Adaptive Error Estimation in Linearized Ocean General Circulation Models
Chechelnitsky, Michael Y.
1999-01-01
Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large
Biohybrid control of general linear systems using the adaptive filter model of cerebellum
Directory of Open Access Journals (Sweden)
Emma D. Wilson
2015-07-01
Full Text Available The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems such as the vestibulo-ocular reflex (VOR and to sensory processing problems such as the adaptive cancellation of reafferent noise. It has also been successfully applied to problems in robotics such as adaptive camera stabilisation and sensor noise cancellation. In previous applications to inverse control problems the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity control of this plant results in unstable learning and control. To be more generally useful in engineering problems it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC scheme, which stabilises the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.
Foundations of linear and generalized linear models
Agresti, Alan
2015-01-01
A valuable overview of the most important ideas and results in statistical analysis Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linear statistical models. The book presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical model building. The book begins by illustrating the fundamentals of linear models,
Ye, Dan; Chen, Mengmeng; Li, Kui
2017-06-22
In this paper, we consider the distributed containment control problem of multi-agent systems with actuator bias faults based on observer method. The objective is to drive the followers into the convex hull spanned by the dynamic leaders, where the input is unknown but bounded. By constructing an observer to estimate the states and bias faults, an effective distributed adaptive fault-tolerant controller is developed. Different from the traditional method, an auxiliary controller gain is designed to deal with the unknown inputs and bias faults together. Moreover, the coupling gain can be adjusted online through the adaptive mechanism without using the global information. Furthermore, the proposed control protocol can guarantee that all the signals of the closed-loop systems are bounded and all the followers converge to the convex hull with bounded residual errors formed by the dynamic leaders. Finally, a decoupled linearized longitudinal motion model of the F-18 aircraft is used to demonstrate the effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Multivariate generalized linear mixed models using R
Berridge, Damon Mark
2011-01-01
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...
Introduction to generalized linear models
Dobson, Annette J
2008-01-01
Introduction Background Scope Notation Distributions Related to the Normal Distribution Quadratic Forms Estimation Model Fitting Introduction Examples Some Principles of Statistical Modeling Notation and Coding for Explanatory Variables Exponential Family and Generalized Linear Models Introduction Exponential Family of Distributions Properties of Distributions in the Exponential Family Generalized Linear Models Examples Estimation Introduction Example: Failure Times for Pressure Vessels Maximum Likelihood Estimation Poisson Regression Example Inference Introduction Sampling Distribution for Score Statistics Taylor Series Approximations Sampling Distribution for MLEs Log-Likelihood Ratio Statistic Sampling Distribution for the Deviance Hypothesis Testing Normal Linear Models Introduction Basic Results Multiple Linear Regression Analysis of Variance Analysis of Covariance General Linear Models Binary Variables and Logistic Regression Probability Distributions ...
Generalized, Linear, and Mixed Models
McCulloch, Charles E; Neuhaus, John M
2011-01-01
An accessible and self-contained introduction to statistical models-now in a modernized new editionGeneralized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects.A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed m
Multivariate covariance generalized linear models
DEFF Research Database (Denmark)
Bonat, W. H.; Jørgensen, Bent
2016-01-01
We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...... measures and longitudinal structures, and the third involves a spatiotemporal analysis of rainfall data. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modelled by means of a link function and a linear predictor. The models...
Qureshi, Nauman Khalid; Naseer, Noman; Noori, Farzan Majeed; Nazeer, Hammad; Khan, Rayyan Azam; Saleem, Sajid
2017-01-01
In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain-computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher ( p classification-performance fNIRS-BCI.
Directory of Open Access Journals (Sweden)
Nauman Khalid Qureshi
2017-07-01
Full Text Available In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS signals utilizable in a two-class [motor imagery (MI and rest; mental rotation (MR and rest] brain–computer interface (BCI is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05. These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.
Discrete linear canonical transform computation by adaptive method.
Zhang, Feng; Tao, Ran; Wang, Yue
2013-07-29
The linear canonical transform (LCT) describes the effect of quadratic phase systems on a wavefield and generalizes many optical transforms. In this paper, the computation method for the discrete LCT using the adaptive least-mean-square (LMS) algorithm is presented. The computation approaches of the block-based discrete LCT and the stream-based discrete LCT using the LMS algorithm are derived, and the implementation structures of these approaches by the adaptive filter system are considered. The proposed computation approaches have the inherent parallel structures which make them suitable for efficient VLSI implementations, and are robust to the propagation of possible errors in the computation process.
Synaptic dynamics: linear model and adaptation algorithm.
Yousefi, Ali; Dibazar, Alireza A; Berger, Theodore W
2014-08-01
In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed. The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation-depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm's accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and
Estimating classification images with generalized linear and additive models.
Knoblauch, Kenneth; Maloney, Laurence T
2008-12-22
Conventional approaches to modeling classification image data can be described in terms of a standard linear model (LM). We show how the problem can be characterized as a Generalized Linear Model (GLM) with a Bernoulli distribution. We demonstrate via simulation that this approach is more accurate in estimating the underlying template in the absence of internal noise. With increasing internal noise, however, the advantage of the GLM over the LM decreases and GLM is no more accurate than LM. We then introduce the Generalized Additive Model (GAM), an extension of GLM that can be used to estimate smooth classification images adaptively. We show that this approach is more robust to the presence of internal noise, and finally, we demonstrate that GAM is readily adapted to estimation of higher order (nonlinear) classification images and to testing their significance.
Gravitational Wave in Linear General Relativity
Cubillos, D. J.
2017-07-01
General relativity is the best theory currently available to describe the interaction due to gravity. Within Albert Einstein's field equations this interaction is described by means of the spatiotemporal curvature generated by the matter-energy content in the universe. Weyl worked on the existence of perturbations of the curvature of space-time that propagate at the speed of light, which are known as Gravitational Waves, obtained to a first approximation through the linearization of the field equations of Einstein. Weyl's solution consists of taking the field equations in a vacuum and disturbing the metric, using the Minkowski metric slightly perturbed by a factor ɛ greater than zero but much smaller than one. If the feedback effect of the field is neglected, it can be considered as a weak field solution. After introducing the disturbed metric and ignoring ɛ terms of order greater than one, we can find the linearized field equations in terms of the perturbation, which can then be expressed in terms of the Dalambertian operator of the perturbation equalized to zero. This is analogous to the linear wave equation in classical mechanics, which can be interpreted by saying that gravitational effects propagate as waves at the speed of light. In addition to this, by studying the motion of a particle affected by this perturbation through the geodesic equation can show the transversal character of the gravitational wave and its two possible states of polarization. It can be shown that the energy carried by the wave is of the order of 1/c5 where c is the speed of light, which explains that its effects on matter are very small and very difficult to detect.
Generalized Linear Models in Vehicle Insurance
Directory of Open Access Journals (Sweden)
Silvie Kafková
2014-01-01
Full Text Available Actuaries in insurance companies try to find the best model for an estimation of insurance premium. It depends on many risk factors, e.g. the car characteristics and the profile of the driver. In this paper, an analysis of the portfolio of vehicle insurance data using a generalized linear model (GLM is performed. The main advantage of the approach presented in this article is that the GLMs are not limited by inflexible preconditions. Our aim is to predict the relation of annual claim frequency on given risk factors. Based on a large real-world sample of data from 57 410 vehicles, the present study proposed a classification analysis approach that addresses the selection of predictor variables. The models with different predictor variables are compared by analysis of deviance and Akaike information criterion (AIC. Based on this comparison, the model for the best estimate of annual claim frequency is chosen. All statistical calculations are computed in R environment, which contains stats package with the function for the estimation of parameters of GLM and the function for analysis of deviation.
Generalized Quadratic Linearization of Machine Models
Parvathy Ayalur Krishnamoorthy; Kamaraj Vijayarajan; Devanathan Rajagopalan
2011-01-01
In the exact linearization of involutive nonlinear system models, the issue of singularity needs to be addressed in practical applications. The approximate linearization technique due to Krener, based on Taylor series expansion, apart from being applicable to noninvolutive systems, allows the singularity issue to be circumvented. But approximate linearization, while removing terms up to certain order, also introduces terms of higher order than those removed into the system. To overcome th...
A Note on the Identifiability of Generalized Linear Mixed Models
DEFF Research Database (Denmark)
Labouriau, Rodrigo
2014-01-01
I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable. The proof is based on the assumptions of generalized linear mixed models on the first and second order moments and some general mild regularity...... conditions, and, therefore, is extensible to quasi-likelihood based generalized linear models. In particular, binomial and Poisson mixed models with dispersion parameter are identifiable when equipped with the standard parametrization...
Imputation by PLS regression for generalized linear mixed models
Guyon, Emilie; Pommeret, Denys
2011-01-01
The problem of handling missing data in generalized linear mixed models with correlated covariates is considered when the missing mechanism concerns both the response variable and the covariates. An imputation algorithm combining multiple imputation and Partial Least Squares (PLS) regression is proposed. The method relies on two steps. In a first step, using a linearization technique, the generalized linear mixed model is approximated by a linear mixed model. A latent variable is introduced a...
Generalized Frequency Domain LMS Adaptive Filter
Directory of Open Access Journals (Sweden)
F. Dohnal
1995-06-01
Full Text Available The most significant problems of acoustic echo canceller (AEC realizations are high computational complexity and insufficient convergence rate of the applied adaptive algorithms. From the analysis of the frequency domain block adaptive filter [2,3] realization and the modified subband acoustic echo canceller [6] the generalized frequency domain adaptive filter [8,9] has been derived. The result of simulations is demonstrated the efficiency of this algorithm for a stationary noise and real speech signal excitation.
Layer potentials for general linear elliptic systems
Directory of Open Access Journals (Sweden)
Ariel Barton
2017-12-01
Full Text Available In this article we construct layer potentials for elliptic differential operators using the Babuska-Lax-Milgram theorem, without recourse to the fundamental solution; this allows layer potentials to be constructed in very general settings. We then generalize several well known properties of layer potentials for harmonic and second order equations, in particular the Green's formula, jump relations, adjoint relations, and Verchota's equivalence between well-posedness of boundary value problems and invertibility of layer potentials.
"A regression error specification test (RESET) for generalized linear models".
Sunil Sapra
2005-01-01
Generalized linear models (GLMs) are generalizations of linear regression models, which allow fitting regression models to response data that follow a general exponential family. GLMs are used widely in social sciences for fitting regression models to count data, qualitative response data and duration data. While a variety of specification tests have been developed for the linear regression model and are routinely applied for testing for misspecification of functional form, omitted variables,...
ADAPTIVE NOISE CANCELLATION OF DOPPLER SHIFTED SIGNALS: A LINEAR FRAMEWORK
Stewart, R. W.; Weiss, S.
1996-01-01
In this paper we investigate the performance of single channel adaptive noise cancellation techniques for situations where the noise signal received by the two microphones cannot be related by a fixed weight canceller's (linear) digital filter due to Doppler shift on the two signals. A mathematical signal model is produced, which shows that the adaptive filter is in fact required to identify a time-varying system which incorporates Doppler shift, and potential rapid variations in signal power...
General linear response analysis of anelasticity
Indian Academy of Sciences (India)
function and generalized compliance) of a system under an applied stress, in terms of the equilibrium strain auto-correlation. These results extend an earlier analysis to cover inhomogeneous stresses and the tensor nature of the variables. For anelasti- city due to point defects, we express the strain compactly in terms of the ...
Linear Perturbation Adaptive Control of Hydraulically Driven Manipulators
DEFF Research Database (Denmark)
Andersen, T.O.; Hansen, M.R.; Conrad, Finn
2004-01-01
A method for synthesis of a robust adaptive scheme for a hydraulically driven manipulator, that takes full advantage of any known system dynamics to simplify the adaptive control problem for the unknown portion of the dynamics is presented. The control method is based on adaptive perturbation...... control.Using the Lyapunov approach, under slowly time-varying assumptions, it is shown that the tracking error and the parameter error remain bounded. This bound is a function of the ideal parameters and a bounded disturbance. The control algorithm decouples and linearizes the manipulator so that each...
Self-characterization of linear and nonlinear adaptive optics systems.
Hampton, Peter J; Conan, Rodolphe; Keskin, Onur; Bradley, Colin; Agathoklis, Pan
2008-01-10
We present methods used to determine the linear or nonlinear static response and the linear dynamic response of an adaptive optics (AO) system. This AO system consists of a nonlinear microelectromechanical systems deformable mirror (DM), a linear tip-tilt mirror (TTM), a control computer, and a Shack-Hartmann wavefront sensor. The system is modeled using a single-input-single-output structure to determine the one-dimensional transfer function of the dynamic response of the chain of system hardware. An AO system has been shown to be able to characterize its own response without additional instrumentation. Experimentally determined models are given for a TTM and a DM.
Equating an adaptive test to a linear test
van der Linden, Willem J.
2005-01-01
Two new methods for the equating of an adaptive test to a linear test are presented. The methods are based on the conditional distributions of the observed scores on the two tests, given the examinee’s ability. They are motivated by the fact that conditioning on the examinee’s ability is necessary
Adaptation-II of the surrogate methods for linear programming ...
African Journals Online (AJOL)
Adaptation-II of the surrogate methods for linear programming problems. SO Oko. Abstract. No Abstract. Global Journal of Mathematical Sciences Vol. 5(1) 2006: 63-71. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT · http://dx.doi.org/10.4314/gjmas.v5i1.21381.
Adaptive ensemble Kalman filtering of non-linear systems
Directory of Open Access Journals (Sweden)
Tyrus Berry
2013-07-01
Full Text Available A necessary ingredient of an ensemble Kalman filter (EnKF is covariance inflation, used to control filter divergence and compensate for model error. There is an on-going search for inflation tunings that can be learned adaptively. Early in the development of Kalman filtering, Mehra (1970, 1972 enabled adaptivity in the context of linear dynamics with white noise model errors by showing how to estimate the model error and observation covariances. We propose an adaptive scheme, based on lifting Mehra's idea to the non-linear case, that recovers the model error and observation noise covariances in simple cases, and in more complicated cases, results in a natural additive inflation that improves state estimation. It can be incorporated into non-linear filters such as the extended Kalman filter (EKF, the EnKF and their localised versions. We test the adaptive EnKF on a 40-dimensional Lorenz96 model and show the significant improvements in state estimation that are possible. We also discuss the extent to which such an adaptive filter can compensate for model error, and demonstrate the use of localisation to reduce ensemble sizes for large problems.
Adaptive feedback linearization applied to steering of ships
Directory of Open Access Journals (Sweden)
Thor I. Fossen
1993-10-01
Full Text Available This paper describes the application of feedback linearization to automatic steering of ships. The flexibility of the design procedure allows the autopilot to be optimized for both course-keeping and course-changing manoeuvres. Direct adaptive versions of both the course-keeping and turning controller are derived. The advantages of the adaptive controllers are improved performance and reduced fuel consumption. The application of nonlinear control theory also allows the designer in a systematic manner to compensate for nonlinearities in the control design.
Generalized Linear Models with Applications in Engineering and the Sciences
Myers, Raymond H; Vining, G Geoffrey; Robinson, Timothy J
2012-01-01
Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities."-Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Ma
Robust Adaptive Control via Neural Linearization and Compensation
Directory of Open Access Journals (Sweden)
Roberto Carmona Rodríguez
2012-01-01
Full Text Available We propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.
A New Method for Solving General Dual Fuzzy Linear Systems
Directory of Open Access Journals (Sweden)
M. Otadi
2013-09-01
Full Text Available . According to fuzzy arithmetic, general dual fuzzy linear system (GDFLS cannot be replaced by a fuzzy linear system (FLS. In this paper, we use new notation of fuzzy numbers and convert a GDFLS to two linear systems in crisp case, then we discuss complexity of the proposed method. Conditions for the existence of a unique fuzzy solution to n × n GDFLS are derived
Dynamic generalized linear models for monitoring endemic diseases
DEFF Research Database (Denmark)
Lopes Antunes, Ana Carolina; Jensen, Dan Børge; Halasa, T.
The objective was to use a Dynamic Generalized Linear Model (DGLM) based on abinomial distribution with a linear trend, for monitoring the PRRS (Porcine Reproductive and Respiratory Syndrome sero-prevalence in Danish swine herds. The DGLM was described and its performance for monitoring control...
Generalizing a Categorization of Students' Interpretations of Linear Kinematics Graphs
Bollen, Laurens; De Cock, Mieke; Zuza, Kristina; Guisasola, Jenaro; van Kampen, Paul
2016-01-01
We have investigated whether and how a categorization of responses to questions on linear distance-time graphs, based on a study of Irish students enrolled in an algebra-based course, could be adopted and adapted to responses from students enrolled in calculus-based physics courses at universities in Flanders, Belgium (KU Leuven) and the Basque…
Rapid, generalized adaptation to asynchronous audiovisual speech.
Van der Burg, Erik; Goodbourn, Patrick T
2015-04-07
The brain is adaptive. The speed of propagation through air, and of low-level sensory processing, differs markedly between auditory and visual stimuli; yet the brain can adapt to compensate for the resulting cross-modal delays. Studies investigating temporal recalibration to audiovisual speech have used prolonged adaptation procedures, suggesting that adaptation is sluggish. Here, we show that adaptation to asynchronous audiovisual speech occurs rapidly. Participants viewed a brief clip of an actor pronouncing a single syllable. The voice was either advanced or delayed relative to the corresponding lip movements, and participants were asked to make a synchrony judgement. Although we did not use an explicit adaptation procedure, we demonstrate rapid recalibration based on a single audiovisual event. We find that the point of subjective simultaneity on each trial is highly contingent upon the modality order of the preceding trial. We find compelling evidence that rapid recalibration generalizes across different stimuli, and different actors. Finally, we demonstrate that rapid recalibration occurs even when auditory and visual events clearly belong to different actors. These results suggest that rapid temporal recalibration to audiovisual speech is primarily mediated by basic temporal factors, rather than higher-order factors such as perceived simultaneity and source identity. © 2015 The Author(s) Published by the Royal Society. All rights reserved.
Linear stability analysis and homoclinic orbit for a generalized non-linear heat transfer
Directory of Open Access Journals (Sweden)
Liu Jun
2012-01-01
Full Text Available This paper studies the linear stability and dynamic structure for a generalized non-linear heat equation, and obtains novel analytic solutions such as homoclinc orbit and breather solitary solutions for the first time based on Hirota method.
Penalized maximum likelihood estimation for generalized linear point processes
DEFF Research Database (Denmark)
Hansen, Niels Richard
2010-01-01
-likelihood. Of particular interest is when the intensity is expressed in terms of a linear filter parametrized by a Sobolev space. Using that the Sobolev spaces are reproducing kernel Hilbert spaces we derive results on the representation of the penalized maximum likelihood estimator in a special case and the gradient......A generalized linear point process is specified in terms of an intensity that depends upon a linear predictor process through a fixed non-linear function. We present a framework where the linear predictor is parametrized by a Banach space and give results on Gateaux differentiability of the log...... of the negative log-likelihood in general. The latter is used to develop a descent algorithm in the Sobolev space. We conclude the paper by extensions to multivariate and additive model specifications. The methods are implemented in the R-package ppstat....
Faraway, Julian J
2005-01-01
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
Adaptive Identification for Switched Nonlinear Systems with Linear Parameterization
Directory of Open Access Journals (Sweden)
Xiaoping Zong
2014-09-01
Full Text Available This paper presents the adaptive control parameter identification of switched nonlinear system that uses model reference adaptive control (MRAC method to track the variation of the state error to approach the ideal values. MRAC is treated as a class of switched nonlinear systems in which the unknown parameters appear linearly. At the same time, switched systems ensure the whole system stay stability and avoid vibration. The update laws are designed to change the controllers with the arbitrary switching signal so that the systems are closed to the model reference system. The parameters approach the real system parameters when the systems are stable, which achieves the control purpose. Simulation results show that the proposed method is validated.
A Matrix Approach for General Higher Order Linear Recurrences
2011-01-01
properties of linear recurrences (such as the well-known Fibonacci and Pell sequences). In [2], Er defined k linear recurring sequences of order at...the nth term of the ith generalized order-k Fibonacci sequence. Communicated by Lee See Keong. Received: March 26, 2009; Revised: August 28, 2009...Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject
Estimation and variable selection for generalized additive partial linear models
Wang, Li
2011-08-01
We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.
Practical likelihood analysis for spatial generalized linear mixed models
DEFF Research Database (Denmark)
Bonat, W. H.; Ribeiro, Paulo Justiniano
2016-01-01
We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are, respectiv......We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are......, respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...... of algorithms and convergence analysis, commonly required by simulation-based methods. © 2016 John Wiley & Sons, Ltd....
The linear model and hypothesis a general unifying theory
Seber, George
2015-01-01
This book provides a concise and integrated overview of hypothesis testing in four important subject areas, namely linear and nonlinear models, multivariate analysis, and large sample theory. The approach used is a geometrical one based on the concept of projections and their associated idempotent matrices, thus largely avoiding the need to involve matrix ranks. It is shown that all the hypotheses encountered are either linear or asymptotically linear, and that all the underlying models used are either exactly or asymptotically linear normal models. This equivalence can be used, for example, to extend the concept of orthogonality in the analysis of variance to other models, and to show that the asymptotic equivalence of the likelihood ratio, Wald, and Score (Lagrange Multiplier) hypothesis tests generally applies.
Thurstonian models for sensory discrimination tests as generalized linear models
DEFF Research Database (Denmark)
Brockhoff, Per B.; Christensen, Rune Haubo Bojesen
2010-01-01
Sensory discrimination tests such as the triangle, duo-trio, 2-AFC and 3-AFC tests produce binary data and the Thurstonian decision rule links the underlying sensory difference 6 to the observed number of correct responses. In this paper it is shown how each of these four situations can be viewed...... as a so-called generalized linear model. The underlying sensory difference 6 becomes directly a parameter of the statistical model and the estimate d' and it's standard error becomes the "usual" output of the statistical analysis. The d' for the monadic A-NOT A method is shown to appear as a standard...... linear contrast in a generalized linear model using the probit link function. All methods developed in the paper are implemented in our free R-package sensR (http://www.cran.r-project.org/package=sensR/). This includes the basic power and sample size calculations for these four discrimination tests...
Item Analysis by the Hierarchical Generalized Linear Model.
Kamata, Akihito
2001-01-01
Presents the hierarchical generalized linear model (HGLM) as an explicit two-level formulation of a multilevel item response model. Shows that the HGLM is equivalent to the Rasch model, and that a characteristic of the HGLM is that person ability can be expressed as a latent regression model with person-characteristic variables. Shows that the…
New Implicit General Linear Method | Ibrahim | Journal of the ...
African Journals Online (AJOL)
A New implicit general linear method is designed for the numerical olution of stiff differential Equations. The coefficients matrix is derived from the stability function. The method combines the single-implicitness or diagonal implicitness with property that the first two rows are implicit and third and fourth row are explicit.
A MIXTURE LIKELIHOOD APPROACH FOR GENERALIZED LINEAR-MODELS
WEDEL, M; DESARBO, WS
1995-01-01
A mixture model approach is developed that simultaneously estimates the posterior membership probabilities of observations to a number of unobservable groups or latent classes, and the parameters of a generalized linear model which relates the observations, distributed according to some member of
Torus quotients of homogeneous spaces of the general linear group ...
Indian Academy of Sciences (India)
... Refresher Courses · Symposia · Live Streaming. Home; Journals; Proceedings – Mathematical Sciences; Volume 119; Issue 1. Torus Quotients of Homogeneous Spaces of the General Linear Group and the Standard Representation of Certain Symmetric Groups. S S Kannan Pranab Sardar. Volume 119 Issue 1 February ...
Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models
Wagler, Amy E.
2014-01-01
Generalized linear mixed models are frequently applied to data with clustered categorical outcomes. The effect of clustering on the response is often difficult to practically assess partly because it is reported on a scale on which comparisons with regression parameters are difficult to make. This article proposes confidence intervals for…
Generalizing a categorization of students’ interpretations of linear kinematics graphs
Directory of Open Access Journals (Sweden)
Laurens Bollen
2016-02-01
Full Text Available We have investigated whether and how a categorization of responses to questions on linear distance-time graphs, based on a study of Irish students enrolled in an algebra-based course, could be adopted and adapted to responses from students enrolled in calculus-based physics courses at universities in Flanders, Belgium (KU Leuven and the Basque Country, Spain (University of the Basque Country. We discuss how we adapted the categorization to accommodate a much more diverse student cohort and explain how the prior knowledge of students may account for many differences in the prevalence of approaches and success rates. Although calculus-based physics students make fewer mistakes than algebra-based physics students, they encounter similar difficulties that are often related to incorrectly dividing two coordinates. We verified that a qualitative understanding of kinematics is an important but not sufficient condition for students to determine a correct value for the speed. When comparing responses to questions on linear distance-time graphs with responses to isomorphic questions on linear water level versus time graphs, we observed that the context of a question influences the approach students use. Neither qualitative understanding nor an ability to find the slope of a context-free graph proved to be a reliable predictor for the approach students use when they determine the instantaneous speed.
Adaptive discontinuous Galerkin methods for non-linear reactive flows
Uzunca, Murat
2016-01-01
The focus of this monograph is the development of space-time adaptive methods to solve the convection/reaction dominated non-stationary semi-linear advection diffusion reaction (ADR) equations with internal/boundary layers in an accurate and efficient way. After introducing the ADR equations and discontinuous Galerkin discretization, robust residual-based a posteriori error estimators in space and time are derived. The elliptic reconstruction technique is then utilized to derive the a posteriori error bounds for the fully discrete system and to obtain optimal orders of convergence. As coupled surface and subsurface flow over large space and time scales is described by (ADR) equation the methods described in this book are of high importance in many areas of Geosciences including oil and gas recovery, groundwater contamination and sustainable use of groundwater resources, storing greenhouse gases or radioactive waste in the subsurface.
Generalized Transformation Techniques for Multi-Choice Linear Programming Problems
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Srikumar ACHARYA
2013-01-01
Full Text Available The multi-choice programming allows the decision maker to consider multiple number of resources for each constraint or goal. Multi-choice linear programming problem can not be solved directly using the traditional linear programming technique. However, to deal with the multi-choice parameters, multiplicative terms of binary variables may be used in the transformed mathematical model. Recently, Biswal and Acharya (2009 have proposed a methodology to transform the multi-choice linear programming problem to an equivalent mathematical programming model, which can accommodate a maximum of eight goals in righthand side of any constraint. In this paper we present two models as generalized transformation of the multi-choice linear programming problem. Using any one of the transformation techniques a decision maker can handle a parameter with nite number of choices. Binary variables are introduced to formulate a non-linear mixed integer programming model. Using a non-linear programming software optimal solution of the proposed model can be obtained. Finally, a numerical example is presented to illustrate the transformation technique and the solution procedure.
Model Based Adaptive Piecewise Linear Controller for Complicated Control Systems
Directory of Open Access Journals (Sweden)
Tain-Sou Tsay
2014-01-01
Full Text Available A model based adaptive piecewise linear control scheme for industry processes with specifications on peak overshoots and rise times is proposed. It is a gain stabilized control technique. Large gain is used for large tracking error to get fast response. Small gain is used between large and small tracking error for good performance. Large gain is used again for small tracking error to cope with large disturbance. Parameters of the three-segment piecewise linear controller are found by an automatic regulating time series which is function of output characteristics of the plant and reference model. The time series will be converged to steady values after the time response of the considered system matching that of the reference model. The proposed control scheme is applied to four numerical examples which have been compensated by PID controllers. Parameters of PID controllers are found by optimization method. It gives an almost command independent response and gives significant improvements for response time and performance.
Testing for one Generalized Linear Single Order Parameter
DEFF Research Database (Denmark)
Ellegaard, Niels Langager; Christensen, Tage Emil; Dyre, Jeppe
We examine a linear single order parameter model for thermoviscoelastic relaxation in viscous liquids, allowing for a distribution of relaxation times. In this model the relaxation of volume and entalpy is completely described by the relaxation of one internal order parameter. In contrast to prior...... work the order parameter may be chosen to have a non-exponential relaxation. The model predictions contradict the general consensus of the properties of viscous liquids in two ways: (i) The model predicts that following a linear isobaric temperature step, the normalized volume and entalpy relaxation...
Regularization Paths for Generalized Linear Models via Coordinate Descent
Directory of Open Access Journals (Sweden)
Jerome Friedman
2010-02-01
Full Text Available We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso, ℓ2 (ridge regression and mixtures of the two (the elastic net. The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, Jerome; Hastie, Trevor; Tibshirani, Rob
2010-01-01
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods. PMID:20808728
General Linearized Theory of Quantum Fluctuations around Arbitrary Limit Cycles.
Navarrete-Benlloch, Carlos; Weiss, Talitha; Walter, Stefan; de Valcárcel, Germán J
2017-09-29
The theory of Gaussian quantum fluctuations around classical steady states in nonlinear quantum-optical systems (also known as standard linearization) is a cornerstone for the analysis of such systems. Its simplicity, together with its accuracy far from critical points or situations where the nonlinearity reaches the strong coupling regime, has turned it into a widespread technique, being the first method of choice in most works on the subject. However, such a technique finds strong practical and conceptual complications when one tries to apply it to situations in which the classical long-time solution is time dependent, a most prominent example being spontaneous limit-cycle formation. Here, we introduce a linearization scheme adapted to such situations, using the driven Van der Pol oscillator as a test bed for the method, which allows us to compare it with full numerical simulations. On a conceptual level, the scheme relies on the connection between the emergence of limit cycles and the spontaneous breaking of the symmetry under temporal translations. On the practical side, the method keeps the simplicity and linear scaling with the size of the problem (number of modes) characteristic of standard linearization, making it applicable to large (many-body) systems.
General Linearized Theory of Quantum Fluctuations around Arbitrary Limit Cycles
Navarrete-Benlloch, Carlos; Weiss, Talitha; Walter, Stefan; de Valcárcel, Germán J.
2017-09-01
The theory of Gaussian quantum fluctuations around classical steady states in nonlinear quantum-optical systems (also known as standard linearization) is a cornerstone for the analysis of such systems. Its simplicity, together with its accuracy far from critical points or situations where the nonlinearity reaches the strong coupling regime, has turned it into a widespread technique, being the first method of choice in most works on the subject. However, such a technique finds strong practical and conceptual complications when one tries to apply it to situations in which the classical long-time solution is time dependent, a most prominent example being spontaneous limit-cycle formation. Here, we introduce a linearization scheme adapted to such situations, using the driven Van der Pol oscillator as a test bed for the method, which allows us to compare it with full numerical simulations. On a conceptual level, the scheme relies on the connection between the emergence of limit cycles and the spontaneous breaking of the symmetry under temporal translations. On the practical side, the method keeps the simplicity and linear scaling with the size of the problem (number of modes) characteristic of standard linearization, making it applicable to large (many-body) systems.
Computation of Optimal Monotonicity Preserving General Linear Methods
Ketcheson, David I.
2009-07-01
Monotonicity preserving numerical methods for ordinary differential equations prevent the growth of propagated errors and preserve convex boundedness properties of the solution. We formulate the problem of finding optimal monotonicity preserving general linear methods for linear autonomous equations, and propose an efficient algorithm for its solution. This algorithm reliably finds optimal methods even among classes involving very high order accuracy and that use many steps and/or stages. The optimality of some recently proposed methods is verified, and many more efficient methods are found. We use similar algorithms to find optimal strong stability preserving linear multistep methods of both explicit and implicit type, including methods for hyperbolic PDEs that use downwind-biased operators.
Genetic parameters for racing records in trotters using linear and generalized linear models.
Suontama, M; van der Werf, J H J; Juga, J; Ojala, M
2012-09-01
Heritability and repeatability and genetic and phenotypic correlations were estimated for trotting race records with linear and generalized linear models using 510,519 records on 17,792 Finnhorses and 513,161 records on 25,536 Standardbred trotters. Heritability and repeatability were estimated for single racing time and earnings traits with linear models, and logarithmic scale was used for racing time and fourth-root scale for earnings to correct for nonnormality. Generalized linear models with a gamma distribution were applied for single racing time and with a multinomial distribution for single earnings traits. In addition, genetic parameters for annual earnings were estimated with linear models on the observed and fourth-root scales. Racing success traits of single placings, winnings, breaking stride, and disqualifications were analyzed using generalized linear models with a binomial distribution. Estimates of heritability were greatest for racing time, which ranged from 0.32 to 0.34. Estimates of heritability were low for single earnings with all distributions, ranging from 0.01 to 0.09. Annual earnings were closer to normal distribution than single earnings. Heritability estimates were moderate for annual earnings on the fourth-root scale, 0.19 for Finnhorses and 0.27 for Standardbred trotters. Heritability estimates for binomial racing success variables ranged from 0.04 to 0.12, being greatest for winnings and least for breaking stride. Genetic correlations among racing traits were high, whereas phenotypic correlations were mainly low to moderate, except correlations between racing time and earnings were high. On the basis of a moderate heritability and moderate to high repeatability for racing time and annual earnings, selection of horses for these traits is effective when based on a few repeated records. Because of high genetic correlations, direct selection for racing time and annual earnings would also result in good genetic response in racing success.
The generalized sidelobe canceller based on quaternion widely linear processing.
Tao, Jian-wu; Chang, Wen-xiu
2014-01-01
We investigate the problem of quaternion beamforming based on widely linear processing. First, a quaternion model of linear symmetric array with two-component electromagnetic (EM) vector sensors is presented. Based on array's quaternion model, we propose the general expression of a quaternion semiwidely linear (QSWL) beamformer. Unlike the complex widely linear beamformer, the QSWL beamformer is based on the simultaneous operation on the quaternion vector, which is composed of two jointly proper complex vectors, and its involution counterpart. Second, we propose a useful implementation of QSWL beamformer, that is, QSWL generalized sidelobe canceller (GSC), and derive the simple expressions of the weight vectors. The QSWL GSC consists of two-stage beamformers. By designing the weight vectors of two-stage beamformers, the interference is completely canceled in the output of QSWL GSC and the desired signal is not distorted. We derive the array's gain expression and analyze the performance of the QSWL GSC in the presence of one type of interference. The advantage of QSWL GSC is that the main beam can always point to the desired signal's direction and the robustness to DOA mismatch is improved. Finally, simulations are used to verify the performance of the proposed QSWL GSC.
The Generalized Sidelobe Canceller Based on Quaternion Widely Linear Processing
Directory of Open Access Journals (Sweden)
Jian-wu Tao
2014-01-01
Full Text Available We investigate the problem of quaternion beamforming based on widely linear processing. First, a quaternion model of linear symmetric array with two-component electromagnetic (EM vector sensors is presented. Based on array’s quaternion model, we propose the general expression of a quaternion semiwidely linear (QSWL beamformer. Unlike the complex widely linear beamformer, the QSWL beamformer is based on the simultaneous operation on the quaternion vector, which is composed of two jointly proper complex vectors, and its involution counterpart. Second, we propose a useful implementation of QSWL beamformer, that is, QSWL generalized sidelobe canceller (GSC, and derive the simple expressions of the weight vectors. The QSWL GSC consists of two-stage beamformers. By designing the weight vectors of two-stage beamformers, the interference is completely canceled in the output of QSWL GSC and the desired signal is not distorted. We derive the array’s gain expression and analyze the performance of the QSWL GSC in the presence of one type of interference. The advantage of QSWL GSC is that the main beam can always point to the desired signal’s direction and the robustness to DOA mismatch is improved. Finally, simulations are used to verify the performance of the proposed QSWL GSC.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
Irincheeva, Irina
2012-08-03
We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
Parametrizing linear generalized Langevin dynamics from explicit molecular dynamics simulations
Gottwald, Fabian; Ivanov, Sergei D; Kühn, Oliver
2015-01-01
Fundamental understanding of complex dynamics in many-particle systems on the atomistic level is of utmost importance. Often the systems of interest are of macroscopic size but can be partitioned into few important degrees of freedom which are treated most accurately and others which constitute a thermal bath. Particular attention in this respect attracts the linear generalized Langevin equation (GLE), which can be rigorously derived by means of a linear projection (LP) technique. Within this framework a complicated interaction with the bath can be reduced to a single memory kernel. This memory kernel in turn is parametrized for a particular system studied, usually by means of time-domain methods based on explicit molecular dynamics data. Here we discuss that this task is most naturally achieved in frequency domain and develop a Fourier-based parametrization method that outperforms its time-domain analogues. Very surprisingly, the widely used rigid bond method turns out to be inappropriate in general. Importa...
Enhanced group analysis of a semi linear generalization of a general bond-pricing equation
Bozhkov, Y.; Dimas, S.
2018-01-01
The enhanced group classification of a semi linear generalization of a general bond-pricing equation is carried out by harnessing the underlying equivalence and additional equivalence transformations. We employ that classification to unearth the particular cases with a larger Lie algebra than the general case and use them to find non trivial invariant solutions under the terminal and the barrier option condition.
An adaptive feedback linearization strategy for variable speed wind energy conversion systems
Energy Technology Data Exchange (ETDEWEB)
Valenciaga, F.; Puleston, P.F.; Battaiotto, P.E.; Mantz, R.J. [Universidad Nacional de La Plata, Depto. de Electrotecnia, La Plata (Argentina)
2000-07-01
This paper presents a control strategy based on adaptive feedback linearization intended for variable speed grid-connected wind energy conversion systems (WECS). The proposed adaptive control law accomplishes energy capture maximization by tracking the wind speed fluctuations. In addition, it linearizes the system even in the presence of turbine model uncertainties, allowing the closed-loop dynamic behaviour to be determined by a simple tuning of the controller parameters. Particularly, the attention is focused on WECS with slip power recovery, which use a power conversion stage as a rotor-controlled double-output induction generator. However, the concepts behind the proposed control strategy are general and can be easily extended to other WECS configurations. (Author)
Electromagnetic axial anomaly in a generalized linear sigma model
Fariborz, Amir H.; Jora, Renata
2017-06-01
We construct the electromagnetic anomaly effective term for a generalized linear sigma model with two chiral nonets, one with a quark-antiquark structure, the other one with a four-quark content. We compute in the leading order of this framework the decays into two photons of six pseudoscalars: π0(137 ), π0(1300 ), η (547 ), η (958 ), η (1295 ) and η (1760 ). Our results agree well with the available experimental data.
Neural Generalized Predictive Control of a non-linear Process
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1998-01-01
The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability qu...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem....
Generalized Maximum Entropy Analysis of the Linear Simultaneous Equations Model
Directory of Open Access Journals (Sweden)
Thomas L. Marsh
2014-02-01
Full Text Available A generalized maximum entropy estimator is developed for the linear simultaneous equations model. Monte Carlo sampling experiments are used to evaluate the estimator’s performance in small and medium sized samples, suggesting contexts in which the current generalized maximum entropy estimator is superior in mean square error to two and three stage least squares. Analytical results are provided relating to asymptotic properties of the estimator and associated hypothesis testing statistics. Monte Carlo experiments are also used to provide evidence on the power and size of test statistics. An empirical application is included to demonstrate the practical implementation of the estimator.
Petit, Cyril; Conan, Jean-Marc; Kulcsár, Caroline; Raynaud, Henri-François
2009-06-01
We present a comprehensive analysis of the linear quadratic Gaussian control approach applied to adaptive optics (AO) and multiconjugated AO (MCAO) based on numerical and experimental validations. The structure of the control law is presented and its main properties discussed. We then propose an extended experimental validation of this control law in AO and a simplified MCAO configuration. Performance is compared with end-to-end numerical simulations. Sensitivity of the performance regarding tuning parameters is tested. Finally, extension to full MCAO and laser tomographic AO (LTAO) through numerical simulation is presented and analyzed.
Classification images and bubbles images in the generalized linear model.
Murray, Richard F
2012-07-09
Classification images and bubbles images are psychophysical tools that use stimulus noise to investigate what features people use to make perceptual decisions. Previous work has shown that classification images can be estimated using the generalized linear model (GLM), and here I show that this is true for bubbles images as well. Expressing the two approaches in terms of a single statistical model clarifies their relationship to one another, makes it possible to measure classification images and bubbles images simultaneously, and allows improvements developed for one method to be used with the other.
A Graphical User Interface to Generalized Linear Models in MATLAB
Directory of Open Access Journals (Sweden)
Peter Dunn
1999-07-01
Full Text Available Generalized linear models unite a wide variety of statistical models in a common theoretical framework. This paper discusses GLMLAB-software that enables such models to be fitted in the popular mathematical package MATLAB. It provides a graphical user interface to the powerful MATLAB computational engine to produce a program that is easy to use but with many features, including offsets, prior weights and user-defined distributions and link functions. MATLAB's graphical capacities are also utilized in providing a number of simple residual diagnostic plots.
Adaptive fuzzy bilinear observer based synchronization design for generalized Lorenz system
Energy Technology Data Exchange (ETDEWEB)
Baek, Jaeho, E-mail: jhbaek97@yeics.yonsei.ac.k [ICS Laboratory (B723), Department of Electrical and Electronic Engineering, Yonsei University, 134, Shinchon-dong, Seodaemun-gu, Seoul, 120-749 (Korea, Republic of); Lee, Heejin [Department of Information and Control Engineering, Electronic Technique Synthesis Institute, Hankyung National University, Ansung, Kyunggi, 456-749 (Korea, Republic of); Kim, Seungwoo [Department of Electrical Information Engineering, Soonchunhyang University, Asan, Chungnam, 336-745 (Korea, Republic of); Park, Mignon [ICS Laboratory (B723), Department of Electrical and Electronic Engineering, Yonsei University, 134, Shinchon-dong, Seodaemun-gu, Seoul, 120-749 (Korea, Republic of)
2009-11-23
This Letter proposes an adaptive fuzzy bilinear observer (FBO) based synchronization design for generalized Lorenz system (GLS). The GLS can be described to TS fuzzy bilinear generalized Lorenz model (FBGLM) with their states immeasurable and their parameters unknown. We design an adaptive FBO based on TS FBGLM for synchronization. Lyapunov theory is employed to guarantee the stability of error dynamic system via linear matrix equalities (LMIs) and to derive the adaptive laws to estimate unknown parameters. Numerical example is given to demonstrate the validity of our proposed adaptive FBO approach for synchronization.
DEFF Research Database (Denmark)
Porto da Silva, Edson; Zibar, Darko
2016-01-01
Simple analytical widely linear complex-valued models for IQ-imbalance and IQ-skew effects in multicarrier transmitters are presented. To compensate for such effects, a 4×4 MIMO widely linear adaptive equalizer is proposed and experimentally validated.......Simple analytical widely linear complex-valued models for IQ-imbalance and IQ-skew effects in multicarrier transmitters are presented. To compensate for such effects, a 4×4 MIMO widely linear adaptive equalizer is proposed and experimentally validated....
Residuals analysis of the generalized linear models for longitudinal data.
Chang, Y C
2000-05-30
The generalized estimation equation (GEE) method, one of the generalized linear models for longitudinal data, has been used widely in medical research. However, the related sensitivity analysis problem has not been explored intensively. One of the possible reasons for this was due to the correlated structure within the same subject. We showed that the conventional residuals plots for model diagnosis in longitudinal data could mislead a researcher into trusting the fitted model. A non-parametric method, named the Wald-Wolfowitz run test, was proposed to check the residuals plots both quantitatively and graphically. The rationale proposedin this paper is well illustrated with two real clinical studies in Taiwan. Copyright 2000 John Wiley & Sons, Ltd.
Generalized space and linear momentum operators in quantum mechanics
Energy Technology Data Exchange (ETDEWEB)
Costa, Bruno G. da, E-mail: bruno.costa@ifsertao-pe.edu.br [Instituto Federal de Educação, Ciência e Tecnologia do Sertão Pernambucano, Campus Petrolina, BR 407, km 08, 56314-520 Petrolina, Pernambuco (Brazil); Instituto de Física, Universidade Federal da Bahia, R. Barão de Jeremoabo s/n, 40170-115 Salvador, Bahia (Brazil); Borges, Ernesto P., E-mail: ernesto@ufba.br [Instituto de Física, Universidade Federal da Bahia, R. Barão de Jeremoabo s/n, 40170-115 Salvador, Bahia (Brazil)
2014-06-15
We propose a modification of a recently introduced generalized translation operator, by including a q-exponential factor, which implies in the definition of a Hermitian deformed linear momentum operator p{sup ^}{sub q}, and its canonically conjugate deformed position operator x{sup ^}{sub q}. A canonical transformation leads the Hamiltonian of a position-dependent mass particle to another Hamiltonian of a particle with constant mass in a conservative force field of a deformed phase space. The equation of motion for the classical phase space may be expressed in terms of the generalized dual q-derivative. A position-dependent mass confined in an infinite square potential well is shown as an instance. Uncertainty and correspondence principles are analyzed.
Polymorphic Uncertain Linear Programming for Generalized Production Planning Problems
Directory of Open Access Journals (Sweden)
Xinbo Zhang
2014-01-01
Full Text Available A polymorphic uncertain linear programming (PULP model is constructed to formulate a class of generalized production planning problems. In accordance with the practical environment, some factors such as the consumption of raw material, the limitation of resource and the demand of product are incorporated into the model as parameters of interval and fuzzy subsets, respectively. Based on the theory of fuzzy interval program and the modified possibility degree for the order of interval numbers, a deterministic equivalent formulation for this model is derived such that a robust solution for the uncertain optimization problem is obtained. Case study indicates that the constructed model and the proposed solution are useful to search for an optimal production plan for the polymorphic uncertain generalized production planning problems.
Nested Generalized Linear Model with Ordinal Response for Correlated Data
Directory of Open Access Journals (Sweden)
Aji H. Wigena
2012-05-01
Full Text Available In this paper, we discuss the generalized linear models with ordinal response for correlated data in nested area. Some basic concepts are described, that is nested spatial, threshold model, and cumulative link function. Due to correlated data used for this modeling, Generalized Estimating Eequation (GEE is used as model parameters estimation method. Nested is shown by the model building and its application on nested spatially data. In this method, some Working Correlation Matrices (WCM are able to be specified depend on the nature and type of the data. In this study, 3 types of WCM and 2 types of parameters estimation covariance are used to see the results of parameters estimation from these combinations. As a conclusion, independent WCM is appropriate to the data.
Adaptive Linear Parameter Varying Control for Aeroservoelastic Suppression Project
National Aeronautics and Space Administration — Adaptive control offers an opportunity to fulfill aircraft safety objectives though automated vehicle recovery while maintaining performance and stability...
Adaptive Linear Parameter Varying Control for Aeroservoelastic Suppression Project
National Aeronautics and Space Administration — Adaptive control offers an opportunity to fulfill present and future aircraft safety objectives though automated vehicle recovery while maintaining performance and...
Bayesian Subset Modeling for High-Dimensional Generalized Linear Models
Liang, Faming
2013-06-01
This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and consistency with the existing sure independence screening (SIS) and iterative sure independence screening (ISIS) procedures. However, since the proposed procedure makes use of joint information from all predictors, it generally outperforms SIS and ISIS in real applications. This article also makes extensive comparisons of BSR with the popular penalized likelihood methods, including Lasso, elastic net, SIS, and ISIS. The numerical results indicate that BSR can generally outperform the penalized likelihood methods. The models selected by BSR tend to be sparser and, more importantly, of higher prediction ability. In addition, the performance of the penalized likelihood methods tends to deteriorate as the number of predictors increases, while this is not significant for BSR. Supplementary materials for this article are available online. © 2013 American Statistical Association.
A Sawmill Manager Adapts To Change With Linear Programming
George F. Dutrow; James E. Granskog
1973-01-01
Linear programming provides guidelines for increasing sawmill capacity and flexibility and for determining stumpagepurchasing strategy. The operator of a medium-sized sawmill implemented improvements suggested by linear programming analysis; results indicate a 45 percent increase in revenue and a 36 percent hike in volume processed.
Testing Hardy-Weinberg disequilibrium using the generalized linear model.
Xu, Shizhong
2012-12-01
Current methods for detecting Hardy-Weinberg disequilibrium (HWD) only deal with one locus at a time. We developed a method that can jointly detect HWD for multiple loci. The method was developed under the generalized linear model (GLM) using the probit link function. When applied to a single locus, the new method is more powerful than the exact test. When applied to two or more loci, the method can reduce false positives caused by linkage disequilibrium (LD). We applied the method to 24 single nucleotide polymorphism (SNP) markers of a single human gene and eliminated several false positive HWDs due to LD. We developed an R package 'hwdglm' for joint HWD detection, which can be downloaded from our personal website (www.statgen.ucr.edu).
