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Abstract: Graph neural networks (GNNs) are good at capturing the intricate topologies and dependencies among components and are outstanding in fault diagnosis tasks of complex industrial process. Bias ...

Abstract: Linking time-varying functional brain activity with underlying neural architecture remains a complex and challenging endeavor. A recent framework for this undertaking is graph signal ...