Kernel Regression in the Presence of Correlated Errors Kernel Regression in the Presence in nonparametric regression is difficult in the presence of correlated errors. There exist a wide variety vector machines for regression. Keywords: nonparametric regression, correlated errors, bandwidth choice
SUBSPACE REGRESSION IN REPRODUCING KERNEL HILBERT SPACE
SUBSPACE REGRESSION IN REPRODUCING KERNEL HILBERT SPACE L. Hoegaerts #3; J.A.K. Suykens #3; J: We focus on three methods for finding a suitable subspace for regression in a reproducing kernel correlation analysis and we demonstrate how this fits within a more general context of subspace regression
Quantile Regression with 1-regularization and Gaussian Kernels
Quantile Regression with 1-regularization and Gaussian Kernels Lei Shi1,2 , Xiaolin Huang1 , Zheng Sciences, Fudan University Shanghai 200433, P. R. China Abstract The quantile regression problem of 1-quantile regression with Gaussian kernels is almost the same as that of the RKHS-based learning
Using the Equivalent Kernel to Understand Gaussian Process Regression
Sollich, Peter
Using the Equivalent Kernel to Understand Gaussian Process Regression Peter Sollich Dept.k.i.williams@ed.ac.uk Abstract The equivalent kernel [1] is a way of understanding how Gaussian pro cess regression works processes. Consider the supervised regression problem for a dataset D with entries (x i ; y i ) for i = 1
Using the Equivalent Kernel to Understand Gaussian Process Regression
Sollich, Peter
Using the Equivalent Kernel to Understand Gaussian Process Regression Peter Sollich Dept.k.i.williams@ed.ac.uk Abstract The equivalent kernel [1] is a way of understanding how Gaussian pro- cess regression works processes. Consider the supervised regression problem for a dataset D with entries (xi, yi) for i = 1
Kernel Machine Based Feature Extraction Algorithms for Regression Problems
Szepesvari, Csaba
Kernel Machine Based Feature Extraction Algorithms for Regression Problems Csaba Szepesv´ari 1 and Andr´as Kocsor and Korn´el Kov´acs 2 Abstract. In this paper we consider two novel kernel machine based performance of the algorithm. The second algo- rithm combines kernel machines with average derivative
Kernel Regression with Correlated Errors K. De Brabanter
Kernel Regression with Correlated Errors K. De Brabanter , J. De Brabanter , , J.A.K. Suykens B: It is a well-known problem that obtaining a correct bandwidth in nonparametric regression is difficult support vector machines for regression. Keywords: nonparametric regression, correlated errors, short
Kernel Regression For Determining Photometric Redshifts From Sloan Broadband Photometry
D. Wang; Y. X. Zhang; C. Liu; Y. H. Zhao
2007-06-20
We present a new approach, kernel regression, to determine photometric redshifts for 399,929 galaxies in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS). In our case, kernel regression is a weighted average of spectral redshifts of the neighbors for a query point, where higher weights are associated with points that are closer to the query point. One important design decision when using kernel regression is the choice of the bandwidth. We apply 10-fold cross-validation to choose the optimal bandwidth, which is obtained as the cross-validation error approaches the minimum. The experiments show that the optimal bandwidth is different for diverse input patterns, the least rms error of photometric redshift estimation arrives at 0.019 using color+eClass as the inputs, the less rms error amounts to 0.020 using ugriz+eClass as the inputs. Here eClass is a galaxy spectra type. Then the little rms scatter is 0.021 with color+r as the inputs.
Kernel regression estimates of time delays between gravitationally lensed fluxes
Otaibi, Sultanah AL; Cuevas-Tello, Juan C; Mandel, Ilya; Raychaudhury, Somak
2015-01-01
Strongly lensed variable quasars can serve as precise cosmological probes, provided that time delays between the image fluxes can be accurately measured. A number of methods have been proposed to address this problem. In this paper, we explore in detail a new approach based on kernel regression estimates, which is able to estimate a single time delay given several datasets for the same quasar. We develop realistic artificial data sets in order to carry out controlled experiments to test of performance of this new approach. We also test our method on real data from strongly lensed quasar Q0957+561 and compare our estimates against existing results.
Functional inverse regression and reproducing kernel Hilbert space
Ren, Haobo
2006-10-30
and Reproducing Kernel Hilbert Space. (August 2005) Haobo Ren, B.S., Peking University; M.S., Peking University Chair of Advisory Committee: Dr. Tailen Hsing The basic philosophy of Functional Data Analysis (FDA) is to think of the observed data functions... component analysis of ? . Duan and Li (1991) and Li (1997) presented more delicate results for analyzing single- index regression by SIR, Hsing and Carroll (1992) and Zhu and Ng (1995) derived the large sample properties of SIR based on ?-delta, Chen and Li...
Parameter-insensitive kernel in extreme learning for non-linear support vector regression
Verleysen, Michel
Parameter-insensitive kernel in extreme learning for non-linear support vector regression Beno Available online 12 May 2011 Keywords: Extreme learning machine Support vector regression ELM kernel Infinite number of neurons a b s t r a c t Support vector regression (SVR) is a state-of-the-art method
A Unified Kernel Regression for Diffusion Wavelets on Manifolds Detects Aging-Related
Chung, Moo K.
for constructing wavelets on manifolds using a complicated machinery employed in previous studies [6,7]. Although as a solution to penalized regressions, which significantly differ from our framework that does not have any] that projects the statistical results to a surface for interpretation. 2 Kernel Regression and Wavelets
Approximate l-fold cross-validation with Least Squares SVM and Kernel Ridge Regression
Edwards, Richard E [ORNL] [ORNL; Zhang, Hao [ORNL] [ORNL; Parker, Lynne Edwards [ORNL] [ORNL; New, Joshua Ryan [ORNL] [ORNL
2013-01-01
Kernel methods have difficulties scaling to large modern data sets. The scalability issues are based on computational and memory requirements for working with a large matrix. These requirements have been addressed over the years by using low-rank kernel approximations or by improving the solvers scalability. However, Least Squares Support VectorMachines (LS-SVM), a popular SVM variant, and Kernel Ridge Regression still have several scalability issues. In particular, the O(n^3) computational complexity for solving a single model, and the overall computational complexity associated with tuning hyperparameters are still major problems. We address these problems by introducing an O(n log n) approximate l-fold cross-validation method that uses a multi-level circulant matrix to approximate the kernel. In addition, we prove our algorithm s computational complexity and present empirical runtimes on data sets with approximately 1 million data points. We also validate our approximate method s effectiveness at selecting hyperparameters on real world and standard benchmark data sets. Lastly, we provide experimental results on using a multi-level circulant kernel approximation to solve LS-SVM problems with hyperparameters selected using our method.
Wisconsin at Madison, University of
for constructing wavelets on manifolds using a complicated machinery employed in previous studies [6, 7]. Although as a solution to penalized regressions, which significantly differ from our framework that does not have any] that projects the statistical results to a surface for interpretation. 2 Kernel Regression and Wavelets
Jaillet, Patrick
Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov comes at a cost of poor scalability in the data size. To improve its scalability, this paper presents with larger data). As a result, our LMA method can trade off between the size of the support set and the or
The curse of dimension in nonparametric regression
Kpotufe, Samory
2010-01-01
3.2.1 Kernel regression . . . . . . . . . . . . . . . . . .3.2.2 k-NN regression . . . . . . . . . . . . . . . . . .1.1 Nonparametric regression . . . . . . . . . . . 1.1.1
Bayesian Analysis of Curves Shape Variation through Registration and Regression
Telesca, Donatello
2015-01-01
Wehrly (1986). Kernel regression estimation using repeatedadaptive Bayesian penalized regression splines (P-splines).self-modeling nonlinear regression. The Annals of Statistics
MULTIVARIATE NONPARAMETRIC REGRESSION AND VISUALIZATION
Klemelä, Jussi
not be available in electronic format. Library of Congress Cataloging-in-Publication Data: Klemel¨a, Jussi AND CLASSIFICATION 1 Overview of Regression and Classification 3 2 Linear Methods and Extensions 77 3 Kernel Methods Visualization xxi I.4 Literature xxiii PART I METHODS OF REGRESSION AND CLASSIFICATION 1 Overview of Regression
Adaptive wiener image restoration kernel
Yuan, Ding (Henderson, NV)
2007-06-05
A method and device for restoration of electro-optical image data using an adaptive Wiener filter begins with constructing imaging system Optical Transfer Function, and the Fourier Transformations of the noise and the image. A spatial representation of the imaged object is restored by spatial convolution of the image using a Wiener restoration kernel.
Matrix kernels for measures on partitions
Eugene Strahov
2008-09-10
We consider the problem of computation of the correlation functions for the z-measures with the deformation (Jack) parameters 2 or 1/2. Such measures on partitions are originated from the representation theory of the infinite symmetric group, and in many ways are similar to the ensembles of Random Matrix Theory of $\\beta=4$ or $\\beta=1$ symmetry types. For a certain class of such measures we show that correlation functions can be represented as Pfaffians including $2\\times 2$ matrix valued kernels, and compute these kernels explicitly. We also give contour integral representations for correlation kernels of closely connected measures on partitions.
Unsupervised State-Space Modelling Using Reproducing Kernels
Tobar, Felipe; Djuri?, Petar M.; Mandic, Danilo P.
2015-06-22
gradient- based methods. These include ridge regression (RR) in the offline case, and least mean square (LMS) and recursive least squares (RLS) in online cases. These linear estimation algorithms are the basis of kernel adaptive filters. [ACCEPTED... in red. the observation signal yt using kernels and an LMS-based update rule; see [41]. Kernel State-Space Model (KSSM): The adaptive version of the proposed method, where the predictions are gener- ated by propagating the particles of the state according...
Jan de Leeuw
2011-01-01
values. And then we perform an ordinary monotone regression.nally mention that monotone regression can be generalized inMONOTONE REGRESSION JAN DE LEEUW Abstract. This is an entry
Linear Regression and Support Vector Regression
Shi, Qinfeng "Javen"
Linear Regression and Support Vector Regression Paul Paisitkriangkrai paulp@cs.adelaide.edu.au The University of Adelaide 18 August 2014 #12;Outlines · Regression overview · Linear regression · Support vector regression · Machine learning tools available #12;Regression Overview CLUSTERING CLASSIFICATION REGRESSION
Reduced Harmonic Representation of Partitions
Michalis Psimopoulos
2011-03-08
In the present article the reduced integral representation of partitions in terms of harmonic products has been derived first by using hypergeometry and the new concept of fractional sum and secondly by studying the Fourier series of the kernel function appearing in the integral representation. Using the method of induction, a generalization of the theory has also been obtained.
Daume III, Hal
Robust RVM Regression Using Sparse Outlier Model Kaushik Mitra, Ashok Veeraraghavan* and Rama, vashok, rama}@umiacs.umd.edu Abstract Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many com- puter
Slicing Regression: Dimension Reduction via Inverse Regression
Ker-Chau Li
2011-01-01
Semiparametric Additive Regression," unpublished manuscript.N . , and L i , K . C. (in press), "Slicing Regression: aLink-Free Regression Method," The Annals of Statistics.
Convex Deep Learning via Normalized Kernels Ozlem Aslan
Schuurmans, Dale
Convex Deep Learning via Normalized Kernels ¨Ozlem Aslan Dept of Computing Science University Deep learning has been a long standing pursuit in machine learning, which until recently was hampered meth- ods while expanding the range of representable structures toward deep models. In this paper, we
Column-Generation Boosting Methods for Mixture of Kernels
Chandy, John A.
Column-Generation Boosting Methods for Mixture of Kernels Jinbo Bi Computer-Aided Diagnosis approach to classification and regres- sion based on column generation using a mixture of ker- nels logistic regression, etc. The idea is to map data into Permission to make digital or hard copies of all
Quantum Energy Regression using Scattering Transforms
Matthew Hirn; Nicolas Poilvert; Stephane Mallat
2015-02-06
We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation. A scattering transform is a deep convolution network computed with a cascade of multiscale wavelet transforms. It possesses appropriate invariant and stability properties for quantum energy regression. This new framework removes fundamental limitations of Coulomb matrix based energy regressions, and numerical experiments give state-of-the-art accuracy over planar molecules.
Quantum Energy Regression using Scattering Transforms
Hirn, Matthew; Mallat, Stephane
2015-01-01
We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation. A scattering transform is a deep convolution network computed with a cascade of multiscale wavelet transforms. It possesses appropriate invariant and stability properties for quantum energy regression. This new framework removes fundamental limitations of Coulomb matrix based energy regressions, and numerical experiments give state-of-the-art accuracy over planar molecules.
Kernel Carpentry for Online Regression using Randomly Varying Coefficient Model
Edakunni, Narayanan U.; Schaal, Stefan; Vijayakumar, Sethu
2006-01-01
We present a Bayesian formulation of locally weighted learning (LWL) using the novel concept of a randomly varying coefficient model. Based on this
Jan de Leeuw
2011-01-01
Stone. Classi?cation and Regression Trees. Wadsworth, 1984.Projection Pursuit Regression. Journal of the AmericanK.C. Li. Sliced Inverse Regression for Dimension Reduction (
Gerber, Samuel
2013-01-01
1984). Classification and Regression Trees. Monterey, CA:Piecewise-polynomial regression trees. Statistica Sinica 4,BART: Bayesian additive regression trees. Ann. Appl. Stat.
Nonlinear models Nonlinear Regression
Penny, Will
Nonlinear models Will Penny Nonlinear Regression Nonlinear Regression Priors Energies Posterior Metropolis-Hasting Proposal density References Nonlinear models Will Penny Bayesian Inference Course, WTCN, UCL, March 2013 #12;Nonlinear models Will Penny Nonlinear Regression Nonlinear Regression Priors
Piecewise Convex Function Estimation: Representations, Duality and Model Selection
functionals correspond to variable halfwidth data-adaptive kernels, i.e. the e#11;ective halfwidth decreasesPiecewise Convex Function Estimation: Representations, Duality and Model Selection Kurt S. Riedel
Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
Chen, Sheng
for costly model evaluation. Index Terms -- orthogonal forward regression, structure identification, cross struc- ture construction process as a cost function in order to op- timize the model generalization introduces a construction algorithm for sparse kernel modelling using the leave-one-out test score also known
Regression problems for magnitudes
Castellaro, Silvia; Mulargia, Francesco; Kagan, Yan Y
2006-01-01
through general orthogonal regression with ? = 0.25 (? m2 =use and misuse of orthogonal regression in linear errors-in-A procedure for linear regression capable of less biased
Choquistic Regression: Generalizing Logistic Regression Using the Choquet Integral
Hüllermeier, Eyke
Choquistic Regression: Generalizing Logistic Regression Using the Choquet Integral Ali Fallah, called choquistic regression, which generalizes conventional logistic regression and takes advantage regression (2) Generalization to choquistic regression (3) First experimental results 2 #12;Logistic
Bruemmer, David J. (Idaho Falls, ID)
2009-11-17
A robot platform includes perceptors, locomotors, and a system controller. The system controller executes a robot intelligence kernel (RIK) that includes a multi-level architecture and a dynamic autonomy structure. The multi-level architecture includes a robot behavior level for defining robot behaviors, that incorporate robot attributes and a cognitive level for defining conduct modules that blend an adaptive interaction between predefined decision functions and the robot behaviors. The dynamic autonomy structure is configured for modifying a transaction capacity between an operator intervention and a robot initiative and may include multiple levels with at least a teleoperation mode configured to maximize the operator intervention and minimize the robot initiative and an autonomous mode configured to minimize the operator intervention and maximize the robot initiative. Within the RIK at least the cognitive level includes the dynamic autonomy structure.
The Augmented Complex Kernel LMS
Bouboulis, Pantelis; Mavroforakis, Michael
2011-01-01
Recently, a unified framework for adaptive kernel based signal processing of complex data was presented by the authors, which, besides offering techniques to map the input data to complex Reproducing Kernel Hilbert Spaces, developed a suitable Wirtinger-like Calculus for general Hilbert Spaces. In this short paper, the extended Wirtinger's calculus is adopted to derive complex kernel-based widely-linear estimation filters. Furthermore, we illuminate several important characteristics of the widely linear filters. We show that, although in many cases the gains from adopting widely linear estimation filters, as alternatives to ordinary linear ones, are rudimentary, for the case of kernel based widely linear filters significant performance improvements can be obtained.
Local Regression and Likelihood
Masci, Frank
Local Regression and Likelihood Clive Loader Springer #12;#12;#12;#12;#12;Preface This book, and the associated software, have grown out of the author's work in the field of local regression over the past- ods and in particular regression, for example at the level of Draper and Smith (1981). The theoretical
Lega, Joceline
Introduction AI/Regression Technology/Search Simple Equations, Deep Understanding How Topics and Alison Cornell Simple Equations, Deep Meaning #12;Introduction AI/Regression Technology/Search Who are we helps out) Qiyam Tung and Alison Cornell Simple Equations, Deep Meaning #12;Introduction AI/Regression
Directed Regression Stanford University
Van Roy, Ben
Directed Regression Yi-hao Kao Stanford University Stanford, CA 94305 yihaokao Stanford, CA 94305 xyan@stanford.edu Abstract When used to guide decisions, linear regression analysis typically involves esti- mation of regression coefficients via ordinary least squares and their subsequent
Slicing Regression: A Link-free Regression Method
Ker-Chau Li
2011-01-01
H. V. (1983). Reverse regression, fairness, and employmentleast squares linear regression. Statistica Sinica FISHER,A. S. (1981). Linear regression after selection. J.
Helices in High Dimensional Regression
Ker-Chau Li
2011-01-01
of "Sliced inverse regression." J. Amer. Statist. Assoc. 86C. (1992). Measurement error regression with unknown link:a projection-pursuit type regression model. Ann. Statist. 19
Kernel-Kernel Communication in a Shared-Memory Multiprocessor
Scott, Michael L.
, in which each instance of the kernel performs local operations directly and uses remote invocation, the Federal University of Rio de Janeiro, and the Brazilian National Research Council. Eliseu Chaves), prakash@transarc.com, marsh@mitl.com, and {leblanc,scott}@cs.rochester.edu. Tech. Rep. 368, Apr. 1991 #12
Fast Kernel-Based Independent Component Analysis
McAuliffe, Jon
Fast Kernel-Based Independent Component Analysis Hao Shen , Stefanie Jegelka and Arthur Gretton instance, sources with near-zero kurtosis). FastKICA (Fast HSIC-based Kernel ICA) is a new optimisation-based ICA algorithms, FastKICA is applicable to any twice differentiable kernel function. Experimental
Principle of Least Squares Regression Equations Residuals Correlation and Regression
Watkins, Joseph C.
Principle of Least Squares Regression Equations Residuals Topic 3 Correlation and Regression Linear Regression I 1 / 15 #12;Principle of Least Squares Regression Equations Residuals Outline Principle of Least Squares Regression Equations Residuals 2 / 15 #12;Principle of Least Squares Regression Equations
Quantum regression theorem for non-Markovian Lindblad equations
Adrian A. Budini
2006-03-13
We find the conditions under which a quantum regression theorem can be assumed valid for non-Markovian master equations consisting in Lindblad superoperators with memory kernels. Our considerations are based on a generalized Born-Markov approximation, which allows us to obtain our results from an underlying Hamiltonian description. We demonstrate that a non-Markovian quantum regression theorem can only be granted in a stationary regime if the dynamics satisfies a quantum detailed balance condition. As an example we study the correlations of a two level system embedded in a complex structured reservoir and driven by an external coherent field.
QuestV: A Vi rtuali zed Multi kernel for Hi ghConfidence Systems Ye Li BostonU niversity ########## #### Matthew Danish BostonU niversity ######## #### Richard West BostonU niversity ############## #### Abstract operating together as a dis tributed system on a chip. QuestV uses virtualization techniques to isolate
Conventional regression models Unlabelled units
McCullagh, Peter
Conventional regression models Unlabelled units Consequences Sampling bias in logistic models Peter effects #12;Conventional regression models Unlabelled units Consequences Outline 1 Conventional regression models Gaussian models Binary regression model Properties of conventional models 2 Unlabelled units Point
B. Bruegmann
1993-12-02
The loop representation plays an important role in canonical quantum gravity because loop variables allow a natural treatment of the constraints. In these lectures we give an elementary introduction to (i) the relevant history of loops in knot theory and gauge theory, (ii) the loop representation of Maxwell theory, and (iii) the loop representation of canonical quantum gravity. (Based on lectures given at the 117. Heraeus Seminar, Bad Honnef, Sept. 1993)
Regression from Local Features for Viewpoint and Pose Estimation Marwan Torki Ahmed Elgammal
Chen, Kuang-Yu
Regression from Local Features for Viewpoint and Pose Estimation Marwan Torki Ahmed Elgammal features in an image. The regression is learned from an embedded representation that reflects the local in computer vision can be formulated as regression problems where the goal is to learn a continu- ous real
Path Integral Representations on the Complex Sphere
Christian Grosche
2007-10-23
In this paper we discuss the path integral representations for the coordinate systems on the complex sphere S3C. The Schroedinger equation, respectively the path integral, separates in exactly 21 orthogonal coordinate systems. We enumerate these coordinate systems and we are able to present the path integral representations explicitly in the majority of the cases. In each solution the expansion into the wave-functions is stated. Also, the kernel and the corresponding Green function can be stated in closed form in terms of the invariant distance on the sphere, respectively on the hyperboloid.
Multivariate calibration with single-index signal regression Paul H.C. Eilers a
Marx, Brian D.
February 2009 Keywords: Multivariate calibration P-splines Projection pursuit regression In general, linearity is assumed to hold in multivariate calibration, but this may not be true. Penalized signal. The higher-dimensional space is never explicitly constructed: it is implied by the kernel function. We
Ordinary Least Square Regression, Orthogonal Regression, Geometric Mean Regression and their
Ordinary Least Square Regression, Orthogonal Regression, Geometric Mean Regression@notes.cc.sunysb.edu Abstract. Regression analysis, especially the ordinary least squares method which assumes that errors for both measurements. In this work, we examine two regression approaches available to accommodate
Pricing Kernel Specification for User Cost of Monetary Assets
Zhou, Wei
2007-04-27
This paper studies the nonlinear asset pricing kernel approximation by using orthonormal polynomials of state variables in which the pricing kernel specification is restricted by preference theory. We approximate the true asset pricing kernel...
Robust Minimax Probability Machine Regression Robust Minimax Probability Machine Regression
Grudic, Greg
Robust Minimax Probability Machine Regression Robust Minimax Probability Machine Regression Thomas of Computer Science University of Colorado Boulder, C0 80309-0430, USA Abstract We formulate regression as maximizing the minimum probability () that the regression model is within ± of all future observations (i
Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral
Hüllermeier, Eyke
Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral Ali Fallah a generalization of logis- tic regression based on the Choquet integral. The basic idea of our approach, referred to as choquistic regression, is to replace the linear function of pre- dictor variables, which is commonly used
The Complex Gaussian Kernel LMS algorithm
Bouboulis, Pantelis
2010-01-01
Although the real reproducing kernels are used in an increasing number of machine learning problems, complex kernels have not, yet, been used, in spite of their potential interest in applications such as communications. In this work, we focus our attention on the complex gaussian kernel and its possible application in the complex Kernel LMS algorithm. In order to derive the gradients needed to develop the complex kernel LMS (CKLMS), we employ the powerful tool of Wirtinger's Calculus, which has recently attracted much attention in the signal processing community. Writinger's calculus simplifies computations and offers an elegant tool for treating complex signals. To this end, the notion of Writinger's calculus is extended to include complex RKHSs. Experiments verify that the CKLMS offers significant performance improvements over the traditional complex LMS or Widely Linear complex LMS (WL-LMS) algorithms, when dealing with nonlinearities.
Nonlinear regression Gordon K. Smyth
Smyth, Gordon K.
Nonlinear regression Gordon K. Smyth Volume 3, pp 14051411 in Encyclopedia of Environmetrics (ISBN, 2002 #12;Nonlinear regression The basic idea of nonlinear regression is the same as that of linear regression, namely to relate a response Y to a vector of predictor variables x D x1, . . . , xk T (see Linear
Logistic regression Weakly informative priors
Gelman, Andrew
Logistic regression Weakly informative priors Conclusions Bayesian generalized linear models default p #12;Logistic regression Weakly informative priors Conclusions Classical logistic regression The problem of separation Bayesian solution Logistic regression -6 -4 -2 0 2 4 6 0.00.20.40.60.81.0 y = logit
GILK: A dynamic instrumentation tool for the Linux Kernel
Pearce, David J.
GILK: A dynamic instrumentation tool for the Linux Kernel David J. Pearce, Paul H.J. Kelly, Tony Abstract. This paper describes a dynamic instrumentation tool for the Linux Kernel which allows a stock Linux kernel to be modi#12;ed while in ex- ecution, with instruments implemented as kernel modules
ARCHITECTURE FOR A PORTABLE OPEN SOURCE REAL TIME KERNEL ENVIRONMENT
Lipari, Giuseppe
to the automotive industry. The set of tools comprises a schedulability analyzer, a kernel library and a kernel con platforms without sacri#28;cing performance. With our tools, the programmer only need to write of an Open Source Real Time Kernel for embedded automotive applications. Developing a kernel
Extension of Wirtinger's Calculus in Reproducing Kernel Hilbert Spaces and the Complex Kernel LMS
Bouboulis, Pantelis
2010-01-01
Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. The primary mathematical tool employed in these methods is the notion of the Reproducing Kernel Hilbert Space. However, so far, the emphasis has been on batch techniques. It is only recently, that online techniques have been considered in the context of adaptive signal processing tasks. Moreover, these efforts have only been focussed on and real valued data sequences. To the best of our knowledge, no kernel-based strategy has been developed, so far, that is able to deal with complex valued signals. In this paper, we present a general framework to attack the problem of adaptive filtering of complex signals, using either real reproducing kernels, taking advantage of a technique called \\textit{complexification} of real RKHSs, or complex reproducing kernels, highlighting the use of the complex gaussian kernel. In order to derive gradients of operators that need to be defined on the associat...
