Graph theory and machine learning are revolutionary approaches to medical image analysis that leverage the structural nuances of medical data for better diagnostic accuracy. This research focuses on ...

Official implementation of Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation by Jun Hyeong Kim*, Seonghwan Kim*, Seokhyun Moon*, Hyeongwoo Kim*, Jeheon Woo*, Woo Youn Kim. [arXiv ...

, Frank Harary, Generalized Ramsey Theory for Graphs. II. Small Diagonal Numbers, Proceedings of the American Mathematical Society, Vol. 32, No. 2 (Apr., 1972), pp ...

"Arc-traceable tournaments and digraphs," (invited) Paths, Cycles and Graph Structures Workshop, University of Colorado Denver, July 2009. "Anti-cycles in directed graphs," (invited) 2009 SIAM Annual ...

Deep neural networks for graphs (DNNGs) represent an emerging field that studies how the deep learning method can be generalized to graph-structured data. Since graphs are a powerful and flexible tool ...

This repository contains the official implementation of our ICML 2024 paper, VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context. VisionGraph, is a benchmark ...

C. W. Groetsch, C. R. Vogel, Asymptotic Theory of Filtering for Linear Operator Equations with Discrete Noisy Data, Mathematics of Computation, Vol. 49, No. 180 (Oct ...

A 17-year-old student has shaken the mathematics world by toppling a theory that stood unchallenged for over 40 years.

Mathematics : a discrete introduction by Schneiderman, Edward R., 1957- Publication date 2000 Topics Mathematics, Computer science -- Mathematics Publisher Pacific Grove, Calif. : Brooks/Cole ...

IIT Guwahati released GATE 2026 Syllabus for all 30 subjects at gate2026.iitg.ac.in. Check the detailed syllabus and exam ...

This important work sets out to identify the neural substrates of associative fear responses in adult zebrafish. Through a compelling and innovative paradigm and analysis, the authors suggest brain ...