Explicit estimating equations for semiparametric generalized linear latent variable models
Ma, Yanyuan
2010-07-05
We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n consistency and asymptotic normality. We explain the computational implementation of our method and illustrate the numerical performance of the estimators in finite sample situations via extensive simulation studies. The advantage of our estimators over the existing likelihood approach is also shown via numerical comparison. We employ the method to analyse a real data example from economics. © 2010 Royal Statistical Society.
Mixed Task and Data Parallel Executions in General Linear Methods
Directory of Open Access Journals (Sweden)
Thomas Rauber
2007-01-01
Full Text Available On many parallel target platforms it can be advantageous to implement parallel applications as a collection of multiprocessor tasks that are concurrently executed and are internally implemented with fine-grain SPMD parallelism. A class of applications which can benefit from this programming style are methods for solving systems of ordinary differential equations. Many recent solvers have been designed with an additional potential of method parallelism, but the actual effectiveness of mixed task and data parallelism depends on the specific communication and computation requirements imposed by the equation to be solved. In this paper we study mixed task and data parallel implementations for general linear methods realized using a library for multiprocessor task programming. Experiments on a number of different platforms show good efficiency results.
dglars: An R Package to Estimate Sparse Generalized Linear Models
Directory of Open Access Journals (Sweden)
Luigi Augugliaro
2014-09-01
Full Text Available dglars is a publicly available R package that implements the method proposed in Augugliaro, Mineo, and Wit (2013, developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method proposed in Efron, Hastie, Johnstone, and Tibshirani (2004. The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve: a predictor-corrector algorithm, proposed in Augugliaro et al. (2013, and a cyclic coordinate descent algorithm, proposed in Augugliaro, Mineo, and Wit (2012. The latter algorithm, as shown here, is significantly faster than the predictor-corrector algorithm. For comparison purposes, we have implemented both algorithms.
Analysis of Robust Quasi-deviances for Generalized Linear Models
Directory of Open Access Journals (Sweden)
Eva Cantoni
2004-04-01
Full Text Available Generalized linear models (McCullagh and Nelder 1989 are a popular technique for modeling a large variety of continuous and discrete data. They assume that the response variables Yi , for i = 1, . . . , n, come from a distribution belonging to the exponential family, such that E[Yi ] = ?i and V[Yi ] = V (?i , and that ?i = g(?i = xiT?, where ? ? IR p is the vector of parameters, xi ? IR p, and g(. is the link function. The non-robustness of the maximum likelihood and the maximum quasi-likelihood estimators has been studied extensively in the literature. For model selection, the classical analysis-of-deviance approach shares the same bad robustness properties. To cope with this, Cantoni and Ronchetti (2001 propose a robust approach based on robust quasi-deviance functions for estimation and variable selection. We refer to that paper for a deeper discussion and the review of the literature.
Wagner, Daniel Robert
Linear matrix inequalities and convex optimization techniques have become popular tools to solve nontrivial problems in the field of adaptive control. Specifically, the stability of adaptive control laws in the presence of actuator dynamics remains as an important open control problem. In this thesis, we present a linear matrix inequalities-based hedging approach and evaluate it for model reference adaptive control of an uncertain dynamical system in the presence of actuator dynamics. The ideal reference dynamics are modified such that the hedging approach allows the correct adaptation without being hindered by the presence of actuator dynamics. The hedging approach is first generalized such that two cases are considered where the actuator output and control effectiveness are known and unknown. We then show the stability of the closed-loop dynamical system using Lyapunov based stability analysis tools and propose a linear matrix inequality-based framework for the computation of the minimum allowable actuator bandwidth limits such that the closed-loop dynamical system remains stable. The results of the linear matrix inequality-based heading approach are then generalized to multiactuator systems with a new linear matrix inequality condition. The minimum actuator bandwidth solutions for closed-loop system stability are theoretically guaranteed to exist in a convex set with a partially convex constraint and then solved numerically using an algorithm in the case where there are multiple actuators. Finally, the efficacy of the results contained in this thesis are demonstrated using several illustrative numerical examples.
Adaptive Non-linear Control of Hydraulic Actuator Systems
DEFF Research Database (Denmark)
Hansen, Poul Erik; Conrad, Finn
1998-01-01
Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF).......Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF)....
DEFF Research Database (Denmark)
Holst, René; Jørgensen, Bent
2015-01-01
The paper proposes a versatile class of multiplicative generalized linear longitudinal mixed models (GLLMM) with additive dispersion components, based on explicit modelling of the covariance structure. The class incorporates a longitudinal structure into the random effects models and retains...... a marginal as well as a conditional interpretation. The estimation procedure is based on a computationally efficient quasi-score method for the regression parameters combined with a REML-like bias-corrected Pearson estimating function for the dispersion and correlation parameters. This avoids...... the multidimensional integral of the conventional GLMM likelihood and allows an extension of the robust empirical sandwich estimator for use with both association and regression parameters. The method is applied to a set of otholit data, used for age determination of fish....
Multivariate statistical modelling based on generalized linear models
Fahrmeir, Ludwig
1994-01-01
This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...
Bayesian inference for generalized linear models for spiking neurons
Directory of Open Access Journals (Sweden)
Sebastian Gerwinn
2010-05-01
Full Text Available Generalized Linear Models (GLMs are commonly used statistical methods for modelling the relationship between neural population activity and presented stimuli. When the dimension of the parameter space is large, strong regularization has to be used in order to fit GLMs to datasets of realistic size without overfitting. By imposing properly chosen priors over parameters, Bayesian inference provides an effective and principled approach for achieving regularization. Here we show how the posterior distribution over model parameters of GLMs can be approximated by a Gaussian using the Expectation Propagation algorithm. In this way, we obtain an estimate of the posterior mean and posterior covariance, allowing us to calculate Bayesian confidence intervals that characterize the uncertainty about the optimal solution. From the posterior we also obtain a different point estimate, namely the posterior mean as opposed to the commonly used maximum a posteriori estimate. We systematically compare the different inference techniques on simulated as well as on multi-electrode recordings of retinal ganglion cells, and explore the effects of the chosen prior and the performance measure used. We find that good performance can be achieved by choosing an Laplace prior together with the posterior mean estimate.
Generalized Functional Linear Models With Semiparametric Single-Index Interactions
Li, Yehua
2010-06-01
We introduce a new class of functional generalized linear models, where the response is a scalar and some of the covariates are functional. We assume that the response depends on multiple covariates, a finite number of latent features in the functional predictor, and interaction between the two. To achieve parsimony, the interaction between the multiple covariates and the functional predictor is modeled semiparametrically with a single-index structure. We propose a two step estimation procedure based on local estimating equations, and investigate two situations: (a) when the basis functions are pre-determined, e.g., Fourier or wavelet basis functions and the functional features of interest are known; and (b) when the basis functions are data driven, such as with functional principal components. Asymptotic properties are developed. Notably, we show that when the functional features are data driven, the parameter estimates have an increased asymptotic variance, due to the estimation error of the basis functions. Our methods are illustrated with a simulation study and applied to an empirical data set, where a previously unknown interaction is detected. Technical proofs of our theoretical results are provided in the online supplemental materials.
Generalized linear isotherm regularity equation of state applied to metals
Directory of Open Access Journals (Sweden)
H. Sun
2012-03-01
Full Text Available A three-parameter equation of state (EOS without physically incorrect oscillations is proposed based on the generalized Lennard-Jones (GLJ potential and the approach in developing linear isotherm regularity (LIR EOS of Parsafar and Mason [J. Phys. Chem., 1994, 49, 3049]. The proposed (GLIR EOS can include the LIR EOS therein as a special case. The three-parameter GLIR, Parsafar and Mason (PM [Phys. Rev. B, 1994, 49, 3049], Shanker, Singh and Kushwah (SSK [Physica B, 1997, 229, 419], Parsafar, Spohr and Patey (PSP [J. Phys. Chem. B, 2009, 113, 11980], and reformulated PM and SSK EOSs are applied to 30 metallic solids within wide pressure ranges. It is shown that the PM, PMR and PSP EOSs for most solids, and the SSK and SSKR EOSs for several solids, have physically incorrect turning points, and pressure becomes negative at high enough pressure. The GLIR EOS is capable not only of overcoming the problem existing in other five EOSs where the pressure becomes negative at high pressure, but also gives results superior to other EOSs
Variational Bayesian Parameter Estimation Techniques for the General Linear Model
Directory of Open Access Journals (Sweden)
Ludger Starke
2017-09-01
Full Text Available Variational Bayes (VB, variational maximum likelihood (VML, restricted maximum likelihood (ReML, and maximum likelihood (ML are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation.
The linearized inversion of the generalized interferometric multiple imaging
Aldawood, Ali
2016-09-06
The generalized interferometric multiple imaging (GIMI) procedure can be used to image duplex waves and other higher order internal multiples. Imaging duplex waves could help illuminate subsurface zones that are not easily illuminated by primaries such as vertical and nearly vertical fault planes, and salt flanks. To image first-order internal multiple, the GIMI framework consists of three datuming steps, followed by applying the zero-lag cross-correlation imaging condition. However, the standard GIMI procedure yields migrated images that suffer from low spatial resolution, migration artifacts, and cross-talk noise. To alleviate these problems, we propose a least-squares GIMI framework in which we formulate the first two steps as a linearized inversion problem when imaging first-order internal multiples. Tests on synthetic datasets demonstrate the ability to localize subsurface scatterers in their true positions, and delineate a vertical fault plane using the proposed method. We, also, demonstrate the robustness of the proposed framework when imaging the scatterers or the vertical fault plane with erroneous migration velocities.
Detection of Fraudulent Transactions Through a Generalized Mixed Linear Models
Directory of Open Access Journals (Sweden)
Jackelyne Gómez–Restrepo
2012-12-01
Full Text Available The detection of bank frauds is a topic which many financial sector companieshave invested time and resources into. However, finding patterns inthe methodologies used to commit fraud in banks is a job that primarily involvesintimate knowledge of customer behavior, with the idea of isolatingthose transactions which do not correspond to what the client usually does.Thus, the solutions proposed in literature tend to focus on identifying outliersor groups, but fail to analyse each client or forecast fraud. This paperevaluates the implementation of a generalized linear model to detect fraud.With this model, unlike conventional methods, we consider the heterogeneityof customers. We not only generate a global model, but also a model for eachcustomer which describes the behavior of each one according to their transactionalhistory and previously detected fraudulent transactions. In particular,a mixed logistic model is used to estimate the probability that a transactionis fraudulent, using information that has been taken by the banking systemsin different moments of time.
Robust Comparison of the Linear Model Structures in Self-tuning Adaptive Control
DEFF Research Database (Denmark)
Zhou, Jianjun; Conrad, Finn
1989-01-01
The Generalized Predictive Controller (GPC) is extended to the systems with a generalized linear model structure which contains a number of choices of linear model structures. The Recursive Prediction Error Method (RPEM) is used to estimate the unknown parameters of the linear model structures......-output behaviour of self-tuning controllers....
General Intelligence as a Domain-Specific Adaptation
Kanazawa, Satoshi
2004-01-01
General intelligence (g) poses a problem for evolutionary psychology's modular view of the human brain. The author advances a new evolutionary psychological theory of the evolution of general intelligence and argues that general intelligence evolved as a domain-specific adaptation for the originally limited sphere of evolutionary novelty in the…
Dyja, Robert; van der Zee, Kristoffer G
2016-01-01
We present an adaptive methodology for the solution of (linear and) non-linear time dependent problems that is especially tailored for massively parallel computations. The basic concept is to solve for large blocks of space-time unknowns instead of marching sequentially in time. The methodology is a combination of a computationally efficient implementation of a parallel-in-space-time finite element solver coupled with a posteriori space-time error estimates and a parallel mesh generator. This methodology enables, in principle, simultaneous adaptivity in both space and time (within the block) domains. We explore this basic concept in the context of a variety of time-steppers including $\\Theta$-schemes and Backward Differentiate Formulas. We specifically illustrate this framework with applications involving time dependent linear, quasi-linear and semi-linear diffusion equations. We focus on investigating how the coupled space-time refinement indicators for this class of problems affect spatial adaptivity. Final...
Directory of Open Access Journals (Sweden)
Kizilkaya Kadir
2005-01-01
Full Text Available Abstract We propose a general Bayesian approach to heteroskedastic error modeling for generalized linear mixed models (GLMM in which linked functions of conditional means and residual variances are specified as separate linear combinations of fixed and random effects. We focus on the linear mixed model (LMM analysis of birth weight (BW and the cumulative probit mixed model (CPMM analysis of calving ease (CE. The deviance information criterion (DIC was demonstrated to be useful in correctly choosing between homoskedastic and heteroskedastic error GLMM for both traits when data was generated according to a mixed model specification for both location parameters and residual variances. Heteroskedastic error LMM and CPMM were fitted, respectively, to BW and CE data on 8847 Italian Piemontese first parity dams in which residual variances were modeled as functions of fixed calf sex and random herd effects. The posterior mean residual variance for male calves was over 40% greater than that for female calves for both traits. Also, the posterior means of the standard deviation of the herd-specific variance ratios (relative to a unitary baseline were estimated to be 0.60 ± 0.09 for BW and 0.74 ± 0.14 for CE. For both traits, the heteroskedastic error LMM and CPMM were chosen over their homoskedastic error counterparts based on DIC values.
Adaptation of generalized Hill inequalities to anisotropic elastic ...
African Journals Online (AJOL)
Hill inequalities. From different type of anisotropic elastic symmetries, numerical examples are given. Constructing bounds on effective eigenvalues provides a deeper understanding about mechanical behavior of anisotropic materials. Generalized Hill inequalities are adapted to all anisotropic elastic symmetries.
Adaptive Generation and Diagnostics of Linear Few-Cycle Light Bullets
Directory of Open Access Journals (Sweden)
Martin Bock
2013-02-01
Full Text Available Recently we introduced the class of highly localized wavepackets (HLWs as a generalization of optical Bessel-like needle beams. Here we report on the progress in this field. In contrast to pulsed Bessel beams and Airy beams, ultrashort-pulsed HLWs propagate with high stability in both spatial and temporal domain, are nearly paraxial (supercollimated, have fringe-less spatial profiles and thus represent the best possible approximation to linear “light bullets”. Like Bessel beams and Airy beams, HLWs show self-reconstructing behavior. Adaptive HLWs can be shaped by ultraflat three-dimensional phase profiles (generalized axicons which are programmed via calibrated grayscale maps of liquid-crystal-on-silicon spatial light modulators (LCoS-SLMs. Light bullets of even higher complexity can either be freely formed from quasi-continuous phase maps or discretely composed from addressable arrays of identical nondiffracting beams. The characterization of few-cycle light bullets requires spatially resolved measuring techniques. In our experiments, wavefront, pulse and phase were detected with a Shack-Hartmann wavefront sensor, 2D-autocorrelation and spectral phase interferometry for direct electric-field reconstruction (SPIDER. The combination of the unique propagation properties of light bullets with the flexibility of adaptive optics opens new prospects for applications of structured light like optical tweezers, microscopy, data transfer and storage, laser fusion, plasmon control or nonlinear spectroscopy.
Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems
Nguyen, Chuong Hoang; Leonessa, Alexander
2017-08-01
In this paper, the problem of characterizing adaptive output feedback control laws for a general class of unknown MIMO linear systems is considered. Specifically, the presented control approach relies on three components, i.e., a predictor, a reference model and a controller. The predictor is designed to predict the system's output with arbitrary accuracy, for any admissible control input. Subsequently, a full state feedback control law is designed to control the predictor output to approach the reference system, while the reference system tracks the desired trajectory. Ultimately, the control objective of driving the actual system output to track the desired trajectories is achieved by showing that the system output, the predictor output and the reference system trajectories all converge to each other.
Non-linear and adaptive control of a refrigeration system
DEFF Research Database (Denmark)
Rasmussen, Henrik; Larsen, Lars F. S.
2011-01-01
In a refrigeration process heat is absorbed in an evaporator by evaporating a flow of liquid refrigerant at low pressure and temperature. Controlling the evaporator inlet valve and the compressor in such a way that a high degree of liquid filling in the evaporator is obtained at all compressor...... are capable of adapting to variety of systems. This paper proposes a novel method for superheat and capacity control of refrigeration systems; namely by controlling the superheat by the compressor speed and capacity by the refrigerant flow. A new low order nonlinear model of the evaporator is developed...... capacities ensures a high energy efficiency. The level of liquid filling is indirectly measured by the superheat. Introduction of variable speed compressors and electronic expansion valves enables the use of more sophisticated control algorithms, giving a higher degree of performance and just as important...
Model reference adaptive control for linear time varying and nonlinear systems
Abida, L.; Kaufman, H.
1982-01-01
Model reference adaptive control is applied to linear time varying systems and to nonlinear systems amenable to virtual linearization. Asymptotic stability is guaranteed even if the perfect model following conditions do not hold, provided that some sufficient conditions are satisfied. Simulations show the scheme to be capable of effectively controlling certain nonlinear systems.
A General Linear Method for Equating with Small Samples
Albano, Anthony D.
2015-01-01
Research on equating with small samples has shown that methods with stronger assumptions and fewer statistical estimates can lead to decreased error in the estimated equating function. This article introduces a new approach to linear observed-score equating, one which provides flexible control over how form difficulty is assumed versus estimated…
Implications of plan-based generalization in sensorimotor adaptation.
McDougle, Samuel D; Bond, Krista M; Taylor, Jordan A
2017-07-01
Generalization is a fundamental aspect of behavior, allowing for the transfer of knowledge from one context to another. The details of this transfer are thought to reveal how the brain represents what it learns. Generalization has been a central focus in studies of sensorimotor adaptation, and its pattern has been well characterized: Learning of new dynamic and kinematic transformations in one region of space tapers off in a Gaussian-like fashion to neighboring untrained regions, echoing tuned population codes in the brain. In contrast to common allusions to generalization in cognitive science, generalization in visually guided reaching is usually framed as a passive consequence of neural tuning functions rather than a cognitive feature of learning. While previous research has presumed that maximum generalization occurs at the instructed task goal or the actual movement direction, recent work suggests that maximum generalization may occur at the location of an explicitly accessible movement plan. Here we provide further support for plan-based generalization, formalize this theory in an updated model of adaptation, and test several unexpected implications of the model. First, we employ a generalization paradigm to parameterize the generalization function and ascertain its maximum point. We then apply the derived generalization function to our model and successfully simulate and fit the time course of implicit adaptation across three behavioral experiments. We find that dynamics predicted by plan-based generalization are borne out in the data, are contrary to what traditional models predict, and lead to surprising implications for the behavioral, computational, and neural characteristics of sensorimotor adaptation.NEW & NOTEWORTHY The pattern of generalization is thought to reveal how the motor system represents learned actions. Recent work has made the intriguing suggestion that maximum generalization in sensorimotor adaptation tasks occurs at the location of the
Local Influence Diagnostics for Generalized Linear Mixed Models With Overdispersion
Rakhmawati, Trias; Molenberghs, Geert; Verbeke, Geert; Faes, Christel
2014-01-01
Since the seminal paper by Cook and Weisberg (1982), local in- fluence, next to case deletion, has gained popularity as a tool to detect influential subjects and measurements for a variety of statistical models. For the linear mixed model the approach leads to easily interpretable and computationally convenient expressions, not only highlighting influential subjects, but also which aspect of their profile leads to undue influence on the model’s fit (Verbeke and Lesaffre 1998). Ouwens, T...
Local Influence Diagnostics for Generalized Linear Mixed Models With Overdispersion
Rakhmawati, Trias Wahyuni; Molenberghs, Geert; Verbeke, Geert; Faes, Christel
2016-01-01
Since the seminal paper by Cook and Weisberg [9 R.D. Cook and S. Weisberg, Residuals and Influence in Regression, Chapman & Hall, London, 1982.], local influence, next to case deletion, has gained popularity as a tool to detect influential subjects and measurements for a variety of statistical models. For the linear mixed model the approach leads to easily interpretable and computationally convenient expressions, not only highlighting influential subjects, but also which aspect of their profi...
Linearly convergent stochastic heavy ball method for minimizing generalization error
Loizou, Nicolas
2017-10-30
In this work we establish the first linear convergence result for the stochastic heavy ball method. The method performs SGD steps with a fixed stepsize, amended by a heavy ball momentum term. In the analysis, we focus on minimizing the expected loss and not on finite-sum minimization, which is typically a much harder problem. While in the analysis we constrain ourselves to quadratic loss, the overall objective is not necessarily strongly convex.
Non-Linear Periodization for General Fitness & Athletes
Fleck, Steven J.
2011-01-01
Periodization of resistance training or planned changes in training volume and intensity are used to maximize strength and fitness gains. Several types of periodized resistance training plans have been developed. The most common of these plans is linear also termed classic or strength/power periodization and nonlinear periodization. The biggest difference between these two types of training plans is with nonlinear periodization changes in training volume and intensity are made more frequently...
Maximum Likelihood in a Generalized Linear Finite Mixture Model by Using the EM Algorithm
Jansen, R.C.
A generalized linear finite mixture model and an EM algorithm to fit the model to data are described. By this approach the finite mixture model is embedded within the general framework of generalized linear models (GLMs). Implementation of the proposed EM algorithm can be readily done in statistical
Generalized linear mixed models modern concepts, methods and applications
Stroup, Walter W
2012-01-01
PART I The Big PictureModeling BasicsWhat Is a Model?Two Model Forms: Model Equation and Probability DistributionTypes of Model EffectsWriting Models in Matrix FormSummary: Essential Elements for a Complete Statement of the ModelDesign MattersIntroductory Ideas for Translating Design and Objectives into ModelsDescribing ""Data Architecture"" to Facilitate Model SpecificationFrom Plot Plan to Linear PredictorDistribution MattersMore Complex Example: Multiple Factors with Different Units of ReplicationSetting the StageGoals for Inference with Models: OverviewBasic Tools of InferenceIssue I: Data
Single image super-resolution using locally adaptive multiple linear regression.
Yu, Soohwan; Kang, Wonseok; Ko, Seungyong; Paik, Joonki
2015-12-01
This paper presents a regularized superresolution (SR) reconstruction method using locally adaptive multiple linear regression to overcome the limitation of spatial resolution of digital images. In order to make the SR problem better-posed, the proposed method incorporates the locally adaptive multiple linear regression into the regularization process as a local prior. The local regularization prior assumes that the target high-resolution (HR) pixel is generated by a linear combination of similar pixels in differently scaled patches and optimum weight parameters. In addition, we adapt a modified version of the nonlocal means filter as a smoothness prior to utilize the patch redundancy. Experimental results show that the proposed algorithm better restores HR images than existing state-of-the-art methods in the sense of the most objective measures in the literature.
A Generalized Linear Mixed Model for Enumerated Sunspots
Riggs, J.
2012-06-01
(Abstract only) Monthly sunspot counts data from consistently submitting AAVSO observers were provided to determine monthly average sunspot numbers and the individual observer parameters that correct each observerâs counts to the monthly average. The data span a fourteen-month period from May 2010 through June 2011. The parameters are determined from a mixed-effects, loglinear model constructed specifically from the fourteen months of Poisson-distributed sunspot numbers. This model differs in the treatment of the data distribution assumptions of the existing linear regression model developed by Shapley (1949). The loglinear model methodology exceeds the correction coefficient performance criteria set by Shapley, and provides a method for determining the relative sunspot number reported monthly by the American Association of Variable Star Observers Solar Section. Model improvements are discussed.
About one non linear generalization of the compression reflection ...
African Journals Online (AJOL)
Generalize method of iteration is proposed, presented as a combination of the classical iteration and proportional division methods, on which the conditions of Boltsano-Cochy theorems are satisfied. Аn evidence of the proposed algorithm's convergence is brought. Originally, the compression reflection operator as a ...
Directory of Open Access Journals (Sweden)
Hussein Abdel-jaber
2015-10-01
Full Text Available Congestion control is one of the hot research topics that helps maintain the performance of computer networks. This paper compares three Active Queue Management (AQM methods, namely, Adaptive Gentle Random Early Detection (Adaptive GRED, Random Early Dynamic Detection (REDD, and GRED Linear analytical model with respect to different performance measures. Adaptive GRED and REDD are implemented based on simulation, whereas GRED Linear is implemented as a discrete-time analytical model. Several performance measures are used to evaluate the effectiveness of the compared methods mainly mean queue length, throughput, average queueing delay, overflow packet loss probability, and packet dropping probability. The ultimate aim is to identify the method that offers the highest satisfactory performance in non-congestion or congestion scenarios. The first comparison results that are based on different packet arrival probability values show that GRED Linear provides better mean queue length; average queueing delay and packet overflow probability than Adaptive GRED and REDD methods in the presence of congestion. Further and using the same evaluation measures, Adaptive GRED offers a more satisfactory performance than REDD when heavy congestion is present. When the finite capacity of queue values varies the GRED Linear model provides the highest satisfactory performance with reference to mean queue length and average queueing delay and all the compared methods provide similar throughput performance. However, when the finite capacity value is large, the compared methods have similar results in regard to probabilities of both packet overflowing and packet dropping.
Non-Linear Cosmological Redshift According to General Relativity
Rabounski, Dmitri
2012-03-01
A new method of calculation of the frequency of a photon is applied. It means solving the scalar geodesic equation (equation of energy) of the photon. In the space of Schwarzschild's mass-point metric, the well-known gravitational redshift has been obtained. No frequency shift has been found in the space of Gödel's metric, and in the space of Einstein's metric (a homogeneous distribution of ideal liquid and physical vacuum). The other obtained solutions manifest a cosmological effect: its magnitude increases with distance travelled by the photon. This is the parabolic cosmological blueshift found in the space of Schwarzschild's metric of a sphere of incompressible liquid, and in the space of a sphere filled with physical vacuum (de Sitter's metric). The exponential cosmological redshift has been found in the expanding space of Friedmann's metric (empty or filled with ideal liquid and physical vacuum). The redshift is non-linear when approaching the event horizon, where it reaches the ultimate hugh value z = e^π ,,= 22.14. This explains the observed accelerate expansion of the Universe. These results were obtained in the purely geometric way, without the use of the Doppler effect. The paper has been submitted to The Abraham Zelmanov Journal.
Generalized synchronization in complex dynamical networks via adaptive couplings
Liu, Hui; Chen, Juan; Lu, Jun-an; Cao, Ming
2010-01-01
This paper investigates generalized synchronization of three typical classes of complex dynamical networks: scale-free networks, small-world networks. and interpolating networks. The proposed synchronization strategy is to adjust adaptively a node's coupling strength based oil the node's local
Model-free adaptive sliding mode controller design for generalized ...
Indian Academy of Sciences (India)
Home; Journals; Pramana – Journal of Physics; Volume 89; Issue 3. Model-free adaptive sliding mode controller design for generalized projective synchronization of the fractional-order chaotic system via radial basis function neural networks. L M WANG. Research Article Volume 89 Issue 3 September 2017 Article ID 38 ...
Model-free adaptive sliding mode controller design for generalized ...
Indian Academy of Sciences (India)
L M WANG
2017-08-16
Aug 16, 2017 ... Abstract. A novel model-free adaptive sliding mode strategy is proposed for a generalized projective synchronization (GPS) between two entirely unknown fractional-order chaotic systems subject to the external disturbances. To solve the difficulties from the little knowledge about the master–slave system ...
Wang, Li-Ming; Tang, Yong-Guang; Chai, Yong-Quan; Wu, Feng
2014-10-01
An adaptive fuzzy sliding mode strategy is developed for the generalized projective synchronization of a fractional-order chaotic system, where the slave system is not necessarily known in advance. Based on the designed adaptive update laws and the linear feedback method, the adaptive fuzzy sliding controllers are proposed via the fuzzy design, and the strength of the designed controllers can be adaptively adjusted according to the external disturbances. Based on the Lyapunov stability theorem, the stability and the robustness of the controlled system are proved theoretically. Numerical simulations further support the theoretical results of the paper and demonstrate the efficiency of the proposed method. Moreover, it is revealed that the proposed method allows us to manipulate arbitrarily the response dynamics of the slave system by adjusting the desired scaling factor λi and the desired translating factor ηi, which may be used in a channel-independent chaotic secure communication.
Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models.
Weber, Alison I; Pillow, Jonathan W
2017-12-01
A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a set of linear filters and a point nonlinearity and are conditionally Poisson spiking. They have desirable statistical properties for fitting and have been widely used to analyze spike trains from electrophysiological recordings. However, the dynamical repertoire of GLMs has not been systematically compared to that of real neurons. Here we show that GLMs can reproduce a comprehensive suite of canonical neural response behaviors, including tonic and phasic spiking, bursting, spike rate adaptation, type I and type II excitation, and two forms of bistability. GLMs can also capture stimulus-dependent changes in spike timing precision and reliability that mimic those observed in real neurons, and can exhibit varying degrees of stochasticity, from virtually deterministic responses to greater-than-Poisson variability. These results show that Poisson GLMs can exhibit a wide range of dynamic spiking behaviors found in real neurons, making them well suited for qualitative dynamical as well as quantitative statistical studies of single-neuron and population response properties.
On Self-Adaptive Method for General Mixed Variational Inequalities
Directory of Open Access Journals (Sweden)
Abdellah Bnouhachem
2008-01-01
Full Text Available We suggest and analyze a new self-adaptive method for solving general mixed variational inequalities, which can be viewed as an improvement of the method of (Noor 2003. Global convergence of the new method is proved under the same assumptions as Noor's method. Some preliminary computational results are given to illustrate the efficiency of the proposed method. Since the general mixed variational inequalities include general variational inequalities, quasivariational inequalities, and nonlinear (implicit complementarity problems as special cases, results proved in this paper continue to hold for these problems.
Adaptive Kronrod-Patterson integration of non-linear finite-element matrices
DEFF Research Database (Denmark)
Janssen, Hans
2010-01-01
. While developed for finite element unsaturated moisture transfer simulation, adaptive integration is similarly applicable for other non-linear problems and other discretization methods, and whereas perhaps outperformed by mesh-adaptive techniques, adaptive integration requires much less implementation......Efficient simulation of unsaturated moisture flow in porous media is of great importance in many engineering fields. The highly non-linear character of unsaturated flow typically gives sharp moving moisture fronts during wetting and drying of materials with strong local moisture permeability...... and capacity variations as result. It is shown that these strong variations conflict with the common preference for low-order numerical integration in finite element simulations of unsaturated moisture flow: inaccurate numerical integration leads to errors that are often far more important than errors from...
Generalization in adaptation to stable and unstable dynamics.
Directory of Open Access Journals (Sweden)
Abdelhamid Kadiallah
Full Text Available Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization.
Directory of Open Access Journals (Sweden)
Domingues M. O.
2013-12-01
Full Text Available We present a new adaptive multiresoltion method for the numerical simulation of ideal magnetohydrodynamics. The governing equations, i.e., the compressible Euler equations coupled with the Maxwell equations are discretized using a finite volume scheme on a two-dimensional Cartesian mesh. Adaptivity in space is obtained via Harten’s cell average multiresolution analysis, which allows the reliable introduction of a locally refined mesh while controlling the error. The explicit time discretization uses a compact Runge–Kutta method for local time stepping and an embedded Runge-Kutta scheme for automatic time step control. An extended generalized Lagrangian multiplier approach with the mixed hyperbolic-parabolic correction type is used to control the incompressibility of the magnetic field. Applications to a two-dimensional problem illustrate the properties of the method. Memory savings and numerical divergences of magnetic field are reported and the accuracy of the adaptive computations is assessed by comparing with the available exact solution.
Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks
Kanevski, Mikhail
2015-04-01
The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press
Domain Generalization and Adaptation using Low Rank Exemplar SVMs.
Li, Wen; Xu, Zheng; Xu, Dong; Dai, Dengxin; Van Gool, Luc
2017-05-16
Domain adaptation between diverse source and target domains is a challenging research problem, especially in the real-world visual recognition tasks where the images and videos consist of significant variations in viewpoints, illuminations, qualities, etc. In this paper, we propose a new approach for domain generalization and domain adaptation based on exemplar SVMs. Specifically, we decompose the source domain into many subdomains, each of which contains only one positive training sample and all negative samples. Each subdomain is relatively less diverse, and is expected to have a simpler distribution. By training one exemplar SVM for each subdomain, we obtain a set of exemplar SVMs. To further exploit the inherent structure of source domain, we introduce a nuclear-norm based regularizer into the objective function in order to enforce the exemplar SVMs to produce a low-rank output on training samples. In the prediction process, the confident exemplar SVM classifiers are selected and reweigted according to the distribution mismatch between each subdomain and the test sample in the target domain. We formulate our approach based on the logistic regression and least square SVM algorithms, which are referred to as low rank exemplar SVMs (LRE-SVMs) and low rank exemplar least square SVMs (LRE-LSSVMs), respectively. A fast algorithm is also developed for accelerating the training of LRE-LSSVMs. We further extend Domain Adaptation Machine (DAM) to learn an optimal target classifier for domain adaptation, and show that our approach can also be applied to domain adaptation with evolving target domain, where the target data distribution is gradually changing. The comprehensive experiments for object recognition and action recognition demonstrate the effectiveness of our approach for domain generalization and domain adaptation with fixed and evolving target domains.
Ko, C. C.; Wen, J.; Chin, F.
1992-12-01
A new algorithm for separating and tracking multiple directional sources in a linear power-inversion array is proposed and investigated. In this algorithm, the sources are separated by using an adaptive beamformer whose responses consist of perfect steerable nulls. By using the LMS algorithm for adaptive processing of the beamformer outputs to minimize the array output power and examining the adaptive weights employed, these nulls can be adjusted to track the sources individually so that the beamformer outputs will be due to different sources in the steady state. With this algorithm, the problem of incidental cancellation is eliminated and the enhancement of multiple moving sources becomes a natural process. Also, since the sources are individually tracked and the beamformer is only updated occasionally when significant changes in the environment are detected, the algorithm possesses fast tracking behavior and its implementation complexity is comparable with that of beamformer-based adaptive arrays using the LMS algorithm.
Lei, Meizhen; Wang, Liqiang
2018-01-01
The halbach-type linear oscillatory motor (HT-LOM) is multi-variable, highly coupled, nonlinear and uncertain, and difficult to get a satisfied result by conventional PID control. An incremental adaptive fuzzy controller (IAFC) for stroke tracking was presented, which combined the merits of PID control, the fuzzy inference mechanism and the adaptive algorithm. The integral-operation is added to the conventional fuzzy control algorithm. The fuzzy scale factor can be online tuned according to the load force and stroke command. The simulation results indicate that the proposed control scheme can achieve satisfied stroke tracking performance and is robust with respect to parameter variations and external disturbance.
The Generalized Logit-Linear Item Response Model for Binary-Designed Items
Revuelta, Javier
2008-01-01
This paper introduces the generalized logit-linear item response model (GLLIRM), which represents the item-solving process as a series of dichotomous operations or steps. The GLLIRM assumes that the probability function of the item response is a logistic function of a linear composite of basic parameters which describe the operations, and the…
Optimization of an Adaptive SPECT System with the Scanning Linear Estimator.
Ghanbari, Nasrin; Clarkson, Eric; Kupinski, Matthew; Li, Xin
2017-09-01
A method for optimization of an adaptive Single Photon Emission Computed Tomography (SPECT) system is presented. Adaptive imaging systems can quickly change their hardware configuration in response to data being generated in order to improve image quality for a specific task. In this work we simulate an adaptive SPECT system and propose a method for finding the adaptation that maximizes the performance on a signal estimation task. To start with, a simulated object model containing a spherical signal is imaged with a scout configuration. A Markov-Chain Monte Carlo (MCMC) technique utilizes the scout data to generate an ensemble of possible objects consistent with the scout data. This object ensemble is imaged by numerous simulated hardware configurations and for each system estimates of signal activity, size and location are calculated via the Scanning Linear Estimator (SLE). A figure of merit, based on a Modified Dice Index (MDI), quantifies the performance of each imaging configuration and it allows for optimization of the adaptive SPECT. This figure of merit is calculated by multiplying two terms: the first term uses the definition of the Dice similarity index to determine the percent of overlap between the actual and the estimated spherical signal, the second term utilizes an exponential function that measures the squared error for the activity estimate. The MDI combines the error in estimates of activity, size, and location, in one convenient metric and it allows for simultaneous optimization of the SPECT system with respect to all the estimated signal parameters. The results of our optimizations indicate that the adaptive system performs better than a non-adaptive one in conditions where the diagnostic scan has a low photon count - on the order of thousand photons per projection. In a statistical study, we optimized the SPECT system for one hundred unique objects and demonstrated that the average MDI on an estimation task is 0.84 for the adaptive system and 0
The Generalization of the Poisson Sum Formula Associated with the Linear Canonical Transform
Directory of Open Access Journals (Sweden)
Jun-Fang Zhang
2012-01-01
Full Text Available The generalization of the classical Poisson sum formula, by replacing the ordinary Fourier transform by the canonical transformation, has been derived in the linear canonical transform sense. Firstly, a new sum formula of Chirp-periodic property has been introduced, and then the relationship between this new sum and the original signal is derived. Secondly, the generalization of the classical Poisson sum formula to the linear canonical transform sense has been obtained.
Adaptive matching of the iota ring linear optics for space charge compensation
Energy Technology Data Exchange (ETDEWEB)
Romanov, A. [Fermilab; Bruhwiler, D. L. [RadiaSoft, Boulder; Cook, N. [RadiaSoft, Boulder; Hall, C. [RadiaSoft, Boulder
2016-10-09
Many present and future accelerators must operate with high intensity beams when distortions induced by space charge forces are among major limiting factors. Betatron tune depression of above approximately 0.1 per cell leads to significant distortions of linear optics. Many aspects of machine operation depend on proper relations between lattice functions and phase advances, and can be i proved with proper treatment of space charge effects. We implement an adaptive algorithm for linear lattice re matching with full account of space charge in the linear approximation for the case of Fermilab’s IOTA ring. The method is based on a search for initial second moments that give closed solution and, at the same predefined set of goals for emittances, beta functions, dispersions and phase advances at and between points of interest. Iterative singular value decomposition based technique is used to search for optimum by varying wide array of model parameters
Temporal adaptation to audiovisual asynchrony generalizes across different sound frequencies
Directory of Open Access Journals (Sweden)
Jordi eNavarra
2012-05-01
Full Text Available The human brain exhibits a highly-adaptive ability to reduce natural asynchronies between visual and auditory signals. Even though this mechanism robustly modulates the subsequent perception of sounds and visual stimuli, it is still unclear how such a temporal realignment is attained. In the present study, we investigated whether or not temporal adaptation generalizes across different sound frequencies. In a first exposure phase, participants adapted to a fixed 220-ms audiovisual asynchrony or else to synchrony for 3min. In a second phase, the participants performed simultaneity judgments (SJs regarding pairs of audiovisual stimuli that were presented at different stimulus onset asynchronies (SOAs and included either the same tone as in the exposure phase (a 250Hz beep, another low-pitched beep (300Hz, or a high-pitched beep (2500Hz. Temporal realignment was always observed (when comparing SJ performance after exposure to asynchrony vs. synchrony, regardless of the frequency of the sound tested. This suggests that temporal recalibration influences the audiovisual perception of sounds in a frequency non-specific manner and may imply the participation of non-primary perceptual areas of the brain that are not constrained by certain physical features such as sound frequency.
Univariate and multivariate general linear models theory and applications with SAS
Kim, Kevin
2006-01-01
Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences.With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regr
Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control
Directory of Open Access Journals (Sweden)
Wenya Zhou
2014-01-01
Full Text Available Attitude dynamic model of unmanned aerial vehicles (UAVs is multi-input multioutput (MIMO, strong coupling, and nonlinear. Model uncertainties and external gust disturbances should be considered during designing the attitude control system for UAVs. In this paper, feedback linearization and model reference adaptive control (MRAC are integrated to design the attitude control system for a fixed wing UAV. First of all, the complicated attitude dynamic model is decoupled into three single-input single-output (SISO channels by input-output feedback linearization. Secondly, the reference models are determined, respectively, according to the performance indexes of each channel. Subsequently, the adaptive control law is obtained using MRAC theory. In order to demonstrate the performance of attitude control system, the adaptive control law and the proportional-integral-derivative (PID control law are, respectively, used in the coupling nonlinear simulation model. Simulation results indicate that the system performance indexes including maximum overshoot, settling time (2% error range, and rise time obtained by MRAC are better than those by PID. Moreover, MRAC system has stronger robustness with respect to the model uncertainties and gust disturbance.
Directory of Open Access Journals (Sweden)
IRNANDA AIKO FIFI DJUUNA
2010-07-01
Full Text Available Djuuna IAF, Abbott LK, Van Niel K (2010 Predicting infectivity of Arbuscular Mycorrhizal fungi from soil variables using Generalized Additive Models and Generalized Linear Models. Biodiversitas 11: 145-150. The objective of this study was to predict the infectivity of arbuscular mycorrhizal fungi (AM fungi, from field soil based on soil properties and land use history using generalized additive models (GAMs and generalized linear models (GLMs. A total of 291 soil samples from a farm in Western Australia near Wickepin were collected and used in this study. Nine soil properties, including elevation, pH, EC, total C, total N, P, K, microbial biomass carbon, and soil texture, and land use history of the farm were used as independent variables, while the percentage of root length colonized (%RLC was used as the dependent variable. GAMs parameterized for the percent of root length colonized suggested skewed quadratic responses to soil pH and microbial biomass carbon; cubic responses to elevation and soil K; and linear responses to soil P, EC and total C. The strength of the relationship between percent root length colonized by AM fungi and environmental variables showed that only elevation, total C and microbial biomass carbon had strong relationships. In general, GAMs and GLMs models confirmed the strong relationship between infectivity of AM fungi (assessed in a glasshouse bioassay for soil collected in summer prior to the first rain of the season and soil properties.
Linear-quadratic-Gaussian control for adaptive optics systems using a hybrid model.
Looze, Douglas P
2009-01-01
This paper presents a linear-quadratic-Gaussian (LQG) design based on the equivalent discrete-time model of an adaptive optics (AO) system. The design model incorporates deformable mirror dynamics, an asynchronous wavefront sensor and zero-order hold operation, and a continuous-time model of the incident wavefront. Using the structure of the discrete-time model, the dimensions of the Riccati equations to be solved are reduced. The LQG controller is shown to improve AO system performance under several conditions.
General purpose graphic processing unit implementation of adaptive pulse compression algorithms
Cai, Jingxiao; Zhang, Yan
2017-07-01
This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.
Accuracy requirements of optical linear algebra processors in adaptive optics imaging systems
Downie, John D.
1990-01-01
A ground-based adaptive optics imaging telescope system attempts to improve image quality by detecting and correcting for atmospherically induced wavefront aberrations. The required control computations during each cycle will take a finite amount of time. Longer time delays result in larger values of residual wavefront error variance since the atmosphere continues to change during that time. Thus an optical processor may be well-suited for this task. This paper presents a study of the accuracy requirements in a general optical processor that will make it competitive with, or superior to, a conventional digital computer for the adaptive optics application. An optimization of the adaptive optics correction algorithm with respect to an optical processor's degree of accuracy is also briefly discussed.
A generalized Prony method for reconstruction of sparse sums of eigenfunctions of linear operators
Peter, Thomas; Plonka, Gerlind
2013-02-01
We derive a new generalization of Prony’s method to reconstruct M-sparse expansions of (generalized) eigenfunctions of linear operators from only O(M) suitable values in a deterministic way. The proposed method covers the well-known reconstruction methods for M-sparse sums of exponentials as well as for the interpolation of M-sparse polynomials by using special linear operators in C({{ {R}}}). Further, we can derive new reconstruction formulas for M-sparse expansions of orthogonal polynomials using the Sturm-Liouville operator. The method is also applied to the recovery of M-sparse vectors in finite-dimensional vector spaces.
Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.
Vidal, Sherry
Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…
GLIMMIX : Software for estimating mixtures and mixtures of generalized linear models
Wedel, M
2001-01-01
GLIMMIX is a commercial WINDOWS-based computer program that implements the EM algorithm (Dempster, Laird and Rubin 1977) for the estimation of finite mixtures and mixtures of generalized linear models. The program allows for the specification of a number of distributions in the exponential family,
Bayesian prediction of spatial count data using generalized linear mixed models
DEFF Research Database (Denmark)
Christensen, Ole Fredslund; Waagepetersen, Rasmus Plenge
2002-01-01
Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, ...
Directory of Open Access Journals (Sweden)
Jen-Yuan Chen
2014-01-01
Full Text Available Continuing from the works of Li et al. (2014, Li (2007, and Kincaid et al. (2000, we present more generalizations and modifications of iterative methods for solving large sparse symmetric and nonsymmetric indefinite systems of linear equations. We discuss a variety of iterative methods such as GMRES, MGMRES, MINRES, LQ-MINRES, QR MINRES, MMINRES, MGRES, and others.
The microcomputer scientific software series 2: general linear model--regression.
Harold M. Rauscher
1983-01-01
The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...
On Extended Exponential General Linear Methods PSQ with S>Q ...
African Journals Online (AJOL)
This paper is concerned with the construction and Numerical Analysis of Extended Exponential General Linear Methods. These methods, in contrast to other methods in literatures, consider methods with the step greater than the stage order (S>Q).Numerical experiments in this study, indicate that Extended Exponential ...
Modeling containment of large wildfires using generalized linear mixed-model analysis
Mark Finney; Isaac C. Grenfell; Charles W. McHugh
2009-01-01
Billions of dollars are spent annually in the United States to contain large wildland fires, but the factors contributing to suppression success remain poorly understood. We used a regression model (generalized linear mixed-model) to model containment probability of individual fires, assuming that containment was a repeated-measures problem (fixed effect) and...
Generalized linear models with random effects unified analysis via H-likelihood
Lee, Youngjo; Pawitan, Yudi
2006-01-01
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors.Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of...
Zhou, Shaohua Kevin; Aggarwal, Gaurav; Chellappa, Rama; Jacobs, David W
2007-02-01
Traditional photometric stereo algorithms employ a Lambertian reflectance model with a varying albedo field and involve the appearance of only one object. In this paper, we generalize photometric stereo algorithms to handle all appearances of all objects in a class, in particular the human face class, by making use of the linear Lambertian property. A linear Lambertian object is one which is linearly spanned by a set of basis objects and has a Lambertian surface. The linear property leads to a rank constraint and, consequently, a factorization of an observation matrix that consists of exemplar images of different objects (e.g., faces of different subjects) under different, unknown illuminations. Integrability and symmetry constraints are used to fully recover the subspace bases using a novel linearized algorithm that takes the varying albedo field into account. The effectiveness of the linear Lambertian property is further investigated by using it for the problem of illumination-invariant face recognition using just one image. Attached shadows are incorporated in the model by a careful treatment of the inherent nonlinearity in Lambert's law. This enables us to extend our algorithm to perform face recognition in the presence of multiple illumination sources. Experimental results using standard data sets are presented.
The linear stability of plane stagnation-point flow against general disturbances
Brattkus, K.; Davis, S. H.
1991-01-01
The linear-stability theory of plane stagnation-point flow against an infinite flat plate is re-examined. Disturbances are generalized from those of Goertler type to include other types of variations along the plate. It is shown that Hiemenz flow is linearly stable and that the Goertler-type modes are those that decay slowest. This work then rationalizes the use of such self-similar disturbances on Hiemenz flow and shows how questions of disturbance structure can be approached on other self-similar flows.
Adaptive Elastic Net for Generalized Methods of Moments.
Caner, Mehmet; Zhang, Hao Helen
2014-01-30
Model selection and estimation are crucial parts of econometrics. This paper introduces a new technique that can simultaneously estimate and select the model in generalized method of moments (GMM) context. The GMM is particularly powerful for analyzing complex data sets such as longitudinal and panel data, and it has wide applications in econometrics. This paper extends the least squares based adaptive elastic net estimator of Zou and Zhang (2009) to nonlinear equation systems with endogenous variables. The extension is not trivial and involves a new proof technique due to estimators lack of closed form solutions. Compared to Bridge-GMM of Caner (2009), we allow for the number of parameters to diverge to infinity as well as collinearity among a large number of variables, also the redundant parameters set to zero via a data dependent technique. This method has the oracle property, meaning that we can estimate nonzero parameters with their standard limit and the redundant parameters are dropped from the equations simultaneously. Numerical examples are used to illustrate the performance of the new method.
Hydrodynamics in full general relativity with conservative adaptive mesh refinement
East, William E.; Pretorius, Frans; Stephens, Branson C.
