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Human Vision and Electronic Imaging 2017, HVEI 2017

作者： Wichmann, Felix A. Janssen, David H.J. Geirhos, Robert Aguilar, Guillermo Schütt, Heiko H. Maertens, Marianne Bethge, Matthias Neural Information Processing Group University of Tübingen Germany Bernstein Center for Computational Neuroscience Tübingen Germany Max Planck Institute for Intelligent Systems Tübingen Germany Faculty of Computer Science and Electrical Engineering TU Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Department of Psychology University of Potsdam Germany Centre for Integrative Neuroscience University of Tübingen Germany

Recent advances in computational models in vision science have considerably furthered our understanding of human visual perception. At the same time, rapid advances in convolutional deep neural networks (DNNs) have resulted in computer vision models of object recognition which, for the first time, rival human object recognition. Furthermore, it has been suggested that DNNs may not only be successful models for computer vision, but may also be good computational models of the monkey and human visual systems. The advances in computational models in both vision science and computer vision pose two challenges in two different and independent domains: First, because the latest computational models have much higher predictive accuracy, and competing models may make similar predictions, we require more human data to be able to statistically distinguish between different models. Thus we would like to have methods to acquire trustworthy human behavioural data fast and easy. Second, we need challenging experiments to ascertain whether models show similar input-output behaviour only near "ceiling" performance, or whether their performance degrades similar to human performance: only then do we have strong evidence that models and human observers may be using similar features and processing strategies. In this paper we address both challenges. © 2017, Society for Imaging science and Technology.

关键词： Deep neural networks

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Comparison of Polarized Radiative Transfer Codes Used by the EHT Collaboration

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The Astrophysical Journal 2023年第1期950卷 35-35页

作者： Ben S. Prather Jason Dexter Monika Moscibrodzka Hung-Yi Pu Thomas Bronzwaer Jordy Davelaar Ziri Younsi Charles F. Gammie Roman Gold George N. Wong Kazunori Akiyama Antxon Alberdi Walter Alef Juan Carlos Algaba Richard Anantua Keiichi Asada Rebecca Azulay Uwe Bach Anne-Kathrin Baczko David Ball Mislav Baloković John Barrett Michi Bauböck Bradford A. Benson Dan Bintley Lindy Blackburn Raymond Blundell Katherine L. Bouman Geoffrey C. Bower Hope Boyce Michael Bremer Christiaan D. Brinkerink Roger Brissenden Silke Britzen Avery E. Broderick Dominique Broguiere Sandra Bustamante Do-Young Byun John E. Carlstrom Chiara Ceccobello Andrew Chael Chi-kwan Chan Dominic O. Chang Koushik Chatterjee Shami Chatterjee Ming-Tang Chen Yongjun Chen Xiaopeng Cheng Ilje Cho Pierre Christian Nicholas S. Conroy John E. Conway James M. Cordes Thomas M. Crawford Geoffrey B. Crew Alejandro Cruz-Osorio Yuzhu Cui Mariafelicia De Laurentis Roger Deane Jessica Dempsey Gregory Desvignes Vedant Dhruv Sheperd S. Doeleman Sean Dougal Sergio A. Dzib Ralph P. Eatough Razieh Emami Heino Falcke Joseph Farah Vincent L. Fish Ed Fomalont H. Alyson Ford Raquel Fraga-Encinas William T. Freeman Per Friberg Christian M. Fromm Antonio Fuentes Peter Galison Roberto García Olivier Gentaz Boris Georgiev Ciriaco Goddi Arturo I. Gómez-Ruiz José L. Gómez Minfeng Gu Mark Gurwell Kazuhiro Hada Daryl Haggard Kari Haworth Michael H. Hecht Ronald Hesper Dirk Heumann Luis C. Ho Paul Ho Mareki Honma Chih-Wei L. Huang Lei Huang David H. Hughes Shiro Ikeda C. M. Violette Impellizzeri Makoto Inoue Sara Issaoun David J. James Buell T. Jannuzi Michael Janssen Britton Jeter Wu Jiang Alejandra Jiménez-Rosales Michael D. Johnson Svetlana Jorstad Abhishek V. Joshi Taehyun Jung Mansour Karami Ramesh Karuppusamy Tomohisa Kawashima Garrett K. Keating Mark Kettenis Dong-Jin Kim Jae-Young Kim Jongsoo Kim Junhan Kim Motoki Kino Jun Yi Koay Prashant Kocherlakota Yutaro Kofuji Shoko Koyama Carsten Kramer Michael Kramer Thomas P. Krichbaum Cheng-Yu Kuo Noemi La Bella Tod R. Lauer Daeyoung L CCS-2 Los Alamos National Laboratory P.O. Box 1663 Los Alamos NM 87545 USA bprather@lanl.gov JILA and Department of Astrophysical and Planetary Sciences University of Colorado Boulder CO 80309 USA Department of Astrophysics/IMAPP Radboud University P.O. Box 9010 6500 GL Nijmegen The Netherlands Department of Physics National Taiwan Normal University No. 88 Sec.4 Tingzhou Road Taipei 116 Taiwan R.O.C. Center of Astronomy and Gravitation National Taiwan Normal University No. 88 Sec. 4 Tingzhou Road Taipei 116 Taiwan R.O.C. Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan R.O.C. Department of Astronomy and Columbia Astrophysics Laboratory Columbia University 550 W. 120th Street New York NY 10027 USA Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New York NY 10010 USA Department of Astrophysics Institute for Mathematics Astrophysics and Particle Physics (IMAPP) Radboud University P.O. Box 9010 6500 GL Nijmegen The Netherlands Mullard Space Science Laboratory University College London Holmbury St. Mary Dorking Surrey RH5 6NT UK Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 D-60438 Frankfurt am Main Germany Department of Physics University of Illinois 1110 West Green Street Urbana IL 61801 USA Department of Astronomy University of Illinois at Urbana-Champaign 1002 West Green Street Urbana IL 61801 USA NCSA University of Illinois 1205 W. Clark Street Urbana IL 61801 USA CP3-Origins University of Southern Denmark Campusvej 55 DK-5230 Odense M Denmark School of Natural Sciences Institute for Advanced Study 1 Einstein Drive Princeton NJ 08540 USA Princeton Gravity Initiative Princeton University Princeton NJ 08544 USA Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road Westford MA 01886 USA National Astronomical Observatory of Japan 2-21-1 Osaw

