A new topology-based method predicts atomic charges in metal-organic frameworks from bond connectivity alone, making large-scale computational screening practical.
Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...
On SWE-Bench Verified, the model achieved a score of 70.6%. This performance is notably competitive when placed alongside significantly larger models; it outpaces DeepSeek-V3.2, which scores 70.2%, ...
A research team led by Professor Wang Hongzhi from the Hefei Institute of Physical Science of the Chinese Academy of Sciences has developed a multi-stage, dual-domain, progressive network with ...
Abstract: Sparse Bayesian learning (SBL) is an advanced statistical framework that dominantly enhances the sparse features of targets of interest in radar imagery. A widely adopted strategy for ...
MoEEG has two variants, Base and Large, which share an identical model architecture but differ in hyperparameter configurations: the Base model features an embedding dimension of 128 with 4 attention ...
MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more ...
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