Difference image analysis: Automatic kernel design using information criteria
Bramich, D M; Alsubai, K A; Bachelet, E; Mislis, D; Parley, N
2015-01-01
We present a selection of methods for automatically constructing an optimal kernel model for difference image analysis which require very few external parameters to control the kernel design. Each method consists of two components; namely, a kernel design algorithm to generate a set of candidate kernel models, and a model selection criterion to select the simplest kernel model from the candidate models that provides a sufficiently good fit to the target image. We restricted our attention to the case of solving for a spatially-invariant convolution kernel composed of delta basis functions, and we considered 19 different kernel solution methods including six employing kernel regularisation. We tested these kernel solution methods by performing a comprehensive set of image simulations and investigating how their performance in terms of model error, fit quality, and photometric accuracy depends on the properties of the reference and target images. We find that the irregular kernel design algorithm employing unreg...
Comparing Methods for Multivariate Nonparametric Regression
Comparing Methods for Multivariate Nonparametric Regression David L. Banks \\Lambda Robert T, of the National Science Foundation or the U.S. government. #12; Keywords: multivariate nonparametric regression, linear regression, stepwise linear regression, additive models, AM, projection pursuit regression, PPR
Fractal Weyl law for Linux Kernel Architecture
L. Ermann; A. D. Chepelianskii; D. L. Shepelyansky
2010-09-16
We study the properties of spectrum and eigenstates of the Google matrix of a directed network formed by the procedure calls in the Linux Kernel. Our results obtained for various versions of the Linux Kernel show that the spectrum is characterized by the fractal Weyl law established recently for systems of quantum chaotic scattering and the Perron-Frobenius operators of dynamical maps. The fractal Weyl exponent is found to be $\
CORRESPONDENCE Reduced major axis regression
CORRESPONDENCE Reduced major axis regression and the island rule The Ôisland ruleÕ describes). The slope of the least-squares regression of Y on X, b, measures the extent to which large body sizes tend- ferent way, as the regression of Y ) X on X, and here the slope (b¢) is generally negative (b¢ = b ) 1
Linear regression issues in astronomy
Babu, G. Jogesh
Linear regression issues in astronomy Eric Feigelson Summer School in astrostatistics References regression Seeking the intrinsic relationship between two properties without specifying `dependent' and `independent' variables OLS(Y|X) OLS(X|Y) (inverse regr) Four symmetrical regression lines #12;Analytical
Simple Linear Regression Basic Ideas
Simple Linear Regression Basic Ideas Some Examples Least Squares Statistical View of Least Squares of Least Squares #12;Simple Linear Regression Basic Ideas Some Examples Least Squares Basic Ideas Suppose #12;Simple Linear Regression Basic Ideas Some Examples Least Squares Basic Ideas Suppose we have two
Representation of Universal Algebra
Aleks Kleyn
2015-02-07
Theory of representations of universal algebra is a natural development of the theory of universal algebra. Morphism of the representation is the map that conserve the structure of the representation. Exploring of morphisms of the representation leads to the concepts of generating set and basis of representation. In the book I considered the notion of tower of representations of $F_i$-algebras, i=1 ..., n, as the set of coordinated representations of $F_i$-algebras.
SVMs with Profile-Based Kernels 1 Support Vector Machines with Profile-Based Kernels for
Pace, Gordon J.
amount of new protein sequences. The resulting sequences describe a protein in terms of the amino acids, the frequency that each amino acid appears in that column. Once a profile is available, a new sequence canSVMs with Profile-Based Kernels 1 Support Vector Machines with Profile-Based Kernels for Remote
Complete regression of melanoma associated with vitiligo
Piqué-Duran, Enric; Palacios-Llopis, Santiago; Martínez-Martín, MªSol; Pérez-Cejudo, Juan A
2011-01-01
melanoma: influence of regression. Histopathology 1983; 7:Varga E, Dobozy A. Tumor regression predicts higher risk ofcutaneous melanoma with regression doesnot require a lower
Regression with Missing X's: A Review
Roderick J. A. Little
2011-01-01
1968), "Missing Data in Regression Analysis," Journal of theTechnometrics, Little: Regression With Missing X's Ibrahim,Influence Multiple Linear Regression with Incomplete Data,"
Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals
Vu, Kevin; Li, Li; Rupp, Matthias; Chen, Brandon F; Khelif, Tarek; Müller, Klaus-Robert; Burke, Kieron
2015-01-01
Accurate approximations to density functionals have recently been obtained via machine learning (ML). By applying ML to a simple function of one variable without any random sampling, we extract the qualitative dependence of errors on hyperparameters. We find universal features of the behavior in extreme limits, including both very small and very large length scales, and the noise-free limit. We show how such features arise in ML models of density functionals.
Facial Action Unit Intensity Prediction via Hard Multi-Task Metric Learning for Kernel Regression
]), characterizing activations of face muscles (AUs) for describing facial expressions. In order to be able, all related to mouth movements. Thus, some features are potentially relevant for all five tasks
Regression analysis with longitudinal measurements
Ryu, Duchwan
2005-08-29
- tinuous response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3 Average fitted values (95% credible intervals) for 20 simulations when sigma2u = 1.0 and n = 200 with continuous response . . . . . . . . . 30 4 Continuous regression... simula- tions when sigma2u = 1.0 and n = 200 with binary response . . . . . . . . 42 7 Binary regression for the real data . . . . . . . . . . . . . . . . . . . 43 8 Directed acyclic graph for Bayesian nonparametric regression un- der Model-0...
Fast generation of sparse random kernel graphs
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore »at most ?(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less
Experimental study of turbulent flame kernel propagation
Mansour, Mohy [National Institute of Laser Enhanced Sciences, Cairo University, Giza (Egypt); Peters, Norbert; Schrader, Lars-Uve [Institute of Combustion Technology, Aachen (Germany)
2008-07-15
Flame kernels in spark ignited combustion systems dominate the flame propagation and combustion stability and performance. They are likely controlled by the spark energy, flow field and mixing field. The aim of the present work is to experimentally investigate the structure and propagation of the flame kernel in turbulent premixed methane flow using advanced laser-based techniques. The spark is generated using pulsed Nd:YAG laser with 20 mJ pulse energy in order to avoid the effect of the electrodes on the flame kernel structure and the variation of spark energy from shot-to-shot. Four flames have been investigated at equivalence ratios, {phi}{sub j}, of 0.8 and 1.0 and jet velocities, U{sub j}, of 6 and 12 m/s. A combined two-dimensional Rayleigh and LIPF-OH technique has been applied. The flame kernel structure has been collected at several time intervals from the laser ignition between 10 {mu}s and 2 ms. The data show that the flame kernel structure starts with spherical shape and changes gradually to peanut-like, then to mushroom-like and finally disturbed by the turbulence. The mushroom-like structure lasts longer in the stoichiometric and slower jet velocity. The growth rate of the average flame kernel radius is divided into two linear relations; the first one during the first 100 {mu}s is almost three times faster than that at the later stage between 100 and 2000 {mu}s. The flame propagation is slightly faster in leaner flames. The trends of the flame propagation, flame radius, flame cross-sectional area and mean flame temperature are related to the jet velocity and equivalence ratio. The relations obtained in the present work allow the prediction of any of these parameters at different conditions. (author)
W.R. Tobler Bidimensional Regression
Tobler, Waldo
W.R. Tobler Bidimensional Regression Since its invention by Francis Galton in 1877 regression. Here the regression coefficients constitute a spatially varying, but coordinate invariant, second projection. In a computer implementation a nonparametric approach allows visualization of the regression
On fusion kernel in Liouville theory
Nikita Nemkov
2014-09-29
We study fusion kernel for non-degenerate conformal blocks in Liouville theory as a solution to the difference equations originating from the pentagon identity. We suggest an approach to these equations based on 'non-perturbative' series expansion which allows to calculate the fusion kernel iteratively. We also find the exact solutions for the cases when the central charge is $c=1+6(b-b^{-1})^2$ and $b~\\in \\mathbb{N}$. For $c = 1$ our result reproduces the formula, obtained earlier from analytical continuation via Painlev\\'e equation. However, in our case it appears in a significantly simplified form.
Black Kernel and White Tip of Rice.
Martin, Alan L. (Alan La Mott); Altstatt, G. E. (George E.)
1940-01-01
. Amer. Jour. Bot. 26 :P46-852. 1939. Taubenhaus, J. J. Rice diseases. In 50th Ann. Rpt. Tex. Agr. Sta., pp. 114-115. 1937. Taubenhaus, J. J., Altstatt, G. E., and Wyche, R. H. Black kernel of rice. In 4Fth Ann. Rpt. Texas Am. Exp. Sta., p. 94. 1935.... Taubenhaus, J. J., and Wyche, R. H. Rice Diseases. In 49th Ann. Rpt. Texas Agr. Exp. Sta., pp. 109-111. 1936. Tullis, E. C. Fungi isolated from discolored rice kernels. U. S. Dept. Agr. Tech. Bull. 540. 1936. ...
Crystal Structure Representations for Machine Learning Models of Formation Energies
Faber, Felix; von Lilienfeld, O Anatole; Armiento, Rickard
2015-01-01
We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization energies of organic molecules have been successful using a Coulomb matrix representation of the molecule. We consider three ways to generalize such representations to periodic systems: (i) a matrix where each element is related to the Ewald sum of the electrostatic interaction between two different atoms in the unit cell repeated over the lattice; (ii) an extended Coulomb-like matrix that takes into account a number of neighboring unit cells; and (iii) an Ansatz that mimics the periodicity and the basic features of the elements in the Ewald sum matrix by using a sine function of the crystal coordinates of the atoms. The representations are compared for a Laplacian kernel with Manhattan norm, trained to reproduce formation energies using a data set of 3938 crystal structures obtained from the Materials Project. For training sets consi...
Linux Kernel Co-Scheduling For Bulk Synchronous Parallel Applications...
Office of Scientific and Technical Information (OSTI)
INFORMATION SCIENCE; ALGORITHMS; DESIGN; IMPLEMENTATION; KERNELS; PERFORMANCE; SUPERCOMPUTERS Operating systems; OS noise; OS interference; co-scheduling; coordinated scheduling...
Olive, David
1D Regression David J. Olive Southern Illinois University August 27, 2004 Abstract Regression regression, Y is independent of x given a single linear combination +T x of the predictors. Special cases of 1D regression include multiple linear regression, logistic regression, generalized linear models
Comprehensive Kernel Instrumentation via Dynamic Binary Translation
Toronto, University of
Comprehensive Kernel Instrumentation via Dynamic Binary Translation Peter Feiner Angela Demke Brown, bug-finding, and security tools. Such tools are currently not available for operating system (OS handlers and device drivers, enabling comprehensive instrumentation of the OS without imposing any overhead
Pluricomplex Green function, pluricomplex Poisson kernel and
Bracci, Filippo
resem- bles completely the one dimensional equation giving rise to the Green function. More or lessPluricomplex Green function, pluricomplex Poisson kernel and applications FILIPPO BRACCI fbracci@mat.uniroma2.it Abstract. This is a small survey about the pluricomplex Green function of M
Measurements of the Thermal Neutron Scattering Kernel
Danon, Yaron
Measurements of the Thermal Neutron Scattering Kernel Li (Emily) Liu, Yaron Danon, Bjorn Becker and discussions Problems and Future study Questions #12;3 M. Mattes and J. Keinert, Thermal Neutron Scattering experimental data used was from 1973-1974! M. Mattes and J. Keinert, Thermal Neutron Scattering Data
Mismatch String Kernels for SVM Protein Classification
Noble, William Stafford
machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure efficiently using a mismatch tree data structure and report experiments on a benchmark SCOP dataset, where we savings. 1 Introduction A fundamental problem in computational biology is the classification of proteins
Is the gasoline tax regressive?
Poterba, James M.
1990-01-01
Claims of the regressivity of gasoline taxes typically rely on annual surveys of consumer income and expenditures which show that gasoline expenditures are a larger fraction of income for very low income households than ...
LOG HAZARD REGRESSION Huiying Sun
Heckman, Nancy E.
LOG HAZARD REGRESSION by Huiying Sun Ph.D, Harbin Institute of Technology, Harbin, CHINA, 1991 .................................................................... .................................................................... .................................................................... .................................................................... THE UNIVERSITY OF BRITISH COLUMBIA September, 1999 c flHuiying Sun, 1999 #12; Abstract We propose using
Supplementary Material for Robust Regression
Supplementary Material for Robust Regression Dong Huang, Ricardo Cabral and Fernando De la Torre the solutions of the subproblems (3)-(6). #12;2 Dong Huang, Ricardo Silveira Cabral and Fernando De la Torre 1
Comparing Methods for Multivariate Nonparametric Regression
Comparing Methods for Multivariate Nonparametric Regression David L. Banks Robert T. Olszewskiy Roy, of the National Science Foundation or the U.S. government. #12;Keywords: multivariate nonparametric regression, linear regression, stepwise linear regression, additive models, AM, projection pursuit regression, PPR
Conventional regression models Auto-generated units
McCullagh, Peter
Conventional regression models Auto-generated units Consequences of auto-generation Inference Royal Statistical Society Feb 6, 2008 Peter McCullagh Auto-generated units #12;Conventional regression Conventional regression models Gaussian models Binary regression model Properties of regression models Problems
Conventional regression models Auto-generated units
McCullagh, Peter
Conventional regression models Auto-generated units Consequences of auto-generation Inference regression models Auto-generated units Consequences of auto-generation Inference and prediction Outline 1 Conventional regression models Gaussian models Binary regression model Properties of regression models Problems
A general approach to statistical modeling of physical laws: nonparametric regression
I. Grabec
2007-04-01
Statistical modeling of experimental physical laws is based on the probability density function of measured variables. It is expressed by experimental data via a kernel estimator. The kernel is determined objectively by the scattering of data during calibration of experimental setup. A physical law, which relates measured variables, is optimally extracted from experimental data by the conditional average estimator. It is derived directly from the kernel estimator and corresponds to a general nonparametric regression. The proposed method is demonstrated by the modeling of a return map of noisy chaotic data. In this example, the nonparametric regression is used to predict a future value of chaotic time series from the present one. The mean predictor error is used in the definition of predictor quality, while the redundancy is expressed by the mean square distance between data points. Both statistics are used in a new definition of predictor cost function. From the minimum of the predictor cost function, a proper number of data in the model is estimated.
Testing regression models with fewer regressors
Judea Pearl; P. Meshkat
2011-01-01
Testing Regression Models W i t h Fewer Regressors JudeaINTRODUCTION Let M be a recursive regression model, that is,a set of regression equations for ordered variables Yi, ,
Review: Logistic regression, Gaussian nave Bayes, linear regression, and their connections
Mitchell, Tom
Review: Logistic regression, Gaussian naïve Bayes, linear regression, and their connections Yi, and feature selection #12;Outline Logistic regression Decision surface (boundary) of classifiers Generative vs. discriminative classifiers Linear regression Bias-variance decomposition and tradeoff
Marcus Hutter -1 -Bayesian Regression of Piecewise Constant Functions Bayesian Regression of
Hutter, Marcus
Marcus Hutter - 1 - Bayesian Regression of Piecewise Constant Functions Bayesian Regression6 June 2006 #12;Marcus Hutter - 2 - Bayesian Regression of Piecewise Constant Functions Table of Contents · Bayesian Regression · Quantities of Interest · Efficient Solutions by Dynamic Programming · Determination
Knowledge representations for sensemaking
Nguyen, Hong-Linh Q
2012-01-01
I discuss the lessons learned during the design and implementation of three knowledge representations systems for sensemaking. The focus is on the tension that exists between a knowledge representation's role as a surrogate ...
On admissible memory kernels for random unitary qubit evolution
Filip A. Wudarski; Pawe? Nale?yty; Gniewomir Sarbicki; Dariusz Chru?ci?ski
2015-04-12
We analyze random unitary evolution of the qubit within memory kernel approach. We provide su?cient conditions which guarantee that the corresponding memory kernel generates physically legitimate quantum evolution. Interestingly, we are able to recover several well known examples and generate new classes of nontrivial qubit evolution. Surprisingly, it turns out that quantum evolution with memory kernel generated by our approach gives rise to vanishing non-Markovianity measure based on the distinguishability of quantum states.
U-086:Linux Kernel "/proc//mem" Privilege Escalation Vulnerability
Broader source: Energy.gov [DOE]
A vulnerability has been discovered in the Linux Kernel, which can be exploited by malicious, local users to gain escalated privileges.
Simultaneous regression and clustering to predict movie ratings
Rodriguez, Matthew
2010-01-01
Testing the Regression Coefficients . . . . . . . . .2 Performing Logistic Regression on the Movielens dataset2.4 Logistic Regression Experiments . . . . . . . . . .
SMOOTHED ESTIMATING EQUATIONS FOR INSTRUMENTAL VARIABLES QUANTILE REGRESSION
Kaplan, David M.; Sun, Yixiao
2012-01-01
Instrumental quantile regression inference for structuralsample inference for quantile regression models. Journal ofmethods for median regression models. Econometrica,
Design of Positive-Definite Quaternion Kernels
Tobar, Felipe; Mandic, Danilo P.
2015-01-01
provided access to 3D data, this has spurred a resurgence in research on quaternion representations of such signals. Indeed, quaternions have become a standard in a number of areas, including computer graphics, quantum physics and aeronautics. When it comes...
On Robust Regression in Photogrammetric Point Clouds
Schindler, Konrad
On Robust Regression in Photogrammetric Point Clouds Konrad Schindler and Horst Bischof Institute,bischof}@icg.tu-graz.ac.at Abstract. Many applications in computer vision require robust linear regression on photogrammetrically for robust regression are based on distance measures from the regression surface to the points
Regression models Consequences of auto-generation
McCullagh, Peter
Regression models Consequences of auto-generation All creatures great and small Inference University of Chicago Statslab Cambridge Nov 14 2008 Peter McCullagh Auto-generated units #12;Regression Regression models Gaussian models Binary regression model Attenuation of treatment effect Problems
Conventional regression models Auto-generated units
McCullagh, Peter
Conventional regression models Auto-generated units Consequences of auto-generation Inference regression models Auto-generated units Consequences of auto-generation Inference and prediction Outline 1 Conventional regression models Gaussian models Binary regression model Attenuation of treatment effect Problems
Conventional regression models Auto-generated units
McCullagh, Peter
Conventional regression models Auto-generated units Consequences of auto-generation Arguments pro-generated units #12;Conventional regression models Auto-generated units Consequences of auto-generation Arguments pro and con Outline 1 Conventional regression models Gaussian models Binary regression model
36-220 Lab #11 Multiple Regression
Spirtes, Peter
36-220 Lab #11 Multiple Regression Please write your name below, tear off this front page and give;36-220 Lab #11 Multiple Regression First let's download the dataset we will need for the lab. Download gas Simple Regression Analysis 1. Fit the least squares regression line to describe the relationship between
Visualizing 1D Regression David J. Olive
Olive, David
Visualizing 1D Regression David J. Olive Abstract. Regression is the study of the conditional distribution of the re- sponse y given the predictors x. In a 1D regression, y is independent of x given a single linear combination T x of the predictors. Special cases of 1D regression include multiple linear
A Formulation for Minimax Probability Machine Regression
Grudic, Greg
A Formulation for Minimax Probability Machine Regression Thomas Strohmann Department of Computer Science University of Colorado, Boulder grudic@cs.colorado.edu Abstract We formulate the regression of the regression model will be within some ± bound of the true regression function. Our formulation is unique
ContentsContents4343Regression and correlation
Vickers, James
ContentsContents4343Regression and correlation 1. Regression 2. Correlation Learning outcomes You. 1 #12;Regression 43.1 Introduction Problems in engineering often involve the exploration of the relationship(s) between two or more variables. The technique of regression analysis is very useful and well
Shadow Kernels: A General Mechanism For Kernel Specialization in Existing Operating Systems
Chick, Oliver R. A.; Carata, Lucian; Snee, James; Balakrishnan, Nikilesh; Sohan, Ripduman
2015-01-01
in systems such as Ar- rakis [13, 14]. Once a process is given ownership of a particular vir- tualized PCI device, the problem of “routing” asynch- ronous tasks towards executing a given shadow kernel becomes solvable: All the code that is executed in ker...
Shape Recipes: Scene Representations that Refer to the Image
Torralba, Antonio
Shape Recipes: Scene Representations that Refer to the Image William T. Freeman and Antonio- resentation, called a scene recipe, that relies on the image itself to de- scribe the complex scene configurations. Shape recipes are an example: these are the regression coefficients that predict the bandpassed
Structured Linux Kernel Projects for Teaching Operating Systems Concepts
Laadan, Oren
Structured Linux Kernel Projects for Teaching Operating Systems Concepts Oren Laadan Dept of a complex, production operating system can be a challenge. We present a structured series of five Linux kernel programming projects suitable for a one semester introductory operating systems course to address
GILK: A dynamic instrumentation tool for the Linux Kernel
Pearce, David J.
GILK: A dynamic instrumentation tool for the Linux Kernel A. Nonymous, B. Nonymous, C.Nonymous No Institute Given Abstract. This document describes a novel instrumentation tool for the Linux Kernel function, and insert user-speci#12;ed instrumentation before or after any basic block. The instruments
A Heat Kernel based Cortical Thickness Estimation Algorithm
Wang, Yalin
thickness, Heat Kernel, Tetrahedral Mesh, Streamline, False Discovery Rate 1 Introduction AlzheimerA Heat Kernel based Cortical Thickness Estimation Algorithm Gang Wang1,2 , Xiaofeng Zhang1.R.China Abstract. Cortical thickness estimation in magnetic resonance imag- ing (MRI) is an important technique
HEAT KERNEL AND GREEN FUNCTION ESTIMATES ON NONCOMPACT SYMMETRIC SPACES
Ji, Lizhen
HEAT KERNEL AND GREEN FUNCTION ESTIMATES ON NONCOMPACT SYMMETRIC12, 43A80, 43A85, * *43A90, 58G11. Key words and phrases. Green function, heat kernel, Iwasawa effi* *ciently to pro- duce sharp and complete results comparable to the Euclidean or the compact case
Mach Kernel Monitor (with applications using the PIE environment)
Mach Kernel Monitor (with applications using the PIE environment) 23 August 1990 Ted Lehr David. Special examples of data obtained by using MKM are shown via the PIE performance monitoring environment programs are scheduled. This manual describes how to use MKM, the Mach (contextswitch) kernel monitor
FRAME BASED KERNEL METHODS FOR AUTOMATIC CLASSIFICATION IN HYPERSPECTRAL DATA
Hirn, Matthew
FRAME BASED KERNEL METHODS FOR AUTOMATIC CLASSIFICATION IN HYPERSPECTRAL DATA John J. Benedetto, instead of (orthonormal) bases. Our frames are data-dependent and are based on endmember demixing schemes propose a new kernel and frame based dimension reduc- ing algorithm by exploiting the synergy between
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels
California at Berkeley, University of
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels Richard Vuduc (LLNL), James Demmel in progress · Written in C (can call from Fortran) #12;Motivation: The Difficulty of Tuning · n = 21216 · nnz data structures Step 2: Make BLAS-like kernel calls int* ptr = ..., *ind = ...; double* val
Partial least squares regression as an alternative to current regression methods used in ecology
Carrascal, Luis M.
Partial least squares regression as an alternative to current regression methods used in ecology regression analysis (PLSR), and its potential utility in ecological studies. This statistical technique with multiple regression (MR) and with a combination of principal component analysis and multiple regression
Regression Analysis with an Unknown Link Function: the Adjoint Projection Pursuit Regression
Naihua Duan
2011-01-01
Misspecified Non- linear Regression Models," Journal of theAdjoint Projection Pursuit Regression also give examples inL is the estimated linear regression of x on bx. Note Remark
The Dynamical Kernel Scheduler - Part 1
Adelmann, Andreas; Suter, Andreas
2015-01-01
Emerging processor architectures such as GPUs and Intel MICs provide a huge performance potential for high performance computing. However developing software using these hardware accelerators introduces additional challenges for the developer such as exposing additional parallelism, dealing with different hardware designs and using multiple development frameworks in order to use devices from different vendors. The Dynamic Kernel Scheduler (DKS) is being developed in order to provide a software layer between host application and different hardware accelerators. DKS handles the communication between the host and device, schedules task execution, and provides a library of built-in algorithms. Algorithms available in the DKS library will be written in CUDA, OpenCL and OpenMP. Depending on the available hardware, the DKS can select the appropriate implementation of the algorithm. The first DKS version was created using CUDA for the Nvidia GPUs and OpenMP for Intel MIC. DKS was further integrated in OPAL (Object-or...
Regression quantiles for time series
Cai, Zongwu
2002-02-01
~see, e+g+, Ibragimov and Linnik, 1971, p+ 316!+ Namely, partition REGRESSION QUANTILES FOR TIME SERIES 187 $1, + + + , n% into 2qn 1 1 subsets with large block of size r 5 rn and small block of size s 5 sn+ Set q 5 qn 5 ? n rn 1 sn? , (A.7) where {x...! are the standard Lindeberg–Feller conditions for asymptotic normality of Qn,1 for the independent setup+ Let us first establish ~A+8!+ To this effect, we define the large-block size rn by rn 5 {~nhn!102} and the small-block size sn 5 {~nhn!1020log n}+ Then, as n r...
Batch-mode Supervised Learning Linear regression
Wehenkel, Louis
Batch-mode Supervised Learning Linear regression Applied inductive learning - Lecture 3 Louis (& Pierre Geurts)AIA... (1/19) #12;Batch-mode Supervised Learning Linear regression Batch-mode Supervised Learning Linear regression Least mean square error solution Regularization and algorithmics Residual
Exact and Approximate REML for Heteroscedastic Regression
Smyth, Gordon K.
