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Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Noname manuscript No. (will be inserted by the editor) Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos Hugo Jair Escalante∗ · Heysem Kaya∗ · Albert Ali Salah∗ · Sergio Escalera · Ya˘gmur G¨u¸cl¨ut¨urk · Umut G¨u¸cl¨u · Xavier Bar´o · Isabelle Guyon · Julio Jacques Junior · Meysam Madadi · Stephane Ayache · Evelyne Viegas · Furkan G¨urpınar · Achmadnoer Sukma Wicaksana · Cynthia C. S. Liem · Marcel A. J. van Gerven · Rob van Lier Received: date / Accepted: date ∗ Means equal contribution by the authors. Hugo Jair Escalante INAOE, Mexico and ChaLearn, USA E-mail: [email protected] Heysem Kaya Namık Kemal University, Department of Computer Engineering, Turkey E-mail: [email protected] Albert Ali Salah Bo˘gazi¸ci University, Dept. of Computer Engineering, Turkey and Nagoya University, FCVRC, Japan E-mail: [email protected] Furkan G¨urpınar Bo˘gazi¸ciUniversity, Computational Science and Engineering, Turkey E-mail: [email protected] Sergio Escalera University of Barcelona and Computer Vision Center, Spain E-mail: [email protected] Meysam Madadi Computer Vision Center, Spain E-mail: [email protected] Ya˘gmur G¨u¸cl¨ut¨urk,Umut G¨u¸cl¨u,Marcel A. J. van Gerven and Rob van Lier Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands E-mail: fy.gucluturk,u.guclu,m.vangerven,[email protected] Xavier Bar´oand Julio Jacques Junior Universitat Oberta de Catalunya and Computer Vision Center, Spain arXiv:1802.00745v3 [cs.CV] 29 Sep 2019 E-mail: fxbaro,[email protected] Isabelle Guyon UPSud/INRIA, Universit´eParis-Saclay, France and ChaLearn, USA 2 H.J. -
Terry Cole (1931-1999)
TERRY COLE (1931-1999) INTERVIEWED BY SHIRLEY K. COHEN October 11, 22 & 30, 1996 Photo by Robert Paz ARCHIVES CALIFORNIA INSTITUTE OF TECHNOLOGY Pasadena, California Subject area Chemistry, Jet Propulsion Laboratory Abstract Interview in three sessions, October 1996, with Terry Cole, senior faculty associate in the Division of Chemistry and Chemical Engineering and senior member of the technical staff of the Jet Propulsion Laboratory. Cole earned his BS in chemistry from the University of Minnesota in 1954 and his PhD from Caltech in 1958 under Don Yost, on magnetic resonance. The following year he moved to the Ford Scientific Research Laboratory, in Dearborn, Michigan, where he rose to head the departments of chemistry and chemical engineering. In 1980 he joined JPL’s Energy & Technology Applications branch; in 1982 he became JPL’s chief technologist, and he was instrumental in establishing JPL’s Microdevices Laboratory and its Center for Space Microelectronic Technology. Interview includes recollections of Lew Allen’s directorship of JPL and a discussion of the origins of the SURF (Summer Undergraduate Research Fellowship) program. http://resolver.caltech.edu/CaltechOH:OH_Cole_T Administrative information Access The interview is unrestricted. Copyright Copyright has been assigned to the California Institute of Technology © 2001, 2003. All requests for permission to publish or quote from the transcript must be submitted in writing to the University Archivist. Preferred citation Cole, Terry. Interview by Shirley K. Cohen. Pasadena, California, October 11, 22, and 30, 1996. Oral History Project, California Institute of Technology Archives. Retrieved [supply date of retrieval] from the World Wide Web: http://resolver.caltech.edu/CaltechOH:OH_Cole_T Contact information Archives, California Institute of Technology Mail Code 015A-74 Pasadena, CA 91125 Phone: (626)395-2704 Fax: (626)793-8756 Email: [email protected] Graphics and content © 2003 California Institute of Technology. -
Interview with Thomas K. Caughey
