A new topology-based method predicts atomic charges in metal-organic frameworks from bond connectivity alone, making large-scale computational screening practical.

Abstract: Incomplete multi-view clustering (IMC) has received increasing attention since missing observations of views are common in real-world applications. Existing approaches often learn similarity ...

Abstract: The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field ...

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 ...

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

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

A research team has successfully implemented a programmable spinor lattice on a photonic integrated circuit (PIC). This platform enables the realization of non-Abelian physics, in which the outcome of ...

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...

MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more ...

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 ...

"The last generation of enterprise software became trillion-dollar companies by owning what happened. The next trillion-dollar opportunity? Owning why it happened." — Foundation Capital Traditional ...