Zobrazeno 1 - 10
of 392
pro vyhledávání: '"LU Yiwen"'
Publikováno v:
Vojnosanitetski Pregled, Vol 80, Iss 11, Pp 915-920 (2023)
Background/Aim. There is growing evidence suggesting that high-sensitivity C-reactive protein (hs-CRP) is a reliable biomarker in patients with hypertension. While the relation-ship between hypertension and age is well established, the connection bet
Externí odkaz:
https://doaj.org/article/3816a2dbfbc14162b1d65cba334a0bc8
Publikováno v:
Shanghai yufang yixue, Vol 34, Iss 3, Pp 252-255 (2022)
ObjectiveTo study the composition and concentration of atmospheric particulate pollutants in four seasons in the industrial and clean living areas, and to provide a scientific basis for the strategy of controlling industrial pollution and atmospher
Externí odkaz:
https://doaj.org/article/8c07711907be45d488b930d8c408a8fb
Autor:
Zhou, Yuan, Zhang, Peng, Song, Mengya, Zheng, Alice, Lu, Yiwen, Liu, Zhiheng, Chen, Yong, Xi, Zhaohan
Large language models (LLMs) have demonstrated remarkable progress in healthcare. However, a significant gap remains regarding LLMs' professionalism in domain-specific clinical practices, limiting their application in real-world diagnostics. In this
Externí odkaz:
http://arxiv.org/abs/2410.02026
This paper considers the output prediction problem for an unknown Linear Time-Invariant (LTI) system. In particular, we focus our attention on the OBF-ARX filter, whose transfer function is a linear combination of Orthogonal Basis Functions (OBFs), w
Externí odkaz:
http://arxiv.org/abs/2409.05390
Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical research and practice. In recent years, deep learning models have been applied to EHRs, but they often rely on massive features, which may not be readily a
Externí odkaz:
http://arxiv.org/abs/2406.05682
Data-Enabled Predictive Control (DeePC) bypasses the need for system identification by directly leveraging raw data to formulate optimal control policies. However, the size of the optimization problem in DeePC grows linearly with respect to the data
Externí odkaz:
http://arxiv.org/abs/2404.16727
Autor:
Ye, Zhen, Ju, Zeqian, Liu, Haohe, Tan, Xu, Chen, Jianyi, Lu, Yiwen, Sun, Peiwen, Pan, Jiahao, Bian, Weizhen, He, Shulin, Liu, Qifeng, Guo, Yike, Xue, Wei
Recent progress in large-scale zero-shot speech synthesis has been significantly advanced by language models and diffusion models. However, the generation process of both methods is slow and computationally intensive. Efficient speech synthesis using
Externí odkaz:
http://arxiv.org/abs/2404.14700
The diffusion-based Singing Voice Conversion (SVC) methods have achieved remarkable performances, producing natural audios with high similarity to the target timbre. However, the iterative sampling process results in slow inference speed, and acceler
Externí odkaz:
http://arxiv.org/abs/2401.01792
In this paper, we introduce a new class of parameterized controllers, drawing inspiration from Model Predictive Control (MPC). The controller resembles a Quadratic Programming (QP) solver of a linear MPC problem, with the parameters of the controller
Externí odkaz:
http://arxiv.org/abs/2312.05332
Activation functions are essential to introduce nonlinearity into neural networks, with the Rectified Linear Unit (ReLU) often favored for its simplicity and effectiveness. Motivated by the structural similarity between a shallow Feedforward Neural N
Externí odkaz:
http://arxiv.org/abs/2309.17194