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arXiv 2022年

作者： Lu, Dening Xie, Qian Wei, Mingqiang Gao, Kyle Xu, Linlin Li, Jonathan The Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen518038 China The Department of Geography and Environmental Management University of Waterloo WaterlooONN2L 3G1 Canada The Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom

关键词： Deep neural networks

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Hamilton–jacobi deep Q-learning for deterministic continuous-time systems with lipschitz continuous controls

arXiv

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arXiv 2020年

作者： Kim, Jeongho Shin, Jaeuk Yang, Insoon Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of

In this paper, we propose Q-learning algorithms for continuous-time deterministic optimal control problems with Lipschitz continuous controls. Our method is based on a new class of Hamilton–Jacobi–Bellman (HJB) equations derived from applying the dynamic programming principle to continuous-time Q-functions. A novel semi-discrete version of the HJB equation is proposed to design a Q-learning algorithm that uses data collected in discrete time without discretizing or approximating the system dynamics. We identify the condition under which the Q-function estimated by this algorithm converges to the optimal Q-function. For practical implementation, we propose the Hamilton–Jacobi DQN, which extends the idea of deep Q-networks (DQN) to our continuous control setting. This approach does not require actor networks or numerical solutions to optimization problems for greedy actions since the HJB equation provides a simple characterization of optimal controls via ordinary differential equations. We empirically demonstrate the performance of our method through benchmark tasks and high-dimensional linear-quadratic problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Continuous time systems

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Granular Computing for Data Analytics:A Manifesto of Human-Centric Computing

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1025-1034页

作者： Witold Pedrycz Department of Electrical&Computer Engineering University of Alberta EdmontonCanadaand also with the Systems Research InstitutePolish Academy of SciencesWarsawPoland

In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the *** advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular *** Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing *** study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.

关键词： Aggregation clustering design of information granules fuzzy sets granular computing information granules,principle of justifiable granularity.

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Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

arXiv

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arXiv 2021年

作者： Zhang, Zijian Wang, Shuai Hong, Yuncong Zhou, Liangkai Hao, Qi Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Sifakis Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Pazhou Lab Guangzhou510330 China Harbin Institute of Technology 92 West Dazhi Street Nan Gang District Harbin150001 China

The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles. This paper proposes a federated learning (FL) based dynamic map fusion framework to achieve high map quality despite unknown numbers of objects in fields of view (FoVs), various sensing and model uncertainties, and missing data labels for online learning. The novelty of this work is threefold: (1) developing a three-stage fusion scheme to predict the number of objects effectively and to fuse multiple local maps with fidelity scores;(2) developing an FL algorithm which fine-tunes feature models (i.e., representation learning networks for feature extraction) distributively by aggregating model parameters;(3) developing a knowledge distillation method to generate FL training labels when data labels are unavailable. The proposed framework is implemented in the Car Learning to Act (CARLA) simulation platform. Extensive experimental results are provided to verify the superior performance and robustness of the developed map fusion and FL schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Simulation platform

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Mission Design of DESTINY+: Toward Active Asteroid (3200) Phaethon

Mission Design of DESTINY+: Toward Active Asteroid (3200) Ph...

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IAF Astrodynamics Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Ozaki, Naoya Yamamoto, Takayuki Pushparaj, Nishanth Gonzalez-Franquesa, Ferran Gutierrez-Ramon, Roger Chikazawa, Takuya Yanagida, Kanta Padilha, Dan Tos, Diogene Alessandro Dei Elik, Onur C. Marmo, Nicola Kawakatsu, Yasuhiro Nishiyama, Kazutaka Takashimai, Takeshi ADepartment of Spacecraft Engineering Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa Sagamihara252-5210 Japan Research and Development Japan Aerospace Exploration Agency Kanagawa Sagamihara252-5210 Japan Department of Space and Astronautical Science Graduate University for Advanced Studies Sokendai Kanagawa Sagamihara252-5210 Japan University of Tokyo Bunkyo-ku Tokyo113-8656 Japan Deimos Space ESA/ESOC Darmstadt64293 Germany University of Glasgow GlasgowG12 8QQ United Kingdom Sapienza University of Rome Via Eudossiana 18 Rome00184 Italy Department of Space Flight Systems Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa Sagamihara252-5210 Japan Department of Spacecraft Engineering Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa Sagamihara252-5210 Japan

ISBN: (纸本)9781713843078

DESTINY+ is an upcoming JAXA Epsilon medium-class mission to be launched in 2024. The spacecraft will demonstrate advanced technologies, including highly efficient solar electric propulsion to enable deep space missions with lower costs and higher frequency. As the nominal science mission, the spacecraft will perform a high-speed flyby observation of (3200) Phaethon. Visits to several other asteroids are part of the extended mission. This paper presents the mission design and flight operation overview of the DESTINY+ mission. Epsilon S launch vehicle has the capability to insert the spacecraft into a near geostationary transfer orbit. The spacecraft will raise its orbit by solar electric propulsion in a spiral-shaped trajectory that will take it to the vicinity of the Moon. A number of lunar gravity assists will allow the spacecraft to fly to an interplanetary orbit that will take it to multiple asteroid flybys. In addition to the mission analysis, this paper includes an operational feasibility study, as it is highly coupled with system constraints. Copyrightc 2021 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Asteroids

