Apple's iPadOS 26 is finally available to all, with much-needed updates to Notes and Files, plus a new windowed multitasking ...

Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

The Annals of Statistics, Vol. 37, No. 3 (Jun., 2009), pp. 1360-1404 (45 pages) We study the problem of nonparametric estimation of a multivariate function $g ...

Unlike traditional approaches that typically test one protein target or drug at a time in hopes of identifying an effective ...

Abstract: Multivariate time series anomaly detection (MTSAD) plays a crucial role in the Internet of Things (IoT) to identify device malfunction or system attacks. Graph neural networks (GNN) are ...

At its core, the SPARKLINE function in Google Sheets takes a row of numbers and turns them into a mini chart that lives right inside a single cell. Instead of inserting a full-sized chart that eats up ...

Abstract: For accurate forecasting of multivariate time series, it is essential to consider the relationship between temporal and spatial dimensions. Graph neural networks (GNNs) have gained ...

Importance Little research has been done on post-COVID symptoms at 24 months postinfection and on the association these may ...

This project applies graph attention networks combined with topological analysis to detect anomalies in multivariate time series. It leverages research in topological graph neural networks and graph ...

The rapid accumulation of protein sequence data, coupled with the slow pace of experimental annotations, creates a critical need for computational methods to predict protein functions. Existing models ...