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International Workshop on Pattern Recognition in NeuroImaging (PRNI)

作者： Alejandro Tabas Emili Balaguer-Ballester Laura Igual Faculty of Science and Technology Bournemouth University Faculty of Science and Technology Bournemouth University UK Berstein Center for Comp. Neuroscience Dept. of Applied Mathematics & Analysis Universitat de Barcelona Computer Vision Center of Barcelona

Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher's Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments.

关键词： Algorithm design and analysis Data mining Noise Brain Independent component analysis Standards computer architecture

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Forecasting a moving target: Ensemble models for ILI case count predictions 14

Forecasting a moving target: Ensemble models for ILI case co...

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14th SIAM International Conference on Data Mining, SDM 2014

作者： Chakraborty, Prithwish Khadivi, Pejman Lewis, Bryan Mahendiran, Aravindan Chen, Jiangzhuo Butler, Patrick Nsoesie, Elaine O. Mekaru, Sumiko R. Brownstein, John S. Marathe, Madhav V. Ramakrishnan, Naren Dept. of Computer Science Virginia Tech BlacksburgVA United States Discovery Analytics Center Virginia Tech BlacksburgVA United States Network Dynamics and Simulation Science Laboratory Virginia Bioinformatics Institute Virginia Tech BlacksburgVA United States Childrens Hospital Informatics Program Boston Childrens Hospital BostonMA United States Department of Pediatrics Harvard Medical School BostonMA United States

ISBN: (纸本)9781510811515

Modern epidemiological forecasts of common illnesses, such as the flu, rely on both traditional surveillance sources as well as digital surveillance data. However, most published studies have been retrospective. Concurrently, the reports about flu activity generally lags by several weeks and even when published are revised for several weeks more. We posit that effectively handling this uncertainty is one of the key challenges for a real-time prediction system in this sphere. In this paper, we present a detailed prospective analysis on the generation of robust quantitative predictions about temporal trends of flu activity, using several surrogate data sources for 15 Latin American countries. We present our findings about the limitations and possible advantages of correcting the uncertainty associated with official flu estimates. We also compare the prediction accuracy between model-level fusion of different surrogate data sources against data-level fusion. Finally, we present a novel matrix factorization approach using neighborhood embedding to predict flu case counts. Comparing our proposed ensemble method against several baseline methods helps us demarcate the importance of different data sources for the countries under consideration. Copyriqht © SIAM.

关键词： Forecasting

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MASS CALIBRATION OF OPTICALLY SELECTED DES CLUSTERS USING A MEASUREMENT OF CMB-CLUSTER LENSING WITH SPTpol DATA

