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Proceedings of the 2016 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

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Learning theory for distribution regression

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Zoltán Szabó Bharath K. Sriperumbudur Barnabás Póczos Arthur Gretton Google MPI for Intelligent Systems Applied Mathematics Department Center for Applied Mathematics École Polytechnique University of Paris-Saclay Palaiseau Cedex France and University College London London UK Department of Statistics Pennsylvania State University University Park PA Machine Learning Department School of Computer Science Carnegie Mellon University Pittsburgh PA University College London London UK

We focus on the distribution regression problem: regressing to vector-valued outputs from probability measures. Many important machine learning and statistical tasks fit into this framework, including multi-instance learning and point estimation problems without analytical solution (such as hyperparameter or entropy estimation). Despite the large number of available heuristics in the literature, the inherent two-stage sampled nature of the problem makes the theoretical analysis quite challenging, since in practice only samples from sampled distributions are observable, and the estimates have to rely on similarities computed between sets of points. To the best of our knowledge, the only existing technique with consistency guarantees for distribution regression requires kernel density estimation as an intermediate step (which often performs poorly in practice), and the domain of the distributions to be compact Euclidean. In this paper, we study a simple, analytically computable, ridge regression-based alternative to distribution regression, where we embed the distributions to a reproducing kernel Hilbert space, and learn the regressor from the embeddings to the outputs. Our main contribution is to prove that this scheme is consistent in the two-stage sampled setup under mild conditions (on separable topological domains enriched with kernels): we present an exact computational-statistical efficiency trade-off analysis showing that our estimator is able to match the one-stage sampled minimax optimal rate (Caponnetto and De Vito, 2007; Steinwart et al., 2009). This result answers a 17-year-old open question, establishing the consistency of the classical set kernel (Haussler, 1999; Gärtner et al., 2002) in regression. We also cover consistency for more recent kernels on distributions, including those due to Christmann and Steinwart (2010).

关键词： Kernel ridge regression mean embedding minimax optimality multi-instance learning two-Stage sampled distribution regression

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Secure communication through jammers jointly optimized in geography and time 15

Secure communication through jammers jointly optimized in ge...

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16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2015

作者： Allouche, Yair Cassuto, Yuval Efrat, Alon Segal, Michael Arkin, Esther M. Grebla, Guy Mitchell, Joseph S. B. Sankararaman, Swaminathan Dept. of Communication Systems Engineering Ben-Gurion University Beer-Sheva Israel Dept. Electrical Engineering Technion – Israel Institute of Technology Haifa32000 Israel Dept. of Computer Science University of Arizona TucsonAZ85721 United States Dept. Applied Mathematics and Statistics Stony Brook University Stony BrookNY11794 United States Dept. Electrical Engineering Columbia University New YorkNY10027 United States Dept. of Applied Mathematics and Statistics Stony Brook University Stony BrookNY11794 United States Akamai Technologies CambridgeMA02142 United States

ISBN: (纸本)9781450334891

Security-sensitive applications, such as patient health monitoring and credit card transactions, are increasingly utilizing wireless communication systems, RFIDs, wireless sensor networks, and other wireless communication systems. The use of interference-emitting jammers to protect these sensitive communications has been recently explored in the literature, and has shown high potential. In this paper we consider optimization problems relating to the temporal distributions of jammers’ activity, and the suitable coding regimes used for communication. Solving the joint problem optimally enables comprehensive security in space, at a low power consumption and low communication overhead. The joint optimization of jamming in space and time is driven by a new framework that uses the bit-error probability as a measure of communication quality. Under this framework, we show how to guarantee information-theoretic security within a geographic region, and with increased flexibility to tailor the coding regime to the problem’s geometry. We present efficient algorithms for different settings, and provide simulations for various scenarios using the bit-error probability functions. These simulations demonstrate the efficiency of the scheme. We believe that our scheme can lead to practical, economical and scalable solutions for providing another layer of protection of sensitive data, in cases where encryption schemes are limited or impractical. Copyright © 2015 ACM.

关键词： Sensitive data

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Measuring dependence powerfully and equitably

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Yakir A. Reshef David N. Reshef Hilary K. Finucane Pardis C. Sabeti Michael Mitzenmacher Google MPI for Intelligent Systems School of Engineering and Applied Sciences Harvard University Cambridge MA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA Department of Mathematics Massachusetts Institute of Technology Cambridge MA Department of Organismic and Evolutionary Biology Harvard University Cambridge MA

Given a high-dimensional data set, we often wish to find the strongest relationships within it. A common strategy is to evaluate a measure of dependence on every variable pair and retain the highest-scoring pairs for follow-up. This strategy works well if the statistic used (a) has good power to detect non-trivial relationships, and (b) is equitable, meaning that for some measure of noise it assigns similar scores to equally noisy relationships regardless of relationship type (e.g., linear, exponential, periodic). In this paper, we define and theoretically characterize two new statistics that together yield an efficient approach for obtaining both power and equitability. To do this, we first introduce a new population measure of dependence and show three equivalent ways that it can be viewed, including as a canonical "smoothing" of mutual information. We then introduce an efficiently computable consistent estimator of our population measure of dependence, and we empirically establish its equitability on a large class of noisy functional relationships. This new statistic has better bias/variance properties and better runtime complexity than a previous heuristic approach. Next, we derive a second, related statistic whose computation is a trivial side-product of our algorithm and whose goal is powerful independence testing rather than equitability. We prove that this statistic yields a consistent independence test and show in simulations that the test has good power against independence. Taken together, our results suggest that these two statistics are a valuable pair of tools for exploratory data analysis.

