Johns Hopkins researchers are building a high-speed imaging system to capture brain signals 50 times faster than current tools—revealing the "hidden" neural processes that drive neurological disorders ...

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

Abstract: This study examines approaches to hyperparameter optimization in machine learning and deep learning systems using genetic algorithms. Today, the success of neural networks and machine ...

Atmospheric aerosols influence climate forcing, air quality, visibility, and human health, but their properties vary widely across space and time. Satellite instruments equipped with multi-angle and ...

Robot perception and cognition often rely on the integration of information from multiple sensory modalities, such as vision, ...

GlassAI Enables Real-Time RAW Video Processing on Snapdragon® 8 Elite (Gen 5) Platforms The Snapdragon 8 Elite (Gen 5) ...

Labs: a synergistic neural AI ecosystem driving the future through integrated research, product monetization, and accelerated AI ...

A team at the University of California, San Diego has redesigned how RRAM operates in an effort to accelerate the execution ...

RIT researchers publish a paper in Nature Scientific Reports on a new tree-based machine learning algorithm used to predict chaos.

The signals that drive many of the brain and body's most essential functions—consciousness, sleep, breathing, heart rate and motion—course through bundles of "white matter" fibers in the brainstem, ...

Machine learning is helping neuroscientists organize vast quantities of cells’ genetic data in the latest neurobiological cartography effort.

Small and dense but filled with vitally important neural fibers, the brainstem has been hard for brain imaging technologies ...