Multivariate wavelet kernel regression method Samir Touzani, Daniel Busbya
Paris-Sud XI, Université de
Multivariate wavelet kernel regression method Samir Touzani, Daniel Busbya a IFP Energies nouvelles multivariate nonparametric regression method, in the framework of wavelet decomposition. We call this method the wavelet kernel ANOVA (WK-ANOVA), which is a wavelet based reproducing kernel Hilbert space (RKHS) method
Kernel Density Based Linear Regression Estimate and Zhibiao Zhao
Zhao, Zhibiao
Kernel Density Based Linear Regression Estimate Weixin Yao and Zhibiao Zhao Abstract For linear regression models with non-normally distributed errors, the least squares estimate (LSE) will lose some words: EM algorithm, Kernel density estimate, Least squares estimate, Linear regression, Maximum
FAST SPEAKER ADAPTION VIA MAXIMUM PENALIZED LIKELIHOOD KERNEL REGRESSION
Tsang Wai Hung "Ivor"
of MLLR using non- linear regression. Specifically, kernel regression is applied with appropriate of Science and Technology Clear Water Bay, Hong Kong ABSTRACT Maximum likelihood linear regression (MLLR) has], and transformation-based methods, most notably, maximum likelihood linear regression (MLLR) adap- tation [3]. However
Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression
Kazhdan, Michael
Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression A. C. Öztireli, G IMLS in terms of Local Kernel Regression (LKR) · Borrowing ideas from robust statistics · Advantages to Implement · Competitive performance #12;Local Kernel Regression · Taylor expansion around the evaluation
Functional inverse regression and reproducing kernel Hilbert space
Ren, Haobo
2006-10-30T23:59:59.000Z
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 for regression which uses the e-sensitive loss and produces sparse models. However, non-linear SVRs are difficult.g. [24]). Used in conjunction with kernels, SVRs are powerful non-linear models for regression which
Representations of the LL BFKL Kernel and the Bootstrap
G. P. Vacca
2005-09-20T23:59:59.000Z
Different forms of the BFKL kernel both in coordinate and momentum representations may appear as a result of different gauge choices and/or inner scalar products of the color singlet states. We study a spectral representation of the BFKL kernel not defined on the Moebius space of functions but on a deformation of it, which provides the usual bootstrap property due to gluon reggeization. In this space the corresponding symmetry is made explicit introducing a deformed realization of the sl(2,C) algebra.
Kernel Regression with Order Preferences Xiaojin Zhu and Andrew B. Goldberg
Zhu, Xiaojin "Jerry"
such knowledge as positive correlation can be difficult in non-linear kernel regression, because of the non-linear, but the exact re- lation is highly non-linear and unknown. We can, however, easily create order preferencesKernel Regression with Order Preferences Xiaojin Zhu and Andrew B. Goldberg Department of Computer
Functional inverse regression and reproducing kernel Hilbert space
Ren, Haobo
2006-10-30T23:59:59.000Z
, such that the denominator in (3.2) is not zero. Property III.9. Let T be finite, and let the matrix determined by kernel K is nonsingu- lar. Then universalf, g element H(K, T): bardblfbardbl2K = summation t,selementT f(t) f(s)K-1(t, s), angbracketleft f, gangbracketright... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 RKHS with Applications in Probability and Statistics . . . . . . 13 III A FUNCTIONAL MULTIPLE-INDEX MODEL . . . . . . . . . . 15 3.1 A Second Order Multiple-Index Model . . . . . . . . . . . . . 15 3.2 Some Facts of RKHS...
Deformed Spectral Representation of the BFKL Kernel and the Bootstrap for Gluon Reggeization
J. bartels; L. N. Lipatov; M. Salvadore; G. P. Vacca
2005-06-23T23:59:59.000Z
We investigate the space of functions in which the BFKL kernel acts. For the amplitudes which describe the scattering of colorless projectiles it is convenient to define, in transverse coordinates, the Moebius space in which the solutions to the BFKL equation vanish as the coordinates of the two reggeized gluons coincide. However, in order to fulfill the bootstrap relation for the BFKL kernel it is necessary to modify the space of functions. We define and investigate a new space of functions and show explicitly that the bootstrap relation is valid for the corresponding spectral form of the kernel. We calculate the generators of the resulting deformed representation of the sl(2,C) algebra.
Bayesian Kernel Shaping for Learning Control
Ting, Jo-Anne; Kalakrishnan, Mrinal; Vijayakumar, Sethu; Schaal, Stefan
2008-01-01T23:59:59.000Z
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of output noise varies spatially. Previous ...
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)
2007-06-05T23:59:59.000Z
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.
The Kernel Recursive Least Squares Algorithm Yaakov Engel
Meir, Ron
prediction and channel equalization. Keywords: on-line learning, kernel methods, non-linear regression Experiments 21 5.1 Non-Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 5 the permission of the authors. #12;Abstract We present a non-linear kernel-based version of the Recursive Least
Kernel Support Vector Regression with imprecise output
2008-01-27T23:59:59.000Z
Jan 27, 2008 ... of a determined car (predictor variables) and an interval score given by a ... First, we compute the predicted interval for the score of each car via ...
Reduced Harmonic Representation of Partitions
Michalis Psimopoulos
2011-03-08T23:59:59.000Z
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.
"(Operating System)" linux "(kernel)"
kernel kernel shell (FIXME) shell (interface) (command line) shell ---(Command Interpreter line ""(word) (meta) command line () IFS shell #12;* (White Space) * (Tab) * (Enter) (command-name) * * (alias) * (function) * shell (built-in) * $PATH 3) echo echo echo command line echo --- command
Risk Bounds for Regularized Least-squares Algorithm with Operator-valued kernels
Vito, Ernesto De
2005-05-16T23:59:59.000Z
We show that recent results in [3] on risk bounds for regularized least-squares on reproducing kernel Hilbert spaces can be straightforwardly extended to the vector-valued regression setting. We first briefly introduce ...
Wavelet Kernel Learning F. Yger and A. Rakotomamonjy1
Paris-Sud XI, Université de
Wavelet Kernel Learning F. Yger and A. Rakotomamonjy1 Universit´e de Rouen, LITIS EA 4108, 76800 from a wavelet representation. Our work aims at building features by selecting wavelet coefficients resulting from signal or image decomposition on a adapted wavelet basis. For this purpose, we jointly learn
West, Mike
of covariates to use in regression or generalized linear models is a ubiquitous problem. The Bayesian paradigm regression and binary re- gression with non-orthogonal design matrices in conjunction with independent "spike and kernel regression (Clyde and George 2004). The generalization of the Gaussian linear model to other
Multi-view kernel construction
Sa, Virginia R.; Gallagher, Patrick W.; Lewis, Joshua M.; Malave, Vicente L.
2010-01-01T23:59:59.000Z
5157-z Multi-view kernel construction Virginia R. de Sa ·multiple different graph construction algorithms. The Ng et
Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees
Anderson, Charles H.
, kernel types, and platforms. 1. INTRODUCTION Due to power consumption and heat dissipation concerns for non-linear regression can be used to estimate timing models from data, capturing the best of both ap
Representation discovery in non-parametric reinforcement learning
Zewdie, Dawit (Dawit Habtamu)
2014-01-01T23:59:59.000Z
Recent years have seen a surge of interest in non-parametric reinforcement learning. There are now practical non-parametric algorithms that use kernel regression to approximate value functions. The correctness guarantees ...
KALMAN FILTERING REPRODUCING KERNEL HILBERT SPACES
Slatton, Clint
KALMAN FILTERING IN REPRODUCING KERNEL HILBERT SPACES Pingping Zhu #12;Outline · Introduction · Related Work · A Novel Extended Kernel Recursive Least Squares · Kernel Kalman Filter based on Conditional · Develop a Kalman filter in the Reproducing kernel Hilbert space (RKHS) Motivation · Kernel methods can
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
Bruemmer, David J. (Idaho Falls, ID)
2009-11-17T23:59:59.000Z
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.
Invariance in Kernel Methods by Haar-Integration Kernels
, regression, clustering, outlier-detection, feature- extraction etc. A powerful battery of algorithms
Aguiar, Pedro M. Q.
2009-01-01T23:59:59.000Z
inherent to the usual bag-of-words representations. In fact, approaches that map data to statistical/09 Nonextensive Information Theoretic Kernels on Measures Andr´e F. T. Martins AFM@CS.CMU.EDU Noah A. Smith, images, and other types of structured data. Some of these kernels are related to classic information
Ksplice: Automatic Rebootless Kernel Updates
Kaashoek, M. Frans
2009-01-01T23:59:59.000Z
Ksplice allows system administrators to apply patches to their operating system kernels without rebooting. Unlike previous hot update systems, Ksplice operates at the object code layer, which allows Ksplice to transform ...
Hypoelliptic heat kernel inequalities on H-type groups
Eldredge, Nathaniel Gilbert Bartsch
2009-01-01T23:59:59.000Z
Hypoelliptic heat kernel inequalities on the HeisenbergHypoelliptic heat kernel inequalities on Lie groups. PhDHypoelliptic heat kernel inequalities on H-type groups A
Least Squares Support Vector Machines for Kernel CCA in Nonlinear State-Space Identification
with multivariable input and output signals. It is of practical interest to be able to determine a state-space for linear time-invariant systems well-established state-space identification methods are available (Ljung to estimate the state-space representation. In this paper we use kernel canonical correlation analysis (KCCA
B. Bruegmann
1993-12-02T23:59:59.000Z
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)
Multivariate calibration with single-index signal regression Paul H.C. Eilers a
Marx, Brian D.
regression can be extended with an explicit link function between linear prediction and response is being estimated by P-splines. Application to simulations and three data sets shows that if a non-linearity from linear algebra, by non-linear functions. The idea is that a non-linear kernel in the linear space
Analytic representations with Theta functions for systems on Z(d) and on S
P. Evangelides; C. Lei; A. Vourdas
2015-02-18T23:59:59.000Z
An analytic representation with Theta functions on a torus, for systems with variables in Z(d), is considered. Another analytic representation with Theta functions on a strip, for systems with positions in a circle S and momenta in Z, is also considered. The reproducing kernel formalism for these two systems is studied. Wigner and Weyl functions in this language, are also studied
Learning Kernels -Tutorial Part IV: Software Solutions
Mohri, Mehryar
Kernel.org http://www.openkernel.org Â· DOGMA (online alg: UFO) http://dogma.sourceforge.net/index.html Â· MKL
'ETALE WILD KERNELS OF EXCEPTIONAL NUMBER FIELDS KEVIN HUTCHINSON
'ETALE WILD KERNELS OF EXCEPTIONAL NUMBER FIELDS KEVIN HUTCHINSON Abstract.We clarify the relationship between higher 'etale wild kernels * *of a number- cyclotomic tower of the field. We also determine the relationship betwee* *n the 'etale wild kernel
T-705: Linux Kernel Weakness in Sequence Number Generation Facilitates...
Broader source: Energy.gov (indexed) [DOE]
5: Linux Kernel Weakness in Sequence Number Generation Facilitates Packet Injection Attacks T-705: Linux Kernel Weakness in Sequence Number Generation Facilitates Packet Injection...
transformations: representations
Nguyen, Dat H.
Overview 1. Number transformations: from one base to another 2. Integer representations 3. Real rate, caches... #12; ECS 50, Discussion on 4/25 2 Integer Transformation: From Decimal to Binary Let, Discussion on 4/25 3 Integer Transformation: From Binary to Decimal Compute the weight of each digit position
Kernelization and Enumeration: New Approaches to Solving Hard Problems
Meng, Jie
2011-08-08T23:59:59.000Z
their sizes. We present a 2k kernel for the cluster editing problem, which improves the previous best kernel of size 4k; We also present a linear kernel of size 7k 2d for the d-cluster editing problem, which is the first linear kernel for the problem...
LOG HAZARD REGRESSION Huiying Sun
Heckman, Nancy E.
LOG HAZARD REGRESSION by Huiying Sun Ph.D, Harbin Institute of Technology, Harbin, CHINA, 1991 regression splines to estimate the two log marginal hazard func tions of bivariate survival times, where, 1995) hazard regression for estimating a univariate survival time. We derive an approach to find
Heat kernel asymptotics for magnetic Schrödinger operators
Bolte, Jens, E-mail: jens.bolte@rhul.ac.uk [Department of Mathematics, Royal Holloway, University of London, Egham TW20 0EX (United Kingdom)] [Department of Mathematics, Royal Holloway, University of London, Egham TW20 0EX (United Kingdom); Keppeler, Stefan, E-mail: stefan.keppeler@uni-tuebingen.de [Mathematisches Institut, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen (Germany)] [Mathematisches Institut, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen (Germany)
2013-11-15T23:59:59.000Z
We explicitly construct parametrices for magnetic Schrödinger operators on R{sup d} and prove that they provide a complete small-t expansion for the corresponding heat kernel, both on and off the diagonal.
Fractal Weyl law for Linux Kernel Architecture
L. Ermann; A. D. Chepelianskii; D. L. Shepelyansky
2010-09-16T23:59:59.000Z
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 $\
Representation of Universal Algebra
Aleks Kleyn
2015-02-07T23:59:59.000Z
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.
Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral
Hüllermeier, Eyke
to as choquistic regression, is to replace the linear function of pre- dictor variables, which is commonly used, it becomes possible to capture non-linear dependencies and in- teractions among predictor variables while regression, including the following ones: · Since the model is essentially linear in the in- put attributes
Kernels and learning curves for Gaussian process regression on random graphs
Sollich, Peter
and Outline Gaussian processes (GPs) have become a standard part of the machine learning toolbox [1]. Learning
Crystal Structure Representations for Machine Learning Models of Formation Energies
Faber, Felix; von Lilienfeld, O Anatole; Armiento, Rickard
2015-01-01T23:59:59.000Z
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...
Accuracy of Reduced and Extended Thin-Wire Kernels
Burke, G J
2008-11-24T23:59:59.000Z
Some results are presented comparing the accuracy of the reduced thin-wire kernel and an extended kernel with exact integration of the 1/R term of the Green's function and results are shown for simple wire structures.
A dynamic kernel modifier for linux
Minnich, R. G. (Ronald G.)
2002-09-03T23:59:59.000Z
Dynamic Kernel Modifier, or DKM, is a kernel module for Linux that allows user-mode programs to modify the execution of functions in the kernel without recompiling or modifying the kernel source in any way. Functions may be traced, either function entry only or function entry and exit; nullified; or replaced with some other function. For the tracing case, function execution results in the activation of a watchpoint. When the watchpoint is activated, the address of the function is logged in a FIFO buffer that is readable by external applications. The watchpoints are time-stamped with the resolution of the processor high resolution timers, which on most modem processors are accurate to a single processor tick. DKM is very similar to earlier systems such as the SunOS trace device or Linux TT. Unlike these two systems, and other similar systems, DKM requires no kernel modifications. DKM allows users to do initial probing of the kernel to look for performance problems, or even to resolve potential problems by turning functions off or replacing them. DKM watchpoints are not without cost: it takes about 200 nanoseconds to make a log entry on an 800 Mhz Pentium-Ill. The overhead numbers are actually competitive with other hardware-based trace systems, although it has less 'Los Alamos National Laboratory is operated by the University of California for the National Nuclear Security Administration of the United States Department of Energy under contract W-7405-ENG-36. accuracy than an In-Circuit Emulator such as the American Arium. Once the user has zeroed in on a problem, other mechanisms with a higher degree of accuracy can be used.
On fusion kernel in Liouville theory
Nikita Nemkov
2014-09-29T23:59:59.000Z
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.
ETALE WILD KERNELS OF EXCEPTIONAL NUMBER FIELDS KEVIN HUTCHINSON
#19; ETALE WILD KERNELS OF EXCEPTIONAL NUMBER FIELDS KEVIN HUTCHINSON Abstract. We clarify the relationship between higher #19;etale wild kernels of a number #12;eld at the prime 2 and the Galois between the #19;etale wild kernel and the group of in#12;nitely divisible elements of H 2 (F; Z 2 (j + 1
ETALE WILD KERNELS OF EXCEPTIONAL NUMBER FIELDS KEVIN HUTCHINSON
Â´ETALE WILD KERNELS OF EXCEPTIONAL NUMBER FIELDS KEVIN HUTCHINSON Abstract. We clarify the relationship between higher Â´etale wild kernels of a number field at the prime 2 and the Galois between the Â´etale wild kernel and the group of infinitely divisible elements of H2 (F, Z2(j + 1)){2}. 1
E. M. Drobyshevski
2002-05-21T23:59:59.000Z
The daemon-stimulated proton decay is capable of providing an appreciable part of the Sun luminosity as well as nonelectron flavor component in the solar neutrino flux. This follows (1) from our experiments on detection of negative daemons in Earth-crossing orbits, which give ~1 microsec for the decay time of a daemon-containing proton, and (2) from an estimate of the total number of daemons which could be captured by the Sun from the Galactic disk (up to \\~2.4E30). Because of their huge mass (~3E-5 g), the captured daemons form in the Sun's center a kernel a few cm in size. The protons diffuse into the kernel to decay there with a release of energy. Physically sound estimates of the parameters of the kernel can be obtained if it consists mainly of negative daemons. Proton decay maintains a high temperature of the daemon gas (up to \\~1E11-1E12 K), which makes it physically collisionless and prevents kernel collapse into a black hole.
Choosing a Kernel for Cross-Validation
Savchuk, Olga
2010-01-14T23:59:59.000Z
methods of bandwidth selection termed: Indirect cross-validation and Robust one-sided cross- validation. The kernels used in the Indirect cross-validation method yield an improvement in the relative bandwidth rate to n^1=4, which is substantially better...
Nonextensive Entropic Kernels Andre F. T. Martins
Aguiar, Pedro M. Q.
. Some of these kernels are related to classic infor- mation theoretic quantities, such as mutual information and the Jensen-Shannon diver- gence. Meanwhile, driven by recent advances in Tsallis statistics on Machine Learning, Helsinki, Finland, 2008. Copy- right 2008 by the author(s)/owner(s). approaches that map
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
Regression analysis Dependent variable (WMMY)
Langseth, Helge
;64 65 #12;66 Multiple Linear Regression 67 Example Â Acid rain in Norwegian lakes Data from a study of the influence of acid rain on Norwegian lakes, made in 1986. Totally 1005 lakes were studied. In this example
Diagnostics for multiple regression problems
Daly, J.C.
1982-03-01T23:59:59.000Z
In the last 10 to 15 years there has been much work done in trying to improve linear regression results. Individuals have analyzed the susceptibility of least-squares results to values far removed from the center of the independent variable observations. They have studied the problem of heavy-tailed residuals, and they have studied the problem of collinearity. From these studies have come ridge regression techniques, robust regression techniques, regression on principal components, etc. However, many practitioners view these methods with suspicion (and ignorance), and prefer to continue using the usual least-squares procedures to fit their models, even though their results might not be answering the question they think. In reaction to this, statisticians are spending more time analyzing how the individual observations affect the least squares results. In the last few years approximately 10 papers and one text have appeared that address the problem of how to study the influence of the individual observations. This report is a study of the recent work done in linear regression diagnostics. It is concerned with analyzing the effect of one case at a time, since the methods to analyze this situation are relatively straight-forward and are not prohibitive computationally.
Visualizing 1D Regression David J. Olive
Olive, David
regression, binary regression and general- ized linear models. If a good estimate ^b of some non a single linear combination T x of the predictors. Special cases of 1D regression include multiple linear(y) = + T x + e. Generalized linear models (GLM's) are also a special case of 1D regression. Some notation
Regression analysis with missing data
Michelli, Frank Anthony
1968-01-01T23:59:59.000Z
: Statistios REGRESSION ANALYSIS WITH MISS1NG DATA A Thesis FRANK ANTHONY MICHELLI Approved as to style and content by: hairman of o ttee Member Head of Department Member Member Zanuary 196B ACZNOWLED ONE NT S I can only begin to express my sincere...
On admissible memory kernels for random unitary qubit evolution
Filip A. Wudarski; Pawe? Nale?yty; Gniewomir Sarbicki; Dariusz Chru?ci?ski
2015-04-12T23:59:59.000Z
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.
T-653: Linux Kernel sigqueueinfo() Process Lets Local Users Send...
Broader source: Energy.gov (indexed) [DOE]
Process Lets Local Users Send Spoofed Signals T-653: Linux Kernel sigqueueinfo() Process Lets Local Users Send Spoofed Signals June 23, 2011 - 4:49am Addthis PROBLEM:...
U-175: Linux Kernel KVM Memory Slot Management Flaw
Broader source: Energy.gov [DOE]
A vulnerability was reported in the Linux Kernel. A local user on the guest operating system can cause denial of service conditions on the host operating system.
Nonlinear Multiple Kernel Learning via Mixture of Probabilistic Kernel Discriminant Analysis
Liu, Huan
.ye@asu.edu Computer Science & Engineering Arizona State University Shipeng Yu shipeng.yu@siemens.com CAD and Knowledge Solutions Siemens Medical Solutions USA, Inc. Huan Liu huan.liu@asu.edu Computer Science & Engineering, and in the transformed space, instances can be better separated. A kernel can be constructed in the transformed space
Intel's Math Kernel Library (MKL) at NERSC
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC)Integrated Codes | National Nuclear Security AdministrationIntegratingIntelVTuneKernel
Regression analysis with longitudinal measurements
Ryu, Duchwan
2005-08-29T23:59:59.000Z
, in the cardiotoxic effects of doxorubicin chemotherapy for the treat- ment of acute lymphoblastic leukemia in childhood (Lipsitz et al., 2002; Fitzmaurice et al., 2003), the design points are not pre-defined but determined by the preceding response. This outcome...-dependent feature of measurements makes biased estimation of regression line. As noticed by Lipsitz et al. (2002); Fitzmaurice et al. (2003), even the least square estimates will be biased, which does not require the distributional assumption of response error...
Extended Kalman Filter Using a Kernel Recursive Least Squares Observer
Slatton, Clint
Extended Kalman Filter Using a Kernel Recursive Least Squares Observer Pingping Zhu, Badong Chen estimation problem combining the extended Kalman filter (EKF) with a kernel recursive least squares (KRLS Kalman filter, EKF and KRLS algorithms. Results demonstrate that the performance of the EKF
Customized Kernel Execution on Reconfigurable Hardware for Embedded Applications
Ziavras, Sotirios G.
is required. This can be facilitated through dynamic adaptation of the silicon resources in reconfigurable reconfiguration overheads can be estimated. Therefore, if the scheduling of time- consuming kernels considers also kernels. Experiments involving EEMBC (EDN Embedded Microprocessor Benchmarking Consortium) and Mi
CDF and Survival Function Estimation with Infinite-Order Kernels
Politis, Dimitris N.
) and the survival function is proposed using infinite-order kernels. Fourier transform theory on generalizedCDF and Survival Function Estimation with Infinite-Order Kernels Arthur Berg and Dimitris N sample sizes these estimators can significantly improve the estimation of the CDF and survival function
Student Representation System Policy Student Representation System Policy
Birmingham, University of
Policy Student Representation System Policy 2013-14 Student Representation System Policy UNIVERSITY OF BIRMINGHAM STUDENT REPRESENTATION SYSTEM POLICY #12;Policy Student Representation System Policy 2013-14 Student Representation System Policy Index of points 1. Introduction 2. Purpose 3. Core Principles 4
Scientific Computing Kernels on the Cell Processor
Williams, Samuel W.; Shalf, John; Oliker, Leonid; Kamil, Shoaib; Husbands, Parry; Yelick, Katherine
2007-04-04T23:59:59.000Z
The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. As a result, the high performance computing community is examining alternative architectures that address the limitations of modern cache-based designs. In this work, we examine the potential of using the recently-released STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. First, we introduce a performance model for Cell and apply it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs. The difficulty of programming Cell, which requires assembly level intrinsics for the best performance, makes this model useful as an initial step in algorithm design and evaluation. Next, we validate the accuracy of our model by comparing results against published hardware results, as well as our own implementations on a 3.2GHz Cell blade. Additionally, we compare Cell performance to benchmarks run on leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1E) architectures. Our work also explores several different mappings of the kernels and demonstrates a simple and effective programming model for Cell's unique architecture. Finally, we propose modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations. Overall results demonstrate the tremendous potential of the Cell architecture for scientific computations in terms of both raw performance and power efficiency.
Thresholding Multivariate Regression and Generalized Principal Components
Sun, Ranye
2014-03-17T23:59:59.000Z
the curse of dimensionality. It is desirable to estimate the regression coefficient matrix by low-rank matrices constructed from its SVD. We reduce such regression problems to sparse SVD problems for cor- related data matrices and generalize the fast...
Regression quantiles for time series
Cai, Zongwu
2002-02-01T23:59:59.000Z
~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...
Regression analysis of cytopathological data
Whittemore, A.S.; McLarty, J.W.; Fortson, N.; Anderson, K.
