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arXiv 2021年

作者： Zhang, Lijun Wang, Yibo Wang, Guanghui Yi, Jinfeng Yang, Tianbao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China College of Computing Georgia Institute of Technology United States JD AI Research Beijing China Department of Computer Science and Engineering Texas A&M University United States

Recently, several universal methods have been proposed for online convex optimization, and attain minimax rates for multiple types of convex functions simultaneously. However, they need to design and optimize one surrogate loss for each type of functions, making it difficult to exploit the structure of the problem and utilize existing algorithms. In this paper, we propose a simple strategy for universal online convex optimization, which avoids these limitations. The key idea is to construct a set of experts to process the original online functions, and deploy a meta-algorithm over the linearized losses to aggregate predictions from experts. Specifically, the meta-algorithm is required to yield a second-order bound with excess losses, so that it can leverage strong convexity and exponential concavity to control the meta-regret. In this way, our strategy inherits the theoretical guarantee of any expert designed for strongly convex functions and exponentially concave functions, up to a double logarithmic factor. As a result, we can plug in off-the-shelf online solvers as black-box experts to deliver problem-dependent regret bounds. For general convex functions, it maintains the minimax optimality and also achieves a small-loss bound. Furthermore, we extend our universal strategy to online composite optimization, where the loss function comprises a time-varying function and a fixed regularizer. To deal with the composite loss functions, we employ a meta-algorithm based on the optimistic online learning framework, which not only possesses a second-order bound, but also can utilize estimations for upcoming loss functions. With appropriate configurations, we demonstrate that the additional regularizer does not contribute to the meta-regret, thus maintaining the universality in the composite setting. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization algorithms

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C1-Conforming Quadrilateral Spectral Element Method for Fourth-Order Equations

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Communications on Applied Mathematics and Computation 2019年第3期1卷 403-434页

作者： Huiyuan Li Weikun Shan Zhimin Zhang State Key Laboratory of Computer Science/Laboratory of Parallel Computing Institute of SoftwareChinese Academy of SciencesBeijing 100190China School of Science Henan University of TechnologyZhengzhou 450001HenanChina Beijing Computational Science Research Center Beijing 100193China Department of Mathematics Wayne State UniversityDetroitMI 48202USA

This paper is devoted to Professor Benyu Guo's open question on the C1-conforming quadrilateral spectral element method for fourth-order equations which has been endeavored for years. Starting with generalized Jacobi polynomials on the reference square, we construct the C1-conforming basis functions using the bilinear mapping from the reference square onto each quadrilateral element which fall into three categories-interior modes, edge modes, and vertex modes. In contrast to the triangular element, compulsively compensatory requirements on the global C1-continuity should be imposed for edge and vertex mode basis functions such that their normal derivatives on each common edge are reduced from rational functions to polynomials, which depend on only parameters of the common edge. It is amazing that the C1-conforming basis functions on each quadrilateral element contain polynomials in primitive variables, the completeness is then guaranteed and further confirmed by the numerical results on the Petrov-Galerkin spectral method for the non-homogeneous boundary value problem of fourth-order equations on an arbitrary quadrilateral. Finally, a C1-conforming quadrilateral spectral element method is proposed for the biharmonic eigenvalue problem, and numerical experiments demonstrate the effectiveness and efficiency of our spectral element method.

关键词： Quadrilateral spectral element method Fourth-order equations Mapped polynomials C1-conforming basis Polynomial inclusion Completeness

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A first look at developers⇔ live chat on gitter

arXiv

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arXiv 2021年

作者： Shi, Lin Jiang, Hanzhi Jiang, Ziyou Chen, Xiao Niu, Nan Yang, Ye Wang, Qing Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Department of EECS University of Cincinnati CincinnatiOH United States School of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China

Modern communication platforms such as Gitter and Slack play an increasingly critical role in supporting software teamwork, especially in open source development. Conversations on such platforms often contain intensive, valuable information that may be used for better understanding OSS developer communication and collaboration. However, little work has been done in this regard. To bridge the gap, this paper reports a first comprehensive empirical study on developers’ live chat, investigating when they interact, what community structures look like, which topics are discussed, and how they interact. We manually analyze 749 dialogs in the first phase, followed by an automated analysis of over 173K dialogs in the second phase. We find that developers tend to converse more often on weekdays, especially on Wednesdays and Thursdays (UTC), that there are three common community structures observed, that developers tend to discuss topics such as API usages and errors, and that six dialog interaction patterns are identified in the live chat communities. Based on the findings, we provide recommendations for individual developers and OSS communities, highlight desired features for platform vendors, and shed light on future research directions. We believe that the findings and insights will enable a better understanding of developers’ live chat, pave the way for other researchers, as well as a better utilization and mining of knowledge embedded in the massive chat history. Copyright © 2021, The Authors. All rights reserved.

