WorldWideScience

Sample records for regularized wmnls estimation

  1. Condition Number Regularized Covariance Estimation.

    Science.gov (United States)

    Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala

    2013-06-01

    Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the "large p small n " setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required.

  2. Condition Number Regularized Covariance Estimation*

    Science.gov (United States)

    Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala

    2012-01-01

    Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the “large p small n” setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required. PMID:23730197

  3. Efficient multidimensional regularization for Volterra series estimation

    Science.gov (United States)

    Birpoutsoukis, Georgios; Csurcsia, Péter Zoltán; Schoukens, Johan

    2018-05-01

    This paper presents an efficient nonparametric time domain nonlinear system identification method. It is shown how truncated Volterra series models can be efficiently estimated without the need of long, transient-free measurements. The method is a novel extension of the regularization methods that have been developed for impulse response estimates of linear time invariant systems. To avoid the excessive memory needs in case of long measurements or large number of estimated parameters, a practical gradient-based estimation method is also provided, leading to the same numerical results as the proposed Volterra estimation method. Moreover, the transient effects in the simulated output are removed by a special regularization method based on the novel ideas of transient removal for Linear Time-Varying (LTV) systems. Combining the proposed methodologies, the nonparametric Volterra models of the cascaded water tanks benchmark are presented in this paper. The results for different scenarios varying from a simple Finite Impulse Response (FIR) model to a 3rd degree Volterra series with and without transient removal are compared and studied. It is clear that the obtained models capture the system dynamics when tested on a validation dataset, and their performance is comparable with the white-box (physical) models.

  4. Convergence and fluctuations of Regularized Tyler estimators

    KAUST Repository

    Kammoun, Abla; Couillet, Romain; Pascal, Frederic; Alouini, Mohamed-Slim

    2015-01-01

    This article studies the behavior of regularized Tyler estimators (RTEs) of scatter matrices. The key advantages of these estimators are twofold. First, they guarantee by construction a good conditioning of the estimate and second, being a derivative of robust Tyler estimators, they inherit their robustness properties, notably their resilience to the presence of outliers. Nevertheless, one major problem that poses the use of RTEs in practice is represented by the question of setting the regularization parameter p. While a high value of p is likely to push all the eigenvalues away from zero, it comes at the cost of a larger bias with respect to the population covariance matrix. A deep understanding of the statistics of RTEs is essential to come up with appropriate choices for the regularization parameter. This is not an easy task and might be out of reach, unless one considers asymptotic regimes wherein the number of observations n and/or their size N increase together. First asymptotic results have recently been obtained under the assumption that N and n are large and commensurable. Interestingly, no results concerning the regime of n going to infinity with N fixed exist, even though the investigation of this assumption has usually predated the analysis of the most difficult N and n large case. This motivates our work. In particular, we prove in the present paper that the RTEs converge to a deterministic matrix when n → ∞ with N fixed, which is expressed as a function of the theoretical covariance matrix. We also derive the fluctuations of the RTEs around this deterministic matrix and establish that these fluctuations converge in distribution to a multivariate Gaussian distribution with zero mean and a covariance depending on the population covariance and the parameter.

  5. Convergence and fluctuations of Regularized Tyler estimators

    KAUST Repository

    Kammoun, Abla

    2015-10-26

    This article studies the behavior of regularized Tyler estimators (RTEs) of scatter matrices. The key advantages of these estimators are twofold. First, they guarantee by construction a good conditioning of the estimate and second, being a derivative of robust Tyler estimators, they inherit their robustness properties, notably their resilience to the presence of outliers. Nevertheless, one major problem that poses the use of RTEs in practice is represented by the question of setting the regularization parameter p. While a high value of p is likely to push all the eigenvalues away from zero, it comes at the cost of a larger bias with respect to the population covariance matrix. A deep understanding of the statistics of RTEs is essential to come up with appropriate choices for the regularization parameter. This is not an easy task and might be out of reach, unless one considers asymptotic regimes wherein the number of observations n and/or their size N increase together. First asymptotic results have recently been obtained under the assumption that N and n are large and commensurable. Interestingly, no results concerning the regime of n going to infinity with N fixed exist, even though the investigation of this assumption has usually predated the analysis of the most difficult N and n large case. This motivates our work. In particular, we prove in the present paper that the RTEs converge to a deterministic matrix when n → ∞ with N fixed, which is expressed as a function of the theoretical covariance matrix. We also derive the fluctuations of the RTEs around this deterministic matrix and establish that these fluctuations converge in distribution to a multivariate Gaussian distribution with zero mean and a covariance depending on the population covariance and the parameter.

  6. Regularized Regression and Density Estimation based on Optimal Transport

    KAUST Repository

    Burger, M.; Franek, M.; Schonlieb, C.-B.

    2012-01-01

    for estimating densities and for preserving edges in the case of total variation regularization. In order to compute solutions of the variational problems, a regularized optimal transport problem needs to be solved, for which we discuss several formulations

  7. Bounded Perturbation Regularization for Linear Least Squares Estimation

    KAUST Repository

    Ballal, Tarig; Suliman, Mohamed Abdalla Elhag; Al-Naffouri, Tareq Y.

    2017-01-01

    This paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded

  8. Estimation of the global regularity of a multifractional Brownian motion

    DEFF Research Database (Denmark)

    Lebovits, Joachim; Podolskij, Mark

    This paper presents a new estimator of the global regularity index of a multifractional Brownian motion. Our estimation method is based upon a ratio statistic, which compares the realized global quadratic variation of a multifractional Brownian motion at two different frequencies. We show that a ...... that a logarithmic transformation of this statistic converges in probability to the minimum of the Hurst functional parameter, which is, under weak assumptions, identical to the global regularity index of the path....

  9. Regularized Regression and Density Estimation based on Optimal Transport

    KAUST Repository

    Burger, M.

    2012-03-11

    The aim of this paper is to investigate a novel nonparametric approach for estimating and smoothing density functions as well as probability densities from discrete samples based on a variational regularization method with the Wasserstein metric as a data fidelity. The approach allows a unified treatment of discrete and continuous probability measures and is hence attractive for various tasks. In particular, the variational model for special regularization functionals yields a natural method for estimating densities and for preserving edges in the case of total variation regularization. In order to compute solutions of the variational problems, a regularized optimal transport problem needs to be solved, for which we discuss several formulations and provide a detailed analysis. Moreover, we compute special self-similar solutions for standard regularization functionals and we discuss several computational approaches and results. © 2012 The Author(s).

  10. Bounded Perturbation Regularization for Linear Least Squares Estimation

    KAUST Repository

    Ballal, Tarig

    2017-10-18

    This paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded norm is allowed into the linear transformation matrix to improve the singular-value structure. Following this, the problem is formulated as a min-max optimization problem. Next, the min-max problem is converted to an equivalent minimization problem to estimate the unknown vector quantity. The solution of the minimization problem is shown to converge to that of the ℓ2 -regularized least squares problem, with the unknown regularizer related to the norm bound of the introduced perturbation through a nonlinear constraint. A procedure is proposed that combines the constraint equation with the mean squared error (MSE) criterion to develop an approximately optimal regularization parameter selection algorithm. Both direct and indirect applications of the proposed method are considered. Comparisons with different Tikhonov regularization parameter selection methods, as well as with other relevant methods, are carried out. Numerical results demonstrate that the proposed method provides significant improvement over state-of-the-art methods.

  11. Regularization and error estimates for nonhomogeneous backward heat problems

    Directory of Open Access Journals (Sweden)

    Duc Trong Dang

    2006-01-01

    Full Text Available In this article, we study the inverse time problem for the non-homogeneous heat equation which is a severely ill-posed problem. We regularize this problem using the quasi-reversibility method and then obtain error estimates on the approximate solutions. Solutions are calculated by the contraction principle and shown in numerical experiments. We obtain also rates of convergence to the exact solution.

  12. Robust regularized least-squares beamforming approach to signal estimation

    KAUST Repository

    Suliman, Mohamed Abdalla Elhag

    2017-05-12

    In this paper, we address the problem of robust adaptive beamforming of signals received by a linear array. The challenge associated with the beamforming problem is twofold. Firstly, the process requires the inversion of the usually ill-conditioned covariance matrix of the received signals. Secondly, the steering vector pertaining to the direction of arrival of the signal of interest is not known precisely. To tackle these two challenges, the standard capon beamformer is manipulated to a form where the beamformer output is obtained as a scaled version of the inner product of two vectors. The two vectors are linearly related to the steering vector and the received signal snapshot, respectively. The linear operator, in both cases, is the square root of the covariance matrix. A regularized least-squares (RLS) approach is proposed to estimate these two vectors and to provide robustness without exploiting prior information. Simulation results show that the RLS beamformer using the proposed regularization algorithm outperforms state-of-the-art beamforming algorithms, as well as another RLS beamformers using a standard regularization approaches.

  13. Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

    KAUST Repository

    Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Kammoun, Abla; Al-Naffouri, Tareq Y.

    2016-01-01

    random matrix theory are applied to derive the near-optimum regularizer that minimizes the mean-squared error of the estimator. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods for various

  14. Fluctuations of the SNR at the output of the MVDR with Regularized Tyler Estimators

    KAUST Repository

    Elkhalil, Khalil; Kammoun, Abla; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

    2016-01-01

    case in which the receiver employs the regularized Tyler estimator in order to estimate the covariance matrix of the interference-plus-noise process using n observations of size N×1N×1. The choice for the regularized Tylor estimator (RTE) is motivated

  15. Supplementary Appendix for: Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

    KAUST Repository

    Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Kammoun, Abla; Alnaffouri, Tareq Y.

    2016-01-01

    In this supplementary appendix we provide proofs and additional simulation results that complement the paper (constrained perturbation regularization approach for signal estimation using random matrix theory).

  16. On primal regularity estimates for set-valued mappings

    Czech Academy of Sciences Publication Activity Database

    Cibulka, R.; Fabian, Marián

    2016-01-01

    Roč. 438, č. 1 (2016), s. 444-464 ISSN 0022-247X R&D Projects: GA ČR(CZ) GAP201/12/0290 Institutional support: RVO:67985840 Keywords : metric regularity * linear openness * one-sided directional derivative Subject RIV: BA - General Mathematics Impact factor: 1.064, year: 2016 http://www.sciencedirect.com/science/article/pii/S0022247X16001402

  17. Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation

    International Nuclear Information System (INIS)

    Bardsley, Johnathan M; Goldes, John

    2009-01-01

    In image processing applications, image intensity is often measured via the counting of incident photons emitted by the object of interest. In such cases, image data noise is accurately modeled by a Poisson distribution. This motivates the use of Poisson maximum likelihood estimation for image reconstruction. However, when the underlying model equation is ill-posed, regularization is needed. Regularized Poisson likelihood estimation has been studied extensively by the authors, though a problem of high importance remains: the choice of the regularization parameter. We will present three statistically motivated methods for choosing the regularization parameter, and numerical examples will be presented to illustrate their effectiveness

  18. Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators

    KAUST Repository

    Kammoun, Abla; Couillet, Romain; Pascal, Frederic; Alouini, Mohamed-Slim

    2017-01-01

    This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.

  19. Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators

    KAUST Repository

    Kammoun, Abla

    2017-10-25

    This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.

  20. Estimation of extreme risk regions under multivariate regular variation

    NARCIS (Netherlands)

    Cai, J.; Einmahl, J.H.J.; de Haan, L.F.M.

    2011-01-01

    When considering d possibly dependent random variables, one is often interested in extreme risk regions, with very small probability p. We consider risk regions of the form {z ∈ Rd : f (z) ≤ β}, where f is the joint density and β a small number. Estimation of such an extreme risk region is difficult

  1. Bayesian estimation of regularization parameters for deformable surface models

    International Nuclear Information System (INIS)

    Cunningham, G.S.; Lehovich, A.; Hanson, K.M.

    1999-01-01

    In this article the authors build on their past attempts to reconstruct a 3D, time-varying bolus of radiotracer from first-pass data obtained by the dynamic SPECT imager, FASTSPECT, built by the University of Arizona. The object imaged is a CardioWest total artificial heart. The bolus is entirely contained in one ventricle and its associated inlet and outlet tubes. The model for the radiotracer distribution at a given time is a closed surface parameterized by 482 vertices that are connected to make 960 triangles, with nonuniform intensity variations of radiotracer allowed inside the surface on a voxel-to-voxel basis. The total curvature of the surface is minimized through the use of a weighted prior in the Bayesian framework, as is the weighted norm of the gradient of the voxellated grid. MAP estimates for the vertices, interior intensity voxels and background count level are produced. The strength of the priors, or hyperparameters, are determined by maximizing the probability of the data given the hyperparameters, called the evidence. The evidence is calculated by first assuming that the posterior is approximately normal in the values of the vertices and voxels, and then by evaluating the integral of the multi-dimensional normal distribution. This integral (which requires evaluating the determinant of a covariance matrix) is computed by applying a recent algorithm from Bai et. al. that calculates the needed determinant efficiently. They demonstrate that the radiotracer is highly inhomogeneous in early time frames, as suspected in earlier reconstruction attempts that assumed a uniform intensity of radiotracer within the closed surface, and that the optimal choice of hyperparameters is substantially different for different time frames

  2. Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

    KAUST Repository

    Suliman, Mohamed Abdalla Elhag

    2016-10-06

    In this work, we propose a new regularization approach for linear least-squares problems with random matrices. In the proposed constrained perturbation regularization approach, an artificial perturbation matrix with a bounded norm is forced into the system model matrix. This perturbation is introduced to improve the singular-value structure of the model matrix and, hence, the solution of the estimation problem. Relying on the randomness of the model matrix, a number of deterministic equivalents from random matrix theory are applied to derive the near-optimum regularizer that minimizes the mean-squared error of the estimator. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods for various estimated signal characteristics. In addition, simulations show that our approach is robust in the presence of model uncertainty.

  3. Perturbation-Based Regularization for Signal Estimation in Linear Discrete Ill-posed Problems

    KAUST Repository

    Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Al-Naffouri, Tareq Y.

    2016-01-01

    Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in ill-posed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. In this work, we propose a new regularization approach and a new regularization parameter selection approach for linear least-squares discrete ill-posed problems. The proposed approach is based on enhancing the singular-value structure of the ill-posed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimated data error, the proposed approach is developed to minimize the mean-squared error of the estimator which is the objective in many typical estimation scenarios. The performance of the proposed approach is demonstrated by applying it to a large set of real-world discrete ill-posed problems. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods in most cases. In addition, the approach also enjoys the lowest runtime and offers the highest level of robustness amongst all the tested benchmark regularization methods.

  4. Perturbation-Based Regularization for Signal Estimation in Linear Discrete Ill-posed Problems

    KAUST Repository

    Suliman, Mohamed Abdalla Elhag

    2016-11-29

    Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in ill-posed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. In this work, we propose a new regularization approach and a new regularization parameter selection approach for linear least-squares discrete ill-posed problems. The proposed approach is based on enhancing the singular-value structure of the ill-posed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimated data error, the proposed approach is developed to minimize the mean-squared error of the estimator which is the objective in many typical estimation scenarios. The performance of the proposed approach is demonstrated by applying it to a large set of real-world discrete ill-posed problems. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods in most cases. In addition, the approach also enjoys the lowest runtime and offers the highest level of robustness amongst all the tested benchmark regularization methods.

  5. Model-based estimation with boundary side information or boundary regularization

    International Nuclear Information System (INIS)

    Chiao, P.C.; Rogers, W.L.; Fessler, J.A.; Clinthorne, N.H.; Hero, A.O.

    1994-01-01

    The authors have previously developed a model-based strategy for joint estimation of myocardial perfusion and boundaries using ECT (Emission Computed Tomography). The authors have also reported difficulties with boundary estimation in low contrast and low count rate situations. In this paper, the authors propose using boundary side information (obtainable from high resolution MRI and CT images) or boundary regularization to improve both perfusion and boundary estimation in these situations. To fuse boundary side information into the emission measurements, the authors formulate a joint log-likelihood function to include auxiliary boundary measurements as well as ECT projection measurements. In addition, the authors introduce registration parameters to align auxiliary boundary measurements with ECT measurements and jointly estimate these parameters with other parameters of interest from the composite measurements. In simulated PET O-15 water myocardial perfusion studies using a simplified model, the authors show that the joint estimation improves perfusion estimation performance and gives boundary alignment accuracy of <0.5 mm even at 0.2 million counts. The authors implement boundary regularization through formulating a penalized log-likelihood function. The authors also demonstrate in simulations that simultaneous regularization of the epicardial boundary and myocardial thickness gives comparable perfusion estimation accuracy with the use of boundary side information

  6. Model-based estimation with boundary side information or boundary regularization [cardiac emission CT].

    Science.gov (United States)

    Chiao, P C; Rogers, W L; Fessler, J A; Clinthorne, N H; Hero, A O

    1994-01-01

    The authors have previously developed a model-based strategy for joint estimation of myocardial perfusion and boundaries using ECT (emission computed tomography). They have also reported difficulties with boundary estimation in low contrast and low count rate situations. Here they propose using boundary side information (obtainable from high resolution MRI and CT images) or boundary regularization to improve both perfusion and boundary estimation in these situations. To fuse boundary side information into the emission measurements, the authors formulate a joint log-likelihood function to include auxiliary boundary measurements as well as ECT projection measurements. In addition, they introduce registration parameters to align auxiliary boundary measurements with ECT measurements and jointly estimate these parameters with other parameters of interest from the composite measurements. In simulated PET O-15 water myocardial perfusion studies using a simplified model, the authors show that the joint estimation improves perfusion estimation performance and gives boundary alignment accuracy of <0.5 mm even at 0.2 million counts. They implement boundary regularization through formulating a penalized log-likelihood function. They also demonstrate in simulations that simultaneous regularization of the epicardial boundary and myocardial thickness gives comparable perfusion estimation accuracy with the use of boundary side information.

  7. Fluctuations of the SNR at the output of the MVDR with Regularized Tyler Estimators

    KAUST Repository

    Elkhalil, Khalil

    2016-12-27

    This paper analyzes the statistical properties of the signal-to-noise ratio (SNR) at the output of the Capon\\'s minimum variance distortionless response (MVDR) beamformers when operating over impulsive noises. Particularly, we consider the supervised case in which the receiver employs the regularized Tyler estimator in order to estimate the covariance matrix of the interference-plus-noise process using n observations of size N×1N×1. The choice for the regularized Tylor estimator (RTE) is motivated by its resilience to the presence of outliers and its regularization parameter that guarantees a good conditioning of the covariance estimate. Of particular interest in this paper is the derivation of the second order statistics of the SINR. To achieve this goal, we consider two different approaches. The first one is based on considering the classical regime, referred to as the n-large regime, in which N is assumed to be fixed while n grows to infinity. The second approach is built upon recent results developped within the framework of random matrix theory and assumes that N and n grow large together. Numerical results are provided in order to compare between the accuracies of each regime under different settings.

  8. Bayesian estimation of regularization and atlas building in diffeomorphic image registration.

    Science.gov (United States)

    Zhang, Miaomiao; Singh, Nikhil; Fletcher, P Thomas

    2013-01-01

    This paper presents a generative Bayesian model for diffeomorphic image registration and atlas building. We develop an atlas estimation procedure that simultaneously estimates the parameters controlling the smoothness of the diffeomorphic transformations. To achieve this, we introduce a Monte Carlo Expectation Maximization algorithm, where the expectation step is approximated via Hamiltonian Monte Carlo sampling on the manifold of diffeomorphisms. An added benefit of this stochastic approach is that it can successfully solve difficult registration problems involving large deformations, where direct geodesic optimization fails. Using synthetic data generated from the forward model with known parameters, we demonstrate the ability of our model to successfully recover the atlas and regularization parameters. We also demonstrate the effectiveness of the proposed method in the atlas estimation problem for 3D brain images.

  9. Temporal regularization of ultrasound-based liver motion estimation for image-guided radiation therapy

    Energy Technology Data Exchange (ETDEWEB)

    O’Shea, Tuathan P., E-mail: tuathan.oshea@icr.ac.uk; Bamber, Jeffrey C.; Harris, Emma J. [Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS foundation Trust, Sutton, London SM2 5PT (United Kingdom)

    2016-01-15

    Purpose: Ultrasound-based motion estimation is an expanding subfield of image-guided radiation therapy. Although ultrasound can detect tissue motion that is a fraction of a millimeter, its accuracy is variable. For controlling linear accelerator tracking and gating, ultrasound motion estimates must remain highly accurate throughout the imaging sequence. This study presents a temporal regularization method for correlation-based template matching which aims to improve the accuracy of motion estimates. Methods: Liver ultrasound sequences (15–23 Hz imaging rate, 2.5–5.5 min length) from ten healthy volunteers under free breathing were used. Anatomical features (blood vessels) in each sequence were manually annotated for comparison with normalized cross-correlation based template matching. Five sequences from a Siemens Acuson™ scanner were used for algorithm development (training set). Results from incremental tracking (IT) were compared with a temporal regularization method, which included a highly specific similarity metric and state observer, known as the α–β filter/similarity threshold (ABST). A further five sequences from an Elekta Clarity™ system were used for validation, without alteration of the tracking algorithm (validation set). Results: Overall, the ABST method produced marked improvements in vessel tracking accuracy. For the training set, the mean and 95th percentile (95%) errors (defined as the difference from manual annotations) were 1.6 and 1.4 mm, respectively (compared to 6.2 and 9.1 mm, respectively, for IT). For each sequence, the use of the state observer leads to improvement in the 95% error. For the validation set, the mean and 95% errors for the ABST method were 0.8 and 1.5 mm, respectively. Conclusions: Ultrasound-based motion estimation has potential to monitor liver translation over long time periods with high accuracy. Nonrigid motion (strain) and the quality of the ultrasound data are likely to have an impact on tracking

  10. Regularization and error estimates for asymmetric backward nonhomogeneous heat equations in a ball

    Directory of Open Access Journals (Sweden)

    Le Minh Triet

    2016-09-01

    Full Text Available The backward heat problem (BHP has been researched by many authors in the last five decades; it consists in recovering the initial distribution from the final temperature data. There are some articles [1,2,3] related the axi-symmetric BHP in a disk but the study in spherical coordinates is rare. Therefore, we wish to study a backward problem for nonhomogenous heat equation associated with asymmetric final data in a ball. In this article, we modify the quasi-boundary value method to construct a stable approximate solution for this problem. As a result, we obtain regularized solution and a sharp estimates for its error. At the end, a numerical experiment is provided to illustrate our method.

  11. Comparative analysis of gradient-field-based orientation estimation methods and regularized singular-value decomposition for fringe pattern processing.

    Science.gov (United States)

    Sun, Qi; Fu, Shujun

    2017-09-20

    Fringe orientation is an important feature of fringe patterns and has a wide range of applications such as guiding fringe pattern filtering, phase unwrapping, and abstraction. Estimating fringe orientation is a basic task for subsequent processing of fringe patterns. However, various noise, singular and obscure points, and orientation data degeneration lead to inaccurate calculations of fringe orientation. Thus, to deepen the understanding of orientation estimation and to better guide orientation estimation in fringe pattern processing, some advanced gradient-field-based orientation estimation methods are compared and analyzed. At the same time, following the ideas of smoothing regularization and computing of bigger gradient fields, a regularized singular-value decomposition (RSVD) technique is proposed for fringe orientation estimation. To compare the performance of these gradient-field-based methods, quantitative results and visual effect maps of orientation estimation are given on simulated and real fringe patterns that demonstrate that the RSVD produces the best estimation results at a cost of relatively less time.

  12. Stochastically Estimating Modular Criticality in Large-Scale Logic Circuits Using Sparsity Regularization and Compressive Sensing

    Directory of Open Access Journals (Sweden)

    Mohammed Alawad

    2015-03-01

    Full Text Available This paper considers the problem of how to efficiently measure a large and complex information field with optimally few observations. Specifically, we investigate how to stochastically estimate modular criticality values in a large-scale digital circuit with a very limited number of measurements in order to minimize the total measurement efforts and time. We prove that, through sparsity-promoting transform domain regularization and by strategically integrating compressive sensing with Bayesian learning, more than 98% of the overall measurement accuracy can be achieved with fewer than 10% of measurements as required in a conventional approach that uses exhaustive measurements. Furthermore, we illustrate that the obtained criticality results can be utilized to selectively fortify large-scale digital circuits for operation with narrow voltage headrooms and in the presence of soft-errors rising at near threshold voltage levels, without excessive hardware overheads. Our numerical simulation results have shown that, by optimally allocating only 10% circuit redundancy, for some large-scale benchmark circuits, we can achieve more than a three-times reduction in its overall error probability, whereas if randomly distributing such 10% hardware resource, less than 2% improvements in the target circuit’s overall robustness will be observed. Finally, we conjecture that our proposed approach can be readily applied to estimate other essential properties of digital circuits that are critical to designing and analyzing them, such as the observability measure in reliability analysis and the path delay estimation in stochastic timing analysis. The only key requirement of our proposed methodology is that these global information fields exhibit a certain degree of smoothness, which is universally true for almost any physical phenomenon.

  13. Near infrared reactance for the estimation of body fatness in regularly exercising individuals.

    Science.gov (United States)

    Evans, J; Lambert, M I; Micklesfield, L K; Goedecke, J H; Jennings, C L; Savides, L; Claassen, A; Lambert, E V

    2013-07-01

    Near infrared reactance (NIR) is used to measure body fat percentage (BF%), but there is little data on its use in non-obese, regularly exercising individuals. Therefore, this study aimed to examine the limits of agreement between NIR compared to dual x-ray absorptiometry (DXA) for the measurement of BF% in 2 cohorts of regularly exercising individuals. BF% was measured using DXA and NIR in a regular exercising (≥3 sessions/week), healthy active cohort (HA; n=57), and in a regularly exercising and resistance trained (≥2 sessions/week) cohort (RT; n=59). The RT cohort had lower BF% than the HA cohort (15.3±5.5% and 25.8±7.1%, Pexercising individuals. However, the rather broad LOA of NIR need to be considered when using NIR to screen for overweight and obesity, or measure and track changes in body composition. © Georg Thieme Verlag KG Stuttgart · New York.

