Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Jiqing Wu"'
Autor:
Jiqing Wu, Viktor H. Koelzer
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 3481-3488 (2024)
Digital twins in biomedical research, i.e. virtual replicas of biological entities such as cells, organs, or entire organisms, hold great potential to advance personalized healthcare. As all biological processes happen in space, there is a growing in
Externí odkaz:
https://doaj.org/article/0e531bf4eb0843bf8534435682e28ae0
Publikováno v:
Advances in Mechanical Engineering, Vol 15 (2023)
During the operation of high-speed railway, the transition-point disappearance phenomenon, which is caused by the deformation of pantograph head, poses a safety threat to the pantograph-catenary system. In this study, the analytical model for transit
Externí odkaz:
https://doaj.org/article/a6e75133fe54434cb8e20005c88cad4a
Autor:
Jiqing Wu, Viktor H. Koelzer
Modeling heterogeneous disease states by data-driven methods has great potential to advance biomedical research. However, a comprehensive analysis of phenotypic heterogeneity is often challenged by the complex nature of biomedical datasets and emergi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::18cced7d867c43dcac9345c6f04ec5c3
https://doi.org/10.1101/2023.02.10.528026
https://doi.org/10.1101/2023.02.10.528026
Autor:
Hanxiang Chen, Shuting Hou, Haoyuan Cui, Chao Wang, Ming Zhang, Hongping Li, Hui Xu, Jiqing Wu, Wenshuai Zhu
Publikováno v:
Journal of Molecular Liquids. 379:121650
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3c5ab546f648b69ad65573ad8fb1988
https://doi.org/10.1016/j.molliq.2023.121650
https://doi.org/10.1016/j.molliq.2023.121650
Autor:
Zhaoyang Zhang, Tong Zheng, Yanxin Zhuang, Pengfei Xing, Jian Kong, Donghui Wei, Wenhui Ma, Kuixian Wei, Jiqing Wu
Publikováno v:
Journal of Cleaner Production. 371:133655
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50e217a31e1bfdaa29839b7b0d716270
https://doi.org/10.1016/j.jclepro.2022.133655
https://doi.org/10.1016/j.jclepro.2022.133655
Autor:
Jiqing Wu, Luc Van Gool, Wen Li, Zhiwu Huang, Janine Thoma, Dinesh Acharya, Danda Pani Paudel
Publikováno v:
CVPR
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is challenging to approximate the WD of high-dimensional distributions. I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::204e083dd4ba1312efa7b179caf66f6c
https://doi.org/10.1109/cvpr.2019.00383
https://doi.org/10.1109/cvpr.2019.00383
Autor:
Radu Timofte, Shuhang Gu, Jiqing Wu, Luc Van Gool, Lei Zhang, Ming-Hsuan Yang, Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita, Shijia Hu, Yijie Bei, Zheng Hui, Xiao Jiang, Yanan Gu, Jie Liu, Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers, Jiahui Yu, Yuchen Fan, Jianchao Yang, Ning Xu, Zhaowen Wang, Xinchao Wang, Thomas S. Huang, Xintao Wang, Ke Yu, Tak-Wai Hui, Chao Dong, Liang Lin, Chen Change Loy, Dongwon Park, Kwanyoung Kim, Se Young Chun, Kai Zhang, Pengjv Liu, Wangmeng Zuo, Shi Guo, Jiye Liu, Jinchang Xu, Yijiao Liu, Fengye Xiong, Yuan Dong, Hongliang Bai, Alexandru Damian, Nikhil Ravi, Sachit Menon, Cynthia Rudin, Junghoon Seo, Taegyun Jeon, Jamyoung Koo, Seunghyun Jeon, Soo Ye Kim, Jae-Seok Choi, Sehwan Ki, Soomin Seo, Hyeonjun Sim, Saehun Kim, Munchurl Kim, Rong Chen, Kun Zeng, Jinkang Guo, Yanyun Qu, Cuihua Li, Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn, Yuan Yuan, Jiawei Zhang, Jiahao Pang, Xiangyu Xu, Yan Zhao, Wei Deng, Sibt Ul Hussain, Muneeb Aadil, Rafia Rahim, Xiaowang Cai, Fang Huang, Yueshu Xu, Pablo Navarrete Michelini, Dan Zhu, Hanwen Liu, Jun-Hyuk Kim, Jong-Seok Lee, Yiwen Huang, Ming Qiu, Liting Jing, Jiehang Zeng, Ying Wang, Manoj Sharma, Rudrabha Mukhopadhyay, Avinash Upadhyay, Sriharsha Koundinya, Ankit Shukla, Santanu Chaudhury, Zhe Zhang, Yu Hen Hu, Lingzhi Fu
Publikováno v:
CVPR Workshops
This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downsc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::84c6886f489dceba6721a20afda21901
https://doi.org/10.1109/cvprw.2018.00130
https://doi.org/10.1109/cvprw.2018.00130
Publikováno v:
Computer Vision – ECCV 2018 ISBN: 9783030012274
ECCV (5)
ECCV (5)
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive quali
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1028cea7739991286415e22d270dded4
https://doi.org/10.1007/978-3-030-01228-1_40
https://doi.org/10.1007/978-3-030-01228-1_40
Publikováno v:
ICCV Workshops
Very recent Galliani et al. [13] proposed a method using a very deep CNN architecture for learned spectral reconstruction and showed large improvements over the recent sparse coding method of Arad et al. [6]. In this paper we defend the shallow learn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f5984f76e64e226fb56efd4965a87b1
https://doi.org/10.1109/iccvw.2017.63
https://doi.org/10.1109/iccvw.2017.63
Publikováno v:
Computer Vision – ACCV 2016 Workshops ISBN: 9783319544069
ACCV Workshops (1)
ACCV Workshops (1)
Also recently, exciting strides forward have been made in the area of image restoration, particularly for image denoising and single image super-resolution. Deep learning techniques contributed to this significantly. The top methods differ in their f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df726147a6e191bdf303010fe417d405
https://doi.org/10.1007/978-3-319-54407-6_11
https://doi.org/10.1007/978-3-319-54407-6_11