惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

J
Java Code Geeks
小众软件
小众软件
博客园 - 叶小钗
宝玉的分享
宝玉的分享
博客园_首页
Hugging Face - Blog
Hugging Face - Blog
人人都是产品经理
人人都是产品经理
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
S
SegmentFault 最新的问题
B
Blog RSS Feed
Engineering at Meta
Engineering at Meta
N
Netflix TechBlog - Medium
Google DeepMind News
Google DeepMind News
U
Unit 42
F
Fortinet All Blogs
IT之家
IT之家
Y
Y Combinator Blog
Martin Fowler
Martin Fowler
T
The Blog of Author Tim Ferriss
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
The GitHub Blog
The GitHub Blog
Stack Overflow Blog
Stack Overflow Blog
Blog — PlanetScale
Blog — PlanetScale
酷 壳 – CoolShell
酷 壳 – CoolShell

cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
Cosmos 1.0: a multidimensional map of the emerging techno...
Xian Gong, Paul X. McCarthy, Colin Griffith, Claire McFarland, M · 2025-05-15 · via cs.SI updates on arXiv.org

This paper introduces the Cosmos 1.0 dataset and describes a novel methodology for creating and mapping a universe of technologies, adjacent concepts, and entities. We utilise various source data that contain a rich diversity and breadth of contemporary knowledge. The Cosmos 1.0 dataset comprises 23,544 technology-adjacent entities (TA23k) with a hierarchical structure and eight categories of external indices. Each entity is represented by a 100-dimensional contextual embedding vector, which we use to assign it to seven thematic tech-clusters (TC7) and three meta tech-clusters (TC3). We manually verify 100 emerging technologies (ET100). This dataset is enriched with additional indices specifically developed to assess the landscape of emerging technologies, including the Technology Awareness Index, Generality Index, Deeptech, and Age of Tech Index. The dataset incorporates extensive metadata sourced from Wikipedia and linked data from third-party sources such as Crunchbase, Google Books, OpenAlex and Google Scholar, which are used to validate the relevance and accuracy of the constructed indices.