Science

Accurately forecasting electric vehicle charging load is crucial for grid stability and efficient energy management as EV adoption accelerates.

Researchers have developed a new automated system to expose the subtle prejudices embedded within text-to-image models, revealing how these systems can perpetuate harmful stereotypes.

Researchers are leveraging advanced network analysis and machine learning to unlock deeper insights into the brain changes associated with Alzheimer’s disease.

Researchers are now able to detect and analyze sensitive and illegal content embedded within Ethereum transactions, revealing a previously unseen risk to blockchain security and privacy.

A new architecture integrates artificial intelligence, semantic technologies, and digital preservation to unlock actionable insights from raw data and accelerate scientific progress.

Researchers are developing image filters that go beyond simple aesthetics, aiming to subtly reshape visuals to evoke specific emotional responses.

Researchers are developing techniques to move beyond obvious visual cues and identify AI-generated images with greater accuracy and reliability.

Researchers are exploring the application of quantum field theory – specifically a disordered ϕ⁴ model – as a novel machine learning approach to forecasting financial time series.

A new approach uses artificial intelligence to model long-term electricity markets and evaluate pathways to ambitious decarbonization goals.

Researchers are enhancing graph node classification by combining graph neural networks with large language models to identify both familiar and entirely new types of data points.