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Proceedings of the 34th International Conference on Neural Information Processing systems

作者： Thiparat Chotibut Fryderyk Falniowski Michał Misiurewicz Georgios Piliouras Chula Intelligent and Complex Systems Department of Physics Faculty of Science Chulalongkorn University Department of Mathematics Cracow University of Economics Department of Mathematical Sciences Indiana Univ.-Purdue Univ. Indianapolis Engineering Systems and Design Singapore Univ. of Technology and Design

ISBN: (纸本)9781713829546

Routing games are amongst the most studied classes of games in game theory. Their most well-known property is that learning dynamics typically converge to equilibria implying approximately optimal performance (low Price of Anarchy). We perform a stress test for these classic results by studying the ubiquitous learning dynamics, Multiplicative Weights Update (MWU), in different classes of congestion games, uncovering intricate non-equilibrium phenomena. We study MWU using the actual game costs without applying cost normalization to [0, 1]. Although this non-standard assumption leads to large regret, it captures realistic agents' behaviors. Namely, as the total demand increases, agents respond more aggressively to unbearably large *** start with the illustrative case of non-atomic routing games with two paths of linear cost, and show that every system has a carrying capacity, above which it becomes unstable. If the equilibrium flow is a symmetric 50 - 50% split, the system exhibits one period-doubling bifurcation. Although the Price of Anarchy is equal to one, in the large population limit the time-average social cost for all but a zero measure set of initial conditions converges to its worst possible value. For asymmetric equilibrium flows, increasing the demand eventually forces the system into Li-Yorke chaos with positive topological entropy and periodic orbits of all possible periods. Remarkably, in all non-equilibrating regimes, the time-average flows on the paths converge exactly to the equilibrium flows, a property akin to no-regret learning in zero-sum games. We extend our results to games with arbitrarily many strategies, polynomial cost functions, non-atomic as well as atomic routing games, and heterogenous users.

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SpinalXNet: Transfer Learning with Modified Fully Connected Layer for X-Ray Image Classification

SpinalXNet: Transfer Learning with Modified Fully Connected ...

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Recent Advances in systems science and engineering (RASSE), IEEE International Conference on

作者： Keshav Kumar Sadia Khanam Md Mahbub Islam Bhuiyan Mohammad Reza Chalak Qazani Subrota Kumar Mondal Houshyar Asadi H M Dipu Kabir Abbas Khorsavi Saeid Nahavandi Centre of Energy Excellence Gyancity Research Lab Motihari India Dhaka Dental College Dhaka Bangladesh Smartify Pty Ltd Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Australia Faculty of Information Technology Macau University of Science and Technology Macao Harvard Paulson School of Engineering and Applied Sciences Harvard University Allston USA

ISBN: (纸本)9781665434423

Over the past year, COVID-19 has become a global pandemic and people across the globe have suffered a lot from this pandemic. The rate of transmitting the coronavirus in people is very quick. A rapid diagnosis can potentially help governments in identifying the pattern of transmission. There are some tests available but those tests take a long time to give the report. So, in this work, we have proposed a model that will distinguish between normal people, COVID affected people, and pneumonia affected people with the help of an X-ray. X-ray images are considered because taking an X-ray image is very little time-consuming. In this work, we have trained the X-ray images with a novel Deep Learning approach with SpinalNet architecture, and that distinguishes normal people, COVID affected people, and pneumonia affected people. After training the model we have achieved a very good accuracy for the SpinalNet architecture that is 96.12% while the traditional model provides 95.50% accuracy. We present precision, recall, and F1 scores of COVID and Pneumonia classes. We also present our results and codes with execution details. This paper contains the link to Kaggle notebooks with execution details. The applied Spinalnet transfer learning code is available in our GitHub repository: https://***/dipuk0506/SpinalNet

关键词： COVID-19 Training Codes Pandemics Pulmonary diseases Transfer learning Government

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ARReSVG: intelligent Multi-UAV Navigation in Partially Observable Spaces Using Adaptive Deep Reinforcement Learning Approach

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IEEE Transactions on Vehicular Technology 2025年

作者： Essaky, Selvam Raja, Gunasekaran Dev, Kapal Niyato, Dusit Anna University MIT Campus NGNLab Department of Computer Technology Chennai600044 India Munster Technological University Department of Computer Science CONNECT Centre Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Auckland Park2006 South Africa Lebanese American University Department of Electrical and Computer Engineering Byblos1111 Lebanon Nanyang Technological University College of Computing and Data Science Nanyang Avenue 639798 Singapore

