Proof-of-concept exploit code has been published for a critical remote code execution flaw in protobuf.js, a widely used ...

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

Abstract: Speech language models (Speech LMs) enable end-to-end speech-text modeling within a single model, offering a promising direction for spoken dialogue systems. The choice of speech-text ...

Spam texts seem to strike when you least expect it. Not only are they frustrating, they also put phone users at risk of phishing and fraud. These messages are typically sent out in bulk with the ...

Summary: Meta’s Fundamental AI Research team has unveiled TRIBE, a groundbreaking foundation model designed to predict how the human brain processes visual and auditory stimuli. Trained on massive ...

Abstract: Objective: Inter-session and inter-subject variability in electroencephalography (EEG) signals, resulting from individual differences and environmental factors, poses a major challenge for ...

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, ...