Zobrazeno 1 - 10
of 12 185
pro vyhledávání: '"LiangYu"'
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:
Nie, Xinfang, Zhu, Xuanran, Fan, Yu-ang, Long, Xinyue, Liu, Hongfeng, Huang, Keyi, Xi, Cheng, Che, Liangyu, Zheng, Yuxuan, Feng, Yufang, Yang, Xiaodong, Lu, Dawei
Publikováno v:
Phys. Rev. Lett. 133, 140602 (2024)
The accurate determination of the electronic structure of strongly correlated materials using first principle methods is of paramount importance in condensed matter physics, computational chemistry, and material science. However, due to the exponenti
Externí odkaz:
http://arxiv.org/abs/2410.07808
Cutting planes (cuts) are crucial for solving Mixed Integer Linear Programming (MILP) problems. Advanced MILP solvers typically rely on manually designed heuristic algorithms for cut selection, which require much expert experience and cannot be gener
Externí odkaz:
http://arxiv.org/abs/2410.03112
The eigenstate thermalization hypothesis (ETH) in chaotic two dimensional CFTs is subtle due to infinitely many conserved KdV charges. Previous works have demonstrated that primary CFT eigenstates have flat entanglement spectrum, which is very differ
Externí odkaz:
http://arxiv.org/abs/2409.19271
We study subsystem entropy in 2d CFTs, for subsystems constituting a finite fraction of the full system. We focus on the extensive contribution, which scales linearly with the subsystem size in the thermodynamic limit. We employ the so-called diagona
Externí odkaz:
http://arxiv.org/abs/2409.19046
Semantic segmentation networks have achieved significant success under the assumption of independent and identically distributed data. However, these networks often struggle to detect anomalies from unknown semantic classes due to the limited set of
Externí odkaz:
http://arxiv.org/abs/2409.17330
Autor:
Chen, Liangyu, Fors, Simon Pettersson, Yan, Zixian, Ali, Anaida, Abad, Tahereh, Osman, Amr, Moschandreou, Eleftherios, Lienhard, Benjamin, Kosen, Sandoko, Li, Hang-Xi, Shiri, Daryoush, Liu, Tong, Hill, Stefan, Amin, Abdullah-Al, Rehammar, Robert, Dahiya, Mamta, Nylander, Andreas, Rommel, Marcus, Roudsari, Anita Fadavi, Caputo, Marco, Leif, Grönberg, Govenius, Joonas, Dobsicek, Miroslav, Giannelli, Michele Faucci, Kockum, Anton Frisk, Bylander, Jonas, Tancredi, Giovanna
The realization of fault-tolerant quantum computing requires the execution of quantum error-correction (QEC) schemes, to mitigate the fragile nature of qubits. In this context, to ensure the success of QEC, a protocol capable of implementing both qub
Externí odkaz:
http://arxiv.org/abs/2409.16748
In recent years, the e-commerce industry has seen a rapid increase in the demand for advanced AI-driven customer service solutions. Traditional cloud-based models face limitations in terms of latency, personalized services, and privacy concerns. Furt
Externí odkaz:
http://arxiv.org/abs/2410.07122
Autor:
Hu, Chang-Kang, Wei, Chao, Liu, Chilong, Che, Liangyu, Zhou, Yuxuan, Xie, Guixu, Qin, Haiyang, Hu, Guantian, Yuan, Haolan, Zhou, Ruiyang, Liu, Song, Tan, Dian, Xin, Tao, Yu, Dapeng
Publikováno v:
Phys. Rev. Lett. 133, 160801 (2024)
Quantum state tomography (QST) via local measurements on reduced density matrices (LQST) is a promising approach but becomes impractical for large systems. To tackle this challenge, we developed an efficient quantum state tomography method inspired b
Externí odkaz:
http://arxiv.org/abs/2409.12614
Finetuning large language models on instruction data is crucial for enhancing pre-trained knowledge and improving instruction-following capabilities. As instruction datasets proliferate, selecting optimal data for effective training becomes increasin
Externí odkaz:
http://arxiv.org/abs/2409.11378