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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
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
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
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
Rao-Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression
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
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
Open source software maturity model based on linear regression and Bayesian analysis
Zhang, Dongmin
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...
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
8. Neural Networks Over the years, linear regression models have attempted to characterise the 0.2% proof stress and
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
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
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
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
Inclusion of Building Envelope Thermal Lag Effects in Linear Regression Models of Daily Basis Building Energy Use Data
Masuda, H.; Claridge, D. E.
Inclusion?of?Building?Envelope?Thermal?Lag? Effects?in?Linear?Regression?Models?of?Daily? Basis?Building?Energy?Use?Data The?12th International?Conference?for?Enhanced?Building?Operations October?22nd?26th,?2012 Manchester,?UK Hiroko...?enhanced?building?operations. October?18?20,?2011,? Brooklyn,?NY. Rabl,?A.?and?Rialhe,?A.?(1992).?Energy?Signature?Models?for?Commercial?Buildings:?Test?with?Measured?Data?and?Interpretation. Energy?and?Buildings,?19,?143?154. Shao,?X.?and?Claridge,?D.E.?(2006).?Use?of?first?law...
Inclusion of Building Envelope Thermal Lag Effects in Linear Regression Models of Daily Basis Building Energy Use Data
Masuda, H.; Claridge, D. E.
Inclusion?of?Building?Envelope?Thermal?Lag? Effects?in?Linear?Regression?Models?of?Daily? Basis?Building?Energy?Use?Data The?12th International?Conference?for?Enhanced?Building?Operations October?22nd?26th,?2012 Manchester,?UK Hiroko...?enhanced?building?operations. October?18?20,?2011,? Brooklyn,?NY. Rabl,?A.?and?Rialhe,?A.?(1992).?Energy?Signature?Models?for?Commercial?Buildings:?Test?with?Measured?Data?and?Interpretation. Energy?and?Buildings,?19,?143?154. Shao,?X.?and?Claridge,?D.E.?(2006).?Use?of?first?law?energy?balance?as?a?screening?tool?for?building?energy...
In-situ prediction on sensor networks using distributed multiple linear regression models
Basha, Elizabeth (Elizabeth Ann)
Within sensor networks for environmental monitoring, a class of problems exists that requires in-situ control and modeling. In this thesis, we provide a solution to these problems, enabling model-driven computation where ...
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
Combining Regressive and Auto-Regressive Models for Spatial-Temporal Dragoljub Pokrajac firstname.lastname@example.com
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
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
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
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
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
Katz, Richard
weather) -- Software R open source statistical programming language: Function glm "Family;(2) Generalized Linear Models Statistical Framework -- Multiple Regression Analysis (Linear model or LM) Response
Non-Linear Continuum Regression Using Genetic Programming email@example.com
Fernandez, Thomas
Non-Linear Continuum Regression Using Genetic Programming Ben McKay firstname.lastname@example.com 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
Parsimonious additive logistic models Logistic regression is a standard tool in statistics for binary classification. The logistic model
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
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
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
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
Rudnyi, Evgenii B.
to apply. The algorithm to maximize a likelihood function in the case of a non-linear physico - the same variances of errors 3.1. One-way classification 3.2. Linear regression 4. Real case (vaporizationSTATISTICAL MODEL OF SYSTEMATIC ERRORS: LINEAR ERROR MODEL E.B. Rudnyi Department of Chemistry
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
Dirichlet Process Mixtures of Generalized Linear Models Lauren A. Hannah David M. Blei Warren B. Powell
Blei, David M.
a linear transformation of co- variates through a possibly non-linear link function to generate a response of generalized linear models (DP-GLMs), a Bayesian nonparametric regression model that combines the advantages of gen- eralized linear models with the flexibility of nonpara- metric regression. A DP-GLM produces
PD Dr. Martin Stetter, Siemens AG 1 Das lineare Modell
Popeea, Corneliu - Chair for Foundations of Software Reliability and Theoretical Computer Science
PD Dr. Martin Stetter, Siemens AG 1 Das lineare Modell · Ausgangspunkt: Lineares Perceptron vorgegeben, werden nicht gelernt #12;PD Dr. Martin Stetter, Siemens AG 2 · Geschrieben als Regressionsmodell Regression: Lineares Modell #12;PD Dr. Martin Stetter, Siemens AG 3 · ML-Parameterschätzung des linearen
Shaon Sahoo; Soumya Kanti Ganguly
Contrary to the actual nonlinear Glauber model (NLGM), the linear Glauber model (LGM) is exactly solvable, although the detailed balance condition is not generally satisfied. This motivates us to address the issue of writing the transition rate ($w_j$) in a best possible linear form such that the mean squared error in satisfying the detailed balance condition is least. The advantage of this work is that, by studying the LGM analytically, we will be able to anticipate how the kinetic properties of an arbitrary Ising system depend on the temperature and the coupling constants. The analytical expressions for the optimal values of the parameters involved in the linear $w_j$ are obtained using a simple Moore-Penrose pseudoinverse matrix. This approach is quite general, in principle applicable to any system and can reproduce the exact results for one dimensional Ising system. In the continuum limit, we get a linear time-dependent Ginzburg-Landau (TDGL) equation from the Glauber's microscopic model of non-conservative dynamics. We analyze the critical and dynamic properties of the model, and show that most of the important results obtained in different studies can be reproduced by our new mathematical approach. We will also show in this paper that the effect of magnetic field can easily be studied within our approach; in particular, we show that the inverse of relaxation time changes quadratically with (weak) magnetic field and that the fluctuation-dissipation theorem is valid for our model.
Paris-Sud XI, Université de
to this family of approaches, non-linear transformation of the original data set [7, 3] too. The auto-associative neural networks can also be view as a non-linear PCA model [2, 27, 4, 19]. In [13] we propose the auto that the projection function is linear and let the regression function be arbitrary. We call the resulting AAM
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
Non-asymptotic Adaptive Prediction in Functional Linear Models Elodie Brunel, Andre Mas, and Angelina Roche
Paris-Sud XI, Université de
Non-asymptotic Adaptive Prediction in Functional Linear Models ´Elodie Brunel, Andr´e Mas, and Angelina Roche I3M, Universit´e Montpellier II Abstract Functional linear regression has recently attracted. Functional linear regression, functional principal components analysis, mean squared prediction error
DOI 10.1007/s10994-013-5423-y Least-squares independence regression for non-linear
Kaski, Samuel
Mach Learn DOI 10.1007/s10994-013-5423-y Least-squares independence regression for non-linear 2011 / Accepted: 9 November 2013 © The Author(s) 2013 Abstract The discovery of non-linear causal method. Keywords Causal inference · Non-linear · Non-Gaussian · Squared-loss mutual information · Least
Evidence for a non-universal Kennicutt-Schmidt relationship using hierarchical Bayesian linear regression
Shetty, Rahul; Bigiel, Frank
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...
The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation
Jahandideh, Sepideh [Department of Hospital Management, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of)], E-mail: email@example.com; Jahandideh, Samad [Department of Medical Physics, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Asadabadi, Ebrahim Barzegari [Department of Biophysics, Faculty of Science, Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Askarian, Mehrdad [Department of Community Medicine, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Movahedi, Mohammad Mehdi [Department of Medical Physics, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Hosseini, Somayyeh [Department of Biochemistry, Division of Genetics, Tabriz University of Medical Sciences, Tabriz (Iran, Islamic Republic of); Jahandideh, Mina [Department of Mathematics, Faculty of Science, Vali-E-Asr University, Rafsanjan (Iran, Islamic Republic of)
Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.
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
Topics in Microeconometrics: Estimation of a Dynamic Model of Occupational Transitions, Wage and Non-Wage Benefits Cross Validation Bandwidth Selection for Derivatives of Various Dimensional Densities Testing the Additive Separability of the Teacher Value Added Effect Semiparametrically
Baird, Matthew David
additively non-separable linear regression model. First,the additively non-separable linear regression model matchesThe additively non-separable linear regression model nests
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
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
Regression models Consequences of auto-generation
McCullagh, Peter
with conventional models 2 Consequences of auto-generation Sampling bias Volunteer samples Joint distributions of Y into two components x1(u) = (variety(u), treatment(u), . . .) affecting the mean x2(u) = (block; ) = Nn(X1, 2 0In + 2 1K[x2])(A) A Rn, K[x] = {K(xi, xj)} block-factor models: K(i, j) = 1 if block
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
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
Censored regression modeling in agricultural economics
Khee-Guan Tan, Andrew
error or omitted variable problem. According to Heckman, it is possible to correct for the above problem by first 10 estimating the omitted variable, X;. Using probit analysis, X; is consistently estimated as the inverse of Mill's ratio, f... is, in essence, an extension of Hausman's asymptotic specification test to the censored model. Hausman's approach required that an estimate of Ex@ ? = N-iE(X'y), say ExY, be compared to an estimator which A is consistent and inefficient relative...
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
Partially linear models with unit roots
Juhl, Ted P.; Xiao, Z. J.
~y tH110021 * H11002 [y tH110021 * !~e t H11002 [e t H11001g~x t !H11002 [g~x t !! Zf t 2 H11001o p ~1!+ PARTIALLY LINEAR MODELS WITH UNIT ROOTS 897 The theorem holds because 1 N 2 ( tH110051 N ~y tH110021 * H11002 [y tH110021 * ! 2 Zf t 2 nE~f 2 !s v... in econometrics+ One type of these models is the following partially linear regression: y t H11005g ' z t H11001g~x t !H11001e t , tH110051,+++,N, (1.1) where g~{! is an unknown real function and g is the vector of unknown param- eters that we want to estimate...
Efficient inference in general semiparametric regression models
Maity, Arnab
. Note that (2.17) means that the non-zero Y-data within an indi- vidual marginally have the same mean R T i ? 2 + ?(Z i ), variance ? 2 + ? 2 u2 and common covariance ? 2 u2 . II.4.2.3. Likelihood Function The collection of parameters is B, consisting... .............................. 4 II.1. Introduction ......................... 4 II.2. Semiparametric Models with a Single Component ..... 8 II.2.1. Main Results .................... 8 II.2.2. General Functions of the Response and Double- Robustness ..................... 11 II.3...
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
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
Agogino, Alice M.
residential and office buildings alike, low-cost, easily-installed retrofit energy. Of this, lighting accounts for 11% of energy use in residential buildings and 25% of the energy use in commercial buildings. Increased energy
Computer Methods and Programs in Biomedicine 65 (2001) 191200 A step-by-step guide to non-linear regression analysis of
Clement, Prabhakar
of this present study was to introduce a simple, easily understood method for carrying out non-linear regression: Microsoft Excel; Non-linear regression; Least squares; Iteration; Goodness of fit; Curve fit wwwComputer Methods and Programs in Biomedicine 65 (2001) 191200 A step-by-step guide to non-linear
Efficient Estimation in a Regression Model with Missing Responses
Crawford, Scott
This article examines methods to efficiently estimate the mean response in a linear model with an unknown error distribution under the assumption that the responses are missing at random. We show how the asymptotic variance is affected...
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
High dimensional linear inverse modelling
Cooper, Fenwick C
We introduce and demonstrate two linear inverse modelling methods for systems of stochastic ODE's with accuracy that is independent of the dimensionality (number of elements) of the state vector representing the system in question. Truncation of the state space is not required. Instead we rely on the principle that perturbations decay with distance or the fact that for many systems, the state of each data point is only determined at an instant by itself and its neighbours. We further show that all necessary calculations, as well as numerical integration of the resulting linear stochastic system, require computational time and memory proportional to the dimensionality of the state vector.
Introduction Improved Model Alternative Statistical Model
Regression Linear "Linear" is for the parameter(s) e.g. yi = 0 +1xi +i Non-linear "Non-linear Square Regression Linear "Linear" is for the parameter(s) e.g. yi = 0 +1xi +i #12;Introduction Improved Model Recall of Ordinary Least-Square Regression Least Square Regression Linear "Linear
Individual heterogeneity in life history processes: Estimation and applications of demographic models to stage-structured arthropod populations
Scranton, Katherine
cois, 2008. Non-linear regression models for approximateparameters. They use non-linear regression of parameters on
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
Deconvolution of antibody affinities and concentrations by non-linear regression analysis of competitive ELISA data.
Stevens, F. J.; Bobrovnik, S. A.; Biosciences Division; Palladin Inst. Biochemistry
Physiological responses of the adaptive immune system are polyclonal in nature whether induced by a naturally occurring infection, by vaccination to prevent infection or, in the case of animals, by challenge with antigen to generate reagents of research or commercial significance. The composition of the polyclonal responses is distinct to each individual or animal and changes over time. Differences exist in the affinities of the constituents and their relative proportion of the responsive population. In addition, some of the antibodies bind to different sites on the antigen, whereas other pairs of antibodies are sterically restricted from concurrent interaction with the antigen. Even if generation of a monoclonal antibody is the ultimate goal of a project, the quality of the resulting reagent is ultimately related to the characteristics of the initial immune response. It is probably impossible to quantitatively parse the composition of a polyclonal response to antigen. However, molecular regression allows further parameterization of a polyclonal antiserum in the context of certain simplifying assumptions. The antiserum is described as consisting of two competing populations of high- and low-affinity and unknown relative proportions. This simple model allows the quantitative determination of representative affinities and proportions. These parameters may be of use in evaluating responses to vaccines, to evaluating continuity of antibody production whether in vaccine recipients or animals used for the production of antisera, or in optimizing selection of donors for the production of monoclonal antibodies.
Journal of Machine Learning Research 1 (2011) 1-33 Submitted 9/09, Revised 7/10, 1/11; Published 5/11 Dirichlet Process Mixtures of Generalized Linear Models
Blei, David M.
characterizes the deviation of the response from its conditional mean. The simplest example is linear regression. Generalized linear models (GLMs) extend linear regression to many types of response variables (Mc a linear function; a non-linear function may be applied to the output of the linear function, but only one
Predictive Linear Regression Model for Microinverter Internal Temperature
Rollins, Andrew M.
, photovoltaic (PV) module temperature, irradiance and AC power data. Time-series environmental, temperature prediction, reliabil- ity, photovoltaic systems. I. INTRODUCTION PV modules equipped with microinverters have system. Reliability of microinverters in harsh and extreme real- world outdoor operating conditions has
Comparison of co-expression measures: mutual information, correlation, and model based indices
Song, Lin; Langfelder, Peter; Horvath, Steve
that non- linear association measures, especially regressioncontrast, regression models capture non-linear gene pairwiseand spline regression models to measure non-linear
Statistics & Gene Expression Data Analysis Note 8: Binary Regression Outcomes and classification probabilities
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
An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models
Harlim, John, E-mail: email@example.com [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: email@example.com [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: email@example.com [Department of Mathematics and Center for Atmosphere and Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 (United States)
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.
Reduced-rank Vector Generalized Linear Models Thomas W. Yee,
Hastie, Trevor
. Keywords: Canonical correspondence analysis; Linear discriminant analysis; Neural networks; Non- parametric the reduced-rank regression idea has been applied to non-Gaussian errors is the MLM. This was proposed such as neural networks, projection pursuit regression, linear discriminant analysis, canonical correspondence
Multi-Anticipative Piecewise-Linear Car-Following Model
Nadir Farhi; Habib Haj-Salem; Jean-Patrick Lebacque
We propose in this article an extension of the piecewise linear car-following model to multi-anticipative driving. As in the one-car-anticipative model, the stability and the stationary regimes are characterized thanks to a variational formulation of the car-dynamics. We study the homogeneous driving case. We show that in term of the stationary regime, the multi-anticipative model guarantees the same macroscopic behavior as for the one-car-anticipative one. Nevertheless, in the transient traffic, the variance in car-velocities and accelerations is mitigated by the multi-anticipative driving, and the car-trajectories are smoothed. A parameter identification of the model is made basing on NGSIM data and using a piecewise linear regression approach.
Multiple linear regression: fixed and multinormal data with missing observations among the independent variables
Johnston, Walter Edward
*) )() 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...
Photon emission within the linear sigma model
F. Wunderlich; B. Kampfer
Soft-photon emission rates are calculated within the linear sigma model. The investigation is aimed at answering the question to which extent the emissivities map out the phase structure of this particular effective model of strongly interacting matter.
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
Statistical Modeling of Daily Energy Consumption in Commercial Buildings Using Multiple Regression and Principal Component Analysis
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...
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
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
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.
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
Mining customer credit by using neural network model with logistic regression approach
Kao, Ling-Jing
. 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...
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
Linear regression analysis of emissions factors when firing fossil fuels and biofuels in a commercial water-tube boiler
Sharon Falcone Miller; Bruce G. Miller [Pennsylvania State University, University Park, PA (United States). Energy Institute
This paper compares the emissions factors for a suite of liquid biofuels (three animal fats, waste restaurant grease, pressed soybean oil, and a biodiesel produced from soybean oil) and four fossil fuels (i.e., natural gas, No. 2 fuel oil, No. 6 fuel oil, and pulverized coal) in Penn State's commercial water-tube boiler to assess their viability as fuels for green heat applications. The data were broken into two subsets, i.e., fossil fuels and biofuels. The regression model for the liquid biofuels (as a subset) did not perform well for all of the gases. In addition, the coefficient in the models showed the EPA method underestimating CO and NOx emissions. No relation could be studied for SO{sub 2} for the liquid biofuels as they contain no sulfur; however, the model showed a good relationship between the two methods for SO{sub 2} in the fossil fuels. AP-42 emissions factors for the fossil fuels were also compared to the mass balance emissions factors and EPA CFR Title 40 emissions factors. Overall, the AP-42 emissions factors for the fossil fuels did not compare well with the mass balance emissions factors or the EPA CFR Title 40 emissions factors. Regression analysis of the AP-42, EPA, and mass balance emissions factors for the fossil fuels showed a significant relationship only for CO{sub 2} and SO{sub 2}. However, the regression models underestimate the SO{sub 2} emissions by 33%. These tests illustrate the importance in performing material balances around boilers to obtain the most accurate emissions levels, especially when dealing with biofuels. The EPA emissions factors were very good at predicting the mass balance emissions factors for the fossil fuels and to a lesser degree the biofuels. While the AP-42 emissions factors and EPA CFR Title 40 emissions factors are easier to perform, especially in large, full-scale systems, this study illustrated the shortcomings of estimation techniques. 23 refs., 3 figs., 8 tabs.
Chemometric calibration of infrared spectrometers: selection and validation of variables by non-linear models
Verleysen, Michel
regression (PCR) and partial least squares regression (PLSR). Then, we will propose to incorporate non-linearChemometric calibration of infrared spectrometers: selection and validation of variables by non-linear (step by step) for the selection of spectral variables, using linear regression or neural networks
Robust Linearization of RF Amplifiers Using NonLinear Internal Model Control Method
Paris-Sud XI, Université de
Robust Linearization of RF Amplifiers Using NonLinear Internal Model Control Method Smail Bachir #1, the nonlinear Internal Model Control (IMC) method is introduced and applied to linearize high frequency Power to be controlled [8]. If the model is a perfect representation of the non linear system, the controller can
Bias in Predicting Annual Energy Use in Commercial Buildings with Regression Models Developed from Short Data Sets
Katipamula, S.; Reddy, T. A.; Claridge, D. E.
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...
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
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
Hastie, Trevor
GENERALIZED LINEAR MODELS WITH REGULARIZATION A DISSERTATION SUBMITTED TO THE DEPARTMENT Park 2006 All Rights Reserved ii #12;I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor
Introduction to Statistical Linear Models Spring 2005
of multivariate data and in the language of matrices and vectors. Broad introduction to MATLAB/Octave, R (SSyllabus Introduction to Statistical Linear Models 960:577:01 Spring 2005 Instructor: Farid Statistical Analysis" Fifth edition, Prentice Hall, 2002. Other sources may be required and will be posted
A Bayesian Bivariate Failure Time Regression Model by Paul Damien
West, Mike
. In engineering studies, reliability is defined as the probability that the system has not failed at time t . The reliability of a system using the model in (1) is given by R(t) = P (T 1 ? t; T 2 ? t) = exp(\\Gammaâ??t)[1 using Markov Chain Monte Carlo methods. Keywords: Gumbel Distribution, Gibbs Sampling, Correlation
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
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
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
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
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
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, Université de
Cooling energy demand evaluation by means of regression models obtained from dynamic simulations Ph, Université 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
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
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
Non-parametric regression and neural-network inll drilling recovery models for carbonate reservoirs
ValkĂł, Peter
, and operations e- ciency. Consequent to the primary recovery, water- ¯ood is often used as a secondary recoveryNon-parametric regression and neural-network in®ll drilling recovery models for carbonate ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in West Texas
Essays on Regression Spline Structural Nonparametric Stochastic Production Frontier Estimation and Inefficiency Analysis Models
Li, Ke
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...
Spectral learning of linear dynamics from generalised-linear observations
a non-linear and non-Gaussian observation process. We use this approach to obtain estimates to the generalised-linear regression model [8]), where the expected value of an observation is given by a monotonicSpectral learning of linear dynamics from generalised-linear observations with application
Datadriven calibration of linear estimators with minimal penalties
This paper tackles the problem of selecting among several linear estimators in non parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge classification, with linear and non linear predictors [37, 36]. A central issue common to all regularization
Data-driven calibration of linear estimators with minimal penalties
Paris-Sud XI, Université de
This paper tackles the problem of selecting among several linear estimators in non- parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge classification, with linear and non- linear predictors [37, 36]. A central issue common to all regularization
IEEE Transactions on Software Engineering (to appear). Empirical Studies of a Prediction Model for Regression Test
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
Diagnostics for multiple regression problems
Daly, J.C.
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.
A near infrared regression model for octane measurements in gasolines which contain MTBE
Maggard, S.M. (Ashland Petroleum Co., KY (USA))
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.
General model selection estimation of a periodic regression with a Gaussian noise
Konev, Victor; 10.1007/s10463-008-0193-1
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.
Adaptive nonparametric estimation in heteroscedastic regression models. Part 1: Sharp non-asymptotic Oracle inequalities
Galtchouk, Leonid
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
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
Factoring Gaussian Precision Matrices for Linear Dynamic Models
Frankel, Joe; King, Simon
The linear dynamic model (LDM), also known as the Kalman filter model, has been the subject of research in the engineering, control, and more recently, machine learning and speech technology communities. The Gaussian noise processes are usually...
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
Lineales: Algebraic Models of Linear Logic from a Categorical
de Paiva, Valeria
Lineales: Algebraic Models of Linear Logic from a Categorical Perspective Valeria de Paiva with category theory. A secondary aim is to argue for the virtues of Lineales as algebraic structures supporting This paper describes algebraic semantics for (intuitionistic and classical) propositional linear logic, using
Data-driven calibration of linear estimators with minimal Sylvain Arlot
This paper tackles the problem of selecting among several linear estimators in non- parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge, with linear and non-linear predictors [19, 18]. A central issue common to all regularization frameworks
Computational Reality XII Linear regression
Berlin,Technische Universität
Abstract We have seen many different examples where the balance equations are solved with some given material relations, to get the response of the matter subject to the loading. In this and the next have three equations to find two unknowns, here C, D. We actually needed two equation to solve two
A Linear Parabolic Trough Solar Collector Performance Model
Qu, M.; Archer, D.; Masson, S.
A performance model has been programmed for solar thermal collector based on a linear, tracking parabolic trough reflector focused on a surface-treated metallic pipe receiver enclosed in an evacuated transparent tube: a Parabolic Trough Solar...
Model Error Correction for Linear Methods in PET Neuroreceptor Measurements
Renaut, Rosemary
Model Error Correction for Linear Methods in PET Neuroreceptor Measurements Hongbin Guo address: email@example.com (Hongbin Guo) Preprint submitted to NeuroImage December 11, 2008 #12;reached. A new
Double Generalized Linear Models: Approximate REML and Diagnostics
Smyth, Gordon K.
added com plication in a generalized linear model setting by adjusting the working vector and working the dispersion will be of direct interest in its own right, to identify the sources of variability
Estimation of the linear-plateau segmented regression model in the presence of measurement error
Grimshaw, Scott D.
/c?) 4(~/o ) I (2-6) where 4(') is the standard normal density. Hence, letting = /m(YW)/o?, (2. 5) can be written as V [1 f fx(t) (( ) dz dt + f? j fx(t) 4( ) dz dt] m As the number of repeated observations is increased, 1im P [misclassif ication...] = / [lim C'(v )] f (t) dt + P [lim m(-v ) ] f (t) dt m x m x m~ by Lease B. l, = 0, since lim @(v ) = @(- ) for t & Y , m lim @(-v ) = @(~) for t & Y m Therefore, in the limit, the probability of misclassification is zero. When the join point, Y...
Nonlinear regression analysis of field emission data
Barry, Scott Wilson
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...
Error Control of Iterative Linear Solvers for Integrated Groundwater Models
Bai, Zhaojun
gradient method or Generalized Minimum RESidual (GMRES) method, is how to choose the residual tolerance for integrated groundwater models, which are implicitly coupled to another model, such as surface water models the correspondence between the residual error in the preconditioned linear system and the solution error. Using
Large scale nuclear sensor monitoring and diagnostics by means of an ensemble of regression models based on Evolving Clustering Methods
Boyer, Edmond
Large scale nuclear sensor monitoring and diagnostics by means of an ensemble of regression models , Enrico Ziob a Institute for Energy Technology, Halden, Norway b Polytechnic of Milan, Milan, Italy actions for safely steering critical situations and preventing accidents. To avoid misleading information
Bias Reduction and Goodness-of-Fit Tests in Conditional Logistic Regression Models
Sun, Xiuzhen
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.
