can see that most of the examples are in the region with the highest probability, while\n",
+    "the anomalous examples are in the regions with lower probabilities.\n",
+    "\n",
+    "To do the visualization of the Gaussian fit, we first estimate the parameters of our assumed Gaussian distribution, then compute the probabilities for each of the points and then visualize both the overall distribution and where each of the points falls in terms of that distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  Estimate my and sigma2\n",
+    "mu, sigma2 = estimateGaussian(X)\n",
+    "\n",
+    "#  Returns the density of the multivariate normal at each data point (row) \n",
+    "#  of X\n",
+    "p = multivariateGaussian(X, mu, sigma2)\n",
+    "\n",
+    "#  Visualize the fit\n",
+    "visualizeFit(X,  mu, sigma2)\n",
+    "pyplot.xlabel('Latency (ms)')\n",
+    "pyplot.ylabel('Throughput (mb/s)')\n",
+    "pyplot.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[1] = estimateGaussian\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "### 1.3 Selecting the threshold, $\\varepsilon$\n",
+    "\n",
+    "Now that you have estimated the Gaussian parameters, you can investigate which examples have a very high probability given this distribution and which examples have a very low probability. The low probability examples are more likely to be the anomalies in our dataset. One way to determine which examples are anomalies is to select a threshold based on a cross validation set. In this part of the exercise, you will implement an algorithm to select the threshold $\\varepsilon$ using the $F_1$ score on a cross validation set.\n",
+    "\n",
+    "\n",
+    "You should now complete the code for the function `selectThreshold`. For this, we will use a cross validation set $\\{ (x_{cv}^{(1)}, y_{cv}^{(1)}), \\dots, (x_{cv}^{(m_{cv})}, y_{cv}^{(m_{cv})})\\}$, where the label $y = 1$ corresponds to an anomalous example, and $y = 0$ corresponds to a normal example. For each cross validation example, we will compute $p\\left( x_{cv}^{(i)}\\right)$. The vector of all of these probabilities $p\\left( x_{cv}^{(1)}\\right), \\dots, p\\left( x_{cv}^{(m_{cv})}\\right)$ is passed to `selectThreshold` in the vector `pval`. The corresponding labels $y_{cv}^{(1)} , \\dots , y_{cv}^{(m_{cv})}$ are passed to the same function in the vector `yval`.\n",
+    "\n",
+    "The function `selectThreshold` should return two values; the first is the selected threshold $\\varepsilon$. If an example $x$ has a low probability $p(x) < \\varepsilon$, then it is considered to be an anomaly. The function should also return the $F_1$ score, which tells you how well you are doing on finding the ground truth\n",
+    "anomalies given a certain threshold. For many different values of $\\varepsilon$, you will compute the resulting $F_1$ score by computing how many examples the current threshold classifies correctly and incorrectly.\n",
+    "\n",
+    "The $F_1$ score is computed using precision ($prec$) and recall ($rec$):\n",
+    "\n",
+    "$$ F_1 = \\frac{2 \\cdot prec \\cdot rec}{prec + rec}, $$\n",
+    "\n",
+    "You compute precision and recall by: \n",
+    "\n",
+    "$$ prec = \\frac{tp}{tp + fp}  $$ \n",
+    "\n",
+    "$$ rec = \\frac{tp}{tp + fn} $$\n",
+    "\n",
+    "where: \n",
+    "\n",
+    "- $tp$ is the number of true positives: the ground truth label says it’s an anomaly and our algorithm correctly classified it as an anomaly.\n",
+    "\n",
+    "-  $fp$ is the number of false positives: the ground truth label says it’s not an anomaly, but our algorithm incorrectly classified it as an anomaly.\n",
+    "- $fn$ is the number of false negatives: the ground truth label says it’s an anomaly, but our algorithm incorrectly classified it as not being anomalous.\n",
+    "\n",
+    "In the provided code `selectThreshold`, there is already a loop that will try many different values of $\\varepsilon$ and select the best $\\varepsilon$ based on the $F_1$ score. You should now complete the code in `selectThreshold`. You can implement the computation of the $F_1$ score using a for-loop over all the cross\n",
+    "validation examples (to compute the values $tp$, $fp$, $fn$). You should see a value for `epsilon` of about 8.99e-05.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note:** In order to compute $tp$, $fp$ and $fn$, you may be able to use a vectorized implementation rather than loop over all the examples. This can be implemented by numpy's equality test\n",
+    "between a vector and a single number. If you have several binary values in an n-dimensional binary vector $v \\in \\{0, 1\\}^n$, you can find out how many values in this vector are 0 by using: np.sum(v == 0). You can also\n",
+    "apply a logical and operator to such binary vectors. For instance, let `cvPredictions` be a binary vector of  size equal to the number of cross validation set, where the $i^{th}$ element is 1 if your algorithm considers\n",
+    "$x_{cv}^{(i)}$ an anomaly, and 0 otherwise. You can then, for example, compute the number of false positives using: `fp = np.sum((cvPredictions == 1) & (yval == 0))`.\n",
+    "</div>\n",
+    "<a id=\"selectThreshold\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def selectThreshold(yval, pval):\n",
+    "    \"\"\"\n",
+    "    Find the best threshold (epsilon) to use for selecting outliers based\n",
+    "    on the results from a validation set and the ground truth.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    yval : array_like\n",
+    "        The ground truth labels of shape (m, ).\n",
+    "    \n",
+    "    pval : array_like\n",
+    "        The precomputed vector of probabilities based on mu and sigma2 parameters. It's shape is also (m, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    bestEpsilon : array_like\n",
+    "        A vector of shape (n,) corresponding to the threshold value.\n",
+    "    \n",
+    "    bestF1 : float\n",
+    "        The value for the best F1 score.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the F1 score of choosing epsilon as the threshold and place the\n",
+    "    value in F1. The code at the end of the loop will compare the\n",
+    "    F1 score for this choice of epsilon and set it to be the best epsilon if\n",
+    "    it is better than the current choice of epsilon.\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    You can use predictions = (pval < epsilon) to get a binary vector\n",
