Hardware assurance (HA) through circuit analysis ensures the highest level of confidence in manufactured ICs to function as intended and are free ... development of artificial intelligence (AI) on ...

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

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 ...

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 ...

Confused about activation functions in neural networks? This video breaks down what they are, why they matter, and the most common types — including ReLU, Sigmoid, Tanh, and more! #NeuralNetworks #Mac ...

Brain-inspired spiking neural networks bring real-time AI to edge devices, boosting performance, reducing power use, and ...

Understanding neural network dynamics is a cornerstone of systems neuroscience, bridging the gap between biological neural networks and artificial neural ...

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 ...

With the emergence of advanced machine learning, graph-guided neural networks have gained attention for their unique ability to represent structured data and relationships among process variables. By ...