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Effect of output coupling ratio on performance of Q-switched fiber laser based on MgQ2 organic saturable absorber

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Optical Fiber Technology 2025年 93卷

作者： Najm, Mustafa Mohammed Najm, Sarah Mohammed Hmood, Jassim K. Yit, Jing Ee Al-Azzawi, Alabbas A. Abdullah, Mohammed Najm Harun, S.W. Photonics Engineering Laboratory Department of Electrical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur50603 Malaysia Technical Instructors Training Institute Middle Technical University Baghdad10074 Iraq Department of Computer Engineering University of Technology Baghdad10066 Iraq Optoelectronics Engineering Department College of Laser and Optoelectronics Engineering University of Technology Baghdad10066 Iraq Department of Mechanical Engineering Faculty of Engineering Universiti Teknologi Petronas 32610 Seri Iskandar Perak Malaysia Research and Development Directorate Ministry of Higher Education and Scientific Research Baghdad10065 Iraq Department of Medical Instrumentation Technical College of Medical Technical Al-Farahidi University Baghdad Iraq

This study experimentally investigates an organic 8-Hydroxyquinoline magnesium (MgQ2) thin-film saturable absorber (SA). The MgQ2-SA is prepared using the drop-casting method to create a solid polymer film. The results show that the optical properties of the MgQ2-SA film include a non-saturable absorption of 30 %, a saturable absorption of 22.5 %, and a saturable intensity of 11.4 MW/cm2. The Q-switched operation using MgQ2-SA film produces pulse widths ranging from 7.45 µs to 1.4 µs, with pulse repetition rates reported from 32 to 135 kHz, particularly in the coupling ratio range of 50 % to 10 %. Q-switched spectral operations were observed at wavelengths of 1530 nm for 10:90 output coupler (OC). Besides, for 50:50 OC, dual-wavelength spectrum is recorded with peaks at 1557 nm, and 1559 nm wavelengths. The proposed system architecture shows the capability to generate unique pulse characteristics across various wavelengths and different coupling ratios. © 2025

关键词： Nonlinear optics

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AI recognition of patient race in medical imaging: a modelling study

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The Lancet Digital Health 2022年第6期4卷 e406-e414页

作者： Gichoya, Judy Wawira Banerjee, Imon Bhimireddy, Ananth Reddy Burns, John L Celi, Leo Anthony Chen, Li-Ching Correa, Ramon Dullerud, Natalie Ghassemi, Marzyeh Huang, Shih-Cheng Kuo, Po-Chih Lungren, Matthew P Palmer, Lyle J Price, Brandon J Purkayastha, Saptarshi Pyrros, Ayis T Oakden-Rayner, Lauren Okechukwu, Chima Seyyed-Kalantari, Laleh Trivedi, Hari Wang, Ryan Zaiman, Zachary Zhang, Haoran Department of Radiology Emory University Atlanta GA United States Department of Computer Science Emory University Atlanta GA United States School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ United States School of Informatics and Computing Indiana University–Purdue University Indianapolis IN United States Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science University of Toronto Toronto ON Canada Stanford University School of Medicine Palo Alto CA United States Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia School of Public Health University of Adelaide Adelaide SA Australia Florida State University College of Medicine Tallahassee FL United States Dupage Medical Group Hinsdale IL United States Department of Computer Science Georgia Institute of Technology Atlanta GA United States Lunenfeld-Tanenbaum Research Institute Sinai Health Toronto ON Canada Vector Institute for Artificial Intelligence Toronto ON Canada

Background: Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods: Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings: In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91–0·99], CT chest imaging [0·87–0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that t

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Hopping-Transport Mechanism for Reconfigurable Logic in Disordered Dopant Networks

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Physical Review Applied 2022年第6期17卷 064025-064025页

作者： Henri Tertilt Jesse Bakker Marlon Becker Bram de Wilde Indrek Klanberg Bernard J. Geurts Wilfred G. van der Wiel Andreas Heuer Peter A. Bobbert Institut für physikalische Chemie Westfälische Wilhelms-Universität Münster Münster Germany NanoElectronics Group University of Twente Enschede Netherlands Multiscale Modeling and Simulation Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) University of Twente Enschede Netherlands Center for Computational Energy Research (CCER) Eindhoven University of Technology Netherlands Fachbereich Physik Westfälische Wilhelms-Universität Münster Münster Germany Department of Applied Physics Eindhoven University of Technology Netherlands

We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic in dopant network processing units. Kinetic Monte Carlo simulations of the hopping process show temperature-dependent current-voltage characteristics and artificially evolved basic Boolean logic gates in very good agreement with experiment. The simulations provide unique insights into the local electrostatic potential and current flow in the dopant network, showing subtle changes induced by control voltages that set the conditions for the logic operation. These insights will be crucial in the systematic further development of this burgeoning technology for unconventional computing. The establishment of the principles underlying the logic functionality of these devices encourages the exploration and utilization of the same principles in other materials and device geometries.