+    "    of 0's and 1's of the outlier predictions\n",
+    "    \"\"\"\n",
+    "    bestEpsilon = 0\n",
+    "    bestF1 = 0\n",
+    "    F1 = 0\n",
+    "   \n",
+    "    for epsilon in np.linspace(1.01*min(pval), max(pval), 1000):\n",
+    "        # ====================== YOUR CODE HERE =======================\n",
+    "        predictions = (pval < epsilon)\n",
+    "        tp = np.sum((predictions == yval) & (yval == 1))\n",
+    "        fp = np.sum((predictions == 1) & (yval == 0))\n",
+    "        fn = np.sum((predictions == 0) & (yval == 1))\n",
+    "        \n",
+    "        prec = tp / (tp + fp)\n",
+    "        rec = tp / (tp + fn)\n",
+    "        \n",
+    "        F1 = 2 * prec * rec / (prec + rec)\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "        # =============================================================\n",
+    "        if F1 > bestF1:\n",
+    "            bestF1 = F1\n",
+    "            bestEpsilon = epsilon\n",
+    "\n",
+    "    return bestEpsilon, bestF1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `selectThreshold`, the next cell will run your anomaly detection code and circle the anomalies in the plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best epsilon found using cross-validation: 9.00e-05\n",
+      "Best F1 on Cross Validation Set:  0.875000\n",
+      "   (you should see a value epsilon of about 8.99e-05)\n",
+      "   (you should see a Best F1 value of  0.875000)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pval = multivariateGaussian(Xval, mu, sigma2)\n",
+    "\n",
+    "epsilon, F1 = selectThreshold(yval, pval)\n",
+    "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n",
+    "print('Best F1 on Cross Validation Set:  %f' % F1)\n",
+    "print('   (you should see a value epsilon of about 8.99e-05)')\n",
+    "print('   (you should see a Best F1 value of  0.875000)')\n",
+    "\n",
+    "#  Find the outliers in the training set and plot the\n",
+    "outliers = p < epsilon\n",
+    "\n",
+    "#  Visualize the fit\n",
+    "visualizeFit(X,  mu, sigma2)\n",
+    "pyplot.xlabel('Latency (ms)')\n",
+    "pyplot.ylabel('Throughput (mb/s)')\n",
+    "pyplot.tight_layout()\n",
+    "\n",
+    "#  Draw a red circle around those outliers\n",
+    "pyplot.plot(X[outliers, 0], X[outliers, 1], 'ro', ms=10, mfc='None', mew=2)\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[2] = selectThreshold\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.4 High dimensional dataset\n",
+    "\n",
+    "The next cell will run the anomaly detection algorithm you implemented on a more realistic and much harder dataset. In this dataset, each example is described by 11 features, capturing many more properties of your compute servers, but only some features indicate whether a point is an outlier. The script will use your code to estimate the Gaussian parameters ($\\mu_i$ and $\\sigma_i^2$), evaluate the probabilities for both the training data `X` from which you estimated the Gaussian parameters, and do so for the the cross-validation set `Xval`. Finally, it will use `selectThreshold` to find the best threshold $\\varepsilon$. You should see a value epsilon of about 1.38e-18, and 117 anomalies found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best epsilon found using cross-validation: 1.38e-18\n",
+      "Best F1 on Cross Validation Set          : 0.615385\n",
+      "\n",
+      "  (you should see a value epsilon of about 1.38e-18)\n",
+      "   (you should see a Best F1 value of      0.615385)\n",
+      "\n",
+      "# Outliers found: 117\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Loads the second dataset. You should now have the\n",
+    "#  variables X, Xval, yval in your environment\n",
+    "data = loadmat(os.path.join('ex8data2.mat'))\n",
+    "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n",
+    "\n",
+    "# Apply the same steps to the larger dataset\n",
+    "mu, sigma2 = estimateGaussian(X)\n",
+    "\n",
+    "#  Training set \n",
+    "p = multivariateGaussian(X, mu, sigma2)\n",
+    "\n",
+    "#  Cross-validation set\n",
+    "pval = multivariateGaussian(Xval, mu, sigma2)\n",
+    "\n",
+    "#  Find the best threshold\n",
+    "epsilon, F1 = selectThreshold(yval, pval)\n",
+    "\n",
+    "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n",
+    "print('Best F1 on Cross Validation Set          : %f\\n' % F1)\n",
+    "print('  (you should see a value epsilon of about 1.38e-18)')\n",
+    "print('   (you should see a Best F1 value of      0.615385)')\n",
+    "print('\\n# Outliers found: %d' % np.sum(p < epsilon))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 Recommender Systems\n",
+    "\n",
+    "In this part of the exercise, you will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings ([MovieLens 100k Dataset](https://grouplens.org/datasets/movielens/) from GroupLens Research). This dataset consists of ratings on a scale of 1 to 5. The dataset has $n_u = 943$ users, and $n_m = 1682$ movies. \n",
+    "\n",
+    "In the next parts of this exercise, you will implement the function `cofiCostFunc` that computes the collaborative filtering objective function and gradient. After implementing the cost function and gradient, you will use `scipy.optimize.minimize` to learn the parameters for collaborative filtering.\n",
+    "\n",
+    "### 2.1 Movie ratings dataset\n",
+    "\n",
+    "The next cell will load the dataset `ex8_movies.mat`, providing the variables `Y` and `R`.\n",
+    "The matrix `Y` (a `num_movies` $\\times$ `num_users` matrix) stores the ratings $y^{(i,j)}$ (from 1 to 5). The matrix `R` is an binary-valued indicator matrix, where $R(i, j) = 1$ if user $j$ gave a rating to movie $i$, and $R(i, j) = 0$ otherwise. The objective of collaborative filtering is to predict movie ratings for the movies that users have not yet rated, that is, the entries with $R(i, j) = 0$. This will allow us to recommend the movies with the highest predicted ratings to the user.\n",
+    "\n",
+    "To help you understand the matrix `Y`, the following cell will compute the average movie rating for the first movie (Toy Story) and print its average rating."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average rating for movie 1 (Toy Story): 3.878319 / 5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load data\n",