THOMAS K. CAUGHEY (1927–2004) INTERVIEWED BY CAROL BUGÉ March 25 and April 2, 1987 Photo Courtesy Caltech’s Engineering & Science ARCHIVES CALIFORNIA INSTITUTE OF TECHNOLOGY Pasadena, California Subject area Engineering Abstract Interview in two sessions in 1987 by Carol Bugé with Thomas Kirk Caughey, Professor of Applied Mechanics and Caltech alumnus (PhD, 1954). Caughey was born and educated in Scotland (bachelor's degree, University of Glasgow, 1948.) Comes to the U.S. with Fulbright to Cornell, where he completes his master's degree in mechanical engineering in 1952. He then earns his PhD at Caltech in 1954. He recalls Caltech’s engineering and physics faculty in the 1950s: H. Frederic Bohnenblust, Arthur Erdelyi, Richard P. Feynman, Tsien Hsue-shen. Begins teaching at Caltech in 1955; recalls Caltech’s Engineering Division under Frederick Lindvall; other engineers and physicists; compares engineering to other disciplines. Return to Cornell and earlier period: outstanding Cornell professors Feynman, Hans Bethe, Barney Rosser, Ed Gunder, Harry Conway; recalls grad student Ross Evan Iwanowski. Problems of physics degree program at Cornell. Professors http://resolver.caltech.edu/CaltechOH:OH_Caughey_T Gray and Bernard Hague at Glasgow University. Comparison between American and European educational systems. His research in dynamics. Earthquake research at Caltech: George Housner and Donald Hudson. Discusses physics and engineering entering a decade of decline; coming fields of genetic engineering, cognitive science and computing, neural networks, and artificial intelligence. Anecdotes about Fritz Zwicky and Charles Richter. Comments on coeducation at Caltech. Caltech personalities: Robert Millikan in his late years; Paul Epstein; Edward Simmons, Richard Gerke; William A. Fowler; further on Zwicky, Hudson; engineers Donald Clark, Alfred Ingersoll; early memories of Earnest Watson. -
Arxiv:2008.08516V1 [Cs.LG] 19 Aug 2020
Automated Machine Learning - a brief review at the end of the early years Hugo Jair Escalante Computer Science Department Instituto Nacional de Astrof´ısica, Optica´ y Electronica,´ Tonanzintla, Puebla, 72840, Mexico E-mail: [email protected] Abstract Automated machine learning (AutoML) is the sub-field of machine learning that aims at automating, to some extend, all stages of the design of a machine learning system. In the context of supervised learning, AutoML is concerned with feature extraction, pre processing, model design and post processing. Major contributions and achievements in AutoML have been taking place during the recent decade. We are there- fore in perfect timing to look back and realize what we have learned. This chapter aims to summarize the main findings in the early years of AutoML. More specifically, in this chapter an introduction to AutoML for supervised learning is provided and an historical review of progress in this field is presented. Likewise, the main paradigms of AutoML are described and research opportunities are outlined. 1 Introduction Automated Machine Learning or AutoML is a term coined by the machine learning community to refer to methods that aim at automating the design and development of machine learning systems and appli- cations [33]. In the context of supervised learning, AutoML aims at relaxing the need of the user in the loop from all stages in the design of supervised learning systems (i.e., any system relying on models for classification, recognition, regression, forecasting, etc.). This is a tangible need at present, as data are be- arXiv:2008.08516v1 [cs.LG] 19 Aug 2020 ing generated vastly and in practically any context and scenario, however, the number of machine learning experts available to analyze such data is overseeded. -
Robust Deep Learning: a Case Study Victor Estrade, Cécile Germain, Isabelle Guyon, David Rousseau
Robust deep learning: A case study Victor Estrade, Cécile Germain, Isabelle Guyon, David Rousseau To cite this version: Victor Estrade, Cécile Germain, Isabelle Guyon, David Rousseau. Robust deep learning: A case study. JDSE 2017 - 2nd Junior Conference on Data Science and Engineering, Sep 2017, Orsay, France. pp.1-5, 2017. hal-01665938 HAL Id: hal-01665938 https://hal.inria.fr/hal-01665938 Submitted on 17 Dec 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Robust deep learning: A case study Victor Estrade, Cecile Germain, Isabelle Guyon, David Rousseau Laboratoire de Recherche en Informatique Abstract. We report on an experiment on robust classification. The literature proposes adversarial and generative learning, as well as feature construction with auto-encoders. In both cases, the context is domain- knowledge-free performance. As a consequence, the robustness quality relies on the representativity of the training dataset wrt the possible perturbations. When domain-specific a priori knowledge is available, as in our case, a specific flavor of DNN called Tangent Propagation is an effective and less data-intensive alternative. Keywords: Domain adaptation, Deep Neural Networks, High Energy Physics 1 Motivation This paper addresses the calibration of a classifier in presence of systematic errors, with an example in High Energy Physics. -