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.
Assessing the reliability of linear dynamic transformer thermal modelling
Assessing the reliability of linear dynamic transformer thermal modelling X. Mao, D.J. Tylavsky and G.A. McCulla Abstract: Improving the utilisation of transformers requires that the hot-spot and top. An alternative method for assessing transformer model reliability is provided. 1 Introduction The maximally
Non-linear approximations for solving 3D-packing MIP models: a ...
MIP models: a heuristic approach ... three-dimensional packing, MIP/MINLP models, linear/non-linear ..... Springer Science + Business Media, New York.
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
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
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
Modeling spray impingement using linear stability theories for droplet shattering
DesJardin, Paul Edward; Yoon, Sam Sukgoo
This paper compares several linear-theory-based models for droplet shattering employed for simulations of spray impingement on flat wall surface or a circular cylinder. Numerical simulations are conducted using a stochastic separated flow (SSF) technique that includes sub-models for droplet dynamics and impact. Results for spray impingement over a flat wall indicate that the linear theory applicable for a single droplet impact over-predicts the number of satellite (or secondary) droplets upon shattering when compared to experimental data. The causes for the observed discrepancies are discussed. Numerical simulation results for spray impingement over for a circular cylinder in cross flow are obtained and discussed.
Jul 31, 2008 ... linear regression model subject to a bound on the l1-norm of the coefficients; .... this strategy scales poorly with the number of non-zero groups.
Regression analysis Dependent variable (WMMY)
Langseth, Helge
;64 65 #12;66 Multiple Linear Regression 67 Example  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
Integer linear programming models for a cement delivery problem
Hertz, Alain
Integer linear programming models for a cement delivery problem Alain Hertz D´epartement de email@example.com and email@example.com April 4, 2011 Abstract We consider a cement delivery problem with an heterogeneous in [14], [15] and [16] and are reviewed in [4]. In this paper, we study a cement delivery problem which
Bootstrap for model selection: linear approximation of the optimism
Verleysen, Michel
Bootstrap for model selection: linear approximation of the optimism G. Simon1 , A. Lendasse2 , M. Lemaître 4, B-1348 Louvain-la-Neuve, Belgium, email@example.com Abstract. The bootstrap resampling, as artificial neural networks. Nevertheless, the use of the bootstrap implies a high computational load
Katz, Richard
) Multisites (Spatial dependence of daily weather) -- Software R open source statistical programming language (Capable of "reproducing" any desired statistic) -- Disadvantages Synthetic weather looks too much like") Not amenable to uncertainty analysis #12;#12;#12;(2) Generalized Linear Models · Statistical Framework
absorption models linear: Topics by E-print Network
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
absorption models linear First Page Previous Page 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Next Page Last Page Topic Index 1 A Simulation of Ly-alpha...
Variable selection using Adaptive Non-linear Interaction Structures in High dimensions
Radchenko, Peter
superior predictive performance over other approaches. Some key words: Non-Linear Regression; InteractionsVariable selection using Adaptive Non-linear Interaction Structures in High dimensions Peter a tra- ditional linear regression model in which the number of predictors, p, is large relative
Efficient Online Classification using an Ensemble of Bayesian Linear Logistic Regressors
Vijayakumar, Sethu
a linear logistic regression as the base classifier with Bayesian learning for the regression The Randomly Varying Coefficient model approximates a multivariate non-linear function using a set of localEfficient Online Classification using an Ensemble of Bayesian Linear Logistic Regressors Narayanan
Phase transition in linear sigma model and disoriented chiral condensate
A. K. Chaudhuri
We have investigated the phase transition and disoriented chiral condensate domain formation in linear sigma model. Solving the equation of motion for the sigma model fields in contact with a heat bath, we have shown that the fields undergo phase transition above a certain critical temperature(T_c). It was also shown that when the fields thermalised at temperature above T_c are cooled down sufficiently rapidly, disoriented chiral condensate domains are formed quite late in the evolution.
Critical comparative analysis, validation and interpretation of SVM and PLS regression models in a QSAR study on HIV-1 protease inhibitors
Ferreira, Márcia M. C.
to be adequate for solving this problem. Besides, PLS allows limited modeling of non-linear relations by using new alternative to the existing linear and non-linear multivariate calibra- tion approaches and structural risk minimization. SVM is able to treat both, linear and non- linear data sets and control or even
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
Attracted to de Sitter: cosmology of the linear Horndeski models
Martin-Moruno, Prado; Lobo, Francisco S N
We consider Horndeski cosmological models, with a minisuperspace Lagrangian linear in the field derivative, that are able to screen any vacuum energy and material content leading to a spatially flat de Sitter vacuum fixed by the theory itself. Furthermore, we investigate particular models with a cosmic evolution independent of the material content and use them to understand the general characteristics of this framework. We also consider more realistic models, which we denote the "term-by-term" and "tripod" models, focusing attention on cases in which the critical point is indeed an attractor solution and the cosmological history is of particular interest.
Attracted to de Sitter: cosmology of the linear Horndeski models
Prado Martin-Moruno; Nelson J. Nunes; Francisco S. N. Lobo
We consider Horndeski cosmological models, with a minisuperspace Lagrangian linear in the field derivative, that are able to screen any vacuum energy and material content leading to a spatially flat de Sitter vacuum fixed by the theory itself. Furthermore, we investigate particular models with a cosmic evolution independent of the material content and use them to understand the general characteristics of this framework. We also consider more realistic models, which we denote the "term-by-term" and "tripod" models, focusing attention on cases in which the critical point is indeed an attractor solution and the cosmological history is of particular interest.
Chapter 16: Log-linear regression for Poisson counts Exposure to ionizing radiation is recognized as a cancer risk. In the United States, EPA
Bardsley, John
as a cancer risk. In the United States, EPA sets guidelines specifying upper limits on the amount of exposure groups than low exposure. The objective of regression analysis is to estimate the rate of cancer deaths cases or deaths attributable to cancer) using a number of explanatory variables believed to be related
Supplementary materials for this article are available online. Please click the JASA link at http://pubs.amstat.org. Linear or Nonlinear? Automatic Structure Discovery
Liu, Yufeng
selection; RKHS; Semiparametric regression; Shrinkage; Smoothing splines. 1. INTRODUCTION Linear to be linear and others to be non- linear. Partially linear models have wide applications in practice due://pubs.amstat.org. Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models Hao Helen ZHANG, Guang
Developing a gas purchasing strategy using a linear model
Alst, K.M. Van [Midland Cogeneration Venture Limited Partnership, Midland, MI (United States)
This paper outlines the process of developing a gas purchasing strategy with the use of a linear programming model. The linear model is used to determine the least cost approach regarding the acquisition of natural gas which has a considerable impact on the company`s financial performance. The author discusses the importance of optimizing gas costs from an end-user`s perspective. The Midland Cogeneration Venture (MCV) is the country`s largest cogeneration facility. The Facility has been certified by FERC (Federal Energy Regulatory Commission) as a Q.F. (Qualifying Facility) under PURPA (Public Utility Regulatory Policies Act of 1978). Unlike utilities, who have the ability to pass costs through to customers, MCV`s revenues are based on long-term contracts with its utility and industrial customers. Therefore, MCV cannot pass costs through to its customers. As such, effectively managing costs is vital to the success of the company.
Holographic transports and stability in anisotropic linear axion model
Xian-Hui Ge; Yi Ling; Chao Niu; Sang-Jin Sin
We study thermoelectric conductivities and shear viscosities in a holographically anisotropic model. Momentum relaxation is realized through perturbing the linear axion field. AC conductivity exhibits a conherent/incoherent metal transition. The longitudinal shear viscosity for prolate anisotropy violates the bound conjectured by Kovtun-Son-Starinets. We also find that thermodynamic and dynamical instabilities are not always equivalent, which provides a counter example of the Gubser-Mitra conjecture.
A Linear Circuit Model For Social Influence Analysis
Xiang, Biao; Liu, Qi; Xiong, Hui
Understanding the behaviors of information propagation is essential for the effective exploitation of social influence in social networks. However, few existing influence models are both tractable and efficient for describing the information propagation process and quantitatively measuring social influence. To this end, in this paper, we develop a linear social influence model, named Circuit due to its close relation to the circuit network. Based on the predefined four axioms of social influence, we first demonstrate that our model can efficiently measure the influence strength between any pair of nodes. Along this line, an upper bound of the node(s)' influence is identified for potential use, e.g., reducing the search space. Furthermore, we provide the physical implication of the Circuit model and also a deep analysis of its relationships with the existing methods, such as PageRank. Then, we propose that the Circuit model provides a natural solution to the problems of computing each single node's authority a...
The use of logistic regression to model the probability of oak wilt occurrence in the Texas hill country using forest stand and site characteristics
Dignum, David Rory
THE USE OF LOGISTIC REGRESSION TO MODEL THE PROBABILITY OF OAK MILT OCCURRENCE IN THE TEXAS HILL COUNTRY USING FOREST STAND AND SITE CHARACTERISTICS A Thesis by DAVID RORY DIGNUM Submitted to the Graduate College of Texas Afdi University... in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May 1988 Maj or Subj cot: Forestry THE USE OF LOGISTIC REGRESSION TO MODEL THE PROBABILITY OF OAK WILT OCCURRENCE IN THE TEXAS HILL COUNTRY USING FOREST STAND AND SITE...
The use of logistic regression to model the probability of oak wilt occurrence in the Texas hill country using forest stand and site characteristics
Dignum, David Rory
THE USE OF LOGISTIC REGRESSION TO MODEL THE PROBABILITY OF OAK MILT OCCURRENCE IN THE TEXAS HILL COUNTRY USING FOREST STAND AND SITE CHARACTERISTICS A Thesis by DAVID RORY DIGNUM Submitted to the Graduate College of Texas Afdi University... in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May 1988 Maj or Subj cot: Forestry THE USE OF LOGISTIC REGRESSION TO MODEL THE PROBABILITY OF OAK WILT OCCURRENCE IN THE TEXAS HILL COUNTRY USING FOREST STAND AND SITE...
BIOMETRICS55, 580-584 A Global Goodness-of-Fit Statistic for Cox Regression Models
Parzen, Michael
of the models would be helpful. Schoenfeld (1980), Moreau, O'Quigley, and Mesbah (1985), and Moreau, O'Quigley
Performance Assessment of Different Control Structures for Generic Reactive Distillation Using Linear and Nonlinear Process Models
Al-Arfaj, Muhammad A.
compares the closed-loop performance of three control structures using an approximate linear model. Responses based on the linear model for various control structures show a good agreement when compared of the linear model is shown to be better in a single-end control system than in a dual-end control system
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
Steam-circuit Model for the Compact Linear Fresnel Reflector , G. L. Morrison1
Steam-circuit Model for the Compact Linear Fresnel Reflector Prototype J. D. Pye1 , G. L. firstname.lastname@example.com Abstract The Compact Linear Fresnel Reflector (CLFR) is a linear-concentrating solar thermal energy system The Compact Linear Fresnel Reflector (CLFR) was first conceived of in 1992-1993 and was patented in 1995
Exploiting separability in large-scale linear support vector machine ...
Aug 7, 2007 ... universum classification, ordinal regression and ?-insensitive regression. .... ear, quadratic and non-linear optimization programmes.
MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification
Murphy, Robert F.
Allocation (LDA) (Blei et al., 2003) is an example of such models for textual documents. LDA posits that each stud- ied in text modeling (McCallum et al., 2006) and im- age analysis (Blei & Jordan, 2003). Recently, super- vised variants of LDA have been proposed, including the supervised LDA (sLDA) (Blei & Mc
Representing and querying regression models in a relational database management system
Thiagarajan, Arvind
Curve fitting is a widely employed, useful modeling tool in several financial, scientific, engineering and data mining applications, and in applications like sensor networks that need to tolerate missing or noisy data. ...
Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles
Kiliccote, Sila
Automated Demand Response in a Large Office Building", CECBuilding Control Strategies and Techniques for Demand Response",Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-Residential Buildings
Impact of Nighttime Shut Down on the Prediction Accuracy of Monthly Regression Models for Energy Consumption in Commercial Buildings
Wang, J.; Claridge, D. E.
the annual prediction error to 0.6% from -6.1 % . The modified heating regression models reduces the annual prediction error to 4.1% from 5.7%. Ec=6.6569+0.1 875(67.044-Tdb)'+0.6756(Tdb-67.044)7 Eh=0.909 1 -.3662(67.04-Tdb)'-.0462(Tdb-67.044)' Ec=5....8505+. 1736(67.7082-Tdb)*+.6794(Tdb-67.708)' ~h=0.97 18-0.341 (67.044-~db)'-0.0458(~db-67.044)+ CONCLUSIONS The results of the four cases studied indicate that when the AHUs operate 24 hours per day, the annual prediction error of the regular cooling...
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
Logistic regression models for predicting trip reporting accuracy in GPS-enhanced household travel surveys
Forrest, Timothy Lee
...................................................................................................... 47 7 Significance Levels of Variables in Each Model......................................... 49 A-1 Example of Household Data File Format (Laredo) ..................................... 60 A...-2 Example of Household Data File Format Codes (Laredo) .......................... 62 A-3 Example of Person Data File Format (Laredo)............................................ 63 A-4 Example...
Limited Model Information Control Design for Linear Discrete-Time Systems with Stochastic Parameters
Johansson, Karl Henrik
Limited Model Information Control Design for Linear Discrete-Time Systems with Stochastic systems with stochastically varying parameters. Recently, there have been studies in optimal control subsystems' parameters. There have been many studies in optimal control design for linear discrete
Examination of temporal DDT trends in Lake Erie fish communities using dynamic linear modeling
Arhonditsis, George B.
Examination of temporal DDT trends in Lake Erie fish communities using dynamic linear modeling 25 July 2013 Communicated by Dr. Erik Christensen Keywords: DDT Bayesian inference Dynamic linear (DDT) was initially heralded for its effectiveness against malaria and agricultural pests
Modelling the e#ects of air pollution on health using Bayesian Dynamic Generalised Linear Models
Bath, University of
Modelling the e#ects of air pollution on health using Bayesian Dynamic Generalised Linear Models 1 Introduction The potential detrimental e#ects of ambient air pollution is a major issue in public (2004)). Large multicity studies such as `Air pollution and health: a European approach' (APHEA
Integration of geographic information systems and logistic multiple regression for aquatic macrophyte modeling
Narumalani, S. [Nebraska Univ., Lincoln, NE (United States). Dept. of Geography; Jensen, J.R.; Althausen, J.D.; Burkhalter, S. [South Carolina Univ., Columbia, SC (United States). Dept. of Geography; Mackey, H.E. Jr. [Westinghouse Savannah River Co., Aiken, SC (United States)
Since aquatic macrophytes have an important influence on the physical and chemical processes of an ecosystem while simultaneously affecting human activity, it is imperative that they be inventoried and managed wisely. However, mapping wetlands can be a major challenge because they are found in diverse geographic areas ranging from small tributary streams, to shrub or scrub and marsh communities, to open water lacustrian environments. In addition, the type and spatial distribution of wetlands can change dramatically from season to season, especially when nonpersistent species are present. This research, focuses on developing a model for predicting the future growth and distribution of aquatic macrophytes. This model will use a geographic information system (GIS) to analyze some of the biophysical variables that affect aquatic macrophyte growth and distribution. The data will provide scientists information on the future spatial growth and distribution of aquatic macrophytes. This study focuses on the Savannah River Site Par Pond (1,000 ha) and L Lake (400 ha) these are two cooling ponds that have received thermal effluent from nuclear reactor operations. Par Pond was constructed in 1958, and natural invasion of wetland has occurred over its 35-year history, with much of the shoreline having developed extensive beds of persistent and non-persistent aquatic macrophytes.
Neural network model of creep strength of austenitic stainless steels
Cambridge, University of
is a parameterised non-linear model which can be used to perform regression, in which case, a very Ż exible, non-linear of the problems encoun- tered with linear regression. In the present study, neural network analysis was applied with a constant input set to unity. Any non-linear function can be used at the hidden units (as long
Fourth standard model family neutrino at future linear colliders
Ciftci, A.K.; Ciftci, R.; Sultansoy, S. [Physics Department, Faculty of Sciences, Ankara University, 06100 Tandogan, Ankara (Turkey); Physics Department, Faculty of Sciences and Arts, Gazi University, 06500 Teknikokullar, Ankara (Turkey)
It is known that flavor democracy favors the existence of the fourth standard model (SM) family. In order to give nonzero masses for the first three-family fermions flavor democracy has to be slightly broken. A parametrization for democracy breaking, which gives the correct values for fundamental fermion masses and, at the same time, predicts quark and lepton Cabibbo-Kobayashi-Maskawa (CKM) matrices in a good agreement with the experimental data, is proposed. The pair productions of the fourth SM family Dirac ({nu}{sub 4}) and Majorana (N{sub 1}) neutrinos at future linear colliders with {radical}(s)=500 GeV, 1 TeV, and 3 TeV are considered. The cross section for the process e{sup +}e{sup -}{yields}{nu}{sub 4}{nu}{sub 4}(N{sub 1}N{sub 1}) and the branching ratios for possible decay modes of the both neutrinos are determined. The decays of the fourth family neutrinos into muon channels ({nu}{sub 4}(N{sub 1}){yields}{mu}{sup {+-}}W{sup {+-}}) provide cleanest signature at e{sup +}e{sup -} colliders. Meanwhile, in our parametrization this channel is dominant. W bosons produced in decays of the fourth family neutrinos will be seen in detector as either di-jets or isolated leptons. As an example, we consider the production of 200 GeV mass fourth family neutrinos at {radical}(s)=500 GeV linear colliders by taking into account di-muon plus four jet events as signatures.
The Electric Sense of the Thornback Ray, Platyrhinoidis triseriata : Linear Dynamic Range in Single-Unit Electrophysiological Recordings in vivo from the Afferent Nerve Fibers of the Ampullae of Lorenzini
Gonzalez, Ivan F
Non-linear regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . .the system. We use non-linear regression to ?t a sinusoidalthe histogram using non-linear regression, we use analysis
The electric sense of the Thornback Ray, Platyrhinoidis triseriata : linear dynamic range in single-unit electrophysiological recordings in vivo from the afferent nerve fibers of the Ampullae of Lorenzini
Gonzalez, Ivan F.
Non-linear regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . .the system. We use non-linear regression to ?t a sinusoidalthe histogram using non-linear regression, we use analysis
II MODEL AND FEEDBACK LINEARIZING CONTROLLER 1 A Multilayer Perceptron Replaces a Feedback
Amaral, José Nelson
II MODEL AND FEEDBACK LINEARIZING CONTROLLER 1 A Multilayer Perceptron Replaces a Feedback Linearization Controller in a Nonlinear Servomechanism Jos'e F. Haffner, Ney T. Meyrer, Jos'e N. Amaral and Lu'is F. A. Pereira Abstract--- A Feedback Linearizing Controller (FLC) is used to train a multilayer
Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong
Paris-Sud XI, Université de
Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong of the dynamics of the robot and propose a new Linear Model Predictive Control scheme which is an improvement are unfortunately severely limited. Model Predictive Control, also known as Receding Horizon Control, is a general
. This approach allows an affordable entry into renewable energy for existing coal-power producers, and allows them to meet the mandatory renewable energy targets set by the government of New South Wales . (Hu et) linear absorbers, achieving higher ground area efficiency. · Receiver is an inverted, trapezoidal, linear
Effects of the Tsallis distribution in the linear sigma model
Masamichi Ishihara
The effects of the Tsallis distribution which has two parameters, $q$ and $T$,on physical quantities are studied using the linear sigma model in chiral phase transitions.The parameter $T$ dependences of the condensate and mass for various $q$ are shown, where $T$ is called temperature. The Tsallis distribution approaches the Boltzmann-Gibbs distribution as $q$ approaches $1$. The critical temperature and energy density are described with digamma function, and the $q$ dependences of these quantities and the extension of Stefan-Boltzmann limit of the energy density are shown. The following facts are clarified. The chiral symmetry restoration for $q>1$ occurs at low temperature, compared with the restoration at $q=1$. The sigma mass and pion mass reflect the restoration. The critical temperature decreases monotonically as $q$ increases. The small deviation from the Boltzmann-Gibbs distribution results in the large deviations of physical quantities, especially the energy density. It is displayed from the energetic point of view that the small deviation from the Boltzmann-Gibbs distribution is realized for $q>1$. The physical quantities are affected by the Tsallis distribution even when $|q-1|$ is small.
On the existence of affine Landau-Ginzburg phases in gauged linear sigma models
Patrick Clarke; Josh Guffin
We prove a simple criterion for the existence of an affine Landau-Ginzburg point in the K\\"ahler moduli space of a gauged linear sigma model.
Ice stream basal conditions from block-wise surface data inversion and simple regression models of ice stream flow: Application to
Boyce, C. Kevin
Ice stream basal conditions from block-wise surface data inversion and simple regression models of ice stream flow: Application to Bindschadler Ice Stream O. V. Sergienko,1 R. A. Bindschadler,2 P. L; published 4 December 2008. [1] Widespread basal conditions controlling ice stream flows are still beyond
Results and Comparison from the SAM Linear Fresnel Technology Performance Model: Preprint
Wagner, M. J.
This paper presents the new Linear Fresnel technology performance model in NREL's System Advisor Model. The model predicts the financial and technical performance of direct-steam-generation Linear Fresnel power plants, and can be used to analyze a range of system configurations. This paper presents a brief discussion of the model formulation and motivation, and provides extensive discussion of the model performance and financial results. The Linear Fresnel technology is also compared to other concentrating solar power technologies in both qualitative and quantitative measures. The Linear Fresnel model - developed in conjunction with the Electric Power Research Institute - provides users with the ability to model a variety of solar field layouts, fossil backup configurations, thermal receiver designs, and steam generation conditions. This flexibility aims to encompass current market solutions for the DSG Linear Fresnel technology, which is seeing increasing exposure in fossil plant augmentation and stand-alone power generation applications.
E-model for Transportation Problem of Linear Stochastic Fractional ...
Abstract: This paper deals with the so-called transportation problem of linear stochastic fractional programming, and ... sophisticated analysis. Stochastic ... circuit board of multi-objective LSFP, algorithm to identify redundant fractional objective ...
Finite-element discretization of a linearized 2 -D model for lubricated oil transportation
Frey, Pascal
Finite-element discretization of a linearized 2 - D model for lubricated oil transportation V acts as a lubricant by coating the wall of the pipeline, thus preventing the oil from adhering is devoted to the numerical simulation of a linearized model for the lubricated trans- portation of heavy
On the Parameter Estimation of Linear Models of Aggregate Power System Loads
Cańizares, Claudio A.
1 On the Parameter Estimation of Linear Models of Aggregate Power System Loads Valery Knyazkin-- This paper addressed some theoretical and practical issues relevant to the problem of power system load, and the corresponding results are used to validate a commonly used linear model of aggre- gate power system load
Summer 2013 Introduction to Statistical Linear Models 26:960:577
Lin, Xiaodong
and Hall/CRC, 2005 An R Companion to Linear Statistical Models, Christopher Hay-Jahans, ISBN 9781439873656-0-387-40270-3 Linear Models with R (Texts in Statistical Science) | Edition: 1, Julian J. Faraway ISBN: 9781584884255 Publications, 2002 R is primarily a command-line language. While usage of R is extremely straightforward, you
Estimation of linear autoregressive models with Markov-switching, the E.M. algorithm revisited
Rynkiewicz, Joseph
This work concerns estimation of linear autoregressive models with Markov-switching using expectation maximisation (E.M.) algorithm. Our method generalise the method introduced by Elliot for general hidden Markov models and avoid to use backward recursion.
Optimisation and comparison of integrated models of direct-drive linear machines for wave energy conversion
Crozier, Richard Carson
Combined electrical and structural models of five types of permanent magnet linear electrical machines suitable for direct-drive power take-off on wave energy applications are presented. Electromagnetic models were ...
Evolution Operators for Linearly Polarized Two-Killing Cosmological Models
J. Fernando Barbero G.; Daniel Gómez Vergel; Eduardo J. S. Villaseńor
We give a general procedure to obtain non perturbative evolution operators in closed form for quantized linearly polarized two Killing vector reductions of general relativity with a cosmological interpretation. We study the representation of these operators in Fock spaces and discuss in detail the conditions leading to unitary evolutions.
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
Direct-Steam Linear Fresnel Performance Model for NREL's System Advisor Model
Wagner, M. J.; Zhu, G.
This paper presents the technical formulation and demonstrated model performance results of a new direct-steam-generation (DSG) model in NREL's System Advisor Model (SAM). The model predicts the annual electricity production of a wide range of system configurations within the DSG Linear Fresnel technology by modeling hourly performance of the plant in detail. The quasi-steady-state formulation allows users to investigate energy and mass flows, operating temperatures, and pressure drops for geometries and solar field configurations of interest. The model includes tools for heat loss calculation using either empirical polynomial heat loss curves as a function of steam temperature, ambient temperature, and wind velocity, or a detailed evacuated tube receiver heat loss model. Thermal losses are evaluated using a computationally efficient nodal approach, where the solar field and headers are discretized into multiple nodes where heat losses, thermal inertia, steam conditions (including pressure, temperature, enthalpy, etc.) are individually evaluated during each time step of the simulation. This paper discusses the mathematical formulation for the solar field model and describes how the solar field is integrated with the other subsystem models, including the power cycle and optional auxiliary fossil system. Model results are also presented to demonstrate plant behavior in the various operating modes.
