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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Yongzhe Jia Xuyun Zhang Hongsheng Hu Kim-Kwang Raymond Choo Lianyong Qi Xiaolong Xu Amin Beheshti Wanchun Dou State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computing Macquarie University Australia School of Information and Physical Sciences University of Newcastle Australia Department of Information Systems and Cyber Security University of Texas at San Antonio USA College of Computer Science and Technology China University of Petroleum (East China) China School of Computer and Software Nanjing University of Information Science and Technology China

ISBN: (纸本)9798331314385

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://***/jyzgh/DapperFL.

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Revisiting Restarts of CDCL: Should the Search Information be Preserved?

arXiv

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arXiv 2024年

作者： Zhang, Xindi Chen, Zhihan Cai, Shaowei Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences China

SAT solvers are indispensable in formal verification for hardware and software with many important applications. CDCL is the most widely used framework for modern SAT solvers, and restart is an essential technique of CDCL. When restarting, CDCL solvers cancel the current variable assignment while maintaining the branching order, variable phases, and learnt clauses. This type of restart is referred to as warm restart in this paper. Although different restart policies have been studied, there is no study on whether such information should be kept after restarts. This work addresses this question and finds some interesting observations. This paper indicates that under this popular warm restart scheme, there is a substantial variation in run-time with different randomized initial orders and phases, which motivates us to forget some learned information periodically to prevent being stuck in a disadvantageous search space. We propose a new type of restart called cold restart, which differs from previous restarts by forgetting some of the learned information. Experiments show that modern CDCL solvers can benefit from periodically conducting cold restarts. Based on the analysis of the cold-restart strategies, we develop a parallel SAT solver. Both the sequential and parallel versions of cold restart are more suitable for satisfiable instances, which suggests that existing CDCL heuristics for information management should be revised if one hopes to construct a satisfiable-oriented SAT solver. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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Multi-Resolution Planar Region Extraction for Uneven Terrains

Multi-Resolution Planar Region Extraction for Uneven Terrain...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yinghan Sun Linfang Zheng Hua Chen Wei Zhang Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Computer Science University of Birmingham UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper studies the problem of extracting planar regions in uneven terrains from unordered point cloud measurements. Such a problem is critical in various robotic applications such as robotic perceptive locomotion. While existing approaches have shown promising results in effectively extracting planar regions from the environment, they often suffer from issues such as low computational efficiency or loss of resolution. To address these issues, we propose a multi-resolution planar region extraction strategy in this paper that balances the accuracy in boundaries and computational efficiency. Our method begins with a pointwise classification preprocessing module, which categorizes all sampled points according to their local geometric properties to facilitate multi-resolution segmentation. Subsequently, we arrange the categorized points using an octree, followed by an in-depth analysis of nodes to finish multi-resolution plane segmentation. The efficiency and robustness of the proposed approach are verified via synthetic and real-world experiments, demonstrating our method’s ability to generalize effectively across various uneven terrains while maintaining real-time performance, achieving frame rates exceeding 35 FPS.

关键词： Point cloud compression Deep learning Accuracy Octrees Noise Robustness Real-time systems

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DisCOV: Distributed COVID-19 Detection on X-Ray Images with Edge-Cloud Collaboration

DisCOV: Distributed COVID-19 Detection on X-Ray Images with ...

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2022 IEEE World Congress on services, services 2022

作者： Xu, Xiaolong Tian, Hao Zhang, Xuyun Qi, Lianyong He, Qiang Dou, Wanchun Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Macquarie University Department of Computing Sydney Australia Qufu Normal University School of Information Science and Engineering Rizhao China Swinburne University of Technology School of Software and Electrical Engineering Melbourne Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

[J1C2 Presentation Abstract at IEEE services 2022 for IEEE Transactions on services computing DOI 10.1109/TSC.2022.3142265]

ISBN: (数字)9781665481311

ISBN: (纸本)9781665481311

[J1C2 Presentation Abstract at IEEE services 2022 for IEEE Transactions on services computing DOI 10.1109/TSC.2022.3142265]

关键词： COVID-19 Image edge detection Collaboration X-ray imaging

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Improved Forecasts of Global Extreme Marine Heatwaves Through a Physics-guided Data-driven Approach

arXiv

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arXiv 2024年

作者： Shu, Ruiqi Wu, Hao Gao, Yuan Xu, Fanghua Gou, Ruijian Huang, Xiaomeng Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China School of Computer Science and Technology University of Science and Technology of China China Key Laboratory of Physical Oceanography Frontiers Science Center for Deep Ocean Multispheres and Earth System Ocean University of China Qingdao China

