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pro vyhledávání: '"Nakata A"'
Autor:
Reza Abbaszadeh, Raheleh Askari-Moghadam, Maryam Moradian, Hojat Mortazaeian, Mohammad Reza Safaei Qomi, Negar Omidi, Yasaman Khalili, Tahmineh Tahouri
Publikováno v:
The Egyptian Heart Journal, Vol 75, Iss 1, Pp 1-6 (2023)
Abstract Background Pulmonary regurgitation is the most common complication after the complete repair of tetralogy of Fallot, and severe pulmonary regurgitation after surgery requires pulmonary valve replacement. In this retrospective observational,
Externí odkaz:
https://doaj.org/article/c227dd006c894f97bd21ea0154329cdb
Akademický článek
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Autor:
ROGERS, JOHN, LEOW, RONALD P.
Publikováno v:
The Modern Language Journal, 2020 Apr 01. 104(1), 309-312.
Externí odkaz:
https://www.jstor.org/stable/45286659
Autor:
Sakaedani, Haruko
Publikováno v:
Journal of Qur'anic Studies. Oct2016, Vol. 18 Issue 3, p131-134. 4p.
Autor:
Yoko, Kawaguchi
Publikováno v:
キリストと世界 = Christ and the World. 33:70-100
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=jairo_______::0696aa28f6c09ac1643b4217cb0dbbc4
http://id.nii.ac.jp/1131/00002526/
http://id.nii.ac.jp/1131/00002526/
Autor:
Yatomi, Go, Nakata, Motoki
Convergence of a matrix decomposition technique, the multi-field singular value decomposition (MFSVD) which efficiently analyzes nonlinear correlations by simultaneously decomposing multiple fields, is investigated. Toward applications in turbulence
Externí odkaz:
http://arxiv.org/abs/2411.03739
We study unitary and state $t$-designs from a computational complexity theory perspective. First, we address the problems of computing frame potentials that characterize (approximate) $t$-designs. We provide a quantum algorithm for computing the fram
Externí odkaz:
http://arxiv.org/abs/2410.23353
Continuous normalizing flows (CNFs) can model data distributions with expressive infinite-length architectures. But this modeling involves computationally expensive process of solving an ordinary differential equation (ODE) during maximum likelihood
Externí odkaz:
http://arxiv.org/abs/2410.09246
The goal of this paper is to improve the performance of pretrained Vision Transformer (ViT) models, particularly DINOv2, in image clustering task without requiring re-training or fine-tuning. As model size increases, high-norm artifacts anomaly appea
Externí odkaz:
http://arxiv.org/abs/2410.04801