Zobrazeno 1 - 10
of 7 392
pro vyhledávání: '"XIE Xiao"'
Autor:
Jin, Ao-Qun, Xiang, Tian-Yu, Zhou, Xiao-Hu, Gui, Mei-Jiang, Xie, Xiao-Liang, Liu, Shi-Qi, Wang, Shuang-Yi, Cao, Yue, Duan, Sheng-Bin, Xie, Fu-Chao, Hou, Zeng-Guang
Current robot learning algorithms for acquiring novel skills often rely on demonstration datasets or environment interactions, resulting in high labor costs and potential safety risks. To address these challenges, this study proposes a skill-learning
Externí odkaz:
http://arxiv.org/abs/2412.09286
Autor:
Huang, De-Xing, Zhou, Xiao-Hu, Gui, Mei-Jiang, Xie, Xiao-Liang, Liu, Shi-Qi, Wang, Shuang-Yi, Li, Hao, Xiang, Tian-Yu, Hou, Zeng-Guang
Iodinated contrast agents are widely utilized in numerous interventional procedures, yet posing substantial health risks to patients. This paper presents CAS-GAN, a novel GAN framework that serves as a "virtual contrast agent" to synthesize X-ray ang
Externí odkaz:
http://arxiv.org/abs/2410.08490
Autor:
Xie, Xiao-Jin, Shi, Jianrong, Yan, Hong-Liang, Chen, Tian-Yi, Prieto, Carlos Allende, Beers, Timothy C., Liu, Shuai, Li, Chun-Qian, Ding, Ming-Yi, Tang, Yao-Jia, Zhang, Ruizhi, Xie, Renjing
Publikováno v:
ApJL, 2024, Volume 970, Number 2, L30
Highly r-process-enhanced stars are rare and usually metal-poor ([Fe/H] < - 1.0), and mainly populate the Milky Way halo and dwarf galaxies. This study presents the discovery of a relatively bright (V = 12.72), highly r-process-enhanced (r-II) star (
Externí odkaz:
http://arxiv.org/abs/2407.11572
Autor:
Huang, De-Xing, Zhou, Xiao-Hu, Xie, Xiao-Liang, Liu, Shi-Qi, Wang, Shuang-Yi, Feng, Zhen-Qiu, Gui, Mei-Jiang, Li, Hao, Xiang, Tian-Yu, Yao, Bo-Xian, Hou, Zeng-Guang
Automatic vessel segmentation is paramount for developing next-generation interventional navigation systems. However, current approaches suffer from suboptimal segmentation performances due to significant challenges in intraoperative images (i.e., lo
Externí odkaz:
http://arxiv.org/abs/2406.19749
Autor:
Ding, Ming-Yi, Shi, Jian-Rong, Yan, Hong-liang, Li, Chun-Qian, Gao, Qi, Chen, Tian-Yi, Zhang, Jing-Hua, Liu, Shuai, Xie, Xiao-Jin, Tang, Yao-Jia, Zhou, Ze-Ming, Wang, Jiang-Tao
Lithium is a fragile but crucial chemical element in the universe, exhibits interesting and complex behaviors. Thanks to the massive spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) medium-resolution surv
Externí odkaz:
http://arxiv.org/abs/2403.01815
Autor:
Huang, De-Xing, Zhou, Xiao-Hu, Xie, Xiao-Liang, Liu, Shi-Qi, Feng, Zhen-Qiu, Gui, Mei-Jiang, Li, Hao, Xiang, Tian-Yu, Liu, Xiu-Ling, Hou, Zeng-Guang
Medical image segmentation takes an important position in various clinical applications. Deep learning has emerged as the predominant solution for automated segmentation of volumetric medical images. 2.5D-based segmentation models bridge computationa
Externí odkaz:
http://arxiv.org/abs/2401.11856
Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way. The emergence of the 3rd wave information has made a proportion of maps are available to be generated ubiquitously, which would significantly e
Externí odkaz:
http://arxiv.org/abs/2312.08600
Autor:
Li, Hao, Zhou, Xiao-Hu, Xie, Xiao-Liang, Liu, Shi-Qi, Feng, Zhen-Qiu, Liu, Xiao-Yin, Gui, Mei-Jiang, Xiang, Tian-Yu, Huang, De-Xing, Yao, Bo-Xian, Hou, Zeng-Guang
Offline reinforcement learning (RL) aims to optimize policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing offline RL challenges due to their capability to mitigate the limitations o
Externí odkaz:
http://arxiv.org/abs/2310.17245
Publikováno v:
Hecheng xiangjiao gongye, Vol 46, Iss 6, Pp 519-524 (2024)
The application of neodymium based cis-1,4-polybutadiene rubber (Nd-BR) BR 9101 N in the formula of support compound of run-flat tire was studied, and compared with those of foreign same type product Nd-BR CB 24. The results showed that Nd-BR BR 9101
Externí odkaz:
https://doaj.org/article/0479a0316eb64f6ea50ab3d31f4f9e0f
Autor:
Liu, Xiao-Yin, Zhou, Xiao-Hu, Gui, Mei-Jiang, Xie, Xiao-Liang, Liu, Shi-Qi, Wang, Shuang-Yi, Li, Hao, Xiang, Tian-Yu, Huang, De-Xing, Hou, Zeng-Guang
Model-based reinforcement learning (RL), which learns environment model from offline dataset and generates more out-of-distribution model data, has become an effective approach to the problem of distribution shift in offline RL. Due to the gap betwee
Externí odkaz:
http://arxiv.org/abs/2309.08925