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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes an O(T max{c,1−c})network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further reduced to O)(log(T)), and the network cumulative constraint violation bound is reduced to O(log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

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Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective

arXiv

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arXiv 2023年

作者： Suo, Dajiang Jayawardana, Vindula Wu, Cathy The Polytechnic School Ira A. Fulton Schools of Engineering Arizona State University TempeAZ85281 United States The Department of Electrical Engineering and Computer Science Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Laboratory for Information & Decision Systems Department of Civil and Environmental Engineering Institute of Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States

An emerging public health application of connected and automated vehicle (CAV) technologies is to reduce response times of emergency medical service (EMS) by indirectly coordinating traffic. Therefore, in this work we study the CAV-assisted corridor clearance for EMS vehicles from a short term deployment perspective. Existing research on this topic often overlooks the impact of EMS vehicle disruptions on regular traffic, assumes 100% CAV penetration, relies on real-time traffic signal timing data and queue lengths at intersections, and makes various assumptions about traffic settings when deriving optimal model-based CAV control strategies. However, these assumptions pose significant challenges for near-term deployment and limit the real-world applicability of such methods. To overcome these challenges and enhance real-world applicability in near-term, we propose a model-free approach employing deep reinforcement learning (DRL) for designing CAV control strategies, showing its reduced overhead in designing and greater scalability and performance compared to model-based methods. Our qualitative analysis highlights the complexities of designing scalable EMS corridor clearance controllers for diverse traffic settings in which DRL controller provides ease of design compared to the model-based methods. In numerical evaluations, the model-free DRL controller outperforms the model-based counterpart by improving traffic flow and even improving EMS travel times in scenarios when a single CAV is present. Across 19 considered settings, the learned DRL controller excels by 25% in reducing the travel time in six instances, achieving an average improvement of 9%. These findings underscore the potential and promise of model-free DRL strategies in advancing EMS response and traffic flow coordination, with a focus on practical near-term deployment. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Risk Identification Method for Smuggled Frozen Meat in Urban Public Safety Awareness and Early Warning

Risk Identification Method for Smuggled Frozen Meat in Urban...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Li, Xueru Zhang, Yan Liu, Lingjie Tian, Ye Zhang, Yue Harbin Engineering University College of Computer Science and Technology Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Zhejiang China

ISBN: (纸本)9798350357677

With the continuous growth in demand for meat and frozen products in the cold chain market, illegally smuggled frozen goods have quickly entered the market due to their low prices, leading to a series of food safety issues. This article proposes a method for identifying the risks of smuggled frozen meat for urban public safety awareness using the YOLOv7 model. The YOLOv7 model is used to detect data captured at various traffic checkpoints, obtaining spatiotemporal information of frozen goods vehicles. This information is then combined with map data to generate vehicle trajectory information. The trajectory similarity between the frozen vehicle trajectories and the specialized database of frozen transportation routes is analyzed using the EDwP distance algorithm. The EDwP algorithm employs a threshold-free approach, dynamically interpolating using the concept of projection, and automatically adapting to non-uniform sampling rates to address the issue of inconsistent trajectory data sampling rates. The experimental results of the YOLOv7-EDwP algorithm were verified by verifying the alerts generated in the recent period through on-site verification by a third-party inspection agency, with a recognition accuracy of 90.77%. This holds significant significance in the detection and warning of smuggled frozen meat. © 2023 IEEE.

关键词： Transportation routes

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Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting

arXiv

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arXiv 2024年

作者： Guo, Pengxin Jin, Pengrong Li, Ziyue Bai, Lei Zhang, Yu The Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong The Department of Mathematics Southern University of Science and Technology Shenzhen518055 China The Department of Information Systems University of Cologne Cologne50923 Germany The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Peng Cheng Laboratory Shenzhen518000 China

Accurate spatial-temporal traffic flow forecasting is crucial in aiding traffic managers in implementing control measures and assisting drivers in selecting optimal travel routes. Traditional deep-learning based methods for traffic flow forecasting typically rely on historical data to train their models, which are then used to make predictions on future data. However, the performance of the trained model usually degrades due to the temporal drift between the historical and future data. To make the model trained on historical data better adapt to future data in a fully online manner, this paper conducts the first study of the online test-time adaptation techniques for spatial-temporal traffic flow forecasting problems. To this end, we propose an Adaptive Double Correction by Series Decomposition (ADCSD) method, which first decomposes the output of the trained model into seasonal and trend-cyclical parts and then corrects them by two separate modules during the testing phase using the latest observed data entry by entry. In the proposed ADCSD method, instead of fine-tuning the whole trained model during the testing phase, a lite network is attached after the trained model, and only the lite network is fine-tuned in the testing process each time a data entry is observed. Moreover, to satisfy that different time series variables may have different levels of temporal drift, two adaptive vectors are adopted to provide different weights for different time series variables. Extensive experiments on four real-world traffic flow forecasting datasets demonstrate the effectiveness of the proposed ADCSD method. The code is available at https://***/Pengxin-Guo/ADCSD. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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An Incentive Mechanism for Privacy Preserved Data Trading with Verifiable Data Disturbance