Exact and Approximate REML for Heteroscedastic Regression Gordon K. Smyth Department of Mathematics, the above het- eroscedastic regression model is the most general model of the type considered by LN98 and SV to estimate the het- eroscedastic regression model by way of two coupled generalized linear models
Nonparametric Regression with Correlated Errors Jean Opsomer
Wang, Yuedong
Nonparametric Regression with Correlated Errors Jean Opsomer Iowa State University Yuedong Wang Nonparametric regression techniques are often sensitive to the presence of correlation in the errors splines and wavelet regression under correlation, both for short-range and long-range dependence
Regression on feature projections H. Altay Guvenir
Güvenir, H. Altay
Regression on feature projections H. Altay Guvenir * Uysal a Department Computer Engineering ######## This paper describes machine learning method, called Regression Feature Projections (RFP), predicting real with KNN based-regression algorithms. Results real sets achieves or comparable accuracy and is both Rule
SHARP THRESHOLDS FOR HYPERGRAPH REGRESSIVE RAMSEY NUMBERS
Lee, Gyesik
SHARP THRESHOLDS FOR HYPERGRAPH REGRESSIVE RAMSEY NUMBERS LORENZO CARLUCCI, GYESIK LEE, AND ANDREAS WEIERMANN Abstract. The f-regressive Ramsey number Rreg f (d, n) is the minimum N such that every colouring regressive Ramsey numbers as defined by Kanamori and McAloon. In this paper we classifiy the growth
Initial-state splitting kernels in cold nuclear matter
Ovanesyan, Grigory; Vitev, Ivan
2015-01-01
We derive medium-induced splitting kernels for energetic partons that undergo interactions in dense QCD matter before a hard-scattering event at large momentum transfer $Q^2$. Working in the framework of the effective theory ${\\rm SCET}_{\\rm G}\\,$, we compute the splitting kernels beyond the soft gluon approximation. We present numerical studies that compare our new results with previous findings. We expect the full medium-induced splitting kernels to be most relevant for the extension of initial-state cold nuclear matter energy loss phenomenology in both p+A and A+A collisions.
1D Regression ... estimates of the linear regression coefficients are relevant to the linear
Olive, David
Chapter 12 1D Regression ... estimates of the linear regression coefficients are relevant and look for a functional form for g(·). Brillinger (1983, p. 98) Regression is the study. The scalar Y is a random variable and x is a random vector. A special case of regression is multiple linear
1D Regression ... estimates of the linear regression coefficients are relevant to the linear
Olive, David
Chapter 15 1D Regression ... estimates of the linear regression coefficients are relevant and look for a functional form for g(·). Brillinger (1983, p. 98) Regression is the study. The scalar Y is a random variable and x is a random vector. A special case of regression is multiple linear
Robust Regression Appendix to An R and S-PLUS Companion to Applied Regression
Masci, Frank
Robust Regression Appendix to An R and S-PLUS Companion to Applied Regression John Fox January 2002 regression, is to employ a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation, introduced by Huber (1964).1 Consider
Scaling regression inputs by dividing by 2 sd Prior distribution for logistic regression
Gelman, Andrew
Scaling regression inputs by dividing by 2 sd Prior distribution for logistic regression Comparing Andrew Gelman Some Recent Progress in Simple Statistical Methods #12;Scaling regression inputs by dividing by 2 sd Prior distribution for logistic regression Comparing the upper third to the lower third
ENGI 3423 Simple Linear Regression Page 12-01 Simple Linear Regression
George, Glyn
ENGI 3423 Simple Linear Regression Page 12-01 Simple Linear Regression Sometimes an experiment predict the value of Y for that value of x . The simple linear regression model is that the predicted for dealing with non-linear regression are available in the course text, but are beyond the scope
Protecting Kernels from Untrusted Modules using Dynamic Binary Instrumentation
Goel, Ashvin
Protecting Kernels from Untrusted Modules using Dynamic Binary Instrumentation University · virtualization · Secure only modules whose source code is available (BGI, · LXFI, etc.) · Many modules is manageable · Data consistency is challenging Dynamic Binary Instrumentation Goals and Approach Challenges Two
Many Molecular Properties from One Kernel in Chemical Space
Ramakrishnan, Raghunathan
2015-01-01
We introduce property-independent kernels for machine learning modeling of arbitrarily many molecular properties. The kernels encode molecular structures for training sets of varying size, as well as similarity measures sufficiently diffuse in chemical space to sample over all training molecules. Corresponding molecular reference properties provided, they enable the instantaneous generation of ML models which can systematically be improved through the addition of more data. This idea is exemplified for single kernel based modeling of internal energy, enthalpy, free energy, heat capacity, polarizability, electronic spread, zero-point vibrational energy, energies of frontier orbitals, HOMO-LUMO gap, and the highest fundamental vibrational wavenumber. Models of these properties are trained and tested using 112 kilo organic molecules of similar size. Resulting models are discussed as well as the kernels' use for generating and using other property models.
RECIPES FOR CLASSICAL KERNEL FUNCTIONS ASSOCIATED TO A
RECIPES FOR CLASSICAL KERNEL FUNCTIONS ASSOCIATED TO A MULTIPLY CONNECTED DOMAIN IN THE PLANE elementary functions that are easy to compute. I shall also give a recipe to compute the functions
Efficient graphlet kernels for large graph comparison Nino Shervashidze
Mehlhorn, Kurt
Efficient graphlet kernels for large graph comparison Nino Shervashidze MPI for Biological such as gSpan (Yan & Han, 2003) have been developed for this task, which use el- egant data structures
Regression analysis for peak designation in pulsatile pressure signals
Scalzo, Fabien; Xu, Peng; Asgari, Shadnaz; Bergsneider, Marvin; Hu, Xiao
2009-01-01
5 ORIGINAL ARTICLE Regression analysis for peak designationwith more versatile regression models. The experimentalof different state-of-the-art regression analysis methods is
RegTools: A Julia Package for Assisting Regression Analysis
Liang, Muzhou
2015-01-01
and R. E. Welsch. 1980. ”Regression Diagnostics: IdentifyingObservation in Linear Regression. ” Techometrics 19:15-18. [Observations in Linear Regression. ” Journal of the American
Automating approximate Bayesian computation by local linear regression
Thornton, Kevin R
2009-01-01
computation by local linear regression Kevin R Thorntonof ABC based on using a linear regression to approximate theimplements the local linear-regression approach to ABC. The
Polymer representations and geometric quantization
Miguel Campiglia
2011-11-02
Polymer representations of the Weyl algebra of linear systems provide the simplest analogues of the representation used in loop quantum gravity. The construction of these representations is algebraic, based on the Gelfand-Naimark-Segal construction. Is it possible to understand these representations from a Geometric Quantization point of view? We address this question for the case of a two dimensional phase space.
On flame kernel formation and propagation in premixed gases
Eisazadeh-Far, Kian; Metghalchi, Hameed [Northeastern University, Mechanical and Industrial Engineering Department, Boston, MA 02115 (United States); Parsinejad, Farzan [Chevron Oronite Company LLC, Richmond, CA 94801 (United States); Keck, James C. [Massachusetts Institute of Technology, Cambridge, MA 02139 (United States)
2010-12-15
Flame kernel formation and propagation in premixed gases have been studied experimentally and theoretically. The experiments have been carried out at constant pressure and temperature in a constant volume vessel located in a high speed shadowgraph system. The formation and propagation of the hot plasma kernel has been simulated for inert gas mixtures using a thermodynamic model. The effects of various parameters including the discharge energy, radiation losses, initial temperature and initial volume of the plasma have been studied in detail. The experiments have been extended to flame kernel formation and propagation of methane/air mixtures. The effect of energy terms including spark energy, chemical energy and energy losses on flame kernel formation and propagation have been investigated. The inputs for this model are the initial conditions of the mixture and experimental data for flame radii. It is concluded that these are the most important parameters effecting plasma kernel growth. The results of laminar burning speeds have been compared with previously published results and are in good agreement. (author)
Modeling Multiple Drugs on Lung Cancer and Normal Cells using Regression
Chen, Michelle
2013-01-01
GLM: Binomial Regression Model . . . . . . . . . .Linear Regression: Least SquaresCourse in Statistics Regression Analysis. Pearson Education,
Regression of Environmental Noise in LIGO Data
Tiwari, Vaibhav; Frolov, Valery; Klimenko, Sergey; Mitselmakher, Guenakh; Necula, Valentin; Prodi, Giovanni; Re, Virginia; Salemi, Francesco; Vedovato, Gabriele; Yakushin, Igor
2015-01-01
We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the gravitational-wave channel from the PEM measurements. One of the most promising regression method is based on the construction of Wiener-Kolmogorov filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the Wiener-Kolmogorov method has been extended, incorporating banks of Wiener filters in the time-frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we presents the first results on regression of the bi-coherent noise in the LIGO data.
Regression of Environmental Noise in LIGO Data
Vaibhav Tiwari; Marco Drago; Valery Frolov; Sergey Klimenko; Guenakh Mitselmakher; Valentin Necula; Giovanni Prodi; Virginia Re; Francesco Salemi; Gabriele Vedovato; Igor Yakushin
2015-03-25
We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the gravitational-wave channel from the PEM measurements. One of the most promising regression method is based on the construction of Wiener-Kolmogorov filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the Wiener-Kolmogorov method has been extended, incorporating banks of Wiener filters in the time-frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we presents the first results on regression of the bi-coherent noise in the LIGO data.
Huang, Su-Yun
Multiclass Support Vector Classification via Regression Multiclass Support Vector Classification via Regression Pei-Chun Chen peichun@stat.sinica.edu.tw Institute of Statistical Science Academia classification is considered and resolved through the mul- tiresponse linear regression approach. Scores are used
Stout, Quentin F.
2008-01-01
In Computational Statistics and Data Analysis 53 (2008), pp. 289297 Unimodal Regression via Prefix Isotonic Regression Quentin F. Stout University of Michigan Ann Arbor, MI 481092121 Abstract This paper gives algorithms for determining real-valued uni- variate unimodal regressions, that is, for determining
T-583: Linux Kernel OSF Partition Table Buffer Overflow Lets Local Users Obtain Information
Broader source: Energy.gov [DOE]
A local user can create a storage device with specially crafted OSF partition tables. When the kernel automatically evaluates the partition tables, a buffer overflow may occur and data from kernel heap space may leak to user-space.
New Frontiers in Representation Discovery
Massachusetts at Amherst, University of
University of Massachusetts, Amherst Collaborators: Mauro Maggioni (Duke), Jeff Johns, Sarah Osentoski, Chang is a representation? (Marr and Nishihara, 1978) · "A representation is a formal system for making explicit certain
Alan Rufty
2007-02-17
This article presents a technique for analytic interpolation over the exterior of a unit disk using complex poles in the interior--as well as corresponding techniques for the exterior of a real unit disk and for the interior of a real and complex unit disk. This is accomplished by developing special kernel spaces labeled dual-access collocation-kernel spaces. Higher order pole and logarithmic point source kernels are also considered. Relationships to Szego and Bergman kernel theory are addressed.
A Library for Locally Weighted Projection Regression
Klanke, Stefan; Vijayakumar, Sethu; Schaal, Stefan
2008-01-01
In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the key features, our ...
Spatial variation decomposition via sparse regression
Zhang, Wangyang
In this paper, we briefly discuss the recent development of a novel sparse regression technique that aims to accurately decompose process variation into two different components: (1) spatially correlated variation, and (2) ...
Developmental Regression in Children with Down Syndrome
Bernad Ripoll, Susana
2011-05-18
communication, social skills, and play skills. Ten children lost some daily living skills, 8 participants had apparent motor skill changes, and 12 developed sleep disturbances. After regression 16 participants received a diagnosis of autism spectrum disorders...
Modifying Kernels Using Label Information Improves SVM Classification Performance
Toronto, University of
, we solve protein remote homology detec- tion problem and handwritten digit classification problem digit classification and two mismatch-string kernels as base ker- nels [6] for protein classification. We present our experi- mental results for digit classification and protein homology detection
PERI - Auto-tuning Memory Intensive Kernels for Multicore
Bailey, David H; Williams, Samuel; Datta, Kaushik; Carter, Jonathan; Oliker, Leonid; Shalf, John; Yelick, Katherine; Bailey, David H
2008-06-24
We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications.
Measurement Denoising Using Kernel Adaptive Filters in the Smart Grid
Qiu, Robert Caiming
Measurement Denoising Using Kernel Adaptive Filters in the Smart Grid Zhe Chen and Robert C. Qiu@ieee.org, rqiu@tntech.edu Abstract--State estimation plays an important role in the smart grid. Conventionally, noisy measurements are directly used for state estimation. Today, in the context of the smart grid
Improving the Energy Efficiency of the MANTIS Kernel
Sreenan, Cormac J.
Improving the Energy Efficiency of the MANTIS Kernel Cormac Duffy1 , Utz Roedig2 , John Herbert1. The event-based TinyOS is more energy efficient than the multi-threaded MANTIS system. However, MANTIS, timeliness can be traded for energy efficiency by choosing the appropriate operating system. In this paper we
ASYMPTOTIC PROPERTIES OF THE HEAT KERNEL ON CONIC MANIFOLDS
Loya, Paul
ASYMPTOTIC PROPERTIES OF THE HEAT KERNEL ON CONIC MANIFOLDS PAUL LOYA Abstract. We derive Foundation Fellowship. 1 #12; 2 PAUL LOYA Trace expansions of cone operators has a long history stemming from on conic manifolds; see for instance, Callias [5], Cheeger [7], Chou [9], BrË?uning--Seeley [3], Br
Kernel spectral clustering for predicting maintenance of industrial machines
status, but the operations are labor inten- sive and prone to human errors. Condition-based maintenance monitoring of machine parts leads to reliable and accurate lifetime predictions, and maintenance operationsKernel spectral clustering for predicting maintenance of industrial machines Rocco Langone1, Carlos
Optical transformation from chirplet to fractional Fourier transformation kernel
Hong-yi Fan; Li-yun Hu
2009-02-11
We find a new integration transformation which can convert a chirplet function to fractional Fourier transformation kernel, this new transformation is invertible and obeys Parseval theorem. Under this transformation a new relationship between a phase space function and its Weyl-Wigner quantum correspondence operator is revealed.
A Forced Sampled Execution Approach to Kernel Rootkit Identification
Chiueh, Tzi-cker
}@symantec.com Abstract. Kernel rootkits are considered one of the most dangerous forms of malware because they reside a forced sampled execution approach to traverse the driver's control flow graph. Through a comprehensive, 11, 4] is a piece of binary code that a computer in- truder, after breaking into a machine, installs
A Kernel Method for Market Clearing Sebastien Lahaie
Sandholm, Tuomas W.
linear prices do not suffice. We first present a procedure that, given a sample of values and costs nonlinear clearing prices with much less than full revelation of values and costs. When the kernel function that, given a sample of values and costs for a set of bundles, computes nonlinear clearing prices using
The Equivalence between Row and Column Linear Regression
Tresp, Volker
The Equivalence between Row and Column Linear Regression: A Surprising Feature of Linear Regression and Communications 81730 M¨unchen, Germany Abstract The rows of the design matrix in linear regression are the inputs of the training data set. Normal regression is row regression: the goal is to predict a training target from
A Fully Bayesian Bayesian Approach to Logistic Regression
Shin, Joanne
2015-01-01
Making . . . . . 2.1 Traditional Logistic Regression . . .2.2 Fully Bayesian Logistic Regression 2.2.1 Learning theFully Bayesian (blue) Logistic Regression for three separate
Identifying and removing sources of imprecision in polynomial regression
Brauner, Neima
Identifying and removing sources of imprecision in polynomial regression Neima Braunera and removal of imprecision in polynomial regression, originating from random errors (noise) in the independent.V. Keywords: Regression; Polynomial; Precision; Noise; Collinearity 1. Introduction Mathematical modeling
IRT Goodness-of-Fit Using Approaches from Logistic Regression
Mair, Patrick; Reise, Steven P.; Bentler, Peter M.
2008-01-01
connect IRT and logistic regression, we represent the matrixApproaches from Logistic Regression us write the log-oddsApproaches from Logistic Regression Cox DR, Snell EJ (1989).
Computationally Efficient Gaussian Process Changepoint Detection and Regression
Computationally Efficient Gaussian Process Changepoint Detection and Regression by Robert Conlin Changepoint Detection and Regression by Robert Conlin Grande Submitted to the Department of Aeronautics in Aerospace Engineering Abstract Most existing GP regression algorithms assume a single generative model
Regression: What's it all about?1 Andrew Gelman2
Gelman, Andrew
Regression: What's it all about?1 Andrew Gelman2 5 Jan 2015 Regression plays three different roles in applied statistics: 1. A specification the different faces of regression modeling after being asked to review the new book
IRT Goodness-of-Fit Using Approaches from Logistic Regression
Patrick Mair; Steven P. Reise; Peter M. Bentler
2011-01-01
connect IRT and logistic regression, we represent the matrixApproaches from Logistic Regression us write the log-oddsApproaches from Logistic Regression Cox DR, Snell EJ (1989).
Distributed Multivariate Regression Using Wavelet-based Collective Data Mining.
Kargupta, Hilol
Distributed Multivariate Regression Using Wavelet-based Collective Data Mining. Daryl E a method for distributed multivariate regression using wavelet- based Collective Data Mining (CDM employed in parametric multivariate regression to provide an effective data mining technique for use
Unit I-5 Matrix representations 1 Matrix representation
Birkett, Stephen
Unit I-5 Matrix representations 1 Unit I-5 Matrix representation of linear maps Unit I-5 Matrix in U Â· if v = a1v1 + ... + anvn then T(v) = a1T(v1) + ... + anT(vn) Unit I-5 Matrix representations 3 Example. Find the unique linear map T: R2 R2 so that T(1,2) = (2,3) and T(0,1) = (1,4). Unit I-5 Matrix
A Sparse Representation for Function Approximation 1 Tomaso Poggio and Federico Girosi
Poggio, Tomaso
A Sparse Representation for Function Approximation 1 Tomaso Poggio and Federico Girosi Center relation to PCA, regularization, sparsity principles and Support Vector Machines. 1 This paper appeared optimal locations. Our standard example throughout the paper is the regression problem of reconstructing
PART IV ? REPRESENTATIONS AND INSTRUCTIONS
National Nuclear Security Administration (NNSA)
on Contracting with Entities Engaging in Certain Activities or Transactions Relating to Iran-Representation and Certification. This provision applies to all solicitations. (xxi)...
A comparative study of spline regression
Nougues, Arnaud
1980-01-01
ta IC 33 6. 00 3. 00 '!. 00 5 00 6. DO 1, 00 Oiaa 9. 00 10. 00 11 00 Il. tla IC. Ita I'1. 00 NUBBER BF BRERKPOiNOB CI al 01 0'3 3. 00 a. OD 5, 00 6. DD 1. 00 Oi tla . OD 10. 0 ~ I I, 30 13, OD 13. 00 10. 00 NUNBFR 0, 33ERKPBiNTB Figure 5... POIRIER'S METHOD FOR COMPUTING LEAST SQUARES SPLINES 4. 2 THE RESTRICTED LEAST SQUARES APPROACH 4. 3 LEAST SQUARES SPLINES USING THE B 15 REPRESENTATION 21 4. 4 LEAST SQUARES SPLINES USING THE TRUNCATED POWER BASIS REPRESENTATION 24 4. 5 A...
Memory Kernel in the Expertise of Chess Players
Schaigorodsky, Ana L; Billoni, Orlando V
2015-01-01
In this work we investigate a mechanism for the emergence of long-range time correlations observed in a chronologically ordered database of chess games. We analyze a modified Yule-Simon preferential growth process proposed by Cattuto et al., which includes memory effects by means of a probabilistic kernel. According to the Hurst exponent of different constructed time series from the record of games, artificially generated databases from the model exhibit similar long-range correlations. In addition, the inter-event time frequency distribution is well reproduced by the model for realistic parameter values. In particular, we find the inter-event time distribution properties to be correlated with the expertise of the chess players through the memory kernel extension. Our work provides new information about the strategies implemented by players with different levels of expertise, showing an interesting example of how popularities and long-range correlations build together during a collective learning process.
Positive curvature property for some hypoelliptic heat kernels
Qian, Bin
2010-01-01
In this note, we look at some hypoelliptic operators arising from nilpotent rank 2 Lie algebras. In particular, we concentrate on the diffusion generated by three Brownian motions and their three L\\'evy areas, which is the simplest extension of the Laplacian on the Heisenberg group $\\mathbb{H}$. In order to study contraction properties of the heat kernel, we show that, as in the case of the Heisenberg group, the restriction of the sub-Laplace operator acting on radial functions (which are defined in some precise way in the core of the paper) satisfies a non negative Ricci curvature condition (more precisely a $CD(0, \\infty)$ inequality), whereas the operator itself does not satisfy any $CD(r,\\infty)$ inequality. From this we may deduce some useful, sharp gradient bounds for the associated heat kernel.
Functional data analysis: classification and regression
Lee, Ho-Jin
2005-11-01
...................... 24 4.5 Example: Medfly Fecundity Data .............. 35 4.6 Summary ........................... 49 V FUNCTIONAL ROBUST REGRESSION ............ 50 5.1 Theoretical Foundation .................... 50 5.2 SMO Algorithm for the Minimization Problem... ....... 56 5.3 Simulation Study ....................... 57 CHAPTER vii Page 5.4 Example: Lipoprotein Density Profiles Data ........ 61 5.5 Discussion ........................... 64 VI CONCLUSION ........................... 65 REFERENCES...
Symbolic Representation of Neural Networks
Liu, Huan
Draft Symbolic Representation of Neural Networks Rudy Setiono and Huan Liu Department,liuhg@iscs.nus.sg Abstract Although backpropagation neural networks generally predict better than decision trees do is needed by human experts. This work drives a sym bolic representation for neural networks to make
Regression Given input data (features), predict value of a
Giger, Christine
Regression #12;Regression ! · Given input data (features), predict value of a continuous quantity;! · Assumption: linear relation between input and output Linear regression y = 0 + 1x y = > x x = 1 x > #12 ^ = (XX> ) 1 Xy etc. #12;! · Many relations are not linear · The complete graph Non-linear regression #12
Regression Given input data (features), predict value of a
Giger, Christine
Regression #12;Regression · Given input data (features), predict value of a continuous quantity;· Assumption: linear relation between input and output Linear regression y = 0 + 1x x = 1 x1 x2 . . . xn y · The complete graph Non-linear regression #12;· Need to fit non-linear functions · example: polynomials Non
Probability Density Function Estimation Using Orthogonal Forward Regression
Chen, Sheng
Probability Density Function Estimation Using Orthogonal Forward Regression S. Chen, X. Hong and C estimation is formulated as a regression problem and the orthogonal forward regression tech- nique is adopted procedure. Two examples are used to demonstrate the ability of this regression- based approach
Quantile Regression for Correlated Observations , Lee-Jen Wei2
Wolfe, Patrick J.
Quantile Regression for Correlated Observations Li Chen1 , Lee-Jen Wei2 , and Michael I. Parzen 1 60637 Abstract In this paper, we consider the problem of regression analysis for data which consist the standard mean regression, we regress various percentiles of each marginal response variable over its
Multiple Regression Introduction to Statistics Using R (Psychology 9041B)
Gribble, Paul
Multiple Regression Introduction to Statistics Using R (Psychology 9041B) Paul Gribble Winter, 2015 1 Correlation, Regression & Multiple Regression 1.1 Bivariate correlation The Pearson product*r*(N-2)) / (1-(r*r)) > pobs Fobs 1 #12;Intro Stats R 1 Correlation, Regression
Local Regression with Meaningful Parameters Rafael A. Irizarry
Irizarry, Rafael A.
Local Regression with Meaningful Parameters Rafael A. Irizarry Abstract Local regression or loess squares. In this paper we will present a version of local regression that fits more general parametric, and financial data are included. KEY WORDS: Local Regression, Harmonic Model, Meaningful Parameters, Sound
JOURNAL OF LATEX CLASS FILES 1 Robust Logistic Regression
Hero, Alfred O.
JOURNAL OF LATEX CLASS FILES 1 Robust Logistic Regression with Bounded Data Uncertainties Patrick L a formulation of robust logistic regression under bounded data uncertainties. The robust estimates are obtained compare the results of 1-Logistic Regression against 1-Robust Logistic Regression on a real gene
Censored Regression Trend Analyses for Ambient Water Quality Data
Smyth, Gordon K.
Censored Regression Trend Analyses for Ambient Water Quality Data Gordon K. Smyth 1 , Melanie Cox 2 regression; logistic distribution; regression splines; seasonal trends. 1 Introduction Water is a very. A censored regression strat- egy is used to accommodate arbitrary detection limits for the indicator
Ridge Regression Estimation Approach to Measurement Error Model
Shalabh
Ridge Regression Estimation Approach to Measurement Error Model A.K.Md. Ehsanes Saleh Carleton of the regression parameters is ill conditioned. We consider the Hoerl and Kennard type (1970) ridge regression (RR) modifications of the five quasi- empirical Bayes estimators of the regression parameters of a measurement error
Deepest Regression in Analytical Chemistry P. J. Rousseeuw,a
Van Aelst, Stefan
Deepest Regression in Analytical Chemistry P. J. Rousseeuw,a , S. Van Aelst,a , B. Rambalib , and J Recently the concept of regression depth has been introduced [1]. The deepest regression (DR) is a method for linear regression which is defined as the fit with the best depth relative to the data. In this paper we
Isotonic Regression for Multiple Independent Variables Quentin F. Stout
Stout, Quentin F.
Isotonic Regression for Multiple Independent Variables Quentin F. Stout Computer Science for determining isotonic regressions for weighted data at a set of points P in multidimensional space isotonic regression with unweighted data. L isotonic regression is not unique, and algorithms are given
REGRESSION BY SELECTING APPROPRIATE 7ROJD$\\GQDQG+$OWD\\*venir
Güvenir, H. Altay
REGRESSION BY SELECTING APPROPRIATE FEATURE(S) 7ROJD$\\GÕQDQG+$OWD\\*üvenir Department of Computer methods, called Regression by Selecting Best Feature Projection (RSBFP) and Regression by Selecting Best linear regression line on each continuous feature. In the case of categorical features, exactly one
A high-order fast method for computing convolution integral with smooth kernel
Qiang, Ji
2010-01-01
of the convolution integral. The downside of these evenfor computing convolution integral with smooth kernel Jicalculate convolution integral with smooth non-periodic
T-653: Linux Kernel sigqueueinfo() Process Lets Local Users Send Spoofed Signals
Broader source: Energy.gov [DOE]
A vulnerability was reported in the Linux Kernel. A local user can send spoofed signals to other processes in certain cases.
