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Robustness Of Stochastic Learning Dynamics To Player Heterogeneity In Games

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Robustness Of Stochastic Learning Dynamics To Player Heterog...

IEEE Annual Conference on Decision and control

作者： Hassan Jaleel Waseem Abbas Jeff S. Shamma Department of Electrical Engineering Lahore University of Management Science (LUMS) Lahore Pakistan Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN USA Robotics Intelligent Systems & Control (RISC) Lab King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

We investigate the impact of players with heterogeneous update rules on the long-term behavior of a population under stochastic learning dynamics. We show that under certain conditions, the presence of even a single heterogeneous player with a different decision making strategy can significantly alter the long-term behavior of the entire population. To quantify the impact of a heterogeneous player, we define a new notion of robustness of stochastic learning dynamics to player heterogeneity. Based on our proposed notion, an action profile that is stochastically stable under the standard setup is robust to player heterogeneity if it can still explain the long-run behavior of all the players other than the heterogeneous players. We consider two types of heterogeneous players: A confused player who randomly updates his actions and a stubborn player who never updates his action. For each of these types, we present a qualitative description of scenarios in which an action profile that is stochastically stable under the standard setup is not robust to the presence of a heterogeneous player of a particular type.

关键词： Robustness Stochastic processes Sociology Statistics Standards Games Resistance

Edge Detection Operator for Underwater Target Image 3

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Edge Detection Operator for Underwater Target Image

3rd IEEE International Conference on Image, Vision and Computing, ICIVC 2018

作者： Xiaoheng, Dong Minghang, Li Jiashu, Miao Zhengyu, Wang Shanghai Marine Electronic Equipment Research Institute Shanghai China Shanghai University Key Lab. of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai China Shanghai Aerospace Control Technology Institute Shanghai China

ISBN: (纸本)9781538649916

Image edge detection is an important basis for image recognition extraction. The traditional segmentation algorithm can't effectively extract important edge information of digital image. In view of the low contrast and edge blur of underwater image, a new feature based on wavelet transform Edge extraction algorithm has been proposed. On the basis of this, an edge detection method based on the details of two-dimensional image wavelet decomposition is further studied. This method eliminates a large number of remaining edges and false edges, and the feature edge of the image is extracted effectively, so it provides reliable information for the subsequent target recognition. © 2018 IEEE.

关键词： Wavelet decomposition

Functional Synthetic Biology

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arXiv 2022年

作者： Aldulijan, Ibrahim Beal, Jacob Billerbeck, Sonja Bouffard, Jeff Chambonnier, Gaël Delkis, Nikolaos Guerreiro, Isaac Holub, Martin Ross, Paul Selvarajah, Vinoo Sprent, Noah Vidal, Gonzalo Vignoni, Alejandro Systems Engineering Stevens Institute of Technology 1 Castle Point Terrace HobokenNJ07030 United States Intelligent Software & Systems Raytheon BBN Technologies 10 Moulton Street CambridgeMA02138 United States Molecular Microbiology Groningen Biomolecular Sciences and Biotechnology Institute University of Groningen Nijenborgh 7 Groningen9747 AG Netherlands Centre for Applied Synthetic Biology Department of Biology Concordia University 7141 Sherbrooke Street West MontréalQCH4B 1R6 Canada Department of Biological Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Plant and Environmental Biotechnology Laboratory Department of Biochemistry and Biotechnology University of Thessaly Viopolis Mezourlo Larissa41500 Greece iGEM Foundation 45 Prospect Street CambridgeMA02139 United States Delft University of Technology Van der Maasweg 9 2629 HZ Netherlands BioStrat Marketing 9965 Harbour Lake Circle Boynton BeachFL33437 United States Department of Chemical Engineering Imperial College London South Kensington Campus Exhibition Road SW7 2AZ United Kingdom Research Group School of Computing Newcastle University Devonshire Building Devonshire Terrace Newcastle Upon TyneNE1 7RU United Kingdom Synthetic Biology and Biosystems Control Lab Instituto de Automatica e Informatica Industrial Universitat Politecnica de Valencia Camino de Vera s/n Valencia46022 Spain

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility, and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability, and reductions in technical risk and cost. © 2022, CC BY.

关键词： Biological systems

Cyberbullying and traditional bullying in Greece: An empirical study

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arXiv 2019年

作者： Papatsimouli, Maria Skordas, John Lazaridis, Lazaros Michailidi, Eleni Saprikis, Vaggelis Fragulis, George F. Lab. of Web Technologies & Applied Control Systems Dept. Of Electrical Engineering Western Macedonia Univ. of Applied Sciences Kozani Hellas Dept. Of Business Administriation Western Macedonia Univ. of Applied Sciences Kozani Hellas Greece

The rapid evolution of technology is a preferred method of interacting. A new world was created for young people who are sending emails, visiting websites, using webcams and chat rooms, and instant messaging through social media. In the past years, people used face-to-face communication though, in recent years, people are using internet technology in order to communicate with each other. As a consequence the communication change, created a new type of bullying, cyberbullying, in which bullying is taking place by using internet technology. Cyberbullying is a phenomenon which is increasing day by day over the world. This was one of the reasons that prompted us to do this survey. Furthermore, bullies aggressive reaction is affected by four factors which are conductive during childhood. Copyright © 2019, The Authors. All rights reserved.

