Abstract: The inability to capture the temporal dynamics of network interactions limits traditional intrusion detection systems (IDSs) in detecting sophisticated threats that evolve over time. This ...

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 ...

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

This repository provides unofficial binary wheels for Pygame for Python on Windows. The files are unofficial (meaning: informal, unrecognized, personal, unsupported, no warranty, no liability, ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Florida's Burmese pythons have reached a level of lore in Florida that perhaps no other animals have held in the state. They're the ultimate of swamp monsters. Pythons are gigantic predators from ...

Representing the brain as a complex network typically involves approximations of both biological detail and network structure. Here, we discuss the sort of biological detail that may improve network ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...