  14. Regularization parameter estimation for underdetermined problems by the χ 2 principle with application to 2D focusing gravity inversion

    International Nuclear Information System (INIS)

    Vatankhah, Saeed; Ardestani, Vahid E; Renaut, Rosemary A

    2014-01-01

    The χ 2 principle generalizes the Morozov discrepancy principle to the augmented residual of the Tikhonov regularized least squares problem. For weighting of the data fidelity by a known Gaussian noise distribution on the measured data, when the stabilizing, or regularization, term is considered to be weighted by unknown inverse covariance information on the model parameters, the minimum of the Tikhonov functional becomes a random variable that follows a χ 2 -distribution with m+p−n degrees of freedom for the model matrix G of size m×n, m⩾n, and regularizer L of size p × n. Then, a Newton root-finding algorithm, employing the generalized singular value decomposition, or singular value decomposition when L = I, can be used to find the regularization parameter α. Here the result and algorithm are extended to the underdetermined case, m 2 algorithms when m 2 and unbiased predictive risk estimator of the regularization parameter are used for the first time in this context. For a simulated underdetermined data set with noise, these regularization parameter estimation methods, as well as the generalized cross validation method, are contrasted with the use of the L-curve and the Morozov discrepancy principle. Experiments demonstrate the efficiency and robustness of the χ 2 principle and unbiased predictive risk estimator, moreover showing that the L-curve and Morozov discrepancy principle are outperformed in general by the other three techniques. Furthermore, the minimum support stabilizer is of general use for the χ 2 principle when implemented without the desirable knowledge of the mean value of the model. (paper)

  15. Mean magnetic susceptibility regularized susceptibility tensor imaging (MMSR-STI) for estimating orientations of white matter fibers in human brain.

    Science.gov (United States)

    Li, Xu; van Zijl, Peter C M

    2014-09-01

    An increasing number of studies show that magnetic susceptibility in white matter fibers is anisotropic and may be described by a tensor. However, the limited head rotation possible for in vivo human studies leads to an ill-conditioned inverse problem in susceptibility tensor imaging (STI). Here we suggest the combined use of limiting the susceptibility anisotropy to white matter and imposing morphology constraints on the mean magnetic susceptibility (MMS) for regularizing the STI inverse problem. The proposed MMS regularized STI (MMSR-STI) method was tested using computer simulations and in vivo human data collected at 3T. The fiber orientation estimated from both the STI and MMSR-STI methods was compared to that from diffusion tensor imaging (DTI). Computer simulations show that the MMSR-STI method provides a more accurate estimation of the susceptibility tensor than the conventional STI approach. Similarly, in vivo data show that use of the MMSR-STI method leads to a smaller difference between the fiber orientation estimated from STI and DTI for most selected white matter fibers. The proposed regularization strategy for STI can improve estimation of the susceptibility tensor in white matter. © 2014 Wiley Periodicals, Inc.

  16. Local digital algorithms for estimating the mean integrated curvature of r-regular sets

    DEFF Research Database (Denmark)

    Svane, Anne Marie

    , no asymptotically unbiased estimator of this type exists in dimension greater than or equal to three, while for stationary isotropic lattices, asymptotically unbiased estimators are plenty. Both results follow from a general formula that we state and prove, describing the asymptotic behavior of hit...

  17. A Probabilistic Model of Visual Working Memory: Incorporating Higher Order Regularities into Working Memory Capacity Estimates

    Science.gov (United States)

    Brady, Timothy F.; Tenenbaum, Joshua B.

    2013-01-01

    When remembering a real-world scene, people encode both detailed information about specific objects and higher order information like the overall gist of the scene. However, formal models of change detection, like those used to estimate visual working memory capacity, assume observers encode only a simple memory representation that includes no…

  18. Regularized Pre-image Estimation for Kernel PCA De-noising

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    The main challenge in de-noising by kernel Principal Component Analysis (PCA) is the mapping of de-noised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed...

  19. Adaptive regularization

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Rasmussen, Carl Edward; Svarer, C.

    1994-01-01

    Regularization, e.g., in the form of weight decay, is important for training and optimization of neural network architectures. In this work the authors provide a tool based on asymptotic sampling theory, for iterative estimation of weight decay parameters. The basic idea is to do a gradient desce...

  20. Minimization and parameter estimation for seminorm regularization models with I-divergence constraints

    International Nuclear Information System (INIS)

    Teuber, T; Steidl, G; Chan, R H

    2013-01-01

    In this paper, we analyze the minimization of seminorms ‖L · ‖ on R n under the constraint of a bounded I-divergence D(b, H · ) for rather general linear operators H and L. The I-divergence is also known as Kullback–Leibler divergence and appears in many models in imaging science, in particular when dealing with Poisson data but also in the case of multiplicative Gamma noise. Often H represents, e.g., a linear blur operator and L is some discrete derivative or frame analysis operator. A central part of this paper consists in proving relations between the parameters of I-divergence constrained and penalized problems. To solve the I-divergence constrained problem, we consider various first-order primal–dual algorithms which reduce the problem to the solution of certain proximal minimization problems in each iteration step. One of these proximation problems is an I-divergence constrained least-squares problem which can be solved based on Morozov’s discrepancy principle by a Newton method. We prove that these algorithms produce not only a sequence of vectors which converges to a minimizer of the constrained problem but also a sequence of parameters which converges to a regularization parameter so that the corresponding penalized problem has the same solution. Furthermore, we derive a rule for automatically setting the constraint parameter for data corrupted by multiplicative Gamma noise. The performance of the various algorithms is finally demonstrated for different image restoration tasks both for images corrupted by Poisson noise and multiplicative Gamma noise. (paper)

  1. Comparison of two recent models for estimating actual evapotranspiration using only regularly recorded data

    Science.gov (United States)

    Ali, M. F.; Mawdsley, J. A.

    1987-09-01

    An advection-aridity model for estimating actual evapotranspiration ET is tested with over 700 days of lysimeter evapotranspiration and meteorological data from barley, turf and rye-grass from three sites in the U.K. The performance of the model is also compared with the API model . It is observed from the test that the advection-aridity model overestimates nonpotential ET and tends to underestimate potential ET, but when tested with potential and nonpotential data together, the tendencies appear to cancel each other. On a daily basis the performance level of this model is found to be of the same order as the API model: correlation coefficients were obtained between the model estimates and lysimeter data of 0.62 and 0.68 respectively. For periods greater than one day, generally the performance of the models are improved. Proposed by Mawdsley and Ali (1979)

  2. Application of selection and estimation regular vine copula on go public company share

    Science.gov (United States)

    Hasna Afifah, R.; Noviyanti, Lienda; Bachrudin, Achmad

    2018-03-01

    The accuracy of financial risk management involving a large number of assets is needed, but information about dependencies among assets cannot be adequately analyzed. To analyze dependencies on a number of assets, several tools have been added to standard multivariate copula. However, these tools have not been adequately used in apps with higher dimensions. The bivariate parametric copula families can be used to solve it. The multivariate copula can be built from the bivariate parametric copula which is connected by a graphical representation to become Pair Copula Constructions (PCCs) or vine copula. The application of C-vine and D-vine copula have been used in some researches, but the use of C-vine and D-vine copula is more limited than R-vine copula. Therefore, this study used R-vine copula to provide flexibility for modeling complex dependencies on a high dimension. Since copula is a static model, while stock values change over time, then copula should be combined with the ARMA- GARCH model for modeling the movement of shares (volatility). The objective of this paper is to select and estimate R-vine copula which is used to analyze PT Jasa Marga (Persero) Tbk (JSMR), PT Waskita Karya (Persero) Tbk (WSKT), and PT Bank Mandiri (Persero) Tbk (BMRI) from august 31, 2014 to august 31, 2017. From the method it is obtained that the selected copulas for 2 edges at the first tree are survival Gumbel and the copula for edge at the second tree is Gaussian.

  3. Evaluating the impact of spatio-temporal smoothness constraints on the BOLD hemodynamic response function estimation: an analysis based on Tikhonov regularization

    International Nuclear Information System (INIS)

    Casanova, R; Yang, L; Hairston, W D; Laurienti, P J; Maldjian, J A

    2009-01-01

    Recently we have proposed the use of Tikhonov regularization with temporal smoothness constraints to estimate the BOLD fMRI hemodynamic response function (HRF). The temporal smoothness constraint was imposed on the estimates by using second derivative information while the regularization parameter was selected based on the generalized cross-validation function (GCV). Using one-dimensional simulations, we previously found this method to produce reliable estimates of the HRF time course, especially its time to peak (TTP), being at the same time fast and robust to over-sampling in the HRF estimation. Here, we extend the method to include simultaneous temporal and spatial smoothness constraints. This method does not need Gaussian smoothing as a pre-processing step as usually done in fMRI data analysis. We carried out two-dimensional simulations to compare the two methods: Tikhonov regularization with temporal (Tik-GCV-T) and spatio-temporal (Tik-GCV-ST) smoothness constraints on the estimated HRF. We focus our attention on quantifying the influence of the Gaussian data smoothing and the presence of edges on the performance of these techniques. Our results suggest that the spatial smoothing introduced by regularization is less severe than that produced by Gaussian smoothing. This allows more accurate estimates of the response amplitudes while producing similar estimates of the TTP. We illustrate these ideas using real data. (note)

  4. Numerical aspects of drift kinetic turbulence: Ill-posedness, regularization and a priori estimates of sub-grid-scale terms

    KAUST Repository

    Samtaney, Ravi

    2012-01-01

    of a simple collisional model, by inclusion of an ad-hoc hyperviscosity or artificial viscosity term or by implicit dissipation in upwind schemes. Comparisons between the various methods and regularizations are presented. We apply a filtering formalism

  5. Numerical aspects of drift kinetic turbulence: Ill-posedness, regularization and a priori estimates of sub-grid-scale terms

    KAUST Repository

    Samtaney, Ravi

    2012-01-01

    We present a numerical method based on an Eulerian approach to solve the Vlasov-Poisson system for 4D drift kinetic turbulence. Our numerical approach uses a conservative formulation with high-order (fourth and higher) evaluation of the numerical fluxes coupled with a fourth-order accurate Poisson solver. The fluxes are computed using a low-dissipation high-order upwind differencing method or a tuned high-resolution finite difference method with no numerical dissipation. Numerical results are presented for the case of imposed ion temperature and density gradients. Different forms of controlled regularization to achieve a well-posed system are used to obtain convergent resolved simulations. The regularization of the equations is achieved by means of a simple collisional model, by inclusion of an ad-hoc hyperviscosity or artificial viscosity term or by implicit dissipation in upwind schemes. Comparisons between the various methods and regularizations are presented. We apply a filtering formalism to the Vlasov equation and derive sub-grid-scale (SGS) terms analogous to the Reynolds stress terms in hydrodynamic turbulence. We present a priori quantifications of these SGS terms in resolved simulations of drift-kinetic turbulence by applying a sharp filter. © 2012 IOP Publishing Ltd.

  6. Numerical aspects of drift kinetic turbulence: ill-posedness, regularization and a priori estimates of sub-grid-scale terms

    International Nuclear Information System (INIS)

    Samtaney, Ravi

    2012-01-01

    We present a numerical method based on an Eulerian approach to solve the Vlasov-Poisson system for 4D drift kinetic turbulence. Our numerical approach uses a conservative formulation with high-order (fourth and higher) evaluation of the numerical fluxes coupled with a fourth-order accurate Poisson solver. The fluxes are computed using a low-dissipation high-order upwind differencing method or a tuned high-resolution finite difference method with no numerical dissipation. Numerical results are presented for the case of imposed ion temperature and density gradients. Different forms of controlled regularization to achieve a well-posed system are used to obtain convergent resolved simulations. The regularization of the equations is achieved by means of a simple collisional model, by inclusion of an ad-hoc hyperviscosity or artificial viscosity term or by implicit dissipation in upwind schemes. Comparisons between the various methods and regularizations are presented. We apply a filtering formalism to the Vlasov equation and derive sub-grid-scale (SGS) terms analogous to the Reynolds stress terms in hydrodynamic turbulence. We present a priori quantifications of these SGS terms in resolved simulations of drift-kinetic turbulence by applying a sharp filter.

  7. Stochastic differential equations as a tool to regularize the parameter estimation problem for continuous time dynamical systems given discrete time measurements.

    Science.gov (United States)

    Leander, Jacob; Lundh, Torbjörn; Jirstrand, Mats

    2014-05-01

    In this paper we consider the problem of estimating parameters in ordinary differential equations given discrete time experimental data. The impact of going from an ordinary to a stochastic differential equation setting is investigated as a tool to overcome the problem of local minima in the objective function. Using two different models, it is demonstrated that by allowing noise in the underlying model itself, the objective functions to be minimized in the parameter estimation procedures are regularized in the sense that the number of local minima is reduced and better convergence is achieved. The advantage of using stochastic differential equations is that the actual states in the model are predicted from data and this will allow the prediction to stay close to data even when the parameters in the model is incorrect. The extended Kalman filter is used as a state estimator and sensitivity equations are provided to give an accurate calculation of the gradient of the objective function. The method is illustrated using in silico data from the FitzHugh-Nagumo model for excitable media and the Lotka-Volterra predator-prey system. The proposed method performs well on the models considered, and is able to regularize the objective function in both models. This leads to parameter estimation problems with fewer local minima which can be solved by efficient gradient-based methods. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  8. Water Residence Time estimation by 1D deconvolution in the form of a l2 -regularized inverse problem with smoothness, positivity and causality constraints

    Science.gov (United States)

    Meresescu, Alina G.; Kowalski, Matthieu; Schmidt, Frédéric; Landais, François

    2018-06-01

    The Water Residence Time distribution is the equivalent of the impulse response of a linear system allowing the propagation of water through a medium, e.g. the propagation of rain water from the top of the mountain towards the aquifers. We consider the output aquifer levels as the convolution between the input rain levels and the Water Residence Time, starting with an initial aquifer base level. The estimation of Water Residence Time is important for a better understanding of hydro-bio-geochemical processes and mixing properties of wetlands used as filters in ecological applications, as well as protecting fresh water sources for wells from pollutants. Common methods of estimating the Water Residence Time focus on cross-correlation, parameter fitting and non-parametric deconvolution methods. Here we propose a 1D full-deconvolution, regularized, non-parametric inverse problem algorithm that enforces smoothness and uses constraints of causality and positivity to estimate the Water Residence Time curve. Compared to Bayesian non-parametric deconvolution approaches, it has a fast runtime per test case; compared to the popular and fast cross-correlation method, it produces a more precise Water Residence Time curve even in the case of noisy measurements. The algorithm needs only one regularization parameter to balance between smoothness of the Water Residence Time and accuracy of the reconstruction. We propose an approach on how to automatically find a suitable value of the regularization parameter from the input data only. Tests on real data illustrate the potential of this method to analyze hydrological datasets.

  9. Modification of Displacement Coefficient Method in Estimation of Target Displacement for Regular Concrete Bridges Based on ASCE 41-06 Standard

    Directory of Open Access Journals (Sweden)

    Seyed Bahram Beheshti-Aval

    2015-06-01

    Full Text Available Displacement Coefficient Method (DCM stipulated in the ASCE 41-06 standard is becoming the preferred method for seismic rehabilitation of buildings in many high-seismic-hazard countries. Applications of the method for non-building constructions such as bridges are beyond the scope of this standard. Thus its application to this kind of structure should be approached with care. Target displacement has reasonable accuracy for buildings with strong columns and weak beams, where there is the development of plastic hinges. Due to high stiffness and strength of the deck relative to the piers in most bridges, this mechanism does not occur, and it is necessary to evaluate the accuracy of DCM for such structures. In this research, an attempt is made to evaluate the credibility of DCM in the ASCE/SEI 41-06 standard for estimating target drifts in concrete regular bridges under strong ground motions. To apply the extension of the method to bridge structures, the definition of new correction factor CB, which should be multiplied to previous coefficients, is required. This novel coefficient can improve the accuracy of the mentioned method in accessing seismic displacement demands. The coefficient is presented for soil types A to D based on NEHRP soil classification. The validity of the modified DCM is examined for several bridges with use of nonlinear dynamic analysis. Good correlation is found between both procedures.

  10. 2-D left ventricular flow estimation by combining speckle tracking with Navier-Stokes-based regularization: an in silico, in vitro and in vivo study.

    Science.gov (United States)

    Gao, Hang; Bijnens, Nathalie; Coisne, Damien; Lugiez, Mathieu; Rutten, Marcel; D'hooge, Jan

    2015-01-01

    Despite the availability of multiple ultrasound approaches to left ventricular (LV) flow characterization in two dimensions, this technique remains in its childhood and further developments seem warranted. This article describes a new methodology for tracking the 2-D LV flow field based on ultrasound data. Hereto, a standard speckle tracking algorithm was modified by using a dynamic kernel embedding Navier-Stokes-based regularization in an iterative manner. The performance of the proposed approach was first quantified in synthetic ultrasound data based on a computational fluid dynamics model of LV flow. Next, an experimental flow phantom setup mimicking the normal human heart was used for experimental validation by employing simultaneous optical particle image velocimetry as a standard reference technique. Finally, the applicability of the approach was tested in a clinical setting. On the basis of the simulated data, pointwise evaluation of the estimated velocity vectors correlated well (mean r = 0.84) with the computational fluid dynamics measurement. During the filling period of the left ventricle, the properties of the main vortex obtained from the proposed method were also measured, and their correlations with the reference measurement were also calculated (radius, r = 0.96; circulation, r = 0.85; weighted center, r = 0.81). In vitro results at 60 bpm during one cardiac cycle confirmed that the algorithm properly measures typical characteristics of the vortex (radius, r = 0.60; circulation, r = 0.81; weighted center, r = 0.92). Preliminary qualitative results on clinical data revealed physiologic flow fields. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

  11. UNFOLDED REGULAR AND SEMI-REGULAR POLYHEDRA

    Directory of Open Access Journals (Sweden)

    IONIŢĂ Elena

    2015-06-01

    Full Text Available This paper proposes a presentation unfolding regular and semi-regular polyhedra. Regular polyhedra are convex polyhedra whose faces are regular and equal polygons, with the same number of sides, and whose polyhedral angles are also regular and equal. Semi-regular polyhedra are convex polyhedra with regular polygon faces, several types and equal solid angles of the same type. A net of a polyhedron is a collection of edges in the plane which are the unfolded edges of the solid. Modeling and unfolding Platonic and Arhimediene polyhedra will be using 3dsMAX program. This paper is intended as an example of descriptive geometry applications.

  12. Coordinate-invariant regularization

    International Nuclear Information System (INIS)

    Halpern, M.B.

    1987-01-01

    A general phase-space framework for coordinate-invariant regularization is given. The development is geometric, with all regularization contained in regularized DeWitt Superstructures on field deformations. Parallel development of invariant coordinate-space regularization is obtained by regularized functional integration of the momenta. As representative examples of the general formulation, the regularized general non-linear sigma model and regularized quantum gravity are discussed. copyright 1987 Academic Press, Inc

  13. Regularity of Minimal Surfaces

    CERN Document Server

    Dierkes, Ulrich; Tromba, Anthony J; Kuster, Albrecht

    2010-01-01

    "Regularity of Minimal Surfaces" begins with a survey of minimal surfaces with free boundaries. Following this, the basic results concerning the boundary behaviour of minimal surfaces and H-surfaces with fixed or free boundaries are studied. In particular, the asymptotic expansions at interior and boundary branch points are derived, leading to general Gauss-Bonnet formulas. Furthermore, gradient estimates and asymptotic expansions for minimal surfaces with only piecewise smooth boundaries are obtained. One of the main features of free boundary value problems for minimal surfaces is t

  14. Regularizing portfolio optimization

    International Nuclear Information System (INIS)

    Still, Susanne; Kondor, Imre

    2010-01-01

    The optimization of large portfolios displays an inherent instability due to estimation error. This poses a fundamental problem, because solutions that are not stable under sample fluctuations may look optimal for a given sample, but are, in effect, very far from optimal with respect to the average risk. In this paper, we approach the problem from the point of view of statistical learning theory. The occurrence of the instability is intimately related to over-fitting, which can be avoided using known regularization methods. We show how regularized portfolio optimization with the expected shortfall as a risk measure is related to support vector regression. The budget constraint dictates a modification. We present the resulting optimization problem and discuss the solution. The L2 norm of the weight vector is used as a regularizer, which corresponds to a diversification 'pressure'. This means that diversification, besides counteracting downward fluctuations in some assets by upward fluctuations in others, is also crucial because it improves the stability of the solution. The approach we provide here allows for the simultaneous treatment of optimization and diversification in one framework that enables the investor to trade off between the two, depending on the size of the available dataset.

  15. Regularizing portfolio optimization

    Science.gov (United States)

    Still, Susanne; Kondor, Imre

    2010-07-01

    The optimization of large portfolios displays an inherent instability due to estimation error. This poses a fundamental problem, because solutions that are not stable under sample fluctuations may look optimal for a given sample, but are, in effect, very far from optimal with respect to the average risk. In this paper, we approach the problem from the point of view of statistical learning theory. The occurrence of the instability is intimately related to over-fitting, which can be avoided using known regularization methods. We show how regularized portfolio optimization with the expected shortfall as a risk measure is related to support vector regression. The budget constraint dictates a modification. We present the resulting optimization problem and discuss the solution. The L2 norm of the weight vector is used as a regularizer, which corresponds to a diversification 'pressure'. This means that diversification, besides counteracting downward fluctuations in some assets by upward fluctuations in others, is also crucial because it improves the stability of the solution. The approach we provide here allows for the simultaneous treatment of optimization and diversification in one framework that enables the investor to trade off between the two, depending on the size of the available dataset.

  16. An iterative regularization method in estimating the transient heat-transfer rate on the surface of the insulation layer of a double circular pipe

    International Nuclear Information System (INIS)

    Chen, W.-L.; Yang, Y.-C.

    2009-01-01

    In this study, a conjugate gradient method based inverse algorithm is applied to estimate the unknown space- and time-dependent heat-transfer rate on the surface of the insulation layer of a double circular pipe heat exchanger using temperature measurements. It is assumed that no prior information is available on the functional form of the unknown heat-transfer rate; hence the procedure is classified as the function estimation in inverse calculation. The temperature data obtained from the direct problem are used to simulate the temperature measurements. The accuracy of the inverse analysis is examined by using simulated exact and inexact temperature measurements. Results show that an excellent estimation on the space- and time-dependent heat-transfer rate can be obtained for the test case considered in this study.

  17. Distance-regular graphs

    NARCIS (Netherlands)

    van Dam, Edwin R.; Koolen, Jack H.; Tanaka, Hajime

    2016-01-01

    This is a survey of distance-regular graphs. We present an introduction to distance-regular graphs for the reader who is unfamiliar with the subject, and then give an overview of some developments in the area of distance-regular graphs since the monograph 'BCN'[Brouwer, A.E., Cohen, A.M., Neumaier,

  18. LL-regular grammars

    NARCIS (Netherlands)

    Nijholt, Antinus

    1980-01-01

    Culik II and Cogen introduced the class of LR-regular grammars, an extension of the LR(k) grammars. In this paper we consider an analogous extension of the LL(k) grammars called the LL-regular grammars. The relation of this class of grammars to other classes of grammars will be shown. Any LL-regular

  19. Race/ethnic disparities in reproductive age: an examination of ovarian reserve estimates across four race/ethnic groups of healthy, regularly cycling women.

    Science.gov (United States)

    Bleil, Maria E; Gregorich, Steven E; Adler, Nancy E; Sternfeld, Barbara; Rosen, Mitchell P; Cedars, Marcelle I

    2014-01-01

    To determine whether reproductive age, as indexed by a validated marker of ovarian reserve (antimüllerian hormone [AMH]), varies among women of different race/ethnic backgrounds. Cross-sectional study. Community-based sample. Multiethnic sample of 947 (277 white, 237 African American, 220 Latina, and 213 Chinese) healthy and regularly cycling premenopausal women, ages 25-45. None. AMH level. A multivariate model was fit examining race/ethnicity, covariates, nonlinear terms for age (age(2), age(3)), and body mass index (BMI(2), BMI(3)), and two-way interactions between race/ethnicity and each of the other predictor variables in relation to AMH. After backward elimination, significant effects included race/ethnicity (F = 8.45), age (F = 349.94), race/ethnicity-by-linear age interaction (F = 4.67), age(2) (F = 31.61), and BMI (F = 10.69). Inspection of the significant race/ethnicity-by-linear age interaction showed AMH levels were consistently lower among Latina women compared with white women across all ages, whereas AMH levels were lower among African American and Chinese women compared with the white women at younger and middle ages, respectively. The AMH levels were higher among African American compared with Latina and Chinese women at older ages. Although the results must be considered preliminary, the findings are twofold: African American women may have lower AMH levels at younger ages but experience less of a reduction in AMH with advancing age, and Latina and Chinese women compared with white women may have lower AMH levels, marking a lower ovarian reserve and a possibly increased risk for earlier menopause. Copyright © 2014 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

  20. The effects of dominance, regular inbreeding and sampling design on Q(ST), an estimator of population differentiation for quantitative traits.

