Zobrazeno 1 - 10
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pro vyhledávání: '"ZHONG, Hui"'
To alleviate energy shortages and environmental impacts caused by transportation, this study introduces EcoFollower, a novel eco-car-following model developed using reinforcement learning (RL) to optimize fuel consumption in car-following scenarios.
Externí odkaz:
http://arxiv.org/abs/2408.03950
We study the elastic electric and magnetic form factors of the proton, neutron and the charged roper resonance ($G_E^p$, $G_M^p$, $G_E^n$, $G_M^n$, $G_E^R$ and $G_M^R$) systematically in a constituent quark model. Three ingredients are crucial in thi
Externí odkaz:
http://arxiv.org/abs/2403.19343
While quantum computing has strong potential in data-driven fields, the privacy issue of sensitive or valuable information involved in the quantum algorithm should be considered. Differential privacy (DP), which is a fundamental privacy tool widely u
Externí odkaz:
http://arxiv.org/abs/2403.09173
Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. To protect quantum computing privacy, pioneering research efforts proposed to redefine differential privacy (DP) in quantum computing, i.e., quantum
Externí odkaz:
http://arxiv.org/abs/2312.14521
Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (QML) may present privacy risks
Externí odkaz:
http://arxiv.org/abs/2312.11126
Quantum computing has been widely applied in various fields, such as quantum physics simulations, quantum machine learning, and big data analysis. However, in the domains of data-driven paradigm, how to ensure the privacy of the database is becoming
Externí odkaz:
http://arxiv.org/abs/2312.08210
Autor:
Chen, Yong-Jian, Gao, Jin-Wei, Han, Jin-Xuan, Yuan, Zhong-Hui, Li, Ruo-Qi, Jiang, Yong-Yuan, Song, Jie
Publikováno v:
Phys.Rev.A 108,022613(2023)
This study investigates the use of orbital angular momentum (OAM) to enhance phase estimation in Mach-Zehnder interferometers (MZIs) by employing non-Gaussian states as input resources in the presence of noise. Our research demonstrates that non-Gaus
Externí odkaz:
http://arxiv.org/abs/2312.01684
Autor:
Wang, Pengqin, Zhu, Meixin, Lu, Hongliang, Zhong, Hui, Chen, Xianda, Shen, Shaojie, Wang, Xuesong, Wang, Yinhai
Prediction, decision-making, and motion planning are essential for autonomous driving. In most contemporary works, they are considered as individual modules or combined into a multi-task learning paradigm with a shared backbone but separate task head
Externí odkaz:
http://arxiv.org/abs/2310.10357
The Metaverse is a virtual world, an immersive experience, a new human-computer interaction, built upon various advanced technologies. How to protect Metaverse personal information and virtual properties is also facing new challenges, such as new att
Externí odkaz:
http://arxiv.org/abs/2310.03162
Recent advances in machine learning and natural language processing have fostered the enormous prosperity of smart voice assistants and their services, e.g., Alexa, Google Home, Siri, etc. However, voice spoofing attacks are deemed to be one of the m
Externí odkaz:
http://arxiv.org/abs/2309.15203