2012-06-01
There is great interest in numerical relativity simulations involving matter due to the likelihood that binary compact objects involving neutron stars will be detected by gravitational wave observatories in the coming years, as well as to the possibility that binary compact object mergers could explain short-duration gamma-ray bursts. We present a code designed for simulations of hydrodynamics coupled to the Einstein field equations targeted toward such applications. This code has recently been used to study eccentric mergers of black hole-neutron star binaries. We evolve the fluid conservatively using high-resolution shock-capturing methods, while the field equations are solved in the generalized-harmonic formulation with finite differences. In order to resolve the various scales that may arise, we use adaptive mesh refinement (AMR) with grid hierarchies based on truncation error estimates. A noteworthy feature of this code is the implementation of the flux correction algorithm of Berger and Colella to ensure that the conservative nature of fluid advection is respected across AMR boundaries. We present various tests to compare the performance of different limiters and flux calculation methods, as well as to demonstrate the utility of AMR flux corrections.
Experimental study on modified linear quadratic Gaussian control for adaptive optics.
Fu, Qiang; Pott, Jörg-Uwe; Peter, Diethard; Shen, Feng; Rao, Changhui; Li, Xinyang
2014-03-10
To achieve high-resolution imaging the standard control algorithm used for classical adaptive optics (AO) is the simple but efficient proportional-integral (PI) controller. The goal is to minimize the rms error of the residual wave front. However, using the PI controller, it is not possible to do this. One possible way to minimize the rms error is to use linear quadratic Gaussian (LQG) control. In practice, however, this control algorithm still encounters an unexpected problem that leads to the divergence of control in AO. This paper proposes a modified LQG (MLQG) to solve this issue. The controller is analyzed explicitly. Laboratory tests shows strong stability and high precision compared to the classical control.
Model-free adaptive fractional order control of stable linear time-varying systems.
Yakoub, Z; Amairi, M; Chetoui, M; Saidi, B; Aoun, M
2017-03-01
This paper presents a new model-free adaptive fractional order control approach for linear time-varying systems. An online algorithm is proposed to determine some frequency characteristics using a selective filtering and to design a fractional PID controller based on the numerical optimization of the frequency-domain criterion. When the system parameters are time-varying, the controller is updated to keep the same desired performances. The main advantage of the proposed approach is that the controller design depends only on the measured input and output signals of the process. The effectiveness of the proposed method is assessed through a numerical example. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Torque ripple reduction of brushless DC motor based on adaptive input-output feedback linearization.
Shirvani Boroujeni, M; Markadeh, G R Arab; Soltani, J
2017-09-01
Torque ripple reduction of Brushless DC Motors (BLDCs) is an interesting subject in variable speed AC drives. In this paper at first, a mathematical expression for torque ripple harmonics is obtained. Then for a non-ideal BLDC motor with known harmonic contents of back-EMF, calculation of desired reference current amplitudes, which are required to eliminate some selected harmonics of torque ripple, are reviewed. In order to inject the reference harmonic currents to the motor windings, an Adaptive Input-Output Feedback Linearization (AIOFBL) control is proposed, which generates the reference voltages for three phases voltage source inverter in stationary reference frame. Experimental results are presented to show the capability and validity of the proposed control method and are compared with the vector control in Multi-Reference Frame (MRF) and Pseudo-Vector Control (P-VC) method results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Deconinck, E; Coomans, D; Vander Heyden, Y
2007-01-04
In general, linear modelling techniques such as multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS), are used to model QSAR data. This type of data can be very complex and linear modelling techniques often model only a limited part of the information captured in the data. In this study, it was tried to combine linear techniques with the flexible non-linear technique multivariate adaptive regression splines (MARS). Models were built using an MLR model, combined with either a stepwise procedure or a genetic algorithm for variable selection, a PCR model or a PLS model as starting points for the MARS algorithm. The descriptive and predictive power of the models was evaluated in a QSAR context and compared to the performances of the individual linear models and the single MARS model. In general, the combined methods resulted in significant improvements compared to the linear models and can be considered valuable techniques in modelling complex QSAR data. For the used data set the best model was obtained using a combination of PLS and MARS. This combination resulted in a model with a Pearson correlation coefficient of 0.90 and a cross-validation error, evaluated with 10-fold cross-validation of 9.9%, pointing at good descriptive and high predictive properties.
Directory of Open Access Journals (Sweden)
Elias Giannakis
2016-10-01
Full Text Available The development of green space along urban rivers could mitigate urban heat island effects, enhance the physical and mental well-being of city dwellers, and improve flood resilience. A linear park has been recently created along the ephemeral Pedieos River in the urban area of Nicosia, Cyprus. Questionnaire surveys and micrometeorological measurements were conducted to explore people’s perceptions and satisfaction regarding the services of the urban park. People’s main reasons to visit the park were physical activity and exercise (67%, nature (13%, and cooling (4%. The micrometeorological measurements in and near the park revealed a relatively low cooling effect (0.5 °C of the park. However, the majority of the visitors (84% were satisfied or very satisfied with the cooling effect of the park. Logistic regression analysis indicated that the odds of individuals feeling very comfortable under a projected 3 °C future increase in temperature would be 0.34 times lower than the odds of feeling less comfortable. The discrepancies between the observed thermal comfort index and people’s perceptions revealed that people in semi-arid environments are adapted to the hot climatic conditions; 63% of the park visitors did not feel uncomfortable at temperatures between 27 °C and 37 °C. Further research is needed to assess other key ecosystems services of this urban green river corridor, such as flood protection, air quality regulation, and biodiversity conservation, to contribute to integrated climate change adaptation planning.
Dang, Qianyu; Mazumdar, Sati; Houck, Patricia R
2008-08-01
The generalized linear mixed model (GLIMMIX) provides a powerful technique to model correlated outcomes with different types of distributions. The model can now be easily implemented with SAS PROC GLIMMIX in version 9.1. For binary outcomes, linearization methods of penalized quasi-likelihood (PQL) or marginal quasi-likelihood (MQL) provide relatively accurate variance estimates for fixed effects. Using GLIMMIX based on these linearization methods, we derived formulas for power and sample size calculations for longitudinal designs with attrition over time. We found that the power and sample size estimates depend on the within-subject correlation and the size of random effects. In this article, we present tables of minimum sample sizes commonly used to test hypotheses for longitudinal studies. A simulation study was used to compare the results. We also provide a Web link to the SAS macro that we developed to compute power and sample sizes for correlated binary outcomes.
Optimisation of substrate blends in anaerobic co-digestion using adaptive linear programming.
García-Gen, Santiago; Rodríguez, Jorge; Lema, Juan M
2014-12-01
Anaerobic co-digestion of multiple substrates has the potential to enhance biogas productivity by making use of the complementary characteristics of different substrates. A blending strategy based on a linear programming optimisation method is proposed aiming at maximising COD conversion into methane, but simultaneously maintaining a digestate and biogas quality. The method incorporates experimental and heuristic information to define the objective function and the linear restrictions. The active constraints are continuously adapted (by relaxing the restriction boundaries) such that further optimisations in terms of methane productivity can be achieved. The feasibility of the blends calculated with this methodology was previously tested and accurately predicted with an ADM1-based co-digestion model. This was validated in a continuously operated pilot plant, treating for several months different mixtures of glycerine, gelatine and pig manure at organic loading rates from 1.50 to 4.93 gCOD/Ld and hydraulic retention times between 32 and 40 days at mesophilic conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.
Tharrey, Marion; Olaya, Gilma A; Fewtrell, Mary; Ferguson, Elaine
2017-12-01
The aim of the study was to use linear programming (LP) analyses to adapt New Complementary Feeding Guidelines (NCFg) designed for infants aged 6 to 12 months living in poor socioeconomic circumstances in Bogota to ensure dietary adequacy for young children aged 12 to 23 months. A secondary data analysis was performed using dietary and anthropometric data collected from 12-month-old infants (n = 72) participating in a randomized controlled trial. LP analyses were performed to identify nutrients whose requirements were difficult to achieve using local foods as consumed; and to test and compare the NCFg and alternative food-based recommendations (FBRs) on the basis of dietary adequacy, for 11 micronutrients, at the population level. Thiamine recommended nutrient intakes for these young children could not be achieved given local foods as consumed. NCFg focusing only on meat, fruits, vegetables, and breast milk ensured dietary adequacy at the population level for only 4 micronutrients, increasing to 8 of 11 modelled micronutrients when the FBRs promoted legumes, dairy, vitamin A-rich vegetables, and chicken giblets. None of the FBRs tested ensured population-level dietary adequacy for thiamine, niacin, and iron unless a fortified infant food was recommended. The present study demonstrated the value of using LP to adapt NCFg for a different age group than the one for which they were designed. Our analyses suggest that to ensure dietary adequacy for 12- to 23-month olds these adaptations should include legumes, dairy products, vitamin A-rich vegetables, organ meat, and a fortified food.
Linear and nonlinear associations between general intelligence and personality in Project TALENT.
Major, Jason T; Johnson, Wendy; Deary, Ian J
2014-04-01
Research on the relations of personality traits to intelligence has primarily been concerned with linear associations. Yet, there are no a priori reasons why linear relations should be expected over nonlinear ones, which represent a much larger set of all possible associations. Using 2 techniques, quadratic and generalized additive models, we tested for linear and nonlinear associations of general intelligence (g) with 10 personality scales from Project TALENT (PT), a nationally representative sample of approximately 400,000 American high school students from 1960, divided into 4 grade samples (Flanagan et al., 1962). We departed from previous studies, including one with PT (Reeve, Meyer, & Bonaccio, 2006), by modeling latent quadratic effects directly, controlling the influence of the common factor in the personality scales, and assuming a direction of effect from g to personality. On the basis of the literature, we made 17 directional hypotheses for the linear and quadratic associations. Of these, 53% were supported in all 4 male grades and 58% in all 4 female grades. Quadratic associations explained substantive variance above and beyond linear effects (mean R² between 1.8% and 3.6%) for Sociability, Maturity, Vigor, and Leadership in males and Sociability, Maturity, and Tidiness in females; linear associations were predominant for other traits. We discuss how suited current theories of the personality-intelligence interface are to explain these associations, and how research on intellectually gifted samples may provide a unique way of understanding them. We conclude that nonlinear models can provide incremental detail regarding personality and intelligence associations. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Meng, Fanwei; Liu, Chengying; Li, Zhijun; Wang, Liping
2013-01-01
Due to low damping ratio, flat permanent magnet linear synchronous motor's vibration is difficult to be damped and the accuracy is limited. The vibration suppressing results are not good enough in the existing research because only the longitudinal direction vibration is considered while the normal direction vibration is neglected. The parameters of the direct-axis current controller are set to be the same as those of the quadrature-axis current controller commonly. This causes contradiction between signal noise and response. To suppress the vibration, the electromagnetic force model of the flat permanent magnet synchronous linear motor is formulated first. Through the analysis of the effect that direct-axis current noise and quadrature-axis current noise have on both direction vibration, it can be declared that the conclusion that longitudinal direction vibration is only related to the quadrature-axis current noise while the normal direction vibration is related to both the quadrature-axis current noise and direct-axis current noise. Then, the simulation test on current loop with a low-pass filter is conducted and the results show that the low-pass filter can not suppress the vibration but makes the vibration more severe. So a vibration suppressing strategy that the proportional gain of direct-axis current controller adapted according to quadrature-axis reference current is proposed. This control strategy can suppress motor vibration by suppressing direct-axis current noise. The experiments results about the effect of K p and T i on normal direction vibration, longitudinal vibration and the position step response show that this strategy suppresses vibration effectively while the motor's motion performance is not affected. The maximum reduction of vibration can be up to 40%. In addition, current test under rated load condition is also conducted and the results show that the control strategy can avoid the conflict between the direct-axis current and the quadrature
A cautionary note on generalized linear models for covariance of unbalanced longitudinal data
Huang, Jianhua Z.
2012-03-01
Missing data in longitudinal studies can create enormous challenges in data analysis when coupled with the positive-definiteness constraint on a covariance matrix. For complete balanced data, the Cholesky decomposition of a covariance matrix makes it possible to remove the positive-definiteness constraint and use a generalized linear model setup to jointly model the mean and covariance using covariates (Pourahmadi, 2000). However, this approach may not be directly applicable when the longitudinal data are unbalanced, as coherent regression models for the dependence across all times and subjects may not exist. Within the existing generalized linear model framework, we show how to overcome this and other challenges by embedding the covariance matrix of the observed data for each subject in a larger covariance matrix and employing the familiar EM algorithm to compute the maximum likelihood estimates of the parameters and their standard errors. We illustrate and assess the methodology using real data sets and simulations. © 2011 Elsevier B.V.
Martin, J; Schneider, F; Kowalewskij, A; Jordan, D; Hapfelmeier, A; Kochs, E F; Wagner, K J; Schulz, C M
2016-12-01
Excessive workload may impact the anaesthetists' ability to adequately process information during clinical practice in the operation room and may result in inaccurate situational awareness and performance. This exploratory study investigated heart rate (HR), linear and non-linear heart rate variability (HRV) metrics and subjective ratings scales for the assessment of workload associated with the anaesthesia stages induction, maintenance and emergence. HR and HRV metrics were calculated based on five min segments from each of the three anaesthesia stages. The area under the receiver operating characteristics curve (AUC) of the investigated metrics was calculated to assess their ability to discriminate between the stages of anaesthesia. Additionally, a multiparametric approach based on logistic regression models was performed to further evaluate whether linear or non-linear heart rate metrics are suitable for the assessment of workload. Mean HR and several linear and non-linear HRV metrics including subjective workload ratings differed significantly between stages of anaesthesia. Permutation Entropy (PeEn, AUC=0.828) and mean HR (AUC=0.826) discriminated best between the anaesthesia stages induction and maintenance. In the multiparametric approach using logistic regression models, the model based on non-linear heart rate metrics provided a higher AUC compared with the models based on linear metrics. In this exploratory study based on short ECG segment analysis, PeEn and HR seem to be promising to separate workload levels between different stages of anaesthesia. The multiparametric analysis of the regression models favours non-linear heart rate metrics over linear metrics. © The Author 2016. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Directory of Open Access Journals (Sweden)
Z. Kolka
1999-04-01
Full Text Available The elementary canonical state models of the third-order autonomous dynamical systems, topologically conjugate to Chua's circuit family, are generalized for any continuous and odd symmetrical piecewise-linear (PWL feedback function. Their state equations are in accordance with the basic form of the Lur'e systems and the corresponding circuit model contains the multiple PWL feedback. The general results are applied for the simplest three-region case defined by three sets of the equivalent eigenvalue parameters. The application of these results is demonstrated on the double-scroll chaotic attractor with global attracting properties. As an example its utilization in synchronized chaos is shown.
Directory of Open Access Journals (Sweden)
Tsung-han Tsai
2013-05-01
Full Text Available There is some confusion in political science, and the social sciences in general, about the meaning and interpretation of interaction effects in models with non-interval, non-normal outcome variables. Often these terms are casually thrown into a model specification without observing that their presence fundamentally changes the interpretation of the resulting coefficients. This article explains the conditional nature of reported coefficients in models with interactions, defining the necessarily different interpretation required by generalized linear models. Methodological issues are illustrated with an application to voter information structured by electoral systems and resulting legislative behavior and democratic representation in comparative politics.
Deformations of the vacuum solutions of general relativity subjected to linear constraints
Molina, C.
2013-12-01
The problem of deforming geometries is particularly important in the context of constructing new exact solutions of Einstein’s equation. This issue often appears when extensions of the general relativity are treated, for instance in brane world scenarios. In this paper we investigate spacetimes in which the energy-momentum tensor obeys a linear constraint. Extensions of the usual vacuum and electrovacuum solutions of general relativity are derived and an exact solution is presented. The classes of geometries obtained include a wide variety of compact objects, among them black holes and wormholes. The general metric derived in this work generalizes several solutions already published in the literature. Perturbations around the exact solution are also considered.
A fully general and adaptive inverse analysis method for cementitious materials
DEFF Research Database (Denmark)
Jepsen, Michael S.; Damkilde, Lars; Lövgren, Ingemar
2016-01-01
The paper presents an adaptive method for inverse determination of the tensile σ - w relationship, direct tensile strength and Young’s modulus of cementitious materials. The method facilitates an inverse analysis with a multi-linear σ - w function. Usually, simple bi- or tri-linear functions are ...
Study on sampling of continuous linear system based on generalized Fourier transform
Li, Huiguang
2003-09-01
In the research of signal and system, the signal's spectrum and the system's frequency characteristic can be discussed through Fourier Transform (FT) and Laplace Transform (LT). However, some singular signals such as impulse function and signum signal don't satisfy Riemann integration and Lebesgue integration. They are called generalized functions in Maths. This paper will introduce a new definition -- Generalized Fourier Transform (GFT) and will discuss generalized function, Fourier Transform and Laplace Transform under a unified frame. When the continuous linear system is sampled, this paper will propose a new method to judge whether the spectrum will overlap after generalized Fourier transform (GFT). Causal and non-causal systems are studied, and sampling method to maintain system's dynamic performance is presented. The results can be used on ordinary sampling and non-Nyquist sampling. The results also have practical meaning on research of "discretization of continuous linear system" and "non-Nyquist sampling of signal and system." Particularly, condition for ensuring controllability and observability of MIMO continuous systems in references 13 and 14 is just an applicable example of this paper.
Unified Einstein-Virasoro master equation in the general non-linear $\\sigma$ model
De Boer, J
1997-01-01
The Virasoro master equation (VME) describes the general affine-Virasoro construction T=L^{ab}J_aJ_b+iD^a \\dif J_a in the operator algebra of the WZW model, where L^{ab} is the inverse inertia tensor and D^a is the improvement vector. In this paper, we generalize this construction to find the general (one-loop) Virasoro construction in the operator algebra of the general non-linear sigma model. The result is a unified Einstein-Virasoro master equation which couples the spacetime spin-two field L^{ab} to the background fields of the sigma model. For a particular solution L_G^{ab}, the unified system reduces to the canonical stress tensors and conventional Einstein equations of the sigma model, and the system reduces to the general affine-Virasoro construction and the VME when the sigma model is taken to be the WZW action. More generally, the unified system describes a space of conformal field theories which is presumably much larger than the sum of the general affine-Virasoro construction and the sigma model w...
Normality of raw data in general linear models: The most widespread myth in statistics
Kery, Marc; Hatfield, Jeff S.
2003-01-01
In years of statistical consulting for ecologists and wildlife biologists, by far the most common misconception we have come across has been the one about normality in general linear models. These comprise a very large part of the statistical models used in ecology and include t tests, simple and multiple linear regression, polynomial regression, and analysis of variance (ANOVA) and covariance (ANCOVA). There is a widely held belief that the normality assumption pertains to the raw data rather than to the model residuals. We suspect that this error may also occur in countless published studies, whenever the normality assumption is tested prior to analysis. This may lead to the use of nonparametric alternatives (if there are any), when parametric tests would indeed be appropriate, or to use of transformations of raw data, which may introduce hidden assumptions such as multiplicative effects on the natural scale in the case of log-transformed data. Our aim here is to dispel this myth. We very briefly describe relevant theory for two cases of general linear models to show that the residuals need to be normally distributed if tests requiring normality are to be used, such as t and F tests. We then give two examples demonstrating that the distribution of the response variable may be nonnormal, and yet the residuals are well behaved. We do not go into the issue of how to test normality; instead we display the distributions of response variables and residuals graphically.
Kun, David William
Unmanned aircraft systems (UASs) are gaining popularity in civil and commercial applications as their lightweight on-board computers become more powerful and affordable, their power storage devices improve, and the Federal Aviation Administration addresses the legal and safety concerns of integrating UASs in the national airspace. Consequently, many researchers are pursuing novel methods to control UASs in order to improve their capabilities, dependability, and safety assurance. The nonlinear control approach is a common choice as it offers several benefits for these highly nonlinear aerospace systems (e.g., the quadrotor). First, the controller design is physically intuitive and is derived from well known dynamic equations. Second, the final control law is valid in a larger region of operation, including far from the equilibrium states. And third, the procedure is largely methodical, requiring less expertise with gain tuning, which can be arduous for a novice engineer. Considering these facts, this thesis proposes a nonlinear controller design method that combines the advantages of adaptive robust control (ARC) with the powerful design tools of linear matrix inequalities (LMI). The ARC-LMI controller is designed with a discontinuous projection-based adaptation law, and guarantees a prescribed transient and steady state tracking performance for uncertain systems in the presence of matched disturbances. The norm of the tracking error is bounded by a known function that depends on the controller design parameters in a known form. Furthermore, the LMI-based part of the controller ensures the stability of the system while overcoming polytopic uncertainties, and minimizes the control effort. This can reduce the number of parameters that require adaptation, and helps to avoid control input saturation. These desirable characteristics make the ARC-LMI control algorithm well suited for the quadrotor UAS, which may have unknown parameters and may encounter external
Generalized linear sampling method for elastic-wave sensing of heterogeneous fractures
Pourahmadian, Fatemeh; Haddar, Houssem
2016-01-01
A theoretical foundation is developed for active seismic reconstruction of fractures endowed with spatially-varying interfacial condition (e.g.~partially-closed fractures, hydraulic fractures). The proposed indicator functional carries a superior localization property with no significant sensitivity to the fracture's contact condition, measurement errors, and illumination frequency. This is accomplished through the paradigm of the $F_\\sharp$-factorization technique and the recently developed Generalized Linear Sampling Method (GLSM) applied to elastodynamics. The direct scattering problem is formulated in the frequency domain where the fracture surface is illuminated by a set of incident plane waves, while monitoring the induced scattered field in the form of (elastic) far-field patterns. The analysis of the well-posedness of the forward problem leads to an admissibility condition on the fracture's (linearized) contact parameters. This in turn contributes toward establishing the applicability of the $F_\\sharp...
Non-cooperative stochastic differential game theory of generalized Markov jump linear systems
Zhang, Cheng-ke; Zhou, Hai-ying; Bin, Ning
2017-01-01
This book systematically studies the stochastic non-cooperative differential game theory of generalized linear Markov jump systems and its application in the field of finance and insurance. The book is an in-depth research book of the continuous time and discrete time linear quadratic stochastic differential game, in order to establish a relatively complete framework of dynamic non-cooperative differential game theory. It uses the method of dynamic programming principle and Riccati equation, and derives it into all kinds of existence conditions and calculating method of the equilibrium strategies of dynamic non-cooperative differential game. Based on the game theory method, this book studies the corresponding robust control problem, especially the existence condition and design method of the optimal robust control strategy. The book discusses the theoretical results and its applications in the risk control, option pricing, and the optimal investment problem in the field of finance and insurance, enriching the...
Robust Adaptive Fuzzy Design for Ship Linear-tracking Control with Input Saturation
Directory of Open Access Journals (Sweden)
Yancai Hu
2017-04-01
Full Text Available A robust adaptive control approach is proposed for underactuated surface ship linear path-tracking control system based on the backstepping control method and Lyapunov stability theory. By employing T-S fuzzy system to approximate nonlinear uncertainties of the control system, the proposed scheme is developed by combining “dynamic surface control” (DSC and “minimal learning parameter” (MLP techniques. The substantial problems of “explosion of complexity” and “dimension curse” existed in the traditional backstepping technique are circumvented, and it is convenient to implement in applications. In addition, an auxiliary system is developed to deal with the effect of input saturation constraints. The control algorithm avoids the singularity problem of controller and guarantees the stability of the closed-loop system. The tracking error converges to an arbitrarily small neighborhood. Finally, MATLAB simulation results are given from an application case of Dalian Maritime University training ship to demonstrate the effectiveness of the proposed scheme.
Directory of Open Access Journals (Sweden)
Muhammad Ammirrul Atiqi Mohd Zainuri
2016-05-01
Full Text Available This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC adaptive linear element (ADALINE neural network with the integration of photovoltaic (PV to shunt active power filters (SAPFs as active current source. Active PV injection in SAPFs should reduce dependency on grid supply current to supply the system. In addition, with a better and faster harmonics extraction algorithm, the SAPF should perform well, especially under dynamic PV and load conditions. The role of the actual injection current from SAPF after connecting PVs will be evaluated, and the better effect of using FAC ADALINE will be confirmed. The proposed SAPF was simulated and evaluated in MATLAB/Simulink first. Then, an experimental laboratory prototype was also developed to be tested with a PV simulator (CHROMA 62100H-600S, and the algorithm was implemented using a TMS320F28335 Digital Signal Processor (DSP. From simulation and experimental results, significant improvements in terms of total harmonic distortion (THD, time response and reduction of source power from grid have successfully been verified and achieved.
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Fikse Freddy
2010-03-01
Full Text Available Abstract Background The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms. Results We propose the use of double hierarchical generalized linear models (DHGLM, where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model. Conclusions We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.
Analysis of dental caries using generalized linear and count regression models
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Javali M. Phil
2013-11-01
Full Text Available Generalized linear models (GLM are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are flexible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero inflated count regression models such as the zero-inflated Poisson (ZIP, zero-inflated negative binomial (ZINB regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and over-dispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.
Métris, Aline; George, Susie M; Ropers, Delphine
2017-01-02
Addition of salt to food is one of the most ancient and most common methods of food preservation. However, little is known of how bacterial cells adapt to such conditions. We propose to use piecewise linear approximations to model the regulatory adaptation of Escherichiacoli to osmotic stress. We apply the method to eight selected genes representing the functions known to be at play during osmotic adaptation. The network is centred on the general stress response factor, sigma S, and also includes a module representing the catabolic repressor CRP-cAMP. Glutamate, potassium and supercoiling are combined to represent the intracellular regulatory signal during osmotic stress induced by salt. The output is a module where growth is represented by the concentration of stable RNAs and the transcription of the osmotic gene osmY. The time course of gene expression of transport of osmoprotectant represented by the symporter proP and of the osmY is successfully reproduced by the network. The behaviour of the rpoS mutant predicted by the model is in agreement with experimental data. We discuss the application of the model to food-borne pathogens such as Salmonella; although the genes considered have orthologs, it seems that supercoiling is not regulated in the same way. The model is limited to a few selected genes, but the regulatory interactions are numerous and span different time scales. In addition, they seem to be condition specific: the links that are important during the transition from exponential to stationary phase are not all needed during osmotic stress. This model is one of the first steps towards modelling adaptation to stress in food safety and has scope to be extended to other genes and pathways, other stresses relevant to the food industry, and food-borne pathogens. The method offers a good compromise between systems of ordinary differential equations, which would be unmanageable because of the size of the system and for which insufficient data are available
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S. Alonso-Quesada
2010-01-01
Full Text Available This paper presents a strategy for designing a robust discrete-time adaptive controller for stabilizing linear time-invariant (LTI continuous-time dynamic systems. Such systems may be unstable and noninversely stable in the worst case. A reduced-order model is considered to design the adaptive controller. The control design is based on the discretization of the system with the use of a multirate sampling device with fast-sampled control signal. A suitable on-line adaptation of the multirate gains guarantees the stability of the inverse of the discretized estimated model, which is used to parameterize the adaptive controller. A dead zone is included in the parameters estimation algorithm for robustness purposes under the presence of unmodeled dynamics in the controlled dynamic system. The adaptive controller guarantees the boundedness of the system measured signal for all time. Some examples illustrate the efficacy of this control strategy.
Adaptation of generalized Hill inequalities to anisotropic elastic ...
African Journals Online (AJOL)
user
Science Reports of the Research Institutes Tohoku University A- Physics Chemistry and. Metallurgy, Vol.19, pp.172. Mehrabadi, M.M., Cowin, S.C., 1995. Anisotropic symmetries of linear elasticity. Appl. Mech. Rev., Vol.48, pp.247-285. Pace, N.G. and Saunders G.A.,1971. Elastic wave propagation in group-VB semimetals.
Kamran, M. Ahmad; Hong, Keum-Shik
2013-10-01
Objective. Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique that measures brain activities by using near-infrared light of 650-950 nm wavelength. The major advantages of fNIRS are its low cost, portability, and good temporal resolution as a plausible solution to real-time imaging. Recent research has shown the great potential of fNIRS as a tool for brain-computer interfaces. Approach. This paper presents the first novel technique for fNIRS-based modelling of brain activities using the linear parameter-varying (LPV) method and adaptive signal processing. The output signal of each channel is assumed to be an output of an LPV system with unknown coefficients that are optimally estimated by the affine projection algorithm. The parameter vector is assumed to be Gaussian. Main results. The general linear model (GLM) is very popular and is a commonly used method for the analysis of functional MRI data, but it has certain limitations in the case of optical signals. The proposed model is more efficient in the sense that it allows the user to define more states. Moreover, unlike most previous models, it is online. The present results, showing improvement, were verified by random finger-tapping tasks in extensive experiments. We used 24 states, which can be reduced or increased depending on the cost of computation and requirements. Significance. The t-statistics were employed to determine the activation maps and to verify the significance of the results. Comparison of the proposed technique and two existing GLM-based algorithms shows an improvement in the estimation of haemodynamic response. Additionally, the convergence of the proposed algorithm is shown by error reduction in consecutive iterations.
Fokkema, M; Smits, N; Zeileis, A; Hothorn, T; Kelderman, H
2017-10-25
Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.
Vector generalized linear and additive models with an implementation in R
Yee, Thomas W
2015-01-01
This book presents a statistical framework that expands generalized linear models (GLMs) for regression modelling. The framework shared in this book allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole. This is possible through the approximately half-a-dozen major classes of statistical models included in the book and the software infrastructure component, which makes the models easily operable. The book’s methodology and accompanying software (the extensive VGAM R package) are directed at these limitations, and this is the first time the methodology and software are covered comprehensively in one volume. Since their advent in 1972, GLMs have unified important distributions under a single umbrella with enormous implications. The demands of practical data analysis, however, require a flexibility that GLMs do not have. Data-driven GLMs, in the form of generalized additive models (GAMs), are also largely confined to the exponential family. This book ...
Dai, James Y.; Chan, Kwun Chuen Gary; Hsu, Li
2014-01-01
Instrumental variable regression is one way to overcome unmeasured confounding and estimate causal effect in observational studies. Built on structural mean models, there has been considerale work recently developed for consistent estimation of causal relative risk and causal odds ratio. Such models can sometimes suffer from identification issues for weak instruments. This hampered the applicability of Mendelian randomization analysis in genetic epidemiology. When there are multiple genetic variants available as instrumental variables, and causal effect is defined in a generalized linear model in the presence of unmeasured confounders, we propose to test concordance between instrumental variable effects on the intermediate exposure and instrumental variable effects on the disease outcome, as a means to test the causal effect. We show that a class of generalized least squares estimators provide valid and consistent tests of causality. For causal effect of a continuous exposure on a dichotomous outcome in logistic models, the proposed estimators are shown to be asymptotically conservative. When the disease outcome is rare, such estimators are consistent due to the log-linear approximation of the logistic function. Optimality of such estimators relative to the well-known two-stage least squares estimator and the double-logistic structural mean model is further discussed. PMID:24863158
Thermodynamic bounds and general properties of optimal efficiency and power in linear responses.
Jiang, Jian-Hua
2014-10-01
We study the optimal exergy efficiency and power for thermodynamic systems with an Onsager-type "current-force" relationship describing the linear response to external influences. We derive, in analytic forms, the maximum efficiency and optimal efficiency for maximum power for a thermodynamic machine described by a N×N symmetric Onsager matrix with arbitrary integer N. The figure of merit is expressed in terms of the largest eigenvalue of the "coupling matrix" which is solely determined by the Onsager matrix. Some simple but general relationships between the power and efficiency at the conditions for (i) maximum efficiency and (ii) optimal efficiency for maximum power are obtained. We show how the second law of thermodynamics bounds the optimal efficiency and the Onsager matrix and relate those bounds together. The maximum power theorem (Jacobi's Law) is generalized to all thermodynamic machines with a symmetric Onsager matrix in the linear-response regime. We also discuss systems with an asymmetric Onsager matrix (such as systems under magnetic field) for a particular situation and we show that the reversible limit of efficiency can be reached at finite output power. Cooperative effects are found to improve the figure of merit significantly in systems with multiply cross-correlated responses. Application to example systems demonstrates that the theory is helpful in guiding the search for high performance materials and structures in energy researches.
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David T Redden
2006-08-01
Full Text Available Individual genetic admixture estimates, determined both across the genome and at specific genomic regions, have been proposed for use in identifying specific genomic regions harboring loci influencing phenotypes in regional admixture mapping (RAM. Estimates of individual ancestry can be used in structured association tests (SAT to reduce confounding induced by various forms of population substructure. Although presented as two distinct approaches, we provide a conceptual framework in which both RAM and SAT are special cases of a more general linear model. We clarify which variables are sufficient to condition upon in order to prevent spurious associations and also provide a simple closed form "semiparametric" method of evaluating the reliability of individual admixture estimates. An estimate of the reliability of individual admixture estimates is required to make an inherent errors-in-variables problem tractable. Casting RAM and SAT methods as a general linear model offers enormous flexibility enabling application to a rich set of phenotypes, populations, covariates, and situations, including interaction terms and multilocus models. This approach should allow far wider use of RAM and SAT, often using standard software, in addressing admixture as either a confounder of association studies or a tool for finding loci influencing complex phenotypes in species as diverse as plants, humans, and nonhuman animals.
Some problems of human adaptation and ecology under the aspect of general pathology
Kaznacheyev, V. P.
1980-01-01
The main problems of human adaptation at the level of the body and the population in connection with the features of current morbidity of the population and certain demographic processes are analyzed. The concepts of health and adaptation of the individual and human populations are determined. The importance of the anthropo-ecological approach to the investigation of the adaptation process of human populations is demonstrated. Certain features of the etiopathogenesis of diseases are considered in connection with the population-ecological regularities of human adaptation. The importance of research on general pathology aspects of adaptation and the ecology of man for planning, and organization of public health protection is discussed.
Cheng, Guang
2014-02-01
We consider efficient estimation of the Euclidean parameters in a generalized partially linear additive models for longitudinal/clustered data when multiple covariates need to be modeled nonparametrically, and propose an estimation procedure based on a spline approximation of the nonparametric part of the model and the generalized estimating equations (GEE). Although the model in consideration is natural and useful in many practical applications, the literature on this model is very limited because of challenges in dealing with dependent data for nonparametric additive models. We show that the proposed estimators are consistent and asymptotically normal even if the covariance structure is misspecified. An explicit consistent estimate of the asymptotic variance is also provided. Moreover, we derive the semiparametric efficiency score and information bound under general moment conditions. By showing that our estimators achieve the semiparametric information bound, we effectively establish their efficiency in a stronger sense than what is typically considered for GEE. The derivation of our asymptotic results relies heavily on the empirical processes tools that we develop for the longitudinal/clustered data. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2014 ISI/BS.
Fan, Yurui; Huang, Guohe; Veawab, Amornvadee
2012-01-01
In this study, a generalized fuzzy linear programming (GFLP) method was developed to deal with uncertainties expressed as fuzzy sets that exist in the constraints and objective function. A stepwise interactive algorithm (SIA) was advanced to solve GFLP model and generate solutions expressed as fuzzy sets. To demonstrate its application, the developed GFLP method was applied to a regional sulfur dioxide (SO2) control planning model to identify effective SO2 mitigation polices with a minimized system performance cost under uncertainty. The results were obtained to represent the amount of SO2 allocated to different control measures from different sources. Compared with the conventional interval-parameter linear programming (ILP) approach, the solutions obtained through GFLP were expressed as fuzzy sets, which can provide intervals for the decision variables and objective function, as well as related possibilities. Therefore, the decision makers can make a tradeoff between model stability and the plausibility based on solutions obtained through GFLP and then identify desired policies for SO2-emission control under uncertainty.
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package
Directory of Open Access Journals (Sweden)
Jarrod Had
2010-02-01
Full Text Available Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package MCMCglmm implements such an algorithm for a range of model fitting problems. More than one response variable can be analyzed simultaneously, and these variables are allowed to follow Gaussian, Poisson, multi(binominal, exponential, zero-inflated and censored distributions. A range of variance structures are permitted for the random effects, including interactions with categorical or continuous variables (i.e., random regression, and more complicated variance structures that arise through shared ancestry, either through a pedigree or through a phylogeny. Missing values are permitted in the response variable(s and data can be known up to some level of measurement error as in meta-analysis. All simu- lation is done in C/ C++ using the CSparse library for sparse linear systems.
Scholz, Stefan; Graf von der Schulenburg, Johann-Matthias; Greiner, Wolfgang
2015-11-17
Regional differences in physician supply can be found in many health care systems, regardless of their organizational and financial structure. A theoretical model is developed for the physicians' decision on office allocation, covering demand-side factors and a consumption time function. To test the propositions following the theoretical model, generalized linear models were estimated to explain differences in 412 German districts. Various factors found in the literature were included to control for physicians' regional preferences. Evidence in favor of the first three propositions of the theoretical model could be found. Specialists show a stronger association to higher populated districts than GPs. Although indicators for regional preferences are significantly correlated with physician density, their coefficients are not as high as population density. If regional disparities should be addressed by political actions, the focus should be to counteract those parameters representing physicians' preferences in over- and undersupplied regions.
Directory of Open Access Journals (Sweden)
Nurdan Cetin
2014-01-01
Full Text Available We consider a multiobjective linear fractional transportation problem (MLFTP with several fractional criteria, such as, the maximization of the transport profitability like profit/cost or profit/time, and its two properties are source and destination. Our aim is to introduce MLFTP which has not been studied in literature before and to provide a fuzzy approach which obtain a compromise Pareto-optimal solution for this problem. To do this, first, we present a theorem which shows that MLFTP is always solvable. And then, reducing MLFTP to the Zimmermann’s “min” operator model which is the max-min problem, we construct Generalized Dinkelbach’s Algorithm for solving the obtained problem. Furthermore, we provide an illustrative numerical example to explain this fuzzy approach.
Shen, Peiping; Zhang, Tongli; Wang, Chunfeng
2017-01-01
This article presents a new approximation algorithm for globally solving a class of generalized fractional programming problems (P) whose objective functions are defined as an appropriate composition of ratios of affine functions. To solve this problem, the algorithm solves an equivalent optimization problem (Q) via an exploration of a suitably defined nonuniform grid. The main work of the algorithm involves checking the feasibility of linear programs associated with the interesting grid points. It is proved that the proposed algorithm is a fully polynomial time approximation scheme as the ratio terms are fixed in the objective function to problem (P), based on the computational complexity result. In contrast to existing results in literature, the algorithm does not require the assumptions on quasi-concavity or low-rank of the objective function to problem (P). Numerical results are given to illustrate the feasibility and effectiveness of the proposed algorithm.
General theories of linear gravitational perturbations to a Schwarzschild black hole
Tattersall, Oliver J.; Ferreira, Pedro G.; Lagos, Macarena
2018-02-01
We use the covariant formulation proposed by Tattersall, Lagos, and Ferreira [Phys. Rev. D 96, 064011 (2017), 10.1103/PhysRevD.96.064011] to analyze the structure of linear perturbations about a spherically symmetric background in different families of gravity theories, and hence study how quasinormal modes of perturbed black holes may be affected by modifications to general relativity. We restrict ourselves to single-tensor, scalar-tensor and vector-tensor diffeomorphism-invariant gravity models in a Schwarzschild black hole background. We show explicitly the full covariant form of the quadratic actions in such cases, which allow us to then analyze odd parity (axial) and even parity (polar) perturbations simultaneously in a straightforward manner.
Generalized linear mixed models: a practical guide for ecology and evolution.
Bolker, Benjamin M; Brooks, Mollie E; Clark, Connie J; Geange, Shane W; Poulsen, John R; Stevens, M Henry H; White, Jada-Simone S
2009-03-01
How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
Generalization of the ordinary state-based peridynamic model for isotropic linear viscoelasticity
Delorme, Rolland; Tabiai, Ilyass; Laberge Lebel, Louis; Lévesque, Martin
2017-02-01
This paper presents a generalization of the original ordinary state-based peridynamic model for isotropic linear viscoelasticity. The viscoelastic material response is represented using the thermodynamically acceptable Prony series approach. It can feature as many Prony terms as required and accounts for viscoelastic spherical and deviatoric components. The model was derived from an equivalence between peridynamic viscoelastic parameters and those appearing in classical continuum mechanics, by equating the free energy densities expressed in both frameworks. The model was simplified to a uni-dimensional expression and implemented to simulate a creep-recovery test. This implementation was finally validated by comparing peridynamic predictions to those predicted from classical continuum mechanics. An exact correspondence between peridynamics and the classical continuum approach was shown when the peridynamic horizon becomes small, meaning peridynamics tends toward classical continuum mechanics. This work provides a clear and direct means to researchers dealing with viscoelastic phenomena to tackle their problem within the peridynamic framework.
Optimal Stochastic Control Problem for General Linear Dynamical Systems in Neuroscience
Directory of Open Access Journals (Sweden)
Yan Chen
2017-01-01
Full Text Available This paper considers a d-dimensional stochastic optimization problem in neuroscience. Suppose the arm’s movement trajectory is modeled by high-order linear stochastic differential dynamic system in d-dimensional space, the optimal trajectory, velocity, and variance are explicitly obtained by using stochastic control method, which allows us to analytically establish exact relationships between various quantities. Moreover, the optimal trajectory is almost a straight line for a reaching movement; the optimal velocity bell-shaped and the optimal variance are consistent with the experimental Fitts law; that is, the longer the time of a reaching movement, the higher the accuracy of arriving at the target position, and the results can be directly applied to designing a reaching movement performed by a robotic arm in a more general environment.
Directory of Open Access Journals (Sweden)
Wen-Min Zhou
2013-01-01
Full Text Available This paper is concerned with the consensus problem of general linear discrete-time multiagent systems (MASs with random packet dropout that happens during information exchange between agents. The packet dropout phenomenon is characterized as being a Bernoulli random process. A distributed consensus protocol with weighted graph is proposed to address the packet dropout phenomenon. Through introducing a new disagreement vector, a new framework is established to solve the consensus problem. Based on the control theory, the perturbation argument, and the matrix theory, the necessary and sufficient condition for MASs to reach mean-square consensus is derived in terms of stability of an array of low-dimensional matrices. Moreover, mean-square consensusable conditions with regard to network topology and agent dynamic structure are also provided. Finally, the effectiveness of the theoretical results is demonstrated through an illustrative example.
Zhang, Hui; Lu, Naiji; Feng, Changyong; Thurston, Sally W; Xia, Yinglin; Zhu, Liang; Tu, Xin M
2011-09-10
The generalized linear mixed-effects model (GLMM) is a popular paradigm to extend models for cross-sectional data to a longitudinal setting. When applied to modeling binary responses, different software packages and even different procedures within a package may give quite different results. In this report, we describe the statistical approaches that underlie these different procedures and discuss their strengths and weaknesses when applied to fit correlated binary responses. We then illustrate these considerations by applying these procedures implemented in some popular software packages to simulated and real study data. Our simulation results indicate a lack of reliability for most of the procedures considered, which carries significant implications for applying such popular software packages in practice. Copyright © 2011 John Wiley & Sons, Ltd.
Sliwinski, M J; Hall, C B
1998-03-01
General slowing (GS) theories are often tested by meta-analysis that model mean latencies of older adults as a function of mean latencies of younger adults. Ordinary least squares (OLS) regression is inappropriate for this purpose because it fails to account for the nested structure of multitask response time (RT) data. Hierarchical linear models (HLM) are an alternative method for analyzing such data. OLS analysis of data from 21 studies that used iterative cognitive tasks supported GS; however, HLM analysis demonstrated significant variance in slowing across experimental tasks and a process-specific effect by showing less slowing for memory scanning than for visual-search and mental-rotation tasks. The authors conclude that HLM is more suitable than OLS methods for meta-analyses of RT data and for testing GS theories.
Adaptation of generalized Hill inequalities to anisotropic elastic ...
African Journals Online (AJOL)
user
Keywords: Generalized Hill Inequalities, Elastic Constants, Anisotropic Elastic Symmetries, ... In literature, Dinçkal and Akgöz (2010) decomposed elastic constant tensor into ...... Science Reports of the Research Institutes Tohoku University A- Physics Chemistry and ... Wright, A., Faraday, C.S.N., White, E.F.T., et al., 1971.
Usefulness of noise adaptive non-linear gaussian filter in FDG-PET study.
Nagayoshi, Makoto; Murase, Kenya; Fujino, Kouichi; Uenishi, Yusuke; Kawamata, Minoru; Nakamura, Yukio; Kitamura, Keishi; Higuchi, Ichiro; Oku, Naohiko; Hatazawa, Jun
2005-09-01
In positron emission tomography (PET) studies, shortening transmission (TR) scan time can improve patient comfort and increase scanner throughput. However, PET images from short TR scans may be degraded due to the statistical noise included in the TR image. The purpose of this study was to apply non-linear Gaussian (NLG) and noise adaptive NLG (ANLG) filters to TR images, and to evaluate the extent of noise reduction by the ANLG filter in comparison with that by the NLG filter using phantom and clinical studies. In phantom studies, pool phantoms of various diameters and injected doses of 2-deoxy-2-[18F]fluoro-D-glucose (FDG) were used and the coefficients of variation (CVs) of the counts in the TR images processed with the NLG and ANLG filters were compared. In clinical studies, two normal volunteers and 13 patients with tumors were studied. In volunteer studies, the CV values in the liver were compared. In patient studies, the standardized uptake values (SUVs) of tumors in the emission images were obtained after processing the TR images using the NLG and ANLG filters. In phantom studies, the CV values in the TR images processed with the ANLG filter were smaller than those in the images processed with the NLG filter. When using the ANLG filter, their dependency on the phantom size, injected dose of FDG and TR scan time was smaller than when using the NLG filter. In volunteer studies, the CV values in the images processed with the ANLG filter were smaller than those in the images processed with the NLG filter, and were almost constant regardless of the TR scan time. In patient studies, there was an excellent correlation between the SUVs obtained from the images with a TR scan time of 7 min processed with the NLG filter (x) and those obtained from the images with a TR scan time of 4 min processed with the ANLG filter (y) (r = 0.995, y = 1.034x - 0.075). Our results suggest that the ANLG filter is effective and useful for noise reduction in TR images and shortening TR
Radiation dose reduction for chest CT with non-linear adaptive filters.
Singh, Sarabjeet; Digumarthy, Subba R; Back, Anni; Shepard, Jo-anne O; Kalra, Mannudeep K
2013-03-01
CT radiation dose reduction results in increased noise or graininess of images which affects the diagnostic information. One of the approaches to lower radiation exposure to patients is to reduce image noise with the use of image processing software in low radiation dose images. To assess image quality and accuracy of non-linear adaptive filters (NLAF) at low dose chest CT. In an IRB approved prospective study, 24 patients (mean age, 63 ± 7.3 years; M:F ratio, 11:13) gave informed consent for acquisition of four additional chest CT image series at 150, 110, 75, and 40 mAs (baseline image series) on a 64-slice MDCT over an identical 10-cm length. NLAF was used to process three low dose (110, 75, and 40 mAs) image series (postprocessed image series). Two radiologists reviewed baseline and postprocessed images in a blinded manner for image quality. Objective noise, CT attenuation values, patient weight, transverse diameters, CTDIvol, and DLP were recorded. Statistical analysis was performed using parametric and non-parametric tests for comparing postprocessed and baseline images. No lesions were missed on baseline or postprocessed CT images (n = 80 lesions, 73 lesions hardening artifacts not affecting diagnostic decision-making (14/22) in both baseline and postprocessed image series. Diagnostic confidence for chest CT was improved to fully confident in postprocessed images at 40 mAs. Compared to baseline images, postprocessing reduced objective noise by 26% (14.2 ± 4.7/19.2 ± 6.4), 31.5% (15.2 ± 4.7/22.2 ± 5.7), and 41.5% (16.9 ± 6/28.9 ± 10.2) at 110 mAs, 75 mAs, and 40 mAs tube current-time product levels. Applications of NLAF can help reduce tube current down to 40 mAs for chest CT while maintaining lesion conspicuity and image quality.
