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Correction to: Well-Balanced Fifth-Order Finite Volume WENO Schemes with Constant Subtraction Technique for Shallow Water Equations

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Journal of Scientific Computing 2025年第1期103卷 1-4页

作者： Zhao, Lidan Tao, Zhanjing Zhang, Min School of Mathematics Jilin University Changchun China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Chongqing Research Institute of Big Data Peking University Chongqing China

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A unifying moving mesh method for curves, surfaces, and domains based on mesh equidistribution and alignment

arXiv

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arXiv 2025年

作者： Zhang, Min Huang, Weizhang National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Chongqing Research Institute of Big Data Peking University Chongqing401121 China Department of Mathematics University of Kansas LawrenceKS66045 United States

A unifying moving mesh method is developed for general m-dimensional geometric objects in d-dimensions (d ≥ 1 and 1 ≤ m ≤ d) including curves, surfaces, and domains. The method is based on mesh equidistribution and alignment and does not require the availability of an analytical parametric representation of the underlying geometric object. Mathematical characterizations of shape and size of m-simplexes and properties of corresponding edge matrices and affine mappings are derived. The equidistribution and alignment conditions are presented in a unifying form for m-simplicial meshes. The equation for mesh movement is defined based on the moving mesh PDE approach, and suitable projection of the nodal mesh velocities is employed to ensure the mesh points stay on the underlying geometric object. The analytical expression for the mesh velocities is obtained in a compact matrix form. The nonsingularity of moving meshes is proved. Numerical results for curves (m = 1) and surfaces (m = 2) in two and three dimensions are presented to demonstrate the ability of the developed method to move mesh points without causing singularity and control their concentration. Copyright © 2025, The Authors. All rights reserved.

关键词： Mesh generation

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Hetu: a highly efficient automatic parallel distributed deep learning system

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Science China(Information Sciences) 2023年第1期66卷 314-315页

作者： Xupeng MIAO Xiaonan NIE Hailin ZHANG Tong ZHAO Bin CUI School of Computer Science Peking University Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications

On July 18, 2021, the PKU-DAIR lab1)(data and Intelligence Research lab at Peking University) openly released the source code of Hetu, a highly efficient and easy-to-use distributed deep learning(DL) framework. Hetu is the first distributed DL system developed by academic groups in Chinese universities, and takes into account both high availability in industry and innovation in academia. Through independent research and development, Hetu is completely decoupled from the existing DL systems and has unique characteristics. The public release of the Hetu system will help researchers and practitioners to carry out frontier MLSys(machine learning system) research and promote innovation and industrial upgrading.

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Robust Indoor Person Re-Identification with Multimodal Training

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IEEE Internet of Things Journal 2025年

作者： Su, Can Xue, Xinlei Ma, Lei Zhang, Xiaolong Yan, Wei Bian, Kaigui Peking University School of Computer Science AI Innovation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing100871 China Peking University School of Computer Science Beijing100871 China Peking University Beijing Academy of Artificial Intelligence National Biomedical Imaging Center College of Future Technology National Key Laboratory for Multimedia Information Processing Beijing100871 China Beihang University Beijing100191 China

Existing person re-identification (ReID) methods mainly rely on images and videos to match persons across cameras, yet visual data captured by cameras are vulnerable to environmental interferences (e.g. illumination and occlusion) or personal appearance changes, leading to performance degradation under such scenes. Meanwhile, the popularization of Wi-Fi networks has allowed probe requests to be captured for mobile sensing applications such as crowd counting and trajectory estimation. However, the MAC address randomization technique adopted by modern devices breaks the association of probe requests and adversely affects the functionality of these applications. In this paper, we propose MaRPA, the first multimodal training approach that incorporates both videos and Wi-Fi probe requests to simultaneously promote tasks of probe requests association and person ReID. MaRPA first distinguishes among pairwise probe request frames through a contrastive learning model. It then matches video and probe request sequences by exploring their similarities from the position and the vision aspects. Matched videos and probe requests provide complementary information and generate more robust features for both tasks. To evaluate MaRPA, we contribute a new dataset containing synchronous videos and probe requests data for probe requests association and person ReID. Experimental results demonstrate the effectiveness of our approach. For probe requests association, it achieves > 85% discrimination accuracy and > 0.90 V-measure score;for person ReID, it achieves 75.8% mAP and 90.6% Rank-1, improving state-of-the-art video-based ReID methods by over 40% © 2014 IEEE.

关键词： Wi-Fi

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Tracking the carbon footprint of global generative artificial intelligence

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Innovation 2025年第5期6卷

作者： Ding, Zhaohao Wang, Jianxiao Song, Yiyang Zheng, Xiaokang He, Guannan Chen, Xiupeng Zhang, Tiance Lee, Wei-Jen Song, Jie State Key Lab of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong999077 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Jan C. Willems Center for Systems and Control University of Groningen Groningen9700AB Netherlands Energy Systems Research Center University of Texas at Arlington ArlingtonTX76019 United States

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Accelerating exploitation and integration of global renewable energy

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Innovation 2025年

作者： Wang, Jianxiao Chen, Xinjiang Zhuang, Minghao Li, Yan Ruan, Ziwen Wang, Yuhan Zhang, Ning Song, Jie He, Kebin Lu, Xi National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Peking University Ordos Research Institute of Energy Ordos017000 China State Key Laboratory of Nutrient Use and Management College of Resources and Environmental Sciences China Agricultural University Beijing100193 China College of Environmental Science and Engineering Tongji University Shanghai200092 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Key Lab of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China

