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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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Erratum: Ultrasound-driven 4D MRI (2018 Phys. Med. Biol. 63 145015)

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Physics in Medicine & Biology 2018年第17期63卷

作者： Alina Giger Marc Stadelmann Frank Preiswerk Christoph Jud Valeria De Luca Zarko Celicanin Oliver Bieri Rares Salomir Philippe C Cattin Department of Biomedical Engineering University of Basel Allschwil Switzerland Center for Medical Image Analysis & Navigation University of Basel Allschwil Switzerland Institute for Surgical Technology & Biomechanics University of Bern Bern Switzerland Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA United States of America Computer Vision Laboratory ETH Zurich Zurich Switzerland Now at the Novartis Institute for BioMedical Research Basel Switzerland Division of Radiological Physics Department of Radiology University of Basel Hospital Basel Switzerland Image Guided Interventions Laboratory Faculty of Medicine Radiology University of Geneva Geneva Switzerland

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Counterattack detection in broadcast soccer videos using camera motion estimation

Counterattack detection in broadcast soccer videos using cam...

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2015 International Symposium on Artificial Intelligence and Signal Processing, AISP 2015

作者： Sigari, Mohamad-Hoseyn Soltanian-Zadeh, Hamid Kiani, Vahid Pourreza, Amid-Reza Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Machine Vision Research Laboratory Computer Engineering Department Faculty of Engineering Ferdowsi University of Mashhad Mashhad Iran

ISBN: (纸本)9781479988174

This paper presents a new method for counterattack detection using estimated camera motion and evaluates some classification methods to detect this event. To this end, video is partitioned to shots and view type of each shot is recognized first. Then, relative pan of the camera during far-view and medium-view shots is estimated. After weighting of pan value of each frame according to the type of shots, the video is partitioned to motion segments. Then, motion segments are refined to achieve better results. Finally, the features extracted from consecutive motion segments are investigated for counterattack detection. We propose two methods for counterattack detection: (1) rule-based (heuristic rules) and (2) SVM-based. Experiments show that the SVM classifier with linear or RBF kernel results in the best results. © 2015 IEEE.

关键词： Motion estimation

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Visualization of changes in the foveal avascular zone in both observed and treated diabetic macular edema using optical coherence tomography angiography

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International Journal of Retina and Vitreous 2017年第1期3卷 19页

作者： Gill, Aditya Cole, Emily D. Novais, Eduardo A. Louzada, Ricardo N. de Carlo, Talisa Duker, Jay S. Waheed, Nadia K. Baumal, Caroline R. Witkin, Andre J. New England Eye Center Tufts Medical Center Tufts University 800 Washington Street Box 450 Boston 02111 MA United States Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge 02139 MA United States Department of Ophthalmology Federal University of São Paulo São Paulo Brazil Ophthalmic Center Reference (CEROF) Federal University of Goiás Goiânia Brazil

Background: Central vision loss in diabetic retinopathy is commonly related to diabetic macular edema (DME). The objective of this study was to describe changes between consecutive visits on optical coherence tomography angiography (OCTA) of the foveal avascular zone (FAZ) in eyes with DME. Methods: 20 eyes from 14 patients with DME were imaged on 2 successive clinic visits separated by at least 1 month. The mean interval between visits was 3.2 months. The only intervention used was intravitreal anti-VEGF in 11 eyes;the others were observed over time without treatment. Two different readers measured FAZ area using a pseudoautomated tool in comparison to a manual tracing tool. Qualitative changes in the appearance of the vasculature surrounding the FAZ were also recorded. The retinal capillary plexus was segmented into deep and superficial plexuses, and FAZ measurements were done on the superficial, deep, and summated plexuses. Results: Pseudo-automated and manual measurements of FAZ area decreased significantly (p < 0.05) between visits in the deep, superficial, and summated plexuses. Qualitative analysis of vasculature surrounding the FAZ showed that most of the vascular changes (65%) over time were visible in the deep plexus, compared to 30 and 20% in the superficial and summated plexuses, respectively. Conclusions: The most significant differences in FAZ size over time were in the summated plexus (p < 0.001), while changes in FAZ appearance were most prominent in the deep plexus. Absolute decrease in FAZ size over visits was largest in the deep plexus. Our results demonstrate that OCTA can effectively be used to measure FAZ area in patients with DME, visualize qualitative changes in retinal vasculature, and visualize the segmentation levels at which these changes can be best appreciated. However, larger studies are needed to evaluate the reproducibility of manual and pseudo-automated measuring techniques. © The Author(s) 2017.