1982-12-01T23:59:59.000Z
Epithelial cells from the human body are frequently labelled according to one of several ordered levels of abnormality, ranging from normal to malignant. The label of the most abnormal cell in a specimen determines the score for the specimen. This paper presents a model for the regression of specimen scores against continuous and discrete variables, as in host exposure to carcinogens. Application to data and tests for adequacy of model fit are illustrated using sputum specimens obtained from a cohort of former asbestos workers.
ENGI 3423 Simple Linear Regression Page 12-01 Simple Linear Regression
George, Glyn
for dealing with non-linear regression are available in the course text, but are beyond the scopeENGI 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
Classification using Intersection Kernel Support Vector Machines is Efficient Subhransu Maji
O'Brien, James F.
Classification using Intersection Kernel Support Vector Machines is Efficient Subhransu Maji EECS classification using kernelized SVMs re- quires evaluating the kernel for a test vector and each of the support vectors. For a class of kernels we show that one can do this much more efficiently. In particular we show
HIGHER WILD KERNELS AND DIVISIBILITY IN THE K-THEORY OF NUMBER FIELDS
Weibel, Charles
HIGHER WILD KERNELS AND DIVISIBILITY IN THE K, 2004 Abstract. The higher wild kernels are finite subgroups of the even K-group* *s of a number field F, generalizing Tate's wild kernel for K2. Each wild kernel contains* * the subgroup
HIGHER WILD KERNELS AND DIVISIBILITY IN THE K-THEORY OF NUMBER FIELDS
HIGHER WILD KERNELS AND DIVISIBILITY IN THE K-THEORY OF NUMBER FIELDS C. Weibel July 15, 2004 Abstract. The higher wild kernels are #12;nite subgroups of the even K-groups of a number #12;eld F , generalizing Tate's wild kernel for K2 . Each wild kernel contains the subgroup of divisible elements
Approximation Methods for Gaussian Process Regression
Williams, Chris
, Microsoft Research Ltd. 7 J J Thomson Avenue, CB3 0FB Cambridge, UK joaquinc@microsoft.com Carl Edward by Rasmussen and Williams (2006). Gaussian processes allow a Bayesian use of kernels for learning proposed by Qui~nonero-Candela and Rasmussen (2005), based on inducing inputs and conditionally independent
Hermeneutics, Information and Representation
Chalmers, Matthew
Hermeneutics, Information and Representation Matthew Chalmers Computing Science, Glasgow University, United Kingdom Abstract By drawing from semiology, epistemology and philosophical hermeneutics, we for computersupported collaborative work (CSCW). We point out similarities between discourse in hermeneutics
MA699: Stochastic Taylor expansions and heat kernel asymptotics
2011-01-17T23:59:59.000Z
Jan 17, 2011 ... where p : (0,+?) × Rn × Rn ? R is a smooth function that is called the heat kernel ... Theorem 2.1 Let us assume that b and ? are smooth, and that their ..... [
KALMAN FILTERING IN REPRODUCING KERNEL HILBERT SPACES PINGPING ZHU
Slatton, Clint
KALMAN FILTERING IN REPRODUCING KERNEL HILBERT SPACES By PINGPING ZHU A DISSERTATION PRESENTED.1.1 Bayesian Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.1.2 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.3 Nonlinear Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.3.1 Extended
Green's kernels for transmission problems in bodies with small inclusions
Vladimir Maz'ya; Alexander Movchan; Michael Nieves
2010-05-24T23:59:59.000Z
The uniform asymptotic approximation of Green's kernel for the transmission problem of antiplane shear is obtained for domains with small inclusions. The remainder estimates are provided. Numerical simulations are presented to illustrate the effectiveness of the approach.
Highly Sparse Reductions to Kernel Spectral Raghvendra Mall1
the traditional k-means techniques. A new spectral clus- tering algorithm based on weighted kernel principal in comparison to the data between clusters. Spectral clustering methods [1,2,3] are generally better than
Kernels for Feedback Arc Set In Tournaments Stephane Bessy
Paris-Sud XI, Université de
Kernels for Feedback Arc Set In Tournaments St´ephane Bessy Fedor V. Fomin Serge Gaspers Christophe´e de Montpellier 2, CNRS, 161 rue Ada, 34392 Montpellier, France. {bessy
complexity of the classical kernel functions of potential theory
1998-08-27T23:59:59.000Z
1991 Mathematics Subject Classification. ...... It is a simple exercise using the argument principle that the ... off the following facts (keep in mind that wk is close to An ? ?n). ...... excellent strategy for computing the Poisson kernel efficiently.
Hereditary kernel identification method of nonlinear polymeric viscoelastic materials
Olodo Emmanuel; Villevo Adanhounme; Mahouton Norbert Hounkonnou
2012-12-26T23:59:59.000Z
This paper deals with a polymeric matrix composite material. The matrix behaviour is described by the modified Rabotnov's nonlinear viscoelastic model assuming the material is nonlinear viscoelastic. The parameters of creep and stress-relaxation kernels of the model are determined. From the experimental data related to kernels approximated by spline functions and by means of the method of weighted residual, the formulas for the determination of viscoelastic parameters are derived.
Polymer representations and geometric quantization
Miguel Campiglia
2011-11-02T23:59:59.000Z
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.
Varying-Coefficient Functional Linear Regression Models
Cardot, Hervé
Varying-Coefficient Functional Linear Regression Models Herv´e Cardot1 and Pascal Sarda2 1, the ability of such non linear functional approaches to produce competitive estimations. Short title : Varying monograph. We propose here another generalization of the functional linear regression model in which
Computational Reality XIII Non-linear regression
Berlin,Technische Universität
Computational Reality XIII Non-linear regression Inverse analysis II B. Emek Abali @ LKM - TU Berlin Abstract Linear regression to fit and determine parameters, shown in the last tutorial, is quite useful and widely implemented, however, there are material models where parameters are coupled non-linearly
Umbra's system representation.
McDonald, Michael James
2005-07-01T23:59:59.000Z
This document describes the Umbra System representation. Umbra System representation, initially developed in the spring of 2003, is implemented in Incr/Tcl using concepts borrowed from Carnegie Mellon University's Architecture Description Language (ADL) called Acme. In the spring of 2004 through January 2005, System was converted to Umbra 4, extended slightly, and adopted as the underlying software system for a variety of Umbra applications that support Complex Systems Engineering (CSE) and Complex Adaptive Systems Engineering (CASE). System is now a standard part Of Umbra 4. While Umbra 4 also includes an XML parser for System, the XML parser and Schema are not described in this document.
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
2015-01-01T23:59:59.000Z
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
2015-03-25T23:59:59.000Z
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.
adaptive regression modeling: Topics by E-print Network
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
Information Sciences Websites Summary: Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers, adaptive metric, cross-vali-...
Stochastic Low-Rank Kernel Learning for Regression Pierre Machart PIERRE.MACHART@LIF.UNIV-MRS.FR
Paris-Sud XI, Université de
, Aix-Marseille Universit´e Sandrine Anthoine ANTHOINE@CMI.UNIV-MRS.FR LATP, CNRS, Aix the hyperparameters inherent to our modeling as well as the complexity of the proposed algorithm. Section 5 reports
Kernel density estimation of a multidimensional efficiency profile
Anton Poluektov
2014-11-20T23:59:59.000Z
Kernel density estimation is a convenient way to estimate the probability density of a distribution given the sample of data points. However, it has certain drawbacks: proper description of the density using narrow kernels needs large data samples, whereas if the kernel width is large, boundaries and narrow structures tend to be smeared. Here, an approach to correct for such effects, is proposed that uses an approximate density to describe narrow structures and boundaries. The approach is shown to be well suited for the description of the efficiency shape over a multidimensional phase space in a typical particle physics analysis. An example is given for the five-dimensional phase space of the $\\Lambda_b^0\\to D^0p\\pi$ decay.
U-226: Linux Kernel SFC Driver TCP MSS Option Handling Denial...
Broader source: Energy.gov (indexed) [DOE]
Vulnerability PLATFORM: Linux Kernel 3.2.x ABSTRACT: The Linux kernel is prone to a remote denial-of-service vulnerability. reference LINKS: Secunia Advisory SA50081 Bugtraq ID:...
Kollegala, Revathi
2012-07-16T23:59:59.000Z
of wavelet functions as kernels with Support Vector Data Description for target detection in hyperspectral images. Specifically, it proposes the Adaptive Wavelet Kernel Support Vector Data Description (AWK-SVDD) that learns the optimal wavelet function...
Geometry, noncommutative algebra and representations
Gordon, Iain
and Deformations 4 Representation Theory 2 Iain Gordon Geometry, noncommutative algebra and representations: analysis, algebra, geometry, number theory (to name four!) 4 Iain Gordon Geometry, noncommutative algebra is a finite field. 6 Iain Gordon Geometry, noncommutative algebra and representations Geometry and Commutative
A Library for Locally Weighted Projection Regression
Klanke, Stefan; Vijayakumar, Sethu; Schaal, Stefan
2008-01-01T23:59:59.000Z
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 ...
Iterative gradient descent approach to multiple regression with fuzzy data
Bargiela, Andrzej
to multiple regression and lay foundation for a further generalisation to multiple non-linear regression dictates adoption of a more general viewpoint, regression variables are given as non-numerical entities of the parameters of the regression model have been derived only for the case of a simple linear regression, i
Math 261A -Spring 2012 M. Bremer Multiple Linear Regression
Keinan, Alon
called non-linear regression models or polynomial regression models, as the regression curveMath 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
A Kalman-Particle Kernel Filter and its Application to Terrain Navigation
Del Moral , Pierre
A Kalman-Particle Kernel Filter and its Application to Terrain Navigation Dinh-Tuan Pham.musso@onera.fr Abstract A new nonlinear filter, the Kalman- Particle Kernel Filter (KPKF) is proposed. Compared. Keywords: Kalman filter, kernel density estimator, regularized particle filter, Inertial navigation System
Sparse Kernel Orthonormalized PLS for feature extraction in large data sets
Sparse Kernel Orthonormalized PLS for feature extraction in large data sets Anonymous Author a novel multivariate analysis method for large scale problems. Our scheme is based on a novel kernel strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS
Kernel k-means, Spectral Clustering and Normalized Cuts Inderjit S. Dhillon
Ghosh, Joydeep
Kernel k-means, Spectral Clustering and Normalized Cuts Inderjit S. Dhillon Dept. of Computer. of Computer Sciences University of Texas at Austin Austin, TX 78712 kulis@cs.utexas.edu ABSTRACT Kernel k-means an ex- plicit theoretical connection between them. We show the generality of the weighted kernel k-means
Small Representation Principle
H. B. F. Nielsen
2014-03-06T23:59:59.000Z
In a previous article Don Bennett and I looked for, found and proposed a game in which the Standard Model Gauge Group $S(U(2) \\times U(3))$ gets singled out as the "winner". This "game" means that the by Nature chosen gauge group should be just that one, which has the maximal value for a quantity, which is a modification of the ratio of the quadratic Casimir for the adjoint representation and that for a "smallest" faithful representation. In a recent article I proposed to extend this "game" to construct a corresponding game between different potential dimensions for space-time. The idea is to formulate, how the same competition as the one between the potential gauge groups would run out, if restricted to the potential Lorentz or Poincare groups achievable for different dimensions of space-time $d$. The remarkable point is, that it is the experimental space-time dimension 4, which wins. It follows that the whole Standard Model is specified by requiring SMALLEST REPRESENTATIONS! Speculatively we even argue that our principle found suggests the group of gauge transformations and some manifold(suggestive of say general relativity).
A comparative study of spline regression
Nougues, Arnaud
1980-01-01T23:59:59.000Z
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...: RATIONALE FOR POIRIER'S METHOD APPENDIX B: A LEAST SQUARES CUBIC SPLINE PROGRAM BASED ON POIRIER'S METHOD ~Pa e 52 55 APPENDIX C: COMPUTATION OF B IN THE RESTRICTED LEAST SQUARES METHOD FOR CUBIC SPLINES WITH A CONTINUOUS SECOND DERIVATIVE APPENDIX...
Part II - Managerial Competencies: Organizational Representation...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
II - Managerial Competencies: Organizational Representation and Liaison Part II - Managerial Competencies: Organizational Representation and Liaison Form for the SES program...
Boyer, Edmond
). The case of stable random elements was also investigated (see for instance Li, Linde (2004), Aurzada, Lifshits, Linde (2009)). Another issue is related to the norm. Indeed in infinite dimensional spaces, norms) or Li and Linde (1993) for instance. A classical example stems from the situation where PX-x0 PX where
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
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
Optical transformation from chirplet to fractional Fourier transformation kernel
Hong-yi Fan; Li-yun Hu
2009-02-11T23:59:59.000Z
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.
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
Kernel spectral clustering for predicting maintenance of industrial machines
to deal with sensory faults have been used [1],[2],[3]: corrective maintenance, preventive maintenance the machine fails, it is expensive and safety and environment issues arise. Preventive maintenance is basedKernel spectral clustering for predicting maintenance of industrial machines Rocco Langone1, Carlos
Kernel Methods for Melanoma Recognition Elisabetta LA TORREa,1
Caputo, Barbara
Kernel Methods for Melanoma Recognition Elisabetta LA TORREa,1 , Tatiana TOMMASIa , Barbara CAPUTOb for computer assisted diagnosis of melanomas. The first is the support vector machines algorithm, a state a sophisticated segmentation technique and a set of features especially designed for melanoma recognition. To our
Melanoma Recognition Using Representative and Discriminative Kernel Classifiers
Caputo, Barbara
Melanoma Recognition Using Representative and Discriminative Kernel Classifiers Tatiana Tommasi1 caputo@nada.kth.se Abstract. Malignant melanoma is the most deadly form of skin lesion. Early diagnosis these algorithms against the (to our knowledge) state-of-the-art method on melanoma recognition, exploring how
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
2008-06-24T23:59:59.000Z
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.
Analysis with Kernel Density Estimation University of Michigan / HERMES Collaboration
Analysis with Kernel Density Estimation S. Gliske University of Michigan / HERMES Collaboration Transverse Parton Structure of the Hadron Yerevan, Armenia 25 June, 2009 Gliske (HERMES / Michigan) Analysis/Smearing Effects SIDIS cos(n) Conclusion Gliske (HERMES / Michigan) Analysis with KDEs TPSH `09 2 / 24 #12
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
Reproducing kernel element method Part III: Generalized enrichment and applications
Li, Shaofan
Reproducing kernel element method Part III: Generalized enrichment and applications Hongsheng Lu enrichment is proposed to construct the global partition polynomials or to enrich global partition polynomial. This is accomplished by either multiplying enrichment functions with the original global partition poly- nomials
Geodesic Regression on Riemannian Manifolds P. Thomas Fletcher
Boyer, Edmond
- ing multiple linear regression in Rn . Here we are interested in the relationship between a non that one could choose, and it provides a direct generalization of linear regression to the manifold setting regression model is linear regression, due to its simplicity, ease of interpretation, and ability to model
Morse-Smale Regression Samuel Gerber, University of Utah
approximated by a linear model. This approach yields regression models that are amenable to interpretation capabilities of non-parametric methods. A classical 1 #12;approach to partition-based regression are regression a piece- wise constant model, treed regression (Alexander and Grimshaw, 1996) proposed linear models
Ensemble Kalman Filtering with Shrinkage Regression Techniques
Eidsvik, Jo
Ensemble Kalman Filtering with Shrinkage Regression Techniques Jon Sætrom & Henning Omre, Norwegian University of Science and Technology; Summary The classical Ensemble Kalman Filter (EnKF) is known;Introduction The Ensemble Kalman Filter (EnKF) is a Bayesian data assimilation method that in recent years has
Calibration via Regression Dean P. Foster
Kakade, Sham M.
Calibration via Regression Dean P. Foster Statistics Department University of Pennsylvania Email-- In the online prediction setting, the concept of calibration entails having the empirical (conditional hard to compare with each other. This paper shows how to get an approximate form of calibration out
Positive curvature property for some hypoelliptic heat kernels
Qian, Bin
2010-01-01T23:59:59.000Z
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.
Kernel-Correlated Levy Field Driven Forward Rate and Application to Derivative Pricing
Bo Lijun [Xidian University, Department of Mathematics (China); Wang Yongjin [Nankai University, School of Business (China); Yang Xuewei, E-mail: xwyangnk@yahoo.com.cn [Nanjing University, School of Management and Engineering (China)
2013-08-01T23:59:59.000Z
We propose a term structure of forward rates driven by a kernel-correlated Levy random field under the HJM framework. The kernel-correlated Levy random field is composed of a kernel-correlated Gaussian random field and a centered Poisson random measure. We shall give a criterion to preclude arbitrage under the risk-neutral pricing measure. As applications, an interest rate derivative with general payoff functional is priced under this pricing measure.
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.
U-080: Linux Kernel XFS Heap Overflow May Let Remote Users Execute Arbitrary Code
Broader source: Energy.gov [DOE]
A vulnerability was reported in the Linux Kernel. A remote user can cause arbitrary code to be executed on the target user's system.
Srinivasan, Ashok
Reuse and Refactoring of GPU Kernels to Design Complex Applications Santonu Sarkar, Sayantan Mitra, Ashok Srinivasan Infosys Labs, Infosys Ltd. Bangalore 560100, India Email: {santonu sarkar01,sayantan
Graph dynamics : learning and representation
Ribeiro, Andre Figueiredo
2006-01-01T23:59:59.000Z
Graphs are often used in artificial intelligence as means for symbolic knowledge representation. A graph is nothing more than a collection of symbols connected to each other in some fashion. For example, in computer vision ...
A Regression Test Selection Technique for Graphical User Interfaces
Chesser, Carl
2012-08-31T23:59:59.000Z
Regression testing is a quality control measure to ensure that the newly modified part of the software still complies with its specified requirements and that the unmodified part has not been affected by the maintenance activity. Regression testing...
Distributed Multivariate Regression Using Wavelet-based Collective Data Mining.
Kargupta, Hilol
an approach to the analysis of distributed, heterogeneous databases with distinct feature spacesDistributed Multivariate Regression Using Wavelet-based Collective Data Mining. Daryl E a method for distributed multivariate regression using wavelet- based Collective Data Mining (CDM
Kernel-based distance metric learning for microarray data classification
Xiong, Huilin; Chen, Xue-wen
2006-06-01T23:59:59.000Z
the test data. We only consider Gaussian kernel function in the proposed and SVM algorithms. 1. ALL-AML Leukemia Data: This data set, taken from the website [17], contains 72 samples of human acute leuke- mia. 47 samples belong to acute lymphoblastic... lymphoblastic leukemia (ALL), 20 of them to mixed line- age leukemia (MLL), a subset of human acute leukemia with a chromosomal translocation, and 28 of the samples are acute myelogenous leukemia (AML). Each sample gives the expression levels of 12582 genes...
Operation of Faddeev-Kernel in Configuration Space
S. Ishikawa
2007-01-16T23:59:59.000Z
We present a practical method to solve Faddeev three-body equations at energies above three-body breakup threshold as integral equations in coordinate space. This is an extension of previously used method for bound states and scattering states below three-body breakup threshold energy. We show that breakup components in three-body reactions produce long-range effects on Faddeev integral kernels in coordinate space, and propose numerical procedures to treat these effects. Using these techniques, we solve Faddeev equations for neutron-deuteron scattering to compare with benchmark solutions.
Regression Given input data (features), predict value of a
Giger, Christine
· The complete graph Non-linear regression #12;· Need to fit non-linear functions · example: polynomials Non-linear of the inputs · After applying z(x), we fit a plane in 3D-space Non-linear regression y = 0 + 1x + 2x2 z(x) = 1 x x2 #12;x y x2 regression from x to y, non-linear lifting from x to z=[x x2] regression from [x x2
Nonlinear regression analysis of field emission data
Barry, Scott Wilson
1992-01-01T23:59:59.000Z
for the zirconium/tungsten cathode data. . Regressed enhancement fa. ctors using the integral model(solid line) and approximate model(dashed line) over a range of fixed work functions for the zirconium/tungsten cathode data. 70 Integral model(solid line...) and linear( dashed line) fitting curves for the zirconium/tungsten cathode data, . 71 33 Integral model(solid line) and linear(dashed line) fitting curves for the zirconium/tungsten cathode data, excluding the last three suspect data points. 72 CHAPTER I...
Quantum Field Theory and Representation Theory
Woit, Peter
Quantum Field Theory and Representation Theory Peter Woit woit@math.columbia.edu Department of Mathematics Columbia University Quantum Field Theory and Representation Theory p.1 #12;Outline of the talk · Quantum Mechanics and Representation Theory: Some History Quantum Field Theory and Representation Theory
A Hybrid GP Approach for Numerically Robust Symbolic Regression
Fernandez, Thomas
expressions encoded in tree structures to perform symbolic regression. A non-linear optimization method very common technique is linear regression, in which the model is a linear combina- tion of given base are polynomials (polynomial regression) or trigonometric poly- nomials (e.g. Fourier series). For general linearly
Non-linear regression models for Approximate Bayesian Computation
Robert, Christian P.
Non-linear regression models for Approximate Bayesian Computation (ABC) Michael Blum Olivier ABC #12;Blum and OF (2009) suggest the use of non-linear conditional heteroscedastic regression models) Linear regression-based ABC can sometimes be improved #12;abc of ABC Using stochastic simulations
The SROV program for data analysis and regression model identification
Brauner, Neima
) regression models comprised of linear combination of independent variables and their non-linear functions. # 2002 Elsevier Science Ltd. All rights reserved. Keywords: Stepwise regression; Colinearity; Non have been intro- duced for selection of the optimal model in linear regression (for detailed reviews
Submitted to the Annals of Statistics FUNCTIONAL ADDITIVE REGRESSION
Radchenko, Peter
extends beyond the standard linear regression setting to fit general non-linear additive models. We extending the classical functional regression model. [25] proposed an index model to implement a non-linear extends the usual linear regression model involving a functional predictor, X(t), and a scalar response, Y
Adaptive sparse polynomial chaos expansion based on least angle regression
Blatman, Geraud, E-mail: geraud.blatman@edf.f [Clermont Universite, IFMA, EA 3867, Laboratoire de Mecanique et Ingenieries, BP 10448, F-63000 Clermont-Ferrand (France); EDF R and D, Departement Materiaux et Mecanique des Composants, Site des Renardieres, 77250 Moret-sur-Loing cedex (France); Sudret, Bruno [Clermont Universite, IFMA, EA 3867, Laboratoire de Mecanique et Ingenieries, BP 10448, F-63000 Clermont-Ferrand (France); Phimeca Engineering, Centre d'Affaires du Zenith, 34 rue de Sarlieve, F-63800 Cournon d'Auvergne (France)
2011-03-20T23:59:59.000Z
Polynomial chaos (PC) expansions are used in stochastic finite element analysis to represent the random model response by a set of coefficients in a suitable (so-called polynomial chaos) basis. The number of terms to be computed grows dramatically with the size of the input random vector, which makes the computational cost of classical solution schemes (may it be intrusive (i.e. of Galerkin type) or non intrusive) unaffordable when the deterministic finite element model is expensive to evaluate. To address such problems, the paper describes a non intrusive method that builds a sparse PC expansion. First, an original strategy for truncating the PC expansions, based on hyperbolic index sets, is proposed. Then an adaptive algorithm based on least angle regression (LAR) is devised for automatically detecting the significant coefficients of the PC expansion. Beside the sparsity of the basis, the experimental design used at each step of the algorithm is systematically complemented in order to avoid the overfitting phenomenon. The accuracy of the PC metamodel is checked using an estimate inspired by statistical learning theory, namely the corrected leave-one-out error. As a consequence, a rather small number of PC terms are eventually retained (sparse representation), which may be obtained at a reduced computational cost compared to the classical 'full' PC approximation. The convergence of the algorithm is shown on an analytical function. Then the method is illustrated on three stochastic finite element problems. The first model features 10 input random variables, whereas the two others involve an input random field, which is discretized into 38 and 30 - 500 random variables, respectively.