+    "data = loadmat(os.path.join('ex8_movies.mat'))\n",
+    "Y, R = data['Y'], data['R']\n",
+    "\n",
+    "# Y is a 1682x943 matrix, containing ratings (1-5) of \n",
+    "# 1682 movies on 943 users\n",
+    "\n",
+    "# R is a 1682x943 matrix, where R(i,j) = 1 \n",
+    "# if and only if user j gave a rating to movie i\n",
+    "\n",
+    "# From the matrix, we can compute statistics like average rating.\n",
+    "print('Average rating for movie 1 (Toy Story): %f / 5' %\n",
+    "      np.mean(Y[0, R[0, :] == 1]))\n",
+    "\n",
+    "# We can \"visualize\" the ratings matrix by plotting it with imshow\n",
+    "pyplot.figure(figsize=(8, 8))\n",
+    "pyplot.imshow(Y)\n",
+    "pyplot.ylabel('Movies')\n",
+    "pyplot.xlabel('Users')\n",
+    "pyplot.grid(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Throughout this part of the exercise, you will also be working with the matrices, `X` and `Theta`:\n",
+    "\n",
+    "$$ \\text{X} = \n",
+    "\\begin{bmatrix}\n",
+    "- \\left(x^{(1)}\\right)^T - \\\\\n",
+    "- \\left(x^{(2)}\\right)^T - \\\\\n",
+    "\\vdots \\\\\n",
+    "- \\left(x^{(n_m)}\\right)^T - \\\\\n",
+    "\\end{bmatrix}, \\quad\n",
+    "\\text{Theta} = \n",
+    "\\begin{bmatrix}\n",
+    "- \\left(\\theta^{(1)}\\right)^T - \\\\\n",
+    "- \\left(\\theta^{(2)}\\right)^T - \\\\\n",
+    "\\vdots \\\\\n",
+    "- \\left(\\theta^{(n_u)}\\right)^T - \\\\\n",
+    "\\end{bmatrix}.\n",
+    "$$\n",
+    "\n",
+    "The $i^{th}$ row of `X` corresponds to the feature vector $x^{(i)}$ for the $i^{th}$ movie, and the $j^{th}$ row of `Theta` corresponds to one parameter vector $\\theta^{(j)}$, for the $j^{th}$ user. Both $x^{(i)}$ and $\\theta^{(j)}$ are n-dimensional vectors. For the purposes of this exercise, you will use $n = 100$, and therefore, $x^{(i)} \\in \\mathbb{R}^{100}$ and $\\theta^{(j)} \\in \\mathbb{R}^{100}$. Correspondingly, `X` is a $n_m \\times 100$ matrix and `Theta` is a $n_u \\times 100$ matrix.\n",
+    "\n",
+    "<a id=\"section3\"></a>\n",
+    "### 2.2 Collaborative filtering learning algorithm\n",
+    "\n",
+    "Now, you will start implementing the collaborative filtering learning algorithm. You will start by implementing the cost function (without regularization).\n",
+    "\n",
+    "The collaborative filtering algorithm in the setting of movie recommendations considers a set of n-dimensional parameter vectors $x^{(1)}, \\dots, x^{(n_m)}$ and $\\theta^{(1)} , \\dots, \\theta^{(n_u)}$, where the model predicts the rating for movie $i$ by user $j$ as $y^{(i,j)} = \\left( \\theta^{(j)} \\right)^T x^{(i)}$. Given a dataset that consists of a set of ratings produced by some users on some movies, you wish to learn the parameter vectors $x^{(1)}, \\dots, x^{(n_m)}, \\theta^{(1)}, \\dots, \\theta^{(n_u)}$ that produce the best fit (minimizes the squared error).\n",
+    "\n",
+    "You will complete the code in `cofiCostFunc` to compute the cost function and gradient for collaborative filtering. Note that the parameters to the function (i.e., the values that you are trying to learn) are `X` and `Theta`. In order to use an off-the-shelf minimizer such as `scipy`'s `minimize` function, the cost function has been set up to unroll the parameters into a single vector called `params`. You had previously used the same vector unrolling method in the neural networks programming exercise.\n",
+    "\n",
+    "#### 2.2.1 Collaborative filtering cost function\n",
+    "\n",
+    "The collaborative filtering cost function (without regularization) is given by\n",
+    "\n",
+    "$$\n",
+    "J(x^{(1)}, \\dots, x^{(n_m)}, \\theta^{(1)}, \\dots,\\theta^{(n_u)}) = \\frac{1}{2} \\sum_{(i,j):r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)} - y^{(i,j)} \\right)^2\n",
+    "$$\n",
+    "\n",
+    "You should now modify the function `cofiCostFunc` to return this cost in the variable `J`. Note that you should be accumulating the cost for user $j$ and movie $i$ only if `R[i,j] = 1`.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note**: We strongly encourage you to use a vectorized implementation to compute $J$, since it will later by called many times by `scipy`'s optimization package. As usual, it might be easiest to first write a non-vectorized implementation (to make sure you have the right answer), and the modify it to become a vectorized implementation (checking that the vectorization steps do not change your algorithm’s output). To come up with a vectorized implementation, the following tip might be helpful: You can use the $R$ matrix to set selected entries to 0. For example, `R * M` will do an element-wise multiplication between `M`\n",
+    "and `R`; since `R` only has elements with values either 0 or 1, this has the effect of setting the elements of M to 0 only when the corresponding value in R is 0. Hence, `np.sum( R * M)` is the sum of all the elements of `M` for which the corresponding element in `R` equals 1.\n",
+    "</div>\n",
+    "\n",
+    "<a id=\"cofiCostFunc\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cofiCostFunc(params, Y, R, num_users, num_movies,\n",
+    "                      num_features, lambda_=0.0):\n",
+    "    \"\"\"\n",
+    "    Collaborative filtering cost function.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    params : array_like\n",
+    "        The parameters which will be optimized. This is a one\n",
+    "        dimensional vector of shape (num_movies x num_users, 1). It is the \n",
+    "        concatenation of the feature vectors X and parameters Theta.\n",
+    "    \n",
+    "    Y : array_like\n",
+    "        A matrix of shape (num_movies x num_users) of user ratings of movies.\n",
+    "    \n",
+    "    R : array_like\n",
+    "        A (num_movies x num_users) matrix, where R[i, j] = 1 if the \n",
+    "        i-th movie was rated by the j-th user.\n",
+    "    \n",
+    "    num_users : int\n",
+    "        Total number of users.\n",
+    "    \n",
+    "    num_movies : int\n",
+    "        Total number of movies.\n",
+    "    \n",
+    "    num_features : int\n",
+    "        Number of features to learn.\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        The regularization coefficient.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The value of the cost function at the given params.\n",
+    "    \n",
