Zobrazeno 1 - 10
of 3 659
pro vyhledávání: '"Xin T"'
Publikováno v:
Journal of Inflammation Research, Vol Volume 17, Pp 1039-1055 (2024)
Xiuqing Yuan,1,* Tiantian Xin,1,* Huanhuan Yu,1 Jian Huang,2 Yaohan Xu,1 Caixin Ou,1 Yongfeng Chen1 1Department of Dermatology, Dermatology Hospital of Southern Medical University, Guangzhou, Guangdong Province, People’s Republic of China;
Externí odkaz:
https://doaj.org/article/c5ce670a763442778cc02d076b986bd1
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Nature-inspired swarm-based algorithms are increasingly applied to tackle high-dimensional and complex optimization problems across disciplines. They are general purpose optimization algorithms, easy to implement and assumption-free. Some co
Externí odkaz:
https://doaj.org/article/64a96450cfa3481d8d70982e323498f2
Publikováno v:
Psychology Research and Behavior Management, Vol Volume 16, Pp 29-38 (2023)
Lunan Gao,1 Jinhong Yang,2 Jiang Liu,1 Tingting Xin,1 Yuxiu Liu1 1School of Nursing, Weifang Medical University, Weifang, People’s Republic of China; 2Department of Oncology, Weifang People’s Hospital, Weifang, People’s Republic of ChinaCorresp
Externí odkaz:
https://doaj.org/article/dd4136a872b84be79ba1711d489f5fba
Selective state space models (SSM), such as Mamba, have gained prominence for their effectiveness in modeling sequential data. Despite their outstanding empirical performance, a comprehensive theoretical understanding of deep selective SSM remains el
Externí odkaz:
http://arxiv.org/abs/2410.03292
Stochastic gradient descent (SGD) is a powerful optimization technique that is particularly useful in online learning scenarios. Its convergence analysis is relatively well understood under the assumption that the data samples are independent and ide
Externí odkaz:
http://arxiv.org/abs/2410.01195
Dynamic mode decomposition (DMD) is a data-driven method of extracting spatial-temporal coherent modes from complex systems and providing an equation-free architecture to model and predict systems. However, in practical applications, the accuracy of
Externí odkaz:
http://arxiv.org/abs/2410.02815
We consider Bayesian inference for image deblurring with total variation (TV) prior. Since the posterior is analytically intractable, we resort to Markov chain Monte Carlo (MCMC) methods. However, since most MCMC methods significantly deteriorate in
Externí odkaz:
http://arxiv.org/abs/2409.09810
Autor:
Liu S, Xing L, Wang Q, Xin T, Mao H, Tao Y, Zhao J, Li X, Li C, Li Q, Dou Y, Li Y, Zhang W, Ning B, Song Q
Publikováno v:
International Journal of General Medicine, Vol Volume 14, Pp 4629-4638 (2021)
Shuyuan Liu,1,2,* Ling Xing,1,3,* Qian Wang,4 Tianyu Xin,2 Handing Mao,1 Ye Tao,5 Jinbao Zhao,2 Xin Li,4 Cong Li,1 Qinghua Li,6 Yan Dou,7 Yixin Li,8 Wei Zhang,9 Bo Ning,10 Qing Song1 1Medical School of Chinese PLA, Beijing, People’s Republi
Externí odkaz:
https://doaj.org/article/c323a0032f83485fb46d810e1610a825
Analyzing dynamical data often requires information of the temporal labels, but such information is unavailable in many applications. Recovery of these temporal labels, closely related to the seriation or sequencing problem, becomes crucial in the st
Externí odkaz:
http://arxiv.org/abs/2406.13635
We examine the infinite-dimensional optimization problem of finding a decomposition of a probability measure into K probability sub-measures to minimize specific loss functions inspired by applications in clustering and user grouping. We analytically
Externí odkaz:
http://arxiv.org/abs/2406.00914