On ascertaining inductively the dimension of the joint kernel of certain commuting linear operators
Shen, Zuowei
On ascertaining inductively the dimension of the joint kernel of certain commuting linear operators), and a collection f`xgx2X of commuting linear maps on some linear space, the family of linear operators whose joint DMS-9000053, DMS-9102857. i #12;proposed running head: dimension of joint kernels Proofs should
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
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
Random Features for Large-Scale Kernel Machines Intel Research Seattle
Kim, Tae-Kyun
Random Features for Large-Scale Kernel Machines Ali Rahimi Intel Research Seattle Seattle, WA 98105 products of the transformed data are approximately equal to those in the feature space of a user specified on their ability to approximate various radial basis kernels, and show that in large-scale classification
Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification , Shih-Fu Chang
Chang, Shih-Fu
Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification Yadong Mu , Gang Hua , Wei the efficiency of non-linear kernel SVM in very large scale visual classification prob- lems. Our key idea be transformed into solving a linear SVM over the hash bits. The proposed Hash-SVM enjoys dramatic storage cost
An improved ECG-Derived Respiration Method using Kernel Principal Component Analysis
) of heart beats generates well-performing ECG- derived respiratory signals (EDR). This study aims at im- proving the performance of EDR signals using kernel PCA (kPCA). Kernel PCA is a generalization of PCA and kPCA is eval- uated by comparing the EDR signals to the reference res- piratory signal. Correlation
Application of Kernel Principal Component Analysis for Single Lead ECG-Derived Respiration
signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA function (RBF) kernel performs the best when deriving EDR signals. Further improvement is carried outPCA is assessed by comparing the EDR signals to a reference respiratory signal, using the correlation
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
Regression analysis of WMATA metering information
Not Available
1983-12-01T23:59:59.000Z
The PEPCO provided a magnetic tape that contained energy usage (pulses) data as given in the PEPCO account. The data had 15-min pulses for 26 traction energy meters which were in operation during 1980. The time span was January 20, 1980, to January 19, 1981. Out of 26 traction metering data provided by PEPCO, 18 meters were in DC, 5 meters were in MD, and 3 meters were in VA jurisdictions. The data were converted into Fortran readable form, using program RU0A09.FOR. The system flow chart is shown. Using A, plots were created of summary statistics, which provided through bar charts information on mean, standard deviation, and maximum of power demand. Using B, regression analyses of power vs. car-miles/hour and degree-days for revenue operating and nonoperating periods were established. Using C, energy consumption histograms on each time period for various meters were created. The regression analysis which was done on PEPCO metering data in order to determine the dependence of traction energy usages on car-miles and daily temperature is described in detail.
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
2010-12-02T23:59:59.000Z
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.
Towards Optimal and Expressive Kernelization for d-Hitting Set
van Bevern, René
2011-01-01T23:59:59.000Z
A sunflower in a hypergraph is a set of hyperedges pairwise intersecting in exactly the same vertex set. Sunflowers are a useful tool in polynomial-time data reduction for problems formalizable as d-Hitting Set, the problem of covering all hyperedges (of cardinality at most d) of a hypergraph by at most k vertices. Additionally, in fault diagnosis, sunflowers yield concise explanations for "highly defective structures". We provide a linear-time algorithm that, by finding sunflowers, transforms an instance of d-Hitting Set into an equivalent instance comprising at most O(k^d) hyperedges and vertices. In terms of parameterized complexity theory, we show a problem kernel with asymptotically optimal size (unless coNP in NP/poly). We show that the number of vertices can be reduced to O(k^(d-1)) with additional processing in O(k^(1.5d)) time---nontrivially combining the sunflower technique with a known problem kernel that uses crown reductions.
Chlipala, Adam
]. Interpreters are also used outside of kernels, such as in Bitcoin's transaction scripting [2]. As an example
Obradovic, Zoran
of accuracy for both linear and non-linear regression models. The obtained experimental results suggest impact on accuracy of an auto-regression model. For non-linear phenomena, learning algorithms that model grid. The proposed method combines linear or non-linear non-spatial and non- temporal regression models
A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts Inderjit Dhillon, Yuqiang Guan proposed to handle data that is not linearly separable. Spectral clustering and kernel k-means are two such methods that are seemingly quite different. In this paper, we show that a general weighted kernel k-means
Camps-Valls, Gustavo
boundaries to the change detection problem by exploiting a kernel-based clustering algorithm. The kernel k-means imagery prove the consistency of the proposed approach. Keywords: Unsupervised change detection, Kernel k-means An example is the algorithmic comparison of the scale invariant Mahalanobis distance between the pixels
Figure 1. Block diagram of the turbo decoder. A Memory-Reduced Log-MAP Kernel for Turbo Decoder
Hung, Shih-Hao
Figure 1. Block diagram of the turbo decoder. A Memory-Reduced Log-MAP Kernel for Turbo Decoder--Generally, the Log-MAP kernel of the turbo decoding consume large memories in hardware implement- tation of the turbo decoder is implemented to verify the proposed memory-reduced Log- MAP kernel in 3.04Ã?3.04mm2 core
An Implementation of Bayesian Adaptive Regression Splines (BARS)
Kass, Rob
extensive experience in fitting curves to neu- rophysiological data (Kass, Ventura, and Brown, 2005; Kass, Ventura, and Cai, 2003; Ventura et al., 2002). A typical data display is given in Figure 1. The raw data smoothers (Ventura et al., 2002), but we came across neurons like the one displayed in Figure 1 where kernel
Representation of noncommutative phase space
Kang Li; Jianhua Wang; Chiyi Chen
2005-08-16T23:59:59.000Z
The representations of the algebra of coordinates and momenta of noncommutative phase space are given. We study, as an example, the harmonic oscillator in noncommutative space of any dimension. Finally the map of Sch$\\ddot{o}$dinger equation from noncommutative space to commutative space is obtained.
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...
Regression Given input data (features), predict value of a
Giger, Christine
^ = (XX> ) 1 Xy etc. #12;! · Many relations are not linear · The complete graph Non-linear regression #12;! · Need to fit non-linear functions · example: polynomials Non-linear regression y = 6 10000 x5 82 10000 x) the dimension of the inputs · After applying z(x), we fit a plane in 3D-space Non-linear regression y = 0 + 1x
Using Enhanced Spherical Images for Object Representation
Smith, David A.
1979-05-01T23:59:59.000Z
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, ...
accretionary forced regressive: Topics by E-print Network
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
is to propose a new classification and regression method for challenging highdimensional data. The proposed new technique casts classification problems (class labels as output)...
acute behavioral regression: Topics by E-print Network
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
is to propose a new classification and regression method for challenging highdimensional data. The proposed new technique casts classification problems (class labels as output)...
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, 1967 Peter Woit (Columbia University) Quantum Mechanics and Representation Theory November 2013 2 / 30
Context Representation for Web Search Results
Baeza-Yates, Ricardo
Context Representation for Web Search Results Jesús 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
IAI : Knowledge Representation John A. Bullinaria, 2005
Bullinaria, John
is a Knowledge Representation? 3. Requirements of a Knowledge Representation 4. Practical Aspects of Good5-5 Practical Aspects of Good Representations In practice, the theoretical requirements for good Dictionary provides as good a definition as any: knowledge, nolij, n. assured belief; that which is known
T-700:Red Hat: kernel security, bug fix, and enhancement update...
Broader source: Energy.gov (indexed) [DOE]
processes to gather confidential information, such as the length of a password used in a process. (CVE-2011-2495, Low) Impact: Bug 690028 - CVE-2011-1182 kernel signal spoofing...
Scalable SMT-Based Verification of GPU Kernel Functions School of Computing, University of Utah
Capecchi, Mario R.
Scalable SMT-Based Verification of GPU Kernel Functions Guodong Li School of Computing, University Satisfiability Modulo Theo- ries (SMT) tools, detecting bugs such as data races, in- correctly synchronized Satisfiabil- ity Modulo Theories (SMT [2
System Response Kernel Calculation for List-mode Reconstruction in Strip PET Detector
Bia?as, P; Strzelecki, A; Bednarski, T; Czerwi?ski, E; Kap?on, ?; Kochanowski, A; Korcyl, G; Kowalski, P; Kozik, T; Krzemie?, W; Molenda, M; Moskal, P; Nied?wiecki, Sz; Pa?ka, M; Pawlik, M; Raczy?ski, L; Rudy, Z; Salabura, P; Sharma, N G; Silarski, M; S?omski, A; Smyrski, J; Wi?licki, W; Zieli?ski, M
2013-01-01T23:59:59.000Z
Reconstruction of the image in Positron Emission Tomographs (PET) requires the knowledge of the system response kernel which describes the contribution of each pixel (voxel) to each tube of response (TOR). This is especially important in list-mode reconstruction systems, where an efficient analytical approximation of such function is required. In this contribution, we present a derivation of the system response kernel for a novel 2D strip PET.
The architecture of a plug-and-play kernel for oilfield software applications
Ward, V.L.; Seaton, C.P. [Schlumberger Dowell, Tulsa, OK (United States)
1996-12-01T23:59:59.000Z
It is now common practice for engineers to use PC software to design and evaluate oilfield services. Rapidly changing technology in PC software has made it necessary for organizations to release new applications quickly to remain competitive. The authors designed a plug-and-play kernel for the computer aided design and evaluation (CADE) applications to reduce development time and time to market. The paper discusses the kernel used in the CADE software in detail.
The effect of stress cracked and broken corn kernels on alkaline processing losses
Jackson, David Scott
1986-01-01T23:59:59.000Z
(~. 70, P&. 06). There were significant differences in COD and DML (KRATIO= 100) between highly damaged corn and the less damaged counterpart of the same hybrid. Stress cracked corn, however, only slightly increased COD and DML. The ease of pericarp... Sigruficance of Com and Cooking Parameters . . . LIST OF FIGURES Page Stress Crack, Pericarp, and Broken Kernel Damage of Corn . . Flow Chart of Procedutes and Differences Between Cook Methods I and H 21 24 Correlation between Thousand Kernel Weight...
Grid polygons from permutations and their enumeration by the kernel method
Toufik Mansour; Simone Severini
2006-03-09T23:59:59.000Z
A grid polygon is a polygon whose vertices are points of a grid. We define an injective map between permutations of length n and a subset of grid polygons on n vertices, which we call consecutive-minima polygons. By the kernel method, we enumerate sets of permutations whose consecutive-minima polygons satisfy specific geometric conditions. We deal with 2-variate and 3-variate generating functions involving derivatives, cases which are not routinely solved by the kernel method.
Temporal Representation in Semantic Graphs
Levandoski, J J; Abdulla, G M
2007-08-07T23:59:59.000Z
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.
Asymptotic Equivalence and Adaptive Estimation for Robust Nonparametric Regression
Zhou, Harrison Huibin
Asymptotic Equivalence and Adaptive Estimation for Robust Nonparametric Regression T. Tony Cai1 and Harrison H. Zhou2 University of Pennsylvania and Yale University Abstract Asymptotic equivalence theory. In this paper we develop asymptotic equivalence results for robust nonparametric regression with unbounded loss
A Library for Locally Weighted Projection Regression --Supplementary Documentation --
problems: · The function to be learnt is non-linear. Otherwise having multiple local models is a waste of resources, and you should rather use ordinary linear regression, or partial least squares (PLS) for the caseA Library for Locally Weighted Projection Regression -- Supplementary Documentation -- Stefan
Model selection and estimation of a component in additive regression
Paris-Sud XI, Université de
on s and is based on non-asymptotic model selection methods. Given some linear spaces collection {Sm, m M}, we proposed and, among them, a widely used is the linear regression Z = µ + k i=1 iX(i) + (2) where µ;drawback of linear regression is its lack of flexibility for modeling more complex dependencies between Z
A DISTRIBUTION WITH GIVEN MARGINALS AND GIVEN REGRESSION CURVE
Cuadras, Carles M.
; and the possibility of using this construction to test nonlinear regression procedures and methods of estimation; mixture of distributions; nonlinear regression; extremal correla tions. AMS(1991) subject classification this construction to the nonlinear case. If ' is a monotone nonlinear function, satisfying some restrictions (e
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
FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS
Keller, Arturo A.
resources resulting in water stress. Effective water management a solution Supply side management Demand side management #12;Developing a regression equation based on cluster analysis for forecasting waterFORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS by Bruce Bishop Professor of Civil
SPAN-4. A Point-Kernel Shield Evaluation Code
Wallace, O.J. [Bettis Atomic Power Lab., West Mifflin, PA, (United States)
1992-03-16T23:59:59.000Z
SPAN4 calculates the fast neutron dose rate, thermal neutron flux, gamma-ray flux, dose rate, and energy-absorption rate in rectangular, cylindrical, and spherical geometries by integrating appropriate exponential kernels over a source distribution. The shield configuration is flexible, a first-level shield mesh, using any one of the three geometries, is specified. Regions of this same geometry or of other geometries, having their own (finer) meshes, may then be embedded between the first-level mesh lines, defining second-level shield meshes. This process is telescopic, third-level shield meshes may be embedded between second-level meshlines in turn. All meshes may have variable spacing. Sources and detectors may be located arbitrarily with respect to any shield mesh. The source is defined by the function: s=s0+s1(a)*s2(b)*s3(c)+s4(a,b)*s3(c)+s5(a,c)*s2(b)+s6(b,c) *s1(a)+s7(a,b,c), where a, b, and c represent coordinates. If any factor is missing, the corresponding terms are zero.
purposes, some conventional spectral clustering techniques are also considered, namely, kernel k- means and min-cuts. Also, standard k-means. The normalized mutual information and adjusted random index metrics Mellon University, as well as to a synthetic example, namely three moving Gaussian clouds. For comparison
Luttman, A.
2012-10-08T23:59:59.000Z
This slide-show discusses the use of the Local Polynomial Approximation (LPA) to smooth signals from photonic Doppler velocimetry (PDV) applying a generalized Peano kernel theorem.
Topics in ordinal logistic regression and its applications
Kim, Hyun Sun
2004-11-15T23:59:59.000Z
Sample size calculation methods for ordinal logistic regression are proposed to test statistical hypotheses. The author was motivated to do this work by the need for statistical analysis of the red imported ?re ants data. The proposed methods...
ON AUTOMORPHY OF CERTAIN GALOIS REPRESENTATIONS OF ...
2015-02-09T23:59:59.000Z
22. 1991 Mathematics Subject Classification. Primary 14F30,14L05. Key words and phrases. Galois representations, automorphy. This materials is based upon ...
Knowledge and Skill Representations for Robotized Production
Malec, Jacek
: Model-based control, Knowledge representation, System architectures, Autonomous control, Industrial robots 1. INTRODUCTION Model-based systems in control are a means to utilize effi- ciently human
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)
2014-11-15T23:59:59.000Z
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.
Scattering in flatland: Efficient representations via wave atoms
Peraire, Jaime
2009 Abstract This paper presents a numerical compression strategy for the boundary integral equation of acoustic scattering in two dimensions. These equations have oscillatory kernels that we represent of the kernel of the double-layer potential for this scatterer, sampled as a 1024x1024 matrix. Right: a zoomed
dos Santos, Pedro G.
2012-12-31T23:59:59.000Z
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...
Lindemer, Terrence [Harbach Engineering and Solutions] [Harbach Engineering and Solutions; Voit, Stewart L [ORNL] [ORNL; Silva, Chinthaka M [ORNL] [ORNL; Besmann, Theodore M [ORNL] [ORNL; Hunt, Rodney Dale [ORNL] [ORNL
2014-01-01T23:59:59.000Z
The U.S. Department of Energy is considering a new nuclear fuel that would be less susceptible to ruptures during a loss-of-coolant accident. The fuel would consist of tristructural isotropic coated particles with large, dense uranium nitride (UN) kernels. This effort explores many factors involved in using gel-derived uranium oxide-carbon microspheres to make large UN kernels. Analysis of recent studies with sufficient experimental details is provided. Extensive thermodynamic calculations are used to predict carbon monoxide and other pressures for several different reactions that may be involved in conversion of uranium oxides and carbides to UN. Experimentally, the method for making the gel-derived microspheres is described. These were used in a microbalance with an attached mass spectrometer to determine details of carbothermic conversion in argon, nitrogen, or vacuum. A quantitative model is derived from experiments for vacuum conversion to an uranium oxide-carbide kernel.
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
Rational Univariate Representation 1 Stickelberger's Theorem
Verschelde, Jan
(RUR) 2 The Elbow Manipulator a spatial robot arm with three links 3 Application of the Newton a rational univariate representation (RUR) 2 The Elbow Manipulator a spatial robot arm with three links 3 a rational univariate representation (RUR) 2 The Elbow Manipulator a spatial robot arm with three links 3
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
Representations of Petri net interactions Pawel Sobocinski
Sobocinski, Pawel
Representations of Petri net interactions Pawel SobociÂ´nski ECS, University of Southampton, UK Abstract. We introduce a novel compositional algebra of Petri nets, as well as a stateful extension In part owing to their intuitive graphical representation, Petri nets [28] are of- ten used both
DNA Motif Representation with Nucleotide Dependency
Chin, Francis Y.L.
DNA Motif Representation with Nucleotide Dependency Francis Chin1 and Henry Leung1 1 Department, these representations cannot model biological binding sites well because they fail to capture nucleotide interdependence. It has been pointed out by many researchers that the nucleotides of the DNA binding site cannot
The congruence kernel of an arithmetic lattice in a rank one algebraic group over a local field
Zalesskii, Pavel
is finite. Otherwise the CSP has an essentially negative answer. The principal result in [S1] is that-called congruence subgroup problem or CSP, has attracted a great deal of attention since the 19th century-)congruence kernel of G. In his terminology [S1] the CSP for this group has an affirmative answer if this kernel
Debray, Saumya
-purpose operating systems on embedded platforms. The problem is complicated by the fact that kernel code tends imple- mentation of our ideas on an Intel x86 platform, applied to a Linux kernel that has been will typically not have a mouse interface); at the software end, they usually support a fixed set of applications
Likas, Aristidis
Abstract-- Kernel k-means is an extension of the standard k- means clustering algorithm associated with this method, in this work we propose the global kernel k-means algorithm, a deterministic a global search proce- dure consisting of several executions of kernel k-means from suitable
Optimized data fusion for kernel k-means clustering Shi Yu, L´eon-Charles Tranchevent, Xinhai Liu, Wolfgang Abstract--This paper presents a novel optimized kernel k-means algo- rithm (OKKC) to combine multiple data squares support vector machine 1 INTRODUCTION We present a novel optimized kernel k-means clustering (OKKC
Shell Element Verification & Regression Problems for DYNA3D
Zywicz, E
2008-02-01T23:59:59.000Z
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.
IEEE TRANSACTIONS ON NEURAL NETWORKS 1 Online Kernel-based Learning
an application in task-space tracking control of redundant robots possible. The model parametrization furtherIEEE TRANSACTIONS ON NEURAL NETWORKS 1 Online Kernel-based Learning for Task-Space Tracking Robot Control Duy Nguyen-Tuong, Jan Peters Abstract--Task-space control of redundant robot systems based
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
Path Integral Control by Reproducing Kernel Hilbert Space Embedding Konrad Rawlik
Vijayakumar, Sethu
of stochastic optimal control problems, of the so called path integral form, into reproducing kernel Hilbert efficiency, are provided. 1 Introduction While solving general non-linear stochastic optimal con- trol (SOC new sam- ples [Theodorou et al., 2010; 2009]. Additionally, the ap- proach remains model
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 Cookeville, TN 38505, USA hexb@tntech.edu Abstract In this paper we introduce SPEK (Storage Performance storage systems at block level. It can be used for both DAS (Direct Attached Storage) and block level
Selection and Properties of Alternative Forming Fluids for TRISO Fuel Kernel Production
Doug Marshall; M. Baker; J. King; B. Gorman
2013-01-01T23:59:59.000Z
Current Very High Temperature Reactor (VHTR) designs incorporate TRi-structural ISOtropic (TRISO) fuel, which consists of a spherical fissile fuel kernel surrounded by layers of pyrolytic carbon and silicon carbide. An internal sol-gel process forms the fuel kernel using wet chemistry to produce uranium oxyhydroxide gel spheres by dropping a cold precursor solution into a hot column of trichloroethylene (TCE). Over time, gelation byproducts inhibit complete gelation, and the TCE must be purified or discarded. The resulting TCE waste stream contains both radioactive and hazardous materials and is thus considered a mixed hazardous waste. Changing the forming fluid to a non-hazardous alternative could greatly improve the economics of TRISO fuel kernel production. Selection criteria for a replacement forming fluid narrowed a list of ~10,800 chemicals to yield ten potential replacement forming fluids: 1-bromododecane, 1- bromotetradecane, 1-bromoundecane, 1-chlorooctadecane, 1-chlorotetradecane, 1-iododecane, 1-iodododecane, 1-iodohexadecane, 1-iodooctadecane, and squalane. The density, viscosity, and surface tension for each potential replacement forming fluid were measured as a function of temperature between 25 °C and 80 °C. Calculated settling velocities and heat transfer rates give an overall column height approximation. 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane show the greatest promise as replacements, and future tests will verify their ability to form satisfactory fuel kernels.
Viability Kernel for Ecosystem Management Models Eladio Oca~na Anaya
Paris-Sud XI, Université de
Viability Kernel for Ecosystem Management Models Eladio Oca~na Anaya Michel De Lara Ricardo task in general. We study the viability of nonlinear generic ecosystem models under preservation in the Peruvian upwelling ecosystem. Key words: control theory; state constraints; viability; predator
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
IEEE SIGNAL PROCESSING MAGAZINE 2013 1 Kernel Multivariate Analysis Framework for
Camps-Valls, Gustavo
IEEE SIGNAL PROCESSING MAGAZINE 2013 1 Kernel Multivariate Analysis Framework for Supervised in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform Correlation Analysis (CCA) and Orthonormalized PLS (OPLS), as well as their non- linear extensions derived
Global Heat Kernel Estimate for Relativistic Stable Processes in Exterior Open Sets
Chen, Zhen-Qing
,1 exterior open sets as well as for half-space-like open sets. The ideas of [8] have been adaptedGlobal Heat Kernel Estimate for Relativistic Stable Processes in Exterior Open Sets Zhen-Qing Chen for the transition densities of relativistic -stable processes with mass m (0, 1] in C1,1 exterior open sets
Definition RX Evaluate Kernels K-2d K-1d Change By definition undefined
Theiler, James
Definition RX Evaluate Kernels K-2d K-1d Change By def·i·ni·tion undefined Adventures in anomaly Alamos National Laboratory Research supported by the United States Department of Energy through the Los Alamos Laboratory Directed Research and Development (LDRD) Program. #12;Theiler LA-UR-14-24429 Definition
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
IEEE SIGNAL PROCESSING LETTER, VOL. , NO. , 2008 1 Image interpolation by blending kernels
interpolation kernel function cannot yield high quality high resolution(HR for short) images in practice, since before being used to interpolate the low resolution(LR for short) image. The main problem is that if we of values at control points to a spline function. This method is also used in practice currently
Using a Secure Java Micro-kernel on Embedded Devices for the Reliable Execution of
Binder, Walter
Using a Secure Java Micro-kernel on Embedded Devices for the Reliable Execution of Dynamically Uploaded Applications Walter Binder and Bal´azs Lichtl CoCo Software Engineering GmbH Margaretenstr. 22 applications. Mobile code is used for application upload, as well as for remote configuration and maintenance
Unified Convolutional/Turbo Decoder Architecture Design Based on Triple-Mode MAP/VA Kernel
Hung, Shih-Hao
Unified Convolutional/Turbo Decoder Architecture Design Based on Triple-Mode MAP/VA Kernel Fan convolutional/ turbo decoder design. According to the triple-mode MAP/VA timing chart and by merging some similar modules in both the Viterbi decoder and the log-MAP turbo code decoder, we build one unified
Shi, Tao
Abstract Polar Cloud Detection using Satellite Data with Analysis and Application of Kernel Professor Bin Yu, Chair Clouds play a major role in Earth's climate and cloud detection is a crucial step climate model studies. Cloud detection is particularly difficult in the snow- and ice-covered polar
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
EXTENSIONS OF LINKING SYSTEMS WITH p-GROUP KERNEL BOB OLIVER AND JOANA VENTURA
Ventura, Joana
EXTENSIONS OF LINKING SYSTEMS WITH p-GROUP KERNEL BOB OLIVER AND JOANA VENTURA Abstract. We study. Oliver is partially supported by UMR 7539 of the CNRS. J. Ventura is partially supported by FCT. #12;2 BOB OLIVER AND JOANA VENTURA enother prolem is tht in generlD when ev is linking system nd A g
EXTENSIONS OF LINKING SYSTEMS WITH p-GROUP KERNEL BOB OLIVER AND JOANA VENTURA
Oliver, Bob
AND JOANA VENTURA Abstract. We study extensions of p-local finite groups where the kernel i* *s of the CNRS. J. Ventura is partially supported by FCT/POCTI/FEDER and grant PDCT/MAT/58497* */2004. Both;2 BOB OLIVER AND JOANA VENTURA Another problem is that in general, when eLis a linking system and A C e
Frequent Pattern Mining for Kernel Trace Data Christopher LaRosa, Li Xiong, Ken Mandelberg
Xiong, Li
, application programmers, operating systems engineers, and security analysts. In the systems area, data miningFrequent Pattern Mining for Kernel Trace Data Christopher LaRosa, Li Xiong, Ken Mandelberg patterns and other recurring runtime execution patterns in operating system trace logs, we employ data
Hu, Yaozhong; Nualart, David
2009-11-09T23:59:59.000Z
argument can be made easily by approximating the Dirac delta function by the heat kernel p"(x) = 1p 2pi" e ?x2/2" as " tends to zero. That is, Gt(h) is the limit in L2(?) as " tends to zero of G"t (h) =?2 ? t 0 ? v 0 #0; p"(Bv ? Bu + h) + p"(Bv ? Bu ? h)? 2...p"(Bv ? Bu) #1; dudv. (2.6) Applying Clark-Ocone formula we can derive the following stochastic integral representation for Gt(h). Proposition 2. The random variable Gt(h) defined in (1.3) can be expressed as Gt(h) = E(Gt(h)) + ? t 0 ut...