Inference for Clustered Mixed Outcomes from a Multivariate Generalized Linear Mixed Model
Chen, Hsiang-Chun
) and E(?i2t?) with their marginal expectations over X, ??1 = EX {E(?i1t)} and ??2 = EX {E(?i2t)}, which are shown in the previous subsections. In other words, the overall total-CC is ?total = KtotalN,1,2 (??1, ??2) KtotalD,1,2 (??1, ??2) . 3.2.4....2 Multivariate Generalized Linear Mixed Model . . . . . . . . . . . . . 6 2.3 Assessing Correlation in Generalized Linear Mixed Model . . . . . . . 8 2.4 Bayesian Method for the Generalized Linear Mixed Model . . . . . . 10 3. ASSESSING CORRELATION...
IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS, PART B: CYBERNETICS, VOL. ?, NO. ?, ? 1 M-estimator and D-optimality Model Construction using
Chen, Sheng
output is a linear combination of non- linear basis functions. Provided that there is a separate and linear algebra are directly applicable. Moreover by applying linear regression statistical techniques-estimator and D-optimality Model Construction using Orthogonal Forward Regression Xia Hong, Senior Member, IEEE
Controller synthesis with very simplified linear constraints in PN model Dideban A. * Zareiee M. * Alla H. **
Paris-Sud XI, Université de
Controller synthesis with very simplified linear constraints in PN model Dideban A. * Zareiee M a controller. A set of linear constraints allow forbidding the reachability of specific states. The number number of control places. A systematic method for constructing very simplified controller is offered
Process Modeling of Ti-6Al-4V Linear Friction Welding (LFW)
Grujicic, Mica
Process Modeling of Ti-6Al-4V Linear Friction Welding (LFW) Mica Grujicic, G. Arakere, B finite-element analysis of the linear friction welding (LFW) process is combined with the basic physical in the open literature revealed that the weld region consists of a thermo- mechanically affected zone (TMAZ
Testing Linear Diagnostics of Ensemble Performance on a Simplified Global Circulation Model
Nelson, Ethan
is inherently flow dependent and that the ensemble predicts potential patterns of forecast errors more reliably than the magnitudes of the errors. A low-resolution global circulation model is implemented to calculate linear diagnostics in the vector space...
Non-linear inversion modeling for Ultrasound Computer Tomography: transition from soft to hard tissues imaging
Paris-Sud XI, Université de
Non-linear inversion modeling for Ultrasound Computer Tomography: transition from soft to hard Marseille cedex 20, France ABSTRACT Ultrasound Computer Tomography (UCT) is an imaging technique which has experiments. Keyword: Ultrasound Computer Tomography, Inverse Born Approximation, Elliptical Projection
Microgrid Reliability Modeling and Battery Scheduling Using Stochastic Linear Programming
Cardoso, Goncalo; Stadler, Michael; Siddiqui, Afzal; Marnay, Chris; DeForest, Nicholas; Barbosa-Povoa, Ana; Ferrao, Paulo
This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6percent.
Characteristics of identifying linear dynamic models from impulse response data using Prony analysis
Trudnowski, D.J.
The purpose of the study was to investigate the characteristics of fitting linear dynamic models to the impulse response of oscillatory dynamic systems using Prony analysis. Many dynamic systems exhibit oscillatory responses with multiple modes of oscillations. Although the underlying dynamics of such systems are often nonlinear, it is frequently possible and very useful to represent the system operating about some set point with a linear model. Derivation of such linear models can be done using two basic approaches: model the system using theoretical derivations and some linearization method such as a Taylor series expansion; or use a curve-fitting technique to optimally fit a linear model to specified system response data. Prony analysis belongs to the second class of system modeling because it is a method of fitting a linear model to the impulse response of a dynamic system. Its parallel formulation inherently makes it well suited for fitting models to oscillatory system data. Such oscillatory dynamic effects occur in large synchronous-generator-based power systems in the form of electromechanical oscillations. To study and characterize these oscillatory dynamics, BPA has developed computer codes to analyze system data using Prony analysis. The objective of this study was to develop a highly detailed understanding of the properties of using Prony analysis to fit models to systems with characteristics often encountered in power systems. This understanding was then extended to develop general ``rules-of-thumb`` for using Prony analysis. The general characteristics were investigated by performing fits to data from known linear models under controlled conditions. The conditions studied include various mathematical solution techniques; different parent system configurations; and a large variety of underlying noise characteristics.
Characteristics of identifying linear dynamic models from impulse response data using Prony analysis
Trudnowski, D.J.
The purpose of the study was to investigate the characteristics of fitting linear dynamic models to the impulse response of oscillatory dynamic systems using Prony analysis. Many dynamic systems exhibit oscillatory responses with multiple modes of oscillations. Although the underlying dynamics of such systems are often nonlinear, it is frequently possible and very useful to represent the system operating about some set point with a linear model. Derivation of such linear models can be done using two basic approaches: model the system using theoretical derivations and some linearization method such as a Taylor series expansion; or use a curve-fitting technique to optimally fit a linear model to specified system response data. Prony analysis belongs to the second class of system modeling because it is a method of fitting a linear model to the impulse response of a dynamic system. Its parallel formulation inherently makes it well suited for fitting models to oscillatory system data. Such oscillatory dynamic effects occur in large synchronous-generator-based power systems in the form of electromechanical oscillations. To study and characterize these oscillatory dynamics, BPA has developed computer codes to analyze system data using Prony analysis. The objective of this study was to develop a highly detailed understanding of the properties of using Prony analysis to fit models to systems with characteristics often encountered in power systems. This understanding was then extended to develop general rules-of-thumb'' for using Prony analysis. The general characteristics were investigated by performing fits to data from known linear models under controlled conditions. The conditions studied include various mathematical solution techniques; different parent system configurations; and a large variety of underlying noise characteristics.
Nonparametric Stochastic Modeling Of Linear Systems With Pre-scribed Variance Of Several Natural Frequencies
Paris-Sud XI, Université de
Nonparametric Stochastic Modeling Of Linear Systems With Pre- scribed Variance Of Several Natural of the inverse of the random matrix. The efficient simulation of sam- ples of random matrices according matrices, maximum entropy, probabilistic model 1 INTRODUCTION The stochastic modeling and simulation
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts
Vijayakumar, Sethu
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts Georgios Petkos for adaptive motor control exist which learn the system's inverse dynamics online and use this single model;II Command Context 1 Context 2 Dynamics models Context n Control Learning Commands Switch / Mix
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts
Toussaint, Marc
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts Georgios Petkos for adaptive motor control exist which learn the system's inverse dynamics online and use this single model version - to appear in ICANN 2006 #12;II Command Context 1 Context 2 Dynamics models Context n Control
Control-Oriented Linear Parameter-Varying Modelling of a Turbocharged Diesel Engine
Cambridge, University of
Control-Oriented Linear Parameter-Varying Modelling of a Turbocharged Diesel Engine Merten Jung-- In this paper, a third order nonlinear model of the airpath of a turbocharged diesel engine is derived, which and to a higher order nonlinear model suggests the validity of this approach. I. INTRODUCTION Modern diesel
Alternative mixed-integer linear programming models of a maritime inventory routing problem
Grossmann, Ignacio E.
Alternative mixed-integer linear programming models of a maritime inventory routing problem Jiang is enhanced by reformulating the time assignment constraints. Next, we present a model based on event points. Sherali et al (1999) formulated a mixed-integer programming model based on a discrete time representation
Linearity Improvement ofHBT-based Doherty Power Amplifiers Based on a Simple Analytical Model
Asbeck, Peter M.
model is based on linear and nonlinear components extracted from a VBIC model for Skyworks InGaP values were extracted from a device model for a Skyworks advanced InGaP/GaAs HBT, using ADS in harmonic
Revising Regulatory Networks: From Expression Data to Linear Causal Models
Langley, Pat
network structure. However, this ignores much ex- isting knowledge because for a given organism and system under study, a biologist may already have a partial model of gene regulation. We propose a method, with expression data. We demonstrate our approach by revising a model of photosynthesis regulation proposed
Wedeward, Kevin
network can affect each other. We consider a simple model of a power system derived from singular analysis of large electric power networks is in- creasingly important as power systems become larger construct minimally complicated dynamical models of power networks as affine nonlinear control systems
Testing Lack-of-Fit of Generalized Linear Models via Laplace Approximation
Glab, Daniel Laurence
, the use of noninformative priors produces a new omnibus lack-of-fit statistic. iv We present a thorough numerical study of the proposed test and the various exist- ing orthogonal series-based tests in the context of the logistic regression model. Simula... . . . . . . . . . . . . . . . 13 1.4.1 The Lack-of-Fit Test . . . . . . . . . . . . . . . . . . . . 14 1.4.2 Smoothing-based Tests of Fit . . . . . . . . . . . . . . . . 15 1.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 II TESTS OF FIT FOR LOGISTIC...
An Analytic Linear Accelerator Source Model for Monte Carlo Dose Calculations. I. Model Representation and Construction
Tian, Zhen; Folkerts, Michael; Shi, Feng; Jiang, Steve B; Jia, Xun
Monte Carlo (MC) simulation is considered as the most accurate method for radiation dose calculations. Accuracy of a source model for a linear accelerator is critical for the overall dose calculation accuracy. In this paper, we presented an analytical source model that we recently developed for GPU-based MC dose calculations. A key concept called phase-space-ring (PSR) was proposed. It contained a group of particles that are of the same type and close in energy and radial distance to the center of the phase-space plane. The model parameterized probability densities of particle location, direction and energy for each primary photon PSR, scattered photon PSR and electron PSR. For a primary photon PSRs, the particle direction is assumed to be from the beam spot. A finite spot size is modeled with a 2D Gaussian distribution. For a scattered photon PSR, multiple Gaussian components were used to model the particle direction. The direction distribution of an electron PSRs was also modeled as a 2D Gaussian distributi...
Robust Constrained Model Predictive Control using Linear Matrix Inequalities \\Lambda
Balakrishnan, Venkataramanan "Ragu"
dynamical systems, such as those encountered in chemical process control in the petrochemical, pulp process models as well as many performance criteria of significance to the process industries can
Robust Constrained Model Predictive Control using Linear Matrix Inequalities
Balakrishnan, Venkataramanan "Ragu"
, such as those encountered in chemical process control in the petrochemical, pulp and paper industries, several process models as well as many performance criteria of significance to the process industries can
Reading list for ST 755 Topic 1: Linear mixed models
Zhang, Daowen
problems. Journal of the American Statistical Association, 72, 320340. 5. Laird, N.M. and Ware, J.H. (1982 models. Journal of the American Statistical Association 88, 925. 3. Breslow, N.E. and Lin, X. (1995 with multiple components of dispersion. Journal of the American Statistical Associ- ation 91, 10071016. 5
A Linear Parabolic Trough Solar Collector Performance Model
Qu, M.; Archer, D.; Masson, S.
through a 6m by 2.3m PTSC with 900 w/m^2 solar insulation and 0 incident angle, the estimated collector efficiency is about 55% The model predictions will be confirmed by the operation of PTSCs now being installed at Carnegie Mellon....
(1) Likelihood mode of inference: definitions and results (i) Reference population and model.
Frangakis, Constantine
linear regression model with non additive effect. pr" ` "! CU# ` sr)¤ P u X }|e5 ~ ra 5 ¤ A B pr models. (1) Normal linear regression model with additive effect. pr"a`"! sU# ` sr)¤ P u X }| 5~ rf 5
Paris-Sud XI, Université de
TIME-VARYING LINEAR MODEL APPROXIMATION: APPLICATION TO THERMAL AND AIRFLOW BUILDING SIMULATION Nowadays, most of the numerical tools dedicated to simulating the thermal behavior of buildings, consider is demonstrated by its application to the simulation of a multi-zones building. THERMAL AND AIRFLOW MODELS
Distributed state estimation and model predictive control of linear interconnected system
Boyer, Edmond
requirements, modern control systems are becoming more and more complex. For these processes, different controlDistributed state estimation and model predictive control of linear interconnected system: In this paper, a distributed and networked control system architecture based on independent Model Predictive
On Some Models in Linear Thermo-Elasticity with Rational Material Laws
Santwana Mukhopadhyay; Rainer Picard; Sascha Trostorff; Marcus Waurick
We shall consider some common models in linear thermo-elasticity within a common structural framework. Due to the flexibility of the structural perspective we will obtain well-posedness results for a large class of generalized models allowing for more general material properties such as anisotropies, inhomogeneities, etc.
Plug-and-play decentralized model predictive control for linear systems
Ferrari-Trecate, Giancarlo
1 Plug-and-play decentralized model predictive control for linear systems Stefano Riverso, Graduate to automatize the design of local controllers so that it can be carried out in parallel by smart actuators. In particular, local controllers exploit tube-based Model Predictive Control (MPC) in order to guarantee
Local Genealogies in a Linear Mixed Model for Genome-Wide Association Mapping in Complex
Schierup, Mikkel Heide
Local Genealogies in a Linear Mixed Model for Genome-Wide Association Mapping in Complex Pedigreed fashion. Here, we present a complementary approach, called `GENMIX (genealogy based mixed model)' which combines advantages from two powerful GWAS methods: genealogy-based haplotype grouping and MMA. Subjects
Non-linear sigma-models and string theories
Sen, A.
The connection between sigma-models and string theories is discussed, as well as how the sigma-models can be used as tools to prove various results in string theories. Closed bosonic string theory in the light cone gauge is very briefly introduced. Then, closed bosonic string theory in the presence of massless background fields is discussed. The light cone gauge is used, and it is shown that in order to obtain a Lorentz invariant theory, the string theory in the presence of background fields must be described by a two-dimensional conformally invariant theory. The resulting constraints on the background fields are found to be the equations of motion of the string theory. The analysis is extended to the case of the heterotic string theory and the superstring theory in the presence of the massless background fields. It is then shown how to use these results to obtain nontrivial solutions to the string field equations. Another application of these results is shown, namely to prove that the effective cosmological constant after compactification vanishes as a consequence of the classical equations of motion of the string theory. 34 refs. (LEW)
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
Three-dimensional finite-difference modeling of non-linear ground notion
Jones, E.M. [Los Alamos National Lab., NM (United States); Olsen, K.B. [California Univ., Santa Barbara, CA (United States). Inst. for Crustal Studies
We present a hybrid finite-difference technique capable of modeling non-linear soil amplification from the 3-D finite-fault radiation pattern for earthquakes in arbitrary earth models. The method is applied to model non-linear effects in the soils of the San Fernando Valley (SFV) from the 17 January 1994 M 6.7 Northridge earthquake. 0-7 Hz particle velocities are computed for an area of 17 km by 19 km immediately above the causative fault and 5 km below the surface where peak strike-parallel, strike-perpendicular, vertical, and total velocities reach values of 71 cm/s, 145 cm/s, 152 cm/s, and 180 cm/s, respectively. Selected Green`s functions and a soil model for the SFV are used to compute the approximate stress level during the earthquake, and comparison to the values for near-surface alluvium at the U.S. Nevada Test Site suggests that the non-linear regime may have been entered. We use selected values from the simulated particle velocity distribution at 5 km depth to compute the non-linear response in a soil column below a site within the Van Norman Complex in SFV, where the strongest ground motion was recorded. Since site-specific non- linear material parameters from the SFV are currently unavailable, values are taken from analyses of observed Test Site ground motions. Preliminary results show significant reduction of spectral velocities at the surface normalized to the peak source velocity due to non-linear effects when the peak velocity increases from 32 cm/s (approximately linear case) to 64 cm/s (30-92%), 93 cm/s (7-83%), and 124 cm/s (2-70%). The largest reduction occurs for frequencies above 1 Hz.
High Dimensional Sparse Econometric Models: An Introduction
Belloni, Alexandre
In this chapter we discuss conceptually high dimensional sparse econometric models as well as estimation of these models using ?1-penalization and post-?1-penalization methods. Focusing on linear and nonparametric regression ...
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. email@example.com 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
Control-relevant Modelling and Linear Analysis of Instabilities in Oxy-fuel Combustion
Foss, Bjarne A.
Control-relevant Modelling and Linear Analysis of Instabilities in Oxy-fuel Combustion Dagfinn combustion have been proposed as an alternative to conventional gas turbine cycles for achieving CO2-capture for CO2 sequestration purposes. While combustion instabilities is a problem in modern conventional gas
Mathematical and numerical analysis of a transient non-linear axisymmetric eddy current model
RodrĂguez, Rodolfo
Mathematical and numerical analysis of a transient non-linear axisymmetric eddy current model the theoretically predicted behavior of the method, are reported. Keywords transient eddy current · axisymmetric is the accurate computation of power losses in the ferromagnetic components of the core due to hysteresis and eddy-current
RodrĂguez, Rodolfo
NUMERICAL SOLUTION OF A TRANSIENT NON-LINEAR AXISYMMETRIC EDDY CURRENT MODEL WITH firstname.lastname@example.com This paper deals with an axisymmetric transient eddy current problem in conductive nonlinear magnetic media of the proposed scheme. Keywords: transient eddy current problem; electromagnetic losses; nonlinear magnetic
A non linear model for combustion instability : analysis and quenching of the oscillations
Boyer, Edmond
- ing the oscillations. 1 Introduction Combustion instabilities phenomena in gas turbine are the focusA non linear model for combustion instability : analysis and quenching of the oscillations Ioan D are reflected by physical boundaries into the combustion process. In term of system interpretation
Large Scale Approximate Inference and Experimental Design for Sparse Linear Models
Seeger, Matthias
Large Scale Approximate Inference and Experimental Design for Sparse Linear Models Matthias W.kyb.tuebingen.mpg.de/bs/people/seeger/ 27 June 2008 Matthias W. Seeger (MPI BioCyb) Large Scale Bayesian Experimental Design 27/6/08 1 / 27 Algorithms 4 Magnetic Resonance Imaging Sequences Matthias W. Seeger (MPI BioCyb) Large Scale Bayesian
Vázquez-Abad, Felisa J.
ON THE LINEAR GROWTH OF THE SPLITÂANDÂMERGE SIMULATION TREE FOR A MULTICOMPONENT AGE REPLACEMENT Functional estimation, splitÂandÂmerge tree, opti mization, maintenance models, age replacement poli cies Abstract We a consider a replacement policy based on age thresholds, for a multicomponent system. We want
Land cover time profiles from linear mixture models applied to MODIS images P. Oliveira a,
Gonçalves, Paulo
the coarse spatial resolution of satellites such as the Advanced Very High Resolution Radiometer (AVHRR) (e and AQUA launched on December 18, 1999 and on May 4, 2002, respectively. MODIS images correspond to high the pixel. In linear models (LMM) the electromagnetic energy interacts with a single component before being
Neural Modeling of Non-Linear Processes: Relevance of the Takens-Ma~ne Theorem
Masulli, Francesco
coupled to a 150 MW steam turbine. 1 Introduction The problem of controlling systems characterized by non to be managed (on a typical steam turbine they are about 576,000/hour). Moreover, so far, there are no availableNeural Modeling of Non-Linear Processes: Relevance of the Takens-Ma~n´e Theorem Francesco Masulli
Linear Free Energy Relationships between Dissolution Rates and Molecular Modeling Energies of Rhombohedral
Linear Free Energy Relationships between Dissolution Rates and Molecular Modeling Energies, and Geochemistry Department, Sandia National Laboratories, Albuquerque, New Mexico 87185-0750 Received July 24, 2003. In Final Form: December 18, 2003 Bulk and surface energies are calculated for endmembers
OFS model-based adaptive control for block-oriented non-linear Systems
Cambridge, University of
) and a heavy oil distillation column (Zhang et al., 2004b). Meanwhile, he has also made some theoretical processes such as distillation, pH neutralization control, hydro-control and chemical reactions linear model predictive control (MPC) based on a Laguerre series and successfully applied the scheme to p
Collinearity in Linear Structural Models of Market Power Jeffrey M. Perloff*
Perloff, Jeffrey M.
if the marginal cost and demand equations are linear. Key Words: collinearity, estimation, market power. JEL that the marginal cost curve is cMC = + w+ r + Q + , (1) where w is the wage, r is the rental rate on capital, Q The well-known structural model used to estimate market power suffers from a severe collinearity problem
Linear Compositional Delay Model for the Timing Analysis of Sub-Powered Combinational Circuits
Linear Compositional Delay Model for the Timing Analysis of Sub-Powered Combinational Circuits the propagation delay through nanometer CMOS circuits is highly desirable. Statistical Static Timing Analysis to accurately capture the circuit behaviour. In view of this we introduce an Inverse Gaussian Distribution (IGD
Job Scheduling Using successive Linear Programming Approximations of a Sparse Model
Paris-Sud XI, Université de
Job Scheduling Using successive Linear Programming Approximations of a Sparse Model Stephane of parallel jobs on a set of processors either in a cluster or in a multiprocessor computer. For the makespan objective, i.e., the comple- tion time of the last job, this problem has been shown to be NP
Poisson loglinear modeling with linear constraints on the expected cell frequencies
Martin, Nirian
In this paper we consider Poisson loglinear models with linear constraints (LMLC) on the expected table counts. Multinomial and product multinomial loglinear models can be obtained by considering that some marginal totals (linear constraints on the expected table counts) have been prefixed in a Poisson loglinear model. Therefore with the theory developed in this paper, multinomial and product multinomial loglinear models can be considered as a particular case. To carry out inferences on the parameters in the LMLC an information-theoretic approach is followed from which the classical maximum likelihood estimators and Pearson chi-square statistics for goodness-of fit are obtained. In addition, nested hypotheses are proposed as a general procedure for hypothesis testing. Through a simulation study the appropriateness of proposed inference tools is illustrated.
Lattice spacing dependence of phase transition temperature in the classical linear sigma model
A. K. Chaudhuri
We have investigated the phase transition properties of classical linear sigma model. The fields were kept in contact with a heat bath for sufficiently long time such that fields are equilibrated at the temperature of the heat bath. It was shown that the sigma model fields undergoes phase transition, but the transition temperature depend crucially on the lattice spacing. In the continuum limit, the transition temperature tends to zero or at least to a very low value.
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
Non-linear Langevin model for the early-stage dynamics of electrospinning jets
Lauricella, Marco; Pisignano, Dario; Succi, Sauro
We present a non-linear Langevin model to investigate the early-stage dynamics of electrified polymer jets in electrospinning experiments. In particular, we study the effects of air drag force on the uniaxial elongation of the charged jet, right after ejection from the nozzle. Numerical simulations show that the elongation of the jet filament close to the injection point is significantly affected by the non-linear drag exerted by the surrounding air. These result provide useful insights for the optimal design of current and future electrospinning experiments.
Linear-optical generation of eigenstates of the two-site XY model
Stefanie Barz; Borivoje Dakic; Yannick Ole Lipp; Frank Verstraete; James D. Whitfield; Philip Walther
Much of the anticipation accompanying the development of a quantum computer relates to its application to simulating dynamics of another quantum system of interest. Here we study the building blocks for simulating quantum spin systems with linear optics. We experimentally generate the eigenstates of the XY Hamiltonian under an external magnetic field. The implemented quantum circuit consists of two CNOT gates, which are realized experimentally by harnessing entanglement from a photon source and by applying a CPhase gate. We tune the ratio of coupling constants and magnetic field by changing local parameters. This implementation of the XY model using linear quantum optics might open the door to the future studies of quenching dynamics using linear optics.
Lattice model of linear telechelic polymer melts. II. Influence of chain stiffness on basic thermodynamic properties
Wen-Sheng Xu; Karl F. Freed
The lattice cluster theory (LCT) for semiflexible linear telechelic melts, developed in paper I, is applied to examine the influence of chain stiffness on the average degree of self-assembly and the basic thermodynamic properties of linear telechelic polymer melts. Our calculations imply that chain stiffness promotes self-assembly of linear telechelic polymer melts that assemble on cooling when either polymer volume fraction $\\phi$ or temperature $T$ is high, but opposes self-assembly when both $\\phi$ and $T$ are sufficiently low. This allows us to identify a boundary line in the $\\phi$-$T$ plane that separates two regions of qualitatively different influence of chain stiffness on self-assembly. The enthalpy and entropy of self-assembly are usually treated as adjustable parameters in classical Flory-Huggins type theories for the equilibrium self-assembly of polymers, but they are demonstrated here to strongly depend on chain stiffness. Moreover, illustrative calculations for the dependence of the entropy density of linear telechelic polymer melts on chain stiffness demonstrate the importance of including semiflexibility within the LCT when exploring the nature of glass formation in models of linear telechelic polymer melts.
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
Paris-Sud XI, Université de
/Simulink simulations. Key words: power system harmonics, power electronic, linear time periodic modeling, PWM, control1 POWER ELECTRONICS HARMONIC ANALYSIS BASED ON THE LINEAR TIME PERIODIC MODELING. APPLICATIONS in power electronic systems. The considered system is described by a set of differential equations, which
Thermal history modelling: HeFTy vs. QTQt Pieter Vermeesch , Yuntao Tian
Crawford, Ian
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 2.2. Linear regression of weakly non-linear data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 2.3. Linear regression of strongly non-linear data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 2. Part I: linear regression
Regression analysis of cytopathological data
Whittemore, A.S.; McLarty, J.W.; Fortson, N.; Anderson, K.
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.
