Late in 2025, we covered the development of an AI system called Evo that was trained on massive numbers of bacterial genomes. So many that, when prompted with sequences from a cluster of related genes ...

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) ...

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 ...

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 ...

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 ...

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, ...

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 ...

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 ...

Thank you very much for your outstanding work. Recently, I came across your research results and was immediately intrigued. I attempted to train a parameter autoencoder using my own set of parameters, ...

ABSTRACT: The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in ...