Zobrazeno 1 - 10
of 1 134
pro vyhledávání: '"LONG Gang"'
Autor:
Zhang, Jun-Jie, Song, Jiahao, Wang, Xiu-Cheng, Li, Fu-Peng, Liu, Zehan, Chen, Jian-Nan, Dang, Haoning, Wang, Shiyao, Zhang, Yiyan, Xu, Jianhui, Shi, Chunxiang, Wang, Fei, Pang, Long-Gang, Cheng, Nan, Zhang, Weiwei, Zhang, Duo, Meng, Deyu
We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five diverse applic
Externí odkaz:
http://arxiv.org/abs/2412.16234
Autor:
Zhang, Jun-Jie, Cheng, Nan, Li, Fu-Peng, Wang, Xiu-Cheng, Chen, Jian-Nan, Pang, Long-Gang, Meng, Deyu
Understanding the mechanisms behind neural network optimization is crucial for improving network design and performance. While various optimization techniques have been developed, a comprehensive understanding of the underlying principles that govern
Externí odkaz:
http://arxiv.org/abs/2409.06402
Publikováno v:
Gong-kuang zidonghua, Vol 44, Iss 1, Pp 84-88 (2018)
In view of problems of randomness and subjectivity in determining weight of existing rockburst prediction methods，a discrete Hopfield neural network (DHNN) model for prediction of classification of rockburst intensity was proposed。The model selec
Externí odkaz:
https://doaj.org/article/d4bca1fd967f4379822749f3eec5a444
Two-dimensional (2D) jet tomography is a promising tool to study jet medium modification in high-energy heavy-ion collisions. It combines gradient (transverse) and longitudinal jet tomography for selection of events with localized initial jet product
Externí odkaz:
http://arxiv.org/abs/2402.00264
Publikováno v:
Chinese Physics Letters 40, 122101 (2023)
In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examinati
Externí odkaz:
http://arxiv.org/abs/2311.07274
Einstein field equations are notoriously challenging to solve due to their complex mathematical form, with few analytical solutions available in the absence of highly symmetric systems or ideal matter distribution. However, accurate solutions are cru
Externí odkaz:
http://arxiv.org/abs/2309.07397
Atomic nonlinear interferometry has wide applications in quantum metrology and quantum information science. Here we propose a nonlinear time-reversal interferometry scheme with high robustness and metrological gain based on the spin squeezing generat
Externí odkaz:
http://arxiv.org/abs/2308.04042
Spin squeezing is vitally important in quantum metrology and quantum information science. The noise reduction resulting from spin squeezing can surpass the standard quantum limit and even reach the Heisenberg Limit (HL) in some special circumstances.
Externí odkaz:
http://arxiv.org/abs/2306.04156
Publikováno v:
Progress in Particle and Nuclear Physics, 104084, 2023
In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review artic
Externí odkaz:
http://arxiv.org/abs/2303.15136
Autor:
Huang, Long-Gang, Zhang, Xuanchen, Wang, Yanzhen, Hua, Zhenxing, Tang, Yuanjiang, Liu, Yong-Chun
Spin squeezing plays a crucial role in quantum metrology and quantum information science. Its generation is the prerequisite for further applications but still faces an enormous challenge since the existing physical systems rarely contain the require
Externí odkaz:
http://arxiv.org/abs/2303.13889