Rothermel, Gregg
for Regression Test Selection Mary Jean Harrold 1 David Rosenblum 2 Gregg Rothermel 3 Elaine Weyuker 4 Abstract Regression testing is an important activity that can account for a large proportion of the cost of software maintenance. One approach to reducing the cost of regression testing is to employ a selective regression
West, Mike
classification, validation, prognosis Binary regression models · Linear regression model based on regression Standard statistical models transform from real-value to (0, 1) using a specified non-linear functionStatistics & Gene Expression Data Analysis Note 8: Binary Regression Outcomes and classification
arXiv:submit/0910499[stat.ML]11Feb2014 Online Nonparametric Regression
Rakhlin, Alexander "Sasha"
learning with squared loss and online nonparametric regression are the same. In addition to a non experts and for online linear regression. 1 Introduction Within the online regression framework, data (x1, starting with the paper of Foster [8], has been almost exclusively on finite-dimensional linear regression
A note on wavelet estimation of the derivatives of a regression function in a
Paris-Sud XI, Université de
A note on wavelet estimation of the derivatives of a regression function in a random design setting of the derivatives of a regression function in the nonparametric regression model with random design. New wavelet. Keywords and phrases: Nonparametric regression, Derivatives function estimation, Wavelets, Besov balls
Davis JE, Eddy MJ, Sutton TM, Altomari TJ
2007-03-01T23:59:59.000Z
Solid modeling computer software systems provide for the design of three-dimensional solid models used in the design and analysis of physical components. The current state-of-the-art in solid modeling representation uses a boundary representation format in which geometry and topology are used to form three-dimensional boundaries of the solid. The geometry representation used in these systems is cubic B-spline curves and surfaces--a network of cubic B-spline functions in three-dimensional Cartesian coordinate space. Many Monte Carlo codes, however, use a geometry representation in which geometry units are specified by intersections and unions of half-spaces. This paper describes an algorithm for converting from a boundary representation to a half-space representation.
Mental Representations Formed From Educational Website Formats
Elizabeth T. Cady; Kimberly R. Raddatz; Tuan Q. Tran; Bernardo de la Garza; Peter D. Elgin
2006-10-01T23:59:59.000Z
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.
Evaluating Hydrology Preservation of Simplified Terrain Representations
Varela, Carlos
Evaluating Hydrology Preservation of Simplified Terrain Representations Ph. D. Student: Christopher captures the hydrology is important for determining the effectiveness of a terrain simplification technique also present a novel ter- rain simplification algorithm based on the compression of hydrology features
Evaluating Hydrology Preservation of Simplified Terrain Representations
Franklin, W. Randolph
Evaluating Hydrology Preservation of Simplified Terrain Representations Jonathan Muckella , Marcus network. A quan- titative measurement of how accurately a drainage network captures the hydrology to preserve the important hydrology features. This method and other simplification schemes are then evaluated
Towards improving phenotype representation in OWL
Loebe, Frank; Stumpf, Frank; Hoehndorf, Robert; Herre, Heinrich
2012-09-21T23:59:59.000Z
PROCEEDINGS Open Access Towards improving phenotype representation in OWL Frank Loebe1*, Frank Stumpf1, Robert Hoehndorf2, Heinrich Herre3 From Ontologies in Biomedicine and Life Sciences (OBML 2011) Berlin, Germany. 6-7 October 2011...
Text representations in digital hypermedia library systems
Lokken, Sveinung Taraldsrud
1993-01-01T23:59:59.000Z
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...
Harmonic Representation of Combinations and Partitions
Michalis Psimopoulos
2011-03-01T23:59:59.000Z
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.
QUASI-REPRESENTATIONS OF SURFACE GROUPS 1 ...
2012-07-13T23:59:59.000Z
C?-algebra. 1. Introduction. Let G be a discrete countable group. In [3,4] the ... conjecture, a unital finite dimensional representation ?: C?(G) ? Mr(C) induces ...
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
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
Storage Device Performance Prediction with Selective Bagging Classification and Regression
Paris-Sud XI, Université de
Storage Device Performance Prediction with Selective Bagging Classification and Regression Tree Lei}@eng.wayne.edu, cheneh@ustc.edu.cn Abstract. Storage device performance prediction is a key element of self-managed storage systems and application planning tasks, such as data assignment and configuration. Based
1Machine Learning, to appear. Least-Squares Independence Regression
Sugiyama, Masashi
1Machine Learning, to appear. Least-Squares Independence Regression for Non-Linear Causal Inference of Technology, Japan. sesejun@cs.titech.ac.jp Abstract The discovery of non-linear causal relationship under Causal inference, Non-Linear, Non-Gaussian, Squared-loss mutual information, Least-Squares Independence
Efficient Locally Weighted Polynomial Regression Predictions Andrew W. Moore
Schneider, Jeff
polynomial regression (LWPR) is a popular instancebased al gorithm for learning continuous nonlinear gorithm for learning continuous nonlinear mappings from realvalued input vectors to realvalued output vectors. It is particularly appropriate for learning com plex highly nonlinear functions of up to about
Bayesian Regularization and Model Choice in Structured Additive Regression
Gerkmann, Ralf
on the regression coefficients with an inno- vative multiplicative parameter expansion that induces desirable shrinkage properties. This thesis points out a possible reason why previous attempts at extending SSVS with locally varying exponential-gamma distributed variances for the differences of the P-spline coefficients
Worldwide Oil Production Michaelis-Menten Kinetics Correlation and Regression
Watkins, Joseph C.
Michaelis-Menten Kinetics Worldwide Oil Production Example. The modern history of petroleum began in the 19Worldwide 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
Breakdown points of Cauchy regression-scale estimators Ivan Mizera
Mizera, Ivan
@stat.ualberta.ca. This work was supported in part by the National Sciences and Engineering Research Council of Canada. 2 of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, T6G 2G1, Canada. Email: mizeraBreakdown points of Cauchy regression-scale estimators Ivan Mizera University of Alberta1
Adaptive matrix distances aiming at optimum regression subspaces
Blanz, Volker
with distances of the associated target space. The formalism of multivariate subspace regression (MSR) is based space, such as linear discriminant analysis (LDA) for dis- crete class labels [3], generalized linear.strickert@uni-siegen.de Abstract. A new supervised adaptive metric approach is introduced for mapping an input vector space
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Swan II, J. Edward
multivariate visual analysis. The current work features an expanded version of MDX that builds on recentA Visual Analytics Approach for Correlation, Classification, and Regression Analysis Chad A. Steeda, Mississippi State University, Stennis Space Center, MS, 39529; cDepartment of Computer Science and Engineering
Polynomial regression with derivative information in nuclear reactor uncertainty quantification*
Anitescu, Mihai
1 Polynomial regression with derivative information in nuclear reactor uncertainty quantification in the outputs. The usual difficulties in modeling the work of the nuclear reactor models include the large size, Argonne National Laboratory, Argonne, IL, USA b Nuclear Engineering Division, Argonne National Laboratory
Bootstrap Tests for Overidentification in Linear Regression Models
Spino, Claude
Bootstrap Tests for Overidentification in Linear Regression Models Russell Davidson Department it impossible to perform reliable inference near the point at which the limit is ill-defined. Several bootstrap are not too weak. We also study the power properties of the bootstrap tests. JEL codes: C10, C12, C15, C30
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
Regression and Causation: A Critical Examination of Six Econometrics Textbooks
California at Los Angeles, University of
Regression and Causation: A Critical Examination of Six Econometrics Textbooks Bryant Chen-1596, USA (310) 825-3243 September 10, 2013 Abstract This report surveys six influential econometric acceptance of the causal content of econometric equations and, uniformly, fail to provide coherent
Representations up to homotopy of Lie algebroids
Abad, Camilo Arias
2009-01-01T23:59:59.000Z
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...
Comparing Single and Multiple Turbine Representations in a Wind Farm Simulation: Preprint
Muljadi, E.; Parsons, B.
2006-03-01T23:59:59.000Z
This paper compares single turbine representation versus multiple turbine representation in a wind farm simulation.
INDECOMPOSABLE REPRESENTATIONS FOR REAL ROOTS OF A WILD QUIVER
Su, Xiuping
INDECOMPOSABLE REPRESENTATIONS FOR REAL ROOTS OF A WILD QUIVER BERNT TORE JENSEN AND XIUPING SU wild quiver. We define operations which act on representations of this quiver, and using representation and the generic representation coincide. We will see that for wild quivers, the situation can
PROPERTIES OF A SOLAR FLARE KERNEL OBSERVED BY HINODE AND SDO
Young, P. R. [College of Science, George Mason University, 4400 University Drive, Fairfax, VA 22030 (United States)] [College of Science, George Mason University, 4400 University Drive, Fairfax, VA 22030 (United States); Doschek, G. A.; Warren, H. P. [Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, DC 20375 (United States)] [Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, DC 20375 (United States); Hara, H. [National Astronomical Observatory of Japan/NINS, 2-21-1 Osawa, Mitaka, Tokyo 181-8588 (Japan)] [National Astronomical Observatory of Japan/NINS, 2-21-1 Osawa, Mitaka, Tokyo 181-8588 (Japan)
2013-04-01T23:59:59.000Z
Flare kernels are compact features located in the solar chromosphere that are the sites of rapid heating and plasma upflow during the rise phase of flares. An example is presented from a M1.1 class flare in active region AR 11158 observed on 2011 February 16 07:44 UT for which the location of the upflow region seen by EUV Imaging Spectrometer (EIS) can be precisely aligned to high spatial resolution images obtained by the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). A string of bright flare kernels is found to be aligned with a ridge of strong magnetic field, and one kernel site is highlighted for which an upflow speed of Almost-Equal-To 400 km s{sup -1} is measured in lines formed at 10-30 MK. The line-of-sight magnetic field strength at this location is Almost-Equal-To 1000 G. Emission over a continuous range of temperatures down to the chromosphere is found, and the kernels have a similar morphology at all temperatures and are spatially coincident with sizes at the resolution limit of the AIA instrument ({approx}<400 km). For temperatures of 0.3-3.0 MK the EIS emission lines show multiple velocity components, with the dominant component becoming more blueshifted with temperature from a redshift of 35 km s{sup -1} at 0.3 MK to a blueshift of 60 km s{sup -1} at 3.0 MK. Emission lines from 1.5-3.0 MK show a weak redshifted component at around 60-70 km s{sup -1} implying multi-directional flows at the kernel site. Significant non-thermal broadening corresponding to velocities of Almost-Equal-To 120 km s{sup -1} is found at 10-30 MK, and the electron density in the kernel, measured at 2 MK, is 3.4 Multiplication-Sign 10{sup 10} cm{sup -3}. Finally, the Fe XXIV {lambda}192.03/{lambda}255.11 ratio suggests that the EIS calibration has changed since launch, with the long wavelength channel less sensitive than the short wavelength channel by around a factor two.
Kwok, James Tin-Yau
-Wing Mak, Member, IEEE, Roger Wend-Huu Hsiao, Student Member, IEEE, Simon Ka-Lung Ho, and James T. Kwok using kernel principal component analysis. A new speaker model is then constructed as a linear
Boyer, Edmond
Kernel for a Semantic Learning Platform with adapted suggestions Ioan SZILAGYI, Radu BALOG {ioan.szilagyi; radu.balog-crisan; ioan.roxin}@univ-fcomte.fr Abstract -- In the context of personalized
Latest Jurassic-early Cretaceous regressive facies, northeast Africa craton
van Houten, F.B.
1980-06-01T23:59:59.000Z
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.
Natural geometric representation for electron local observables
Minogin, V.G., E-mail: minogin@isan.troitsk.ru
2014-03-15T23:59:59.000Z
An existence of the quartic identities for the electron local observables that define orthogonality relations for the 3D quantities quadratic in the electron observables is found. It is shown that the joint solution of the quartic and bilinear identities for the electron observables defines a unique natural representation of the observables. In the natural representation the vector type electron local observables have well-defined fixed positions with respect to a local 3D orthogonal reference frame. It is shown that the natural representation of the electron local observables can be defined in six different forms depending on a choice of the orthogonal unit vectors. The natural representation is used to determine the functional dependence of the electron wave functions on the local observables valid for any shape of the electron wave packet. -- Highlights: •Quartic identities that define the orthogonality relations for the electron local observables are found. •Joint solution of quartic and bilinear identities defines a unique natural representation of the electron local observables. •Functional dependence of the electron wave functions on the electron local observables is determined.
Effect of Pre and Post-Harvest Treatments on Characteristics of ‘Pawnee’ Pecan Kernels
Mansur, Zainab J
2014-04-17T23:59:59.000Z
(Venkatachalam, 2004). Pecans are considered a healthy food because of the high monounsaturated fatty acid content (Villarreal-Lozoya et al., 2007) and high concentrations of phenolics, flavonoids, and proanthocyanidins, which are phytochemicals with strong...., 2004). Pecan kernels have a wide range of possible uses. They can be sold in shell or shelled, and used as a main ingredient for confectionery, dairy and bakery products. Other uses include: incorporation into snack bars in raw form, sweetening...
Poisson Regression Analysis of Illness and Injury Surveillance Data
Frome E.L., Watkins J.P., Ellis E.D.
2012-12-12T23:59:59.000Z
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.
Fission product release and survivability of UN-kernel LWR TRISO fuel
T. M. Besmann; M. K. Ferber; H.-T. Lin; B. P. Collin
2014-05-01T23:59:59.000Z
A thermomechanical assessment of the LWR application of TRISO fuel with UN kernels was performed. Fission product release under operational and transient temperature conditions was determined by extrapolation from fission product recoil calculations and limited data from irradiated UN pellets. Both fission recoil and diffusive release were considered and internal particle pressures computed for both 650 and 800 um diameter kernels as a function of buffer layer thickness. These pressures were used in conjunction with a finite element program to compute the radial and tangential stresses generated within a TRISO particle undergoing burnup. Creep and swelling of the inner and outer pyrolytic carbon layers were included in the analyses. A measure of reliability of the TRISO particle was obtained by computing the probability of survival of the SiC barrier layer and the maximum tensile stress generated in the pyrolytic carbon layers from internal pressure and thermomechanics of the layers. These reliability estimates were obtained as functions of the kernel diameter, buffer layer thickness, and pyrolytic carbon layer thickness. The value of the probability of survival at the end of irradiation was inversely proportional to the maximum pressure.
Phenomenological memory-kernel master equations and time-dependent Markovian processes
L. Mazzola; E. -M. Laine; H. -P. Breuer; S. Maniscalco; J. Piilo
2011-03-03T23:59:59.000Z
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.
Fast transform from an adaptive multi-wavelet representation to a partial Fourier representation
Jia, Jun [ORNL; Harrison, Robert J [ORNL; Fann, George I [ORNL
2010-01-01T23:59:59.000Z
We present a fast algorithm to compute the partial transformation of a function represented in an adaptive pseudo-spectral multi-wavelet representation to a partial Fourier representation. Such fast transformations are useful in many contexts in physics and engineering, where changes of representation from a piece wise polynomial basis to a Fourier basis. The algorithm is demonstrated for a Gaussian in one and in three dimensions. For 2D, we apply this approach to a Gaussian in a periodic domain. The accuracy and the performance of this method is compared with direct summation.
Photovoltaic Array Condition Monitoring Based on Online Regression of Performance Model
Teodorescu, Remus
Photovoltaic Array Condition Monitoring Based on Online Regression of Performance Model Sergiu Abstract -- Photovoltaic (PV) system performance can be degraded by a series of factors affecting the PV monitoring, fault detection, performance model, photovoltaic systems, regression analysis. I. INTRODUCTION
Galtchouk, Leonid
2008-01-01T23:59:59.000Z
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
2010-01-01T23:59:59.000Z
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
Solving three-body scattering problem in the momentum lattice representation
V. N. Pomerantsev; V. I. Kukulin; O. A. Rubtsova
2008-12-02T23:59:59.000Z
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.
Representation Issues in Neighborhood Search and
Whitley, Darrell
. Yet, we also know that no search method is better than another over all possible problems is better than any other over all possible discrete functions. Radcliffe and Surry (1995) extend these notions to also cover the idea that all representations are equivalent when their behavior is considered
ACQUIRED EQUIVALENCE CHANGES STIMULUS REPRESENTATIONS , D. SHOHAMY
Shohamy, Daphna
ACQUIRED EQUIVALENCE CHANGES STIMULUS REPRESENTATIONS M. MEETER 1 , D. SHOHAMY 2 , AND C.E. MYERS 3 UNIVERSITY 3 DEPT. OF PSYCHOLOGY, RUTGERS UNIVERSITY Acquired equivalence is a paradigm in which of feature salience. A different way of conceptualizing acquired equivalence is in terms of strategic
Theta Representations Of Odd Orthogonal Groups
Bump, Daniel
Theta Representations Of Odd Orthogonal Groups This is a report on three papers of Daniel Bump'ns of semisimple G \\Theta SLn were given in a uniform way by Kazhdan and Savin (1989) when G is simplylaced (all
FROM NORLUND MATRICES TO LAPLACE REPRESENTATIONS
Sinnamon, Gordon J.
(and not too large) on the line Re z = 0, the Laplace transform LF is just the (Poisson extension of the) Fourier transform of F. It is therefore appropriate to view the power series representation¨orlund matrices and corresponding convolution operators on the line. Analogous inequalities are proved for power
THE BREUILMEZARD CONJECTURE FOR POTENTIALLY BARSOTTITATE REPRESENTATIONS.
Kisin, Mark
THE BREUILM´EZARD CONJECTURE FOR POTENTIALLY BARSOTTITATE REPRESENTATIONS. TOBY GEE AND MARK, proving a variety of results including the BuzzardDiamondJarvis conjecture. Contents Overview. 2.3. Patching 14 3.4. Potential diagonalizability 18 3.5. Local results 20 4. The BuzzardDiamondJarvis
Issues in Temporal Representation of Multimedia Documents
Joseph Fourier Grenoble-I, Université
Issues in Temporal Representation of Multimedia Documents Nabil Layaïda OPERA project, INRIA Rhône the means of increasing the rich ness of information contained in electronic documents. One of the goals of the Opera team is designing an authoring environment for multimedia documents, called MADEUS, which meets
Neuronal Representations of Learning Sensorimotor Skills
of learning-related changes: additional controls and simulations....68-74 V. Viewing and Doing: SimilarNeuronal Representations of Learning Sensorimotor Skills Thesis submitted for the degree "Doctor.......................................................................................26-47 II. Preparatory Activity in Motor Cortex Reflects Learning of Local Visuomotor Skills
Bayes Nets Representation: joint distribution and conditional
Mitchell, Tom
Bayes Nets Representation: joint distribution and conditional independence Yi Zhang 10-701, Machine joint distribution of BNs Infer C. I. from factored joint distributions D-separation (motivation) 2 structure All about the joint distribution of variables ! Conditional independence assumptions are useful
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
Video Indexing Based on Mosaic Representations
Shapiro, Ehud
1 Video Indexing Based on Mosaic Representations Michal Irani P. Anandan Abstract| Video is a rich is implicitly buried inside the raw video data, and is provided with the cost of very high temporal redundancy. While the standard sequential form of video storage is ad- equate for viewing in a "movie mode
ModelicaXML A Modelica XML representation
Zhao, Yuxiao
>email@none.ro person> ... person job="manager"> Classified person> persons> #12;6November 04 for XML document validation person-job-attribute "job (programmer|manager) #REQUIREDModelicaXML A Modelica XML representation with Applications Adrian Pop, Peter Fritzson Programming
Sample size for logistic regression with small response probability
Whittemore, A S
1980-03-01T23:59:59.000Z
The Fisher information matrix for the estimated parameters in a multiple logistic regression can be approximated by the augmented Hessian matrix of the moment generating function for the covariates. The approximation is valid when the probability of response is small. With its use one can obtain a simple closed form estimate of the asymptotic covariance matrix of the maximum likelihood parameter estimates, and thus approximate sample sizes needed to test hypotheses about the parameters. The method is developed for selected distributions of a single covariate, and for a class of exponential-type distributions of several covariates. It is illustrated with an example concerning risk factors for coronary heart disease.
The Impact of Test Suite Granularity on the CostEffectiveness of Regression Testing
Rothermel, Gregg
The Impact of Test Suite Granularity on the CostÂEffectiveness of Regression Testing Gregg,pkallakug@cse.unl.edu ABSTRACT Regression testing is an expensive testing process used to validate software following modi#12;cations. The cost-e#11;ective- ness of regression testing techniques varies with characteris- tics of test
The Impact of Test Suite Granularity on the CostEffectiveness of Regression Testing
Rothermel, Gregg
The Impact of Test Suite Granularity on the CostÂEffectiveness of Regression Testing Gregg,pkallakug@cse.unl.edu ABSTRACT Regression testing is an expensive testing process used to validate software following modifications. The costÂeffectiveÂ ness of regression testing techniques varies with characterisÂ tics of test
The Impact of Test Suite Granularity on the Cost-Effectiveness of Regression Testing
Rothermel, Gregg
The Impact of Test Suite Granularity on the Cost-Effectiveness of Regression Testing Gregg,pkallakug@cse.unl.edu ABSTRACT Regression testing is an expensive testing process used to validate software following modi cations. The cost-e ective- ness of regression testing techniques varies with characteris- tics of test
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
Structure based chemical shift prediction using Random Forests non-linear regression
Langmead, Christopher James
Structure based chemical shift prediction using Random Forests non-linear regression K. Arun-ordinates will permit close study of this relationship. This paper presents a novel non-linear regression based ap, regression, Random Forests #12;Abstract Protein nuclear magnetic resonance (NMR) chemical shifts are among
COMPSTAT'2004 Symposium c Physica-Verlag/Springer 2004 ROBUST REGRESSION QUANTILES WITH
to robustly estimate linear regression quantiles with censored data. We adjust the estimator recently points. 1 Introduction We consider the linear regression setting, in which we have to model the re, the deepest regression estimator [5], which has been defined for non censored data. In Section 4 we
Non-Linear Continuum Regression Using Genetic Programming Ben.McKay@ncl.ac.uk
Fernandez, Thomas
Non-Linear Continuum Regression Using Genetic Programming Ben McKay Ben.McKay@ncl.ac.uk Mark Willis In this contribution, genetic programming is combined with continuum regression to produce two novel non-linear-based' strategy. Having discussed continuum regression, the modifications required to extend the algorithm for non-linear
Consistency of the posterior distribution and MLE for piecewise linear regression
Paris-Sud XI, Université de
Consistency of the posterior distribution and MLE for piecewise linear regression Tristan Launay1 and that of the Bayes estimator for a two-phase piecewise linear regression mdoel where the break-point is unknown and be the unknown regression coefficient of the non-zero phase. The observations X1:n = (X1, . . . , Xn) depend
Statistical prediction of aircraft trajectory: regression methods vs point-mass model
Paris-Sud XI, Université de
the altitude of climbing aircraft. In addition to the standard linear regression model, two common non-linear, BADA, linear regression, neural networks, Loess. INTRODUCTION Predicting aircraft trajectoriesStatistical prediction of aircraft trajectory: regression methods vs point-mass model M. Ghasemi
Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method
Cichocki, Andrzej
1 Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method Qibin Regression (PLSR) - a multivariate method which, in contrast to Multiple Linear Regression (MLR. A standard way to optimize the model parameters is the Non- linear Iterative Partial Least Squares (NIPALS
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 regression model extends several familiar non-linear time series models such as the exponential
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
Zander, Jessica Selene
2012-01-01T23:59:59.000Z
Narratives of Contamination: Representations of Race,Fall 2012 Narratives of Contamination: Representations ofAbstract Narratives of Contamination: Representations of
Representation of Energy Use in the Food Products Industry
Elliott, N. R.
2007-01-01T23:59:59.000Z
Traditional representations of energy in the manufacturing sector have tended to represent energy end-uses rather than actual energy service demands. While this representation if quite adequate for understanding how energy is used today...
Preservice teachers' knowledge of linear functions within multiple representation modes
You, Zhixia
2009-05-15T23:59:59.000Z
and algebraic representations; (3) flexibility within visual representations; (4) flexibility with real-life situations, and (5) procedural skills. In terms of pedagogical content knowledge, two aspects were examined across five corresponding components...
