Science

New research reveals that artificial intelligence can reliably estimate the severity of post-traumatic stress disorder from patient-provided narratives.

Researchers have developed a framework that moves beyond analyzing genes in isolation to predict how cells will react to genetic changes by modeling coordinated gene programs.

A new Bayesian method boosts the accuracy of combined forecasts even when experts participate irregularly, offering a solution for real-world survey panels.

Researchers have developed a hybrid verification method that significantly speeds up the process of ensuring neural network reliability and safety.

A new forecasting model uses spatial awareness and transformer networks to predict traffic patterns with greater accuracy.

A new framework dramatically reduces data acquisition time for millimeter-wave and terahertz channel sounding, enabling more practical large-scale measurements for future 6G wireless systems.

A new approach leverages compositional data analysis to monitor evolving software systems and maintain reliable observability.

Researchers have developed a novel framework that combines geometric modeling with in-context learning to achieve more accurate and adaptable predictions for complex time series data.

Researchers are harnessing the power of multimodal data and large-scale pretraining to dramatically improve the accuracy and robustness of time series forecasting and anomaly detection.

New research reveals that performance gains from increasing datasets aren’t guaranteed in materials modeling, challenging established scaling laws.