Zobrazeno 1 - 10
of 192
pro vyhledávání: '"Jinyang, Guo"'
In the domain of photorealistic avatar generation, the fidelity of audio-driven lip motion synthesis is essential for realistic virtual interactions. Existing methods face two key challenges: a lack of vivacity due to limited diversity in generated l
Externí odkaz:
http://arxiv.org/abs/2409.09326
Autor:
Jinyang Guo, Yanzhao Xu, Chao Huang, Mingbo Wang, Fan Zhang, Zhao Liu, Zhenhua Li, Huilai Lv, Ziqiang Tian
Publikováno v:
Asian Journal of Surgery, Vol 47, Iss 8, Pp 3478-3484 (2024)
Objective: To investigate the effectiveness of the original oblique conformal anastomosis presented in this research in reducing the incidence of cervical anastomotic leak after performing totally minimally invasive esophagectomy (TMIE). Methods: The
Externí odkaz:
https://doaj.org/article/d3e18cfc40ab4dbba43f535b6fef4642
Autor:
Zhuanzhuan Liu, Qingxin Zhang, Liya Li, Junjie He, Jinyang Guo, Zichen Wang, Yige Huang, Zimeng Xi, Fei Yuan, Yiji Li, Tingting Li
Publikováno v:
Frontiers in Cellular and Infection Microbiology, Vol 13 (2023)
Externí odkaz:
https://doaj.org/article/7094008eb2114545a4b38c7fe8eba25b
Autor:
Zhuanzhuan Liu, Qingxin Zhang, Liya Li, Junjie He, Jinyang Guo, Zichen Wang, Yige Huang, Zimeng Xi, Fei Yuan, Yiji Li, Tingting Li
Publikováno v:
Frontiers in Cellular and Infection Microbiology, Vol 13 (2023)
Dengue is prevalent in tropical and subtropical regions. As an arbovirus disease, it is mainly transmitted by Aedes aegypti and Aedes albopictus. According to the previous studies, temperature is closely related to the survival of Aedes mosquitoes, t
Externí odkaz:
https://doaj.org/article/5ad84eeba9dc4839a9e011e1ac2dad61
Publikováno v:
IEEE Access, Vol 11, Pp 33324-33335 (2023)
Visual object detection is a computer vision technique based on deep learning. However, due to privacy issues, it is highly challenging to build an object detection model according to the current methods on the centrally stored training dataset. Fede
Externí odkaz:
https://doaj.org/article/da7f04b25d714e419016aa3dd6e00faa
Publikováno v:
Substance Abuse Treatment, Prevention, and Policy, Vol 17, Iss 1, Pp 1-13 (2022)
Abstract Background This study aimed to evaluate the effects of the World Anti-Doping Agency's e-learning programme for anti-doping education on knowledge of, explicit and implicit attitudes towards, and likelihood of doping among Chinese college ath
Externí odkaz:
https://doaj.org/article/80a83989a5294fad9a7471a267acb767
Autor:
Jinyang Guo, Cong Luo, Christian Wittkowski, Ingo Fehr, Zhikai Chong, Magdalena Kitzberger, Ayah Alassali, Xuezhi Zhao, Ralf Leineweber, Yujun Feng, Kerstin Kuchta
Publikováno v:
Polymers, Vol 15, Iss 9, p 2220 (2023)
One of the major applications (40% in Europe) of plastic is packaging, which is often printed to display required information and to deliver an attractive aesthetic for marketing purposes. However, printing ink can cause contamination in the mechanic
Externí odkaz:
https://doaj.org/article/0eab46558d3a4b7cb0e4ee046bd58dea
Publikováno v:
Frontiers in Public Health, Vol 10 (2022)
BackgroundProlonged time on sedentary behavior, especially screen-based sitting time, is associated with unfavorable health indicators in children and adolescents. However, the effects of sedentary behavior on cognitive function remain to be elucidat
Externí odkaz:
https://doaj.org/article/5cdd843241c34588a833f2a23f9a9de3
Autor:
José Romero Hung, Chao Li, Taolei Wang, Jinyang Guo, Pengyu Wang, Chuanming Shao, Jing Wang, Guoyong Shi, Xiangwen Liu, Hanqing Wu
Publikováno v:
ACM Transactions on Design Automation of Electronic Systems. 28:1-27
Despite the extraordinary applicative potentiality that dynamic graph inference may entail, its practical-physical implementation has been a topic seldom explored in literature. Although graph inference through neural networks has received plenty of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::138708c12426fe6a054a9510db0980aa
https://doi.org/10.1145/3524124
https://doi.org/10.1145/3524124
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-15
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::edb4d2dc996532c68efc926fd634be92
https://doi.org/10.1109/tnnls.2023.3266435
https://doi.org/10.1109/tnnls.2023.3266435