PRODUCT REPRESENTATION IN LIGHTWEIGHT FORMATS FOR PRODUCT LIFECYCLE MANAGEMENT (PLM)
Rzepa, Henry S.
PRODUCT REPRESENTATION IN LIGHTWEIGHT FORMATS FOR PRODUCT LIFECYCLE MANAGEMENT (PLM) Lian Ding environments and the entire product lifecycle. There are new requirements for product representations, including: platform/application independence, support for the product lifecycle, rapidly sharing information
Cyclic transgressive and regressive sequences, Paleocene Suite, Sirte basin, Libya
Abushagur, S.A.
1986-05-01T23:59:59.000Z
The Farrud lithofacies represent the main reservoir rock of the Ghani oil field and Western Concession Eleven of the Sirte basin, Libya. Eight microfacies are recognized in the Farrud lithofacies in the Ghani field area: (1) bryozoan-bioclastic (shallow, warm, normal marine shelf deposits); (2) micrite (suggesting quiet, low-energy conditions such as may have existed in a well-protected lagoon); (3) dasycladacean (very shallow, normal marine environment); (4) bioclastic (very shallow, normal marine environment with moderate to vigorous energy); (5) mgal (very shallow, normal marine environment in a shelf lagoon); (6) pelletal-skeletal (deposition within slightly agitated waters of a sheltered lagoon with restricted circulation); (7) dolomicrite (fenestrate structures indicating a high intertidal environment of deposition); and (8) anhydrite (supratidal environment). The Paleocene suite of the Farrud lithofacies generally shows a prograding, regressive sequence of three facies: (1) supratidal facies, characterized by nonfossiliferous anhydrite, dolomite, and dolomitic pelletal carbonate mudstone; (2) intertidal to very shallow subtidal facies, characterized by fossiliferous, pelletal, carbonate mudstone and skeletal calcarenite; and (3) subtidal facies, characterized by a skeletal, pelletal, carbonate mudstone. Source rocks were primarily organic-rich shales overlying the Farrud reservoir rock. Porosity and permeability were developed in part by such processes as dolomitization, leaching, and fracturing in the two progradational, regressive carbonate facies. Hydrocarbons were trapped by a supratidal, anhydrite cap rock.
Fractal Representation of Planar Shapes Wladyslaw Skarbek3
Vialatte, FranÃ§ois
Fractal Representation of Planar Shapes Wladyslaw Skarbek3 Frontier Research Program RIKEN, ABS of planar shapes, (e.g. characters in the given font) a fractal operator is de#12;ned which when applied on the fractal representation. Signi#12;cant compression of data volume was obtained for shape representation
Representation Theory, Geometry & Combinatorics Organizer: M. Haiman and N. Reshetikhin
Haiman, Mark D.
Representation Theory, Geometry & Combinatorics Seminar Organizer: M. Haiman and N. Reshetikhin course: Representation theory and the X-ray transform The X-ray transform (also called the Funk transform tools from complex analysis and the representation theory of Lie groups. Lecture 1: Differential
Anomaly-free representations of the holonomy-flux algebra
SangChul Yoon
2008-09-07T23:59:59.000Z
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.
Extremely local MR representations: Youngmi Hur1 & Amos Ron2
Maryland at College Park, University of
Extremely local MR representations: L-CAMP Youngmi Hur1 & Amos Ron2 Workshop on sparse representations: UMD, May 2005 1 Math, UW-Madison 2 CS, UW-Madison #12;Wavelet and framelet constructions History of all local MR representations #12;L-CAMP: Extremely local MR constructions Bird's view of the CAP
Hall, Sharon J.
Figure 3. Socioeconomics drive biomass too. Simple regression with untrans- formed variables. Solid line represents the estimated regression line, whereas the dashed lines represent the 95% confidence metropolitan area. I hypothesized that income is the driving factor of vegetation coverage, primarily affecting
The effect of artificial drying temperature on the quality of early harvested pecan kernels
McLean, Roy William
1988-01-01T23:59:59.000Z
process was complete, the pecans were shelled, sealed in 8303 cans under 27inHg vacuum, and stored at 0 F until analyses were performed. Sixty whole kernels were randomly selected from each sample, objectively evaluated for color and then cold pressed... harvesting entails shaking the tree after the husks have split and allowing them to dry naturally. Once the nuts 10 were dried, they were shelled and sealed in ()303 cans, sealed under vacuum and stored at 0 F until analyses were performed. Methods...
Animal representations and animal remains at Çatalhöyük
Russell, Nerissa; Meece, Stephanie
2006-01-01T23:59:59.000Z
(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...
Inverse operator representations of quantum phase
G. M. Saxena
2008-03-14T23:59:59.000Z
We define quantum phase in terms of inverses of annihilation and creation operators. We show that like Susskind - Glogower phase operators, the measured phase operators and the unitary phase operators can be defined in terms of the inverse operators. However, for the unitary phase operator the Hilbert space includes the negative energy states. The quantum phase in inverse operator representation may find the applications in the field of quantum optics particularly in the squeezed states.
Virasoro Representations on (Diff S1)/S1 Coadjoint Orbits
Washington Taylor IV
1992-04-28T23:59:59.000Z
A new set of realizations of the Virasoro algebra on a bosonic Fock space are found by explicitly computing the Virasoro representations associated with coadjoint orbits of the form (Diff S1) / S1. Some progress is made in understanding the unitary structure of these representations. The characters of these representations are exactly the bosonic partition functions calculated previously by Witten using perturbative and fixed-point methods. The representations corresponding to the discrete series of unitary Virasoro representations with c <= 1 are found to be reducible in this formulation, confirming a conjecture by Aldaya and Navarro-Salas.
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)
2013-01-01T23:59:59.000Z
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.
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)
2012-02-01T23:59:59.000Z
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.
Slatton, Clint
function (SDF) is proposed. The SDF is a well known 2- D correlation filter for object recognition. The proposed non- linear version of the SDF is derived from kernel-based lear- ning. The kernel SDF the performance of the linear SDF by incorporating the image's class higher order moments. We show that this ker
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
Support vector methods for survival analysis: a comparison between ranking and regression
techniques for the estimation of non-linear transformation models for the analysis of survival data. Methods is the use of non-linear kernels im- plementing automatically non-parametric effects of the covariates the advantage that they are easily extendable towards non-linear models without the need to check non-linearities
GPU Kernels for High-Speed 4-Bit Astrophysical Data Processing
Klages, Peter; Denman, Nolan; Recnik, Andre; Sievers, Jonathan; Vanderlinde, Keith
2015-01-01T23:59:59.000Z
Interferometric radio telescopes often rely on computationally expensive O(N^2) correlation calculations; fortunately these computations map well to massively parallel accelerators such as low-cost GPUs. This paper describes the OpenCL kernels developed for the GPU based X-engine of a new hybrid FX correlator. Channelized data from the F-engine is supplied to the GPUs as 4-bit, offset-encoded real and imaginary integers. Because of the low bit width of the data, two values may be packed into a 32-bit register, allowing multiplication and addition of more than one value with a single fused multiply-add instruction. With this data and calculation packing scheme, as many as 5.6 effective tera-operations per second (TOPS) can be executed on a 4.3 TOPS GPU. The kernel design allows correlations to scale to large numbers of input elements, limited only by maximum buffer sizes on the GPU. This code is currently working on-sky with the CHIME Pathfinder Correlator in BC, Canada.
Validi, AbdoulAhad, E-mail: validiab@msu.edu
2014-03-01T23:59:59.000Z
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.
Representations of groups of order 16
McCarthy, Edmond Robert
1966-01-01T23:59:59.000Z
, 16) K - {10, 14) The analysis al. so shows that G contains three subgroups H. of order 2; only one of which is normal. This is the i subgroup consisting of the elements (1, 5}. The factor group G/H is isomorphic to C 4 C2. Since the Cayley table..., 13 10, 14 11, 15 37 With this correspondence established we need only refer back to Table II, the character table of C 4 C2, to begin writing out representations of Group Six. For example, in Table II we find D&(3) = -1. If TK...
Real-time semiparametric regression BY J. LUTS1, T. BRODERICK2 AND M.P. WAND1
Wand, Matt
regression refers to a large class of regression models that provide for non-linear predictor effects using regression is quite broad and includes, as special cases, generalized linear mixed models, generalizedReal-time semiparametric regression BY J. LUTS1, T. BRODERICK2 AND M.P. WAND1 1 School
The Econometric Analysis of Interval-valued Data and Adaptive Regression Splines
Lin, Wei
2013-01-01T23:59:59.000Z
and Miller, D. (2000). Econometric Foundations. CambridgeRegression Models,” Econometric Reviews. Vol. 8, pp. 217-De- pendent Bootstrap,” Econometric Reviews. Vol. 23, pp.
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
BASHIR et al.: GAIT REPRESENTATION USING FLOW FIELDS 1 Gait Representation Using Flow Fields
Gong, Shaogang
the human body configuration (e.g. 2D/3D skeletons) and the model parameters estimated over time encode approaches such as Gait Energy Image (GEI) and Motion Silhouettes Image (MSI) capture only the motion inten unchanged freely in print or electronic forms. #12;2 BASHIR et al.: GAIT REPRESENTATION USING FLOW FIELDS
Fusion Algebras Induced by Representations of the Modular Group
W. Eholzer
1992-11-27T23:59:59.000Z
Using the representation theory of the subgroups SL_2(Z_p) of the modular group we investigate the induced fusion algebras in some simple examples. Only some of these representations lead to 'good' fusion algebras. Furthermore, the conformal dimensions and the central charge of the corresponding rational conformal field theories are calculated. Two series of representations which can be realized by unitary theories are presented. We show that most of the fusion algebras induced by admissible representations are realized in well known rational models.
Kernel polynomial method for a nonorthogonal electronic-structure calculation of amorphous diamond
Roeder, H.; Silver, R.N. [Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States); Drabold, D.A.; Dong, J.J. [Department of Physics and Astronomy, Condensed Matter and Surface Science Program, Ohio University, Athens, Ohio 45701 (United States)
1997-06-01T23:59:59.000Z
The Kernel polynomial method (KPM) has been successfully applied to tight-binding electronic-structure calculations as an O(N) method. Here we extend this method to nonorthogonal basis sets with a sparse overlap matrix {bold S} and a sparse Hamiltonian {bold H}. Since the KPM method utilizes matrix vector multiplications it is necessary to apply {bold S}{sup {minus}1}{bold H} onto a vector. The multiplication of {bold S}{sup {minus}1} is performed using a preconditioned conjugate-gradient method and does not involve the explicit inversion of {bold S}. Hence the method scales the same way as the original KPM method, i.e., O(N), although there is an overhead due to the additional conjugate-gradient part. We apply this method to a large scale electronic-structure calculation of amorphous diamond. {copyright} {ital 1997} {ital The American Physical Society}
Power and Torque Characteristics of Diesel Engine Fuelled by Palm-Kernel Oil Biodiesel
Oguntola J Alamu; Ezra A Adeleke; Nurudeen O. Adekunle; Salam O; Oguntola J Alamu; Ezra A Adeleke; Nurudeen O Adekunle; Salam O Ismaila
Short-term engine performance tests were carried out on test diesel engine fuelled with Palm kernel oil (PKO) biodiesel. The biodiesel fuel was produced through transesterification process using 100g PKO, 20.0 % ethanol (wt%), 1.0 % potassium hydroxide catalyst at 60°C reaction temperature and 90min. reaction time. The diesel engine was attached to a general electric dynamometer. Torque and power delivered by the engine were monitored throughout the 24-hour test duration at 1300, 1500, 1700, 2000, 2250 and 2500rpm. At all engine speeds tested, results showed that torque and power outputs for PKO biodiesel were generally lower than those for petroleum diesel. Also, Peak torque for PKO biodiesel occurred at a lower engine speed compared to diesel.
Collins, J.L.
2004-12-02T23:59:59.000Z
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.
Why You Should Care About Quantile Regression Augusto Born de Oliveira
Tomkins, Andrew
demonstrate the successful application of quantile regression, a recent development in statistics, to computer, with the additional benefit of be- ing applicable to data from any distribution. This property makes it especially Evaluation, ANOVA, Quantile Regression 1. Introduction Both academia and industry use performance evaluation
Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net
Low, Steven H.
. Furthermore, un- like the Lasso, the Elastic Net can yield a sparse esti- mate with more than n non-zero477 Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net Alexander to regression regulariza- tion called the Pairwise Elastic Net is pro- posed. Like the Elastic Net, it simultane
Combining Regression Trees and Radial Basis Function Networks Mark Orr, John Hallam,
Edinburgh, University of
a model using linear regression. The non-linear transformation is controlled by a set of m basis functions, 1988] transform the n- dimensional inputs non-linearly to an m-dimensional space and then estimate and radii and the second estimates the weights, fw j g m j=1 , of the linear regression model f(x) = m X j=1
Gas Plume Species Identification in LWIR Hyperspectral Imagery by Regression Analyses
Salvaggio, Carl
of the algorithm is a stepwise linear regression technique that only includes a basis vector in the model such as atmospheric compensation, gas absorption and emission, background modeling, and fitting a linear regression to a non-linear radiance model were addressed in order to generate the matrix of basis vectors. Synthetic
A regression model with a hidden logistic process for feature extraction from time series
Chamroukhi, Faicel
operation. The switch operations signals can be seen as time series presenting non-linearities and various changes in regime. Basic linear regression can not be adopted for this type of sig- nals because a constant linear relationship is not adapted. As alternative to linear regression, some authors use
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression Sham M) provide powerful generalizations of linear regression, where the target variable is assumed to be a (possibly unknown) 1-dimensional function of a linear predictor. In gen- eral, these problems entail non
Sufficient reductions in regressions with elliptically contoured1 inverse predictors2
Bura, Efstathia
for21 the regression of Y on X comprises of a linear and a non-linear component.22 1 Introduction23 There are two general approaches based on inverse regression for estimating the linear sufficient9 reductions with18 parameters (µY , ) and density gY , there is no linear non-trivial sufficient reduction except
Practical High Breakdown Regression David J. Olive and Douglas M. Hawkins
Olive, David
Practical High Breakdown Regression David J. Olive and Douglas M. Hawkins Southern Illinois breakdown n consistent regression es- timators exist. The response plot of the fitted values versus@umn.edu), School of Statistics, University of Minnesota, Minneapolis, MN 55455-0493, USA. Their work was supported
PD Dr. Martin Stetter, Siemens AG 1 Neuronale Verfahren zur Regression
Popeea, Corneliu - Chair for Foundations of Software Reliability and Theoretical Computer Science
PD Dr. Martin Stetter, Siemens AG 1 Neuronale Verfahren zur Regression · Lineares Modell · Vom-Netze Regression #12;PD Dr. Martin Stetter, Siemens AG 2 Das lineare Modell · Ausgangspunkt: Lineares Perceptron vorgegeben, werden nicht gelernt #12;PD Dr. Martin Stetter, Siemens AG 3 · Geschrieben als Regressionsmodell
Mohanty, Saraju P.
Fast Optimization of Nano-CMOS Voltage-Controlled Oscillator using Polynomial Regression in a current-starved 50nm voltage-controlled oscillator (VCO). Accurate polynomial-regression based models have-CMOS), Voltage-Controlled Oscillator (VCO). 1. Introduction Digital design exploration and optimization is highly
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
Regional regression models of watershed suspended-sediment discharge for the eastern United States
Vogel, Richard M.
: Sediment transport Regression Water quality Ungaged GAGES SPARROW s u m m a r y Estimates of mean annual Streamflow (GAGES) database. The resulting regional regression models summarized for major US water resources contaminants including pesticides, met- als, and polycyclic aromatic hydrocarbons (PAHs) readily sorb
Comparing Data Mining Methods with Logistic Regression in Childhood Obesity Prediction
Tjortjis, Christos
1 Comparing Data Mining Methods with Logistic Regression in Childhood Obesity Prediction Shaoyan young children at risk of obesity be identified from their early growth records?" Pilot work using logistic regression to predict overweight and obese children demonstrated relatively limited success. Hence
LR: Compact connectivity representation for triangle meshes
Gurung, T; Luffel, M; Lindstrom, P; Rossignac, J
2011-01-28T23:59:59.000Z
We propose LR (Laced Ring) - a simple data structure for representing the connectivity of manifold triangle meshes. LR provides the option to store on average either 1.08 references per triangle or 26.2 bits per triangle. Its construction, from an input mesh that supports constant-time adjacency queries, has linear space and time complexity, and involves ordering most vertices along a nearly-Hamiltonian cycle. LR is best suited for applications that process meshes with fixed connectivity, as any changes to the connectivity require the data structure to be rebuilt. We provide an implementation of the set of standard random-access, constant-time operators for traversing a mesh, and show that LR often saves both space and traversal time over competing representations.
Representations of the groups of order 24
Strange, John Billy
1967-01-01T23:59:59.000Z
'y 0 1 0 0 0 1 z z 1 0 0 x m g22 ~ ( x, y-x, z) -1 1 0 y = y-x 0 0 1 z z a( 1, -1, 0) 0 1 0 1 0 0 0 0 1 x y z z g3 (y? ) 24 1 -1 0 0 -1 0 0 0 1 z z g24- (x-X -V g. O 2W ~RI 3 g3 JR I a' i6 L t 5 y Jt 9 l3 Figure 3. Six... in the representation tables are the exponents of m in k cos kn/12 + i sin k&/12. CAYLEY TABLE FOR GROUP NUHBER 2 3 10 ll 3 11 12 1 2 12 10 7 8 9 10 11 12 4 5 8 9 7 lj 12 10 5 6 9 7 8 12 10 ll 6 4 10 jl 12 7 11 12 10 8 12 10...
Holographic Representation of Higher Spin Gauge Fields
Debajyoti Sarkar; Xiao Xiao
2014-11-17T23:59:59.000Z
Extending the results of \\cite{Heem}, \\cite{KLRS} on the holographic representation of local gauge field operators in anti de Sitter space, here we construct the bulk operators for higher spin gauge fields in the leading order of $\\frac{1}{N}$ expansion. Working in holographic gauge for higher spin gauge fields, we show that gauge field operators with integer spin $s>1$ can be represented by an integration over a ball region, which is the interior region of the spacelike bulk lightcone on the boundary. The construction is shown to be AdS-covariant up to gauge transformations, and the two-point function between higher spin gauge fields and boundary higher spin current exhibit singularities on both bulk and boundary lightcones. We also comment on possible extension to the level of three-point functions and carry out a causal construction for higher spin fields in de Sitter spacetime.
The representation theory of cyclotomic BMW algebras
Rui, H
2008-01-01T23:59:59.000Z
In this paper, we go on Rui-Xu's work on cyclotomic Birman-Wenzl algebras $\\W_{r, n}$ in \\cite{RX}. In particular, we use the representation theory of cellular algebras in \\cite{GL} to classify the irreducible $\\W_{r, n}$-modules for all positive integers $r$ and $n$. By constructing cell filtrations for all cell modules of $\\W_{r, n}$, we compute the discriminants associated to all cell modules for $\\W_{r, n} $. Via such discriminats together with induction and restriction functors given in section~5, we determine explicitly when $\\W_{r, n}$ is semisimple over a field. This generalizes our previous result on Birman-Wenzl algebras in \\cite{RS1}.
Deep Learning Representation using Autoencoder for 3D Shape Retrieval
benchmarks. I. INTRODUCTION With the fast development of 3D printer, Microsoft Kinect sensor and laserDeep 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
Infrared regular representation of the three dimensional massless Nelson model
Infrared regular representation of the three dimensional massless Nelson model J#19;ozsef L this Gaussian measure space. KEYWORDS: Nelson's scalar #12;eld model, infrared regular representation, ground] of a spinless electron coupled to a scalar massless Bose #12;eld is infrared divergent in 3 space dimensions
Decomposition of Representations of CAR Induced by Bogoliubov Endomorphisms
Jens B{ö}ckenhauer
1994-10-04T23:59:59.000Z
In a Fock representation, a non-surjective Bogoliubov transformation of CAR leads to a reducible representation. For the case that the corresponding Bogoliubov operator has finite corank, the decomposition into irreducible subrepresentations is clarified. In particular, it turns out that the number of appearing subrepresentations is completely determined by the corank.
Wigner representation of the rotational dynamics of rigid tops
Dmitry V. Zhdanov; Tamar Seideman
2014-06-15T23:59:59.000Z
We propose the general methodology to design the Wigner representations with the desired dynamical and semiclassical properties in the phase spaces with nontrivial topology. As an illustration, two representations of molecular rotations are developed to suit the computational demands of contemporary applications of laser alignment, diagnostics of reaction dynamics, studies of scattering and dissipative processes.
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
SEMANTIC DATA INTEGRATION IN A MULTIPLE REPRESENTATION ENVIRONMENT
KÃ¶bben, Barend
SEMANTIC DATA INTEGRATION IN A MULTIPLE REPRESENTATION ENVIRONMENT J.E. Stotera , R.L.G. Lemmensa: Semantic data integration, Multi Representation Database, Generalisation, ontologies, machine ontology at the different scales are semantically integrated, 2) objects in the different scales representing the same real
Fast Multipole Representation of Diffusion Curves and Points Timothy Sun
Grinspun, Eitan
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
An alternative proof of the Bryant representation Jose A. Galveza
GÃ¡lvez, JosÃ© Antonio
An alternative proof of the Bryant representation JosÂ´e A. GÂ´alveza and Pablo Mirab a Departamento Keywords: constant mean curvature, Bryant surfaces, Liouville equation. 1 Introduction In his famous 1987 paper, R. Bryant [Bry] established a meromorphic representation for the surfaces with constant mean
An Ontology for Semantic Representation of an Urban Virtual Environment
Paris-Sud XI, Université de
An Ontology for Semantic Representation of an Urban Virtual Environment K. Harkouken Saiah1, N of semantic representation of a dynamic virtual environment. Our model is embodied into a simulation with the semantic state of their environment. The idea is to represent the services offered by the environment
Virasoro representations and fusion for general augmented minimal models
Holger Eberle; Michael Flohr
2006-04-13T23:59:59.000Z
In this paper we present explicit results for the fusion of irreducible and higher rank representations in two logarithmically conformal models, the augmented c_{2,3} = 0 model as well as the augmented Yang-Lee model at c_{2,5} = -22/5. We analyse their spectrum of representations which is consistent with the symmetry and associativity of the fusion algebra. We also describe the first few higher rank representations in detail. In particular, we present the first examples of consistent rank 3 indecomposable representations and describe their embedding structure. Knowing these two generic models we also conjecture the general representation content and fusion rules for general augmented c_{p,q} models.
Efficient implementation and the product state representation of numbers.
Benioff, P.; Physics
2001-10-12T23:59:59.000Z
The relation between the requirement of efficient implementability and the product-state representation of numbers is examined. Numbers are defined to be any model of the axioms of number theory or arithmetic. Efficient implementability (EI) means that the basic arithmetic operations are physically implementable and the space-time and thermodynamic resources needed to carry out the implementations are polynomial in the range of numbers considered. Different models of numbers are described to show the independence of both EI and the product-state representation from the axioms. The relation between EI and the product-state representation is examined. It is seen that the condition of a product-state representation does not imply EI. Arguments used to refute the converse implication, EI implies a product-state representation, seem reasonable; but they are not conclusive. Thus this implication remains an open question.
Human Responses to Climate Change: Social Representation, Identity and Socio-Psychological Action
Royal Holloway, University of London
and methodological approaches. Keywords: climate change; communication; social representation; identity; social representations and applies them to the issue of climate change communication, focusing in particularHuman Responses to Climate Change: Social Representation, Identity and Socio- Psychological Action
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
2009-06-28T23:59:59.000Z
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.
Vargas Verdesoto, Milton X. [Centro Oncologico de Chihuahua, Hacienda de la Esperanza 6304, Chihuahua, Chihuahua (Mexico); Alvarez Romero, Jose T. [SSDL, Departamento de Metrologia de Radiaciones Ionizantes, Instituto Nacional de Investigaciones Nucleares, La Marquesa, Ocoyoacac, Estado de Mexico 52750 (Mexico)
2010-12-07T23:59:59.000Z
A planar multienergetic pencil beam kernel with rotational symmetry is calculated for a stereotactic radiosurgery system, SRS, BrainLAB with cones, employing the deconvolution method of the off axis ratio profile, OAR, corresponding to the cone of 35 mm in diameter for a 6 MV photon beam produced by a linear accelerator Varian 2100 C/D. Before the deconvolution, the experimental OAR is corrected for beam divergence and variations of the spectral fluence {Phi}, using a boundary function BF. The function BF and the fluence {Phi} are transformed to the conjugate space with the zero order Hankel function, which is the appropriate transform due to the radial symmetry of the circular beams generated by the cones. The kernel in the conjugate space is obtained as the ratio of the transform of BF to the transform of {Phi}, therefore the kernel in the real space is calculated as the inverse transform of the kernel in the conjugate space. To validate the kernel in the real space, it is convolved with the fluence of the cones of 7.5, 12.5, 15, 17.5, 20, 22.5, 25, 30 and 35 mm in diameter. The comparison of the OARs calculated and measured shows a maximum difference of 4.5% in the zones of high gradient of dose, and a difference less than 2% in the regions of low gradient of dose. Finally, the expanded uncertainty of the kernel is estimated and reported.