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Man Li, Xinghua Luo, Bin Ren, Yanbing Miao, Yinbin Liu, Ximeng Deng, Robert H. State Key Laboratory of Integrated Services Networks the School of Cyber Engineering Xidian University Xi’an710071 China Engineering Research Center of Big data Security Ministry of Education Xi’an710071 China The College of Mathematics and Computer Science Fuzhou University Fujian350108 China The Department of Information Systems Singapore Management University 80 Stamford Road Singapore

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer’s (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs’s local disturbance’s privacy and verifiability with our proposed three-level verification algorithm. Subsequently, we model the trading as a game and disturbance verification results determine the compensation and punishment for trading bilateral utilities following Nash Equilibrium where DOs honestly disturb data. Theoretical analysis shows that DOs are motivated to honestly disturb data and their raw data privacy is preserved. Extensive experiments using the real-world dataset demonstrate that the deviating DOs in our scheme can be verified with a probability of more than 90% and the statistical result accuracy can be improved by more than 80% compared with the existing works. © 2004-2012 IEEE. All rights reserved.

关键词： Differential privacy

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Part-guided Relational Transformers for Fine-grained Visual Recognition

arXiv

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arXiv 2022年

作者： Zhao, Yifan Li, Jia Chen, Xiaowu Tian, Yonghong The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Department of Computer Science and Technology Peking University Beijing100871 China The Pengcheng Laboratory Shenzhen518055 China

Fine-grained visual recognition is to classify objects with visually similar appearances into subcategories, which has made great progress with the development of deep CNNs. However, handling subtle differences between different subcategories still remains a challenge. In this paper, we propose to solve this issue in one unified framework from two aspects, i.e., constructing feature-level interrelationships, and capturing part-level discriminative features. This framework, namely PArt-guided Relational Transformers (PART), is proposed to learn the discriminative part features with an automatic part discovery module, and to explore the intrinsic correlations with a feature transformation module by adapting the Transformer models from the field of natural language processing. The part discovery module efficiently discovers the discriminative regions which are highly-corresponded to the gradient descent procedure. Then the second feature transformation module builds correlations within the global embedding and multiple part embedding, enhancing spatial interactions among semantic pixels. Moreover, our proposed approach does not rely on additional part branches in the inference time and reaches state-of-the-art performance on 3 widely-used fine-grained object recognition benchmarks. Experimental results and explainable visualizations demonstrate the effectiveness of our proposed approach. Code can be found at https://***/iCVTEAM/PART. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Exact solution of the longitudinally-graded rectangular plate under mixed instantaneous shock loading

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Waves in Random and Complex Media 2023年

作者： Amari, Abdelfattah Salman, Hayder Mahmood Department of Chemical Engineering College of Engineering King Khalid University Abha Saudi Arabia Research Laboratory of Processes Energetics Environment and Electrical Systems National School of Engineers of Gabes Gabes University Gabes Tunisia Department of Computer Science Al-Turath University College Baghdad Iraq

This study follows a novel path toward analysis of the thermoelastic reaction of the longitudinally-graded graphene-nanoplates enriched composite (LG-GNPEC) rectangular-shaped plates exposed to the mixed-form instantaneous shock. Three-dimensional form of the linear thermoelasticity theory is picked as the base for establishing the governing equations. Lord-Shulman theorem and energy balance relations are employed to determine the time-dependency of the system’s behavior. Due to simply-supported boundary conditions, solving the governing equations through an analytical solution on the bases of Navier’s approach is performed. Accuracy of the applied methods is verified by comparing the obtained results with those presented in the published studies. Results of this research can be directly considered in the analysis of the thermoelastic behavior of the advanced composite materials exposed to the mixed-form thermomechanical shocks. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Graphene

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Partial 3D Image Reconstruction by Cuboids Using Stable Computation of Hahn Polynomials 6th

Partial 3D Image Reconstruction by Cuboids Using Stable Comp...

