Zobrazeno 1 - 10
of 10 177
pro vyhledávání: '"Yu, Gang"'
Autor:
Yu, Gang
Publikováno v:
Comptes Rendus. Mathématique, Vol 361, Iss G3, Pp 609-616 (2023)
In this note, we investigate the $L_1$-norms of Barker polynomials and, more generally, Littlewood polynomials over the unit circle, and give improvements to some existing results.
Externí odkaz:
https://doaj.org/article/ea0cfcc72a094a2eb06631da01b9246f
The cluster structures of the $0^+$ states in $^{12}\mathrm{C}$, including the ground state, the Hoyle state, and the recently identified $0_3^+$ and $0_4^+$ states, are analyzed to explore the cluster configurations and $3\alpha$ correlations withou
Externí odkaz:
http://arxiv.org/abs/2501.10664
Autor:
Tu, Bingsheng, Xue, Nan, Liu, Jialin, Guo, Qi, Wu, Yuanbin, Liu, Zuoye, Pálffy, Adriana, Yang, Yang, Yao, Ke, Wei, Baoren, Zou, Yaming, Kong, Xiangjin, Ma, Yu-Gang
Nuclear excitation by electron capture (NEEC) is an important nuclear excitation mechanism which still lacks conclusive experimental verification. This is primarily attributed to strong background x-/$\gamma$-ray noise and competing nuclear excitatio
Externí odkaz:
http://arxiv.org/abs/2501.05217
The nuclear structures of $^7$Li($\alpha+n+n+p$) and $^7$Be($\alpha+p+p+n$) are studied within the microscopic cluster model, in which the clustering fragments e.g., triton, $^3$He, and even the single nucleons around the core are studied in the $^7$
Externí odkaz:
http://arxiv.org/abs/2501.05216
Machine learning offers a powerful framework for validating and predicting atomic mass. We compare three improved neural network methods for representation and extrapolation for atomic mass prediction. The powerful method, adopting a macroscopic-micr
Externí odkaz:
http://arxiv.org/abs/2501.01352
Autor:
Shao, Tianhao, Chen, Jinhui, Pochodzalla, Josef, Achenbach, Patrick, Christmann, Mirco, Distler, Michael O., Doria, Luca, Esser, Anselm, Geratz, Julian, Helmel, Christian, Hoek, Matthias, Kino, Ryoko, Klag, Pascal, Ma, Yu-Gang, Markus, David, Merkel, Harald, Mihovilovič, Miha, Müller, Ulrich, Nagao, Sho, Nakamura, Satoshi N., Nishi, Kotaro, Nishida, Ken, Oura, Fumiya, Pätschke, Jonas, Schlimme, Björn Sören, Sfienti, Concettina, Steger, Daniel, Steinen, Marcell, Thiel, Michaela, Wilczek, Andrzej, Wilhelm, Luca
For the first time the neutron-rich hydrogen isotope $\rm ^{6}H$ was produced in an electron scattering experiment in the reaction $\rm ^{7}Li(e,~e'p\pi^{+})^{6}H$ using the spectrometer facility of the A1 Collaboration at the Mainz Microtron acceler
Externí odkaz:
http://arxiv.org/abs/2501.01232
The exceptional generative capability of text-to-image models has raised substantial safety concerns regarding the generation of Not-Safe-For-Work (NSFW) content and potential copyright infringement. To address these concerns, previous methods safegu
Externí odkaz:
http://arxiv.org/abs/2501.01125
Transferable adversarial examples highlight the vulnerability of deep neural networks (DNNs) to imperceptible perturbations across various real-world applications. While there have been notable advancements in untargeted transferable attacks, targete
Externí odkaz:
http://arxiv.org/abs/2501.01106
Dynamic 3D scene representation and novel view synthesis from captured videos are crucial for enabling immersive experiences required by AR/VR and metaverse applications. However, this task is challenging due to the complexity of unconstrained real-w
Externí odkaz:
http://arxiv.org/abs/2412.20720
Autor:
Li, Zhangxun, Zhao, Mengyang, Yang, Xuan, Liu, Yang, Sheng, Jiamu, Zeng, Xinhua, Wang, Tian, Wu, Kewei, Jiang, Yu-Gang
Video anomaly detection (VAD) has been extensively researched due to its potential for intelligent video systems. However, most existing methods based on CNNs and transformers still suffer from substantial computational burdens and have room for impr
Externí odkaz:
http://arxiv.org/abs/2412.20084