arXiv

引用

arXiv 2018年

作者： Raghunathan, S. Patil, S. Baxter, E. Benson, B.A. Bleem, L.E. Chou, T.L. Crawford, T.M. Holder, G.P. McClintock, T. Reichardt, C.L. Rozo, E. Varga, T.N. Abbott, T.M.C. Ade, P.A.R. Allam, S. Anderson, A.J. Annis, J. Austermann, J.E. Avila, S. Beall, J.A. Bechtol, K. Bender, A.N. Bernstein, G. Bertin, E. Bianchini, F. Brooks, D. Burke, D.L. Carlstrom, J.E. Carretero, J. Chang, C.L. Chiang, H.C. Cho, H.-M. Citron, R. Crites, A.T. Cunha, C.E. da Costa, L.N. Davis, C. Desai, S. Diehl, H.T. Dietrich, J.P. Dobbs, M.A. Doel, P. Eifler, T.F. Everett, W. Evrard, A.E. Flaugher, B. Fosalba, P. Frieman, J. Gallicchio, J. García-Bellido, J. Gaztanaga, E. George, E.M. Gilbert, A. Gruen, D. Gruendl, R.A. Gschwend, J. Gupta, N. Gutierrez, G. de Haan, T. Halverson, N.W. Harrington, N. Hartley, W.G. Henning, J.W. Hilton, G.C. Hollowood, D.L. Holzapfel, W.L. Honscheid, K. Hou, Z. Hoyle, B. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. James, D.J. Jeltema, T. Kim, A.G. Carrasco Kind, M. Knox, L. Kovacs, A. Kuehn, K. Kuropatkin, N. Lee, A.T. Li, T.S. Lima, M. Maia, M.A.G. Marshall, J.L. McMahon, J.J. Melchior, P. Menanteau, F. Meyer, S.S. Miller, C.J. Miquel, R. Mocanu, L. Montgomery, J. Nadolski, A. Natoli, T. Nibarger, J.P. Novosad, V. Padin, S. Plazas, A.A. Pryke, C. Rapetti, D. Romer, A.K. Carnero Rosell, A. Ruhl, J.E. Saliwanchik, B.R. Sanchez, E. Sayre, J.T. Scarpine, V. Schaffer, K.K. Schubnell, M. Serrano, S. Sevilla-Noarbe, I. Smecher, G. Smith, R.C. Soares-Santos, M. Sobreira, F. Stark, A.A. Story, K.T. Suchyta, E. Swanson, M.E.C. Tarle, G. Thomas, D. Tucker, C. Vanderlinde, K. de Vicente, J. Vieira, J.D. Wang, G. Whitehorn, N. Wu, W.L.K. Zhang, Y. School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Physics and Astronomy University of California Los AngelesCA90095 United States Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States High Energy Physics Division Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States Department of Physics University of Chicago ChicagoIL60637 United States Department of Physics McGill University 3600 Rue University MontrealQCH3A 2T8 Canada Canadian Institute for Advanced Research CIFAR Program in Cosmology and Gravity TorontoONM5G 1Z8 Canada Department of Physics University of Arizona TucsonAZ85721 United States Max Planck Institute for Extraterrestrial Physics Giessenbachstrasse Garching85748 Germany Universitäts-Sternwarte Fakultät für Physik LudwigMaximilians Universität München Scheinerstr. 1 München81679 Germany Cerro Tololo Inter-American Observatory National Optical Astronomy Observatory Casilla 603 La Serena Chile Cardiff University CardiffCF10 3XQ United Kingdom Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Institute of Cosmology & Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom LSST 933 North Cherry Avenue TucsonAZ85721 United States CNRS UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Department of Physics & Astronomy University College London Gower

We use cosmic microwave background (CMB) temperature maps from the 500 deg2 SPTpol survey to measure the stacked lensing convergence of galaxy clusters from the Dark Energy Survey (DES) Year-3 redMaPPer (RM) cluster catalog. The lensing signal is extracted through a modified quadratic estimator designed to be unbiased by the thermal Sunyaev-Zel’dovich (tSZ) effect. The modified estimator uses a tSZ-free map, constructed from the SPTpol 95 and 150 GHz datasets, to estimate the background CMB gradient. For lensing reconstruction, we employ two versions of the RM catalog: a flux-limited sample containing 4003 clusters and a volume-limited sample with 1741 clusters. We detect lensing at a significance of 8.7σ(6.7σ) with the flux(volume)-limited sample. By modeling the reconstructed convergence using the Navarro-Frenk-White profile, we find the average lensing masses to be M200m = (1.62+0−0..3225 [stat.] ± 0.04 [sys.]) and (1.28+0−0..1418 [stat.] ± 0.03 [sys.]) ×1014 M☉ for the volume- and flux-limited samples respectively. The systematic error budget is much smaller than the statistical uncertainty and is dominated by the uncertainties in the RM cluster centroids. We use the volume-limited sample to calibrate the normalization of the mass-richness scaling relation, and find a result consistent with the galaxy weak-lensing measurements from DES (McClintock et al. 2018). Copyright © 2018, The Authors. All rights reserved.

