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Anytime motion planning on large dense roadmaps with expensive edge evaluations

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arXiv 2017年

作者： Choudhury, Shushman Salzman, Oren Choudhury, Sanjiban Dellin, Christopher M. Srinivasa, Siddhartha S. Department of Computer Science Stanford University Robotics Institute Carnegie Mellon University Paul G. Allen School of Computer Science and Engineering University of Washington

We propose an algorithmic framework for efficient anytime motion planning on large dense geometric roadmaps, in domains where collision checks and therefore edge evaluations are computationally expensive. A large dense roadmap (graph) can typically ensure the existence of high quality solutions for most motion-planning problems, but the size of the roadmap, particularly in high-dimensional spaces, makes existing search-based planning algorithms computationally expensive. We deal with the challenges of expensive search and collision checking in two ways. First, we frame the problem of anytime motion planning on roadmaps as searching for the shortest path over a sequence of subgraphs of the entire roadmap graph, generated by some densification strategy. This lets us achieve bounded sub-optimality with bounded worst-case planning effort. Second, for searching each subgraph, we develop an anytime planning algorithm which uses a belief model to compute the collision probability of unknown configurations and searches for paths that are Pareto-optimal in path length and collision probability. This algorithm is efficient with respect to collision checks as it searches for successively shorter paths. We theoretically analyze both our ideas and evaluate them individually on high-dimensional motion-planning problems. Finally, we apply both of these ideas together in our algorithmic framework for anytime motion planning, and show that it outperforms BIT* on high-dimensional hypercube problems. Copyright © 2017, The Authors. All rights reserved.

关键词： Motion planning

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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iCLAP: Shape recognition by combining proprioception and touch sensing

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arXiv 2018年

作者： Luo, Shan Mou, Wenxuan Althoefer, Kaspar Liu, Hongbin Center for Robotics Research Department of Informatics King's College London LondonWC2R 2LS United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Multimedia and Vision Research Group School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Engineering and Materials Science Queen Mary University of London LondonE1 4NS United Kingdom

For humans, both the proprioception and touch sensing are highly utilized when performing haptic perception. However, most approaches in robotics use only either proprioceptive data or touch data in haptic object recognition. In this paper, we present a novel method named Iterative Closest Labeled Point (iCLAP) to link the kinesthetic cues and tactile patterns fundamentally and also introduce its extensions to recognize object shapes. In the training phase, the iCLAP first clusters the features of tactile readings into a codebook and assigns these features with distinct label numbers. A 4D point cloud of the object is then formed by taking the label numbers of the tactile features as an additional dimension to the 3D sensor positions;hence, the two sensing modalities are merged to achieve a synthesized perception of the touched object. Furthermore, we developed and validated hybrid fusion strategies, product based and weighted sum based, to combine decisions obtained from iCLAP and single sensing modalities. Extensive experimentation demonstrates a dramatic improvement of object recognition using the proposed methods and it shows great potential to enhance robot perception ability. Copyright © 2018, The Authors. All rights reserved.

关键词： Object recognition

Polaritonic frequency-comb generation and breather propagation in a negative-index metamaterial with a cold four-level atomic medium

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arXiv 2019年

作者： Asgarnezhad-Zorgabad, Saeid Berini, Pierre Sanders, Barry C. Department of Physics Sharif University of Technology Tehran11365 11155 Iran Institute for Quantum Science and Technology University of Calgary CalgaryABT2N 1N4 Canada Department of Physics University of Ottawa 150 Louis-Pasteur OttawaONK1N 6N5 Canada Centre for Research in Photonics University of Ottawa 25 Templeton St. OttawaONK1N 6N5 Canada School of Electrical Engineering and Computer Science 700 King Edward St. OttawaONK1N 6N5 Canada Program in Quantum Information Science Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada

We develop a concept for a waveguide that exploits spatial control of nonlinear surface-polaritonic waves. Our scheme includes an optical cavity with four-level N-type atoms in a lossless dielectric placed above a negative-index metamaterial layer. We propose exciting a polaritonic Akhmediev breather at a certain position of the interface between the atomic medium and the metamaterial by modifying laser-field intensities and detunings. Furthermore, we propose generating position-dependent polaritonic frequency combs by engineering widths of the electromagnetically induced transparency window commensurate with the surface-polaritonic modulation instability. Therefore, this waveguide acts as a high-speed polaritonic modulator and position-dependent frequency-comb generator, which can be applied to compact photonic chips. Copyright © 2019, The Authors. All rights reserved.

