Nvidia's GB10 and GB300 chips power a new class of PCs that will redefine how professionals work with AI. But that's just the start.

Explore 20 essential activation functions implemented in Python for deep neural networks—including ELU, ReLU, Leaky ReLU, ...

A review by researchers at Tongji University and the University of Technology Sydney published in Frontiers of Computer Science, highlights the powerful role of graph neural networks (GNNs) in ...

At ARVO 2025, in Salt Lake City, Utah, Patipol Tiyajamorn, talked about his poster on using graph neural networks to identify ...

High-entropy alloys (HEAs) offer tunable compositions and surface structures, presenting significant potential for creating novel active sites to enhance CO2 reduction (CO2RR) catalysis, a key process ...

GNN, a framework to train robust GNNs under noisy conditions. Soft-GNN mitigates label noise impact through dynamic ...

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for ...

Prosthetic limbs have been slow to evolve from simple motionless replicas of human body parts to moving, active devices. A major part of this is that controlling the many joints of a prosthetic is ...

As a powerful tool for graph analytics, graph neural networks (GNNs) have recently gained wide attention due to its end-to-end processing capabilities. With the proliferation of cloud computing, it is ...

But neural networks only predict based on patterns from the past—what happens when the weather does something that's unprecedented ... Swarming is one of the principal forms of bacterial ...