关键词： Open source software

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Improving read performance of lsm-tree based KV stores via dual grained caches 21

Improving read performance of lsm-tree based KV stores via d...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Li, Xiang Xu, Guangping Fan, Hao Lu, Hongli Tang, Bo Xue, Yanbing Zong, Ziliang School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Department of Computer Science Texas State University San MarcosTX United States

ISBN: (纸本)9781728120584

key-value (KV) stores based on the Log-Structure Merge tree (LSM-tree) have been widely used in modern storage systems (e.g. LevelDB and RocksDB) to achieve high write throughput. However, conventional LSM-tree design has high latency for random read operations, especially in the workloads with concurrent read and write operations. This paper proposes a dual-grained cache scheme to improve the performance of LSM-tree based KV stores, in which a coarse-grained cache improves read performance by leveraging spatial locality and a fine-grained cache tracks the frequently accessed KV pairs. We implement the dual-grained caching on a LSM-tree simulator and evaluate its performance using various Yahoo! Cloud Service Benchmark (YCSB) workloads. Our experimental results show that the proposed way of dual-grained caches can significantly improve the read performance of KV stores without sacrificing the advantages of the LSM-tree for write-intensive workloads. © 2019 IEEE.

关键词： Compaction Throughput Conferences Indexes computer science Benchmark testing Distributed databases

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The Quantum Internet (Technical Version)

arXiv

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arXiv 2025年

作者： Rohde, Peter P. Huang, Zixin Ouyang, Yingkai Huang, He-Liang Su, Zu-En Devitt, Simon Ramakrishnan, Rohit Mantri, Atul Tan, Si-Hui Liu, Nana Harrison, Scott Radhakrishnan, Chandrashekar Brennen, Gavin K. Baragiola, Ben Q. Dowling, Jonathan P. Byrnes, Tim Munro, William J. Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Hearne Institute for Theoretical Physics Louisiana State University Baton Rouge United States School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia School of Mathematical and Physical Sciences University of Sheffield SheffieldS3 7RH United Kingdom Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science & Technology of China Hefei China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information & Quantum Physics University of Science & Technology of China Hefei China CAS-Alibaba Quantum Computing Laboratory Shanghai China InstituteQ Aalto University Espoo02150 Finland Indian Institute of Science Bangalore India Singapore University of Technology & Design Singapore Centre for Quantum Technologies National University of Singapore Singapore Horizon Quantum Ireland 24 Fitzwilliam Place Dublin 2 D02 T296 Ireland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Leibniz Institute for Research & Information in Education Frankfurt am Main Germany Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Shanghai200124 China Centre of Excellence in Engineered Quantum Systems Macquarie University NSW Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University VIC

The desire to share and unite remote digital assets motivated the development of the classical internet, the enabler of the entire 21st century economy and our modern way of life. As we enter the quantum era, it is to be expected there will be a similar demand for networking quantum assets, motivating a global quantum internet for bringing together the world's quantum resources, leveraging off their exponential trajectory in capability. We present models for quantum networking, how they might be applied in the future, and the implications they will have. Socially, economically, politically and geostrategically, the upcoming era of quantum supremacy will be as significant for the 21st century as the transistor was for the 20th. The inherently different scaling in the computational power of quantum computers fundamentally changes the dynamics of how they will operate in the future. Given their high expected initial cost, a client/server model for outsourcing computation will be essential for enabling the accessibility and proliferation of this technology, and ensuring its economic viability. We therefore anticipate the emergence of cloud quantum computing, a model for outsourcing quantum computations to the network. We argue that economic efficiency will mandate that all future quantum computers be united into a single global virtual quantum computer, offering exponentially more power to all network participants than if they were to keep their resources to themselves. This model for the allocation of computational resources is uniquely quantum, with no classical analogue, completely altering the economic landscape for the future of computation. Given the sensitivity of much of the data to which future quantum computers are going to foreseeably be applied, protocols for encrypted quantum computation will be essential - the outsourcing of computations that neither an eavesdropper nor even the server performing the computation can spy upon. This will enable new models fo

关键词： Outsourcing

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Group Distributionally Robust Optimization with Flexible Sample Queries

arXiv

引用

arXiv 2025年

作者： Bai, Haomin Yu, Dingzhi Li, Shuai Luo, Haipeng Zhang, Lijun National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China John Hopcroft Center Shanghai Jiao Tong University China Thomas Department of Computer Science University of Southern California United States

Group distributionally robust optimization (GDRO) aims to develop models that perform well across m distributions simultaneously. Existing GDRO algorithms can only process a fixed number of samples per iteration, either 1 or m, and therefore can not support scenarios where the sample size varies dynamically. To address this limitation, we investigate GDRO with flexible sample queries and cast it as a two-player game: one player solves an online convex optimization problem, while the other tackles a prediction with limited advice (PLA) problem. Within such a game, we propose a novel PLA algorithm, constructing appropriate loss estimators for cases where the sample size is either 1 or not, and updating the decision using follow-the-regularized-leader. Then, we establish the first high-probability regret bound for non-oblivious PLA. Building upon the above approach, we develop a GDRO algorithm that allows an arbitrary and varying sample size per round, achieving a high-probability optimization error bound of O (Formula Present) , where rt denotes the sample size at round t. This result demonstrates that the optimization error decreases as the number of samples increases and implies a consistent sample complexity of O(m log(m)/ϵ2) for any fixed sample size r ∈ [m], aligning with existing bounds for cases of r = 1 or m. We validate our approach on synthetic binary and real-world multi-class datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results

