The Money Trail That Never Stops: Understanding Daisy Chains in Payment Networks

Daisy chains are different. Their signal is structural. What matters is not one transaction, but the sequence. Imagine money ...

HydroGraphNet boosts watershed predictions of daily flow and nitrogen in sparse data regions

Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of ...

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 ...
Medical Dialogues

Medical Bulletin 31/March/2026 - Video

New Study Identifies Exercise Type That Significantly Improves Sleep Quality A new meta-analysis published in Sleep and Biological Rhythms suggests ...
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 ...
IEEE

DAHBM-GCN: A Flexible Graph Convolution Network Accelerator With Multiple Dataflows and HBM

Abstract: Graph-structured data has been widely applied in transportation, molecular, and e-commerce networks, etc. Graph Convolutional Network (GCN) has emerged as an efficient approach to processing ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...