Abstract: Quantifying the complexity of biomedical signals offers critical insight into underlying physiological and pathological dynamics. This study systematically evaluates compression-based ...

Abstract: Graph convolutional networks (GCNs) are emerging neural network models designed to process graph-structured data. Due to massively parallel computations using irregular data structures by ...

PFAS method development must reconcile trace sensitivity with quantitative robustness despite ion suppression, coelution, and retention challenges spanning ultra-short-chain to long-chain analytes.

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Matrix multiplication is a key operation in scientific computing and machine learning, with GPU libraries like NVIDIA Cutlass and cuBLAS providing optimized implementations of the three nested loop ...