Zobrazeno 1 - 10
of 3 247
pro vyhledávání: '"HUANG Xuan"'
This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts, particularly for student constructed responses in science assessments. The research addresses a key gap in the field by ex
Externí odkaz:
http://arxiv.org/abs/2411.07407
Autor:
Chen, Xiuji, Liu, Zipeng, Chen, Si, Gu, Duan, Huang, Xuan, Qian, Houjun, Wang, Dong, Deng, Haixiao
In the past decade, the fourth-generation light source based on the combination of Energy Recovery Linac (ERL) and Free-Electron Laser (FEL) using superconducting linear accelerators has garnered significant attention. It holds immense potential, par
Externí odkaz:
http://arxiv.org/abs/2410.17660
Autor:
Huang, Xuan, Li, Hanhui, Liu, Wanquan, Liang, Xiaodan, Yan, Yiqiang, Cheng, Yuhao, Gao, Chengqiang
In this paper, we propose to create animatable avatars for interacting hands with 3D Gaussian Splatting (GS) and single-image inputs. Existing GS-based methods designed for single subjects often yield unsatisfactory results due to limited input views
Externí odkaz:
http://arxiv.org/abs/2410.08840
Autor:
Huang, Xuan, Miao, Haichao, Kim, Hyojin, Townsend, Andrew, Champley, Kyle, Tringe, Joseph, Pascucci, Valerio, Bremer, Peer-Timo
Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and
Externí odkaz:
http://arxiv.org/abs/2408.11957
Autor:
Panta, Aashish, Huang, Xuan, McCurdy, Nina, Ellsworth, David, Gooch, Amy, Scorzelli, Giorgio, Torres, Hector, Klein, Patrice, Ovando-Montejo, Gustavo, Pascucci, Valerio
Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environment
Externí odkaz:
http://arxiv.org/abs/2408.11831
Autor:
Zhong, Yu-Jie, Huang, Xuan-Fu, Chen, Ting-Zhen, Zhang, Jia-Ren, Li, Jia-Cheng, Huang, Angus, Hsu, Hsiu-Chuan, Ortix, Carmine, Chang, Ching-Hao
We theoretically demonstrate that carbon nanoscrolls -- spirally wrapped graphene layers with open endpoints -- can be characterized by a large positive magnetoconductance. We show that when a carbon nanoscroll is subject to an axial magnetic field o
Externí odkaz:
http://arxiv.org/abs/2408.03518
Atrous convolutions are employed as a method to increase the receptive field in semantic segmentation tasks. However, in previous works of semantic segmentation, it was rarely employed in the shallow layers of the model. We revisit the design of atro
Externí odkaz:
http://arxiv.org/abs/2406.03702
Autor:
Shi, Shuyao, Ling, Neiwen, Jiang, Zhehao, Huang, Xuan, He, Yuze, Zhao, Xiaoguang, Yang, Bufang, Bian, Chen, Xia, Jingfei, Yan, Zhenyu, Yeung, Raymond, Xing, Guoliang
Recently,smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar,
Externí odkaz:
http://arxiv.org/abs/2404.13786
Neural radiance fields (NeRFs) are promising 3D representations for scenes, objects, and humans. However, most existing methods require multi-view inputs and per-scene training, which limits their real-life applications. Moreover, current methods foc
Externí odkaz:
http://arxiv.org/abs/2401.00979
Autor:
Gao, Xin, Qiu, Tianheng, Zhang, Xinyu, Bai, Hanlin, Liu, Kang, Huang, Xuan, Wei, Hu, Zhang, Guoying, Liu, Huaping
Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however, in the context of deep learning, existing multi-scale algorithms not only require the use of complex modules for feature fusion of low-scale RGB images and dee
Externí odkaz:
http://arxiv.org/abs/2401.00027