The Lancet

Decoding the language of sleep with artificial intelligence

Sleep is one of medicine's underused data streams. Clinically, disturbed sleep has often been treated as a symptom of a disorder, but sleep is also a physiological state in which brain, cardiac, ...
Security Boulevard

NDSS 2025 – Defending Against Backdoor Attacks On Graph Neural Networks Via Discrepancy Learning

Hao Yu (National University of Defense Technology), Chuan Ma (Chongqing University), Xinhang Wan (National University of Defense Technology), Jun Wang (National University of Defense Technology), Tao ...
IEEE

FROG: A Fine-Grained Spatiotemporal Graph Neural Network With Self-Supervised Guidance for Early Diagnosis of Alzheimer's Disease

Abstract: Functional magnetic resonance imaging (fMRI) has demonstrated significant potential in the early diagnosis and study of pathological mechanisms of Alzheimer's disease (AD). To fit subtle ...
The New York Times

The Data on Self-Driving Cars Is Clear. We Have to Change Course.

Dr. Slotkin is a neurosurgeon. I recently got called to see a teenager ejected in a rollover car crash. The trauma team rushed him into surgery to stop major abdominal bleeding, but we all knew. When ...
Frontiers

Effective and efficient self-supervised masked model based on mixed feature training

Under the influence of Masked Language Modeling (MLM), Masked Image Modeling (MIM) employs an attention mechanism to perform masked training on images. However, processing a single image requires ...
IEEE

Multi-Channel Equilibrium Graph Neural Network for Multi-View Semi-Supervised Learning

Abstract: In practical applications, the difficulty of multi-view data annotation poses a challenge for multi-view semi-supervised learning. Although some graph-based approaches have been proposed for ...
Frontiers

Self-supervised learning analysis of multi-FISH labeled cell-type map in thick brain slices

Accurate mapping of the spatial distribution of diverse cell types is essential for understanding the cellular organization of brain. However, the cellular heterogeneity and the substantial cost of ...
Phys.org

Self-learning neural network cracks iconic black holes

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...
EurekAlert!

Soft-GNN: towards robust graph neural networks via self-adaptive data utilization

Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...