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 …

Photonic neural networks perform brain-inspired computations using photons instead of
electrons to achieve substantially improved computing performance. However, existing …

Graph signal processing (GSP) deals with the representation, analysis, and processing of
structured data, ie graph signals that are defined on the vertex set of a generic graph. A …

Abstract Background and Objectives Graph neural networks (GNN) have demonstrated
remarkable encoding capabilities in the context of brain network classification tasks. They …

Pinpointing the sources of dementia is crucial to the effective treatment of
neurodegenerative diseases. In this paper, we propose a diffusion model with impulsive …

Graphs are fundamental mathematical structures used in various fields to model statistical
and physical relationships between data, signals, and processes. In some applications, such …

To study the relationship between brain network and shooting performance during shooting
aiming, we collected electroencephalogram (EEG) signals from 40 skilled shooters during …

Although most dramatic structural changes occur in the perinatal period, a growing body of
evidences demonstrates that adolescence and early adulthood are also important for …

Background Longitudinal neuroimaging studies have demonstrated that adolescence is a
crucial developmental period of continued brain growth and change. Motivated by both …

Do users from Carnegie Mellon University form social communities on Facebook? Do signal
processing researchers tightly collaborate with each other? Do Chinese restaurants in …