The creaminess of custard. The fizz of foam. The slurpability of soup. Texture is just as essential to our eating experience ...

We propose GTAE-IDS, a novel unsupervised packet-based graph neural network framework aimed at early and precise anomaly detection in network traffic. GTAE-IDS employs graph embeddings to capture and ...

Learns the normal patterns in network traffic data Automatically removes highly correlated features Detects anomalies based on reconstruction error Provides ...

such as masked autoencoder (MAE) reconstruction and contrastive learning, offer a promising solution by reducing reliance on labeled data. Nonetheless, transformer-based MAEs are computationally ...

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent ...

Understanding neural network dynamics is a cornerstone of systems neuroscience, bridging the gap between biological neural networks and artificial neural ...

Brain-inspired spiking neural networks bring real-time AI to edge devices, boosting performance, reducing power use, and ...

Rose Yu has drawn on the principles of fluid dynamics to improve deep learning systems that predict traffic, model the ...

Despite advances in machine vision, processing visual data requires substantial computing resources and energy, limiting deployment in edge devices. Now, researchers from Japan have developed a ...

and reveals a novel neural-glia-fibroblast-lymphatic regulatory axis. This provides a new framework for understanding how the brain adapts its lymphatic network based on functional needs ...