Zobrazeno 1 - 10
of 323
pro vyhledávání: '"Li, Yaohang"'
Complex networks, which are the abstractions of many real-world systems, present a persistent challenge across disciplines for people to decipher their underlying information. Recently, hyperbolic geometry of latent spaces has gained traction in netw
Externí odkaz:
http://arxiv.org/abs/2405.16928
Autor:
Almaeen, Manal, Alghamdi, Tareq, Kriesten, Brandon, Adams, Douglas, Li, Yaohang, Liuti, Huey-Wen Lin ans Simonetta
We develop a new methodology for extracting Compton form factors (CFFs) in from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM).
Externí odkaz:
http://arxiv.org/abs/2405.05826
Autonomous virtual agents are often limited by their singular mode of interaction with real-world environments, restricting their versatility. To address this, we propose the Multi-Modal Agent Collaboration framework (MMAC-Copilot), a framework utili
Externí odkaz:
http://arxiv.org/abs/2404.18074
Human leukocyte antigen (HLA) is an important molecule family in the field of human immunity, which recognizes foreign threats and triggers immune responses by presenting peptides to T cells. In recent years, the synthesis of tumor vaccines to induce
Externí odkaz:
http://arxiv.org/abs/2208.04314
Autor:
Almaeen, Manal, Grigsby, Jake, Hoskins, Joshua, Kriesten, Brandon, Li, Yaohang, Lin, Huey-Wen, Liuti, Simonetta
We develop a framework to establish benchmarks for machine learning and deep neural networks analyses of exclusive scattering cross sections (FemtoNet). Within this framework we present an extraction of Compton form factors for deeply virtual Compton
Externí odkaz:
http://arxiv.org/abs/2207.10766
Autor:
Alanazi, Yasir, Sato, N., Ambrozewicz, Pawel, Blin, Astrid N. Hiller, Melnitchouk, W., Battaglieri, Marco, Liu, Tianbo, Li, Yaohang
Publikováno v:
Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21) Survey Track, p. 4286 (2021)
Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions. We survey the state-of-the-art of machine learning (ML) efforts at building physics event generators. We review ML gene
Externí odkaz:
http://arxiv.org/abs/2106.00643
Autor:
Alanazi, Yasir, Sato, N., Liu, Tianbo, Melnitchouk, W., Ambrozewicz, Pawel, Hauenstein, Florian, Kuchera, Michelle P., Pritchard, Evan, Robertson, Michael, Strauss, Ryan, Velasco, Luisa, Li, Yaohang
Publikováno v:
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) Main Track, p. 2126 (2021)
We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of effici
Externí odkaz:
http://arxiv.org/abs/2001.11103
Publikováno v:
In Journal of Biomedical Informatics July 2023 143
Matrix completion is a widely used technique for image inpainting and personalized recommender system, etc. In this work, we focus on accelerating the matrix completion using faster randomized singular value decomposition (rSVD). Firstly, two fast ra
Externí odkaz:
http://arxiv.org/abs/1810.06860
In this article, we consider the general problem of checking the correctness of matrix multiplication. Given three $n \times n$ matrices $A$, $B$, and $C$, the goal is to verify that $A \times B=C$ without carrying out the computationally costly oper
Externí odkaz:
http://arxiv.org/abs/1705.10449