    Science.gov (United States)

    Goudet, Jérôme; Büchi, Lucie

    2006-02-01

    To test whether quantitative traits are under directional or homogenizing selection, it is common practice to compare population differentiation estimates at molecular markers (F(ST)) and quantitative traits (Q(ST)). If the trait is neutral and its determinism is additive, then theory predicts that Q(ST) = F(ST), while Q(ST) > F(ST) is predicted under directional selection for different local optima, and Q(ST) sampling designs and find that it is always best to sample many populations (>20) with few families (five) rather than few populations with many families. Provided that estimates of Q(ST) are derived from individuals originating from many populations, we conclude that the pattern Q(ST) > F(ST), and hence the inference of directional selection for different local optima, is robust to the effect of nonadditive gene actions.

  1. Regular Expression Pocket Reference

    CERN Document Server

    Stubblebine, Tony

    2007-01-01

    This handy little book offers programmers a complete overview of the syntax and semantics of regular expressions that are at the heart of every text-processing application. Ideal as a quick reference, Regular Expression Pocket Reference covers the regular expression APIs for Perl 5.8, Ruby (including some upcoming 1.9 features), Java, PHP, .NET and C#, Python, vi, JavaScript, and the PCRE regular expression libraries. This concise and easy-to-use reference puts a very powerful tool for manipulating text and data right at your fingertips. Composed of a mixture of symbols and text, regular exp

  2. A regularized stationary mean-field game

    KAUST Repository

    Yang, Xianjin

    2016-01-01

    In the thesis, we discuss the existence and numerical approximations of solutions of a regularized mean-field game with a low-order regularization. In the first part, we prove a priori estimates and use the continuation method to obtain the existence of a solution with a positive density. Finally, we introduce the monotone flow method and solve the system numerically.

  3. A regularized stationary mean-field game

    KAUST Repository

    Yang, Xianjin

    2016-04-19

    In the thesis, we discuss the existence and numerical approximations of solutions of a regularized mean-field game with a low-order regularization. In the first part, we prove a priori estimates and use the continuation method to obtain the existence of a solution with a positive density. Finally, we introduce the monotone flow method and solve the system numerically.

  4. Regularization by External Variables

    DEFF Research Database (Denmark)

    Bossolini, Elena; Edwards, R.; Glendinning, P. A.

    2016-01-01

    Regularization was a big topic at the 2016 CRM Intensive Research Program on Advances in Nonsmooth Dynamics. There are many open questions concerning well known kinds of regularization (e.g., by smoothing or hysteresis). Here, we propose a framework for an alternative and important kind of regula......Regularization was a big topic at the 2016 CRM Intensive Research Program on Advances in Nonsmooth Dynamics. There are many open questions concerning well known kinds of regularization (e.g., by smoothing or hysteresis). Here, we propose a framework for an alternative and important kind...

  5. Regular expressions cookbook

    CERN Document Server

    Goyvaerts, Jan

    2009-01-01

    This cookbook provides more than 100 recipes to help you crunch data and manipulate text with regular expressions. Every programmer can find uses for regular expressions, but their power doesn't come worry-free. Even seasoned users often suffer from poor performance, false positives, false negatives, or perplexing bugs. Regular Expressions Cookbook offers step-by-step instructions for some of the most common tasks involving this tool, with recipes for C#, Java, JavaScript, Perl, PHP, Python, Ruby, and VB.NET. With this book, you will: Understand the basics of regular expressions through a

  6. Regularities of Multifractal Measures

    Indian Academy of Sciences (India)

    First, we prove the decomposition theorem for the regularities of multifractal Hausdorff measure and packing measure in R R d . This decomposition theorem enables us to split a set into regular and irregular parts, so that we can analyze each separately, and recombine them without affecting density properties. Next, we ...

  7. Stochastic analytic regularization

    International Nuclear Information System (INIS)

    Alfaro, J.

    1984-07-01

    Stochastic regularization is reexamined, pointing out a restriction on its use due to a new type of divergence which is not present in the unregulated theory. Furthermore, we introduce a new form of stochastic regularization which permits the use of a minimal subtraction scheme to define the renormalized Green functions. (author)

  8. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

    2014-01-01

    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse

  9. Regular expression containment

    DEFF Research Database (Denmark)

    Henglein, Fritz; Nielsen, Lasse

    2011-01-01

    We present a new sound and complete axiomatization of regular expression containment. It consists of the conventional axiomatiza- tion of concatenation, alternation, empty set and (the singleton set containing) the empty string as an idempotent semiring, the fixed- point rule E* = 1 + E × E......* for Kleene-star, and a general coin- duction rule as the only additional rule. Our axiomatization gives rise to a natural computational inter- pretation of regular expressions as simple types that represent parse trees, and of containment proofs as coercions. This gives the axiom- atization a Curry......-Howard-style constructive interpretation: Con- tainment proofs do not only certify a language-theoretic contain- ment, but, under our computational interpretation, constructively transform a membership proof of a string in one regular expres- sion into a membership proof of the same string in another regular expression. We...

  10. Supersymmetric dimensional regularization

    International Nuclear Information System (INIS)

    Siegel, W.; Townsend, P.K.; van Nieuwenhuizen, P.

    1980-01-01

    There is a simple modification of dimension regularization which preserves supersymmetry: dimensional reduction to real D < 4, followed by analytic continuation to complex D. In terms of component fields, this means fixing the ranges of all indices on the fields (and therefore the numbers of Fermi and Bose components). For superfields, it means continuing in the dimensionality of x-space while fixing the dimensionality of theta-space. This regularization procedure allows the simple manipulation of spinor derivatives in supergraph calculations. The resulting rules are: (1) First do all algebra exactly as in D = 4; (2) Then do the momentum integrals as in ordinary dimensional regularization. This regularization procedure needs extra rules before one can say that it is consistent. Such extra rules needed for superconformal anomalies are discussed. Problems associated with renormalizability and higher order loops are also discussed

  11. Regularized maximum correntropy machine

    KAUST Repository

    Wang, Jim Jing-Yan; Wang, Yunji; Jing, Bing-Yi; Gao, Xin

    2015-01-01

    In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two challenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.

  12. Regularized maximum correntropy machine

    KAUST Repository

    Wang, Jim Jing-Yan

    2015-02-12

    In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two challenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.

  13. Energy functions for regularization algorithms

    Science.gov (United States)

    Delingette, H.; Hebert, M.; Ikeuchi, K.

    1991-01-01

    Regularization techniques are widely used for inverse problem solving in computer vision such as surface reconstruction, edge detection, or optical flow estimation. Energy functions used for regularization algorithms measure how smooth a curve or surface is, and to render acceptable solutions these energies must verify certain properties such as invariance with Euclidean transformations or invariance with parameterization. The notion of smoothness energy is extended here to the notion of a differential stabilizer, and it is shown that to void the systematic underestimation of undercurvature for planar curve fitting, it is necessary that circles be the curves of maximum smoothness. A set of stabilizers is proposed that meet this condition as well as invariance with rotation and parameterization.

  14. Maximum mutual information regularized classification

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-09-07

    In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

  15. Maximum mutual information regularized classification

    KAUST Repository

    Wang, Jim Jing-Yan; Wang, Yi; Zhao, Shiguang; Gao, Xin

    2014-01-01

    In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

  16. Manifold Regularized Reinforcement Learning.

    Science.gov (United States)

    Li, Hongliang; Liu, Derong; Wang, Ding

    2018-04-01

    This paper introduces a novel manifold regularized reinforcement learning scheme for continuous Markov decision processes. Smooth feature representations for value function approximation can be automatically learned using the unsupervised manifold regularization method. The learned features are data-driven, and can be adapted to the geometry of the state space. Furthermore, the scheme provides a direct basis representation extension for novel samples during policy learning and control. The performance of the proposed scheme is evaluated on two benchmark control tasks, i.e., the inverted pendulum and the energy storage problem. Simulation results illustrate the concepts of the proposed scheme and show that it can obtain excellent performance.

  17. Diverse Regular Employees and Non-regular Employment (Japanese)

    OpenAIRE

    MORISHIMA Motohiro

    2011-01-01

    Currently there are high expectations for the introduction of policies related to diverse regular employees. These policies are a response to the problem of disparities between regular and non-regular employees (part-time, temporary, contract and other non-regular employees) and will make it more likely that workers can balance work and their private lives while companies benefit from the advantages of regular employment. In this paper, I look at two issues that underlie this discussion. The ...

  18. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-04-17

    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

  19. 'Regular' and 'emergency' repair

    International Nuclear Information System (INIS)

    Luchnik, N.V.

    1975-01-01

    Experiments on the combined action of radiation and a DNA inhibitor using Crepis roots and on split-dose irradiation of human lymphocytes lead to the conclusion that there are two types of repair. The 'regular' repair takes place twice in each mitotic cycle and ensures the maintenance of genetic stability. The 'emergency' repair is induced at all stages of the mitotic cycle by high levels of injury. (author)

  20. Regularization of divergent integrals

    OpenAIRE

    Felder, Giovanni; Kazhdan, David

    2016-01-01

    We study the Hadamard finite part of divergent integrals of differential forms with singularities on submanifolds. We give formulae for the dependence of the finite part on the choice of regularization and express them in terms of a suitable local residue map. The cases where the submanifold is a complex hypersurface in a complex manifold and where it is a boundary component of a manifold with boundary, arising in string perturbation theory, are treated in more detail.

  1. Regular Single Valued Neutrosophic Hypergraphs

    Directory of Open Access Journals (Sweden)

    Muhammad Aslam Malik

    2016-12-01

    Full Text Available In this paper, we define the regular and totally regular single valued neutrosophic hypergraphs, and discuss the order and size along with properties of regular and totally regular single valued neutrosophic hypergraphs. We also extend work on completeness of single valued neutrosophic hypergraphs.

  2. The geometry of continuum regularization

    International Nuclear Information System (INIS)

    Halpern, M.B.

    1987-03-01

    This lecture is primarily an introduction to coordinate-invariant regularization, a recent advance in the continuum regularization program. In this context, the program is seen as fundamentally geometric, with all regularization contained in regularized DeWitt superstructures on field deformations

  3. Annotation of Regular Polysemy

    DEFF Research Database (Denmark)

    Martinez Alonso, Hector

    Regular polysemy has received a lot of attention from the theory of lexical semantics and from computational linguistics. However, there is no consensus on how to represent the sense of underspecified examples at the token level, namely when annotating or disambiguating senses of metonymic words...... and metonymic. We have conducted an analysis in English, Danish and Spanish. Later on, we have tried to replicate the human judgments by means of unsupervised and semi-supervised sense prediction. The automatic sense-prediction systems have been unable to find empiric evidence for the underspecified sense, even...

  4. Regularities of radiation heredity

    International Nuclear Information System (INIS)

    Skakov, M.K.; Melikhov, V.D.

    2001-01-01

    One analyzed regularities of radiation heredity in metals and alloys. One made conclusion about thermodynamically irreversible changes in structure of materials under irradiation. One offers possible ways of heredity transmittance of radiation effects at high-temperature transformations in the materials. Phenomenon of radiation heredity may be turned to practical use to control structure of liquid metal and, respectively, structure of ingot via preliminary radiation treatment of charge. Concentration microheterogeneities in material defect structure induced by preliminary irradiation represent the genetic factor of radiation heredity [ru

  5. Incremental projection approach of regularization for inverse problems

    Energy Technology Data Exchange (ETDEWEB)

    Souopgui, Innocent, E-mail: innocent.souopgui@usm.edu [The University of Southern Mississippi, Department of Marine Science (United States); Ngodock, Hans E., E-mail: hans.ngodock@nrlssc.navy.mil [Naval Research Laboratory (United States); Vidard, Arthur, E-mail: arthur.vidard@imag.fr; Le Dimet, François-Xavier, E-mail: ledimet@imag.fr [Laboratoire Jean Kuntzmann (France)

    2016-10-15

    This paper presents an alternative approach to the regularized least squares solution of ill-posed inverse problems. Instead of solving a minimization problem with an objective function composed of a data term and a regularization term, the regularization information is used to define a projection onto a convex subspace of regularized candidate solutions. The objective function is modified to include the projection of each iterate in the place of the regularization. Numerical experiments based on the problem of motion estimation for geophysical fluid images, show the improvement of the proposed method compared with regularization methods. For the presented test case, the incremental projection method uses 7 times less computation time than the regularization method, to reach the same error target. Moreover, at convergence, the incremental projection is two order of magnitude more accurate than the regularization method.

  6. Regularized Discriminant Analysis: A Large Dimensional Study

    KAUST Repository

    Yang, Xiaoke

    2018-04-28

    In this thesis, we focus on studying the performance of general regularized discriminant analysis (RDA) classifiers. The data used for analysis is assumed to follow Gaussian mixture model with different means and covariances. RDA offers a rich class of regularization options, covering as special cases the regularized linear discriminant analysis (RLDA) and the regularized quadratic discriminant analysis (RQDA) classi ers. We analyze RDA under the double asymptotic regime where the data dimension and the training size both increase in a proportional way. This double asymptotic regime allows for application of fundamental results from random matrix theory. Under the double asymptotic regime and some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that only depends on the data statistical parameters and dimensions. This result not only implicates some mathematical relations between the misclassification error and the class statistics, but also can be leveraged to select the optimal parameters that minimize the classification error, thus yielding the optimal classifier. Validation results on the synthetic data show a good accuracy of our theoretical findings. We also construct a general consistent estimator to approximate the true classification error in consideration of the unknown previous statistics. We benchmark the performance of our proposed consistent estimator against classical estimator on synthetic data. The observations demonstrate that the general estimator outperforms others in terms of mean squared error (MSE).

  7. Bypassing the Limits of Ll Regularization: Convex Sparse Signal Processing Using Non-Convex Regularization

    Science.gov (United States)

    Parekh, Ankit

    Sparsity has become the basis of some important signal processing methods over the last ten years. Many signal processing problems (e.g., denoising, deconvolution, non-linear component analysis) can be expressed as inverse problems. Sparsity is invoked through the formulation of an inverse problem with suitably designed regularization terms. The regularization terms alone encode sparsity into the problem formulation. Often, the ℓ1 norm is used to induce sparsity, so much so that ℓ1 regularization is considered to be `modern least-squares'. The use of ℓ1 norm, as a sparsity-inducing regularizer, leads to a convex optimization problem, which has several benefits: the absence of extraneous local minima, well developed theory of globally convergent algorithms, even for large-scale problems. Convex regularization via the ℓ1 norm, however, tends to under-estimate the non-zero values of sparse signals. In order to estimate the non-zero values more accurately, non-convex regularization is often favored over convex regularization. However, non-convex regularization generally leads to non-convex optimization, which suffers from numerous issues: convergence may be guaranteed to only a stationary point, problem specific parameters may be difficult to set, and the solution is sensitive to the initialization of the algorithm. The first part of this thesis is aimed toward combining the benefits of non-convex regularization and convex optimization to estimate sparse signals more effectively. To this end, we propose to use parameterized non-convex regularizers with designated non-convexity and provide a range for the non-convex parameter so as to ensure that the objective function is strictly convex. By ensuring convexity of the objective function (sum of data-fidelity and non-convex regularizer), we can make use of a wide variety of convex optimization algorithms to obtain the unique global minimum reliably. The second part of this thesis proposes a non-linear signal

  8. Effective field theory dimensional regularization

    International Nuclear Information System (INIS)

    Lehmann, Dirk; Prezeau, Gary

    2002-01-01

    A Lorentz-covariant regularization scheme for effective field theories with an arbitrary number of propagating heavy and light particles is given. This regularization scheme leaves the low-energy analytic structure of Greens functions intact and preserves all the symmetries of the underlying Lagrangian. The power divergences of regularized loop integrals are controlled by the low-energy kinematic variables. Simple diagrammatic rules are derived for the regularization of arbitrary one-loop graphs and the generalization to higher loops is discussed

  9. Effective field theory dimensional regularization

    Science.gov (United States)

    Lehmann, Dirk; Prézeau, Gary

    2002-01-01

    A Lorentz-covariant regularization scheme for effective field theories with an arbitrary number of propagating heavy and light particles is given. This regularization scheme leaves the low-energy analytic structure of Greens functions intact and preserves all the symmetries of the underlying Lagrangian. The power divergences of regularized loop integrals are controlled by the low-energy kinematic variables. Simple diagrammatic rules are derived for the regularization of arbitrary one-loop graphs and the generalization to higher loops is discussed.

  10. 75 FR 76006 - Regular Meeting

    Science.gov (United States)

    2010-12-07

    ... FARM CREDIT SYSTEM INSURANCE CORPORATION Regular Meeting AGENCY: Farm Credit System Insurance Corporation Board. ACTION: Regular meeting. SUMMARY: Notice is hereby given of the regular meeting of the Farm Credit System Insurance Corporation Board (Board). Date and Time: The meeting of the Board will be held...

  11. Selection of regularization parameter for l1-regularized damage detection

    Science.gov (United States)

    Hou, Rongrong; Xia, Yong; Bao, Yuequan; Zhou, Xiaoqing

    2018-06-01

    The l1 regularization technique has been developed for structural health monitoring and damage detection through employing the sparsity condition of structural damage. The regularization parameter, which controls the trade-off between data fidelity and solution size of the regularization problem, exerts a crucial effect on the solution. However, the l1 regularization problem has no closed-form solution, and the regularization parameter is usually selected by experience. This study proposes two strategies of selecting the regularization parameter for the l1-regularized damage detection problem. The first method utilizes the residual and solution norms of the optimization problem and ensures that they are both small. The other method is based on the discrepancy principle, which requires that the variance of the discrepancy between the calculated and measured responses is close to the variance of the measurement noise. The two methods are applied to a cantilever beam and a three-story frame. A range of the regularization parameter, rather than one single value, can be determined. When the regularization parameter in this range is selected, the damage can be accurately identified even for multiple damage scenarios. This range also indicates the sensitivity degree of the damage identification problem to the regularization parameter.

  12. Ensemble manifold regularization.

    Science.gov (United States)

    Geng, Bo; Tao, Dacheng; Xu, Chao; Yang, Linjun; Hua, Xian-Sheng

    2012-06-01

    We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, cross validation is applied, but it does not necessarily scale up. Other problems derive from the suboptimality incurred by discrete grid search and the overfitting. Therefore, we develop an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR carefully so it 1) learns both the composite manifold and the semi-supervised learner jointly, 2) is fully automatic for learning the intrinsic manifold hyperparameters implicitly, 3) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption, and 4) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Furthermore, we prove the convergence property of EMR to the deterministic matrix at rate root-n. Extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework.

  13. Improvements in GRACE Gravity Fields Using Regularization

    Science.gov (United States)

    Save, H.; Bettadpur, S.; Tapley, B. D.

    2008-12-01

    The unconstrained global gravity field models derived from GRACE are susceptible to systematic errors that show up as broad "stripes" aligned in a North-South direction on the global maps of mass flux. These errors are believed to be a consequence of both systematic and random errors in the data that are amplified by the nature of the gravity field inverse problem. These errors impede scientific exploitation of the GRACE data products, and limit the realizable spatial resolution of the GRACE global gravity fields in certain regions. We use regularization techniques to reduce these "stripe" errors in the gravity field products. The regularization criteria are designed such that there is no attenuation of the signal and that the solutions fit the observations as well as an unconstrained solution. We have used a computationally inexpensive method, normally referred to as "L-ribbon", to find the regularization parameter. This paper discusses the characteristics and statistics of a 5-year time-series of regularized gravity field solutions. The solutions show markedly reduced stripes, are of uniformly good quality over time, and leave little or no systematic observation residuals, which is a frequent consequence of signal suppression from regularization. Up to degree 14, the signal in regularized solution shows correlation greater than 0.8 with the un-regularized CSR Release-04 solutions. Signals from large-amplitude and small-spatial extent events - such as the Great Sumatra Andaman Earthquake of 2004 - are visible in the global solutions without using special post-facto error reduction techniques employed previously in the literature. Hydrological signals as small as 5 cm water-layer equivalent in the small river basins, like Indus and Nile for example, are clearly evident, in contrast to noisy estimates from RL04. The residual variability over the oceans relative to a seasonal fit is small except at higher latitudes, and is evident without the need for de-striping or

  14. Consistent Partial Least Squares Path Modeling via Regularization.

    Science.gov (United States)

    Jung, Sunho; Park, JaeHong

    2018-01-01

    Partial least squares (PLS) path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc), designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables. PLSc has yet no remedy for this multicollinearity problem, which can cause loss of statistical power and accuracy in parameter estimation. Thus, a ridge type of regularization is incorporated into PLSc, creating a new technique called regularized PLSc. A comprehensive simulation study is conducted to evaluate the performance of regularized PLSc as compared to its non-regularized counterpart in terms of power and accuracy. The results show that our regularized PLSc is recommended for use when serious multicollinearity is present.

  15. Adaptive Regularization of Neural Classifiers

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Larsen, Jan; Hansen, Lars Kai

    1997-01-01

    We present a regularization scheme which iteratively adapts the regularization parameters by minimizing the validation error. It is suggested to use the adaptive regularization scheme in conjunction with optimal brain damage pruning to optimize the architecture and to avoid overfitting. Furthermo......, we propose an improved neural classification architecture eliminating an inherent redundancy in the widely used SoftMax classification network. Numerical results demonstrate the viability of the method...

  16. A short proof of increased parabolic regularity

    Directory of Open Access Journals (Sweden)

    Stephen Pankavich

    2015-08-01

    Full Text Available We present a short proof of the increased regularity obtained by solutions to uniformly parabolic partial differential equations. Though this setting is fairly introductory, our new method of proof, which uses a priori estimates and an inductive method, can be extended to prove analogous results for problems with time-dependent coefficients, advection-diffusion or reaction diffusion equations, and nonlinear PDEs even when other tools, such as semigroup methods or the use of explicit fundamental solutions, are unavailable.

  17. Regularized multivariate regression models with skew-t error distributions

    KAUST Repository

    Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi

    2014-01-01

    We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both

  18. 75 FR 53966 - Regular Meeting

    Science.gov (United States)

    2010-09-02

    ... FARM CREDIT SYSTEM INSURANCE CORPORATION Regular Meeting AGENCY: Farm Credit System Insurance Corporation Board. SUMMARY: Notice is hereby given of the regular meeting of the Farm Credit System Insurance Corporation Board (Board). DATE AND TIME: The meeting of the Board will be held at the offices of the Farm...

  19. Online co-regularized algorithms

    NARCIS (Netherlands)

    Ruijter, T. de; Tsivtsivadze, E.; Heskes, T.

    2012-01-01

    We propose an online co-regularized learning algorithm for classification and regression tasks. We demonstrate that by sequentially co-regularizing prediction functions on unlabeled data points, our algorithm provides improved performance in comparison to supervised methods on several UCI benchmarks

  20. Regularity of the Maxwell equations in heterogeneous media and Lipschitz domains

    KAUST Repository

    Bonito, Andrea

    2013-12-01

    This note establishes regularity estimates for the solution of the Maxwell equations in Lipschitz domains with non-smooth coefficients and minimal regularity assumptions. The argumentation relies on elliptic regularity estimates for the Poisson problem with non-smooth coefficients. © 2013 Elsevier Ltd.

  1. Continuum-regularized quantum gravity

    International Nuclear Information System (INIS)

    Chan Huesum; Halpern, M.B.

    1987-01-01

    The recent continuum regularization of d-dimensional Euclidean gravity is generalized to arbitrary power-law measure and studied in some detail as a representative example of coordinate-invariant regularization. The weak-coupling expansion of the theory illustrates a generic geometrization of regularized Schwinger-Dyson rules, generalizing previous rules in flat space and flat superspace. The rules are applied in a non-trivial explicit check of Einstein invariance at one loop: the cosmological counterterm is computed and its contribution is included in a verification that the graviton mass is zero. (orig.)

  2. New regular black hole solutions

    International Nuclear Information System (INIS)

    Lemos, Jose P. S.; Zanchin, Vilson T.

    2011-01-01

    In the present work we consider general relativity coupled to Maxwell's electromagnetism and charged matter. Under the assumption of spherical symmetry, there is a particular class of solutions that correspond to regular charged black holes whose interior region is de Sitter, the exterior region is Reissner-Nordstroem and there is a charged thin-layer in-between the two. The main physical and geometrical properties of such charged regular black holes are analyzed.

  3. Regular variation on measure chains

    Czech Academy of Sciences Publication Activity Database

    Řehák, Pavel; Vitovec, J.

    2010-01-01

    Roč. 72, č. 1 (2010), s. 439-448 ISSN 0362-546X R&D Projects: GA AV ČR KJB100190701 Institutional research plan: CEZ:AV0Z10190503 Keywords : regularly varying function * regularly varying sequence * measure chain * time scale * embedding theorem * representation theorem * second order dynamic equation * asymptotic properties Subject RIV: BA - General Mathematics Impact factor: 1.279, year: 2010 http://www.sciencedirect.com/science/article/pii/S0362546X09008475

  4. Manifold Regularized Correlation Object Tracking

    OpenAIRE

    Hu, Hongwei; Ma, Bo; Shen, Jianbing; Shao, Ling

    2017-01-01

    In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped fr...

  5. On geodesics in low regularity

    Science.gov (United States)

    Sämann, Clemens; Steinbauer, Roland

    2018-02-01

    We consider geodesics in both Riemannian and Lorentzian manifolds with metrics of low regularity. We discuss existence of extremal curves for continuous metrics and present several old and new examples that highlight their subtle interrelation with solutions of the geodesic equations. Then we turn to the initial value problem for geodesics for locally Lipschitz continuous metrics and generalize recent results on existence, regularity and uniqueness of solutions in the sense of Filippov.

  6. Geometric continuum regularization of quantum field theory

    International Nuclear Information System (INIS)

    Halpern, M.B.