Katipamula, S.; Reddy, T. A.; Claridge, D. E.
1994-01-01T23:59:59.000Z
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...
Continuous representation for shell models of turbulence
Alexei A. Mailybaev
2014-09-16T23:59:59.000Z
In this work we construct and analyze continuous hydrodynamic models in one space dimension, which are induced by shell models of turbulence. After Fourier transformation, such continuous models split into an infinite number of uncoupled subsystems, which are all identical to the same shell model. The two shell models, which allow such a construction, are considered: the dyadic (Desnyansky--Novikov) model with the intershell ratio $\\lambda = 2^{3/2}$ and the Sabra model of turbulence with $\\lambda = \\sqrt{2+\\sqrt{5}} \\approx 2.058$. The continuous models allow understanding various properties of shell model solutions and provide their interpretation in physical space. We show that the asymptotic solutions of the dyadic model with Kolmogorov scaling correspond to the shocks (discontinuities) for the induced continuous solutions in physical space, and the finite-time blowup together with its viscous regularization follow the scenario similar to the Burgers equation. For the Sabra model, we provide the physical space representation for blowup solutions and intermittent turbulent dynamics.
Graph representation of protein free energy landscape
Li, Minghai; Duan, Mojie; Fan, Jue; Huo, Shuanghong, E-mail: shuo@clarku.edu [Gustaf H. Carlson School of Chemistry and Biochemistry, Clark University, 950 Main Street, Worcester, Massachusetts 01610 (United States)] [Gustaf H. Carlson School of Chemistry and Biochemistry, Clark University, 950 Main Street, Worcester, Massachusetts 01610 (United States); Han, Li [Department of Mathematics and Computer Science, Clark University, 950 Main Street, Worcester, Massachusetts 01610 (United States)] [Department of Mathematics and Computer Science, Clark University, 950 Main Street, Worcester, Massachusetts 01610 (United States)
2013-11-14T23:59:59.000Z
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.
Bias Reduction and Goodness-of-Fit Tests in Conditional Logistic Regression Models
Sun, Xiuzhen
2011-10-21T23:59:59.000Z
in conditional logistic regression by solving a modified score equation. The resultant estimator not only reduces bias but also can prevent producing infinite value. Furthermore, we propose a method to calculate the standard error of the resultant estimator. A...
Modeling Personalized Email Prioritization: Classification-based and Regression-based Approaches
Yoo S.; Yang, Y.; Carbonell, J.
2011-10-24T23:59:59.000Z
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.
PD Dr. Martin Stetter, Siemens AG 1 Neuronale Verfahren zur Regression
Popeea, Corneliu - Chair for Foundations of Software Reliability and Theoretical Computer Science
PD Dr. Martin Stetter, Siemens AG 1 Neuronale Verfahren zur Regression · Lineares Modell · Vom: Optimierungsverfahren #12;PD Dr. Martin Stetter, Siemens AG 2 Optimierung konvexer Funktionen · Häufiges Problem bei
A regression approach to infer electricity consumption of legacy telecom equipment
Fisher, Kathleen
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
Reddy, T. A.; Claridge, D.; Wu, J.
analysis to identify these models. However, such models tend to suffer from physically unreasonable regression coefficients and instability due to the fact that the predictor variables (i.e., climatic parameters, building internal loads, etc...
Open source software maturity model based on linear regression and Bayesian analysis
Zhang, Dongmin
2009-05-15T23:59:59.000Z
based on Bayesian statistics. More importantly, an updating rule is established through Bayesian analysis to improve the joint distribution, and thus the objectivity of the coefficients in the linear multiple-regression model, according to new incoming...
Mining customer credit by using neural network model with logistic regression approach
Kao, Ling-Jing
2001-01-01T23:59:59.000Z
. 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...
Predicting Turbulence using Partial Least Squares Regression and an Artificial Neural Network
Lakshmanan, Valliappa
Predicting Turbulence using Partial Least Squares Regression and an Artificial Neural Network in the dataset. Then, the transformed data are pre- sented to a neural network whose output node has a sigmoid
Regression Based Investigation of Pumping Limits and Springflow Within the Edwards Aquifer
McCarl, Bruce A.
Regression Based Investigation of Pumping Limits and Springflow Within the Edwards Aquifer K . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 A Model to Study the Effects of Pumping Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Investigation of the Effects of Pumping Allocations on Springflow
Frank, M. D.; Beattie, B. R.
1979-01-01T23:59:59.000Z
inventory, value of livestock inventory and miscellaneous expenditures. Using 1969 Census of Agriculture data, each regional function was statistically fit using both ordinary least squares (OLS) and ridge regression. As expected, parameter estimates under...
New Approaches in Testing Common Assumptions for Regressions with Missing Data
Chown, Justin Andrew
2014-07-30T23:59:59.000Z
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2.... EFFICIENTLY ESTIMATING THE ERROR DISTRIBUTION IN NON- PARAMETRIC REGRESSION WITH RESPONSES MISSING AT RAN- DOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1 Efficiency...
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)
2012-05-01T23:59:59.000Z
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.
Optical Spectral Observations of a Flickering White-Light Kernel in a C1 Solar Flare
Kowalski, Adam F; Fletcher, Lyndsay
2014-01-01T23:59:59.000Z
We analyze optical spectra of a two-ribbon, long duration C1.1 flare that occurred on 18 Aug 2011 within AR 11271 (SOL2011-08-18T15:15). The impulsive phase of the flare was observed with a comprehensive set of space-borne and ground-based instruments, which provide a range of unique diagnostics of the lower flaring atmosphere. Here we report the detection of enhanced continuum emission, observed in low-resolution spectra from 3600 \\AA\\ to 4550 \\AA\\ acquired with the Horizontal Spectrograph at the Dunn Solar Telescope. A small, $\\le$0''.5 ($10^{15}$ cm$^2$) penumbral/umbral kernel brightens repeatedly in the optical continuum and chromospheric emission lines, similar to the temporal characteristics of the hard X-ray variation as detected by the Gamma-ray Burst Monitor (GBM) on the Fermi spacecraft. Radiative-hydrodynamic flare models that employ a nonthermal electron beam energy flux high enough to produce the optical contrast in our flare spectra would predict a large Balmer jump in emission, indicative of h...
Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors
Sarkar, Abhra
2014-06-24T23:59:59.000Z
BAYESIAN SEMIPARAMETRIC DENSITY DECONVOLUTION AND REGRESSION IN THE PRESENCE OF MEASUREMENT ERRORS A Dissertation by ABHRA SARKAR Submitted to the Office of Graduate and Professional Studies of Texas A&M University in partial fulfillment... Copyright 2014 Abhra Sarkar ABSTRACT Although the literature on measurement error problems is quite extensive, so- lutions to even the most fundamental measurement error problems like density de- convolution and regression with errors...
Deng, Yangyang; Parajuli, Prem B.
2011-08-10T23:59:59.000Z
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.
On the Representation of Physical Quantities in Natural Language Text
Forbus, Kenneth D.
On the Representation of Physical Quantities in Natural Language Text Sven E. Kuehne (skuehne processes from natural language text. In an earlier analysis (Kuehne & Forbus, 2002) we presented a scheme
Surface Dependent Representations for Illumination Insensitive Image Comparison
Lindenbaum, Michael
. Lighting variation significantly affects surface appearance and makes image comparison difficultSurface Dependent Representations for Illumination Insensitive Image Comparison Margarita Osadchy the problem of matching images to tell whether they come from the same scene viewed under different lighting
Generating Tensor Representation from Concept Tree in Meaning Based Search
Panigrahy, Jagannath
2011-08-08T23:59:59.000Z
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...
Containing the opposition : selective representation in Jordan and Turkey
Wakeman, Raffaela Lisette
2009-01-01T23:59:59.000Z
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 ...
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
Fast quantum algorithms for approximating some irreducible representations of groups
Stephen P. Jordan
2009-04-21T23:59:59.000Z
We consider the quantum complexity of estimating matrix elements of unitary irreducible representations of groups. For several finite groups including the symmetric group, quantum Fourier transforms yield efficient solutions to this problem. Furthermore, quantum Schur transforms yield efficient solutions for certain irreducible representations of the unitary group. Beyond this, we obtain poly(n)-time quantum algorithms for approximating matrix elements from all the irreducible representations of the alternating group A_n, and all the irreducible representations of polynomial highest weight of U(n), SU(n), and SO(n). These quantum algorithms offer exponential speedup in worst case complexity over the fastest known classical algorithms. On the other hand, we show that average case instances are classically easy, and that the techniques analyzed here do not offer a speedup over classical computation for the estimation of group characters.
Representation of Energy Use in the Food Products Industry
Elliott, N. R.
2007-01-01T23:59:59.000Z
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...
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. ...
The Role of Knowledge in Visual Shape Representation
Saund, Eric
1988-10-01T23:59:59.000Z
This report shows how knowledge about the visual world can be built into a shape representation in the form of a descriptive vocabulary making explicit the important geometrical relationships comprising objects' shapes. ...
Representation and compression of multidimensional piecewise functions using surflets
Chandrasekaran, Venkat
We study the representation, approximation, and compression of functions in M dimensions that consist of constant or smooth regions separated by smooth (M-1)-dimensional discontinuities. Examples include images containing ...
Incorporating Representation of Agricultural Ecosystems and Management Within IBIS
Incorporating Representation of Agricultural Ecosystems and Management Within IBIS: The development of Agro-IBIS Chris Kucharik Department of Agronomy & Center for Sustainability and the Global Environment balance Soil and canopy physics Leaf physiology Minutes Phenology Budburst, senescence, dormancy Daily
An Executable Representation of Distance and Direction Richard Johnson
Pingali, Keshav K.
that permits imperative updates to memory. The dependence ow graph sub- sumes other representations between statements that 1This research was supported by an NSF Presidential Young Investigator award (NSF
Concise representation of the saturation properties for pure compounds
Borrelli, Leslie Kieffer
1982-01-01T23:59:59.000Z
CONCISE REPRESENTATION QF THE SATURATIQN PROPERTIES FQR PURE CQHPQUN DS A Thesis by LESLIE KIEFFER BQRRELLI Submitted to the Graduate College of Texas A&M University in partial fulfillment of the r equirement for the degree of NASTER... of Depa ent) May 1982 ABSTRACT CONCISE REPRESENTATION QF THE SATURATION PROPERTIES FOR PURE COMPOUNDS (May 1982) Leslie Kieffer Borrelli, B. S. , Texas ARM University Chair man of Advisor y Committee: Dr . Kenneth R. Hall Vapor pr essure...
Contescu, Cristian I [ORNL
2006-01-01T23:59:59.000Z
This report supports the effort for development of small scale fabrication of UCO (a mixture of UO{sub 2} and UC{sub 2}) fuel kernels for the generation IV high temperature gas reactor program. In particular, it is focused on optimization of dispersion conditions of carbon black in the broths from which carbon-containing (UO{sub 2} {center_dot} H{sub 2}O + C) gel spheres are prepared by internal gelation. The broth results from mixing a hexamethylenetetramine (HMTA) and urea solution with an acid-deficient uranyl nitrate (ADUN) solution. Carbon black, which is previously added to one or other of the components, must stay dispersed during gelation. The report provides a detailed description of characterization efforts and results, aimed at identification and testing carbon black and surfactant combinations that would produce stable dispersions, with carbon particle sizes below 1 {micro}m, in aqueous HMTA/urea and ADUN solutions. A battery of characterization methods was used to identify the properties affecting the water dispersability of carbon blacks, such as surface area, aggregate morphology, volatile content, and, most importantly, surface chemistry. The report introduces the basic principles for each physical or chemical method of carbon black characterization, lists the results obtained, and underlines cross-correlations between methods. Particular attention is given to a newly developed method for characterization of surface chemical groups on carbons in terms of their acid-base properties (pK{sub a} spectra) based on potentiometric titration. Fourier-transform infrared (FTIR) spectroscopy was used to confirm the identity of surfactants, both ionic and non-ionic. In addition, background information on carbon black properties and the mechanism by which surfactants disperse carbon black in water is also provided. A list of main physical and chemical properties characterized, samples analyzed, and results obtained, as well as information on the desired trend or range of values generally associated with better dispersability, is provided in the Appendix. Special attention was given to characterization of several surface-modified carbon blacks produced by Cabot Corporation through proprietary diazonium salts chemistry. As demonstrated in the report, these advanced carbons offer many advantages over traditional dispersions. They disperse very easily, do not require intensive mechanical shearing or sonication, and the particle size of the dispersed carbon black aggregates is in the target range of 0.15-0.20 {micro}m. The dispersions in water and HMTA/urea solutions are stable for at least 30 days; in conditions of simulated broth, the dispersions are stable for at least 6 hours. It is proposed that the optimization of the carbon black dispersing process is possible by replacing traditional carbon blacks and surfactants with surface-modified carbon blacks having suitable chemical groups attached on their surface. It is recognized that the method advanced in this report for optimizing the carbon black dispersion process is based on a limited number of tests made in aqueous and simulated broth conditions. The findings were corroborated by a limited number of tests carried out with ADUN solutions by the Nuclear Science and Technology Division at Oak Ridge National Laboratory (ORNL). More work is necessary, however, to confirm the overall recommendation based on the findings discussed in this report: namely, that the use of surface-modified carbon blacks in the uranium-containing broth will not adversely impact the chemistry of the gelation process, and that high quality uranium oxicarbide (UCO) kernels will be produced after calcination.
On the heat kernel and the Dirichlet form of Liouville Brownian Motion
Christophe Garban; Rémi Rhodes; Vincent Vargas
2014-10-16T23:59:59.000Z
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.
The Dissimilarity Representation as a Tool for Three-Way Data Classification: A 2D Measure
Duin, Robert P.W.
Â´ia (Ingeominas), Colombia jmakario@ingeominas.gov.co Abstract. The dissimilarity representation has demonstrated
WILD ALGEBRAS HAVE ONE-POINT EXTENSIONS OF REPRESENTATION DIMENSION AT LEAST FOUR
Oppermann, Steffen
WILD ALGEBRAS HAVE ONE-POINT EXTENSIONS OF REPRESENTATION DIMENSION AT LEAST FOUR STEFFEN OPPERMANN Abstract. We show that any wild algebra has a one-point exten- sion of representation dimension at least between tame and wild representation type is another way of saying "how infinite" the representation
A Graphic Representation of States for Quantum Copying Machines
Sara Felloni; Giuliano Strini
2006-09-29T23:59:59.000Z
The aim of this paper is to introduce a new graphic representation of quantum states by means of a specific application: the analysis of two models of quantum copying machines. The graphic representation by diagrams of states offers a clear and detailed visualization of quantum information's flow during the unitary evolution of not too complex systems. The diagrams of states are exponentially more complex in respect to the standard representation and this clearly illustrates the discrepancy of computational power between quantum and classical systems. After a brief introductive exposure of the general theory, we present a constructive procedure to illustrate the new representation by means of concrete examples. Elementary diagrams of states for single-qubit and two-qubit systems and a simple scheme to represent entangled states are presented. Quantum copying machines as imperfect cloners of quantum states are introduced and the quantum copying machines of Griffiths and Niu and of Buzek and Hillery are analyzed, determining quantum circuits of easier interpretation. The method has indeed shown itself to be extremely successful for the representation of the involved quantum operations and it has allowed to point out the characteristic aspects of the quantum computations examined.
Some unitary representations of Thompson's groups F and T
Vaughan F. R. Jones
2014-12-24T23:59:59.000Z
In a "naive" attempt to create algebraic quantum field theories on the circle, we obtain a family of unitary representations of Thompson's groups T and F for any subfactor. The Thompson group elements are the "local scale transformations" of the theory. In a simple case the coefficients of the representations are polynomial invariants of links. We show that all links arise and introduce new "oriented" subgroups $\\overrightarrow F
Clifford, Dirac, and Majorana algebras, and their representations
Salingaros, N.
1981-04-01T23:59:59.000Z
We show that the Dirac algebra is an algebra in five dimensions. It has traditionally been confused with the two distinct algebras in four dimensions, which we have identified as the Majorana algebra and the Clifford algebra in Minkowski space-time. A careful discussion of the subtle inter-relationship between these three algebras is achieved by employing a basis of differential forms. In addition, we provide for the first time a 4 x 4 complex matrix representation of the Clifford algebra in Minkowski spacetime, and compare it to the matrix representations of the Dirac and Majorana algebras. A remark on Eddington's E-numbers is included.
Calibrating Bayesian Network Representations of Social-Behavioral Models
Whitney, Paul D.; Walsh, Stephen J.
2010-04-08T23:59:59.000Z
While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empirical comparison with data taken from the Minorities at Risk Organizational Behaviors database.
Adaptive Sampling for Estimating a Scalar Field using a Robotic Boat and a Sensor Network
Zhang, Bin; Sukhatme, Gaurav
2007-01-01T23:59:59.000Z
LOCAL REGRESSION A. Linear Local Regression Non-parametricnon- parametric estimators. Kernel estimators include Nadaraya- Waston, Gasser-Muller and local linear regression
FSR Methods for Second-Order Regression Models Hugh B. Crews
Boos, Dennis
approach to forward selection by using different -to-enter values for first-order and second-order terms-order linear regression models. Often, interaction and quadratic terms are also of interest, but the number-order terms. Method performance is compared through Monte Carlo simulation, and an illustration is provided
FSR Methods for Second-Order Regression Models Hugh B. Crews1
Boos, Dennis
-order linear regression models. Often, interaction and quadratic terms are also of interest, but the number first-order and second-order terms. Method performance is compared through Monte Carlo simulation optimization, selecting interaction and quadratic terms is important. In such applications, second-order terms
What is the Chance that the Equity Premium Varies Evidence from Predictive Regressions
Kahana, Michael J.
Regressions Abstract We examine the evidence on excess stock return predictability in a Bayesian setting by the historical time series of returns and predictor variables. We find that taking into account the stochastic;1 Introduction This paper investigates the evidence in favor of stock return predictability from a model
Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net
Blei, David M.
, the Elastic Net can yield a sparse esti- mate with more than n non-zero weights (Efron et al., 2004). One canExploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net Alexander Lorbert- tion called the Pairwise Elastic Net is pro- posed. Like the Elastic Net, it simultane- ously performs
Teasing Out the Effect of Tutorials via Multiple Regression Stephanie V. Chasteen
Colorado at Boulder, University of
Teasing Out the Effect of Tutorials via Multiple Regression Stephanie V. Chasteen Science Education instruction, did not achieve the same impacts. Keywords: physics education research, course reform, electricity and magnetism, assessment PACS: 01.30.Ib, 01.40.Di, 01.40.Fk, 01.40.G-, 01.40.gb INTRODUCTION
Towards a Generalized Regression Model for On-body Energy Prediction from Treadmill Walking
Sukhatme, Gaurav S.
Towards a Generalized Regression Model for On-body Energy Prediction from Treadmill Walking sensor data to energy expenditure is the ques- tion of normalizating across physiological parameters. Common approaches such as weight scaling require validation for each new population. An alternative
SPIDAR calibration based on regression methods M'hamed Frad, 1
Paris-Sud XI, Université de
SPIDAR calibration based on regression methods 1,2 M'hamed Frad, 1 Hichem Maaref, 1 Samir Otmane, 2 calibrated. The driving idea of this work is to derive easy-to-use calibration algorithms that can be used to calibrate our haptic device and to add therefore adaptability to the system behavior. We make use
Cubic Spline Regression for the Open-Circuit Potential Curves of a Lithium-Ion Battery
Cubic Spline Regression for the Open-Circuit Potential Curves of a Lithium-Ion Battery Qingzhi Guo-circuit potential OCP of an inter- calation electrode in a lithium-ion battery on the lithium concentra- tion reaction at an electrode in a lithium- ion battery depends exponentially on the difference between
Localized Regression Analysis as a Method for Detecting Erroneous Measurements in Geospatial
Ward, Karen
Localized Regression Analysis as a Method for Detecting Erroneous Measurements in Geospatial at El Paso, El Paso, TX 79968, USA Abstract Geospatial databases generally consist of measurements dealing with many other types of point data. Keywords: Geospatial Databases; Error Detection; Gravity Data
A Functional Regression Approach for Prediction in a District-Heating System
Paris-Sud XI, Université de
A Functional Regression Approach for Prediction in a District-Heating System Aldo Goia Dipartimento in a district heating sys- tem. Our dataset consists of four separated periods, with 198 days each period and 24 load forecasting, district heat- ing system Introduction Among the activities of support
Hybrid K-Means: Combining Regression-Wise and Centroid-Based Criteria for QSAR
Mirkin, Boris
Hybrid K-Means: Combining Regression-Wise and Centroid-Based Criteria for QSAR Robert Stanforth1 traditional methods of cluster-analysis such as K-Means clustering may not work very well because they capture in the space of input variables. The combined clustering criterion is referred to as the hybrid K-means cri
A spatiotemporal auto-regressive moving average model for solar radiation
Stone, J. V.
A spatiotemporal auto-regressive moving average model for solar radiation C.A. Glasbey and D 1). Solar radiation, averaged over ten minute intervals, was recorded at each site for two years otherwise there are too many parameters to be estimated. As we wish to simulate solar radiation on a network
Cooling energy demand evaluation by means of regression models obtained from dynamic simulations
Paris-Sud XI, UniversitÃ© de
Cooling energy demand evaluation by means of regression models obtained from dynamic simulations Ph, UniversitÃ© Lyon1, FRANCE ABSTRACT The forecast of the energy heating/cooling demand would be a good indicator between simple and complex methods of evaluating the cooling energy demand we have proposed to use energy
Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines
CaÃ±izares, Claudio A.
1 Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines H. In this paper, the MARS technique is applied to forecast the hourly Ontario energy price (HOEP). The MARS models values of the latest pre- dispatch price and demand information, made available by the Ontario
LS-SVM based regression and spectral clustering for predicting maintenance of machines
with sensory3 faults have been used [? ],[? ],[? ]: corrective maintenance, preventive main-4 tenance, manual the machine fails, it is expensive and6 safety and environmental issues arise. Preventive maintenance is basedLS-SVM based regression and spectral clustering for predicting maintenance of machines Rocco
Predicting Turbulence Using Partial Least Squares Regression and an Artificial Neural Network
Lakshmanan, Valliappa
Predicting Turbulence Using Partial Least Squares Regression and an Artificial Neural Network #12;Neural Network Neural Network Architecture 6 inputs (the 6 transformed components) 1 output (0 Lakshmanan et. al (OU/NSSL) PLS and NN 8th Conf. on AI, Atlanta, GA 9 / 15 #12;Neural Network Validation
Permeation of Gases in Polymers: Parameter Identification and Nonlinear Regression Analysis
Scheichl, Robert
Permeation of Gases in Polymers: Parameter Identification and Nonlinear Regression Analysis Robert at PARAOPE, Heidelberg, June 30th, 2004 #12;Overview Â· Permeation of gases in polymers Â Application areas for diffusion in polymers Â Description of the experimental device Â Mathematical model Â· Parameter
Paris-Sud XI, UniversitÃ© de
A Regression Algorithm for the Smart Prognosis of a Reversed Polarity Fault in a Photovoltaic database containing sample data is used for simulation purposes. Keywords--Photovoltaic generator, SVR, k-NNR, reversed polarity fault, diagnosis, prognosis. NOMENCLATURE PV = Photovoltaic; SVM = Support Vector
SPATIO-TEMPORAL REGRESSION MODELS FOR DEFORESTATION IN THE BRAZILIAN AMAZON Giovana M. de Espindolaa
Camara, Gilberto
SPATIO-TEMPORAL REGRESSION MODELS FOR DEFORESTATION IN THE BRAZILIAN AMAZON Giovana M. de change, spatial simultaneous autoregression ABSTRACT: Deforestation in the Brazilian Amazon has sharply of deforestation in a selected area by relating data from 2002-2008 to a number of explanatory variables, part
Air Leakage of US Homes: Regression Analysis and Improvements from Retrofit
Air Leakage of US Homes: Regression Analysis and Improvements from Retrofit Wanyu R. Chan, Jeffrey,000 single-family detached homes have sufficient information for the analysis of air leakage in relation variability in normalized leakage. ResDB also contains the before and after retrofit air leakage measurements
Learning Dynamic Models of Compartment Systems by Combining Symbolic Regression with Fuzzy Vector
Fernandez, Thomas
. Categories and Subject Descriptors I.2.1 [Pattern Recognition]: Models--Fuzzy Set; I.2.6 [ArtificialLearning Dynamic Models of Compartment Systems by Combining Symbolic Regression with Fuzzy Vector and fuzzy represen- tation. We need differential capabilities because, in a dy- namic environment, models
Blood Glucose Level Prediction using Physiological Models and Support Vector Regression
Bunescu, Razvan C.