A Gibbs Sampler for Multivariate Linear Regression
Mantz, Adam B
2015-01-01
Kelly (2007, hereafter K07) described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modeled by a flexible mixture of Gaussians rather than assumed to be uniform. Here I extend the K07 algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Second, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamica...
ccsd-00085858,version1-16Jul2006 SUPERCONNECTION AND FAMILY BERGMAN KERNELS
Boyer, Edmond
ccsd-00085858,version1-16Jul2006 SUPERCONNECTION AND FAMILY BERGMAN KERNELS XIAONAN MA AND WEIPING is to use the superconnection as in the local family index theorem. Superconnexion et noyaux de Bergman en;SUPERCONNECTION AND FAMILY BERGMAN KERNELS 3 Let T 2 (T RW) TRX be the tensor defined in the following way
Differentially Private Learning with Kernels Prateek Jain prajain@microsoft.com
Rajamani, Sriram K.
framework for differ- entially private regularized empirical risk minimization (ERM) that guarantees privacy. Chaudhuri et al. (2011) and Rubinstein et al. (2009) briefly looked at kernel ERMS where the dimensionality, we study the problem of differentially private learning using kernel ERM (kERM), where ac- cess
Supplemental Material for "Classifying Video with Kernel Dynamic Antoni B. Chan and Nuno Vasconcelos
Vasconcelos, Nuno M.
Supplemental Material for "Classifying Video with Kernel Dynamic Textures" Antoni B. Chan and Nuno Vasconcelos SVCL-TR 2007/03 April 2007 #12;#12;Supplemental Material for "Classifying Video with Kernel This is the supplemental material for "Classifying Video with Dynamic Textures" [1]. It contains information about
Kernel Level Energy-Efficient 3G Background Traffic Shaper for Android Smartphones
Kernel Level Energy-Efficient 3G Background Traffic Shaper for Android Smartphones Ekhiotz Jon the Android platform, and measures its energy footprint. The total energy savings of our implementation range study. Index Terms--transmission energy; 3G; kernel; Android I. INTRODUCTION Mobile users have been
JKernelMachines: A Simple Framework for Kernel Machines David Picard PICARD@ENSEA.FR
Paris-Sud XI, Université de
. Description of the Library The backbone of the library is the definition of data types and kernels. In order to use any type of input space, the library makes heavy use o, France Editor: Abstract JKernelMachines is a Java library for learning with kernels. It is primarily
Kernel de Tiempo Real para Control de Procesos Oscar Miranda Gomez, Pedro Mejia Alvarez
Mejia-Alvarez, Pedro
´afico de simulaci´on de tareas de tiempo real. El Kernel de Tiempo Real es peque~no (de tama~no 20.5Kb) y procesador y un peque~no sistema operativo empotrado el cual es capaz de controlar todo el hardware de manera de tiempo real el principal compo- nente lo constituye el sistema operativo o Kernel. Un sistema
A GEOMETRIC CHARACTERIZATION: COMPLEX ELLIPSOIDS AND THE BOCHNER-MARTINELLI KERNEL
Bolt, Michael
A GEOMETRIC CHARACTERIZATION: COMPLEX ELLIPSOIDS AND THE BOCHNER-MARTINELLI KERNEL MICHAEL BOLT;2 MICHAEL BOLT Theorem 1 follows from an analogous characterization of real ellipsoids. The following-Fantappi`e kernel that is constructed using supporting hyperplanes. Theorem 2. For a strictly convex domain Cn
HW Componentizing Kernel: A New Approach to address the Mega Complexity of Future Automotive CPS
Rajkumar, Ragunathan "Raj"
HW Componentizing Kernel: A New Approach to address the Mega Complexity of Future Automotive CPS of CPS (Cyber Physical System). However, current software development process in the automotive industry automotive software devel- opment process in the perspective of CPS and proposes a new kernel-based approach
Comparing Kernels For Predicting Protein Binding Sites From Amino Acid Sequence
Honavar, Vasant
in on all three tasks, with the substitution kernel based on amino acid substitution matrices that take into account structural or evolutionary conservation or physicochemical properties of amino acids yields modestComparing Kernels For Predicting Protein Binding Sites From Amino Acid Sequence Feihong Wu1
Jumping Neptune Can Explain the Kuiper Belt Kernel
Nesvorny, David
2015-01-01
The Kuiper belt is a population of icy bodies beyond the orbit of Neptune. A particularly puzzling and up-to-now unexplained feature of the Kuiper belt is the so-called `kernel', a concentration of orbits with semimajor axes a~44 AU, eccentricities e~0.05, and inclinations ibelt kernel can be explained if Neptune's otherwise smooth migration was interrupted by a discontinuous change of Neptune's semimajor axis when Neptune reached ~28 AU. Before the discontinuity happened, planetesimals located at ~40 AU were swept into Neptune's 2:1 resonance, and were carried with the migrating resonance outwards. The 2:1 resonance was at ~44 AU when Neptune reached ~28 AU. If Neptune's semimajor axis changed by fraction of AU at this point, perhaps because Neptune was scattered off of another planet, the 2:1 population would have been released at ~44 AU, and would remain there to this day. We show that the orbital distribution of bodies produced in this model provides a good match to...
TORCH Computational Reference Kernels - A Testbed for Computer Science Research
Kaiser, Alex; Williams, Samuel Webb; Madduri, Kamesh; Ibrahim, Khaled; Bailey, David H.; Demmel, James W.; Strohmaier, Erich
2010-12-02
For decades, computer scientists have sought guidance on how to evolve architectures, languages, and programming models in order to improve application performance, efficiency, and productivity. Unfortunately, without overarching advice about future directions in these areas, individual guidance is inferred from the existing software/hardware ecosystem, and each discipline often conducts their research independently assuming all other technologies remain fixed. In today's rapidly evolving world of on-chip parallelism, isolated and iterative improvements to performance may miss superior solutions in the same way gradient descent optimization techniques may get stuck in local minima. To combat this, we present TORCH: A Testbed for Optimization ResearCH. These computational reference kernels define the core problems of interest in scientific computing without mandating a specific language, algorithm, programming model, or implementation. To compliment the kernel (problem) definitions, we provide a set of algorithmically-expressed verification tests that can be used to verify a hardware/software co-designed solution produces an acceptable answer. Finally, to provide some illumination as to how researchers have implemented solutions to these problems in the past, we provide a set of reference implementations in C and MATLAB.
Efficient Learning and Feature Selection in High Dimensional Regression
Ting, Jo-Anne; D'Souza, Aaron; Vijayakumar, Sethu; Schaal, Stefan
2010-01-01
We present a novel algorithm for efficient learning and feature selection in high-dimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a probabilistic version...
Testing regression models with residuals as data by Xia Hua.
Hua, Xia, Ph. D. Massachusetts Institute of Technology
2010-01-01
Abstract In polynomial regression ... . In this thesis, I developed a residual based test, the turning point test for residuals, which tests the hypothesis that the kth order polynomial regression holds with ... while the ...
Local Dimensionality Reduction for Non-Parametric Regression
Hoffmann, Heiko; Schaal, Stefan; Vijayakumar, Sethu
2009-01-01
Locally-weighted regression is a computationally-efficient technique for non-linear regression. However, for high-dimensional data, this technique becomes numerically brittle and computationally too expensive if many ...
Honest Confidence Intervals for the Error Variance in Stepwise Regression
Stine, Robert A.
Honest Confidence Intervals for the Error Variance in Stepwise Regression Dean P. Foster and Robert alternatives are used. These simpler algorithms (e.g., forward or backward stepwise regression) obtain
Moment Based Dimension Reduction for Multivariate Response Regression
Bura, Efstathia
Moment Based Dimension Reduction for Multivariate Response Regression Xiangrong Yin Efstathia Bura January 20, 2005 Abstract Dimension reduction aims to reduce the complexity of a regression without re- quiring a pre-specified model. In the case of multivariate response regressions, covariance
Ridge Regression Estimation for Survey Samples Mingue Park
Yang, Min
Ridge Regression Estimation for Survey Samples Mingue Park and Min Yang Korea University and University of Missouri Abstract A procedure for constructing a vector of regression weights is considered. Under the re- gression superpopulation model, the ridge regression estimator that has minimum model mean
A note on modelling cross correlations: hyperbolic secant regression
Smyth, Gordon K.
A note on modelling cross correlations: hyperbolic secant regression Gordon K. Smyth Department of size one. This al- lows regression type modelling of the correlation without unnecessary loss ~ is given in Section 2 that has useful performance down to samples of size one. This allows regression type
Nonparametric Regression in Exponential Families Lawrence D. Brown1
Zhou, Harrison Huibin
Nonparametric Regression in Exponential Families Lawrence D. Brown1 , T. Tony Cai2 and Harrison H. Zhou3 University of Pennsylvania and Yale University Abstract Most results in nonparametric regression we consider nonparametric regression in exponential families which include, for example, Poisson
An Efficient Algorithm for REML in Heteroscedastic Regression
Smyth, Gordon K.
An Efficient Algorithm for REML in Heteroscedastic Regression Gordon K. Smyth Walter and Eliza Hall of covariates relevant for predicting the variance and and are vectors of regression coefficients. The model). Heteroscedastic regression models have an extensive literature going back to Park (1966), Rutemiller & Bowers
NONPARAMETRIC REGRESSION ESTIMATION OF HOUSEHOLD VMT Young-Jun Kweon
Kockelman, Kara M.
NONPARAMETRIC REGRESSION ESTIMATION OF HOUSEHOLD VMT Young-Jun Kweon (Corresponding Author Transportation Survey. The results are density functions and regression surfaces for VMT, in relation regressions perform better than their ordinary-least squares counterparts in many ways: they permit full
Practical Regression and Anova using R Julian J. Faraway
Practical Regression and Anova using R Julian J. Faraway July 2002 #12;1 Copyright c 1999, 2000 There are many books on regression and analysis of variance. These books expect different levels of pre is on the practice of regression and analysis of variance. The objective is to learn what methods are available
Conditional Regression Forests for Human Pose Estimation Pushmeet Kohli
Kohli, Pushmeet
Conditional Regression Forests for Human Pose Estimation Min Sun Pushmeet Kohli Jamie Shotton estimation from depth images. The conditional regression model proposed in the paper is general and can body joint prediction as a regression problem which avoids intermediate body part classification
AUTOMATED REGRESSION TESTING AND VERIFICATION OF COMPLEX CODE
Roychoudhury, Abhik
AUTOMATED REGRESSION TESTING AND VERIFICATION OF COMPLEX CODE CHANGES DOCTORAL THESIS MARCEL B¨OHME NATIONAL UNIVERSITY OF SINGAPORE 2014 #12;AUTOMATED REGRESSION TESTING AND VERIFICATION OF COMPLEX CODE : Automated Regression Testing and Verification of Complex Code Changes Abstract How can we check software
Analysis of Some Methods for Reduced Rank Gaussian Process Regression
Analysis of Some Methods for Reduced Rank Gaussian Process Regression Joaquin Qui~nonero-Candela1 there is strong motivation for using Gaussian Pro- cesses (GPs) due to their excellent performance in regression-effective ap- proximations to GPs, both for classification and for regression. In this paper we analyze one
Nonparametric Regression in Exponential Families Lawrence D. Brown1
Brown, Lawrence D.
Nonparametric Regression in Exponential Families Lawrence D. Brown1 , T. Tony Cai1 and Harrison H. Zhou2 University of Pennsylvania and Yale University Abstract Most results in nonparametric regression we consider nonparametric regression in exponential families with the main focus on the natural
Robust Regression Analysis: Some Popular Statistical Package Options
Masci, Frank
1 Robust Regression Analysis: Some Popular Statistical Package Options By Robert A. Yaffee Meyer of SAS, Institute for their gracious assistance. Robust regression analysis provides an alternative to a least squares regression model when fundamental assumptions are unfulfilled by the nature
Math 261A -Spring 2012 M. Bremer Multiple Linear Regression
Keinan, Alon
Math 261A - Spring 2012 M. Bremer Multiple Linear Regression So far, we have seen the concept of simple linear regression where a single predictor variable X was used to model the response variable Y . In many applications, there is more than one factor that influences the response. Multiple regression
Classifying the phase transition threshold for unordered regressive Ramsey numbers
Weiermann, Andreas
Classifying the phase transition threshold for unordered regressive Ramsey numbers Florian the notion of unordered regressive Ramsey numbers or unordered Kanamori-McAloon numbers. We show that these are of Ackermannian growth rate. For a given number- theoretic function f we consider unordered f-regressive Ramsey
Matrix Representation of Special Relativity
Wolfgang Koehler
2007-03-08
I compare the matrix representation of the basic statements of Special Relativity with the conventional vector space representation. It is shown, that the matrix form reproduces all equations in a very concise and elegant form, namely: Maxwell equations, Lorentz-force, energy-momentum tensor, Dirac-equation and Lagrangians. The main thesis is, however, that both forms are nevertheless not equivalent, but matrix representation is superior and gives a deeper insight into physical reality, because it is based on much less assumptions. It allows a better understanding of Minkowski spacetime on the basis of matrix algebra. An escpecially remarkable result of the consequent usage of this alge- braic concept is the formulation of Diracs equation in a novel matrix form. This equation can be generalized to include a new variant of Yang-Mills gauge fields, which possibly express unified electro-weak interactions in a new way.
Improving the Fisher Kernel for Large-Scale Image Classification
Kim, Tae-Kyun
classification the FK was shown to extend the popular bag-of-visual-words (BOV) by going beyond count statistics. However, in practice, this enriched representation has not yet shown its superiority over the BOV classification to date has been to describe images with bag-of-visual-words (BOV) histograms and to classify them
Terminologies Trivial extensions and representations
Iyama, Osamu
. For an artin algebra A over a commutative artinian ring C, D := HomC (-, E0 C (top C)), A = DTrA, -1 A = Tr of Algebra at University of Tsukuba The view from the old seminar at Univesity of Tsukuba Jun-ichi Miyachi November 14, 2013, Nagoya Jun-ichi Miyachi Researches on the Representation Theory of Algebra at Univers
Neural representation of transparent overlay
von der Heydt, Rüdiger
the corners of the light and dark squares are rounded off (Fig. 1c), transparency is no longer perceivedNeural representation of transparent overlay Fangtu T Qiu & Ru¨diger von der Heydt Perceptual transparency is a surprising phenomenon in which a number of regions of different shades organize
Xiaowei Yang; Qing Shen; Hongquan Xu; Steven Shoptaw
2011-01-01
1997. 8. Faraway, JJ. Regression analysis for a functionalLocally Weighted Regression and Smoothing Scatterplots,Functional Regression Analysis using an F Test for
Penalized-regression-based multimarker genotype analysis of Genetic Analysis Workshop 17 data
Ayers, Kristin L; Mamasoula, Chrysovalanto; Cordell, Heather J
2011-01-01
via penalized logistic regression. Genet Epidemiol 2010, 34:The group LASSO for logistic regression. J R Stat Soc Ser Bvariants by penalized regression. Bioinformatics 2010, 26:
Representation of Limited Rights Data and Restricted Computer...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
Representation of Limited Rights Data and Restricted Computer Software Representation of Limited Rights Data and Restricted Computer Software Representation of Limited Rights Data...
CRYSTALLINE REPRESENTATIONS AND F -CRYSTALS. Mark Kisin
Kisin, Mark
CRYSTALLINE REPRESENTATIONS AND F -CRYSTALS. Mark and finite flat group schemes, con* *jectured by Breuil, and to show that a crystalline representation. In this paper we generalize Breuil's theory to describe crystalline represen* *tations of higher weight or
Using Enhanced Spherical Images for Object Representation
Smith, David A.
1979-05-01
The processes involved in vision, manipulation, and spatial reasoning depend greatly on the particular representation of three-dimensional objects used. A novel representation, based on concepts of differential geometry, ...
Quantum Mechanics and Representation Theory Columbia University
Woit, Peter
Quantum Mechanics and Representation Theory Peter Woit Columbia University Texas Tech, November 21 2013 Peter Woit (Columbia University) Quantum Mechanics and Representation Theory November 2013 1 / 30 #12;Does Anyone Understand Quantum Mechanics? "No One Understands Quantum Mechanics" "I think
Collision kernels from velocity-selective optical pumping with magnetic depolarization
Bhamre, T.
We experimentally demonstrate how magnetic depolarization of velocity-selective optical pumping can be used to single out the collisional cusp kernel best describing spin- and velocity-relaxing collisions between potassium ...
A Gaussian-Like Immersed Boundary Kernel with Improved Translational Invariance and Smoothness
Bao, Yuan-Xun; Peskin, Charles S
2015-01-01
The immersed boundary (IB) method is a general mathematical framework for studying problems involving fluid-structure interactions in which an elastic structure is immersed in a viscous incompressible fluid. In the IB formulation, the fluid described by Eulerian variables is coupled with the immersed structure described by Lagrangian variables via the use of the Dirac delta function. From a numerical standpoint, the Lagrangian force spreading and the Eulerian velocity interpolation are carried out by a regularized, compactly supported discrete delta function or kernel. Immersed-boundary kernels are derived from a certain set of postulates to achieve approximate grid translational invariance, interpolation accuracy and computational efficiency. In this note, we present a new 6-point immersed-boundary kernel that is $\\mathcal{C}^3$ and features a substantially improved translational invariance compared to other common IB kernels.
CPM: A Graph Pattern Matching Kernel with Diffusion for Accurate Graph Classification
Kansas, University of
(the Molecular Library Initiative project) that aims to determine and publicize the biological activ chemical structure data sets and have compared our methods to all major graph kernel functions that we know
Weighted Bergman Kernel Functions and the Lu Qi-keng Problem
Jacobson, Robert Lawrence
2012-07-16
The classical Lu Qi-keng Conjecture asks whether the Bergman kernel function for every domain is zero free. The answer is no, and several counterexamples exist in the literature. However, the more general Lu Qi-keng Problem, ...
Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping
Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping distances between proteins. This method shows significant improvement in protein fold recognition. Overall March 2014 Keywords: Protein sequence Fold recognition Alignment method Feature extraction
CLASSIFICATION OF FOOD KERNELS WITH IMPACT ACOUSTICS TIME-1 FREQUENCY PATTERNS2
Minnesota, University of
, Classification.24 25 INTRODUCTION26 Food kernel damage caused by insects, fungi and mold are major sources degrades the quality and value of wheat and is one of the most difficult29 #12;2 defects to detect
Context Representation for Web Search Results
Baeza-Yates, Ricardo
Context Representation for Web Search Results JesÃºs Vegas Department of Computer Science U. Valladolid Context Representation for Web Search Results 2 Outline Intro Web search results in the web site and Future work #12;Context Representation for Web Search Results 3 Introduction Searching the web is one
STORM: A STatistical Object Representation Model
Rafanelli, M. ); Shoshani, A. )
1989-11-01
In this paper we explore the structure and semantic properties of the entities stored in statistical databases. We call such entities statistical objects'' (SOs) and propose a new statistical object representation model,'' based on a graph representation. We identify a number of SO representational problems in current models and propose a methodology for their solution. 11 refs.
Atomic Representations of Rank 2 Graph Algebras
Davidson, Ken
Atomic Representations of Rank 2 Graph Algebras Kenneth R. Davidson a , Stephen C. Power b , Dilian University, Lancaster LA1 4YF, U.K. Abstract We provide a detailed analysis of atomic -representations- posed into a direct sum or direct integral of irreducible atomic representations. The building blocks
Quantum Control and Representation Theory
A. Ibort; J. M. Pérez-Pardo
2012-03-11
A new notion of controllability for quantum systems that takes advantage of the linear superposition of quantum states is introduced. We call such notion von Neumann controllabilty and it is shown that it is strictly weaker than the usual notion of pure state and operator controlability. We provide a simple and effective characterization of it by using tools from the theory of unitary representations of Lie groups. In this sense we are able to approach the problem of control of quantum states from a new perspective, that of the theory of unitary representations of Lie groups. A few examples of physical interest and the particular instances of compact and nilpotent dynamical Lie groups are discussed.
Temporal Representation in Semantic Graphs
Levandoski, J J; Abdulla, G M
2007-08-07
A wide range of knowledge discovery and analysis applications, ranging from business to biological, make use of semantic graphs when modeling relationships and concepts. Most of the semantic graphs used in these applications are assumed to be static pieces of information, meaning temporal evolution of concepts and relationships are not taken into account. Guided by the need for more advanced semantic graph queries involving temporal concepts, this paper surveys the existing work involving temporal representations in semantic graphs.
EXTENSIONS OF CRYSTALLINE REPRESENTATIONS Matthew Emerton and Mark Kisin
Emerton, Matthew
EXTENSIONS OF CRYSTALLINE REPRESENTATIONS Matthew Emerton. 27 4.Extensions of Crystalline Representations. 4.1 Continuous Galois Cohomology of Galois representations, and especia* *lly extensions of crystalline and semi-stable representations
Infinite-Dimensional Representations of 2-Groups
John C. Baez; Aristide Baratin; Laurent Freidel; Derek K. Wise
2011-02-09
A "2-group" is a category equipped with a multiplication satisfying laws like those of a group. Just as groups have representations on vector spaces, 2-groups have representations on "2-vector spaces", which are categories analogous to vector spaces. Unfortunately, Lie 2-groups typically have few representations on the finite-dimensional 2-vector spaces introduced by Kapranov and Voevodsky. For this reason, Crane, Sheppeard and Yetter introduced certain infinite-dimensional 2-vector spaces called "measurable categories" (since they are closely related to measurable fields of Hilbert spaces), and used these to study infinite-dimensional representations of certain Lie 2-groups. Here we continue this work. We begin with a detailed study of measurable categories. Then we give a geometrical description of the measurable representations, intertwiners and 2-intertwiners for any skeletal measurable 2-group. We study tensor products and direct sums for representations, and various concepts of subrepresentation. We describe direct sums of intertwiners, and sub-intertwiners - features not seen in ordinary group representation theory. We study irreducible and indecomposable representations and intertwiners. We also study "irretractable" representations - another feature not seen in ordinary group representation theory. Finally, we argue that measurable categories equipped with some extra structure deserve to be considered "separable 2-Hilbert spaces", and compare this idea to a tentative definition of 2-Hilbert spaces as representation categories of commutative von Neumann algebras.
Breast Cancer Prognosis via Gaussian Mixture Regression Tiago H. Falk
Shatkay, Hagit
compares the performance of classification and regression trees (CART), multivariate adaptive regression]. For each pa- tient, 30 cellular features are extracted from digital images of cells taken from the tumor (e algorithms, namely, classification and re- gression trees (CART) [4] and multivariate adaptive regres- sion
Feature selection in high dimensional regression problems for genomics
Paris-Sud XI, Université de
Feature selection in high dimensional regression problems for genomics Julie Hamon1,2,3 , Clarisse, France julien.jacques@lifl.fr Abstract. In the context of genomic selection in animal breeding and "closed to real" datasets. Keywords: Feature selection, combinatorial optimization, regression, genomic. 1
Inference for Covariate Adjusted Regression via Varying Coefficient Models 1
Müller, Hans-Georg
¸ENT¨URK AND HANS-GEORG M¨ULLER University of California, Davis We consider covariate adjusted regression (CAR consider a variant of (1), where one observes contaminated versions of predictors and response. Contamination of the variables in the regression model occurs through a multiplicative factor that is determined
MULTIVARIATE REGRESSION S-ESTIMATORS FOR ROBUST ESTIMATION AND INFERENCE
Van Aelst, Stefan
1 MULTIVARIATE REGRESSION S-ESTIMATORS FOR ROBUST ESTIMATION AND INFERENCE Stefan Van Aelst-estimators for multivariate regression. We study the robustness of the estimators in terms of their breakdown point and in and multivariate location and scatter. Furthermore we develop a fast and robust bootstrap method
Proportional Hazards Regression with Unknown Link Function By WEI WANG
Wang, Jane-Ling
Proportional Hazards Regression with Unknown Link Function By WEI WANG Harvard Medical School@amss.ac.cn The University of Hong Kong, Hong Kong qhwang@hku.hk Summary Proportional hazards regression model assumes of the covariates. Traditional ap- proaches, such as the Cox proportional hazards model, focus on estimating
Estimators in step regression models Ursula U. Muller
Wefelmeyer, Wolfgang
Estimators in step regression models Ursula U. M¨uller Texas A&M University Anton Schick Binghamton function but smooth; see Schick and Wefelmeyer (2012) and (2013). Here the regression function has jumps. Then the corresponding convolution estimator can have the rate n-1/2 of an empirical estimator; see again Schick
KNOWMESH --Meshless geometry with knowledge representation
Franklin, W. Randolph
(aka path, motion) planning · Drainage analysis Commercial product's inconsistent layer representation setup Closeup of tank Dense grid of scanner points · model with homogeneous lifts
Optimization Online - Minimal Representation of Insurance Prices
Oct 27, 2012 ... Minimal Representation of Insurance Prices. Alois Pichler (alois.pichler ***at*** univie.ac.at) Alexander Shapiro (ashapiro ***at*** ...
dos Santos, Pedro G.
2012-12-31
This dissertation provides insights on what influences women's descriptive representation in state legislatures in Brazil. The study of female representation in Brazil provides for a good case study as the country uses a gender quota system...
Online Sparse Matrix Gaussian Process Regression and Vision Applications
Yang, Ming-Hsuan
in learning nonlinear mappings between high dimensional data and their low dimensional representations the use of matrix downdating using hyper- bolic rotations to also learn the hyperparameters of the GP
Regression analysis of oncology drug licensing deal values
Hawkins, Paul Allen
2006-01-01
This work is an attempt to explain wide variations in drug licensing deal value by using regression modeling to describe and predict the relationship between oncology drug deal characteristics and their licensing deal ...
SMOOTHED ESTIMATING EQUATIONS FOR INSTRUMENTAL VARIABLES QUANTILE REGRESSION
Kaplan, David M.; Sun, Yixiao
2012-01-01
Yale University. DAVID M. KAPLAN AND YIXIAO SUN Phillips, P.QUANTILE REGRESSION DAVID M. KAPLAN AND YIXIAO SUN Abstract.htr{E(AA )} . h DAVID M. KAPLAN AND YIXIAO SUN In view of (
Topics on Regularization of Parameters in Multivariate Linear Regression
Chen, Lianfu
2012-02-14
My dissertation mainly focuses on the regularization of parameters in the multivariate linear regression under different assumptions on the distribution of the errors. It consists of two topics where we develop iterative procedures to construct...