Interpretation of resolved polarized images of black holes by the Event Horizon Telescope (EHT) requires predictions of the polarized emission observable by an Earth-based instrument for a particular model of the black hole accretion system. Such predictions are generated by general relativistic radiative transfer (GRRT) codes, which integrate the equations of polarized radiative transfer in curved spacetime. A selection of ray-tracing GRRT codes used within the EHT Collaboration is evaluated for accuracy and consistency in producing a selection of test images, demonstrating that the various methods and implementations of radiative transfer calculations are highly consistent. When imaging an analytic accretion model, we find that all codes produce images similar within a pixel-wise normalized mean squared error (NMSE) of 0.012 in the worst case. When imaging a snapshot from a cell-based magnetohydrodynamic simulation, we find all test images to be similar within NMSEs of 0.02, 0.04, 0.04, and 0.12 in Stokes I, Q, U, and V, respectively. We additionally find the values of several image metrics relevant to published EHT results to be in agreement to much better precision than measurement uncertainties.

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Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

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Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

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DCAR: A Discriminative and Compact Audio Representation for Audio processing

DCAR: A Discriminative and Compact Audio Representation for ...

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作者： Jing, Liping Liu, Bo Choi, Jaeyoung Janin, Adam Bernd, Julia Mahoney, Michael W. Friedland, Gerald Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China College of Information Science and Technology Agricultural University of Hebei Hebei071000 China International Computer Science Institute BerkeleyCA94704 United States Delft University of Technology Delft2628 Netherlands Department of Statistics University of California Berkeley BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States Lawrence Livermore National Laboratory LivermoreCA94550 United States

