Researchers have developed a scalable method to build comprehensive firm-to-firm production networks from publicly available data, offering unprecedented insight into economic dependencies.
A new approach combines satellite imagery, road network details, and accident history to significantly improve the prediction of traffic accidents and identify key contributing factors.
Researchers have developed a novel framework to detect and mitigate a critical vulnerability – atomicity violations – within the code that powers decentralized applications.
A new approach to graph neural networks explicitly quantifies prediction uncertainty, enhancing robustness in challenging data scenarios.
As data science projects become increasingly complex, traditional risk management approaches fall short, overlooking critical ethical and societal considerations.
A new framework, GraphMatch, combines the power of natural language processing with graph neural networks to significantly improve recommendations in fast-moving, two-sided digital marketplaces.
A novel framework uses artificial intelligence to reconcile on-chain data with issuer reports, providing a more comprehensive view of stablecoin credibility.
As artificial intelligence moves beyond isolated tools and into interconnected networks, a new landscape of systemic risks emerges, demanding a fresh approach to safety and governance.
New research harnesses the power of advanced artificial intelligence to create near-real-time global flood maps with unprecedented accuracy and speed.
A new framework leverages large language models to extract and track key events from financial news, improving the accuracy of stock price forecasting.