Zobrazeno 1 - 10
of 546
pro vyhledávání: '"ZHOU Yipeng"'
Publikováno v:
Frontiers in Immunology, Vol 15 (2024)
Neutrophils are essential components of the innate immune system that defend against the invading pathogens, such as bacteria, viruses, and fungi, as well as having regulatory roles in various conditions, including tissue repair, cancer immunity, and
Externí odkaz:
https://doaj.org/article/67c12d258c764edb973210fdf2eab692
Publikováno v:
Gongye shui chuli, Vol 44, Iss 6, Pp 34-39 (2024)
The harmless and resourceful treatment of uranium-containing wastewater generated from uranium and uranium-polymetallic ore mining and metallurgy is a hot research topic in the field of nuclear resources and environment. This paper reviewed the curre
Externí odkaz:
https://doaj.org/article/79824386cdc7444e9ecf757c629d76db
Autor:
Zhang, Xianzhi, Zhou, Yipeng, Hu, Miao, Wu, Di, Liao, Pengshan, Guizani, Mohsen, Sheng, Michael
To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private feder
Externí odkaz:
http://arxiv.org/abs/2412.02934
Bilevel optimization, crucial for hyperparameter tuning, meta-learning and reinforcement learning, remains less explored in the decentralized learning paradigm, such as decentralized federated learning (DFL). Typically, decentralized bilevel methods
Externí odkaz:
http://arxiv.org/abs/2410.14115
This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability. While promising approaches exist, they are often not publicly available, revolve around pedestrian datasets excl
Externí odkaz:
http://arxiv.org/abs/2409.01971
Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. As users stream their preferred online videos, their requests are autom
Externí odkaz:
http://arxiv.org/abs/2408.14735
Pre-training exploits public datasets to pre-train an advanced machine learning model, so that the model can be easily tuned to adapt to various downstream tasks. Pre-training has been extensively explored to mitigate computation and communication re
Externí odkaz:
http://arxiv.org/abs/2408.09478
To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To enhance the protection of model gradients in FL, differentially privat
Externí odkaz:
http://arxiv.org/abs/2408.08642
Caching content at the edge network is a popular and effective technique widely deployed to alleviate the burden of network backhaul, shorten service delay and improve service quality. However, there has been some controversy over privacy violations
Externí odkaz:
http://arxiv.org/abs/2405.01844
Publikováno v:
MATEC Web of Conferences, Vol 355, p 02054 (2022)
In the industrial area, the deployment of deep learning models in object detection and tracking are normally too large, also, it requires appropriate trade-offs between speed and accuracy. In this paper, we present a compressed object identification
Externí odkaz:
https://doaj.org/article/ccf0a3059c464ed2bf9b7b2e7b92922f