关键词： Doping effects Hopping transport Neuromorphic computing Devices for digital logic, storage & processing Disordered systems Doped semiconductors Genetic algorithm Monte Carlo methods

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The IBEX Imaging Knowledge-Base: A Community Resource Enabling Adoption and development of Immunofluoresence Imaging Methods

arXiv

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arXiv 2024年

作者： Yaniv, Ziv Anidi, Ifeanyichukwu U. Arakkal, Leanne Arroyo-Mejías, Armando J. Beuschel, Rebecca T. Börner, Katy Chu, Colin J. Clark, Beatrice Clatworthy, Menna R. Colautti, Jake Coscia, Fabian Croteau, Joshua Denha, Saven Dever, Rose Dutra, Walderez O. Fritzsche, Sonja Fullam, Spencer Gerner, Michael Y. Gola, Anita Gollob, Kenneth J. Hernandez, Jonathan M. Hor, Jyh Liang Ichise, Hiroshi Jing, Zhixin Jonigk, Danny Kandov, Evelyn Kastenmüller, Wolfgang Koenig, Joshua F.E. Kothurkar, Aanandita Kortekaas, Rosa K. Kreins, Alexandra Y. Lamborn, Ian T. Lin, Yuri Morais, Katia Luciano Pereira Lunich, Aleksandra Luz, Jean C.S. MacDonald, Ryan B. Makranz, Chen Maltez, Vivien I. McDonough, John E. Moriarty, Ryan V. Ocampo-Godinez, Juan M. Olyntho, Vitoria M. Oxenius, Annette Padhan, Kartika Remmert, Kirsten Richoz, Nathan Schrom, Edward C. Shang, Wanjing Shi, Lihong Shih, Rochelle M. Speranza, Emily Stierli, Salome Teichmann, Sarah A. Veres, Tibor Z. Vierhout, Megan Wachter, Brianna T. Williams, Margaret Zangger, Nathan Germain, Ronald N. Radtke, Andrea J. Bioinformatics and Computational Bioscience Branch National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Critical Care Medicine and Pulmonary Branch National Heart Lung and Blood Institute National Institutes of Health BethesdaMD United States Lymphocyte Biology Section Laboratory of Immune System Biology NIAID NIH BethesdaMD United States Department of Intelligent Systems Engineering Indiana University BloomingtonIN United States UCL Institute of Ophthalmology NIHR Moorfields Biomedical Research Centre London United Kingdom Cambridge Institute for Therapeutic Immunology and Infectious Diseases University of Cambridge Department of Medicine Molecular Immunity Unit Laboratory of Molecular Biology Cambridge United Kingdom McMaster Immunology Research Centre Schroeder Allergy and Immunology Research Institute Department of Medicine Faculty of Health Sciences McMaster University HamiltonON Canada Spatial Proteomics Group Berlin Germany Department of Business Development BioLegend Inc. San DiegoCA United States Functional Immunogenomics Unit National Institute of Arthritis and Musculoskeletal and Skin Diseases National Institutes of Health BethesdaMD United States Laboratory of Cell-Cell Interactions Department of Morphology Institute of Biological Sciences Universidade Federal de Minas Gerais MG Belo Horizonte Brazil Division of Rheumatology Rush University Medical Center ChicagoIL United States Department of Immunology University of Washington School of Medicine SeattleWA United States Robin Chemers Neustein Laboratory of Mammalian Cell Biology and Development The Rockefeller University New YorkNY United States Hospital Israelita Albert Einstein SP Sao Paulo Brazil Surgical Oncology Program National Cancer Institute National Institutes of Health BethesdaMD United States Institute of Pathology Aachen Medical University RWTH Aachen Aachen Germany Hannover Germany Würzburg Institute

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, e.g., primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data. © 2024, CC BY.

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Crowdsourcing Urban Narratives for a Post-Pandemic World

Crowdsourcing Urban Narratives for a Post-Pandemic World

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International Conference on computer Supported Cooperative Work in Design

作者： Ramon Chaves Daniel Schneider Claudia Motta António Correia Hugo Paredes Bárbara Caetano Jano Moreira de Souza Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) Federal University of Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro UTAD Vila Real Portugal Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil

ISBN: (纸本)9781728165974;9781728165981

Over the past decades, the use of digital technologies to support participatory urban planning and design has been repeatedly described as a crucial instrument and critical building block for tackling historical problems of participation in such processes. Social media, e-participation platforms, and crowdsourcing applications are examples of technologies that can involve citizens in decision-making processes and thus leverage the benefits of collective intelligence. However, despite the extensive use of social media platforms, old problems related to engagement and participation still occur in digital initiatives. Successful collaboration examples between citizens, policymakers, and strategic stakeholders are still scarce based on online social practices. This study aims to introduce a collective intelligence model, which combines crowdsourcing and social storytelling to support participatory urban planning and design from a bottom-up perspective. The paper concludes by discussing a scenario where citizens can engage in mapping, taking photos, sending ideas, or even creating collective stories about their university issues in a post-pandemic future.

关键词： Crowdsourcing Social networking (online) Smart cities Instruments Urban planning Collective intelligence Tools

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