When data is productized and semantics encoded, you unlock agentic AI at scale. Instead of assistants, you get executors: ...

We present a knowledge‐guided machine learning framework for operational hydrologic forecasting at the catchment scale. Our approach, a Factorized Hierarchical Neural Network (FHNN), has two main ...

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are ...

Discover what context graphs are, why they're revolutionizing AI systems, and who's building this trillion-dollar technology ...

Loosh launches a cognitive engine giving AI memory, ethics, and decentralized intelligence for real-world autonomy.

CARE-ACE supports autonomy through bounded agentic reasoning, in which diagnostic, prognostic, planning, and risk-assessment ...

The final, formatted version of the article will be published soon. Background Biomedical knowledge graphs (KGs), such as the Data Distillery Knowledge Graph (DDKG), capture known relationships among ...

Official implementation of our CleanPose, the first solution to mitigate the confoundering effect in category-level pose estimation via causal learning and knowledge distillation. You can generate the ...

A team led by Guoyin Yin at Wuhan University and the Shanghai Artificial Intelligence Laboratory recently proposed a modular machine learning ...

Chinese and Singaporean researchers have developed a defense mechanism that poisons proprietary knowledge graph data, making ...

A decorated UVA engineer collects early-career honors for his leadership and contributions to the data mining field.

Accurate smartphone positioning in dense urban environments remains challenging due to signal blockage, multipath effects, and unreliable satellite visibility. This study presents a new positioning ...