Cross-Modal Data Understanding Advances Through Bukun Ren’s Review of Visual Language Models

A study on visual language models explores how shared semantic frameworks improve image–text understanding across ...
Climate Compass on MSN

The science behind earthquake prediction efforts

Every year, the Earth shakes thousands of times. Most of those tremors go unnoticed, felt only by sensitive instruments ...
Frontiers

DWGCN: distance-weighted graph convolutional network for robust spatial domain identification in spatial transcriptomics

Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...
IEEE

Graph Tensor Convolutional Network

Abstract: A dynamic graph (DG) is commonly encountered in many big data-related application scenarios, like cryptocurrency transaction analysis. A dynamic graph convolutional network (GCN) can ...
Frontiers

RS-STGCN: Regional-Synergy Spatio-Temporal Graph Convolutional Network for emotion recognition

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...
C&EN

SFTGNN: An Efficient Spherical Fourier Transform-Enhanced Graph Neural Network for High-Accuracy Crystal Property Prediction

The prediction of the properties of crystal materials has always been a core issue in materials science and solid-state physics. With the rapid development of computer simulation techniques and ...