+    "    grad : array_like\n",
+    "        The gradient vector of the cost function at the given params.\n",
+    "        grad has a shape (num_movies x num_users, 1)\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost function and gradient for collaborative filtering.\n",
+    "    Concretely, you should first implement the cost function (without\n",
+    "    regularization) and make sure it is matches our costs. After that,\n",
+    "    you should implement thegradient and use the checkCostFunction routine \n",
+    "    to check that the gradient is correct. Finally, you should implement\n",
+    "    regularization.\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    - The input params will be unraveled into the two matrices:\n",
+    "        X : (num_movies  x num_features) matrix of movie features\n",
+    "        Theta : (num_users  x num_features) matrix of user features\n",
+    "\n",
+    "    - You should set the following variables correctly:\n",
+    "\n",
+    "        X_grad : (num_movies x num_features) matrix, containing the \n",
+    "                 partial derivatives w.r.t. to each element of X\n",
+    "        Theta_grad : (num_users x num_features) matrix, containing the \n",
+    "                     partial derivatives w.r.t. to each element of Theta\n",
+    "\n",
+    "    - The returned gradient will be the concatenation of the raveled \n",
+    "      gradients X_grad and Theta_grad.\n",
+    "    \"\"\"\n",
+    "    # Unfold the U and W matrices from params\n",
+    "    X = params[:num_movies*num_features].reshape(num_movies, num_features)\n",
+    "    Theta = params[num_movies*num_features:].reshape(num_users, num_features)\n",
+    "\n",
+    "    # You need to return the following values correctly\n",
+    "    J = 0\n",
+    "    X_grad = np.zeros(X.shape)\n",
+    "    Theta_grad = np.zeros(Theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    J = (1 / 2) * np.sum(np.square((X.dot(Theta.T) - Y) * R)) + (lambda_ / 2) * np.sum(np.square(X)) + \\\n",
+    "                                                                (lambda_ / 2) * np.sum(np.square(Theta))\n",
+    "    \n",
+    "    for i in range(R.shape[0]):\n",
+    "        \n",
+    "        idx = np.where(R[i, :] == 1)[0]\n",
+    "        Theta_temp = Theta[idx, :]\n",
+    "        Y_temp = Y[i, idx]\n",
+    "        X_grad[i, :] = np.dot(np.dot(X[i, :], Theta_temp.T) - Y_temp, Theta_temp) + lambda_ * X[i, :]\n",
+    "        \n",
+    "    for j in range(R.shape[1]):\n",
+    "        \n",
+    "        idx = np.where(R[:, j] == 1)[0]\n",
+    "        X_temp = X[idx, :]\n",
+    "        Y_temp = Y[idx, j]\n",
+    "        Theta_grad[j, :] = np.dot(np.dot(X_temp, Theta[j, :]) - Y_temp, X_temp) + lambda_ * Theta[j, :]\n",
+    "    \n",
+    "    \n",
+    "    # =============================================================\n",
+    "    \n",
+    "    grad = np.concatenate([X_grad.ravel(), Theta_grad.ravel()])\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After you have completed the function, the next cell will run your cost function. To help you debug your cost function, we have included set of weights that we trained on that.  You should expect to see an output of 22.22."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at loaded parameters:  22.22 \n",
+      "(this value should be about 22.22)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Load pre-trained weights (X, Theta, num_users, num_movies, num_features)\n",
+    "data = loadmat(os.path.join('ex8_movieParams.mat'))\n",
+    "X, Theta, num_users, num_movies, num_features = data['X'],\\\n",
+    "        data['Theta'], data['num_users'], data['num_movies'], data['num_features']\n",
+    "\n",
+    "#  Reduce the data set size so that this runs faster\n",
+    "num_users = 4\n",
+    "num_movies = 5\n",
+    "num_features = 3\n",
+    "\n",
+    "X = X[:num_movies, :num_features]\n",
+    "Theta = Theta[:num_users, :num_features]\n",
+    "Y = Y[:num_movies, 0:num_users]\n",
+    "R = R[:num_movies, 0:num_users]\n",
+    "\n",
+    "#  Evaluate cost function\n",
+    "J, _ = cofiCostFunc(np.concatenate([X.ravel(), Theta.ravel()]),\n",
+    "                    Y, R, num_users, num_movies, num_features)\n",
+    "           \n",
+    "print('Cost at loaded parameters:  %.2f \\n(this value should be about 22.22)' % J)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[3] = cofiCostFunc\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section4\"></a>\n",
+    "#### 2.2.2 Collaborative filtering gradient\n",
+    "\n",
+    "Now you should implement the gradient (without regularization). Specifically, you should complete the code in `cofiCostFunc` to return the variables `X_grad` and `Theta_grad`. Note that `X_grad` should be a matrix of the same size as `X` and similarly, `Theta_grad` is a matrix of the same size as\n",
+    "`Theta`. The gradients of the cost function is given by:\n",
+    "\n",
+    "$$ \\frac{\\partial J}{\\partial x_k^{(i)}} = \\sum_{j:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)} - y^{(i,j)} \\right) \\theta_k^{(j)} $$\n",
+    "\n",
+    "$$ \\frac{\\partial J}{\\partial \\theta_k^{(j)}} = \\sum_{i:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)}- y^{(i,j)} \\right) x_k^{(j)} $$\n",
+    "\n",
+    "Note that the function returns the gradient for both sets of variables by unrolling them into a single vector. After you have completed the code to compute the gradients, the next cell run a gradient check\n",
+    "(available in `utils.checkCostFunction`) to numerically check the implementation of your gradients (this is similar to the numerical check that you used in the neural networks exercise. If your implementation is correct, you should find that the analytical and numerical gradients match up closely.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note:** You can get full credit for this assignment without using a vectorized implementation, but your code will run much more slowly (a small number of hours), and so we recommend that you try to vectorize your implementation. To get started, you can implement the gradient with a for-loop over movies\n",
