The ability to predict brain activity from words before they occur can be explained by information shared between neighbouring words, without requiring next-word prediction by the brain.

Schug has written extensively on the role of AI and data science in analytical chemistry in the LCGC Blog. In a recent ...

This technique can be used out-of-the-box, requiring no model training or special packaging. It is code-execution free, which ...

This special issue focuses on the latest research progress in 6G technology development, standard formulation, and engineering practice, based on our previous special issue titled 6G Requirements, ...

This study presents a valuable application of a video-text alignment deep neural network model to improve neural encoding of naturalistic stimuli in fMRI. The authors provide convincing evidence that ...

Now that anyone can use AI to generate keywords and spin up a paid search campaign in minutes, it’s easy to assume the hard work is done. But creating structured, scalable performance still requires a ...

Deep learning methods such as multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) have been applied to predict the complex traits in animal and plant breeding. However, it remains ...

Abstract: Recent advancements in deep learning for semantic communication have been significant, yet fixed-length encoding techniques struggle to capture the complex and variable nature of semantic ...

Recent studies suggest that working memory (WM) temporarily stores and processes bindings, while semantically related WM processing interacts with long-term memory (LTM). Semantic information can be ...