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349 results on '"Wang, Zizheng"'

3. Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation

Author

Wang, Xinhua, Yue, Houping, Wang, Zizheng, Xu, Liancheng, and Zhang, Jinyu

Subjects
Computer Science - Information Retrieval
Abstract

Cross-domain sequential recommenders (CSRs) are gaining considerable research attention as they can capture user sequential preference by leveraging side information from multiple domains. However, these works typically follow an ideal setup, i.e., different domains obey similar data distribution, which ignores the bias brought by asymmetric interaction densities (a.k.a. the inter-domain density bias). Besides, the frequently adopted mechanism (e.g., the self-attention network) in sequence encoder only focuses on the interactions within a local view, which overlooks the global correlations between different training batches. To this end, we propose an External Attention-enhanced Graph Contrastive Learning framework, namely EA-GCL. Specifically, to remove the impact of the inter-domain density bias, an auxiliary Self-Supervised Learning (SSL) task is attached to the traditional graph encoder under a multi-task learning manner. To robustly capture users' behavioral patterns, we develop an external attention-based sequence encoder that contains an MLP-based memory-sharing structure. Unlike the self-attention mechanism, such a structure can effectively alleviate the bias interference from the batch-based training scheme. Extensive experiments on two real-world datasets demonstrate that EA-GCL outperforms several state-of-the-art baselines on CSR tasks. The source codes and relevant datasets are available at https://github.com/HoupingY/EA-GCL., Comment: 9 pages, 4 figures, accepted by ICDM 2023 (workshop-GML4Rec)

Published
2023
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5. Optimal Efficiency Point Control of Wireless Charging Systems for Electric Vehicles

Author

Shi, Qianqian, Song, Xiaotong, Chen, Jiaqi, Wang, Zizheng, Lu, Yiwei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, Li, Zewen, editor, and Luo, An, editor

Published
2024
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21. A Multi-Dimensional Reverse Auction Mechanism for Volatile Federated Learning in the Mobile Edge Computing Systems.

Author

Hong, Yiming, Zheng, Zhaohua, and Wang, Zizheng

Subjects
MACHINE learning, FEDERATED learning, MOBILE computing, EDGE computing, COMPUTER systems
Abstract

Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients' volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets. [ABSTRACT FROM AUTHOR]

Published
2024
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26. International consensus guidelines on robotic pancreatic surgery in 2023

Author

Liu, Rong, primary, Abu Hilal, Mohammed, additional, Besselink, Marc G., additional, Hackert, Thilo, additional, Palanivelu, Chinnusamy, additional, Zhao, Yupei, additional, He, Jin, additional, Boggi, Ugo, additional, Jang, Jin-Young, additional, Panaro, Fabrizio, additional, Goh, Brian K. P., additional, Efanov, Mikhail, additional, Nagakawa, Yuichi, additional, Kim, Hong-Jin, additional, Yin, Xiaoyu, additional, Zhao, Zhiming, additional, Shyr, Yi-Ming, additional, Iyer, Shridhar, additional, Kakiashvili, Eli, additional, Han, Ho-Seong, additional, Lee, Jae Hoon, additional, Croner, Roland, additional, Wang, Shin-E, additional, Marino, Marco Vito, additional, Prasad, Arun, additional, Wang, Wei, additional, He, Songqing, additional, Yang, Kehu, additional, Liu, Qu, additional, Wang, Zizheng, additional, Li, Mengyang, additional, Xu, Shuai, additional, Wei, Kongyuan, additional, Deng, Zhaoda, additional, Jia, Yuze, additional, and van Ramshorst, Tess M. E., additional

Published
2024
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46. 胰管(修复)外科的理论及实践

Author

Liu, Rong, primary, Liu, Qu, additional, Wang, Zizheng, additional, Zhang, Xiuping, additional, Zhang, Zhuochao, additional, Chang, Zhenyu, additional, Wang, Fei, additional, Wang, Chao, additional, and Zhang, Gong, additional

Published
2023
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47. Synergistic sensitization effects of single-atom gold and cerium dopants on mesoporous SnO2nanospheres for enhanced volatile sulfur compound sensingElectronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4mh00507d

Author

Li, Ping, Wang, Zizheng, Feng, Youyou, Feng, Bingxi, Cheng, Dong, and Wei, Jing

Abstract

The real-time monitoring of volatile sulfur compounds is indispensable; however, it continues to pose a significant challenge due to issues such as limited performance towards parts-per-billion (ppb)-level gas. Herein, a concept of synergistic sensitization effects involving single-atom gold (Au) and cerium (Ce) dopants is proposed to boost the sensing performance of allyl mercaptan, a common volatile sulfur compound. As a proof-of-concept, a chemiresistive gas sensor based on mesoporous SnO2nanospheres with single-atom Au decoration and Ce dopant (denoted Au/Ce–SnO2) is successfully synthesized. The synthesis of Au/Ce–SnO2is achieved through the utilization of a self-template strategy, employing metal–phenolic hybrids as a precursor. The obtained materials exhibit high specific surface area (89.4 m2g−1), and small particle size (∼86 nm). The gas sensor reveals unprecedented sensitivity (0.097 ppb−1) and ultra-low detection limit (0.74 ppb), surpassing all state-of-the-art allyl mercaptan gas sensors. Furthermore, a wireless gas sensor is constructed for highly selective and real-time monitoring of allyl mercaptan. The decoration of single-atom Au facilitates the adsorption and dissociation of oxygen and target gases. Simultaneously, the Ce dopant enhances the oxidation of allyl mercaptan. The sensing performance is boosted by the mesoporous framework of SnO2, as well as the synergistic sensitization effects resulting from single-atom Au decoration and Ce doping, thereby facilitating its potential application in environmental and health-related domains.

Published
2024
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