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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Ultra-thin, High-efficiency Mid-Infrared Transmissive Huygens Meta-Optics

arXiv

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arXiv 2017年

作者： Zheng, Hanyu Ding, Jun Zhang, Li An, Sensong Lin, Hongtao Zheng, Bowen Du, Qingyang Yin, Gufan Michon, Jerome Zhang, Yifei Fang, Zhuoran Deng, Longjiang Gu, Tian Zhang, Hualiang Hu, Juejun State Key Laboratory of Electronic Thin Films and Integrated Devices National Engineering Research Center of Electromagnetic Radiation Control Materials University of Electronic Science and Technology of China ChengduSichuan China Department of Materials Science & Engineering Massachusetts Institute of Technology CambridgeMA United States Department of Electrical & Computer Engineering University of Massachusetts Lowell LowellMA United States

The mid-infrared (mid-IR) is a strategically important band for numerous applications ranging from night vision to biochemical sensing. Unlike visible or near-infrared optical parts which are commonplace and economically available off-the-shelf, mid-IR optics often requires exotic materials or complicated processing, which accounts for their high cost and inferior quality compared to their visible or near-infrared counterparts. Here we theoretically analyzed and experimentally realized a Huygens metasurface platform capable of fulfilling a diverse cross-section of optical functions in the mid-IR. The meta-optical elements were constructed using high-index chalcogenide films deposited on fluoride substrates: the choices of wide-band transparent materials allow the design to be scaled across a broad infrared spectrum. Capitalizing on a novel two-component Huygens’ meta-atom design, the meta-optical devices feature an ultra-thin profile (λ0/8 in thickness, where λ is the free-space wavelength) and measured optical efficiencies up to 75% in transmissive mode, both of which represent major improvements over state-of-the-art. We have also demonstrated, for the first time, mid-IR transmissive meta-lenses with diffraction-limited focusing and imaging performance. The projected size, weight and power advantages, coupled with the manufacturing scalability leveraging standard microfabrication technologies, make the Huygens meta-optical devices promising for next-generation mid-IR system applications. Copyright © 2017, The Authors. All rights reserved.

关键词： Infrared devices

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

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arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation

arXiv

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arXiv 2017年

作者： Cong, Runmin Lei, Jianjun Fu, Huazhu Huang, Qingming Cao, Xiaochun Hou, Chunping School of Electrical and Information Engineering Tianjin University Tianjin300072 China Ocular Imaging Department Institute for Infocomm Research Agency for Science Technology and Research Singapore School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100093 China

Co-saliency detection aims at extracting the common salient regions from an image group containing two or more relevant images. It is a newly emerging topic in computer vision community. Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency. First, the intra saliency map for each image is generated by the single image saliency model, while the inter saliency map is calculated based on the multi-constraint feature matching, which represents the constraint relationship among multiple images. Then, the optimization scheme, namely Cross Label Propagation (CLP), is used to refine the intra and inter saliency maps in a cross way. Finally, all the original and optimized saliency maps are integrated to generate the final co-saliency result. The proposed method introduces the depth information and multi-constraint feature matching to improve the performance of co-saliency detection. Moreover, the proposed method can effectively exploit any existing single image saliency model to work well in co-saliency scenarios. Experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed model. Copyright © 2017, The Authors. All rights reserved.

关键词： Image enhancement

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An amateur drone surveillance system based on cognitive internet of things

arXiv

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arXiv 2017年

作者： Ding, Guoru Wu, Qihui Zhang, Linyuan Lin, Yun Tsiftsis, Theodoros Yao, Yu-Dong National Mobile Communications Research Laboratory Southeast University Nanjing210018 College of Communications Engineering Pla University of Science and Technology Nanjing210007 Department of Electronics and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210007 College of Information and Communication Engineering Harbin Engineering University Harbin School of Engineering Nazarbayev University Astana010000 Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

Drones, also known as mini-unmanned aerial vehicles, have attracted increasing attention due to their boundless applications in communications, photography, agriculture, surveillance and numerous public services. However, the deployment of amateur drones poses various safety, security and privacy threats. To cope with these challenges, amateur drone surveillance becomes a very important but largely unexplored topic. In this article, we firstly present a brief survey to show the state-of-the-art studies on amateur drone surveillance. Then, we propose a vision, named Dragnet, by tailoring the recent emerging cognitive internet of things framework for amateur drone surveillance. Next, we discuss the key enabling techniques for Dragnet in details, accompanied with the technical challenges and open issues. Furthermore, we provide an exemplary case study on the detection and classification of authorized and unauthorized amateur drones, where, for example, an important event is being held and only authorized drones are allowed to fly over. Copyright © 2017, The Authors. All rights reserved.