    1989-01-01

    An overview of the continuum regularization program is given. The program is traced from its roots in stochastic quantization, with emphasis on the examples of regularized gauge theory, the regularized general nonlinear sigma model and regularized quantum gravity. In its coordinate-invariant form, the regularization is seen as entirely geometric: only the supermetric on field deformations is regularized, and the prescription provides universal nonperturbative invariant continuum regularization across all quantum field theory. 54 refs

  7. Metric regularity and subdifferential calculus

    International Nuclear Information System (INIS)

    Ioffe, A D

    2000-01-01

    The theory of metric regularity is an extension of two classical results: the Lyusternik tangent space theorem and the Graves surjection theorem. Developments in non-smooth analysis in the 1980s and 1990s paved the way for a number of far-reaching extensions of these results. It was also well understood that the phenomena behind the results are of metric origin, not connected with any linear structure. At the same time it became clear that some basic hypotheses of the subdifferential calculus are closely connected with the metric regularity of certain set-valued maps. The survey is devoted to the metric theory of metric regularity and its connection with subdifferential calculus in Banach spaces

  8. Manifold Regularized Correlation Object Tracking.

    Science.gov (United States)

    Hu, Hongwei; Ma, Bo; Shen, Jianbing; Shao, Ling

    2018-05-01

    In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches.

  9. Dimensional regularization in configuration space

    International Nuclear Information System (INIS)

    Bollini, C.G.; Giambiagi, J.J.

    1995-09-01

    Dimensional regularization is introduced in configuration space by Fourier transforming in D-dimensions the perturbative momentum space Green functions. For this transformation, Bochner theorem is used, no extra parameters, such as those of Feynman or Bogoliubov-Shirkov are needed for convolutions. The regularized causal functions in x-space have ν-dependent moderated singularities at the origin. They can be multiplied together and Fourier transformed (Bochner) without divergence problems. The usual ultraviolet divergences appear as poles of the resultant functions of ν. Several example are discussed. (author). 9 refs

  10. Regular algebra and finite machines

    CERN Document Server

    Conway, John Horton

    2012-01-01

    World-famous mathematician John H. Conway based this classic text on a 1966 course he taught at Cambridge University. Geared toward graduate students of mathematics, it will also prove a valuable guide to researchers and professional mathematicians.His topics cover Moore's theory of experiments, Kleene's theory of regular events and expressions, Kleene algebras, the differential calculus of events, factors and the factor matrix, and the theory of operators. Additional subjects include event classes and operator classes, some regulator algebras, context-free languages, communicative regular alg

  11. Matrix regularization of 4-manifolds

    OpenAIRE

    Trzetrzelewski, M.

    2012-01-01

    We consider products of two 2-manifolds such as S^2 x S^2, embedded in Euclidean space and show that the corresponding 4-volume preserving diffeomorphism algebra can be approximated by a tensor product SU(N)xSU(N) i.e. functions on a manifold are approximated by the Kronecker product of two SU(N) matrices. A regularization of the 4-sphere is also performed by constructing N^2 x N^2 matrix representations of the 4-algebra (and as a byproduct of the 3-algebra which makes the regularization of S...

  12. Regularized forecasting of chaotic dynamical systems

    International Nuclear Information System (INIS)

    Bollt, Erik M.

    2017-01-01

    While local models of dynamical systems have been highly successful in terms of using extensive data sets observing even a chaotic dynamical system to produce useful forecasts, there is a typical problem as follows. Specifically, with k-near neighbors, kNN method, local observations occur due to recurrences in a chaotic system, and this allows for local models to be built by regression to low dimensional polynomial approximations of the underlying system estimating a Taylor series. This has been a popular approach, particularly in context of scalar data observations which have been represented by time-delay embedding methods. However such local models can generally allow for spatial discontinuities of forecasts when considered globally, meaning jumps in predictions because the collected near neighbors vary from point to point. The source of these discontinuities is generally that the set of near neighbors varies discontinuously with respect to the position of the sample point, and so therefore does the model built from the near neighbors. It is possible to utilize local information inferred from near neighbors as usual but at the same time to impose a degree of regularity on a global scale. We present here a new global perspective extending the general local modeling concept. In so doing, then we proceed to show how this perspective allows us to impose prior presumed regularity into the model, by involving the Tikhonov regularity theory, since this classic perspective of optimization in ill-posed problems naturally balances fitting an objective with some prior assumed form of the result, such as continuity or derivative regularity for example. This all reduces to matrix manipulations which we demonstrate on a simple data set, with the implication that it may find much broader context.

  13. Regularization of Nonmonotone Variational Inequalities

    International Nuclear Information System (INIS)

    Konnov, Igor V.; Ali, M.S.S.; Mazurkevich, E.O.

    2006-01-01

    In this paper we extend the Tikhonov-Browder regularization scheme from monotone to rather a general class of nonmonotone multivalued variational inequalities. We show that their convergence conditions hold for some classes of perfectly and nonperfectly competitive economic equilibrium problems

  14. Lattice regularized chiral perturbation theory

    International Nuclear Information System (INIS)

    Borasoy, Bugra; Lewis, Randy; Ouimet, Pierre-Philippe A.

    2004-01-01

    Chiral perturbation theory can be defined and regularized on a spacetime lattice. A few motivations are discussed here, and an explicit lattice Lagrangian is reviewed. A particular aspect of the connection between lattice chiral perturbation theory and lattice QCD is explored through a study of the Wess-Zumino-Witten term

  15. 76 FR 3629 - Regular Meeting

    Science.gov (United States)

    2011-01-20

    ... Meeting SUMMARY: Notice is hereby given of the regular meeting of the Farm Credit System Insurance Corporation Board (Board). Date and Time: The meeting of the Board will be held at the offices of the Farm... meeting of the Board will be open to the [[Page 3630

  16. Forcing absoluteness and regularity properties

    NARCIS (Netherlands)

    Ikegami, D.

    2010-01-01

    For a large natural class of forcing notions, we prove general equivalence theorems between forcing absoluteness statements, regularity properties, and transcendence properties over L and the core model K. We use our results to answer open questions from set theory of the reals.

  17. Globals of Completely Regular Monoids

    Institute of Scientific and Technical Information of China (English)

    Wu Qian-qian; Gan Ai-ping; Du Xian-kun

    2015-01-01

    An element of a semigroup S is called irreducible if it cannot be expressed as a product of two elements in S both distinct from itself. In this paper we show that the class C of all completely regular monoids with irreducible identity elements satisfies the strong isomorphism property and so it is globally determined.

  18. Fluid queues and regular variation

    NARCIS (Netherlands)

    Boxma, O.J.

    1996-01-01

    This paper considers a fluid queueing system, fed by N independent sources that alternate between silence and activity periods. We assume that the distribution of the activity periods of one or more sources is a regularly varying function of index ¿. We show that its fat tail gives rise to an even

  19. Fluid queues and regular variation

    NARCIS (Netherlands)

    O.J. Boxma (Onno)

    1996-01-01

    textabstractThis paper considers a fluid queueing system, fed by $N$ independent sources that alternate between silence and activity periods. We assume that the distribution of the activity periods of one or more sources is a regularly varying function of index $zeta$. We show that its fat tail

  20. Empirical laws, regularity and necessity

    NARCIS (Netherlands)

    Koningsveld, H.

    1973-01-01

    In this book I have tried to develop an analysis of the concept of an empirical law, an analysis that differs in many ways from the alternative analyse's found in contemporary literature dealing with the subject.

    1 am referring especially to two well-known views, viz. the regularity and

  1. Interval matrices: Regularity generates singularity

    Czech Academy of Sciences Publication Activity Database

    Rohn, Jiří; Shary, S.P.

    2018-01-01

    Roč. 540, 1 March (2018), s. 149-159 ISSN 0024-3795 Institutional support: RVO:67985807 Keywords : interval matrix * regularity * singularity * P-matrix * absolute value equation * diagonally singilarizable matrix Subject RIV: BA - General Mathematics Impact factor: 0.973, year: 2016

  2. Regularization in Matrix Relevance Learning

    NARCIS (Netherlands)

    Schneider, Petra; Bunte, Kerstin; Stiekema, Han; Hammer, Barbara; Villmann, Thomas; Biehl, Michael

    A In this paper, we present a regularization technique to extend recently proposed matrix learning schemes in learning vector quantization (LVQ). These learning algorithms extend the concept of adaptive distance measures in LVQ to the use of relevance matrices. In general, metric learning can

  3. Constrained least squares regularization in PET

    International Nuclear Information System (INIS)

    Choudhury, K.R.; O'Sullivan, F.O.

    1996-01-01

    Standard reconstruction methods used in tomography produce images with undesirable negative artifacts in background and in areas of high local contrast. While sophisticated statistical reconstruction methods can be devised to correct for these artifacts, their computational implementation is excessive for routine operational use. This work describes a technique for rapid computation of approximate constrained least squares regularization estimates. The unique feature of the approach is that it involves no iterative projection or backprojection steps. This contrasts with the familiar computationally intensive algorithms based on algebraic reconstruction (ART) or expectation-maximization (EM) methods. Experimentation with the new approach for deconvolution and mixture analysis shows that the root mean square error quality of estimators based on the proposed algorithm matches and usually dominates that of more elaborate maximum likelihood, at a fraction of the computational effort

  4. Regular and conformal regular cores for static and rotating solutions

    Energy Technology Data Exchange (ETDEWEB)

    Azreg-Aïnou, Mustapha

    2014-03-07

    Using a new metric for generating rotating solutions, we derive in a general fashion the solution of an imperfect fluid and that of its conformal homolog. We discuss the conditions that the stress–energy tensors and invariant scalars be regular. On classical physical grounds, it is stressed that conformal fluids used as cores for static or rotating solutions are exempt from any malicious behavior in that they are finite and defined everywhere.

  5. Regular and conformal regular cores for static and rotating solutions

    International Nuclear Information System (INIS)

    Azreg-Aïnou, Mustapha

    2014-01-01

    Using a new metric for generating rotating solutions, we derive in a general fashion the solution of an imperfect fluid and that of its conformal homolog. We discuss the conditions that the stress–energy tensors and invariant scalars be regular. On classical physical grounds, it is stressed that conformal fluids used as cores for static or rotating solutions are exempt from any malicious behavior in that they are finite and defined everywhere.

  6. Physical model of dimensional regularization

    Energy Technology Data Exchange (ETDEWEB)

    Schonfeld, Jonathan F.

    2016-12-15

    We explicitly construct fractals of dimension 4-ε on which dimensional regularization approximates scalar-field-only quantum-field theory amplitudes. The construction does not require fractals to be Lorentz-invariant in any sense, and we argue that there probably is no Lorentz-invariant fractal of dimension greater than 2. We derive dimensional regularization's power-law screening first for fractals obtained by removing voids from 3-dimensional Euclidean space. The derivation applies techniques from elementary dielectric theory. Surprisingly, fractal geometry by itself does not guarantee the appropriate power-law behavior; boundary conditions at fractal voids also play an important role. We then extend the derivation to 4-dimensional Minkowski space. We comment on generalization to non-scalar fields, and speculate about implications for quantum gravity. (orig.)

  7. Regularized strings with extrinsic curvature

    International Nuclear Information System (INIS)

    Ambjoern, J.; Durhuus, B.

    1987-07-01

    We analyze models of discretized string theories, where the path integral over world sheet variables is regularized by summing over triangulated surfaces. The inclusion of curvature in the action is a necessity for the scaling of the string tension. We discuss the physical properties of models with extrinsic curvature terms in the action and show that the string tension vanishes at the critical point where the bare extrinsic curvature coupling tends to infinity. Similar results are derived for models with intrinsic curvature. (orig.)

  8. Circuit complexity of regular languages

    Czech Academy of Sciences Publication Activity Database

    Koucký, Michal

    2009-01-01

    Roč. 45, č. 4 (2009), s. 865-879 ISSN 1432-4350 R&D Projects: GA ČR GP201/07/P276; GA MŠk(CZ) 1M0545 Institutional research plan: CEZ:AV0Z10190503 Keywords : regular languages * circuit complexity * upper and lower bounds Subject RIV: BA - General Mathematics Impact factor: 0.726, year: 2009

  9. General inverse problems for regular variation

    DEFF Research Database (Denmark)

    Damek, Ewa; Mikosch, Thomas Valentin; Rosinski, Jan

    2014-01-01

    Regular variation of distributional tails is known to be preserved by various linear transformations of some random structures. An inverse problem for regular variation aims at understanding whether the regular variation of a transformed random object is caused by regular variation of components ...

  10. Semisupervised Support Vector Machines With Tangent Space Intrinsic Manifold Regularization.

    Science.gov (United States)

    Sun, Shiliang; Xie, Xijiong

    2016-09-01

    Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces. Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms.

  11. Regularization of Instantaneous Frequency Attribute Computations

    Science.gov (United States)

    Yedlin, M. J.; Margrave, G. F.; Van Vorst, D. G.; Ben Horin, Y.

    2014-12-01

    We compare two different methods of computation of a temporally local frequency:1) A stabilized instantaneous frequency using the theory of the analytic signal.2) A temporally variant centroid (or dominant) frequency estimated from a time-frequency decomposition.The first method derives from Taner et al (1979) as modified by Fomel (2007) and utilizes the derivative of the instantaneous phase of the analytic signal. The second method computes the power centroid (Cohen, 1995) of the time-frequency spectrum, obtained using either the Gabor or Stockwell Transform. Common to both methods is the necessity of division by a diagonal matrix, which requires appropriate regularization.We modify Fomel's (2007) method by explicitly penalizing the roughness of the estimate. Following Farquharson and Oldenburg (2004), we employ both the L curve and GCV methods to obtain the smoothest model that fits the data in the L2 norm.Using synthetic data, quarry blast, earthquakes and the DPRK tests, our results suggest that the optimal method depends on the data. One of the main applications for this work is the discrimination between blast events and earthquakesFomel, Sergey. " Local seismic attributes." , Geophysics, 72.3 (2007): A29-A33.Cohen, Leon. " Time frequency analysis theory and applications." USA: Prentice Hall, (1995).Farquharson, Colin G., and Douglas W. Oldenburg. "A comparison of automatic techniques for estimating the regularization parameter in non-linear inverse problems." Geophysical Journal International 156.3 (2004): 411-425.Taner, M. Turhan, Fulton Koehler, and R. E. Sheriff. " Complex seismic trace analysis." Geophysics, 44.6 (1979): 1041-1063.

  12. Lavrentiev regularization method for nonlinear ill-posed problems

    International Nuclear Information System (INIS)

    Kinh, Nguyen Van

    2002-10-01

    In this paper we shall be concerned with Lavientiev regularization method to reconstruct solutions x 0 of non ill-posed problems F(x)=y o , where instead of y 0 noisy data y δ is an element of X with absolut(y δ -y 0 ) ≤ δ are given and F:X→X is an accretive nonlinear operator from a real reflexive Banach space X into itself. In this regularization method solutions x α δ are obtained by solving the singularly perturbed nonlinear operator equation F(x)+α(x-x*)=y δ with some initial guess x*. Assuming certain conditions concerning the operator F and the smoothness of the element x*-x 0 we derive stability estimates which show that the accuracy of the regularized solutions is order optimal provided that the regularization parameter α has been chosen properly. (author)

  13. Regularization Techniques for Linear Least-Squares Problems

    KAUST Repository

    Suliman, Mohamed

    2016-04-01

    Linear estimation is a fundamental branch of signal processing that deals with estimating the values of parameters from a corrupted measured data. Throughout the years, several optimization criteria have been used to achieve this task. The most astonishing attempt among theses is the linear least-squares. Although this criterion enjoyed a wide popularity in many areas due to its attractive properties, it appeared to suffer from some shortcomings. Alternative optimization criteria, as a result, have been proposed. These new criteria allowed, in one way or another, the incorporation of further prior information to the desired problem. Among theses alternative criteria is the regularized least-squares (RLS). In this thesis, we propose two new algorithms to find the regularization parameter for linear least-squares problems. In the constrained perturbation regularization algorithm (COPRA) for random matrices and COPRA for linear discrete ill-posed problems, an artificial perturbation matrix with a bounded norm is forced into the model matrix. This perturbation is introduced to enhance the singular value structure of the matrix. As a result, the new modified model is expected to provide a better stabilize substantial solution when used to estimate the original signal through minimizing the worst-case residual error function. Unlike many other regularization algorithms that go in search of minimizing the estimated data error, the two new proposed algorithms are developed mainly to select the artifcial perturbation bound and the regularization parameter in a way that approximately minimizes the mean-squared error (MSE) between the original signal and its estimate under various conditions. The first proposed COPRA method is developed mainly to estimate the regularization parameter when the measurement matrix is complex Gaussian, with centered unit variance (standard), and independent and identically distributed (i.i.d.) entries. Furthermore, the second proposed COPRA

  14. Consistent Partial Least Squares Path Modeling via Regularization

    Directory of Open Access Journals (Sweden)

    Sunho Jung

    2018-02-01

    Full Text Available Partial least squares (PLS path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc, designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables. PLSc has yet no remedy for this multicollinearity problem, which can cause loss of statistical power and accuracy in parameter estimation. Thus, a ridge type of regularization is incorporated into PLSc, creating a new technique called regularized PLSc. A comprehensive simulation study is conducted to evaluate the performance of regularized PLSc as compared to its non-regularized counterpart in terms of power and accuracy. The results show that our regularized PLSc is recommended for use when serious multicollinearity is present.

  15. Regularized Statistical Analysis of Anatomy

    DEFF Research Database (Denmark)

    Sjöstrand, Karl

    2007-01-01

    This thesis presents the application and development of regularized methods for the statistical analysis of anatomical structures. Focus is on structure-function relationships in the human brain, such as the connection between early onset of Alzheimer’s disease and shape changes of the corpus...... and mind. Statistics represents a quintessential part of such investigations as they are preluded by a clinical hypothesis that must be verified based on observed data. The massive amounts of image data produced in each examination pose an important and interesting statistical challenge...... efficient algorithms which make the analysis of large data sets feasible, and gives examples of applications....

  16. Regularization methods in Banach spaces

    CERN Document Server

    Schuster, Thomas; Hofmann, Bernd; Kazimierski, Kamil S

    2012-01-01

    Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle inverse and ill-posed problems. Usually the mathematical model of an inverse problem consists of an operator equation of the first kind and often the associated forward operator acts between Hilbert spaces. However, for numerous problems the reasons for using a Hilbert space setting seem to be based rather on conventions than on an approprimate and realistic model choice, so often a Banach space setting would be closer to reality. Furthermore, sparsity constraints using general Lp-norms or the B

  17. Academic Training Lecture - Regular Programme

    CERN Multimedia

    PH Department

    2011-01-01

    Regular Lecture Programme 9 May 2011 ACT Lectures on Detectors - Inner Tracking Detectors by Pippa Wells (CERN) 10 May 2011 ACT Lectures on Detectors - Calorimeters (2/5) by Philippe Bloch (CERN) 11 May 2011 ACT Lectures on Detectors - Muon systems (3/5) by Kerstin Hoepfner (RWTH Aachen) 12 May 2011 ACT Lectures on Detectors - Particle Identification and Forward Detectors by Peter Krizan (University of Ljubljana and J. Stefan Institute, Ljubljana, Slovenia) 13 May 2011 ACT Lectures on Detectors - Trigger and Data Acquisition (5/5) by Dr. Brian Petersen (CERN) from 11:00 to 12:00 at CERN ( Bldg. 222-R-001 - Filtration Plant )

  18. RES: Regularized Stochastic BFGS Algorithm

    Science.gov (United States)

    Mokhtari, Aryan; Ribeiro, Alejandro

    2014-12-01

    RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is proposed to solve convex optimization problems with stochastic objectives. The use of stochastic gradient descent algorithms is widespread, but the number of iterations required to approximate optimal arguments can be prohibitive in high dimensional problems. Application of second order methods, on the other hand, is impracticable because computation of objective function Hessian inverses incurs excessive computational cost. BFGS modifies gradient descent by introducing a Hessian approximation matrix computed from finite gradient differences. RES utilizes stochastic gradients in lieu of deterministic gradients for both, the determination of descent directions and the approximation of the objective function's curvature. Since stochastic gradients can be computed at manageable computational cost RES is realizable and retains the convergence rate advantages of its deterministic counterparts. Convergence results show that lower and upper bounds on the Hessian egeinvalues of the sample functions are sufficient to guarantee convergence to optimal arguments. Numerical experiments showcase reductions in convergence time relative to stochastic gradient descent algorithms and non-regularized stochastic versions of BFGS. An application of RES to the implementation of support vector machines is developed.

  19. Regularized Label Relaxation Linear Regression.

    Science.gov (United States)

    Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung; Fang, Bingwu

    2018-04-01

    Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on -norm and -norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.

  20. On primal regularity estimates for single-valued mappings

    Czech Academy of Sciences Publication Activity Database

    Cibulka, R.; Fabian, Marián; Ioffe, A. D.

    2015-01-01

    Roč. 17, č. 1 (2015), s. 187-208 ISSN 1661-7738 R&D Projects: GA ČR(CZ) GAP201/12/0290 Institutional support: RVO:67985840 Keywords : Clarke's inverse function theorem * confinal family * contingent tangent cone * linear openness Subject RIV: BA - General Mathematics Impact factor: 0.540, year: 2015 http://link.springer.com/article/10.1007%2Fs11784-015-0240-5

  1. Robust regularized least-squares beamforming approach to signal estimation

    KAUST Repository

    Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Al-Naffouri, Tareq Y.

    2017-01-01

    In this paper, we address the problem of robust adaptive beamforming of signals received by a linear array. The challenge associated with the beamforming problem is twofold. Firstly, the process requires the inversion of the usually ill

  2. Electricity markets volatility: estimates, regularities and risk management applications

    Energy Technology Data Exchange (ETDEWEB)

    Nakamura, Masao [Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, BC, V6 T 1Z2 (Canada)]. E-mail: masao.nakamura@sauder.ubc.ca; Nakashima, Tomoaki [Powerex Corp., Vancouver, BC, V6C 2X8 (Canada); Niimura, Takahide [TNC Systems Technologies, Burnaby, BC, V5 H 2W4 (Canada)

    2006-09-15

    The recent deregulation of the market for electric power in many parts of the US and Canada has expanded the set of potential tools for managing the types of risks faced by both generators and consumers of electric power. In particular manufacturing and other firms whose operations are powered by electricity now face, on a continuing basis, the engineering management decisions concerning whether they should buy or produce electricity, and if they are to buy or sell electricity, what types of contracts are optimum. These types of risk management decisions typically involve futures, forwards, options and other financial derivatives. The price and volatility of electric power are known to play an essential role in determining which of these instruments should be used. However, electricity as a commodity possesses certain special features not shared by other commodities and hence its risk properties are not yet well understood. In this paper we consider and test certain hypotheses about the properties of electricity price using recent market data. We find that electricity prices possess certain volatility and other systematic properties that can be characterized by the type and method of delivery of electricity. These properties can be used by firms in formulating their optimal demand and supply schedules of electric power.

  3. Electricity markets volatility: estimates, regularities and risk management applications

    International Nuclear Information System (INIS)

    Nakamura, Masao; Nakashima, Tomoaki; Niimura, Takahide

    2006-01-01

    The recent deregulation of the market for electric power in many parts of the US and Canada has expanded the set of potential tools for managing the types of risks faced by both generators and consumers of electric power. In particular manufacturing and other firms whose operations are powered by electricity now face, on a continuing basis, the engineering management decisions concerning whether they should buy or produce electricity, and if they are to buy or sell electricity, what types of contracts are optimum. These types of risk management decisions typically involve futures, forwards, options and other financial derivatives. The price and volatility of electric power are known to play an essential role in determining which of these instruments should be used. However, electricity as a commodity possesses certain special features not shared by other commodities and hence its risk properties are not yet well understood. In this paper we consider and test certain hypotheses about the properties of electricity price using recent market data. We find that electricity prices possess certain volatility and other systematic properties that can be characterized by the type and method of delivery of electricity. These properties can be used by firms in formulating their optimal demand and supply schedules of electric power

  4. Directional Total Generalized Variation Regularization for Impulse Noise Removal

    DEFF Research Database (Denmark)

    Kongskov, Rasmus Dalgas; Dong, Yiqiu

    2017-01-01

    this regularizer for directional images is highly advantageous. In order to estimate directions in impulse noise corrupted images, which is much more challenging compared to Gaussian noise corrupted images, we introduce a new Fourier transform-based method. Numerical experiments show that this method is more...

  5. From inactive to regular jogger

    DEFF Research Database (Denmark)

    Lund-Cramer, Pernille; Brinkmann Løite, Vibeke; Bredahl, Thomas Viskum Gjelstrup

    study was conducted using individual semi-structured interviews on how a successful long-term behavior change had been achieved. Ten informants were purposely selected from participants in the DANO-RUN research project (7 men, 3 women, average age 41.5). Interviews were performed on the basis of Theory...... of Planned Behavior (TPB) and The Transtheoretical Model (TTM). Coding and analysis of interviews were performed using NVivo 10 software. Results TPB: During the behavior change process, the intention to jogging shifted from a focus on weight loss and improved fitness to both physical health, psychological......Title From inactive to regular jogger - a qualitative study of achieved behavioral change among recreational joggers Authors Pernille Lund-Cramer & Vibeke Brinkmann Løite Purpose Despite extensive knowledge of barriers to physical activity, most interventions promoting physical activity have proven...

  6. Tessellating the Sphere with Regular Polygons

    Science.gov (United States)

    Soto-Johnson, Hortensia; Bechthold, Dawn

    2004-01-01

    Tessellations in the Euclidean plane and regular polygons that tessellate the sphere are reviewed. The regular polygons that can possibly tesellate the sphere are spherical triangles, squares and pentagons.

  7. On the equivalence of different regularization methods

    International Nuclear Information System (INIS)

    Brzezowski, S.

