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Reinforcement learning meets minority game: Toward optimal resource allocation

Physical Review E 2019年第3期99卷 032302-032302页

作者： Si-Ping Zhang Jia-Qi Dong Li Liu Zi-Gang Huang Liang Huang Ying-Cheng Lai The Key Laboratory of Biomedical Information Engineering of Ministry of Education The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou 730000 China Institute of Theoretical Physics Key Laboratory of Theoretical Physics Chinese Academy of Sciences P.O. Box 2735 Beijing 100190 China School of Software Engineering Chongqing University Chongqing 400044 People's Republic of China School of Electrical Computer and Energy Engineering Department of Physics Arizona State University Tempe Arizona 85287 USA

The main point of this paper is to provide an affirmative answer through exploiting reinforcement learning (RL) in artificial intelligence (AI) for eliminating herding without any external control in complex resource allocation systems. In particular, we demonstrate that when agents are empowered with RL (e.g., the popular Q-learning algorithm in AI) in that they get familiar with the unknown game environment gradually and attempt to deliver the optimal actions to maximize the payoff, herding can effectively be eliminated. Furthermore, computations reveal the striking phenomenon that, regardless of the initial state, the system evolves persistently and relentlessly toward the optimal state in which all resources are used efficiently. However, the evolution process is not without interruptions: there are large fluctuations that occur but only intermittently in time. The statistical distribution of the time between two successive fluctuating events is found to depend on the parity of the evolution, i.e., whether the number of time steps in between is odd or even. We develop a physical analysis and derive mean-field equations to gain an understanding of these phenomena. Since AI is becoming increasingly widespread, we expect our RL empowered minority game system to have broad applications.

关键词： Collective dynamics Competition Self-organized systems Mean field theory

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Incident detection based on mobile crowd sensing for smart city

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Journal of computers (Taiwan) 2019年第1期30卷 96-104页

作者： Zhang, Peng Zhang, Zhenjiang Chao, Han-Chieh Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China Department of Software Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Computer Science and Information Engineering National Ilan University Yilan County260 Taiwan

In recent years, mobile devices have taken a significant role in improving the quality of people’s life. In order to enhance the usability of those devices, more and more sensors have been built in. Furthermore, the data storage and data processing capacity are becoming larger and larger. To capture more information about the city conditions and make full use of the computing resource, mobile crowd sensing has been proposed. Incident detection is an important component of the safety monitoring system, however, existing incident detection methods are mostly based on computer vision which is vulnerability to environmental impacts. This paper proposes an automatic incident detection model based on Mobile Crowd Sensing to deal with the problem above. The incident detection algorithm in the model is based on the human activity recognition and the crowd density. The simulation results show that this model is feasible and effective. 2019 © computer Society of the Republic of China. All Rights Reserved.

关键词： Environmental impact

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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UAV-assisted Data Dissemination With Proactive Caching and File Sharing in V2X Networks

UAV-assisted Data Dissemination With Proactive Caching and F...

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IEEE Global Communications Conference

作者： Rui Lu Rongqing Zhang Xiang Cheng Liuqing Yang Department of Electronics State Key Laboratory of Advanced Optical Communication Systems and Networks School of EECS Peking University Beijing China Shenzhen Research Institute of Big Data (SRIBD) Shenzhen China School of Software Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering Colorado State University CO USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Vehicle-to-Everything (V2X) communications refers to an intelligent and connected vehicular network where all vehicles and infrastructure systems are interconnected with each other. Data dissemination is playing an increasingly significant role in enhancing the network connectivity and data transmission performance. However, conventional scenarios and protocols cannot satisfy the growing pluralistic and superior quality of services (QoS) requirements of included vehicles. Therefore, in this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted data dissemination protocol with proactive caching at the vehicles and an advanced file sharing strategy for revolutionizing communications. Specifically, in the proactive caching phase, we employ UAVs to act as flying base stations (BSs) for information interactions. Considering the time-variant network topology, we further propose a spatial scheduling (SS) algorithm for the trajectory optimization of each UAV, which can expedite the caching process and boost the system throughput. Then in the file sharing phase, based on the previous caching status, we provide a relay ordering algorithm to enhance the network transmission performance. Numerical results verify that our proposed UAV-assisted data transmission protocol can achieve a desirable system performance in terms of the downloading process, network throughput, and average data delivery delay.

关键词： Data dissemination Relays Protocols quality of service Scheduling Throughput Wireless communication

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Simultaneous spectral-spatial feature selection and extraction for hyperspectral images

arXiv

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arXiv 2019年

作者： Zhang, Lefei Zhang, Qian Du, Bo Huang, Xin Tang, Yuan Yan Tao, Dacheng State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China Beijing Samsung Telecom R&D Center Beijing100028 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China Department of Computer and Information Science University of Macau Macau999078 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectralspatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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Relay Selection Strategy (RSS) Design for In-Vehicle Storage (IVS) System

Relay Selection Strategy (RSS) Design for In-Vehicle Storage...

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IEEE Global Communications Conference

作者： Binbin Hu Rongqing Zhang Luoyang Fang Xiang Cheng Liuqing Yang State Key Laboratory of Advanced Optical Communication Systems & Networks School of EECS Peking University Beijing China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Software Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering Colorado State University Fort Collins CO USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In recent years, autonomous driving has attracted a vast amount of attention from both industry and academia, which has introduced large amounts of region-related data. In order to release the burden in core networks caused by the communication demands for such data, various in-vehicle storage (IVS) systems have been widely studied to bring contents closer to users. To cope with the mobility issue hindering the realization of IVS systems, in this paper, we propose a relay selection strategy (RSS) consisting of the relay map construction (RMC) algorithm and the relay pair matching (RPM) algorithm with the assistance of the vehicle route information. By considering both the potential transmission amount and the waiting time, the proposed RSS generates an overall optimal relay assignment for the IVS system. The performance gain of the proposed RSS compared with the baseline is evaluated by a realistic simulator in terms of the relay failure ratio, the retrieval throughput, and the RSU consumption ratio. Simulation results show that the proposed RSS achieves higher efficiency and robustness than the baseline.