关键词： Galaxies

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Deep Gaussian processes 16

Deep Gaussian processes

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16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013

作者： Damianou, Andreas C. Lawrence, Neil D. Dept. of Computer Science Sheffield Institute for Translational Neuroscience University of Sheffield Sheffield United Kingdom

In this paper we introduce deep Gaussian process (GP) models. Deep GPS are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We perform inference in the model by approximate variational marginalization. This results in a strict lower bound on the marginal likelihood of the model which we use for model selection (number of layers and nodes per layer). Deep belief networks are typically applied to relatively large data sets using stochastic gradient descent for optimization. Our fully Bayesian treatment allows for the application of deep models even when data is scarce. Model selection by our variational bound shows that a five layer hierarchy is justified even when modelling a digit data set containing only 150 examples. Copyright 2013 by the authors.

关键词： Gaussian distribution

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Dark energy survey year 1 results: Methodology and projections for joint analysis of galaxy Clustering, galaxy lensing, and CMB lensing two-point functions

arXiv

引用

arXiv 2018年

作者： Baxter, E.J. Omori, Y. Chang, C. Giannantonio, T. Kirk, D. Krause, E. Blazek, J. Bleem, L. Choi, A. Crawford, T.M. Dodelson, S. Gruen, D. Holder, G.P. Jain, B. Jarvis, M. MacCrann, N. Nicola, A. Pandey, S. Prat, J. Samuroff, S. Sánchez, C. Sheldon, E. Troxel, M.A. Zuntz, J. Abbott, T.M.C. Abdalla, F.B. Annis, J. Avila, S. Bechtol, K. Bertin, E. Brooks, D. Buckley-Geer, E. Burke, D.L. Rosell, A. Carnero Kind, M. Carrasco Carretero, J. Castander, F.J. Cawthon, R. Cunha, C.E. D'Andrea, C.B. Da Costa, L.N. Davis, C. De Vicente, J. DePoy, D.L. Diehl, H.T. Doel, P. Eifler, T.F. Estrada, J. Evrard, A.E. Flaugher, B. Fosalba, P. Frieman, J. García-Bellido, J. Gaztanaga, E. Gerdes, D.W. Gruendl, R.A. Gschwend, J. Gutierrez, G. Hartley, W.G. Hollowood, D. Hoyle, B. James, D.J. Kent, S. Kuehn, K. Kuropatkin, N. Lahav, O. Lima, M. Maia, M.A.G. March, M. Marshall, J.L. Melchior, P. Menanteau, F. Miquel, R. Plazas, A.A. Roodman, A. Rykoff, E.S. Sanchez, E. Schindler, R. Schubnell, M. Sevilla-Noarbe, I. Smith, M. Smith, R.C. Soares-Santos, M. Sobreira, F. Suchyta, E. Swanson, M.E.C. Tarle, G. Walker, A.R. Weller, J. Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Dept. of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United States Department of Physics McGill Space Institute McGill University MontrealQCH3A 2T8 Canada Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians Universität München Scheinerstr. 1 München81679 Germany Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Department of Astronomy/Steward Observatory 933 North Cherry Avenue TucsonAZ85721-0065 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. PasadenaCA91109 United States Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Observatoire de Sauverny Versoix1290 Switzerland Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Argonne National Laboratory 9700 South Cass Avenue LemontIL60439 United States Scottish Universities Physics Alliance Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Center for Cosmology and AstroParticle Physics Ohio State University 191 West Woodruff Avenue ColumbusOH43210 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Observatories of the Carnegie Institution of Washington 813 Santa Barbara St. PasadenaCA91101 United States Kavli Institute for Particle