The linear model and experimentally observed resonant field amplification in tokamaks and reversed field pinches
Pustovitov, V. D. [Russian Research Centre Kurchatov Institute, Institute of Tokamak Physics (Russian Federation)
A review is given of the experimentally observed effects related to the resonant field amplification (RFA) and the Resistive Wall Mode (RWM) instability in tokamaks and reversed field pinches (RFPs). This includes the feedback rotation of RWM in RFX-mod RFP, dependence of the RWM growth rate on the plasma-wall separation observed in JT-60U, appearance of the slowly growing RWM precursors in JT-60U and similar phenomena in other devices. The experimental results are compared with theoretical predictions based on the model comprising the Maxwell equations, Ohm's law for the conducting wall, the boundary conditions and assumption of linear plasma response to the external magnetic perturbations. The model describes the plasma reaction to the error field as essentially depending on two factors: the plasma proximity to the RWM stability threshold and the natural rotation frequency of the plasma mode. The linear response means that these characteristics are determined by the plasma equilibrium parameters only. It is shown that the mentioned effects in different devices under different conditions can be described on a common basis with only assumption that the plasma behaves as a linear system. To extend the range of the model validation, some predictions are derived with proposals for experimental studies of the RFA dynamics.
Variable-Speed Wind Turbine Controller Systematic Design Methodology: A Comparison of Non-Linear and Linear Model-Based Designs
Hand, M. M.
Variable-speed, horizontal axis wind turbines use blade-pitch control to meet specified objectives for three regions of operation. This paper focuses on controller design for the constant power production regime. A simple, rigid, non-linear turbine model was used to systematically perform trade-off studies between two performance metrics. Minimization of both the deviation of the rotor speed from the desired speed and the motion of the actuator is desired. The robust nature of the proportional-integral-derivative (PID) controller is illustrated, and optimal operating conditions are determined. Because numerous simulation runs may be completed in a short time, the relationship of the two opposing metrics is easily visualized. Traditional controller design generally consists of linearizing a model about an operating point. This step was taken for two different operating points, and the systematic design approach was used. A comparison of the optimal regions selected using the n on-linear model and the two linear models shows similarities. The linearization point selection does, however, affect the turbine performance slightly. Exploitation of the simplicity of the model allows surfaces consisting of operation under a wide range of gain values to be created. This methodology provides a means of visually observing turbine performance based upon the two metrics chosen for this study. Design of a PID controller is simplified, and it is possible to ascertain the best possible combination of controller parameters. The wide, flat surfaces indicate that a PID controller is very robust in this variable-speed wind turbine application.
On linear stability and dispersion for crystals in the Schroedinger-Poisson model
Alexander Komech; Elena Kopylova
We consider the Schr\\"odinger-Poisson-Newton equations as a model of crystals. Our main results are the well posedness and dispersion decay for the linearized dynamics at the ground state. This linearization is a Hamilton system with nonselfadjoint (and even nonsymmetric) generator. We diagonalize this Hamilton generator using our theory of spectral resolution of the Hamilton operators with positive definite energy which is a special version of the M. Krein - H. Langer theory of selfadjoint operators in the Hilbert spaces with indefinite metric. Using this spectral resolution, we establish the well posedness and the dispersion decay of the linearized dynamics with positive energy. The key result of present paper is the energy positivity for the linearized dynamics with small elementary charge $e>0$ under a novel Wiener-type condition on the ions positions and their charge densitities. We give examples of the crystals satisfying this condition. The main difficulty in the proof ofr the positivity is due to the fact that for $e=0$ the minimal spectral point $E_0=0$ is an eigenvalue of infinite multiplicity for the energy operator. To prove the positivity we study the asymptotics of the ground state as $e\\to 0$ and show that the zero eigenvalue $E_0=0$ bifurcates into $E_e\\sim e^2$.
Fracture permeability and seismic wave scattering--Poroelastic linear-slip interface model for heterogeneous fractures
Nakagawa, S.; Myer, L.R.
Schoenberg's Linear-slip Interface (LSI) model for single, compliant, viscoelastic fractures has been extended to poroelastic fractures for predicting seismic wave scattering. However, this extended model results in no impact of the in-plane fracture permeability on the scattering. Recently, we proposed a variant of the LSI model considering the heterogeneity in the in-plane fracture properties. This modified model considers wave-induced, fracture-parallel fluid flow induced by passing seismic waves. The research discussed in this paper applies this new LSI model to heterogeneous fractures to examine when and how the permeability of a fracture is reflected in the scattering of seismic waves. From numerical simulations, we conclude that the heterogeneity in the fracture properties is essential for the scattering of seismic waves to be sensitive to the permeability of a fracture.
Poisson Regression Analysis of Illness and Injury Surveillance Data
Frome E.L., Watkins J.P., Ellis E.D.
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.
A Mixed-Integer Linear Programming Model for Optimizing the Scheduling and Assignment of Tank Farm Operations
Grossmann, Ignacio E.
1 A Mixed-Integer Linear Programming Model for Optimizing the Scheduling and Assignment of Tank, Midland, MI 48674, USA Abstract This paper presents a novel mixed-integer linear programming (MILP multi-product processing lines and the assignment of dedicated storage tanks to finished products
A comparative study on a non-linear turbulent heat transfer model for separating and reattaching flows
Jia, S.; Chung, B.T.F. [Univ. of Akron, OH (United States). Dept. of Mechanical Engineering
Based on a previously proposed non-linear turbulence model, a turbulent heat transfer model is formulated in the present study using the concept of Generalized Gradient Diffusion (GGD) hypothesis. Under this hypothesis, an anisotropic thermal diffusivity can be obtained through the proposed non-linear turbulent model which is applied to the turbulent flow and heat transfer in a sudden expansion pipe with a constant heat flux through the pipe wall. The numerical results are compared with the available experimental data for both turbulent and thermal quantities, with an emphasis on the non-linear heat transfer predictions. The improved results are obtained for the bulk temperature distribution showing that the present non-linear heat transfer model is capable of predicting the anisotropic turbulent heat transfer for the pipe expansion flow. Some limits of the proposed model are also identified and discussed.
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 email@example.com Abstract. A new supervised adaptive metric approach is introduced for mapping an input vector space
Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models
Yock, Adam D., E-mail: email@example.com; Kudchadker, Rajat J. [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States)] [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States); Rao, Arvind [Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and the Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States)] [Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and the Graduate School of Biomedical Sciences, the University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States); Dong, Lei [Scripps Proton Therapy Center, San Diego, California 92121 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States)] [Scripps Proton Therapy Center, San Diego, California 92121 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States); Beadle, Beth M.; Garden, Adam S. [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States)] [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Court, Laurence E. [Department of Radiation Physics and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States)] [Department of Radiation Physics and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and The Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas 77030 (United States)
Purpose: The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Methods: Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. Results: In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: ?11.6%–23.8%) and 14.6% (range: ?7.3%–27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: ?6.8%–40.3%) and 13.1% (range: ?1.5%–52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: ?11.1%–20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. Conclusions: A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.
Angular momentum transport modeling: achievements of a gyrokinetic quasi-linear approach
Cottier, P; Camenen, Y; Gurcan, O D; Casson, F J; Garbet, X; Hennequin, P; Tala, T
QuaLiKiz, a model based on a local gyrokinetic eigenvalue solver is expanded to include momentum flux modeling in addition to heat and particle fluxes. Essential for accurate momentum flux predictions, the parallel asymmetrization of the eigenfunctions is successfully recovered by an analytical fluid model. This is tested against self-consistent gyrokinetic calculations and allows for a correct prediction of the ExB shear impact on the saturated potential amplitude by means of a mixing length rule. Hence, the effect of the ExB shear is recovered on all the transport channels including the induced residual stress. Including these additions, QuaLiKiz remains ~10 000 faster than non-linear gyrokinetic codes allowing for comparisons with experiments without resorting to high performance computing. The example is given of momentum pinch calculations in NBI modulation experiments.
Statistical physics of a model binary genetic switch with linear feedback
Paolo Visco; Rosalind J. Allen; Martin R. Evans
We study the statistical properties of a simple genetic regulatory network that provides heterogeneity within a population of cells. This network consists of a binary genetic switch in which stochastic flipping between the two switch states is mediated by a "flipping" enzyme. Feedback between the switch state and the flipping rate is provided by a linear feedback mechanism: the flipping enzyme is only produced in the on switch state and the switching rate depends linearly on the copy number of the enzyme. This work generalises the model of [Phys. Rev. Lett., 101, 118104] to a broader class of linear feedback systems. We present a complete analytical solution for the steady-state statistics of the number of enzyme molecules in the on and off states, for the general case where the enzyme can mediate flipping in either direction. For this general case we also solve for the flip time distribution, making a connection to first passage and persistence problems in statistical physics. We show that the statistics of the model are non-Poissonian, leading to a peak in the flip time distribution. The occurrence of such a peak is analysed as a function of the parameter space. We present a new relation between the flip time distributions measured for two relevant choices of initial condition. We also introduce a new correlation measure to show that this model can exhibit long-lived temporal correlations, thus providing a primitive form of cellular memory. Motivated by DNA replication as well as by evolutionary mechanisms involving gene duplication, we study the case of two switches in the same cell. This results in correlations between the two switches; these can either positive or negative depending on the parameter regime.
Non-linear Dynamics, Emergent Behaviors and Controlled Expansions: Towards Effective Modeling of the Congested Traffic
Bo Yang; Xihua Xu; John Z. F. Pang; Christopher Monterola
We propose a framework for constructing microscopic traffic models from microscopic acceleration patterns that can in principle be experimental measured and proper averaged. The exact model thus obtained can be used to justify the consistency of various popular models in the literature. Assuming analyticity of the exact model, we suggest that a controlled expansion around the constant velocity, uniform headway "ground state" is the proper way of constructing various different effective models. Assuming a unique ground state for any fixed average density, we discuss the universal properties of the resulting effective model, focusing on the emergent quantities of the coupled non-linear ODEs. These include the maximum and minimum headway that give the coexistence curve in the phase diagram, as well as an emergent intrinsic scale that characterizes the strength of interaction between clusters, leading to non-trivial cluster statistics when the unstable ground state is randomly perturbed. Utilizing the universal properties of the emergent quantities, a simple algorithm for constructing an effective traffic model is also presented. The algorithm tunes the model with statistically well-defined quantities extracted from the flow-density plot, and the resulting effective model naturally captures and predicts many quantitative and qualitative empirical features of the highway traffic, especially in the presence of an on-ramp bottleneck. The simplicity of the effective model provides strong evidence that stochasticity, diversity of vehicle types and modeling of complicated individual driving behaviors are \\emph{not} fundamental to many observations of the complex spatiotemporal patterns in the real traffic dynamics. We also propose the nature of the congested phase can be well characterized by the long lasting transient states of the effective model, from which the wide moving jams evolve.
The 2-dimensional non-linear sigma-model on a random latice
B. Alles; M. Beccaria
The O(n) non-linear $\\sigma$-model is simulated on 2-dimensional regular and random lattices. We use two different levels of randomness in the construction of the random lattices and give a detailed explanation of the geometry of such lattices. In the simulations, we calculate the mass gap for $n=3, 4$ and 8, analysing the asymptotic scaling of the data and computing the ratio of Lambda parameters $\\Lambda_{\\rm random}/\\Lambda_{\\rm regular}$. These ratios are in agreement with previous semi-analytical calculations. We also numerically calculate the topological susceptibility by using the cooling method.
Application of bi-linear loglog SN model to strain-controlled fatigue data of aluminum alloys and its effect on life predictions
Fatemi, Ali
Application of bi-linear loglog SN model to strain-controlled fatigue data of aluminum alloyslog model is applied to stress amplitude versus fatigue life data of 14 aluminum alloys. It is shown-life curves are discussed. Life predictions of aluminum alloys based on linear and bi-linear models are also
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
Modeling and experiments on differential pumping in linear plasma generators operating at high gas flows
Eck, H. J. N. van; Koppers, W. R.; Rooij, G. J. van; Goedheer, W. J.; Cardozo, N. J. Lopes; Kleyn, A. W. [FOM-Institute for Plasma Physics Rijnhuizen, Association EURATOM-FOM, Trilateral Euregio Cluster, P.O. Box 1207, 3430 BE Nieuwegein (Netherlands); Engeln, R.; Schram, D. C. [Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands)
The direct simulation Monte Carlo (DSMC) method was used to investigate the efficiency of differential pumping in linear plasma generators operating at high gas flows. Skimmers are used to separate the neutrals from the plasma beam, which is guided from the source to the target by a strong axial magnetic field. In this way, the neutrals are prevented to reach the target region. The neutral flux to the target must be lower than the plasma flux to enable ITER relevant plasma-surface interaction (PSI) studies. It is therefore essential to control the neutral gas dynamics. The DSMC method was used to model the expansion of a hot gas in a low pressure vessel where a small discrepancy in shock position was found between the simulations and a well-established empirical formula. Two stage differential pumping was modeled and applied in the linear plasma devices Pilot-PSI and PLEXIS. In Pilot-PSI a factor of 4.5 pressure reduction for H{sub 2} has been demonstrated. Both simulations and experiments showed that the optimum skimmer position depends on the position of the shock and therefore shifts for different gas parameters. The shape of the skimmer has to be designed such that it has a minimum impact on the shock structure. A too large angle between the skimmer and the forward direction of the gas flow leads to an influence on the expansion structure. A pressure increase in front of the skimmer is formed and the flow of the plasma beam becomes obstructed. It has been shown that a skimmer with an angle around 53 deg. gives the best performance. The use of skimmers is implemented in the design of the large linear plasma generator Magnum-PSI. Here, a three stage differentially pumped vacuum system is used to reach low enough neutral pressures near the target, opening a door to PSI research in the ITER relevant regime.
Modelling and Linear Control of a Buoyancy-Driven Airship Xiaotao WU Claude H. MOOG and Yueming HU
Paris-Sud XI, Université de
Modelling and Linear Control of a Buoyancy-Driven Airship Xiaotao WU Claude H. MOOG and Yueming HU Abstract-- We describe the modelling and control of a new- kind airship which is propelled by buoyancy gliders and aircraft, a 6DOF nonlinear mathematical model of a buoyancy-driven airship is derived
Experimental characterization and modeling of non-linear coupling of the LHCD power on Tore Supra
Preynas, M. [Max Planck Institut für Plasmaphysik, EURATOM Association, D-17491 Greifswald (Germany); Goniche, M.; Hillairet, J.; Litaudon, X.; Ekedahl, A. [CEA, IRFM, F-13108 Saint Paul lez Durance (France)
To achieve steady state operation on future tokamaks, in particular on ITER, the unique capability of a LHCD system to efficiently drive off-axis non-inductive current is needed. In this context, it is of prime importance to study and master the coupling of LH wave to the core plasma at high power density (tens of MW/m{sup 2}). In some specific conditions, deleterious effects on the LHCD coupling are sometimes observed on Tore Supra. At high power the waves may modify the edge parameters that change the wave coupling properties in a non-linear manner. In this way, dedicated LHCD experiments have been performed using the LHCD system of Tore Supra, composed of two different conceptual designs of launcher: the Fully Active Multijunction (FAM) and the new Passive Active Multijunction (PAM) antennas. A nonlinear interaction between the electron density and the electric field has been characterized in a thin plasma layer in front of the two LHCD antennas. The resulting dependence of the power reflection coefficient with the LHCD power, leading occasionally to trips in the output power, is not predicted by the standard linear theory of the LH wave coupling. Therefore, it is important to investigate and understand the possible origin of such non-linear effects in order to avoid their possible deleterious consequences. The PICCOLO-2D code, which self-consistently treats the wave propagation in the antenna vicinity and its interaction with the local edge plasma density, is used to simulate Tore Supra discharges. The simulation reproduces very well the occurrence of a non-linear behavior in the coupling observed in the LHCD experiments. The important differences and trends between the FAM and the PAM antennas, especially a larger increase in RC for the FAM, are also reproduced by the PICCOLO-2D simulation. The working hypothesis of the contribution of the ponderomotive effect in the non-linear observations of LHCD coupling is therefore validated through this comprehensive modeling for the first time on the FAM and PAM antennas on Tore Supra.
Non-linear model of particle acceleration at colliding shock flows
Bykov, A M; Osipov, S M
Powerful stellar winds and supernova explosions with intense energy release in the form of strong shock waves can convert a sizeable part of the kinetic energy release into energetic particles. The starforming regions are argued as a favorable site of energetic particle acceleration and could be efficient sources of nonthermal emission. We present here a non-linear time-dependent model of particle acceleration in the vicinity of two closely approaching fast magnetohydrodynamic (MHD) shocks. Such MHD flows are expected to occur in rich young stellar cluster where a supernova is exploding in the vicinity of a strong stellar wind of a nearby massive star. We find that the spectrum of the high energy particles accelerated at the stage of two closely approaching shocks can be harder than that formed at a forward shock of an isolated supernova remnant. The presented method can be applied to model particle acceleration in a variety of systems with colliding MHD flows.
Non-Linear Poisson-Boltzmann Theory of a Wigner-Seitz Model for Swollen Clays
R. J. F. Leote de Carvalho; E. Trizac; J. -P. Hansen
Swollen stacks of finite-size disc-like Laponite clay platelets are investigated within a Wigner-Seitz cell model. Each cell is a cylinder containing a coaxial platelet at its centre, together with an overall charge-neutral distribution of microscopic co and counterions, within a primitive model description. The non-linear Poisson-Boltzmann (PB) equation for the electrostatic potential profile is solved numerically within a highly efficient Green's function formulation. Previous predictions of linearised Poisson-Boltzmann (LPB) theory are confirmed at a qualitative level, but large quantitative differences between PB and LPB theories are found at physically relevant values of the charge carried by the platelets. A hybrid theory treating edge effect at the linearised level yields good potential profiles. The force between two coaxial platelets, calculated within PB theory, is an order of magnitude smaller than predicted by LPB theory
Neutral Higgs boson pair production at the linear collider in the noncommutative standard model
Das, Prasanta Kumar; Prakash, Abhishodh; Mitra, Anupam [Birla Institute of Technology and Science-Pilani, K.K. Birla Goa Campus, NH-17B, Zuarinagar, Goa-403726 (India)
We study the Higgs boson pair production at the linear collider in the noncommutative extension of the standard model using the Seiberg-Witten map of this to the first order of the noncommutative parameter {Theta}{sub {mu}{nu}}. Unlike the standard model (where the process is forbidden) here the Higgs boson pair directly interacts with the photon. We find that the pair production cross section can be quite significant for the noncommutative scale {Lambda} lying in the range 0.5 TeV to 1.0 TeV. Using the experimental (LEP 2, Tevatron, and global electroweak fit) bound on the Higgs mass, we obtain 626 GeV{<=}{Lambda}{<=}974 GeV.
Multiple Linear Regression Models: Predicting the Texas Windstrom Insurance Association Claim Payout and Ratio Versus the Appraised Value of Commercial Buildings from Hurricae Ike
Kim, Ji Myong
). This estimate does not cover indirect costs such as insurance compensation from the United States government or indirect costs to companies and individuals. Moreover, Hurricane Andrew, in August of 1992, created insured losses of $150 million in a single...
Abstract--In order to design a model based controller availability of a linear model of the system to be controlled is
Boyer, Edmond
-elastic simulators. The use of such linearized models describing the WT dynamics was a real breakthrough, since of the resulting models is unnecessarily high for describing the plant dynamics, including non- observable modes, mechanical modelling, electrical models, etc. Based on these researches, some advanced WT simulation tools
Steady state and transient model of a linear solar concentrator with cylindrical absorber
Ecevit, A.
A linear parabolic collector with integrated absorber pipe assembly is one of the main elements of a solar energy collection system that produces electricity or process heat. This kind of a system must geometrically and thermally be optimized so that a reasonable operating efficiency can be reached. A linear parabolic collector having an absorber, encircled with a cylindrical cavity, has been studied and the geometrical parameters of the system have been optimized before the collector was built and put into operation. The collector having dimensions of 200X95 sq.cm and having a focal length of 60 cm. is built under the view of the optimization procedure. The collector is oriented EW horizontal in the NS tracking mode and the longitudinal deviations of the focal line is examined. The energy distribution along the focal line of the collector is measured using a laser together with a wattmeter. The effects of the thermal and optical parameters on the performance of the system is studied by the use of a theoretical model that is built for the collector absorber system. The value of each parameter is changed from a minimum to a maximum, keeping the other parameters at their average values.
Abstract--In this contribution GPTIPS, a free, open source MATLAB toolbox for performing symbolic regression by
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
Limited Dependent Variable Correlated Random Coefficient Panel Data Models
Liang, Zhongwen
for the average slopes of a linear CRC model with a general nonparametric correlation between regressors and random coefficients. I construct a sqrt(n) consistent estimator for the average slopes via varying coefficient regression. The identification of binary...
Functional dependence of axial anomaly via mesonic fluctuations in the three flavor linear sigma model
Fejos, G
Temperature dependence of the $U_A(1)$ anomaly is investigated by taking into account mesonic fluctuations in the $U(3)\\times U(3)$ linear sigma model. A field dependent anomaly coefficient function of the effective potential is calculated within the finite temperature functional renormalization group approach. The applied approximation scheme is a generalization of the chiral invariant expansion technique developed in [G. Fej\\H{o}s, Phys. Rev. D 90, 096011 (2014)]. We provide an analytic expression and also numerical evidence that depending on the relationship between the two quartic couplings, mesonic fluctuations can either strengthen of weaken the anomaly as a function of the temperature. Role of the six-point invariant of the $U(3)\\times U(3)$ group, and therefore the stability of the chiral expansion is also discussed in detail.
Functional dependence of axial anomaly via mesonic fluctuations in the three flavor linear sigma model
G. Fejos
Temperature dependence of the $U_A(1)$ anomaly is investigated by taking into account mesonic fluctuations in the $U(3)\\times U(3)$ linear sigma model. A field dependent anomaly coefficient function of the effective potential is calculated within the finite temperature functional renormalization group approach. The applied approximation scheme is a generalization of the chiral invariant expansion technique developed in [G. Fejos, Phys. Rev. D 90, 096011 (2014)]. We provide an analytic expression and also numerical evidence that depending on the relationship between the two quartic couplings, mesonic fluctuations can either strengthen of weaken the anomaly as a function of the temperature. Role of the six-point invariant of the $U(3)\\times U(3)$ group, and therefore the stability of the chiral expansion is also discussed in detail.
The Component Slope Linear Model for Calculating Intensive Partial Molar Properties: Application to Waste Glasses
Reynolds, Jacob G. [Washington River Protection Solutions, Richland, WA (United States)
Partial molar properties are the changes occurring when the fraction of one component is varied while the fractions of all other component mole fractions change proportionally. They have many practical and theoretical applications in chemical thermodynamics. Partial molar properties of chemical mixtures are difficult to measure because the component mole fractions must sum to one, so a change in fraction of one component must be offset with a change in one or more other components. Given that more than one component fraction is changing at a time, it is difficult to assign a change in measured response to a change in a single component. In this study, the Component Slope Linear Model (CSLM), a model previously published in the statistics literature, is shown to have coefficients that correspond to the intensive partial molar properties. If a measured property is plotted against the mole fraction of a component while keeping the proportions of all other components constant, the slope at any given point on a graph of this curve is the partial molar property for that constituent. Actually plotting this graph has been used to determine partial molar properties for many years. The CSLM directly includes this slope in a model that predicts properties as a function of the component mole fractions. This model is demonstrated by applying it to the constant pressure heat capacity data from the NaOH-NaAl(OH{sub 4}H{sub 2}O system, a system that simplifies Hanford nuclear waste. The partial molar properties of H{sub 2}O, NaOH, and NaAl(OH){sub 4} are determined. The equivalence of the CSLM and the graphical method is verified by comparing results detennined by the two methods. The CSLM model has been previously used to predict the liquidus temperature of spinel crystals precipitated from Hanford waste glass. Those model coefficients are re-interpreted here as the partial molar spinel liquidus temperature of the glass components.
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
Measurement and Modeling of Solute Diffusion Coefficients in Unsaturated Soils
Chou, Hsin-Yi
data and the non-linear regression fitted lines of Olsen anddata and the non-linear regression fitted lines of powerdata and the non-linear regression fitted lines of the two-
N=(4,4) Gauged Linear Sigma Models for Defect Five-branes
Kimura, Tetsuji
We study two-dimensional ${\\cal N}=(4,4)$ gauged linear sigma model (GLSM). Its low energy effective theory is a nonlinear sigma model whose target space gives rise to a configuration of five-branes in string theory. In this article we focus on sigma models for NS5-branes, KK5-branes and an exotic $5^2_2$-brane. In particular, we carefully analyze the GLSM for an exotic $5^2_2$-brane whose background configuration is multi-valued. The exotic $5^2_2$-brane is a concrete example of nongeometric configuration in string theory. We find that the exotic feature originates from the string winding coordinate in a very clear way. In order to complete this analysis, we propose a duality transformation formula which converts an ${\\cal N}=(2,2)$ chiral superfield in F-term to a twisted chiral superfield coupled to an unconstrained complex superfield. This article is a short review based on arXiv:1304.4061 in collaboration with Shin Sasaki.
Linear and Nonlinear Modeling of a Traveling-Wave Thermoacoustic Heat Engine
Scalo, Carlo; Hesselink, Lambertus
We have carried out three-dimensional Navier-Stokes simulations, from quiescent conditions to the limit cycle, of a traveling-wave thermoacoustic heat engine (TAE) composed of a long variable-area resonator shrouding a smaller annular tube, which encloses the hot (HHX) and ambient (AHX) heat-exchangers, and the regenerator (REG). Simulations are wall-resolved, with no-slip and adiabatic conditions enforced at all boundaries, while the heat transfer and drag due to the REG and HXs are modeled. HHX temperatures have been investigated in the range 440K - 500K with AHX temperature fixed at 300K. The initial exponential growth of acoustic energy is due to a network of traveling waves amplified by looping around the REG/HX unit in the direction of the imposed temperature gradient. A simple analytical model demonstrates that such thermoacoustic instability is a Lagrangian thermodynamic process resembling a Stirling cycle. A system-wide linear stability model based on Rott's theory is able to accurately predict the f...
