Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Yu Inatsu"'
Autor:
Keiichi Inoue, Masayuki Karasuyama, Ryoko Nakamura, Masae Konno, Daichi Yamada, Kentaro Mannen, Takashi Nagata, Yu Inatsu, Hiromu Yawo, Kei Yura, Oded Béjà, Hideki Kandori, Ichiro Takeuchi
Publikováno v:
Communications Biology, Vol 4, Iss 1, Pp 1-11 (2021)
Inoue, Takeuchi and colleagues propose a machine learning-based protocol to screen rhodopsins for their likelihood to be red-shifted. After experimental verification, their tool shows remarkable success at identifying rhodopsins that showed red-shift
Externí odkaz:
https://doaj.org/article/a7253888b2914466b0761002b36b7c01
Publikováno v:
IEEE Access, Vol 8, Pp 203982-203993 (2020)
In the manufacturing industry, it is often necessary to repeat expensive operational testing of machine in order to identify the range of input conditions under which the machine operates properly. Since it is often difficult to accurately control th
Externí odkaz:
https://doaj.org/article/41aad1ce369f490a9eedb0e5991b1b0a
Autor:
Keiichi Inoue, Masayuki Karasuyama, Ryoko Nakamura, Masae Konno, Daichi Yamada, Kentaro Mannen, Takashi Nagata, Yu Inatsu, Hiromu Yawo, Kei Yura, Oded Béjà, Hideki Kandori, Ichiro Takeuchi
Publikováno v:
Communications Biology, Vol 4, Iss 1, Pp 1-1 (2021)
Externí odkaz:
https://doaj.org/article/8d010b8059064fb49a1c945ab2876721
Autor:
Yu Inatsu1 yu.inatsu@riken.jp, Daisuke Sugita2 sugita.d.mllab.nit@gmail.com, Kazuaki Toyoura1,3 toyoura.kazuaki.5r@kyoto-u.ac.jp, Ichiro Takeuchi1,2,4 takeuchi.ichiro@nitech.ac.jp
Publikováno v:
Neural Computation. Oct2020, Vol. 32 Issue 10, p2032-2068. 37p. 1 Color Photograph, 1 Chart, 5 Graphs.
Autor:
Yu Inatsu1 yu.inatsu@riken.jp, Masayuki Karasuyama2,3,4 karasuyama@nitech.ac.jp, Keiichi Inoue5 inoue@issp.u-tokyo.ac.jp, Hideki Kandori2 kandori@nitech.ac.jp, Ichiro Takeuchi1,2,4 takeuchi.ichiro@nitech.ac.jp
Publikováno v:
Neural Computation. Oct2020, Vol. 32 Issue 10, p1998-2031. 33p. 1 Chart, 5 Graphs.
Publikováno v:
IEEE Access, Vol 8, Pp 203982-203993 (2020)
In the manufacturing industry, it is often necessary to repeat expensive operational testing of machine in order to identify the range of input conditions under which the machine operates properly. Since it is often difficult to accurately control th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d17fcc687e9f50d228192b15ff27a1ea
https://doi.org/10.1109/access.2020.3036863
https://doi.org/10.1109/access.2020.3036863
Autor:
Yu Inatsu, Takashi Nagata, Kei Yura, Hideki Kandori, Oded Béjà, Daichi Yamada, Kentaro Mannen, Ichiro Takeuchi, Masayuki Karasuyama, Masae Konno, Hiromu Yawo, Keiichi Inoue, Ryoko Nakamura
Publikováno v:
Communications Biology
Communications Biology, Vol 4, Iss 1, Pp 1-11 (2021)
Communications Biology, Vol 4, Iss 1, Pp 1-11 (2021)
Microbial rhodopsins are photoreceptive membrane proteins, which are used as molecular tools in optogenetics. Here, a machine learning (ML)-based experimental design method is introduced for screening rhodopsins that are likely to be red-shifted from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d05c4effb8fd30d117159a96f792862
http://europepmc.org/articles/PMC7979833
http://europepmc.org/articles/PMC7979833
In practical data analysis under noisy environment, it is common to first use robust methods to identify outliers, and then to conduct further analysis after removing the outliers. In this paper, we consider statistical inference of the model estimat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6aa594083160f439504bd623acf01f1e
Publikováno v:
Neural computation. 32(12)
Testing under what conditions a product satisfies the desired properties is a fundamental problem in manufacturing industry. If the condition and the property are respectively regarded as the input and the output of a black-box function, this task ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e11f7ff5074047f38bc19d9334f60e1
https://pubmed.ncbi.nlm.nih.gov/33080163
https://pubmed.ncbi.nlm.nih.gov/33080163
Publikováno v:
CVPR
Image segmentation is one of the most fundamental tasks of computer vision. In many practical applications, it is essential to properly evaluate the reliability of individual segmentation results. In this study, we propose a novel framework to provid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b685312cd63cdd4396850ae231634749
https://doi.org/10.1109/cvpr42600.2020.00957
https://doi.org/10.1109/cvpr42600.2020.00957