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GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
With the recent study of deep learning in scientific computation, the Physics-Informed Neural Networks (PINNs) method has drawn widespread attention for solving Partial Differential Equations (PDEs).
Structural characteristics in network control of molecular multiplex networks
Here, we focus on the molecular multiplex networks coupled by the transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network, exploring the controllability and energy requiring in these types of molecular multiplex networks.
Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans
To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models without leakage of data privacy.
