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Unsupervised Balanced Hash Codes Learning With Multichannel Feature Fusion

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 2816-2825页

作者： Chen, Yaxiong Zhao, Dongjie Lu, Xiongbo Xiong, Shengwu Wang, Huangting Wuhan Univ Technol Sch Comp & Artificial Intelligence Wuhan 430070 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Wuhan Univ Technol Chongqing Res Inst Chongqing 401122 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol CAS Xian 710119 Peoples R China

Unsupervised hashingalgorithms are widely used in large-scale remote sensing images (RSIs) retrieval task. However, existing RSI retrieval algorithms fail to capture the multichannel characteristic of multispectral RSIs and the balanced property of hash codes, which lead the poor performance of RSI retrieval. To tackle these issues, we develop an unsupervised hashing algorithm, namely, variational autoencoder balanced hashing (VABH), to leverage multichannel feature fusion and multiscale context information to perform RSI retrieval task. First, multichannel feature fusion module is designed to extract RSI feature information by leveraging the multichannel properties of multispectral RSI. Second, multiscale learning module is developed to learn the multiscale context information of RSIs. Finally, a novel objective function is designed to capture the discrimination and balanced property of hash codes in the hashing learning process. Comprehensive experiments on diverse benchmark have well demonstrated the reasonableness and effectiveness of the proposed VABH algorithm.

关键词： Feature extraction Codes Data mining Convolution Linear programming approximation algorithms Semantics Deep hash codes multichannel feature fusion multiscale context information unsupervised hashing learning

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Toward Optimal Deployment for Full-View Point Coverage in Camera Sensor Networks

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IEEE INTERNET OF THINGS JOURNAL 2022年第21期9卷 22008-22021页

作者： Shi, Kun Liu, Shuxian Li, Chao Liu, Haoyu He, Shibo Zhang, Qi Chen, Jiming Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China China State Shipbldg Corp Syst Engn Res Inst Beijing 100036 Peoples R China NetEase Games Fuxi AI Lab Hangzhou 310052 Peoples R China

Recent years have witnessed the fast proliferation of camera sensors networks (CSNs) in numerous Internet of Things (IoT) applications. In order to a capture distinct image of targets from interesting directions, we leverage a special type of coverage called full-view coverage. Full-view coverage guarantees to obtain the images of a point from every direction, whereas it demands much more sensors than a conventional coverage. To this end, we investigate the problem of deploying the minimum number of rotatable camera sensors to achieve the full-view coverage of a set of target points, namely, optimal deployment for the full-view point coverage (OFP) problem. In this work, camera sensors are capable of rotating freely with infinite orientations, thus not only the deployment locations but also the orientations for each camera sensor are required to be optimized. To tackle this challenging problem, we first prove that the OFP problem is NP-hard. Then, we propose two approximation algorithms-iterative screening algorithm (ISA) and improved ISA (IISA) to solve the OFP. We further perform extensive simulations and conduct physical testings to demonstrate the superiority and effectiveness of our proposed solutions. Experimental results show that IISA can generally reduce the total number of required camera sensors by more than 20% compared with the state-of-the-art work.

关键词： Cameras Sensors Internet of Things approximation algorithms Testing Redundancy Image recognition Camera sensor network full-view coverage optimal deployment

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From Spectrum Bonding to Contiguous-Resource Batching Task Scheduling

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IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第3期30卷 1245-1254页

作者： Shi, Hehuan Chen, Lin Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

We formulate and analyze a generic task scheduling problem: a set of tasks need to be executed on a pool of continuous resource such as spectrum and memory, each requiring a certain amount of time and contiguous resource;some tasks can be executed simultaneously in batch by sharing the resource, while others requiring exclusive use of the resource. We seek an optimal resource allocation and the related scheduling policy maximizing the overall system utility. This problem, termed as the contiguous-resource batching task scheduling problem, arises in a variety of engineering fields, where communication and storage resources are potential bottlenecks and thus need to be carefully scheduled. Two motivating examples are the spectrum bonding problem in dynamic spectrum access systems and the dynamic storage allocation problem in computer systems. In this paper, we investigate both offline and online scheduling settings. We first establish the NP-hardness of the offline setting and the inapproximability of the online setting in its generic form. Given the theoretical performance limit, we then develop approximation algorithms with mathematically proven performance guarantee in terms of approximation and competitive ratios for the offline and online settings.

