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pro vyhledávání: '"Kim, JinHo"'
In modeling a relativistic disk around a compact object, the self-gravity of the disk is often neglected while it needs to be incorporated for more accurate descriptions in several circumstances. Extending the Komatsu-Eriguchi-Hachisu self-consistent
Externí odkaz:
http://arxiv.org/abs/2406.00945
This study accelerates MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3T and 0.55T. Thirty healthy volunteers underwent conventional two-fold MRCP scans at field strengths of 3T or 0.55T. We trained a
Externí odkaz:
http://arxiv.org/abs/2405.03732
Autor:
Kim, Jinho, Kim, Taehoon
Publikováno v:
Hitotsubashi Journal of Economics, 2024 Jun 01. 65(1), 94-115.
Externí odkaz:
https://www.jstor.org/stable/27310116
Autor:
Park, Jungwuk, Han, Dong-Jun, Kim, Jinho, Wang, Shiqiang, Brinton, Christopher G., Moon, Jaekyun
Traditional federated learning (FL) algorithms operate under the assumption that the data distributions at training (source domains) and testing (target domain) are the same. The fact that domain shifts often occur in practice necessitates equipping
Externí odkaz:
http://arxiv.org/abs/2311.00227
We show that the quasicategory defined as the localization of the category of (simple) graphs at the class of A-homotopy equivalences does not admit colimits. In particular, we settle in the negative the question of whether the A-homotopy equivalence
Externí odkaz:
http://arxiv.org/abs/2306.02219
Autor:
Garain, Sudip K, Kim, Jinho
We study the time evolution of sub-Keplerian transonic accretion flow onto a non-rotating black hole using a three-dimensional, inviscid hydrodynamics simulation code. Prior two-dimensional simulations show that centrifugal barrier in the accreting m
Externí odkaz:
http://arxiv.org/abs/2212.08310
Black-box models, such as deep neural networks, exhibit superior predictive performances, but understanding their behavior is notoriously difficult. Many explainable artificial intelligence methods have been proposed to reveal the decision-making pro
Externí odkaz:
http://arxiv.org/abs/2209.15409
Publikováno v:
IEEE Access, vol. 12, pp. 15438-15446, Jan. 2024
Graph neural networks (GNNs) are powerful tools for handling graph-structured data. However, their design often limits them to learning only higher-order feature interactions, leaving low-order feature interactions overlooked. To address this problem
Externí odkaz:
http://arxiv.org/abs/2204.03225