Two Species of Vortices in a massive Gauged Non-linear Sigma Model
Alberto Alonso-Izquierdo; Wifredo Garcia Fuertes; Juan Mateos Guilarte
Non-linear sigma models with scalar fields taking values on $\\mathbb{C}\\mathbb{P}^n$ complex manifolds are addressed. In the simplest $n=1$ case, where the target manifold is the $\\mathbb{S}^2$ sphere, we describe the scalar fields by means of stereographic maps. In this case when the $\\mathbb{U}(1)$ symmetry is gauged and Maxwell and mass terms are allowed, the model accommodates stable self-dual vortices of two kinds with different energies per unit length and where the Higgs field winds at the cores around the two opposite poles of the sphere. Allowing for dielectric functions in the magnetic field, similar and richer self-dual vortices of different species in the south and north charts can be found by slightly modifying the potential. Two different situations are envisaged: either the vacuum orbit lies on a parallel in the sphere, or one pole and the same parallel form the vacuum orbit. Besides the self-dual vortices of two species, there exist BPS domain walls in the second case. Replacing the Maxwell contribution of the gauge field to the action by the second Chern-Simons secondary class, only possible in $(2+1)$-dimensional Minkowski space-time, new BPS topological defects of two species appear. Namely, both BPS vortices and domain ribbons in the south and the north charts exist because the vacuum orbit consits of the two poles and one parallel. Formulation of the gauged $\\mathbb{C}\\mathbb{P}^2$ model in a Reference chart shows a self-dual structure such that BPS semi-local vortices exist. The transition functions to the second or third charts break the $\\mathbb{U}(1)\\times\\mathbb{S}\\mathbb{U}(2)$ semi-local symmetry, but there is still room for standard self-dual vortices of the second species. The same structures encompassing $N$ complex scalar fields are easily generalized to gauged $\\mathbb{C}\\mathbb{P}^N$ models.
Bulk viscosity and the phase transition of the linear sigma model
Antonio Dobado; Juan M. Torres-Rincon
In this work we deal with the critical behavior of the bulk viscosity in the linear sigma model (LSM) as an example of a system which can be treated by using different techniques. Starting from the Boltzmann-Uehling-Uhlenbeck equation we compute the bulk viscosity over entropy density of the LSM in the large-N limit. We search for a possible maximum of the bulk viscosity over entropy density at the critical temperature of the chiral phase transition. The information about this critical temperature, as well as the effective masses, is obtained from the effective potential. We find that the expected maximum (as a measure of the conformality loss) is absent in the large N in agreement with other models in the same limit. However, this maximum appears when, instead of the large-N limit, the Hartree approximation within the Cornwall-Jackiw-Tomboulis (CJT) formalism is used. Nevertheless, this last approach to the LSM does not give rise to the Goldstone theorem and also predicts a first order phase transition instead of the expected second order one. Therefore both, the large-N limit and the CJT-Hartree approximations, should be considered as complementary for the study of the critical behavior of the bulk viscosity in the LSM.
The two-phase issue in the O(n) non-linear $?$-model: A Monte Carlo study
B. Alles; A. Buonanno; G. Cella
We have performed a high statistics Monte Carlo simulation to investigate whether the two-dimensional O(n) non-linear sigma models are asymptotically free or they show a Kosterlitz- Thouless-like phase transition. We have calculated the mass gap and the magnetic susceptibility in the O(8) model with standard action and the O(3) model with Symanzik action. Our results for O(8) support the asymptotic freedom scenario.
Efficient modelling of particle collisions using a non-linear viscoelastic contact force
Shouryya Ray; Tobias Kempe; Jochen Fröhlich
In this paper the normal collision of spherical particles is investigated. The particle interaction is modelled in a macroscopic way using the Hertzian contact force with additional linear damping. The goal of the work is to develop an efficient approximate solution of sufficient accuracy for this problem which can be used in soft-sphere collision models for Discrete Element Methods and for particle transport in viscous fluids. First, by the choice of appropriate units, the number of governing parameters of the collision process is reduced to one, thus providing a dimensionless parameter that characterizes all such collisions up to dynamic similitude. It is a simple combination of known material parameters as well as initial conditions. A rigorous calculation of the collision time and restitution coefficient from the governing equations, in the form of a series expansion in this parameter is provided. Such a first principles calculation is particularly interesting from a theoretical perspective. Since the governing equations present some technical difficulties, the methods employed are also of interest from the point of view of the analytical technique. Using further approximations, compact expressions for the restitution coefficient and the collision time are then provided. These are used to implement an approximate algebraic rule for computing the desired stiffness and damping in the framework of the adaptive collision model (Kempe & Fr\\"ohlich, J. Fluid Mech., 709: 445-489, 2012). Numerical tests with binary as well as multiple particle collisions are included that illustrate the accuracy of the proposed method and its superiority in terms of numerical efficiency.
Gauged Linear Sigma Model with F-term for A-type ALE Space
Tetsuji Kimura; Masaya Yata
We construct yet another ${\\mathcal N}=(4,4)$ gauged linear sigma model for the $A_N$-type ALE space. In our construction the toric data of the ALE space are manifest. Due to the $SU(2)_R$ symmetry, the F-term is automatically determined. The toric data, which govern the K\\"{a}hler structures of the ALE space, are embedded into $U(1)$ charges of charged hypermultiplets. The F-term is also inevitable to determine the complex structures of the ALE space. In the IR limit, we obtain the K\\"{a}hler potential of the $A_N$-type ALE space. We also find the origin of the ${\\mathbb Z}_{N+1}$ orbifold symmetry in the singular limit of the $A_N$-type ALE space. In a special case, we reproduce an explicit form of the K\\"{a}hler potential of the $A_1$-type ALE space, i.e., the Eguchi-Hanson space.
Calculation of probabilities of transfer, recurrence intervals, and positional indices for linear compartment models. Environmental Sciences Division Publication no. 1544
Carney, J.H.; DeAngelis, D.L.; Gardner, R.H.; Mankin, J.B.; Post, W.M.
Six indices are presented for linear compartment systems that quantify the probable pathways of matter or energy transfer, the likelihood of recurrence if the model contains feedback loops, and the number of steps (transfers) through the system. General examples are used to illustrate how these indices can simplify the comparison of complex systems or organisms in unrelated systems.
A O(n^8) X O(n^7) Linear Programming Model of the Quadratic Assignment Problem
Diaby, Moustapha
In this paper, we propose a linear programming (LP) formulation of the Quadratic Assignment Problem (QAP) with O(n^8) variables and O(n^7) constraints, where n is the number of assignments. A small experimentation that was undertaken in order to gain some rough indications about the computational performance of the model is discussed.
Lagrangian theory of gravitational instability of Friedman-Lemaitre cosmologies - generic third-order model for non-linear clustering
Thomas Buchert
The Lagrangian perturbation theory on Friedman-Lemaitre cosmologies investigated and solved up to the second order in earlier papers (Buchert 1992, Buchert \\& Ehlers 1993) is evaluated up to the third order. On its basis a model for non-linear clustering applicable to the modeling of large-scale structure in the Universe for generic initial conditions is formulated. A truncated model is proposed which represents the ``main body'' of the perturbation sequence in the early non-linear regime by neglecting all gravitational sources which describe interaction of the perturbations. However, I also give the irrotational solutions generated by the interaction terms to the third order, which induce vorticity in Lagrangian space. The consequences and applicability of the solutions are put into perspective. In particular, the model presented enables the study of previrialization effects in gravitational clustering and the onset of non-dissipative gravitational turbulence within the cluster environment.
Estimation of Building Parameters Using Simplified Energy Balance Model and Metered Whole Building Energy Use
Masuda, H.; Claridge, D.
, cooling and heating and weather data using multiple linear regression models based on the simplified steady-state energy balance for a whole building. Two approaches using different response variables: the energy balance load (EBL) and the building thermal...
Meta-Analysis for Longitudinal Data Models using Multivariate Mixture Priors
West, Mike
of multivariate normals, accomodating population heterogeneity, out- liers and non-linearity in regression. First, the random e#11;ects model is a exible mixture of multivariate normals, accomodating population
Estimation of Building Parameters Using Simplified Energy Balance Model and Metered Whole Building Energy Use
Masuda, H.; Claridge, D.
, cooling and heating and weather data using multiple linear regression models based on the simplified steady-state energy balance for a whole building. Two approaches using different response variables: the energy balance load (EBL) and the building thermal...
Single-Hop Case Typically, linear regression (using the last
temperature, voltage, etc... (30-100 ppm) Christoph Lenzen, Philipp Sommer, Roger Wattenhofer Computer of the 2nd International Conference on Embedded Networked Sensor Systems, 2004. [Sommer09] P. Sommer and R International Conference on Information Processing in Sensor Networks, 2009. [Lenzen09] C. Lenzen, P. Sommer
On a three-layer Hele-Shaw model of enhanced oil recovery with a linear viscous profile
Daripa, Prabir; Meneses, Rodrigo
We present a non-standard eigenvalue problem that arises in the linear stability of a three-layer Hele-Shaw model of enhanced oil recovery. A nonlinear transformation is introduced which allows reformulation of the non-standard eigenvalue problem as a boundary value problem for Kummer's equation when the viscous profile of the middle layer is linear. Using the existing body of works on Kummer's equation, we construct an exact solution of the eigenvalue problem and provide the dispersion relation implicitly through the existence criterion for the non-trivial solution. We also discuss the convergence of the series solution. It is shown that this solution reduces to the physically relevant solutions in two asymptotic limits: (i) when the linear viscous profile approaches a constant viscous profile; or (ii) when the length of the middle layer approaches zero.
Piecewise Linear Instrumental Variable Estimation of Causal Influence Richard Scheines
Spirtes, Peter
studies show that when the causal influence of X on Y is non-linear, the piecewise linear linear IV-estimator. In the final section, we describe an experiment comparing regular regression, linearPiecewise Linear Instrumental Variable Estimation of Causal Influence Richard Scheines Dept
Kalman filtering and smoothing for linear wave equations with model error This article has been downloaded from IOPscience. Please scroll down to see the full text article.
Stuart, Andrew
Kalman filtering and smoothing for linear wave equations with model error This article has been:10.1088/0266-5611/27/9/095008 Kalman filtering and smoothing for linear wave equations with model an online approach to state estimation inverse problems when data are acquired sequentially. The Kalman
Modeling, Estimation and Control of Distributed Parameter Systems: Application to Transportation Networks
Blandin, Sebastien
in the case of non-linear regression since there is nocan be extended to non-linear regression methods through the
Vacuum models with a linear and a quadratic term in H: structure formation and number counts analysis
Adria Gomez-Valent; Joan Sola
We focus on the class of cosmological models with a time-evolving vacuum energy density of the form $\\rho_\\Lambda=C_0+C_1 H+C_2 H^2$, where $H$ is the Hubble rate. Higher powers of $H$ could be important for the early inflationary epoch, but are irrelevant afterwards. We study these models at the background level and at the perturbations level, both at the linear and at the nonlinear regime. We find that those with $C_0=0$ are seriously hampered, as they are unable to fit simultaneously the current observational data on Hubble expansion and the linear growth rate of clustering. This is in contrast to the $C_0\
Predictions of monthly energy consumption and annual patterns of energy usage for convenience stores by using multiple and nonlinear regression models
Muendej, Krisanee
Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply plans. The models...
Lattice and off-lattice side chain models of protein folding: Linear time structure prediction better than 86% of optimal
Hart, W.E.; Istrail, S. [Sandia National Labs., Albuquerque, NM (United States). Algorithms and Discrete Mathematics Dept.
This paper considers the protein structure prediction problem for lattice and off-lattice protein folding models that explicitly represent side chains. Lattice models of proteins have proven extremely useful tools for reasoning about protein folding in unrestricted continuous space through analogy. This paper provides the first illustration of how rigorous algorithmic analyses of lattice models can lead to rigorous algorithmic analyses of off-lattice models. The authors consider two side chain models: a lattice model that generalizes the HP model (Dill 85) to explicitly represent side chains on the cubic lattice, and a new off-lattice model, the HP Tangent Spheres Side Chain model (HP-TSSC), that generalizes this model further by representing the backbone and side chains of proteins with tangent spheres. They describe algorithms for both of these models with mathematically guaranteed error bounds. In particular, the authors describe a linear time performance guaranteed approximation algorithm for the HP side chain model that constructs conformations whose energy is better than 865 of optimal in a face centered cubic lattice, and they demonstrate how this provides a 70% performance guarantee for the HP-TSSC model. This is the first algorithm in the literature for off-lattice protein structure prediction that has a rigorous performance guarantee. The analysis of the HP-TSSC model builds off of the work of Dancik and Hannenhalli who have developed a 16/30 approximation algorithm for the HP model on the hexagonal close packed lattice. Further, the analysis provides a mathematical methodology for transferring performance guarantees on lattices to off-lattice models. These results partially answer the open question of Karplus et al. concerning the complexity of protein folding models that include side chains.
Regression analysis with missing data
Michelli, Frank Anthony
: 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...
Development of a Regression Kriging Model Conditioned with Sequential Gaussian Simulation to Predict the Spatial Distribution of Site Index for The Savannah River Site.
Edwards, Lloyd [USDA Forest Service, Southern Research Station] [USDA Forest Service, Southern Research Station; Parresol, Bernie [USDA Forest Service, Southern Research Station] [USDA Forest Service, Southern Research Station
The primary research objective of the project is to determine an optimum model to spatially interpolate point derived tree site index (SI). This optimum model will use relevant data from 635 measured sample points to create continuous 40 meter SI raster layer of entire study extent.
Shell Element Verification & Regression Problems for DYNA3D
Zywicz, E
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.
Towards Analytic Solutions of Step-Wise Safe Switching for Known Affine-Linear Models
Koumboulis, Fotis N.; Tzamtzi, Maria P. [Department of Automation, Halkis Institute of Technology, 34400 Psahna, Evia (Greece)
In the present work we establish conditions which guarantee safe transitions for the closed-loop system produced by the application of the Step-Wise Safe Switching control approach to an affine linear system when the nonlinear description of the plant is known. These conditions are based on the local Input to State Stability (ISS) properties of the nonlinear system around the plant's nominal operating points.
Predictions of monthly energy consumption and annual patterns of energy usage for convenience stores by using multiple and nonlinear regression models
Muendej, Krisanee
Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply ...
A Modeling and Filtering Framework for Linear Differential-Algebraic Equations
Schön, Thomas
, Dymola, the SimMechanics toolbox for MATLAB, and Modelica [14], [20]. Such modeling software makes
Longitudinal Control Of A Platoon Of Vehicles. I, Linear Model (ucb/erl M89/106)
Sheikholeslam, Shahab; Desoer, Charles A.
We propose the following linear control law for longitudinalgoing to use the proposed linear control law for the firstgoing to use the proposed linear control law for the second
Theoretical and practical aspects of linear and nonlinear model order reduction techniques
Vasilyev, Dmitry Missiuro
Model order reduction methods have proved to be an important technique for accelerating time-domain simulation in a variety of computer-aided design tools. In this study we present several new techniques for model reduction ...
A trajectory piecewise-linear approach to model order reduction of nonlinear dynamical systems
RewieĹ ski, MichaĹ Jerzy, 1975-
(cont.) Finally, we present projection schemes which result in improved accuracy of the reduced order TPWL models, as well as discuss approaches leading to guaranteed stable and passive TPWL reduced-order models.
ORIGINAL ARTICLE A Constitutive Model For the Warp-Weft Coupled Non-linear
Reddy, Batmanathan Dayanand "Daya"
) on the development of airship fabrics. However, the first real model for fabric forces was presented by Peirce (1937
Nonparametric Identification of Linearizations and Uncertainty using Gaussian Process Models Application to Robust Wheel Slip Control
Williamson, John
models, taking into account their uncertainty. The approach is applied to a simulated wheel slip control task illustrating controller development based on a nonparametric model of the unknown friction of the nonlinear models' derivatives. I. INTRODUCTION Robust control is a fairly mature field, in particular
Regression analysis with longitudinal measurements
Ryu, Duchwan
, 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...
Transformer procurement and relocation at a large electric utility: A mixed 0-1 linear programming model
Leung, L.C. [Chinese Univ. of Hong Kong, Shatin (Hong Kong). Decision Science and Managerial Economics] [Chinese Univ. of Hong Kong, Shatin (Hong Kong). Decision Science and Managerial Economics; Khator, S.K. [Univ. of South Florida, Tampa, FL (United States). Industrial and Management Systems Engineering] [Univ. of South Florida, Tampa, FL (United States). Industrial and Management Systems Engineering
The Power Delivery Substation Division at Florida Power and Light (FPL) must plan and provide logistical support for about 2,000 transformers located at roughly 400 substations. Each year, to meet new transformer requirements, the Division must make the decision of procuring and/or relocating transformers. Due to the large number of transformers and substations involved, there is a strong need for a systematic approach to determine optimally the decisions for transformer procurement and relocation, as well as their schedules. In this paper, a mixed 0-1 linear programming model is developed for that purpose.
Dynamic Retrospective Regression for Functional Daniel Gervini
Gervini, Daniel
synchronization as an intrinsic part of the model, and then attains better predictive power than ordinary linear counts over time in HIV patients can be mod- eled as functions of viral load trajectories (Liang et al. 2003, Wu and Liang 2004, Wu and Müller 2011); gene expression profiles of insects at the pupal stage
The Econometric Analysis of Interval-valued Data and Adaptive Regression Splines
Lin, Wei
and Miller, D. (2000). Econometric Foundations. CambridgeRegression Models," Econometric Reviews. Vol. 8, pp. 217-De- pendent Bootstrap," Econometric Reviews. Vol. 23, pp.
Bounding A Protein's Free Energy In Lattice Models Via Linear Programming
Newman, Alantha
in understanding protein structure prediction. In these models, a protein folds to maximize H-H contacts (minimize [4], abstracts the dominant force in protein folding: the hydrophobic interaction. The hydrophobicity of protein folding in the Hydro- phobic-Hydrophilic (HP) model. We formulate several di#11;erent integer
A non-linear behavior model for SiC/SiC composites
Kibler, J.J.; Jones, M.L.; Yen, C.F. [Materials Sciences Corp., Fort Washington, PA (United States)
An interactive analytical model has been developed for modeling the behavior of Continuous Fiber reinforced Ceramic matrix Composites (CFCC). The model integrates a large number of micromechanics solution to problems associated with the microstructure of CFCC materials into an easy to use tool for predicting properties, strengths, and stress states for these materials in unidirectional and laminated forms. Particulate reinforcement and voids can be included in the material description. Inherent in the code is a model for handling the accumulation of micro cracks within the matrix as loading is increased, resulting in a nonlinear stress-strain response of the composite. Sufficient material characteristics are retained within the model to enable sensitivity studies to identify principal causes for material behavior.
Least-Order Torsion-Gravity for Fermion Fields, and the Non-Linear Potentials in the Standard Models
Luca Fabbri
We will consider the least-order torsional completion of gravity for a spacetime filled with fermionic Dirac matter fields, and we study the effects of the background-induced non-linear potentials for the matter field themselves in view of their effects for both standard models of physics: from the one of cosmology to that of particles, we will discuss the mechanisms of generation of the cosmological constant and particle masses as well as the phenomenology of leptonic weak-like forces and neutrino oscillations, the problem of zero-point energy, how there can be neutral massive fields as candidates for dark matter, and avoidance of gravitational singularity formation; we will show the way in which all these different effects can nevertheless be altogether described in terms of just a single model, which will be thoroughly discussed in the end.
Thresholding Multivariate Regression and Generalized Principal Components
Sun, Ranye
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...
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
Non-linear load-deflection models for seafloor interaction with steel catenary risers
Jiao, Yaguang
or attached to the riser would be washed away. 10 2.1.4 Model Tests of Steel Catenary Riser A full scale mode test of a steel catenary riser was conducted as part of the STRIDE III JIP, by 2H Offshore Engineering Ltd to investigate the effects of fluid...) developed advanced soil stiffness and soil suction models using STRIDE and CARISIMA JIP test data and other published literature data. This newer model describes the load-deflection response of the soil-pipe interaction associated with the riser vertical...
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts
Petkos, Georgios; Toussaint, Marc; Vijayakumar, Sethu
For stationary systems, efficient techniques for adaptive motor control exist which learn the system's inverse dynamics online and use this single model for control. However, in realistic domains the system dynamics often ...
A Linear Discrete Dynamic System Model for Temporal Gene Interaction and Regulatory
Song, Joe
Influence in Response to Bioethanol Conversion Inhibitor HMF for Ethanologenic Yeast Mingzhou (Joe) Song1 significantly expressed genes in response to bioethanol conversion inhibitor 5-hydroxymethylfurfural in detoxification for bioethanol conversion by yeast. 1 Introduction Computational modeling of gene regulatory
Non-Linear Drying Diffusion and Viscoelastic Drying Shrinkage Modeling in Hardened Cement Pastes
Leung, Chin K.
modeling with an average diffusion coefficient and with determined viscoelastic parameters from creep tests agreed well compared to the shrinkage data from experiments, indicating that drying shrinkage of cement paste may be considered as a poroviscoelastic...
TEA - a linear frequency domain finite element model for tidal embayment analysis
Westerink, Joannes J.
A frequency domain (harmonic) finite element model is developed for the numerical prediction of depth average circulation within small embayments. Such embayments are often characterized by irregular boundaries and bottom ...
ab initio Electronic Transport Model with Explicit Solution to the Linearized Boltzmann Transport Equation
Faghaninia, Alireza; Lo, Cynthia S
Accurate models of carrier transport are essential for describing the electronic properties of semiconductor materials. To the best of our knowledge, the current models following the framework of the Boltzmann transport equation (BTE) either rely heavily on experimental data (i.e., semi-empirical), or utilize simplifying assumptions, such as the constant relaxation time approximation (BTE-cRTA). While these models offer valuable physical insights and accurate calculations of transport properties in some cases, they often lack sufficient accuracy -- particularly in capturing the correct trends with temperature and carrier concentration. We present here a general transport model for calculating low-field electrical drift mobility and Seebeck coefficient of n-type semiconductors, by explicitly considering all relevant physical phenomena (i.e. elastic and inelastic scattering mechanisms). We first rewrite expressions for the rates of elastic scattering mechanisms, in terms of ab initio properties, such as the ban...
Comparison of Single, Double, and Triple Linear Flow Models for Shale Gas/Oil Reservoirs
Tivayanonda, Vartit
reservoirs effectively. Verification and derivation of asymptotic and associated equations from the Laplace space for dual porosity and triple porosity models are performed in order to generate analysis equations. Theories and practical applications...
Regression quantiles for time series
Cai, Zongwu
~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...
Effect of Fractionation in Stereotactic Body Radiation Therapy Using the Linear Quadratic Model
Yang, Jun, E-mail: email@example.com [Department of Radiation Oncology, Drexel University, Philadelphia, Pennsylvania (United States) [Department of Radiation Oncology, Drexel University, Philadelphia, Pennsylvania (United States); Philadelphia Cyberknife, Havertown, Pennsylvania (United States); Lamond, John [Department of Radiation Oncology, Drexel University, Philadelphia, Pennsylvania (United States) [Department of Radiation Oncology, Drexel University, Philadelphia, Pennsylvania (United States); Philadelphia Cyberknife, Havertown, Pennsylvania (United States); Fowler, Jack [Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin (United States)] [Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin (United States); Lanciano, Rachelle [Department of Radiation Oncology, Drexel University, Philadelphia, Pennsylvania (United States) [Department of Radiation Oncology, Drexel University, Philadelphia, Pennsylvania (United States); Philadelphia Cyberknife, Havertown, Pennsylvania (United States); Feng, Jing [Department of Radiation Oncology, Drexel University, Philadelphia, Pennsylvania (United States)] [Department of Radiation Oncology, Drexel University, Philadelphia, Pennsylvania (United States); Brady, Luther [Department of Radiation Oncology, Drexel University, Philadelphia, Pennsylvania (United States) [Department of Radiation Oncology, Drexel University, Philadelphia, Pennsylvania (United States); Philadelphia Cyberknife, Havertown, Pennsylvania (United States)
Purpose: To examine the fractionation effect of stereotactic body radiation therapy with a heterogeneous dose distribution. Methods: Derived from the linear quadratic formula with measurements from a hypothetical 2-cm radiosurgical tumor, the threshold percentage was defined as (?/?{sub tissue}/?/?{sub tumor}), the balance ?/? ratio was defined as (prescription dose/tissue tolerance*?/?{sub tumor}), and the balance dose was defined as (tissue tolerance/threshold percentage). Results: With increasing fractions and equivalent peripheral dose to the target, the biological equivalent dose of "hot spots" in a target decreases. The relative biological equivalent doses of serial organs decrease only when the relative percentage of its dose to the prescription dose is above the threshold percentage. The volume of parallel organs at risk decreases only when the tumor's ?/? ratio is above the balance ?/? ratio and the prescription dose is lower than balance dose. Conclusions: The potential benefits of fractionation in stereotactic body radiation therapy depend on the complex interplay between the total dose, ?/? ratios, and dose differences between the target and the surrounding normal tissues.