关键词： Task analysis Bonding Time-frequency analysis approximation algorithms Resource management Dynamic scheduling Heuristic algorithms Channel bonding batching task scheduling contiguous resource allocation offline and online approximation algorithm design

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Robust and Adaptive Sequential Submodular Optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第1期67卷 89-104页

作者： Tzoumas, Vasileios Jadbabaie, Ali Pappas, George J. Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA MIT Lab Informat & Decis Syst Cambridge MA 02139 USA MIT Inst Data Syst & Soc Cambridge MA 02139 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA

Emerging applications of control, estimation, and machine learning, from target tracking to decentralized model fitting, pose resource constraints that limit which of the available sensors, actuators, or data can be simultaneously used across time. Therefore, many researchers have proposed solutions within discrete optimization frameworks where the optimization is performed over finite sets. By exploiting notions of discrete convexity, such as submodularity, the researchers have been able to provide scalable algorithms with provable suboptimality bounds. In this article, we consider such problems but in adversarial environments, where in every step a number of the chosen elements in the optimization is removed due to failures/attacks. Specifically, we consider for the first time a sequential version of the problem that allows us to observe the failures and adapt, while the attacker also adapts to our response. We call the novel problem robust sequential submodular maximization (RSM). Generally, the problem is computationally hard and no scalable algorithm is known for its solution. However, in this article, we propose robust and adaptive maximization (RAM), the first scalable algorithm. RAM runs in an online fashion, adapting in every step to the history of failures. Also, it guarantees a near-optimal performance, even against any number of failures among the used elements. Particularly, RAM has both provable per-instance a priori bounds and tight and/or optimal a posteriori bounds. Finally, we demonstrate RAM's near-optimality in simulations across various application scenarios, along with its robustness against several failure types, from worst-case to random.

关键词： Sensors Random access memory Optimization History Target tracking Greedy algorithms Actuators Algorithm design and analysis approximation algorithms autonomous systems combinatorial mathematics control estimation fault-tolerant systems mobile robots multirobot systems

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Learning From Oracle Demonstrations-A New Approach to Develop Autonomous Intersection Management Control algorithms Based on Multiagent Deep Reinforcement Learning

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IEEE ACCESS 2022年 10卷 53601-53613页

作者： Guillen-Perez, Antonio Cano, Maria-Dolores Univ Politecn Cartagena Dept Informat & Commun Technol Cartagena 30202 Spain

Worldwide, many companies are working towards safe and innovative control systems for Autonomous Vehicles (AVs). A key component is Autonomous Intersection Management (AIM) systems, which operate at the level of traffic intersections and manage the right-of-way for AVs, thereby improving flow and safety. AIM traditionally uses control policies based on simple rules. However, Deep Reinforcement Learning (DRL) can provide advanced control policies with the advantage of proactively reacting and forecasting hazardous situations. The main drawback of DRL is the training time, which is fast in simple tasks but not negligible when addressing real-world problems with multiple agents. Learning from Demonstrations (LfD) emerged to solve this problem, significantly speeding up training, and reducing the exploration problem. The challenge is that LfD requires an expert to extract new demonstrations. Therefore, in this paper, we propose the use of an agent, previously trained by imitation learning, to act as an expert to leverage LfD. We named this new agent Oracle, and our new approach was called Learning from Oracle Demonstrations (LfOD). We implemented this novel method over the DRL TD3 algorithm, incorporating significant changes to TD3 that allowed the use of Oracle demonstrations. The complete version was called TD3fOD. The results obtained in the AIM training scenario showed that TD3fOD notably improves the learning process compared with TD3 and DDPGfD, speeding up learning to 5-6 times, while the policy found offered both significantly lower variance and better learning ability. The testing scenario also showed a significant improvement in multiple key performance metrics compared with other vehicle control techniques on AIM, such as reducing waiting time by more than 90% and significantly decreasing fuel or electricity consumption and emissions, highlighting the benefits of LfOD.