Non-linear climate feedback analysis in an atmospheric general circulation model
Energy Technology Data Exchange (ETDEWEB)
Colman, R.A.; Power, S.B.; McAvaney, B.J. [Bureau of Meteorology, Melbourne, VIC (Australia). Research Centre
1997-10-01
A method is described for evaluating the `partial derivatives` of globally averaged top-of-atmosphere (TOA) radiation changes with respect to basicclimate model physical parameters. This method is used to analyse feedbacks in the Australian bureau of meteorology research centre general circulation model. The parameters considered are surface temperature, water vapour, lapse rate and cloud cover. The climate forcing which produces the changes is a globally uniform sea surface temperature (SST) perturbation. The first and second order differentials of model parameters with respect to the forcing (i.e. SST changes) are estimated from quadratic least square fitting. Except for total cloud cover, variables are found to be strong functions of global SST. Strongly nonlinear variations of lapse rate and high cloud amount and height appear to relate to the nonlinear response in penetrative convection. Globally averaged TOA radiation differentials with respect to model parameters are also evaluated. With the exception of total cloud contributions, a high correlation is generally found to exist, on the global mean level, between TOA radiation and the respective parameter perturbations. The largest nonlinear terms contributing to radiative changes are those due to lapse rate and high cloud. The contributions of linear and nonlinear terms to the overall radiative response from a 4 K SST perturbation are assessed. Significant nonlinear responses are found to be associated with lapse rate, water vapour and cloud changes. Although the exact magnitude of these responses is likely to be a function of the particular model as well as the imposed SST perturbation pattern, the present experiments flag these as processes which cannot properly be understood from linear theory in the evaluation of climate change sensitivity. (orig.) With 6 figs., 4 tabs., 28 refs.
Directory of Open Access Journals (Sweden)
Enrique Calderín-Ojeda
2017-11-01
Full Text Available Generalized linear models might not be appropriate when the probability of extreme events is higher than that implied by the normal distribution. Extending the method for estimating the parameters of a double Pareto lognormal distribution (DPLN in Reed and Jorgensen (2004, we develop an EM algorithm for the heavy-tailed Double-Pareto-lognormal generalized linear model. The DPLN distribution is obtained as a mixture of a lognormal distribution with a double Pareto distribution. In this paper the associated generalized linear model has the location parameter equal to a linear predictor which is used to model insurance claim amounts for various data sets. The performance is compared with those of the generalized beta (of the second kind and lognorma distributions.
Ultra Linear Low-loss Varactors & Circuits for Adaptive RF Systems
Huang, C.
2010-01-01
With the evolution of wireless communication, varactors can play an important role in enabling adaptive transceivers as well as phase-diversity systems. This thesis presents various varactor diode-based circuit topologies that facilitate RF adaptivity. The proposed varactor configurations can act as
Generalization of Hindi OCR Using Adaptive Segmentation and Font Files
Agrawal, Mudit; Ma, Huanfeng; Doermann, David
In this chapter, we describe an adaptive Indic OCR system implemented as part of a rapidly retargetable language tool effort and extend work found in [20, 2]. The system includes script identification, character segmentation, training sample creation, and character recognition. For script identification, Hindi words are identified in bilingual or multilingual document images using features of the Devanagari script and support vector machine (SVM). Identified words are then segmented into individual characters, using a font-model-based intelligent character segmentation and recognition system. Using characteristics of structurally similar TrueType fonts, our system automatically builds a model to be used for the segmentation and recognition of the new script, independent of glyph composition. The key is a reliance on known font attributes. In our recognition system three feature extraction methods are used to demonstrate the importance of appropriate features for classification. The methods are tested on both Latin and non-Latin scripts. Results show that the character-level recognition accuracy exceeds 92% for non-Latin and 96% for Latin text on degraded documents. This work is a step toward the recognition of scripts of low-density languages which typically do not warrant the development of commercial OCR, yet often have complete TrueType font descriptions.
DALDABAN, Ferhat; USTKOYUNCU, Nurettin
2010-01-01
In this paper, a new method based on adaptive neuro-fuzzy inference system (ANFIS) to estimate the phase inductance of linear switched reluctance motors (LSRMs) is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the back-propagation (BP) algorithm and the least square method (LSM), is used to identify the parameters of ANFIS. The translator position and the p...
DALDABAN, Ferhat; USTKOYUNCU, Nurettin
2010-01-01
In this paper, a new method based on adaptive neuro-fuzzy inference system (ANFIS) to estimate the phase inductance of linear switched reluctance motors (LSRMs) is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the back-propagation (BP) algorithm and the least square method (LSM), is used to identify the parameters of ANFIS. The translator position and the phase...
Automated Clutch of AMT Vehicle Based on Adaptive Generalized Minimum Variance Controller
National Research Council Canada - National Science Library
Ze Li; Xinhao Yang
2014-01-01
... of the automated clutch of automatic mechanical transmission vehicle. In this paper, an adaptive generalized minimum variance controller is applied to the automated clutch, which is driven by a brushless DC motor...
Generalized linear model for mapping discrete trait loci implemented with LASSO algorithm.
Directory of Open Access Journals (Sweden)
Jun Xing
Full Text Available Generalized estimating equation (GEE algorithm under a heterogeneous residual variance model is an extension of the iteratively reweighted least squares (IRLS method for continuous traits to discrete traits. In contrast to mixture model-based expectation-maximization (EM algorithm, the GEE algorithm can well detect quantitative trait locus (QTL, especially large effect QTLs located in large marker intervals in the manner of high computing speed. Based on a single QTL model, however, the GEE algorithm has very limited statistical power to detect multiple QTLs because of ignoring other linked QTLs. In this study, the fast least absolute shrinkage and selection operator (LASSO is derived for generalized linear model (GLM with all possible link functions. Under a heterogeneous residual variance model, the LASSO for GLM is used to iteratively estimate the non-zero genetic effects of those loci over entire genome. The iteratively reweighted LASSO is therefore extended to mapping QTL for discrete traits, such as ordinal, binary, and Poisson traits. The simulated and real data analyses are conducted to demonstrate the efficiency of the proposed method to simultaneously identify multiple QTLs for binary and Poisson traits as examples.
Østergaard, Jacob; Kramer, Mark A; Eden, Uri T
2018-01-01
To understand neural activity, two broad categories of models exist: statistical and dynamical. While statistical models possess rigorous methods for parameter estimation and goodness-of-fit assessment, dynamical models provide mechanistic insight. In general, these two categories of models are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input current. We then fit these spike train data with a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured.
An Adaptive Multialphabet Arithmetic Coding Based on Generalized Virtual Sliding Window
DEFF Research Database (Denmark)
Belyaev, Evgeny; Forchhammer, Søren; Liu, Kai
2017-01-01
We propose a novel efficient multialphabet multiplication-free adaptive arithmetic coder. First, we generalize probability estimation via virtual sliding window for the multialphabet case and show that it does not require multiplications and provides a tradeoff between the probability adaptation...... speed and the precision of the probability estimation. Second, we show how the generalized virtual sliding window can be used to eliminate multiplications and divisions. Finally, we demonstrate that the proposed arithmetic coder provides better compression performance than existing implementations based...
No Evidence for a Low Linear Energy Transfer Adaptive Response in Irradiated RKO Cells
Energy Technology Data Exchange (ETDEWEB)
Sowa, Marianne B.; Goetz, Wilfried; Baulch, Janet E.; Lewis, Adam J.; Morgan, William F.
2011-01-06
It has become increasingly evident from reports in the literature that there are many confounding factors that are capable of modulating radiation induced non-targeted responses such as the bystander effect and the adaptive response. In this paper we examine recent data that suggest that the observation of non-targeted responses may not be universally observable for differing radiation qualities. We have conducted a study of the adaptive response following low LET exposures for human colon carcinoma cells and failed to observe adaption for the endpoints of clonogenic survival or micronucleus formation.
Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J
2016-05-01
Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) . © 2015 John Wiley & Sons Ltd/London School of Economics.
Directory of Open Access Journals (Sweden)
Haodong Yuan
2017-01-01
Full Text Available A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC and adaptive PSO-optimized support vector machine (SVM is proposed. In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases. To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed. Firstly, preliminary features are obtained by wavelet packet node energy. Then, dictionary learning with class-wise K-SVD algorithm is implemented. Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm. Finally, SVM optimized by adaptive particle swarm optimization (PSO is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized. In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and K-means are also conducted for comparison. The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC. The dictionary learned by class-wise K-SVD achieves the best performance. Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM.
Development and validation of a general purpose linearization program for rigid aircraft models
Duke, E. L.; Antoniewicz, R. F.
1985-01-01
This paper discusses a FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft models. The program LINEAR numerically determines a linear systems model using nonlinear equations of motion and a user-supplied, nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model. Also, included in the report is a comparison of linear and nonlinear models for a high-performance aircraft.
Elliott, J.; de Souza, R. S.; Krone-Martins, A.; Cameron, E.; Ishida, E. E. O.; Hilbe, J.
2015-04-01
Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the underlying physical processes of the data. In this article, and the companion papers of this series, we present the set of Generalized Linear Models (GLMs) as a fast alternative method for tackling general astronomical problems, including the ones related to the machine learning paradigm. To demonstrate the applicability of GLMs to inherently positive and continuous physical observables, we explore their use in estimating the photometric redshifts of galaxies from their multi-wavelength photometry. Using the gamma family with a log link function we predict redshifts from the PHoto-z Accuracy Testing simulated catalogue and a subset of the Sloan Digital Sky Survey from Data Release 10. We obtain fits that result in catastrophic outlier rates as low as ∼1% for simulated and ∼2% for real data. Moreover, we can easily obtain such levels of precision within a matter of seconds on a normal desktop computer and with training sets that contain merely thousands of galaxies. Our software is made publicly available as a user-friendly package developed in Python, R and via an interactive web application. This software allows users to apply a set of GLMs to their own photometric catalogues and generates publication quality plots with minimum effort. By facilitating their ease of use to the astronomical community, this paper series aims to make GLMs widely known and to encourage their implementation in future large-scale projects, such as the Large Synoptic Survey Telescope.
MGMRES: A generalization of GMRES for solving large sparse nonsymmetric linear systems
Energy Technology Data Exchange (ETDEWEB)
Young, D.M.; Chen, J.Y. [Univ. of Texas, Austin, TX (United States)
1994-12-31
The authors are concerned with the solution of the linear system (1): Au = b, where A is a real square nonsingular matrix which is large, sparse and non-symmetric. They consider the use of Krylov subspace methods. They first choose an initial approximation u{sup (0)} to the solution {bar u} = A{sup {minus}1}B of (1). They also choose an auxiliary matrix Z which is nonsingular. For n = 1,2,{hor_ellipsis} they determine u{sup (n)} such that u{sup (n)} {minus} u{sup (0)}{epsilon}K{sub n}(r{sup (0)},A) where K{sub n}(r{sup (0)},A) is the (Krylov) subspace spanned by the Krylov vectors r{sup (0)}, Ar{sup (0)}, {hor_ellipsis}, A{sup n{minus}1}r{sup 0} and where r{sup (0)} = b{minus}Au{sup (0)}. If ZA is SPD they also require that (u{sup (n)}{minus}{bar u}, ZA(u{sup (n)}{minus}{bar u})) be minimized. If, on the other hand, ZA is not SPD, then they require that the Galerkin condition, (Zr{sup n}, v) = 0, be satisfied for all v{epsilon}K{sub n}(r{sup (0)}, A) where r{sup n} = b{minus}Au{sup (n)}. In this paper the authors consider a generalization of GMRES. This generalized method, which they refer to as `MGMRES`, is very similar to GMRES except that they let Z = A{sup T}Y where Y is a nonsingular matrix which is symmetric by not necessarily SPD.
Regularization and a general linear model for event-related potential estimation.
Kristensen, Emmanuelle; Guerin-Dugué, Anne; Rivet, Bertrand
2017-12-01
The usual event-related potential (ERP) estimation is the average across epochs time-locked on stimuli of interest. These stimuli are repeated several times to improve the signal-to-noise ratio (SNR) and only one evoked potential is estimated inside the temporal window of interest. Consequently, the average estimation does not take into account other neural responses within the same epoch that are due to short inter stimuli intervals. These adjacent neural responses may overlap and distort the evoked potential of interest. This overlapping process is a significant issue for the eye fixation-related potential (EFRP) technique in which the epochs are time-locked on the ocular fixations. The inter fixation intervals are not experimentally controlled and can be shorter than the neural response's latency. To begin, the Tikhonov regularization, applied to the classical average estimation, was introduced to improve the SNR for a given number of trials. The generalized cross validation was chosen to obtain the optimal value of the ridge parameter. Then, to deal with the issue of overlapping, the general linear model (GLM), was used to extract all neural responses inside an epoch. Finally, the regularization was also applied to it. The models (the classical average and the GLM with and without regularization) were compared on both simulated data and real datasets from a visual scene exploration in co-registration with an eye-tracker, and from a P300 Speller experiment. The regularization was found to improve the estimation by average for a given number of trials. The GLM was more robust and efficient, its efficiency actually reinforced by the regularization.
Generalized Jeans' Escape of Pick-Up Ions in Quasi-Linear Relaxation
Moore, T. E.; Khazanov, G. V.
2011-01-01
Jeans escape is a well-validated formulation of upper atmospheric escape that we have generalized to estimate plasma escape from ionospheres. It involves the computation of the parts of particle velocity space that are unbound by the gravitational potential at the exobase, followed by a calculation of the flux carried by such unbound particles as they escape from the potential well. To generalize this approach for ions, we superposed an electrostatic ambipolar potential and a centrifugal potential, for motions across and along a divergent magnetic field. We then considered how the presence of superthermal electrons, produced by precipitating auroral primary electrons, controls the ambipolar potential. We also showed that the centrifugal potential plays a small role in controlling the mass escape flux from the terrestrial ionosphere. We then applied the transverse ion velocity distribution produced when ions, picked up by supersonic (i.e., auroral) ionospheric convection, relax via quasi-linear diffusion, as estimated for cometary comas [1]. The results provide a theoretical basis for observed ion escape response to electromagnetic and kinetic energy sources. They also suggest that super-sonic but sub-Alfvenic flow, with ion pick-up, is a unique and important regime of ion-neutral coupling, in which plasma wave-particle interactions are driven by ion-neutral collisions at densities for which the collision frequency falls near or below the gyro-frequency. As another possible illustration of this process, the heliopause ribbon discovered by the IBEX mission involves interactions between the solar wind ions and the interstellar neutral gas, in a regime that may be analogous [2].
Test for interactions between a genetic marker set and environment in generalized linear models.
Lin, Xinyi; Lee, Seunggeun; Christiani, David C; Lin, Xihong
2013-09-01
We consider in this paper testing for interactions between a genetic marker set and an environmental variable. A common practice in studying gene-environment (GE) interactions is to analyze one single-nucleotide polymorphism (SNP) at a time. It is of significant interest to analyze SNPs in a biologically defined set simultaneously, e.g. gene or pathway. In this paper, we first show that if the main effects of multiple SNPs in a set are associated with a disease/trait, the classical single SNP-GE interaction analysis can be biased. We derive the asymptotic bias and study the conditions under which the classical single SNP-GE interaction analysis is unbiased. We further show that, the simple minimum p-value-based SNP-set GE analysis, can be biased and have an inflated Type 1 error rate. To overcome these difficulties, we propose a computationally efficient and powerful gene-environment set association test (GESAT) in generalized linear models. Our method tests for SNP-set by environment interactions using a variance component test, and estimates the main SNP effects under the null hypothesis using ridge regression. We evaluate the performance of GESAT using simulation studies, and apply GESAT to data from the Harvard lung cancer genetic study to investigate GE interactions between the SNPs in the 15q24-25.1 region and smoking on lung cancer risk.
Establishment of a new initial dose plan for vancomycin using the generalized linear mixed model.
Kourogi, Yasuyuki; Ogata, Kenji; Takamura, Norito; Tokunaga, Jin; Setoguchi, Nao; Kai, Mitsuhiro; Tanaka, Emi; Chiyotanda, Susumu
2017-04-08
When administering vancomycin hydrochloride (VCM), the initial dose is adjusted to ensure that the steady-state trough value (Css-trough) remains within the effective concentration range. However, the Css-trough (population mean method predicted value [PMMPV]) calculated using the population mean method (PMM) often deviate from the effective concentration range. In this study, we used the generalized linear mixed model (GLMM) for initial dose planning to create a model that accurately predicts Css-trough, and subsequently assessed its prediction accuracy. The study included 46 subjects whose trough values were measured after receiving VCM. We calculated the Css-trough (Bayesian estimate predicted value [BEPV]) from the Bayesian estimates of trough values. Using the patients' medical data, we created models that predict the BEPV and selected the model with minimum information criterion (GLMM best model). We then calculated the Css-trough (GLMMPV) from the GLMM best model and compared the BEPV correlation with GLMMPV and with PMMPV. The GLMM best model was {[0.977 + (males: 0.029 or females: -0.081)] × PMMPV + 0.101 × BUN/adjusted SCr - 12.899 × SCr adjusted amount}. The coefficients of determination for BEPV/GLMMPV and BEPV/PMMPV were 0.623 and 0.513, respectively. We demonstrated that the GLMM best model was more accurate in predicting the Css-trough than the PMM.
Gauge coupling of non-linear σ-model and a generalized Mazur identity
Carter, B.
An inversionsymmetric class of non-linear σ-models is constructed. The original pure model with field values in the coset space of a classical matrix group G with respect to an isotropy subgroup under the adjoint action is generalized to a minimally gauge coupled model in which the field is a section in a bundle with group G acting on the coset space as fibre with a nontrivial connection of (for example) Yang-Mills type. It is shown that the gauge coupled models admit a natural generalisation of the identities originally constructed by Mazur for the pure nonlinear σ-models whereby the divergence of a quantity whose surface integral vanishes when suitable boundary conditions are satisfied is shown to be equal to a functional of the difference between two sets of field variables that is positive definite in many relevant situation. In such cases, which occur when the base-space metric is positive definite (so that the system is of elliptic type) and the isotropy subgroup is compact, the identities lead directly to uniqueness theorems for the solutions.
Use of the generalized linear models in data related to dental caries index
Directory of Open Access Journals (Sweden)
Javali S
2007-01-01
Full Text Available The aim of this study is to encourage and initiate the application of generalized linear models (GLMs in the analysis of the covariates of decayed, missing, and filled teeth (DMFT index data, which is not necessarily normally distributed. GLMs can be performed assuming underlying many distributions; in fact Poisson distribution with log built-in link function and binomial distribution with Logit and Probit built-in link functions are considered. The Poisson model is used for modeling the DMFT index data and the Logit and Probit models are employed to model the dichotomous outcome of DMFT = 0 and DMFT ≠ 0 (caries free/caries present. The data comprised 7188 subjects aged 18-30 years from the study on the oral health status of Karnataka state conducted by SDM College of Dental Sciences and Hospital, Dharwad, Karnataka, India. The Poisson model and binomial models (Logit and Probit displayed dissimilarity in the outcome of results at 5% level of significance ( P < 0.05. The binomial models were a poor fit, whereas the Poisson model showed a good fit for the DMFT index data. Therefore, a suitable modeling approach for DMFT index data is to use a Poisson model for the DMFT response and a binomial model for the caries free and caries present (DMFT = 0 and DMFT ≠ 0. These GLMs allow separate estimation of those covariates which influence the magnitude of caries.
Population Decoding of Motor Cortical Activity using a Generalized Linear Model with Hidden States
Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas G.; Paninski, Liam
2010-01-01
Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (lowering the Mean Square Error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. PMID:20359500
The overlooked potential of Generalized Linear Models in astronomy, I: Binomial regression
de Souza, R. S.; Cameron, E.; Killedar, M.; Hilbe, J.; Vilalta, R.; Maio, U.; Biffi, V.; Ciardi, B.; Riggs, J. D.
2015-09-01
Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific enquiry increases as more subtle relationships are sought. Contemporary data analysis problems often elude the capabilities of classical statistical techniques, suggesting the use of cutting edge statistical methods. In this light, astronomers have overlooked a whole family of statistical techniques for exploratory data analysis and robust regression, the so-called Generalized Linear Models (GLMs). In this paper-the first in a series aimed at illustrating the power of these methods in astronomical applications-we elucidate the potential of a particular class of GLMs for handling binary/binomial data, the so-called logit and probit regression techniques, from both a maximum likelihood and a Bayesian perspective. As a case in point, we present the use of these GLMs to explore the conditions of star formation activity and metal enrichment in primordial minihaloes from cosmological hydro-simulations including detailed chemistry, gas physics, and stellar feedback. We predict that for a dark mini-halo with metallicity ≈ 1.3 × 10-4Z⨀, an increase of 1.2 × 10-2 in the gas molecular fraction, increases the probability of star formation occurrence by a factor of 75%. Finally, we highlight the use of receiver operating characteristic curves as a diagnostic for binary classifiers, and ultimately we use these to demonstrate the competitive predictive performance of GLMs against the popular technique of artificial neural networks.
Directory of Open Access Journals (Sweden)
Miguel Flores
2016-11-01
Full Text Available This work aims to classify the DNA sequences of healthy and malignant cancer respectively. For this, supervised and unsupervised classification methods from a functional context are used; i.e. each strand of DNA is an observation. The observations are discretized, for that reason different ways to represent these observations with functions are evaluated. In addition, an exploratory study is done: estimating the mean and variance of each functional type of cancer. For the unsupervised classification method, hierarchical clustering with different measures of functional distance is used. On the other hand, for the supervised classification method, a functional generalized linear model is used. For this model the first and second derivatives are used which are included as discriminating variables. It has been verified that one of the advantages of working in the functional context is to obtain a model to correctly classify cancers by 100%. For the implementation of the methods it has been used the fda.usc R package that includes all the techniques of functional data analysis used in this work. In addition, some that have been developed in recent decades. For more details of these techniques can be consulted Ramsay, J. O. and Silverman (2005 and Ferraty et al. (2006.
Directory of Open Access Journals (Sweden)
Tülin Acar
2012-01-01
Full Text Available The aim of this research is to compare the result of the differential item functioning (DIF determining with hierarchical generalized linear model (HGLM technique and the results of the DIF determining with logistic regression (LR and item response theory–likelihood ratio (IRT-LR techniques on the test items. For this reason, first in this research, it is determined whether the students encounter DIF with HGLM, LR, and IRT-LR techniques according to socioeconomic status (SES, in the Turkish, Social Sciences, and Science subtest items of the Secondary School Institutions Examination. When inspecting the correlations among the techniques in terms of determining the items having DIF, it was discovered that there was significant correlation between the results of IRT-LR and LR techniques in all subtests; merely in Science subtest, the results of the correlation between HGLM and IRT-LR techniques were found significant. DIF applications can be made on test items with other DIF analysis techniques that were not taken to the scope of this research. The analysis results, which were determined by using the DIF techniques in different sample sizes, can be compared.
An adaptive algorithm for separating and tracking multiple directional sources in linear arrays
Ko, C. C.; Chin, Francois; Foo, S. S.
1992-03-01
A new algorithm for spatially filtering out, enhancing, and tracking individual directional sources in an adaptive array is proposed and investigated. In this algorithm, the sources are separated by using an adaptive beamformer whose outputs are processed by employing the LMS algorithm to track distinct sources individually. From the LMS weights employed, the source locations can be estimated and whenever significant changes in these are detected, the beamformer is updated so that its outputs will be due to different sources in the steady state. With this algorithm, the problems of look direction errors in look-direction constrained arrays and of large signal power in power inversion arrays are eliminated, and the enhancement of multiple moving sources becomes a natural process. Furthermore, because the sources are individually tracked and the beamformer is only updated occasionally, the algorithm possesses fast tracking behavior, and its implementation complexity is comparable to that of beamformer-based adaptive arrays using the LMS algorithm.
DEFF Research Database (Denmark)
Hundebøll, Martin; Pedersen, Morten Videbæk; Roetter, Daniel Enrique Lucani
2014-01-01
This work studies the potential and impact of the FRANC network coding protocol for delivering high quality Dynamic Adaptive Streaming over HTTP (DASH) in wireless networks. Although DASH aims to tailor the video quality rate based on the available throughput to the destination, it relies...
Yock, Adam D.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Kudchadker, Rajat J.; Court, Laurence E.
2014-01-01
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
Energy Technology Data Exchange (ETDEWEB)
Yock, Adam D., E-mail: ADYock@mdanderson.org; 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); 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); 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); Beadle, Beth M.; Garden, Adam S. [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)
2014-05-15
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
Robust speech perception: recognize the familiar, generalize to the similar, and adapt to the novel.
Kleinschmidt, Dave F; Jaeger, T Florian
2015-04-01
Successful speech perception requires that listeners map the acoustic signal to linguistic categories. These mappings are not only probabilistic, but change depending on the situation. For example, one talker's /p/ might be physically indistinguishable from another talker's /b/ (cf. lack of invariance). We characterize the computational problem posed by such a subjectively nonstationary world and propose that the speech perception system overcomes this challenge by (a) recognizing previously encountered situations, (b) generalizing to other situations based on previous similar experience, and (c) adapting to novel situations. We formalize this proposal in the ideal adapter framework: (a) to (c) can be understood as inference under uncertainty about the appropriate generative model for the current talker, thereby facilitating robust speech perception despite the lack of invariance. We focus on 2 critical aspects of the ideal adapter. First, in situations that clearly deviate from previous experience, listeners need to adapt. We develop a distributional (belief-updating) learning model of incremental adaptation. The model provides a good fit against known and novel phonetic adaptation data, including perceptual recalibration and selective adaptation. Second, robust speech recognition requires that listeners learn to represent the structured component of cross-situation variability in the speech signal. We discuss how these 2 aspects of the ideal adapter provide a unifying explanation for adaptation, talker-specificity, and generalization across talkers and groups of talkers (e.g., accents and dialects). The ideal adapter provides a guiding framework for future investigations into speech perception and adaptation, and more broadly language comprehension. (c) 2015 APA, all rights reserved).
Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel
Kleinschmidt, Dave F.; Jaeger, T. Florian
2016-01-01
Successful speech perception requires that listeners map the acoustic signal to linguistic categories. These mappings are not only probabilistic, but change depending on the situation. For example, one talker’s /p/ might be physically indistinguishable from another talker’s /b/ (cf. lack of invariance). We characterize the computational problem posed by such a subjectively non-stationary world and propose that the speech perception system overcomes this challenge by (1) recognizing previously encountered situations, (2) generalizing to other situations based on previous similar experience, and (3) adapting to novel situations. We formalize this proposal in the ideal adapter framework: (1) to (3) can be understood as inference under uncertainty about the appropriate generative model for the current talker, thereby facilitating robust speech perception despite the lack of invariance. We focus on two critical aspects of the ideal adapter. First, in situations that clearly deviate from previous experience, listeners need to adapt. We develop a distributional (belief-updating) learning model of incremental adaptation. The model provides a good fit against known and novel phonetic adaptation data, including perceptual recalibration and selective adaptation. Second, robust speech recognition requires listeners learn to represent the structured component of cross-situation variability in the speech signal. We discuss how these two aspects of the ideal adapter provide a unifying explanation for adaptation, talker-specificity, and generalization across talkers and groups of talkers (e.g., accents and dialects). The ideal adapter provides a guiding framework for future investigations into speech perception and adaptation, and more broadly language comprehension. PMID:25844873
Cressman, Erin K.
2015-01-01
Visuomotor learning results in changes in both motor and sensory systems (Cressman EK, Henriques DY. J Neurophysiol 102: 3505–3518, 2009), such that reaches are adapted and sense of felt hand position recalibrated after reaching with altered visual feedback of the hand. Moreover, visuomotor learning has been shown to generalize such that reach adaptation achieved at a trained target location can influence reaches to novel target directions (Krakauer JW, Pine ZM, Ghilardi MF, Ghez C. J Neurosci 20: 8916–8924, 2000). We looked to determine whether proprioceptive recalibration also generalizes to novel locations. Moreover, we looked to establish the relationship between reach adaptation and changes in sense of felt hand position by determining whether proprioceptive recalibration generalizes to novel targets in a similar manner as reach adaptation. On training trials, subjects reached to a single target with aligned or misaligned cursor-hand feedback, in which the cursor was either rotated or scaled in extent relative to hand movement. After reach training, subjects reached to the training target and novel targets (including targets from a second start position) without visual feedback to assess generalization of reach adaptation. Subjects then performed a proprioceptive estimation task, in which they indicated the position of their hand relative to visual reference markers placed at similar locations as the trained and novel reach targets. Results indicated that shifts in hand position generalized across novel locations, independent of reach adaptation. Thus these distinct sensory and motor generalization patterns suggest that reach adaptation and proprioceptive recalibration arise from independent error signals and that changes in one system cannot guide adjustments in the other. PMID:25972587
Gorissen, B.L.; Blanc, J.P.C.; den Hertog, D.; Ben-Tal, A.
We propose a new way to derive tractable robust counterparts of a linear program based on the duality between the robust (“pessimistic”) primal problem and its “optimistic” dual. First we obtain a new convex reformulation of the dual problem of a robust linear program, and then show how to construct
Generalized linear mixed models can detect unimodal species-environment relationships
Jamil, Tahira; Braak, ter C.J.F.
2013-01-01
Niche theory predicts that species occurrence and abundance show non-linear, unimodal relationships with respect to environmental gradients. Unimodal models, such as the Gaussian (logistic) model, are however more difficult to fit to data than linear ones, particularly in a multi-species context in
The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.
Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun
2017-01-01
Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.
Directory of Open Access Journals (Sweden)
Parnchit Wattanasaruch
2012-09-01
Full Text Available The analyses of clinical and epidemiologic studies are often based on some kind of regression analysis, mainly linearregression and logistic models. These analyses are often affected by the fact that one or more of the predictors are measuredwith error. The error in the predictors is also known to bias the estimates and hypothesis testing results. One of the proceduresfrequently used to handle such problem in order to reduce the measurement errors is the method of regression calibration forpredicting the continuous covariate. The idea is to predict the true value of error-prone predictor from the observed data, thento use the predicted value for the analyses. In this research we develop four calibration procedures, namely probit, complementary log-log, logit, and logistic calibration procedures for corrections of the measurement error and/or the misclassification error to predict the true values for the misclassification explanatory variables used in generalized linear models. Theprocesses give the predicted true values of a binary explanatory variable using the calibration techniques then use thesepredicted values to fit the three models such that the probit, the complementary log-log, and the logit models under the binaryresponse. All of which are investigated by considering the mean square error (MSE in 1,000 simulation studies in each caseof the known parameters and conditions. The results show that the proposed working calibration techniques that can performadequately well are the probit, logistic, and logit calibration procedures. Both the probit calibration procedure and the probitmodel are superior to the logistic and logit calibrations due to the smallest MSE. Furthermore, the probit model-parameterestimates also improve the effects of the misclassification explanatory variable. Only the complementary log-log model andits calibration technique are appropriate when measurement error is moderate and sample size is high.
Power analysis for generalized linear mixed models in ecology and evolution.
Johnson, Paul C D; Barry, Sarah J E; Ferguson, Heather M; Müller, Pie
2015-02-01
'Will my study answer my research question?' is the most fundamental question a researcher can ask when designing a study, yet when phrased in statistical terms - 'What is the power of my study?' or 'How precise will my parameter estimate be?' - few researchers in ecology and evolution (EE) try to answer it, despite the detrimental consequences of performing under- or over-powered research. We suggest that this reluctance is due in large part to the unsuitability of simple methods of power analysis (broadly defined as any attempt to quantify prospectively the 'informativeness' of a study) for the complex models commonly used in EE research. With the aim of encouraging the use of power analysis, we present simulation from generalized linear mixed models (GLMMs) as a flexible and accessible approach to power analysis that can account for random effects, overdispersion and diverse response distributions.We illustrate the benefits of simulation-based power analysis in two research scenarios: estimating the precision of a survey to estimate tick burdens on grouse chicks and estimating the power of a trial to compare the efficacy of insecticide-treated nets in malaria mosquito control. We provide a freely available R function, sim.glmm, for simulating from GLMMs.Analysis of simulated data revealed that the effects of accounting for realistic levels of random effects and overdispersion on power and precision estimates were substantial, with correspondingly severe implications for study design in the form of up to fivefold increases in sampling effort. We also show the utility of simulations for identifying scenarios where GLMM-fitting methods can perform poorly.These results illustrate the inadequacy of standard analytical power analysis methods and the flexibility of simulation-based power analysis for GLMMs. The wider use of these methods should contribute to improving the quality of study design in EE.
Hubbard, Rebecca A; Johnson, Eric; Chubak, Jessica; Wernli, Karen J; Kamineni, Aruna; Bogart, Andy; Rutter, Carolyn M
2017-06-01
Exposures derived from electronic health records (EHR) may be misclassified, leading to biased estimates of their association with outcomes of interest. An example of this problem arises in the context of cancer screening where test indication, the purpose for which a test was performed, is often unavailable. This poses a challenge to understanding the effectiveness of screening tests because estimates of screening test effectiveness are biased if some diagnostic tests are misclassified as screening. Prediction models have been developed for a variety of exposure variables that can be derived from EHR, but no previous research has investigated appropriate methods for obtaining unbiased association estimates using these predicted probabilities. The full likelihood incorporating information on both the predicted probability of exposure-class membership and the association between the exposure and outcome of interest can be expressed using a finite mixture model. When the regression model of interest is a generalized linear model (GLM), the expectation-maximization algorithm can be used to estimate the parameters using standard software for GLMs. Using simulation studies, we compared the bias and efficiency of this mixture model approach to alternative approaches including multiple imputation and dichotomization of the predicted probabilities to create a proxy for the missing predictor. The mixture model was the only approach that was unbiased across all scenarios investigated. Finally, we explored the performance of these alternatives in a study of colorectal cancer screening with colonoscopy. These findings have broad applicability in studies using EHR data where gold-standard exposures are unavailable and prediction models have been developed for estimating proxies.
Predicting stem borer density in maize using RapidEye data and generalized linear models
Abdel-Rahman, Elfatih M.; Landmann, Tobias; Kyalo, Richard; Ong'amo, George; Mwalusepo, Sizah; Sulieman, Saad; Ru, Bruno Le
2017-05-01
Average maize yield in eastern Africa is 2.03 t ha-1 as compared to global average of 6.06 t ha-1 due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In eastern Africa, maize yield losses due to stem borers are currently estimated between 12% and 21% of the total production. The objective of the present study was to explore the possibility of RapidEye spectral data to assess stem borer larva densities in maize fields in two study sites in Kenya. RapidEye images were acquired for the Bomet (western Kenya) test site on the 9th of December 2014 and on 27th of January 2015, and for Machakos (eastern Kenya) a RapidEye image was acquired on the 3rd of January 2015. Five RapidEye spectral bands as well as 30 spectral vegetation indices (SVIs) were utilized to predict per field maize stem borer larva densities using generalized linear models (GLMs), assuming Poisson ('Po') and negative binomial ('NB') distributions. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were used to assess the models performance using a leave-one-out cross-validation approach. The Zero-inflated NB ('ZINB') models outperformed the 'NB' models and stem borer larva densities could only be predicted during the mid growing season in December and early January in both study sites, respectively (RMSE = 0.69-1.06 and RPD = 8.25-19.57). Overall, all models performed similar when all the 30 SVIs (non-nested) and only the significant (nested) SVIs were used. The models developed could improve decision making regarding controlling maize stem borers within integrated pest management (IPM) interventions.
Gaillard, Emmanuelle; Lieber, Jean; Nauer, Emmanuel
2015-01-01
International audience; This paper presents the participation of the Taaable team to the 2015 Computer Cooking Contest. The Taaable system addresses the mixology and the sandwich challenges. For the mixology challenge, the 2014 Taaable system was extended in two ways. First, a formal concept analysis approach is used to improve the ingredient substitution, which must take into account a limited set of available foods. Second, the adaptation of the ingredient quantities has also been improved ...
High-Accuracy Methods for Numerical Flow Analysis Using Adaptive Non-Linear Wavelets
2012-08-01
of one dimensional grid system Data representation of two dimensional grid system is as shown in Fig. 1-3. Original flow data is the NACA0012 ...restriction technique, we checked the overall enhancement in computational efficiency of NACA0012 flow problems. Here, the AUSMPW+ scheme [21] is...adaptive wavelet method with a 2nd level resolution, respectively. Table 2-1 Test cases and results for the flow at NACA0012 airfoil NACA0012 Airfoil
Robust Adaptive Dynamic Programming of Two-Player Zero-Sum Games for Continuous-Time Linear Systems.
Fu, Yue; Fu, Jun; Chai, Tianyou
2015-12-01
In this brief, an online robust adaptive dynamic programming algorithm is proposed for two-player zero-sum games of continuous-time unknown linear systems with matched uncertainties, which are functions of system outputs and states of a completely unknown exosystem. The online algorithm is developed using the policy iteration (PI) scheme with only one iteration loop. A new analytical method is proposed for convergence proof of the PI scheme. The sufficient conditions are given to guarantee globally asymptotic stability and suboptimal property of the closed-loop system. Simulation studies are conducted to illustrate the effectiveness of the proposed method.
DEFF Research Database (Denmark)
Bergami, Leonardo; Poulsen, Niels Kjølstad
2015-01-01
The paper proposes a smart rotor configuration where adaptive trailing edge flaps (ATEFs) are employed for active alleviation of the aerodynamic loads on the blades of the NREL 5 MW reference turbine. The flaps extend for 20% of the blade length and are controlled by a linear quadratic (LQ....... The effects of active flap control are assessed with aeroelastic simulations of the turbine in normal operation conditions, as prescribed by the International Electrotechnical Commission standard. The turbine lifetime fatigue damage equivalent loads provide a convenient summary of the results achieved...
Jackson, Kate; Correia, Carlos; Lardière, Olivier; Andersen, Dave; Bradley, Colin
2015-01-15
We use a theoretical framework to analytically assess temporal prediction error functions on von-Kármán turbulence when a zonal representation of wavefronts is assumed. The linear prediction models analyzed include auto-regressive of an order up to three, bilinear interpolation functions, and a minimum mean square error predictor. This is an extension of the authors' previously published work Correia et al. [J. Opt. Soc. Am. A31, 101 (2014)JOAOD61084-752910.1364/JOSAA.31.000101], in which the efficacy of various temporal prediction models was established. Here we examine the tolerance of these algorithms to specific forms of model errors, thus defining the expected change in behavior of the previous results under less ideal conditions. Results show that ±100% wind speed error and ±50 deg are tolerable before the best linear predictor delivers poorer performance than the no-prediction case.
Adaptions of ArcGIS' Linear Referencing System to the Coastal Environment
DEFF Research Database (Denmark)
Balstrøm, Thomas
2008-01-01
For many years it has been problematic to store information for the coastal environment in a GIS. However, a system named "Linear referencing System" based upon a dynamic segmentation principle implemented in ESRIs ArcGIS 9 software has now made it possible to store and analyze information refere...... referenced to the coastline. This presentation demonstrates how to initialize this system, how to incorporate data into it and how to perform analysis queries hereon.......For many years it has been problematic to store information for the coastal environment in a GIS. However, a system named "Linear referencing System" based upon a dynamic segmentation principle implemented in ESRIs ArcGIS 9 software has now made it possible to store and analyze information...
Directory of Open Access Journals (Sweden)
Wanfang Shen
2012-01-01
Full Text Available The mathematical formulation for a quadratic optimal control problem governed by a linear quasiparabolic integrodifferential equation is studied. The control constrains are given in an integral sense: Uad={u∈X;∫ΩUu⩾0, t∈[0,T]}. Then the a posteriori error estimates in L∞(0,T;H1(Ω-norm and L2(0,T;L2(Ω-norm for both the state and the control approximation are given.
Perceptual adaptation of vowels generalizes across the phonology and does not require local context.
Chládková, Kateřina; Podlipský, Václav Jonáš; Chionidou, Anastasia
2017-02-01
Listeners usually understand without difficulty even speech that sounds atypical. When they encounter noncanonical realizations of speech sounds, listeners can make short-term adjustments of their long-term representations of those sounds. Previous research, focusing mostly on adaptation in consonants, has suggested that for perceptual adaptation to take place some local cues (lexical, phonotactic, or visual) have to guide listeners' interpretation of the atypical sounds. In the present experiment we investigated perceptual adaptation in vowels. Our first aim was to show whether perceptual adaptation generalizes to unexposed but phonologically related vowels. To this end, we exposed Greek listeners to words or nonwords containing manipulated /i/ or /e/, and tested whether they adapted their perception of the /i/-/e/ contrast, as well as the unexposed /u/-/o/ contrast, which represents the same phonological height distinction. Our second aim was to test whether perceptual adaptation in vowels requires local context. Thus, a half of our listeners heard the manipulated vowels in real Greek words, while the other half heard them in nonwords providing no phonotactic cues on vowel identity. The results showed similar adjustment of /i/-/e/ categorization and of /u/-/o/ categorization, which indicates generalization of perceptual adaptation across phonologically related vowels. Furthermore, adaptation occurred irrespective of whether local context cues were present or not, suggesting that, at least in vowels, adaptation can be based on the distribution of auditory properties in the input. Our findings, confirming that fast perceptual adaptation in adult listeners occurs even for vowels, highlight the role of phonological abstraction in speech perception. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Holdaway, Daniel; Kent, James
2015-01-01
The linearity of a selection of common advection schemes is tested and examined with a view to their use in the tangent linear and adjoint versions of an atmospheric general circulation model. The schemes are tested within a simple offline one-dimensional periodic domain as well as using a simplified and complete configuration of the linearised version of NASA's Goddard Earth Observing System version 5 (GEOS-5). All schemes which prevent the development of negative values and preserve the shape of the solution are confirmed to have nonlinear behaviour. The piecewise parabolic method (PPM) with certain flux limiters, including that used by default in GEOS-5, is found to support linear growth near the shocks. This property can cause the rapid development of unrealistically large perturbations within the tangent linear and adjoint models. It is shown that these schemes with flux limiters should not be used within the linearised version of a transport scheme. The results from tests using GEOS-5 show that the current default scheme (a version of PPM) is not suitable for the tangent linear and adjoint model, and that using a linear third-order scheme for the linearised model produces better behaviour. Using the third-order scheme for the linearised model improves the correlations between the linear and non-linear perturbation trajectories for cloud liquid water and cloud liquid ice in GEOS-5.
The Adapted Ordering Method for Lie algebras and superalgebras and their generalizations
Energy Technology Data Exchange (ETDEWEB)
Gato-Rivera, Beatriz [Instituto de Matematicas y Fisica Fundamental, CSIC, Serrano 123, Madrid 28006 (Spain); NIKHEF-H, Kruislaan 409, NL-1098 SJ Amsterdam (Netherlands)
2008-02-01
In 1998 the Adapted Ordering Method was developed for the representation theory of the superconformal algebras in two dimensions. It allows us to determine maximal dimensions for a given type of space of singular vectors, to identify all singular vectors by only a few coefficients, to spot subsingular vectors and to set the basis for constructing embedding diagrams. In this paper we present the Adapted Ordering Method for general Lie algebras and superalgebras and their generalizations, provided they can be triangulated. We also review briefly the results obtained for the Virasoro algebra and for the N = 2 and Ramond N = 1 superconformal algebras.
DEFF Research Database (Denmark)
Abrahamsen, Trine Julie; Hansen, Lars Kai
2011-01-01
is typically not bijective, pre-image estimation is inherently illposed. In many applications, including functional magnetic resonance imaging (fMRI) data which is the application used for illustration in the present work, it is of interest to denoise a sparse signal. To meet this objective we investigate......-linearity of the kernel embedding. The latter result provides evidence of signal manifold non-linearity in the specific fMRI case study....
Ma, Yaping; Xiao, Yegui; Wei, Guo; Sun, Jinwei
2016-01-01
In this paper, a multichannel nonlinear adaptive noise canceller (ANC) based on the generalized functional link artificial neural network (FLANN, GFLANN) is proposed for fetal electrocardiogram (FECG) extraction. A FIR filter and a GFLANN are equipped in parallel in each reference channel to respectively approximate the linearity and nonlinearity between the maternal ECG (MECG) and the composite abdominal ECG (AECG). A fast scheme is also introduced to reduce the computational cost of the FLANN and the GFLANN. Two (2) sets of ECG time sequences, one synthetic and one real, are utilized to demonstrate the improved effectiveness of the proposed nonlinear ANC. The real dataset is derived from the Physionet non-invasive FECG database (PNIFECGDB) including 55 multichannel recordings taken from a pregnant woman. It contains two subdatasets that consist of 14 and 8 recordings, respectively, with each recording being 90 s long. Simulation results based on these two datasets reveal, on the whole, that the proposed ANC does enjoy higher capability to deal with nonlinearity between MECG and AECG as compared with previous ANCs in terms of fetal QRS (FQRS)-related statistics and morphology of the extracted FECG waveforms. In particular, for the second real subdataset, the F1-measure results produced by the PCA-based template subtraction (TSpca) technique and six (6) single-reference channel ANCs using LMS- and RLS-based FIR filters, Volterra filter, FLANN, GFLANN, and adaptive echo state neural network (ESN a ) are 92.47%, 93.70%, 94.07%, 94.22%, 94.90%, 94.90%, and 95.46%, respectively. The same F1-measure statistical results from five (5) multi-reference channel ANCs (LMS- and RLS-based FIR filters, Volterra filter, FLANN, and GFLANN) for the second real subdataset turn out to be 94.08%, 94.29%, 94.68%, 94.91%, and 94.96%, respectively. These results indicate that the ESN a and GFLANN perform best, with the ESN a being slightly better than the GFLANN but about four times more
Directory of Open Access Journals (Sweden)
Dauda GuliburYAKUBU
2012-12-01
Full Text Available Accurate solutions to initial value systems of ordinary differential equations may be approximated efficiently by Runge-Kutta methods or linear multistep methods. Each of these has limitations of one sort or another. In this paper we consider, as a middle ground, the derivation of continuous general linear methods for solution of stiff systems of initial value problems in ordinary differential equations. These methods are designed to combine the advantages of both Runge-Kutta and linear multistep methods. Particularly, methods possessing the property of A-stability are identified as promising methods within this large class of general linear methods. We show that the continuous general linear methods are self-starting and have more ability to solve the stiff systems of ordinary differential equations, than the discrete ones. The initial value systems of ordinary differential equations are solved, for instance, without looking for any other method to start the integration process. This desirable feature of the proposed approach leads to obtaining very high accuracy of the solution of the given problem. Illustrative examples are given to demonstrate the novelty and reliability of the methods.
Power Allocation Optimization: Linear Precoding Adapted to NB-LDPC Coded MIMO Transmission
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Tarek Chehade
2015-01-01
Full Text Available In multiple-input multiple-output (MIMO transmission systems, the channel state information (CSI at the transmitter can be used to add linear precoding to the transmitted signals in order to improve the performance and the reliability of the transmission system. This paper investigates how to properly join precoded closed-loop MIMO systems and nonbinary low density parity check (NB-LDPC. The q elements in the Galois field, GF(q, are directly mapped to q transmit symbol vectors. This allows NB-LDPC codes to perfectly fit with a MIMO precoding scheme, unlike binary LDPC codes. The new transmission model is detailed and studied for several linear precoders and various designed LDPC codes. We show that NB-LDPC codes are particularly well suited to be jointly used with precoding schemes based on the maximization of the minimum Euclidean distance (max-dmin criterion. These results are theoretically supported by extrinsic information transfer (EXIT analysis and are confirmed by numerical simulations.
Lari, Zahra; Habib, Ayman
2014-07-01
Laser scanning systems have been established as leading tools for the collection of high density three-dimensional data over physical surfaces. The collected point cloud does not provide semantic information about the characteristics of the scanned surfaces. Therefore, different processing techniques have been developed for the extraction of useful information from this data which could be applied for diverse civil, industrial, and military applications. Planar and linear/cylindrical features are among the most important primitive information to be extracted from laser scanning data, especially those collected in urban areas. This paper introduces a new approach for the identification, parameterization, and segmentation of these features from laser scanning data while considering the internal characteristics of the utilized point cloud - i.e., local point density variation and noise level in the dataset. In the first step of this approach, a Principal Component Analysis of the local neighborhood of individual points is implemented to identify the points that belong to planar and linear/cylindrical features and select their appropriate representation model. For the detected planar features, the segmentation attributes are then computed through an adaptive cylinder neighborhood definition. Two clustering approaches are then introduced to segment and extract individual planar features in the reconstructed parameter domain. For the linear/cylindrical features, their directional and positional parameters are utilized as the segmentation attributes. A sequential clustering technique is proposed to isolate the points which belong to individual linear/cylindrical features through directional and positional attribute subspaces. Experimental results from simulated and real datasets demonstrate the feasibility of the proposed approach for the extraction of planar and linear/cylindrical features from laser scanning data.