+    "(for computing $\\frac{\\partial J}{\\partial x^{(i)}_k}$) and a for-loop over users (for computing $\\frac{\\partial J}{\\theta_k^{(j)}}$). When you first implement the gradient, you might start with an unvectorized version, by implementing another inner for-loop that computes each element in the summation. After you have completed the gradient computation this way, you should try to vectorize your implementation (vectorize the inner for-loops), so that you are left with only two for-loops (one for looping over movies to compute $\\frac{\\partial J}{\\partial x_k^{(i)}}$ for each movie, and one for looping over users to compute $\\frac{\\partial J}{\\partial \\theta_k^{(j)}}$ for each user).\n",
+    "</div>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Tip:** To perform the vectorization, you might find this helpful: You should come up with a way to compute all the derivatives associated with $x_1^{(i)} , x_2^{(i)}, \\dots , x_n^{(i)}$ (i.e., the derivative terms associated with the feature vector $x^{(i)}$) at the same time. Let us define the derivatives for the feature vector of the $i^{th}$ movie as:\n",
+    "\n",
+    "$$ \\left(X_{\\text{grad}} \\left(i, :\\right)\\right)^T = \n",
+    "\\begin{bmatrix}\n",
+    "\\frac{\\partial J}{\\partial x_1^{(i)}} \\\\\n",
+    "\\frac{\\partial J}{\\partial x_2^{(i)}} \\\\\n",
+    "\\vdots \\\\\n",
+    "\\frac{\\partial J}{\\partial x_n^{(i)}}\n",
+    "\\end{bmatrix} = \\quad\n",
+    "\\sum_{j:r(i,j)=1} \\left( \\left( \\theta^{(j)} \\right)^T x^{(i)} - y^{(i,j)} \\right) \\theta^{(j)}\n",
+    "$$\n",
+    "\n",
+    "To vectorize the above expression, you can start by indexing into `Theta` and `Y` to select only the elements of interests (that is, those with `r[i, j] = 1`). Intuitively, when you consider the features for the $i^{th}$ movie, you only need to be concerned about the users who had given ratings to the movie, and this allows you to remove all the other users from `Theta` and `Y`. <br/><br/>\n",
+    "\n",
+    "\n",
+    "Concretely, you can set `idx = np.where(R[i, :] == 1)[0]` to be a list of all the users that have rated movie $i$. This will allow you to create the temporary matrices `Theta_temp = Theta[idx, :]` and `Y_temp = Y[i, idx]` that index into `Theta` and `Y` to give you only the set of users which have rated the $i^{th}$ movie. This will allow you to write the derivatives as: <br>\n",
+    "\n",
+    "`X_grad[i, :] = np.dot(np.dot(X[i, :], Theta_temp.T) - Y_temp, Theta_temp)`\n",
+    "\n",
+    "<br><br>\n",
+    "Note that the vectorized computation above returns a row-vector instead. After you have vectorized the computations of the derivatives with respect to $x^{(i)}$, you should use a similar method to vectorize the derivatives with respect to $θ^{(j)}$ as well.\n",
+    "</div>\n",
+    "\n",
+    "[Click here to go back to the function `cofiCostFunc` to update it](#cofiCostFunc). \n",
+    "\n",
+    "<font color=\"red\"> Do not forget to re-execute the cell containg the function `cofiCostFunc` so that it is updated with your implementation of the gradient computation.</font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.32223061  0.32223061]\n",
+      " [-5.09583159 -5.09583159]\n",
+      " [-6.00992322 -6.00992322]\n",
+      " [-0.76625854 -0.76625854]\n",
+      " [-3.80982461 -3.80982461]\n",
+      " [-4.42725561 -4.42725561]\n",
+      " [-1.72003132 -1.72003132]\n",
+      " [-6.15955831 -6.15955831]\n",
+      " [-5.01843531 -5.01843531]\n",
+      " [ 0.3084587   0.3084587 ]\n",
+      " [-1.92019009 -1.92019009]\n",
+      " [-2.64112539 -2.64112539]\n",
+      " [-2.51368282 -2.51368282]\n",
+      " [ 0.44279257  0.44279257]\n",
+      " [-3.41810596 -3.41810596]\n",
+      " [ 0.83386632  0.83386632]\n",
+      " [ 1.51998354  1.51998354]\n",
+      " [ 1.94280327  1.94280327]\n",
+      " [-3.57679451 -3.57679451]\n",
+      " [-1.72630652 -1.72630652]\n",
+      " [-5.22093163 -5.22093163]\n",
+      " [-5.09799565 -5.09799565]\n",
+      " [-4.71389622 -4.71389622]\n",
+      " [-8.66112211 -8.66112211]\n",
+      " [ 0.52087948  0.52087948]\n",
+      " [ 0.81792346  0.81792346]\n",
+      " [ 0.91256629  0.91256629]]\n",
+      "\n",
+      "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n",
+      "\n",
+      "Relative Difference: 6.90949e-13\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Check gradients by running checkcostFunction\n",
+    "checkCostFunction(cofiCostFunc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[4] = cofiCostFunc\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section5\"></a>\n",
+    "#### 2.2.3 Regularized cost function\n",
+    "\n",
+    "The cost function for collaborative filtering with regularization is given by\n",
+    "\n",
+    "$$ J(x^{(1)}, \\dots, x^{(n_m)}, \\theta^{(1)}, \\dots, \\theta^{(n_u)}) = \\frac{1}{2} \\sum_{(i,j):r(i,j)=1} \\left( \\left( \\theta^{(j)} \\right)^T x^{(i)} - y^{(i,j)} \\right)^2 + \\left( \\frac{\\lambda}{2} \\sum_{j=1}^{n_u} \\sum_{k=1}^{n} \\left( \\theta_k^{(j)} \\right)^2  \\right) + \\left( \\frac{\\lambda}{2} \\sum_{i=1}^{n_m} \\sum_{k=1}^n \\left(x_k^{(i)} \\right)^2 \\right) $$\n",
+    "\n",
+    "You should now add regularization to your original computations of the cost function, $J$. After you are done, the next cell will run your regularized cost function, and you should expect to see a cost of about 31.34.\n",
+    "\n",
+    "[Click here to go back to the function `cofiCostFunc` to update it](#cofiCostFunc)\n",
+    "<font color=\"red\"> Do not forget to re-execute the cell containing the function `cofiCostFunc` so that it is updated with your implementation of regularized cost function.</font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at loaded parameters (lambda = 1.5): 31.34\n",
+      "              (this value should be about 31.34)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Evaluate cost function\n",
+    "J, _ = cofiCostFunc(np.concatenate([X.ravel(), Theta.ravel()]),\n",
+    "                    Y, R, num_users, num_movies, num_features, 1.5)\n",
+    "           \n",
+    "print('Cost at loaded parameters (lambda = 1.5): %.2f' % J)\n",
+    "print('              (this value should be about 31.34)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[5] = cofiCostFunc\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section6\"></a>\n",
+    "#### 2.2.4 Regularized gradient\n",
+    "\n",
+    "Now that you have implemented the regularized cost function, you should proceed to implement regularization for the gradient. You should add to your implementation in `cofiCostFunc` to return the regularized gradient\n",