Optical imaging surveys measure both the galaxy density and the gravitational lensing-induced shear fields across the sky. Recently, the Dark Energy Survey (DES) collaboration used a joint fit to two-point correlations between these observables to place tight constraints on cosmology (DES Collaboration et al. 2018). In this work, we develop the methodology to extend the DES year one joint probes analysis to include cross-correlations of the optical survey observables with gravitational lensing of the cosmic microwave background (CMB) as measured by the South Pole Telescope (SPT) and Planck. Using simulated analyses, we show how the resulting set of five two-point functions increases the robustness of the cosmological constraints to systematic errors in galaxy lensing shear calibration. Additionally, we show that contamination of the SPT+Planck CMB lensing map by the thermal Sunyaev-Zel'dovich effect is a potentially large source of systematic error for two-point function analyses, but show that it can be reduced to acceptable levels in our analysis by masking clusters of galaxies and imposing angular scale cuts on the two-point functions. The methodology developed here will be applied to the analysis of data from the DES, the SPT, and Planck in a companion work. Copyright © 2018, The Authors. All rights reserved.

关键词： Galaxies

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EHMTI-0174. Dural vessel hemodynamics during normal behavior and migraine attack

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The Journal of Headache and Pain 2014年第1期15卷 1-1页

作者： Y Gao P Drew Neuroscience Program Pennsylvania State University University Park USA Dept. Engineering Science and Mechanics & Dept. Neurosurgery Pennsylvania State University University Park USA

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CMB polarization B-mode delensing with SPTpol and Herschel

arXiv

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arXiv 2017年

作者： Manzotti, A. Story, K.T. Wu, W.L.K. Austermann, J.E. Beall, J.A. Bender, A.N. Benson, B.A. Bleem, L.E. Bock, J.J. Carlstrom, J.E. Chang, C.L. Chiang, H.C. Cho, H.-M. Citron, R. Conley, A. Crawford, T.M. Crites, A.T. de Haan, T. Dobbs, M.A. Dodelson, S. Everett, W. Gallicchio, J. George, E.M. Gilbert, A. Halverson, N.W. Harrington, N. Henning, J.W. Hilton, G.C. Holder, G.P. Holzapfel, W.L. Hoover, S. Hou, Z. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. Keisler, R. Knox, L. Lee, A.T. Leitch, E.M. Li, D. McMahon, J.J. Meyer, S.S. Mocanu, L.M. Natoli, T. Nibarger, J.P. Novosad, V. Padin, S. Pryke, C. Reichardt, C.L. Ruhl, J.E. Saliwanchik, B.R. Sayre, J.T. Schaffer, K.K. Smecher, G. Stark, A.A. Vanderlinde, K. Vieira, J.D. Viero, M.P. Wang, G. Whitehorn, N. Yefremenko, V. Zemcov, M. Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Dept. of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United States Department of Physics University of California BerkeleyCA94720 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States High Energy Physics Division Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States California Institute of Technology MS 367-17 1200 E. California Blvd. PasadenaCA91125 United States Jet Propulsion Laboratory PasadenaCA91109 United States Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Durban South Africa SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Department of Physics McGill University 3600 Rue University MontrealQCH3A 2T8 Canada Canadian Institute for Advanced Research CIFAR Program in Cosmology and Gravity TorontoONM5G 1Z8 Canada Department of Astrophysical and Planetary Sciences University of Colorado BoulderCO80309 United States Harvey Mudd College 301 Platt Blvd. ClaremontCA91711 United States Department of Physics University of Colorado BoulderCO80309 United States Astronomy Department University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States Department of Physics University of Illinois Urbana-Champ

We present a demonstration of delensing the observed cosmic microwave background (CMB) B-mode polarization anisotropy. This process of reducing the gravitational-lensing generated B-mode component will become increasingly important for improving searches for the B modes produced by primordial gravitational waves. In this work, we delens B-mode maps constructed from multi-frequency SPTpol observations of a 90 deg2 patch of sky by subtracting a B-mode template constructed from two inputs: SPTpol E-mode maps and a lensing potential map estimated from the Herschel 500 µm map of the CIB. We find that our delensing procedure reduces the measured B-mode power spectrum by 28% in the multipole range 300 Copyright © 2017, The Authors. All rights reserved.