Raymond L. Czaplewski
1973-01-01
A generalized, non-linear population dynamics model of an ecosystem is used to investigate the direction of selective pressures upon a mutant by studying the competition between parent and mutant populations. The model has the advantages of considering selection as operating on the phenotype, of retaining the interaction of the mutant population with the ecosystem as a...
Directory of Open Access Journals (Sweden)
Bahita Mohamed
2011-01-01
Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.
Yoo, Yun Joo; Sun, Lei; Poirier, Julia G; Paterson, Andrew D; Bull, Shelley B
2017-02-01
By jointly analyzing multiple variants within a gene, instead of one at a time, gene-based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster-specific effects in a quadratic sum of squares and cross-products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well-powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P-value, variance-component, and principal-component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene-specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome-wide analysis. The cluster construction of the MLC test statistics helps reveal within-gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations. © 2016 The Authors Genetic Epidemiology Published by Wiley Periodicals, Inc.
Eichardt, Roland; Baumgarten, Daniel; Petković, Bojana; Wiekhorst, Frank; Trahms, Lutz; Haueisen, Jens
2012-10-01
The problem of estimating magnetic nanoparticle distributions from magnetorelaxometric measurements is addressed here. The objective of this work was to identify source grid parameters that provide a good condition for the related linear inverse problem. The parameters investigated here were the number of sources, the extension of the source grid, and the source direction. A new measure of the condition, the ratio between the largest and mean singular value of the lead field matrix, is proposed. Our results indicated that the source grids should be larger than the sensor area. The sources and, consequently, the magnetic excitation field, should be directed toward the Z-direction. For underdetermined linear inverse problems, such as in our application, the number of sources affects the condition to a relatively small degree. Overdetermined magnetostatic linear inverse problems, however, benefit from a reduction in the number of sources, which considerably improves the condition. The adapted source grids proposed here were used to estimate the magnetostatic dipole from simulated data; the L2-norm, residual, and distances between the estimated and simulated sources were significantly reduced.
Energy Technology Data Exchange (ETDEWEB)
Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)
2009-12-28
This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.
A Generalization of Order Statistic Filters: The Alpha-Trimmed Linear Filter.
1987-01-01
T24 -T=2 FREE CW/7Zpi) Figure 14 cx-TL HPF3500 L15 Transfer Functions plus Ideal Frequency Response 42 is zero mean and Gaussian and is, therefore...Filtering Using Linear Combinations of Order Statistics." IEEE Transactions on Acoustic. Spech . and kgW na L nj ing, Vol. ASSP-31, No. 6, Dec 1983
Directory of Open Access Journals (Sweden)
Nengjun Yi
2011-12-01
Full Text Available Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/.
Yi, Nengjun; Liu, Nianjun; Zhi, Degui; Li, Jun
2011-12-01
Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants) for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).
Sejnowski, Terrence J.
2014-01-01
The brain processes sensory and motor information in a wide range of coordinate systems, ranging from retinal coordinates in vision to body-centered coordinates in areas that control musculature. Here we focus on the coordinate system used in the motor cortex to guide actions and examine physiological and psychophysical evidence for an allocentric reference frame based on spatial coordinates. When the equations of motion governing reaching dynamics are expressed as spatial vectors, each term is a vector cross product between a limb-segment position and a velocity or acceleration. We extend this computational framework to motor adaptation, in which the cross-product terms form adaptive bases for canceling imposed perturbations. Coefficients of the velocity- and acceleration-dependent cross products are assumed to undergo plastic changes to compensate the force-field or visuomotor perturbations. Consistent with experimental findings, each of the cross products had a distinct reference frame, which predicted how an acquired remapping generalized to untrained location in the workspace. In response to force field or visual rotation, mainly the coefficients of the velocity- or acceleration-dependent cross products adapted, leading to transfer in an intrinsic or extrinsic reference frame, respectively. The model further predicted that remapping of visuomotor rotation should under- or overgeneralize in a distal or proximal workspace. The cross-product bases can explain the distinct patterns of generalization in visuomotor and force-field adaptation in a unified way, showing that kinematic and dynamic motor adaptation need not arise through separate neural substrates. PMID:25429111
DEFF Research Database (Denmark)
Klinting, Emil Lund; Thomsen, Bo; Godtliebsen, Ian Heide
of non-linear parameters that are often contained in more specialized fit-basis functions. We have tested different fit-basis functions including Morse and double-well shapes, which are clearly superior to the standard polynomial type fit-basis functions to represent these kinds of potentials...... to employ different types of fit-basis functions to provide more adequate fits. This becomes even more pronounced in an iterative n-mode expansion scheme such as the adaptive density-guided approach (ADGA), where it is essential to capture the underlying physics quickly or risk introducing unnecessary...... coordinates, of which the polyspherical coordinates are a special class, can in this perspective provide even better conditions for the use of general fit-basis functions....
Large deformation image classification using generalized locality-constrained linear coding.
Zhang, Pei; Wee, Chong-Yaw; Niethammer, Marc; Shen, Dinggang; Yap, Pew-Thian
2013-01-01
Magnetic resonance (MR) imaging has been demonstrated to be very useful for clinical diagnosis of Alzheimer's disease (AD). A common approach to using MR images for AD detection is to spatially normalize the images by non-rigid image registration, and then perform statistical analysis on the resulting deformation fields. Due to the high nonlinearity of the deformation field, recent studies suggest to use initial momentum instead as it lies in a linear space and fully encodes the deformation field. In this paper we explore the use of initial momentum for image classification by focusing on the problem of AD detection. Experiments on the public ADNI dataset show that the initial momentum, together with a simple sparse coding technique-locality-constrained linear coding (LLC)--can achieve a classification accuracy that is comparable to or even better than the state of the art. We also show that the performance of LLC can be greatly improved by introducing proper weights to the codebook.
Generalized solutions to linearized equations of thermoelastic solid and viscous thermofluid
Directory of Open Access Journals (Sweden)
Anvarbek M. Meirmanov
2007-03-01
Full Text Available Within the framework of continuum mechanics, the full description of joint motion of elastic bodies and compressible viscous fluids with taking into account thermal effects is given by the system consisting of the mass, momentum, and energy balance equations, the first and the second laws of thermodynamics, and an additional set of thermomechanical state laws. The present paper is devoted to the investigation of this system. Assuming that variations of the physical characteristics of the thermomechanical system of the fluid and the solid are small about some rest state, we derive the linearized non-stationary dynamical model, prove its well-posedness, establish additional refined global integral bounds for solutions, and further deduce the linearized incompressible models and models incorporating absolutely rigid skeleton, as asymptotic limits.
Continuity and general perturbation of the Drazin inverse for closed linear operators
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N. Castro González
2002-01-01
Full Text Available We study perturbations and continuity of the Drazin inverse of a closed linear operator A and obtain explicit error estimates in terms of the gap between closed operators and the gap between ranges and nullspaces of operators. The results are used to derive a theorem on the continuity of the Drazin inverse for closed operators and to describe the asymptotic behavior of operator semigroups.
Non-linear partial differential equations an algebraic view of generalized solutions
Rosinger, Elemer E
1990-01-01
A massive transition of interest from solving linear partial differential equations to solving nonlinear ones has taken place during the last two or three decades. The availability of better computers has often made numerical experimentations progress faster than the theoretical understanding of nonlinear partial differential equations. The three most important nonlinear phenomena observed so far both experimentally and numerically, and studied theoretically in connection with such equations have been the solitons, shock waves and turbulence or chaotical processes. In many ways, these phenomen
Ko, Gaëlle K.; Neveu, Pierre; N'Tsoukpoe, K. Edem; Coulibaly, Yezouma
2017-06-01
An optimized and low cost linear Fresnel collector, adapted to the West Africa context, is being designed using local mankind and locally available materials. There is an already built collector. The concentrator has 7.5 m2 of mirrors segmented in equal width of 0.1 m. The receiver has a trapezoidal cavity with seven copper absorber tubes. The sun tracking system operates in open loop and the sun position is calculated using an algorithm. The components of the collector were selected purposely to reduce production cost while optimizing its performances. The cost of collector is €275 / m2 of mirrors and €255 / kWth for solar field and receiver respectively. The material and method for the collector characterization are outlined. An organic Rankine cycle will be coupled with the collector for electricity generation. Dynamic modelling, of an organic Rankine cycle, is being investigated using Gibbs equivalents systems method.
An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising
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Chuan He
2014-01-01
Full Text Available We propose an adaptive total generalized variation (TGV based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising. The variable splitting (VS and the classical augmented Lagrangian method (ALM are used to solve the proposed model. With the proposed adaptive model and ALM, the regularization parameter, which balances the data fidelity and the regularizer, is refreshed with a closed form in each iterate, and the image denoising can be accomplished without manual interference. Numerical results indicate that our method is effective in staircasing effect suppression and holds superiority over some other state-of-the-art methods both in quantitative and in qualitative assessment.
Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models
DEFF Research Database (Denmark)
Lanne, Markku; Nyberg, Henri
We propose a new generalized forecast error variance decomposition with the property that the proportions of the impact accounted for by innovations in each variable sum to unity. Our decomposition is based on the well-established concept of the generalized impulse response function. The use...
International Organization for Standardization. Geneva
1989-01-01
This part is intended to simplify drawing indications and specifies general tolerances in four tolerance classes. It applies to the dimensions of workpieces that are produced by metal removal or are formed from sheet metal. It contains three tables and an informative annex with regard to concepts behind general tolerancing of dimensions.
Motor learning and general adaptation syndrome Aprendizaje motor y síndrome general de adaptación
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E. M. Ordoño
2010-09-01
Full Text Available
This work examines the General Adaptation Syndrome like a suitable framework to explain motor learning processes. Human motor behaviour is viewed like a complex system continuously interacting in the environment. Motor learning is proposed as an adaptation process to the tasks constraints. Training loads and practice load are also considered analogous. Practice is the vehicle of learning, but it must be applied with the enough amount of load to produce an adaptation to a new level of performance. The principles of sport training are presented related to motor learning topics. Common principles are proposed to explain the learning of motor skills, regardless of the level of complexity, and level of the performer, and providing basic criteria that should help to design learning tasks.
Key Words: Motor learning, adaptation, complex systems, training, motor skills.
Este trabajo examina las posibilidades del Síndrome General de Adaptación como un marco de referencia para explicar y predecir los cambios producidos por el Aprendizaje Motor. Se parte de la consideración del ser humano como un sistema complejo en continua interacción con su entorno y el aprendizaje como un proceso de adaptación a las condiciones impuestas por la tarea. Se propone el concepto de carga de práctica análogo al de carga de entrenamiento, considerando que la práctica, vehículo del aprendizaje, debe aplicarse como una estimulación suficiente como para desencadenar en el aprendiz una adaptación a un nuevo nivel de rendimiento. En base a esta propuesta, se relacionan los principios del entrenamiento deportivo con el aprendizaje de habilidades motrices. Se formula una perspectiva teórica que trata de explicar de forma común los procesos de modificación de los patrones motores independientemente del nivel de complejidad, conllevando los mismos
Darken, Patrick F
2004-08-01
A review of graphical and test based methods for evaluating assumptions underlying the use of least squares analysis with the general linear model is presented along with some discussion of robustness. Alternative analyses are described for situations where there is evidence that the assumptions are not reasonable. Evaluation of the assumptions is illustrated through the use of an example from a clinical trial used for US registration purposes. It is recommended that: (1) most assumptions required for the least squares analysis of data using the general linear model can be judged using residuals graphically without the need for formal testing, (2) it is more important to normalize data or to use nonparametric methods when there is heterogeneous variance between treatment groups, and (3) nonparametric analyses can be used to demonstrate robustness of results and that it is best to specify these analyses prior to unblinding.
Energy Technology Data Exchange (ETDEWEB)
Rida, S.Z., E-mail: szagloul@yahoo.co [Department of Mathematics, Faculty of Science, South Valley University, Qena (Egypt)
2010-01-25
This Letter deals with the solution of unified fractional reaction-diffusion systems in an infinite domain. The results are obtained in compact and elegant forms in terms of generalized Mittag-Leffler functions, which are suitable for numerical computation.
de Souza, Juliana Bottoni; Reisen, Valdério Anselmo; Santos, Jane Méri; Franco, Glaura Conceição
2014-01-01
OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit. PMID:25119940
Souza, Juliana Bottoni de; Reisen, Valdério Anselmo; Santos, Jane Méri; Franco, Glaura Conceição
2014-06-01
OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive - while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model - principal component analysis, showed better results in estimating relative risk and quality of fit.
Directory of Open Access Journals (Sweden)
Juliana Bottoni de Souza
2014-06-01
Full Text Available OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit.
Goloviznin, V. M.; Karabasov, S. A.; Kozubskaya, T. K.; Maksimov, N. V.
2009-12-01
A generalization of the CABARET finite difference scheme is proposed for linearized one-dimensional Euler equations based on the characteristic decomposition into local Riemann invariants. The new method is compared with several central finite difference schemes that are widely used in computational aeroacoustics. Numerical results for the propagation of an acoustic wave in a homogeneous field and the refraction of this wave through a contact discontinuity obtained on a strongly nonuniform grid are presented.
Adaptive-Anisotropic Wavelet Collocation Method on general curvilinear coordinate systems
Brown-Dymkoski, Eric; Vasilyev, Oleg V.
2017-03-01
A new general framework for an Adaptive-Anisotropic Wavelet Collocation Method (A-AWCM) for the solution of partial differential equations is developed. This proposed framework addresses two major shortcomings of existing wavelet-based adaptive numerical methodologies, namely the reliance on a rectangular domain and the ;curse of anisotropy;, i.e. drastic over-resolution of sheet- and filament-like features arising from the inability of the wavelet refinement mechanism to distinguish highly correlated directional information in the solution. The A-AWCM addresses both of these challenges by incorporating coordinate transforms into the Adaptive Wavelet Collocation Method for the solution of PDEs. The resulting integrated framework leverages the advantages of both the curvilinear anisotropic meshes and wavelet-based adaptive refinement in a complimentary fashion, resulting in greatly reduced cost of resolution for anisotropic features. The proposed Adaptive-Anisotropic Wavelet Collocation Method retains the a priori error control of the solution and fully automated mesh refinement, while offering new abilities through the flexible mesh geometry, including body-fitting. The new A-AWCM is demonstrated for a variety of cases, including parabolic diffusion, acoustic scattering, and unsteady external flow.
Impact of co-channel interference on the performance of adaptive generalized transmit beamforming
Radaydeh, Redha Mahmoud Mesleh
2011-08-01
The impact of co-channel interference on the performance of adaptive generalized transmit beamforming for low-complexity multiple-input single-output (MISO) configuration is investigated. The transmit channels are assumed to be sufficiently separated and undergo Rayleigh fading conditions. Due to the limited space, a single receive antenna is employed to capture desired user transmission. The number of active transmit channels is adjusted adaptively based on statistically unordered and/or ordered instantaneous signal-to-noise ratios (SNRs), where the transmitter has no information about the statistics of undesired signals. The adaptation thresholds are identified to guarantee a target performance level, and the adaptation schemes with enhanced spectral efficiency or power efficiency are studied and their performance are compared under various channels conditions. To facilitate comparison studies, results for the statistics of instantaneous combined signal-to-interference-plus-noise ratio (SINR) are derived, which can be applied for different fading conditions of interfering signals. The statistics for combined SNR and combined SINR are then used to quantify various performance measures, considering the impact of non-ideal estimation of the desired user channel state information (CSI) and the randomness in the number of active interferers. Numerical and simulation comparisons for the achieved performance of the adaptation schemes are presented. © 2006 IEEE.
The effects of general mental ability and memory on adaptive transfer in work settings
Directory of Open Access Journals (Sweden)
Barbara Frank
2017-10-01
Full Text Available To handle complex technical operations, operators acquire skills in vocational training. Most of these skills are not used immediately but at some point later; this is called temporal transfer. Our previous research showed that cognitive abilities such as general mental ability (GMA and memory are good predictors of temporal transfer. In addition to temporal transfer, operators also have to solve non-routine and abnormal upcoming problems using their skill set; this type of transfer is called adaptive transfer. Based on previous findings, it is assumed that GMA and memory will affect adaptive transfer as well. Thirty-three engineering students learned how to operate a complex technical system in normal operation with either a fixed or a contingent sequence. After two weeks, all participants had to adapt their learned skills to handle the adaptive transfer task, which was not initially trained. It was shown that high GMA positively predicted adaptive transfer, but no effect of memory was found. This implies that GMA is required to solve new complex tasks using a learned skill set. The findings are in line with studies that showed an effect of GMA on temporal transfer.
Directory of Open Access Journals (Sweden)
Shibing Wang
2016-02-01
Full Text Available This paper introduces a new memristor-based hyperchaotic complex Lü system (MHCLS and investigates its adaptive complex generalized synchronization (ACGS. Firstly, the complex system is constructed based on a memristor-based hyperchaotic real Lü system, and its properties are analyzed theoretically. Secondly, its dynamical behaviors, including hyperchaos, chaos, transient phenomena, as well as periodic behaviors, are explored numerically by means of bifurcation diagrams, Lyapunov exponents, phase portraits, and time history diagrams. Thirdly, an adaptive controller and a parameter estimator are proposed to realize complex generalized synchronization and parameter identification of two identical MHCLSs with unknown parameters based on Lyapunov stability theory. Finally, the numerical simulation results of ACGS and its applications to secure communication are presented to verify the feasibility and effectiveness of the proposed method.
Nemeth, Michael P.; Schultz, Marc R.
2012-01-01
A detailed exact solution is presented for laminated-composite circular cylinders with general wall construction and that undergo axisymmetric deformations. The overall solution is formulated in a general, systematic way and is based on the solution of a single fourth-order, nonhomogeneous ordinary differential equation with constant coefficients in which the radial displacement is the dependent variable. Moreover, the effects of general anisotropy are included and positive-definiteness of the strain energy is used to define uniquely the form of the basis functions spanning the solution space of the ordinary differential equation. Loading conditions are considered that include axisymmetric edge loads, surface tractions, and temperature fields. Likewise, all possible axisymmetric boundary conditions are considered. Results are presented for five examples that demonstrate a wide range of behavior for specially orthotropic and fully anisotropic cylinders.
Fitzmaurice, G M; Laird, N M; Shneyer, L
2001-04-15
This paper considers the mixture model methodology for handling non-ignorable drop-outs in longitudinal studies with continuous outcomes. Recently, Hogan and Laird have developed a mixture model for non-ignorable drop-outs which is a standard linear mixed effects model except that the parameters which characterize change over time depend also upon time of drop-out. That is, the mean response is linear in time, other covariates and drop-out time, and their interactions. One of the key attractions of the mixture modelling approach to drop-outs is that it is relatively easy to explore the sensitivity of results to model specification. However, the main drawback of mixture models is that the parameters that are ordinarily of interest are not immediately available, but require marginalization of the distribution of outcome over drop-out times. Furthermore, although a linear model is assumed for the conditional mean of the outcome vector given time of drop out, after marginalization, the unconditional mean of the outcome vector is not, in general, linear in the regression parameters. As a result, it is not possible to parsimoniously describe the effects of covariates on the marginal distribution of the outcome in terms of regression coefficients. The need to explicitly average over the distribution of the drop-out times and the absence of regression coefficients that describe the effects of covariates on the outcome are two unappealing features of the mixture modelling approach. In this paper we describe a particular parameterization of the general linear mixture model that circumvents both of these problems. Copyright 2001 John Wiley & Sons, Ltd.
Iterative solution of general sparse linear systems on clusters of workstations
Energy Technology Data Exchange (ETDEWEB)
Lo, Gen-Ching; Saad, Y. [Univ. of Minnesota, Minneapolis, MN (United States)
1996-12-31
Solving sparse irregularly structured linear systems on parallel platforms poses several challenges. First, sparsity makes it difficult to exploit data locality, whether in a distributed or shared memory environment. A second, perhaps more serious challenge, is to find efficient ways to precondition the system. Preconditioning techniques which have a large degree of parallelism, such as multicolor SSOR, often have a slower rate of convergence than their sequential counterparts. Finally, a number of other computational kernels such as inner products could ruin any gains gained from parallel speed-ups, and this is especially true on workstation clusters where start-up times may be high. In this paper we discuss these issues and report on our experience with PSPARSLIB, an on-going project for building a library of parallel iterative sparse matrix solvers.
M. Nool (Margreet); A. van der Ploeg (Auke)
1997-01-01
textabstractWe study the solution of generalized eigenproblems generated by a model which is used for stability investigation of tokamak plasmas. The eigenvalue problems are of the form $A x = lambda B x$, in which the complex matrices $A$ and $B$ are block tridiagonal, and $B$ is Hermitian positive
Fracchia, F.; Filippi, Claudia; Amovilli, C.
2012-01-01
We propose a new class of multideterminantal Jastrow–Slater wave functions constructed with localized orbitals and designed to describe complex potential energy surfaces of molecular systems for use in quantum Monte Carlo (QMC). Inspired by the generalized valence bond formalism, we elaborate a
Akbarzadeh, Alireza; Danner, Aaron J
2010-12-01
The Hamiltonian of an optical medium is important in both the design and the description of optical devices in the geometrical optics limit. The results calculated in this article show in detail how ray tracing in anisotropic materials in arbitrary coordinate systems and curved spaces can be carried out. Writing Maxwell's equations in the most general form, we derive a coordinate-free form for the eikonal equation and hence the Hamiltonian of a general purpose medium. The expression works for both orthogonal and non-orthogonal coordinate systems, and we show how it can be simplified for biaxial and uniaxial media in orthogonal coordinate systems. In order to show the utility of the equations in a real case, we study both the isotropic and the uniaxially transmuted birefringent Eaton lens and derive the ray trajectories in spherical coordinates for each case.
A Generalized q-Mittag-Leffler Function by q-Captuo Fractional Linear Equations
Directory of Open Access Journals (Sweden)
Thabet Abdeljawad
2012-01-01
Full Text Available Some Caputo q-fractional difference equations are solved. The solutions are expressed by means of a new introduced generalized type of q-Mittag-Leffler functions. The method of successive approximation is used to obtain the solutions. The obtained q-version of Mittag-Leffler function is thought as the q-analogue of the one introduced previously by Kilbas and Saigo (1995.
A Generalized q-Mittag-Leffler Function by q-Captuo Fractional Linear Equations
Thabet Abdeljawad; Betül Benli; Dumitru Baleanu
2012-01-01
Some Caputo q-fractional difference equations are solved. The solutions are expressed by means of a new introduced generalized type of q-Mittag-Leffler functions. The method of successive approximation is used to obtain the solutions. The obtained q-version of Mittag-Leffler function is thought as the q-analogue of the one introduced previously by Kilbas and Saigo (1995).
A generalized linear mixed model for longitudinal binary data with a marginal logit link function
Parzen, Michael; Ghosh, Souparno; Lipsitz, Stuart; Sinha, Debajyoti; Fitzmaurice, Garrett M.; Mallick, Bani K.; Ibrahim, Joseph G.
2011-01-01
Longitudinal studies of a binary outcome are common in the health, social, and behavioral sciences. In general, a feature of random effects logistic regression models for longitudinal binary data is that the marginal functional form, when integrated over the distribution of the random effects, is no longer of logistic form. Recently, Wang and Louis [Biometrika 90 (2003) 765--775] proposed a random intercept model in the clustered binary data setting where the marginal model has a logistic for...
Sokolov, I M
2006-06-01
The work by Barbi, Bologna, and Grigolini [Phys. Rev. Lett. 95, 220601 (2005)] discusses a response to alternating external field of a non-Markovian two-state system, where the waiting time between the two attempted changes of state follows a power law. It introduced a new instrument for description of such situations based on a stochastic master equation with reset. In the present Brief Report we provide an alternative description of the situation within the framework of a generalized master equation. The results of our analytical approach are corroborated by direct numerical simulations of the system.
Time evolution of linear and generalized Heisenberg algebra nonlinear Pöschl-Teller coherent states
Rego-Monteiro, M. A.; Curado, E. M. F.; Rodrigues, Ligia M. C. S.
2017-11-01
We analyze the time evolution of two kinds of coherent states for a particle in a Pöschl-Teller potential. We find a pair of canonically conjugate operators and compare the behavior of their time evolution for both coherent states. The nonlinear ones are more localized. The trajectory in the phase space of the mean values of these two operators is a kind of generalization of the Rose algebraic curves. The new pair of canonically conjugate variables leads to a fourth-order Schrödinger equation which has the same energy spectrum as the Pöschl-Teller system.
Prediction on the distribution of dividends in Argentina using a mixed generalized linear model
González, Mariana Verónica; Moneta Pizarro, Adrián Maximiliano
2017-01-01
Durante las últimas décadas, la política de dividendos seguida por las empresas ha sido un tema de interés en diversas investigaciones, con apreciaciones distintas sobre los resultados conseguidos. En general, se admite que la decisión de distribuir dividendos por parte de una empresa es el resultado de un conjunto de factores relacionados, desde limitaciones de carácter jurídico hasta cuestiones vinculadas a la estructura financiera de la entidad y su situación de liquidez, pasando por la ca...
Aldao, Amelia; Mennin, Douglas S
2012-02-01
Recent models of generalized anxiety disorder (GAD) have expanded on Borkovec's avoidance theory by delineating emotion regulation deficits associated with the excessive worry characteristic of this disorder (see Behar, DiMarco, Hekler, Mohlman, & Staples, 2009). However, it has been difficult to determine whether emotion regulation is simply a useful heuristic for the avoidant properties of worry or an important extension to conceptualizations of GAD. Some of this difficulty may arise from a focus on purported maladaptive regulation strategies, which may be confounded with symptomatic distress components of the disorder (such as worry). We examined the implementation of adaptive regulation strategies by participants with and without a diagnosis of GAD while watching emotion-eliciting film clips. In a between-subjects design, participants were randomly assigned to accept, reappraise, or were not given specific regulation instructions. Implementation of adaptive regulation strategies produced differential effects in the physiological (but not subjective) domain across diagnostic groups. Whereas participants with GAD demonstrated lower cardiac flexibility when implementing adaptive regulation strategies than when not given specific instructions on how to regulate, healthy controls showed the opposite pattern, suggesting they benefited from the use of adaptive regulation strategies. We discuss the implications of these findings for the delineation of emotion regulation deficits in psychopathology. Copyright © 2011 Elsevier Ltd. All rights reserved.
National Research Council Canada - National Science Library
William Alvarez Gaviria
2004-01-01
...; for him, climacteric is the final response to fatigue or the third stage of the general adaptation syndrome, just as in elderly people there is a loss of the capacity of proliferation of fibroblasts...
Generalized Monge-Kantorovich optimization for grid generation and adaptation in LP
Energy Technology Data Exchange (ETDEWEB)
Delzanno, G L [Los Alamos National Laboratory; Finn, J M [Los Alamos National Laboratory
2009-01-01
The Monge-Kantorovich grid generation and adaptation scheme of is generalized from a variational principle based on L{sub 2} to a variational principle based on L{sub p}. A generalized Monge-Ampere (MA) equation is derived and its properties are discussed. Results for p > 1 are obtained and compared in terms of the quality of the resulting grid. We conclude that for the grid generation application, the formulation based on L{sub p} for p close to unity leads to serious problems associated with the boundary. Results for 1.5 {approx}< p {approx}< 2.5 are quite good, but there is a fairly narrow range around p = 2 where the results are close to optimal with respect to grid distortion. Furthermore, the Newton-Krylov methods used to solve the generalized MA equation perform best for p = 2.
Directory of Open Access Journals (Sweden)
Dongyul Lee
2014-01-01
Full Text Available The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC with adaptive modulation and coding (AMC provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm.
Lee, Dongyul; Lee, Chaewoo
2014-01-01
The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC) with adaptive modulation and coding (AMC) provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs) to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP) and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm.
Buttenfield, B.P.; Stanislawski, L.V.; Brewer, C.A.
2011-01-01
This paper reports on generalization and data modeling to create reduced scale versions of the National Hydrographic Dataset (NHD) for dissemination through The National Map, the primary data delivery portal for USGS. Our approach distinguishes local differences in physiographic factors, to demonstrate that knowledge about varying terrain (mountainous, hilly or flat) and varying climate (dry or humid) can support decisions about algorithms, parameters, and processing sequences to create generalized, smaller scale data versions which preserve distinct hydrographic patterns in these regions. We work with multiple subbasins of the NHD that provide a range of terrain and climate characteristics. Specifically tailored generalization sequences are used to create simplified versions of the high resolution data, which was compiled for 1:24,000 scale mapping. Results are evaluated cartographically and metrically against a medium resolution benchmark version compiled for 1:100,000, developing coefficients of linear and areal correspondence.
1976-10-13
TR 5501 14. R. E. Hartwig, "Resultants and the Solution of AX-XB : -C," SIAM J. Appl. Math., vol. 23, no. 1, July 1972, pp. 104-117. 15. V. Kucera ...34The Matrix Equation AX+XB = C," SIAM J. Appl. Math., vol. 26, no. 1, Jan . 1974, pD. 15-25. 16. R. E. Hartwig, "AX-XB = C, Resultants and Generalized...Inverses," SIAM J. Appl. Math., vol. 28, no. 1, Jan . 1975, pp. 154-183. 17. S. Barnett, "Simplification of Certain Linear Matrix Equations," IEEE Trans
General and craniofacial development are complex adaptive processes influenced by diversity.
Brook, A H; O'Donnell, M Brook; Hone, A; Hart, E; Hughes, T E; Smith, R N; Townsend, G C
2014-06-01
Complex systems are present in such diverse areas as social systems, economies, ecosystems and biology and, therefore, are highly relevant to dental research, education and practice. A Complex Adaptive System in biological development is a dynamic process in which, from interacting components at a lower level, higher level phenomena and structures emerge. Diversity makes substantial contributions to the performance of complex adaptive systems. It enhances the robustness of the process, allowing multiple responses to external stimuli as well as internal changes. From diversity comes variation in outcome and the possibility of major change; outliers in the distribution enhance the tipping points. The development of the dentition is a valuable, accessible model with extensive and reliable databases for investigating the role of complex adaptive systems in craniofacial and general development. The general characteristics of such systems are seen during tooth development: self-organization; bottom-up emergence; multitasking; self-adaptation; variation; tipping points; critical phases; and robustness. Dental findings are compatible with the Random Network Model, the Threshold Model and also with the Scale Free Network Model which has a Power Law distribution. In addition, dental development shows the characteristics of Modularity and Clustering to form Hierarchical Networks. The interactions between the genes (nodes) demonstrate Small World phenomena, Subgraph Motifs and Gene Regulatory Networks. Genetic mechanisms are involved in the creation and evolution of variation during development. The genetic factors interact with epigenetic and environmental factors at the molecular level and form complex networks within the cells. From these interactions emerge the higher level tissues, tooth germs and mineralized teeth. Approaching development in this way allows investigation of why there can be variations in phenotypes from identical genotypes; the phenotype is the outcome
A generalized linear mixed model for longitudinal binary data with a marginal logit link function.
Parzen, Michael; Ghosh, Souparno; Lipsitz, Stuart; Sinha, Debajyoti; Fitzmaurice, Garrett M; Mallick, Bani K; Ibrahim, Joseph G
2011-01-01
Longitudinal studies of a binary outcome are common in the health, social, and behavioral sciences. In general, a feature of random effects logistic regression models for longitudinal binary data is that the marginal functional form, when integrated over the distribution of the random effects, is no longer of logistic form. Recently, Wang and Louis (2003) proposed a random intercept model in the clustered binary data setting where the marginal model has a logistic form. An acknowledged limitation of their model is that it allows only a single random effect that varies from cluster to cluster. In this paper, we propose a modification of their model to handle longitudinal data, allowing separate, but correlated, random intercepts at each measurement occasion. The proposed model allows for a flexible correlation structure among the random intercepts, where the correlations can be interpreted in terms of Kendall's τ. For example, the marginal correlations among the repeated binary outcomes can decline with increasing time separation, while the model retains the property of having matching conditional and marginal logit link functions. Finally, the proposed method is used to analyze data from a longitudinal study designed to monitor cardiac abnormalities in children born to HIV-infected women.
A generalized linear mixed model for longitudinal binary data with a marginal logit link function
Parzen, Michael; Ghosh, Souparno; Lipsitz, Stuart; Sinha, Debajyoti; Fitzmaurice, Garrett M.; Mallick, Bani K.; Ibrahim, Joseph G.
2010-01-01
Summary Longitudinal studies of a binary outcome are common in the health, social, and behavioral sciences. In general, a feature of random effects logistic regression models for longitudinal binary data is that the marginal functional form, when integrated over the distribution of the random effects, is no longer of logistic form. Recently, Wang and Louis (2003) proposed a random intercept model in the clustered binary data setting where the marginal model has a logistic form. An acknowledged limitation of their model is that it allows only a single random effect that varies from cluster to cluster. In this paper, we propose a modification of their model to handle longitudinal data, allowing separate, but correlated, random intercepts at each measurement occasion. The proposed model allows for a flexible correlation structure among the random intercepts, where the correlations can be interpreted in terms of Kendall’s τ. For example, the marginal correlations among the repeated binary outcomes can decline with increasing time separation, while the model retains the property of having matching conditional and marginal logit link functions. Finally, the proposed method is used to analyze data from a longitudinal study designed to monitor cardiac abnormalities in children born to HIV-infected women. PMID:21532998
A Self-adaptive Bit-level Color Image Encryption Algorithm Based on Generalized Arnold Map
Directory of Open Access Journals (Sweden)
Ye Rui-Song
2017-01-01
Full Text Available A self-adaptive bit-level color image encryption algorithm based on generalized Arnold map is proposed. The red, green, blue components of the plain-image with height H and width W are decomposed into 8-bit planes and one three-dimensional bit matrix with size ze H×W×24 is obtained. The generalized Arnold map is used to generate pseudo-random sequences to scramble the resulted three-dimensional bit matrix by sort-based approach. The scrambled 3D bit matrix is then rearranged to be one scrambled color image. Chaotic sequences produced by another generalized Arnold map are used to diffuse the resulted red, green, blue components in a cross way to get more encryption effects. Self-adaptive strategy is adopted in both the scrambling stage and diffusion stage, meaning that the key streams are all related to the content of the plain-image and therefore the encryption algorithm show strong robustness against known/chosen plaintext attacks. Some other performances are carried out, including key space, key sensitivity, histogram, correlation coefficients between adjacent pixels, information entropy and difference attack analysis, etc. All the experimental results show that the proposed image encryption algorithm is secure and effective for practical application.
Generalized Uncertainty Quantification for Linear Inverse Problems in X-ray Imaging
Energy Technology Data Exchange (ETDEWEB)
Fowler, Michael James [Clarkson Univ., Potsdam, NY (United States)
2014-04-25
In industrial and engineering applications, X-ray radiography has attained wide use as a data collection protocol for the assessment of material properties in cases where direct observation is not possible. The direct measurement of nuclear materials, particularly when they are under explosive or implosive loading, is not feasible, and radiography can serve as a useful tool for obtaining indirect measurements. In such experiments, high energy X-rays are pulsed through a scene containing material of interest, and a detector records a radiograph by measuring the radiation that is not attenuated in the scene. One approach to the analysis of these radiographs is to model the imaging system as an operator that acts upon the object being imaged to produce a radiograph. In this model, the goal is to solve an inverse problem to reconstruct the values of interest in the object, which are typically material properties such as density or areal density. The primary objective in this work is to provide quantitative solutions with uncertainty estimates for three separate applications in X-ray radiography: deconvolution, Abel inversion, and radiation spot shape reconstruction. For each problem, we introduce a new hierarchical Bayesian model for determining a posterior distribution on the unknowns and develop efficient Markov chain Monte Carlo (MCMC) methods for sampling from the posterior. A Poisson likelihood, based on a noise model for photon counts at the detector, is combined with a prior tailored to each application: an edge-localizing prior for deconvolution; a smoothing prior with non-negativity constraints for spot reconstruction; and a full covariance sampling prior based on a Wishart hyperprior for Abel inversion. After developing our methods in a general setting, we demonstrate each model on both synthetically generated datasets, including those from a well known radiation transport code, and real high energy radiographs taken at two U. S. Department of Energy
Jiang, Fei; Ma, Yanyuan; Wang, Yuanjia
We propose a generalized partially linear functional single index risk score model for repeatedly measured outcomes where the index itself is a function of time. We fuse the nonparametric kernel method and regression spline method, and modify the generalized estimating equation to facilitate estimation and inference. We use local smoothing kernel to estimate the unspecified coefficient functions of time, and use B-splines to estimate the unspecified function of the single index component. The covariance structure is taken into account via a working model, which provides valid estimation and inference procedure whether or not it captures the true covariance. The estimation method is applicable to both continuous and discrete outcomes. We derive large sample properties of the estimation procedure and show different convergence rate of each component of the model. The asymptotic properties when the kernel and regression spline methods are combined in a nested fashion has not been studied prior to this work even in the independent data case.
Szadkowski, Zbigniew; Fraenkel, E. D.; van den Berg, Ad M.
2013-01-01
We present the FPGA/NIOS implementation of an adaptive finite impulse response (FIR) filter based on linear prediction to suppress radio frequency interference (RFI). This technique will be used for experiments that observe coherent radio emission from extensive air showers induced by
Moeller, Friederike; LeVan, Pierre; Gotman, Jean
2011-02-01
Most EEG-fMRI studies in epileptic patients are analyzed using the general linear model (GLM), which assumes a known hemodynamic response function (HRF) to epileptic spikes. In contrast, independent component analysis (ICA) can extract blood-oxygenation level dependent (BOLD) responses without imposing constraints on the HRF. ICA might therefore detect responses that vary in time and shape, and that are not detected in the GLM analysis. In this study, we compared the findings of ICA and GLM analyses in 12 patients with idiopathic generalized epilepsy. Spatial ICA was used to extract independent components from the functional magnetic resonance imaging (fMRI) data. A deconvolution method identified component time courses significantly related to the generalized EEG discharges, without constraining the shape of the HRF. The results from the ICA analysis were compared to those from the GLM analysis. GLM maps and ICA maps showed significant correlation and revealed BOLD responses in the thalamus, caudate nucleus, and default mode areas. In patients with a low rate of discharges per minute, the GLM analysis detected BOLD signal changes within the thalamus and the caudate nucleus that were not revealed by the ICA. In conclusion, ICA is a viable alternative technique to GLM analyses in EEG-fMRI studies related to generalized discharges. This study demonstrated that the BOLD response largely resembles the standard HRF and that GLM analysis is adequate. However, ICA is more dependent on a sufficient number of events than GLM analysis. Copyright © 2010 Wiley-Liss, Inc.
Zhang, Chenglong; Guo, Ping
2017-10-01
The vague and fuzzy parametric information is a challenging issue in irrigation water management problems. In response to this problem, a generalized fuzzy credibility-constrained linear fractional programming (GFCCFP) model is developed for optimal irrigation water allocation under uncertainty. The model can be derived from integrating generalized fuzzy credibility-constrained programming (GFCCP) into a linear fractional programming (LFP) optimization framework. Therefore, it can solve ratio optimization problems associated with fuzzy parameters, and examine the variation of results under different credibility levels and weight coefficients of possibility and necessary. It has advantages in: (1) balancing the economic and resources objectives directly; (2) analyzing system efficiency; (3) generating more flexible decision solutions by giving different credibility levels and weight coefficients of possibility and (4) supporting in-depth analysis of the interrelationships among system efficiency, credibility level and weight coefficient. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. Therefore, optimal irrigation water allocation solutions from the GFCCFP model can be obtained. Moreover, factorial analysis on the two parameters (i.e. λ and γ) indicates that the weight coefficient is a main factor compared with credibility level for system efficiency. These results can be effective for support reasonable irrigation water resources management and agricultural production.
Xiao, Lin; Liao, Bolin; Li, Shuai; Chen, Ke
2018-02-01
In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this paper proposes two nonlinear recurrent neural networks based on two nonlinear activation functions. According to Lyapunov theory, such two nonlinear recurrent neural networks are proved to be convergent within finite-time. Besides, by solving differential equation, the upper bounds of the finite convergence time are determined analytically. Compared with existing recurrent neural networks, the proposed two nonlinear recurrent neural networks have a better convergence property (i.e., the upper bound is lower), and thus the accurate solutions of general time-varying LMEs can be obtained with less time. At last, various different situations have been considered by setting different coefficient matrices of general time-varying LMEs and a great variety of computer simulations (including the application to robot manipulators) have been conducted to validate the better finite-time convergence of the proposed two nonlinear recurrent neural networks. Copyright © 2017 Elsevier Ltd. All rights reserved.
Pekkanen, Jami; Lappi, Otto
2017-12-18
We introduce a conceptually novel method for eye-movement signal analysis. The method is general in that it does not place severe restrictions on sampling frequency, measurement noise or subject behavior. Event identification is based on segmentation that simultaneously denoises the signal and determines event boundaries. The full gaze position time-series is segmented into an approximately optimal piecewise linear function in O(n) time. Gaze feature parameters for classification into fixations, saccades, smooth pursuits and post-saccadic oscillations are derived from human labeling in a data-driven manner. The range of oculomotor events identified and the powerful denoising performance make the method useable for both low-noise controlled laboratory settings and high-noise complex field experiments. This is desirable for harmonizing the gaze behavior (in the wild) and oculomotor event identification (in the laboratory) approaches to eye movement behavior. Denoising and classification performance are assessed using multiple datasets. Full open source implementation is included.
Leng, Chenlei; Liang, Hua; Martinson, Neil
2011-01-01
To study significant predictors of condom use in HIV-infected adults, we propose the use of generalized partially linear models and develop a variable selection procedure incorporating a least squares approximation. Local polynomial regression and spline smoothing techniques are used to estimate the baseline nonparametric function. The asymptotic normality of the resulting estimate is established. We further demonstrate that, with the proper choice of the penalty functions and the regularization parameter, the resulting estimate performs as well as an oracle procedure. Finite sample performance of the proposed inference procedure is assessed by Monte Carlo simulation studies. An application to assess condom use by HIV-infected patients gains some interesting results, which can not be obtained when an ordinary logistic model is used. PMID:21465515
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Fang-Rong Yan
Full Text Available This article provides a fully bayesian approach for modeling of single-dose and complete pharmacokinetic data in a population pharmacokinetic (PK model. To overcome the impact of outliers and the difficulty of computation, a generalized linear model is chosen with the hypothesis that the errors follow a multivariate Student t distribution which is a heavy-tailed distribution. The aim of this study is to investigate and implement the performance of the multivariate t distribution to analyze population pharmacokinetic data. Bayesian predictive inferences and the Metropolis-Hastings algorithm schemes are used to process the intractable posterior integration. The precision and accuracy of the proposed model are illustrated by the simulating data and a real example of theophylline data.
Garcia-Jacas, Cesar R; Marrero-Ponce, Yovani; Barigye, Stephen J; Valdes-Martini, Jose R; Rivera-Borroto, Oscar M; Olivero-Verbel, Jesus
2014-01-01
The present manuscript introduces, for the first time, a novel 3D-QSAR alignment free method (QuBiLS-MIDAS) based on tensor concepts through the use of the three-linear and four-linear algebraic forms as specific cases of n-linear maps. To this end, the k(th) three-tuple and four-tuple spatial-(dis)similarity matrices are defined, as tensors of order 3 and 4, respectively, to represent 3Dinformation among "three and four" atoms of the molecular structures. Several measures (multi-metrics) to establish (dis)-similarity relations among "three and four" atoms are discussed, as well as, normalization schemes proposed for the n-tuple spatial-(dis)similarity matrices based on the simple-stochastic and mutual probability algebraic transformations. To consider specific interactions among atoms, both for the global and local indices, n-tuple path and length cut-off constraints are introduced. This algebraic scaffold can also be seen as a generalization of the vector-matrix-vector multiplication procedure (which is a matrix representation of the traditional linear, quadratic and bilinear forms) for the calculation of molecular descriptors and is thus a new theoretical approach with a methodological contribution. A variability analysis based on Shannon's entropy reveals that the best distributions are achieved with the ternary and quaternary measures corresponding to the bond and dihedral angles. In addition, the proposed indices have superior entropy behavior than the descriptors calculated by other programs used in chemo-informatics studies, such as, DRAGON, PADEL, Mold2, and so on. A principal component analysis shows that the novel 3D n-tuple indices codify the same information captured by the DRAGON 3D-indices, as well as, information not codified by the latter. A QSAR study to obtain deeper criteria on the contribution of the novel molecular parameters was performed for the binding affinity to the corticosteroid-binding globulin, using Cramer's steroid database. The
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Biman Jana
2016-08-01
Full Text Available A structure-based model of myosin motor is built in the same spirit of our early work for kinesin-1 and Ncd towards physical understanding of its mechanochemical cycle. We find a structural adaptation of the motor head domain in post-powerstroke state that signals faster ADP release from it compared to the same from the motor head in the pre-powerstroke state. For dimeric myosin, an additional forward strain on the trailing head, originating from the postponed powerstroke state of the leading head in the waiting state of myosin, further increases the rate of ADP release. This coordination between the two heads is the essence of the processivity of the cycle. Our model provides a structural description of the powerstroke step of the cycle as an allosteric transition of the converter domain in response to the Pi release. Additionally, the variation in structural elements peripheral to catalytic motor domain is the deciding factor behind diverse directionalities of myosin motors (myosin V & VI. Finally, we observe that there are general rules for functional molecular motors across the different families. Allosteric structural adaptation of the catalytic motor head in different nucleotide states is crucial for mechanochemistry. Strain-mediated coordination between motor heads is essential for processivity and the variation of peripheral structural elements is essential for their diverse functionalities.
Cross-Cultural adaptation of the General Functioning Scale of the Family
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Thiago Pires
2016-01-01
Full Text Available ABSTRACT OBJECTIVE To describe the process of cross-cultural adaptation of the General Functioning Scale of the Family, a subscale of the McMaster Family Assessment Device, for the Brazilian population. METHODS The General Functioning Scale of the Family was translated into Portuguese and administered to 500 guardians of children in the second grade of elementary school in public schools of Sao Gonçalo, Rio de Janeiro, Southeastern Brazil. The types of equivalences investigated were: conceptual and of items, semantic, operational, and measurement. The study involved discussions with experts, translations and back-translations of the instrument, and psychometric assessment. Reliability and validity studies were carried out by internal consistency testing (Cronbach’s alpha, Guttman split-half correlation model, Pearson correlation coefficient, and confirmatory factor analysis. Associations between General Functioning of the Family and variables theoretically associated with the theme (father’s or mother’s drunkenness and violence between parents were estimated by odds ratio. RESULTS Semantic equivalence was between 90.0% and 100%. Cronbach’s alpha ranged from 0.79 to 0.81, indicating good internal consistency of the instrument. Pearson correlation coefficient ranged between 0.303 and 0.549. Statistical association was found between the general functioning of the family score and the theoretically related variables, as well as good fit quality of the confirmatory analysis model. CONCLUSIONS The results indicate the feasibility of administering the instrument to the Brazilian population, as it is easy to understand and a good measurement of the construct of interest.