+    "by adding the contributions from the regularization terms. Note that the gradients for the regularized cost function is given by:\n",
+    "\n",
+    "$$ \\frac{\\partial J}{\\partial x_k^{(i)}} = \\sum_{j:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)} - y^{(i,j)} \\right) \\theta_k^{(j)} + \\lambda x_k^{(i)} $$\n",
+    "\n",
+    "$$ \\frac{\\partial J}{\\partial \\theta_k^{(j)}} = \\sum_{i:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)}- y^{(i,j)} \\right) x_k^{(j)} + \\lambda \\theta_k^{(j)} $$\n",
+    "\n",
+    "This means that you just need to add $\\lambda x^{(i)}$ to the `X_grad[i,:]` variable described earlier, and add $\\lambda \\theta^{(j)}$ to the `Theta_grad[j, :]` variable described earlier.\n",
+    "\n",
+    "[Click here to go back to the function `cofiCostFunc` to update it](#cofiCostFunc)\n",
+    "<font color=\"red\"> Do not forget to re-execute the cell containing the function `cofiCostFunc` so that it is updated with your implementation of the gradient for the regularized cost function.</font>\n",
+    "\n",
+    "After you have completed the code to compute the gradients, the following cell will run another gradient check (`utils.checkCostFunction`) to numerically check the implementation of your gradients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-1.9102557  -1.9102557 ]\n",
+      " [-0.95991112 -0.95991112]\n",
+      " [ 0.08908727  0.08908727]\n",
+      " [ 1.0946103   1.0946103 ]\n",
+      " [ 1.73678501  1.73678501]\n",
+      " [ 2.74312564  2.74312564]\n",
+      " [ 2.71074002  2.71074002]\n",
+      " [-0.7373052  -0.7373052 ]\n",
+      " [-5.52252451 -5.52252451]\n",
+      " [-3.38321578 -3.38321578]\n",
+      " [-3.22283851 -3.22283851]\n",
+      " [-5.07614337 -5.07614337]\n",
+      " [-4.08092075 -4.08092075]\n",
+      " [-2.62845883 -2.62845883]\n",
+      " [-2.09802439 -2.09802439]\n",
+      " [-0.41663339 -0.41663339]\n",
+      " [ 0.1344823   0.1344823 ]\n",
+      " [ 3.30598185  3.30598185]\n",
+      " [-2.15782753 -2.15782753]\n",
+      " [ 0.51292493  0.51292493]\n",
+      " [ 5.31459884  5.31459884]\n",
+      " [ 2.0158988   2.0158988 ]\n",
+      " [ 0.57751327  0.57751327]\n",
+      " [ 0.55228461  0.55228461]\n",
+      " [-1.62312447 -1.62312447]\n",
+      " [ 3.93522416  3.93522416]\n",
+      " [ 9.15770134  9.15770134]]\n",
+      "\n",
+      "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n",
+      "\n",
+      "Relative Difference: 2.06392e-12\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Check gradients by running checkCostFunction\n",
+    "checkCostFunction(cofiCostFunc, 1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[6] = cofiCostFunc\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 Learning movie recommendations \n",
+    "\n",
+    "After you have finished implementing the collaborative filtering cost function and gradient, you can now start training your algorithm to make movie recommendations for yourself. In the next cell, you can enter your own movie preferences, so that later when the algorithm runs, you can get your own movie recommendations! We have filled out some values according to our own preferences, but you should change this according to your own tastes. The list of all movies and their number in the dataset can be found listed in the file `Data/movie_idx.txt`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New user ratings:\n",
+      "-----------------\n",
+      "Rated 4 stars: Toy Story (1995)\n",
+      "Rated 3 stars: Twelve Monkeys (1995)\n",
+      "Rated 5 stars: Usual Suspects, The (1995)\n",
+      "Rated 4 stars: Outbreak (1995)\n",
+      "Rated 5 stars: Shawshank Redemption, The (1994)\n",
+      "Rated 3 stars: While You Were Sleeping (1995)\n",
+      "Rated 5 stars: Forrest Gump (1994)\n",
+      "Rated 2 stars: Silence of the Lambs, The (1991)\n",
+      "Rated 4 stars: Alien (1979)\n",
+      "Rated 5 stars: Die Hard 2 (1990)\n",
+      "Rated 5 stars: Sphere (1998)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Before we will train the collaborative filtering model, we will first\n",
+    "#  add ratings that correspond to a new user that we just observed. This\n",
+    "#  part of the code will also allow you to put in your own ratings for the\n",
+    "#  movies in our dataset!\n",
+    "movieList = loadMovieList()\n",
+    "n_m = len(movieList)\n",
+    "\n",
+    "#  Initialize my ratings\n",
+    "my_ratings = np.zeros(n_m)\n",
+    "\n",
+    "# Check the file movie_idx.txt for id of each movie in our dataset\n",
+    "# For example, Toy Story (1995) has ID 1, so to rate it \"4\", you can set\n",
+    "# Note that the index here is ID-1, since we start index from 0.\n",
+    "my_ratings[0] = 4\n",
+    "\n",
+    "# Or suppose did not enjoy Silence of the Lambs (1991), you can set\n",
+    "my_ratings[97] = 2\n",
+    "\n",
+    "# We have selected a few movies we liked / did not like and the ratings we\n",
+    "# gave are as follows:\n",
+    "my_ratings[6] = 3\n",
+    "my_ratings[11]= 5\n",
+    "my_ratings[53] = 4\n",
+    "my_ratings[63] = 5\n",
+    "my_ratings[65] = 3\n",
+    "my_ratings[68] = 5\n",
+    "my_ratings[182] = 4\n",
+    "my_ratings[225] = 5\n",
+    "my_ratings[354] = 5\n",
+    "\n",
+    "print('New user ratings:')\n",
+    "print('-----------------')\n",
+    "for i in range(len(my_ratings)):\n",
+    "    if my_ratings[i] > 0:\n",
+    "        print('Rated %d stars: %s' % (my_ratings[i], movieList[i]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.3.1 Recommendations\n",
+    "\n",
+    "After the additional ratings have been added to the dataset, the script\n",
+    "will proceed to train the collaborative filtering model. This will learn the\n",
+    "parameters X and Theta. To predict the rating of movie i for user j, you need to compute (θ (j) ) T x (i) . The next part of the script computes the ratings for\n",
+    "all the movies and users and displays the movies that it recommends (Figure\n",
+    "4), according to ratings that were entered earlier in the script. Note that\n",
+    "you might obtain a different set of the predictions due to different random\n",
+    "initializations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Recommender system learning completed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Now, you will train the collaborative filtering model on a movie rating \n",
