Discover how homomorphic encryption (HE) enhances privacy-preserving model context sharing in AI, ensuring secure data handling and compliance for MCP deployments.

Professor Junmo Kim and Ph.D. candidate Minchan Kwon, School of Electrical Engineering >No matter how much data they learn, why do Artificial ...

The authors provide a useful integrated analytical approach to investigating MASLD focused on diverse multiomic integration methods. The strength of evidence for this new resource is solid, as ...

This potentially valuable cross-sectional longitudinal study leverages high-definition transcranial direct current stimulation to the left dorsolateral prefrontal cortex to examine its effect on ...

Tellurium nanowire transistors switch between boosting and suppressing their light response through voltage alone, enabling ...

v0 is the best vibe coding tools for generating sleek and attractive design elements. Having been built by gold standard ...

AI into web searches, but the AI chatbot now offers far more. It is extremely capable in complex problem solving, deep ...

Picking and packing errors are mistakes that occur during the order fulfillment process in a warehouse. Picking errors ...

No matter how much data they learn, why do artificial intelligence (AI) models often miss the mark on human intent?

Explore how AI-driven threat detection can secure Model Context Protocol (MCP) deployments from data manipulation attempts, with a focus on post-quantum security.

The Data Project has identified three focus areas addressing challenges in research data management: education in data management, data storage, and data sharing. Three working groups were established ...