arXiv

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arXiv 2022年

作者： Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Biao, Jiang Yuhan, Ding Zhang, Li Wan Xiaoyue, Feng Sijing, Chen Tianheng, Zhong Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang University of Science and Technology of China China Shanghai Ai Laboratory China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Macau China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology China School of Artificial Intelligence Xidian University China School of Remote Sensing and Information Engineering Wuhan University China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Harbin Institute of Technology China Changsha Research Institute of Mining and Metallurgy China Tianjin University China University of Wurzburg Computer Vision Lab Germany Couger Inc. Japan School of Statistics and Data Science Nankai University China Founding Minds Software India Department of Electronics and Communication Srm University Fudan University China Shenyang Aerospace University China Nanjing University of Science and Technology China

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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A benchmarking study of embedding-based entity alignment for knowledge graphs

arXiv

引用

arXiv 2020年

作者： Sun, Zequn Zhang, Qingheng Hu, Wei Wang, Chengming Chen, Muhao Akrami, Farahnaz Li, Chengkai State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science University of California Los Angeles United States Department of Computer Science and Engineering University of Texas Arlington United States

Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings. In this paper, we conduct a comprehensive experimental study of this emerging field. This study surveys 23 recent embedding-based entity alignment approaches and categorizes them based on their techniques and characteristics. We further observe that current approaches use different datasets in evaluation, and the degree distributions of entities in these datasets are inconsistent with real KGs. Hence, we propose a new KG sampling algorithm, with which we generate a set of dedicated benchmark datasets with various heterogeneity and distributions for a realistic evaluation. This study also produces an open-source library, which includes 12 representative embedding-based entity alignment approaches. We extensively evaluate these approaches on the generated datasets, to understand their strengths and limitations. Additionally, for several directions that have not been explored in current approaches, we perform exploratory experiments and report our preliminary findings for future studies. The benchmark datasets, open-source library and experimental results are all accessible online and will be duly maintained. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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Boosting API recommendation with implicit feedback

arXiv

引用

arXiv 2020年

作者： Zhou, Yu Yang, Xinying Chen, Taolue Huang, Zhiqiu Ma, Xiaoxing Gall, Harald College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science University of Surrey United Kingdom Department of Informatics University of Zurich Switzerland

Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast list of candidates. To ease the burden, a multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of user feedback into the recommendation loop. In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance. By exploiting user feedback information, we train a learning-to-rank model to re-rank the recommendation results. In addition, we speed up the feedback learning process with active learning. Existing query-based API recommendation approaches can be plugged into BRAID. We select three stateof- the-art API recommendation approaches as baselines to demonstrate the performance enhancement of BRAID measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with acceptable overheads, the recommendation performance improves steadily and substantially with the increasing percentage of feedback data, comparing with the baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Natural language processing systems

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Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Liang, Pengchen Huang, Haishan Pu, Bin Chen, Jianguo Hua, Xiang Zhang, Jing Ma, Weibo Chen, Zhuangzhuang Li, Yiwei Chang, Qing Department Shanghai Key Laboratory of Gastric Neoplasms Department of Surgery Shanghai Institute of Digestive Surgery Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai200025 China School of Microelectronics Shanghai University Shanghai201800 China School of Software Engineering Sun Yat-sen University Zhuhai519000 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine Shanghai200025 China School of Public Administration East China Normal University Shanghai200062 China Department of Nuclear Medicine Shanghai Children’s Hospital School of Medicine Shanghai Jiao Tong University Shanghai200062 China

Large-scale pre-trained models, such as Vision Foundation Models (VFMs), have demonstrated impressive performance across various downstream tasks by transferring generalized knowledge, especially when target data is limited. However, their high computational cost and the domain gap between natural and medical images limit their practical application in medical segmentation tasks. Motivated by this, we pose the following important question:"How can we effectively utilize the knowledge of large pre-trained VFMs to train a small, task-specific model for medical image segmentation when training data is limited?" To address this problem, we propose a novel and generalizable task-specific knowledge distillation framework. Our method fine-tunes the VFM on the target segmentation task to capture task-specific features before distilling the knowledge to smaller models, leveraging Low-Rank Adaptation (LoRA) to reduce the computational cost of fine-tuning. Additionally, we incorporate synthetic data generated by diffusion models to augment the transfer set, enhancing model performance in data-limited scenarios. Experimental results across five medical image datasets demonstrate that our method consistently outperforms task-agnostic knowledge distillation and self-supervised pretraining approaches like MoCo v3 and Masked Autoencoders (MAE). For example, on the KidneyUS dataset, our method achieved a 28% higher Dice score than task-agnostic KD using 80 labeled samples for fine-tuning. On the CHAOS dataset, it achieved an 11% improvement over MAE with 100 labeled samples. These results underscore the potential of task-specific knowledge distillation to train accurate, efficient models for medical image segmentation in data-constrained settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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