By extending the scope of a key insight behind Fermat’s Last Theorem, four mathematicians have made great strides toward ...

This useful study employs optogenetics, genetically-encoded dopamine and serotonin sensors, and patch-clamp electrophysiology to investigate modulations of neurotransmitter release between striatal ...

Graph signals are signals with an irregular structure that can be described by a graph. Graph neural networks (GNNs) are information processing architectures tailored to these graph signals and made ...

Yahoo Finance is chronicling the latest news and updates on Trump's tariffs.

President Trump last week touted a $550 billion investment in the US that Japan made as part of trade negotiations "to lower their tariffs a little bit," as he described it. On Saturday, Japanese ...

Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision ...

ParGNN: A Scalable Graph Neural Network Training Framework on multi-GPUs ParGNN is accepted by DAC 2025. ParGNN, an efficient full-batch training system for GNNs, which adopts a profiler-guided ...

The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Recently, machine learning has been ...

SMDA is a minimalist recursive disassembler library that is optimized for accurate Control Flow Graph (CFG) recovery from memory dumps.