引用

6th International Conference on Wireless Technologies, Embedded and Intelligent systems, WITS 2020

作者： Tahiri, Mohamed Amine Karmouni, Hicham Tahiri, Ahmed Sayyouri, Mhamed Qjidaa, Hassan CED-ST STIC Laboratory of Electronic Signals and Systems of Information LESSI Dhar El Mahrez Faculty of Science Sidi Mohamed Ben Abdellah-Fez University Fez Morocco Laboratory of Computer Science and Interdisciplnary Physics LIPI Fez Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory of Engineering Sciences for Energy LabSIPE National School of Applied Sciences of El Jadida Chouaïb Doukkali University PO Box 1166 Jadida Plateau EL24004 Morocco

ISBN: (纸本)9789813368927

In this paper, we will present a new method of partial reconstruction of the 3D image. The latter is based on the following two concepts: one is the stable discrete orthogonal Hahn polynomials (DOHPs), which significantly reduces numerical defects. The other is the 3D image cuboid representation (ICR) to accelerate the computation time of discrete orthogonal Hahn moments (DOHMs) and improve the quality of 3D image reconstruction. The results of the simulation confirm the ability of the 3D image reconstruction using the proposed process. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Faster Convergence of Local SGD for Over-Parameterized Models

arXiv

引用

arXiv 2022年

作者： Qin, Tiancheng Rasoul Etesami, S. Uribe, Cesár A. Department of Industrial and Systems Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Rice University United States

Modern machine learning architectures are often highly expressive. They are usually overparameterized and can interpolate the data by driving the empirical loss close to zero. We analyze the convergence of Local SGD (or FedAvg) for such over-parameterized models in the heterogeneous data setting and improve upon the existing literature by establishing the following convergence rates. For general convex loss functions, we establish an error bound of O(1/T) under a mild data similarity assumption and an error bound of O(K/T) otherwise, where K is the number of local steps and T is the total number of iterations. For non-convex loss functions we prove an _error bound of O(K/T). These bounds improve upon the best previous bound of O(1/√nT) in both cases, where n is the number of nodes, when no assumption on the model being over-parameterized is made. We complete our results by providing problem instances in which our established convergence rates are tight to a constant factor with a reasonably small stepsize. Finally, we validate our theoretical results by performing large-scale numerical experiments that reveal the convergence behavior of Local SGD for practical over-parameterized deep learning models, in which the O(1/T) convergence rate of Local SGD is clearly shown. Copyright © 2022, The Authors. All rights reserved.

关键词： Parameterization

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Multi-residual 2D network integrating spatial correlation for whole heart segmentation

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computers in Biology and Medicine 2024年 172卷 108261-108261页

作者： Huang, Yan Yang, Jinzhu Sun, Qi Yuan, Yuliang Li, Honghe Hou, Yang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Computer Science and Engineering Northeastern University Liaoning Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang China Department of Radiology Shengjing Hospital of China Medical University Liaoning Shenyang China

Whole heart segmentation (WHS) has significant clinical value for cardiac anatomy, modeling, and analysis of cardiac function. This study aims to address the WHS accuracy on cardiac CT images, as well as the fast inference speed and low graphics processing unit (GPU) memory consumption required by practical clinical applications. Thus, we propose a multi-residual two-dimensional (2D) network integrating spatial correlation for WHS. The network performs slice-by-slice segmentation on three-dimensional cardiac CT images in a 2D encoder–decoder manner. In the network, a convolutional long short-term memory skip connection module is designed to perform spatial correlation feature extraction on the feature maps at different resolutions extracted by the sub-modules of the pre-trained ResNet-based encoder. Moreover, a decoder based on the multi-residual module is designed to analyze the extracted features from the perspectives of multi-scale and channel attention, thereby accurately delineating the various substructures of the heart. The proposed method is verified on a dataset of the multi-modality WHS challenge, an in-house WHS dataset, and a dataset of the abdominal organ segmentation challenge. The dice, Jaccard, average symmetric surface distance, Hausdorff distance, inference time, and maximum GPU memory of the WHS are 0.914, 0.843, 1.066 mm, 15.778 mm, 9.535 s, and 1905 MB, respectively. The proposed network has high accuracy, fast inference speed, minimal GPU memory consumption, strong robustness, and good generalization. It can be deployed to clinical practical applications for WHS and can be effectively extended and applied to other multi-organ segmentation fields. The source code is publicly available at https://***/nancy1984yan/MultiResNet-SC. © 2024 Elsevier Ltd

关键词： Graphics processing unit

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