Zobrazeno 1 - 10
of 8 753
pro vyhledávání: '"ZHOU, CHEN"'
The Non-Equilibrium Green's Function (NEGF) method combined with ab initio calculations has been widely used to study charge transport in molecular junctions. However, the significant computational demands of high-resolution calculations for all devi
Externí odkaz:
http://arxiv.org/abs/2412.06300
Autor:
Zhou, Chen, Neubert, Marlen, Koide, Yuri, Zhang, Yumeng, Vuong, Van-Quan, Schlöder, Tobias, Dehnen, Stefanie, Friederich, Pascal
Constructing datasets representative of the target domain is essential for training effective machine learning models. Active learning (AL) is a promising method that iteratively extends training data to enhance model performance while minimizing dat
Externí odkaz:
http://arxiv.org/abs/2412.00401
Autor:
Wu, Hengkui, Chi, Panpan, Zhu, Yongfeng, Liu, Liujiang, Hu, Shuyang, Wang, Yuexin, Zhou, Chen, Wang, Qihao, Xin, Yingsi, Liu, Bruce, Liang, Dahao, Jia, Xinglong, Ruan, Manqi
For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encount
Externí odkaz:
http://arxiv.org/abs/2412.00129
Autor:
Zhou, Chen, Cheng, Peng, Fang, Junfeng, Zhang, Yifan, Yan, Yibo, Jia, Xiaojun, Xu, Yanyan, Wang, Kun, Cao, Xiaochun
Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task. It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the
Externí odkaz:
http://arxiv.org/abs/2411.18288
Autor:
Gao, Leyun, Ruzi, Alim, Li, Qite, Zhou, Chen, Chen, Liangwen, Zhang, Xueheng, Sun, Zhiyu, Li, Qiang
Entanglement is a fundamental pillar of quantum mechanics. Probing quantum entanglement and testing Bell inequality with muons can be a significant leap forward, as muon is arguably the only massive elementary particle that can be manipulated and det
Externí odkaz:
http://arxiv.org/abs/2411.12518
Autor:
Fang, Yaquan, Gao, Christina, Li, Ying-Ying, Shu, Jing, Wu, Yusheng, Xing, Hongxi, Xu, Bin, Xu, Lailin, Zhou, Chen
Numerous challenges persist in High Energy Physics (HEP), the addressing of which requires advancements in detection technology, computational methods, data analysis frameworks, and phenomenological designs. We provide a concise yet comprehensive ove
Externí odkaz:
http://arxiv.org/abs/2411.11294
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