Zobrazeno 1 - 10
of 3 445
pro vyhledávání: '"Meng, Hao"'
We prove the Hardy-Littlewood-Sobolev type $L^p$ estimates for the gain term of the Boltzmann collision operator including Maxwellian molecule, hard potential and hard sphere models. Combining with the results of Alonso et al. [2] for the soft potent
Externí odkaz:
http://arxiv.org/abs/2404.05517
In the field of digital content creation, generating high-quality 3D characters from single images is challenging, especially given the complexities of various body poses and the issues of self-occlusion and pose ambiguity. In this paper, we present
Externí odkaz:
http://arxiv.org/abs/2402.17214
Autor:
Fan Pu, Weiran Chen, Chenxi Li, Jingqiao Fu, Weijing Gao, Chao Ma, Xingqi Cao, Lingzhi Zhang, Meng Hao, Jin Zhou, Rong Huang, Yanan Ma, Kejia Hu, Zuyun Liu
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-11 (2024)
Abstract Complicated associations between multiplexed environmental factors and aging are poorly understood. We manipulated aging using multidimensional metrics such as phenotypic age, brain age, and brain volumes in the UK Biobank. Weighted quantile
Externí odkaz:
https://doaj.org/article/6da1979f1c4e438d922a059ed202b5b9
Autor:
Deng, Yu-Hao, Gu, Yi-Chao, Liu, Hua-Liang, Gong, Si-Qiu, Su, Hao, Zhang, Zhi-Jiong, Tang, Hao-Yang, Jia, Meng-Hao, Xu, Jia-Min, Chen, Ming-Cheng, Qin, Jian, Peng, Li-Chao, Yan, Jiarong, Hu, Yi, Huang, Jia, Li, Hao, Li, Yuxuan, Chen, Yaojian, Jiang, Xiao, Gan, Lin, Yang, Guangwen, You, Lixing, Li, Li, Zhong, Han-Sen, Wang, Hui, Liu, Nai-Le, Renema, Jelmer J., Lu, Chao-Yang, Pan, Jian-Wei
We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for the characterization of
Externí odkaz:
http://arxiv.org/abs/2304.12240
Autor:
Deng, Yu-Hao, Gong, Si-Qiu, Gu, Yi-Chao, Zhang, Zhi-Jiong, Liu, Hua-Liang, Su, Hao, Tang, Hao-Yang, Xu, Jia-Min, Jia, Meng-Hao, Chen, Ming-Cheng, Zhong, Han-Sen, Wang, Hui, Yan, Jiarong, Hu, Yi, Huang, Jia, Zhang, Wei-Jun, Li, Hao, Jiang, Xiao, You, Lixing, Wang, Zhen, Li, Li, Liu, Nai-Le, Lu, Chao-Yang, Pan, Jian-Wei
Gaussian boson sampling (GBS) is not only a feasible protocol for demonstrating quantum computational advantage, but also mathematically associated with certain graph-related and quantum chemistry problems. In particular, it is proposed that the gene
Externí odkaz:
http://arxiv.org/abs/2302.00936
Inspired by the success of recent vision transformers and large kernel design in convolutional neural networks (CNNs), in this paper, we analyze and explore essential reasons for their success. We claim two factors that are critical for 3D large-scal
Externí odkaz:
http://arxiv.org/abs/2301.06962
Publikováno v:
Ziyuan Kexue, Vol 46, Iss 2, Pp 235-248 (2024)
[Objective] The reduction of urban and rural construction land is an important proposition with the tendency of population decline in the latter stages of urbanization. Our research provided reference for optimizing the allocation of urban and rural
Externí odkaz:
https://doaj.org/article/462e506217ba4e57bf6d7c276b0738ae
Autor:
Meng Kong, Changtong Gao, Xiaona Luan, Cuiying Fan, Meng Hao, Canghai Jin, Jiangning Zhao, Hongyan Li, Jindong Zhao, Jian Luan, Yong Lin, Qiang Li
Publikováno v:
BMC Musculoskeletal Disorders, Vol 25, Iss 1, Pp 1-11 (2024)
Abstract Background Teriparatide (TPTD) is a widely used anabolic agent for the treatment of osteoporosis. Several factors have been identified to be related to bone mineral density (BMD) increase in anti-osteoporosis treatment with other agents; how
Externí odkaz:
https://doaj.org/article/129cf2c9a0a44d56b657ac879bdfdfb0
Publikováno v:
Phys. Rev. B 106, 174502 (2022)
Based on the Bogoliubov-de Gennes equations, we investigate the transport of the Josephson current in a S/$f_L$-F$_1$-$f_C$-F$_2$-$f_R$/S junction, where S and F$_{1,2}$ are superconductors and ferromagnets, and $f_{L, C, R}$ are the left, central, a
Externí odkaz:
http://arxiv.org/abs/2210.12718
We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to the efficiency of self-attention in encoding spatial information. In t
Externí odkaz:
http://arxiv.org/abs/2209.08575