+    "#  dataset of 1682 movies and 943 users\n",
+    "\n",
+    "#  Load data\n",
+    "data = loadmat(os.path.join('ex8_movies.mat'))\n",
+    "Y, R = data['Y'], data['R']\n",
+    "\n",
+    "#  Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by \n",
+    "#  943 users\n",
+    "\n",
+    "#  R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a\n",
+    "#  rating to movie i\n",
+    "\n",
+    "#  Add our own ratings to the data matrix\n",
+    "Y = np.hstack([my_ratings[:, None], Y])\n",
+    "R = np.hstack([(my_ratings > 0)[:, None], R])\n",
+    "\n",
+    "#  Normalize Ratings\n",
+    "Ynorm, Ymean = normalizeRatings(Y, R)\n",
+    "\n",
+    "#  Useful Values\n",
+    "num_movies, num_users = Y.shape\n",
+    "num_features = 10\n",
+    "\n",
+    "# Set Initial Parameters (Theta, X)\n",
+    "X = np.random.randn(num_movies, num_features)\n",
+    "Theta = np.random.randn(num_users, num_features)\n",
+    "\n",
+    "initial_parameters = np.concatenate([X.ravel(), Theta.ravel()])\n",
+    "\n",
+    "# Set options for scipy.optimize.minimize\n",
+    "options = {'maxiter': 100}\n",
+    "\n",
+    "# Set Regularization\n",
+    "lambda_ = 10\n",
+    "res = optimize.minimize(lambda x: cofiCostFunc(x, Ynorm, R, num_users,\n",
+    "                                               num_movies, num_features, lambda_),\n",
+    "                        initial_parameters,\n",
+    "                        method='TNC',\n",
+    "                        jac=True,\n",
+    "                        options=options)\n",
+    "theta = res.x\n",
+    "\n",
+    "# Unfold the returned theta back into U and W\n",
+    "X = theta[:num_movies*num_features].reshape(num_movies, num_features)\n",
+    "Theta = theta[num_movies*num_features:].reshape(num_users, num_features)\n",
+    "\n",
+    "print('Recommender system learning completed.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After training the model, you can now make recommendations by computing the predictions matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top recommendations for you:\n",
+      "----------------------------\n",
+      "Predicting rating 5.0 for movie Prefontaine (1997)\n",
+      "Predicting rating 5.0 for movie They Made Me a Criminal (1939)\n",
+      "Predicting rating 5.0 for movie Saint of Fort Washington, The (1993)\n",
+      "Predicting rating 5.0 for movie Great Day in Harlem, A (1994)\n",
+      "Predicting rating 5.0 for movie Star Kid (1997)\n",
+      "Predicting rating 5.0 for movie Entertaining Angels: The Dorothy Day Story (1996)\n",
+      "Predicting rating 5.0 for movie Someone Else's America (1995)\n",
+      "Predicting rating 5.0 for movie Santa with Muscles (1996)\n",
+      "Predicting rating 5.0 for movie Marlene Dietrich: Shadow and Light (1996)\n",
+      "Predicting rating 5.0 for movie Aiqing wansui (1994)\n",
+      "\n",
+      "Original ratings provided:\n",
+      "--------------------------\n",
+      "Rated 4 for Toy Story (1995)\n",
+      "Rated 3 for Twelve Monkeys (1995)\n",
+      "Rated 5 for Usual Suspects, The (1995)\n",
+      "Rated 4 for Outbreak (1995)\n",
+      "Rated 5 for Shawshank Redemption, The (1994)\n",
+      "Rated 3 for While You Were Sleeping (1995)\n",
+      "Rated 5 for Forrest Gump (1994)\n",
+      "Rated 2 for Silence of the Lambs, The (1991)\n",
+      "Rated 4 for Alien (1979)\n",
+      "Rated 5 for Die Hard 2 (1990)\n",
+      "Rated 5 for Sphere (1998)\n"
+     ]
+    }
+   ],
+   "source": [
+    "p = np.dot(X, Theta.T)\n",
+    "my_predictions = p[:, 0] + Ymean\n",
+    "\n",
+    "movieList =loadMovieList()\n",
+    "\n",
+    "ix = np.argsort(my_predictions)[::-1]\n",
+    "\n",
+    "print('Top recommendations for you:')\n",
+    "print('----------------------------')\n",
+    "for i in range(10):\n",
+    "    j = ix[i]\n",
+    "    print('Predicting rating %.1f for movie %s' % (my_predictions[j], movieList[j]))\n",
+    "\n",
+    "print('\\nOriginal ratings provided:')\n",
+    "print('--------------------------')\n",
+    "for i in range(len(my_ratings)):\n",
+    "    if my_ratings[i] > 0:\n",
+    "        print('Rated %d for %s' % (my_ratings[i], movieList[i]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#utils\n",
+    "def normalizeRatings(Y, R):\n",
+    "    \"\"\"\n",
+    "    Preprocess data by subtracting mean rating for every movie (every row).\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    Y : array_like\n",
+    "        The user ratings for all movies. A matrix of shape (num_movies x num_users).\n",
+    "    R : array_like\n",
+    "        Indicator matrix for movies rated by users. A matrix of shape (num_movies x num_users).\n",
+    "    Returns\n",
+    "    -------\n",
+    "    Ynorm : array_like\n",
+    "        A matrix of same shape as Y, after mean normalization.\n",
+    "    Ymean : array_like\n",
+    "        A vector of shape (num_movies, ) containing the mean rating for each movie.\n",
+    "    \"\"\"\n",
+    "    m, n = Y.shape\n",
+    "    Ymean = np.zeros(m)\n",
+    "    Ynorm = np.zeros(Y.shape)\n",
+    "\n",
+    "    for i in range(m):\n",
+    "        idx = R[i, :] == 1\n",
+    "        Ymean[i] = np.mean(Y[i, idx])\n",
+    "        Ynorm[i, idx] = Y[i, idx] - Ymean[i]\n",
+    "\n",
+    "    return Ynorm, Ymean\n",
+    "\n",
+    "\n",
+    "def loadMovieList():\n",
+    "    \"\"\"\n",
+    "    Reads the fixed movie list in movie_ids.txt and returns a list of movie names.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    movieNames : list\n",
+    "        A list of strings, representing all movie names.\n",
+    "    \"\"\"\n",
+    "    # Read the fixed movieulary list\n",
+    "    with open(('movie_ids.txt'),  encoding='ISO-8859-1') as fid:\n",
+    "        movies = fid.readlines()\n",
+    "\n",
+    "    movieNames = []\n",
+    "    for movie in movies:\n",
+    "        parts = movie.split()\n",
+    "        movieNames.append(' '.join(parts[1:]).strip())\n",
+    "    return movieNames\n",
+    "\n",
+    "\n",
+    "def computeNumericalGradient(J, theta, e=1e-4):\n",
+    "    \"\"\"\n",
+    "    Computes the gradient using \"finite differences\" and gives us a numerical estimate of the\n",
+    "    gradient.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    J : func\n",