    1985-01-01

    The R-circunflex-operation preceded by the regularization procedure is discussed. Some arguments are given, according to which the results may depend on the method of regularization, introduced in order to avoid divergences in perturbation calculations. 10 refs. (author)

  8. The uniqueness of the regularization procedure

    International Nuclear Information System (INIS)

    Brzezowski, S.

    1981-01-01

    On the grounds of the BPHZ procedure, the criteria of correct regularization in perturbation calculations of QFT are given, together with the prescription for dividing the regularized formulas into the finite and infinite parts. (author)

  9. Borderline personality disorder and regularly drinking alcohol before sex.

    Science.gov (United States)

    Thompson, Ronald G; Eaton, Nicholas R; Hu, Mei-Chen; Hasin, Deborah S

    2017-07-01

    Drinking alcohol before sex increases the likelihood of engaging in unprotected intercourse, having multiple sexual partners and becoming infected with sexually transmitted infections. Borderline personality disorder (BPD), a complex psychiatric disorder characterised by pervasive instability in emotional regulation, self-image, interpersonal relationships and impulse control, is associated with substance use disorders and sexual risk behaviours. However, no study has examined the relationship between BPD and drinking alcohol before sex in the USA. This study examined the association between BPD and regularly drinking before sex in a nationally representative adult sample. Participants were 17 491 sexually active drinkers from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Logistic regression models estimated effects of BPD diagnosis, specific borderline diagnostic criteria and BPD criterion count on the likelihood of regularly (mostly or always) drinking alcohol before sex, adjusted for controls. Borderline personality disorder diagnosis doubled the odds of regularly drinking before sex [adjusted odds ratio (AOR) = 2.26; confidence interval (CI) = 1.63, 3.14]. Of nine diagnostic criteria, impulsivity in areas that are self-damaging remained a significant predictor of regularly drinking before sex (AOR = 1.82; CI = 1.42, 2.35). The odds of regularly drinking before sex increased by 20% for each endorsed criterion (AOR = 1.20; CI = 1.14, 1.27) DISCUSSION AND CONCLUSIONS: This is the first study to examine the relationship between BPD and regularly drinking alcohol before sex in the USA. Substance misuse treatment should assess regularly drinking before sex, particularly among patients with BPD, and BPD treatment should assess risk at the intersection of impulsivity, sexual behaviour and substance use. [Thompson Jr RG, Eaton NR, Hu M-C, Hasin DS Borderline personality disorder and regularly drinking alcohol

  10. Application of Turchin's method of statistical regularization

    Science.gov (United States)

    Zelenyi, Mikhail; Poliakova, Mariia; Nozik, Alexander; Khudyakov, Alexey

    2018-04-01

    During analysis of experimental data, one usually needs to restore a signal after it has been convoluted with some kind of apparatus function. According to Hadamard's definition this problem is ill-posed and requires regularization to provide sensible results. In this article we describe an implementation of the Turchin's method of statistical regularization based on the Bayesian approach to the regularization strategy.

  11. Regular extensions of some classes of grammars

    NARCIS (Netherlands)

    Nijholt, Antinus

    Culik and Cohen introduced the class of LR-regular grammars, an extension of the LR(k) grammars. In this report we consider the analogous extension of the LL(k) grammers, called the LL-regular grammars. The relations of this class of grammars to other classes of grammars are shown. Every LL-regular

  12. Sparse reconstruction by means of the standard Tikhonov regularization

    International Nuclear Information System (INIS)

    Lu Shuai; Pereverzev, Sergei V

    2008-01-01

    It is a common belief that Tikhonov scheme with || · ||L 2 -penalty fails in sparse reconstruction. We are going to show, however, that this standard regularization can help if the stability measured in L 1 -norm will be properly taken into account in the choice of the regularization parameter. The crucial point is that now a stability bound may depend on the bases with respect to which the solution of the problem is assumed to be sparse. We discuss how such a stability can be estimated numerically and present the results of computational experiments giving the evidence of the reliability of our approach.

  13. Class of regular bouncing cosmologies

    Science.gov (United States)

    Vasilić, Milovan

    2017-06-01

    In this paper, I construct a class of everywhere regular geometric sigma models that possess bouncing solutions. Precisely, I show that every bouncing metric can be made a solution of such a model. My previous attempt to do so by employing one scalar field has failed due to the appearance of harmful singularities near the bounce. In this work, I use four scalar fields to construct a class of geometric sigma models which are free of singularities. The models within the class are parametrized by their background geometries. I prove that, whatever background is chosen, the dynamics of its small perturbations is classically stable on the whole time axis. Contrary to what one expects from the structure of the initial Lagrangian, the physics of background fluctuations is found to carry two tensor, two vector, and two scalar degrees of freedom. The graviton mass, which naturally appears in these models, is shown to be several orders of magnitude smaller than its experimental bound. I provide three simple examples to demonstrate how this is done in practice. In particular, I show that graviton mass can be made arbitrarily small.

  14. Regularized semiclassical limits: Linear flows with infinite Lyapunov exponents

    KAUST Repository

    Athanassoulis, Agissilaos; Katsaounis, Theodoros; Kyza, Irene

    2016-01-01

    Semiclassical asymptotics for Schrödinger equations with non-smooth potentials give rise to ill-posed formal semiclassical limits. These problems have attracted a lot of attention in the last few years, as a proxy for the treatment of eigenvalue crossings, i.e. general systems. It has recently been shown that the semiclassical limit for conical singularities is in fact well-posed, as long as the Wigner measure (WM) stays away from singular saddle points. In this work we develop a family of refined semiclassical estimates, and use them to derive regularized transport equations for saddle points with infinite Lyapunov exponents, extending the aforementioned recent results. In the process we answer a related question posed by P.L. Lions and T. Paul in 1993. If we consider more singular potentials, our rigorous estimates break down. To investigate whether conical saddle points, such as -|x|, admit a regularized transport asymptotic approximation, we employ a numerical solver based on posteriori error control. Thus rigorous upper bounds for the asymptotic error in concrete problems are generated. In particular, specific phenomena which render invalid any regularized transport for -|x| are identified and quantified. In that sense our rigorous results are sharp. Finally, we use our findings to formulate a precise conjecture for the condition under which conical saddle points admit a regularized transport solution for the WM. © 2016 International Press.

  15. Regularized semiclassical limits: Linear flows with infinite Lyapunov exponents

    KAUST Repository

    Athanassoulis, Agissilaos

    2016-08-30

    Semiclassical asymptotics for Schrödinger equations with non-smooth potentials give rise to ill-posed formal semiclassical limits. These problems have attracted a lot of attention in the last few years, as a proxy for the treatment of eigenvalue crossings, i.e. general systems. It has recently been shown that the semiclassical limit for conical singularities is in fact well-posed, as long as the Wigner measure (WM) stays away from singular saddle points. In this work we develop a family of refined semiclassical estimates, and use them to derive regularized transport equations for saddle points with infinite Lyapunov exponents, extending the aforementioned recent results. In the process we answer a related question posed by P.L. Lions and T. Paul in 1993. If we consider more singular potentials, our rigorous estimates break down. To investigate whether conical saddle points, such as -|x|, admit a regularized transport asymptotic approximation, we employ a numerical solver based on posteriori error control. Thus rigorous upper bounds for the asymptotic error in concrete problems are generated. In particular, specific phenomena which render invalid any regularized transport for -|x| are identified and quantified. In that sense our rigorous results are sharp. Finally, we use our findings to formulate a precise conjecture for the condition under which conical saddle points admit a regularized transport solution for the WM. © 2016 International Press.

  16. Adaptive regularization of noisy linear inverse problems

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Madsen, Kristoffer Hougaard; Lehn-Schiøler, Tue

    2006-01-01

    In the Bayesian modeling framework there is a close relation between regularization and the prior distribution over parameters. For prior distributions in the exponential family, we show that the optimal hyper-parameter, i.e., the optimal strength of regularization, satisfies a simple relation: T......: The expectation of the regularization function, i.e., takes the same value in the posterior and prior distribution. We present three examples: two simulations, and application in fMRI neuroimaging....

  17. Higher derivative regularization and chiral anomaly

    International Nuclear Information System (INIS)

    Nagahama, Yoshinori.

    1985-02-01

    A higher derivative regularization which automatically leads to the consistent chiral anomaly is analyzed in detail. It explicitly breaks all the local gauge symmetry but preserves global chiral symmetry and leads to the chirally symmetric consistent anomaly. This regularization thus clarifies the physics content contained in the consistent anomaly. We also briefly comment on the application of this higher derivative regularization to massless QED. (author)

  18. Regularity effect in prospective memory during aging

    Directory of Open Access Journals (Sweden)

    Geoffrey Blondelle

    2016-10-01

    Full Text Available Background: Regularity effect can affect performance in prospective memory (PM, but little is known on the cognitive processes linked to this effect. Moreover, its impacts with regard to aging remain unknown. To our knowledge, this study is the first to examine regularity effect in PM in a lifespan perspective, with a sample of young, intermediate, and older adults. Objective and design: Our study examined the regularity effect in PM in three groups of participants: 28 young adults (18–30, 16 intermediate adults (40–55, and 25 older adults (65–80. The task, adapted from the Virtual Week, was designed to manipulate the regularity of the various activities of daily life that were to be recalled (regular repeated activities vs. irregular non-repeated activities. We examine the role of several cognitive functions including certain dimensions of executive functions (planning, inhibition, shifting, and binding, short-term memory, and retrospective episodic memory to identify those involved in PM, according to regularity and age. Results: A mixed-design ANOVA showed a main effect of task regularity and an interaction between age and regularity: an age-related difference in PM performances was found for irregular activities (older < young, but not for regular activities. All participants recalled more regular activities than irregular ones with no age effect. It appeared that recalling of regular activities only involved planning for both intermediate and older adults, while recalling of irregular ones were linked to planning, inhibition, short-term memory, binding, and retrospective episodic memory. Conclusion: Taken together, our data suggest that planning capacities seem to play a major role in remembering to perform intended actions with advancing age. Furthermore, the age-PM-paradox may be attenuated when the experimental design is adapted by implementing a familiar context through the use of activities of daily living. The clinical

  19. Regularity effect in prospective memory during aging

    OpenAIRE

    Blondelle, Geoffrey; Hainselin, Mathieu; Gounden, Yannick; Heurley, Laurent; Voisin, Hélène; Megalakaki, Olga; Bressous, Estelle; Quaglino, Véronique

    2016-01-01

    Background: Regularity effect can affect performance in prospective memory (PM), but little is known on the cognitive processes linked to this effect. Moreover, its impacts with regard to aging remain unknown. To our knowledge, this study is the first to examine regularity effect in PM in a lifespan perspective, with a sample of young, intermediate, and older adults.Objective and design: Our study examined the regularity effect in PM in three groups of participants: 28 young adults (18–30), 1...

  20. Regularization and error assignment to unfolded distributions

    CERN Document Server

    Zech, Gunter

    2011-01-01

    The commonly used approach to present unfolded data only in graphical formwith the diagonal error depending on the regularization strength is unsatisfac-tory. It does not permit the adjustment of parameters of theories, the exclusionof theories that are admitted by the observed data and does not allow the com-bination of data from different experiments. We propose fixing the regulariza-tion strength by a p-value criterion, indicating the experimental uncertaintiesindependent of the regularization and publishing the unfolded data in additionwithout regularization. These considerations are illustrated with three differentunfolding and smoothing approaches applied to a toy example.

  1. Iterative Regularization with Minimum-Residual Methods

    DEFF Research Database (Denmark)

    Jensen, Toke Koldborg; Hansen, Per Christian

    2007-01-01

    subspaces. We provide a combination of theory and numerical examples, and our analysis confirms the experience that MINRES and MR-II can work as general regularization methods. We also demonstrate theoretically and experimentally that the same is not true, in general, for GMRES and RRGMRES their success......We study the regularization properties of iterative minimum-residual methods applied to discrete ill-posed problems. In these methods, the projection onto the underlying Krylov subspace acts as a regularizer, and the emphasis of this work is on the role played by the basis vectors of these Krylov...... as regularization methods is highly problem dependent....

  2. Iterative regularization with minimum-residual methods

    DEFF Research Database (Denmark)

    Jensen, Toke Koldborg; Hansen, Per Christian

    2006-01-01

    subspaces. We provide a combination of theory and numerical examples, and our analysis confirms the experience that MINRES and MR-II can work as general regularization methods. We also demonstrate theoretically and experimentally that the same is not true, in general, for GMRES and RRGMRES - their success......We study the regularization properties of iterative minimum-residual methods applied to discrete ill-posed problems. In these methods, the projection onto the underlying Krylov subspace acts as a regularizer, and the emphasis of this work is on the role played by the basis vectors of these Krylov...... as regularization methods is highly problem dependent....

  3. An adaptive regularization parameter choice strategy for multispectral bioluminescence tomography

    Energy Technology Data Exchange (ETDEWEB)

    Feng Jinchao; Qin Chenghu; Jia Kebin; Han Dong; Liu Kai; Zhu Shouping; Yang Xin; Tian Jie [Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China); College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124 (China); Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China); Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China) and School of Life Sciences and Technology, Xidian University, Xi' an 710071 (China)

    2011-11-15

    rather than monochromatic data. Furthermore, the study conducted using an adaptive regularization parameter demonstrated our ability to accurately localize the bioluminescent source. With the adaptively estimated regularization parameter, the reconstructed center position of the source was (20.37, 31.05, 12.95) mm, and the distance to the real source was 0.63 mm. The results of the dual-source experiments further showed that our algorithm could localize the bioluminescent sources accurately. The authors then presented experimental evidence that the proposed algorithm exhibited its calculated efficiency over the heuristic method. The effectiveness of the new algorithm was also confirmed by comparing it with the L-curve method. Furthermore, various initial speculations regarding the regularization parameter were used to illustrate the convergence of our algorithm. Finally, in vivo mouse experiment further illustrates the effectiveness of the proposed algorithm. Conclusions: Utilizing numerical, physical phantom and in vivo examples, we demonstrated that the bioluminescent sources could be reconstructed accurately with automatic regularization parameters. The proposed algorithm exhibited superior performance than both the heuristic regularization parameter choice method and L-curve method based on the computational speed and localization error.

  4. Reducing errors in the GRACE gravity solutions using regularization

    Science.gov (United States)

    Save, Himanshu; Bettadpur, Srinivas; Tapley, Byron D.

    2012-09-01

    The nature of the gravity field inverse problem amplifies the noise in the GRACE data, which creeps into the mid and high degree and order harmonic coefficients of the Earth's monthly gravity fields provided by GRACE. Due to the use of imperfect background models and data noise, these errors are manifested as north-south striping in the monthly global maps of equivalent water heights. In order to reduce these errors, this study investigates the use of the L-curve method with Tikhonov regularization. L-curve is a popular aid for determining a suitable value of the regularization parameter when solving linear discrete ill-posed problems using Tikhonov regularization. However, the computational effort required to determine the L-curve is prohibitively high for a large-scale problem like GRACE. This study implements a parameter-choice method, using Lanczos bidiagonalization which is a computationally inexpensive approximation to L-curve. Lanczos bidiagonalization is implemented with orthogonal transformation in a parallel computing environment and projects a large estimation problem on a problem of the size of about 2 orders of magnitude smaller for computing the regularization parameter. Errors in the GRACE solution time series have certain characteristics that vary depending on the ground track coverage of the solutions. These errors increase with increasing degree and order. In addition, certain resonant and near-resonant harmonic coefficients have higher errors as compared with the other coefficients. Using the knowledge of these characteristics, this study designs a regularization matrix that provides a constraint on the geopotential coefficients as a function of its degree and order. This regularization matrix is then used to compute the appropriate regularization parameter for each monthly solution. A 7-year time-series of the candidate regularized solutions (Mar 2003-Feb 2010) show markedly reduced error stripes compared with the unconstrained GRACE release 4

  5. Lipschitz Regularity of Solutions for Mixed Integro-Differential Equations

    OpenAIRE

    Barles, Guy; Chasseigne, Emmanuel; Ciomaga, Adina; Imbert, Cyril

    2011-01-01

    We establish new Hoelder and Lipschitz estimates for viscosity solutions of a large class of elliptic and parabolic nonlinear integro-differential equations, by the classical Ishii-Lions's method. We thus extend the Hoelder regularity results recently obtained by Barles, Chasseigne and Imbert (2011). In addition, we deal with a new class of nonlocal equations that we term mixed integro-differential equations. These equations are particularly interesting, as they are degenerate both in the loc...

  6. Regularity and mass conservation for discrete coagulation–fragmentation equations with diffusion

    KAUST Repository

    Cañ izo, J.A.; Desvillettes, L.; Fellner, K.

    2010-01-01

    We present a new a priori estimate for discrete coagulation-fragmentation systems with size-dependent diffusion within a bounded, regular domain confined by homogeneous Neumann boundary conditions. Following from a duality argument, this a priori

  7. EEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression

    KAUST Repository

    Tian, Tian Siva; Huang, Jianhua Z.; Shen, Haipeng; Li, Zhimin

    2013-01-01

    In this work, we propose a spatial-temporal two-way regularized regression method for reconstructing neural source signals from EEG/MEG time course measurements. The proposed method estimates the dipole locations and amplitudes simultaneously

  8. On infinite regular and chiral maps

    OpenAIRE

    Arredondo, John A.; Valdez, Camilo Ramírez y Ferrán

    2015-01-01

    We prove that infinite regular and chiral maps take place on surfaces with at most one end. Moreover, we prove that an infinite regular or chiral map on an orientable surface with genus can only be realized on the Loch Ness monster, that is, the topological surface of infinite genus with one end.

  9. From recreational to regular drug use

    DEFF Research Database (Denmark)

    Järvinen, Margaretha; Ravn, Signe

    2011-01-01

    This article analyses the process of going from recreational use to regular and problematic use of illegal drugs. We present a model containing six career contingencies relevant for young people’s progress from recreational to regular drug use: the closing of social networks, changes in forms...

  10. Automating InDesign with Regular Expressions

    CERN Document Server

    Kahrel, Peter

    2006-01-01

    If you need to make automated changes to InDesign documents beyond what basic search and replace can handle, you need regular expressions, and a bit of scripting to make them work. This Short Cut explains both how to write regular expressions, so you can find and replace the right things, and how to use them in InDesign specifically.

  11. Regularization modeling for large-eddy simulation

    NARCIS (Netherlands)

    Geurts, Bernardus J.; Holm, D.D.

    2003-01-01

    A new modeling approach for large-eddy simulation (LES) is obtained by combining a "regularization principle" with an explicit filter and its inversion. This regularization approach allows a systematic derivation of the implied subgrid model, which resolves the closure problem. The central role of

  12. 29 CFR 779.18 - Regular rate.

    Science.gov (United States)

    2010-07-01

    ... employee under subsection (a) or in excess of the employee's normal working hours or regular working hours... Relating to Labor (Continued) WAGE AND HOUR DIVISION, DEPARTMENT OF LABOR STATEMENTS OF GENERAL POLICY OR... not less than one and one-half times their regular rates of pay. Section 7(e) of the Act defines...

  13. Local regularity analysis of strata heterogeneities from sonic logs

    Directory of Open Access Journals (Sweden)

    S. Gaci

    2010-09-01

    Full Text Available Borehole logs provide geological information about the rocks crossed by the wells. Several properties of rocks can be interpreted in terms of lithology, type and quantity of the fluid filling the pores and fractures.

    Here, the logs are assumed to be nonhomogeneous Brownian motions (nhBms which are generalized fractional Brownian motions (fBms indexed by depth-dependent Hurst parameters H(z. Three techniques, the local wavelet approach (LWA, the average-local wavelet approach (ALWA, and Peltier Algorithm (PA, are suggested to estimate the Hurst functions (or the regularity profiles from the logs.

    First, two synthetic sonic logs with different parameters, shaped by the successive random additions (SRA algorithm, are used to demonstrate the potential of the proposed methods. The obtained Hurst functions are close to the theoretical Hurst functions. Besides, the transitions between the modeled layers are marked by Hurst values discontinuities. It is also shown that PA leads to the best Hurst value estimations.

    Second, we investigate the multifractional property of sonic logs data recorded at two scientific deep boreholes: the pilot hole VB and the ultra deep main hole HB, drilled for the German Continental Deep Drilling Program (KTB. All the regularity profiles independently obtained for the logs provide a clear correlation with lithology, and from each regularity profile, we derive a similar segmentation in terms of lithological units. The lithological discontinuities (strata' bounds and faults contacts are located at the local extrema of the Hurst functions. Moreover, the regularity profiles are compared with the KTB estimated porosity logs, showing a significant relation between the local extrema of the Hurst functions and the fluid-filled fractures. The Hurst function may then constitute a tool to characterize underground heterogeneities.

  14. Poisson image reconstruction with Hessian Schatten-norm regularization.

    Science.gov (United States)

    Lefkimmiatis, Stamatios; Unser, Michael

    2013-11-01

    Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupted by Poisson noise. In this paper, we propose an efficient framework for Poisson image reconstruction, under a regularization approach, which depends on matrix-valued regularization operators. In particular, the employed regularizers involve the Hessian as the regularization operator and Schatten matrix norms as the potential functions. For the solution of the problem, we propose two optimization algorithms that are specifically tailored to the Poisson nature of the noise. These algorithms are based on an augmented-Lagrangian formulation of the problem and correspond to two variants of the alternating direction method of multipliers. Further, we derive a link that relates the proximal map of an l(p) norm with the proximal map of a Schatten matrix norm of order p. This link plays a key role in the development of one of the proposed algorithms. Finally, we provide experimental results on natural and biological images for the task of Poisson image deblurring and demonstrate the practical relevance and effectiveness of the proposed framework.

  15. An iterative method for Tikhonov regularization with a general linear regularization operator

    NARCIS (Netherlands)

    Hochstenbach, M.E.; Reichel, L.

    2010-01-01

    Tikhonov regularization is one of the most popular approaches to solve discrete ill-posed problems with error-contaminated data. A regularization operator and a suitable value of a regularization parameter have to be chosen. This paper describes an iterative method, based on Golub-Kahan

  16. Multiple graph regularized protein domain ranking

    KAUST Repository

    Wang, Jim Jing-Yan

    2012-11-19

    Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

  17. Multiple graph regularized protein domain ranking

    KAUST Repository

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2012-01-01

    Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

  18. Hierarchical regular small-world networks

    International Nuclear Information System (INIS)

    Boettcher, Stefan; Goncalves, Bruno; Guclu, Hasan

    2008-01-01

    Two new networks are introduced that resemble small-world properties. These networks are recursively constructed but retain a fixed, regular degree. They possess a unique one-dimensional lattice backbone overlaid by a hierarchical sequence of long-distance links, mixing real-space and small-world features. Both networks, one 3-regular and the other 4-regular, lead to distinct behaviors, as revealed by renormalization group studies. The 3-regular network is planar, has a diameter growing as √N with system size N, and leads to super-diffusion with an exact, anomalous exponent d w = 1.306..., but possesses only a trivial fixed point T c = 0 for the Ising ferromagnet. In turn, the 4-regular network is non-planar, has a diameter growing as ∼2 √(log 2 N 2 ) , exhibits 'ballistic' diffusion (d w = 1), and a non-trivial ferromagnetic transition, T c > 0. It suggests that the 3-regular network is still quite 'geometric', while the 4-regular network qualifies as a true small world with mean-field properties. As an engineering application we discuss synchronization of processors on these networks. (fast track communication)

  19. Multiple graph regularized protein domain ranking.

    Science.gov (United States)

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2012-11-19

    Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

  20. Coupling regularizes individual units in noisy populations

    International Nuclear Information System (INIS)

    Ly Cheng; Ermentrout, G. Bard

    2010-01-01

    The regularity of a noisy system can modulate in various ways. It is well known that coupling in a population can lower the variability of the entire network; the collective activity is more regular. Here, we show that diffusive (reciprocal) coupling of two simple Ornstein-Uhlenbeck (O-U) processes can regularize the individual, even when it is coupled to a noisier process. In cellular networks, the regularity of individual cells is important when a select few play a significant role. The regularizing effect of coupling surprisingly applies also to general nonlinear noisy oscillators. However, unlike with the O-U process, coupling-induced regularity is robust to different kinds of coupling. With two coupled noisy oscillators, we derive an asymptotic formula assuming weak noise and coupling for the variance of the period (i.e., spike times) that accurately captures this effect. Moreover, we find that reciprocal coupling can regularize the individual period of higher dimensional oscillators such as the Morris-Lecar and Brusselator models, even when coupled to noisier oscillators. Coupling can have a counterintuitive and beneficial effect on noisy systems. These results have implications for the role of connectivity with noisy oscillators and the modulation of variability of individual oscillators.

  1. Multiple graph regularized protein domain ranking

    Directory of Open Access Journals (Sweden)

    Wang Jim

    2012-11-01

    Full Text Available Abstract Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

  2. A Priori Regularity of Parabolic Partial Differential Equations

    KAUST Repository

    Berkemeier, Francisco

    2018-05-13

    In this thesis, we consider parabolic partial differential equations such as the heat equation, the Fokker-Planck equation, and the porous media equation. Our aim is to develop methods that provide a priori estimates for solutions with singular initial data. These estimates are obtained by understanding the time decay of norms of solutions. First, we derive regularity results for the heat equation by estimating the decay of Lebesgue norms. Then, we apply similar methods to the Fokker-Planck equation with suitable assumptions on the advection and diffusion. Finally, we conclude by extending our techniques to the porous media equation. The sharpness of our results is confirmed by examining known solutions of these equations. The main contribution of this thesis is the use of functional inequalities to express decay of norms as differential inequalities. These are then combined with ODE methods to deduce estimates for the norms of solutions and their derivatives.

