Abstract: Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., ...

scVAG is an innovative framework that integrates Variational Autoencoder (VAE) and Graph Attention Autoencoder (GATE) models for enhanced analysis of single-cell gene expression data. Built upon the ...

Abstract: As a commonly used model for anomaly detection, the autoencoder model for anomaly detection does not train the objective for extracted features, which is a downside of autoencoder model. In ...

The 2025 SANS SOC Survey shows AI use is rising, but many SOCs lack integration, customization, and clear validation ...

Codes for training of Multiple Operator Kolmogorov-Arnold Network (MultiOKAN). The present framework delivers end-to-end, data-driven prediction of multiphase flow evolution from initial ...