Zobrazeno 21 - 30
of 861
pro vyhledávání: '"Luan, Yi"'
Autor:
Zhang, Ningning, Tang, Wenwen, Torres, Lidiane, Wang, Xujun, Ajaj, Yasmeen, Zhu, Li, Luan, Yi, Zhou, Hongyue, Wang, Yadong, Zhang, Dingyao, Kurbatov, Vadim, Khan, Sajid A., Kumar, Priti, Hidalgo, Andres, Wu, Dianqing, Lu, Jun
Publikováno v:
In Cell 15 February 2024 187(4):846-860
Autor:
Ran, Jiangjun, Liu, Lin, Zhang, Guoqing, Shum, C.K., Qiu, Jiahui, Hu, Ruigang, Li, Jianping, Peng, Junhuan, Hwang, Cheinway, Luan, Yi, Sun, Yue, Xu, Min, Chen, Dingmei, Ding, Jun, Zhong, Yulong
Publikováno v:
In Science of the Total Environment 15 January 2024 908
Autor:
Ali, Shoaib, Ran, Jiangjun, Luan, Yi, Khorrami, Behnam, Xiao, Yun, Tangdamrongsub, Natthachet
Publikováno v:
In Science of the Total Environment 15 January 2024 908
Publikováno v:
In Materials Today Chemistry January 2024 35
Publikováno v:
Energies, Vol 17, Iss 10, p 2278 (2024)
In order to improve the multi-class assessment performance of transient stability in power systems, a multi-class assessment model that combines the CLV-GAN algorithm with an improved error-correcting output coding technique is proposed in the paper.
Externí odkaz:
https://doaj.org/article/8413a4f267eb44c88f5e3f6a532000c4
Autor:
Luan, Yi, Wang, Qingling, Li, Songnan, Gu, Chen, Liu, Rui, Ge, Qingfeng, Yu, Hai, Wu, Mangang
Publikováno v:
In Food Chemistry: X 30 December 2023 20
Publikováno v:
陆军军医大学学报, Vol 45, Iss 5, Pp 462-467 (2023)
Objective To investigate the diagnostic value of contrast-enhanced ultrasound liver imaging reporting and data system (CEUS LI-RADS) and European CEUS guidelines for liver malignant tumors in high-risk patients with hepatocellular carcinoma (HCC). Me
Externí odkaz:
https://doaj.org/article/98502b322007475c8c4d925ccf347af6
Dual encoders perform retrieval by encoding documents and queries into dense lowdimensional vectors, scoring each document by its inner product with the query. We investigate the capacity of this architecture relative to sparse bag-of-words models an
Externí odkaz:
http://arxiv.org/abs/2005.00181
We present a method to represent input texts by contextualizing them jointly with dynamically retrieved textual encyclopedic background knowledge from multiple documents. We apply our method to reading comprehension tasks by encoding questions and pa
Externí odkaz:
http://arxiv.org/abs/2004.12006
We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by enumerating, refining
Externí odkaz:
http://arxiv.org/abs/1909.03546