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determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer
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, neuromorphic electronics based on spiking neural networks (SNNs) and the compute-in-memory (CIM) paradigm is emerging as the most promising path. While the research community has so far favored resistive Compute
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al., “Deep Transfer Learning for Fault Diagnosis”, IEEE Transactions on Industrial Electronics, 2020. • Zhang C. et al., “Graph Neural Networks for Power Systems”, Electric Power Systems Research, 2023
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on these characteristics (and, if necessary, more advanced characteristics), prediction models will be developed, ranging from simple statistical relationships to more flexible models such as artificial neural networks
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neural network tool to design multilayered compound metasurfaces for multifunctional operation. • Collaborate with nanofabrication teams to prototype and validate designs. • Conduct optical