Most of you have used a navigation app like Google Maps for your travels at some point. These apps rely on algorithms that compute shortest paths through vast networks. Now imagine scaling that task ...
ABSTRACT: Aiming at the problems of intensity inhomogeneity, boundary blurring and noise interference in the segmentation of three-dimensional volume data (such as medical images and industrial CT ...
This paper studies the fair influence maximization problem with efficient algorithms. In particular, given a graph G, a community structure C consisting of disjoint communities, and a budget k, the ...
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Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States ...
ABSTRACT: Missing data remains a persistent and pervasive challenge across a wide range of domains, significantly impacting data analysis pipelines, predictive modeling outcomes, and the reliability ...
Submodular maximization is a significant area of interest in combinatorial optimization, with numerous real-world applications. A research team led by Xiaoming SUN from the State Key Lab of Processors ...
In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function ...
Driven by a Vision to Deliver Smarter, Longer-Lasting Energy Solutions, Flux Power Integrates AI and Software Intelligence to Meet the Growing Demand for Adaptive, Data-Driven Electrification VISTA, ...
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