Zobrazeno 1 - 10
of 28 780
pro vyhledávání: '"XU, Jun"'
We have investigated the performance of anisotropic flows $\langle v_n^2 \rangle$, transverse momentum fluctuations $\langle \delta p_T^2 \rangle $, and their correlations $\langle v_n^2 \delta p_T \rangle$ in central collisions at relativistic energ
Externí odkaz:
http://arxiv.org/abs/2409.02452
Estimation of the response probability distributions of computer simulators in the presence of randomness is a crucial task in many fields. However, achieving this task with guaranteed accuracy remains an open computational challenge, especially for
Externí odkaz:
http://arxiv.org/abs/2409.00407
Autor:
Smirnov, Maksim, Gushchin, Aleksandr, Antsiferova, Anastasia, Vatolin, Dmitry, Timofte, Radu, Jia, Ziheng, Zhang, Zicheng, Sun, Wei, Qian, Jiaying, Cao, Yuqin, Sun, Yinan, Zhu, Yuxin, Min, Xiongkuo, Zhai, Guangtao, De, Kanjar, Luo, Qing, Zhang, Ao-Xiang, Zhang, Peng, Lei, Haibo, Jiang, Linyan, Li, Yaqing, Meng, Wenhui, Tan, Xiaoheng, Wang, Haiqiang, Xu, Xiaozhong, Liu, Shan, Chen, Zhenzhong, Cheng, Zhengxue, Xiao, Jiahao, Xu, Jun, He, Chenlong, Zheng, Qi, Zhu, Ruoxi, Li, Min, Fan, Yibo, Tu, Zhengzhong
Video quality assessment (VQA) is a crucial task in the development of video compression standards, as it directly impacts the viewer experience. This paper presents the results of the Compressed Video Quality Assessment challenge, held in conjunctio
Externí odkaz:
http://arxiv.org/abs/2408.11982
Reciprocal recommender systems~(RRS), conducting bilateral recommendations between two involved parties, have gained increasing attention for enhancing matching efficiency. However, the majority of existing methods in the literature still reuse conve
Externí odkaz:
http://arxiv.org/abs/2408.09748
Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampl
Externí odkaz:
http://arxiv.org/abs/2408.08736
Nowadays, many recommender systems encompass various domains to cater to users' diverse needs, leading to user behaviors transitioning across different domains. In fact, user behaviors across different domains reveal changes in preference toward reco
Externí odkaz:
http://arxiv.org/abs/2408.08209
Autor:
Chen, Jie, Chen, Zhipeng, Wang, Jiapeng, Zhou, Kun, Zhu, Yutao, Jiang, Jinhao, Min, Yingqian, Zhao, Wayne Xin, Dou, Zhicheng, Mao, Jiaxin, Lin, Yankai, Song, Ruihua, Xu, Jun, Chen, Xu, Yan, Rui, Wei, Zhewei, Hu, Di, Huang, Wenbing, Wen, Ji-Rong
Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. To make the CPT approach more traceable, this paper presents a technical report for continually pre-training Llama-3 (8B), which si
Externí odkaz:
http://arxiv.org/abs/2407.18743
Autor:
Li, Guang-Shuai, Su, Jun, Terashima, Satoru, Zhao, Jian-Wei, Xiao, Er-Xi, Zhang, Ji-Chao, He, Liu-Chun, Guo, Ge, Lin, Wei-Ping, Lin, Wen-Jian, Liu, Chuan-Ye, Lu, Chen-Gui, Mei, Bo, Pang, Dan-Yang, Sun, Ye-Lei, Sun, Zhi-Yu, Wang, Meng, Wang, Feng, Wang, Jing, Wang, Shi-Tao, Wei, Xiu-Lin, Xu, Xiao-Dong, Xu, Jun-Yao, Zhu, Li-Hua, Zheng, Yong, Zhang, Mei-Xue, Zhang, Xue-Heng
We report on the first measurement of the elemental fragmentation cross sections (EFCSs) of $^{29-33}\mathrm{Si}$ on a carbon target at $\sim$230~MeV/nucleon. The experimental data covering charge changes of $\Delta Z$ = 1-4 are reproduced well by th
Externí odkaz:
http://arxiv.org/abs/2407.14697
Autor:
Tang, Jiakai, Dai, Sunhao, Sun, Zexu, Chen, Xu, Xu, Jun, Yu, Wenhui, Hu, Lantao, Jiang, Peng, Li, Han
In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. However, most existing GCL models rely on heuristic approaches and usually a
Externí odkaz:
http://arxiv.org/abs/2407.10184
Controllable learning (CL) emerges as a critical component in trustworthy machine learning, ensuring that learners meet predefined targets and can adaptively adjust without retraining according to the changes in those targets. We provide a formal def
Externí odkaz:
http://arxiv.org/abs/2407.06083