A multi-regression analysis of airline indirect operating costs
Taneja, Nawal K.
1968-01-01
A multiple regression analysis of domestic and local airline indirect costs was carried out to formulate cost estimating equations for airline indirect costs. Data from CAB and FAA sources covering the years 1962-66 was ...
Error bars for linear and nonlinear neural network regression models
Penny, Will
Error bars for linear and nonlinear neural network regression models William D. Penny and Stephen J College of Science, Technology and Medicine, London SW7 2BT., U.K. w.penny@ic.ac.uk, s
Robust regression on noisy data for fusion scaling laws
Verdoolaege, Geert, E-mail: geert.verdoolaege@ugent.be [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Laboratoire de Physique des Plasmas de l'ERM - Laboratorium voor Plasmafysica van de KMS (LPP-ERM/KMS), Ecole Royale Militaire - Koninklijke Militaire School, B-1000 Brussels (Belgium)
2014-11-15
We introduce the method of geodesic least squares (GLS) regression for estimating fusion scaling laws. Based on straightforward principles, the method is easily implemented, yet it clearly outperforms established regression techniques, particularly in cases of significant uncertainty on both the response and predictor variables. We apply GLS for estimating the scaling of the L-H power threshold, resulting in estimates for ITER that are somewhat higher than predicted earlier.
Median regression and the missing information principle Ian W. McKeague
McKeague, Ian
Median regression and the missing information principle Ian W. McKeague Department of Statistics regression analysis has robustness properties which make it an attractive alternative to regression based of the median regression model, leading to a new estimator for the regression parameters. Our approach adapts
Regression Benchmarking with Simple Middleware Benchmarks Lubomr Bulej1,2
Regression Benchmarking with Simple Middleware Benchmarks Lubomír Bulej1,2 , Tomás Kalibera1 , Petr.tuma}@mff.cuni.cz Abstract The paper introduces the concept of regression benchmarking as a variant of regression testing fo- cused at detecting performance regressions. Applying the regression benchmarking in the area of middle
Cho, Young Hyun
1 Optimization of Vertical and Horizontal Beamforming Kernels on the PowerPC G4 Processor with Alti-time on a Sun UltraSPARC II server with 16 333-MHz processors by utilizing the Visual Instruction Set (VIS beamforming kernels to use AltiVec SIMD extension for the PowerPC. AltiVec can execute up to four 32-bit
Noise Kernel for Self-similar Tolman Bondi Metric: Fluctuations on Cauchy Horizon
Seema Satin; Kinjalk Lochan; Sukratu Barve
2013-04-12
We attempt to calculate the point separated Noise Kernel for self similar Tolman Bondi metric, using a method similar to that developed by Eftekharzadeh et. al for ultra-static spacetimes referring to the work by Page. In case of formation of a naked singularity, the Noise Kernel thus obtained is found to be regular except on the Cauchy horizon, where it diverges. The behavior of the noise in case of the formation of a covered singularity is found to be regular. This result seemingly renders back reaction non-negligible which questions the stability of the results obtained from the semiclassical treatment of the self similar Tolman Bondi metric.
Gene- or region-based association study via kernel principal component analysis
Gao, Qingsong; He, Yungang; Yuan, Zhongshang; Zhao, Jinghua; Zhang, Bingbing; Xue, Fuzhong
2011-08-26
RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, et al: Complement factor H polymorphism in age-related macular degeneration. Science 2005, 308(5720):385-389. 2. Maraganore DM, de Andrade M, Lesnick... information processing systems 1999, 11(1):536-542. 28. Schlkopf B, Smola A, Müller K: Kernel principal component analysis. Artificial Neural Networks¡ªICANN’97 1997, 583-588. 29. Scholkopf B, Smola A, Muller KR: Nonlinear component analysis as a kernel...
Heat kernels on 2d Liouville quantum gravity: a numerical study
Grigory Bonik; Joe P. Chen; Alexander Teplyaev
2014-11-06
We numerically compute the heat kernel on a square lattice torus equipped with the measure corresponding to Liouville quantum gravity (LQG). From the on-diagonal heat kernel we verify that the spectral dimension of LQG is 2. Furthermore, when diffusion is started from a high point of the underlying Gaussian free field, our numerics indicates superdiffusive space-time scaling with respect to the Euclidean metric in the small space-to-time regime. The implications of this result require further investigation, but seem to coincide with the notion that the Euclidean metric is not the right geodesic for characterizing the geometry of LQG.
Representable states on quasilocal quasi *-algebras
Bagarello, F.; Trapani, C.; Triolo, S.
2011-01-15
Continuing a previous analysis originally motivated by physics, we consider representable states on quasilocal quasi *-algebras, starting with examining the possibility for a compatible family of local states to give rise to a global state. Some properties of local modifications of representable states and some aspects of their asymptotic behavior are also considered.
RAMIFICATION OF CRYSTALLINE REPRESENTATIONS SHIN HATTORI
Hattori, Shin
RAMIFICATION OF CRYSTALLINE REPRESENTATIONS SHIN HATTORI Abstract. This is a survey on integral p-adic Hodge theory, especially on the Fontaine-Laffaille theory, and a ramification bound for crystalline-Laffaille modules 4 2.2. Associated Galois representations 8 2.3. Relation to crystalline cohomology 12 2
Knowledge representation in process engineering Ulrike Sattler
Baader, Franz
Knowledge representation in process engineering Ulrike Sattler RWTH Aachen, uli, the tasks we are concerned with in process engineering are described as well as how knowledge representation@cantor.informatik.rwthaachen.de Abstract Process engineering is surely no pure con figuration application, but modeling the structure
N-representability is QMA-complete
Y. -K. Liu; M. Christandl; F. Verstraete
2006-09-17
We study the computational complexity of the N-representability problem in quantum chemistry. We show that this problem is QMA-complete, which is the quantum generalization of NP-complete. Our proof uses a simple mapping from spin systems to fermionic systems, as well as a convex optimization technique that reduces the problem of finding ground states to N-representability.
Topics in Representation Theory: The Heisenberg Algebra
Woit, Peter
Topics in Representation Theory: The Heisenberg Algebra We'll now turn to a topic which is a precise analog of the previous discussion of the Clifford algebra and spinor representations. By replacing a new algebra, the Heisenberg algebra. The group of automor- phism of this algebra is now a symplectic
Arhonditsis, George B.
Multiple regression models: A methodology for evaluating trihalomethane concentrations in drinking of these substances on human health. A multiple regression model was developed to estimate THM concentrations Science Ltd. All rights reserved. Keywords: Multiple regression model; Trihalomethanes; Drinking water
Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression
Chen, Wan-Ping
2015-01-01
in Multi-response Linear Regression Problem Set-up and theList of Figures The linear regression model Y ? X?, with p nin high-dimensional multivariate regression. ” The Annals of
Linear Regression Sample Proportions Interpretation of the Confidence Interval Interval Estimation
Watkins, Joseph C.
Linear Regression Sample Proportions Interpretation of the Confidence Interval Topic 16 Interval Estimation Additional Topics 1 / 9 #12;Linear Regression Sample Proportions Interpretation of the Confidence Interval Outline Linear Regression Sample Proportions Interpretation of the Confidence Interval 2 / 9 #12
Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares
Kwok, James Tin-Yau
Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares Shutao Li1 , Chen Clear Water Bay, Hong Kong shutao li@yahoo.com.cn, lc337199@sina.com, jamesk@cs.ust.hk Abstract. In this paper, we propose a gene extraction method by us- ing two standard feature extraction methods, namely
3DString: A Feature String Kernel for 3D Object Classification on Voxelized Data
Kriegel, Hans-Peter
3DString: A Feature String Kernel for 3D Object Classification on Voxelized Data Johannes AÃ?falg-Maximilians-University Munich, Germany {assfalg|kb|kriegel}@dbs.ifi.lmu.de ABSTRACT Classification of 3D objects remains which allows to combine it with an M-tree for handling of large volumes of data. Classification
Moulin, Pierre
Image Set Classification Using Holistic Multiple Order Statistics Features and Localized Multi-Kernel Metric Learning Jiwen Lu1 , Gang Wang1,2 , and Pierre Moulin3 1 Advanced Digital Sciences Center viewpoints or under varying illuminations. While a number of image set classification methods have been
Structure of an Aspergillus flavus population from maize kernels in northern Italy Antonio Mauro a
Cotty, Peter J.
; Piva et al., 2006). Aflatoxins are secondary metabolites produced by several mem- bers of AspergillusStructure of an Aspergillus flavus population from maize kernels in northern Italy Antonio Mauro, a Institute of Entomology and Plant Pathology, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84
AN EFFICIENT INTEGRAL TRANSFORM TECHNIQUE OF A SINGULAR WIRE ANTENNA KERNEL
Park, Seong-Ook
AN EFFICIENT INTEGRAL TRANSFORM TECHNIQUE OF A SINGULAR WIRE ANTENNA KERNEL S.-O. Park Department-348, South of Korea Abstract-This paper presents an efficient integral transform tech- nique for evaluating transforma- tions, the original double integral 1/-Rs with a singular kernal can be represented as a finite
Integral operators with the generalized sine-kernel on the real axis
N. A. Slavnov
2010-05-27
The asymptotic properties of integral operators with the generalized sine kernel acting on the real axis are studied. The formulas for the resolvent and the Fredholm determinant are obtained in the large x limit. Some applications of the results obtained to the theory of integrable models are considered.
Predictable Interrupt Management for Real Time Kernels over conventional PC Hardware1
Mejia-Alvarez, Pedro
which this integrated model improves the traditional model. The design of a flexible and portable kernel-CONACyT 42151-Y, and CONACYT 42449-Y Mexico. Abstract In this paper we analyze the traditional model on real-time systems. As a result of this analysis, we propose a model that integrates interrupts
Geotagged Photo Recognition using Corresponding Aerial Photos with Multiple Kernel Learning
Yanai, Keiji
Geotagged Photo Recognition using Corresponding Aerial Photos with Multiple Kernel Learning Keita for geotagged photos, we have already proposed ex- ploiting aerial photos around geotag places as addi- tional image features for visual recognition of geo- tagged photos. In the previous work, to fuse two kinds
Optimizing Kernel Block Memory Operations Michael Calhoun, Scott Rixner, Alan L. Cox
Optimizing Kernel Block Memory Operations Michael Calhoun, Scott Rixner, Alan L. Cox Rice University Houston, TX 77005 {calhoun,rixner,alc}@rice.edu Abstract-- This paper investigates the performance of block memory operations in the operating system, including memory copies, page zeroing, interprocess
A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities
A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities Ke Xu of the technique and its potential for nowcasting weather radar reflectivities. Key Words: Bayesian, dilation to nowcasting weather radar reflectivities into two general categories. The first is the use of simple
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space
Scott, Clayton
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space Robert A. Vandermeulen is not straightforward and is a topic we explore in this paper. To construct a robust KDE we scale the traditional KDE and project it to its nearest weighted KDE in the L2 norm. This yields a scaled and projected KDE (SPKDE
Vasconcelos, Nuno M.
Supplemental Material for ``Classifying Video with Kernel Dynamic Textures'' Antoni B. Chan and Nuno Vasconcelos SVCLTR 2007/03 April 2007 #12; #12; Supplemental Material for ``Classifying Video This is the supplemental material for ``Classifying Video with Dynamic Textures'' [1]. It contains information about
Aguiar, Pedro M. Q.
COMBINING FREE ENERGY SCORE SPACES WITH INFORMATION THEORETIC KERNELS: APPLICATION TO SCENE recent and top performing tools in each of the steps: (i) the free energy score space; (ii) non embeddings can be found in [1, 2, 13]. A very recent approach, termed free energy score space (FESS) [13, 14
A FOOD IMAGE RECOGNITION SYSTEM WITH MULTIPLE KERNEL LEARNING Taichi Joutou and Keiji Yanai
Yanai, Keiji
A FOOD IMAGE RECOGNITION SYSTEM WITH MULTIPLE KERNEL LEARNING Taichi Joutou and Keiji Yanai 182-8585 JAPAN ABSTRACT Since health care on foods is drawing people's attention re- cently, a system that can record everyday meals easily is being awaited. In this paper, we propose an automatic food image
SPEK: A Storage Performance Evaluation Kernel Module for Block Level Storage Systems under
Yang, Qing "Ken"
1 SPEK: A Storage Performance Evaluation Kernel Module for Block Level Storage Systems under Faulty), for evaluating the performance of block-level storage systems in the presence of faults as well as under normal operations. SPEK can work on both Direct Attached Storage (DAS) and block level networked storage systems
SPEK: A Storage Performance Evaluation Kernel Module for Block Level Storage Systems
He, Xubin "Ben"
SPEK: A Storage Performance Evaluation Kernel Module for Block Level Storage Systems Ming Zhang storage systems at block level. It can be used for both DAS (Direct Attached Storage) and block level networked storage systems. Each SPEK tool consists of a controller, several workers, and one or more probers
Yang, Junfeng
page frame sharing can be leveraged for the complete circumven- tion of software and hardware kernel, and kGuard. We also discuss techniques for constructing reliable ret2dir exploits against x86, x86-level software has become much harder, as recent versions of popular OSes come with nu- merous protections
KLAS FreeBSD GNU C A Kernel Profiler based on Aspect-orientation
Chiba, Shigeru
in the C language. That code fragment is automatically executed for collecting detailed performance data KLAS C KLAS KLAS KLAS FreeBSD GNU C A Kernel Profiler based on Aspect-orientation Yoshisato corresponding to the member accesses. We have implemented this feature by extending the GNU C compiler
UNCORRECTED 2 Kernel-based distance metric learning for content-based image retrieval
Yeung, Dit-Yan
UNCORRECTED PROOF 1 2 Kernel-based distance metric learning for content-based image retrieval 3 and Technology, Clear Water Bay, Kowloon, Hong Kong 5 Received 24 August 2005; received in revised form 16 images, the performance of a content-based image retrieval (CBIR) system 9 depends critically
A New Kernel Method for Object Recognition: Spin Glass-Markov Random Fields
Caputo, Barbara
A New Kernel Method for Object Recognition: Spin Glass-Markov Random Fields BARBARA CAPUTO Doctoral table, our car in a parking lot, and so on. While this task is performed with great accuracy with energy function derived from results of statistical physics of spin glasses. Markov random fields
Shell Element Verification & Regression Problems for DYNA3D
Zywicz, E
2008-02-01
A series of quasi-static regression/verification problems were developed for the triangular and quadrilateral shell element formulations contained in Lawrence Livermore National Laboratory's explicit finite element program DYNA3D. Each regression problem imposes both displacement- and force-type boundary conditions to probe the five independent nodal degrees of freedom employed in the targeted formulation. When applicable, the finite element results are compared with small-strain linear-elastic closed-form reference solutions to verify select aspects of the formulations implementation. Although all problems in the suite depict the same geometry, material behavior, and loading conditions, each problem represents a unique combination of shell formulation, stabilization method, and integration rule. Collectively, the thirty-six new regression problems in the test suite cover nine different shell formulations, three hourglass stabilization methods, and three families of through-thickness integration rules.
Non-smooth brownian martingales and stochastic integral representations
Wroblewski, David M.
2007-01-01
and M. Yor. On stochastic integral representations ofmotion by stochas- tic integrals. Ann. Math. Statist. , 41:Martingales and Stochastic Integral Representations A
Harmonic Representation of Combinations and Partitions
Michalis Psimopoulos
2011-03-01
In the present article a new method of deriving integral representations of combinations and partitions in terms of harmonic products has been established. This method may be relevant to statistical mechanics and to number theory.
Mental Representations Formed From Educational Website Formats
Elizabeth T. Cady; Kimberly R. Raddatz; Tuan Q. Tran; Bernardo de la Garza; Peter D. Elgin
2006-10-01
The increasing popularity of web-based distance education places high demand on distance educators to format web pages to facilitate learning. However, limited guidelines exist regarding appropriate writing styles for web-based distance education. This study investigated the effect of four different writing styles on reader’s mental representation of hypertext. Participants studied hypertext written in one of four web-writing styles (e.g., concise, scannable, objective, and combined) and were then administered a cued association task intended to measure their mental representations of the hypertext. It is hypothesized that the scannable and combined styles will bias readers to scan rather than elaborately read, which may result in less dense mental representations (as identified through Pathfinder analysis) relative to the objective and concise writing styles. Further, the use of more descriptors in the objective writing style will lead to better integration of ideas and more dense mental representations than the concise writing style.
Text representations in digital hypermedia library systems
Lokken, Sveinung Taraldsrud
1993-01-01
The advent of the digital library poses a great number of challenging research questions in the areas of hypermedia, computer-human interaction, information retrieval, and information science. Choosing a representation for text converted from...
Representation, Organization, Classification, and Meaning-Making
Toronto, University of
1 Representation, Organization, Classification, and Meaning-Making Description Fundamental epistemological and ontological issues in the use of knowledge and information in human activities. Analysis, department store, grocery store, children's library, a menu, a store catalogue) and analyze that organization
The moduli of representations with Borel mold
Nakamoto, Kazunori
2010-01-01
The author constructs the moduli of representations whose images generate the subalgebra of upper triangular matrices (up to inner automorphisms) of the full matrix ring for any groups and any monoids.
Lyapunov Function Synthesis using Handelman Representations.
Sankaranarayanan, Sriram
Lyapunov Function Synthesis using Handelman Representations. Sriram Sankaranarayanan Xin Chen investigate linear programming relaxations to synthesize Lyapunov functions that es- tablish the stability approach searches for a Lyapunov function, given a parametric form with unknown coefficients
Reiner, Dora; Blaickner, Matthias; Rattay, Frank
2009-11-15
Purpose: Radiopharmaceuticals administered in targeted radionuclide therapy (TRT) rely to a great extent not only on beta-emitting nuclides but also on emitters of monoenergetic electrons. Recent advances like combined PET/CT devices, the consequential coregistration of both data, the concept of using beta couples for diagnosis and therapy, respectively, as well as the development of voxel models offer a great potential for developing TRT dose calculation systems similar to those available for external beam treatment planning. The deterministic algorithms in question for this task are based on the convolution of three-dimensional matrices, one representing the activity distribution and the other the dose point kernel. This study aims to report on three-dimensional kernel matrices for various nuclides used in TRT. Methods: The Monte Carlo code MCNP5 was used to calculate discrete dose kernels of beta particles including the contributions from their respective secondary radiation in soft tissue for the following nuclides: {sup 32}P, {sup 33}P, {sup 67}Cu, {sup 89}Sr, {sup 90}Y, {sup 103}Rh{sup m}, {sup 131}I, {sup 177}Lu, {sup 186}Re, and {sup 188}Re. For each nuclide a kernel cube of 10x10x10 mm{sup 3} was calculated, the dimensions of a voxel being 1 mm{sup 3}. Additional kernels with voxel sizes of 3x3x3 mm{sup 3} were simulated. Results: Comparison with the S-value data regarding {sup 32}P, {sup 89}Sr, {sup 90}Y, and {sup 131}I of the MIRD committee which were calculated with the EGS4 code showed a very good agreement, the secondary particle transport of {sup 90}Y being the only exception. Documented analytical kernels on the other side show deviations very close and very far to the source. Conclusions: The good accordance with the only discrete dose kernels published up to date justifies the method chosen. Together with the additional six nuclides, this report provides a considerable database for three-dimensional kernel matrices with regard to beta radionuclides applied in TRT. In contrast to analytical dose point kernels, the discrete kernels elude the problem of overestimation near the source and take energy depositions into account, which occur beyond the range of the continuous-slowing-down approximation (csda range). Recalculation of the 1x1x1 mm{sup 3} kernels to other dose kernels with varying voxel dimensions, cubic or noncubic, is shown to be easily manageable and thereby provides a resolution-independent system of dose calculation.
Quantum Prisoner's Dilemma in the new representation
Jinshan Wu
2004-06-06
Using the representation introduced in our another paper\\cite{frame}, the well-known Quantum Prisoner's Dilemma proposed in \\cite{jens}, is reexpressed and calculated. By this example and the works in \\cite{frame} on classical games and Quantum Penny Flip game, which first proposed in \\cite{meyer}, we show that our new representation can be a general framework for games originally in different forms.
Estimating the error distribution function in semiparametric regression
Mueller, Uschi
Schick, Wolfgang Wefelmeyer Abstract We prove a stochastic expansion for a residual-based estimator linear smoother, i.i.d. representation, Donsker class, effi- ciency. #12;2 M¨uller - Schick - Wefelmeyer estimators of ; see e.g. Schick (1996). Given such an estimator ^ of , we estimate by a local linear smoother
Evaluation of Regressive Methods for Automated Generation of Test Trajectories
Cukic, Bojan
. In simple terms,a test trajectoiy is defined as a series of data points, with each (possiblymultidimensional of test generation and the percentage of "acceptable" trajectories, measured by the domain specificEvaluation of Regressive Methods for Automated Generation of Test Trajectories Brian J. Taylor
Scalable Regression Tree Learning on Hadoop using Computer Science Department
Hwang, Kai
to build prediction models. Regression tree is a popular learning model that combines decision trees. This pushes the envelope on the traditional theoretical, empirical and computational sciences to allow novel be monitored by the utility company using Advanced metering infrastructure (AMI), also known as smart meters
DYNA3D/ParaDyn Regression Test Suite Inventory
Lin, J I
2011-01-25
The following table constitutes an initial assessment of feature coverage across the regression test suite used for DYNA3D and ParaDyn. It documents the regression test suite at the time of production release 10.1 in September 2010. The columns of the table represent groupings of functionalities, e.g., material models. Each problem in the test suite is represented by a row in the table. All features exercised by the problem are denoted by a check mark in the corresponding column. The definition of ''feature'' has not been subdivided to its smallest unit of user input, e.g., algorithmic parameters specific to a particular type of contact surface. This represents a judgment to provide code developers and users a reasonable impression of feature coverage without expanding the width of the table by several multiples. All regression testing is run in parallel, typically with eight processors. Many are strictly regression tests acting as a check that the codes continue to produce adequately repeatable results as development unfolds, compilers change and platforms are replaced. A subset of the tests represents true verification problems that have been checked against analytical or other benchmark solutions. Users are welcomed to submit documented problems for inclusion in the test suite, especially if they are heavily exercising, and dependent upon, features that are currently underrepresented.
Climate Multi-model Regression Using Spatial Smoothing Karthik Subbian
Banerjee, Arindam
Climate Multi-model Regression Using Spatial Smoothing Karthik Subbian Arindam Banerjee Abstract There are several Global Climate Models (GCMs) reported by var- ious countries to the Intergovernmental Panel on Climate Change (IPCC). Due to the varied nature of the GCM assumptions, the fu- ture projections
LogisticLDA: Regularizing Latent Dirichlet Allocation by Logistic Regression
: upstream and downstream models. In "upstream" models, hidden topics are generated by conditioning 160169 #12;a document). Examples of upstream models include the Dirichlet Multinomial Regression model (DMR) (Mimno and McCallum, 2008), and the Theme Model (Li and Perona, 2005). Although upstream
Worldwide Oil Production Michaelis-Menten Kinetics Correlation and Regression
Watkins, Joseph C.
Worldwide Oil Production Michaelis-Menten Kinetics Topic 4 Correlation and Regression Transformed Variables 1 / 13 #12;Worldwide Oil Production Michaelis-Menten Kinetics Outline Worldwide Oil Production Michaelis-Menten Kinetics Lineweaver-Burke double reciprocal plot 2 / 13 #12;Worldwide Oil Production
AIRCRAFT PARAMETRIC STRUCTURAL LOAD MONITORING USING GAUSSIAN PROCESS REGRESSION
Boyer, Edmond
cases. KEYWORDS : Structural Health and Usage Monitoring, Gaussian Process Regression, Fatigue, 1 in the remaining useful life. If the error is too 7th European Workshop on Structural Health Monitoring July 8 manuscript, published in "EWSHM - 7th European Workshop on Structural Health Monitoring (2014)" #12
FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS
Keller, Arturo A.
Winter (November - April) water demand Developed by Limaye et al. 1993 Residential water demand f {PPHFORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS by Bruce Bishop Professor of Civil resources resulting in water stress. Effective water management a solution Supply side management Demand
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
Hartemink, Alexander
. Experimental results on standard benchmark data sets attest to the accuracy, sparsity, and efficiency the relevance vector machine (RVM) [35], the sparse probit regression (SPR) algorithm [10], [11], sparse online Gaussian processes [6], the informative vector machine (IVM) [22], and the joint classifier and feature
Efficiency transfer for regression models with responses missing at random
Mueller, Uschi
Efficiency transfer for regression models with responses missing at random Ursula U. M that characteristics of the con- ditional distribution of Y given X can be estimated efficiently using complete case analysis. One can simply omit incomplete cases and work with an appro- priate efficient estimator without
ESTIMATING THE ERROR DISTRIBUTION FUNCTION IN NONPARAMETRIC REGRESSION WITH MULTIVARIATE
Schick, Anton
URSULA U. M¨ULLER, ANTON SCHICK AND WOLFGANG WEFELMEYER Abstract. We consider nonparametric regression function. This problem was already addressed by M¨uller, Schick and Wefelmeyer (2007) in the case m = 11, . . . , xm) Rm . Anton Schick was supported by NSF Grant DMS 0405791. 1 #12;2 URSULA U. M
Mining Simulation Metrics for Failure Triage in Regression Testing
Veneris, Andreas
are grouped by applying data-mining clustering algorithms. Fi- nally, the generated failure clustersMining Simulation Metrics for Failure Triage in Regression Testing Zissis Poulos1 , Andreas Veneris of failures can be exposed. These failures need to be properly grouped and distributed among engineers
Nonstationary Logistic Regression William D. Penny and Stephen J. Roberts
Roberts, Stephen
Nonstationary Logistic Regression William D. Penny and Stephen J. Roberts Technical Report, Neural, Technology and Medicine, London SW7 2BT., U.K. w.penny@ic.ac.uk, s.j.roberts@ic.ac.uk May 5, 1999 Abstract We
Representations up to homotopy of Lie algebroids
Abad, Camilo Arias
2009-01-01
This is the first in a series of papers devoted to the study of the cohomology of classifying spaces. The aim of this paper is to introduce and study the notion of representation up to homotopy and to make sense of the adjoint representation of a Lie algebroid. Our construction is inspired by Quillen's notion of superconnection and fits into the general theory of structures up to homotopy. The advantage of considering such representations is that they are flexible and general enough to contain interesting examples which are the correct generalization of the corresponding notions for Lie algebras. They also allow one to identify seemingly ad-hoc constructions and cohomology theories as instances of the cohomology with coefficients in representations (up to homotopy). In particular, we show that the adjoint representation of a Lie algebroid makes sense as a representation up to homotopy and that, similar to the case of Lie algebras, the resulting cohomology controls the deformations of the Lie algebroid (i.e. i...
arXiv:submit/0910499[stat.ML]11Feb2014 Online Nonparametric Regression
Rakhlin, Alexander "Sasha"
arXiv:submit/0910499[stat.ML]11Feb2014 Online Nonparametric Regression Alexander Rakhlin University for online regression for arbitrary classes of regression functions in terms of the sequential entropy learning with squared loss and online nonparametric regression are the same. In addition to a non
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression Divyanshu Vats of selecting tuning parame- ters for high-dimensional sparse regression. We propose a simple and computation-dependent sparse regression algorithm into an asymptotically tuning-free sparse regression algorithm. More specifi
Regression Test Selection for AspectJ Software Ohio State University
Xu, Guoqing "Harry"
Regression Test Selection for AspectJ Software Guoqing Xu Ohio State University Atanas Rountev Ohio, the new program needs to be regression tested to validate these changes. To reduce the cost of regression test- ing, a regression-test-selection technique can be used to se- lect only a necessary subset
Use of Regression Equations 1 Running head: Equations from summary data
Crawford, John R.