Blood Glucose Level Prediction using Physiological Models and Support Vector Regression Razvan continually monitor their blood glucose levels and adjust insulin doses, striving to keep blood glucose levels as close to normal as possible. Blood glucose levels that deviate from the normal range can lead to serious
HIGH DIMENSIONAL REGRESSION USING THE SPARSE MATRIX TRANSFORM (SMT) Guangzhi Cao
HIGH DIMENSIONAL REGRESSION USING THE SPARSE MATRIX TRANSFORM (SMT) Guangzhi Cao GE Healthcare decorrelating the high dimensional observation vector using the sparse matrix transform (SMT) estimate vector us- ing the sparse matrix transform (SMT) estimate of the covari- ance [5]. To improve
Prediction of tree diameter growth using quantile regression and mixed-effects models
Cao, Quang V.
Prediction of tree diameter growth using quantile regression and mixed-effects models Som B. Bohora diameter predictions for the same tree in the future. Another approach considered in this study involved and mixed-effects models in predicting tree diameter growth. Tree diameter at the end of each growth period
Non-parametric regression and neural-network inll drilling recovery models for carbonate reservoirs
ValkÃ³, Peter
, and operations e- ciency. Consequent to the primary recovery, water- Â¯ood is often used as a secondary recoveryNon-parametric regression and neural-network inÂ®ll drilling recovery models for carbonate ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in West Texas
A sliced inverse regression approach for data stream Marie Chavent1,2
Paris-Sud XI, Université de
regression model involving a common EDR (Effective Dimension Reduction) direction is assumed in each block consists of pooling all the observed blocks and estimating the EDR direction by the SIR (Sliced Inverse.e., drift in the EDR direction or aberrant blocks in the data stream. In a simulation study, we illustrate
A sliced inverse regression approach for data stream Marie Chavent1,2
Paris-Sud XI, Université de
regression model involving a common EDR (Effective Dimension Reduction) direction is assumed in each block consists of pooling all the observed blocks and estimating the EDR direction by the SIR (Sliced Inverse. A graphical tool is also provided in order to detect changes in the underlying model, i.e., drift in the EDR
A sliced inverse regression approach for data stream Marie Chavent1,2
Paris-Sud XI, Université de
by block in a stream. A semiparametric regression model involving a common EDR (Effective Dimension of a new block. A simple direct approach consists in pooling all the observed blocks and estimate the EDR provide a graphical tool in order to detect if a drift occurs in the EDR direction or if some aberrant
Scott, David W.
Nonparametric Regression for Geographic Visualization and Analysis of Environmental Policy Gerald August 9-13, 1998. The views expressed are the author's, and do not necessarily represent policies; averaged shifted histogram; environmental economics The U.S. Department of Agriculture administers
Eugene Lytvynov; Lin Mei
2006-08-14T23:59:59.000Z
Let $X$ be a locally compact, second countable Hausdorff topological space. We consider a family of commuting Hermitian operators $a(\\Delta)$ indexed by all measurable, relatively compact sets $\\Delta$ in $X$ (a quantum stochastic process over $X$). For such a family, we introduce the notion of a correlation measure. We prove that, if the family of operators possesses a correlation measure which satisfies some condition of growth, then there exists a point process over $X$ having the same correlation measure. Furthermore, the operators $a(\\Delta)$ can be realized as multiplication operators in the $L^2$-space with respect to this point process. In the proof, we utilize the notion of $\\star$-positive definiteness, proposed in [Y. G. Kondratiev and T.\\ Kuna, {\\it Infin. Dimens. Anal. Quantum Probab. Relat. Top.} {\\bf 5} (2002), 201--233]. In particular, our result extends the criterion of existence of a point process from that paper to the case of the topological space $X$, which is a standard underlying space in the theory of point processes. As applications, we discuss particle densities of the quasi-free representations of the CAR and CCR, which lead to fermion, boson, fermion-like, and boson-like (e.g. para-fermions and para-bosons of order 2) point processes. In particular, we prove that any fermion point process corresponding to a Hermitian kernel may be derived in this way.
Representations and Properties of Generalized $A_r$ Statistics
Mohammed Daoud
2006-06-20T23:59:59.000Z
A generalization of $A_r$ statistics is proposed and developed. The generalized $A_r$ quantum statistics is completely specified by a set of Jacobson generators satisfying a set of triple algebraic relations. Fock-Hilbert representations and Bargmann-Fock realizations are derived.
A Simple Representation Technique to Improve GA Performance
's performance by allowing a more rapid search through the hypothesis space. This is achieved by the prior be very different than their parents, thus increasing the speed in which the hypothesis space is searchedA Simple Representation Technique to Improve GA Performance Steven L. Keast Department of Computer
Music Representation in a Digital Music Library Donald Byrd
Indiana University
Music Representation in a Digital Music Library Donald Byrd School of Music Indiana University Bloomington, IN 47401 812-855-0296 isaacso@indiana.edu Abstract The Variations2 digital music library users. 1. Introduction Variations2 is a large-scale digital music library project under development
Rendering Optical Effects Based on Spectra Representation in Complex Scenes
Paris-Sud XI, Université de
Rendering Optical Effects Based on Spectra Representation in Complex Scenes Weiming Dong Project ALICE, INRIA Lorraine/Loria, France Weiming.Dong@loria.fr Abstract. Rendering the structural color sensitive phenomena. Achieving the rendering of complex scenes with both the full spectra and RGB light
Path-transformations in probability and representation theory
Jordan, Jonathan
Path-transformations in probability and representation theory Neil O'Connell University of Warwick Biane and Philippe Bougerol Neil O'Connell Path-transforms in probability and rep. theory #12;Pitman is a three-dimensional Bessel process. Neil O'Connell Path-transforms in probability and rep. theory #12;The
FROM NO"RLUND MATRICES TO LAPLACE REPRESENTATIONS
Sinnamon, Gordon J.
(and not * *too large) on the line Re z = 0, the Laplace transform LF is just the (Poisson exte* *nsion of the) Fourier transform of F . It is therefore appropriate to view the power * *series. Analogous inequalities are proved for power series representations * *of functions in weighted Hardy
Knowledge representation in process engineering Franz Baader and Ulrike Sattler
Baader, Franz
Knowledge representation in process engineering Franz Baader and Ulrike Sattler RWTH Aachen to represent the relevant process engi neering knowledge. The application domain Process engineering knowledge engineer), and not automatically in ferred from the definition of the class. As the complexity
Subspace Segmentation with A Minimal Squared Frobenius Norm Representation
Yu, Yizhou
Minimal Squared Frobenius Norm Representa- tion (MSFNR). MSFNR performs data clustering by solving benchmark [9]. However, LRR in- volves nuclear norm1 minimization. Solving LRR re- quires computing multiple Representation (MSFNR). It employs convex optimization to perform subspace clustering. The method minimizes
Attention and biased competition in multi-voxel object representations
VanRullen, Rufin
. In a multidimensional voxel space, the response to simultaneously-presented categories was well described as a weighted.e., univariate) fMRI methods (13Â15), these recently discov- ered multivariate pattern analysis techniques have to understand attentional influences in large-scale multivariate representations of simultaneously
Analytical Representation of the Longitudinal Hadronic Shower Development
Y. A. Kulchitsky; V. B. Vinogradov
1999-03-12T23:59:59.000Z
The analytical representation of the longitudinal hadronic shower development from the face of a calorimeter is presented and compared with experimental data. The suggested formula is particularly useful at designing, testing and calibration of huge calorimeter complex like in ATLAS at LHC.
Analytical representation of the longitudinal hadronic shower development
Kulchitskii, Yu A
1998-01-01T23:59:59.000Z
The analytical representation of the longitudinal hadronic shower development from the face of a calorimeter is presented and compared with experimental data. The suggested formula is particularly useful at designing, testing and calibration of huge calorimeter complex like in ATLAS at LHC.
Security Requirements Engineering: A Framework for Representation and Analysis
Nuseibeh, Bashar
Security Requirements Engineering: A Framework for Representation and Analysis Charles B. Haley Abstract--This paper presents a framework for security requirements elicitation and analysis. The framework is based on constructing a context for the system, representing security requirements as constraints
Compact Representation of Coordinated Sampling Policies for Body Sensor Networks
Panangadan, Anand
.Talukder@jpl.nasa.gov Abstract Embedded sensors of a Body Sensor Network need to efficiently utilize their energy resources Department of Electrical Engineering Los Angeles, CA 90089, USA 1-213-821-0871 {lius,raghu}@usc.edu 2 of a compact representation is feasible with little loss in performance. The global optimal policy is computed
CAPlets: wavelet representations without wavelets Youngmi Hur Amos Ron
Liblit, Ben
CAPlets: wavelet representations without wavelets Youngmi Hur Amos Ron Department of Mathematics" between each two consecutive layers is recorded in terms of detail coefficients. Wavelet decomposition of wavelet decompositions is their implementation and inversion by a fast algorithm, the so-called fast
Interactive Dimensions in the Construction of Mental Representations for Text
Patel, Aniruddh D.
Interactive Dimensions in the Construction of Mental Representations for Text David N. Rapp be as critical to the construction of complex mental models as the discrete dimensions themselves. In the present a bead on Specify again. Incredibly, the horse was still rolling along. A pang of fear went through Woolf
TORSION, AS A FUNCTION ON THE SPACE OF REPRESENTATIONS
Haller, Stefan
triangulation, and the dynamical torsion to a vector field with the properties listed in section 2TORSION, AS A FUNCTION ON THE SPACE OF REPRESENTATIONS DAN BURGHELEA AND STEFAN HALLER Abstract functions on Rep M (#; V ) called in this paper complex valued Ray--Singer torsion, Milnor--Turaev torsion
Brief Communications Unstable Representation of Sound: A Biological Marker of
Brief Communications Unstable Representation of Sound: A Biological Marker of Dyslexia Jane and reading skills. Children with dyslexia, who often exhibit impairments in auditory-based perceptual skills manifestations of auditory impairments in dyslexia include impaired perception of speech in background noise
Representation Theory, Geometry & Combinatorics Organizer: M. Haiman and N. Reshetikhin
Haiman, Mark D.
Representation Theory, Geometry & Combinatorics Seminar Organizer: M. Haiman and N. Reshetikhin Wednesday, 4:00Â6:00pm, 939 Evans Apr. 14 Ivan Shestakov, IME-USP, S~ao Paolo Nonassociative Lie Theory The theory of Lie algebras is one of the corner-stones of modern mathematics and physics. They usually relate
CONTEXTUALLY ADAPTIVE SIGNAL REPRESENTATION USING CONDITIONAL PRINCIPAL COMPONENT ANALYSIS
Rajashekar, Umesh
is the construction of bases that are adapted to individual signal in- stances. Here we develop a new framework. Index Terms-- Adaptive basis, conditional PCA, self-similarities, image modeling, image representation traditional methods, primarily due to the cost of encoding the indices of selected basis elements
Case Generation Using Rough Sets with Fuzzy Representation
Mitra, Pabitra
, granular computing, rough-fuzzy hybridization, soft computing, pattern recognition, data mining. Ã¦ 1 for case generation. Fuzzy set theory is used for linguistic representation of patterns, thereby producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules which model
BACK TO OFFICE REPORT Enhancing the Representation of Environment and
BACK TO OFFICE REPORT Enhancing the Representation of Environment and Natural Resources in Poverty be established when PRSPs are combined with Sector-Wide Approaches to funding development through Medium-Term Expenditure Frameworks (MTEF). Environment and Natural Resources (ENR) are recognised by the international
Network sampling and classification: An investigation of network model representations
Needleman, Daniel
only one or two connectivity patterns of an observed network--such as degree distribution, or diameterNetwork sampling and classification: An investigation of network model representations Edoardo M: Connectivity pattern Network type Network metrics Network sampling Network classification Methods
Towards Event Sequence Representation, Reasoning and Visualization for EHR Data
Golbeck, Jennifer
Towards Event Sequence Representation, Reasoning and Visualization for EHR Data Cui Tao Dept will provide a comprehensive environment for users to visualize inferred temporal relationships from EHR data the narrative, temporal reasoning is also needed in order to analyze the trends in time. Manually assessing tens
Nucleon-nucleon potentials in phase-space representation
H. Feldmeier; T. Neff; D. Weber
2014-12-02T23:59:59.000Z
A phase-space representation of nuclear interactions, which depends on the distance $\\vec{r}$ and relative momentum $\\vec{p}$ of the nucleons, is presented. A method is developed that permits to extract the interaction $V(\\vec{r},\\vec{p})$ from antisymmetrized matrix elements given in a spherical basis with angular momentum quantum numbers, either in momentum or coordinate space representation. This representation visualizes in an intuitive way the non-local behavior introduced by cutoffs in momentum space or renormalization procedures that are used to adapt the interaction to low momentum many-body Hilbert spaces, as done in the unitary correlation operator method or with the similarity renormalization group. It allows to develop intuition about the various interactions and illustrates how the softened interactions reduce the short-range repulsion in favor of non-locality or momentum dependence while keeping the scattering phase shifts invariant. It also reveals that these effective interactions can have undesired complicated momentum dependencies at momenta around and above the Fermi momentum. Properties, similarities and differences of the phase-space representations of the Argonne and the N3LO chiral potential, and their UCOM and SRG derivatives are discussed.
THE CATEGORICAL WEIL REPRESENTATION SHAMGAR GUREVICH AND RONNY HADANI
Gurevich, Shamgar
))f(y), where G(B, ) is an appropriate Gauss sum normalization. 0.2. Canonical vector space. In [10, 11 of odd characteristic: There exists a canonical system of intertwining operators between the Lagrangian models of the Heisenberg representation. This defines a canonical vector space H(V ) associated
Utah, University of
The Flux OSKit: A Substrate for Kernel and Language Research Bryan Ford Godmar Back Greg Benson Jay group to implement even a basic useful OS core---e.g., the functionÂ ality traditionally found are interesting for research purposes. For examÂ Ford, Back, and Lepreau are at the Univ. of Utah (baford
Shivers, Olin
The Flux OSKit: A Substrate for Kernel and Language Research Bryan Ford Godmar Back Greg Benson Jay for a small group to implement even a basic useful OS core---e.g., the functionality traditionally found, memory management suited for physical memory and its Ford, Back, and Lepreau are at the Univ. of Utah
Utah, University of
The Flux OSKit: A Substrate for Kernel and Language Research Bryan Ford Godmar Back Greg Benson Jay is catalyzing research and development in operating systems and programÂ ming languages. Ford, Back, and Lepreau to expand and diversify, it is increasingly impractical for a small group to implement even a basic useful
Romero, Christopher 1978-
2012-12-03T23:59:59.000Z
-depth analytic review of calculus textbooks, we cannot know whether the books are likely to help students learn (McNeely, 1997). The roles of representations (Cunningham, 2005; Knuth, 2000; Porzio, 1999) and representational transfer (Keller & Hirsch, 1998...
Laforcade, Pierre
Niigata (Japan), July 18-20, 2007 Pierre Laforcade 1 Graphical representation of abstract (Japan), July 18-20, 2007 Pierre Laforcade 2 Outline 1. Research context 2. Experimentation: the UML4LD proposition 3. Summary and ongoing work #12;Niigata (Japan), July 18-20, 2007 Pierre Laforcade 3 Research
Cambridge, University of
30 8. Neural Networks Over the years, linear regression models have attempted to characterise the 0 interact. A more powerful alternative is the use of neural networks [40,42], a non-linear modelling prediction uncertainties. #12;31 In linear regression, the sum of each input xi multiplied with a weight wi
Fernandez, Thomas
regression [5], [6] that evolves linear combinations of non-linear transformations of the input Manuscript non-linear transformations of the input variables. The functionality of GPTIPS is demonstrated regression by genetic programming (GP) is introduced. GPTIPS is specifically designed to evolve mathematical
Boyer, Edmond
a linear regression model. A generalization is the additive logistic model, which replaces each linear term, removes irrelevant variables, and identifies non linear trends. The estimates are computed via the usualParsimonious additive logistic models Logistic regression is a standard tool in statistics
Fusion rules for admissible representations of affine algebras: the case of $A_2^{(1)}$
P. Furlan; A. Ch. Ganchev; V. B. Petkova
1997-10-26T23:59:59.000Z
We derive the fusion rules for a basic series of admissible representations of $\\hat{sl}(3)$ at fractional level $3/p-3$. The formulae admit an interpretation in terms of the affine Weyl group introduced by Kac and Wakimoto. It replaces the ordinary affine Weyl group in the analogous formula for the fusion rules multiplicities of integrable representations. Elements of the representation theory of a hidden finite dimensional graded algebra behind the admissible representations are briefly discussed.
Lu, Jianfeng
Multipole Representation of the Fermi Operator with Application to the Electronic Structure, Princeton University, Princeton, NJ 08544 We propose a multipole representation of the Fermi-Dirac function temperature Green's functions in many-body physics [13]. It is natural to consider a multipole representation
Faculty of Engineering and Natural Sciences Knowledge Representation and Automated Reasoning
Gozuacik, Devrim
Faculty of Engineering and Natural Sciences AI Day on Knowledge Representation and Automated representations of knowledge manipulated by reasoning engines are an integral and crucial component of intelligent Reasoning Wednesday, 21 May 2008 13:4015:30, FENS G035 15:4017:00, FENS G029 Knowledge Representation
Krysl, Svatopluk
C -algebras Oscillator or Segal-Shale-Weil representation Geometry: Associating the oscillator or Segal-Shale-Weil representation Geometry: Associating the oscillator to symplectic manifolds Global and (x) = 0 implies x = 0 2 S. KrÃ½sl #12;C -algebras Oscillator or Segal-Shale-Weil representation
A special irreducible matrix representation of the real Clifford algebra C,,3,1...
Scharnhorst, Klaus
a particularly symmetric real representation of 4 4 Dirac matrices Majorana representation which may prove useful represen- tations of these Clifford algebras, a set of real 4 4 Dirac matrices Majorana representation Received 1 May 1998; accepted for publication 29 October 1998 4 4 Dirac gamma matrices irreducible matrix
A special irreducible matrix representation of the real Clifford algebra C '' 3,1...
Scharnhorst, Klaus
a particularly symmetric real representation of 434 Dirac matrices ~Majorana representation! which may prove matrices ~Majorana representation!, which we will be interested in, can only be obtained for the Clifford, Germany ~Received 1 May 1998; accepted for publication 29 October 1998! 434 Dirac ~gamma! matrices
Shetty, Rahul; Bigiel, Frank
2012-01-01T23:59:59.000Z
We develop a Bayesian linear regression method which rigorously treats measurement uncertainties, and accounts for hierarchical data structure for investigating the relationship between the star formation rate and gas surface density. The method simultaneously estimates the intercept, slope, and scatter about the regression line of each individual subject (e.g. a galaxy) and the population (e.g. an ensemble of galaxies). Using synthetic datasets, we demonstrate that the Bayesian method accurately recovers the parameters of both the individuals and the population, especially when compared to commonly employed least squares methods, such as the bisector. We apply the Bayesian method to estimate the Kennicutt-Schmidt (KS) parameters of a sample of spiral galaxies compiled by Bigiel et al. (2008). We find significant variation in the KS parameters, indicating that no single KS relationship holds for all galaxies. This suggests that the relationship between molecular gas and star formation differs between galaxies...
Li, Ke
2012-02-14T23:59:59.000Z
of the requirements for the degree of DOCTOR OF PHILOSOPHY December 2010 Major Subject: Agricultural Economics Essays on Regression Spline Structural Nonparametric Stochastic Production Frontier Estimation and Ine ciency Analysis Models Copyright 2010 Ke Li... of the requirements for the degree of DOCTOR OF PHILOSOPHY Approved by: Chair of Committee, Ximing Wu Committee Members, David Bessler H. Alan Love Qi Li Head of Department, John P. Nichols December 2010 Major Subject: Agricultural Economics iii ABSTRACT...
Straub, John E.
Journal of Molecular Graphics and Modelling 22 (2004) 441Â450 Continuous anisotropic representation improved performance. Novel graphical representations are developed and used to depict the orientational
EMP (electromagnetic pulse) representational tools for personal workstations
Riley, A.; Shafer, D.
1987-01-01T23:59:59.000Z
The ability to rapidly provide a visual representation of a problem set, its accompanying environment, and the variables that directly impact the analysis is of enormous value to the weapons analyst. Parametric, first-principle tools are directly and immediately usable by the analyst to represent the systems under investigation and the effects on those systems by the weapons under analysis. The three tools described, GEOREP, 3-AXIS, and G RANGE, provide these visual, analytic tools directly to the analyst on personal computer workstations. The simplicity and rapidity with which these tools may be used are especially beneficial to weapons analysts dealing with complex phenomena such as EMP. The potential flexibility of these representational tools is shown through examples of notional weapons applications. Use of GEOREP, 3-AXIS, and G RANGE, which augment, rather than supplant, complex weapons effects physics codes, can help provide the necessary, cost-effective guidance for making decisions on detailed case studies.
Shifted-elementary-mode representation for partially coherent vectorial fields
Tervo, Jani; Vahimaa, Pasi; Wyrowski, Frank
2010-01-01T23:59:59.000Z
A representation of partially spatially coherent and partially polarized stationary electromagnetic fields is given in terms of mutually uncorrelated, transversely shifted, fully coherent and polarized elementary electric-field modes. This representation allows one to propagate non-paraxial partially coherent vector fields using techniques for spatially fully coherent fields, which are numerically far more efficient than methods for propagating correlation functions. A procedure is given to determine the elementary modes from the radiant intensity and the far-zone polarization properties of the entire field. The method is applied to quasihomogeneous fields with rotationally symmetric cosine-modulated radiant intensity distributions. This is an adequate model for fields emitted by, e.g., many light-emitting diodes.
Improved data representation for three-dimensional analysis
Olah, Desiree Jeanine
1992-01-01T23:59:59.000Z
. OB JECTIVES The purpose of this research was to evaluate the capabilities of spatially-oriented software to improve data representation for three-dimensional analysis. The specific objectives were to: I) Select appropriate spatial analysis... to perform their study. Manley and Tallet (1990) found that the 1VM gave them the capability to calculate volumes between complex surfaces within user defined regions and to provide them with information about specific features or water masses that are far...
The bosonic Fock representation and a generalized Shale theorem
P. L. Robinson
2012-03-26T23:59:59.000Z
We detail a new approach to the bosonic Fock representation of a complex Hilbert space V: our account places the bosonic Fock space S[V] between the symmetric algebra SV and its full antidual SV'; in addition to providing a context in which arbitrary (not necessarily restricted) real symplectic automorphisms of V are implemented, it offers simplified proofs of many standard results of the theory.
The bosonic Fock representation and a generalized Shale theorem
Robinson, P L
2012-01-01T23:59:59.000Z
We detail a new approach to the bosonic Fock representation of a complex Hilbert space V: our account places the bosonic Fock space S[V] between the symmetric algebra SV and its full antidual SV'; in addition to providing a context in which arbitrary (not necessarily restricted) real symplectic automorphisms of V are implemented, it offers simplified proofs of many standard results of the theory.
Mixing of fermions and spectral representation of propagator
Kaloshin, A E
2015-01-01T23:59:59.000Z
We develop the spectral representation of propagator for $n$ mixing fermion fields in case of $\\mathsf{P}$-parity violation. Solving of the eigenstate problem for inverse matrix propagator allows to build the system of orthogonal projectors and to represent the matrix propagator as a sum of poles with positive and negative energy. The procedure of multiplicative renormalization is investigated, the renormalization matrices are obtained in a closed form without using of perturbation theory.
Hydrogen atom in phase space: The Wigner representation
L. Praxmeyer; J. Mostowski; K. Wodkiewicz
2005-04-06T23:59:59.000Z
We have found an effective method of calculating the Wigner function, being a quantum analogue of joint probability distribution of position and momentum, for bound states of nonrelativistic hydrogen atom. The formal similarity between the eigenfunctions of nonrelativistic hydrogen atom in the momentum representation and Klein-Gordon propagators has allowed the calculation of the Wigner function for an arbitrary bound state of the hydrogen atom. These Wigner functions for some low lying states are depicted and discussed.
Quasi-probability representations of quantum theory with applications to quantum information science
Christopher Ferrie
2011-10-15T23:59:59.000Z
This article comprises a review of both the quasi-probability representations of infinite-dimensional quantum theory (including the Wigner function) and the more recently defined quasi-probability representations of finite-dimensional quantum theory. We focus on both the characteristics and applications of these representations with an emphasis toward quantum information theory. We discuss the recently proposed unification of the set of possible quasi-probability representations via frame theory and then discuss the practical relevance of negativity in such representations as a criteria for quantumness.
