Science

A new study reveals improved methods for predicting abrupt changes in dynamic systems subjected to slow, repeating forces.

A growing body of research demonstrates that topological methods offer a powerful new lens for understanding organization and change in complex systems, moving beyond traditional approaches.

A new approach combines the power of deep learning with interpretable statistics to better predict mortgage defaults and understand the factors driving credit risk.

Artificial intelligence is transforming network defense, but its performance isn’t guaranteed in the face of evolving threats and real-world data challenges.

A new framework quantifies plasticity by linking network structure to dynamical regimes, offering a measurable way to understand a system’s responsiveness to change.

Researchers have developed a graphical approach to reliably identify causal relationships in systems where multiple factors interact and confounding variables obscure the true drivers.

Researchers have developed a self-supervised learning framework that enhances the reliability of AI systems handling multiple data types, making them more resilient to errors and anomalies.

New research shows a deep learning model can predict health risks across multiple body systems simply by analyzing 3D skeletal motion.

Researchers have developed a novel reinforcement learning framework that dramatically improves traffic signal control, promising smoother commutes and reduced congestion in complex urban environments.

A new framework offers a path toward reliable depression detection from audio while prioritizing user data security.