Use of Regression Equations 1 Running head: Equations from summary data Neuropsychology, in press the final version published in the APA journal. It is not the copy of record Using regression equations.crawford@abdn.ac.uk #12;Use of Regression Equations 2 Abstract Regression equations have many useful roles
Fortin, Marie Josee
. Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR this assumption using a local modelling technique, geographically weighted regression (GWR), not previously used weighted regression, logistic regression, non-stationarity, Northwest Atlantic, spatial modelling. Received
357 09/2010 Assessing Representations for Moving Object
Güting, Ralf Hartmut
INFORMATIK BERICHTE 357 09/2010 Assessing Representations for Moving Object Histories Christian Hagen #12;1 Assessing Representations for Moving Object Histories Christian D¨untgen, Thomas Behr of the histories of moving objects in databases: the Compact Representation, the Unit Representation and the Hybrid
Complete Representations in Algebraic Logic Robin Hirsch & Ian Hodkinson \\Lambda
Hodkinson, Ian
Complete Representations in Algebraic Logic Robin Hirsch & Ian Hodkinson \\Lambda Abstract A boolean algebra is shown to be completely representable if and only if it is atomic, whereas it is shown that neither the class of completely representable relation algebras nor the class of completely representable
Shalabh
1 Chapter 14 Logistic Regression Models In the linear regression model X + , there are two types two possible values 0 and 1. In such a case, the logistic regression is used. For example, y can function of a random variable. In particular, the logistic distribution, whose cumulative distribution
CrossWalk: A Tool for Performance Profiling Across the UserKernel Boundary (ParCo Preprint)
Miller, Barton P.
significant amount of their time in the operating system. As a result, conventional userlevel profilers canWalk. By drilling down into the kernel, we were able to identify the ultimate cause of its performance problems
Azcona, J; Burguete, J
2014-06-01
Purpose: To obtain the pencil beam kernels that characterize a megavoltage photon beam generated in a FFF linac by experimental measurements, and to apply them for dose calculation in modulated fields. Methods: Several Kodak EDR2 radiographic films were irradiated with a 10 MV FFF photon beam from a Varian True Beam (Varian Medical Systems, Palo Alto, CA) linac, at the depths of 5, 10, 15, and 20cm in polystyrene (RW3 water equivalent phantom, PTW Freiburg, Germany). The irradiation field was a 50 mm diameter circular field, collimated with a lead block. Measured dose leads to the kernel characterization, assuming that the energy fluence exiting the linac head and further collimated is originated on a point source. The three-dimensional kernel was obtained by deconvolution at each depth using the Hankel transform. A correction on the low dose part of the kernel was performed to reproduce accurately the experimental output factors. The kernels were used to calculate modulated dose distributions in six modulated fields and compared through the gamma index to their absolute dose measured by film in the RW3 phantom. Results: The resulting kernels properly characterize the global beam penumbra. The output factor-based correction was carried out adding the amount of signal necessary to reproduce the experimental output factor in steps of 2mm, starting at a radius of 4mm. There the kernel signal was in all cases below 10% of its maximum value. With this correction, the number of points that pass the gamma index criteria (3%, 3mm) in the modulated fields for all cases are at least 99.6% of the total number of points. Conclusion: A system for independent dose calculations in modulated fields from FFF beams has been developed. Pencil beam kernels were obtained and their ability to accurately calculate dose in homogeneous media was demonstrated.
Stein's method, heat kernel, and traces of powers of elements of compact Lie groups
Jason Fulman
2010-05-07
Combining Stein's method with heat kernel techniques, we show that the trace of the jth power of an element of U(n,C), USp(n,C) or SO(n,R) has a normal limit with error term of order j/n. In contrast to previous works, here j may be growing with n. The technique should prove useful in the study of the value distribution of approximate eigenfunctions of Laplacians.
Phenomenological memory-kernel master equations and time-dependent Markovian processes
L. Mazzola; E. -M. Laine; H. -P. Breuer; S. Maniscalco; J. Piilo
2011-03-03
Do phenomenological master equations with memory kernel always describe a non-Markovian quantum dynamics characterized by reverse flow of information? Is the integration over the past states of the system an unmistakable signature of non-Markovianity? We show by a counterexample that this is not always the case. We consider two commonly used phenomenological integro-differential master equations describing the dynamics of a spin 1/2 in a thermal bath. By using a recently introduced measure to quantify non-Markovianity [H.-P. Breuer, E.-M. Laine, and J. Piilo, Phys. Rev. Lett. 103, 210401 (2009)] we demonstrate that as far as the equations retain their physical sense, the key feature of non-Markovian behavior does not appear in the considered memory kernel master equations. Namely, there is no reverse flow of information from the environment to the open system. Therefore, the assumption that the integration over a memory kernel always leads to a non-Markovian dynamics turns out to be vulnerable to phenomenological approximations. Instead, the considered phenomenological equations are able to describe time-dependent and uni-directional information flow from the system to the reservoir associated to time-dependent Markovian processes.
Unifying Geometrical Representations of Gauge Theory
Scott T Alsid; Mario A Serna
2014-10-28
We unify three approaches within the vast body of gauge-theory research that have independently developed distinct representations of a geometrical surface-like structure underlying the vector-potential. The three approaches that we unify are: those who use the compactified dimensions of Kaluza-Klein theory, those who use Grassmannian models (also called gauge theory embedding or $CP^{N-1}$ models) to represent gauge fields, and those who use a hidden spatial metric to replace the gauge fields. In this paper we identify a correspondence between the geometrical representations of the three schools.Each school was mostly independently developed, does not compete with other schools, and attempts to isolate the gauge-invariant geometrical surface-like structures that are responsible for the resulting physics. By providing a mapping between geometrical representations, we hope physicists can now isolate representation-dependent physics from gauge-invariant physical results and share results between each school. We provide visual examples of the geometrical relationships between each school for $U(1)$ electric and magnetic fields. We highlight a first new result: in all three representations a static electric field (electric field from a fixed ring of charge or a sphere of charge) has a hidden gauge-invariant time dependent surface that is underlying the vector potential.
Latest Jurassic-early Cretaceous regressive facies, northeast Africa craton
van Houten, F.B.
1980-06-01
Nonmarine to paralic detrital deposits accumulated in six large basins between Algeria and the Arabo-Nubian shield during major regression in latest Jurassic and Early Cretaceous time. The Ghadames Sirte (north-central Libya), and Northern (Egypt) basins lay along the cratonic margin of northeastern Africa. The Murzuk, Kufra, and Southern (Egypt) basins lay in the south within the craton. Data for reconstructing distribution, facies, and thickness of relevant sequences are adequate for the three northern basins only. High detrital influx near the end of Jurassic time and in mid-Cretaceous time produced regressive nubian facies composed largely of low-sinuosity stream and fahdelta deposits. In the west and southwest the Ghadames, Murzuk, and Kufra basins were filled with a few hundred meters of detritus after long-continued earlier Mesozoic aggradation. In northern Egypt the regressive sequence succeeded earlier Mesozoic marine sedimentation; in the Sirte and Southern basins correlative deposits accumulated on Precambrian and Variscan terranes after earlier Mesozoic uplift and erosion. Waning of detrital influx into southern Tunisia and adjacent Libya in the west and into Israel in the east initiated an Albian to early Cenomanian transgression of Tethys. By late Cenomanian time it had flooded the entire cratonic margin, and spread southward into the Murzuk and Southern basins, as well as onto the Arabo-Nubian shield. Latest Jurassic-earliest Cretaceous, mid-Cretaceous, and Late Cretaceous transgressions across northeastern Africa recorded in these sequences may reflect worldwide eustatic sea-level rises. In contrast, renewed large supply of detritus during each regression and a comparable subsidence history of intracratonic and marginal basins imply regional tectonic control. 6 figures.
Doing data regression in the TI-85 Entering the data
Torres, Rodolfo
of the data.DRAW Press to get a plot of the latest regression computed. To forecast or compute a value Press the range (press ) according to the data entered.WIND Press and then in the STAT menu.PLOT Select .Plot1 and then after in the menu.) STAT VARS To forecast or compute a value Press in the menu (you may need to press
Poisson Regression Analysis of Illness and Injury Surveillance Data
Frome E.L., Watkins J.P., Ellis E.D.
2012-12-12
The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra-Poisson variation. The R open source software environment for statistical computing and graphics is used for analysis. Additional details about R and the data that were used in this report are provided in an Appendix. Information on how to obtain R and utility functions that can be used to duplicate results in this report are provided.
Taylor Expansion Diagrams: A Canonical Representation for
Kalla, Priyank
Taylor series expansion that allows one to model word-level signals as algebraic symbols. This power systems has made it essential to address verification issues at early stages of the design cycle representations. TEDs are applicable to modeling, symbolic simulation, and equivalence verification of dataflow
Low Light Image Enhancement via Sparse Representations
Tsakalides, Panagiotis
Low Light Image Enhancement via Sparse Representations Konstantina Fotiadou , Grigorios,greg,tsakalid}@ics.forth.gr Abstract. Enhancing the quality of low light images is a critical pro- cessing function both from images captured under low illumination conditions based on the mathematical framework of Sparse
Sparseness and Expansion in Sensory Representations
. In addition, the low dimensionality of the input layer generates overlaps between the induced representations., 2003), and the electrosensory system of electric fish (Chacron et al., 2011). The ubiquity of this phenomenon suggests that sparse and expansive transformations entail a fundamental computational advantage
Pictorial Representation of Parallel Programs Susan Stepney
Stepney, Susan
of its tools, GRAIL. 1 Introduction Parallel programs have considerably more complicated structures than been developed as part of the Alvey ParSiFal project, for use by one of its tools, GRAIL [1 design. 3 Two-Dimensional Display The pictorial representation used in GRAIL is two dimensional
Radon Transform Inversion using the Shearlet Representation
Labate, Demetrio
Radon Transform Inversion using the Shearlet Representation Flavia Colonna Department The inversion of the Radon transform is a classical ill-posed inverse problem where some method-optimal rate of convergence in estimating a large class of images from noisy Radon data. This is achieved
Adaptive Representation of Specular Light Flux
Montréal, Université de
Adaptive Representation of Specular Light Flux Normand Bri`ere Pierre Poulin D´epartement d in all but the simplest con- figurations. To capture their appearance, we present an adaptive approach based upon light beams. The coher- ence between light rays forming a light beam greatly re- duces
Shape Recipes: Scene Representations that Refer
Freeman, William T.
Shape Recipes: Scene Representations that Refer to the Image William T. Freeman and Antonio to estimate and store. We propose a low-dimensional rep- resentation, called a scene recipe, that relies on the image itself to de- scribe the complex scene configurations. Shape recipes are an example
Memory Space Representation Heterogeneous Network Process Migration
Sun, Xian-He
Memory Space Representation for Heterogeneous Network Process Migration Kasidit Chanchio Xian@bit.csc.lsu.edu http://www.csc.lsu.edu/~scs/ Abstract A major difficulty of heterogeneous process migration is how and effective for heterogeneous network process migration. 1. Introduction As network computing becomes
Computational Neuroimaging: Color Representations and Processing
Wandell, Brian A.
Computational Neuroimaging: Color Representations and Processing £ Brian A. Wandell Neuroscience the retinal image as a collection of objects having various perceptual features, including color, motion the retinal image. This chapter reviews the main ideas concerning how color appearance is derived from
Galois representations with quaternion multiplication associated to ...
A.O.L. Atkin; Wen-Ching Winnie Li; Tong Liu; Ling Long
2013-09-18
Aug 19, 2013 ... Modularity of Scholl representations when d = 1. 6228. 4.2. ... 1. Introduction. To a d-dimensional space S?(?) of cusp forms of weight ? > 2 for a noncongru- ence subgroup ? ...... MR2038777 (2004m:11089). [BLTDR10] T.
Solving three-body scattering problem in the momentum lattice representation
V. N. Pomerantsev; V. I. Kukulin; O. A. Rubtsova
2008-12-02
A brief description of the novel approach towards solving few-body scattering problems in a finite-dimensional functional space of the $L_2$-type is presented. The method is based on the complete few-body continuum discretization in the basis of stationary wave packets. This basis, being transformed to the momentum representation, leads to the cell-lattice-like discretization of the momentum space. So the initial scattering problem can be formulated on the multi-dimensional momentum lattice which makes it possible to reduce the solution of any scattering problem above the breakup threshold (where the integral kernels include, in general, some complicated moving singularities) to convenient simple matrix equations which can be solved on the real energy axis. The phase shifts and inelasticity parameters for the three-body $nd$ elastic scattering with MT I-III $NN$ potential both below and above the three-body breakup threshold calculated with the proposed wave-packet technique are in a very good agreement with the previous accurate benchmark calculation results.
Nonparametric Regression using the Concept of Minimum Energy
Mike Williams
2011-07-12
It has recently been shown that an unbinned distance-based statistic, the energy, can be used to construct an extremely powerful nonparametric multivariate two sample goodness-of-fit test. An extension to this method that makes it possible to perform nonparametric regression using multiple multivariate data sets is presented in this paper. The technique, which is based on the concept of minimizing the energy of the system, permits determination of parameters of interest without the need for parametric expressions of the parent distributions of the data sets. The application and performance of this new method is discussed in the context of some simple example analyses.
Do, Hyunsook
Infrastructure Support for Controlled Experimentation with Software Testing and Regression Testing@cse.unl.edu January 18, 2004 Abstract Where the development, understanding, and assessment of software testing infrastructure to support controlled experimentation with software testing and regression testing techniques
Do, Hyunsook
Infrastructure Support for Controlled Experimentation with Software Testing and Regression Testing@cse.unl.edu April 13, 2005 Abstract Where the development, understanding, and assessment of software testing infrastructure to support controlled experimentation with software testing and regression testing techniques
Teodorescu, Remus
Aalborg Universitet Photovoltaic Array Condition Monitoring Based on Online Regression of the 39th IEEE Photovoltaic Specialists Conference, PVSC 2013 DOI (link to publication from Publisher): 10., & Teodorescu, R. (2013). Photovoltaic Array Condition Monitoring Based on Online Regression of Performance
Galtchouk, Leonid
2008-01-01
An adaptive nonparametric estimation procedure is constructed for the estimation problem of heteroscedastic regression when the noise variance depends on the unknown regression. A non-asymptotic upper bound for a quadratic risk (an oracle inequality) is constructed.
Sharp non-asymptotic oracle inequalities for nonparametric heteroscedastic regression models
Galtchouk, Leonid
2010-01-01
An adaptive nonparametric estimation procedure is constructed for heteroscedastic regression when the noise variance depends on the unknown regression. A non-asymptotic upper bound for a quadratic risk (oracle inequality) is obtained
Using Sensitivity Analysis to Create Simplified Economic Models for Regression Testing
Rothermel, Gregg
Keywords Regression testing, test case prioritization, regression test selection, economic models tradeoffs. Economic models can help practitioners and re- searchers assess methodologies relative sensitivity anal- ysis to examine our model analytically and assess the factors that are most important
Zander, Jessica Selene
2012-01-01
Narratives of Contamination: Representations of Race,Fall 2012 Narratives of Contamination: Representations ofAbstract Narratives of Contamination: Representations of
ECOLOGIC REGRESSION ANALYSIS AND THE STUDY OF THE INFLUENCE OF AIR QUALITY ON MORTALITY
Selvin, S.
2014-01-01
Orcutt, An empirical analysis of air pollution dose-responseIf ecologic regression analysis of air quality and mortality
Integral representations for a generalized Hermite linear functional
R. S. Costas-Santos; Ridha Sfaxi
2008-07-08
In this paper we find new integral representations for the {\\it generalized Hermite linear functional} in the real line and the complex plane. As application, new integral representations for the Euler Gamma function are given.
Lexical semantics and knowledge representation in multilingual sentence generation
Toronto, University of
Lexical semantics and knowledge representation in multilingual sentence generation by Manfred Stede semantics and knowledge representation in multilingual sentence generation Manfred Stede Doctor approach to automatic language generation that focuses on the need to produce a range of di erent
Mental representation and processing of syntactic structure: evidence from Chinese
Cai, Zhenguang
2011-06-29
From the perspective of cognitive psychology, our knowledge of language can be viewed as mental representations and our use of language can be understood as the computation or processing of mental representations. This ...
TURBULENCE-INDUCED RELATIVE VELOCITY OF DUST PARTICLES. IV. THE COLLISION KERNEL
Pan, Liubin [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Padoan, Paolo, E-mail: lpan@cfa.harvard.edu, E-mail: ppadoan@icc.ub.edu [ICREA and Institut de Ciències del Cosmos, Universitat de Barcelona, IEEC-UB, Martí Franquès 1, E-08028 Barcelona (Spain)
2014-12-20
Motivated by its importance for modeling dust particle growth in protoplanetary disks, we study turbulence-induced collision statistics of inertial particles as a function of the particle friction time, ?{sub p}. We show that turbulent clustering significantly enhances the collision rate for particles of similar sizes with ?{sub p} corresponding to the inertial range of the flow. If the friction time, ?{sub p,} {sub h}, of the larger particle is in the inertial range, the collision kernel per unit cross section increases with increasing friction time, ?{sub p,} {sub l}, of the smaller particle and reaches the maximum at ?{sub p,} {sub l} = ?{sub p,} {sub h}, where the clustering effect peaks. This feature is not captured by the commonly used kernel formula, which neglects the effect of clustering. We argue that turbulent clustering helps alleviate the bouncing barrier problem for planetesimal formation. We also investigate the collision velocity statistics using a collision-rate weighting factor to account for higher collision frequency for particle pairs with larger relative velocity. For ?{sub p,} {sub h} in the inertial range, the rms relative velocity with collision-rate weighting is found to be invariant with ?{sub p,} {sub l} and scales with ?{sub p,} {sub h} roughly as ? ?{sub p,h}{sup 1/2}. The weighting factor favors collisions with larger relative velocity, and including it leads to more destructive and less sticking collisions. We compare two collision kernel formulations based on spherical and cylindrical geometries. The two formulations give consistent results for the collision rate and the collision-rate weighted statistics, except that the spherical formulation predicts more head-on collisions than the cylindrical formulation.
Logistic Regression and Bayesian Model Selection in Estimation of Probability of Success
Shemyakin, Arkady
1 1 Logistic Regression and Bayesian Model Selection in Estimation of Probability of Success Arkady ABSTRACT Logistic regression and linear discriminant analysis are used to estimate probability of success X is analyzed as an explanatory variable. A comparison is made between logistic regression technique
Logistic Regression and Artificial Neural Networks for Classification of Ovarian Tumors
Logistic Regression and Artificial Neural Networks for Classification of Ovarian Tumors C. Lu1 , J to generate and evaluate both logistic regression models and artificial neural network (ANN) models to predict, including explorative univariate and multivariate analysis, and the development of the logistic regression
Logistic Regression Advanced Methods for Data Analysis (36-402/36-608)
Tibshirani, Ryan
Logistic Regression Advanced Methods for Data Analysis (36-402/36-608) Spring 2014 1 Classification. In this lecture we will learn one of the most common tools: logistic regression. You should know function) 1.2 Why not just use least squares? · Before we present logistic regression, we address
Modeling Animal-Vehicle Collisions Using Diagonal Inflated Bivariate Poisson Regression
Washington at Seattle, University of
1 Modeling Animal-Vehicle Collisions Using Diagonal Inflated Bivariate Poisson Regression of highway AVCs, this study adopts a diagonal inflated bivariate Poisson regression method, an inflated version of bivariate Poisson regression model, to fit the reported AVC and carcass removal data sets
Regression Model for Daily Maximum Stream Temperature David W. Neumann1
Balaji, Rajagopalan
Regression Model for Daily Maximum Stream Temperature David W. Neumann1 ; Balaji Rajagopalan2 for the summer period. The model is created using a stepwise linear regression procedure to select significant-9372 2003 129:7 667 CE Database subject headings: Decision support systems; Regression models; California
Statistica Sinica 19 (2009), 801-817 VARIABLE SELECTION IN QUANTILE REGRESSION
Liu, Yufeng
2009-01-01
Statistica Sinica 19 (2009), 801-817 VARIABLE SELECTION IN QUANTILE REGRESSION Yichao Wu and Yufeng inception in Koenker and Bassett (1978), quantile regression has become an important and widely used- lection aspect of penalized quantile regression. Under some mild conditions, we demonstrate the oracle
Regression of Multicomponent Sticking Probabilities Using a Genetic Algorithm Ian J. Laurenzi*
Regression of Multicomponent Sticking Probabilities Using a Genetic Algorithm Ian J. LaurenziVania, Philadelphia, PennsylVania 19104 A genetic algorithm (GA) was developed for the purpose of regressing processes were then simulated under physiological conditions via Monte Carlo. The GA successfully regressed
Estimating Litter Decomposition Rate in Single-Pool Models Using Nonlinear Beta Regression
Thomas, David D.
Estimating Litter Decomposition Rate in Single-Pool Models Using Nonlinear Beta Regression Etienne the performance of nonlinear regression using the beta distribution, which is well-suited to bounded data and this type of heteroscedasticity, to standard nonlinear regression (normal errors) on simulated and real
REGRESSION PERFORMANCE OF GROUP LASSO FOR ARBITRARY DESIGN MATRICES Marco F. Duarte,1,
Bajwa, Waheed U.
REGRESSION PERFORMANCE OF GROUP LASSO FOR ARBITRARY DESIGN MATRICES Marco F. Duarte,1, Waheed U.duarte,w.bajwa,robert.calderbank}@duke.edu ABSTRACT In many linear regression problems, explanatory variables are activated in groups or clusters; group lasso has been proposed for regression in such cases. This paper studies the non- asymptotic
Autologistic Regression Analysis of Spatial-Temporal Binary Data via Monte Carlo
Aukema, Brian
Autologistic Regression Analysis of Spatial-Temporal Binary Data via Monte Carlo Maximum Likelihood regression analysis of binary data that are measured on a spatial lattice and repeatedly over discrete time points. We propose a spatial- temporal autologistic regression model and draw statistical inference via
Kutas, Marta
Regression-based estimation of ERP waveforms: II. Nonlinear effects, overlap correction regression-based approach to estimating ERP waveforms. Here, we build on this foundation, showing how rERP framework provides a flexible way to adapt well-known regression techniques to the problem of estimating
Crawford, John R.
Using Regression Equations Built From Summary Data in the Neuropsychological Assessment of the Individual Case John R. Crawford University of Aberdeen Paul H. Garthwaite The Open University Regression that there is a large reservoir of published data that could be used to build regression equations; these equations
Regression Analysis Using Weighted Least Squares Robert Drucker, University of Washington (May 1996)
Washington at Seattle, University of
Regression Analysis Using Weighted Least Squares Robert Drucker, University of Washington (May 1996) 1. Theory The jth order leastsquares regression problem can be stated in matrix notation as Y = Xb regression analysis assumes that the residuals are independent identically dis tributed (iid) random
Estimating Salinity between 25 and 45S in the Atlantic Ocean Using Local Regression
Estimating Salinity between 25° and 45°S in the Atlantic Ocean Using Local Regression W. C. THACKER and temperature in the South Atlantic is quantified with the aid of local regression. To capture the spatial fitting regression models to the profile data is considerably more work than using published clima
Multiple regression on distance matrices: a multivariate spatial analysis tool Jeremy W. Lichstein
Lichstein, Jeremy W.
Multiple regression on distance matrices: a multivariate spatial analysis tool Jeremy W. Lichstein, Spatial autocorrelation Abstract I explore the use of multiple regression on distance matrices (MRM regression of a response matrix on any number of explanatory matrices, where each matrix contains distances
Efficient Regression of General-Activity Human Poses from Depth Images Ross Girshick
Kohli, Pushmeet
Efficient Regression of General-Activity Human Poses from Depth Images Ross Girshick Jamie Shotton of several decision-tree training ob- jectives. Key aspects of our work include: regression di- rectly from the regression-based ap- proaches that have been a staple of monocular 2D human pose estimation [1, 19, 10, 15
Practical Selection of SVM Parameters and Noise Estimation for SVM Regression
Cherkassky, Vladimir
1 Practical Selection of SVM Parameters and Noise Estimation for SVM Regression Vladimir Cherkassky 55455, USA Abstract We investigate practical selection of meta-parameters for SVM regression (that is using several low-dimensional and high-dimensional regression problems. Further, we point out
Adaptive Regression Testing Strategy: An Empirical Study Md. Junaid Arafeen and Hyunsook Do
Do, Hyunsook
Adaptive Regression Testing Strategy: An Empirical Study Md. Junaid Arafeen and Hyunsook Do in different versions. These factors can affect the costs and benefits of regression testing techniques in different ways, and thus, there may be no single regression testing technique that is the most cost
Regression Models with Interval Censoring Jian Huang and Jon A. Wellner 1
Wellner, Jon A.
