Zobrazeno 1 - 10
of 4 370
pro vyhledávání: '"LIU, Bei"'
The $f$-electron materials have many unique properties under pressure, thus of great interest in high-pressure physics and related industrial fields. However, the $f$-electrons pose a substantial challenge to simulations since the electron correlatio
Externí odkaz:
http://arxiv.org/abs/2410.09498
The Special Affine Fourier Transformation(SAFT), which generalizes several well-known unitary transformations, has been demonstrated as a valuable tool in signal processing and optics. In this paper, we explore the multivariate dynamical sampling pro
Externí odkaz:
http://arxiv.org/abs/2409.08506
When parametrizing multi-Higgs potentials, it is desirable to express its coefficients via observables. This is routinely done for the 2HDM, but this approach often fails in more elaborate models. Here, we show that the scalar sector of the CP4 3HDM,
Externí odkaz:
http://arxiv.org/abs/2409.05992
Autor:
Tang, Yuanjiang, Wang, Chenyang, Liu, Bei, Peng, Jin, Liang, Chao, Li, Yaohua, Zhao, Xian, Lu, Cuicui, Zhang, Shuang, Liu, Yong-Chun
Spontaneous symmetry breaking plays a pivotal role in physics ranging from the emergence of elementary particles to the phase transitions of matter. The spontaneous breaking of continuous time translation symmetry leads to a novel state of matter nam
Externí odkaz:
http://arxiv.org/abs/2407.07697
Vortex states of photons, electrons, and other particles are freely propagating wave packets with helicoidal wave fronts winding around the axis of a phase vortex. A particle prepared in a vortex state possesses a non-zero orbital angular momentum pr
Externí odkaz:
http://arxiv.org/abs/2406.06795
Modern speaker verification (SV) systems typically demand expensive storage and computing resources, thereby hindering their deployment on mobile devices. In this paper, we explore adaptive neural network quantization for lightweight speaker verifica
Externí odkaz:
http://arxiv.org/abs/2406.05359
Parameter quantization for Large Language Models (LLMs) has attracted increasing attentions recently in reducing memory costs and improving computational efficiency. Early approaches have been widely adopted. However, the existing methods suffer from
Externí odkaz:
http://arxiv.org/abs/2405.17233
Autor:
Wu, Bo, Liu, Peiye, Cheng, Wen-Huang, Liu, Bei, Zeng, Zhaoyang, Wang, Jia, Huang, Qiushi, Luo, Jiebo
Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts, leveraging vast amounts of multimodal data available on social media platforms. Studying and investigating so
Externí odkaz:
http://arxiv.org/abs/2405.10497
Modeling generalized robot control policies poses ongoing challenges for language-guided robot manipulation tasks. Existing methods often struggle to efficiently utilize cross-dataset resources or rely on resource-intensive vision-language models, th
Externí odkaz:
http://arxiv.org/abs/2403.07312
Robotic motor control necessitates the ability to predict the dynamics of environments and interaction objects. However, advanced self-supervised pre-trained visual representations (PVRs) in robotic motor control, leveraging large-scale egocentric vi
Externí odkaz:
http://arxiv.org/abs/2403.05304