Zobrazeno 1 - 10
of 1 296
pro vyhledávání: '"Qu Liang"'
Autor:
Yan-wen LI, Xun BAO, Yue-ting DING, Qu LIANG, Qiang-ling ZHANG, Yan LU, Lei XIA, Ya-wei LIU, Xue ZOU, Chao-qun HUANG, Cheng-yin SHEN, Yan-nan CHU
Publikováno v:
Zhipu Xuebao, Vol 45, Iss 5, Pp 624-630 (2024)
Atmospheric organic aerosols consist of both gas-phase organic compounds and particle-phase organic compounds. The components of aerosol particles (solid or liquid droplets) can undergo exchange or chemical reactions with gas-phase components, influe
Externí odkaz:
https://doaj.org/article/cf5e240f43414dc68bb4ecd350ea8847
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
As the physical power information system undergoes continual advancement, mobile energy storage has become a pivotal component in the planning and orchestration of multi-component distribution networks. Furthermore, the evolution and enhancement of b
Externí odkaz:
https://doaj.org/article/d48fb5c85b34494bbe3a84c4791db34c
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
The analysis of the links and uncertainties between independent variables and dependent variables is effective. Then, the factor analysis method is applied to establish a comprehensive performance evaluation system that takes into account four dimens
Externí odkaz:
https://doaj.org/article/ccdd759e4bb4442b8a68f09131575afc
Recommender systems typically represent users and items by learning their embeddings, which are usually set to uniform dimensions and dominate the model parameters. However, real-world recommender systems often operate in streaming recommendation sce
Externí odkaz:
http://arxiv.org/abs/2407.15411
Sequential recommender systems have made significant progress. Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning for sequential recommendation, a.k.a., Federated Sequential Recommender
Externí odkaz:
http://arxiv.org/abs/2406.05387
The application of deep learning techniques to medical problems has garnered widespread research interest in recent years, such as applying convolutional neural networks to medical image classification tasks. However, data in the medical field is oft
Externí odkaz:
http://arxiv.org/abs/2404.15585
The embedding-based architecture has become the dominant approach in modern recommender systems, mapping users and items into a compact vector space. It then employs predefined similarity metrics, such as the inner product, to calculate similarity sc
Externí odkaz:
http://arxiv.org/abs/2404.11818
To make room for privacy and efficiency, the deployment of many recommender systems is experiencing a shift from central servers to personal devices, where the federated recommender systems (FedRecs) and decentralized collaborative recommender system
Externí odkaz:
http://arxiv.org/abs/2404.01177
Federated Recommender Systems (FedRecs) have garnered increasing attention recently, thanks to their privacy-preserving benefits. However, the decentralized and open characteristics of current FedRecs present two dilemmas. First, the performance of F
Externí odkaz:
http://arxiv.org/abs/2403.20107
Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating model parameters/gradients to the server. Nevertheless, the currently
Externí odkaz:
http://arxiv.org/abs/2401.17630