Regression Models with Interval Censoring Jian Huang and Jon A. Wellner 1 University of Washington October 6, 1993 Abstract In this paper we discuss estimation in semiparametric regression models with interval censoring, with emphasis on estimation of the regression parameter . The first section surveys
A regression model with a hidden logistic process for feature extraction from time series
Chamroukhi, Faicel
A regression model with a hidden logistic process for feature extraction from time series Faicel from time series is proposed in this paper. This approach consists of a specific regression model Reweighted Least-Squares (IRLS) algorithm. A piecewise regression algorithm and its iterative variant have
On Test Suite Composition and Cost-Effective Regression Testing Gregg Rothermel
Rothermel, Gregg
On Test Suite Composition and Cost-Effective Regression Testing Gregg Rothermel , Sebastian Elbaum}@cse.unl.edu August 31, 2004 Abstract Regression testing is an expensive testing process used to re-validate software as it evolves. Various methodologies for improving regression testing processes have been explored, but the cost
On Test Suite Composition and Cost-Effective Regression Testing. Gregg Rothermel
Rothermel, Gregg
On Test Suite Composition and Cost-Effective Regression Testing. Gregg Rothermel , Sebastian Elbaum}@cse.unl.edu August 30, 2003 Abstract Regression testing is an expensive testing process used to re-validate software as it evolves. Various methodologies for improving regression testing processes have been explored, but the cost
A regression model with a hidden logistic process for signal parametrization
Chamroukhi, Faicel
A regression model with a hidden logistic process for signal parametrization F. Chamroukhi, A. Samé/UTC-France) ESANN 2009 April 24 2009 2 / 21 #12;Context Context: Predictive maintenance of the French railway 2009 6 / 21 #12;The proposed regression approach A regression model with a hidden logistic process
Schick, Anton
in nonparametric regression Anton Schick and Wolfgang Wefelmeyer Abstract. Consider a nonparametric regression statistic, local U-statistic, local polynomial smoother, monotone regression function. Anton Schick was supported by NSF Grant DMS 0906551. 1 #12;2 ANTON SCHICK AND WOLFGANG WEFELMEYER where Kb(t) = K
IBLStreams: A System for Instance-Based Classification and Regression on Data Streams
Hüllermeier, Eyke
IBLStreams: A System for Instance-Based Classification and Regression on Data Streams Ammar Shaker to classification and regression problems. In comparison to model-based methods for learning on data streams. Keywords: Data streams, classification, regression, instance-based learn- ing, concept drift. 1 #12
Feature Selection for Support Vector Regression in the Application of Building Energy Prediction
Paris-Sud XI, Université de
Feature Selection for Support Vector Regression in the Application of Building Energy Prediction--When using support vector regression to predict building energy consumption, since the energy influence and reduces the computational time. Keywords-support vector regression; feature selection; build- ing; energy
Spatial autocorrelation approaches to testing residuals from least squares regression
Chen, Yanguang
2015-01-01
In statistics, the Durbin-Watson test is always employed to detect the presence of serial correlation of residuals from a least squares regression analysis. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the Durbin-Watson will be ineffectual because the value of Durbin-Watson's statistic depends on the sequences of data point arrangement. Based on the ideas from spatial autocorrelation, this paper presents two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then on the analogy of the Durbin-Watson statistic, a serial correlation index is constructed. As a case, the two statistics are applied to the spatial sample of 29 China's regions. These results show th...
arXiv:1412.2844v1[stat.CO]9Dec2014 Optimal Reduced Isotonic Regression
Stout, Quentin F.
arXiv:1412.2844v1[stat.CO]9Dec2014 Optimal Reduced Isotonic Regression Janis Hardwick and Quentin F regression is a shape-constrained nonparametric regression in which the regression is an increasing step function. For n data points, the number of steps in the isotonic regression may be as large as n
Anomaly-free representations of the holonomy-flux algebra
SangChul Yoon
2008-09-07
We work on the uniqueness, gr-qc/0504147, of representations of the holonomy-flux algebra in loop quantum gravity. We argue that for analytic diffeomorphisms, the flux operators can be only constants as functions on the configuration space in representations with no anomaly, which are zero in the standard representation.
Representation Theory of Generalized Deformed Oscillator Algebras
C. Quesne; N. Vansteenkiste
1997-01-28
The representation theory of the generalized deformed oscillator algebras (GDOA's) is developed. GDOA's are generated by the four operators ${1,a,a^{\\dag},N}$. Their commutators and Hermiticity properties are those of the boson oscillator algebra, except for $[a, a^{\\dag}]_q = G(N)$, where $[a,b]_q = a b - q b a$ and $G(N)$ is a Hermitian, analytic function. The unitary irreductible representations are obtained by means of a Casimir operator $C$ and the semi-positive operator $a^{\\dag} a$. They may belong to one out of four classes: bounded from below (BFB), bounded from above (BFA), finite-dimentional (FD), unbounded (UB). Some examples of these different types of unirreps are given.
Huang, Yi-Zhi
Quantum Hall systems Representation theory of vertex operator algebras Applications The end Quantum Science, CAS #12;Quantum Hall systems Representation theory of vertex operator algebras Applications to a fundamental conjecture #12;Quantum Hall systems Representation theory of vertex operator algebras Applications
Huang, Yi-Zhi
Quantum Hall systems Representation theory of vertex operator algebras Applications The end Quantum;Quantum Hall systems Representation theory of vertex operator algebras Applications The end Outline 1 An approach to a fundamental conjecture #12;Quantum Hall systems Representation theory of vertex operator
Group representations, error bases and quantum codes
Knill, E
1996-01-01
This report continues the discussion of unitary error bases and quantum codes. Nice error bases are characterized in terms of the existence of certain characters in a group. A general construction for error bases which are non-abelian over the center is given. The method for obtaining codes due to Calderbank et al. is generalized and expressed purely in representation theoretic terms. The significance of the inertia subgroup both for constructing codes and obtaining the set of transversally implementable operations is demonstrated.
Animal representations and animal remains at Çatalhöyük
Russell, Nerissa; Meece, Stephanie
2006-01-01
(Level VII). Volcano above town plan, leopard skin above geometric design, or other representations? Level VI paintings lack fully convinc ing animal depictions. A patch of painting on the east wall of building VIA.66 includes a number of geomet ric... the centrepieces of the north walls of two rather similar buildings. In a sense they parallel the situation in the faunal assemblage, where cattle are not terribly common, but figure prominently in cer emonial consumption (see Russell & Martin, Volume 4...
Ford, Bryan
The Flux OSKit: A Substrate for Kernel and Language Research Bryan Ford Godmar Back Greg Benson Jay a basic useful OS core--e.g., the functionality traditionally found in the Unix kernel--entirely from suited for physical memory and its Ford, Back, and Lepreau are at the Univ. of Utah (baford
Brown, Angela Demke
The Flux OSKiti A Substrate for Kernel and Language Research Bryan Ford Godmar Back Greg Benson Jay a basic useful OS core-eg., the functionality traditionally found in the Unix kernel-entirely from scratch for physical memory and its Ford, Back, and Lqreau are at the Univ. of Utah @aford,gback,lepreau- @cs
Sérgio Szpigel; Varese S. Timóteo
2012-07-26
We apply the subtracted kernel method (SKM), a renormalization approach based on recursive multiple subtractions performed in the kernel of the scattering equation, to the chiral nucleon-nucleon (NN) interactions up to next-to-next-to-leading-order (NNLO). We evaluate the phase-shifts in the 1S0 channel at each order in Weinberg's power counting scheme and in a modified power counting scheme which yields a systematic power-law improvement. We also explicitly demonstrate that the SKM procedure is renormalization group invariant under the change of the subtraction scale through a non-relativistic Callan-Symanzik flow equation for the evolution of the renormalized NN interactions.
Noise kernel for a quantum field in Schwarzschild spacetime under the Gaussian approximation
A. Eftekharzadeh; Jason D. Bates; Albert Roura; Paul R. Anderson; B. L. Hu
2011-10-31
A method is given to compute an approximation to the noise kernel, defined as the symmetrized connected 2-point function of the stress tensor, for the conformally invariant scalar field in any spacetime conformal to an ultra-static spacetime for the case in which the field is in a thermal state at an arbitrary temperature. The most useful applications of the method are flat space where the approximation is exact and Schwarzschild spacetime where the approximation is better than it is in most other spacetimes. The two points are assumed to be separated in a timelike or spacelike direction. The method involves the use of a Gaussian approximation which is of the same type as that used by Page to compute an approximate form of the stress tensor for this field in Schwarzschild spacetime. All components of the noise kernel have been computed exactly for hot flat space and one component is explicitly displayed. Several components have also been computed for Schwarzschild spacetime and again one component is explicitly displayed.
Liu, Derek Sloboda, Ron S.
2014-05-15
Purpose: Boyer and Mok proposed a fast calculation method employing the Fourier transform (FT), for which calculation time is independent of the number of seeds but seed placement is restricted to calculation grid points. Here an interpolation method is described enabling unrestricted seed placement while preserving the computational efficiency of the original method. Methods: The Iodine-125 seed dose kernel was sampled and selected values were modified to optimize interpolation accuracy for clinically relevant doses. For each seed, the kernel was shifted to the nearest grid point via convolution with a unit impulse, implemented in the Fourier domain. The remaining fractional shift was performed using a piecewise third-order Lagrange filter. Results: Implementation of the interpolation method greatly improved FT-based dose calculation accuracy. The dose distribution was accurate to within 2% beyond 3 mm from each seed. Isodose contours were indistinguishable from explicit TG-43 calculation. Dose-volume metric errors were negligible. Computation time for the FT interpolation method was essentially the same as Boyer's method. Conclusions: A FT interpolation method for permanent prostate brachytherapy TG-43 dose calculation was developed which expands upon Boyer's original method and enables unrestricted seed placement. The proposed method substantially improves the clinically relevant dose accuracy with negligible additional computation cost, preserving the efficiency of the original method.
Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Huang, Hao; Yoo, Shinjae; Yu, Dantong; Qin, Hong
2015-06-01
Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\\ models the heat diffusion traces along the entire time scale, somore »it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.« less
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)
2012-02-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
Robust linear regression with broad distributions of errors
Postnikov, Eugene B
2015-01-01
We consider the problem of linear fitting of noisy data in the case of broad (say $\\alpha$-stable) distributions of random impacts ("noise"), which can lack even the first moment. This situation, common in statistical physics of small systems, in Earth sciences, in network science or in econophysics, does not allow for application of conventional Gaussian maximum-likelihood estimators resulting in usual least-squares fits. Such fits lead to large deviations of fitted parameters from their true values due to the presence of outliers. The approaches discussed here aim onto the minimization of the width of the distribution of residua. The corresponding width of the distribution can either be defined via the interquantile distance of the corresponding distributions or via the scale parameter in its characteristic function. The methods provide the robust regression even in the case of short samples with large outliers, and are equivalent to the normal least squares fit for the Gaussian noises. Our discussion is il...
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)
2013-01-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today s increasing complex, multivariate data sets. In this paper, a visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today s data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. This chapter provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees
Anderson, Charles H.
, energy efficiency, production costs, etc. This evo- lution has profoundly altered the landscape- proaches. A statistically-derived model offers the speed of a model-based approach, with the generality, kernel types, and platforms. 1. INTRODUCTION Due to power consumption and heat dissipation concerns
Validi, AbdoulAhad, E-mail: validiab@msu.edu
2014-03-01
This study introduces a non-intrusive approach in the context of low-rank separated representation to construct a surrogate of high-dimensional stochastic functions, e.g., PDEs/ODEs, in order to decrease the computational cost of Markov Chain Monte Carlo simulations in Bayesian inference. The surrogate model is constructed via a regularized alternative least-square regression with Tikhonov regularization using a roughening matrix computing the gradient of the solution, in conjunction with a perturbation-based error indicator to detect optimal model complexities. The model approximates a vector of a continuous solution at discrete values of a physical variable. The required number of random realizations to achieve a successful approximation linearly depends on the function dimensionality. The computational cost of the model construction is quadratic in the number of random inputs, which potentially tackles the curse of dimensionality in high-dimensional stochastic functions. Furthermore, this vector-valued separated representation-based model, in comparison to the available scalar-valued case, leads to a significant reduction in the cost of approximation by an order of magnitude equal to the vector size. The performance of the method is studied through its application to three numerical examples including a 41-dimensional elliptic PDE and a 21-dimensional cavity flow.
QoS prediction for web service compositions using kernel-based quantile estimation with online, such as WS-BPEL,2 focus on combining web services into aggregate services that satisfy the needs of clients planning web service can be created by composing services for hotel booking, airline booking, payment, etc
Growth of Hereford-Kedah Kelantan calves fed oil palm fronds and palm kernel cake based diet
Paris-Sud XI, Université de
million hectares of land under oil palm cultivation. The palm oil mills yield a number of by-products, the important by- product is the oil palm frond (OPF) which can be utilised fresh or ensiled. HerefordGrowth of Hereford-Kedah Kelantan calves fed oil palm fronds and palm kernel cake based diet I
T-567: Linux Kernel Buffer Overflow in ldm_frag_add() May Let Local Users Gain Elevated Privileges
Broader source: Energy.gov [DOE]
A vulnerability was reported in the Linux Kernel. A local user may be able to obtain elevated privileges on the target system. A physically local user can connect a storage device with a specially crafted LDM partition table to trigger a buffer overflow in the ldm_frag_add() function in 'fs/partitions/ldm.c' and potentially execute arbitrary code with elevated privileges.
Nicholas G Phillips; B. L. Hu
2002-09-17
In Paper II [N. G. Phillips and B. L. Hu, previous abstract] we presented the details for the regularization of the noise kernel of a quantum scalar field in optical spacetimes by the modified point separation scheme, and a Gaussian approximation for the Green function. We worked out the regularized noise kernel for two examples: hot flat space and optical Schwarzschild metric. In this paper we consider noise kernels for a scalar field in the Schwarzschild black hole. Much of the work in the point separation approach is to determine how the divergent piece conformally transforms. For the Schwarzschild metric we find that the fluctuations of the stress tensor of the Hawking flux in the far field region checks with the analytic results given by Campos and Hu earlier [A. Campos and B. L. Hu, Phys. Rev. D {\\bf 58} (1998) 125021; Int. J. Theor. Phys. {\\bf 38} (1999) 1253]. We also verify Page's result [D. N. Page, Phys. Rev. {\\bf D25}, 1499 (1982)] for the stress tensor, which, though used often, still lacks a rigorous proof, as in his original work the direct use of the conformal transformation was circumvented. However, as in the optical case, we show that the Gaussian approximation applied to the Green function produces significant error in the noise kernel on the Schwarzschild horizon. As before we identify the failure as occurring at the fourth covariant derivative order.
Data regression using the TI's calculators TI 82 and TI 83
Torres, Rodolfo
Data regression using the TI's calculators TI 82 and TI 83 TI 85 TI 86 Doing data regression in the TI-82 or TI-83 Entering the data Press to get the statistics menu. Select and press .Edit Enter the x-data in L1 and the y-data in L2. Doing the regression Press to get the statistics menu. Select and select
Reisslein, Martin
Teaching With Concrete and Abstract Visual Representations: Effects on Students' Problem Solving/or abstract visual problem representa- tions during instruction on students' problem-solving practice, near outperformed Groups A and C on problem-solving practice in Experiments 1 and 2 and outperformed Group C
Collins, J.L.
2004-12-02
The main objective of the Depleted UO{sub 2} Kernels Production Task at Oak Ridge National Laboratory (ORNL) was to conduct two small-scale production campaigns to produce 2 kg of UO{sub 2} kernels with diameters of 500 {+-} 20 {micro}m and 3.5 kg of UO{sub 2} kernels with diameters of 350 {+-} 10 {micro}m for the U.S. Department of Energy Advanced Fuel Cycle Initiative Program. The final acceptance requirements for the UO{sub 2} kernels are provided in the first section of this report. The kernels were prepared for use by the ORNL Metals and Ceramics Division in a development study to perfect the triisotropic (TRISO) coating process. It was important that the kernels be strong and near theoretical density, with excellent sphericity, minimal surface roughness, and no cracking. This report gives a detailed description of the production efforts and results as well as an in-depth description of the internal gelation process and its chemistry. It describes the laboratory-scale gel-forming apparatus, optimum broth formulation and operating conditions, preparation of the acid-deficient uranyl nitrate stock solution, the system used to provide uniform broth droplet formation and control, and the process of calcining and sintering UO{sub 3} {center_dot} 2H{sub 2}O microspheres to form dense UO{sub 2} kernels. The report also describes improvements and best past practices for uranium kernel formation via the internal gelation process, which utilizes hexamethylenetetramine and urea. Improvements were made in broth formulation and broth droplet formation and control that made it possible in many of the runs in the campaign to produce the desired 350 {+-} 10-{micro}m-diameter kernels, and to obtain very high yields.
Regression-based estimates of the rate of accumulation of anthropogenic CO2 in the ocean: A fresh February 2012 Available online 23 February 2012 Keywords: Carbon dioxide Regression MLR eMLR Regression and guidelines for improvement are presented. Following these guidelines leads to a local two- regression method
Representation-transparent Matrix Algorithms with Scalable Performance
Wise, David Stephen
Representation-transparent Matrix Algorithms with Scalable Performance Peter Gottschling Indiana been suspended since the next round of Moore's-law improvements will be delivered via CMPs. Programmers
Regression analysis of technical parameters affecting nuclear power plant performances
Ghazy, R.; Ricotti, M. E.; Trueco, P.
2012-07-01
Since the 80's many studies have been conducted in order to explicate good and bad performances of commercial nuclear power plants (NPPs), but yet no defined correlation has been found out to be totally representative of plant operational experience. In early works, data availability and the number of operating power stations were both limited; therefore, results showed that specific technical characteristics of NPPs were supposed to be the main causal factors for successful plant operation. Although these aspects keep on assuming a significant role, later studies and observations showed that other factors concerning management and organization of the plant could instead be predominant comparing utilities operational and economic results. Utility quality, in a word, can be used to summarize all the managerial and operational aspects that seem to be effective in determining plant performance. In this paper operational data of a consistent sample of commercial nuclear power stations, out of the total 433 operating NPPs, are analyzed, mainly focusing on the last decade operational experience. The sample consists of PWR and BWR technology, operated by utilities located in different countries, including U.S. (Japan)) (France)) (Germany)) and Finland. Multivariate regression is performed using Unit Capability Factor (UCF) as the dependent variable; this factor reflects indeed the effectiveness of plant programs and practices in maximizing the available electrical generation and consequently provides an overall indication of how well plants are operated and maintained. Aspects that may not be real causal factors but which can have a consistent impact on the UCF, as technology design, supplier, size and age, are included in the analysis as independent variables. (authors)
A representation formula for maps on supermanifolds
Helein, Frederic [Institut de Mathematiques de Jussieu, UMR 7586, Universite Denis Diderot-Paris 7, Case 7012, 2 place Jussieu, 75251 Paris Cedex 5 (France)
2008-02-15
We analyze the notion of morphisms of rings of superfunctions which is the basic concept underlying the definition of supermanifolds as ringed spaces (i.e., following Berezin, Leites, Manin, etc.). We establish a representation formula for all (pull-back) morphisms from the algebra of functions on an ordinary manifolds to the superalgebra of functions on an open subset of a superspace. We then derive two consequences of this result. The first one is that we can integrate the data associated with a morphism in order to get a (nonunique) map defined on an ordinary space (and uniqueness can be achieved by restriction to a scheme). The second one is a simple and intuitive recipe to compute pull-back images of a function on a manifold M by a map from a superspace to M.
Reality - an emerging representation of the world
Martin A. Green
2009-03-11
Some unique source -- the world, W -- must underlie all the information realized in the universe throughout time. Perceived reality, R, is a progressively emerging representation of W in the form of the geometrical universe. Time corresponds to the process of emergence. When first represented in R, information about W is expressed in a non-localized, quantum manner. As the emergence proceeds, most information becomes inaccessible (entropy), supporting the robust, redundant encoding of accessible records. The past is encoded in and inferred from present records; the anticipated future will preserve present information and reveal unpredictable new information about W. Emergence of the future demands non-unitary reduction of quantum states and increased Kolmogorov complexity of the quasi-classical records in terms of which the quantum states are known. Given the limited information content of records, the quasi-classical universe lacks fine details; whereas the future must be uncertain to admit new information.
Functional Coefficient Regression Models for Non-linear Time Series: A Polynomial
Shen, Haipeng
Functional Coefficient Regression Models for Non-linear Time Series: A Polynomial Spline Approach of functional coefficient regression models for non-linear time series. Consistency and rate of convergence to estimate the coefficient functions. Cai et al. (2000) and Chen & Liu (2001) used the local linear method
Distribution to Distribution Regression Junier B. Oliva joliva@cs.cmu.edu
Schneider, Jeff
Distribution to Distribution Regression Junier B. Oliva joliva@cs.cmu.edu Barnab´as P `Distribution to Distribution re- gression' where one is regressing a mapping where both the covariate (inputs) and re- sponse (outputs) are distributions. No pa- rameters on the input or output distributions
Rothermel, Gregg
Regression testing is an important activity that can account for a large proportion of the cost of software testing technique that (1) chooses a subset of a test suite that was used to test the software before the modifications, and then (2) uses this subset to test the modified software. Selective regression testing
Ungar, Lyle H.
boolean and real-valued, are generated by structured search in the space of queries to the database of features automatically generated from a relational database. Structural Logistic Regression is an "upgradeIn Multi-Relational Data Mining Workshop at KDD-2003. Structural Logistic Regression for Link
WIND FARM PROXIMITY AND PROPERTY VALUES: APOOLED HEDONIC REGRESSION ANALYSIS OF
Branoff, Theodore J.
WIND FARM PROXIMITY AND PROPERTY VALUES: APOOLED HEDONIC REGRESSION ANALYSIS OF PROPERTY VALUES IN CENTRAL ILLINOIS Jennifer L. Hinman #12;Hinman, J.L. (2010) Wind Farm Proximity and Property Values Page 2 of 143 WIND FARM PROXIMITY AND PROPERTY VALUES: APOOLED HEDONIC REGRESSION ANALYSIS OF PROPERTY VALUES
Doing data regression in the TI82 or TI83 Entering the data
Torres, Rodolfo
RegEQ. ffl Press ENTER to get Y1= the regression function. ffl Press GRAPH To forecast or compute. To forecast or compute a value ffl Press FCST in the STAT menu. ffl Enter the value of X= and move to Y=. ffl the answer. Plotting the data and the regression curve ffl Press GRAPH . ffl Clear the Y= and set WIND
Ahn, Hongshik
-dimensional data using the logistic regression model as a base clas- sifier. CERP is similar to random subspace (Ho classification problems using ensem- bles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit
Uniform convergence of convolution estimators for the response density in nonparametric regression
Wefelmeyer, Wolfgang
Anton Schick and Wolfgang Wefelmeyer Abstract. We consider a nonparametric regression model Y = r, functional central limit theorem, efficient influence function, efficient estimator. Anton Schick was supported by NSF Grant DMS 0906551. 1 #12;2 ANTON SCHICK AND WOLFGANG WEFELMEYER (R) The unknown regression
Non-parametric regression and neural-network inll drilling recovery models for carbonate reservoirs
Valkó, Peter
Non-parametric regression and neural-network in®ll drilling recovery models for carbonate This work introduces non-parametric regression and neural network models for forecasting the in®ll drilling variables. This situation mandates proper selection of independent variables for the in®ll drilling recovery
Regression-based estimation of ERP waveforms: I. The rERP framework
Kutas, Marta
Regression-based estimation of ERP waveforms: I. The rERP framework NATHANIEL J. SMITHa AND MARTA Science and Neurosciences, University of California, San Diego, San Diego, California, USA Abstract ERP of experimental designs. We introduce the regression-based rERP framework, which extends ERP averaging to handle
Deep Learning Representation using Autoencoder for 3D Shape Retrieval
Deep Learning Representation using Autoencoder for 3D Shape Retrieval Zhuotun Zhu, Xinggang Wang@hust.edu.cn Abstract--We study the problem of how to build a deep learning representation for 3D shape. Deep learning the features learned on 2D images. In addition, we show the proposed deep learning feature is complementary
Systematic Approach to the Design of RepresentationChanging Algorithms
Fink, Eugene
The performance of all problemsolving sys tems depends crucially on problem representa tion. The same problem representation in an AI problemsolving system is the input to the system. In most problem solving systems problemsolving systems de pends crucially on problem representation. The same problem may be easy or di
SEMANTIC DATA INTEGRATION IN A MULTIPLE REPRESENTATION ENVIRONMENT
Köbben, Barend
SEMANTIC DATA INTEGRATION IN A MULTIPLE REPRESENTATION ENVIRONMENT J.E. Stotera , R.L.G. Lemmensa in a multiple representation environment. Important principles of this environment are 1) the data sets of a new technology in the semantic data integration process of different data sets: machine ontology using
Bringing Together Human and Robotic Environment Representations A Pilot Study
Bringing Together Human and Robotic Environment Representations A Pilot Study Elin A. Topp, Helge and Communication (CSC) Royal Institute of Technology (KTH) 10044 Stockholm, Sweden Email: {topp,hehu,hic,kse}@csc.kth.se Abstract-- Human interaction with a service robot requires a shared representation of the environment
Using Compact Data Representations for Languages Based on Catamorphisms
Fegaras, Leonidas
Using Compact Data Representations for Languages Based on Catamorphisms OGI, Techreport #95 on using a compact vector representation for the recursive structure over which the catamorphism operates program performance, in particular by performing program fusion [13, 3, 7, 1]. In this paper, we explore
`Linear' representations of the polycyclic monoid Mikls Hartmann
Gould, Victoria
`Linear' representations of the polycyclic monoid MiklÃ³s Hartmann University of York October 15, 2012 Based on joint work with M. V. Lawson MiklÃ³s Hartmann `Linear' representations of the polycyclic-1 i aj = 0, i = j Required properties They are congruence-free. (If n > 1.) MiklÃ³s Hartmann `Linear
GRAIL : Graphical Representation of Activity, Interconnection and Loading
Stepney, Susan
GRAIL : Graphical Representation of Activity, Interconnection and Loading Susan Stepney GEC One of the tools being developed as part of ParSiFal is GRAIL. It represents an occam program flow of control, not communication channels): Susan Stepney. "GRAIL: Graphical Representation
ILD 10 Name: ____________________ Tutorial section _______ Representation as Communication: Fields
Maryland at College Park, University of
for a class discussion. On the grids below create a representation of the electric field in the region of each" and follow your instructor's directions. B. Representation: Weather Maps. In the two figures below are shown is the first map better? In what ways is the second map better? 3. Electric Fields. In lecture we have
Utilizing Structured Representations and CSPs in Conformant Probabilistic Planning
Bacchus, Fahiem
Utilizing Structured Representations and CSPs in Conformant Probabilistic Planning Nathanael Hyafil and Fahiem Bacchus1 Abstract. A CSP based algorithm for the conformant probabilistic planning problem (CPP. In this work we revisit this algorithm and develop a version that utilizes a structured representation
2004-09-16 | Greg Jane 1 Semantic representation
Janée, Greg
2004-09-16 | Greg Janée 1 Semantic representation · Semantic definitions explicit representations property rights programmatic interpretation would be beneficial · Here's a common framework for both #12;2004-09-16 | Greg Janée 2 Archival item facets · (Identifier) · Metadata · Data · Intellectual property rights #12;2004
Fast Multipole Representation of Diffusion Curves and Points Timothy Sun
Columbia University
Fast Multipole Representation of Diffusion Curves and Points Timothy Sun Papoj Thamjaroenporn performed on the fast multipole representation. Abstract We propose a new algorithm for random-access evaluation of diffu- sion curve images (DCIs) using the fast multipole method. Unlike all previous methods
Document Representation and Query Expansion Models for Blog Recommendation
Callan, Jamie
Document Representation and Query Expansion Models for Blog Recommendation Jaime Arguello document representation models and two query expansion models for the task of recommend- ing blogs to a user in response to a query. Blog relevance ranking differs from traditional document ranking in ad
Data Representation and Exploration with Geometric Wavelets Eric E. Monson
Maggioni, Mauro
Data Representation and Exploration with Geometric Wavelets Eric E. Monson Duke Visualization Maggioni Duke Mathematics and Computer Science ABSTRACT Geometric Wavelets is a new multi-scale data representation tech- nique which is useful for a variety of applications such as data com- pression
Using Graphical Representations to Support the Calculation of Infusion Parameters
Subramanian, Sriram
Using Graphical Representations to Support the Calculation of Infusion Parameters Sandy J. J. Gould in which participants were asked to solve a num- ber of infusion parameter problems that were represented representations transfer to actual workplace settings. Keywords: Graphical reasoning, infusion pumps, re
Parameterized versus Generative Representations in Structural Design: An Empirical Comparison
George Mason University
Parameterized versus Generative Representations in Structural Design: An Empirical Comparison Rafal results of a large number of design experiments in which parameterized and generative representations were of structural design problems, including the design of a wind bracing system and the design of an entire
Spoken word recognition and lexical representation in very young children
Makous, Walter
Spoken word recognition and lexical representation in very young children Daniel Swingley*, Richard March 2000 Abstract Although children's knowledge of the sound patterns of words has been a focus of debate for many years, little is known about the lexical representations very young children use in word
Linear-Time Algorithms for Proportional Contact Graph Representations
Kobourov, Stephen G.
