Zobrazeno 1 - 10
of 143
pro vyhledávání: '"TANG Chenyu"'
Publikováno v:
Xibei zhiwu xuebao, Vol 44, Iss 11, Pp 1789-1800 (2024)
[Objective] To reveal the regional variation of adaptation strategies of common species Populus alba, it can provide data support for predicting plant adaptation potential under the background of climate change. [Methods] Nine state-owned forest fa
Externí odkaz:
https://doaj.org/article/3aaa581882b3481b8ef1a4749fdc1d38
Autor:
Tang, Chenyu, Yi, Wentian, Xu, Muzi, Jin, Yuxuan, Zhang, Zibo, Chen, Xuhang, Liao, Caizhi, Smielewski, Peter, Occhipinti, Luigi G.
In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin cou
Externí odkaz:
http://arxiv.org/abs/2408.00753
Autor:
Xu, Han, Cui, Taoyong, Tang, Chenyu, Zhou, Dongzhan, Li, Yuqiang, Gao, Xiang, Gong, Xingao, Ouyang, Wanli, Zhang, Shufei, Su, Mao
Machine learning interatomic potentials (MLIPs) have been widely used to facilitate large scale molecular simulations with ab initio level accuracy. However, MLIP-based molecular simulations frequently encounter the issue of collapse due to decreased
Externí odkaz:
http://arxiv.org/abs/2407.13994
Large language models (LLMs) often improve their performance in downstream tasks when they generate Chain of Thought reasoning text before producing an answer. We investigate how LLMs recover from errors in Chain of Thought. Through analysis of error
Externí odkaz:
http://arxiv.org/abs/2405.15092
Autor:
Cui, Taoyong, Tang, Chenyu, Zhou, Dongzhan, Li, Yuqiang, Gong, Xingao, Ouyang, Wanli, Su, Mao, Zhang, Shufei
Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, distribution shift between training and test data
Externí odkaz:
http://arxiv.org/abs/2405.08308
Autor:
Liu, Yong, Kang, Mengtian, Gao, Shuo, Zhang, Chi, Liu, Ying, Li, Shiming, Qi, Yue, Nathan, Arokia, Xu, Wenjun, Tang, Chenyu, Occhipinti, Edoardo, Yusufu, Mayinuer, Wang, Ningli, Bai, Weiling, Occhipinti, Luigi
Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis has several advantages, such as hi
Externí odkaz:
http://arxiv.org/abs/2404.13388
Autor:
Wang, Jiaqi, Kang, Mengtian, Liu, Yong, Zhang, Chi, Liu, Ying, Li, Shiming, Qi, Yue, Xu, Wenjun, Tang, Chenyu, Occhipinti, Edoardo, Yusufu, Mayinuer, Wang, Ningli, Bai, Weiling, Gao, Shuo, Occhipinti, Luigi G.
Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results. However, current methods are commonly based on supervise
Externí odkaz:
http://arxiv.org/abs/2404.13386
Autor:
Tang, Chenyu, Xu, Muzi, Yi, Wentian, Zhang, Zibo, Occhipinti, Edoardo, Dong, Chaoqun, Ravenscroft, Dafydd, Jung, Sung-Min, Lee, Sanghyo, Gao, Shuo, Kim, Jong Min, Occhipinti, Luigi G.
Publikováno v:
npj Flexible Electronics (2024)
Our research presents a wearable Silent Speech Interface (SSI) technology that excels in device comfort, time-energy efficiency, and speech decoding accuracy for real-world use. We developed a biocompatible, durable textile choker with an embedded gr
Externí odkaz:
http://arxiv.org/abs/2311.15683
Autor:
Cui, Taoyong, Tang, Chenyu, Su, Mao, Zhang, Shufei, Li, Yuqiang, Bai, Lei, Dong, Yuhan, Gong, Xingao, Ouyang, Wanli
Publikováno v:
Published in Nature Machine Intelligence 2024
Machine learning interatomic potentials (MLIPs) enables molecular dynamics (MD) simulations with ab initio accuracy and has been applied to various fields of physical science. However, the performance and transferability of MLIPs are limited by insuf
Externí odkaz:
http://arxiv.org/abs/2309.15718
Autor:
Wang, Shengbo, Gao, Shuo, Tang, Chenyu, Occhipinti, Edoardo, Li, Cong, Wang, Shurui, Wang, Jiaqi, Zhao, Hubin, Hu, Guohua, Nathan, Arokia, Dahiya, Ravinder, Occhipinti, Luigi
Publikováno v:
Nat Commun, vol. 15, no. 1, p. 4671, May 2024
Efficient operation of intelligent machines in the real world requires methods that allow them to understand and predict the uncertainties presented by the unstructured environments with good accuracy, scalability and generalization, similar to human
Externí odkaz:
http://arxiv.org/abs/2309.08835