This paper presents a novel two-phase method for audio representation, discriminative and compact audio representation (DCAR), and evaluates its performance at detecting events and scenes in consumer-produced videos. In the first phase of DCAR, each audio track is modeled using a Gaussian mixture model (GMM) that includes several components to capture the variability within that track. The second phase takes into account both global structure and local structure. In this phase, the components are rendered more discriminative and compact by formulating an optimization problem on a Grassmannian manifold. The learned components can effectively represent the structure of audio. Our experiments used the YLI-MED and DCASE Acoustic Scenes datasets. The results show that variants on the proposed DCAR representation consistently outperform four popular audio representations (mv-vector, i-vector, GMM, and HEM-GMM). The advantage is significant for both easier and harder discrimination tasks;we discuss how these performance differences across tasks follow from how each type of model leverages (or does not leverage) the intrinsic structure of the data. © 2017 IEEE.

关键词： Gaussian distribution

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Generalized Fréchet bounds for cell entries in multidimensional contingency tables

arXiv

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arXiv 2017年

作者： Uhler, Caroline Richards, Donald Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Department of Statistics Pennsylvania State University University ParkPA16802 United States

We consider the lattice, L, of all subsets of a multidimensional contingency table and establish the properties of monotonicity and supermodularity for the marginalization function, n(·), on L. We derive from the supermodularity of n(·) some generalized Fréchet inequalities complementing and extending inequalities of Dobra and Fienberg. Further, we construct new monotonic and supermodular functions from n(·), and we remark on the connection between supermodularity and some correlation inequalities for probability distributions on lattices. We also apply an inequality of Ky Fan to derive a new approach to Fréchet inequalities for multidimensional contingency *** 2010 subject classifications: Primary 06D05, 62H17;Secondary 05A20, *** Codes 06D05, 62H17 (Primary), 05A20, 62J12 (Secondary) Copyright © 2017, The Authors. All rights reserved.

关键词： Probability distributions

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Fast Deformable Model for Pedestrian Detection with Haar-like features

Fast Deformable Model for Pedestrian Detection with Haar-lik...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Kuang-Pen Chou Mukesh Prasad Deepak Puthal Ping-Hung Chen Dinesh Kumar Vishwakarma Suresh Sundarami Chin-Teng Lin Wen-Chieh Lin Institute of Computer Science and Engineering National Chiao-Tung University Hsinchu Taiwan Centre for Artificial Intelligence University of Technology Sydney Australia School of Electrical and Data Engineering University of Technology Sydney Australia Institute of Electrical and Control Engineering National Chiao-Tung University Hsinchu Taiwan Department of Electronics & Communication Engineering Delhi Technological University India Nanyang Technological University Singapore Singapore SG Department of Computer Science National Chiao-Tung University Hsinchu Taiwan

This paper proposes a novel Fast Deformable Model for Pedestrian Detection (FDMPD) to detect the pedestrians efficiently and accurately in the crowded environment. Despite of multiple detection methods available, detection becomes difficult due to variety of human postures and perspectives. The proposed study is divided into two parts. First part trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. Second part uses these six response feature maps with full-body model to produce spatial deep features. The combined deep features are used as an input to SVM to judge the existence of pedestrian. As per the experiments conducted on the INRIA person dataset, the proposed FDMPD approach shows greater than 44.75 % improvement compared to other state-of-the-art methods in terms of efficiency and robustness.

关键词： Feature extraction Support vector machines Magnetic heads Computational modeling Knee Head Adaptation models

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Graphene-Based Josephson-Junction Single-Photon Detector

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Physical Review Applied 2017年第2期8卷 024022-024022页

作者： Evan D. Walsh Dmitri K. Efetov Gil-Ho Lee Mikkel Heuck Jesse Crossno Thomas A. Ohki Philip Kim Dirk Englund Kin Chung Fong Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA ICFO-Institut de Ciències Fotòniques The Barcelona Institute of Science and Technology 08860 Castelldefels Barcelona Spain Department of Physics Harvard University Cambridge Massachusetts 02138 USA Department of Physics Pohang University of Science and Technology Pohang 790-784 Republic of Korea Raytheon BBN Technologies Quantum Information Processing Group Cambridge Massachusetts 02138 USA

We propose to use graphene-based Josephson junctions (GJJs) to detect single photons in a wide electromagnetic spectrum from visible to radio frequencies. Our approach takes advantage of the exceptionally low electronic heat capacity of monolayer graphene and its constricted thermal conductance to its phonon degrees of freedom. Such a system could provide high-sensitivity photon detection required for research areas including quantum information processing and radio astronomy. As an example, we present our device concepts for GJJ single-photon detectors in both the microwave and infrared regimes. The dark count rate and intrinsic quantum efficiency are computed based on parameters from a measured GJJ, demonstrating feasibility within existing technologies.