Linear-Time Algorithms for Proportional Contact Graph Representations Technical Report CS-2011. In a proportional contact representation of a planar graph, each vertex is represented by a simple polygon with area proportional to a given weight, and edges are represented by adjacencies between the corresponding pairs
Proportional Contact Representations of Planar Graphs Md. J. Alam1
Kobourov, Stephen G.
Proportional Contact Representations of Planar Graphs Md. J. Alam1 , T. Biedl2 , S. Felsner3 , M-contact between the corresponding polygons. Specifically, we consider proportional contact representations, where, the cartographic error, and the unused area. We describe construc- tive algorithms for proportional contact
PURE MATHEMATICS Representation theory(Baranov, Pirashvili, Snashall)
and theoretical physics. Much of the development of modern representation theory was motivated by representations'. A surprising amount of classical ge- ometric techniques and concepts (such as smoothness, homo- geneous spaces mathematical notion has its roots in mathematical physics, in particular exactly soluble models of statistical
Closing the Learning-Planning Loop with Predictive State Representations
Guestrin, Carlos
Closing the Learning-Planning Loop with Predictive State Representations Byron Boots Machine and sta- tistically consistent spectral algorithm for learning the pa- rameters of a Predictive State the essential features of the environment. This representation allows accurate prediction with a small number
On the representation of many-body interactions in water
Medders, Gregory R; Morales, Miguel A; Paesani, Francesco
2015-01-01
Recent work has shown that the many-body expansion of the interaction energy can effectively be used to develop analytical representations of global potential energy surfaces (PESs) for water. In this study, the role of short- and long-range contri- butions at different orders is investigated by analyzing water potentials that treat the leading terms of the many-body expansion through implicit (i.e., TTM3-F and TTM4-F PESs) and explicit (i.e., WHBB and MB-pol PESs) representations. It is found that explicit short-range representations of 2-body and 3-body interactions along with a physically correct integration of short- and long-range contributions are necessary for an accurate representation of the water interactions from the gas to the condensed phase. Similarly, a complete many-body representation of the dipole moment surface is found to be crucial to reproducing the correct intensities of the infrared spectrum of liquid water.
CHARACTERISTIC SIZE OF FLARE KERNELS IN THE VISIBLE AND NEAR-INFRARED CONTINUA
Xu, Yan; Jing, Ju; Wang, Haimin [Space Weather Research Lab, Center for Solar-Terrestrial Research, New Jersey Institute of Technology, 323 Martin Luther King Blvd, Newark, NJ 07102-1982 (United States); Cao, Wenda, E-mail: yx2@njit.edu [Big Bear Solar Observatory, Center for Solar-Terrestrial Research, New Jersey Institute of Technology, 323 Martin Luther King Blvd, Newark, NJ 07102-1982 (United States)
2012-05-01
In this Letter, we present a new approach to estimate the formation height of visible and near-infrared emission of an X10 flare. The sizes of flare emission cores in three wavelengths are accurately measured during the peak of the flare. The source size is the largest in the G band at 4308 A and shrinks toward longer wavelengths, namely the green continuum at 5200 A and NIR at 15600 A, where the emission is believed to originate from the deeper atmosphere. This size-wavelength variation is likely explained by the direct heating model as electrons need to move along converging field lines from the corona to the photosphere. Therefore, one can observe the smallest source, which in our case is 0.''65 {+-} 0.''02 in the bottom layer (represented by NIR), and observe relatively larger kernels in upper layers of 1.''03 {+-} 0.''14 and 1.''96 {+-} 0.''27, using the green continuum and G band, respectively. We then compare the source sizes with a simple magnetic geometry to derive the formation height of the white-light sources and magnetic pressure in different layers inside the flare loop.
C.P.Oertel; J.R.Giles
2009-11-01
Characterization of radionuclide concentrations in soil profiles requires accurate evaluation of the depth distribution of the concentrations as measured by gamma emissions. An ongoing study based on 137Cs activity has shown that such concentration data generally follow an exponential trend when the fraction of radioactivity below depth is plotted against the depth. The slope of the exponential regression fit is defined as alpha/rho, the depth profile parameter. A weighted exponential regression procedure has been developed to compute a mean ??? for a group of related soil samples. Regression results from different areas or from different time periods can be used to compare representative radionuclide concentrations for the specified groupings.
Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles
Yamaguchi, Nobuyuki; Han, Junqiao; Ghatikar, Girish; Piette, Mary Ann; Asano, Hiroshi; Kiliccote, Sila
2009-06-28
This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables. The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial customers who participated in the 2008 Critical Peak Pricing program including Manual and Automated Demand Response.
The momentum map representation of images
M. Bruveris; F. Gay-Balmaz; D. D. Holm; T. S. Ratiu
2015-04-08
This paper discusses the mathematical framework for designing methods of large deformation matching (LDM) for image registration in computational anatomy. After reviewing the geometrical framework of LDM image registration methods, a theorem is proved showing that these methods may be designed by using the actions of diffeomorphisms on the image data structure to define their associated momentum representations as (cotangent lift) momentum maps. To illustrate its use, the momentum map theorem is shown to recover the known algorithms for matching landmarks, scalar images and vector fields. After briefly discussing the use of this approach for Diffusion Tensor (DT) images, we explain how to use momentum maps in the design of registration algorithms for more general data structures. For example, we extend our methods to determine the corresponding momentum map for registration using semidirect product groups, for the purpose of matching images at two different length scales. Finally, we discuss the use of momentum maps in the design of image registration algorithms when the image data is defined on manifolds instead of vector spaces.
Graph representation of protein free energy landscape
Li, Minghai; Duan, Mojie; Fan, Jue; Huo, Shuanghong; Han, Li
2013-11-14
The thermodynamics and kinetics of protein folding and protein conformational changes are governed by the underlying free energy landscape. However, the multidimensional nature of the free energy landscape makes it difficult to describe. We propose to use a weighted-graph approach to depict the free energy landscape with the nodes on the graph representing the conformational states and the edge weights reflecting the free energy barriers between the states. Our graph is constructed from a molecular dynamics trajectory and does not involve projecting the multi-dimensional free energy landscape onto a low-dimensional space defined by a few order parameters. The calculation of free energy barriers was based on transition-path theory using the MSMBuilder2 package. We compare our graph with the widely used transition disconnectivity graph (TRDG) which is constructed from the same trajectory and show that our approach gives more accurate description of the free energy landscape than the TRDG approach even though the latter can be organized into a simple tree representation. The weighted-graph is a general approach and can be used on any complex system.
Koh, Wonryull
2009-05-15
This dissertation studies two problems related to geometric representation of neuroanatomical data: (i) spatial representation and organization of individual neurons, and (ii) reconstruction of three-dimensional neuroanatomical ...
Sinc function representation and three-loop master diagrams
Easther, Richard; Guralnik, Gerald; Hahn, Stephen
2001-04-15
We test the Sinc function representation, a novel method for numerically evaluating Feynman diagrams, by using it to evaluate the three-loop master diagrams. Analytical results have been obtained for all these diagrams, and we find excellent agreement between our calculations and the exact values. The Sinc function representation converges rapidly, and it is straightforward to obtain accuracies of 1 part in 10{sup 6} for these diagrams and with longer runs we found results better than 1 part in 10{sup 12}. Finally, this paper extends the Sinc function representation to diagrams containing massless propagators.
Katipamula, S.; Reddy, T. A.; Claridge, D. E.
1994-01-01
An empirical or regression modeling approach is simple to develop and easy to use compared to use of detailed hourly simulations. Therefore, regression analysis has become a widely used tool in the determination of annual energy savings accruing...
Modeling Personalized Email Prioritization: Classification-based and Regression-based Approaches
Yoo S.; Yang, Y.; Carbonell, J.
2011-10-24
Email overload, even after spam filtering, presents a serious productivity challenge for busy professionals and executives. One solution is automated prioritization of incoming emails to ensure the most important are read and processed quickly, while others are processed later as/if time permits in declining priority levels. This paper presents a study of machine learning approaches to email prioritization into discrete levels, comparing ordinal regression versus classier cascades. Given the ordinal nature of discrete email priority levels, SVM ordinal regression would be expected to perform well, but surprisingly a cascade of SVM classifiers significantly outperforms ordinal regression for email prioritization. In contrast, SVM regression performs well -- better than classifiers -- on selected UCI data sets. This unexpected performance inversion is analyzed and results are presented, providing core functionality for email prioritization systems.
Forrest, Timothy Lee
2007-04-25
This thesis presents a methodology for conducting logistic regression modeling of trip and household information obtained from household travel surveys and vehicle trip information obtained from global positioning systems (GPS) to better understand...
Mining customer credit by using neural network model with logistic regression approach
Kao, Ling-Jing
2001-01-01
. The objective of this research was to investigate the methodologies to mine customer credit history for the bank industry. Particularly, combination of logistic regression model and neural network technique are proposed to determine if the predictive capability...
A regression approach to infer electricity consumption of legacy telecom equipment
Greenberg, Albert
A regression approach to infer electricity consumption of legacy telecom equipment [Extended and communications technology accounts for a significant fraction of worldwide electricity consumption. Given inferring the electricity consumption of different components of the installed base of telecommu- nications
Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors
Sarkar, Abhra
2014-06-24
Although the literature on measurement error problems is quite extensive, solutions to even the most fundamental measurement error problems like density deconvolution and regression with errors-in-covariates are available ...
Reddy, T. A.; Claridge, D.; Wu, J.
1992-01-01
Statistical models of energy use in commercial buildings are being increasingly used not only for predicting retrofit savings but also for identifying improper operation of HVAC systems. The conventional approach involves using multiple regression...
Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression
Chen, Wan-Ping
2015-01-01
in high-dimensional multivariate regression. ” The Annals ofSupport Union Recovery in Multivariate Multi-Response LinearSupport Union Recovery in Multivariate Multi-Response Linear
Greenberg, Albert
Iterative Multivariate Regression Model for Correlated Responses Prediction S. Tom Au, Guangqin Ma- tive procedure to model multiple responses prediction into correlated multivariate predicting scheme, which is always favorable for responses separations in our multivariate prediction. We also point out
Generating Fo contours from ToBI labels using linear regression
Black, Alan W; Hunt, Andrew
This paper describes a method for generating F0 contours from ToBI labelled utterances. The method uses linear regression to predict F0 target values for the start, mid-vowel and end of every syllable, using features ...
Robust Regression Dong Huang, Ricardo Cabral and Fernando De la Torre
Robust Regression Dong Huang, Ricardo Cabral and Fernando De la Torre Robotics Institute, Carnegie represents the jth column of the matrix D. Non-bold letters #12;2 Dong Huang, Ricardo Cabral and Fernando De
Obradovic, Zoran
SPATIALLY PENALIZED REGRESSION FOR DEPENDENCE ANALYSIS OF RARE EVENTS: A STUDY IN PRECIPITATION, USA ABSTRACT Discovery of dependence structure between precipitation extremes and other climate can be different for different locations. Secondly, the dependence structure between the precipitation
Variational sequences, representation sequences and applications in physics
M. Palese; O. Rossi; E. Winterroth; J. Musilova
2015-08-07
This paper is a review containing new original results on the finite order variational sequence and its different representations with emphasis on applications in the theory of variational symmetries and conservation laws in physics.
Containing the opposition : selective representation in Jordan and Turkey
Wakeman, Raffaela Lisette
2009-01-01
How does elite manipulation of election mechanisms affect the representation of political regime opponents? While the spread of elections has reached all the continents, the number of actual democracies has not increased ...
Ground state and functional integral representations of the CCR algebra
o#11;elholz, Berufsakademie, Leipzig G. Morchio, Dipartimento di Fisica, Universita' di Pisa and INFN, Pisa F. Strocchi Scuola Normale Superiore and INFN, Pisa Abstract The ground state representations
Beyond pixels : exploring new representations and applications for motion analysis
Liu, Ce, Ph. D. Massachusetts Institute of Technology
2009-01-01
The focus of motion analysis has been on estimating a flow vector for every pixel by matching intensities. In my thesis, I will explore motion representations beyond the pixel level and new applications to which these ...
Audio-visual frameworks for design process representation
Soares, Gonçalo Ducla, 1977-
2004-01-01
The design process is based on a recursive and iterative feedback between a designer's ideas and their physical representation. In most practices, this feedback takes place upon one single medium, which endows the designer ...
POLYHEDRAL REPRESENTATION OF DISCRETE MORSE ETHAN D. BLOCH
Bloch, Ethan
POLYHEDRAL REPRESENTATION OF DISCRETE MORSE FUNCTIONS ETHAN D. BLOCH Abstract. It is proved during a sabbatical when parts of this paper were written. 1 #12;2 ETHAN D. BLOCH Forman defines an index
Variational sequences, representation sequences and applications in physics
M. Palese; O. Rossi; E. Winterroth; J. Musilova
2015-09-21
This paper is a review containing new original results on the finite order variational sequence and its different representations with emphasis on applications in the theory of variational symmetries and conservation laws in physics.
Topological conditions for the representation of preorders by continuous utilities
E. Minguzzi
2011-12-13
We remove the Hausdorff condition from Levin's theorem on the representation of preorders by families of continuous utilities. We compare some alternative topological assumptions in a Levin's type theorem, and show that they are equivalent to a Polish space assumption.
Effects of Representations in Engineering Idea Generation Process
Cherickal Viswanathan, Vimal Kumar
2012-02-14
. This research study is focused on the engineering idea generation. The representations of ideas have an important impact on the idea generation process. Design concepts may be represented in a variety of forms like sketches, physical models or computer based...
Karhunen-Loeve representation of stochastic ocean waves
Sclavounos, Paul D.
A new stochastic representation of a seastate is developed based on the Karhunen–Loeve spectral decomposition of stochastic signals and the use of Slepian prolate spheroidal wave functions with a tunable bandwidth parameter. ...
Representation of Energy Use in the Food Products Industry
Elliott, N. R.
2007-01-01
such as combined heat and power (CHP). This paper discusses the differences between energy end-uses and service demands, proposes an approach for approximating service demands and discusses the ramifications of this alternative representation to energy modeling...
SEMANTICS OF PROGRAM REPRESENTATION GRAPHS G. RAMALINGAM and THOMAS REPS
Reps, Thomas W.
dependence graphs (PDGs) [Kuck81, Ferr87, Horw89]. They have also been used in a new algorithm for merging representation in various appli cations such as vectorization, parallelization [Kuck81], and merging program
The Teleost Anatomy Ontology: Anatomical Representation for the Genomics Age
Dahdul, Wasila M.; Lundberg, John G.; Midford, Peter E.; Balhoff, James P.; Lapp, Hilmar; Vision, Todd J.; Haendel, Melissa A.; Westerfield, Monte; Mabee, Paula M.
2010-03-29
of capturing anatomical data in a systematic and computable manner. An ontology is a formal representation of a set of concepts with defined relationships between those concepts. Multispecies anatomy ontologies in particular are an efficient way to represent...
Generating Tensor Representation from Concept Tree in Meaning Based Search
Panigrahy, Jagannath
2011-08-08
to a representation that can be stored and compared efficiently on computers. Meaning of objects can be adequately captured in terms of a hierarchical composition structure called concept tree. This thesis describes the design and development...
LATINO DESCRIPTIVE REPRESENTATION IN LOCAL PUBLIC OFFICE IN TEXAS
Holstein, Carlos 1983-
2012-05-03
This purpose of this study is to document and evaluate descriptive Latino representation at the local level, over time, and as it relates to characteristics of the Latino population. This study has important implications, particularly within...
Structured representation of images for language generation and image retrieval
Elliott, Desmond
2015-06-29
A photograph typically depicts an aspect of the real world, such as an outdoor landscape, a portrait, or an event. The task of creating abstract digital representations of images has received a great deal of attention ...
Stochastic Roadmap Simulation: Efficient Representation and Algorithms for
Brutlag, Doug
Stochastic Roadmap Simulation: Efficient Representation and Algorithms for the Analysis Roadmap Simulation (SRS) #12;Stochastic Roadmap Simulation (SRS) Multiple paths at once; #12;Stochastic Roadmap Simulation (SRS) Multiple paths at once; No local minimum problem; #12;Stochastic Roadmap
Graphical representation of canonical proof: two case studies
Heijltjes, Willem Bernard
2012-06-25
An interesting problem in proof theory is to find representations of proof that do not distinguish between proofs that are ‘morally’ the same. For many logics, the presentation of proofs in a traditional formalism, such ...
CGOL - an Alternative External Representation For LISP users
Pratt, Vaughan R.
Advantages of the standard external representation of LISP include its simple definition, its economical implementation and its convenient extensibility. These advantages have been gained by trading off syntactic variety ...
Deng, Yangyang; Parajuli, Prem B.
2011-08-10
Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.
Tank, David
. The linear regression method presented here is valid for both fixation and low head velocity VOR dataLinear Regression of Eye Velocity on Eye Position and Head Velocity Suggests a Common Oculomotor Aksay, David W. Tank, and H. S. Seung. Linear regression of eye velocity on eye position and head
arXiv:1103.0628v1[astro-ph.IM]3Mar2011 Bivariate least squares linear regression
Masci, Frank
arXiv:1103.0628v1[astro-ph.IM]3Mar2011 Bivariate least squares linear regression: towards a unified squares linear regression, the classical ap- proach pursued for functional models in earlier attempts are regression lines in the general case of correlated errors in X and in Y for heteroscedastic data
Considering representational choices of fourth graders when solving division problems
Gilbert, Mary Chiles
2007-09-17
CONSIDERING REPRESENTATIONAL CHOICES OF FOURTH GRADERS WHEN SOLVING DIVISION PROBLEMS A Thesis by MARY CHILES GILBERT Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree... of MASTER OF SCIENCE May 2006 Major Subject: Curriculum & Instruction CONSIDERING REPRESENTATIONAL CHOICES OF FOURTH GRADERS WHEN SOLVING DIVISION PROBLEMS A Thesis by MARY CHILES GILBERT Submitted to the Office of Graduate...
Crossdressing Cinema: An Analysis of Transgender Representation in Film
Miller, Jeremy Russell
2012-10-19
CINEMA: AN ANALYSIS OF TRANSGENDER REPRESENTATION IN FILM A Dissertation by JEREMY RUSSELL MILLER Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR... OF PHILOSOPHY August 2012 Major Subject: Communication CROSSDRESSING CINEMA: AN ANALYSIS OF TRANSGENDER REPRESENTATION IN FILM A Dissertation by JEREMY RUSSELL MILLER Submitted to the Office of Graduate Studies of Texas A...
On the heat kernel and the Dirichlet form of Liouville Brownian Motion
Christophe Garban; Rémi Rhodes; Vincent Vargas
2014-10-16
In \\cite{GRV}, a Feller process called Liouville Brownian motion on $\\R^2$ has been introduced. It can be seen as a Brownian motion evolving in a random geometry given formally by the exponential of a (massive) Gaussian Free Field $e^{\\gamma X}$ and is the right diffusion process to consider regarding 2d-Liouville quantum gravity. In this note, we discuss the construction of the associated Dirichlet form, following essentially \\cite{fuku} and the techniques introduced in \\cite{GRV}. Then we carry out the analysis of the Liouville resolvent. In particular, we prove that it is strong Feller, thus obtaining the existence of the Liouville heat kernel via a non-trivial theorem of Fukushima and al. One of the motivations which led to introduce the Liouville Brownian motion in \\cite{GRV} was to investigate the puzzling Liouville metric through the eyes of this new stochastic process. One possible approach was to use the theory developed for example in \\cite{stollmann,sturm1,sturm2}, whose aim is to capture the "geometry" of the underlying space out of the Dirichlet form of a process living on that space. More precisely, under some mild hypothesis on the regularity of the Dirichlet form, they provide an intrinsic metric which is interpreted as an extension of Riemannian geometry applicable to non differential structures. We prove that the needed mild hypotheses are satisfied but that the associated intrinsic metric unfortunately vanishes, thus showing that renormalization theory remains out of reach of the metric aspect of Dirichlet forms.
Nicholas G Phillips; B. L. Hu
2002-09-17
Continuing our investigation of the regularization of the noise kernel in curved spacetimes [N. G. Phillips and B. L. Hu, Phys. Rev. D {\\bf 63}, 104001 (2001)] we adopt the modified point separation scheme for the class of optical spacetimes using the Gaussian approximation for the Green functions a la Bekenstein-Parker-Page. In the first example we derive the regularized noise kernel for a thermal field in flat space. It is useful for black hole nucleation considerations. In the second example of an optical Schwarzschild spacetime we obtain a finite expression for the noise kernel at the horizon and recover the hot flat space result at infinity. Knowledge of the noise kernel is essential for studying issues related to black hole horizon fluctuations and Hawking radiation backreaction. We show that the Gaussian approximated Green function which works surprisingly well for the stress tensor at the Schwarzschild horizon produces significant error in the noise kernel there. We identify the failure as occurring at the fourth covariant derivative order.
Bansal, R.M.; Kothari, L.S.; Tewari, S.P.
1980-10-01
A new scattering kernel for heavy water has been proposed. The kernel takes into account the chemical binding energy effects and also includes the rotational and intramolecular vibrational modes. Using this scattering kernel, various neutron transport processes in the temperature range 5 to 60/sup 0/C have been studied and compared with the corresponding experimental results. The calculated results include total neutron scattering cross section at 20/sup 0/C; asymptotic decay of neutron pulses in the temperature range 5 to 60/sup 0/C and temperature variation of the diffusion coefficient and diffusion cooling coefficient; timedependent spectra inside finite-sized assemblies of heavy water at 20 and 43.3/sup 0/C thermalization time; and diffusion length and space-dependent study in pure and poisoned assemblies of heavy water. The calculated results are in good agreement with the experimental results. At some places notable differences are observed between the results obtained using our scattering kernel and those based on the Honeck kernel.
Baghi, Q; Bergé, J; Christophe, B; Touboul, P; Rodrigues, M
2015-01-01
The analysis of physical measurements often copes with highly correlated noises and interruptions caused by outliers, saturation events or transmission losses. We assess the impact of missing data on the performance of linear regression analysis involving the fit of modeled or measured time series. We show that data gaps can significantly alter the precision of the regression parameter estimation in the presence of colored noise, due to the frequency leakage of the noise power. We present a regression method which cancels this effect and estimates the parameters of interest with a precision comparable to the complete data case, even if the noise power spectral density (PSD) is not known a priori. The method is based on an autoregressive (AR) fit of the noise, which allows us to build an approximate generalized least squares estimator approaching the minimal variance bound. The method, which can be applied to any similar data processing, is tested on simulated measurements of the MICROSCOPE space mission, whos...