关键词： Relays Roads Robustness Prediction algorithms Mathematical model Wireless sensor networks Micromechanical devices

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A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

arXiv

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arXiv 2021年

作者： Li, Zekun Zhao, Wei Shi, Feng Qi, Lei Xie, Xingzhi Wei, Ying Ding, Zhongxiang Gao, Yang Wu, Shangjie Liu, Jun Shi, Yinghuan Shen, Dinggang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University Nanjing210046 China School of Computer Science and Artificial Intelligence Southeast University Nanjing210018 China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai201807 China School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of Department of Radiology Second Xiangya Hospital Central South University Hunan410011 China Department of Radiology Quality Control Center Changsha410011 China Department of Radiology Hangzhou First People's Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China Department of Pulmonary and Critical Care Medicine Second Xiangya Hospital Central South University Changsha410011 China

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues - weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works. Copyright © 2021, The Authors. All rights reserved.

关键词： computerized tomography

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Aspect-Based Sentiment Analysis with the Multiple-Element Attention and Part of Speech

Aspect-Based Sentiment Analysis with the Multiple-Element At...

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IEEE International Conference on Ubi-Media Computing

作者： Ning Liu Bo Shen Kun Mi Mingdong Sun Naiyue Chen School of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing China Beijing Huayu Software Co. Ltd Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9781728128207

ISBN: (纸本)9781728128214

Aspect-based Sentiment analysis (ABSA) is a rapidly growing field of research in natural language processing. ABSA is a fine-grained task of Sentiment analysis. How to capture precise sentiment expressions in a sentence towards the specific aspect remains a challenge. In this paper, we propose a novel neural network, named Multiple-element Attention LSTM (MEA-LSTM) to alleviate the problem of self-attention or binary-element attention used in the ABSA task. These attention mechanisms mentioned above are weak attention, they ignore the information of aspect target or sentence representation. To capture the precise sentiment expressions, we make use of multiple-element attention to assign different importance degrees of different words in a sentence. To store these informative aspect-dependent representations, extra representation memory is designed. Part of speech (POS) is an important feature in identifying the sentiment expressions in the ABSA task. We combine POS with the LSTM in the proposed MEA-LSTM. Experimental results show that our proposed model acquires state-of-the-art accuracy at both restaurant and laptop datasets. Besides, a rule of thumb about choosing the number of hops is given on both datasets.

关键词：

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Pain-Free Blood Glucose Monitoring Using Wearable Sensors: Recent Advancements and Future Prospects

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IEEE Reviews in Biomedical engineering 2018年 11卷 21-35页

作者： Siddiqui, Sarah Ali Zhang, Yuan Lloret, Jaime Song, Houbing Obradovic, Zoran Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Integrated Management Coastal Research Institute Universidad Politecnica de Valencia Valencia46022 Spain Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Department of Computer and Information Sciences Temple University PhiladelphiaPA19122 United States

Keeping track of blood glucose levels non-invasively is now possible due to diverse breakthroughs in wearable sensors technology coupled with advanced biomedical signal processing. However, each user might have different requirements and priorities when it comes to selecting a self-monitoring solution. After extensive research and careful selection, we have presented a comprehensive survey on noninvasive/pain-free blood glucose monitoring methods from the recent five years (2012-2016). Several techniques, from bioinformatics, computer science, chemical engineering, microwave technology, etc., are discussed in order to cover a wide variety of solutions available for different scales and preferences. We categorize the noninvasive techniques into nonsample- and sample-based techniques, which we further grouped into optical, nonoptical, intermittent, and continuous. The devices manufactured or being manufactured for noninvasive monitoring are also compared in this paper. These techniques are then analyzed based on certain constraints, which include time efficiency, comfort, cost, portability, power consumption, etc., a user might experience. Recalibration, time, and power efficiency are the biggest challenges that require further research in order to satisfy a large number of users. In order to solve these challenges, artificial intelligence (AI) has been employed by many researchers. AI-based estimation and decision models hold the future of noninvasive glucose monitoring in terms of accuracy, cost effectiveness, portability, efficiency, etc. The significance of this paper is twofold: first, to bridge the gap between IT and medical field;and second, to bridge the gap between end users and the solutions (hardware and software). © 2008-2011 IEEE.

关键词： Cost effectiveness

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A Study on Tree-Based Code Extraction Method for Transplantation

A Study on Tree-Based Code Extraction Method for Transplanta...

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IEEE International Conference on software engineering and Service Sciences (ICSESS)

作者： Lupeng Liu Xiaoguang Mao College of Computer National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9781538665664;9781538665657

Making use of open source software is always a hot topic. Code transplantation is a new idea just proposed for several years, several tools has been built, but no one can solve the problem of open source software transplantation. Abstract syntax tree is a useful way to analyze source code, base on current research, this paper proposes a new method to deal with the code extraction part of transplantation. The experiment result reveals that our method can reduce programmers work of analyze code, make code extraction more convenient and can be used to real word software.

关键词： Syntactics Open source software Tools Java Surgery Cloning

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