A new research model called PiGRAND merges physics guidance with graph neural diffusion to predict and control AM processes.

A team of researchers has built a new protein sequencing workflow that pairs mirror proteases with deep learning software to ...

Fringe movements are using games and other online platforms to draw growing numbers of children to their causes, new data and dozens of interviews show. By Pranav Baskar Taking a page from the child ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Computational neuroscience has traditionally focused on isolated scales, limiting understanding of brain function across multiple levels. While microscopic models capture biophysical details of ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

The prediction of the properties of crystal materials has always been a core issue in materials science and solid-state physics. With the rapid development of computer simulation techniques and ...

Abstract: In this letter, we propose a meta-learning-based fast adversarial training method to address the vulnerability of graph neural network (GNN) based resource allocation method to adversarial ...