This valuable study presents a plastic recurrent spiking network model that spontaneously generates repeating neuronal sequences under unstructured inputs. The authors provide solid evidence that, ...

Heterogeneous NPU designs bring together multiple specialized compute engines to support the range of operators required by ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather ...

CoinDesk Research maps five crypto privacy approaches and examines which models hold up as AI improves. Full coverage of ...

Systematic reviews with network meta-analysis (NMA) have potential biases in their conduct, analysis, and interpretation. If the results or conclusions of an NMA are integrated into policy or practice ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

摘要: Machine learning (ML) has become an increasingly central component of high-energy physics (HEP), providing computational frameworks to address the growing complexity of theoretical calculations ...

ABSTRACT: Machine learning (ML) has become an increasingly central component of high-energy physics (HEP), providing computational frameworks to address the growing complexity of theoretical ...

Abstract: In this letter, we propose a meta-learning-based fast adversarial training method to address the vulnerability of graph neural network (GNN) based resource allocation method to adversarial ...

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The interaction between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) plays ...