关键词： Training Reinforcement learning Task analysis approximation algorithms Behavioral sciences Autonomous vehicles Proposals Autonomous intersection management intelligent transport systems intersection traffic management learning from demonstrations multi-agent deep reinforcement learning

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Finite-Time Distributed Approximate Optimization algorithms of Higher Order Multiagent Systems via Penalty-Function-Based Method

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第10期52卷 6174-6182页

作者： Li, Guipu Wang, Xiangyu Li, Shihua Nanjing Inst Technol Sch Automat Nanjing 211167 Peoples R China Southeast Univ Minist Educ Sch Automat Nanjing 210096 Peoples R China Southeast Univ Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Peoples R China

This article investigates the finite-time distributed approximate optimization problem of higher order multiagent systems, where the local cost functions are considered to be quadratic functions. This problem is solved via penalty-function-based method. First, by the penalty-function method, a global approximate cost function is constructed. Second, nonlinear distributed optimization algorithms are proposed for higher order multiagent systems by the tool of adding a power integrator technique. In the optimization algorithms design, the gradients of the approximate cost function are utilized. Under the proposed optimization algorithms, the agents approach the approximate optimal solution in finite time. Although there exist errors (they may be called approximation errors) between the approximate minimizers and the global accurate minimizer, the approximation errors can be regulated by penalty parameter and the relationship between the bound of the approximation errors and the penalty parameter is given explicitly. Furthermore, the proposed distributed approximate optimization algorithms are applied to the optimal rendezvous problem of wheeled multimobile robots, making the mobile robots achieve approximate optimization rendezvous in finite time. The effectiveness of the proposed distributed optimization algorithms and their applications to optimal rendezvous problem are validated by simulations.

关键词： Optimization Multi-agent systems approximation algorithms Cost function approximation error Mobile robots Convergence Adding a power integrator distributed approximate optimization finite-time control higher order multiagent systems penalty-function-based method

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Ranking with submodular functions on a budget

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DATA MINING AND KNOWLEDGE DISCOVERY 2022年第3期36卷 1197-1218页

作者： Zhang, Guangyi Tatti, Nikolaj Gionis, Aristides KTH Royal Inst Technol Stockholm Sweden Univ Helsinki HIIT Helsinki Finland

Submodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. However, the study of maximizing submodular functions has mainly been restricted in the context of selecting a set of items. On the other hand, many real-world applications require a solution that is a ranking over a set of items. The problem of ranking in the context of submodular function maximization has been considered before, but to a much lesser extent than item-selection formulations. In this paper, we explore a novel formulation for ranking items with submodular valuations and budget constraints. We refer to this problem as max-submodular ranking (MSR). In more detail, given a set of items and a set of non-decreasing submodular functions, where each function is associated with a budget, we aim to find a ranking of the set of items that maximizes the sum of values achieved by all functions under the budget constraints. For the MSR problem with cardinality- and knapsack-type budget constraints we propose practical algorithms with approximation guarantees. In addition, we perform an empirical evaluation, which demonstrates the superior performance of the proposed algorithms against strong baselines.