Hughes, Vanessa K; Langlois, Neil E I
2010-12-01
Bruises can have medicolegal significance such that the age of a bruise may be an important issue. This study sought to determine if colorimetry or reflectance spectrophotometry could be employed to objectively estimate the age of bruises. Based on a previously described method, reflectance spectrophotometric scans were obtained from bruises using a Cary 100 Bio spectrophotometer fitted with a fibre-optic reflectance probe. Measurements were taken from the bruise and a control area. Software was used to calculate the first derivative at 490 and 480 nm; the proportion of oxygenated hemoglobin was calculated using an isobestic point method and a software application converted the scan data into colorimetry data. In addition, data on factors that might be associated with the determination of the age of a bruise: subject age, subject sex, degree of trauma, bruise size, skin color, body build, and depth of bruise were recorded. From 147 subjects, 233 reflectance spectrophotometry scans were obtained for analysis. The age of the bruises ranged from 0.5 to 231.5 h. A General Linear Model analysis method was used. This revealed that colorimetric measurement of the yellowness of a bruise accounted for 13% of the bruise age. By incorporation of the other recorded data (as above), yellowness could predict up to 32% of the age of a bruise-implying that 68% of the variation was dependent on other factors. However, critical appraisal of the model revealed that the colorimetry method of determining the age of a bruise was affected by skin tone and required a measure of the proportion of oxygenated hemoglobin, which is obtained by spectrophotometric methods. Using spectrophotometry, the first derivative at 490 nm alone accounted for 18% of the bruise age estimate. When additional factors (subject sex, bruise depth and oxygenation of hemoglobin) were included in the General Linear Model this increased to 31%-implying that 69% of the variation was dependent on other factors. This
Energy Technology Data Exchange (ETDEWEB)
Treuer, Harald; Hoevels, Moritz; Luyken, Klaus; Visser-Vandewalle, Veerle; Wirths, Jochen; Ruge, Maximilian [University Hospital Cologne, Department of Stereotaxy and Functional Neurosurgery, Cologne (Germany); Kocher, Martin [University Hospital Cologne, Department of Radiotherapy, Cologne (Germany)
2014-11-22
Stereotactic radiosurgery with an adapted linear accelerator (linac-SRS) is an established therapy option for brain metastases, benign brain tumors, and arteriovenous malformations. We intended to investigate whether the dosimetric quality of treatment plans achieved with a CyberKnife (CK) is at least equivalent to that for linac-SRS with circular or micromultileaf collimators (microMLC). A random sample of 16 patients with 23 target volumes, previously treated with linac-SRS, was replanned with CK. Planning constraints were identical dose prescription and clinical applicability. In all cases uniform optimization scripts and inverse planning objectives were used. Plans were compared with respect to coverage, minimal dose within target volume, conformity index, and volume of brain tissue irradiated with ≥ 10 Gy. Generating the CK plan was unproblematic with simple optimization scripts in all cases. With the CK plans, coverage, minimal target volume dosage, and conformity index were significantly better, while no significant improvement could be shown regarding the 10 Gy volume. Multiobjective comparison for the irradiated target volumes was superior in the CK plan in 20 out of 23 cases and equivalent in 3 out of 23 cases. Multiobjective comparison for the treated patients was superior in the CK plan in all 16 cases. The results clearly demonstrate the superiority of the irradiation plan for CK compared to classical linac-SRS with circular collimators and microMLC. In particular, the average minimal target volume dose per patient, increased by 1.9 Gy, and at the same time a 14 % better conformation index seems to be an improvement with clinical relevance. (orig.) [German] Stereotaktische Radiochirurgie mit einem adaptierten Linearbeschleuniger (Linac-SRS) ist eine erfolgreiche und etablierte Therapieoption fuer Hirnmetastasen, benigne Hirntumoren und arteriovenoese Malformationen. Ziel war es, zu untersuchen, ob die mit einem CyberKnife (CK) erreichbare
Autocorrelation-based generalized coherence factor for low-complexity adaptive beamforming.
Shen, Che-Chou; Xing, Yong-Qi; Jeng, Gency
2016-12-01
Generalized coherence factor (GCF) can be adaptively estimated from channel data to suppress sidelobe artifacts. Conventionally, Fast Fourier Transform (FFT) is utilized to calculate the full channel spectrum and suffers from high computation load. In this work, autocorrelation (AR)-based algorithm is utilized to provide the spectral parameters of channel data for GCF estimation with reduced complexity. Autocorrelation relies on the phase difference among neighboring channel pairs to estimate the mean frequency and bandwidth of channel spectrum. Based on these two parameters, the spectral power within the defined range of main lobe direction can be analytically computed from a pseudo spectrum with the presumed shape as the GCF weighting value. A bandwidth factor Q can be further included in the formulation of pseudo channel spectrum to optimize the performance. While the GCF computation complexity of a N-channel system reduces from O(Nlog2N) with FFT to O(N) with AR, the lateral side-lobe level is effectively suppressed in the GCF-AR method. In B-mode speckle imaging, the GCF-AR method can provide a higher image contrast together with a relatively low speckle variation. The resultant Contrast-to-Noise Ratio (CNR) improves from 6.7 with GCF-FFT method to 9.0 with GCF-AR method. GCF-AR method reduces the computation complexity of adaptive imaging while providing superior image quality. GCF-AR method is more resistant to the speckle black-region artifacts near strong reflectors and thus improves the overall image contrast. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.
Said-Houari, Belkacem
2017-01-01
This self-contained, clearly written textbook on linear algebra is easily accessible for students. It begins with the simple linear equation and generalizes several notions from this equation for the system of linear equations and introduces the main ideas using matrices. It then offers a detailed chapter on determinants and introduces the main ideas with detailed proofs. The third chapter introduces the Euclidean spaces using very simple geometric ideas and discusses various major inequalities and identities. These ideas offer a solid basis for understanding general Hilbert spaces in functional analysis. The following two chapters address general vector spaces, including some rigorous proofs to all the main results, and linear transformation: areas that are ignored or are poorly explained in many textbooks. Chapter 6 introduces the idea of matrices using linear transformation, which is easier to understand than the usual theory of matrices approach. The final two chapters are more advanced, introducing t...
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Xiuchun Li
2013-01-01
Full Text Available When the parameters of both drive and response systems are all unknown, an adaptive sliding mode controller, strongly robust to exotic perturbations, is designed for realizing generalized function projective synchronization. Sliding mode surface is given and the controlled system is asymptotically stable on this surface with the passage of time. Based on the adaptation laws and Lyapunov stability theory, an adaptive sliding controller is designed to ensure the occurrence of the sliding motion. Finally, numerical simulations are presented to verify the effectiveness and robustness of the proposed method even when both drive and response systems are perturbed with external disturbances.
McTaggart-Cowan, Helen M; O'Cathain, Alicia; Tsuchiya, Aki; Brazier, John E
2012-04-01
To understand the effect of an adaptation exercise (AE) on general population values for rheumatoid arthritis (RA) states. A sequential mixed methods design was employed: an analysis of a dataset to develop RA states for valuing in later phases of the study; a qualitative interview study with members of the general population to identify how an AE affected valuing of the RA states and to help design a questionnaire for the final phase; and a quantitative quasi-experimental study to identify factors that influence change in values after being informed about adaptation. Three RA states were developed using Rasch and cluster analyses. Participants in the qualitative phase identified a range of ways in which information about adaptation affected their values. For example, they realized they could adapt to RA because their family and friends who had RA, or similar conditions, could cope. A 25-item questionnaire was developed and used during the final phase to identify that younger and healthier individuals were more likely to increase their values after being informed about disease adaptation. The qualitative findings were revisited and found to support the quantitative results. This approach facilitated understanding of whether and how an AE affected valuing of health states. Each phase affected the next phase of the study, leading to the conclusion that general population respondents who have little experience of disease will likely increase their health state values after being informed about adaptation because they understand that they could cope with the disease.
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Cyril R Pernet
2014-01-01
Full Text Available This tutorial presents several misconceptions related to the use the General Linear Model (GLM in functional Magnetic Resonance Imaging (fMRI. The goal is not to present mathematical proofs but to educate using examples and computer code (in Matlab. In particular, I address issues related to (i model parameterization (modelling baseline or null events and scaling of the design matrix; (ii hemodynamic modelling using basis functions, and (iii computing percentage signal change. Using a simple controlled block design and an alternating block design, I first show why 'baseline' should not be modelled (model over-parameterization, and how this affects effect sizes. I also show that, depending on what is tested; over-parameterization does not necessarily impact upon statistical results. Next, using a simple periodic vs. random event related design, I show how the haemodynamic model (haemodynamic function only or using derivatives can affects parameter estimates, as well as detail the role of orthogonalization. I then relate the above results to the computation of percentage signal change. Finally, I discuss how these issues affect group analysis and give some recommendations.
Planeta, Josef; Karásek, Pavel; Hohnová, Barbora; Sťavíková, Lenka; Roth, Michal
2012-08-10
Biphasic solvent systems composed of an ionic liquid (IL) and supercritical carbon dioxide (scCO(2)) have become frequented in synthesis, extractions and electrochemistry. In the design of related applications, information on interphase partitioning of the target organics is essential, and the infinite-dilution partition coefficients of the organic solutes in IL-scCO(2) systems can conveniently be obtained by supercritical fluid chromatography. The data base of experimental partition coefficients obtained previously in this laboratory has been employed to test a generalized predictive model for the solute partition coefficients. The model is an amended version of that described before by Hiraga et al. (J. Supercrit. Fluids, in press). Because of difficulty of the problem to be modeled, the model involves several different concepts - linear solvation energy relationships, density-dependent solvent power of scCO(2), regular solution theory, and the Flory-Huggins theory of athermal solutions. The model shows a moderate success in correlating the infinite-dilution solute partition coefficients (K-factors) in individual IL-scCO(2) systems at varying temperature and pressure. However, larger K-factor data sets involving multiple IL-scCO(2) systems appear to be beyond reach of the model, especially when the ILs involved pertain to different cation classes. Copyright © 2012 Elsevier B.V. All rights reserved.
Li, Chung-I; Shyr, Yu
2016-12-01
As RNA-seq rapidly develops and costs continually decrease, the quantity and frequency of samples being sequenced will grow exponentially. With proteomic investigations becoming more multivariate and quantitative, determining a study's optimal sample size is now a vital step in experimental design. Current methods for calculating a study's required sample size are mostly based on the hypothesis testing framework, which assumes each gene count can be modeled through Poisson or negative binomial distributions; however, these methods are limited when it comes to accommodating covariates. To address this limitation, we propose an estimating procedure based on the generalized linear model. This easy-to-use method constructs a representative exemplary dataset and estimates the conditional power, all without requiring complicated mathematical approximations or formulas. Even more attractive, the downstream analysis can be performed with current R/Bioconductor packages. To demonstrate the practicability and efficiency of this method, we apply it to three real-world studies, and introduce our on-line calculator developed to determine the optimal sample size for a RNA-seq study.
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M. Ilić
2008-01-01
Full Text Available This study considers the solution of a class of linear systems related with the fractional Poisson equation (FPE (−∇2α/2φ=g(x,y with nonhomogeneous boundary conditions on a bounded domain. A numerical approximation to FPE is derived using a matrix representation of the Laplacian to generate a linear system of equations with its matrix A raised to the fractional power α/2. The solution of the linear system then requires the action of the matrix function f(A=A−α/2 on a vector b. For large, sparse, and symmetric positive definite matrices, the Lanczos approximation generates f(Ab≈β0Vmf(Tme1. This method works well when both the analytic grade of A with respect to b and the residual for the linear system are sufficiently small. Memory constraints often require restarting the Lanczos decomposition; however this is not straightforward in the context of matrix function approximation. In this paper, we use the idea of thick-restart and adaptive preconditioning for solving linear systems to improve convergence of the Lanczos approximation. We give an error bound for the new method and illustrate its role in solving FPE. Numerical results are provided to gauge the performance of the proposed method relative to exact analytic solutions.
Foam: Multi-dimensional general purpose Monte Carlo generator with self-adapting simplical grid
Jadach, S.
2000-08-01
A new general purpose Monte Carlo event generator with self-adapting grid consisting of simplices is described. In the process of initialization, the simplex-shaped cells divide into daughter subcells in such a way that: (a) cell density is biggest in areas where integrand is peaked, (b) cells elongate themselves along hyperspaces where integrand is enhanced/singular. The grid is anisotropic, i.e. memory of the axes directions of the primary reference frame is lost. In particular, the algorithm is capable of dealing with distributions featuring strong correlation among variables (like ridge along diagonal). The presented algorithm is complementary to others known and commonly used in the Monte Carlo event generators. It is, in principle, more effective than any other one for distributions with very complicated patterns of singularities - the price to pay is that it is memory-hungry. It is therefore aimed at a small number of integration dimensions ( <10 ). It should be combined with other methods for higher dimension. The source code in Fortran 77 is available from http://home.cern.ch/ hadach.
Foam Multi-Dimensional General Purpose Monte Carlo Generator With Self-Adapting Symplectic Grid
Jadach, Stanislaw
2000-01-01
A new general purpose Monte Carlo event generator with self-adapting grid consisting of simplices is described. In the process of initialization, the simplex-shaped cells divide into daughter subcells in such a way that: (a) cell density is biggest in areas where integrand is peaked, (b) cells elongate themselves along hyperspaces where integrand is enhanced/singular. The grid is anisotropic, i.e. memory of the axes directions of the primary reference frame is lost. In particular, the algorithm is capable of dealing with distributions featuring strong correlation among variables (like ridge along diagonal). The presented algorithm is complementary to others known and commonly used in the Monte Carlo event generators. It is, in principle, more effective then any other one for distributions with very complicated patterns of singularities - the price to pay is that it is memory-hungry. It is therefore aimed at a small number of integration dimensions (<10). It should be combined with other methods for higher ...
What do we call Adaptive Management? A general characterization from a global sample
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T. Espigares
2008-03-01
Full Text Available This study presents a characterisation of the implementation of Adaptive Management (AM from the analysis of 35 projects around the world. Our results reveal that AM projects are usually aimed at ecosystem management, conservation and restoration. Also, they mainly act upon forest or epicontinental water ecosystems and their goal is generally species exploitation and in most cases these projects act at a local scale. From a methodological point of view, most AM cases use an active approach and monitoring programs and were at the phase of problem identification. We found differences in the implementation of AM between developed and developing countries that were present in our samples in the following way: AM projects in developed countries were typically carried out by state agencies, and focused on solving problems concerning epicontinental waters and the public use of ecosystems. They had the support of national funds and used modelling techniques. In contrast, the AM projects from developing countries were mainly aimed at the conservation of natural protected areas and at the mitigation of environmental impacts derived from mining activities. The financial support of these projects was frequently provided by international organizations, and the use of modelling techniques was uncommon. For a better exploitation of all the possibilities of AM, we suggest the use of criteria to be customized to the specific needs of the socio-economic reality of every country and to monitor the results at a global scale to continuously improve this practice.
A general hybrid radiation transport scheme for star formation simulations on an adaptive grid
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Klassen, Mikhail; Pudritz, Ralph E. [Department of Physics and Astronomy, McMaster University 1280 Main Street W, Hamilton, ON L8S 4M1 (Canada); Kuiper, Rolf [Max Planck Institute for Astronomy Königstuhl 17, D-69117 Heidelberg (Germany); Peters, Thomas [Institut für Computergestützte Wissenschaften, Universität Zürich Winterthurerstrasse 190, CH-8057 Zürich (Switzerland); Banerjee, Robi; Buntemeyer, Lars, E-mail: klassm@mcmaster.ca [Hamburger Sternwarte, Universität Hamburg Gojenbergsweg 112, D-21029 Hamburg (Germany)
2014-12-10
Radiation feedback plays a crucial role in the process of star formation. In order to simulate the thermodynamic evolution of disks, filaments, and the molecular gas surrounding clusters of young stars, we require an efficient and accurate method for solving the radiation transfer problem. We describe the implementation of a hybrid radiation transport scheme in the adaptive grid-based FLASH general magnetohydrodyanmics code. The hybrid scheme splits the radiative transport problem into a raytracing step and a diffusion step. The raytracer captures the first absorption event, as stars irradiate their environments, while the evolution of the diffuse component of the radiation field is handled by a flux-limited diffusion solver. We demonstrate the accuracy of our method through a variety of benchmark tests including the irradiation of a static disk, subcritical and supercritical radiative shocks, and thermal energy equilibration. We also demonstrate the capability of our method for casting shadows and calculating gas and dust temperatures in the presence of multiple stellar sources. Our method enables radiation-hydrodynamic studies of young stellar objects, protostellar disks, and clustered star formation in magnetized, filamentary environments.
DEFF Research Database (Denmark)
Yang, Z.; Izadi-Zamanabadi, Roozbeh; Blanke, M.
2000-01-01
Based on the model-matching strategy, an adaptive control reconfiguration method for a class of nonlinear control systems is proposed by using the multiple-model scheme. Instead of requiring the nominal and faulty nonlinear systems to match each other directly in some proper sense, three sets...
Dias, Sofia; Sutton, Alex J; Ades, A E; Welton, Nicky J
2013-07-01
We set out a generalized linear model framework for the synthesis of data from randomized controlled trials. A common model is described, taking the form of a linear regression for both fixed and random effects synthesis, which can be implemented with normal, binomial, Poisson, and multinomial data. The familiar logistic model for meta-analysis with binomial data is a generalized linear model with a logit link function, which is appropriate for probability outcomes. The same linear regression framework can be applied to continuous outcomes, rate models, competing risks, or ordered category outcomes by using other link functions, such as identity, log, complementary log-log, and probit link functions. The common core model for the linear predictor can be applied to pairwise meta-analysis, indirect comparisons, synthesis of multiarm trials, and mixed treatment comparisons, also known as network meta-analysis, without distinction. We take a Bayesian approach to estimation and provide WinBUGS program code for a Bayesian analysis using Markov chain Monte Carlo simulation. An advantage of this approach is that it is straightforward to extend to shared parameter models where different randomized controlled trials report outcomes in different formats but from a common underlying model. Use of the generalized linear model framework allows us to present a unified account of how models can be compared using the deviance information criterion and how goodness of fit can be assessed using the residual deviance. The approach is illustrated through a range of worked examples for commonly encountered evidence formats.
Widyaningsih, Yekti; Saefuddin, Asep; Notodiputro, Khairil A.; Wigena, Aji H.
2012-05-01
The objective of this research is to build a nested generalized linear mixed model using an ordinal response variable with some covariates. There are three main jobs in this paper, i.e. parameters estimation procedure, simulation, and implementation of the model for the real data. At the part of parameters estimation procedure, concepts of threshold, nested random effect, and computational algorithm are described. The simulations data are built for 3 conditions to know the effect of different parameter values of random effect distributions. The last job is the implementation of the model for the data about poverty in 9 districts of Java Island. The districts are Kuningan, Karawang, and Majalengka chose randomly in West Java; Temanggung, Boyolali, and Cilacap from Central Java; and Blitar, Ngawi, and Jember from East Java. The covariates in this model are province, number of bad nutrition cases, number of farmer families, and number of health personnel. In this modeling, all covariates are grouped as ordinal scale. Unit observation in this research is sub-district (kecamatan) nested in district, and districts (kabupaten) are nested in province. For the result of simulation, ARB (Absolute Relative Bias) and RRMSE (Relative Root of mean square errors) scale is used. They show that prov parameters have the highest bias, but more stable RRMSE in all conditions. The simulation design needs to be improved by adding other condition, such as higher correlation between covariates. Furthermore, as the result of the model implementation for the data, only number of farmer family and number of medical personnel have significant contributions to the level of poverty in Central Java and East Java province, and only district 2 (Karawang) of province 1 (West Java) has different random effect from the others. The source of the data is PODES (Potensi Desa) 2008 from BPS (Badan Pusat Statistik).
Camilo, Daniela Castro
2017-08-30
Grid-based landslide susceptibility models at regional scales are computationally demanding when using a fine grid resolution. Conversely, Slope-Unit (SU) based susceptibility models allows to investigate the same areas offering two main advantages: 1) a smaller computational burden and 2) a more geomorphologically-oriented interpretation. In this contribution, we generate SU-based landslide susceptibility for the Sado Island in Japan. This island is characterized by deep-seated landslides which we assume can only limitedly be explained by the first two statistical moments (mean and variance) of a set of predictors within each slope unit. As a consequence, in a nested experiment, we first analyse the distributions of a set of continuous predictors within each slope unit computing the standard deviation and quantiles from 0.05 to 0.95 with a step of 0.05. These are then used as predictors for landslide susceptibility. In addition, we combine shape indices for polygon features and the normalized extent of each class belonging to the outcropping lithology in a given SU. This procedure significantly enlarges the size of the predictor hyperspace, thus producing a high level of slope-unit characterization. In a second step, we adopt a LASSO-penalized Generalized Linear Model to shrink back the predictor set to a sensible and interpretable number, carrying only the most significant covariates in the models. As a result, we are able to document the geomorphic features (e.g., 95% quantile of Elevation and 5% quantile of Plan Curvature) that primarily control the SU-based susceptibility within the test area while producing high predictive performances. The implementation of the statistical analyses are included in a parallelized R script (LUDARA) which is here made available for the community to replicate analogous experiments.
Radaydeh, Redha Mahmoud Mesleh
2010-09-01
The impact of co-channel interference and nonideal estimation of the desired user channel state information (CSI) on the performance of an adaptive threshold-based generalized transmit diversity for low-complexity multiple-input single-output configuration is investigated. The adaptation to channel conditions is assumed to be based on the desired user CSI, and the number of active transmit antennas is adjusted accordingly to guarantee predetermined target performance. To facilitate comparisons between different adaptation schemes, new analytical results for the statistics of combined signal-to-interference-plus-noise ratio (SINR) are derived, which can be applied for different fading conditions of interfering signals. Selected numerical results are presented to validate the analytical development and to compare the outage performance of the considered adaptation schemes. © 2010 IEEE.
Directory of Open Access Journals (Sweden)
Minar Naomi Damanik-Ambarita
2016-07-01
Full Text Available The biotic integrity of the Guayas River basin in Ecuador is at environmental risk due to extensive anthropogenic activities. We investigated the potential impacts of hydromorphological and chemical variables on biotic integrity using macroinvertebrate-based bioassessments. The bioassessment methods utilized included the Biological Monitoring Working Party adapted for Colombia (BMWP-Col and the average score per taxon (ASPT, via an extensive sampling campaign that was completed throughout the river basin at 120 sampling sites. The BMWP-Col classification ranged from very bad to good, and from probable severe pollution to clean water based on the ASPT scores. Generalized linear models (GLMs and sensitivity analysis were used to relate the bioassessment index to hydromorphological and chemical variables. It was found that elevation, nitrate-N, sediment angularity, logs, presence of macrophytes, flow velocity, turbidity, bank shape, land use and chlorophyll were the key environmental variables affecting the BMWP-Col. From the analyses, it was observed that the rivers at the upstream higher elevations of the river basin were in better condition compared to lowland systems and that a higher flow velocity was linked to a better BMWP-Col score. The nitrate concentrations were very low in the entire river basin and did not relate to a negative impact on the macroinvertebrate communities. Although the results of the models provided insights into the ecosystem, cross fold model development and validation also showed that there was a level of uncertainty in the outcomes. However, the results of the models and sensitivity analysis can support water management actions to determine and focus on alterable variables, such as the land use at different elevations, monitoring of nitrate and chlorophyll concentrations, macrophyte presence, sediment transport and bank stability.
Boulehouache, Soufiane; Maamri, Ramdane; Sahnoun, Zaidi
2015-01-01
The Pedagogical Agents (PAs) for Mobile Learning (m-learning) must be able not only to adapt the teaching to the learner knowledge level and profile but also to ensure the pedagogical efficiency within unpredictable changing runtime contexts. Therefore, to deal with this issue, this paper proposes a Context-aware Self-Adaptive Fractal Component…
Cross-Cultural adaptation of the General Functioning Scale of the Family.
Pires, Thiago; Assis, Simone Gonçalves de; Avanci, Joviana Quintes; Pesce, Renata Pires
2016-06-27
To describe the process of cross-cultural adaptation of the General Functioning Scale of the Family, a subscale of the McMaster Family Assessment Device, for the Brazilian population. The General Functioning Scale of the Family was translated into Portuguese and administered to 500 guardians of children in the second grade of elementary school in public schools of Sao Gonçalo, Rio de Janeiro, Southeastern Brazil. The types of equivalences investigated were: conceptual and of items, semantic, operational, and measurement. The study involved discussions with experts, translations and back-translations of the instrument, and psychometric assessment. Reliability and validity studies were carried out by internal consistency testing (Cronbach's alpha), Guttman split-half correlation model, Pearson correlation coefficient, and confirmatory factor analysis. Associations between General Functioning of the Family and variables theoretically associated with the theme (father's or mother's drunkenness and violence between parents) were estimated by odds ratio. Semantic equivalence was between 90.0% and 100%. Cronbach's alpha ranged from 0.79 to 0.81, indicating good internal consistency of the instrument. Pearson correlation coefficient ranged between 0.303 and 0.549. Statistical association was found between the general functioning of the family score and the theoretically related variables, as well as good fit quality of the confirmatory analysis model. The results indicate the feasibility of administering the instrument to the Brazilian population, as it is easy to understand and a good measurement of the construct of interest. Descrever o processo de adaptação transcultural da escala de Funcionamento Geral da Família, subescala da McMaster Family Assessment Device, para a população brasileira. A escala de Funcionamento Geral da Família, original no idioma inglês, foi traduzida para o português e aplicada a 500 responsáveis de crianças do segundo ano do ensino
Cadmium-hazard mapping using a general linear regression model (Irr-Cad) for rapid risk assessment.
Simmons, Robert W; Noble, Andrew D; Pongsakul, P; Sukreeyapongse, O; Chinabut, N
2009-02-01
Research undertaken over the last 40 years has identified the irrefutable relationship between the long-term consumption of cadmium (Cd)-contaminated rice and human Cd disease. In order to protect public health and livelihood security, the ability to accurately and rapidly determine spatial Cd contamination is of high priority. During 2001-2004, a General Linear Regression Model Irr-Cad was developed to predict the spatial distribution of soil Cd in a Cd/Zn co-contaminated cascading irrigated rice-based system in Mae Sot District, Tak Province, Thailand (Longitude E 98 degrees 59'-E 98 degrees 63' and Latitude N 16 degrees 67'-16 degrees 66'). The results indicate that Irr-Cad accounted for 98% of the variance in mean Field Order total soil Cd. Preliminary validation indicated that Irr-Cad 'predicted' mean Field Order total soil Cd, was significantly (p channels and subsequent inter-field irrigation flows. This in turn determines Field Order in Irrigation Sequence (Field Order(IS)). Mean Field Order total soil Cd represents the mean total soil Cd (aqua regia-digested) for a given Field Order(IS). In 2004-2005, Irr-Cad was utilized to evaluate the spatial distribution of total soil Cd in a 'high-risk' area of Mae Sot District. Secondary validation on six randomly selected field groups verified that Irr-Cad predicted mean Field Order total soil Cd and was significantly (p strategic sampling of all primary fields and laboratory based determination of total soil Cd (T-Cd(P)) and the use of a weighed coefficient for Cd (Coeff(W)). The use of primary fields as the basis for Irr-Cad is also an important practical consideration due to their inherent ease of identification and vital role in the classification of fields in terms of Field Order(IS). The inclusion of mean field order soil pH (1:5(water)) to the Irr-Cad model accounted for over 79% of the variation in mean Field Order bio-available (DTPA (diethylenetriaminepentaacetic acid)-extractable) soil Cd. Rice is the
Directory of Open Access Journals (Sweden)
Kyle A McQuisten
Full Text Available BACKGROUND: Exogenous short interfering RNAs (siRNAs induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models. PRINCIPAL FINDINGS: Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs, General Linear Models (GLMs and Support Vector Machines (SVMs. Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation. CONCLUSIONS: The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features
Rahmim, Arman; Zhou, Yun; Tang, Jing; Lu, Lijun; Sossi, Vesna; Wong, Dean F.
2012-01-01
Due to high noise levels in the voxel kinetics, development of reliable parametric imaging algorithms remains as one of most active areas in dynamic brain PET imaging, which in the vast majority of cases involves receptor/transporter studies with reversibly binding tracers. As such, the focus of this work has been to develop a novel direct 4D parametric image reconstruction scheme for such tracers. Based on a relative equilibrium (RE) graphical analysis formulation (Zhou et al., 2009b), we developed a closed-form 4D EM algorithm to directly reconstruct distribution volume (DV) parametric images within a plasma input model, as well as DV ratio (DVR) images within a reference tissue model scheme (wherein an initial reconstruction was used to estimate the reference tissue time-activity-curves). A particular challenge with the direct 4D EM formulation is that the intercept parameters in graphical (linearized) analysis of reversible tracers (e.g. Logan or RE analysis) are commonly negative (unlike for irreversible tracers; e.g. using Patlak analysis). Subsequently, we focused our attention on the AB-EM algorithm, derived by Byrne (1998) to allow inclusion of prior information about the lower (A) and upper (B) bounds for image values. We then generalized this algorithm to the 4D EM framework thus allowing negative intercept parameters. Furthermore, our 4D AB-EM algorithm incorporated, and emphasized the use of spatially varying lower bounds to achieve enhanced performance. As validation, the means of parameters estimated from 55 human 11C-raclopride dynamic PET studies were used for extensive simulations using a mathematical brain phantom. Images were reconstructed using conventional indirect as well as proposed direct parametric imaging methods. Noise vs. bias quantitative measurements were performed in various regions of the brain. Direct 4D EM reconstruction resulted in notable qualitative and quantitative accuracy improvements (over 35% noise reduction, with matched
Rahmim, Arman; Zhou, Yun; Tang, Jing; Lu, Lijun; Sossi, Vesna; Wong, Dean F.
2012-02-01
Due to high noise levels in the voxel kinetics, development of reliable parametric imaging algorithms remains one of most active areas in dynamic brain PET imaging, which in the vast majority of cases involves receptor/transporter studies with reversibly binding tracers. As such, the focus of this work has been to develop a novel direct 4D parametric image reconstruction scheme for such tracers. Based on a relative equilibrium (RE) graphical analysis formulation (Zhou et al 2009b Neuroimage 44 661-70), we developed a closed-form 4D EM algorithm to directly reconstruct distribution volume (DV) parametric images within a plasma input model, as well as DV ratio (DVR) images within a reference tissue model scheme (wherein an initial reconstruction was used to estimate the reference tissue time-activity curves). A particular challenge with the direct 4D EM formulation is that the intercept parameters in graphical (linearized) analysis of reversible tracers (e.g. Logan or RE analysis) are commonly negative (unlike for irreversible tracers, e.g. using Patlak analysis). Subsequently, we focused our attention on the AB-EM algorithm, derived by Byrne (1998, Inverse Problems 14 1455-67) to allow inclusion of prior information about the lower (A) and upper (B) bounds for image values. We then generalized this algorithm to the 4D EM framework, thus allowing negative intercept parameters. Furthermore, our 4D AB-EM algorithm incorporated and emphasized the use of spatially varying lower bounds to achieve enhanced performance. As validation, the means of parameters estimated from 55 human 11C-raclopride dynamic PET studies were used for extensive simulations using a mathematical brain phantom. Images were reconstructed using conventional indirect as well as proposed direct parametric imaging methods. Noise versus bias quantitative measurements were performed in various regions of the brain. Direct 4D EM reconstruction resulted in notable qualitative and quantitative accuracy
Namburu, R. R.; Tamma, K. K.
1991-01-01
The applicability and evaluation of a generalized gamma(T) family of flux-based representations are examined for two different thermal analysis formulations for structures and materials which exhibit no phase change effects. The so-called H-theta and theta forms are demonstrated for numerous test models and linear and higher-order elements. The results show that the theta form with flux-based representations is generally superior to traditional approaches.
High-fidelity linear time-invariant model of a smart rotor with adaptive trailing edge flaps
DEFF Research Database (Denmark)
Bergami, Leonardo; Hansen, Morten Hartvig
2017-01-01
aero-servo-elastic model support the design, systematic tuning and model synthesis of smart rotor control systems. As an example application, the gains of an individual flap controller are tuned using the Ziegler-Nichols method for the full-order poles. The flap controller is based on feedback...... of inverse Coleman transformed and low-pass filtered flapwise blade root moments to the cyclic flap angles through two proportional-integral controllers. The load alleviation potential of the active flap control, anticipated by the frequency response of the linear closed-loop model, is also confirmed by non...
Chen, Haiwen
2012-01-01
In this article, linear item response theory (IRT) observed-score equating is compared under a generalized kernel equating framework with Levine observed-score equating for nonequivalent groups with anchor test design. Interestingly, these two equating methods are closely related despite being based on different methodologies. Specifically, when…
Wang, Ji; Pi, Yangjun; Hu, Yumei; Zhu, Zhencai; Zeng, Lingbin
2017-11-01
In this paper, a new motion and vibration synthesized control system-a linear quadratic regulator/strain rate feedback controller (LQR/SRF) with adaptive disturbance attenuation is presented for a multi flexible-link mechanism subjected to uncertain harmonic disturbances with arbitrary frequencies and unknown magnitudes. In the proposed controller, nodal strain rates are introduced into the model of the multi flexible-link mechanism, based upon which a synthesized LQR controller where both rigid-body motion and elastic deformation are considered is designed. The uncertain harmonic disturbances would be canceled in the feedback loop by its approximated value which is computed online via an adaptive update law. Asymptotic stability of the closed-loop system is proved by the Lyapunov analysis. The effectiveness of the proposed controller is shown via simulation.
Szadkowski, Zbigniew; Fraenkel, E. D.; van den Berg, Ad M.
2013-10-01
We present the FPGA/NIOS implementation of an adaptive finite impulse response (FIR) filter based on linear prediction to suppress radio frequency interference (RFI). This technique will be used for experiments that observe coherent radio emission from extensive air showers induced by ultra-high-energy cosmic rays. These experiments are designed to make a detailed study of the development of the electromagnetic part of air showers. Therefore, these radio signals provide information that is complementary to that obtained by water-Cherenkov detectors which are predominantly sensitive to the particle content of an air shower at ground. The radio signals from air showers are caused by the coherent emission due to geomagnetic and charge-excess processes. These emissions can be observed in the frequency band between 10-100 MHz. However, this frequency range is significantly contaminated by narrow-band RFI and other human-made distortions. A FIR filter implemented in the FPGA logic segment of the front-end electronics of a radio sensor significantly improves the signal-to-noise ratio. In this paper we discuss an adaptive filter which is based on linear prediction. The coefficients for the linear predictor (LP) are dynamically refreshed and calculated in the embedded NIOS processor, which is implemented in the same FPGA chip. The Levinson recursion, used to obtain the filter coefficients, is also implemented in the NIOS and is partially supported by direct multiplication in the DSP blocks of the logic FPGA segment. Tests confirm that the LP can be an alternative to other methods involving multiple time-to-frequency domain conversions using an FFT procedure. These multiple conversions draw heavily on the power consumption of the FPGA and are avoided by the linear prediction approach. Minimization of the power consumption is an important issue because the final system will be powered by solar panels. The FIR filter has been successfully tested in the Altera development kits
DEFF Research Database (Denmark)
Yang, Z.; Izadi-Zamanabadi, Roozbeh; Blanke, M.
2000-01-01
Based on the model-matching strategy, an adaptive control reconfiguration method for a class of nonlinear control systems is proposed by using the multiple-model scheme. Instead of requiring the nominal and faulty nonlinear systems to match each other directly in some proper sense, three sets...... of LTI models are employed to approximate the faulty, reconfigured and nominal nonlinear systems respectively with respect to the on-line information of the operating system, and a set of compensating modules are proposed and designed so as to make the local LTI model approximating to the reconfigured...... corresponding to the updating of local LTI models, which validations are determined by the model approximation errors and the optimal index of local design. The test on a nonlinear ship propulsion system shows the promising potential of this method for system reconfiguration...
Lundahl, P. Johan
2011-01-01
This article presents a new design of flow-orientation device for the study of bio-macromolecules, including DNA and protein complexes, as well as aggregates such as amyloid fibrils and liposome membranes, using Linear Dichroism (LD) spectroscopy. The design provides a number of technical advantages that should make the device inexpensive to manufacture, easier to use and more reliable than existing techniques. The degree of orientation achieved is of the same order of magnitude as that of the commonly used concentric cylinders Couette flow cell, however, since the device exploits a set of flat strain-free quartz plates, a number of problems associated with refraction and birefringence of light are eliminated, increasing the sensitivity and accuracy of measurement. The device provides similar shear rates to those of the Couette cell but is superior in that the shear rate is constant across the gap. Other major advantages of the design is the possibility to change parts and vary sample volume and path length easily and at a low cost. © 2011 The Royal Society of Chemistry.
Udvari-Solner, Alice
This manual offers definitions, techniques, and strategies to generate curricular adaptations to meet the needs of students with a range of intellectual abilities, and thereby increase the practice of inclusive schooling in which all children learn together and multiplicity of learning styles is valued. First, an in-depth definition of…
William Alvarez Gaviria
2004-01-01
The origin of climacteric has been subject of debate. Most opinions agree in that it arises exclusively from natural selection. In this paper the author argues that, besides this reason there is another, even more important; for him, climacteric is the final response to fatigue or the third stage of the general adaptation syndrome, just as in elderly people there is a loss of the capacity of proliferation of fibroblasts and lack of response to insulin. From a genetic point of view, this corre...
Directory of Open Access Journals (Sweden)
Eusebio Eduardo Hernández Martinez
2013-01-01
Full Text Available In robotics, solving the direct kinematics problem (DKP for parallel robots is very often more difficult and time consuming than for their serial counterparts. The problem is stated as follows: given the joint variables, the Cartesian variables should be computed, namely the pose of the mobile platform. Most of the time, the DKP requires solving a non-linear system of equations. In addition, given that the system could be non-convex, Newton or Quasi-Newton (Dogleg based solvers get trapped on local minima. The capacity of such kinds of solvers to find an adequate solution strongly depends on the starting point. A well-known problem is the selection of such a starting point, which requires a priori information about the neighbouring region of the solution. In order to circumvent this issue, this article proposes an efficient method to select and to generate the starting point based on probabilistic learning. Experiments and discussion are presented to show the method performance. The method successfully avoids getting trapped on local minima without the need for human intervention, which increases its robustness when compared with a single Dogleg approach. This proposal can be extended to other structures, to any non-linear system of equations, and of course, to non-linear optimization problems.
Zeghlache, Samir; Benslimane, Tarak; Bouguerra, Abderrahmen
2017-11-01
In this paper, a robust controller for a three degree of freedom (3 DOF) helicopter control is proposed in presence of actuator and sensor faults. For this purpose, Interval type-2 fuzzy logic control approach (IT2FLC) and sliding mode control (SMC) technique are used to design a controller, named active fault tolerant interval type-2 Fuzzy Sliding mode controller (AFTIT2FSMC) based on non-linear adaptive observer to estimate and detect the system faults for each subsystem of the 3-DOF helicopter. The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller. Exponential stability of the closed loop is guaranteed by using the Lyapunov method. The simulation results show that the AFTIT2FSMC can greatly alleviate the chattering effect, providing good tracking performance, even in presence of actuator and sensor faults. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Brunner, D.; Kuang, A. Q.; LaBombard, B.; Burke, W.
2017-07-01
A new servomotor drive system has been developed for the horizontal reciprocating probe on the Alcator C-Mod tokamak. Real-time measurements of plasma temperature and density—through use of a mirror Langmuir probe bias system—combined with a commercial linear servomotor and controller enable self-adaptive position control. Probe surface temperature and its rate of change are computed in real time and used to control probe insertion depth. It is found that a universal trigger threshold can be defined in terms of these two parameters; if the probe is triggered to retract when crossing the trigger threshold, it will reach the same ultimate surface temperature, independent of velocity, acceleration, or scrape-off layer heat flux scale length. In addition to controlling the probe motion, the controller is used to monitor and control all aspects of the integrated probe drive system.
Fogolari, Federico; Corazza, Alessandra; Esposito, Gennaro
2015-04-05
The generalized Born model in the Onufriev, Bashford, and Case (Onufriev et al., Proteins: Struct Funct Genet 2004, 55, 383) implementation has emerged as one of the best compromises between accuracy and speed of computation. For simulations of nucleic acids, however, a number of issues should be addressed: (1) the generalized Born model is based on a linear model and the linearization of the reference Poisson-Boltmann equation may be questioned for highly charged systems as nucleic acids; (2) although much attention has been given to potentials, solvation forces could be much less sensitive to linearization than the potentials; and (3) the accuracy of the Onufriev-Bashford-Case (OBC) model for nucleic acids depends on fine tuning of parameters. Here, we show that the linearization of the Poisson Boltzmann equation has mild effects on computed forces, and that with optimal choice of the OBC model parameters, solvation forces, essential for molecular dynamics simulations, agree well with those computed using the reference Poisson-Boltzmann model. © 2015 Wiley Periodicals, Inc.
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Bagherpoor, H M; Salmasi, Farzad R
2015-07-01
In this paper, robust model reference adaptive tracking controllers are considered for Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) linear systems containing modeling uncertainties, unknown additive disturbances and actuator fault. Two new lemmas are proposed for both SISO and MIMO, under which dead-zone modification rule is improved such that the tracking error for any reference signal tends to zero in such systems. In the conventional approach, adaption of the controller parameters is ceased inside the dead-zone region which results tracking error, while preserving the system stability. In the proposed scheme, control signal is reinforced with an additive term based on tracking error inside the dead-zone which results in full reference tracking. In addition, no Fault Detection and Diagnosis (FDD) unit is needed in the proposed approach. Closed loop system stability and zero tracking error are proved by considering a suitable Lyapunov functions candidate. It is shown that the proposed control approach can assure that all the signals of the close loop system are bounded in faulty conditions. Finally, validity and performance of the new schemes have been illustrated through numerical simulations of SISO and MIMO systems in the presence of actuator faults, modeling uncertainty and output disturbance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Heng, Henry H
2017-02-01
Big-data-omics have promised the success of precision medicine. However, most common diseases belong to adaptive systems where the precision is all but difficult to achieve. In this commentary, I propose a heterogeneity-mediated cellular adaptive model to search for the general model of diseases, which also illustrates why in most non-infectious non-Mendelian diseases the involvement of cellular evolution is less predictable when gene profiles are used. This synthesis is based on the following new observations/concepts: 1) the gene only codes "parts inheritance" while the genome codes "system inheritance" or the entire blueprint; 2) the nature of somatic genetic coding is fuzzy rather than precise, and genetic alterations are not just the results of genetic error but are in fact generated from internal adaptive mechanisms in response to environmental dynamics; 3) stress-response is less specific within cellular evolutionary context when compared to known biochemical specificities; and 4) most medical interventions have their unavoidable uncertainties and often can function as negative harmful stresses as trade-offs. The acknowledgment of diseases as adaptive systems calls for the action to integrate genome- (not simply individual gene-) mediated cellular evolution into molecular medicine. © 2016 John Wiley & Sons, Ltd.
Jäntschi, Lorentz; Bálint, Donatella; Bolboacă, Sorana D
2016-01-01
Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution, the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for finding the coefficients on linear models with two predictors without any constrictive assumptions on the distribution of the errors. The algorithm was developed, implemented, and tested as proof-of-concept using fourteen sets of compounds by investigating the link between activity/property (as outcome) and structural feature information incorporated by molecular descriptors (as predictors). The results on real data demonstrated that in all investigated cases the power of the error is significantly different by the convenient value of two when the Gauss-Laplace distribution was used to relax the constrictive assumption of the normal distribution of the error. Therefore, the Gauss-Laplace distribution of the error could not be rejected while the hypothesis that the power of the error from Gauss-Laplace distribution is normal distributed also failed to be rejected.
Jain, Amit; Kuhls-Gilcrist, Andrew T; Gupta, Sandesh K; Bednarek, Daniel R; Rudin, Stephen
2010-03-01
The MTF, NNPS, and DQE are standard linear system metrics used to characterize intrinsic detector performance. To evaluate total system performance for actual clinical conditions, generalized linear system metrics (GMTF, GNNPS and GDQE) that include the effect of the focal spot distribution, scattered radiation, and geometric unsharpness are more meaningful and appropriate. In this study, a two-dimensional (2D) generalized linear system analysis was carried out for a standard flat panel detector (FPD) (194-micron pixel pitch and 600-micron thick CsI) and a newly-developed, high-resolution, micro-angiographic fluoroscope (MAF) (35-micron pixel pitch and 300-micron thick CsI). Realistic clinical parameters and x-ray spectra were used. The 2D detector MTFs were calculated using the new Noise Response method and slanted edge method and 2D focal spot distribution measurements were done using a pin-hole assembly. The scatter fraction, generated for a uniform head equivalent phantom, was measured and the scatter MTF was simulated with a theoretical model. Different magnifications and scatter fractions were used to estimate the 2D GMTF, GNNPS and GDQE for both detectors. Results show spatial non-isotropy for the 2D generalized metrics which provide a quantitative description of the performance of the complete imaging system for both detectors. This generalized analysis demonstrated that the MAF and FPD have similar capabilities at lower spatial frequencies, but that the MAF has superior performance over the FPD at higher frequencies even when considering focal spot blurring and scatter. This 2D generalized performance analysis is a valuable tool to evaluate total system capabilities and to enable optimized design for specific imaging tasks.
DEFF Research Database (Denmark)
Guo, Meng; Elmedyb, Thomas Bo; Jensen, Søren Holdt
2011-01-01
In this paper, we analyze a general multiple-microphone and single-loudspeaker system, where an adaptive algorithm is used to cancel acoustic feedback/echo and a beamformer processes the feedback/echo canceled signals. This system can be viewed as part of a typical hearing aid system and....../or a traditional acoustic echo cancelation system. We introduce and derive an approximation of a useful frequency domain measure - the power transfer function - and show how to predict the system stability bound, convergence rate and the steady-state behavior across time and frequency. Furthermore, we show how...... the derived expressions can be used to determine e.g. the step size parameter in the adaptive algorithms to achieve a desired system property e.g. convergence rate at a specific frequency....
Steinbrecher, György; Weyssow, B
2004-03-26
The extreme heavy tail and the power-law decay of the turbulent flux correlation observed in hot magnetically confined plasmas are modeled by a system of coupled Langevin equations describing a continuous time linear randomly amplified stochastic process where the amplification factor is driven by a superposition of colored noises which, in a suitable limit, generate a fractional Brownian motion. An exact analytical formula for the power-law tail exponent beta is derived. The extremely small value of the heavy tail exponent and the power-law distribution of laminar times also found experimentally are obtained, in a robust manner, for a wide range of input values, as a consequence of the (asymptotic) self-similarity property of the noise spectrum. As a by-product, a new representation of the persistent fractional Brownian motion is obtained.
Zou, Ding; Djordjevic, Ivan B.
2016-02-01
Forward error correction (FEC) is as one of the key technologies enabling the next-generation high-speed fiber optical communications. In this paper, we propose a rate-adaptive scheme using a class of generalized low-density parity-check (GLDPC) codes with a Hamming code as local code. We show that with the proposed unified GLDPC decoder architecture, a variable net coding gains (NCGs) can be achieved with no error floor at BER down to 10-15, making it a viable solution in the next-generation high-speed fiber optical communications.
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Vrugt, Jasper A.; Madsen, Henrik
2008-01-01
within the context of Monte Carlo (MC) analysis coupled with Bayesian estimation and propagation of uncertainty. Because of its flexibility, ease of implementation and its suitability for parallel implementation on distributed computer systems, the GLUE method has been used in a wide variety...... that require significant computational time to run and produce the desired output. In this paper we improve the computational efficiency of GLUE by sampling the prior parameter space using an adaptive Markov Chain Monte Carlo scheme (the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm). Moreover, we......In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit...
Development of Adaptive Mesh Refinement Techniques for an Ocean General Circulation Model
Herrnstein, A.
2003-12-01
A problem faced by all computational physicists is the trade off between run time and grid resolution. The number of time steps and cost per time step drastically increases as finer and finer grids are used. In effort to combat this dilemma, research areas such as fluid dynamics use a modeling technique known as Adaptive Mesh Refinement (AMR). This involves increasing grid resolution in localized areas of the domain while preserving the structured nature of the original grid. The concept of AMR has been around for nearly two decades, but only in recent years have software packages become available to aid in many of the details. Atmospheric and ocean models simulate phenomena with motion derived from fluid dynamics. This suggests AMR is feasible for global climate modeling. The success of nested regional models also supports this hypothesis. In this research, techniques are developed which will allow AMR to perform on an existing ocean model known as the Modular Ocean Model (MOM). Of particular interest is the ability to use AMR on a staggered grid with centered differencing numerics. These are two characteristics of MOM which reduce computational noise and run time respectively. To aid AMR implementation, the software package SAMRAI (Structured Adaptive Mesh Refinement Application Infrastructure) is used. Refinement is based on solution gradients and possibly other criteria as needed. In addition, regions of critical topography might be refined if necessary. Current tools include time integrators which implement Leap Frog, Predictor Corrector, and Runge Kutta numerics. Velocities are defined at nodes and all other quantities at cell centers. A bench mark simulation of a barotropic modon will be presented.
