Zobrazeno 1 - 10
of 25 072
pro vyhledávání: '"Guo , Jun"'
Autor:
Chen, Jing, Chen, Ji-Yuan, Chen, Jun-Feng, Chen, Xiang, Fu, Chang-Bo, Guo, Jun, Guo, Yi-Han, Khaw, Kim Siang, Li, Jia-Lin, Li, Liang, Li, Shu, Lin, Yu-ming, Liu, Dan-Ning, Liu, Kang, Liu, Kun, Liu, Qi-Bin, Liu, Zhi, Lu, Ze-Jia, Lv, Meng, Song, Si-Yuan, Sun, Tong, Tang, Jian-Nan, Wan, Wei-Shi, Wang, Dong, Wang, Xiao-Long, Wang, Yu-Feng, Wang, Zhen, Wang, Zi-Rui, Wu, Wei-Hao, Yang, Hai-Jun, Yang, Lin, Yang, Yong, Yu, Dian, Yuan, Rui, Zhang, Jun-Hua, Zhang, Yu-Lei, Zhang, Yun-Long, Zhao, Zhi-Yu, Zhou, Bai-Hong, Zhu, Chun-Xiang, Zhu, Xu-Liang, Zhu, Yi-Fan
DarkSHINE is a newly proposed fixed-target experiment initiative to search for the invisible decay of Dark Photon via missing energy/momentum signatures, based on the high repetition rate electron beam to be deployed/delivered by the Shanghai High re
Externí odkaz:
http://arxiv.org/abs/2411.09345
In the realm of Artificial Intelligence Generated Content (AIGC), flow-matching models have emerged as a powerhouse, achieving success due to their robust theoretical underpinnings and solid ability for large-scale generative modeling. These models h
Externí odkaz:
http://arxiv.org/abs/2410.19310
Publikováno v:
NeurIPS 2024
Despite their strong performances on many generative tasks, diffusion models require a large number of sampling steps in order to generate realistic samples. This has motivated the community to develop effective methods to distill pre-trained diffusi
Externí odkaz:
http://arxiv.org/abs/2410.16794
Primordial black holes (PBHs), originating from the gravitational collapse of large overdensities in the early Universe, emerge as a compelling dark matter (DM) candidate across a broad mass range. Of particular interest are ultra-light PBHs with mas
Externí odkaz:
http://arxiv.org/abs/2409.14731
The CNN has achieved excellent results in the automatic classification of medical images. In this study, we propose a novel deep residual 3D attention non-local network (NL-RAN) to classify CT images included COVID-19, common pneumonia, and normal to
Externí odkaz:
http://arxiv.org/abs/2408.04300
Autor:
Ye, Zilyu, Liu, Jinxiu, Peng, Ruotian, Cao, Jinjin, Chen, Zhiyang, Zhang, Yiyang, Xuan, Ziwei, Zhou, Mingyuan, Shen, Xiaoqian, Elhoseiny, Mohamed, Liu, Qi, Qi, Guo-Jun
Recent image generation models excel at creating high-quality images from brief captions. However, they fail to maintain consistency of multiple instances across images when encountering lengthy contexts. This inconsistency is largely due to in exist
Externí odkaz:
http://arxiv.org/abs/2408.03695
Face super-resolution aims to reconstruct a high-resolution face image from a low-resolution face image. Previous methods typically employ an encoder-decoder structure to extract facial structural features, where the direct downsampling inevitably in
Externí odkaz:
http://arxiv.org/abs/2407.19768
Physics-informed neural networks (PINNs) integrate fundamental physical principles with advanced data-driven techniques, driving significant advancements in scientific computing. However, PINNs face persistent challenges with stiffness in gradient fl
Externí odkaz:
http://arxiv.org/abs/2407.19421
Autor:
Zhao, Zhiyu, Liu, Qibin, Chen, Jiyuan, Chen, Jing, Chen, Junfeng, Chen, Xiang, Fu, Changbo, Guo, Jun, Khaw, Kim Siang, Li, Liang, Li, Shu, Liu, Danning, Liu, Kun, Song, Siyuan, Sun, Tong, Tang, Jiannan, Wang, Yufeng, Wang, Zhen, Wu, Weihao, Yang, Haijun, Lin, Yuming, Yuan, Rui, Zhang, Yulei, Zhang, Yunlong, Zhou, Baihong, Zhu, Xuliang, Zhu, Yifan
This paper presents the design and optimization of a LYSO crystal electromagnetic calorimeter (ECAL) for the DarkSHINE experiment, which aims to search for dark photons as potential mediators of dark forces. The ECAL design was evaluated through comp
Externí odkaz:
http://arxiv.org/abs/2407.17800