Mlcheck– Property-Driven Testing of Machine Learning Models
MLCheck– Property-Driven Testing of Machine Learning Models Arnab Sharma Caglar Demir Department of Computer Science Data Science Group University of Oldenburg Paderborn University Oldenburg, Germany Paderborn, Germany [email protected] [email protected] Axel-Cyrille Ngonga Ngomo Heike Wehrheim Data Science Group Department of Computer Science Paderborn University University of Oldenburg Paderborn, Germany Oldenburg, Germany [email protected] [email protected] ABSTRACT 1 INTRODUCTION In recent years, we observe an increasing amount of software with The importance of quality assurance for applications developed machine learning components being deployed. This poses the ques- using machine learning (ML) increases steadily as they are being tion of quality assurance for such components: how can we validate deployed in a growing number of domains and sites. Supervised ML whether specified requirements are fulfilled by a machine learned algorithms “learn” their behaviour as generalizations of training software? Current testing and verification approaches either focus data using sophisticated statistical or mathematical methods. Still, on a single requirement (e.g., fairness) or specialize on a single type developers need to make sure that their software—whether learned of machine learning model (e.g., neural networks). or programmed—satisfies certain specified requirements. Currently, In this paper, we propose property-driven testing of machine two orthogonal approaches can be followed to achieve this goal: (A) learning models. Our approach MLCheck encompasses (1) a lan- employing an ML algorithm guaranteeing some requirement per guage for property specification, and (2) a technique for system- design, or (B) validating the requirement on the model generated atic test case generation. -
Conformational Transition in Immunoglobulin MOPC 460" by Correction. in Themembership List of the National Academy of Scien
Corrections Proc. Natl. Acad. Sci. USA 74 (1977) 1301 Correction. In the article "Kinetic evidence for hapten-induced Correction. In the membership list of the National Academy conformational transition in immunoglobulin MOPC 460" by of Sciences that appeared in the October 1976 issue of Proc. D. Lancet and I. Pecht, which appeared in the October 1976 Natl. Acad. Sci. USA 73,3750-3781, please note the following issue of Proc. Nati. Acad. Sci. USA 73,3549-3553, the authors corrections: H. E. Carter, Britton Chance, Seymour S. Cohen, have requested the following changes. On p. 3550, right-hand E. A. Doisy, Gerald M. Edelman, and John T. Edsall are affil- column, second line from bottom, and p. 3551, left-hand col- iated with the Section ofBiochemistry (21), not the Section of umn, fourth line from the top, "Fig. 2" should be "Fig. 1A." Botany (25). In the legend of Table 2, third line, note (f) should read "AG, = -RTlnKj." On p. 3553, left-hand column, third paragraph, fifth line, "ko" should be replaced by "Ko." Correction. In the Author Index to Volume 73, January-De- cember 1976, which appeared in the December 1976 issue of Proc. Natl. Acad. Sci. USA 73, 4781-4788, the limitations of Correction. In the article "Amino-terminal sequences of two computer alphabetization resulted in the listing of one person polypeptides from human serum with nonsuppressible insu- as the author of another's paper. On p. 4786, it should indicate lin-like and cell-growth-promoting activities: Evidence for that James Christopher Phillips had an article beginning on p. -
The Optical Mouse: Early Biomimetic Embedded Vision