Real-Time Forcast Model Analysis of Daily Average Building Load for a Thermal Storage System Control
Song, L.; Joo, I. S.; Guwana, S.
of a building and three real-time building load forecasting models were developed. They are first-order autogressive model, random walk model and linear regression model. Finally, the comparison of results show the random walk model provides the best...
Modeling the Non-linear Viscoelastic Response of High Temperature Polyimides
Karra, Satish
A constitutive model is developed to predict the viscoelastic response of polyimide resins that are used in high temperature applications. This model is based on a thermodynamic framework that uses the notion that the `natural configuration' of a body evolves as the body undergoes a process and the evolution is determined by maximizing the rate of entropy production in general and the rate of dissipation within purely mechanical considerations. We constitutively prescribe forms for the specific Helmholtz potential and the rate of dissipation (which is the product of density, temperature and the rate of entropy production), and the model is derived by maximizing the rate of dissipation with the constraint of incompressibility, and the reduced energy dissipation equation is also regarded as a constraint in that it is required to be met in every process that the body undergoes. The efficacy of the model is ascertained by comparing the predictions of the model with the experimental data for PMR-15 and HFPE-II-52 ...
Modeling the Non-linear Viscoelastic Response of High Temperature Polyimides
Satish Karra; K. R. Rajagopal
A constitutive model is developed to predict the viscoelastic response of polyimide resins that are used in high temperature applications. This model is based on a thermodynamic framework that uses the notion that the `natural configuration' of a body evolves as the body undergoes a process and the evolution is determined by maximizing the rate of entropy production in general and the rate of dissipation within purely mechanical considerations. We constitutively prescribe forms for the specific Helmholtz potential and the rate of dissipation (which is the product of density, temperature and the rate of entropy production), and the model is derived by maximizing the rate of dissipation with the constraint of incompressibility, and the reduced energy dissipation equation is also regarded as a constraint in that it is required to be met in every process that the body undergoes. The efficacy of the model is ascertained by comparing the predictions of the model with the experimental data for PMR-15 and HFPE-II-52 polyimide resins.
3-D Model of Broadband Emission from Supernova Remnants Undergoing Non-linear Diffusive Shock Acceleration
Lee, Shiu-Hang; Kamae, Tuneyoshi; Ellison, Donald C.
We present a 3-dimensional model of supernova remnants (SNRs) where the hydrodynamical evolution of the remnant is modeled consistently with nonlinear diffusive shock acceleration occurring at the outer blast wave. The model includes particle escape and diffusion outside of the forward shock, and particle interactions with arbitrary distributions of external ambient material, such as molecular clouds. We include synchrotron emission and cooling, bremsstrahlung radiation, neutral pion production, inverse-Compton (IC), and Coulomb energy-loss. Boardband spectra have been calculated for typical parameters including dense regions of gas external to a 1000 year old SNR. In this paper, we describe the details of our model but do not attempt a detailed fit to any specific remnant. We also do not include magnetic field amplification (MFA), even though this effect may be important in some young remnants. In this first presentation of the model we don't attempt a detailed fit to any specific remnant. Our aim is to develop a flexible platform, which can be generalized to include effects such as MFA, and which can be easily adapted to various SNR environments, including Type Ia SNRs, which explode in a constant density medium, and Type II SNRs, which explode in a pre-supernova wind. When applied to a specific SNR, our model will predict cosmic-ray spectra and multi-wavelength morphology in projected images for instruments with varying spatial and spectral resolutions. We show examples of these spectra and images and emphasize the importance of measurements in the hard X-ray, GeV, and TeV gamma-ray bands for investigating key ingredients in the acceleration mechanism, and for deducing whether or not TeV emission is produced by IC from electrons or pion-decay from protons.
Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models
Roweis, Sam
, Matthew J. Beal, and Sam T. Roweis Department of Computer Science University of Toronto Toronto, Ontario, . . . , xn-1), is generated according to some stochastic transition model. We observe y = (y0, . . . , yn-1), with each yt being generated from the corresponding xt according to some stochastic ob- servation process
Maximum Likelihood Estimation for Probit-Linear Mixed Models with Correlated Random Effects
Du, Jie
Jennifer S. K. Chan and Anthony Y. C. Kuk Department of Statistics, University of New South Wales, Sydney 2052, Australia The probit-normal model for binary data (McCulloch, 1994, Journal of the American function, one has to integrate out the random effects, which, except for a few special cases, cannot
Some Useful Matlab and Control Systems Toolbox Functions Creating and converting linear models
Abate, Alessandro
). step - Step response. impulse - Impulse response. lsim - Response to arbitrary inputs. bode - Bode-zero map. damp - Natural frequency and damping of system poles. ltiview - Response analysis GUI (LTI Viewer diagrams of the frequency response. ctrb - Controllability matrix (for ss models). obsv - Observability
Integrated Dynamic Optimization and Control in Reservoir Engineering using Locally Identified Linear Models
Van den Hof, Paul
on dynamic real-time optimization (D- RTO) of waterflooding strategies in petroleum reservoirs haveIntegrated Dynamic Optimization and Control in Reservoir Engineering using Locally Identified, the used large-scale, nonlinear, physics-based reservoir models suffer from vast parametric uncertainty
Linear and Quasilinear Model for Pressure-Driven Interchange and Entropy Modes in a !
Mauel, Michael E.
, the quasilinear particle and heat flux are calculated and show turbulent self-organization that drives profiles that Regulate Interchange Motion Vasyliunas, "Mathematical Models of Magnetospheric Convection and Its Coupling at times of an active magnetospheric dynamo (e.g. during substorms). Figure 3. Dynamo forces, auroral
Accurate coarse-grained models for mixtures of colloids and linear polymers under good-solvent conditions
Giuseppe D'Adamo; Andrea Pelissetto; Carlo Pierleoni
A coarse-graining strategy, previously developed for polymer solutions, is extended here to mixtures of linear polymers and hard-sphere colloids. In this approach groups of monomers are mapped onto a single pseudoatom (a blob) and the effective blob-blob interactions are obtained by requiring the model to reproduce some large-scale structural properties in the zero-density limit. We show that an accurate parametrization of the polymer-colloid interactions is obtained by simply introducing pair potentials between blobs and colloids. For the coarse-grained model in which polymers are modelled as four-blob chains (tetramers), the pair potentials are determined by means of the iterative Boltzmann inversion scheme, taking full-monomer pair correlation functions at zero-density as targets. For a larger number $n$ of blobs, pair potentials are determined by using a simple transferability assumption based on the polymer self-similarity. We validate the model by comparing its predictions with full-monomer results for the interfacial properties of polymer solutions in the presence of a single colloid and for thermodynamic and structural properties in the homogeneous phase at finite polymer and colloid density. The tetramer model is quite accurate for $q\\lesssim 1$ ($q=\\hat{R}_g/R_c$, where $\\hat{R}_g$ is the zero-density polymer radius of gyration and $R_c$ is the colloid radius) and reasonably good also for $q=2$. For $q=2$ an accurate coarse-grained description is obtained by using the $n=10$ blob model. We also compare our results with those obtained by using single-blob models with state-dependent potentials.
A reduced order model for the study of asymmetries in linear gas chromatography for homogeneous tubular columns.
Whiting, Joshua J.; Romero, Louis Anthony; Parks, Michael L.
In gas chromatography, a chemical sample separates into its constituent components as it travels along a long thin column. As the component chemicals exit the column they are detected and identified, allowing the chemical makeup of the sample to be determined. For correct identification of the component chemicals, the distribution of the concentration of each chemical along the length of the column must be nearly symmetric. The prediction and control of asymmetries in gas chromatography has been an active research area since the advent of the technique. In this paper, we develop from first principles a general model for isothermal linear chromatography. We use this model to develop closed-form expressions for terms related to the first, second, and third moments of the distribution of the concentration, which determines the velocity, diffusion rate, and asymmetry of the distribution. We show that for all practical experimental situations, only fronting peaks are predicted by this model, suggesting that a nonlinear chromatography model is required to predict tailing peaks. For situations where asymmetries arise, we analyze the rate at which the concentration distribution returns to a normal distribution. Numerical examples are also provided.
Adjoint-based linear analysis in reduced order thermo-acoustic models
Magri, Luca; Juniper, Matthew P.
is reported in the supplementary material of Luchini and Bottaro [37]. To find the adjoint operator with the CA approach we have to perform integration by parts of (7). The above relation is an elaboration of the generalized Green's identity [30, 38... Instabilities in Gas Turbine Engines: Operational Experience, Fundamental Mechanisms, and Modeling. American Institute of Aeronautics and Astronautics, Inc., 2005. [3] F. E. C. Culick. Unsteady motions in combustion chambers for propulsion systems. RTO...
A Generalized Linear Transport Model for Spatially-Correlated Stochastic Media
Anthony B. Davis; Feng Xu
We formulate a new model for transport in stochastic media with long-range spatial correlations where exponential attenuation (controlling the propagation part of the transport) becomes power law. Direct transmission over optical distance $\\tau(s)$, for fixed physical distance $s$, thus becomes $(1+\\tau(s)/a)^{-a}$, with standard exponential decay recovered when $a\\to\\infty$. Atmospheric turbulence phenomenology for fluctuating optical properties rationalizes this switch. Foundational equations for this generalized transport model are stated in integral form for $d=1,2,3$ spatial dimensions. A deterministic numerical solution is developed in $d=1$ using Markov Chain formalism, verified with Monte Carlo, and used to investigate internal radiation fields. Standard two-stream theory, where diffusion is exact, is recovered when $a=\\infty$. Differential diffusion equations are not presently known when $a<\\infty$, nor is the integro-differential form of the generalized transport equation. Monte Carlo simulations are performed in $d=2$, as a model for transport on random surfaces, to explore scaling behavior of transmittance $T$ when transport optical thickness $\\tau_\\text{t} \\gg 1$. Random walk theory correctly predicts $T \\propto \\tau_\\text{t}^{-\\min\\{1,a/2\\}}$ in the absence of absorption. Finally, single scattering theory in $d=3$ highlights the model's violation of angular reciprocity when $a<\\infty$, a desirable property at least in atmospheric applications. This violation is traced back to a key trait of generalized transport theory, namely, that we must distinguish more carefully between two kinds of propagation: one that ends in a virtual or actual detection, the other in a transition from one position to another in the medium.
The component slope linear model for calculating intensive partial molar properties /application to waste glasses and aluminate solutions
Reynolds, Jacob G. [Washington River Protection Solutions, LLC, Richland, WA (United States)
Partial molar properties are the changes occurring when the fraction of one component is varied while the fractions of all other component mole fractions change proportionally. They have many practical and theoretical applications in chemical thermodynamics. Partial molar properties of chemical mixtures are difficult to measure because the component mole fractions must sum to one, so a change in fraction of one component must be offset with a change in one or more other components. Given that more than one component fraction is changing at a time, it is difficult to assign a change in measured response to a change in a single component. In this study, the Component Slope Linear Model (CSLM), a model previously published in the statistics literature, is shown to have coefficients that correspond to the intensive partial molar properties. If a measured property is plotted against the mole fraction of a component while keeping the proportions of all other components constant, the slope at any given point on a graph of this curve is the partial molar property for that constituent. Actually plotting this graph has been used to determine partial molar properties for many years. The CSLM directly includes this slope in a model that predicts properties as a function of the component mole fractions. This model is demonstrated by applying it to the constant pressure heat capacity data from the NaOH-NaAl(OH){sub 4}-H{sub 2}O system, a system that simplifies Hanford nuclear waste. The partial molar properties of H{sub 2}O, NaOH, and NaAl(OH){sub 4} are determined. The equivalence of the CSLM and the graphical method is verified by comparing results determined by the two methods. The CSLM model has been previously used to predict the liquidus temperature of spinel crystals precipitated from Hanford waste glass. Those model coefficients are re-interpreted here as the partial molar spinel liquidus temperature of the glass components.
Goodness-of-Fit Test Issues in Generalized Linear Mixed Models
Chen, Nai-Wei
checking of Case 1 for (1)ZSm and (2)cS tran m . . . 58 13 Results of the type I error rate of Sm by using local polynomial smoothed residuals are computed based on the scaled chi-squared distribution cSm...-cluster interaction term of fixed effects between two con- tinuous covariates when the alternative model (4.6) is assumed. . . . 64 17 Results of controlling type I error rate of Sm by using local poly- nomial smoothed residuals are computed based on cSm when...
An Analytic Linear Accelerator Source Model for Monte Carlo dose calculations. II. Model Utilization in a GPU-based Monte Carlo Package and Automatic Source Commissioning
Tian, Zhen; Li, Yongbao; Shi, Feng; Jiang, Steve B; Jia, Xun
We recently built an analytical source model for GPU-based MC dose engine. In this paper, we present a sampling strategy to efficiently utilize this source model in GPU-based dose calculation. Our source model was based on a concept of phase-space-ring (PSR). This ring structure makes it effective to account for beam rotational symmetry, but not suitable for dose calculations due to rectangular jaw settings. Hence, we first convert PSR source model to its phase-space let (PSL) representation. Then in dose calculation, different types of sub-sources were separately sampled. Source sampling and particle transport were iterated. So that the particles being sampled and transported simultaneously are of same type and close in energy to alleviate GPU thread divergence. We also present an automatic commissioning approach to adjust the model for a good representation of a clinical linear accelerator . Weighting factors were introduced to adjust relative weights of PSRs, determined by solving a quadratic minimization ...
WAXS fat subtraction model to estimate differential linear scattering coefficients of fatless breast tissue: Phantom materials evaluation
Tang, Robert Y., E-mail: firstname.lastname@example.com [Biomolecular Sciences Program, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6 (Canada); Laamanen, Curtis, E-mail: email@example.com; McDonald, Nancy, E-mail: email@example.com [Department of Physics, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6 (Canada)] [Department of Physics, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6 (Canada); LeClair, Robert J., E-mail: firstname.lastname@example.com [Department of Physics, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6, Canada and Biomolecular Sciences Program, Laurentian University, 935 Ramsey Lake Road, Sudbury, Ontario P3E 2C6 (Canada)
Purpose: Develop a method to subtract fat tissue contributions to wide-angle x-ray scatter (WAXS) signals of breast biopsies in order to estimate the differential linear scattering coefficients ?{sub s} of fatless tissue. Cancerous and fibroglandular tissue can then be compared independent of fat content. In this work phantom materials with known compositions were used to test the efficacy of the WAXS subtraction model. Methods: Each sample 5 mm in diameter and 5 mm thick was interrogated by a 50 kV 2.7 mm diameter beam for 3 min. A 25 mm{sup 2} by 1 mm thick CdTe detector allowed measurements of a portion of the ? = 6° scattered field. A scatter technique provided means to estimate the incident spectrum N{sub 0}(E) needed in the calculations of ?{sub s}[x(E, ?)] where x is the momentum transfer argument. Values of ?{sup Ż}{sub s} for composite phantoms consisting of three plastic layers were estimated and compared to the values obtained via the sum ?{sup Ż}{sub s}{sup ?}(x)=?{sub 1}?{sub s1}(x)+?{sub 2}?{sub s2}(x)+?{sub 3}?{sub s3}(x), where ?{sub i} is the fractional volume of the ith plastic component. Water, polystyrene, and a volume mixture of 0.6 water + 0.4 polystyrene labelled as fibphan were chosen to mimic cancer, fat, and fibroglandular tissue, respectively. A WAXS subtraction model was used to remove the polystyrene signal from tissue composite phantoms so that the ?{sub s} of water and fibphan could be estimated. Although the composite samples were layered, simulations were performed to test the models under nonlayered conditions. Results: The well known ?{sub s} signal of water was reproduced effectively between 0.5 < x < 1.6 nm{sup ?1}. The ?{sup Ż}{sub s} obtained for the heterogeneous samples agreed with ?{sup Ż}{sub s}{sup ?}. Polystyrene signals were subtracted successfully from composite phantoms. The simulations validated the usefulness of the WAXS models for nonlayered biopsies. Conclusions: The methodology to measure ?{sub s} of homogeneous samples was quantitatively accurate. Simple WAXS models predicted the probabilities for specific x-ray scattering to occur from heterogeneous biopsies. The fat subtraction model can allow ?{sub s} signals of breast cancer and fibroglandular tissue to be compared without the effects of fat provided there is an independent measurement of the fat volume fraction ?{sub f}. Future work will consist of devising a quantitative x-ray digital imaging method to estimate ?{sub f} in ex vivo breast samples.
Sequential Change--Point Detection in GARCH(p; q) Models \\Lambda
Kokoszka, Piotr
Sequential Change--Point Detection in GARCH(p; q) Models \\Lambda Istv'an Berkes y A. R 84322Â3900, USA We suggest a sequential monitoring scheme to detect changes in the parameters of a GARCH. Unlike for linear regression models, the squared residuals of nonlinear time series models like GARCH do
ANALYTICAL EMISSION MODELS FOR SIGNALISED ARTERIALS Bruce Hellinga, Mohammad Ali Khan, and Liping Fu
Hellinga, Bruce
ANALYTICAL EMISSION MODELS FOR SIGNALISED ARTERIALS Bruce Hellinga, Mohammad Ali Khan, and Liping for quantifying vehicle tailpipe emissions. In this paper we present non-linear regression models that can be used for emission data is examined using field data. The proposed models have adjusted R 2 values ranging from 0
4 Neural Network Modelling 2 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Cambridge, University of
of reality. Neural networks form a general method of non{linear regression. Their exibility enables them non{linear function is tted to experimental data, Fig 4.1 as in linear regression, the input variable are derived. The general form of the equation developed using linear regression is a sum of the products
Non-linear quantitative structure-activity relationship for adenine derivatives as competitive inhibitors of adenosine deaminase
Sadat Hayatshahi, Sayyed Hamed [Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box: 14115/175, Tehran (Iran, Islamic Republic of) ; Abdolmaleki, Parviz [Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box: 14115/175, Tehran (Iran, Islamic Republic of) ]. E-mail: email@example.com; Safarian, Shahrokh [Department of Biology, Faculty of Science, Tehran University, P.O. Box: 13155-6455, Tehran (Iran, Islamic Republic of) ; Khajeh, Khosro [Department of Biochemistry, Faculty of Science, Tarbiat Modares University, P.O. Box: 14115/175, Tehran (Iran, Islamic Republic of)
Logistic regression and artificial neural networks have been developed as two non-linear models to establish quantitative structure-activity relationships between structural descriptors and biochemical activity of adenosine based competitive inhibitors, toward adenosine deaminase. The training set included 24 compounds with known k {sub i} values. The models were trained to solve two-class problems. Unlike the previous work in which multiple linear regression was used, the highest of positive charge on the molecules was recognized to be in close relation with their inhibition activity, while the electric charge on atom N1 of adenosine was found to be a poor descriptor. Consequently, the previously developed equation was improved and the newly formed one could predict the class of 91.66% of compounds correctly. Also optimized 2-3-1 and 3-4-1 neural networks could increase this rate to 95.83%.
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
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
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.
Fast Optimization of Nano-CMOS Voltage-Controlled Oscillator using Polynomial Regression and Genetic Algorithm
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
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
W. B. Vasantha Kandasamy; Florentin Smarandache
In this book, the authors introduce the notion of Super linear algebra and super vector spaces using the definition of super matrices defined by Horst (1963). This book expects the readers to be well-versed in linear algebra. Many theorems on super linear algebra and its properties are proved. Some theorems are left as exercises for the reader. These new class of super linear algebras which can be thought of as a set of linear algebras, following a stipulated condition, will find applications in several fields using computers. The authors feel that such a paradigm shift is essential in this computerized world. Some other structures ought to replace linear algebras which are over a century old. Super linear algebras that use super matrices can store data not only in a block but in multiple blocks so it is certainly more powerful than the usual matrices. This book has 3 chapters. Chapter one introduces the notion of super vector spaces and enumerates a number of properties. Chapter two defines the notion of super linear algebra, super inner product spaces and super bilinear forms. Several interesting properties are derived. The main application of these new structures in Markov chains and Leontief economic models are also given in this chapter. The final chapter suggests 161 problems mainly to make the reader understand this new concept and apply them.
Southworth, Frank [ORNL; Garrow, Dr. Laurie [Georgia Institute of Technology
This chapter describes the principal types of both passenger and freight demand models in use today, providing a brief history of model development supported by references to a number of popular texts on the subject, and directing the reader to papers covering some of the more recent technical developments in the area. Over the past half century a variety of methods have been used to estimate and forecast travel demands, drawing concepts from economic/utility maximization theory, transportation system optimization and spatial interaction theory, using and often combining solution techniques as varied as Box-Jenkins methods, non-linear multivariate regression, non-linear mathematical programming, and agent-based microsimulation.
Construction of reduced order models for the non-linear Navier-Stokes equations using the proper orthogonal fecomposition (POD)/Galerkin method.
Fike, Jeffrey A.
The construction of stable reduced order models using Galerkin projection for the Euler or Navier-Stokes equations requires a suitable choice for the inner product. The standard L2 inner product is expected to produce unstable ROMs. For the non-linear Navier-Stokes equations this means the use of an energy inner product. In this report, Galerkin projection for the non-linear Navier-Stokes equations using the L2 inner product is implemented as a first step toward constructing stable ROMs for this set of physics.
A new sliced inverse regression method for multivariate response regression
Paris-Sud XI, Université de
) for estimating the effective dimension reduction (EDR) space without requiring a prespecified parametric model. The convergence at rate n of the estimated EDR space is shown. We discuss the choice of the dimension of the EDR space. Moreover, we provide a way to cluster components of y related to the same EDR space. One can thus
A new sliced inverse regression method for multivariate response regression
reduction (EDR) space without requiring a prespecified parametric model. The convergence at rate n of the estimated EDR space is shown. We discuss the choice of the dimension of the EDR space. The numerical a way to cluster components of y related to the same EDR space. One can thus apply properly multivariate
Design of active suspension control based upon use of tubular linear motor and quarter-car model
Allen, Justin Aaron
The design, fabrication, and testing of a quarter-car facility coupled with various control algorithms are presented in this thesis. An experimental linear tubular motor, capable of producing a 52-N force, provides control ...
A Library for Locally Weighted Projection Regression
Klanke, Stefan; Vijayakumar, Sethu; Schaal, Stefan
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 ...
Boyer, Edmond
PAVEMENT PREDICTION PERFORMANCE MODELS AND RELATION WITH TRAFFIC FATALITIES AND INJURIES V. email@example.com ABSTRACT This paper presents some results of a study, which aimed at modelling pavement evolution, pavement characteristics and age. In a second part, non-linear regressions were used in view of obtaining
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
STA 6246 Linear Models Instructor James Hobert, 221 Griffin-Floyd Hall; Office Hours: Monday & Wednesday 4th
Hobert, James P.
Statistics with S (4th edition, 2002), Springer. We will use the statistical computing language R (which can at Chapter 4. If you prefer to use other statistical languages or statistical packages and do not intend level; · a one-year sequence in theoretical statistics at the graduate level; · a course in linear
Special set linear algebra and special set fuzzy linear algebra
W. B. Vasantha Kandasamy; Florentin Smarandache; K. Ilanthenral
The authors in this book introduce the notion of special set linear algebra and special set fuzzy Linear algebra, which is an extension of the notion set linear algebra and set fuzzy linear algebra. These concepts are best suited in the application of multi expert models and cryptology. This book has five chapters. In chapter one the basic concepts about set linear algebra is given in order to make this book a self contained one. The notion of special set linear algebra and their fuzzy analogue is introduced in chapter two. In chapter three the notion of special set semigroup linear algebra is introduced. The concept of special set n-vector spaces, n greater than or equal to three is defined and their fuzzy analogue is their fuzzy analogue is given in chapter four. The probable applications are also mentioned. The final chapter suggests 66 problems.
Liblit, Ben
-linear model to build a partly exible model for multivariate Bernoulli data. The joint distribution the association between outcome variables. A numer- ical scheme based on the block one-step SOR-Leibler) distance. It is used to adaptively select smoothing parameters in each block one-step SOR itera- tion
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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 |Is Your HomeLatestCenter (LMI-EFRC) -Choices toLeeLinear Accelerator
Ensemble Kalman Filtering with Shrinkage Regression Techniques
Eidsvik, Jo
Ensemble Kalman Filtering with Shrinkage Regression Techniques Jon Sćtrom & 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
Materials Science & Metallurgy Master of Philosophy, Materials Modelling, Course MP9, Information Theory, H. K. D. H. Bhadeshia and T. Sourmail
Cambridge, University of
) A neural network representation of linear regression. (b) A nonlinear network representation. output y Theory, H. K. D. H. Bhadeshia and T. Sourmail Lecture 1: Neural Networks Linear Regression Most with ordinary linear regression analysis as fol- lows: #12;(a) A relationship has to be chosen before analysis
Non-linear Predictors for Facial Feature Tracking Across Pose and Expression
Bowden, Richard
Non-linear Predictors for Facial Feature Tracking Across Pose and Expression Tim Sheerman Email: t.sheerman-chase,e.ong,firstname.lastname@example.com Abstract--This paper proposes a non-linear predictor facial feature. Linear regression is only effective when pose changes are limited. As pose variations
Using Linear Genetic Programming to Develop a C/C++ Simulation Model of a Waste Incinerator. Larry M Deschaine PE
Fernandez, Thomas
/C++ computer simulation model that mimics the performance of the concentrations of carbon dioxide. It #12;involves variable input material properties (solids, liquids and gaseous), high temperature, large
Inverse Modeling Using a Wireless Sensor Network (WSN) for Personalized Daylight Harvesting
Agogino, Alice M.