关键词： Diabetic macular edema (DME) Foveal avascular zone (FAZ) Optical coherence tomography (OCT) Optical coherence tomography angiography (OCTA)

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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The Large Area Detector onboard the eXTP mission

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Proceedings of SPIE - The International Society for Optical engineering 2018年 10699卷

作者： Feroci, Marco Ahangarianabhari, Mahdi Ambrosi, Giovanni Ambrosino, Filippo Argan, Andrea Barbera, Marco Bayer, Joerg Bellutti, Pierluigi Bertucci, Bruna Bertuccio, Giuseppe Borghi, Giacomo Bozzo, Enrico Cadeaux, Franck Campana, Riccardo Ceraudo, Francesco Chen, Tianxiang Cirrincione, Daniela De Rosa, Alessandra Del Monte, Ettore DI Cosimo, Sergio DIebold, Sebastian Evangelista, Yuri Fan, Qingmei Favre, Yannick Ficorella, Francesco Fuschino, Fabio Gevin, Olivier Grassi, Marco Hong, Bin Mao, Hanqi Karas, Vladimir Kennedy, Tom Labanti, Claudio Limousin, Olivier Lo Cicero, Ugo Lu, Fangjun Luo, Tao Malcovati, Piero Martindale, Adrian Meuris, Aline Michalska, Malgrozata Morbidini, Alfredo Muleri, Fabio Orleanski, Piotr Paltani, Stephane Pan, Teng Perinati, Emanuele Picciotto, Antonino Pohl, Martin Rashvskaia, Irina Santangelo, Andrea Schanne, Stéphane Skup, Kornad Svoboda, Jiri Tenzer, Chris Vacchi, Andrea Walton, Dave Winter, Berend Wu, Xin Xu, Yupeng Zampa, Gianluigi Zampa, Nicola Zane, Silvia Zdziarski, Andrzej Zhang, Long Zhang, Shu Zhang, Shuangnan Zhang, Wenda Zhang, Xiaoli Zhou, Yupeng Zorzi, Nicola INAF Istituto di Astrofisica e Planetologia Spaziali Via Fosso del Cavaliere 100 Rome00133 Italy INFN Sezione Roma Tor Vergata Via della Ricerca Scientifica Rome00133 Italy Politecnico di Milano Polo di Como Via Anzani 41 Como Italy INFN Sezione di Perugia Via Alessandro Pascoli 23c Perugia06123 Italy Università di Palermo Dipartimento di Fisica e Chmica Via Archirafi 36 Palermo90123 Italy INAF Osservatorio Astronomico di Palermo Piazza del Parlamento 1 Palermo90134 Italy Institut fur Astronomie und Astrophysik Eberhard Karls Universitat Tuebingen72076 Germany Fondazione Bruno Kessler Via Sommarive Povo Trento38123 Italy University of Perugia Dip. Fisica e Geologia P.zza Università Perugia06123 Italy Department of Astronomy University of Geneva Chemin d'Ecogia 16 Versoix1290 Switzerland Department of Nuclear and Particle Physics University of Geneva CH-1211 Switzerland INAF Osservatorio di Astrofisica e Scienza Dello Spazio di Bologna Via P. Gobetti 101 Bologna Italy CEA Paris-Saclay DRF/IRFU Gif sur Yvette91191 France Key Laboratory for Particle Astrophysics Institute of High Energy Physics CAS Beijing100049 China University of Udine Via delle Scienze 206 Udine33100 Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Via Ferrata 5 Pavia27100 Italy North Night Vision Technology Co. Ltd Nanjing211106 China Astronomical Institute Czech Academy of Sciences Bocni II 1401 PragueCZ-14100 Czech Republic Mullard Space Science Laboratory UCL Holmbury St Mary Dorking SurreyRH56NT United Kingdom Space Research Centre Department of Physics and Astronomy University of Leicester LeicesterLE17RH United Kingdom Space Research Center Polish Academy of Sciences Bartycka 18a Warszawa00-716 Poland TIFPA Istituto Nazionale di Fisica Nucleare Via Sommarive 14 Povo Trento38123 Italy Nicolaus Copernicus Astronomical Center Polish Academy of Sciences Bartycka 18 WarszawaPL-00-716 Poland INFN

ISBN: (纸本)9781510619517

The eXTP (enhanced X-ray Timing and Polarimetry) mission is a major project of the Chinese Academy of Sciences (CAS) and China National Space Administration (CNSA) currently performing an extended phase A study and proposed for a launch by 2025 in a low-earth orbit. The eXTP scientific payload envisages a suite of instruments (Spectroscopy Focusing Array, Polarimetry Focusing Array, Large Area Detector and Wide Field Monitor) offering unprecedented simultaneous wide-band X-ray spectral, timing and polarimetry sensitivity. A large European consortium is contributing to the eXTP study and it is expected to provide key hardware elements, including a Large Area Detector (LAD). The LAD instrument for eXTP is based on the design originally proposed for the LOFT mission within the ESA context. The eXTP/LAD envisages a deployed 3.4 m2 effective area in the 2-30 keV energy range, achieved through the technology of the large-area Silicon Drift Detectors - offering a spectral resolution of up to 200 eV FWHM at 6 keV - and of capillary plate collimators - limiting the field of view to about 1 degree. In this paper we provide an overview of the LAD instrument design, including new elements with respect to the earlier LOFT configuration. © 2018 SPIE.

关键词： X ray detectors Buildings Earth (planet) Ellipsometry Gamma rays Orbits Polarimeters Silicon detectors Space telescopes X rays Capillary plate Chinese Academy of Sciences Large area detector Scientific payloads Silicon Drift Detector Space administration Timing X ray astronomy

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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