关键词： Cosmology

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Evaluation of label dependency for the prediction of HLA genes 13

Evaluation of label dependency for the prediction of HLA gen...

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2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013

作者： Paunić, Vanja Steinbach, Michael Madbouly, Abeer Kumar, Vipin Dept. of Computer Science University of Minnesota Minneapolis MN 55455 United States Bioinformatics Research National Marrow Donor Program Minneapolis MN 55413 United States

ISBN: (纸本)9781450324342

The Human Leukocyte Antigen (HLA) gene system plays a crucial role in hematopoietic stem cell transplantation, where patients and donors are matched with respect to their HLA genes in order to maximize the chances of a successful transplant. It is the most polymorphic region of the human genome with some of the strongest associations with autoimmune, infectious, and inammatory diseases. The availability of HLA data is, therefore, of high importance to clinicians and researchers. However, due to its high polymorphism, obtaining it is time- And cost-prohibitive. We previously described a method for the prediction of HLA genes from widely available Single Nucleotide Polymorphism (SNP) data. In this paper we show that using HLA gene dependency information improves prediction performance on multiple real-world data sets. More specifically, we propose and evaluate different approaches for integrating HLA gene dependency into the prediction process. The results from experiments on two real data sets show that adding dependency information is a valuable asset for HLA gene prediction, particularly for smaller data sets. Copyright © 2007 by the Association for Computing Machinery.

关键词： Forecasting

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From size perception to counting: An evolutionary computation point of view

From size perception to counting: An evolutionary computatio...

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15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

作者： Katz, Gali Barabash Benbassat, Amit Diesendruck, Liana Sipper, Moshe Henik, Avishai Dept. of Cognitive Science Ben-Gurion University of Negev Beer-Sheva Israel Dept. of Computer Science Ben-Gurion University of Negev Beer-Sheva Israel National Center for Supercomputing Applications University of Illinois Urbana- Champaign IL United States Dept. of Psychology Zlotowski Center for Neuroscience Ben-Gurion University of Negev Beer-Sheva Israel

ISBN: (纸本)9781450319645

The ability to perceive size is shared by humans and animals. Babies present this basic ability from birth, and it improves with age. Counting, on the other hand, is a more complex task than size perception. We examined the theory that the counting system evolved from a more primitive system of size perception (the leading alternative being that the two systems evolved separately). By using evolutionary computation techniques, we generated artificial neural networks (ANNs) that excelled in size perception and presented a significant advantage in evolving the ability to count over those that evolved this ability from scratch. This advantage was observed also when evolving from ANNs that master other simple classification tasks. We also show that ANNs who train to perceive size of continuous stimuli present better counting skills than those that train with discrete stimuli.

关键词： Neural networks

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Social media communications networks and pharmacovigilance: SequelAE-2.0

Social media communications networks and pharmacovigilance: ...

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2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013

作者： Burger, Eric W. Federoff, Howard J. Fiandaca, Massimo S. Frieder, Ophir Goharian, Nazli Yates, Andrew Computer Science at Georgetown University Technology Officer of SequelAE2.0 Inc. United States Health Sciences and Executive School of Medicine Georgetown University United States Dept. of Neurology and Neuroscience Georgetown University Medical Center Executive Officer of SequelAE2.0 Inc. United States Department of Computer Science Georgetown University Georgetown University Medical Center United States Dept. of Computer Science Georgetown University United States Computer Science Georgetown University United States

ISBN: (纸本)9781467358019

We discuss a system for tracking a patient's response during a clinical trial or post-marketing survey that leverages social media and other Internet-based sharing technologies. The system has many modalities. This work-in-progress paper describes a modality where an informational item is posted to a social media platform, inducing patients to post trial-related responses. The system aggregates the patient's responses and other environmental information to obtain deeper knowledge related to the trial or post-marketing survey, sooner than would be possible using traditional methods. © 2013 IEEE.

关键词： Marketing

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