Representation of analysis results involving aleatory and epistemic uncertainty.
Johnson, Jay Dean (ProStat, Mesa, AZ); Helton, Jon Craig (Arizona State University, Tempe, AZ); Oberkampf, William Louis; Sallaberry, Cedric J.
2008-08-01T23:59:59.000Z
Procedures are described for the representation of results in analyses that involve both aleatory uncertainty and epistemic uncertainty, with aleatory uncertainty deriving from an inherent randomness in the behavior of the system under study and epistemic uncertainty deriving from a lack of knowledge about the appropriate values to use for quantities that are assumed to have fixed but poorly known values in the context of a specific study. Aleatory uncertainty is usually represented with probability and leads to cumulative distribution functions (CDFs) or complementary cumulative distribution functions (CCDFs) for analysis results of interest. Several mathematical structures are available for the representation of epistemic uncertainty, including interval analysis, possibility theory, evidence theory and probability theory. In the presence of epistemic uncertainty, there is not a single CDF or CCDF for a given analysis result. Rather, there is a family of CDFs and a corresponding family of CCDFs that derive from epistemic uncertainty and have an uncertainty structure that derives from the particular uncertainty structure (i.e., interval analysis, possibility theory, evidence theory, probability theory) used to represent epistemic uncertainty. Graphical formats for the representation of epistemic uncertainty in families of CDFs and CCDFs are investigated and presented for the indicated characterizations of epistemic uncertainty.
Transition representations of quantum evolution with application to scattering resonances
Strauss, Y. [Einstein Institute of Mathematics, Hebrew University of Jerusalem, Jerusalem 91904 (Israel)
2011-03-15T23:59:59.000Z
A Lyapunov operator is a self-adjoint quantum observable whose expectation value varies monotonically as time increases and may serve as a marker for the flow of time in a quantum system. In this paper it is shown that the existence of a certain type of Lyapunov operator leads to representations of the quantum dynamics, termed transition representations, in which an evolving quantum state {psi}(t) is decomposed into a sum {psi}(t) ={psi}{sup b}(t) +{psi}{sup f}(t) of a backward asymptotic component and a forward asymptotic component such that the evolution process is represented as a transition from {psi}{sup b}(t) to {psi}{sup f}(t). When applied to the evolution of scattering resonances, such transition representations separate the process of decay of a scattering resonance from the evolution of outgoing waves corresponding to the probability 'released' by the resonance and carried away to spatial infinity. This separation property clearly exhibits the spatial probability distribution profile of a resonance. Moreover, it leads to the definition of exact resonance states as elements of the physical Hilbert space corresponding to the scattering problem. These resonance states evolve naturally according to a semigroup law of evolution.
Paris-Sud XI, UniversitÃ© de
Nachteil tritt nicht auf, wenn die Regression der zukÃ¼nftigen TÃ¶chter auf die durchsch- nittliche Differenz
A near infrared regression model for octane measurements in gasolines which contain MTBE
Maggard, S.M. (Ashland Petroleum Co., KY (USA))
1990-01-01T23:59:59.000Z
Near infrared (NIR) spectroscopy has emerged as a superior technique for the on-line determination of octane during the blending of gasoline. This results from the numerous advantages that NIR spectroscopy has over conventional on-line instrumentation. Methyl t-butyl ether (MTBE) is currently the oxygenated blending component of choice. MTBE is advantageous because it has a high blending octane, a low Reid vapor pressure, is relatively cheap, and does not form peroxides (1). The goal of this project was to develop a NIR regression model that could be used to predict pump octanes regardless of whether they contained MTBE.
Hofland, G.S.; Barton, C.C.
1990-10-01T23:59:59.000Z
The computer program FREQFIT is designed to perform regression and statistical chi-squared goodness of fit analysis on one-dimensional or two-dimensional data. The program features an interactive user dialogue, numerous help messages, an option for screen or line printer output, and the flexibility to use practically any commercially available graphics package to create plots of the program`s results. FREQFIT is written in Microsoft QuickBASIC, for IBM-PC compatible computers. A listing of the QuickBASIC source code for the FREQFIT program, a user manual, and sample input data, output, and plots are included. 6 refs., 1 fig.
U. T. Henshaw
This study investigated the use of soya bean oil and palm kernel oil as alternatives to mineral oil in a transformer system. Crude samples of these oils and their blend in varied proportions were tested for dielectric strength, pour point, flash point, kinematic viscosity, density and moisture content. The results showed that soya bean oil and palm kernel oil have good properties to act as insulating and cooling liquid in a transformer. These properties could be further improved when the oils are refined and purified. Soya bean oil and palm kernel oil have dielectric strengths of 39 kV and 25 kV respectively in their crude states compared with transformer (mineral) oil which has a maximum dielectric strength of 50 kV. Blend of soya bean oil and palm kernel oil showed synergy only in pour point and viscosity. The results of the study further showed that soya bean oil and palm kernel oil and their blends have very high flash points of 234°C and 242°C respectively. In terms of economic costs and environmental considerations, soya bean oil and palm kernel oil appear to be viable alternatives to transformer oils
VB-algebroids and representation theory of Lie algebroids
Gracia-Saz, Alfonso
2008-01-01T23:59:59.000Z
A VB-algebroid is essentially defined as a Lie algebroid object in the category of vector bundles. There is a one-to-one correspondence between VB-algebroids and certain flat Lie algebroid superconnections, up to a natural notion of equivalence. In this setting, we are able to construct characteristic classes, which in special cases reproduce characteristic classes constructed by Crainic and Fernandes. We give a complete classification of regular VB-algebroids, and in the process we obtain another characteristic class of Lie algebroids that does not appear in the ordinary representation theory of Lie algebroids.
Separable Representation of Proton-Nucleus Optical Potentials
L. Hlophe; V. Eremenko; Ch. Elster; F. M. Nunes; G. Arbanas; J. E. Escher; I. J. Thompson
2014-09-14T23:59:59.000Z
Recently, a new approach for solving the three-body problem for (d,p) reactions in the Coulomb-distorted basis in momentum space was proposed. Important input quantities for such calculations are the scattering matrix elements for proton- and neutron-nucleus scattering. We present a generalization of the Ernst-Shakin-Thaler scheme in which a momentum space separable representation of proton-nucleus scattering matrix elements can be calculated in the Coulomb basis. The viability of this method is demonstrated by comparing S-matrix elements obtained for p$+^{48}$Ca and p$+^{208}$Pb for a phenomenological optical potential with corresponding coordinate space calculations.
Hyperforests on the Complete Hypergraph by Grassmann Integral Representation
Bedini, Andrea; Sportiello, Andrea
2008-01-01T23:59:59.000Z
We study the generating function of rooted and unrooted hyperforests in a general complete hypergraph with n vertices by using a novel Grassmann representation of their generating functions. We show that this new approach encodes the known results about the exponential generating functions for the different number of vertices. We consider also some applications as counting hyperforests in the k-uniform complete hypergraph and the one complete in hyperedges of all dimensions. Some general feature of the asymptotic regimes for large number of connected components is discussed.
Hyperforests on the Complete Hypergraph by Grassmann Integral Representation
Andrea Bedini; Sergio Caracciolo; Andrea Sportiello
2008-02-11T23:59:59.000Z
We study the generating function of rooted and unrooted hyperforests in a general complete hypergraph with n vertices by using a novel Grassmann representation of their generating functions. We show that this new approach encodes the known results about the exponential generating functions for the different number of vertices. We consider also some applications as counting hyperforests in the k-uniform complete hypergraph and the one complete in hyperedges of all dimensions. Some general feature of the asymptotic regimes for large number of connected components is discussed.
Review of structure representation and reconstruction on mesoscale and microscale
Li, Dongsheng
2014-05-01T23:59:59.000Z
Structure representation and reconstruction on mesoscale and microscale is critical in material design, advanced manufacturing and multiscale modeling. Microstructure reconstruction has been applied in different areas of materials science and technology, structural materials, energy materials, geology, hydrology, etc. This review summarizes the microstructure descriptors and formulations used to represent and algorithms to reconstruct structures at microscale and mesoscale. In the stochastic methods using correlation function, different optimization approaches have been adapted for objective function minimization. A variety of reconstruction approaches are compared in efficiency and accuracy.
Gupta, N
2008-04-22T23:59:59.000Z
3013 containers are designed in accordance with the DOE-STD-3013-2004. These containers are qualified to store plutonium (Pu) bearing materials such as PuO2 for 50 years. DOT shipping packages such as the 9975 are used to store the 3013 containers in the K-Area Material Storage (KAMS) facility at Savannah River Site (SRS). DOE-STD-3013-2004 requires that a comprehensive surveillance program be set up to ensure that the 3013 container design parameters are not violated during the long term storage. To ensure structural integrity of the 3013 containers, thermal analyses using finite element models were performed to predict the contents and component temperatures for different but well defined parameters such as storage ambient temperature, PuO{sub 2} density, fill heights, weights, and thermal loading. Interpolation is normally used to calculate temperatures if the actual parameter values are different from the analyzed values. A statistical analysis technique using regression methods is proposed to develop simple polynomial relations to predict temperatures for the actual parameter values found in the containers. The analysis shows that regression analysis is a powerful tool to develop simple relations to assess component temperatures.
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Collins, John; Rogers, Ted
2015-04-01T23:59:59.000Z
There is considerable controversy about the size and importance of non-perturbative contributions to the evolution of transverse momentum dependent (TMD) parton distribution functions. Standard fits to relatively high-energy Drell-Yan data give evolution that when taken to lower Q is too rapid to be consistent with recent data in semi-inclusive deeply inelastic scattering. Some authors provide very different forms for TMD evolution, even arguing that non-perturbative contributions at large transverse distance bT are not needed or are irrelevant. Here, we systematically analyze the issues, both perturbative and non-perturbative. We make a motivated proposal for the parameterization of the non-perturbative part ofmore »the TMD evolution kernel that could give consistency: with the variety of apparently conflicting data, with theoretical perturbative calculations where they are applicable, and with general theoretical non-perturbative constraints on correlation functions at large distances. We propose and use a scheme- and scale-independent function A(bT) that gives a tool to compare and diagnose different proposals for TMD evolution. We also advocate for phenomenological studies of A(bT) as a probe of TMD evolution. The results are important generally for applications of TMD factorization. In particular, they are important to making predictions for proposed polarized Drell- Yan experiments to measure the Sivers function.« less
John Collins; Ted Rogers
2015-05-08T23:59:59.000Z
There is considerable controversy about the size and importance of nonperturbative contributions to the evolution of transverse-momentum-dependent (TMD) parton distribution functions. Standard fits to relatively high-energy Drell-Yan data give evolution that when taken to lower Q is too rapid to be consistent with recent data in semi-inclusive deeply inelastic scattering. Some authors provide very different forms for TMD evolution, even arguing that nonperturbative contributions at large transverse distance b_T are not needed or are irrelevant. Here, we systematically analyze the issues, both perturbative and nonperturbative. We make a motivated proposal for the parameterization of the nonperturbative part of the TMD evolution kernel that could give consistency: with the variety of apparently conflicting data, with theoretical perturbative calculations where they are applicable, and with general theoretical nonperturbative constraints on correlation functions at large distances. We propose and use a scheme- and scale-independent function A(b_T) that gives a tool to compare and diagnose different proposals for TMD evolution. We also advocate for phenomenological studies of A(b_T) as a probe of TMD evolution. The results are important generally for applications of TMD factorization. In particular, they are important to making predictions for proposed polarized Drell-Yan experiments to measure the Sivers function.
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Collins, John; Rogers, Ted
2015-04-01T23:59:59.000Z
There is considerable controversy about the size and importance of non-perturbative contributions to the evolution of transverse momentum dependent (TMD) parton distribution functions. Standard fits to relatively high-energy Drell-Yan data give evolution that when taken to lower Q is too rapid to be consistent with recent data in semi-inclusive deeply inelastic scattering. Some authors provide very different forms for TMD evolution, even arguing that non-perturbative contributions at large transverse distance bT are not needed or are irrelevant. Here, we systematically analyze the issues, both perturbative and non-perturbative. We make a motivated proposal for the parameterization of the non-perturbative part of the TMD evolution kernel that could give consistency: with the variety of apparently conflicting data, with theoretical perturbative calculations where they are applicable, and with general theoretical non-perturbative constraints on correlation functions at large distances. We propose and use a scheme- and scale-independent function A(bT) that gives a tool to compare and diagnose different proposals for TMD evolution. We also advocate for phenomenological studies of A(bT) as a probe of TMD evolution. The results are important generally for applications of TMD factorization. In particular, they are important to making predictions for proposed polarized Drell- Yan experiments to measure the Sivers function.
Global Representation of the Fine Structure Constant and its Variation
Michael Edmund Tobar
2005-02-06T23:59:59.000Z
The fine structure constant, alpha, is shown to be proportional to the ratio of the quanta of electric and magnetic flux of force of the electron, and provides a new representation, which is global across all unit systems. Consequently, a variation in alpha was shown to manifest due to a differential change in the fraction of the quanta of electric and magnetic flux of force, while a variation in hcross.c was shown to manifest due to the common mode change. The representation is discussed with respect to the running of the fine structure constant at high energies (small distances), and a putative temporal drift. It is shown that the running of the fine structure constant is due to equal components of electric screening (polarization of vacuum) and magnetic anti-screening (magnetization of vacuum), which cause the perceived quanta of electric charge to increase at small distances, while the magnetic flux quanta decreases. This introduces the concept of the bare magnetic flux quanta as well as the bare electric charge. With regards to temporal drift, it is confirmed that it is impossible to determine which fundamental constant is varying if alpha varies.
Brutlag, Doug
Stochastic Roadmap Simulation: An efficient Representation and Algorithm for Analyzing Molecular. This paper introduces Stochastic Roadmap Simulation (SRS) as a new computational approach for explor- ing
Method of Equivalencing for a Large Wind Power Plant with Multiple Turbine Representation: Preprint
Muljadi, E.; Pasupulati, S.; Ellis, A.; Kosterov, D.
2008-07-01T23:59:59.000Z
This paper focuses on our effort to develop an equivalent representation of a Wind Power Plant collector system for power system planning studies.
How Does Representation Modality Affect User-Experience of Data Artifacts?
Hornecker, Eva
. Representations in the form of demographic statistics, financial reports, environmental data, economic trends' experience of technology (cf. [2]). Phenomenology, from a philosophical perspective, is concerned with people
Johnston, Walter Edward
1965-01-01T23:59:59.000Z
*) )() yx V = y* ? ie x* yx 2 n 2 2 n 21 Z (yp-y*) -g ?Z (x -x") J/n, yx rl l n x* Q x(f ~ y fyiq) * n& ', n)(j Thus the estimates of p, tT, and p are obtained by solving (B. 4) 2 y y These estimates are (s. s) m y))) +P (x x*) Y yx 2 rl q /2 *2... factors, which are either fixed or follow a multinormal distribution, employed in the n repetitions of the experiment, B = a (p x 1) matrix of unknown partial regression coeffictents to be estimated. The approach to the problem of missing data...
Using Focused Regression for Accurate Time-Constrained Scaling of Scientific Applications
Barnes, B; Garren, J; Lowenthal, D; Reeves, J; de Supinski, B; Schulz, M; Rountree, B
2010-01-28T23:59:59.000Z
Many large-scale clusters now have hundreds of thousands of processors, and processor counts will be over one million within a few years. Computational scientists must scale their applications to exploit these new clusters. Time-constrained scaling, which is often used, tries to hold total execution time constant while increasing the problem size along with the processor count. However, complex interactions between parameters, the processor count, and execution time complicate determining the input parameters that achieve this goal. In this paper we develop a novel gray-box, focused median prediction errors are less than 13%. regression-based approach that assists the computational scientist with maintaining constant run time on increasing processor counts. Combining application-level information from a small set of training runs, our approach allows prediction of the input parameters that result in similar per-processor execution time at larger scales. Our experimental validation across seven applications showed that median prediction errors are less than 13%.
General model selection estimation of a periodic regression with a Gaussian noise
Konev, Victor; 10.1007/s10463-008-0193-1
2010-01-01T23:59:59.000Z
This paper considers the problem of estimating a periodic function in a continuous time regression model with an additive stationary gaussian noise having unknown correlation function. A general model selection procedure on the basis of arbitrary projective estimates, which does not need the knowledge of the noise correlation function, is proposed. A non-asymptotic upper bound for quadratic risk (oracle inequality) has been derived under mild conditions on the noise. For the Ornstein-Uhlenbeck noise the risk upper bound is shown to be uniform in the nuisance parameter. In the case of gaussian white noise the constructed procedure has some advantages as compared with the procedure based on the least squares estimates (LSE). The asymptotic minimaxity of the estimates has been proved. The proposed model selection scheme is extended also to the estimation problem based on the discrete data applicably to the situation when high frequency sampling can not be provided.
Rao, Pramod; Escudier, Bernard; Baere, Thierry de, E-mail: debaere@igr.fr [Institut Gustave Roussy, Department of Interventional Radiology (France)
2011-04-15T23:59:59.000Z
We report two cases of spontaneous regression of multiple pulmonary metastases occurring after radiofrequency ablation (RFA) of a single lung metastasis. To the best of our knowledge, these are the first such cases reported. These two patients presented with lung metastases progressive despite treatment with interleukin-2, interferon, or sorafenib but were safely ablated with percutaneous RFA under computed tomography guidance. Percutaneous RFA allowed control of the targeted tumors for >1 year. Distant lung metastases presented an objective response despite the fact that they received no targeted local treatment. Local ablative techniques, such as RFA, induce the release of tumor-degradation product, which is probably responsible for an immunologic reaction that is able to produce a response in distant tumors.
Sample size for logistic regression with small response probability. Technical report No. 33
Whittemore, A S
1980-03-01T23:59:59.000Z
The Fisher information matrix for the estimated parameters in a multiple logistic regression can be approximated by the augmented Hessian matrix of the moment generating function for the covariates. The approximation is valid when the probability of response is small. With its use one can obtain a simple closed-form estimate of the asymptotic covariance matrix of the maximum-likelihood parameter estimates, and thus approximate sample sizes needed to test hypotheses about the parameters. The method is developed for selected distributions of a single covariate, and for a class of exponential-type distributions of several covariates. It is illustrated with an example concerning risk factors for coronary heart disease. 2 figures, 2 tables.
Soldan, D. L.; Ahmed, N.; Stearns, S. D.
1980-01-01T23:59:59.000Z
The use of the sequential regression (SER) algorithm (Electron. Lett., 14, 118(1978); 13, 446(1977)) for long-term processing applications is limited by two problems that can occur when an SER predictor has more weights than required to predict the input signal. First, computational difficulties related to updating the autocorrelation matrix inverse could arise, since no unique least-squares solution exists. Second, the predictor strives to remove very low-level components in the input, and hence could implement a gain function that is essentially zero over the entire passband. The predictor would then tend to become a no-pass filter which is undesirable in certain applications, e.g., intrusion detection (SAND--78-1032). Modifications to the SER algorithm that overcome the above problems are presented, which enable its use for long-term signal processing applications. 3 figures.
Trilce Estrada-Piedra; Juan Pablo Torres-Papaqui; Roberto Terlevich; Olac Fuentes; Elena Terlevich
2003-11-28T23:59:59.000Z
We present a new technique to segregate old and young stellar populations in galactic spectra using machine learning methods. We used an ensemble of classifiers, each classifier in the ensemble specializes in young or old populations and was trained with locally weighted regression and tested using ten-fold cross-validation. Since the relevant information concentrates in certain regions of the spectra we used the method of sequential floating backward selection offline for feature selection. The application to Seyfert galaxies proved that this technique is very insensitive to the dilution by the Active Galactic Nucleus (AGN) continuum. Comparing with exhaustive search we concluded that both methods are similar in terms of accuracy but the machine learning method is faster by about two orders of magnitude.
Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, the Pennsylvania State University, University Park, PA 16802, Unites States (United States)] [Department of Mathematics and Department of Meteorology, the Pennsylvania State University, University Park, PA 16802, Unites States (United States); Mahdi, Adam, E-mail: amahdi@ncsu.edu [Department of Mathematics, North Carolina State University, Raleigh, NC 27695 (United States)] [Department of Mathematics, North Carolina State University, Raleigh, NC 27695 (United States); Majda, Andrew J., E-mail: jonjon@cims.nyu.edu [Department of Mathematics and Center for Atmosphere and Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 (United States)
2014-01-15T23:59:59.000Z
A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partial noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model.
Atsumi, Kazushige [Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan); Shioyama, Yoshiyuki, E-mail: shioyama@radiol.med.kyushu-u.ac.jp [Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan); Arimura, Hidetaka [Department of Health Sciences, Kyushu University, Fukuoka (Japan); Terashima, Kotaro [Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan); Matsuki, Takaomi [Department of Health Sciences, Kyushu University, Fukuoka (Japan); Ohga, Saiji; Yoshitake, Tadamasa; Nonoshita, Takeshi; Tsurumaru, Daisuke; Ohnishi, Kayoko; Asai, Kaori; Matsumoto, Keiji [Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan); Nakamura, Katsumasa [Department of Radiology, Kyushu University Hospital at Beppu, Oita (Japan); Honda, Hiroshi [Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan)
2012-04-01T23:59:59.000Z
Purpose: To determine clinical factors for predicting the frequency and severity of esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer. Methods and Materials: The study group consisted of 109 patients with esophageal cancer of T1-4 and Stage I-III who were treated with definitive radiotherapy and achieved a complete response of their primary lesion at Kyushu University Hospital between January 1998 and December 2007. Esophageal stenosis was evaluated using esophagographic images within 3 months after completion of radiotherapy. We investigated the correlation between esophageal stenosis after radiotherapy and each of the clinical factors with regard to tumors and therapy. For validation of the correlative factors for esophageal stenosis, an artificial neural network was used to predict the esophageal stenotic ratio. Results: Esophageal stenosis tended to be more severe and more frequent in T3-4 cases than in T1-2 cases. Esophageal stenosis in cases with full circumference involvement tended to be more severe and more frequent than that in cases without full circumference involvement. Increases in wall thickness tended to be associated with increases in esophageal stenosis severity and frequency. In the multivariate analysis, T stage, extent of involved circumference, and wall thickness of the tumor region were significantly correlated to esophageal stenosis (p = 0.031, p < 0.0001, and p = 0.0011, respectively). The esophageal stenotic ratio predicted by the artificial neural network, which learned these three factors, was significantly correlated to the actual observed stenotic ratio, with a correlation coefficient of 0.864 (p < 0.001). Conclusion: Our study suggested that T stage, extent of involved circumference, and esophageal wall thickness of the tumor region were useful to predict the frequency and severity of esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer.
Graphics processing units accelerated semiclassical initial value representation molecular dynamics
Tamascelli, Dario; Dambrosio, Francesco Saverio [Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133 Milano (Italy)] [Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133 Milano (Italy); Conte, Riccardo [Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322 (United States)] [Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322 (United States); Ceotto, Michele, E-mail: michele.ceotto@unimi.it [Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano (Italy)] [Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano (Italy)
2014-05-07T23:59:59.000Z
This paper presents a Graphics Processing Units (GPUs) implementation of the Semiclassical Initial Value Representation (SC-IVR) propagator for vibrational molecular spectroscopy calculations. The time-averaging formulation of the SC-IVR for power spectrum calculations is employed. Details about the GPU implementation of the semiclassical code are provided. Four molecules with an increasing number of atoms are considered and the GPU-calculated vibrational frequencies perfectly match the benchmark values. The computational time scaling of two GPUs (NVIDIA Tesla C2075 and Kepler K20), respectively, versus two CPUs (Intel Core i5 and Intel Xeon E5-2687W) and the critical issues related to the GPU implementation are discussed. The resulting reduction in computational time and power consumption is significant and semiclassical GPU calculations are shown to be environment friendly.
Enhancement of Solar Energy Representation in the GCAM Model
Smith, Steven J.; Volke, April C.; Delgado Arias, Sabrina
2010-02-01T23:59:59.000Z
The representation of solar technologies in a research version of the GCAM (formerly MiniCAM) integrated assessment model have been enhanced to add technologies, improve the underlying data, and improve the interaction with the rest of the model. We find that the largest potential impact from the inclusion of thermal Concentrating Solar Power plants, which supply a substantial portion of electric generation in sunny regions of the world. Drawing on NREL research, domestic Solar Hot Water technologies have also been added in the United States region where this technology competes with conventional electric and gas technologies. PV technologies are as implemented in the CCTP scenarios, drawing on NREL cost curves for the United States, extrapolated to other world regions using a spatial analysis of population and solar resources.