Inverse Modeling Using a Wireless Sensor Network (WSN) for Personalized Daylight Harvesting Ryan: predictive: daylight harvesting: piecewise linear regression: building energy efficiency Abstract: Smart light levels, discretized by sub-hourly sun angles. Applied on two days of daylight and ten days
School of Mathematical Sciences MTH5120 Statistical Modelling I Practical 8 and Assignment 7
Bogacka, Barbara
1 Here we are interested in predicting the petrol consumption of a car from its size and engine charac- teristics. The data are on the web-site in file Petrol.txt. There are 38 cars and measurements regression model for the petrol consumption as a linear function of all available variables. (b) Obtain
Chan, T.
Modeling for variable rock properties and discontinuities5.2.1. Laboratory rock properties 5.2.2. Discontinuities andand Board, M. 1980. "Rock Properties and Their on Thermally
Models of thin discs and spheroidal haloes with masses in a linear relationship: mass estimates for NGC4389 and UGC6969
Guillermo A. González; J. Ibáńez; Jerson I. Reina
A family of models of thin discs and spheroidal haloes with masses in a linear relationship is presented. The models are obtained by considering the gravitational potential as the superposition of two independent components, a potential generated by the thin galactic disc and a potential generated by the spheroidal halo. The models leads to an expression for the circular velocity that can be adjusted very accurately to the observed rotation curves of some specific galaxies, in such a way that the models are stable against radial and vertical perturbations. Two particular models for galaxies NGC4389 and UGC6969 are obtained by adjusting the circular velocity with data taken from the recent paper by Verheijen & Sancici (2001). The values of the halo mass, the disc mass and the total mass for these two galaxies are computed in such a way that we obtain a very narrow interval of values for these quantities. Furthermore, the values of masses here obtained are in perfect agreement with the expected order of magnitude and with the relative order of magnitude between the halo mass and the disc mass.
A Regression Test Selection Technique for Graphical User Interfaces
Chesser, Carl
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
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
The International Symposium on Transportation and Traffic Theory 00 (2013) 119 Linear-Quadratic Model Predictive Control
Nazarathy, Yoni
al., 1996). The more recent works on traffic control systems have adopted results of modern control responsibility of Delft University of Technology Keywords: Model Predictive Control, Intelligent Transport System, Congestion Control 1. Introduction Increasing population and economic activities in modern societies have led
Misspecified Mean Function Regression: Making Good Use of Regression Models that are Wrong
Brown, Lawrence D.
the relevant research was "skeptical that the death penalty [as practiced in the United States] can ever decreases, increases, or has no eect on homi- cide rates. ... Consequently, claims that research demonstrates that capital punishment decreases or increases the homicide rate by a specific amount or has
Misspecified Mean Function Regression: Making Good Use of Regression Models that are Wrong
Zhao, Linda
that the death penalty [as practiced in the United States] can ever be subjected to the kind of statistical is not informative about whether capital punishment decreases, increases, or has no eect on homi- cide rates rate by a specific amount or has no eect on the homicide rate should not influence policy judgments
Evaluation of syngas production unit cost of bio-gasification facility using regression analysis techniques
Deng, Yangyang; Parajuli, Prem B.
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.
Journal of Materials Processing Technology 189 (2007) 192198 Modelling of surface finish and tool flank wear in turning
Ozel, Tugrul
flank wear in turning of AISI D2 steel with ceramic wiper inserts Tugrul ¨Ozela,, Yigit Karpata, Lu (60 HRC) using ceramic wiper (multi-radii) design inserts. Multiple linear regression models. Experimental results indicate that surface roughness Ra values as low as 0.180.20 m are attainable with wiper
Spin-chain with PSU(2|2)xU(1)^3 and Non-linear Sigma-model with D(2,1;gamma)
Shogo Aoyama; Yuco Honda
We propose that the spin-chain with the PSU(2|2)xU(1)^3 symmetry is equivalent to the non-linear sigma-model on PSU(2|2)xU(1)^3/{HxU(1)} with a certain subgroup. To this end we show that the spin-variable of the former theory is identified as the Killing scalar of the latter and their correlation functions can have the same integrability. It is crucial to think that the respective theory gets the PSU(2|2)xU(1)^3 symmetry by a symmetry reduction the exceptional supergroup D(2,1;gamma), rather than by an extension of PSU(2|2).
Regression analysis of WMATA metering information
Not Available
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.
Application of linear multiple model predictive control (MMPC) framework towards dynamic maximazation of oxygen yield in an elevated-pressure air separation unit
Mahapatra, P.; Zitney, S.; Bequette, B. Wayne
In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the flowrate of liquid nitrogen (LN2) stream connecting high-pressure and low-pressure ASU columns plays an important role in the total oxygen yield. It has been observed that this yield reaches a maximum at a certain optimal flowrate of LN2 stream. At nominal full-load operation, the flowrate of LN2 stream is maintained near this optimum value, whereas at part-load conditions this flowrate is typically modified in proportion with the load-change (oxygen demand) through a ratio/feed-forward controller. Due to nonlinearity in the entire ASU process, the ratio-modified LN2 flowrate does not guarantee an optimal oxygen yield at part-load conditions. This is further exacerbated when process disturbances in form of "cold-box" heat-leaks enter the system. To address this problem of dynamically maximizing the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. This approach has been used in previous studies to handle large ramp-rates of oxygen demand posed by the gasifier in an IGCC plant. In this study, the proposed algorithm uses linear step-response "blackbox" models surrounding the operating points corresponding to maximum oxygen yield points at different loads. It has been shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost compared to a firstprinciple based nonlinear MPC. Introduction
T. T. Moh
goal of this course is to enable you to recognize linear algebra problems ... descriptions of other people's solutions to problems that use linear algebra and to
Image segmentation using local spectral histograms and linear regression Jiangye Yuan a,
Wang, DeLiang "Leon"
, Center for Cognitive Science, The Ohio State University, Columbus, OH 43210, United States a r t i c l e filterbanks to decompose an image into a set of sub-bands. Filtering methods have received experimental sup- ports on human texture perception and have shown impressive per- formance for texture segmentation
MONT 102N Environmental Mathematics Lab on Linear Regression, Correlation, and Data Analysis
Little, John B.
to Plot Residuals and Line Fit and check 1 #12;the box for labels. Press OK and a new "sheet. · To generate the y-versus-x scatter plot, highlight the cells in those columns, then press the Insert tab on the top, look for the Scatter option under the Charts category and press that button. You can experiment
Waste management under multiple complexities: Inexact piecewise-linearization-based fuzzy flexible programming
Sun Wei [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); Huang, Guo H., E-mail: email@example.com [Institute for Energy, Environment and Sustainability Research, UR-NCEPU, North China Electric Power University, Beijing 102206 (China); Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); Lv Ying; Li Gongchen [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada)
Highlights: Black-Right-Pointing-Pointer Inexact piecewise-linearization-based fuzzy flexible programming is proposed. Black-Right-Pointing-Pointer It's the first application to waste management under multiple complexities. Black-Right-Pointing-Pointer It tackles nonlinear economies-of-scale effects in interval-parameter constraints. Black-Right-Pointing-Pointer It estimates costs more accurately than the linear-regression-based model. Black-Right-Pointing-Pointer Uncertainties are decreased and more satisfactory interval solutions are obtained. - Abstract: To tackle nonlinear economies-of-scale (EOS) effects in interval-parameter constraints for a representative waste management problem, an inexact piecewise-linearization-based fuzzy flexible programming (IPFP) model is developed. In IPFP, interval parameters for waste amounts and transportation/operation costs can be quantified; aspiration levels for net system costs, as well as tolerance intervals for both capacities of waste treatment facilities and waste generation rates can be reflected; and the nonlinear EOS effects transformed from objective function to constraints can be approximated. An interactive algorithm is proposed for solving the IPFP model, which in nature is an interval-parameter mixed-integer quadratically constrained programming model. To demonstrate the IPFP's advantages, two alternative models are developed to compare their performances. One is a conventional linear-regression-based inexact fuzzy programming model (IPFP2) and the other is an IPFP model with all right-hand-sides of fussy constraints being the corresponding interval numbers (IPFP3). The comparison results between IPFP and IPFP2 indicate that the optimized waste amounts would have the similar patterns in both models. However, when dealing with EOS effects in constraints, the IPFP2 may underestimate the net system costs while the IPFP can estimate the costs more accurately. The comparison results between IPFP and IPFP3 indicate that their solutions would be significantly different. The decreased system uncertainties in IPFP's solutions demonstrate its effectiveness for providing more satisfactory interval solutions than IPFP3. Following its first application to waste management, the IPFP can be potentially applied to other environmental problems under multiple complexities.
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
Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines
Cañizares, 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
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 · Permeation of gases in polymers  Application areas for diffusion in polymers  Description of the experimental device  Mathematical model · Parameter
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
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
Enabling efficiency in data center systems
McCullough, John Carleton; McCullough, John Carleton
models . . . 5.4.2 Non-linear regression models EvaluationLasso regression [126]. Non-linear regression models Linearlinear regression based models often perform poorly, and more complex non-
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)...
Optimal spline regression utilizing steepest descent
Flora, Eric Shirley
. Computing The Gradient 3. Steepest Descent V, THE PROGRAM 25 27 34 REFERENCES APPENDIX 38 39 VITA 75 vi LIST OF FIGURES k-1 1. g (s, t) = (s-t) as a function of s Page 2. Normalized linear B-splines, N equally spaced knots 3. Normalized... points. Figure &. shows N with unequally spaced knots and two t coincident interior knots. If k points of v are allowed to coincide then Mi k and N are arbitrarily defined to be right continuous. Hence, 1 i+1 i ' i ? i+1 (2 4) Mi I (t) 0 otherwise...
Least Squares Linear Discriminant Analysis Jieping Ye email@example.com
Liu, Huan
Analysis (LDA) is a well-known method for dimensionality reduc- tion and classification. LDA in the binaryLeast Squares Linear Discriminant Analysis Jieping Ye firstname.lastname@example.com Department of Computer stud- ies have shown certain relationship between multivariate linear regression and LDA for the multi
Bond length and electric current oscillation of long linear carbon chains: Density functional theory, MpB model, and quantum spin transport studies
Oeiras, R. Y.; Silva, E. Z. da [Institute of Physics "Gleb Wataghin", University of Campinas-Unicamp, 13083-859 Campinas, SP (Brazil)] [Institute of Physics "Gleb Wataghin", University of Campinas-Unicamp, 13083-859 Campinas, SP (Brazil)
Carbon linear atomic chains attached to graphene have experimentally been produced. Motivated by these results, we study the nature of the carbon bonds in these nanowires and how it affects their electrical properties. In the present study we investigate chains with different numbers of atoms and we observe that nanowires with odd number of atoms present a distinct behavior than the ones with even numbers. Using graphene nanoribbons as leads, we identify differences in the quantum transport of the chains with the consequence that even and odd numbered chains have low and high electrical conduction, respectively. We also noted a dependence of current with the wire size. We study this unexpected behavior using a combination of first principles calculations and simple models based on chemical bond theory. From our studies, the electrons of carbon nanowires present a quasi-free electron behavior and this explains qualitatively the high electrical conduction and the bond lengths with unexpected values for the case of odd nanowires. Our study also allows the understanding of the electric conduction dependence with the number of atoms and their parity in the chain. In the case of odd number chains a proposed ?-bond (MpB) model describes unsaturated carbons that introduce a mobile ?-bond that changes dramatically the structure and transport properties of these wires. Our results indicate that the nature of bonds plays the main role in the oscillation of quantum electrical conduction for chains with even and odd number of atoms and also that nanowires bonded to graphene nanoribbons behave as a quasi-free electron system, suggesting that this behavior is general and it could also remain if the chains are bonded to other materials.
Crop yield estimation model for Iowa using remote sensing and surface parameters
Singh, Ramesh P.
and prediction using piecewise linear regression method with breakpoint. Crop production environment consists of inherent sources of heterogeneity and their non-linear behavior. A non-linear Quasi-Newton multi
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
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
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 email@example.com Abstract. In the context of genomic selection in animal breeding and "closed to real" datasets. Keywords: Feature selection, combinatorial optimization, regression, genomic. 1
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
Generalized Linear Quadratic Control
Gattami, Ather Said
We consider the problem of stochastic finite- and infinite-horizon linear quadratic control under power constraints. The calculations of the optimal control law can be done off-line as in the classical linear quadratic ...
The Microscopic Linear Dynamics
Penny, Will
The Microscopic Brain Will Penny Linear Dynamics Exponentials Matrix Exponential Eigendecomposition Dynamical Modes Nodes State Space Saddles Oscillations Spirals Centres Offsets Retinal Circuit Nullclines Stability Spiking Neurons Fitzhugh-Nagumo Nonlinear Dynamics Linearization Nonlinear Oscillation Excitable
Zeghib, Abdelghani
Introduction Results Linear Dynamics Lorentz Dynamics Actions of discrete groups on stationary Piccione) Geodeycos Meeting, Lyon, 28-30 April 2010 Abdelghani Zeghib Dynamics on Lorentz manifolds #12;Introduction Results Linear Dynamics Lorentz Dynamics Motivations and questions Examples 1 Introduction
Introduction to Linear Relaxations
Introduction to Linear Relaxations by R. Baker Kearfott Department of Mathematics University relaxations; . discuss validation of linear relaxations. Intro. Linear Relaxations December, 2003 Taylor, . . . , m 2 , where # : R n # R and c i , g i : R n # R are guaranteed to be within one of the x # that has
ARTICLE IN PRESS EJMFLU:2254 Please cite this article as: Y. Zheng, H. Struchtrup, A linearization of Mieussens's discrete velocity model for kinetic equations, European
Struchtrup, Henning
, University of Victoria, Victoria, V8W 3P6, Canada Received 14 September 2005; received in revised form 23 March 2006; accepted 31 August 2006 Abstract A linearization is developed for Mieussens's discrete
Linearized theory of peridynamic states.
Silling, Stewart Andrew
A state-based peridynamic material model describes internal forces acting on a point in terms of the collective deformation of all the material within a neighborhood of the point. In this paper, the response of a state-based peridynamic material is investigated for a small deformation superposed on a large deformation. The appropriate notion of a small deformation restricts the relative displacement between points, but it does not involve the deformation gradient (which would be undefined on a crack). The material properties that govern the linearized material response are expressed in terms of a new quantity called the modulus state. This determines the force in each bond resulting from an incremental deformation of itself or of other bonds. Conditions are derived for a linearized material model to be elastic, objective, and to satisfy balance of angular momentum. If the material is elastic, then the modulus state is obtainable from the second Frechet derivative of the strain energy density function. The equation of equilibrium with a linearized material model is a linear Fredholm integral equation of the second kind. An analogue of Poincare's theorem is proved that applies to the infinite dimensional space of all peridynamic vector states, providing a condition similar to irrotationality in vector calculus.
Low-Dimensional Models for PCA and Regression
Omidiran, Christian Ladapo
Dwight Gasol, Pau Odom, Lamar Hilario, Nene Evans, Jeremyvery highly of Pau Gasol and Lamar Odom, two of Kobe Bryant'
Estimation in Hazard Regression Models under Ordered Departures from Proportionality
Bhattacharjee, Arnab
SK prgho wkurxjk ghwhfwlrq ri #4; Lt| Lu |#4;i *#16;|ih@|#3;hi hiuiht |L t#3;U#4; igiU|t @t |#16;4i#19;#15;@h)#16;?} igiU|t#21; i` Thiuih |L #3;ti |#4;i |ih#19; Hvwlpdwlrq lq Rughuhg Kd}dug Uhjuhvvlrq Prghov 6 djh0ydu|lqj fryduldwh h#14;hfwv +vhh... frqwlqxrxv fryduldwhv1 Hvwl0pdwlrq phwkrgv duh sursrvhg lq Vhfwlrq 61 Lq Vhfwlrq 7/ zh looxvwudwh wkh xvh riwkh hvwlpdwruv xvlqj vlpxodwlrqv dqg wzr uhdo olih dssolfdwlrqv1 Vhfwlrq 7 froohfwv wkhfrqfoxglqj uhpdunv1 2 #24;@h|#16;@* Lh_ih#16;?} #22;#16;|#4; hitTi...
Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models
Washington at Seattle, University of
diagnosed, quantifying the severity of the ab- normality is much more subjective and not a standard part
Probabilistic Clustering of Extratropical Cyclones Using Regression Mixture Models
Ghil, Michael
), The Earth Institute at Columbia University, Palisades, NY, USA 3Department of Computer Science, University of California, Irvine, CA, USA 4Department of Atmospheric and Oceanic Sciences and IGPP, University of California, Los Angeles, CA 5Additional affiliation: D´epartement Terre-Atmosph`ere-Oc´ean and Laboratoire de
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-...
15.060 Data, Models, and Decisions, Fall 2002
Freund, Robert Michael
Introduces students to the basic tools in using data to make informed management decisions. Covers introductory probability, decision analysis, basic statistics, regression, simulation, linear and nonlinear optimization, ...
Best Linear Unbiased Estimate Motivation for BLUE
Fowler, Mark
1 Chapter 6 Best Linear Unbiased Estimate (BLUE) #12;2 Motivation for BLUE Except for Linear Model to a sub-optimal estimate BLUE is one such sub-optimal estimate Idea for BLUE: 1. Restrict estimate) Advantage of BLUE:Needs only 1st and 2nd moments of PDF Mean & Covariance Disadvantages of BLUE: 1. Sub
Hierarchical Linear Discriminant Analysis for Beamforming
Park, Haesun
model of h-LDA by relating it to the two-way multivariate analysis of variance (MANOVA), which fits well dimension reduction, hierarchical linear discriminant analysis (h-LDA) to a well-known spatial localization1 Hierarchical Linear Discriminant Analysis for Beamforming Jaegul Choo , Barry L. Drake
Slatton, Clint
1 LOCAL LINEAR PID CONTROLLERS FOR NONLINEAR CONTROL Jing Lan1, Jeongho Cho1, Deniz Erdogmus2, email@example.com, firstname.lastname@example.com, firstname.lastname@example.com Abstract Nonlinear PID design is difficult if one approaches modeling approach with traditional linear PID controller design techniques to arrive at a principled
Demmel, James; Holtz, Olga; Dumitriu, Ioana
than other basic linear algebra subroutines. AcknowledgmentsApplied Numerical Linear Algebra. SIAM, 1997. [23] J.algorithms in numerical linear algebra. SIAM Review, 20:740–
Adaptive sparse polynomial chaos expansion based on least angle regression
Blatman, Geraud, E-mail: email@example.com [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)
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.
Joint Quantile Regression through Bayesian Semiparametrics
of tropical cyclones in the North Atlantic. Keywords: Bayesian inference, Gaussian process, Tropical cyclones by specifying a separate model for each candidate value of the quantile. In some applications, a unique quantile, it is not at all clear how to combine these separate analysis to form a coherent single view of the effect
Topics in ordinal logistic regression and its applications
Kim, Hyun Sun
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...
Robust regression on noisy data for fusion scaling laws
Verdoolaege, Geert, E-mail: firstname.lastname@example.com [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)
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.
The Computational Complexity of Linear Optics
Aaronson, Scott
We give new evidence that quantum computers---moreover, rudimentary quantum computers built entirely out of linear-optical elements---cannot be efficiently simulated by classical computers. In particular, we define a model ...
Fault tolerant linear actuator
Tesar, Delbert
In varying embodiments, the fault tolerant linear actuator of the present invention is a new and improved linear actuator with fault tolerance and positional control that may incorporate velocity summing, force summing, or a combination of the two. In one embodiment, the invention offers a velocity summing arrangement with a differential gear between two prime movers driving a cage, which then drives a linear spindle screw transmission. Other embodiments feature two prime movers driving separate linear spindle screw transmissions, one internal and one external, in a totally concentric and compact integrated module.
Using Linearity Web Copyright 2007
Rodriguez, Carlos
Using Linearity Web Rev. 2.0 May 2007 Copyright © 2007 #12;Using Linearity Web i Contents Introduction to Linearity Web.............................................................................1 Features, Benefits, and Value of Linearity Web..............................................1 Before You
QPOs: Einstein's gravity non-linear resonances
Paola Rebusco; Marek A. Abramowicz
There is strong evidence that the observed kHz Quasi Periodic Oscillations (QPOs) in the X-ray flux of neutron star and black hole sources in LMXRBs are linked to Einstein's General Relativity. Abramowicz&Klu\\'zniak (2001) suggested a non-linear resonance model to explain the QPOs origin: here we summarize their idea and the development of a mathematical toy-model which begins to throw light on the nature of Einstein's gravity non-linear oscillations.
Appropriateness of mechanistic and non-mechanistic models for the application of ultrafiltration to mixed waste
Foust, Henry; Ghosehajra, Malay
This study asks two questions: (1) How appropriate is the use of a basic filtration equation to the application of ultrafiltration of mixed waste, and (2) How appropriate are non-parametric models for permeate rates (volumes)? To answer these questions, mechanistic and non-mechanistic approaches are developed for permeate rates and volumes associated with an ultrafiltration/mixed waste system in dia-filtration mode. The mechanistic approach is based on a filtration equation which states that t/V vs. V is a linear relationship. The coefficients associated with this linear regression are composed of physical/chemical parameters of the system and based the mass balance equation associated with the membrane and associated developing cake layer. For several sets of data, a high correlation is shown that supports the assertion that t/V vs. V is a linear relationship. It is also shown that non-mechanistic approaches, i.e., the use of regression models to are not appropriate. One models considered is Q(p) = a*ln(Cb)+b. Regression models are inappropriate because the scale-up from a bench scale (pilot scale) study to full-scale for permeate rates (volumes) is not simply the ratio of the two membrane surface areas. (authors)
Stochastic bridges of linear systems
Yongxin Chen; Tryphon Georgiou
We study a generalization of the Brownian bridge as a stochastic process that models the position and velocity of inertial particles between the two end-points of a time interval. The particles experience random acceleration and are assumed to have known states at the boundary. Thus, the movement of the particles can be modeled as an Ornstein-Uhlenbeck process conditioned on position and velocity measurements at the two end-points. It is shown that optimal stochastic control provides a stochastic differential equation (SDE) that generates such a bridge as a degenerate diffusion process. Generalizations to higher order linear diffusions are considered.
Comprehensive transcriptome analysis of the highly complex Pisum sativum genome using next generation sequencing
Franssen, Susanne U; Shrestha, Roshan P; Bräutigam, Andrea; Bornberg-Bauer, Erich; Weber, Andreas PM
was modeled with non-linear regression fitting y = (ax)/(b+points were fitted by non-linear regression with the model y
Bitmead, Bob
, a number of modern model-based control design methods sought to deal with system constraints directlyOPTIMAL CONTROL OF LINEAR SYSTEMS WITH STATE EQUALITY CONSTRAINTS Sangho Ko , Robert R. Bitmead 1 with the optimal control problem for systems with state linear equality constraints. For deterministic linear
Linear Motor Powered Transportation
Thornton, Richard D.
This special issue on linear-motor powered transportation covers both supporting technologies and innovative transport systems in various parts of the World, as this technology moves from the lab to commercial operations. ...
A new sliced inverse regression method for multivariate response R. Coudreta, S. Girardb, J. Saraccoa,c,
Paris-Sud XI, Université de
on sliced inverse regression (SIR) for estimating the effective dimension reduction (EDR) space without requiring a prespecified para- metric model. The convergence at rate n of the estimated EDR space is shown. The choice of the dimension of the EDR space is discussed. Moreover, a way to cluster components of y related
automata linear rules: Topics by E-print Network
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
the same Sontag, Eduardo 25 CHARACTERIZATION OF NON-LINEAR CELLULAR AUTOMATA MODEL FOR PATTERN RECOGNITION Computer Technologies and Information Sciences Websites Summary:...
New Semidefinite Programming Relaxations for the Linear Ordering ...
Her model is based on the observation that linear orderings can be fully described by a series of cuts. .... [48] and input-output analysis [39]), sociology (
Optimization Online - Linear-quadratic control problem with a linear ...
L Faybusovich
Dec 19, 2003 ... Abstract: We describe a complete solution of the linear-quaratic control problem with the linear term in the objective function on a semiinfinite ...
6, 74277469, 2006 Linear ozone
Boyer, Edmond
ACPD 6, 74277469, 2006 Linear ozone photochemistry parametrizations A. J. Geer et al. Title Page Chemistry and Physics Discussions Evaluation of linear ozone photochemistry parametrizations Linear ozone photochemistry parametrizations A. J. Geer et al. Title Page Abstract Introduction
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 firstname.lastname@example.com, email@example.com 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
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
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
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
Analysis on the Inverse problem Statistical analysis of the inverse problem
regression This is a non-linear regression model. Assumption : we have equal variance measurement errors and trigonometric forms. #12;Analysis on the Inverse problem Introduction Non-linear regression This is a non-linear on the Inverse problem Introduction Linear and non-linear regression Examples : Linear model y = 0 + 1x + 2x2 y
Robust Linear Optimization With Recourse
We propose an approach to two-stage linear optimization with recourse that does ... Linear optimization with recourse was first introduced by Dantzig in [17] as a ...
Linear Quantum Feedback Networks
J. Gough; R. Gohm; M. Yanagisawa
The mathematical theory of quantum feedback networks has recently been developed for general open quantum dynamical systems interacting with bosonic input fields. In this article we show, for the special case of linear dynamical systems Markovian systems with instantaneous feedback connections, that the transfer functions can be deduced and agree with the algebraic rules obtained in the nonlinear case. Using these rules, we derive the the transfer functions for linear quantum systems in series, in cascade, and in feedback arrangements mediated by beam splitter devices.
