Science

As artificial intelligence systems become increasingly complex, effectively managing inherent uncertainty is crucial for safe and dependable operation.

A new approach leveraging graph neural networks and spatial clustering dramatically improves how we model and optimize interconnected energy systems.

New research tackles the challenge of accurately forecasting human movement, even when data is incomplete or noisy.

Researchers have developed a method to rigorously verify the stability of identified neural network circuits, enhancing their trustworthiness and predictive power.

A new hardware generator efficiently compresses sparse data streams, unlocking the potential of graph neural networks for high-speed data analysis in particle physics and beyond.

A new approach combines physics-based modeling with machine learning to detect and diagnose faults in complex thermal-hydraulic processes.

New research identifies effective deep learning models for detecting distributed denial-of-service attacks targeting Internet of Things devices, prioritizing both performance and interpretability.

A new analysis reveals that today’s concerns about online misinformation have deep historical roots in earlier psychological research on memory distortion and suggest that understanding this lineage is key to addressing the current crisis.

New research demonstrates the potential of data-driven models to anticipate complications during continuous renal replacement therapy, paving the way for more proactive patient care.

A new framework enhances the ability of distributed machine learning systems to provide trustworthy predictions, even when data and models vary significantly across different sources.