graph neural networks

PrecipGNN: Physics-Informed Spatiotemporal Graph Neural Networks for High-Resolution Precipitation Nowcasting at Continental Scale

PrecipGNN: Physics-Informed Spatiotemporal Graph Neural Networks for High-Resolution Precipitation Nowcasting at Continental Scale

Neurostructural Correlates and Impulsive Mechanisms of Exercise Dependence in Middle-Aged and Elderly Adults: Evidence from Gray Matter and Structural Covariance Networks

Initial evidence of neurostructural markers underlying exercise dependence in the middle-aged and elderly adults are provided, demonstrating associations with regional GMV and whole-brain structural covariance networks.

GNMLC-Net: a graph neural inference and multi-layer context-aware fusion network for high-precision detection of avionics thermistor solder joints

A Cascaded Refinement and Context-Gated Semantic Injection mechanism is designed to enhance sensitivity to small targets by refining key feature information and improving the quality of feature representations, which effectively mitigates the semantic-spatial mismatch issue in deep networks while enhancing multi-scale...

Convergent pathology, divergent pathways: Structural network dissociation of trait and cognitive disinhibition in cocaine use disorder.

This study provides a dimensional framework for understanding the neurobiological organization of behavioral dysregulation in addiction by demonstrating that distinct dimensions of disinhibition in CUD are associated with partially overlapping but largely separable structural connectome architectures.

Vision transformer and graph neural networks based SuryaNet for yoga pose quality assessment

SuryaNet is introduced, a multi-task model with four technical contributions that establishes a new state-of-the-art in terms of quality assessment, while it also provides users with quality feedback.

TcfDyG: Cryptocurrency Network Anomaly Detection Based on Time-aware Channel Fusion Dynamic Graph Neural Network

Process-Aware Frequency-Enhanced Conditional VAE With Graph Neural Networks for Anomaly Detection of Industrial Multivariate Time Series

This article presents a novel anomaly detection method for industrial multivariate time series (IMTS) based on a process-aware frequency-enhanced conditional variational autoencoder (FCVAE) and graph neural networks (GNN). The proposed FCVAE-GNN method consists of three main stages: frequency-enhanced representation le...

GNN-RL APF Switching Controller for Power Quality Enhancement in Renewable Integrated Grids

The integration of renewable or green energy sources in the grids introduce several problems like power quality issues such as harmonic distortion, voltage fluctuations and unbalanced reactive power flow etc. Active Power Filters (APF) can effectively work on these issues. However, the key performance of APFs depend on...

A Graph-Based Transfer Learning Approach for Short-Term Bus Passenger Flow Prediction

Bus passenger flow prediction is a crucial task in bus transportation management and optimization, characterized by strong spatiotemporal dependencies and influenced by external factors such as weather and holidays. However, existing methods face challenges in responding to sudden events, modeling dynamic changes in bu...

Identifying urban water spatial patterns and their underlying mechanism through graph neural network

A feasible probability and graph model for memory engrams

The capacity of long-term memory seems to be extremely large, capable of storing information spanning almost a lifetime. Why does it have such a vast capacity? Why are some memories so enduring? What is the actual physical form of long-term memory? In the movie Inside Out, it is depicted as individual orbs containing i...