关键词： Computer crime

Detection of Leak Site in Sloped Transmission Lines

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Detection of Leak Site in Sloped Transmission Lines

International Conference on control, engineering & Information Technology (CEIT)

作者： Masoud Derakhshan Mohammad Haeri Advanced Control Systems Lab. Electrical Engineering Department Sharif University of Technology Tehran Iran

ISBN: (纸本)9781538676424

In this paper a method is presented to estimate the leak site in sloped transmission lines using a closed solution of the transmission line equations and linear observer. First, the equations are solved for a constant input state, and then the initial estimation of the leak site is presented for variable input state and the leak and pressure coefficient in the leak site is determined with the help of equations obtained from the closed solution using the estimation of the leak site. Then, the nonlinear model is determined by discretizing the transmission line equations with respect to the site variable considering a floating point for the leak site in the observer, designing a linear observer using the initial estimation obtained from the closed solution, and linearizing the observers equations at the obtained point.

关键词： Mathematical model Power transmission lines Observers Friction Pipelines Computational modeling

Black-box Identification and Iterative Learning control for Quadcopter

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Black-box Identification and Iterative Learning Control for ...

International Conference on control, engineering & Information Technology (CEIT)

作者： Yasin Abdolahi Amin Rezaeizadeh Advanced Control Systems Lab. Electrical Engineering Department Sharif University of Technology Tehran Iran

ISBN: (纸本)9781538676424

This paper describes the black-box system identification and iterative learning control algorithm which apply to a quadcopter. At first, two feedback control loops from angles and angular velocities are implemented to stabilize the system, then the experimental data and the models of roll and pitch are identified via a black-box routine. For more accuracy of angles measurement, the complementary filter is used to fuse the data of the inertial measurement unit (IMU). An iterative learning control (ILC) method is then applied to the closed loop system with the aim of following trajectories of the roll and the pitch angles. The designed controller was first applied to the roll and pitch model separately and then applied to both angles simultaneously. The experimental result is presented and discussed.

关键词： Trajectory Mathematical model Iterative learning control Angular velocity Data models control engineering Information technology

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

Robust on-line ADP-based solution of a class of hierarchical nonlinear differential game

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arXiv 2019年

作者： Satouri, Mohammad Reza Kebriaei, Hamed Razminia, Abolhassan Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering College of Engineering University of Tehran P.O. Box 4395-515 Tehran Iran Research Lab. Department of Electrical Engineering School of Engineering Persian Gulf University P.O. Box 75169 Boushehr Iran School of Electrical and Computer Engineering College of Engineering University of Tehran P.O. Box 4395-515 Tehran Iran Control & Intelligent Processing Center of Excellence

In this paper a hierarchical one-leader-multi-followers game for a class of continuous-time nonlinear systems with disturbance is investigated by a novel policy iteration reinforcement learning technique in which, the game model consists both of the zero-sum and nonzero-sum games, simultaneously. An adaptive dynamic programming (ADP) method is developed to achieve optimal control strategy under worst case of disturbance. This algorithm reduces the number of neural networks which are used for estimation for about thirty percent. Proposed algorithm uses neural networks to estimate value functions, control policies and disturbances. Convergence analysis of the estimations is investigated using Lyapunov theory and exploiting properties of the Nemytskii operator. Finally the simulation results will show effectiveness of developed ADP *** Codes 91A65, 49N70, 68T05, Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear systems

Towards Multi-Pose Guided Virtual Try-On Network

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Towards Multi-Pose Guided Virtual Try-On Network

International Conference on Computer Vision (ICCV)

作者： Haoye Dong Xiaodan Liang Xiaohui Shen Bochao Wang Hanjiang Lai Jia Zhu Zhiting Hu Jian Yin School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R.China School of Intelligent Systems Engineering Sun Yat-sen University ByteDance AI Lab. School of Computer Science South China Normal University Guangzhou Key Laboratory of Big Data and Intelligent Education Carnegie Mellon University

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Virtual try-on systems under arbitrary human poses have significant application potential, yet also raise extensive challenges, such as self-occlusions, heavy misalignment among different poses, and complex clothes textures. Existing virtual try-on methods can only transfer clothes given a fixed human pose, and still show unsatisfactory performances, often failing to preserve person identity or texture details, and with limited pose diversity. This paper makes the first attempt towards a multi-pose guided virtual try-on system, which enables clothes to transfer onto a person with diverse poses. Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-On Network (MG-VTON) generates a new person image after fitting the desired clothes into the person and manipulating the pose. MG-VTON is constructed with three stages: 1) a conditional human parsing network is proposed that matches both the desired pose and the desired clothes shape; 2) a deep Warping Generative Adversarial Network (Warp-GAN) that warps the desired clothes appearance into the synthesized human parsing map and alleviates the misalignment problem between the input human pose and the desired one; 3) a refinement render network recovers the texture details of clothes and removes artifacts, based on multi-pose composition masks. Extensive experiments on commonly-used datasets and our newly-collected largest virtual try-on benchmark demonstrate that our MG-VTON significantly outperforms all state-of-the-art methods both qualitatively and quantitatively, showing promising virtual try-on performances.

关键词： Shape Three-dimensional displays Generators Hair Task analysis Face Gallium nitride