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

This manuscript presents important findings that challenge traditional models of speech processing by demonstrating that theta-gamma phase-amplitude coupling in the auditory cortex is primarily a ...

Ever opened a file and seen strange symbols or jumbled text? That’s usually an encoding problem; your software isn’t reading the data correctly. The good news is that Microsoft Office makes it easy to ...

Model-based design tools are often used to design safety-critical embedded software. Consequently, generating correct code from such models is crucial. We tackle this challenge on Lustre, a dataflow ...

The application of data science in agriculture enables the analysis of diverse datasets using methods such as machine learning, deep learning, computer vision, text mining (Drury and Roche, 2019), and ...

This was a topic discussed in the last GDExtension meeting, I was advised to open an issue by @dsnopek. Class methods that return non-RefCounted objects do not have clear ownership semantics. This is ...

An amble through the neighborhoods of North Berkeley often turns into an introspection and a treasure hunt. On one such stroll, I and my fellow amblers stumbled across an empty, upturned flower pot, ...

Abstract: The use of deep learning methods to decode visual perception images from brain activity recorded by fMRI has received a lot of attention. However, limited fMRI data make the task of visual ...

Abstract: Despite deep learning's progress in semantic communication, traditional fixed-length encoding does not adequately address the variable complexity of semantic content, often leading to loss ...