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Snapshot boosting: a fast ensemble framework for deep neural networks

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Science China(Information Sciences) 2020年第1期63卷 77-88页

作者： Wentao ZHANG Jiawei JIANG Yingxia SHAO Bin CUI Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications Department of Computer Science Beijing Key Lab of Intelligent Telecommunications Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Key Lab of High Confidence Software Technologies Department of Computer SciencePeking University

Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combining a sufficient number of weak learners. However, it is rarely used in deep learning due to the high training budget of the neural network. Another method named snapshot ensemble can significantly reduce the training budget, but it is hard to balance the tradeoff between training costs and diversity. Inspired by the ideas of snapshot ensemble and boosting, we propose a method named snapshot boosting. A series of operations are performed to get many base models with high diversity and accuracy, such as the use of the validation set, the boosting-based training framework, and the effective ensemble strategy. Last, we evaluate our method on the computer vision(CV) and the natural language processing(NLP) tasks, and the results show that snapshot boosting can get a more balanced trade-off between training expenses and ensemble accuracy than other well-known ensemble methods.

关键词： ensemble learning deep learning boosting neural network snapshot ensemble

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CATIRI:An Efficient Method for Content-and-Text Based Image Retrieval

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Journal of Computer Science & Technology 2019年第2期34卷 287-304页

作者： Mengqi Zeng Bin Yao Zhi-Jie Wang Yanyan Shen Feifei Li Jianfeng Zhang Hao Lin Minyi Guo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Data and Computer Science Sun Yat-sen University Guangzhou 510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871 China School of Computing University of Utah Salt Lake City 84112 U.S.A. Alibaba Group Hangzhou 311121 China

The combination of visual and textual information in image retrieval remarkably alleviates the semantic gap of traditional image retrieval methods,and thus it has attracted much attention *** retrieval based on such a combination is usually called the content-and-text based image retrieval(CTBIR).Nevertheless,existing studies in CTBIR mainly make efforts on improving the retrieval *** the best of our knowledge,little attention has been focused on how to enhance the retrieval ***,image data is widespread and expanding rapidly in our daily ***,it is important and interesting to investigate the retrieval *** this end,this paper presents an efficient image retrieval method named CATIRI(content-and-text based image retrieval using indexing).CATIRI follows a three-phase solution framework that develops a new indexing structure called *** MHIM-tree seamlessly integrates several elements including Manhattan Hashing,Inverted index,and *** use our MHIM-tree wisely in the query,we present a set of important metrics and reveal their inherent *** on them,we develop a top-k query algorithm for *** results based on benchmark image datasets demonstrate that CATIRI outperforms the competitors by an order of magnitude.

关键词： image retrieval text-and-visual feature indexing top-k

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TJOSConf: Automatic and Safe System Environment Operations Platform 2022

TJOSConf: Automatic and Safe System Environment Operations P...

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11th International Conference on Software and Computer applications, ICSCA 2022

作者： Wang, Yida Jiang, Shuangshuang Cui, Bin School of Cs & National Engineering Laboratory for Big Data Analysis and Applications Peking University China Alibaba Group China

ISBN: (纸本)9781450385770

With exploding number of servers in large IT corporations, system environment management of servers at scale is a big challenge. System environment needs to be regularly updated to meet the system demand of services or fix emerging bugs. It is of vital importance to ensure stability of services during these system updates. However, the heterogeneity of workload and diversity of update scripts make it difficult to estimate how a system update affects the servers and services running on them. Unexpected failures caused by various reasons can also arise during or after update execution, making the situation even worse. This paper aims to solve the system management challenge with an operations platform called TJOSConf. During system updates, TJOSConf is able to interact with services to make sure that expected impacts and unexpected failures can be recognized and handled in time, making system update highly automatic and safe. Various system update scripts can be easily incorporated into TJOSConf as plugins, making the platform scalable. Use cases of TJOSConf in Alibaba prove its effectiveness. © 2022 ACM.

关键词： System environment management operations platform rolling upgrade

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Bridging prediction and decision: Advances and challenges in data-driven optimization

Nexus

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Nexus 2025年第1期2卷

作者： Yanzhi Wang Jianxiao Wang Haoran Zhang Jie Song Department of Industrial Engineering and Management College of Engineering Peking University Beijing 100871 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing 100871 China PKU-Changsha Institute for Computing and Digital Economy Changsha 410000 China School of Urban Planning and Design Peking University Shenzhen Guangdong 518055 China

data-driven approaches have revolutionized traditional optimization methods by integrating prediction with decision-making. This review examines the theoretical foundations, strengths, recent advancements, and limitations of three key methods—sequential optimization, end-to-end learning, and direct learning—highlighting their practical applications in power grid scheduling, operations management, and intelligent autonomous control. A multidimensional comparison is presented, followed by a discussion of the challenges in data-centric methodology, optimization methodology, and decision-making application. This paper offers a methodological guide and outlines future directions for academia and industry to enhance decision-making in complex data environments.

关键词： convex optimization data-centric AI data-driven decision-making end-to-end learning intelligent autonomous control machine learning operations management power grid scheduling reinforcement learning uncertainty estimation

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