  3. Diagrammatic methods in phase-space regularization

    International Nuclear Information System (INIS)

    Bern, Z.; Halpern, M.B.; California Univ., Berkeley

    1987-11-01

    Using the scalar prototype and gauge theory as the simplest possible examples, diagrammatic methods are developed for the recently proposed phase-space form of continuum regularization. A number of one-loop and all-order applications are given, including general diagrammatic discussions of the nogrowth theorem and the uniqueness of the phase-space stochastic calculus. The approach also generates an alternate derivation of the equivalence of the large-β phase-space regularization to the more conventional coordinate-space regularization. (orig.)

  4. J-regular rings with injectivities

    OpenAIRE

    Shen, Liang

    2010-01-01

    A ring $R$ is called a J-regular ring if R/J(R) is von Neumann regular, where J(R) is the Jacobson radical of R. It is proved that if R is J-regular, then (i) R is right n-injective if and only if every homomorphism from an $n$-generated small right ideal of $R$ to $R_{R}$ can be extended to one from $R_{R}$ to $R_{R}$; (ii) R is right FP-injective if and only if R is right (J, R)-FP-injective. Some known results are improved.

  5. Generalized regular genus for manifolds with boundary

    Directory of Open Access Journals (Sweden)

    Paola Cristofori

    2003-05-01

    Full Text Available We introduce a generalization of the regular genus, a combinatorial invariant of PL manifolds ([10], which is proved to be strictly related, in dimension three, to generalized Heegaard splittings defined in [12].

  6. Geometric regularizations and dual conifold transitions

    International Nuclear Information System (INIS)

    Landsteiner, Karl; Lazaroiu, Calin I.

    2003-01-01

    We consider a geometric regularization for the class of conifold transitions relating D-brane systems on noncompact Calabi-Yau spaces to certain flux backgrounds. This regularization respects the SL(2,Z) invariance of the flux superpotential, and allows for computation of the relevant periods through the method of Picard-Fuchs equations. The regularized geometry is a noncompact Calabi-Yau which can be viewed as a monodromic fibration, with the nontrivial monodromy being induced by the regulator. It reduces to the original, non-monodromic background when the regulator is removed. Using this regularization, we discuss the simple case of the local conifold, and show how the relevant field-theoretic information can be extracted in this approach. (author)

  7. The Role of Personality in a Regular Cognitive Monitoring Program.

    Science.gov (United States)

    Sadeq, Nasreen A; Valdes, Elise G; Harrison Bush, Aryn L; Andel, Ross

    2018-02-20

    This study examines the role of personality in cognitive performance, adherence, and satisfaction with regular cognitive self-monitoring. One hundred fifty-seven cognitively healthy older adults, age 55+, completed the 44-item Big-Five Inventory and were subsequently engaged in online monthly cognitive monitoring using the Cogstate Brief Battery for up to 35 months (M=14 mo, SD=7 mo). The test measures speed and accuracy in reaction time, visual learning, and working memory tasks. Neuroticism, although not related to cognitive performance overall (P>0.05), was related to a greater increase in accuracy (estimate=0.07, P=0.04) and speed (estimate=-0.09, P=0.03) on One Card Learning. Greater conscientiousness was related to faster overall speed on Detection (estimate=-1.62, P=0.02) and a significant rate of improvement in speed on One Card Learning (estimate=-0.10, Pconscientiousness were observed. Participants volunteering for regular cognitive monitoring may be quite uniform in terms of personality traits, with personality traits playing a relatively minor role in adherence and satisfaction. The more neurotic may exhibit better accuracy and improve in speed with time, whereas the more conscientious may perform faster overall and improve in speed on some tasks, but the effects appear small.

  8. Fast and compact regular expression matching

    DEFF Research Database (Denmark)

    Bille, Philip; Farach-Colton, Martin

    2008-01-01

    We study 4 problems in string matching, namely, regular expression matching, approximate regular expression matching, string edit distance, and subsequence indexing, on a standard word RAM model of computation that allows logarithmic-sized words to be manipulated in constant time. We show how...... to improve the space and/or remove a dependency on the alphabet size for each problem using either an improved tabulation technique of an existing algorithm or by combining known algorithms in a new way....

  9. Regular-fat dairy and human health

    DEFF Research Database (Denmark)

    Astrup, Arne; Bradley, Beth H Rice; Brenna, J Thomas

    2016-01-01

    In recent history, some dietary recommendations have treated dairy fat as an unnecessary source of calories and saturated fat in the human diet. These assumptions, however, have recently been brought into question by current research on regular fat dairy products and human health. In an effort to......, cheese and yogurt, can be important components of an overall healthy dietary pattern. Systematic examination of the effects of dietary patterns that include regular-fat milk, cheese and yogurt on human health is warranted....

  10. Deterministic automata for extended regular expressions

    Directory of Open Access Journals (Sweden)

    Syzdykov Mirzakhmet

    2017-12-01

    Full Text Available In this work we present the algorithms to produce deterministic finite automaton (DFA for extended operators in regular expressions like intersection, subtraction and complement. The method like “overriding” of the source NFA(NFA not defined with subset construction rules is used. The past work described only the algorithm for AND-operator (or intersection of regular languages; in this paper the construction for the MINUS-operator (and complement is shown.

  11. Regularities of intermediate adsorption complex relaxation

    International Nuclear Information System (INIS)

    Manukova, L.A.

    1982-01-01

    The experimental data, characterizing the regularities of intermediate adsorption complex relaxation in the polycrystalline Mo-N 2 system at 77 K are given. The method of molecular beam has been used in the investigation. The analytical expressions of change regularity in the relaxation process of full and specific rates - of transition from intermediate state into ''non-reversible'', of desorption into the gas phase and accumUlation of the particles in the intermediate state are obtained

  12. Online Manifold Regularization by Dual Ascending Procedure

    OpenAIRE

    Sun, Boliang; Li, Guohui; Jia, Li; Zhang, Hui

    2013-01-01

    We propose a novel online manifold regularization framework based on the notion of duality in constrained optimization. The Fenchel conjugate of hinge functions is a key to transfer manifold regularization from offline to online in this paper. Our algorithms are derived by gradient ascent in the dual function. For practical purpose, we propose two buffering strategies and two sparse approximations to reduce the computational complexity. Detailed experiments verify the utility of our approache...

  13. Iterative regularization in intensity-modulated radiation therapy optimization

    International Nuclear Information System (INIS)

    Carlsson, Fredrik; Forsgren, Anders

    2006-01-01

    A common way to solve intensity-modulated radiation therapy (IMRT) optimization problems is to use a beamlet-based approach. The approach is usually employed in a three-step manner: first a beamlet-weight optimization problem is solved, then the fluence profiles are converted into step-and-shoot segments, and finally postoptimization of the segment weights is performed. A drawback of beamlet-based approaches is that beamlet-weight optimization problems are ill-conditioned and have to be regularized in order to produce smooth fluence profiles that are suitable for conversion. The purpose of this paper is twofold: first, to explain the suitability of solving beamlet-based IMRT problems by a BFGS quasi-Newton sequential quadratic programming method with diagonal initial Hessian estimate, and second, to empirically show that beamlet-weight optimization problems should be solved in relatively few iterations when using this optimization method. The explanation of the suitability is based on viewing the optimization method as an iterative regularization method. In iterative regularization, the optimization problem is solved approximately by iterating long enough to obtain a solution close to the optimal one, but terminating before too much noise occurs. Iterative regularization requires an optimization method that initially proceeds in smooth directions and makes rapid initial progress. Solving ten beamlet-based IMRT problems with dose-volume objectives and bounds on the beamlet-weights, we find that the considered optimization method fulfills the requirements for performing iterative regularization. After segment-weight optimization, the treatments obtained using 35 beamlet-weight iterations outperform the treatments obtained using 100 beamlet-weight iterations, both in terms of objective value and of target uniformity. We conclude that iterating too long may in fact deteriorate the quality of the deliverable plan

  14. A two-way regularization method for MEG source reconstruction

    KAUST Repository

    Tian, Tian Siva; Huang, Jianhua Z.; Shen, Haipeng; Li, Zhimin

    2012-01-01

    The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source time course is smooth in time (smoothness). The focality and smoothness of the reconstructed signals are ensured respectively by imposing a sparsity-inducing penalty and a roughness penalty in the data fitting criterion. A two-stage algorithm is developed for fast computation, where a raw estimate of the source time course is obtained in the first stage and then refined in the second stage by the two-way regularization. The proposed method is shown to be effective on both synthetic and real-world examples. © Institute of Mathematical Statistics, 2012.

  15. Determining the flexibility of regular and chaotic attractors

    International Nuclear Information System (INIS)

    Marhl, Marko; Perc, Matjaz

    2006-01-01

    We present an overview of measures that are appropriate for determining the flexibility of regular and chaotic attractors. In particular, we focus on those system properties that constitute its responses to external perturbations. We deploy a systematic approach, first introducing the simplest measure given by the local divergence of the system along the attractor, and then develop more rigorous mathematical tools for estimating the flexibility of the system's dynamics. The presented measures are tested on the regular Brusselator and chaotic Hindmarsh-Rose model of an excitable neuron with equal success, thus indicating the overall effectiveness and wide applicability range of the proposed theory. Since responses of dynamical systems to external signals are crucial in several scientific disciplines, and especially in natural sciences, we discuss several important aspects and biological implications of obtained results

  16. Major earthquakes occur regularly on an isolated plate boundary fault.

    Science.gov (United States)

    Berryman, Kelvin R; Cochran, Ursula A; Clark, Kate J; Biasi, Glenn P; Langridge, Robert M; Villamor, Pilar

    2012-06-29

    The scarcity of long geological records of major earthquakes, on different types of faults, makes testing hypotheses of regular versus random or clustered earthquake recurrence behavior difficult. We provide a fault-proximal major earthquake record spanning 8000 years on the strike-slip Alpine Fault in New Zealand. Cyclic stratigraphy at Hokuri Creek suggests that the fault ruptured to the surface 24 times, and event ages yield a 0.33 coefficient of variation in recurrence interval. We associate this near-regular earthquake recurrence with a geometrically simple strike-slip fault, with high slip rate, accommodating a high proportion of plate boundary motion that works in isolation from other faults. We propose that it is valid to apply time-dependent earthquake recurrence models for seismic hazard estimation to similar faults worldwide.

  17. A two-way regularization method for MEG source reconstruction

    KAUST Repository

    Tian, Tian Siva

    2012-09-01

    The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source time course is smooth in time (smoothness). The focality and smoothness of the reconstructed signals are ensured respectively by imposing a sparsity-inducing penalty and a roughness penalty in the data fitting criterion. A two-stage algorithm is developed for fast computation, where a raw estimate of the source time course is obtained in the first stage and then refined in the second stage by the two-way regularization. The proposed method is shown to be effective on both synthetic and real-world examples. © Institute of Mathematical Statistics, 2012.

  18. Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

    Directory of Open Access Journals (Sweden)

    Cheng-Yuan Shih

    2010-01-01

    Full Text Available This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA and quadratic discriminant analysis (QDA. It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

  19. Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data.

    Science.gov (United States)

    Dazard, Jean-Eudes; Rao, J Sunil

    2012-07-01

    The paper addresses a common problem in the analysis of high-dimensional high-throughput "omics" data, which is parameter estimation across multiple variables in a set of data where the number of variables is much larger than the sample size. Among the problems posed by this type of data are that variable-specific estimators of variances are not reliable and variable-wise tests statistics have low power, both due to a lack of degrees of freedom. In addition, it has been observed in this type of data that the variance increases as a function of the mean. We introduce a non-parametric adaptive regularization procedure that is innovative in that : (i) it employs a novel "similarity statistic"-based clustering technique to generate local-pooled or regularized shrinkage estimators of population parameters, (ii) the regularization is done jointly on population moments, benefiting from C. Stein's result on inadmissibility, which implies that usual sample variance estimator is improved by a shrinkage estimator using information contained in the sample mean. From these joint regularized shrinkage estimators, we derived regularized t-like statistics and show in simulation studies that they offer more statistical power in hypothesis testing than their standard sample counterparts, or regular common value-shrinkage estimators, or when the information contained in the sample mean is simply ignored. Finally, we show that these estimators feature interesting properties of variance stabilization and normalization that can be used for preprocessing high-dimensional multivariate data. The method is available as an R package, called 'MVR' ('Mean-Variance Regularization'), downloadable from the CRAN website.

  20. Low-Complexity Regularization Algorithms for Image Deblurring

    KAUST Repository

    Alanazi, Abdulrahman

    2016-11-01

    Image restoration problems deal with images in which information has been degraded by blur or noise. In practice, the blur is usually caused by atmospheric turbulence, motion, camera shake, and several other mechanical or physical processes. In this study, we present two regularization algorithms for the image deblurring problem. We first present a new method based on solving a regularized least-squares (RLS) problem. This method is proposed to find a near-optimal value of the regularization parameter in the RLS problems. Experimental results on the non-blind image deblurring problem are presented. In all experiments, comparisons are made with three benchmark methods. The results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and structural similarity, as well as the visual quality of the deblurred images. To reduce the complexity of the proposed algorithm, we propose a technique based on the bootstrap method to estimate the regularization parameter in low and high-resolution images. Numerical results show that the proposed technique can effectively reduce the computational complexity of the proposed algorithms. In addition, for some cases where the point spread function (PSF) is separable, we propose using a Kronecker product so as to reduce the computations. Furthermore, in the case where the image is smooth, it is always desirable to replace the regularization term in the RLS problems by a total variation term. Therefore, we propose a novel method for adaptively selecting the regularization parameter in a so-called square root regularized total variation (SRTV). Experimental results demonstrate that our proposed method outperforms the other benchmark methods when applied to smooth images in terms of PSNR, SSIM and the restored image quality. In this thesis, we focus on the non-blind image deblurring problem, where the blur kernel is assumed to be known. However, we developed algorithms that also work

  1. Regular Expression Matching and Operational Semantics

    Directory of Open Access Journals (Sweden)

    Asiri Rathnayake

    2011-08-01

    Full Text Available Many programming languages and tools, ranging from grep to the Java String library, contain regular expression matchers. Rather than first translating a regular expression into a deterministic finite automaton, such implementations typically match the regular expression on the fly. Thus they can be seen as virtual machines interpreting the regular expression much as if it were a program with some non-deterministic constructs such as the Kleene star. We formalize this implementation technique for regular expression matching using operational semantics. Specifically, we derive a series of abstract machines, moving from the abstract definition of matching to increasingly realistic machines. First a continuation is added to the operational semantics to describe what remains to be matched after the current expression. Next, we represent the expression as a data structure using pointers, which enables redundant searches to be eliminated via testing for pointer equality. From there, we arrive both at Thompson's lockstep construction and a machine that performs some operations in parallel, suitable for implementation on a large number of cores, such as a GPU. We formalize the parallel machine using process algebra and report some preliminary experiments with an implementation on a graphics processor using CUDA.

  2. Regularities, Natural Patterns and Laws of Nature

    Directory of Open Access Journals (Sweden)

    Stathis Psillos

    2014-02-01

    Full Text Available  The goal of this paper is to sketch an empiricist metaphysics of laws of nature. The key idea is that there are regularities without regularity-enforcers. Differently put, there are natural laws without law-makers of a distinct metaphysical kind. This sketch will rely on the concept of a natural pattern and more significantly on the existence of a network of natural patterns in nature. The relation between a regularity and a pattern will be analysed in terms of mereology.  Here is the road map. In section 2, I will briefly discuss the relation between empiricism and metaphysics, aiming to show that an empiricist metaphysics is possible. In section 3, I will offer arguments against stronger metaphysical views of laws. Then, in section 4 I will motivate nomic objectivism. In section 5, I will address the question ‘what is a regularity?’ and will develop a novel answer to it, based on the notion of a natural pattern. In section 6, I will raise the question: ‘what is a law of nature?’, the answer to which will be: a law of nature is a regularity that is characterised by the unity of a natural pattern.

  3. GLOBAL OPTIMIZATION METHODS FOR GRAVITATIONAL LENS SYSTEMS WITH REGULARIZED SOURCES

    International Nuclear Information System (INIS)

    Rogers, Adam; Fiege, Jason D.

    2012-01-01

    Several approaches exist to model gravitational lens systems. In this study, we apply global optimization methods to find the optimal set of lens parameters using a genetic algorithm. We treat the full optimization procedure as a two-step process: an analytical description of the source plane intensity distribution is used to find an initial approximation to the optimal lens parameters; the second stage of the optimization uses a pixelated source plane with the semilinear method to determine an optimal source. Regularization is handled by means of an iterative method and the generalized cross validation (GCV) and unbiased predictive risk estimator (UPRE) functions that are commonly used in standard image deconvolution problems. This approach simultaneously estimates the optimal regularization parameter and the number of degrees of freedom in the source. Using the GCV and UPRE functions, we are able to justify an estimation of the number of source degrees of freedom found in previous work. We test our approach by applying our code to a subset of the lens systems included in the SLACS survey.

  4. Fractional Regularization Term for Variational Image Registration

    Directory of Open Access Journals (Sweden)

    Rafael Verdú-Monedero

    2009-01-01

    Full Text Available Image registration is a widely used task of image analysis with applications in many fields. Its classical formulation and current improvements are given in the spatial domain. In this paper a regularization term based on fractional order derivatives is formulated. This term is defined and implemented in the frequency domain by translating the energy functional into the frequency domain and obtaining the Euler-Lagrange equations which minimize it. The new regularization term leads to a simple formulation and design, being applicable to higher dimensions by using the corresponding multidimensional Fourier transform. The proposed regularization term allows for a real gradual transition from a diffusion registration to a curvature registration which is best suited to some applications and it is not possible in the spatial domain. Results with 3D actual images show the validity of this approach.

  5. Regular non-twisting S-branes

    International Nuclear Information System (INIS)

    Obregon, Octavio; Quevedo, Hernando; Ryan, Michael P.

    2004-01-01

    We construct a family of time and angular dependent, regular S-brane solutions which corresponds to a simple analytical continuation of the Zipoy-Voorhees 4-dimensional vacuum spacetime. The solutions are asymptotically flat and turn out to be free of singularities without requiring a twist in space. They can be considered as the simplest non-singular generalization of the singular S0-brane solution. We analyze the properties of a representative of this family of solutions and show that it resembles to some extent the asymptotic properties of the regular Kerr S-brane. The R-symmetry corresponds, however, to the general lorentzian symmetry. Several generalizations of this regular solution are derived which include a charged S-brane and an additional dilatonic field. (author)

  6. Online Manifold Regularization by Dual Ascending Procedure

    Directory of Open Access Journals (Sweden)

    Boliang Sun

    2013-01-01

    Full Text Available We propose a novel online manifold regularization framework based on the notion of duality in constrained optimization. The Fenchel conjugate of hinge functions is a key to transfer manifold regularization from offline to online in this paper. Our algorithms are derived by gradient ascent in the dual function. For practical purpose, we propose two buffering strategies and two sparse approximations to reduce the computational complexity. Detailed experiments verify the utility of our approaches. An important conclusion is that our online MR algorithms can handle the settings where the target hypothesis is not fixed but drifts with the sequence of examples. We also recap and draw connections to earlier works. This paper paves a way to the design and analysis of online manifold regularization algorithms.

  7. Regularization by fractional filter methods and data smoothing

    International Nuclear Information System (INIS)

    Klann, E; Ramlau, R

    2008-01-01

    This paper is concerned with the regularization of linear ill-posed problems by a combination of data smoothing and fractional filter methods. For the data smoothing, a wavelet shrinkage denoising is applied to the noisy data with known error level δ. For the reconstruction, an approximation to the solution of the operator equation is computed from the data estimate by fractional filter methods. These fractional methods are based on the classical Tikhonov and Landweber method, but avoid, at least partially, the well-known drawback of oversmoothing. Convergence rates as well as numerical examples are presented

  8. Parameter identification for continuous point emission source based on Tikhonov regularization method coupled with particle swarm optimization algorithm.

    Science.gov (United States)

    Ma, Denglong; Tan, Wei; Zhang, Zaoxiao; Hu, Jun

    2017-03-05

    In order to identify the parameters of hazardous gas emission source in atmosphere with less previous information and reliable probability estimation, a hybrid algorithm coupling Tikhonov regularization with particle swarm optimization (PSO) was proposed. When the source location is known, the source strength can be estimated successfully by common Tikhonov regularization method, but it is invalid when the information about both source strength and location is absent. Therefore, a hybrid method combining linear Tikhonov regularization and PSO algorithm was designed. With this method, the nonlinear inverse dispersion model was transformed to a linear form under some assumptions, and the source parameters including source strength and location were identified simultaneously by linear Tikhonov-PSO regularization method. The regularization parameters were selected by L-curve method. The estimation results with different regularization matrixes showed that the confidence interval with high-order regularization matrix is narrower than that with zero-order regularization matrix. But the estimation results of different source parameters are close to each other with different regularization matrixes. A nonlinear Tikhonov-PSO hybrid regularization was also designed with primary nonlinear dispersion model to estimate the source parameters. The comparison results of simulation and experiment case showed that the linear Tikhonov-PSO method with transformed linear inverse model has higher computation efficiency than nonlinear Tikhonov-PSO method. The confidence intervals from linear Tikhonov-PSO are more reasonable than that from nonlinear method. The estimation results from linear Tikhonov-PSO method are similar to that from single PSO algorithm, and a reasonable confidence interval with some probability levels can be additionally given by Tikhonov-PSO method. Therefore, the presented linear Tikhonov-PSO regularization method is a good potential method for hazardous emission

  9. Centered Differential Waveform Inversion with Minimum Support Regularization

    KAUST Repository

    Kazei, Vladimir

    2017-05-26

    Time-lapse full-waveform inversion has two major challenges. The first one is the reconstruction of a reference model (baseline model for most of approaches). The second is inversion for the time-lapse changes in the parameters. Common model approach is utilizing the information contained in all available data sets to build a better reference model for time lapse inversion. Differential (Double-difference) waveform inversion allows to reduce the artifacts introduced into estimates of time-lapse parameter changes by imperfect inversion for the baseline-reference model. We propose centered differential waveform inversion (CDWI) which combines these two approaches in order to benefit from both of their features. We apply minimum support regularization commonly used with electromagnetic methods of geophysical exploration. We test the CDWI method on synthetic dataset with random noise and show that, with Minimum support regularization, it provides better resolution of velocity changes than with total variation and Tikhonov regularizations in time-lapse full-waveform inversion.

  10. A Large Dimensional Analysis of Regularized Discriminant Analysis Classifiers

    KAUST Repository

    Elkhalil, Khalil

    2017-11-01

    This article carries out a large dimensional analysis of standard regularized discriminant analysis classifiers designed on the assumption that data arise from a Gaussian mixture model with different means and covariances. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under mild assumptions, we show that the asymptotic classification error approaches a deterministic quantity that depends only on the means and covariances associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized discriminant analsysis, in practical large but finite dimensions, and can be used to determine and pre-estimate the optimal regularization parameter that minimizes the misclassification error probability. Despite being theoretically valid only for Gaussian data, our findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from the popular USPS data base, thereby making an interesting connection between theory and practice.

  11. Image deblurring using a perturbation-basec regularization approach

    KAUST Repository

    Alanazi, Abdulrahman

    2017-11-02

    The image restoration problem deals with images in which information has been degraded by blur or noise. In this work, we present a new method for image deblurring by solving a regularized linear least-squares problem. In the proposed method, a synthetic perturbation matrix with a bounded norm is forced into the discrete ill-conditioned model matrix. This perturbation is added to enhance the singular-value structure of the matrix and hence to provide an improved solution. A method is proposed to find a near-optimal value of the regularization parameter for the proposed approach. To reduce the computational complexity, we present a technique based on the bootstrapping method to estimate the regularization parameter for both low and high-resolution images. Experimental results on the image deblurring problem are presented. Comparisons are made with three benchmark methods and the results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and SSIM values.

  12. Image deblurring using a perturbation-basec regularization approach

    KAUST Repository

    Alanazi, Abdulrahman; Ballal, Tarig; Masood, Mudassir; Al-Naffouri, Tareq Y.

    2017-01-01

    The image restoration problem deals with images in which information has been degraded by blur or noise. In this work, we present a new method for image deblurring by solving a regularized linear least-squares problem. In the proposed method, a synthetic perturbation matrix with a bounded norm is forced into the discrete ill-conditioned model matrix. This perturbation is added to enhance the singular-value structure of the matrix and hence to provide an improved solution. A method is proposed to find a near-optimal value of the regularization parameter for the proposed approach. To reduce the computational complexity, we present a technique based on the bootstrapping method to estimate the regularization parameter for both low and high-resolution images. Experimental results on the image deblurring problem are presented. Comparisons are made with three benchmark methods and the results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and SSIM values.

  13. Regular transport dynamics produce chaotic travel times.

    Science.gov (United States)

    Villalobos, Jorge; Muñoz, Víctor; Rogan, José; Zarama, Roberto; Johnson, Neil F; Toledo, Benjamín; Valdivia, Juan Alejandro

    2014-06-01

    In the hope of making passenger travel times shorter and more reliable, many cities are introducing dedicated bus lanes (e.g., Bogota, London, Miami). Here we show that chaotic travel times are actually a natural consequence of individual bus function, and hence of public transport systems more generally, i.e., chaotic dynamics emerge even when the route is empty and straight, stops and lights are equidistant and regular, and loading times are negligible. More generally, our findings provide a novel example of chaotic dynamics emerging from a single object following Newton's laws of motion in a regularized one-dimensional system.

  14. Regularity of difference equations on Banach spaces

    CERN Document Server

    Agarwal, Ravi P; Lizama, Carlos

    2014-01-01

    This work introduces readers to the topic of maximal regularity for difference equations. The authors systematically present the method of maximal regularity, outlining basic linear difference equations along with relevant results. They address recent advances in the field, as well as basic semigroup and cosine operator theories in the discrete setting. The authors also identify some open problems that readers may wish to take up for further research. This book is intended for graduate students and researchers in the area of difference equations, particularly those with advance knowledge of and interest in functional analysis.

