Science

Researchers have developed a novel deep learning framework to improve the resolution of 4D Flow MRI data, even when data characteristics differ between training and real-world scans.

A new approach to predictive maintenance leverages the power of foundation models alongside traditional time-series analysis to pinpoint equipment anomalies with greater accuracy.

New research reveals that simply disclosing AI involvement doesn’t guarantee funding success, and how creators can strategically communicate about AI to build trust with backers.

A new data-driven approach is revealing fundamental relationships within scattering amplitudes, potentially simplifying complex calculations and leading to new discoveries.

New research reveals how automated deleveraging mechanisms in perpetual futures markets can lead to unexpected instability and proposes smoother approaches to mitigate risk.

New research reveals an automated method for identifying subtle biases in the reward models that guide artificial intelligence learning.

A new method analyzes the underlying grammatical structure of text to reliably identify content generated by artificial intelligence.

A new framework leverages the power of quantum annealing and deep generative models to create molecules with enhanced properties, exceeding the limitations of conventional training datasets.

New research introduces an efficient algorithm for identifying suspicious patterns in transaction networks by rapidly pinpointing dense, temporally-linked flows.

As real-world data becomes increasingly scarce and expensive, researchers are turning to simulation and digital twins to generate the datasets needed to build robust and intelligent agents.