Abstract: Deep-learning-based health prognostics is receiving ever-increasing attention. Most existing methods leverage advanced neural networks for prognostics performance improvement, providing ...

The project trains a Bayesian CNN (a CNN with dropout used as approximate Bayesian inference via MC-dropout) and uses its predictive uncertainty to decide which unlabelled images are most worth ...

Abstract: Aiming at a cleaner future power system, many regimes in the world have proposed their ambitious decarbonizing plan, with increasing penetration of renewable energy sources (RES) playing an ...

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A deep reinforcement learning framework optimizes silicon-based photonic crystal fiber modulators, achieving ultra-low ...

(A) Photo of the lensfree holography system. (B) Physical and digital workflow of the automated HER2 scoring system using lensfree holography. (C) Visualization of the whole-slide lensfree holographic ...

Researchers at the University of California, Los Angeles have developed a compact, cost-effective diagnostic platform ...

Micron and Anthropic have formed a strategic partnership to enhance AI memory and storage infrastructure. The collaboration includes a supply agreement and adoption of Anthropic's Claude models within ...

Leaf functional traits—chlorophyll (CHL), carotenoid (CAR), equivalent water thickness (EWT), nitrogen (N), and leaf mass per ...

Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...