Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

The study also breaks down the full financial toll, revealing the hardest-hit countries and the most effective scam tactics ...

Light has always carried more than brightness. In this case, it also carries direction and twist. That mix may open a new ...

A recent publication from IMDEA Materials Institute and the Technical University of Madrid (UPM) presents a major step ...

Check out this new 6G semantic communication framework from Engineering! It uses explicit semantic bases to fix the flaws of ...

NVIDIA researchers have proposed a neural compression method for material textures that enables random-access lookups and ...

Intel has shared new details about Texture Set Neural Compression, or TSNC, and the company now positions it as a practical solution instead of just a research demo, while moving the technology into a ...

Deep learning models for decoding intracortical neural activity during attempted speech into text. This repository contains our team's implementation for the COMP 433 Fall 2025 course project, ...

Abstract: Neural shape representation generally refers to representing 3D geometry using neural networks, e.g., computing a signed distance or occupancy value at a specific spatial position. In this ...

Abstract: X-ray computed tomography (CT) is a popular diagnostic imaging tool that has caused public concern over potential radiation risks to the patient. Monte Carlo (MC) simulations are the most ...