关键词： Ranking Submodular maximization Dynamic programming approximation algorithms

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Stochastic makespan minimization in structured set systems

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MATHEMATICAL PROGRAMMING 2022年第1-2期192卷 597-630页

作者： Gupta, Anupam Kumar, Amit Nagarajan, Viswanath Shen, Xiangkun Carnegie Mellon Univ Pittsburgh PA 15213 USA Indian Instiute Technol Delhi New Delhi India Univ Michigan Ann Arbor MI 48109 USA NYC Yahoo Res New York NY USA

We study stochastic combinatorial optimization problems where the objective is to minimize the expected maximum load (a.k.a. the makespan). In this framework, we have a set of n tasks and m resources, where each task j uses some subset of the resources. Tasks have random sizes X-j, and our goal is to non-adaptively select t tasks to minimize the expected maximum load over all resources, where the load on any resource i is the total size of all selected tasks that use i. For example, when resources are points and tasks are intervals in a line, we obtain an O(log log m)approximation algorithm. Our technique is also applicable to other problems with some geometric structure in the relation between tasks and resources;e.g., packing paths, rectangles, and "fat" objects. Our approach uses a strong LP relaxation using the cumulant generating functions of the random variables. We also show that this LP has an Omega(log* m) integrality gap, even for the problem of selecting intervals on a line;here log* m is the iterated logarithm function.

关键词： Stochastic optimization approximation algorithms Geometric packing Linear programming

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PPD: A Scalable and Efficient Parallel Primal-Dual Coordinate Descent Algorithm

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第4期34卷 1958-1966页

作者： Wu, Hejun Huang, Xinchuan Luo, Qiong Yang, Zhongheng Sun Yat Sen Univ Dept Comp Sci Guangdong Key Lab Big Data Anal & Proc Guangzhou 510275 Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Comp & Engn Hong Kong Peoples R China

Dual Coordinate Descent (DCD) is one of the most popular optimization methods. The parallelization of DCD is difficult, as DCD is sequential in nature. As such, simultaneously running multiple DCD threads on batches of data elements causes result inaccuracy and slow convergence, due to the concurrent updates of multiple coordinates. Some parallelization methods adopt separable approximate functions that depend on the degree of parallelism. Such dependencies result in both poor scalability and slow-convergence. To address these challenges, in this paper we present a new parallel primal-dual algorithm for DCD, called PPD. In PPD, the block data distribution is utilized to obtain a new approximate function that is independent from the parallelism. Moreover, PPD is designed with a novel primal-dual acceleration scheme to approach the optimal solution closely and quickly. We demonstrate the advantages of PPD in terms of scalability and efficiency through experiments.

关键词： Optimization approximation algorithms Convergence Parallel processing Partitioning algorithms Scalability Message systems Parallel computing dual coordinate descent convex optimization classification machine learning

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A technique for obtaining true approximations for k-center with covering constraints

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MATHEMATICAL PROGRAMMING 2022年第1-2期192卷 3-27页

作者： Anegg, Georg Angelidakis, Haris Kurpisz, Adam Zenklusen, Rico Swiss Fed Inst Technol Dept Math Zurich Switzerland TU Eindhoven Dept Math & Comp Sci Eindhoven Netherlands

There has been a recent surge of interest in incorporating fairness aspects into classical clustering problems. Two recently introduced variants of the k-Center problem in this spirit are Colorful k-Center, introduced by Bandyapadhyay, Inamdar, Pai, and Varadarajan, and lottery models, such as the Fair Robust k-Center problem introduced by Harris, Pensyl, Srinivasan, and Trinh. To address fairness aspects, these models, compared to traditional k-Center, include additional covering constraints. Prior approximation results for thesemodels require to relax some of the normally hard constraints, like the number of centers to be opened or the involved covering constraints, and therefore, only obtain constant-factor pseudo-approximations. In this paper, we introduce a new approach to deal with such covering constraints that leads to (true) approximations, including a 4-approximation for Colorful k-Center with constantly many colors-settling an open question raised by Bandyapadhyay, Inamdar, Pai, and Varadarajan-and a 4-approximation for Fair Robust k-Center, for which the existence of a (true) constant-factor approximation was also open. We complement our results by showing that if one allows an unbounded number of colors, then Colorful k-Center admits no approximation algorithm with finite approximation guarantee, assuming that P not equal NP. Moreover, under the Exponential Time Hypothesis, the problem is inapproximable if the number of colors grows faster than logarithmic in the size of the ground set.

关键词： approximation algorithms k-Center Clustering Polyhedral techniques

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