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pro vyhledávání: '"Liu Yuting"'
Autor:
LIU Yuting, YU Wanqi, HONG Wen, KANG Sang, LI Xinni, DANZENG Quyang, XIAO Huoyuan, PAN Jingwei
Publikováno v:
Shanghai Jiaotong Daxue xuebao. Yixue ban, Vol 44, Iss 5, Pp 599-605 (2024)
Objective·To investigate the predictive value of the Clinical Frailty Scale (CFS) in the long term outcomes in acute myocardial infarction (AMI) patients who completed in-hospital cardiac rehabilitation (CR).Methods·A total of 501 AMI patients trea
Externí odkaz:
https://doaj.org/article/57b1fbab23714a2f8d4bd9dfc67bdf54
Autor:
Fan Yuru, Liu Yuting
Publikováno v:
Redai dili, Vol 42, Iss 8, Pp 1335-1348 (2022)
With the implementation of the rural land circulation policy, the scale and rate of land circulation in rural areas have grown rapidly. The spatial pattern of farmland circulation has also been changing, which is an important characterization of the
Externí odkaz:
https://doaj.org/article/56c7776215d04f5d9ee4720f7d1731bd
Publikováno v:
Redai dili, Vol 42, Iss 4, Pp 509-518 (2022)
The key factor that influences the progress and operational efficiency of a reserved land redevelopment program is the rationality of land value increment allocation. Scaling-up has become a type of spatial governance instrument. During land redevelo
Externí odkaz:
https://doaj.org/article/8426ef7db0db4ed395e576aa04199836
Autor:
Liu, Yuting, Zhang, Jinghao, Dang, Yizhou, Liang, Yuliang, Liu, Qiang, Guo, Guibing, Zhao, Jianzhe, Wang, Xingwei
Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating collaborative features with text tokens into a unified sequence input an
Externí odkaz:
http://arxiv.org/abs/2408.10645
Autor:
Zhao, Chu, Yang, Enneng, Liang, Yuliang, Lan, Pengxiang, Liu, Yuting, Zhao, Jianzhe, Guo, Guibing, Wang, Xingwei
Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. However, this assumption often fails in the presence of out-of-distribut
Externí odkaz:
http://arxiv.org/abs/2408.00490
Publikováno v:
中西医结合护理, Vol 7, Iss 4, Pp 60-62 (2021)
Under the regular prevention and control of COVID-19, a set of interventions such as systematic prevention and control of COVID-19, home-based care, comprehensive follow-up management had been carried out to ensure the safety and quality of life of p
Externí odkaz:
https://doaj.org/article/7c0fc2efcc634aaf9a8b68cb8597f85c
Autor:
Lan, Pengxiang, Yang, Enneng, Liu, Yuting, Guo, Guibing, Jiang, Linying, Zhao, Jianzhe, Wang, Xingwei
Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, whereby downstream tasks can be well adapted by merely learning the emb
Externí odkaz:
http://arxiv.org/abs/2405.11464
Autor:
Liu, Yuting, Hong, Huibo, Xiang, Xiao, Quan, Runai, Liu, Tao, Cao, Mingtao, Zhang, Shougang, Dong, Ruifang
A dynamic temperature compensation method is presented to stabilize the wavelength of the entangled biphoton source, which is generated via the spontaneous parametric down-conversion based on a MgO: PPLN waveguide. Utilizing the dispersive Fourier tr
Externí odkaz:
http://arxiv.org/abs/2404.18686
Autor:
Liu, Yuting, Dang, Yizhou, Liang, Yuliang, Liu, Qiang, Guo, Guibing, Zhao, Jianzhe, Wang, Xingwei
Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (i.e., links in a graph) with items. To accommodate the different seman
Externí odkaz:
http://arxiv.org/abs/2403.08246
Autor:
Dang, Yizhou, Liu, Yuting, Yang, Enneng, Guo, Guibing, Jiang, Linying, Wang, Xingwei, Zhao, Jianzhe
Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted technique for two main reasons: 1) The vast majority of models can only
Externí odkaz:
http://arxiv.org/abs/2403.06372