Zobrazeno 1 - 10
of 16 515
pro vyhledávání: '"DONG, JUN"'
In the fifth-generation (5G) era, eliminating communication interference sources is crucial for maintaining network performance. Interference often originates from unauthorized or malfunctioning antennas, and radio monitoring agencies must address nu
Externí odkaz:
http://arxiv.org/abs/2412.03055
Autor:
Wen, Jin-Lu, Tang, Jia-Dong, Lv, Ya-Nan, Sun, Yu R., Zou, Chang-Ling, Dong, Jun-Feng, Hu, Shui-Ming
Post-selecting output states in measurements can effectively amplify weak signals and improve precision. However, post-selection effects may also introduce unintended biases in precision measurements. Here, we investigate the influence of post-select
Externí odkaz:
http://arxiv.org/abs/2411.09958
Autor:
Mei, Yongsheng, Yuan, Liangqi, Han, Dong-Jun, Chan, Kevin S., Brinton, Christopher G., Lan, Tian
Federated learning (FL) has become a cornerstone in decentralized learning, where, in many scenarios, the incoming data distribution will change dynamically over time, introducing continuous learning (CL) problems. This continual federated learning (
Externí odkaz:
http://arxiv.org/abs/2411.06618
Publikováno v:
The Astrophysical Journal, 976: 174 (10pp), 2024 December 1
The prompt emission mechanism of gamma-ray bursts (GRBs) is a long-standing open question, and GRBs have been considered as potential sources of high-energy neutrinos. Despite many years of search for the neutrino events associated with GRBs from Ice
Externí odkaz:
http://arxiv.org/abs/2410.09438
Autor:
Chang, Zhan-Lun, Han, Dong-Jun, Parasnis, Rohit, Hosseinalipour, Seyyedali, Brinton, Christopher G.
While most existing federated learning (FL) approaches assume a fixed set of clients in the system, in practice, clients can dynamically leave or join the system depending on their needs or interest in the specific task. This dynamic FL setting intro
Externí odkaz:
http://arxiv.org/abs/2410.05662
Autor:
Zhang, Daoan, Lan, Guangchen, Han, Dong-Jun, Yao, Wenlin, Pan, Xiaoman, Zhang, Hongming, Li, Mingxiao, Chen, Pengcheng, Dong, Yu, Brinton, Christopher, Luo, Jiebo
Reinforcement learning from human feedback (RLHF) methods are emerging as a way to fine-tune diffusion models (DMs) for visual generation. However, commonly used on-policy strategies are limited by the generalization capability of the reward model, w
Externí odkaz:
http://arxiv.org/abs/2410.05255
While traditional federated learning (FL) typically focuses on a star topology where clients are directly connected to a central server, real-world distributed systems often exhibit hierarchical architectures. Hierarchical FL (HFL) has emerged as a p
Externí odkaz:
http://arxiv.org/abs/2409.18448
Over the past several years, various federated learning (FL) methodologies have been developed to improve model accuracy, a primary performance metric in machine learning. However, to utilize FL in practical decision-making scenarios, beyond consider
Externí odkaz:
http://arxiv.org/abs/2409.04901
Autor:
Han, Dong-Jun, Fang, Wenzhi, Hosseinalipour, Seyyedali, Chiang, Mung, Brinton, Christopher G.
Devices located in remote regions often lack coverage from well-developed terrestrial communication infrastructure. This not only prevents them from experiencing high quality communication services but also hinders the delivery of machine learning se
Externí odkaz:
http://arxiv.org/abs/2408.09522
Autor:
Zhao, Xingyue, Li, Zhongyu, Luo, Xiangde, Li, Peiqi, Huang, Peng, Zhu, Jianwei, Liu, Yang, Zhu, Jihua, Yang, Meng, Chang, Shi, Dong, Jun
Recent advances in deep learning have greatly facilitated the automated segmentation of ultrasound images, which is essential for nodule morphological analysis. Nevertheless, most existing methods depend on extensive and precise annotations by domain
Externí odkaz:
http://arxiv.org/abs/2404.14852