Hals, Ingrid K; Bruerberg, Simon Gustafson; Ma, Zuheng; Scholz, Hanne; Björklund, Anneli; Grill, Valdemar
2015-01-01
To provide novel insights on mitochondrial respiration in β-cells and the adaptive effects of hypoxia. Insulin-producing INS-1 832/13 cells were exposed to 18 hours of hypoxia followed by 20-22 hours re-oxygenation. Mitochondrial respiration was measured by high-resolution respirometry in both intact and permeabilized cells, in the latter after establishing three functional substrate-uncoupler-inhibitor titration (SUIT) protocols. Concomitant measurements included proteins of mitochondrial complexes (Western blotting), ATP and insulin secretion. Intact cells exhibited a high degree of intrinsic uncoupling, comprising about 50% of oxygen consumption in the basal respiratory state. Hypoxia followed by re-oxygenation increased maximal overall respiration. Exploratory experiments in peremabilized cells could not show induction of respiration by malate or pyruvate as reducing substrates, thus glutamate and succinate were used as mitochondrial substrates in SUIT protocols. Permeabilized cells displayed a high capacity for oxidative phosphorylation for both complex I- and II-linked substrates in relation to maximum capacity of electron transfer. Previous hypoxia decreased phosphorylation control of complex I-linked respiration, but not in complex II-linked respiration. Coupling control ratios showed increased coupling efficiency for both complex I- and II-linked substrates in hypoxia-exposed cells. Respiratory rates overall were increased. Also previous hypoxia increased proteins of mitochondrial complexes I and II (Western blotting) in INS-1 cells as well as in rat and human islets. Mitochondrial effects were accompanied by unchanged levels of ATP, increased basal and preserved glucose-induced insulin secretion. Exposure of INS-1 832/13 cells to hypoxia, followed by a re-oxygenation period increases substrate-stimulated respiratory capacity and coupling efficiency. Such effects are accompanied by up-regulation of mitochondrial complexes also in pancreatic islets
Diffractive generalized phase contrast for adaptive phase imaging and optical security
DEFF Research Database (Denmark)
Palima, Darwin; Glückstad, Jesper
2012-01-01
We analyze the properties of Generalized Phase Contrast (GPC) when the input phase modulation is implemented using diffractive gratings. In GPC applications for patterned illumination, the use of a dynamic diffractive optical element for encoding the GPC input phase allows for onthe- fly optimiza...... security applications and can be used to create phasebased information channels for enhanced information security....
Lipparini, Filippo; Scalmani, Giovanni; Lagardère, Louis; Stamm, Benjamin; Cancès, Eric; Maday, Yvon; Piquemal, Jean-Philip; Frisch, Michael J; Mennucci, Benedetta
2014-11-14
We present the general theory and implementation of the Conductor-like Screening Model according to the recently developed ddCOSMO paradigm. The various quantities needed to apply ddCOSMO at different levels of theory, including quantum mechanical descriptions, are discussed in detail, with a particular focus on how to compute the integrals needed to evaluate the ddCOSMO solvation energy and its derivatives. The overall computational cost of a ddCOSMO computation is then analyzed and decomposed in the various steps: the different relative weights of such contributions are then discussed for both ddCOSMO and the fastest available alternative discretization to the COSMO equations. Finally, the scaling of the cost of the various steps with respect to the size of the solute is analyzed and discussed, showing how ddCOSMO opens significantly new possibilities when cheap or hybrid molecular mechanics/quantum mechanics methods are used to describe the solute.
Lipparini, Filippo; Scalmani, Giovanni; Lagardère, Louis; Stamm, Benjamin; Cancès, Eric; Maday, Yvon; Piquemal, Jean-Philip; Frisch, Michael J.; Mennucci, Benedetta
2014-11-01
We present the general theory and implementation of the Conductor-like Screening Model according to the recently developed ddCOSMO paradigm. The various quantities needed to apply ddCOSMO at different levels of theory, including quantum mechanical descriptions, are discussed in detail, with a particular focus on how to compute the integrals needed to evaluate the ddCOSMO solvation energy and its derivatives. The overall computational cost of a ddCOSMO computation is then analyzed and decomposed in the various steps: the different relative weights of such contributions are then discussed for both ddCOSMO and the fastest available alternative discretization to the COSMO equations. Finally, the scaling of the cost of the various steps with respect to the size of the solute is analyzed and discussed, showing how ddCOSMO opens significantly new possibilities when cheap or hybrid molecular mechanics/quantum mechanics methods are used to describe the solute.
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Lipparini, Filippo, E-mail: flippari@uni-mainz.de [Sorbonne Universités, UPMC Univ. Paris 06, UMR 7598, Laboratoire Jacques-Louis Lions, F-75005 Paris (France); Sorbonne Universités, UPMC Univ. Paris 06, UMR 7616, Laboratoire de Chimie Théorique, F-75005 Paris (France); Sorbonne Universités, UPMC Univ. Paris 06, Institut du Calcul et de la Simulation, F-75005 Paris (France); Scalmani, Giovanni; Frisch, Michael J. [Gaussian, Inc., 340 Quinnipiac St. Bldg. 40, Wallingford, Connecticut 06492 (United States); Lagardère, Louis [Sorbonne Universités, UPMC Univ. Paris 06, Institut du Calcul et de la Simulation, F-75005 Paris (France); Stamm, Benjamin [Sorbonne Universités, UPMC Univ. Paris 06, UMR 7598, Laboratoire Jacques-Louis Lions, F-75005 Paris (France); CNRS, UMR 7598 and 7616, F-75005 Paris (France); Cancès, Eric [Université Paris-Est, CERMICS, Ecole des Ponts and INRIA, 6 and 8 avenue Blaise Pascal, 77455 Marne-la-Vallée Cedex 2 (France); Maday, Yvon [Sorbonne Universités, UPMC Univ. Paris 06, UMR 7598, Laboratoire Jacques-Louis Lions, F-75005 Paris (France); Institut Universitaire de France, Paris, France and Division of Applied Maths, Brown University, Providence, Rhode Island 02912 (United States); Piquemal, Jean-Philip [Sorbonne Universités, UPMC Univ. Paris 06, UMR 7616, Laboratoire de Chimie Théorique, F-75005 Paris (France); CNRS, UMR 7598 and 7616, F-75005 Paris (France); Mennucci, Benedetta [Dipartimento di Chimica e Chimica Industriale, Università di Pisa, Via Risorgimento 35, 56126 Pisa (Italy)
2014-11-14
We present the general theory and implementation of the Conductor-like Screening Model according to the recently developed ddCOSMO paradigm. The various quantities needed to apply ddCOSMO at different levels of theory, including quantum mechanical descriptions, are discussed in detail, with a particular focus on how to compute the integrals needed to evaluate the ddCOSMO solvation energy and its derivatives. The overall computational cost of a ddCOSMO computation is then analyzed and decomposed in the various steps: the different relative weights of such contributions are then discussed for both ddCOSMO and the fastest available alternative discretization to the COSMO equations. Finally, the scaling of the cost of the various steps with respect to the size of the solute is analyzed and discussed, showing how ddCOSMO opens significantly new possibilities when cheap or hybrid molecular mechanics/quantum mechanics methods are used to describe the solute.
Monod before Monod: enzymatic adaptation, Lwoff, and the legacy of general biology.
Loison, Laurent
2013-01-01
For most of his scientific career, Jacques Monod appeared to be a man of a single problem: the formation of enzymes and the regulation of their properties. His ability to produce theoretical models led him to play a major role in both the discovery of the operon regulation and the model of allosteric transitions. The successes of Monod, from the 1950s to the Noble Prize (1965), are already well documented. In this paper, I will focus on the Monod before Monod, that is, the Monod who, during the 1940s, tried to explain the fundamental phenomenon of enzymatic adaptation. To begin with, however, I will survey how this phenomenon was discovered and explained by French Pasteurians at the very beginning of the twentieth century. This first explanation took place amidst an entrenched Lamarckian atmosphere in French thought, which was still alive during the 1920s and the 1930s, when Monod commenced the study of biology at the Sorbonne. Because of his will to construct a scientific biology free from teleology, Monod always tried to break from the legacy of this traditional background of Lamarckism, and he consequently developed ways of thinking that, in the main, were not part of the French biological tradition. Nevertheless, one point did link Monod to French history: his fruitful interactions with André Lwoff. As we shall see, these interactions were necessary for the development of Monod's science, both technically and intellectually speaking.
Generalizing DTW to the multi-dimensional case requires an adaptive approach
Hu, Bing; Jin, Hongxia; Wang, Jun; Keogh, Eamonn
2017-01-01
In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW’s efficiency, together with multiple empirical studies showing that DTW-based classifiers at least equal (and generally surpass) the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways, dependent or independent warping. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant. In this work, we show that this is not the case. The two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we should trust at the time of classification. Our method allows us to ensure that classification results are at least as accurate as the better of the two rival methods, and, in many cases, our method is significantly more accurate. We demonstrate our ideas with the most extensive set of multi-dimensional time series classification experiments ever attempted. PMID:29104448
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Vivek Singh Bawa
2017-06-01
Full Text Available Advanced driver assistance systems (ADAS have been developed to automate and modify vehicles for safety and better driving experience. Among all computer vision modules in ADAS, 360-degree surround view generation of immediate surroundings of the vehicle is very important, due to application in on-road traffic assistance, parking assistance etc. This paper presents a novel algorithm for fast and computationally efficient transformation of input fisheye images into required top down view. This paper also presents a generalized framework for generating top down view of images captured by cameras with fish-eye lenses mounted on vehicles, irrespective of pitch or tilt angle. The proposed approach comprises of two major steps, viz. correcting the fish-eye lens images to rectilinear images, and generating top-view perspective of the corrected images. The images captured by the fish-eye lens possess barrel distortion, for which a nonlinear and non-iterative method is used. Thereafter, homography is used to obtain top-down view of corrected images. This paper also targets to develop surroundings of the vehicle for wider distortion less field of view and camera perspective independent top down view, with minimum computation cost which is essential due to limited computation power on vehicles.
Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza
2017-09-01
In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.
Indian Academy of Sciences (India)
Page S20: NMR compound 4i. Page S22: NMR compound 4j. General: Chemicals were purchased from Fluka, Merck and Aldrich Chemical Companies. All the products were characterized by comparison of their IR, 1H NMR and 13C NMR spectroscopic data and their melting points with reported values. General procedure ...
Deconinck, E; Zhang, M H; Petitet, F; Dubus, E; Ijjaali, I; Coomans, D; Vander Heyden, Y
2008-02-18
The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.
Hapugoda, J. C.; Sooriyarachchi, M. R.
2017-09-01
Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.
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Pérez-Páramo María
2010-01-01
Full Text Available Abstract Background Generalized anxiety disorder (GAD is a prevalent mental health condition which is underestimated worldwide. This study carried out the cultural adaptation into Spanish of the 7-item self-administered GAD-7 scale, which is used to identify probable patients with GAD. Methods The adaptation was performed by an expert panel using a conceptual equivalence process, including forward and backward translations in duplicate. Content validity was assessed by interrater agreement. Criteria validity was explored using ROC curve analysis, and sensitivity, specificity, predictive positive value and negative value for different cut-off values were determined. Concurrent validity was also explored using the HAM-A, HADS, and WHO-DAS-II scales. Results The study sample consisted of 212 subjects (106 patients with GAD with a mean age of 50.38 years (SD = 16.76. Average completion time was 2'30''. No items of the scale were left blank. Floor and ceiling effects were negligible. No patients with GAD had to be assisted to fill in the questionnaire. The scale was shown to be one-dimensional through factor analysis (explained variance = 72%. A cut-off point of 10 showed adequate values of sensitivity (86.8% and specificity (93.4%, with AUC being statistically significant [AUC = 0.957-0.985; p 0.001. Limitations Elderly people, particularly those very old, may need some help to complete the scale. Conclusion After the cultural adaptation process, a Spanish version of the GAD-7 scale was obtained. The validity of its content and the relevance and adequacy of items in the Spanish cultural context were confirmed.
Liu, Huiquan; Li, Yang; Chen, Daipeng; Qi, Zhaomei; Wang, Qinhu; Wang, Jianhua; Jiang, Cong; Xu, Jin-Rong
2017-09-12
Although fungi lack adenosine deaminase acting on RNA (ADAR) enzymes, adenosine to inosine (A-to-I) RNA editing was reported recently in Fusarium graminearum during sexual reproduction. In this study, we profiled the A-to-I editing landscape and characterized its functional and adaptive properties in the model filamentous fungus Neurospora crassa A total of 40,677 A-to-I editing sites were identified, and approximately half of them displayed stage-specific editing or editing levels at different sexual stages. RNA-sequencing analysis with the Δstc-1 and Δsad-1 mutants confirmed A-to-I editing occurred before ascus development but became more prevalent during ascosporogenesis. Besides fungal-specific sequence and secondary structure preference, 63.5% of A-to-I editing sites were in the coding regions and 81.3% of them resulted in nonsynonymous recoding, resulting in a significant increase in the proteome complexity. Many genes involved in RNA silencing, DNA methylation, and histone modifications had extensive recoding, including sad-1, sms-3, qde-1, and dim-2. Fifty pseudogenes harbor premature stop codons that require A-to-I editing to encode full-length proteins. Unlike in humans, nonsynonymous editing events in N. crassa are generally beneficial and favored by positive selection. Almost half of the nonsynonymous editing sites in N. crassa are conserved and edited in Neurospora tetrasperma Furthermore, hundreds of them are conserved in F. graminearum and had higher editing levels. Two unknown genes with editing sites conserved between Neurospora and Fusarium were experimentally shown to be important for ascosporogenesis. This study comprehensively analyzed A-to-I editing in N. crassa and showed that RNA editing is stage-specific and generally adaptive, and may be functionally related to repeat induced point mutation and meiotic silencing by unpaired DNA.
Linear Generalized Nash Equilibrium Problems
Sudermann-Merx, Nathan Georg
2016-01-01
In der vorliegenden Arbeit werden verallgemeinerte Nash Spiele (LGNEPs) unter Linearitätsannahmen eingeführt und untersucht. Durch Ausnutzung der speziellen Struktur lassen sich theoretische und algorithmische Resultate erzielen, die weit über die Ergebnisse für allgemeine LGNEPs hinausgehen.
Nakagawa, Shinichi; Johnson, Paul C D; Schielzeth, Holger
2017-09-01
The coefficient of determination R(2) quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R(2) for generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R(2) that we called [Formula: see text] for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs. In this paper, we generalize our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen's inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen's inequality has important implications for biologically meaningful interpretation of GLMMs, whereas the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. However, our method can be used across disciplines and regardless of statistical environments. © 2017 The Author(s).
Energy Technology Data Exchange (ETDEWEB)
Manrique, John Peter O.; Costa, Alessandro M., E-mail: johnp067@usp.br, E-mail: amcosta@usp.br [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil)
2016-07-01
The spectral distribution of megavoltage X-rays used in radiotherapy departments is a fundamental quantity from which, in principle, all relevant information required for radiotherapy treatments can be determined. To calculate the dose delivered to the patient who make radiation therapy, are used treatment planning systems (TPS), which make use of convolution and superposition algorithms and which requires prior knowledge of the photon fluence spectrum to perform the calculation of three-dimensional doses and thus ensure better accuracy in the tumor control probabilities preserving the normal tissue complication probabilities low. In this work we have obtained the photon fluence spectrum of X-ray of the SIEMENS ONCOR linear accelerator of 6 MV, using an character-inverse method to the reconstruction of the spectra of photons from transmission curves measured for different thicknesses of aluminum; the method used for reconstruction of the spectra is a stochastic technique known as generalized simulated annealing (GSA), based on the work of quasi-equilibrium statistic of Tsallis. For the validation of the reconstructed spectra we calculated the curve of percentage depth dose (PDD) for energy of 6 MV, using Monte Carlo simulation with Penelope code, and from the PDD then calculate the beam quality index TPR{sub 20/10}. (author)
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William Alvarez Gaviria
2004-09-01
Full Text Available The origin of climacteric has been subject of debate. Most opinions agree in that it arises exclusively from natural selection. In this paper the author argues that, besides this reason there is another, even more important; for him, climacteric is the final response to fatigue or the third stage of the general adaptation syndrome, just as in elderly people there is a loss of the capacity of proliferation of fibroblasts and lack of response to insulin. From a genetic point of view, this corresponds to an antagonic pleiotropy: the genetic program that has made the human adrenergic and corticotropic systems hyperactive, has also caused that they do not reach senescence intact. High concentrations of stress hormones during youth and adulthood in humans, as compared to chimpanzees, gorillas and orangutans, and the hormonal cascade reactions elicited by them are meaningfully related to our most conspicuous illnesses, our genotype/phenotype and, in the long term, with climacteric. Se ha conjeturado a menudo sobre las razones del climaterio y la mayoría de los autores sostiene que es un fenómeno que surge exclusivamente de la selección natural. Aquí asumimos que, aunque esa sea parte de la explicación, no es la razón primordial. Así como con la edad se da la pérdida, por ejemplo, de la capacidad proliferativa de los fibroblastos y de la sensibilidad a la insulina, el climaterio podría corresponder no más que a la fatiga o tercera etapa del Síndrome de Adaptación General. En un enfoque genético correspondería, pues, a una pleiotropía antagónica: el programa genético que ha hecho hiperactivos a los sistemas adrenérgico y corticotrópico del ser humano, evitaría también que llegara incólume al punto final de senescencia. Las altas concentraciones de hormonas de estrés en la juventud y la edad adulta que distinguen a nuestra especie, comparada con el chimpancé, el gorila y el orangután, y las reacciones hormonales en cascada que
Zheng, Xueying; Qin, Guoyou; Tu, Dongsheng
2017-05-30
Motivated by the analysis of quality of life data from a clinical trial on early breast cancer, we propose in this paper a generalized partially linear mean-covariance regression model for longitudinal proportional data, which are bounded in a closed interval. Cholesky decomposition of the covariance matrix for within-subject responses and generalized estimation equations are used to estimate unknown parameters and the nonlinear function in the model. Simulation studies are performed to evaluate the performance of the proposed estimation procedures. Our new model is also applied to analyze the data from the cancer clinical trial that motivated this research. In comparison with available models in the literature, the proposed model does not require specific parametric assumptions on the density function of the longitudinal responses and the probability function of the boundary values and can capture dynamic changes of time or other interested variables on both mean and covariance of the correlated proportional responses. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Piccardo, Matteo; Bloino, Julien; Barone, Vincenzo
2015-08-05
Models going beyond the rigid-rotor and the harmonic oscillator levels are mandatory for providing accurate theoretical predictions for several spectroscopic properties. Different strategies have been devised for this purpose. Among them, the treatment by perturbation theory of the molecular Hamiltonian after its expansion in power series of products of vibrational and rotational operators, also referred to as vibrational perturbation theory (VPT), is particularly appealing for its computational efficiency to treat medium-to-large systems. Moreover, generalized (GVPT) strategies combining the use of perturbative and variational formalisms can be adopted to further improve the accuracy of the results, with the first approach used for weakly coupled terms, and the second one to handle tightly coupled ones. In this context, the GVPT formulation for asymmetric, symmetric, and linear tops is revisited and fully generalized to both minima and first-order saddle points of the molecular potential energy surface. The computational strategies and approximations that can be adopted in dealing with GVPT computations are pointed out, with a particular attention devoted to the treatment of symmetry and degeneracies. A number of tests and applications are discussed, to show the possibilities of the developments, as regards both the variety of treatable systems and eligible methods. © 2015 Wiley Periodicals, Inc.
Anikhovskaya, I A; Dvoenosov, V G; Zhdanov, R I; Koubatiev, A A; Mayskiy, I A; Markelova, M M; Meshkov, M V; Oparina, O N; Salakhov, I M; Yakovlev, M Yu
2015-01-01
General adaptation syndrome (GAS), the basis of the development of which is stress phenomenon, is an essential component of the pathogenesis of many diseases and syndromes. However, the patho genesis of GAS hitherto is considered exclusively from the endocrinological viewpoint. This relates primarily to the initial phase of the GAS, a clinical model for the study of which may be psycho-emotional stress (PES), which we studied using three groups of volunteers. The first one consists of 25 students who were waiting for unaccustomed physical activity (17 men) and play debut on the stage (8 women). The second group consists of 48 children (2-14 years) who expected for "planned" surgery. The third group of volunteers is made up of 80 students (41 women and 39 men) during the first exam. The concentration of cortisol, endotoxin (ET), the activity of antiendotoxin immunity (AEI) and the haemostatic system parameters were determined in the blood serum of volunteers in various combinations. We found laboratory evidence for PES at 92% of students of the first group, 58% of children of the second one and in 21% of students of the third group of volunteers (mostly women). The concentration of ET increased at 13 (52%) volunteers of the first group with a significant increase of average indicators in the whole group (from 0.84 ± 0.06 to 1.19 ± 0.04 EU/ml). At children of the second group, the average concentration of ET increased even more significantly (from 0.42 ± 0.02 to 1.63 ± 0.11 EU/ml), which was accompanied by the activation of the hemostasis system. A degree of the activation was directly dependent on the level of ET in the general circulation and on an activity of AEI. Examination stress in the third group of volunteers is accompanied by activation of plasma hemostasis (increased initial thrombosis rate and reduced the time it starts, lag-period) in 26% of female students and 15% of male students. We suggest that it is possible to use the PES as a clinical model
Simon, Patrick; Schneider, Peter
2017-08-01
In weak gravitational lensing, weighted quadrupole moments of the brightness profile in galaxy images are a common way to estimate gravitational shear. We have employed general adaptive moments (GLAM ) to study causes of shear bias on a fundamental level and for a practical definition of an image ellipticity. The GLAM ellipticity has useful properties for any chosen weight profile: the weighted ellipticity is identical to that of isophotes of elliptical images, and in absence of noise and pixellation it is always an unbiased estimator of reduced shear. We show that moment-based techniques, adaptive or unweighted, are similar to a model-based approach in the sense that they can be seen as imperfect fit of an elliptical profile to the image. Due to residuals in the fit, moment-based estimates of ellipticities are prone to underfitting bias when inferred from observed images. The estimation is fundamentally limited mainly by pixellation which destroys information on the original, pre-seeing image. We give an optimised estimator for the pre-seeing GLAM ellipticity and quantify its bias for noise-free images. To deal with images where pixel noise is prominent, we consider a Bayesian approach to infer GLAM ellipticity where, similar to the noise-free case, the ellipticity posterior can be inconsistent with the true ellipticity if we do not properly account for our ignorance about fit residuals. This underfitting bias, quantified in the paper, does not vary with the overall noise level but changes with the pre-seeing brightness profile and the correlation or heterogeneity of pixel noise over the image. Furthermore, when inferring a constant ellipticity or, more relevantly, constant shear from a source sample with a distribution of intrinsic properties (sizes, centroid positions, intrinsic shapes), an additional, now noise-dependent bias arises towards low signal-to-noise if incorrect prior densities for the intrinsic properties are used. We discuss the origin of this
Greedy controllability of finite dimensional linear systems
Lazar, Martin; Zuazua, Enrique
2016-01-01
We analyse the problem of controllability for parameter-dependent linear finite-dimensional systems. The goal is to identify the most distinguished realisations of those parameters so to better describe or approximate the whole range of controls. We adapt recent results on greedy and weak greedy algorithms for parameter depending PDEs or, more generally, abstract equations in Banach spaces. Our results lead to optimal approximation procedures that, in particular, perform better than simply sa...
Linearization of ancestral multichromosomal genomes.
Maňuch, Ján; Patterson, Murray; Wittler, Roland; Chauve, Cedric; Tannier, Eric
2012-01-01
Recovering the structure of ancestral genomes can be formalized in terms of properties of binary matrices such as the Consecutive-Ones Property (C1P). The Linearization Problem asks to extract, from a given binary matrix, a maximum weight subset of rows that satisfies such a property. This problem is in general intractable, and in particular if the ancestral genome is expected to contain only linear chromosomes or a unique circular chromosome. In the present work, we consider a relaxation of this problem, which allows ancestral genomes that can contain several chromosomes, each either linear or circular. We show that, when restricted to binary matrices of degree two, which correspond to adjacencies, the genomic characters used in most ancestral genome reconstruction methods, this relaxed version of the Linearization Problem is polynomially solvable using a reduction to a matching problem. This result holds in the more general case where columns have bounded multiplicity, which models possibly duplicated ancestral genes. We also prove that for matrices with rows of degrees 2 and 3, without multiplicity and without weights on the rows, the problem is NP-complete, thus tracing sharp tractability boundaries. As it happened for the breakpoint median problem, also used in ancestral genome reconstruction, relaxing the definition of a genome turns an intractable problem into a tractable one. The relaxation is adapted to some biological contexts, such as bacterial genomes with several replicons, possibly partially assembled. Algorithms can also be used as heuristics for hard variants. More generally, this work opens a way to better understand linearization results for ancestral genome structure inference.
Directory of Open Access Journals (Sweden)
A. A. Zarei
2016-03-01
Full Text Available Winter dens are one of the important components of brown bear's (Ursus arctos syriacus habitat, affecting their reproduction and survival. Therefore identification of factors affecting the habitat selection and suitable denning areas in the conservation of our largest carnivore is necessary. We used Geographically Weighted Logistic Regression (GWLR and Generalized Linear Model (GLM for modeling suitability of denning habitat in Kouhkhom region in Fars province. In the present research, 20 dens (presence locations and 20 caves where signs of bear were not found (absence locations were used as dependent variables and six environmental factors were used for each location as independent variables. The results of GLM showed that variables of distance to settlements, altitude, and distance to water were the most important parameters affecting suitability of the brown bear's denning habitat. The results of GWLR showed the significant local variations in the relationship between occurrence of brown bear dens and the variable of distance to settlements. Based on the results of both models, suitable habitats for denning of the species are impassable areas in the mountains and inaccessible for humans.
Zhang, Tingting; Pham, Minh; Sun, Jianhui; Yan, Guofen; Li, Huazhang; Sun, Yinge; Gonzalez, Marlen Z; Coan, James A
2017-12-26
The focus of this paper is on evaluating brain responses to different stimuli and identifying brain regions with different responses using multi-subject, stimulus-evoked functional magnetic resonance imaging (fMRI) data. To jointly model many brain voxels' responses to designed stimuli, we present a new low-rank multivariate general linear model (LRMGLM) for stimulus-evoked fMRI data. The new model not only is flexible to characterize variation in hemodynamic response functions (HRFs) across different regions and stimulus types, but also enables information "borrowing" across voxels and uses much fewer parameters than typical nonparametric models for HRFs. To estimate the proposed LRMGLM, we introduce a new penalized optimization function, which leads to temporally and spatially smooth HRF estimates. We develop an efficient optimization algorithm to minimize the optimization function and identify the voxels with different responses to stimuli. We show that the proposed method can outperform several existing voxel-wise methods by achieving both high sensitivity and specificity. We apply the proposed method to the fMRI data collected in an emotion study, and identify anterior dACC to have different responses to a designed threat and control stimuli. Copyright © 2017. Published by Elsevier Inc.
Oktem, Figen S; Ozaktas, Haldun M
2010-08-01
Linear canonical transforms (LCTs) form a three-parameter family of integral transforms with wide application in optics. We show that LCT domains correspond to scaled fractional Fourier domains and thus to scaled oblique axes in the space-frequency plane. This allows LCT domains to be labeled and ordered by the corresponding fractional order parameter and provides insight into the evolution of light through an optical system modeled by LCTs. If a set of signals is highly confined to finite intervals in two arbitrary LCT domains, the space-frequency (phase space) support is a parallelogram. The number of degrees of freedom of this set of signals is given by the area of this parallelogram, which is equal to the bicanonical width product but usually smaller than the conventional space-bandwidth product. The bicanonical width product, which is a generalization of the space-bandwidth product, can provide a tighter measure of the actual number of degrees of freedom, and allows us to represent and process signals with fewer samples.
Directory of Open Access Journals (Sweden)
Daniel E. Rio
2013-01-01
Full Text Available A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI blood-oxygen level-dependent (BOLD multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF where prewhitening of the data is attempted using autoregressive (AR models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain. This is especially important for experimental designs involving multiple states (either stimulus or drug induced that may alter the form of the response function.
Spence, Jeffrey S; Brier, Matthew R; Hart, John; Ferree, Thomas C
2013-03-01
Linear statistical models are used very effectively to assess task-related differences in EEG power spectral analyses. Mixed models, in particular, accommodate more than one variance component in a multisubject study, where many trials of each condition of interest are measured on each subject. Generally, intra- and intersubject variances are both important to determine correct standard errors for inference on functions of model parameters, but it is often assumed that intersubject variance is the most important consideration in a group study. In this article, we show that, under common assumptions, estimates of some functions of model parameters, including estimates of task-related differences, are properly tested relative to the intrasubject variance component only. A substantial gain in statistical power can arise from the proper separation of variance components when there is more than one source of variability. We first develop this result analytically, then show how it benefits a multiway factoring of spectral, spatial, and temporal components from EEG data acquired in a group of healthy subjects performing a well-studied response inhibition task. Copyright © 2011 Wiley Periodicals, Inc.
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Xavi Puig
2005-12-01
Full Text Available En este trabajo se muestra cómo los modelos lineales generalizados permiten describir eficientemente diferentes patrones de evolución temporal de datos de mortalidad y, a su vez, llevar a cabo una fácil interpretación. Como aplicación práctica se analiza la evolución de la mortalidad por cáncer de mama en las mujeres de Cataluña entre 1986 y 2000. De los resultados destaca que la mortalidad por cáncer de mama experimenta un aumento y un posterior descenso para todos los grupos de edad. El año en que se inicia el descenso es más reciente en los grupos de edad mayor.In this work it is shown how generalized linear models allow one to describe different patterns of temporary evolution of mortality data, while at the same time allow for an easy interpretation. As a practical application, the evolution of the female breast cancer mortality in Catalonia from 1986 to 2000 is analyzed. Remarkably, the mortality from breast cancer first increases and then decreases for all age groups. Moreover, the year in which the cancer rate starts decreasing is more recent in the older age groups.
Lieberman, Lauren; Lucas, Mark; Jones, Jeffery; Humphreys, Dan; Cody, Ann; Vaughn, Bev; Storms, Tommie
2013-01-01
"Helping General Physical Educators and Adapted Physical Educators Address the Office of Civil Rights Dear Colleague Guidance Letter: Part IV--Sport Groups" provides the the following articles: (1) "Sport Programming Offered by Camp Abilities and the United States Association for Blind Athletes" (Lauren Lieberman and Mark…
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Wendy Wing Tak Lam
Full Text Available Illness perceptions are linked to individual help-seeking and preventive behaviors. Previous illness perception studies have identified five dimensions of illness-related experience and behaviour. The Revised Illness Perception Questionnaire (IPQ-R for genetic predisposition (IPQ-R-GP was developed to measure illness perceptions in those genetically-predisposed to blood disease. We adapted the IPQ-R-GP to measure perceptions of generalized cancer predisposition. This paper describes the development and validation of the Cancer Predisposition Perception Scale (CPPS.The draft CPPS scale was first administered to 167 well Hepatitis B carriers and 123 other healthy individuals and the factor structure was examined using Exploratory Factor Analysis. Then the factor structure was confirmed in a second sample comprising 148 healthy controls, 150 smokers and 152 passive smokers using Confirmatory Factor Analysis (CFA.Six-factors comprising 26 items provided optimal fit by eigen and scree-plot methods, accounting for 58.9% of the total variance. CFA indicated good fit of the six-factor model after further excluding three items. The six factors, Emotional representation (5 items, Illness coherence (4 items, Treatment control (3 items, Consequences (5 items, Internal locus of control (2 items and External locus of control (4 items demonstrated adequate-to-good subscale internal consistency (Cronbach's α = 0.63-0.90. Divergent validity was suggested by low correlations with optimism, self-efficacy, and scales for measuring physical and psychological health symptoms.The CPPS appears to be a valid measure of perceived predisposition to generic cancer risks and can be used to examine cancer-risk-related cognitions in individuals at higher and lower cancer risk.
Molaee-Ardekani, B; Senhadji, L; Shamsollahi, M B; Vosoughi-Vahdat, B; Wodey, E
2007-10-01
In this paper, an enhanced local mean-field model that is suitable for simulating the electroencephalogram (EEG) in different depths of anesthesia is presented. The main building elements of the model (e.g., excitatory and inhibitory populations) are taken from Steyn-Ross [M. L. Steyn-Ross, Phys. Rev. E 64, 011917 (2001), D. A. Steyn-Ross, Phys. Rev. E 64, 011918 (2001)] and Bojak and Liley [I. Bojak and D. T. Liley, Phys. Rev. E 71, 041902 (2005)] mean-field models and a new slow ionic mechanism is included in the main model. Generally, in mean-field models, some sigmoid-shape functions determine firing rates of neural populations according to their mean membrane potentials. In the enhanced model, the sigmoid function corresponding to excitatory population is redefined to be also a function of the slow ionic mechanism. This modification adapts the firing rate of neural populations to slow ionic activities of the brain. When an anesthetic drug is administered, the slow mechanism may induce neural cells to alternate between two levels of activity referred to as up and down states. Basically, the frequency of up-down switching is in the delta band (0-4 Hz) and this is the main reason behind high amplitude, low frequency fluctuations of EEG signals in anesthesia. Our analyses show that the enhanced model may have different working states driven by anesthetic drug concentration. The model is settled in the up state in the waking period, it may switch to up and down states in moderate anesthesia while in deep anesthesia it remains in the down state.
Lin, Zi-Jing; Li, Lin; Cazzell, Mary; Liu, Hanli
2014-08-01
Diffuse optical tomography (DOT) is a variant of functional near infrared spectroscopy and has the capability of mapping or reconstructing three dimensional (3D) hemodynamic changes due to brain activity. Common methods used in DOT image analysis to define brain activation have limitations because the selection of activation period is relatively subjective. General linear model (GLM)-based analysis can overcome this limitation. In this study, we combine the atlas-guided 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with risk decision-making processes. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The Balloon Analog Risk Task (BART) is a valid experimental model and has been commonly used to assess human risk-taking actions and tendencies while facing risks. We have used the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making from 37 human participants (22 males and 15 females). Voxel-wise GLM analysis was performed after a human brain atlas template and a depth compensation algorithm were combined to form atlas-guided DOT images. In this work, we wish to demonstrate the excellence of using voxel-wise GLM analysis with DOT to image and study cognitive functions in response to risk decision-making. Results have shown significant hemodynamic changes in the dorsal lateral prefrontal cortex (DLPFC) during the active-choice mode and a different activation pattern between genders; these findings correlate well with published literature in functional magnetic resonance imaging (fMRI) and fNIRS studies. Copyright © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.
Schüle, Steffen Andreas; Gabriel, Katharina M A; Bolte, Gabriele
2017-06-01
The environmental justice framework states that besides environmental burdens also resources may be social unequally distributed both on the individual and on the neighbourhood level. This ecological study investigated whether neighbourhood socioeconomic position (SEP) was associated with neighbourhood public green space availability in a large German city with more than 1 million inhabitants. Two different measures were defined for green space availability. Firstly, percentage of green space within neighbourhoods was calculated with the additional consideration of various buffers around the boundaries. Secondly, percentage of green space was calculated based on various radii around the neighbourhood centroid. An index of neighbourhood SEP was calculated with principal component analysis. Log-gamma regression from the group of generalized linear models was applied in order to consider the non-normal distribution of the response variable. All models were adjusted for population density. Low neighbourhood SEP was associated with decreasing neighbourhood green space availability including 200m up to 1000m buffers around the neighbourhood boundaries. Low neighbourhood SEP was also associated with decreasing green space availability based on catchment areas measured from neighbourhood centroids with different radii (1000m up to 3000 m). With an increasing radius the strength of the associations decreased. Social unequally distributed green space may amplify environmental health inequalities in an urban context. Thus, the identification of vulnerable neighbourhoods and population groups plays an important role for epidemiological research and healthy city planning. As a methodical aspect, log-gamma regression offers an adequate parametric modelling strategy for positively distributed environmental variables. Copyright © 2017 Elsevier GmbH. All rights reserved.
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Valsasina, P.; Agosta, F.; Filippi, M. [Scientific Institute Ospedale San Raffaele, Neuroimaging Research Unit, Milan (Italy); Caputo, D. [Scientific Institute Fondazione Don Gnocchi, Department of Neurology, Milan (Italy); Stroman, P.W. [Queen' s University, Department of Diagnostic Radiology, Centre for Neuroscience Studies, Kingston, ON (Canada)
2008-10-15
Functional MRI (fMRI) of the spinal cord is able to provide maps of neuronal activity. Spinal fMRI data have been analyzed in previous studies by calculating the cross-correlation (CC) between the stimulus and the time course of every voxel and, more recently, by using the general linear model (GLM). The aim of this study was to compare three different approaches (CC analysis, GLM and independent component analysis (ICA)) for analyzing fMRI scans of the cervical spinal cord. We analyzed spinal fMRI data from healthy subjects during a proprioceptive and a tactile stimulation by using two model-based approaches, i.e., CC analysis between the stimulus shape and the time course of every voxel, and the GLM. Moreover, we applied independent component analysis, a model-free approach which decomposes the data in a set of source signals. All methods were able to detect cervical cord areas of activity corresponding to the expected regions of neuronal activations. Model-based approaches (CC and GLM) revealed similar patterns of activity. ICA could identify a component correlated to fMRI stimulation, although with a lower statistical threshold than model-based approaches, and many components, consistent across subjects, which are likely to be secondary to noise present in the data. Model-based approaches seem to be more robust for estimating task-related activity, whereas ICA seems to be useful for eliminating noise components from the data. Combined use of ICA and GLM might improve the reliability of spinal fMRI results. (orig.)
Vilar, Lara; Gómez, Israel; Martínez-Vega, Javier; Echavarría, Pilar; Riaño, David; Martín, M Pilar
2016-01-01
The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment.
Stochl, Jan; Böhnke, Jan R; Pickett, Kate E; Croudace, Tim J
2016-05-20
Recent developments in psychometric modeling and technology allow pooling well-validated items from existing instruments into larger item banks and their deployment through methods of computerized adaptive testing (CAT). Use of item response theory-based bifactor methods and integrative data analysis overcomes barriers in cross-instrument comparison. This paper presents the joint calibration of an item bank for researchers keen to investigate population variations in general psychological distress (GPD). Multidimensional item response theory was used on existing health survey data from the Scottish Health Education Population Survey (n = 766) to calibrate an item bank consisting of pooled items from the short common mental disorder screen (GHQ-12) and the Affectometer-2 (a measure of "general happiness"). Computer simulation was used to evaluate usefulness and efficacy of its adaptive administration. A bifactor model capturing variation across a continuum of population distress (while controlling for artefacts due to item wording) was supported. The numbers of items for different required reliabilities in adaptive administration demonstrated promising efficacy of the proposed item bank. Psychometric modeling of the common dimension captured by more than one instrument offers the potential of adaptive testing for GPD using individually sequenced combinations of existing survey items. The potential for linking other item sets with alternative candidate measures of positive mental health is discussed since an optimal item bank may require even more items than these.
Md Tanwir Uddin Haider,; Singh, M P; U.S.Triar
2012-01-01
This paper presents the results of experiment conducted to investigate the correlation, if any, between the learning style and cognitive traits which include working memory capacity of technical learners. Further, the experiment aimed to investigate the correlation between the learning style and performance of technical learners in web-based quizzes based on their styles over different subjects for making the generalized semantic framework for adaptive online learning system. The results indi...
Directory of Open Access Journals (Sweden)
João C. F. Borges Júnior
2008-09-01
Full Text Available Linear programming models are effective tools to support initial or periodic planning of agricultural enterprises, requiring, however, technical coefficients that can be determined using computer simulation models. This paper, presented in two parts, deals with the development, application and tests of a methodology and of a computational modeling tool to support planning of irrigated agriculture activities. Part I aimed at the development and application, including sensitivity analysis, of a multiyear linear programming model to optimize the financial return and water use, at farm level for Jaíba irrigation scheme, Minas Gerais State, Brazil, using data on crop irrigation requirement and yield, obtained from previous simulation with MCID model. The linear programming model outputted a crop pattern to which a maximum total net present value of R$ 372,723.00 for the four years period, was obtained. Constraints on monthly water availability, labor, land and production were critical in the optimal solution. In relation to the water use optimization, it was verified that an expressive reductions on the irrigation requirements may be achieved by small reductions on the maximum total net present value.Modelos de programação linear são ferramentas eficazes de suporte ao planejamento inicial ou periódico de empreendimentos agrícolas, requerendo, todavia, coeficientes técnicos que podem ser obtidos por modelos computacionais de simulação. Este trabalho, dividido em duas partes, aborda o desenvolvimento, a aplicação e os testes de metodologia e da modelagem computacional de uma ferramenta de auxílio ao planejamento da exploração agrícola irrigada. Teve-se o objetivo de desenvolver e aplicar, com análise de sensibilidade, um modelo de programação linear plurianual para otimização do retorno financeiro e uso da água, em nível de propriedade rural no perímetro de irrigação do Jaíba - MG, utilizando dados de requerimento de irriga
Dai, Hao; Si, Gangquan; Jia, Lixin; Zhang, Yanbin
2013-11-01
This paper investigates generalized function matrix projective lag synchronization between fractional-order and integer-order complex networks with delayed coupling, non-identical topological structures and different dimensions. Based on Lyapunov stability theory, generalized function matrix projective lag synchronization criteria are derived by using the adaptive control method. In addition, the three-dimensional fractional-order chaotic system and the four-dimensional integer-order hyperchaotic system as the nodes of the drive and the response networks, respectively, are analyzed in detail, and numerical simulation results are presented to illustrate the effectiveness of the theoretical results.
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Kostoglotov Andrey Aleksandrovich
2016-01-01
Full Text Available The proposed procedure for the synthesis of the filter of the state estimation is based on a new mathematical model of the dynamic controlled system. It is based on the maximum condition of the function of the generalized power. The optimization boundary problem can be solved using the invariant embedding procedure. The result differs from the known ones as it has lower dimensionality, non-linear structure and provides a more accurate estimation.
Wang, L. M.
2017-09-01
A novel model-free adaptive sliding mode strategy is proposed for a generalized projective synchronization (GPS) between two entirely unknown fractional-order chaotic systems subject to the external disturbances. To solve the difficulties from the little knowledge about the master-slave system and to overcome the bad effects of the external disturbances on the generalized projective synchronization, the radial basis function neural networks are used to approach the packaged unknown master system and the packaged unknown slave system (including the external disturbances). Consequently, based on the slide mode technology and the neural network theory, a model-free adaptive sliding mode controller is designed to guarantee asymptotic stability of the generalized projective synchronization error. The main contribution of this paper is that a control strategy is provided for the generalized projective synchronization between two entirely unknown fractional-order chaotic systems subject to the unknown external disturbances, and the proposed control strategy only requires that the master system has the same fractional orders as the slave system. Moreover, the proposed method allows us to achieve all kinds of generalized projective chaos synchronizations by turning the user-defined parameters onto the desired values. Simulation results show the effectiveness of the proposed method and the robustness of the controlled system.
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Szadkowski, Zbigniew [University of Lodz, Department of Physics and Applied Informatics, 90-236 Lodz, (Poland)
2015-07-01
We present the new approach to a filtering of radio frequency interferences (RFI) in the Auger Engineering Radio Array (AERA) which study the electromagnetic part of the Extensive Air Showers. The radio stations can observe radio signals caused by coherent emissions due to geomagnetic radiation and charge excess processes. AERA observes frequency band from 30 to 80 MHz. This range is highly contaminated by human-made RFI. In order to improve the signal to noise ratio RFI filters are used in AERA to suppress this contamination. The first kind of filter used by AERA was the Median one, based on the Fast Fourier Transform (FFT) technique. The second one, which is currently in use, is the infinite impulse response (IIR) notch filter. The proposed new filter is a finite impulse response (FIR) filter based on a linear prediction (LP). A periodic contamination hidden in a registered signal (digitized in the ADC) can be extracted and next subtracted to make signal cleaner. The FIR filter requires a calculation of n=32, 64 or even 128 coefficients (dependent on a required speed or accuracy) by solving of n linear equations with coefficients built from the covariance Toeplitz matrix. This matrix can be solved by the Levinson recursion, which is much faster than the Gauss procedure. The filter has been already tested in the real AERA radio stations on Argentinean pampas with a very successful results. The linear equations were solved either in the virtual soft-core NIOSR processor (implemented in the FPGA chip as a net of logic elements) or in the external Voipac PXA270M ARM processor. The NIOS processor is relatively slow (50 MHz internal clock), calculations performed in an external processor consume a significant amount of time for data exchange between the FPGA and the processor. Test showed a very good efficiency of the RFI suppression for stationary (long-term) contaminations. However, we observed a short-time contaminations, which could not be suppressed either by the
Carr, Joseph
1996-01-01
The linear IC market is large and growing, as is the demand for well trained technicians and engineers who understand how these devices work and how to apply them. Linear Integrated Circuits provides in-depth coverage of the devices and their operation, but not at the expense of practical applications in which linear devices figure prominently. This book is written for a wide readership from FE and first degree students, to hobbyists and professionals.Chapter 1 offers a general introduction that will provide students with the foundations of linear IC technology. From chapter 2 onwa
Willis, Thomas A; Hartley, Suzanne; Glidewell, Liz; Farrin, Amanda J; Lawton, Rebecca; McEachan, Rosemary R C; Ingleson, Emma; Heudtlass, Peter; Collinson, Michelle; Clamp, Susan; Hunter, Cheryl; Ward, Vicky; Hulme, Claire; Meads, David; Bregantini, Daniele; Carder, Paul; Foy, Robbie
2016-02-29
There are recognised gaps between evidence and practice in general practice, a setting which provides particular challenges for implementation. We earlier screened clinical guideline recommendations to derive a set of 'high impact' indicators based upon criteria including potential for significant patient benefit, scope for improved practice and amenability to measurement using routinely collected data. We aim to evaluate the effectiveness and cost-effectiveness of a multifaceted, adaptable intervention package to implement four targeted, high impact recommendations in general practice. The research programme Action to Support Practice Implement Research Evidence (ASPIRE) includes a pair of pragmatic cluster-randomised trials which use a balanced incomplete block design. Clusters are general practices in West Yorkshire, United Kingdom (UK), recruited using an 'opt-out' recruitment process. The intervention package adapted to each recommendation includes combinations of audit and feedback, educational outreach visits and computerised prompts with embedded behaviour change techniques selected on the basis of identified needs and barriers to change. In trial 1, practices are randomised to adapted interventions targeting either diabetes control or risky prescribing and those in trial 2 to adapted interventions targeting either blood pressure control in patients at risk of cardiovascular events or anticoagulation in atrial fibrillation. The respective primary endpoints comprise achievement of all recommended target levels of haemoglobin A1c (HbA1c), blood pressure and cholesterol in patients with type 2 diabetes, a composite indicator of risky prescribing, achievement of recommended blood pressure targets for specific patient groups and anticoagulation prescribing in patients with atrial fibrillation. We are also randomising practices to a fifth, non-intervention control group to further assess Hawthorne effects. Outcomes will be assessed using routinely collected data
Carlson, James E.
2014-01-01
Many aspects of the geometry of linear statistical models and least squares estimation are well known. Discussions of the geometry may be found in many sources. Some aspects of the geometry relating to the partitioning of variation that can be explained using a little-known theorem of Pappus and have not been discussed previously are the topic of…
Gorissen, B.L.; Ben-Tal, A.; Blanc, J.P.C.; den Hertog, D.
2012-01-01
Abstract: We propose a new way to derive tractable robust counterparts of a linear conic optimization problem by using the theory of Beck and Ben-Tal [2] on the duality between the robust (“pessimistic”) primal problem and its “optimistic” dual. First, we obtain a new convex reformulation of the
Stoll, R R
1968-01-01
Linear Algebra is intended to be used as a text for a one-semester course in linear algebra at the undergraduate level. The treatment of the subject will be both useful to students of mathematics and those interested primarily in applications of the theory. The major prerequisite for mastering the material is the readiness of the student to reason abstractly. Specifically, this calls for an understanding of the fact that axioms are assumptions and that theorems are logical consequences of one or more axioms. Familiarity with calculus and linear differential equations is required for understand
Linear Algebra and Linear Models
Indian Academy of Sciences (India)
This monograph provides an introduction to the basic aspects of the theory oflinear estima- tion and that of testing linear hypotheses. The primary objective is to provide a basic knowledge of analysis of linear models to advanced undergraduate or first year Master's students. The second edition virtually covers the same ...
Cai, Hongzhu; Hu, Xiangyun; Xiong, Bin; Zhdanov, Michael S.
2017-12-01
The induced polarization (IP) method has been widely used in geophysical exploration to identify the chargeable targets such as mineral deposits. The inversion of the IP data requires modeling the IP response of 3D dispersive conductive structures. We have developed an edge-based finite-element time-domain (FETD) modeling method to simulate the electromagnetic (EM) fields in 3D dispersive medium. We solve the vector Helmholtz equation for total electric field using the edge-based finite-element method with an unstructured tetrahedral mesh. We adopt the backward propagation Euler method, which is unconditionally stable, with semi-adaptive time stepping for the time domain discretization. We use the direct solver based on a sparse LU decomposition to solve the system of equations. We consider the Cole-Cole model in order to take into account the frequency-dependent conductivity dispersion. The Cole-Cole conductivity model in frequency domain is expanded using a truncated Padé series with adaptive selection of the center frequency of the series for early and late time. This approach can significantly increase the accuracy of FETD modeling.
