Zobrazeno 1 - 10
of 13 416
pro vyhledávání: '"Zhou Chen"'
Autor:
Wang, Yuexin, Liang, Hao, Zhu, Yongfeng, Che, Yuzhi, Xia, Xin, Qu, Huilin, Zhou, Chen, Zhuang, Xuai, Ruan, Manqi
We propose one-to-one correspondence reconstruction for electron-positron Higgs factories. For each visible particle, one-to-one correspondence aims to associate relevant detector hits with only one reconstructed particle and accurately identify its
Externí odkaz:
http://arxiv.org/abs/2411.06939
Autor:
Su, Aofeng, Wang, Aowen, Ye, Chao, Zhou, Chen, Zhang, Ga, Chen, Gang, Zhu, Guangcheng, Wang, Haobo, Xu, Haokai, Chen, Hao, Li, Haoze, Lan, Haoxuan, Tian, Jiaming, Yuan, Jing, Zhao, Junbo, Zhou, Junlin, Shou, Kaizhe, Zha, Liangyu, Long, Lin, Li, Liyao, Wu, Pengzuo, Zhang, Qi, Huang, Qingyi, Yang, Saisai, Zhang, Tao, Ye, Wentao, Zhu, Wufang, Hu, Xiaomeng, Gu, Xijun, Sun, Xinjie, Li, Xiang, Yang, Yuhang, Xiao, Zhiqing
The emergence of models like GPTs, Claude, LLaMA, and Qwen has reshaped AI applications, presenting vast new opportunities across industries. Yet, the integration of tabular data remains notably underdeveloped, despite its foundational role in numero
Externí odkaz:
http://arxiv.org/abs/2411.02059
Autor:
Gao, Leyun, Wang, Zijian, Liu, Cheng-en, Li, Jinning, Ruzi, Alim, Li, Qite, Zhou, Chen, Li, Qiang
This work proposes a new yet economical experiment to probe the charged lepton flavor violation (CLFV) process mediated by an extra massive neutron gauge boson $Z^\prime$ beyond the standard model, by extending a recently proposed muon dark matter pr
Externí odkaz:
http://arxiv.org/abs/2410.20323
Autor:
Kiriliouk, Anna, Zhou, Chen
This book chapter illustrates how to apply extreme value statistics to financial time series data. Such data often exhibits strong serial dependence, which complicates assessment of tail risks. We discuss the two main approches to tail risk estimatio
Externí odkaz:
http://arxiv.org/abs/2409.18643
Autor:
Prabhushankar, Mohit, Kokilepersaud, Kiran, Quesada, Jorge, Yarici, Yavuz, Zhou, Chen, Alotaibi, Mohammad, AlRegib, Ghassan, Mustafa, Ahmad, Kumakov, Yusufjon
Crowdsourcing annotations has created a paradigm shift in the availability of labeled data for machine learning. Availability of large datasets has accelerated progress in common knowledge applications involving visual and language data. However, spe
Externí odkaz:
http://arxiv.org/abs/2408.11185
Autor:
Chen, Liujun, Zhou, Chen
When applying multivariate extreme values statistics to analyze tail risk in compound events defined by a multivariate random vector, one often assumes that all dimensions share the same extreme value index. While such an assumption can be tested usi
Externí odkaz:
http://arxiv.org/abs/2407.20491
Autor:
Zhu, Yongfeng, Zhuang, Weifeng, Qian, Chen, Ma, Yunheng, Liu, Dong E., Ruan, Manqi, Zhou, Chen
Exploring the application of quantum technologies to fundamental sciences holds the key to fostering innovation for both sides. In high-energy particle collisions, quarks and gluons are produced and immediately form collimated particle sprays known a
Externí odkaz:
http://arxiv.org/abs/2407.09056
Forecasting human trajectories in traffic scenes is critical for safety within mixed or fully autonomous systems. Human future trajectories are driven by two major stimuli, social interactions, and stochastic goals. Thus, reliable forecasting needs t
Externí odkaz:
http://arxiv.org/abs/2404.06971
Annotators exhibit disagreement during data labeling, which can be termed as annotator label uncertainty. Annotator label uncertainty manifests in variations of labeling quality. Training with a single low-quality annotation per sample induces model
Externí odkaz:
http://arxiv.org/abs/2403.10190
Autor:
Chun, Changbum, Neta, Beny
Publikováno v:
In Mathematics and Computers in Simulation March 2015 109:74-91