The Optical Mouse: Early Biomimetic Embedded Vision Richard F. Lyon Abstract The 1980 Xerox optical mouse invention, and subsequent product, was a successful deployment of embedded vision, as well as of the Mead–Conway VLSI design methodology that we developed at Xerox PARC in the late 1970s. The de- sign incorporated an interpretation of visual lateral inhibition, essentially mimicking biology to achieve a wide dynamic range, or light-level-independent operation. Con- ceived in the context of a research group developing VLSI design methodologies, the optical mouse chip represented an approach to self-timed semi-digital design, with the analog image-sensing nodes connecting directly to otherwise digital logic using a switch-network methodology. Using only a few hundred gates and pass tran- sistors in 5-micron nMOS technology, the optical mouse chip tracked the motion of light dots in its field of view, and reported motion with a pair of 2-bit Gray codes for x and y relative position—just like the mechanical mice of the time. Besides the chip, the only other electronic components in the mouse were the LED illuminators. Fig. 1 The Xerox optical mouse chip in its injection- molded dual-inline package (DIP) of clear plastic, with pins stuck into a conductive packaging foam. The bond wires connecting the chip’s pads to the lead frame are (barely) visible. Richard F. Lyon Google Inc., Mountain View CA, e-mail: [email protected] 1 2 Richard F. Lyon Fig. 2 The Winter 1982 Xe- rox World internal magazine cover featuring the Electron- ics Division and their 3-button mechanical and optical mouse developments, among other electronic developments. -
CVPR 2019 Outstanding Reviewers We Are Pleased to Recognize the Following Researchers As “CVPR 2019 Outstanding Reviewers”
CVPR 2019 Cover Page CVPR 2019 Organizing Committee Message from the General and Program Chairs Welcome to the 32nd meeting of the IEEE/CVF Conference on papers, which allowed them to be free of conflict in the paper review Computer Vision and Pattern Recognition (CVPR 2019) at Long process. To accommodate the growing number of papers and Beach, CA. CVPR continues to be one of the best venues for attendees while maintaining the three-day length of the conference, researchers in our community to present their most exciting we run oral sessions in three parallel tracks and devote the entire advances in computer vision, pattern recognition, machine learning, technical program to accepted papers. robotics, and artificial intelligence. With oral and poster We would like to thank everyone involved in making CVPR 2019 a presentations, tutorials, workshops, demos, an ever-growing success. This includes the organizing committee, the area chairs, the number of exhibitions, and numerous social events, this is a week reviewers, authors, demo session participants, donors, exhibitors, that everyone will enjoy. The co-location with ICML 2019 this year and everyone else without whom this meeting would not be brings opportunity for more exchanges between the communities. possible. The program chairs particularly thank a few unsung heroes Accelerating a multi-year trend, CVPR continued its rapid growth that helped us tremendously: Eric Mortensen for mentoring the with a record number of 5165 submissions. After three months’ publication chairs and managing camera-ready and program efforts; diligent work from the 132 area chairs and 2887 reviewers, including Ming-Ming Cheng for quickly updating the website; Hao Su for those reviewers who kindly helped in the last minutes serving as working overtime as both area chair and AC meeting chair; Walter emergency reviewers, 1300 papers were accepted, yielding 1294 Scheirer for managing finances and serving as the true memory of papers in the final program after a few withdrawals. -
National Academy of Sciences July 1, 1973
NATIONAL ACADEMY OF SCIENCES JULY 1, 1973 OFFICERS Term expires President-PHILIP HANDLER June 30, 1975 Vice-President-SAUNDERS MAC LANE June 30, 1977 Home Secretary-ALLEN V. ASTIN June 30, 1975 Foreign Secretary-HARRISON BROWN June 30, 1974 Treasurer- E. R. PIORE June 30, 1976 Executive Officer Comptroller John S. Coleman Aaron Rosenthal Buiness Manager Bernard L. Kropp COUNCIL *Astin, Allen V. (1975) Marshak, Robert E. (1974) Babcock, Horace W. (1976) McCarty, Maclyn (1976) Bloch, Konrad E. (1974) Pierce, John R. (1974) Branscomb, Lewis M. (1975) *Piore, E. R. (1976) *Brown, Harrison (1974) Pitzer, Kenneth S. (1976) *Cloud, Preston (1975) *Shull, Harrison (1974) Eagle, Harry (1975) Westheimer, Frank H. (1975) *Handler, Philip (1975) Williams, Carroll