Linear Programming Environmental
Nagurney, Anna
Linear Program to control air pollution was developed in 1968 by Teller, which minimized cost Fall 2006 #12;Topics · Introduction · Background · Air · Land · Water #12;Introduction · "The United States spends more than 2% of its gross domestic product on pollution control, and this is more than any
W. B. Vasantha Kandasamy; Florentin Smarandache
In this book we use only special types of intervals and introduce the notion of different types of interval linear algebras and interval vector spaces using the intervals of the form [0, a] where the intervals are from Zn or Z+ \\cup {0} or Q+ \\cup {0} or R+ \\cup {0}. A systematic development is made starting from set interval vector spaces to group interval vector spaces. Vector spaces are taken as interval polynomials or interval matrices or just intervals over suitable sets or semigroups or groups. Main feature of this book is the authors have given over 350 examples. This book has six chapters. Chapter one is introductory in nature. Chapter two introduces the notion of set interval linear algebras of type one and two. Set fuzzy interval linear algebras and their algebras and their properties are discussed in chapter three. Chapter four introduces several types of interval linear bialgebras and bivector spaces and studies them. The possible applications are given in chapter five. Chapter six suggests nearly 110 problems of all levels.
June 2226,1992 Toronto, Ont., Canada Sponsored By
to linear and additivemodels for regression problems and to linear logistic and additive logistic models solutions by parametric procedures such as linear regression, logistic regression, and discriminant analysis. CART is a --based non-parametricstatisticalprocedure for application to classification
Smith, Nathaniel J.
Non-linear regression . . . . . . . . . . . . . . . . . . .4.2 often helps. Non-linear regression The models we'vea huge literature on non-linear regression; our goal is to
The vitamin E analog alpha-tocopheryloxyacetic acid enhances the anti-tumor activity of trastuzumab against HER2/neu-expressing breast cancer
media alone). Non-linear regression analysis was performednon- linear mixed model. The mean size was represented by a 4-parameter logistic regression
Latest Jurassic-early Cretaceous regressive facies, northeast Africa craton
van Houten, F.B.
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.
Moment-linear stochastic systems and their applications
Roy, Sandip, 1978-
Our work is motivated by the need for tractable stochastic models for complex network and system dynamics. With this motivation in mind, we develop a class of discrete-time Markov models, called moment-linear stochastic ...
Designing AC Power Grids using Integer Linear Programming
view of recent developments in integer linear programming, we revisit a less known .... optimal solution of the DC model was the minimum cost circuit of the network. ... Andersson, G.: Modelling and Analysis of Electric Power Systems (
Nonlinear GARCH model and 1/f noise
Kononovicius, Aleksejus
Auto-regressive conditionally heteroskedastic (ARCH) family models are still used, by practitioners in business and economic policy making, as a conditional volatility forecasting models. Furthermore ARCH models still are attracting an interest of the researchers. In this contribution we consider the well known GARCH(1,1) process and its nonlinear modifications, reminiscent of NGARCH model. We investigate the possibility to reproduce power law statistics, probability density function and power spectral density, using ARCH family models. For this purpose we derive stochastic differential equations from the GARCH processes in consideration. We find the obtained equations to be similar to a general class of stochastic differential equations known to reproduce power law statistics. We show that linear GARCH(1,1) process has power law distribution, but its power spectral density is Brownian noise-like. However, the nonlinear modifications exhibit both power law distribution and power spectral density of the power ...
Non-linear force-free field modeling of a solar active region around the time of a major flare and coronal mass ejection
C. J. Schrijver; M. L. DeRosa; T. Metcalf; G. Barnes; B. Lites; T. Tarbell; J. McTiernan; G. Valori; T. Wiegelmann; M. S. Wheatland; T. Amari; G. Aulanier; P. Demoulin; M. Fuhrmann; K. Kusano; S. Regnier; J. K. Thalmann
Solar flares and coronal mass ejections are associated with rapid changes in field connectivity and powered by the partial dissipation of electrical currents in the solar atmosphere. A critical unanswered question is whether the currents involved are induced by the motion of pre-existing atmospheric magnetic flux subject to surface plasma flows, or whether these currents are associated with the emergence of flux from within the solar convective zone. We address this problem by applying state-of-the-art nonlinear force-free field (NLFFF) modeling to the highest resolution and quality vector-magnetographic data observed by the recently launched Hinode satellite on NOAA Active Region 10930 around the time of a powerful X3.4 flare. We compute 14 NLFFF models with 4 different codes and a variety of boundary conditions. We find that the model fields differ markedly in geometry, energy content, and force-freeness. We discuss the relative merits of these models in a general critique of present abilities to model the coronal magnetic field based on surface vector field measurements. For our application in particular, we find a fair agreement of the best-fit model field with the observed coronal configuration, and argue (1) that strong electrical currents emerge together with magnetic flux preceding the flare, (2) that these currents are carried in an ensemble of thin strands, (3) that the global pattern of these currents and of field lines are compatible with a large-scale twisted flux rope topology, and (4) that the ~10^32 erg change in energy associated with the coronal electrical currents suffices to power the flare and its associated coronal mass ejection.
Henneaux, Marc; Teitelboim, Claudio [Physique Theorique et Mathematique and International Solvay Institutes, Universite Libre de Bruxelles, Campus Plaine C. P. 231, B-1050 Brussels (Belgium) and Centro de Estudios Cientificos (CECS), Valdivia (Chile); Centro de Estudios Cientificos (CECS), Valdivia (Chile)
We show that duality transformations of linearized gravity in four dimensions, i.e., rotations of the linearized Riemann tensor and its dual into each other, can be extended to the dynamical fields of the theory so as to be symmetries of the action and not just symmetries of the equations of motion. Our approach relies on the introduction of two superpotentials, one for the spatial components of the spin-2 field and the other for their canonically conjugate momenta. These superpotentials are two-index, symmetric tensors. They can be taken to be the basic dynamical fields and appear locally in the action. They are simply rotated into each other under duality. In terms of the superpotentials, the canonical generator of duality rotations is found to have a Chern-Simons-like structure, as in the Maxwell case.
Buttram, M.T.; Ginn, J.W.
A linear induction accelerator includes a plurality of adder cavities arranged in a series and provided in a structure which is evacuated so that a vacuum inductance is provided between each adder cavity and the structure. An energy storage system for the adder cavities includes a pulsed current source and a respective plurality of bipolar converting networks connected thereto. The bipolar high-voltage, high-repetition-rate square pulse train sets and resets the cavities. 4 figs.
Combustion powered linear actuator
Fischer, Gary J. (Albuquerque, NM)
The present invention provides robotic vehicles having wheeled and hopping mobilities that are capable of traversing (e.g. by hopping over) obstacles that are large in size relative to the robot and, are capable of operation in unpredictable terrain over long range. The present invention further provides combustion powered linear actuators, which can include latching mechanisms to facilitate pressurized fueling of the actuators, as can be used to provide wheeled vehicles with a hopping mobility.
Jacques Carolan; Chris Harrold; Chris Sparrow; Enrique Martín-López; Nicholas J. Russell; Joshua W. Silverstone; Peter J. Shadbolt; Nobuyuki Matsuda; Manabu Oguma; Mikitaka Itoh; Graham D. Marshall; Mark G. Thompson; Jonathan C. F. Matthews; Toshikazu Hashimoto; Jeremy L. O'Brien; Anthony Laing
Linear optics underpins tests of fundamental quantum mechanics and computer science, as well as quantum technologies. Here we experimentally demonstrate the longstanding goal of a single reprogrammable optical circuit that is sufficient to implement all possible linear optical protocols up to the size of that circuit. Our six-mode universal system consists of a cascade of 15 Mach-Zehnder interferometers with 30 thermo-optic phase shifters integrated into a single photonic chip that is electrically and optically interfaced for arbitrary setting of all phase shifters, input of up to six photons and their measurement with a 12 single-photon detector system. We programmed this system to implement heralded quantum logic and entangling gates, boson sampling with verification tests, and six-dimensional complex Hadamards. We implemented 100 Haar random unitaries with average fidelity 0.999 $\\pm$ 0.001. Our system is capable of switching between these and any other linear optical protocol in seconds. These results point the way to applications across fundamental science and quantum technologies.
The International Linear Collider
Barish, Barry
In this article, we describe the key features of the recently completed technical design for the International Linear Collider (ILC), a 200-500 GeV linear electron-positron collider (expandable to 1 TeV) that is based on 1.3 GHz superconducting radio-frequency (SCRF) technology. The machine parameters and detector characteristics have been chosen to complement the Large Hadron Collider physics, including the discovery of the Higgs boson, and to further exploit this new particle physics energy frontier with a precision instrument. The linear collider design is the result of nearly twenty years of R&D, resulting in a mature conceptual design for the ILC project that reflects an international consensus. We summarize the physics goals and capability of the ILC, the enabling R&D and resulting accelerator design, as well as the concepts for two complementary detectors. The ILC is technically ready to be proposed and built as a next generation lepton collider, perhaps to be built in stages beginning as a Hig...
Linear optimization Linear programming using the Simplex method
McCready, Mark J.
Linear optimization Linear programming using the Simplex method Maximize M = 40 x1 + 60 x2 subject, that increasing x2 is the way to get the biggest impact. The idea of the simplex method is to move only
Shrink-Wrapping trajectories for Linear Programming
May 30, 2010 ... In particular, we analyze the geometry of these trajectories in the ... convexity that does not rely on complex variables; in Section 3 we ..... otal observation for building Shrink-Wrapping framework for linear programming ... In applications, these three types of problems provide an extremely powerful modeling.
Diabetes Mellitus Glucose Prediction by Linear and
Diabetes Mellitus Glucose Prediction by Linear and Bayesian Ensemble Modeling Fredrik St was diagnosed with diabetes type 1. Being an engineer with a control and systems oriented curriculum I realized of diabetes glucose metabolism, and bringing new hope of technical solutions to support the management
ABSTRACT: This paper describes one Learning in Optimal Non-linear Control *
Moore, John Barratt
Functional ABSTRACT: This paper describes one Learning in Optimal Non-linear Control * ~. Irlicht of nonfinear systems is to apply f{.~wiback control based on plant linearization and application of linear qll)irve robustness in optimization control working with a linearized .tatr-depenrlent plant model. I;vel] wit 1
Functional inverse regression and reproducing kernel Hilbert space
Ren, Haobo
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...
Sample size for logistic regression with small response probability
Whittemore, A S
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.
Park, Joung Won
In this work, a highly linear broadband Low Noise Amplifier (LNA) is presented. The linearity issue in broadband Radio Frequency (RF) front-end is introduced, followed by an analysis of the specifications and requirements of a broadband LNA through...
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,email@example.com 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,firstname.lastname@example.com ABSTRACT Regression testing is an expensive testing process used to validate software following modifications. The costÂeffective ness of regression testing techniques varies with characteris 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,firstname.lastname@example.com 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
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 email@example.com 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 email@example.com 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
Wave functions of linear systems
Tomasz Sowinski
Complete analysis of quantum wave functions of linear systems in an arbitrary number of dimensions is given. It is shown how one can construct a complete set of stationary quantum states of an arbitrary linear system from purely classical arguments. This construction is possible because for linear systems classical dynamics carries the whole information about quantum dynamics.
Cyclic transgressive and regressive sequences, Paleocene Suite, Sirte basin, Libya
Abushagur, S.A.
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.
Model of crack propagation in a clay soil
Carriere, Patrick Edwidge
in elevation of the soil surface were recorded over time of drying for each of the treatments. A logarithmic model to predict the crack depth, the crack width, and the drop in elevation of the soil surface expressed by the equation y = A + C*logt, was found... 2 MEANS procedure results for crack depth. 3 ANOVA results for crack depth. 19 29 30 4 Values of A and C obtained from linear regression analysis for crack depth. 35 5 Selection of combinations of independent variables for maximum R...
Gupta, N
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.
5-loop Konishi from linearized TBA and the XXX magnet
Janos Balog; Arpad Hegedus
Using the linearized TBA equations recently obtained in [arXiv:1002.1711] we show analytically that the 5-loop anomalous dimension of the Konishi operator agrees with the result obtained previously from the generalized Luscher formulae. The proof is based on the relation between this linear system and the XXX model TBA equations.
THE DIFFUSION APPROXIMATION FOR THE LINEAR BOLTZMANN EQUATION WITH VANISHING SCATTERING COEFFICIENT equation, Diffusion approximation, Neutron transport equation, Radiative transfer equation subject, 23], neutron transport theory [27]. A typical model linear Boltzmann equation is (t +· x)f(t,x,)= 1
Output regulation problem for differentiable families of linear systems
Politècnica de Catalunya, Universitat
The output regulation problem arose as one of the main research topics in linear control theory in the 1970s regulation when modeled by a global or a local differentiable family. Partially supported by DGICYT n.PB97Output regulation problem for differentiable families of linear systems Albert Compta and Marta Pe
MATH 100 Introduction to the Profession Linear Equations in MATLAB
Fasshauer, Greg
's input-output model in economics, electric circuit problems, the steady-state analysis of a systemMATH 100 Â Introduction to the Profession Linear Equations in MATLAB Greg Fasshauer Department;Chapter 5 of Experiments with MATLAB Where do systems of linear equations come up? email@example.com MATH
Figure 3. Socioeconomics drive biomass too. Simple regression with untrans-formed variables. Solid line represents the estimated regression line, whereas
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
Positrons for linear colliders
Ecklund, S.
The requirements of a positron source for a linear collider are briefly reviewed, followed by methods of positron production and production of photons by electromagnetic cascade showers. Cross sections for the electromagnetic cascade shower processes of positron-electron pair production and Compton scattering are compared. A program used for Monte Carlo analysis of electromagnetic cascades is briefly discussed, and positron distributions obtained from several runs of the program are discussed. Photons from synchrotron radiation and from channeling are also mentioned briefly, as well as positron collection, transverse focusing techniques, and longitudinal capture. Computer ray tracing is then briefly discussed, followed by space-charge effects and thermal heating and stress due to showers. (LEW)
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)
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)
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.
Physics at the e+ e- Linear Collider
Moortgat-Pick, G; Battaglia, M; Belanger, G; Fujii, K; Kalinowski, J; Heinemeyer, S; Kiyo, Y; Olive, K; Simon, F; Uwer, P; Wackeroth, D; Zerwas, P M; Arbey, A; Asano, M; Bechtle, P; Bharucha, A; Brau, J; Brummer, F; Choi, S Y; Denner, A; Desch, K; Dittmaier, S; Ellis, J; Ellwanger, U; Englert, C; Freitas, A; Ginzburg, I; Godfrey, S; Greiner, N; Grojean, C; Grunewald, M; Heisig, J; Hocker, A; Kanemura, S; Kawagoe, K; Kogler, R; Krawczyk, M; Kronfeld, A S; Kroseberg, J; Liebler, S; List, J; Mahmoudi, F; Mambrini, Y; Matsumoto, S; Mnich, J; Monig, K; Muhlleitner, M M; Poschl, R; Porod, W; Porto, S; Rolbiecki, K; Schlatter, D; Schmitt, M; Serpico, P; Stanitzki, M; Stĺl, O; Stefaniak, T; Stockinger, D; Wagner, A; Weiglein, G; Wilson, G W; Zeune, L; Moortgat, F; Xella, S
A comprehensive review of physics at an e+e- Linear Collider in the energy range of sqrt{s}=92 GeV--3 TeV is presented in view of recent and expected LHC results, experiments from low energy as well as astroparticle physics.The report focuses in particular on Higgs boson, Top quark and electroweak precision physics, but also discusses several models of beyond the Standard Model physics such as Supersymmetry, little Higgs models and extra gauge bosons. The connection to cosmology has been analyzed as well.
Physics at the e+ e- Linear Collider
G. Moortgat-Pick; H. Baer; M. Battaglia; G. Belanger; K. Fujii; J. Kalinowski; S. Heinemeyer; Y. Kiyo; K. Olive; F. Simon; P. Uwer; D. Wackeroth; P. M. Zerwas; A. Arbey; M. Asano; P. Bechtle; A. Bharucha; J. Brau; F. Brummer; S. Y. Choi; A. Denner; K. Desch; S. Dittmaier; U. Ellwanger; C. Englert; A. Freitas; I. Ginzburg; S. Godfrey; N. Greiner; C. Grojean; M. Grunewald; J. Heisig; A. Hocker; S. Kanemura; K. Kawagoe; R. Kogler; M. Krawczyk; A. S. Kronfeld; J. Kroseberg; S. Liebler; J. List; F. Mahmoudi; Y. Mambrini; S. Matsumoto; J. Mnich; K. Monig; M. M. Muhlleitner; R. Poschl; W. Porod; S. Porto; K. Rolbiecki; M. Schmitt; P. Serpico; M. Stanitzki; O. Stĺl; T. Stefaniak; D. Stockinger; G. Weiglein; G. W. Wilson; L. Zeune; F. Moortgat; S. Xella
A comprehensive review of physics at an e+e- Linear Collider in the energy range of sqrt{s}=92 GeV--3 TeV is presented in view of recent and expected LHC results, experiments from low energy as well as astroparticle physics.The report focuses in particular on Higgs boson, Top quark and electroweak precision physics, but also discusses several models of beyond the Standard Model physics such as Supersymmetry, little Higgs models and extra gauge bosons. The connection to cosmology has been analyzed as well.
Short-term load forecasting using generalized regression and probabilistic neural networks in the electricity market
Tripathi, M.M.; Upadhyay, K.G.; Singh, S.N.
For the economic and secure operation of power systems, a precise short-term load forecasting technique is essential. Modern load forecasting techniques - especially artificial neural network methods - are particularly attractive, as they have the ability to handle the non-linear relationships between load, weather temperature, and the factors affecting them directly. A test of two different ANN models on data from Australia's Victoria market is promising. (author)
Optimization of a dual acting, magnetically driven, linear actuator
Willerton, Justin Ryan
In this study the geometry of a dual acting, magnetically driven, linear motion actuator will be optimized. This will be accomplished by modeling the system through a set of differential equations to be solved in Matlab. ...
Linear Supply Function Equilibrium: Generalizations, Application, and Limitations
California at Berkeley. University of
reforms in England and Wales (E&W). Green (1996) used a linear version of this model and applied in the electricity industry. Recent reforms of the electricity industry around the world have stimulated numerous
6, 66276694, 2006 linearized ozone
Boyer, Edmond
ACPD 6, 66276694, 2006 CHEM2D-OPP linearized ozone photochemistry J. P. McCormack et al. Title Chemistry and Physics Discussions CHEM2D-OPP: A new linearized gas-phase ozone firstname.lastname@example.com) 6627 #12;ACPD 6, 66276694, 2006 CHEM2D-OPP linearized ozone photochemistry J. P. McCormack et al
Kliman, G.B.; Brynsvold, G.V.; Jahns, T.M.
A winding and method of winding for a submersible linear pump for pumping liquid sodium are disclosed. The pump includes a stator having a central cylindrical duct preferably vertically aligned. The central vertical duct is surrounded by a system of coils in slots. These slots are interleaved with magnetic flux conducting elements, these magnetic flux conducting elements forming a continuous magnetic field conduction path along the stator. The central duct has placed therein a cylindrical magnetic conducting core, this core having a cylindrical diameter less than the diameter of the cylindrical duct. The core once placed to the duct defines a cylindrical interstitial pumping volume of the pump. This cylindrical interstitial pumping volume preferably defines an inlet at the bottom of the pump, and an outlet at the top of the pump. Pump operation occurs by static windings in the outer stator sequentially conveying toroidal fields from the pump inlet at the bottom of the pump to the pump outlet at the top of the pump. The winding apparatus and method of winding disclosed uses multiple slots per pole per phase with parallel winding legs on each phase equal to or less than the number of slots per pole per phase. The slot sequence per pole per phase is chosen to equalize the variations in flux density of the pump sodium as it passes into the pump at the pump inlet with little or no flux and acquires magnetic flux in passage through the pump to the pump outlet. 4 figs.
Kliman, Gerald B. (Schenectady, NY); Brynsvold, Glen V. (San Jose, CA); Jahns, Thomas M. (Schenectady, NY)
A winding and method of winding for a submersible linear pump for pumping liquid sodium is disclosed. The pump includes a stator having a central cylindrical duct preferably vertically aligned. The central vertical duct is surrounded by a system of coils in slots. These slots are interleaved with magnetic flux conducting elements, these magnetic flux conducting elements forming a continuous magnetic field conduction path along the stator. The central duct has placed therein a cylindrical magnetic conducting core, this core having a cylindrical diameter less than the diameter of the cylindrical duct. The core once placed to the duct defines a cylindrical interstitial pumping volume of the pump. This cylindrical interstitial pumping volume preferably defines an inlet at the bottom of the pump, and an outlet at the top of the pump. Pump operation occurs by static windings in the outer stator sequentially conveying toroidal fields from the pump inlet at the bottom of the pump to the pump outlet at the top of the pump. The winding apparatus and method of winding disclosed uses multiple slots per pole per phase with parallel winding legs on each phase equal to or less than the number of slots per pole per phase. The slot sequence per pole per phase is chosen to equalize the variations in flux density of the pump sodium as it passes into the pump at the pump inlet with little or no flux and acquires magnetic flux in passage through the pump to the pump outlet.
Quantization of general linear electrodynamics
Rivera, Sergio; Schuller, Frederic P. [Albert Einstein Institute, Max Planck Institute for Gravitational Physics, Am Muehlenberg 1, 14476 Potsdam (Germany)
General linear electrodynamics allow for an arbitrary linear constitutive relation between the field strength 2-form and induction 2-form density if crucial hyperbolicity and energy conditions are satisfied, which render the theory predictive and physically interpretable. Taking into account the higher-order polynomial dispersion relation and associated causal structure of general linear electrodynamics, we carefully develop its Hamiltonian formulation from first principles. Canonical quantization of the resulting constrained system then results in a quantum vacuum which is sensitive to the constitutive tensor of the classical theory. As an application we calculate the Casimir effect in a birefringent linear optical medium.
Identifying Redundant Linear Constraints in Systems of Linear ...
Jun 22, 2006 ... redundant linear constraints from the system (2.1) and (2.2). ... It is informative to note that in the above theorem, the optimal ..... S. Boyd and L. El Ghaoui, "Linear Matrix Inequalities in System and Control Theory", SIAM, vol.
Linear Algebra 1: Computing canonical forms in exact linear
Pernet, Clément
Linear Algebra 1: Computing canonical forms in exact linear algebra Clément PERNET, LIG, where U is invertible Reduced echelon form: E = 1 0 0 1 0 1 Gauss-Jordan elimination #12 a field: B = U-1 AU Frobenius normal form (or canonical rational form): F = CP0 CP1 ... CPk
Optimal portfolios using Linear Programming models
Feb 12, 2003 ... The problem. The portfolio manager Sigma wants to construct an optimal portfolio for a customer. .... It is easy to show that it is also possible ...
A computer program for linear models
Zerbe, Manfred Rudolf
whe re: (X'X) ! s the sum of squares and crossproducts ~atrix of the X matrix. (X'Y);s the sum of crossprcducts matrix of the x's with Y. I2 C is (X'X) It should be noted that (X'X), hence (X'X) , is a symmetric matrix and is in many practical.... Courant, Differential and Inte ral Calculus &New York: Interscience Publishers, Inc. , I%61) p. 2 18 12 2 22 3 13 4 n(n+I} n(n+I) nn It is possible to obtain the subscript of an element in the X array given the subscripts of the element of the A...
Prediction Intervals in Generalized Linear Mixed Models
Yang, Cheng-Hsueh
3.1. BLP Based Prediction Intervals………………………………………..……3.2. BP Based Prediction Intervals………………..………………………..……4.1.1. BLP Based Prediction Interval………………………………………. 4.1.2.
Linear Modeling Optimization for Workload Assignments
Smith, Casey G.
.2843 X92 0.9082 1.0000 No Upper Limit X95 0.0716 1.0000 No Upper Limit X97 No Lower Limit 1.0000 1.0132 X914 0.9868 1...
Linear ParameterVarying versus Linear TimeInvariant Control Design for a Pressurized Water Reactor
Bodenheimer, Bobby
. The plant can thus have widely varying dynamics over the operating range. The controllers designed perform to a description of the problem statement. Section 4 describes the identification and modelling of the plant. Se the worstÂcase time variation of a measurable parameter which enters the plant in a linear fractional manner
Machine Learning Srihari Neural Networks
Models · Linear Models for Regression and Classification have form where x is a D-dimensional vector j (x regression y(x,w) = f w j j (x) j=1 M f (a) = 1 1+ e-a Linear Regression Generalized Linear Regression · SVM · Involves non-linear optimization · Objective function is convex · Leads to straightforward
Considering Error Propagation in Stepwise Polynomial Regression Neima Brauner*
Brauner, Neima
of thermophysical properties,2 the maximal poly- nomial order for solid, liquid, or gas heat capacity was set as a complete empirical model (heat capacity, for example) or as a complement to theory-based models
Adapted from EE363: Linear Dynamical Systems, Winter 2005-2006 Stephen Boyd -Stanford University The Kalman filter
Wehenkel, Louis
Lecture 7 The Kalman filter · Linear system driven by stochastic process · Statistical steady-state · Linear Gauss-Markov model · Kalman filter · Steady-state Kalman filter 71 #12;Linear system driven.e., the means propagate by the same linear dynamical system The Kalman filter 72 #12;now let's consider

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