关键词： Internet of things

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Cellular automata applications in shortest path problem

arXiv

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arXiv 2017年

作者： Tsompanas, Michail-Antisthenis I. Dourvas, Nikolaos I. Ioannidis, Konstantinos Sirakoulis, Georgios Ch. Hoffmann, Rolf Adamatzky, Andrew University of the West of England BristolBS16 1QY United Kingdom Laboratory of Electronics Department of Electrical and Computer Engineering Democritus University of Thrace XanthiGR67100 Greece Information Technologies Institute Centre for Research and Technology Hellas 6th km CharilaouThermi Road Thermi Thessaloniki57001 Greece Department of Computer Science Technical University of Darmstadt Hochschulstrae 10 DarmstadtD-64289 Germany

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given. The CA-based model results are found in very good agreement with the computation results produced by the in-vivo exper

关键词： Cellular automata

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Nonlinear Model Predictive Formation Control for Quadcopters

引用

IFAC-PapersOnLine 2015年第19期48卷 39-44页

作者： Ribeiro, Tiago T. Conceição, André G.S. Sa, Inkyu Corke, Peter LaR - Robotics Laboratory Department of Electrical Engineering Federal University of Bahia Salvador Brazil Australian Centre for Robotic Vision School of Electrical Engineering and Computer Science Queensland University of Technology Australia

This paper presents a Nonlinear Model Predictive Formation Control for a group of Quadcopters. In this approach the controllers are distributed to the quadcopters and the coupling is done through its objective function. The path following problem is expressed through a dynamic model of the quadcopter's state errors relative to a Serret-Frenet frame, and the coordination problem is solved through adjustments in the robots' speeds combined with the path update rate for each robot. A strategy based on constraints relaxing is proposed to solve the problem of high state and formation errors, factors that directly affect feasibility and stability. Simulation results considering a realistic scenario are provided to demonstrate the performance of the proposed control strategy. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Problem solving Model predictive control Nonlinear systems Predictive control systems Unmanned aerial vehicles (UAV) Control strategies Coordination problems Formation control Nonlinear model predictive control Objective functions Path following problems Realistic scenario Serret Frenet frame

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Sensor localization using hybrid RF/optical wireless communications for an aerial data mule

Sensor localization using hybrid RF/optical wireless communi...

引用

American Control Conference

作者： Patricio J. Cruz Brian M. Sadler Rafael Fierro Department of Electrical and Computer Engineering University of New Mexico Albuquerque 87131 USA Army Research Laboratory Adelphi MD 20783 USA

ISBN: (纸本)9781467386838

In this paper we consider the problem of pairing a ground sensor with an aerial vehicle, both equipped with a hybrid communication system - radio frequency for non-line of sight transmission and optical for line-of-sight transmission. These communication technologies are complementary and by coordinating them it is possible to mitigate their respective weaknesses. A challenging problem is positioning the flying robot within optical communication range, especially when the distance is large and the sensor location is unknown. In this work, we propose a solution to the problem of autonomously localizing the sensor node relative to the aerial vehicle. We take advantage of the hybrid communication scheme by developing a control strategy that uses the radio signal to guide the aerial platform to the sensor node. Once the optical-based signal strength is over a desired threshold the robot hovers within optical range. The control strategy is demonstrated through simulations that incorporate a realistic model for the hybrid communication link.

关键词： Robot sensing systems Optical fiber communication Radio frequency Optical transmitters Optical sensors Optical receivers Vehicles vehicle Manipulator sensing systems radiofrequency Optical transmitters Optical receivers Optical fibre communication optical sensors control strategy Sensors aerial platforms aerospace robotics sensor placement RADIO SIGNALS

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Editorial

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International Journal of Social robotics 2015年第1期7卷 1-2页

作者： Ge, Shuzhi Sam Khatib, Oussama and the Robotics Research Lab Department of Electrical & Computer Engineering The National University of Singapore Singapore Singapore University of Electronic Science and Technology of China Chengdu China Artificial Intelligence Laboratory Department of Computer Science Stanford University StanfordCA United States

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Nonlinear Model Predictive Formation Control for Quadcopters

Nonlinear Model Predictive Formation Control for Quadcopters

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关键词： Unmanned aerial vehicles (UAV)

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