M. (1976) *Mac Lane, Saunders (1977) * Members of the Executive Committee of the Council of the Academy. SECTIONS The Academy is divided into the following Sections, to which members are assigned at their own choice: (1) Mathematics (10) Microbiology (2) Astronomy (11) Anthropology (3) Physics (12) Psychology (4) Engineering (13) Geophysics (5) Chemistry (14) Biochemistry (6) Geology (15) Applied Biology (7) Botany (16) Applied Physical and Mathematical Sciences (8) Zoology (17) Medical Sciences (9) Physiology (18) Genetics (19) Social, Economic, and Political Sciences In the alphabetical list of members, the number in parentheses, following year of election, indicates the Section to WCiph the member belongs. 3009 Downloaded by guest on September 25, 2021 3010 Members N.A.S. Organization CLASSES Ames, Bruce Nathan, 1972 (14), Department of Biochem- istry, University of California, Berkeley, California The members of Sections are grouped in the following Classes: 94720 Anderson, Carl David, 1938 (3), California Institute of I. -
Curriculum Vitae John J. Hopfield
Curriculum Vitae John J. Hopfield, Professor Department of Molecular Biology Princeton University Princeton NJ 08544 Tel: (609) 258-1239 Fax: (609) 258-6730 Email: [email protected] Education Degree Year(s) Field Of Study Swarthmore College A.B 1954 Physics Cornell University Ph.D. 1958 Physics Research Experience 1958-1960 Member of Technical Staff, Bell Laboratories 1960-1961 Research Physicist, Ecole Normale Superieure, Paris 1961-1964 Assistant/Associate Professor of Physics, University of California, Berkeley 1964-1980 Professor of Physics, Princeton University 1973-1989 Member of Technical Staff, Bell Laboratories 1980-1997 Professor of Chemistry and Biology, California Institute of Technology 1997- Professor, Department of Molecular Biology, Princeton University Honors 1962-1964 Alfred Sloan Fellow 1969 Guggenheim Fellow (Cavendish, Cambridge, England) 1969 Buckley Prize, Am. Phys. Soc. 1983-88 John and Catherine T. MacArthur Award 1985 APS Prize in Biophysics 1988 Michelson-Morley Award, Case-Western Univ. 1989 The Wright Prize, Harvey Mudd College 1991 California Scientist of the Year, California Museum of Science and Industry 1992 Hon. D. Sci. Swarthmore College 1997 Neural Network Pioneer Award, IEEE 1999 International Neural Network Society Helmholtz Award 2001 Dirac Medal and Prize, International Center for Theoretical Physics, Trieste 2002 Pender Award, Moore School of Engineering, University of Pennsylvania 1979-80 Eugene Higgins Professor of Physics, Princeton 1980-97 Roscoe Gilkey Dickinson Professor, California -
Safety and Robustness Certification of Neural Networks with Abstract
AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri∗, Martin Vechev Department of Computer Science ETH Zurich, Switzerland Abstract—We present AI2, the first sound and scalable an- Attack Original Perturbed Diff alyzer for deep neural networks. Based on overapproximation, AI2 can automatically prove safety properties (e.g., robustness) FGSM [12], = 0:3 of realistic neural networks (e.g., convolutional neural networks). The key insight behind AI2 is to phrase reasoning about safety and robustness of neural networks in terms of classic abstract Brightening, δ = 0:085 interpretation, enabling us to leverage decades of advances in that area. Concretely, we introduce abstract transformers that capture the behavior of fully connected and convolutional neural Fig. 1: Attacks applied to MNIST images [25]. network layers with rectified linear unit activations (ReLU), as well as max pooling layers. This allows us to handle real-world neural networks, which are often built out of those types of layers. The FGSM [12] attack perturbs an image by adding to it a 2 We present a complete implementation of AI together with particular noise vector multiplied by a small number (in an extensive evaluation on 20 neural networks. Our results Fig. 1, = 0:3). The brightening attack (e.g., [32]) perturbs demonstrate that: (i) AI2 is precise enough to prove useful specifications (e.g., robustness), (ii) AI2 can be used to certify an image by changing all pixels above the threshold 1 − δ to the effectiveness of state-of-the-art defenses for neural networks, the brightest possible value (in Fig.