Unmanned Aerial Vehicles (UAVs) have brought about a revolution in various applications worldwide. Applications such as aerial surveillance, search and rescue, and operations beyond the line of sight rely on the autonomous navigation capabilities of multi-UAVs. This paper introduces a novel framework, Adaptive Rapid Recurrent Stochastic Valued Gradient (ARReSVG), empowered by Deep Reinforcement Learning (DRL) to address navigational challenges faced by UAVs in partially observable spaces. The proposed ARReSVG framework incorporates an adaptive learning approach to address issues related to partial observability-based autonomous navigation effectively. Compared with traditional obstacle avoidance techniques, the proposed navigation framework utilizes a distributed Multi-UAV Consensus Path Planning (MUCP2) mechanism to perform collaborative path planning for multiple UAVs to ensure collision avoidance trajectories. Additionally, the learned value-action policy implemented in the UAV alleviates the computational demands of real-time map-building. The ARReSVG framework leverages a Gated Recurrent Unit (GRU) to reduce computing complexity and accurately approximate the action-value function. The simulation results show that the ARReSVG framework achieves an impressive 94% © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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mdx: A Cloud Platform for Supporting data science and Cross-Disciplinary Research Collaborations

arXiv

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arXiv 2022年

作者： Suzumura, Toyotaro Sugiki, Akiyoshi Takizawa, Hiroyuki Imakura, Akira Nakamura, Hiroshi Taura, Kenjiro Kudoh, Tomohiro Hanawa, Toshihiro Sekiya, Yuji Kobayashi, Hiroki Matsushima, Shin Kuga, Yohei Nakamura, Ryo Jiang, Renhe Kawase, Junya Hanai, Masatoshi Miyazaki, Hiroshi Ishizaki, Tsutomu Shimotoku, Daisuke Miyamoto, Daisuke Aida, Kento Takefusa, Atsuko Kurimoto, Takashi Sasayama, Koji Kitagawa, Naoya Fujiwara, Ikki Tanimura, Yusuke Aoki, Takayuki Endo, Toshio Ohshima, Satoshi Fukazawa, Keiichiro Date, Susumu Uchibayashi, Toshihiro Information Technology Center The University of Tokyo Japan Information Initiative Center Hokkaido University Japan Cyberscience Center Tohoku University Japan Faculty of Engineering Information and Systems University of Tsukuba Japan Division for Information and Communication Systems The University of Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Japan Center for Cloud Research and Development National Institute of Informatics Japan Research and Development Center for Academic Networks National Institute of Informatics Japan Research Center for Open Science and Data Platform National Institute of Informatics Japan Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Japan Global Scientific Information and Computing Center Tokyo Institute of Technology Japan Information Technology Center Nagoya University Japan Academic Center for Computing and Media Studies Kyoto University Japan Cybermedia Center Osaka University Japan Research Institute for Information Technology Kyushu University Japan

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform’s virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an the overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans. © 2022, CC BY.

关键词： Internet of things

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Two-DOF Speed Control of Permanent Magnet Synchronous Machine with a novel Parameter Identification Method

Two-DOF Speed Control of Permanent Magnet Synchronous Machin...

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Chinese Control Conference (CCC)

作者： Mingjin Hu Wei Hua Xudong Wu Chengdong Wu Dazhi Wang Zhentao Liu Xiaofang Yuan Wennong Zhang School of Electrical Engineering Southeast University Nanjing Faculty of Robot Science and Engineering Northeastern University Shenyang College of Information Science and Engineering Northeastern University Shenyang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 College of Electrical and Information Engineering Hunan University Changsha Inovance Technology Co Ltd Shenzhen

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, a two-degree-of-freedom (2-DOF) speed control scheme for permanent magnet synchronous machines is developed. Both the reference-tracking and disturbance rejection performance are taken into account and only a single parameter related to expected bandwidth determines the dynamics. To improve the performance of speed control, a novel identification method for mechanical parameters is also proposed. The effectiveness of the proposed method is verified by simulations.

关键词： Bandwidth Velocity control Torque Simulation Transfer functions Rotors Parameter estimation

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Evolution of diabetes prediction using the fusion of ANOVA, ADASYN technique and XGBoost based on body composition data

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Journal of diabetes and metabolic disorders 2025年第2期24卷 151页

作者： Mohammad Ali Nematollahi Javad Hassannataj Joloudari Omid Zare Mohammad Maftoun Nima Shadkam Danial Sharifrazi Roohallah Alizadehsani Arefeh Asadollahi Department of Computer Sciences Fasa University Fasa Iran. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran. Department of Computer Engineering Babol Branch Islamic Azad University Babol Iran. Department of Computer Engineering Technical and Vocational University (TVU) Tehran *** Iran. Department of Computer Science University of Verona Verona Italy. Department of Artificial Intelligence Technical and Engineering Faculty South Tehran Branch Islamic Azad University Tehran Iran. Department of Computer Engineering Technical and Engineering Faculty West Tehran Branch Islamic Azad University Tehran Iran. Institute for Intelligent Systems Research and Innovation Deakin University Geelong VIC 3216 Australia. Noncommunicable Diseases Research Center Fasa University of Medical Sciences Fasa Iran.

