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Intelligence prediction from brain connectivity is significantly improved by a novel explainable deep learning framework that uncovers a dual-layered network architecture of localized hubs and distributed relay pathways.
A novel polynomial approximation for matrix logarithm normalization achieves faster and more accurate results in fine-grained recognition, challenging the reliance on eigendecomposition methods.