Liesen, Jörg
2015-01-01
This self-contained textbook takes a matrix-oriented approach to linear algebra and presents a complete theory, including all details and proofs, culminating in the Jordan canonical form and its proof. Throughout the development, the applicability of the results is highlighted. Additionally, the book presents special topics from applied linear algebra including matrix functions, the singular value decomposition, the Kronecker product and linear matrix equations. The matrix-oriented approach to linear algebra leads to a better intuition and a deeper understanding of the abstract concepts, and therefore simplifies their use in real world applications. Some of these applications are presented in detailed examples. In several ‘MATLAB-Minutes’ students can comprehend the concepts and results using computational experiments. Necessary basics for the use of MATLAB are presented in a short introduction. Students can also actively work with the material and practice their mathematical skills in more than 300 exerc...
Searle, Shayle R
2012-01-01
This 1971 classic on linear models is once again available--as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.
Solow, Daniel
2014-01-01
This text covers the basic theory and computation for a first course in linear programming, including substantial material on mathematical proof techniques and sophisticated computation methods. Includes Appendix on using Excel. 1984 edition.
Berberian, Sterling K
2014-01-01
Introductory treatment covers basic theory of vector spaces and linear maps - dimension, determinants, eigenvalues, and eigenvectors - plus more advanced topics such as the study of canonical forms for matrices. 1992 edition.
Christofilos, N.C.; Polk, I.J.
1959-02-17
Improvements in linear particle accelerators are described. A drift tube system for a linear ion accelerator reduces gap capacity between adjacent drift tube ends. This is accomplished by reducing the ratio of the diameter of the drift tube to the diameter of the resonant cavity. Concentration of magnetic field intensity at the longitudinal midpoint of the external sunface of each drift tube is reduced by increasing the external drift tube diameter at the longitudinal center region.
Vardanjani, Hossein Molavi; Baneshi, Mohammad Reza; Haghdoost, AliAkbar
2015-01-01
Due to the lack of nationwide population-based cancer registration, the total cancer prevalence in Iran is unknown. Our previous work in which we used a basic network scale-up (NSU) method, failed to provide plausible estimates of total cancer prevalence in Kerman. The aim of the present study was to estimate total and partial prevalence of cancer in southeastern Iran using an adapted version of the generalized network scale-up method. A survey was conducted in 2014 using multi-stage cluster sampling. A total of 1995 face-to-face gender-matched interviews were performed based on an adapted version of the NSU questionnaire. Interviewees were asked about their family cancer history. Total and partial prevalence were estimated using a generalized NSU estimator. The Monte Carlo method was adopted for the estimation of upper/lower bounds of the uncertainty range of point estimates. One-yr, 2-3 yr, and 4-5 yr prevalence (per 100,000 people) was respectively estimated at 78 (95%CI, 66, 90), 128 (95%CI, 118, 147), and 59 (95%CI, 49, 70) for women, and 48 (95%CI, 38, 58), 78 (95%CI, 66, 91), and 42 (95%CI, 32, 52) for men. The 5-yr prevalence of all cancers was estimated at 0.18 percent for men, and 0.27 percent for women. This study showed that the generalized familial network scale-up method is capable of estimating cancer prevalence, with acceptable precision.
Burgin, G. H.; Fogel, L. J.; Phelps, J. P.
1975-01-01
A technique for computer simulation of air combat is described. Volume 1 decribes the computer program and its development in general terms. Two versions of the program exist. Both incorporate a logic for selecting and executing air combat maneuvers with performance models of specific fighter aircraft. In the batch processing version the flight paths of two aircraft engaged in interactive aerial combat and controlled by the same logic are computed. The realtime version permits human pilots to fly air-to-air combat against the adaptive maneuvering logic (AML) in Langley Differential Maneuvering Simulator (DMS). Volume 2 consists of a detailed description of the computer programs.
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Laurent Marchal-Bertrand
2016-05-01
Full Text Available Sexual dysfunctions are a highly prevalent problem. It is necessary to have instruments adapted to the Colombian population in order to evaluate their sexual functioning because to date none of them have been validated. The aim of this study was to adapt and validate the Massachusetts General Hospital-Sexual Functioning Questionnaire in Colombian population, and compare it with a similar sample from Spain. Two different samples were used in this study. On one hand, a sample of expert judges who performed the cultural adaptation and the evaluation of the scale, and on the other hand, a second end sample of 1117 participants -men and women of both nationalities- who answered the questionnaire -together with others- through a virtual platform. Some of the items were adjusted based on the initial results of the evaluation by the expert judges. Cronbach's alpha between .81 and .92 were obtained after the application of the test. The psychometric properties of the scale are adequate and this instrument properly correlates with other criterion variables. Construct validity was evaluated using factorial invariance. The unidimensional configural model for men (RMSEA = .000; CFI = 1 and for women (RMSEA = .048, CFI = .997 had an adequate fit, and a level of strict invariance was also reached. Screening can be performed with this first validated scale in order to evaluate the sexual difficulties of the Colombian population and compare them with the Spanish population.
Acoustic Model Adaptation for Speech Recognition
Shinoda, Koichi
Statistical speech recognition using continuous-density hidden Markov models (CDHMMs) has yielded many practical applications. However, in general, mismatches between the training data and input data significantly degrade recognition accuracy. Various acoustic model adaptation techniques using a few input utterances have been employed to overcome this problem. In this article, we survey these adaptation techniques, including maximum a posteriori (MAP) estimation, maximum likelihood linear regression (MLLR), and eigenvoice. We also present a schematic view called the adaptation pyramid to illustrate how these methods relate to each other.
Sahai, Vivek
2013-01-01
Beginning with the basic concepts of vector spaces such as linear independence, basis and dimension, quotient space, linear transformation and duality with an exposition of the theory of linear operators on a finite dimensional vector space, this book includes the concept of eigenvalues and eigenvectors, diagonalization, triangulation and Jordan and rational canonical forms. Inner product spaces which cover finite dimensional spectral theory and an elementary theory of bilinear forms are also discussed. This new edition of the book incorporates the rich feedback of its readers. We have added new subject matter in the text to make the book more comprehensive. Many new examples have been discussed to illustrate the text. More exercises have been included. We have taken care to arrange the exercises in increasing order of difficulty. There is now a new section of hints for almost all exercises, except those which are straightforward, to enhance their importance for individual study and for classroom use.
Edwards, Harold M
1995-01-01
In his new undergraduate textbook, Harold M Edwards proposes a radically new and thoroughly algorithmic approach to linear algebra Originally inspired by the constructive philosophy of mathematics championed in the 19th century by Leopold Kronecker, the approach is well suited to students in the computer-dominated late 20th century Each proof is an algorithm described in English that can be translated into the computer language the class is using and put to work solving problems and generating new examples, making the study of linear algebra a truly interactive experience Designed for a one-semester course, this text adopts an algorithmic approach to linear algebra giving the student many examples to work through and copious exercises to test their skills and extend their knowledge of the subject Students at all levels will find much interactive instruction in this text while teachers will find stimulating examples and methods of approach to the subject
Directory of Open Access Journals (Sweden)
Mohammad Akram Alzu'bi
2014-04-01
Full Text Available The study aimed at analyzing English questions of the Jordanian Secondary Certificate Examinations via Blooms' cognitive levels. An analysis sheet was prepared by the researcher for the purpose of the study, which was ensured to be valid and reliable. The whole questions of the general secondary examinations for English course in both levels (level three and level four during 2010-2013 composed the sample of the study. Frequencies and percentages were tabulated to facilitate the analysis of the results. The result of the study revealed that the total percentage of the first three levels (comprehension, knowledge, and analysis is (69.6 but the total percentage of the last three levels (application, synthesis, and evaluation is (30.4 so it indicated that the English questions included in general secondary examinations emphasize low order thinking levels. The researcher recommended that the questions designers should improve their questioning techniques in writing questions of exams.
Directory of Open Access Journals (Sweden)
Irina-Alina Preda
2008-11-01
Full Text Available In this article, we analyze the causes that have led to the improvement of the Romanian general accounting plan according to the Activity- Based Costing (ABC method. We explain the advantages presented by the dissociated organization of management accounting, in contrast with the tabular- statistical form. The article also describes the methodological steps to be taken in the process of recording book entries, according to the Activity-Based Costing (ABC method in Romania.
Allenby, Reg
1995-01-01
As the basis of equations (and therefore problem-solving), linear algebra is the most widely taught sub-division of pure mathematics. Dr Allenby has used his experience of teaching linear algebra to write a lively book on the subject that includes historical information about the founders of the subject as well as giving a basic introduction to the mathematics undergraduate. The whole text has been written in a connected way with ideas introduced as they occur naturally. As with the other books in the series, there are many worked examples.Solutions to the exercises are available onlin
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Yu. L. Sayenko
2016-12-01
Full Text Available Purpose. To perform structural and parametric identification of generalized load equivalent circuit of three-phase three-wire load in the network in the space of phase components. Methodology. Underlying structural identification methods are matrix analysis of electrical circuits. Parametric identification is based on the basic laws of electrical engineering. Results. The structure of a generalized load equivalent circuit is composed in three independent nodes. An approximate method for determining its parameters is proposed. The estimation error determination undistorted and distorted parts of the parameters of generalized load equivalent circuit. Originality. Approximate determination of equivalent circuit parameters are based on the results of a single measurement of voltages and phase currents. Practical value. The proposed replacement structure and a method for determining its parameters of the circuit can be used in the problem of the distribution of actual contributions at the point of common coupling.
Tatarinova, Tatiana; Neely, Michael; Bartroff, Jay; van Guilder, Michael; Yamada, Walter; Bayard, David; Jelliffe, Roger; Leary, Robert; Chubatiuk, Alyona; Schumitzky, Alan
2013-04-01
Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian (B) approaches. In this paper we discuss the nonparametric case using both maximum likelihood and Bayesian approaches. We present two nonparametric methods for estimating the unknown joint population distribution of model parameter values in a pharmacokinetic/pharmacodynamic (PK/PD) dataset. The first method is the NP Adaptive Grid (NPAG). The second is the NP Bayesian (NPB) algorithm with a stick-breaking process to construct a Dirichlet prior. Our objective is to compare the performance of these two methods using a simulated PK/PD dataset. Our results showed excellent performance of NPAG and NPB in a realistically simulated PK study. This simulation allowed us to have benchmarks in the form of the true population parameters to compare with the estimates produced by the two methods, while incorporating challenges like unbalanced sample times and sample numbers as well as the ability to include the covariate of patient weight. We conclude that both NPML and NPB can be used in realistic PK/PD population analysis problems. The advantages of one versus the other are discussed in the paper. NPAG and NPB are implemented in R and freely available for download within the Pmetrics package from www.lapk.org.
Krishnamurthy, S L; Sharma, P C; Sharma, D K; Ravikiran, K T; Singh, Y P; Mishra, V K; Burman, D; Maji, B; Mandal, S; Sarangi, S K; Gautam, R K; Singh, P K; Manohara, K K; Marandi, B C; Padmavathi, G; Vanve, P B; Patil, K D; Thirumeni, S; Verma, O P; Khan, A H; Tiwari, S; Geetha, S; Shakila, M; Gill, R; Yadav, V K; Roy, S K B; Prakash, M; Bonifacio, J; Ismail, Abdelbagi; Gregorio, G B; Singh, Rakesh Kumar
2017-08-11
In the present study, a total of 53 promising salt-tolerant genotypes were tested across 18 salt-affected diverse locations for three years. An attempt was made to identify ideal test locations and mega-environments using GGE biplot analysis. The CSSRI sodic environment was the most discriminating location in individual years as well as over the years and could be used to screen out unstable and salt-sensitive genotypes. Genotypes CSR36, CSR-2K-219, and CSR-2K-262 were found ideal across years. Overall, Genotypes CSR-2K-219, CSR-2K-262, and CSR-2K-242 were found superior and stable among all genotypes with higher mean yields. Different sets of genotypes emerged as winners in saline soils but not in sodic soils; however, Genotype CSR-2K-262 was the only genotype that was best under both saline and alkaline environments over the years. The lack of repeatable associations among locations and repeatable mega-environment groupings indicated the complexity of soil salinity. Hence, a multi-location and multi-year evaluation is indispensable for evaluating the test sites as well as identifying genotypes with consistently specific and wider adaptation to particular agro-climatic zones. The genotypes identified in the present study could be used for commercial cultivation across edaphically challenged areas for sustainable production.
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Zhengyuan Wang
Full Text Available Intestinal stem cells play a pivotal role in the epithelial tissue renewal, homeostasis and cancer development. The lack of a general marker for intestinal stem cells across species has hampered analysis of stem cell number in different species and their adaptive changes upon intestinal lesions or during development of cancer. Here a two-dimensional model, named STORM, has been developed to address this issue. By optimizing epithelium renewal dynamics, the model examines the epithelial stem cell number by taking experimental input information regarding epithelium proliferation and differentiation. As the results suggest, there are 2.0-4.1 epithelial stem cells on each pocket section of zebrafish intestine, 2.0-4.1 stem cells on each crypt section of murine small intestine and 1.8-3.5 stem cells on each crypt section of human duodenum. The model is able to provide quick results for stem cell number and its adaptive changes, which is not easy to measure through experiments. Its general applicability to different species makes it a valuable tool for analysis of intestinal stem cells under various pathological conditions.
Petit, Andrew S; Subotnik, Joseph E
2014-07-07
In this paper, we develop a surface hopping approach for calculating linear absorption spectra using ensembles of classical trajectories propagated on both the ground and excited potential energy surfaces. We demonstrate that our method allows the dipole-dipole correlation function to be determined exactly for the model problem of two shifted, uncoupled harmonic potentials with the same harmonic frequency. For systems where nonadiabatic dynamics and electronic relaxation are present, preliminary results show that our method produces spectra in better agreement with the results of exact quantum dynamics calculations than spectra obtained using the standard ground-state Kubo formalism. As such, our proposed surface hopping approach should find immediate use for modeling condensed phase spectra, especially for expensive calculations using ab initio potential energy surfaces.
Monahan, John F
2008-01-01
Preface Examples of the General Linear Model Introduction One-Sample Problem Simple Linear Regression Multiple Regression One-Way ANOVA First Discussion The Two-Way Nested Model Two-Way Crossed Model Analysis of Covariance Autoregression Discussion The Linear Least Squares Problem The Normal Equations The Geometry of Least Squares Reparameterization Gram-Schmidt Orthonormalization Estimability and Least Squares Estimators Assumptions for the Linear Mean Model Confounding, Identifiability, and Estimability Estimability and Least Squares Estimators F
Widely Linear Equalization for IQ Imbalance and Skew Compensation in Optical Coherent Receivers
DEFF Research Database (Denmark)
Porto da Silva, Edson; Zibar, Darko
2016-01-01
In this paper, an alternative approach to design linear equalization algorithms for optical coherent receivers is introduced. Using widely linear complex analysis, a general analytical model it is shown, where In-phase/quadrature (IQ) imbalances and IQ skew at the coherent receiver front......, it is shown that, by applying the widely linear complex analysis, one can derive a complex-valued adaptive equalizer structure which is able to compensate for linear IQ-mixing effects at the receiver front-end. By extensive numerical simulations, the performance versus complexity of the proposed equalizer...
Karloff, Howard
1991-01-01
To this reviewer’s knowledge, this is the first book accessible to the upper division undergraduate or beginning graduate student that surveys linear programming from the Simplex Method…via the Ellipsoid algorithm to Karmarkar’s algorithm. Moreover, its point of view is algorithmic and thus it provides both a history and a case history of work in complexity theory. The presentation is admirable; Karloff's style is informal (even humorous at times) without sacrificing anything necessary for understanding. Diagrams (including horizontal brackets that group terms) aid in providing clarity. The end-of-chapter notes are helpful...Recommended highly for acquisition, since it is not only a textbook, but can also be used for independent reading and study. —Choice Reviews The reader will be well served by reading the monograph from cover to cover. The author succeeds in providing a concise, readable, understandable introduction to modern linear programming. —Mathematics of Computing This is a textbook intend...
Weiss, M
1987-02-01
The present approach enables a noncompartmental assessment of log-concave plasma concentration-time profiles following oral drug administration. Observed log-concavity corresponds to a nonparametric class of residence time distributions with the following properties: (1) The fractional rate of elimination kB(t) (failure rate of the distribution) increases monotonically until reaching the terminal exponential coefficient kB,Z. (2) The relative dispersion of body residence times CVB2 (ratio of variance to the squared mean, VBRT/MBRT2) acts as a shape parameter of the curve. The role of the input process in determining the shape of the concentration profile is discussed. In this connection evidence is provided for the importance of log-concave percent undissolved versus time plots, introducing the general concept of a time-varying fractional rate of dissolution. The governing factor for the appearance of log-concavity is the ratio of mean absorption time to mean disposition residence time (MAT/MDRT); this factor exceeds a particular threshold value which depends on the distributional properties of the drug. Generalizing previous approaches which are valid for first-order input processes, the "flip-flop" phenomenon and the problem of "vanishing of exponential terms" are explained using fewer assumptions. Upper bounds for the elimination time (more than 90% eliminated) and the cutoff error in AUC determination are presented. The concept of log-concavity reveals general features of the pharmacokinetic behavior of oral dosage forms exhibiting a dominating influence of the absorption/dissolution process.
Banach, S
1987-01-01
This classic work by the late Stefan Banach has been translated into English so as to reach a yet wider audience. It contains the basics of the algebra of operators, concentrating on the study of linear operators, which corresponds to that of the linear forms a1x1 + a2x2 + ... + anxn of algebra.The book gathers results concerning linear operators defined in general spaces of a certain kind, principally in Banach spaces, examples of which are: the space of continuous functions, that of the pth-power-summable functions, Hilbert space, etc. The general theorems are interpreted in various mathematical areas, such as group theory, differential equations, integral equations, equations with infinitely many unknowns, functions of a real variable, summation methods and orthogonal series.A new fifty-page section (``Some Aspects of the Present Theory of Banach Spaces'''') complements this important monograph.
Ma, Qiang; Huang, Dawen; Yang, Jianhua
The theory of general scale transformation (GST) is put forward and used in the second-order underdamped bistable system to extract weak signal features submerged into strong noise. An adaptive stochastic resonance (SR) with GST is proposed and realized by the quantum particle swarm optimization (QPSO) algorithm. The harmonic signal and experimental signal are considered to compare GST with normalized scale transformation (NST) in the second-order system. The results show that detection effect of the adaptive SR with GST is better than the NST SR. In addition, the output signal-to-noise ratio (SNR) is significantly improved in the GST method. Meanwhile, the dependence of the signal extraction efficiency on the noise intensity is researched. The output SNR is decreased with the increase of the noise intensity in two methods. However, the proposed method is always superior to the NST. Moreover, the superiority of the Brown particle oscillation in the single well is discussed. The proposed method has certain reference value in the extraction of the weak signal under the strong noise background.
González-Díaz, Humberto; Arrasate, Sonia; Gómez-SanJuan, Asier; Sotomayor, Nuria; Lete, Esther; Besada-Porto, Lina; Ruso, Juan M
2013-01-01
In general perturbation methods starts with a known exact solution of a problem and add "small" variation terms in order to approach to a solution for a related problem without known exact solution. Perturbation theory has been widely used in almost all areas of science. Bhor's quantum model, Heisenberg's matrix mechanincs, Feyman diagrams, and Poincare's chaos model or "butterfly effect" in complex systems are examples of perturbation theories. On the other hand, the study of Quantitative Structure-Property Relationships (QSPR) in molecular complex systems is an ideal area for the application of perturbation theory. There are several problems with exact experimental solutions (new chemical reactions, physicochemical properties, drug activity and distribution, metabolic networks, etc.) in public databases like CHEMBL. However, in all these cases, we have an even larger list of related problems without known solutions. We need to know the change in all these properties after a perturbation of initial boundary conditions. It means, when we test large sets of similar, but different, compounds and/or chemical reactions under the slightly different conditions (temperature, time, solvents, enzymes, assays, protein targets, tissues, partition systems, organisms, etc.). However, to the best of our knowledge, there is no QSPR general-purpose perturbation theory to solve this problem. In this work, firstly we review general aspects and applications of both perturbation theory and QSPR models. Secondly, we formulate a general-purpose perturbation theory for multiple-boundary QSPR problems. Last, we develop three new QSPR-Perturbation theory models. The first model classify correctly >100,000 pairs of intra-molecular carbolithiations with 75-95% of Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp). The model predicts probabilities of variations in the yield and enantiomeric excess of reactions due to at least one perturbation in boundary conditions (solvent, temperature
Bourlès, Henri
2013-01-01
Linear systems have all the necessary elements (modeling, identification, analysis and control), from an educational point of view, to help us understand the discipline of automation and apply it efficiently. This book is progressive and organized in such a way that different levels of readership are possible. It is addressed both to beginners and those with a good understanding of automation wishing to enhance their knowledge on the subject. The theory is rigorously developed and illustrated by numerous examples which can be reproduced with the help of appropriate computation software. 60 exe
Tyson, Robert
2010-01-01
History and BackgroundIntroductionHistoryPhysical OpticsTerms in Adaptive OpticsSources of AberrationsAtmospheric TurbulenceThermal BloomingNonatmospheric SourcesAdaptive Optics CompensationPhase ConjugationLimitations of Phase ConjugationArtificial Guide StarsLasers for Guide StarsCombining the LimitationsLinear AnalysisPartial Phase ConjugationAdaptive Optics SystemsAdaptive Optics Imaging SystemsBeam Propagation Syst
Skuse, David H; Mandy, William; Steer, Colin; Miller, Laura L; Goodman, Robert; Lawrence, Kate; Emond, Alan; Golding, Jean
2009-02-01
The proportion of schoolchildren with mild social communicative deficits far exceeds the number diagnosed with an autistic spectrum disorder (ASD). We aimed to ascertain both the population distribution of such deficits and their association with functional adaptation and cognitive ability in middle childhood. The parent-report Social and Communication Disorders Checklist was administered to participants (n = 8,094) in the Avon Longitudinal Study of Parents and Children. We correlated impairment severity with independent clinical diagnoses of ASD, cognitive abilities, and teacher-rated maladaptive behavior. Social and Communication Disorders Checklist scores were continuously distributed in the general population; boys had mean scores 30% higher than girls. Social communicative deficits were associated with functional impairment at school, especially in domains of hyperactivity and conduct disorders. A sex-by-verbal IQ interaction effect occurred: verbal IQ was protective against social communication impairments across the range of abilities in female subjects only. In male subjects, this protective effect did not exist for those with above-average verbal IQ. Social communicative deficits are of prognostic significance, in terms of behavioral adjustment at school, for boys and girls. Their high general population prevalence emphasizes the importance of measuring such traits among clinically referred children who do not meet diagnostic ASD criteria. Above-average verbal IQ seems to confer protection against social communication impairments in female subjects but not in male subjects.
BLAS (Basic Linear Algebra Subroutines), Linear Algebra Modules and Supercomputers.
1984-12-31
Linear Algebra Subroutines (BLAS) and linear algebra software modules in general. The need for these extensions and new sets of modules is largely due...potential computin .p"er. The participants represented most active groups in ilecar algebral , ware an were about equally divided among industry...discussions. Section 2 describes seven proposals for linear algebra software modules and Sec- tion 3 describes four presentations on the use of such
Poulin, David; Martinez, David; Aenchbacher, Amy; Aiello, Rocco; Doyle, Mike; Hilgenbrinck, Linda; Busse, Sean; Cappuccio, Jim
2013-01-01
In Part III of the feature, physical educators and adapted physical educators offer current best practices as models of implementation for readers. Contributions included are: (1) Answer to the Dear Colleague Letter from the Anchorage School District's Adapted Sport Program (David Poulin); (2) Georgia's Adapted Physical Educators Response to the…
Mingotti, Nicola
2015-01-01
This work aims at the exposition of two different results we have obtained in Functional Data Analysis. The first is a variable selection method in Functional Regression which is an adaptation of the well known Lasso technique. The second is a brand new Random Walk test for Functional Time Series. Being the results afferent to different areas of Functional Data Analysis, as well as of general Statistics, the introduction will be divided in three parts. Firstly we expose the fundament...
Blyth, T S
2002-01-01
Most of the introductory courses on linear algebra develop the basic theory of finite dimensional vector spaces, and in so doing relate the notion of a linear mapping to that of a matrix. Generally speaking, such courses culminate in the diagonalisation of certain matrices and the application of this process to various situations. Such is the case, for example, in our previous SUMS volume Basic Linear Algebra. The present text is a continuation of that volume, and has the objective of introducing the reader to more advanced properties of vector spaces and linear mappings, and consequently of matrices. For readers who are not familiar with the contents of Basic Linear Algebra we provide an introductory chapter that consists of a compact summary of the prerequisites for the present volume. In order to consolidate the student's understanding we have included a large num ber of illustrative and worked examples, as well as many exercises that are strategi cally placed throughout the text. Solutions to the ex...
Campagnoli, Patrizia; Petris, Giovanni
2009-01-01
State space models have gained tremendous popularity in as disparate fields as engineering, economics, genetics and ecology. Introducing general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. It illustrates the fundamental steps needed to use dynamic linear models in practice, using R package.
Improved LMS algorithm for adaptive beamforming
Godara, Lal C.
1990-01-01
Two adaptive algorithms which make use of all the available samples to estimate the required gradient are proposed and studied. The first algorithm is referred to as the recursive LMS (least mean squares) and is applicable to a general array. The second algorithm is referred to as the improved LMS algorithm and exploits the Toeplitz structure of the ACM (array correlation matrix); it can be used only for an equispaced linear array.
Using generalized linear (mixed) models in HCI
Kaptein, M.C.
2016-01-01
In HCI we often encounter dependent variables which are not (conditionally) normally distributed: we measure response-times, mouse-clicks, or the number of dialog steps it took a user to complete a task. Furthermore, we often encounter nested or grouped data; users are grouped within companies or
Linear Programming Problems for Generalized Uncertainty
Thipwiwatpotjana, Phantipa
2010-01-01
Uncertainty occurs when there is more than one realization that can represent an information. This dissertation concerns merely discrete realizations of an uncertainty. Different interpretations of an uncertainty and their relationships are addressed when the uncertainty is not a probability of each realization. A well known model that can handle…
Generalized Ultrametric Semilattices of Linear Signals
2014-01-23
companies : Bosch, National Instruments, and Toyota. 1 There is, however, a different, yet equally important notion of approximation that stems from a...from the National Science Foundation (NSF awards \\#0720882 ( CSR -EHS: PRET), \\#0931843 (CPS: Large: ActionWebs), and \\#1035672 (CPS: Medium: Timing...Centric Software)), the Naval Research Laboratory (NRL \\#N0013-12-1-G015), and the following companies : Bosch, National Instruments, and Toyota
Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo
2017-11-01
The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.
He, Y. Z.; Liu, Y. M.; Bao, C. G.
2015-03-01
A generalized Gross-Pitaevskii equation adapted to the U(5 )⊃SO(5 )⊃SO(3 ) symmetry has been derived and solved for the spin-2 condensates. The spin-textile and the degeneracy of the ground state (g.s.) together with the factors affecting the stability of the g.s., such as the gap and the level density in the neighborhood of the g.s., have been studied. Based on a rigorous treatment of the spin-degrees of freedom, the spin-textiles can be understood in an N -body language. In addition to the ferro, polar, and cyclic phases, the g.s. might in a mixture of them when |M | is not equal to 0 and 2 N (M is the total magnetization). The great difference in the stability and degeneracy of the g.s. caused by varying φ (which marks the features of the interaction) and M is notable. Since the root-mean-square radius Rrms is an observable, efforts have been made to derive a set of formulas to relate Rrms and N ,ω (frequency of the trap), and φ . These formulas provide a way to check the theories with experimental data.
Directory of Open Access Journals (Sweden)
Fabrice G. Renaud
2012-04-01
Full Text Available Water is the primary medium through which climate change influences the Earth’s ecosystems and therefore people’s livelihoods and wellbeing. Besides climatic change, current demographic trends, economic development and related land use changes have direct impact on increasing demand for freshwater resources. Taken together, the net effect of these supply and demand changes is affecting the vulnerability of water resources. The concept of ‘vulnerability’ is not straightforward as there is no universally accepted approach for assessing vulnerability. In this study, we review the evolution of approaches to vulnerability assessment related to water resources. From the current practices, we identify research gaps, and approaches to overcome these gaps a generalized assessment framework is developed. A feasibility study is then presented in the context of the Lower Brahmaputra River Basin (LBRB. The results of the feasibility study identify the current main constraints (e.g., lack of institutional coordination and opportunities (e.g., adaptation of LBRB. The results of this study can be helpful for innovative research and management initiatives and the described framework can be widely used as a guideline for the vulnerability assessment of water resources systems, particularly in developing countries.
Madhombiro, Munyaradzi; Dube-Marimbe, Bazondlile; Dube, Michelle; Chibanda, Dixon; Zunza, Moleen; Rusakaniko, Simbarashe; Stewart, David; Seedat, Soraya
2017-01-28
Interventions for alcohol use disorders (AUDs) in HIV infected individuals have been primarily targeted at HIV risk reduction and improved antiretroviral treatment adherence. However, reduction in alcohol use is an important goal. Alcohol use affects other key factors that may influence treatment course and outcome. In this study the authors aim to administer an adapted intervention for AUDs to reduce alcohol use in people living with HIV/AIDS (PLWHA). This study is a cluster randomised controlled trial at 16 HIV care clinics. A motivational interviewing and cognitive behavioural therapy based intervention for AUDs, developed through adaptation and piloted in Zimbabwe, will be administered to PLWHA with AUDs recruited at HIV clinics. The intervention will be administered over 16 sessions at 8 HIV clinics. This intervention will be compared with an equal attention control in the form of the World Health Organization Mental Health Gap Action Programme (WHO mhGAP) guide, adapted for the Zimbabwean context. General function, quality of life, and adherence to highly active antiretroviral treatment (HAART) will be secondary outcomes. Booster sessions will be administered to both groups at 3 and 6 months respectively. The primary outcome measure will be the Alcohol Use Disorder Identification Test (AUDIT) score. The World Health Organisation Disability Assessment Schedule 2.0 (WHODAS 2.0), World Health Organisation Quality of Life (WHOQoL) HIV, viral load, and CD4 counts will be secondary outcome measures. Outcome assessments will be administered at baseline, 3, 6, and 12 months. Moderating factors such as perceived social support, how people cope with difficult situations and post-traumatic exposure and experience will be assessed at baseline. Trained research assistants will recruit participants. The outcome assessors who will be trained in administering the outcome and moderating tools will be blinded to the treatment arms allocated to the participants. However, the
Equivalent linearization of nonlinear forces
Meng, Guang; Xue, Zhongqing
1987-07-01
A method used for equivalent linearization of the two orthogonal squeeze-film forces is extended here to the general case of n degrees of freedom and n components of nonlinear forces, and the expressions for equivalent linear coefficients are derived. Nonlinear forces can be linearized by the methods of Fourier expansion, active and reactive powers, or mean-square error. The n components of nonlinear forces can all be expressed formally as the sum of an average force, a linear spring force, and a linear damping force. This paper also gives a flow chart for calculating the steady-state responses of a nonlinear system with many degrees of freedom, using the method of equivalent linearization. The resulting saving in computation time is demonstrated by a numerical example of a flexible rotor-bearing system with a noncentralized squeeze-film damper.
Petersen, M.A.; Groenvold, M.; Aaronson, N.K.; Chie, W.C.; Conroy, T.; Costantini, A.; Fayers, P.; Helbostad, J.; Holzner, B.; Kaasa, S.; Singer, S.; Velikova, G.; Young, T.
2010-01-01
Background Health-related quality of life (HRQOL) questionnaires should ideally be adapted to the individual patient and at the same time scores should be directly comparable across patients. This is achievable using a computerised adaptive test (CAT). Basing the CAT on an existing instrument
DEFF Research Database (Denmark)
Petersen, Morten Aa; Groenvold, Mogens; Aaronson, Neil K
2010-01-01
Health-related quality of life (HRQOL) questionnaires should ideally be adapted to the individual patient and at the same time scores should be directly comparable across patients. This is achievable using a computerised adaptive test (CAT). Basing the CAT on an existing instrument enables measur...
Linear Algebra and Smarandache Linear Algebra
Vasantha, Kandasamy
2003-01-01
The present book, on Smarandache linear algebra, not only studies the Smarandache analogues of linear algebra and its applications, it also aims to bridge the need for new research topics pertaining to linear algebra, purely in the algebraic sense. We have introduced Smarandache semilinear algebra, Smarandache bilinear algebra and Smarandache anti-linear algebra and their fuzzy equivalents. Moreover, in this book, we have brought out the study of linear algebra and ve...
An Approach to Stable Gradient-Descent Adaptation of Higher Order Neural Units.
Bukovsky, Ivo; Homma, Noriyasu
2017-09-01
Stability evaluation of a weight-update system of higher order neural units (HONUs) with polynomial aggregation of neural inputs (also known as classes of polynomial neural networks) for adaptation of both feedforward and recurrent HONUs by a gradient descent method is introduced. An essential core of the approach is based on the spectral radius of a weight-update system, and it allows stability monitoring and its maintenance at every adaptation step individually. Assuring the stability of the weight-update system (at every single adaptation step) naturally results in the adaptation stability of the whole neural architecture that adapts to the target data. As an aside, the used approach highlights the fact that the weight optimization of HONU is a linear problem, so the proposed approach can be generally extended to any neural architecture that is linear in its adaptable parameters.
Adaptive filtering prediction and control
Goodwin, Graham C
2009-01-01
Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o
Numerical Hydrodynamics in General Relativity
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Font José A.
2000-05-01
Full Text Available The current status of numerical solutions for the equations of ideal general relativistic hydrodynamics is reviewed. Different formulations of the equations are presented, with special mention of conservative and hyperbolic formulations well-adapted to advanced numerical methods. A representative sample of available numerical schemes is discussed and particular emphasis is paid to solution procedures based on schemes exploiting the characteristic structure of the equations through linearized Riemann solvers. A comprehensive summary of relevant astrophysical simulations in strong gravitational fields, including gravitational collapse, accretion onto black holes and evolution of neutron stars, is also presented.
Labrada-Martagón, Vanessa; Méndez-Rodríguez, Lia C; Mangel, Marc; Zenteno-Savín, Tania
2013-09-01
Generalized linear models were fitted to evaluate the relationship between 17β-estradiol (E2), testosterone (T) and thyroxine (T4) levels in immature East Pacific green sea turtles (Chelonia mydas) and their body condition, size, mass, blood biochemistry parameters, handling time, year, season and site of capture. According to external (tail size) and morphological (Hormone levels, assessed on sea turtles subjected to a capture stress protocol, were hormone concentrations and blood biochemistry parameters (e.g. glucose, cholesterol) and the potential effect of environmental variables (season and study site). The variables handling time and year did not contribute significantly to explain hormone levels. Differences in sex steroids between season and study sites found by the models coincided with specific nutritional, physiological and body condition differences related to the specific habitat conditions. The models correctly predicted the median levels of the measured hormones in green sea turtles, which confirms the fitted model's utility. It is suggested that quantitative predictions could be possible when the model is tested with additional data. Copyright © 2013 Elsevier Inc. All rights reserved.
Ferencz, Donald C.; Viterna, Larry A.
1991-01-01
ALPS is a computer program which can be used to solve general linear program (optimization) problems. ALPS was designed for those who have minimal linear programming (LP) knowledge and features a menu-driven scheme to guide the user through the process of creating and solving LP formulations. Once created, the problems can be edited and stored in standard DOS ASCII files to provide portability to various word processors or even other linear programming packages. Unlike many math-oriented LP solvers, ALPS contains an LP parser that reads through the LP formulation and reports several types of errors to the user. ALPS provides a large amount of solution data which is often useful in problem solving. In addition to pure linear programs, ALPS can solve for integer, mixed integer, and binary type problems. Pure linear programs are solved with the revised simplex method. Integer or mixed integer programs are solved initially with the revised simplex, and the completed using the branch-and-bound technique. Binary programs are solved with the method of implicit enumeration. This manual describes how to use ALPS to create, edit, and solve linear programming problems. Instructions for installing ALPS on a PC compatible computer are included in the appendices along with a general introduction to linear programming. A programmers guide is also included for assistance in modifying and maintaining the program.
A Unified Approach to High-Gain Adaptive Controllers
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Ian A. Gravagne
2009-01-01
Full Text Available It has been known for some time that proportional output feedback will stabilize MIMO, minimum-phase, linear time-invariant systems if the feedback gain is sufficiently large. High-gain adaptive controllers achieve stability by automatically driving up the feedback gain monotonically. More recently, it was demonstrated that sample-and-hold implementations of the high-gain adaptive controller also require adaptation of the sampling rate. In this paper, we use recent advances in the mathematical field of dynamic equations on time scales to unify and generalize the discrete and continuous versions of the high-gain adaptive controller. We prove the stability of high-gain adaptive controllers on a wide class of time scales.
Spectral analyses of asteroids' linear features
Longobardo, A.; Palomba, E.; Scully, J. E. C.; De Sanctis, M. C.; Capaccioni, F.; Tosi, F.; Zinzi, A.; Galiano, A.; Ammannito, E.; Filacchione, G.; Ciarniello, M.; Raponi, A.; Zambon, F.; Capria, M. T.; Erard, S.; Bockelee-Morvan, D.; Leyrat, C.; Dirri, F.; Nardi, L.; Raymond, C. A.
2017-09-01
Linear features are commonly found on small bodies and can have a geomorphic or tectonic origin. Generally, these features are studied by means of morphological analyses. Here we propose a spectroscopic analyses of linear features of different asteroids visited by space missions, in order to search for correspondence between spectral properties and origin of linear features.
On Systems of Linear Quaternion Functions
Ell, Todd A.
2007-01-01
A method of reducing general quaternion functions of first degree, i.e., linear quaternion functions, to quaternary canonical form is given. Linear quaternion functions, once reduced to canonical form, can be maintained in this form under functional composition. furthermore, the composition operation is symbolically identical to quaternion multiplication, making manipulation and reduction of systems of linear quaternion functions straight forward.
Linearly constrained minimax optimization
DEFF Research Database (Denmark)
Madsen, Kaj; Schjær-Jacobsen, Hans
1978-01-01
We present an algorithm for nonlinear minimax optimization subject to linear equality and inequality constraints which requires first order partial derivatives. The algorithm is based on successive linear approximations to the functions defining the problem. The resulting linear subproblems...
Bolton, W
1995-01-01
This book is concerned with linear equations and matrices, with emphasis on the solution of simultaneous linear equations. The solution of simultaneous linear equations is applied to electric circuit analysis and structural analysis.
Reciprocity in Linear Deterministic Networks under Linear Coding
Raja, Adnan; Viswanath, Pramod
2009-01-01
The linear deterministic model has been used recently to get a first order understanding of many wireless communication network problems. In many of these cases, it has been pointed out that the capacity regions of the network and its reciprocal (where the communication links are reversed and the roles of the sources and the destinations are swapped) are the same. In this paper, we consider a linear deterministic communication network with multiple unicast information flows. For this model and under the restriction to the class of linear coding, we show that the rate regions for a network and its reciprocal are the same. This can be viewed as a generalization of the linear reversibility of wireline networks, already known in the network coding literature.
Directory of Open Access Journals (Sweden)
Marcos Hernández Suárez
Full Text Available There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA or Factor Analysis (FA have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID algorithm and Artificial Neural Network (ANN models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain. Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit's goodness
Hernández Suárez, Marcos; Astray Dopazo, Gonzalo; Larios López, Dina; Espinosa, Francisco
2015-01-01
There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA) or Factor Analysis (FA) have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID) algorithm and Artificial Neural Network (ANN) models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain). Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID) tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit's goodness between 44 and 100
Stochastic Finite Element Analysis of Non-Linear Structures Modelled by Plasticity Theory
DEFF Research Database (Denmark)
Frier, Christian; Sørensen, John Dalsgaard
2003-01-01
A Finite Element Reliability Method (FERM) is introduced to perform reliability analyses on two-dimensional structures in plane stress, modeled by non-linear plasticity theory. FERM is a coupling between the First Order Reliability Method (FORM) and the Finite Element Method (FEM). FERM can be used...... Method (DDM), here adapted to work with a generally formulated plasticity based constitutive model. The approach is exemplified with a steel plate with a hole in bending subjected to a displacement based limit state function....
Adaptive Vehicle Traction Control
Lee, Hyeongcheol; Tomizuka, Masayoshi
1995-01-01
This report presents two different control algorithms for adaptive vehicle traction control, which includes wheel slip control, optimal time control, anti-spin acceleration and anti-skid control, and longitudinal platoon control. The two control algorithms are respectively based on adaptive fuzzy logic control and sliding mode control with on-line road condition estimation. Simulations of the two control methods are conducted using a complex nonlinear vehicle model as well as a simple linear ...
Inferential Models for Linear Regression
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Zuoyi Zhang
2011-09-01
Full Text Available Linear regression is arguably one of the most widely used statistical methods in applications. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs in the linear regression context. In general, an IM is able to produce meaningful probabilistic summaries of the statistical evidence for and against assertions about the unknown parameter of interest and, moreover, these summaries are shown to be properly calibrated in a frequentist sense. Here we demonstrate, using simple examples, that the IM framework is promising for linear regression analysis --- including model checking, variable selection, and prediction --- and for uncertain inference in general.
Extreme bosonic linear channels
Holevo, A. S.
2013-02-01
The set of all channels with a fixed input and output is convex. We first give a convenient formulation of the necessary and sufficient condition for a channel to be an extreme point of this set in terms of the complementary channel, a notion of great importance in quantum information theory. This formulation is based on the general approach to extremality of completely positive maps in an operator algebra in the spirit of Arveson. We then use this formulation to prove our main result: under certain nondegeneracy conditions, environmental purity is necessary and sufficient for the extremality of a bosonic linear (quasifree) channel. It hence follows that a Gaussian channel between finite-mode bosonic systems is extreme if and only if it has minimum noise.
CULTURAL ADAPTATION OF PRODUCTS
Catalin Mihail BARBU
2011-01-01
In this paper I discussed the factors that influence the cultural adaptation of products. Globalization determines the companies to operate abroad; therefore the firms sell their products to markets where the consumer patterns might differ from their national market. It is of high importance to be able to understand and to adapt to local consumer habits. The culture has a strong influence on products adaptation in particular, and on international marketing in general. Companies must be able t...
Yamasaki, Tadashi; Houseman, Gregory; Hamling, Ian; Postek, Elek
2010-05-01
We have developed a new parallelized 3-D numerical code, OREGANO_VE, for the solution of the general visco-elastic problem in a rectangular block domain. The mechanical equilibrium equation is solved using the finite element method for a (non-)linear Maxwell visco-elastic rheology. Time-dependent displacement and/or traction boundary conditions can be applied. Matrix assembly is based on a tetrahedral element defined by 4 vertex nodes and 6 nodes located at the midpoints of the edges, and within which displacement is described by a quadratic interpolation function. For evaluating viscoelastic relaxation, an explicit time-stepping algorithm (Zienkiewicz and Cormeau, Int. J. Num. Meth. Eng., 8, 821-845, 1974) is employed. We test the accurate implementation of the OREGANO_VE by comparing numerical and analytic (or semi-analytic half-space) solutions to different problems in a range of applications: (1) equilibration of stress in a constant density layer after gravity is switched on at t = 0 tests the implementation of spatially variable viscosity and non-Newtonian viscosity; (2) displacement of the welded interface between two blocks of differing viscosity tests the implementation of viscosity discontinuities, (3) displacement of the upper surface of a layer under applied normal load tests the implementation of time-dependent surface tractions (4) visco-elastic response to dyke intrusion (compared with the solution in a half-space) tests the implementation of all aspects. In each case, the accuracy of the code is validated subject to use of a sufficiently small time step, providing assurance that the OREGANO_VE code can be applied to a range of visco-elastic relaxation processes in three dimensions, including post-seismic deformation and post-glacial uplift. The OREGANO_VE code includes a capability for representation of prescribed fault slip on an internal fault. The surface displacement associated with large earthquakes can be detected by some geodetic observations
Linear combination of forecasts with numerical adjustment via MINIMAX non-linear programming
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Jairo Marlon Corrêa
2016-03-01
Full Text Available This paper proposes a linear combination of forecasts obtained from three forecasting methods (namely, ARIMA, Exponential Smoothing and Artificial Neural Networks whose adaptive weights are determined via a multi-objective non-linear programming problem, which seeks to minimize, simultaneously, the statistics: MAE, MAPE and MSE. The results achieved by the proposed combination are compared with the traditional approach of linear combinations of forecasts, where the optimum adaptive weights are determined only by minimizing the MSE; with the combination method by arithmetic mean; and with individual methods
Directory of Open Access Journals (Sweden)
V. I. Djigan
2007-12-01
Full Text Available This paper considers the application of the linear constraints and RLS inverse QR decomposition in adaptive arrays based on constant modulus criterion. The computational procedures of adaptive algorithms are presented. Linearly constrained least squares adaptive arrays, constant modulus adaptive arrays and linearly constrained constant modulus adaptive arrays are compared via simulation. It is demonstrated, that a constant phase shift in the array output signal, caused by desired signal orientation and array weights, is compensated in a simple way in linearly constrained constant modulus adaptive arrays.
NSGIC Regional | GIS Inventory — Hydrography dataset current as of 1997. This Layer is a subset of GDOT's statewide Georgia DLG-F Linear Hydrographic Features dataset used as a cartographic layer in...
Laurence, Caroline O; Heywood, Troy; Bell, Janice; Atkinson, Kaye; Karnon, Jonathan
2017-09-08
Health workforce planning models have been developed to estimate the future health workforce requirements for a population whom they serve and have been used to inform policy decisions. To adapt and further develop a need-based GP workforce simulation model to incorporate current and estimated geographic distribution of patients and GPs. A need-based simulation model that estimates the supply of GPs and levels of services required in South Australia (SA) was adapted and applied to the Western Australian (WA) workforce. The main outcome measure was the differences in the number of full-time equivalent (FTE) GPs supplied and required from 2013 to 2033. The base scenario estimated a shortage of GPs in WA from 2019 onwards with a shortage of 493 FTE GPs in 2033, while for SA, estimates showed an oversupply over the projection period. The WA urban and rural models estimated an urban shortage of GPs over this period. A reduced international medical graduate recruitment scenario resulted in estimated shortfalls of GPs by 2033 for WA and SA. The WA-specific scenarios of lower population projections and registrar work value resulted in a reduced shortage of FTE GPs in 2033, while unfilled training places increased the shortfall of FTE GPs in 2033. The simulation model incorporates contextual differences to its structure that allows within and cross jurisdictional comparisons of workforce estimations. It also provides greater insights into the drivers of supply and demand and the impact of changes in workforce policy, promoting more informed decision-making.
Canuto, V
2015-01-01
This is an English translation of the Italian version of an encyclopedia chapter that appeared in the Italian Encyclopedia of the Physical Sciences, edited by Bruno Bertotti (1994). Following requests from colleagues we have decided to make it available to a more general readership. We present the motivation for constructing General Relativity, provide a short discussion of tensor algebra, and follow the set up of Einstein equations. We discuss briefly the initial value problem, the linear approximation and how should non gravitational physics be described in curved spacetime.
Correct Linearization of Einstein's Equations
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Rabounski D.
2006-06-01
Full Text Available Regularly Einstein's equations can be reduced to a wave form (linearly dependent from the second derivatives of the space metric in the absence of gravitation, the space rotation and Christoffel's symbols. As shown here, the origin of the problem is that one uses the general covariant theory of measurement. Here the wave form of Einstein's equations is obtained in the terms of Zelmanov's chronometric invariants (physically observable projections on the observer's time line and spatial section. The obtained equations depend on solely the second derivatives even if gravitation, the space rotation and Christoffel's symbols. The correct linearization proves: the Einstein equations are completely compatible with weak waves of the metric.
Tuey, R. C.
1972-01-01
Computer solutions of linear programming problems are outlined. Information covers vector spaces, convex sets, and matrix algebra elements for solving simultaneous linear equations. Dual problems, reduced cost analysis, ranges, and error analysis are illustrated.