关键词： Metamaterials

Eavesdropper’s ability to attack a free-space quantum-key-distribution receiver in atmospheric turbulence

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arXiv 2019年

作者： Chaiwongkhot, Poompong Kuntz, Katanya B. Zhang, Yanbao Huang, Anqi Bourgoin, Jean-Philippe Sajeed, Shihan Lütkenhaus, Norbert Jennewein, Thomas Makarov, Vadim Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada NTT Basic Research Laboratories NTT Corporation AtsugiKanagawa Japan NTT Research Center for Theoretical Quantum Physics NTT Corporation AtsugiKanagawa Japan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Aegis Quantum WaterlooON Canada Department of Electrical and Computer Engineering University of Toronto M5S 3G4 Canada Quantum Information Science Program Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada Russian Quantum Center Skolkovo Moscow143025 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

The ability of an eavesdropper (Eve) to perform an intercept-resend attack on a free-space quantum key distribution (QKD) receiver by precisely controlling the incidence angle of an attack laser has been previously demonstrated. However, such an attack could be ineffective in the presence of atmospheric turbulence due to beam wander and spatial mode aberrations induced by the air’s varying index of refraction. We experimentally investigate the impact turbulence has on Eve’s attack on a free-space polarization-encoding QKD receiver by emulating atmospheric turbulence with a spatial light modulator. Our results identify how well Eve would need to compensate for turbulence to perform a successful attack by either reducing her distance to the receiver, or using beam wavefront correction via adaptive optics. Furthermore, we use an entanglement-breaking scheme to find a theoretical limit on the turbulence strength that hinders Eve’s attack. Copyright © 2019, The Authors. All rights reserved.

关键词： Atmospheric turbulence

Stretchable Electronics: Nylon Fabric Enabled Tough and Flaw Insensitive Stretchable Electronics (Adv. Mater. Technol. 4/2019)

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Advanced Materials Technologies 2019年第4期4卷

作者： Kyoseung Sim Yang Gao Zhou Chen Jizhou Song Cunjiang Yu Materials Science and Engineering Program University of Houston Houston TX 77204 USA Department of Mechanical Engineering University of Houston Houston TX 77204 USA School of Mechanical and Power Engineering East China University of Science and Technology Shanghai 200237 China Department of Engineering Mechanics Soft Matter Research Center and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou Zhejiang 310027 China Department of Electrical and Computer Engineering Department of Biomedical Engineering University of Houston Houston TX 77204 USA

Yet another tutorial of disturbance observer： robust stabilization and recovery of nominal performance

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Control Theory and Technology 2016年第3期14卷 237-249页

作者： Hyungbo SHIM Gyunghoon PARK Youngjun JOO Juhoon BACK Nam Hoon JO ASRI Department of Electrical and Computer Engineering Seoul National University Korea Department of Electrical Engineering and Computer Science University of Central Florida U.S.A. School of Robotics Kwangwoon University Korea Department of Electrical Engineering Soongsil University Korea

This paper presents a tutorial-style review on the recent results about the disturbance observer （DOB） in view of robust stabilization and recovery of the nominal performance. The analysis is based on the case when the bandwidth of Q-filter is large, and it is explained in a pedagogical manner that, even in the presence of plant uncertainties and disturbances, the behavior of real uncertain plant can be made almost similar to that of disturbance-free nominal system both in the transient and in the steady-state. The conventional DOB is interpreted in a new perspective, and its restrictions and extensions are discussed.

关键词： Disturbance observer robust stabilization robust transient response disturbance rejection

Molybdenum Disulfide: Scalable Fabrication of Molybdenum Disulfide Nanostructures and their Assembly (Adv. Mater. 43/2020)

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Advanced Materials 2020年第43期32卷

作者： Yun Huang Kang Yu Huaizhi Li Kai Xu Zexi Liang Debora Walker Paulo Ferreira Peer Fischer Donglei (Emma) Fan Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA The Department of Advanced Electron Microscopy Imaging and Spectroscopy International Iberian Nanotechnology Laboratory Braga 4715‐330 Portugal Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Mechanical Engineering Department and IDMEC Instituto Superior Técnico University of Lisbon Av. Rovisco Pais Lisboa 1049‐001 Portugal Max‐Planck‐Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart 70569 Germany Institute of Physical Chemistry University of Stuttgart Pfaffenwaldring 55 Stuttgart 70569 Germany Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA

Increasing accuracy of classification physical activity based on smartphone using ensemble logistic regression with boosting method

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Journal of Physics: Conference Series 2019年第4期1341卷

作者： Lawi, A Aziz, F Wungo, S L Department of Computer Science Universitas Hasanuddin Makassar Indonesia. 90245 Faculty of Mathematics and Natural Sciences Universitas Pancasakti Makassar Indonesia. 90132 Program Study Informatics Engineering STMIK Kreatindo Monokwari Indonesia. 98300

At present, the smartphone is equipped with several sensors such as Accelerometer, Gravity sensor, and Gyroscope which can be used to recognize human physical activities such as walking upstair and walking downstairs etc. Machine learning is needed to group data and get information. Statistical methods have poor performance in classifying because procedures must be met. To overcome this, an ensemble technique was used. This study proposes the application of the gradientboost ensemble method to classify walking upstair and walking downstairs. The *** system is designed for data retrieval using a smartphone. Then, the dataset will be partitioned into 70% training data and 30% test data. The results show that the performance of the ensemble boosting method produces 81.82% accuracy, 86.11% sensitivity and 77.50% specificity.

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