关键词： equitability maximal information coefficient mutual information statistical power total information coefficient

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Towards end-user driven power saving control in Android devices

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14th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and systems, NEW2AN 2014 and 7th Conference on Internet of Things and Smart Spaces, ruSMART 2014

作者： Bernardo, Vitor Correia, Bruno Curado, Marilia Braun, Torsten Ingo Center for Informatics and Systems University of Coimbra Coimbra Portugal Institute for Computer Science and Applied Mathematics University of Bern Bern Switzerland

ISBN: (纸本)9783319103525

During the last decade mobile communications increasingly became part of people's daily routine. Such usage raises new challenges regarding devices' battery lifetime management when using most popular wireless access technologies, such as IEEE 802.11. This paper investigates the energy/delay trade-off of using an end-user driven power saving approach, when compared with the standard IEEE 802.11 power saving algorithms. The assessment was conducted in a real testbed using an Android mobile phone and high-precision energy measurement hardware. The results show clear energy benefits of employing user-driven power saving techniques, when compared with other standard approaches. © 2014 Springer International Publishing.

关键词： Energy efficiency

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Four data visualization heuristics to facilitate reflection in personal informatics

Four data visualization heuristics to facilitate reflection ...

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8th International Conference on Universal Access in Human-computer Interaction, UAHCI 2014 - Held as Part of 16th International Conference on Human-computer Interaction, HCI International 2014

作者： Cuttone, Andrea Petersen, Michael Kai Larsen, Jakob Eg Department of Applied Mathematics and Computer Science Cognitive Systems Section Matematiktorvet 303B 2800 Kgs Lyngby Denmark

ISBN: (纸本)9783319075082

In this paper we discuss how to facilitate the process of reflection in Personal Informatics and Quantified Self systems through interactive data visualizations. Four heuristics for the design and evaluation of such systems have been identified through analysis of self-tracking devices and apps. Dashboard interface paradigms in specific self-tracking devices (Fitbit and Basis) are discussed as representative examples of state of the art in feedback and reflection support. By relating to existing work in other domains, such as event related representation of time series multivariate data in financial analytics, it is discussed how the heuristics could guide designs that would further facilitate reflection in self-tracking personal informatics systems. © 2014 Springer International Publishing.

关键词： Data visualization

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Lyapunov function as potential function: A dynamical equivalence

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Chinese Physics B 2014年第1期23卷 136-141页

作者：袁若石马易安苑波敖平 School of Biomedical Engineering Shanghai Jiao Tong University Shanghai Center for Systems Biomedicine Shanghai Jiao Tong University Department of Applied Mathematics University of Washington Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Physics Shanghai Jiao Tong University

For a physical system, regardless of time reversal symmetry, a potential function serves also as a Lyapunov function, providing convergence and stability information. In this paper, the converse is constructively proved that any dynamics with a Lyapunov function has a corresponding physical realization： a friction force, a Lorentz force, and a potential function. Such construction establishes a set of equations with physical meaning for Lyapunov function and suggests new approaches on the significant unsolved problem namely to construct Lyapunov functions for general nonlinear systems. In addition, a connection is found that the Lyapunov equation is a reduced form of a generalized Einstein relation for linear systems, revealing further insights of the construction.

关键词： Lyapunov function potential function stochastic process generalized Einstein relation

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Artificial Neural Network and Mobile Applications in Medical Diagnosis

Artificial Neural Network and Mobile Applications in Medical...

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International Conference on computer Modeling and Simulation, UKSIM

作者： Gillian Pearce Julian Wong Lela Mirtskhulava Salah Al-Majeed Koba Bakuria Nana Gulua School of Engineering and Applied Science Aston University Aston Birmingham United Kingdom Department of Computer Science Javakhishvili Tbilisi State University Tbilisi Georgia Department of Information Technologies Georgian technical University Tbilisi Georgia Department of Cardiac National University Heart Centre Singapore Systems Engineering Department Military Technological College Muscat Oman Faculty of Mathematics and Computer Science Sokhumi State University Tbilisi Georgia

