MIT Technology Review

This startup wants to change how mathematicians do math

Axiom Math is giving away a powerful new AI tool. But it remains to be seen if it speeds up research as much as the company ...

Electronics of the future: Ultra-efficient graphene switch developed at nanometer scale

A team of researchers from Tel Aviv University, in collaboration with colleagues from Japan, has taken an important step ...
Scientific Research Publishing

From Econometrics to e.conometrics ()

Despite significant mathematical refinements, econometrics has shown the weaknesses of its logical underpinnings, primarily ...
IEEE

A Novel Hierarchical Graph Neural Network with Heterogeneous Multi-Sensor Data for Fault Diagnosis of Complex Mechanical Equipment

Abstract: Modern mechanical equipment increasingly relies on multi-sensor data for health monitoring, which generates complex, heterogeneous, non-Euclidean data that conventional methods struggle to ...
Live Science

'Thermodynamic computer' can mimic AI neural networks — using orders of magnitude less energy to generate images

Researchers generated images from noise, using orders of magnitude less energy than current generative AI models require. When you purchase through links on our site, we may earn an affiliate ...
IEEE

FedHiGraphSAGE: A Hierarchical Graph Neural Network Model for Multi-Level Anomaly Detection in Federated Cloud Environments

Abstract: Anomaly detection in cloud computing environments is increasingly challenging due to the complexity of distributed architectures, the heterogeneity of infrastructure components, and the high ...
GitHub

CHAMP: A Coupled Hierarchical Atom-Motif Predictor

This repository provides the official PyTorch implementation for CHAMP (Coupled Hierarchical Atom-Motif Predictor), a novel hierarchical Graph Neural Network framework designed to achieve state-of-the ...
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 ...
GitHub

CHAMP: A Coupled Hierarchical Atom-Motif Predictor

CHAMP: A Coupled Hierarchical Atom-Motif Predictor This repository provides the official PyTorch implementation for CHAMP (Coupled Hierarchical Atom-Motif Predictor), a novel hierarchical Graph Neural ...
Finextra

Graph Neural Networks and GraphRAG: Navigating Open-World Complexity in Finance

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...