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 ...

Like many of us, [Tim]’s seen online videos of circuit sculptures containing illuminated LED filaments. Unlike most of us, however, he went a step further by using graph theory to design glowing ...

MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat ...

Representation learning lies at the core of modern artificial intelligence, enabling neural networks to uncover meaningful, ...

Aiomics announces the integration of a Hybrid GraphRAG engine into its clinical platform. By anchoring artificial ...

International inflation linked securities are re-emerging as a compelling portfolio building block for US investors. Read ...

We structured the STRONG AYA case-mix and core outcome measures concepts and their properties as knowledge graphs. Having identified the corresponding standard terminologies, we developed a semantic ...

The article studies the main factors affecting the pile drum of a cotton cleaning device for removing small impurities. As a result of the study, graphs were constructed, and the influence of the pile ...

Abstract: Representing and exploiting multivariate signals requires capturing relations between variables, which we can represent by graphs. Graph dictionaries allow to describe complex relational ...

Abstract: Postural tremor of hands, a typical symptom of Parkinson’s disease (PD), is assessed using the Movement Disorder Society-sponsored revision of the Unified PD Rating Scale (MDS-UPDRS).

ABSTRACT: Foot-and-Mouth Disease (FMD) remains a critical threat to global livestock industries, causing severe economic losses and trade restrictions. This paper proposes a novel application of ...

GDF-MIL (Graph-Driven Fusion Multiple Instance Learning) is a novel graph-driven multi-instance learning framework that adaptively balances topology modeling and semantic feature preservation through ...