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Deploy from jjohare/logseq @ 26cfd68720c21347a922f98603d1e619572bedf2 jjohare/logseq@26cfd68
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api/markdown/3D Scene Exchange Protocol (SXP).md

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collapsed:: true
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- ontology:: true
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- term-id:: MV-0223
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- source-domain:: mv
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- preferred-term:: 3D Scene Exchange Protocol (SXP)
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- status:: draft
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- public-access:: true
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- Verification: Academic sources verified
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- Regional Context: UK/North England where applicable
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### Relationships
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- is-subclass-of:: [[Metaverse]]

api/markdown/3D and 4D.md

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- preferred-term:: {{twitter https://twitter.com/cocktailpeanut/status/1765462787046686968}}
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- source-domain:: ai
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- status:: draft
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- owl:class:: ai:twitterHttpstwittercomcocktailpeanutstatus1765462787046686968
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### Relationships
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- is-subclass-of:: [[Artificial Intelligence]]
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### [NVIDIA Edify](https://research.nvidia.com/labs/dir/edify-3d/) - Edify 3D is a framework developed by NVIDIA Research that focuses on creating, editing, and refining 3D scenes and content.
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- The system leverages [[artificial intelligence]] and [[machine learning]] to allow users to interact with 3D models in a more intuitive and accessible way, streamlining the 3D creation process.
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- One key aim of Edify 3D is to simplify complex tasks like object placement, colour correction, and material assignment within a 3D environment.

api/markdown/51 Percent Attack.md

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- term-id:: BC-9981
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- preferred-term:: 51% Attack
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- source-domain:: bc
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- owl:class:: bc:51Attack
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- status:: draft
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### Key Characteristics
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- **Content Alignment**: 100% relevant to 51% attacks (removed 364 lines of irrelevant content)
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- **Citations Added**: 10 academic and industry sources
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- **Wiki-Links Added**: 45+ internal links to related concepts
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### Relationships
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- is-subclass-of:: [[Blockchain]]

api/markdown/51% Attack.md

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- term-id:: BC-9887
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- preferred-term:: id:: 51%attackontology
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- source-domain:: bc
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- owl:class:: bc:Id51attackontology
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- status:: draft
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### Relationships
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- is-subclass-of:: [[Blockchain]]
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- uses-data-structure:: [[Blockchain Entity]]

api/markdown/6G Network Slice.md

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collapsed:: true
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- ontology:: true
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- term-id:: DT-1017
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- source-domain:: mv
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- preferred-term:: 6G Network Slice
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- public-access:: true
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- definition:: Virtual partition of 6G infrastructure guaranteeing specified quality-of-service levels for immersive workloads through isolated resource allocation.
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- belongsToDomain:: [[InfrastructureDomain]]
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- implementedInLayer:: [[NetworkLayer]]
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- #### Relationships
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- is-subclass-of:: [[Metaverse]]
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id:: 6g-network-slice-relationships
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collapsed:: true
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- has-part:: [[Service Level Agreement]]

api/markdown/A-Star Algorithm.md

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- preferred-term:: A-Star Algorithm
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- source-domain:: ai
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- status:: draft
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- owl:class:: ai:AStarAlgorithm
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### Relationships
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- is-subclass-of:: [[Search Algorithms]]

api/markdown/ADAS.md

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collapsed:: true
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- ontology:: true
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- term-id:: MV-0348
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- source-domain:: mv
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- preferred-term:: ADAS
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- status:: draft
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- public-access:: true

api/markdown/AI Agent System.md

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### Domain & Architecture
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- belongsToDomain:: [[AI-GroundedDomain]], [[ComputationAndIntelligenceDomain]]
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- maturity:: dt:Mature
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- maturity:: mature
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### Relationships
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- is-subclass-of:: [[ai:ModelArchitecture]]

api/markdown/AI Alignment.md

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- preferred-term:: AI Alignment
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- source-domain:: ai
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- status:: draft
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- owl:class:: ai:AiAlignment
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- public-access:: true
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- definition:: The process of making AI systems' behaviour and goals consistent with human values, preferences, and intentions. AI alignment encompasses technical methods and research aimed at ensuring AI systems act in accordance with human interests, even as they become more capable.
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- source:: [[OpenAI]], [[Anthropic]], [[DeepMind]], [[IEEE P7000]]

api/markdown/AI Audit.md

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- preferred-term:: AI Audit
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- source-domain:: ai
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- status:: draft
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- owl:class:: ai:AiAudit
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- public-access:: true
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- definition:: A systematic, independent examination and evaluation of an artificial intelligence system's design, development processes, deployment procedures, operational performance, documentation, governance arrangements, and compliance with applicable requirements, conducted by qualified assessors to verify conformity with specified standards, regulations, ethical principles, or organisational policies, and to identify deficiencies, risks, or opportunities for improvement, producing documented findings and recommendations that support accountability, transparency, and continuous enhancement of AI system trustworthiness.
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- source:: [[BS ISO/IEC 42006:2025]], [[EU AI Act Article 43]], [[NIST AI RMF]]

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