Dynamic graph algorithms and data structures represent a vital research frontier in computer science, underpinning applications from network analysis to real-time system monitoring.

Graphs, visual representations outlining the relationships between different entities, concepts or variables, can be very effective in summarizing complex patterns and information. Past psychology ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides ...

Fomography is an exciting indie exploration title available for PC, PS5, Xbox Series X/S, and Nintendo Switch. This single-player, casual puzzle-solving game features 24 trophies, which are ...

Big Data Data Governance Frameworks: Definition, Importance, and Examples Unlock the power of your data with an effective data governance framework for security, compliance, and decision-making.

Learning the gene coexpression pattern is a central challenge for high-dimensional gene expression analysis. Recently, sparse singular value decomposition (SVD) has been used to achieve this goal.

In this study, we propose a spatio-temporal hybrid attentive graph network (ST-HAG) for diagnosing autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) from fMRI data.