Zobrazeno 1 - 10
of 1 261
pro vyhledávání: '"Cheng Ze"'
Autor:
Wei Meng, Huang Ya-di, Cao Wei-bo, Zhao Ru-dong, Cheng Ze-wei, Jun Ou Yang, Yan Ze-peng, Chen Chuan-qi, Liang Yi-ze, Sun Dan-ping, Yu Wen-bin
Publikováno v:
Frontiers in Oncology, Vol 13 (2023)
ObjectiveTo evaluate the safety and clinical effect of tubular esophagogastric anastomosis in laparoscopic radical proximal gastrectomy.MethodsA retrospective analysis was conducted involving 191 patients who underwent laparoscopic radical proximal g
Externí odkaz:
https://doaj.org/article/55aba51e07544db78f9a72d04697f4d2
Autor:
Xu Nan, Cheng Ze-Di, Tang Jin-Dao, Lv Xiao-Min, Li Tong, Guo Meng-Lin, Wang You, Song Hai-Zhi, Zhou Qiang, Deng Guang-Wei
Publikováno v:
Nanophotonics, Vol 10, Iss 9, Pp 2265-2281 (2021)
Nano-opto-electro-mechanical systems (NOEMS), considered as new platforms to study electronic and mechanical freedoms in the field of nanophotonics, have gained rapid progress in recent years. NOEMS offer exciting opportunities to manipulate informat
Externí odkaz:
https://doaj.org/article/50fe3249951f4f0abb6a6b10128d00c2
Autor:
Qu-Zhen Danzeng, Na Cui, Hao Wang, Wen-Jun Pan, Yun Long, Yang-Zong Deji, Cheng Ze, Zeng Ren, Peng Lyu
Publikováno v:
Chinese Medical Journal, Vol 132, Iss 10, Pp 1154-1158 (2019)
Abstract. Background:. At present, there is no available delirium translated assessment method for 3.3 million Tibetans. This study aimed to provide a method for delirium assessment for Tibetan patients speaking this language by validating a translat
Externí odkaz:
https://doaj.org/article/99a2a06f0a224c65926964926b046874
Autor:
Cheng, Ze, Hao, Zhongkai, Wang, Xiaoqiang, Huang, Jianing, Wu, Youjia, Liu, Xudan, Zhao, Yiru, Liu, Songming, Su, Hang
For partial differential equations on domains of arbitrary shapes, existing works of neural operators attempt to learn a mapping from geometries to solutions. It often requires a large dataset of geometry-solution pairs in order to obtain a sufficien
Externí odkaz:
http://arxiv.org/abs/2405.17509
Publikováno v:
MATEC Web of Conferences, Vol 355, p 03050 (2022)
In this paper, a large gain variable range, high linearity, low noise, low DC offset VGAs with a simple gain-dB variable circuit are introduced. In the VGAs chain, the last and the first VGAs employ Bipolar transistors, to improve the linearity and n
Externí odkaz:
https://doaj.org/article/ad0558583cd1408bbbec02f61b7ddcf7
Autor:
Pan, Wei-Wei, Liu, Xiao, Xu, Xiao-Ye, Wang, Qin-Qin, Cheng, Ze-Di, Wang, Jian, Liu, Zhao-Di, Chen, Geng, Zhou, Zong-Quan, Li, Chuan-Feng, Guo, Guang-Can, Dressel, Justin, Vaidman, Lev
We report an experimental realization of a modified counterfactual communication protocol that eliminates the dominant environmental trace left by photons passing through the transmission channel. Compared to Wheeler's criterion for inferring past pa
Externí odkaz:
http://arxiv.org/abs/2308.10165
The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs. However, when faced with real-world physical data, which are often highly non-uniformly distributed, it is challenging to use mesh-based techniqu
Externí odkaz:
http://arxiv.org/abs/2305.18694
Understanding whether self-supervised learning methods can scale with unlimited data is crucial for training large-scale models. In this work, we conduct an empirical study on the scaling capability of masked image modeling (MIM) methods (e.g., MAE)
Externí odkaz:
http://arxiv.org/abs/2305.15248
Autor:
Hao, Zhongkai, Wang, Zhengyi, Su, Hang, Ying, Chengyang, Dong, Yinpeng, Liu, Songming, Cheng, Ze, Song, Jian, Zhu, Jun
Learning partial differential equations' (PDEs) solution operators is an essential problem in machine learning. However, there are several challenges for learning operators in practical applications like the irregular mesh, multiple input functions,
Externí odkaz:
http://arxiv.org/abs/2302.14376
Contrastive Masked Autoencoder (CMAE), as a new self-supervised framework, has shown its potential of learning expressive feature representations in visual image recognition. This work shows that CMAE also trivially generalizes well on video action r
Externí odkaz:
http://arxiv.org/abs/2301.06018