The unusually warm sea surface temperature events known as marine heatwaves (MHWs) have a profound impact on marine ecosystems. Accurate prediction of extreme MHWs has significant scientific and financial worth. However, existing methods still have certain limitations, especially in the most extreme MHWs. In this study, to address these issues, based on the physical nature of MHWs, we created a novel deep learning neural network that is capable of accurate 10-day MHW forecasting. Our framework significantly improves the forecast ability of extreme MHWs through two specially designed modules inspired by numerical models: a coupler and a probabilistic data argumentation. The coupler simulates the driving effect of atmosphere on MHWs while the probabilistic data argumentation approaches significantly boost the forecast ability of extreme MHWs based on the idea of ensemble forecast. Compared with traditional numerical prediction, our framework has significantly higher accuracy and requires fewer computational resources. What’s more, explainable AI methods show that wind forcing is the primary driver of MHW evolution and reveal its relation with air-sea heat exchange. Overall, our model provides a framework for understanding MHWs’ driving processes and operational forecasts in the future. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Detail-Enhancing Framework for Reference-Based Image Super-Resolution

arXiv

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arXiv 2024年

作者： Wang, Zihan Xiong, Ziliang Tang, Hongying Yuan, Xiaobing Shanghai Institution of Micro-system and Information Technology University of Chinese Academy of Science China Computer Vision Laboratory Department of Electrical Engineering LiU Sweden School of Information Science and Technology Shanghai Tech University China

Recent years have witnessed the prosperity of reference-based image super-resolution (Ref-SR). By importing the high-resolution (HR) reference images into the single image super-resolution (SISR) approach, the ill-posed nature of this long-standing field has been alleviated with the assistance of texture transferred from reference images. Although the significant improvement in quantitative and qualitative results has verified the superiority of Ref-SR methods, the presence of misalignment before texture transfer indicates room for further performance improvement. Existing methods tend to neglect the significance of details in the context of comparison, therefore not fully leveraging the information contained within low-resolution (LR) images. In this paper, we propose a Detail-Enhancing Framework (DEF) for reference-based super-resolution, which introduces the diffusion model to generate and enhance the underlying detail in LR images. If corresponding parts are present in the reference image, our method can facilitate rigorous alignment. In cases where the reference image lacks corresponding parts, it ensures a fundamental improvement while avoiding the influence of the reference image. Extensive experiments demonstrate that our proposed method achieves superior visual results while maintaining comparable numerical outcomes. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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Incentive Sharing: A Consumer-Data Privacy Trusted Sharing Paradigm Via Proximal Policy Optimization in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Cai, Ting Hu, Ying Li, Xiang Chen, Wuhui Wu, Yuxin Ye, Zhiwei Zheng, Zibin Hubei University of Technology China China Hubei University of Technology School of Computer Science The Provincial Key Laboratory of Green Intelligent Computing Network Wuhan430068 China Sun Yat-sen University China Pengcheng Laboratory China Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Sun Yat-sen University School of Software Engineering Zhuhai519000 China

Industry 5.0 emphasizes consumer data sharing to drive data-driven innovation and enhance user experiences in consumer electronics. Blockchain-integrated federated learning (blockchained FL) has recently emerged as a privacy-Trusted paradigm to facilitate sharing. However, most blockchained FL sharing paradigms cannot adapt to the complexity or variability of consumer data and network conditions such as: 1) inadequate balancing of the trade-off between data quality and training costs during device selection;2) incentives are designed to maximize instantaneous profit without considering the long-Term dynamics of electronic devices. These limitations lead to low-quality model training and security degradation. This paper proposes a novel blockchained FL incentive-sharing paradigm that dynamically selects consumer devices from a long-Term perspective while balancing data quality and training costs. Specifically, we first present a reputation strategy to enhance security by applying a predefined threshold to prevent malicious and low-quality consumers from participating in FL. Then, to optimize device selection under complex and dynamic consumer environments with high-dimensional system states, a Proximal Policy Optimization (PPO)-based incentive mechanism is proposed, which includes: 1) building an 'intelligent engine' to select the optimal consumer devices for each round of FL based on data quality and costs, and 2) implementing incentives to achieve long-Term maximization of the system's social welfare. Extensive simulations show that our sharing paradigm has, on average, 46.97% higher social welfare and 77.27% lower social costs than baselines and can guarantee high data quality in a trustless environment. © 1975-2011 IEEE.

