Zobrazeno 1 - 10
of 14 803
pro vyhledávání: '"Cheng Peng"'
Autor:
Lai, Phu, Xiang, Wei, Lukito, William Damario, Phan, Khoa Tran, Cheng, Peng, Liu, Chang, Mao, Guoqiang
Cell-free massive multiple-input multiple-output (CFmMIMO) coordinates a great number of distributed access points (APs) with central processing units (CPUs), effectively reducing interference and ensuring uniform service quality for user equipment (
Externí odkaz:
http://arxiv.org/abs/2412.15475
Autor:
Feng, Yicheng, Chen, Yuetao, Chen, Kaiwen, Li, Jingzong, Wu, Tianyuan, Cheng, Peng, Wu, Chuan, Wang, Wei, Ho, Tsung-Yi, Xu, Hong
Simulation offers unique values for both enumeration and extrapolation purposes, and is becoming increasingly important for managing the massive machine learning (ML) clusters and large-scale distributed training jobs. In this paper, we build Echo to
Externí odkaz:
http://arxiv.org/abs/2412.12487
Working with the $29$ available data on the ratio of proton electric and magnetic form factors, $\mu_p G_E^p(Q^2)/ G_M^p(Q^2)$, and independent of any model or theory of strong interactions, we use the Schlessinger point method to objectively address
Externí odkaz:
http://arxiv.org/abs/2412.10598
Estimating spatial distributions is important in data analysis, such as traffic flow forecasting and epidemic prevention. To achieve accurate spatial distribution estimation, the analysis needs to collect sufficient user data. However, collecting dat
Externí odkaz:
http://arxiv.org/abs/2412.06541
Ridesharing services play an essential role in modern transportation, which significantly reduces traffic congestion and exhaust pollution. In the ridesharing problem, improving the sharing rate between riders can not only save the travel cost of dri
Externí odkaz:
http://arxiv.org/abs/2412.06335
Autor:
Wang, Tianyi, Wang, Zichen, Wang, Cong, Shu, Yuanchao, Deng, Ruilong, Cheng, Peng, Chen, Jiming
Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible perturbat
Externí odkaz:
http://arxiv.org/abs/2412.02171
Autor:
Zhou, Chen, Cheng, Peng, Fang, Junfeng, Zhang, Yifan, Yan, Yibo, Jia, Xiaojun, Xu, Yanyan, Wang, Kun, Cao, Xiaochun
Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task. It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the
Externí odkaz:
http://arxiv.org/abs/2411.18288
Autor:
Pan, Feihao, Sun, Songnan, Kolesnikov, Alexander I., Stone, Matthew B., Huang, Jiale, Xu, Daye, Shang, Chenglin, Shi, Bingxian, Gui, Xuejuan, Sun, Zhongcen, Wang, Jinchen, Liu, Juanjuan, Zhang, Hongxia, Liu, Zhengxin, Cheng, Peng
Publikováno v:
Phys. Rev. B 110(2024)174448
Triangular lattice antiferromagnets are prototypes for frustrated magnetism and may potentially realize novel quantum magnetic states such as a quantum spin liquid ground state. A recent work suggests NdTa$_7$O$_{19}$ with rare-earth triangular latti
Externí odkaz:
http://arxiv.org/abs/2411.18045
It is well-known that a diverse corpus is critical for training large language models, which are typically constructed from a mixture of various domains. In general, previous efforts resort to sampling training data from different domains with static
Externí odkaz:
http://arxiv.org/abs/2411.14318
Differential privacy (DP) has recently been introduced into episodic reinforcement learning (RL) to formally address user privacy concerns in personalized services. Previous work mainly focuses on two trust models of DP: the central model, where a ce
Externí odkaz:
http://arxiv.org/abs/2411.11647