ISBN: (纸本)9781479987146

The aim of this paper is to present a pilot study regarding the application of an ANN to stroke recognition and diagnosis. Our system makes use of a (i) a neural network that can be trained to recognize normal limb movements (for individual patients), which may then be coupled to (ii) a physical grid mattress that can be used in the patient's home. Any changes in the patient's movement could potentially indicate that stroke has occurred are transmitted to a mobile phone app. The latter in turn alerts a relative or ambulance to render rapid assistance to the individual. When stroke has occurred it is essential to transfer the patient to hospital very quickly in order that treatment can be given promptly. In the case of strokes that have arisen due to a blood clot in the cerebral circulation of the brain, a drug called Alteplase (an anti-thrombolytic) must be given within 4.5 hours of the stroke occurring to be maximally effective. Therefore it is important to know the exact time on stroke onset. Our system would record the time of onset of the stroke, by recognizing and recording abnormal changes in the individual's limb movements. A Feed forward neural network was used in our modeling.

关键词： Computational modeling Mathematical model computer science Surgery Information technology

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The status of KAGRA underground cryogenic gravitational wave telescope

arXiv

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arXiv 2017年

作者： Akutsu, T. Ando, M. Araya, A. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Barton, M.A. Cannon, K. Craig, K. Creus, W. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Furuhata, T. Haino, S. Hasegawa, K. Hashino, K. Hayama, K. Hirobayashi, S. Hirose, E. Hsieh, B.H. Inoue, Y. Ioka, K. Itoh, Y. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kambara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kang, G. Kasuya, J. Kataoka, Y. Kawai, N. Kawamura, S. Kim, C. Kim, H. Kim, J. Kim, Y. Kimura, N. Kinugawa, T. Kirii, S. Kitaoka, Y. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kumar, R. Lee, H. Lee, H. Liu, Y. Luca, N. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsushima, F. Michimura, Y. Miyakawa, O. Miyamoto, T. Miyamoto, A. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Morozumi, T. Musha, M. Nagano, S. Nagano, K. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakao, K. Narikawa, T. Nguyen Quynh, L. Ni, W.-T. Ochi, T. Oh, J. Oh, S. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Oohara, K. Peña Alleano, F.E. Pinto, I. Sago, N. Saijo, M. Saito, Y. Sakai, K. Sakai, Y. Sasaki, Y. Sasaki, M. Sato, S. Sato, T. Sekiguchi, Y. Seto, N. Shibata, M. Shimoda, T. Shinkai, H. Shoda, A. Somiya, K. Son, E. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. Tomaru, T. Tomura, T. Travasso, F. Tsubono, K. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueki, S. Ueno, K. Ushiba, T. van Putten, M.H.P.M. Vocca, H. Wada, S. Wakamatsu, T. Yamada, T. Yamamoto, S. Yamamoto, T. Yamamoto, K. Yamamoto, A. Yokoyama, J. Yokozawa, T. Yoon, T.H. Yuzurihara, H. Zeidler, S. Zhu, Z.-H. National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan Department of Physics University of Tokyo Bunkyo Tokyo113-0033 Japan Earthquake Research Institute University of Tokyo Bunkyo Tokyo113-0032 Japan Department of Advanced Physics Hirosaki University Hirosaki Aomori036-8561 Japan National Astronomical Observatory of Japan Hida Gifu506-1205 Japan Department of Physics Tokyo Institute of Technology Meguro Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Hida Gifu506-1205 Japan Research Center for the Early Universe University of Tokyo Bunkyo Tokyo113-0033 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa Chiba277-8582 Japan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics University of Toyama Toyama Toyama930-8555 Japan Faculty of Engineering University of Toyama Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Sakyo Kyoto606-8502 Japan Department of Physics Osaka City University Sumiyosi Osaka558-8585 Japan Department of Physics Osaka University Toyonaka Osaka560-0043 Japan Korea Institute of Science and Technology Information Yuseong Daejeon34141 Korea Republic of Daejeon Yuseong34055 Korea Republic of National Institute for Mathematical Sciences Daejeon34047 Korea Republic of Department of Computer Simulation Inje University Gimhae Gyeongsangnam50834 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of High Energy Accelerator Research Organization Tsukuba Ibaraki305-0801 Japan Department of Physical Science Hiroshima University Higashihiroshima Hiroshima903-0213 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka814-0180 Japan Department of Advanced Materials Science University of Tokyo Kashiwa Chiba277-8582 Japan Istituto Nazionale di Fisica Nucleare Sapienza University Roma00185 Italy D

KAGRA is a 3-km interferometric gravitational wave telescope located in the Kamioka mine in Japan. It is the first km-class gravitational wave telescope constructed underground to reduce seismic noise, and the first km-class telescope to use cryogenic cooling of test masses to reduce thermal noise. The construction of the infrastructure to house the interferometer in the tunnel, and the initial phase operation of the interferometer with a simple 3-km Michelson configuration have been completed. The first cryogenic operation is expected in 2018, and the observing runs with a full interferometer are expected in 2020s. The basic interferometer configuration and the current status of KAGRA are described. Copyright © 2017, The Authors. All rights reserved.

关键词： Interferometers

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