New research decodes hidden bias in health care LLMs

Artificial intelligence is increasingly used as a tool in many health care settings, from writing physicians' notes to making ...
IEEE

Autoencoder-Enhanced Ensemble Neural Network for Forced Oscillation Source Location in Renewable-Rich Power Systems

Abstract: Conventional energy-based methods struggle to determine the origin of Forced Oscillations (FOs) in renewable-rich systems because of their dependency on Dissipating Energy Flow (DEF) ...
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 ...
Albuquerque Journal

Sandia researchers build AI system to detect anomalies across the electrical grid

The electrical grid is a crucial, sometimes fragile, piece of infrastructure. As connectivity to the grid increases, so too does its vulnerability. Public Service Company of New Mexico, the state’s ...
T&D

Sandia National Laboratories’ Research Team Develop AI Algorithms to Detect Physical Issues, Cyberattacks within Grid

Researchers at Sandia National Laboratories have developed AI algorithms to detect physical problems, cyberattacks and both at the same time within the grid. “As more disturbances occur, whether from ...
IEEE

Autoencoder-Augmented Graph Neural Networks for Accurate and Scalable Structure Recognition in Analog/Mixed-Signal Schematics

Abstract: The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, ...
Phys.org

Common feature between forest fires and neural networks reveals universal framework

Researchers from the University of Tokyo in collaboration with Aisin Corporation have demonstrated that universal scaling laws, which describe how the properties of a system change with size and scale ...
marktechpost

How Latent Vector Fields Reveal the Inner Workings of Neural Autoencoders

Neural networks are designed to learn compressed representations of high-dimensional data, and autoencoders (AEs) are a widely-used example of such models. These systems employ an encoder-decoder ...