关键词： Electron thermal conductivity Hybrid quantum systems Photonics Quantum measurements Thermal conductivity Graphene Josephson junctions Photon counting

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Unveiling the role of Co-O-Mg bond in magnetic anisotropy of Pt/Co/MgO using atomically controlled deposition and in situ electrical measurement

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Physical Review B 2017年第9期95卷 094417-094417页

作者： Yumeng Yang Jiaren Yuan Long Qi Ying Wang Yanjun Xu Xiaowei Wang Yuanping Feng Baoxi Xu Lei Shen Yihong Wu Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Singapore College of Science Nanjing University of Aeronautics and Astronautics Nanjing 210016 People's Republic of China Department of Physics Centre for Advanced Two-dimensional Materials National University of Singapore Singapore 117542 Singapore Data Storage Institute Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way 08-01 Innovis Singapore 138634 Singapore Department of Mechanical Engineering Engineering Science Programme Faculty of Engineering National University of Singapore Singapore 117575 Singapore

Despite the crucial role of interfacial perpendicular magnetic anisotropy in a Co(Fe)/MgO-based magnetic tunnel junction, the underlying mechanism is still being debated. Here, we report an anatomical study of the oxygen and Mg effect on Pt/Co bilayers through repeated in situ anomalous Hall effect measurements, controlled oxygen exposure, and Mg deposition in an ultrahigh vacuum system. We found that chemisorbed oxygen not only quenches the effective magnetic moment of the Co surface layer, but also softens its magnetic anisotropy. However, a subsequent Mg dusting on the oxygen preexposed Pt/Co surface can recover the magnetic anisotropy. The ab initio calculations on the exchange splitting and orbital hybridization near the Fermi level give a clear physical explanation of the experimental observations. Our results suggest that the Co(Fe)-O-M bond plays a more important role than the widely perceived Co(Fe)-O bond does in realizing interfacial perpendicular magnetic anisotropy in Co(Fe)/MgO heterostructures.

关键词： Magnetic anisotropy Spintronics Ab initio calculations

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Spectral Structure of Elastic Neumann–Poincaré Operators

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Journal of Physics: Conference Series 2018年第1期965卷

作者： Yoshihisa Miyanishi Kazunori Ando Hyeonbae Kang Center for Mathematical Modeling and Data Science Osaka University Osaka 560-8631 Japan Department of Electrical and Electronic Engineering and Computer Science Ehime University Ehime 790-8577 Japan Department of Mathematics and Institute of Applied Mathematics Inha University Incheon 22212 S. Korea

In [1, 2], it is proved that the elastic Neumann–Poincaré operator defined on the smooth boundary of a bounded domain, which is known to be non-compact, is in fact polynomially compact. As a consequence, it is shown that the spectrum of the elastic Neumann-Poincaré operator consists of non-empty sets of eigenvalues accumulating to certain numbers determined by Lamé parameters. The purpose of this paper is to review these results and their proofs, and to discuss about some questions related to these results.

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Mathematics of deep learning

arXiv

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arXiv 2017年

作者： Vidal, René Bruna, Joan Giryes, Raja Soatto, Stefano Center for Imaging Science Biomedical Engineering Johns Hopkins University Baltimore United States Courant Institute of Mathematical Sciences Center for Data Science New York University United States School of Electrical Engineering Tel-Aviv University Tel Aviv Israel Department of Computer Science University of California Los Angeles United States

Recently there has been a dramatic increase in the performance of recognition systems due to the introduction of deep architectures for representation learning and classification. However, the mathematical reasons for this success remain elusive. This tutorial will review recent work that aims to provide a mathematical justification for several properties of deep networks, such as global optimality, geometric stability, and invariance of the learned representations. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

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