Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Shun Kataoka"'
Publikováno v:
The Journal of Physical Chemistry C. 126:8113-8120
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b70152eb4db3fdb344bc31d9605edd9
https://doi.org/10.1021/acs.jpcc.2c01181
https://doi.org/10.1021/acs.jpcc.2c01181
Publikováno v:
Physical Chemistry Chemical Physics. 23:18595-18601
When an aqueous solution freezes at temperatures above the eutectic point, a freeze concentrated solution (FCS) is separated from the ice phase. Reactions of environmental importance often occur in the FCS and, in some cases, are accelerated compared
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8e1518f480a29c96256125dfb99ef9f
https://doi.org/10.1039/d1cp01655e
https://doi.org/10.1039/d1cp01655e
Autor:
Shun Kataoka, Muneki Yasuda
Publikováno v:
The Review of Socionetwork Strategies. 13:267-280
In this paper, we propose a fast image denoising method based on discrete Markov random fields and the fast Fourier transform. The purpose of the image denoising is to infer the original noiseless image from a noise corrupted image. We consider the c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9fd6b0f793080a58878ba126f318cdb6
https://doi.org/10.1007/s12626-019-00043-3
https://doi.org/10.1007/s12626-019-00043-3
Autor:
Shun Kataoka
Publikováno v:
IEICE ESS Fundamentals Review. 11:256-265
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ba1fb0f7b9e08e485b738accacbc569
https://doi.org/10.1587/essfr.11.4_256
https://doi.org/10.1587/essfr.11.4_256
Publikováno v:
The Proceedings of the Dynamics & Design Conference. 2021:426
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a89d0015860ee79997f991a4990666c
https://doi.org/10.1299/jsmedmc.2021.426
https://doi.org/10.1299/jsmedmc.2021.426
Publikováno v:
The Proceedings of the Dynamics & Design Conference. 2020:406
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12b3e770efe11d7f380eb1cd9d62b295
https://doi.org/10.1299/jsmedmc.2020.406
https://doi.org/10.1299/jsmedmc.2020.406
A new Bayesian modeling method is proposed by combining the maximization of the marginal likelihood with a momentum-space renormalization group transformation for Gaussian graphical models. Moreover, we present a scheme for computint the statistical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a3252d9026bc41b03471bd9e9007d31
Autor:
Toshiki Saitoh, Kazuyuki Tanaka, Shun Kataoka, Hiro Ito, Takuya Kida, Yuya Higashikawa, Naoki Katoh, Tetsuo Shibuya, Yushi Uno
Publikováno v:
The Review of Socionetwork Strategies. 13:99-100
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::79e5a87df279c15c6ce91dddc659a742
https://doi.org/10.1007/s12626-019-00058-w
https://doi.org/10.1007/s12626-019-00058-w
In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm. Our method can solve Bayesian image denoising problems, including hyperparameter estimation, in $O(n)$-ti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0063cc6cf36ec4e9f8f6ffdecbc8bcbf
We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure together with the vertex attribute data, that is, the information assi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e27b78a7a0d4e358c655b1c62eeefe9