+    "        The cost function which will be used to estimate its numerical gradient.\n",
+    "    theta : array_like\n",
+    "        The one dimensional unrolled network parameters. The numerical gradient is computed at\n",
+    "         those given parameters.\n",
+    "    e : float (optional)\n",
+    "        The value to use for epsilon for computing the finite difference.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    numgrad : array_like\n",
+    "        The numerical gradient with respect to theta. Has same shape as theta.\n",
+    "    Notes\n",
+    "    -----\n",
+    "    The following code implements numerical gradient checking, and\n",
+    "    returns the numerical gradient. It sets `numgrad[i]` to (a numerical\n",
+    "    approximation of) the partial derivative of J with respect to the\n",
+    "    i-th input argument, evaluated at theta. (i.e., `numgrad[i]` should\n",
+    "    be the (approximately) the partial derivative of J with respect\n",
+    "    to theta[i].)\n",
+    "    \"\"\"\n",
+    "    numgrad = np.zeros(theta.shape)\n",
+    "    perturb = np.diag(e * np.ones(theta.shape))\n",
+    "    for i in range(theta.size):\n",
+    "        loss1, _ = J(theta - perturb[:, i])\n",
+    "        loss2, _ = J(theta + perturb[:, i])\n",
+    "        numgrad[i] = (loss2 - loss1)/(2*e)\n",
+    "    return numgrad\n",
+    "\n",
+    "\n",
+    "def checkCostFunction(cofiCostFunc, lambda_=0.):\n",
+    "    \"\"\"\n",
+    "    Creates a collaborative filtering problem to check your cost function and gradients.\n",
+    "    It will output the  analytical gradients produced by your code and the numerical gradients\n",
+    "    (computed using computeNumericalGradient). These two gradient computations should result\n",
+    "    in very similar values.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    cofiCostFunc: func\n",
+    "        Implementation of the cost function.\n",
+    "    lambda_ : float, optional\n",
+    "        The regularization parameter.\n",
+    "    \"\"\"\n",
+    "    # Create small problem\n",
+    "    X_t = np.random.rand(4, 3)\n",
+    "    Theta_t = np.random.rand(5, 3)\n",
+    "\n",
+    "    # Zap out most entries\n",
+    "    Y = np.dot(X_t, Theta_t.T)\n",
+    "    Y[np.random.rand(*Y.shape) > 0.5] = 0\n",
+    "    R = np.zeros(Y.shape)\n",
+    "    R[Y != 0] = 1\n",
+    "\n",
+    "    # Run Gradient Checking\n",
+    "    X = np.random.randn(*X_t.shape)\n",
+    "    Theta = np.random.randn(*Theta_t.shape)\n",
+    "    num_movies, num_users = Y.shape\n",
+    "    num_features = Theta_t.shape[1]\n",
+    "\n",
+    "    params = np.concatenate([X.ravel(), Theta.ravel()])\n",
+    "    numgrad = computeNumericalGradient(\n",
+    "        lambda x: cofiCostFunc(x, Y, R, num_users, num_movies, num_features, lambda_), params)\n",
+    "\n",
+    "    cost, grad = cofiCostFunc(params, Y, R, num_users,num_movies, num_features, lambda_)\n",
+    "\n",
+    "    print(np.stack([numgrad, grad], axis=1))\n",
+    "    print('\\nThe above two columns you get should be very similar.'\n",
+    "          '(Left-Your Numerical Gradient, Right-Analytical Gradient)')\n",
+    "\n",
+    "    diff = np.linalg.norm(numgrad-grad)/np.linalg.norm(numgrad+grad)\n",
+    "    print('If your cost function implementation is correct, then '\n",
+    "          'the relative difference will be small (less than 1e-9).')\n",
+    "    print('\\nRelative Difference: %g' % diff)\n",
+    "\n",
+    "\n",
+    "def multivariateGaussian(X, mu, Sigma2):\n",
+    "    \"\"\"\n",
+    "    Computes the probability density function of the multivariate gaussian distribution.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n). Where there are m examples of n-dimensions.\n",
+    "    mu : array_like\n",
+    "        A vector of shape (n,) contains the means for each dimension (feature).\n",
+    "    Sigma2 : array_like\n",
+    "        Either a vector of shape (n,) containing the variances of independent features\n",
+    "        (i.e. it is the diagonal of the correlation matrix), or the full\n",
+    "        correlation matrix of shape (n x n) which can represent dependent features.\n",
+    "    Returns\n",
+    "    ------\n",
+    "    p : array_like\n",
+    "        A vector of shape (m,) which contains the computed probabilities at each of the\n",
+    "        provided examples.\n",
+    "    \"\"\"\n",
+    "    k = mu.size\n",
+    "\n",
+    "    # if sigma is given as a diagonal, compute the matrix\n",
+    "    if Sigma2.ndim == 1:\n",
+    "        Sigma2 = np.diag(Sigma2)\n",
+    "\n",
+    "    X = X - mu\n",
+    "    p = (2 * np.pi) ** (- k / 2) * np.linalg.det(Sigma2) ** (-0.5)\\\n",
+    "        * np.exp(-0.5 * np.sum(np.dot(X, np.linalg.pinv(Sigma2)) * X, axis=1))\n",
+    "    return p\n",
+    "\n",
+    "\n",
+    "def visualizeFit(X, mu, sigma2):\n",
+    "    \"\"\"\n",
+    "    Visualize the dataset and its estimated distribution.\n",
+    "    This visualization shows you the  probability density function of the Gaussian distribution.\n",
+    "    Each example has a location (x1, x2) that depends on its feature values.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x 2). Where there are m examples of 2-dimensions. We need at most\n",
+    "        2-D features to be able to visualize the distribution.\n",
+    "    mu : array_like\n",
+    "        A vector of shape (n,) contains the means for each dimension (feature).\n",
+    "    sigma2 : array_like\n",
+    "        Either a vector of shape (n,) containing the variances of independent features\n",
+    "        (i.e. it is the diagonal of the correlation matrix), or the full\n",
+    "        correlation matrix of shape (n x n) which can represent dependent features.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    X1, X2 = np.meshgrid(np.arange(0, 35.5, 0.5), np.arange(0, 35.5, 0.5))\n",
+    "    Z = multivariateGaussian(np.stack([X1.ravel(), X2.ravel()], axis=1), mu, sigma2)\n",
+    "    Z = Z.reshape(X1.shape)\n",
+    "\n",
+    "    pyplot.plot(X[:, 0], X[:, 1], 'bx', mec='b', mew=2, ms=8)\n",
+    "\n",
+    "    if np.all(abs(Z) != np.inf):\n",
+    "        pyplot.contour(X1, X2, Z, levels=10**(np.arange(-20., 1, 3)), zorder=100)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}