关键词： Differential privacy

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IntNet: A novel framework using reconstructed mutation samples and a biologically informed neural network for pathway analysis

IntNet: A novel framework using reconstructed mutation sampl...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Li Zhou Jie Li Weilong Tan School of Computer Science and Technology Harbin Institute of Technology Harbin China College of Computing Harbin Institute of Technology National Key Laboratory of Smart Farming Technology and Systems Harbin China Center for Medicines Nanjing Bioengineering (Gene) Technology Center for Medicines Nanjing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Most pathway analysis methods based on simple nucleotide variations overlook mutations occurring in genes outside the pathway, fail to address mutation heterogeneity with targeted measures, and do not consider the functional relationships among gene components within biomolecular networks when modeling the contributions of biomarkers to pathways. So, we proposed a novel framework, named IntNet, for pathway analysis based on simple nucleotide variations. The core components of IntNet are sample reconstruction (SC) and a new biologically informed neural network model (BINN). To address the difficulty of extracting effective features caused by the mutation heterogeneity, IntNet reconstructs mutation samples based on the hypothesis that mutations in patients with the same phenotype may cluster in similar network regions. BINN uses the human gene network and expanded biological pathways to construct its neural network architecture. BINN implements mutation signaling propagation along the human gene network and integration within the pathways. IntNet's performance was validated using multiple cancer datasets. Experimental results demonstrated that IntNet could identify more accurate and reliable pathways.

关键词： Analytical models Accuracy Phenotypes Biological system modeling Biomarkers Feature extraction Reliability Biological neural networks Nuclear measurements Cancer

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Cooperative and Competitive Multi-Agent systems:From Optimization to Games

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 763-783页

作者： Jianrui Wang Yitian Hong Jiali Wang Jiapeng Xu Yang Tang Qing-Long Han Jürgen Kurths Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Potsdam Institute for Climate Impact Research 14473 Potsdam Institute of Physics Humboldt University of Berlin12489 BerlinGermany

Multi-agent systems can solve scientific issues related to complex systems that are difficult or impossible for a single agent to solve through mutual collaboration and cooperation *** a multi-agent system,agents with a certain degree of autonomy generate complex interactions due to the correlation and coordination,which is manifested as cooperative/competitive *** survey focuses on multi-agent cooperative optimization and cooperative/non-cooperative *** from cooperative optimization,the studies on distributed optimization and federated optimization are *** survey mainly focuses on distributed online optimization and its application in privacy protection,and overviews federated optimization from the perspective of privacy protection ***,cooperative games and non-cooperative games are introduced to expand the cooperative optimization problems from two aspects of minimizing global costs and minimizing individual costs,***-agent cooperative and non-cooperative behaviors are modeled by games from both static and dynamic aspects,according to whether each player can make decisions based on the information of other ***,future directions for cooperative optimization,cooperative/non-cooperative games,and their applications are discussed.

关键词： Cooperative games counterfactual regret minimization distributed optimization federated optimization fictitious

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A Novel Multi-layered Minutiae Extractor Based on OCT Fingerprints 16th

A Novel Multi-layered Minutiae Extractor Based on OCT Finge...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Zeng, Wenfeng Zhang, Wentian Liu, Feng Tan, Xu Li, Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Senzhen China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9783031202322

Fingerprints of Optical Coherence Tomography (OCT) imaging provide 3D volume data which have the nature property of multi-layered tissue structure. This paper, for the first time, attempts to extract minutiae for OCT-based fingerprint by making full use of the merits multi-layered structure of OCT imaging and powerful convolution al neural network (CNN). In particular, a novel multi-layered feature fusion minutiae extraction network is proposed, involving a multi-layered feature extractor to integrate the rich information of multiple subsurface fingerprints, and a two-stage object detection framework using reweighted concatenation feature to detect minutiae points. Compared with the results achieved by typical traditional and learning-based methods, our method outperforms the best one by about 2% of F1-score in minutiae extraction, and achieves the improvement of 0.09% than the best one in matching performance, which further demonstrate the effectiveness and robustness of our proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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