Zobrazeno 1 - 10
of 23 584
pro vyhledávání: '"YU, Min"'
Autor:
Tseng, Po-Yan, Yeh, Yu-Min
We introduce a neutrino-scalar dark matter (DM) $\nu{\text{-}}\phi$ interaction and consider Diffuse Supernova Neutrino Background (DSNB) and Active Galactic Nuclei (AGN) representing distinctive neutrino sources. We focus on interaction mediated by
Externí odkaz:
http://arxiv.org/abs/2412.08537
$f(Q)$ and $f(T)$ gravity are based on fundamentally different geometric frameworks, yet they exhibit many similar properties. In this article, we identify two types of background-dependent and classical correspondences between these two theories of
Externí odkaz:
http://arxiv.org/abs/2412.01104
Low Earth orbit (LEO) satellites are capable of gathering abundant Earth observation data (EOD) to enable different Internet of Things (IoT) applications. However, to accomplish an effective EOD processing mechanism, it is imperative to investigate:
Externí odkaz:
http://arxiv.org/abs/2410.13602
We propose a novel method to construct ghost-free multiple scalar-tensor theories. The key idea is to use the geometric quantities of hypersurfaces defined by the scalar fields, rather than the covariant derivatives of scalar fields or spacetime curv
Externí odkaz:
http://arxiv.org/abs/2410.12680
We investigate the strong coupling problem in modified teleparallel gravity theories using the effective field theory (EFT) approach, demonstrating that it is possible to shift the emergence of new degrees of freedom (DoFs) to lower orders in perturb
Externí odkaz:
http://arxiv.org/abs/2410.03422
Data annotation refers to the labeling or tagging of textual data with relevant information. A large body of works have reported positive results on leveraging LLMs as an alternative to human annotators. However, existing studies focus on classic NLP
Externí odkaz:
http://arxiv.org/abs/2410.03254
In this paper, a novel generative adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association in NTNs. Traditional reinforcement learn
Externí odkaz:
http://arxiv.org/abs/2409.18718
Autor:
Xia, Kequan, Yu, Min
Recently, low-frequency mechanical energy harvesters based on solid-liquid contact electrification have garnered widespread attention for their unique advantages in wear resistance, high charge transfer efficiency, and novel insights into electron-io
Externí odkaz:
http://arxiv.org/abs/2409.03604
Wave energy harvesting is critical for advancing the development and utilization of marine resources. In this study, we present a novel multi-roller structure triboelectric nanogenerator (MR-TENG) designed specifically for efficient water wave energy
Externí odkaz:
http://arxiv.org/abs/2409.03601
Autor:
Liu, Ran, Liu, Ming, Yu, Min, Jiang, Jianguo, Li, Gang, Zhang, Dan, Li, Jingyuan, Meng, Xiang, Huang, Weiqing
Pre-trained language models are increasingly being used in multi-document summarization tasks. However, these models need large-scale corpora for pre-training and are domain-dependent. Other non-neural unsupervised summarization approaches mostly rel
Externí odkaz:
http://arxiv.org/abs/2408.10115