  15. PET regularization by envelope guided conjugate gradients

    International Nuclear Information System (INIS)

    Kaufman, L.; Neumaier, A.

    1996-01-01

    The authors propose a new way to iteratively solve large scale ill-posed problems and in particular the image reconstruction problem in positron emission tomography by exploiting the relation between Tikhonov regularization and multiobjective optimization to obtain iteratively approximations to the Tikhonov L-curve and its corner. Monitoring the change of the approximate L-curves allows us to adjust the regularization parameter adaptively during a preconditioned conjugate gradient iteration, so that the desired solution can be reconstructed with a small number of iterations

  16. Matrix regularization of embedded 4-manifolds

    International Nuclear Information System (INIS)

    Trzetrzelewski, Maciej

    2012-01-01

    We consider products of two 2-manifolds such as S 2 ×S 2 , embedded in Euclidean space and show that the corresponding 4-volume preserving diffeomorphism algebra can be approximated by a tensor product SU(N)⊗SU(N) i.e. functions on a manifold are approximated by the Kronecker product of two SU(N) matrices. A regularization of the 4-sphere is also performed by constructing N 2 ×N 2 matrix representations of the 4-algebra (and as a byproduct of the 3-algebra which makes the regularization of S 3 also possible).

  17. Solving ill-posed control problems by stabilized finite element methods: an alternative to Tikhonov regularization

    Science.gov (United States)

    Burman, Erik; Hansbo, Peter; Larson, Mats G.

    2018-03-01

    Tikhonov regularization is one of the most commonly used methods for the regularization of ill-posed problems. In the setting of finite element solutions of elliptic partial differential control problems, Tikhonov regularization amounts to adding suitably weighted least squares terms of the control variable, or derivatives thereof, to the Lagrangian determining the optimality system. In this note we show that the stabilization methods for discretely ill-posed problems developed in the setting of convection-dominated convection-diffusion problems, can be highly suitable for stabilizing optimal control problems, and that Tikhonov regularization will lead to less accurate discrete solutions. We consider some inverse problems for Poisson’s equation as an illustration and derive new error estimates both for the reconstruction of the solution from the measured data and reconstruction of the source term from the measured data. These estimates include both the effect of the discretization error and error in the measurements.

  18. Regular use of alcohol and tobacco in India and its association with age, gender, and poverty.

    Science.gov (United States)

    Neufeld, K J; Peters, D H; Rani, M; Bonu, S; Brooner, R K

    2005-03-07

    This study provides national estimates of regular tobacco and alcohol use in India and their associations with gender, age, and economic group obtained from a representative survey of 471,143 people over the age of 10 years in 1995-96, the National Sample Survey. The national prevalence of regular use of smoking tobacco is estimated to be 16.2%, chewing tobacco 14.0%, and alcohol 4.5%. Men were 25.5 times more likely than women to report regular smoking, 3.7 times more likely to regularly chew tobacco, and 9.7 times more likely to regularly use alcohol. Respondents belonging to scheduled castes and tribes (recognized disadvantaged groups) were significantly more likely to report regular use of alcohol as well as smoking and chewing tobacco. People from rural areas had higher rates compared to urban dwellers, as did those with no formal education. Individuals with incomes below the poverty line had higher relative odds of use of chewing tobacco and alcohol compared to those above the poverty line. The regular use of both tobacco and alcohol also increased significantly with each diminishing income quintile. Comparisons are made between these results and those found in the United States and elsewhere, highlighting the need to address control of these substances on the public health agenda.

  19. Regular and promotional sales in new product life-cycle: A competitive approach

    OpenAIRE

    Guidolin, Mariangela; Guseo, Renato; Mortarino, Cinzia

    2016-01-01

    In this paper, we consider the application of the Lotka-Volterra model with churn effects, LVch, (Guidolin and Guseo, 2015) to the case of a confectionary product produced in Italy and recently commercialized in a European country. Weekly time series, referring separately to quantities of regular and promotional sales, are available. Their joint inspection highlighted the presence of compensatory dynamics suggesting the study with the LVch to estimate whether competition between regular and p...

  20. On a correspondence between regular and non-regular operator monotone functions

    DEFF Research Database (Denmark)

    Gibilisco, P.; Hansen, Frank; Isola, T.

    2009-01-01

    We prove the existence of a bijection between the regular and the non-regular operator monotone functions satisfying a certain functional equation. As an application we give a new proof of the operator monotonicity of certain functions related to the Wigner-Yanase-Dyson skew information....

  1. Regularity and irreversibility of weekly travel behavior

    NARCIS (Netherlands)

    Kitamura, R.; van der Hoorn, A.I.J.M.

    1987-01-01

    Dynamic characteristics of travel behavior are analyzed in this paper using weekly travel diaries from two waves of panel surveys conducted six months apart. An analysis of activity engagement indicates the presence of significant regularity in weekly activity participation between the two waves.

  2. Regular and context-free nominal traces

    DEFF Research Database (Denmark)

    Degano, Pierpaolo; Ferrari, Gian-Luigi; Mezzetti, Gianluca

    2017-01-01

    Two kinds of automata are presented, for recognising new classes of regular and context-free nominal languages. We compare their expressive power with analogous proposals in the literature, showing that they express novel classes of languages. Although many properties of classical languages hold ...

  3. Faster 2-regular information-set decoding

    NARCIS (Netherlands)

    Bernstein, D.J.; Lange, T.; Peters, C.P.; Schwabe, P.; Chee, Y.M.

    2011-01-01

    Fix positive integers B and w. Let C be a linear code over F 2 of length Bw. The 2-regular-decoding problem is to find a nonzero codeword consisting of w length-B blocks, each of which has Hamming weight 0 or 2. This problem appears in attacks on the FSB (fast syndrome-based) hash function and

  4. Complexity in union-free regular languages

    Czech Academy of Sciences Publication Activity Database

    Jirásková, G.; Masopust, Tomáš

    2011-01-01

    Roč. 22, č. 7 (2011), s. 1639-1653 ISSN 0129-0541 Institutional research plan: CEZ:AV0Z10190503 Keywords : Union-free regular language * one-cycle-free-path automaton * descriptional complexity Subject RIV: BA - General Mathematics Impact factor: 0.379, year: 2011 http://www.worldscinet.com/ijfcs/22/2207/S0129054111008933.html

  5. Regular Gleason Measures and Generalized Effect Algebras

    Science.gov (United States)

    Dvurečenskij, Anatolij; Janda, Jiří

    2015-12-01

    We study measures, finitely additive measures, regular measures, and σ-additive measures that can attain even infinite values on the quantum logic of a Hilbert space. We show when particular classes of non-negative measures can be studied in the frame of generalized effect algebras.

  6. Regularization of finite temperature string theories

    International Nuclear Information System (INIS)

    Leblanc, Y.; Knecht, M.; Wallet, J.C.

    1990-01-01

    The tachyonic divergences occurring in the free energy of various string theories at finite temperature are eliminated through the use of regularization schemes and analytic continuations. For closed strings, we obtain finite expressions which, however, develop an imaginary part above the Hagedorn temperature, whereas open string theories are still plagued with dilatonic divergences. (orig.)

  7. A Sim(2 invariant dimensional regularization

    Directory of Open Access Journals (Sweden)

    J. Alfaro

    2017-09-01

    Full Text Available We introduce a Sim(2 invariant dimensional regularization of loop integrals. Then we can compute the one loop quantum corrections to the photon self energy, electron self energy and vertex in the Electrodynamics sector of the Very Special Relativity Standard Model (VSRSM.

  8. Continuum regularized Yang-Mills theory

    International Nuclear Information System (INIS)

    Sadun, L.A.

    1987-01-01

    Using the machinery of stochastic quantization, Z. Bern, M. B. Halpern, C. Taubes and I recently proposed a continuum regularization technique for quantum field theory. This regularization may be implemented by applying a regulator to either the (d + 1)-dimensional Parisi-Wu Langevin equation or, equivalently, to the d-dimensional second order Schwinger-Dyson (SD) equations. This technique is non-perturbative, respects all gauge and Lorentz symmetries, and is consistent with a ghost-free gauge fixing (Zwanziger's). This thesis is a detailed study of this regulator, and of regularized Yang-Mills theory, using both perturbative and non-perturbative techniques. The perturbative analysis comes first. The mechanism of stochastic quantization is reviewed, and a perturbative expansion based on second-order SD equations is developed. A diagrammatic method (SD diagrams) for evaluating terms of this expansion is developed. We apply the continuum regulator to a scalar field theory. Using SD diagrams, we show that all Green functions can be rendered finite to all orders in perturbation theory. Even non-renormalizable theories can be regularized. The continuum regulator is then applied to Yang-Mills theory, in conjunction with Zwanziger's gauge fixing. A perturbative expansion of the regulator is incorporated into the diagrammatic method. It is hoped that the techniques discussed in this thesis will contribute to the construction of a renormalized Yang-Mills theory is 3 and 4 dimensions

  9. Gravitational lensing by a regular black hole

    International Nuclear Information System (INIS)

    Eiroa, Ernesto F; Sendra, Carlos M

    2011-01-01

    In this paper, we study a regular Bardeen black hole as a gravitational lens. We find the strong deflection limit for the deflection angle, from which we obtain the positions and magnifications of the relativistic images. As an example, we apply the results to the particular case of the supermassive black hole at the center of our galaxy.

  10. Gravitational lensing by a regular black hole

    Energy Technology Data Exchange (ETDEWEB)

    Eiroa, Ernesto F; Sendra, Carlos M, E-mail: eiroa@iafe.uba.ar, E-mail: cmsendra@iafe.uba.ar [Instituto de Astronomia y Fisica del Espacio, CC 67, Suc. 28, 1428, Buenos Aires (Argentina)

    2011-04-21

    In this paper, we study a regular Bardeen black hole as a gravitational lens. We find the strong deflection limit for the deflection angle, from which we obtain the positions and magnifications of the relativistic images. As an example, we apply the results to the particular case of the supermassive black hole at the center of our galaxy.

  11. Analytic stochastic regularization and gange invariance

    International Nuclear Information System (INIS)

    Abdalla, E.; Gomes, M.; Lima-Santos, A.

    1986-05-01

    A proof that analytic stochastic regularization breaks gauge invariance is presented. This is done by an explicit one loop calculation of the vaccum polarization tensor in scalar electrodynamics, which turns out not to be transversal. The counterterm structure, Langevin equations and the construction of composite operators in the general framework of stochastic quantization, are also analysed. (Author) [pt

  12. Annotation of regular polysemy and underspecification

    DEFF Research Database (Denmark)

    Martínez Alonso, Héctor; Pedersen, Bolette Sandford; Bel, Núria

    2013-01-01

    We present the result of an annotation task on regular polysemy for a series of seman- tic classes or dot types in English, Dan- ish and Spanish. This article describes the annotation process, the results in terms of inter-encoder agreement, and the sense distributions obtained with two methods...

  13. Stabilization, pole placement, and regular implementability

    NARCIS (Netherlands)

    Belur, MN; Trentelman, HL

    In this paper, we study control by interconnection of linear differential systems. We give necessary and sufficient conditions for regular implementability of a-given linear, differential system. We formulate the problems of stabilization and pole placement as problems of finding a suitable,

  14. 12 CFR 725.3 - Regular membership.

    Science.gov (United States)

    2010-01-01

    ... UNION ADMINISTRATION CENTRAL LIQUIDITY FACILITY § 725.3 Regular membership. (a) A natural person credit....5(b) of this part, and forwarding with its completed application funds equal to one-half of this... 1, 1979, is not required to forward these funds to the Facility until October 1, 1979. (3...

  15. Supervised scale-regularized linear convolutionary filters

    DEFF Research Database (Denmark)

    Loog, Marco; Lauze, Francois Bernard

    2017-01-01

    also be solved relatively efficient. All in all, the idea is to properly control the scale of a trained filter, which we solve by introducing a specific regularization term into the overall objective function. We demonstrate, on an artificial filter learning problem, the capabil- ities of our basic...

  16. On regular riesz operators | Raubenheimer | Quaestiones ...

    African Journals Online (AJOL)

    The r-asymptotically quasi finite rank operators on Banach lattices are examples of regular Riesz operators. We characterise Riesz elements in a subalgebra of a Banach algebra in terms of Riesz elements in the Banach algebra. This enables us to characterise r-asymptotically quasi finite rank operators in terms of adjoint ...

  17. Complexity in union-free regular languages

    Czech Academy of Sciences Publication Activity Database

    Jirásková, G.; Masopust, Tomáš

    2011-01-01

    Roč. 22, č. 7 (2011), s. 1639-1653 ISSN 0129-0541 Institutional research plan: CEZ:AV0Z10190503 Keywords : Union-free regular language * one-cycle-free- path automaton * descriptional complexity Subject RIV: BA - General Mathematics Impact factor: 0.379, year: 2011 http://www.worldscinet.com/ijfcs/22/2207/S0129054111008933.html

  18. Bit-coded regular expression parsing

    DEFF Research Database (Denmark)

    Nielsen, Lasse; Henglein, Fritz

    2011-01-01

    the DFA-based parsing algorithm due to Dub ´e and Feeley to emit the bits of the bit representation without explicitly materializing the parse tree itself. We furthermore show that Frisch and Cardelli’s greedy regular expression parsing algorithm can be straightforwardly modified to produce bit codings...

  19. Tetravalent one-regular graphs of order 4p2

    DEFF Research Database (Denmark)

    Feng, Yan-Quan; Kutnar, Klavdija; Marusic, Dragan

    2014-01-01

    A graph is one-regular if its automorphism group acts regularly on the set of its arcs. In this paper tetravalent one-regular graphs of order 4p2, where p is a prime, are classified.......A graph is one-regular if its automorphism group acts regularly on the set of its arcs. In this paper tetravalent one-regular graphs of order 4p2, where p is a prime, are classified....

  20. Regularized inversion of controlled source and earthquake data

    International Nuclear Information System (INIS)

    Ramachandran, Kumar

    2012-01-01

    Estimation of the seismic velocity structure of the Earth's crust and upper mantle from travel-time data has advanced greatly in recent years. Forward modelling trial-and-error methods have been superseded by tomographic methods which allow more objective analysis of large two-dimensional and three-dimensional refraction and/or reflection data sets. The fundamental purpose of travel-time tomography is to determine the velocity structure of a medium by analysing the time it takes for a wave generated at a source point within the medium to arrive at a distribution of receiver points. Tomographic inversion of first-arrival travel-time data is a nonlinear problem since both the velocity of the medium and ray paths in the medium are unknown. The solution for such a problem is typically obtained by repeated application of linearized inversion. Regularization of the nonlinear problem reduces the ill posedness inherent in the tomographic inversion due to the under-determined nature of the problem and the inconsistencies in the observed data. This paper discusses the theory of regularized inversion for joint inversion of controlled source and earthquake data, and results from synthetic data testing and application to real data. The results obtained from tomographic inversion of synthetic data and real data from the northern Cascadia subduction zone show that the velocity model and hypocentral parameters can be efficiently estimated using this approach. (paper)

  1. A general framework for regularized, similarity-based image restoration.

    Science.gov (United States)

    Kheradmand, Amin; Milanfar, Peyman

    2014-12-01

    Any image can be represented as a function defined on a weighted graph, in which the underlying structure of the image is encoded in kernel similarity and associated Laplacian matrices. In this paper, we develop an iterative graph-based framework for image restoration based on a new definition of the normalized graph Laplacian. We propose a cost function, which consists of a new data fidelity term and regularization term derived from the specific definition of the normalized graph Laplacian. The normalizing coefficients used in the definition of the Laplacian and associated regularization term are obtained using fast symmetry preserving matrix balancing. This results in some desired spectral properties for the normalized Laplacian such as being symmetric, positive semidefinite, and returning zero vector when applied to a constant image. Our algorithm comprises of outer and inner iterations, where in each outer iteration, the similarity weights are recomputed using the previous estimate and the updated objective function is minimized using inner conjugate gradient iterations. This procedure improves the performance of the algorithm for image deblurring, where we do not have access to a good initial estimate of the underlying image. In addition, the specific form of the cost function allows us to render the spectral analysis for the solutions of the corresponding linear equations. In addition, the proposed approach is general in the sense that we have shown its effectiveness for different restoration problems, including deblurring, denoising, and sharpening. Experimental results verify the effectiveness of the proposed algorithm on both synthetic and real examples.

  2. Adaptive discretizations for the choice of a Tikhonov regularization parameter in nonlinear inverse problems

    International Nuclear Information System (INIS)

    Kaltenbacher, Barbara; Kirchner, Alana; Vexler, Boris

    2011-01-01

    Parameter identification problems for partial differential equations usually lead to nonlinear inverse problems. A typical property of such problems is their instability, which requires regularization techniques, like, e.g., Tikhonov regularization. The main focus of this paper will be on efficient methods for determining a suitable regularization parameter by using adaptive finite element discretizations based on goal-oriented error estimators. A well-established method for the determination of a regularization parameter is the discrepancy principle where the residual norm, considered as a function i of the regularization parameter, should equal an appropriate multiple of the noise level. We suggest to solve the resulting scalar nonlinear equation by an inexact Newton method, where in each iteration step, a regularized problem is solved at a different discretization level. The proposed algorithm is an extension of the method suggested in Griesbaum A et al (2008 Inverse Problems 24 025025) for linear inverse problems, where goal-oriented error estimators for i and its derivative are used for adaptive refinement strategies in order to keep the discretization level as coarse as possible to save computational effort but fine enough to guarantee global convergence of the inexact Newton method. This concept leads to a highly efficient method for determining the Tikhonov regularization parameter for nonlinear ill-posed problems. Moreover, we prove that with the so-obtained regularization parameter and an also adaptively discretized Tikhonov minimizer, usual convergence and regularization results from the continuous setting can be recovered. As a matter of fact, it is shown that it suffices to use stationary points of the Tikhonov functional. The efficiency of the proposed method is demonstrated by means of numerical experiments. (paper)

  3. Using Tikhonov Regularization for Spatial Projections from CSR Regularized Spherical Harmonic GRACE Solutions

    Science.gov (United States)

    Save, H.; Bettadpur, S. V.

    2013-12-01

    It has been demonstrated before that using Tikhonov regularization produces spherical harmonic solutions from GRACE that have very little residual stripes while capturing all the signal observed by GRACE within the noise level. This paper demonstrates a two-step process and uses Tikhonov regularization to remove the residual stripes in the CSR regularized spherical harmonic coefficients when computing the spatial projections. We discuss methods to produce mass anomaly grids that have no stripe features while satisfying the necessary condition of capturing all observed signal within the GRACE noise level.

  4. Extreme values, regular variation and point processes

    CERN Document Server

    Resnick, Sidney I

    1987-01-01

    Extremes Values, Regular Variation and Point Processes is a readable and efficient account of the fundamental mathematical and stochastic process techniques needed to study the behavior of extreme values of phenomena based on independent and identically distributed random variables and vectors It presents a coherent treatment of the distributional and sample path fundamental properties of extremes and records It emphasizes the core primacy of three topics necessary for understanding extremes the analytical theory of regularly varying functions; the probabilistic theory of point processes and random measures; and the link to asymptotic distribution approximations provided by the theory of weak convergence of probability measures in metric spaces The book is self-contained and requires an introductory measure-theoretic course in probability as a prerequisite Almost all sections have an extensive list of exercises which extend developments in the text, offer alternate approaches, test mastery and provide for enj...

  5. Stream Processing Using Grammars and Regular Expressions

    DEFF Research Database (Denmark)

    Rasmussen, Ulrik Terp

    disambiguation. The first algorithm operates in two passes in a semi-streaming fashion, using a constant amount of working memory and an auxiliary tape storage which is written in the first pass and consumed by the second. The second algorithm is a single-pass and optimally streaming algorithm which outputs...... as much of the parse tree as is semantically possible based on the input prefix read so far, and resorts to buffering as many symbols as is required to resolve the next choice. Optimality is obtained by performing a PSPACE-complete pre-analysis on the regular expression. In the second part we present...... Kleenex, a language for expressing high-performance streaming string processing programs as regular grammars with embedded semantic actions, and its compilation to streaming string transducers with worst-case linear-time performance. Its underlying theory is based on transducer decomposition into oracle...

  6. Describing chaotic attractors: Regular and perpetual points

    Science.gov (United States)

    Dudkowski, Dawid; Prasad, Awadhesh; Kapitaniak, Tomasz

    2018-03-01

    We study the concepts of regular and perpetual points for describing the behavior of chaotic attractors in dynamical systems. The idea of these points, which have been recently introduced to theoretical investigations, is thoroughly discussed and extended into new types of models. We analyze the correlation between regular and perpetual points, as well as their relation with phase space, showing the potential usefulness of both types of points in the qualitative description of co-existing states. The ability of perpetual points in finding attractors is indicated, along with its potential cause. The location of chaotic trajectories and sets of considered points is investigated and the study on the stability of systems is shown. The statistical analysis of the observing desired states is performed. We focus on various types of dynamical systems, i.e., chaotic flows with self-excited and hidden attractors, forced mechanical models, and semiconductor superlattices, exhibiting the universality of appearance of the observed patterns and relations.

  7. Chaos regularization of quantum tunneling rates

    International Nuclear Information System (INIS)

    Pecora, Louis M.; Wu Dongho; Lee, Hoshik; Antonsen, Thomas; Lee, Ming-Jer; Ott, Edward

    2011-01-01

    Quantum tunneling rates through a barrier separating two-dimensional, symmetric, double-well potentials are shown to depend on the classical dynamics of the billiard trajectories in each well and, hence, on the shape of the wells. For shapes that lead to regular (integrable) classical dynamics the tunneling rates fluctuate greatly with eigenenergies of the states sometimes by over two orders of magnitude. Contrarily, shapes that lead to completely chaotic trajectories lead to tunneling rates whose fluctuations are greatly reduced, a phenomenon we call regularization of tunneling rates. We show that a random-plane-wave theory of tunneling accounts for the mean tunneling rates and the small fluctuation variances for the chaotic systems.

  8. Least square regularized regression in sum space.

    Science.gov (United States)

    Xu, Yong-Li; Chen, Di-Rong; Li, Han-Xiong; Liu, Lu

    2013-04-01

    This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed. We measure the complexity of the sum space by its covering number and demonstrate that the covering number can be bounded by the product of the covering numbers of basic RKHSs. For sum space of RKHSs with Gaussian kernels, by choosing appropriate parameters, we tradeoff the sample error and regularization error, and obtain a polynomial learning rate, which is better than that in any single RKHS. The utility of this method is illustrated with two simulated data sets and five real-life databases.

  9. Contour Propagation With Riemannian Elasticity Regularization

    DEFF Research Database (Denmark)

    Bjerre, Troels; Hansen, Mads Fogtmann; Sapru, W.

    2011-01-01

    Purpose/Objective(s): Adaptive techniques allow for correction of spatial changes during the time course of the fractionated radiotherapy. Spatial changes include tumor shrinkage and weight loss, causing tissue deformation and residual positional errors even after translational and rotational image...... the planning CT onto the rescans and correcting to reflect actual anatomical changes. For deformable registration, a free-form, multi-level, B-spline deformation model with Riemannian elasticity, penalizing non-rigid local deformations, and volumetric changes, was used. Regularization parameters was defined...... on the original delineation and tissue deformation in the time course between scans form a better starting point than rigid propagation. There was no significant difference of locally and globally defined regularization. The method used in the present study suggests that deformed contours need to be reviewed...

  10. Thin accretion disk around regular black hole

    Directory of Open Access Journals (Sweden)

    QIU Tianqi

    2014-08-01

    Full Text Available The Penrose′s cosmic censorship conjecture says that naked singularities do not exist in nature.So,it seems reasonable to further conjecture that not even a singularity exists in nature.In this paper,a regular black hole without singularity is studied in detail,especially on its thin accretion disk,energy flux,radiation temperature and accretion efficiency.It is found that the interaction of regular black hole is stronger than that of the Schwarzschild black hole. Furthermore,the thin accretion will be more efficiency to lost energy while the mass of black hole decreased. These particular properties may be used to distinguish between black holes.

  11. Convex nonnegative matrix factorization with manifold regularization.

    Science.gov (United States)

    Hu, Wenjun; Choi, Kup-Sze; Wang, Peiliang; Jiang, Yunliang; Wang, Shitong

    2015-03-01

    Nonnegative Matrix Factorization (NMF) has been extensively applied in many areas, including computer vision, pattern recognition, text mining, and signal processing. However, nonnegative entries are usually required for the data matrix in NMF, which limits its application. Besides, while the basis and encoding vectors obtained by NMF can represent the original data in low dimension, the representations do not always reflect the intrinsic geometric structure embedded in the data. Motivated by manifold learning and Convex NMF (CNMF), we propose a novel matrix factorization method called Graph Regularized and Convex Nonnegative Matrix Factorization (GCNMF) by introducing a graph regularized term into CNMF. The proposed matrix factorization technique not only inherits the intrinsic low-dimensional manifold structure, but also allows the processing of mixed-sign data matrix. Clustering experiments on nonnegative and mixed-sign real-world data sets are conducted to demonstrate the effectiveness of the proposed method. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Regular black hole in three dimensions

    OpenAIRE

    Myung, Yun Soo; Yoon, Myungseok

    2008-01-01

    We find a new black hole in three dimensional anti-de Sitter space by introducing an anisotropic perfect fluid inspired by the noncommutative black hole. This is a regular black hole with two horizons. We compare thermodynamics of this black hole with that of non-rotating BTZ black hole. The first-law of thermodynamics is not compatible with the Bekenstein-Hawking entropy.

