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Preface

CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

Energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave communications with SWIPT

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arXiv 2020年

作者： Hao, Wanming Sun, Gangcan Zhou, Fuhui Mi, De Shi, Jia Xiao, Pei Leung, Victor C.M. School of Information Engineering Zhengzhou University Zhengzhou450001 China 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China State Key Laboratory of Integrated Services Networks Xi'dian University Xian710071 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T1Z4 Canada

In this paper, we investigate the energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. We adopt two sparse radio frequency chain antenna structures at the base station (BS), i.e., fully-connected and subarray structures, and design the codebook-based analog precoding according to the different structures. Then, we formulate a joint digital multicast, unicast precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at the BS and minimum harvested energy at receivers are considered. Due to its difficulty to directly solve the formulated problem, we equivalently transform the fractional objective function into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques and propose an iterative algorithm to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop a zero forcing (ZF) technique-based low complexity iterative algorithm. Specifically, the ZF technique is applied to cancel the inter-unicast interference and the first order Taylor approximation is used for the convexification of the non-convex constraints in the original problem. Finally, simulation results are provided to compare the performance of the proposed algorithms under different schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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

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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

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Corrigendum to “Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques” [Renew Sustain Energy Rev 144 (2021) 11096]

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Renewable and Sustainable Energy Reviews 2021年 145卷

作者： Ghezlane Halhoul Merabet Mohamed Essaaidi Mohamed Ben Haddou Basheer Qolomany Junaid Qadir Muhammad Anan Ala Al-Fuqaha Mohamed Riduan Abid Driss Benhaddou Smart Systems Laboratory (SSL) ENSIAS Mohammed V University in Rabat 713 Morocco Department of Engineering Technology University of Houston TX 77204 USA MENTIS Consulting SA 13 rue de Congrès 1000 Brussels Belgium Department of Cyber Systems College of Business and Technology University of Nebraska at Kearney (UNK) Kearney NE 68849 USA Information Technology University Lahore 54000 Pakistan Software Engineering Department Alfaisal University-Riyadh Saudi Arabia Information and Computing Technology (ICT) Division College of Science and Engineering (CSE) Hamad Bin Khalifa University Doha Qatar Department of Computer Science Western Michigan University Kalamazoo MI 49008 USA School of Science and Engineering Alakhawayn University in Ifrane 1005 Morocco

Reinforcement learning meets minority game: Toward optimal resource allocation

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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

AI-skin: Skin disease recognition based on self-learning and wide data collection through a closed loop framework

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arXiv 2019年

作者： Chen, Min Zhou, Ping Wu, Di Hu, Long Hassan, Mohammad Mehedi Alamri, Atif School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Chair of Pervasive and Mobile Computing Software Engineering Department CCIS King Saud University Riyadh11543 Saudi Arabia

There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of selfconfidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system's computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective. Copyright © 2019, The Authors. All rights reserved.

关键词： Data acquisition

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Preface

CEUR Workshop Proceedings 2021年 2899卷

作者： Kovalev, Igor Semenkin, Eugene Gulyaev, Yuri Barakaev, Nusratilla Shaidurov, Vladimir Škraba, Andrej Tsarev, Roman Legalov, Alexander Saramud, Mikhail Grosspietsch, Karl-Erwin Stupina, Alena Department of Computer Science Siberian Federal University Krasnoyarsk Russia Department of system analysis and operations research of Reshetnev University Krasnoyarsk Russia International and Russian Union of Scientific and Engineering Associations Moscow Russia Rector of Bukhara Engineering Technological Institute Bukhara Uzbekistan Institute of Computational Modeling SB RAS Krasnoyarsk Russia University of Maribor UM Faculty of Organizational Sciences - Cybernetics and Decision Support Systems Laboratory Slovenia Department of Informatics Siberian Federal University Krasnoyarsk Russia Faculty of Computer Science School of Software Engineering HSE University Moscow Russia Department of Informatics Siberian Federal University Krasnoyarsk Russia European Organization for Information and Microelectronics Germany The Siberian Federal University Krasnoyarsk Russia

Correction to: Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

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Archives of Computational Methods in engineering 2021年第7期28卷 5039-5039页

作者： Ismail, Leila Materwala, Huned Tayefi, Maryam Ngo, Phuong Karduck, Achim P. Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Norwegian Centre for E-Health Research Tromsø Norway Faculty of Informatics Furtwangen University Furtwangen Germany