Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

AI outputs vary because confidence varies. Corroboration and entity optimization turn inconsistent AI visibility into consistent presence.

Claude Sonnet 4.6 features improved skills in coding, computer use, long-context reasoning, agent planning, knowledge work, ...

Our weekly simulation for U.S. Treasury yields and spreads. Read the latest update in the article series, as of February 6, 2026.

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 ...

The goal is to learn the network characteristics and disease dynamics of the pandemic occurred in Sweden during 2009, commonly known as swine flu. As a secondary goal, we develop an algorithm to ...

Microsoft Corp. today is expanding its Fabric data platform with the addition of native graph database and geospatial mapping capabilities, saying the enhancements enhance Fabric’s capacity to power ...

Abstract: Vision Graph Neural Network (ViG) is the first graph neural network model capable of directly processing image data. The community primarily focuses on the model structures to improve ViG's ...

Abstract: We study a family of random graph models - termed subgraph generated models (SUGMs) - initially developed by Chandrasekhar and Jackson in [1] in which higher-order structures are explicitly ...

We investigate signatures of quantum chaos and complexity in the quantum annealing Ising model on random Erdős-Rényi graphs. By tuning the connectivity of the graph, the dynamics can be driven from a ...