Diabetes is known as a chronic illness with severe consequences. The rising morbidity rates predict a stunning growth in the global diabetes population, approaching 642 million by 2040, implying that one out of every ten people will be affected. This worrying number highlights the critical need for collaborative efforts from industry and academics to accelerate innovation and foster growth in diabetes risk prediction, eventually saving lives. As the frequency of life-threatening diseases, such as diabetes, rises, Medical Decision Support systems (MDSS) continue to prove their usefulness in supporting healthcare professionals, particularly physicians, in clinical decision-making procedures. Due to the advancement of technology, machine-learning techniques have made headlines in the early prediction of diabetes. In this paper, we employed machine learning techniques and the Analysis of Variance (ANOVA) method to explore associations between regional body fat distribution and diabetes mellitus in a community adult population, aiming to assess predictive capabilities. We used individual standard classifiers and ensemble learning methods to conduct a retrospective analysis of a portion of data based on body composition. To address the class imbalance problem in the target variable, we also applied three oversampling methods to provide more accurate predictions via learning algorithms. The results demonstrate that XGBoost, based on the Adaptive Synthetic Sampling (ADASYN) method, outperforms the state-of-the-art by achieving an accuracy value of 92.04%. This model exhibits more effectiveness for diabetes prediction compared to other models.

关键词： ADASYN ANOVA Diabetes prediction Machine learning

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Passivity properties for regulation of DC networks with stochastic load demand

arXiv

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arXiv 2020年

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M.A. Yazdanpanah, Mohammad Javad Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper we present new (stochastic) passivity properties for Direct Current (DC) power networks, where the unknown and unpredictable load demand is modelled by a stochastic process. More precisely, the considered power network consists of distributed generation units supplying ZIP loads, i.e., nonlinear loads comprised of impedance (Z), current (I) and power (P) components. Differently from the majority of the results in the literature, where each of these components is assumed to be constant, we consider time-varying loads whose dynamics are described by a class of stochastic differential equations. Finally, we prove that an existing distributed control scheme achieving current sharing and (average) voltage regulation ensures the asymptotic stochastic stability of the controlled network. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power transmission networks

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EAO-SLAM: Monocular semi-dense object SLAM based on ensemble data association

arXiv

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arXiv 2020年

作者： Wu, Yanmin Zhang, Yunzhou Zhu, Delong Feng, Yonghui Coleman, Sonya Kerr, Dermot Faculty of Robot Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang110819 China The Department of Electronic Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong School of Computing and Intelligent Systems Ulster University N. Ireland United Kingdom

Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms state-of-the-art techniques in accuracy and robustness. The source code is available on https://***/yanmin-wu/ EAO-SLAM. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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Output regulation for load frequency control

arXiv

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arXiv 2020年

Motivated by the inadequacy of the existing control strategies for power systems affected by time-varying uncontrolled power injections such as loads and the increasingly widespread renewable energy sources, this paper proposes two control schemes based on the well-known output regulation control methodology. The first one is designed based on the classical output regulation theory and addresses the so-called Load Frequency Control (LFC) problem in presence of time-varying uncontrolled power injections. Then, in order to also minimize the generation costs, we use an approximate output regulation method that solves numerically only the partial differential equation of the regulator equation and propose a controller based on this solution, minimizing an appropriate penalty function. An extensive case study shows excellent performance of the proposed control schemes in different and critical scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling

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Domain Adaptation and Image Classification via Deep Conditional Adaptation Network

arXiv

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arXiv 2020年

作者： Ge, Pengfei Ren, Chuan-Xian Dai, Dao-Qing Yan, Hong The School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou China The Intelligent Data Center School of Mathematics Sun Yat-Sen University Guangzhou China The Department of Electrical Engineering City University of Hong Kong Hong Kong Hong Kong

Unsupervised domain adaptation (UDA) aims to generalize the supervised model trained on a source domain to an unlabeled target domain. Feature space alignment-based methods are widely used to reduce the domain discrepancy between the source and target domains. However, existing feature space alignment-based methods often ignore the conditional dependence between features and labels when aligning feature spaces, which may suffer from negative transfer. In this paper, we propose a novel UDA method, Deep Conditional Adaptation Network (DCAN), based on conditional distribution alignment of feature spaces. To be specific, we reduce the domain discrepancy by minimizing the Conditional Maximum Mean Discrepancy between the conditional distributions of deep features on the source and target domains, and extract the discriminant information from target domain by maximizing the mutual information between samples and the prediction labels. In addition, DCAN can be used to address a special scenario, Partial UDA, where the target domain category is a subset of the source domain category. Experiments on both UDA and Partial UDA show that DCAN achieves superior classification performance over state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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