Abstract: Maximum likelihood (ML) decoding is the optimal decoding algorithm for arbitrary linear block codes and can be written as an integer programming (IP) problem. Feldman relaxed this IP problem ...

Syndrome-based neural decoding is a promising approach for soft-decision decoding of short, high-rate codes, but the field is still wide open. Performance often lags behind classical decoders like OSD ...

When I started looking into erasure coding techniques, I found many implementations of traditional block codes like Reed-Solomon codes or fountain codes like Raptor codes, though no implementation of ...

The room we are in is locked. It is windowless and lit from above by a fluorescent bulb. In the hallway outside—two stories beneath the city of London—attendants in dark suits patrol silently, giving ...

Abstract: Guessing random additive noise decoding (GRAND) has been recently proposed as a code-agnostic decoding technique for linear block codes, which attempts to ...

Recently, several deep learning methods have been applied to decoding in task-related fMRI, and their advantages have been exploited in a variety of ways. However, this paradigm is sometimes ...

Two stereoscopic cues that underlie the perception of motion-in-depth (MID) are changes in retinal disparity over time (CD) and interocular velocity differences (IOVD). These cues have independent ...

College of Artificial Intelligence, National University of Defense Technology, Changsha, China. Since the end of the 20 th century, with the development and growth of the Internet, people’s demand for ...

The vestibular system plays a crucial role in important fundamental as well as high cognitive functions. Supporting this, vestibular signals have been found widely distributed in the brain including ...

Humans are remarkably efficient at categorizing natural scenes. In fact, scene categories can be decoded from functional MRI (fMRI) data throughout the ventral visual cortex, including the primary ...