AIFU stock rated Sell: earnings declines, negative technicals, and China insurance commission caps pressure margins. Click ...

Advances in mechanistic modeling, machine learning, and biomedical data integration are making it possible to move beyond “one-size-fits-all” evidence and ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract ...

The Green party has made gains under its leader – but there is also uncertainty ahead ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Abstract: Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications where annotations are severely scarce and multimodal ...

This project implements and compares Temporal Graph Neural Networks (TGNNs) for detecting fraudulent transactions in financial networks. We've built a complete end-to-end system including model ...

Introduction: The importance of social environment for the mental health of older adults is gaining increasing attention, while the mediating role of social environment has not yet been thoroughly ...

Abstract: Graph Convolutional Networks (GCNs) rely heavily on the quality of the input graph, which is often defined by a fixed neighborhood size. This work explores two complementary strategies to ...