  13. Sparse regularization for force identification using dictionaries

    Science.gov (United States)

    Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng

    2016-04-01

    The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.

  14. Analytic stochastic regularization and gauge theories

    International Nuclear Information System (INIS)

    Abdalla, E.; Gomes, M.; Lima-Santos, A.

    1987-04-01

    We prove that analytic stochatic regularization braks gauge invariance. This is done by an explicit one loop calculation of the two three and four point vertex functions of the gluon field in scalar chromodynamics, which turns out not to be geuge invariant. We analyse the counter term structure, Langevin equations and the construction of composite operators in the general framework of stochastic quantization. (author) [pt

  15. Preconditioners for regularized saddle point matrices

    Czech Academy of Sciences Publication Activity Database

    Axelsson, Owe

    2011-01-01

    Roč. 19, č. 2 (2011), s. 91-112 ISSN 1570-2820 Institutional research plan: CEZ:AV0Z30860518 Keywords : saddle point matrices * preconditioning * regularization * eigenvalue clustering Subject RIV: BA - General Mathematics Impact factor: 0.533, year: 2011 http://www.degruyter.com/view/j/jnma.2011.19.issue-2/jnum.2011.005/jnum.2011.005. xml

  16. Analytic stochastic regularization: gauge and supersymmetry theories

    International Nuclear Information System (INIS)

    Abdalla, M.C.B.

    1988-01-01

    Analytic stochastic regularization for gauge and supersymmetric theories is considered. Gauge invariance in spinor and scalar QCD is verified to brak fown by an explicit one loop computation of the two, theree and four point vertex function of the gluon field. As a result, non gauge invariant counterterms must be added. However, in the supersymmetric multiplets there is a cancellation rendering the counterterms gauge invariant. The calculation is considered at one loop order. (author) [pt

  17. Minimal length uncertainty relation and ultraviolet regularization

    Science.gov (United States)

    Kempf, Achim; Mangano, Gianpiero

    1997-06-01

    Studies in string theory and quantum gravity suggest the existence of a finite lower limit Δx0 to the possible resolution of distances, at the latest on the scale of the Planck length of 10-35 m. Within the framework of the Euclidean path integral we explicitly show ultraviolet regularization in field theory through this short distance structure. Both rotation and translation invariance can be preserved. An example is studied in detail.

  18. Regularity and chaos in cavity QED

    International Nuclear Information System (INIS)

    Bastarrachea-Magnani, Miguel Angel; López-del-Carpio, Baldemar; Chávez-Carlos, Jorge; Lerma-Hernández, Sergio; Hirsch, Jorge G

    2017-01-01

    The interaction of a quantized electromagnetic field in a cavity with a set of two-level atoms inside it can be described with algebraic Hamiltonians of increasing complexity, from the Rabi to the Dicke models. Their algebraic character allows, through the use of coherent states, a semiclassical description in phase space, where the non-integrable Dicke model has regions associated with regular and chaotic motion. The appearance of classical chaos can be quantified calculating the largest Lyapunov exponent over the whole available phase space for a given energy. In the quantum regime, employing efficient diagonalization techniques, we are able to perform a detailed quantitative study of the regular and chaotic regions, where the quantum participation ratio (P R ) of coherent states on the eigenenergy basis plays a role equivalent to the Lyapunov exponent. It is noted that, in the thermodynamic limit, dividing the participation ratio by the number of atoms leads to a positive value in chaotic regions, while it tends to zero in the regular ones. (paper)

  19. Solution path for manifold regularized semisupervised classification.

    Science.gov (United States)

    Wang, Gang; Wang, Fei; Chen, Tao; Yeung, Dit-Yan; Lochovsky, Frederick H

    2012-04-01

    Traditional learning algorithms use only labeled data for training. However, labeled examples are often difficult or time consuming to obtain since they require substantial human labeling efforts. On the other hand, unlabeled data are often relatively easy to collect. Semisupervised learning addresses this problem by using large quantities of unlabeled data with labeled data to build better learning algorithms. In this paper, we use the manifold regularization approach to formulate the semisupervised learning problem where a regularization framework which balances a tradeoff between loss and penalty is established. We investigate different implementations of the loss function and identify the methods which have the least computational expense. The regularization hyperparameter, which determines the balance between loss and penalty, is crucial to model selection. Accordingly, we derive an algorithm that can fit the entire path of solutions for every value of the hyperparameter. Its computational complexity after preprocessing is quadratic only in the number of labeled examples rather than the total number of labeled and unlabeled examples.

  20. Regularizations: different recipes for identical situations

    International Nuclear Information System (INIS)

    Gambin, E.; Lobo, C.O.; Battistel, O.A.

    2004-03-01

    We present a discussion where the choice of the regularization procedure and the routing for the internal lines momenta are put at the same level of arbitrariness in the analysis of Ward identities involving simple and well-known problems in QFT. They are the complex self-interacting scalar field and two simple models where the SVV and AVV process are pertinent. We show that, in all these problems, the conditions to symmetry relations preservation are put in terms of the same combination of divergent Feynman integrals, which are evaluated in the context of a very general calculational strategy, concerning the manipulations and calculations involving divergences. Within the adopted strategy, all the arbitrariness intrinsic to the problem are still maintained in the final results and, consequently, a perfect map can be obtained with the corresponding results of the traditional regularization techniques. We show that, when we require an universal interpretation for the arbitrariness involved, in order to get consistency with all stated physical constraints, a strong condition is imposed for regularizations which automatically eliminates the ambiguities associated to the routing of the internal lines momenta of loops. The conclusion is clean and sound: the association between ambiguities and unavoidable symmetry violations in Ward identities cannot be maintained if an unique recipe is required for identical situations in the evaluation of divergent physical amplitudes. (author)

  1. Sparsity regularization for parameter identification problems

    International Nuclear Information System (INIS)

    Jin, Bangti; Maass, Peter

    2012-01-01

    The investigation of regularization schemes with sparsity promoting penalty terms has been one of the dominant topics in the field of inverse problems over the last years, and Tikhonov functionals with ℓ p -penalty terms for 1 ⩽ p ⩽ 2 have been studied extensively. The first investigations focused on regularization properties of the minimizers of such functionals with linear operators and on iteration schemes for approximating the minimizers. These results were quickly transferred to nonlinear operator equations, including nonsmooth operators and more general function space settings. The latest results on regularization properties additionally assume a sparse representation of the true solution as well as generalized source conditions, which yield some surprising and optimal convergence rates. The regularization theory with ℓ p sparsity constraints is relatively complete in this setting; see the first part of this review. In contrast, the development of efficient numerical schemes for approximating minimizers of Tikhonov functionals with sparsity constraints for nonlinear operators is still ongoing. The basic iterated soft shrinkage approach has been extended in several directions and semi-smooth Newton methods are becoming applicable in this field. In particular, the extension to more general non-convex, non-differentiable functionals by variational principles leads to a variety of generalized iteration schemes. We focus on such iteration schemes in the second part of this review. A major part of this survey is devoted to applying sparsity constrained regularization techniques to parameter identification problems for partial differential equations, which we regard as the prototypical setting for nonlinear inverse problems. Parameter identification problems exhibit different levels of complexity and we aim at characterizing a hierarchy of such problems. The operator defining these inverse problems is the parameter-to-state mapping. We first summarize some

  2. Learning Sparse Visual Representations with Leaky Capped Norm Regularizers

    OpenAIRE

    Wangni, Jianqiao; Lin, Dahua

    2017-01-01

    Sparsity inducing regularization is an important part for learning over-complete visual representations. Despite the popularity of $\\ell_1$ regularization, in this paper, we investigate the usage of non-convex regularizations in this problem. Our contribution consists of three parts. First, we propose the leaky capped norm regularization (LCNR), which allows model weights below a certain threshold to be regularized more strongly as opposed to those above, therefore imposes strong sparsity and...

  3. Temporal regularity of the environment drives time perception

    OpenAIRE

    van Rijn, H; Rhodes, D; Di Luca, M

    2016-01-01

    It’s reasonable to assume that a regularly paced sequence should be perceived as regular, but here we show that perceived regularity depends on the context in which the sequence is embedded. We presented one group of participants with perceptually regularly paced sequences, and another group of participants with mostly irregularly paced sequences (75% irregular, 25% regular). The timing of the final stimulus in each sequence could be var- ied. In one experiment, we asked whether the last stim...

  4. Regularized Biot-Savart Laws for Modeling Magnetic Flux Ropes

    Science.gov (United States)

    Titov, Viacheslav; Downs, Cooper; Mikic, Zoran; Torok, Tibor; Linker, Jon A.

    2017-08-01

    Many existing models assume that magnetic flux ropes play a key role in solar flares and coronal mass ejections (CMEs). It is therefore important to develop efficient methods for constructing flux-rope configurations constrained by observed magnetic data and the initial morphology of CMEs. As our new step in this direction, we have derived and implemented a compact analytical form that represents the magnetic field of a thin flux rope with an axis of arbitrary shape and a circular cross-section. This form implies that the flux rope carries axial current I and axial flux F, so that the respective magnetic field is a curl of the sum of toroidal and poloidal vector potentials proportional to I and F, respectively. The vector potentials are expressed in terms of Biot-Savart laws whose kernels are regularized at the rope axis. We regularized them in such a way that for a straight-line axis the form provides a cylindrical force-free flux rope with a parabolic profile of the axial current density. So far, we set the shape of the rope axis by tracking the polarity inversion lines of observed magnetograms and estimating its height and other parameters of the rope from a calculated potential field above these lines. In spite of this heuristic approach, we were able to successfully construct pre-eruption configurations for the 2009 February13 and 2011 October 1 CME events. These applications demonstrate that our regularized Biot-Savart laws are indeed a very flexible and efficient method for energizing initial configurations in MHD simulations of CMEs. We discuss possible ways of optimizing the axis paths and other extensions of the method in order to make it more useful and robust.Research supported by NSF, NASA's HSR and LWS Programs, and AFOSR.

  5. Regular-, irregular-, and pseudo-character processing in Chinese: The regularity effect in normal adult readers

    Directory of Open Access Journals (Sweden)

    Dustin Kai Yan Lau

    2014-03-01

    Full Text Available Background Unlike alphabetic languages, Chinese uses a logographic script. However, the pronunciation of many character’s phonetic radical has the same pronunciation as the character as a whole. These are considered regular characters and can be read through a lexical non-semantic route (Weekes & Chen, 1999. Pseudocharacters are another way to study this non-semantic route. A pseudocharacter is the combination of existing semantic and phonetic radicals in their legal positions resulting in a non-existing character (Ho, Chan, Chung, Lee, & Tsang, 2007. Pseudocharacters can be pronounced by direct derivation from the sound of its phonetic radical. Conversely, if the pronunciation of a character does not follow that of the phonetic radical, it is considered as irregular and can only be correctly read through the lexical-semantic route. The aim of the current investigation was to examine reading aloud in normal adults. We hypothesized that the regularity effect, previously described for alphabetical scripts and acquired dyslexic patients of Chinese (Weekes & Chen, 1999; Wu, Liu, Sun, Chromik, & Zhang, 2014, would also be present in normal adult Chinese readers. Method Participants. Thirty (50% female native Hong Kong Cantonese speakers with a mean age of 19.6 years and a mean education of 12.9 years. Stimuli. Sixty regular-, 60 irregular-, and 60 pseudo-characters (with at least 75% of name agreement in Chinese were matched by initial phoneme, number of strokes and family size. Additionally, regular- and irregular-characters were matched by frequency (low and consistency. Procedure. Each participant was asked to read aloud the stimuli presented on a laptop using the DMDX software. The order of stimuli presentation was randomized. Data analysis. ANOVAs were carried out by participants and items with RTs and errors as dependent variables and type of stimuli (regular-, irregular- and pseudo-character as repeated measures (F1 or between subject

  6. The use of regularization in inferential measurements

    International Nuclear Information System (INIS)

    Hines, J. Wesley; Gribok, Andrei V.; Attieh, Ibrahim; Uhrig, Robert E.

    1999-01-01

    Inferential sensing is the prediction of a plant variable through the use of correlated plant variables. A correct prediction of the variable can be used to monitor sensors for drift or other failures making periodic instrument calibrations unnecessary. This move from periodic to condition based maintenance can reduce costs and increase the reliability of the instrument. Having accurate, reliable measurements is important for signals that may impact safety or profitability. This paper investigates how collinearity adversely affects inferential sensing by making the results inconsistent and unrepeatable; and presents regularization as a potential solution (author) (ml)

  7. Regularization ambiguities in loop quantum gravity

    International Nuclear Information System (INIS)

    Perez, Alejandro

    2006-01-01

    One of the main achievements of loop quantum gravity is the consistent quantization of the analog of the Wheeler-DeWitt equation which is free of ultraviolet divergences. However, ambiguities associated to the intermediate regularization procedure lead to an apparently infinite set of possible theories. The absence of an UV problem--the existence of well-behaved regularization of the constraints--is intimately linked with the ambiguities arising in the quantum theory. Among these ambiguities is the one associated to the SU(2) unitary representation used in the diffeomorphism covariant 'point-splitting' regularization of the nonlinear functionals of the connection. This ambiguity is labeled by a half-integer m and, here, it is referred to as the m ambiguity. The aim of this paper is to investigate the important implications of this ambiguity. We first study 2+1 gravity (and more generally BF theory) quantized in the canonical formulation of loop quantum gravity. Only when the regularization of the quantum constraints is performed in terms of the fundamental representation of the gauge group does one obtain the usual topological quantum field theory as a result. In all other cases unphysical local degrees of freedom arise at the level of the regulated theory that conspire against the existence of the continuum limit. This shows that there is a clear-cut choice in the quantization of the constraints in 2+1 loop quantum gravity. We then analyze the effects of the ambiguity in 3+1 gravity exhibiting the existence of spurious solutions for higher representation quantizations of the Hamiltonian constraint. Although the analysis is not complete in 3+1 dimensions - due to the difficulties associated to the definition of the physical inner product - it provides evidence supporting the definitions quantum dynamics of loop quantum gravity in terms of the fundamental representation of the gauge group as the only consistent possibilities. If the gauge group is SO(3) we find

  8. Effort variation regularization in sound field reproduction

    DEFF Research Database (Denmark)

    Stefanakis, Nick; Jacobsen, Finn; Sarris, Ioannis

    2010-01-01

    In this paper, active control is used in order to reproduce a given sound field in an extended spatial region. A method is proposed which minimizes the reproduction error at a number of control positions with the reproduction sources holding a certain relation within their complex strengths......), and adaptive wave field synthesis (AWFS), both under free-field conditions and in reverberant rooms. It is shown that effort variation regularization overcomes the problems associated with small spaces and with a low ratio of direct to reverberant energy, improving thus the reproduction accuracy...

  9. New regularities in mass spectra of hadrons

    International Nuclear Information System (INIS)

    Kajdalov, A.B.

    1989-01-01

    The properties of bosonic and baryonic Regge trajectories for hadrons composed of light quarks are considered. Experimental data agree with an existence of daughter trajectories consistent with string models. It is pointed out that the parity doubling for baryonic trajectories, observed experimentally, is not understood in the existing quark models. Mass spectrum of bosons and baryons indicates to an approximate supersymmetry in the mass region M>1 GeV. These regularities indicates to a high degree of symmetry for the dynamics in the confinement region. 8 refs.; 5 figs

  10. Total-variation regularization with bound constraints

    International Nuclear Information System (INIS)

    Chartrand, Rick; Wohlberg, Brendt

    2009-01-01

    We present a new algorithm for bound-constrained total-variation (TV) regularization that in comparison with its predecessors is simple, fast, and flexible. We use a splitting approach to decouple TV minimization from enforcing the constraints. Consequently, existing TV solvers can be employed with minimal alteration. This also makes the approach straightforward to generalize to any situation where TV can be applied. We consider deblurring of images with Gaussian or salt-and-pepper noise, as well as Abel inversion of radiographs with Poisson noise. We incorporate previous iterative reweighting algorithms to solve the TV portion.

  11. Bayesian regularization of diffusion tensor images

    DEFF Research Database (Denmark)

    Frandsen, Jesper; Hobolth, Asger; Østergaard, Leif

    2007-01-01

    Diffusion tensor imaging (DTI) is a powerful tool in the study of the course of nerve fibre bundles in the human brain. Using DTI, the local fibre orientation in each image voxel can be described by a diffusion tensor which is constructed from local measurements of diffusion coefficients along...... several directions. The measured diffusion coefficients and thereby the diffusion tensors are subject to noise, leading to possibly flawed representations of the three dimensional fibre bundles. In this paper we develop a Bayesian procedure for regularizing the diffusion tensor field, fully utilizing...

  12. Indefinite metric and regularization of electrodynamics

    International Nuclear Information System (INIS)

    Gaudin, M.

    1984-06-01

    The invariant regularization of Pauli and Villars in quantum electrodynamics can be considered as deriving from a local and causal lagrangian theory for spin 1/2 bosons, by introducing an indefinite metric and a condition on the allowed states similar to the Lorentz condition. The consequences are the asymptotic freedom of the photon's propagator. We present a calcultion of the effective charge to the fourth order in the coupling as a function of the auxiliary masses, the theory avoiding all mass divergencies to this order [fr

  13. Strategies for regular segmented reductions on GPU

    DEFF Research Database (Denmark)

    Larsen, Rasmus Wriedt; Henriksen, Troels

    2017-01-01

    We present and evaluate an implementation technique for regular segmented reductions on GPUs. Existing techniques tend to be either consistent in performance but relatively inefficient in absolute terms, or optimised for specific workloads and thereby exhibiting bad performance for certain input...... is in the context of the Futhark compiler, the implementation technique is applicable to any library or language that has a need for segmented reductions. We evaluate the technique on four microbenchmarks, two of which we also compare to implementations in the CUB library for GPU programming, as well as on two...

  14. Regularity theory for mean-field game systems

    CERN Document Server

    Gomes, Diogo A; Voskanyan, Vardan

    2016-01-01

    Beginning with a concise introduction to the theory of mean-field games (MFGs), this book presents the key elements of the regularity theory for MFGs. It then introduces a series of techniques for well-posedness in the context of mean-field problems, including stationary and time-dependent MFGs, subquadratic and superquadratic MFG formulations, and distinct classes of mean-field couplings. It also explores stationary and time-dependent MFGs through a series of a-priori estimates for solutions of the Hamilton-Jacobi and Fokker-Planck equation. It shows sophisticated a-priori systems derived using a range of analytical techniques, and builds on previous results to explain classical solutions. The final chapter discusses the potential applications, models and natural extensions of MFGs. As MFGs connect common problems in pure mathematics, engineering, economics and data management, this book is a valuable resource for researchers and graduate students in these fields.

  15. Regularity Theory for Mean-Field Game Systems

    KAUST Repository

    Gomes, Diogo A.

    2016-09-14

    Beginning with a concise introduction to the theory of mean-field games (MFGs), this book presents the key elements of the regularity theory for MFGs. It then introduces a series of techniques for well-posedness in the context of mean-field problems, including stationary and time-dependent MFGs, subquadratic and superquadratic MFG formulations, and distinct classes of mean-field couplings. It also explores stationary and time-dependent MFGs through a series of a-priori estimates for solutions of the Hamilton-Jacobi and Fokker-Planck equation. It shows sophisticated a-priori systems derived using a range of analytical techniques, and builds on previous results to explain classical solutions. The final chapter discusses the potential applications, models and natural extensions of MFGs. As MFGs connect common problems in pure mathematics, engineering, economics and data management, this book is a valuable resource for researchers and graduate students in these fields.

  16. Regularized multivariate regression models with skew-t error distributions

    KAUST Repository

    Chen, Lianfu

    2014-06-01

    We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.

  17. Regularity Theory for Mean-Field Game Systems

    KAUST Repository

    Gomes, Diogo A.; Pimentel, Edgard A.; Voskanyan, Vardan K.

    2016-01-01

    Beginning with a concise introduction to the theory of mean-field games (MFGs), this book presents the key elements of the regularity theory for MFGs. It then introduces a series of techniques for well-posedness in the context of mean-field problems, including stationary and time-dependent MFGs, subquadratic and superquadratic MFG formulations, and distinct classes of mean-field couplings. It also explores stationary and time-dependent MFGs through a series of a-priori estimates for solutions of the Hamilton-Jacobi and Fokker-Planck equation. It shows sophisticated a-priori systems derived using a range of analytical techniques, and builds on previous results to explain classical solutions. The final chapter discusses the potential applications, models and natural extensions of MFGs. As MFGs connect common problems in pure mathematics, engineering, economics and data management, this book is a valuable resource for researchers and graduate students in these fields.

  18. Efficient regularization with wavelet sparsity constraints in photoacoustic tomography

    Science.gov (United States)

    Frikel, Jürgen; Haltmeier, Markus

    2018-02-01

    In this paper, we consider the reconstruction problem of photoacoustic tomography (PAT) with a flat observation surface. We develop a direct reconstruction method that employs regularization with wavelet sparsity constraints. To that end, we derive a wavelet-vaguelette decomposition (WVD) for the PAT forward operator and a corresponding explicit reconstruction formula in the case of exact data. In the case of noisy data, we combine the WVD reconstruction formula with soft-thresholding, which yields a spatially adaptive estimation method. We demonstrate that our method is statistically optimal for white random noise if the unknown function is assumed to lie in any Besov-ball. We present generalizations of this approach and, in particular, we discuss the combination of PAT-vaguelette soft-thresholding with a total variation (TV) prior. We also provide an efficient implementation of the PAT-vaguelette transform that leads to fast image reconstruction algorithms supported by numerical results.

  19. Emotion regulation deficits in regular marijuana users.

    Science.gov (United States)

    Zimmermann, Kaeli; Walz, Christina; Derckx, Raissa T; Kendrick, Keith M; Weber, Bernd; Dore, Bruce; Ochsner, Kevin N; Hurlemann, René; Becker, Benjamin

    2017-08-01

    Effective regulation of negative affective states has been associated with mental health. Impaired regulation of negative affect represents a risk factor for dysfunctional coping mechanisms such as drug use and thus could contribute to the initiation and development of problematic substance use. This study investigated behavioral and neural indices of emotion regulation in regular marijuana users (n = 23) and demographically matched nonusing controls (n = 20) by means of an fMRI cognitive emotion regulation (reappraisal) paradigm. Relative to nonusing controls, marijuana users demonstrated increased neural activity in a bilateral frontal network comprising precentral, middle cingulate, and supplementary motor regions during reappraisal of negative affect (P marijuana users relative to controls. Together, the present findings could reflect an unsuccessful attempt of compensatory recruitment of additional neural resources in the context of disrupted amygdala-prefrontal interaction during volitional emotion regulation in marijuana users. As such, impaired volitional regulation of negative affect might represent a consequence of, or risk factor for, regular marijuana use. Hum Brain Mapp 38:4270-4279, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  20. Supporting Regularized Logistic Regression Privately and Efficiently

    Science.gov (United States)

    Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

    2016-01-01

    As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738

  1. Supporting Regularized Logistic Regression Privately and Efficiently.

    Science.gov (United States)

    Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

    2016-01-01

    As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.

  2. Multiple graph regularized nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan

    2013-10-01

    Non-negative matrix factorization (NMF) has been widely used as a data representation method based on components. To overcome the disadvantage of NMF in failing to consider the manifold structure of a data set, graph regularized NMF (GrNMF) has been proposed by Cai et al. by constructing an affinity graph and searching for a matrix factorization that respects graph structure. Selecting a graph model and its corresponding parameters is critical for this strategy. This process is usually carried out by cross-validation or discrete grid search, which are time consuming and prone to overfitting. In this paper, we propose a GrNMF, called MultiGrNMF, in which the intrinsic manifold is approximated by a linear combination of several graphs with different models and parameters inspired by ensemble manifold regularization. Factorization metrics and linear combination coefficients of graphs are determined simultaneously within a unified object function. They are alternately optimized in an iterative algorithm, thus resulting in a novel data representation algorithm. Extensive experiments on a protein subcellular localization task and an Alzheimer\\'s disease diagnosis task demonstrate the effectiveness of the proposed algorithm. © 2013 Elsevier Ltd. All rights reserved.

  3. Accelerating Large Data Analysis By Exploiting Regularities

    Science.gov (United States)

    Moran, Patrick J.; Ellsworth, David

    2003-01-01

    We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical to Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid-body transformation of another. Our algorithms can also discover rigid-body motion of meshes in time-series data. Next, we describe a data model where we can utilize the results from the discovery process in order to accelerate large data visualizations. Where possible, we replace general curvilinear zones with rectilinear or cylindrical zones. In rigid-body motion cases we replace a time-series of meshes with a transformed mesh object where a reference mesh is dynamically transformed based on a given time value in order to satisfy geometry requests, on demand. The data model enables us to make these substitutions and dynamic transformations transparently with respect to the visualization algorithms. We present results with large data sets where we combine our mesh replacement and transformation techniques with out-of-core paging in order to achieve significant speed-ups in analysis.

  4. Supporting Regularized Logistic Regression Privately and Efficiently.

    Directory of Open Access Journals (Sweden)

    Wenfa Li

    Full Text Available As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.

  5. Multiview Hessian regularization for image annotation.

    Science.gov (United States)

    Liu, Weifeng; Tao, Dacheng

    2013-07-01

    The rapid development of computer hardware and Internet technology makes large scale data dependent models computationally tractable, and opens a bright avenue for annotating images through innovative machine learning algorithms. Semisupervised learning (SSL) therefore received intensive attention in recent years and was successfully deployed in image annotation. One representative work in SSL is Laplacian regularization (LR), which smoothes the conditional distribution for classification along the manifold encoded in the graph Laplacian, however, it is observed that LR biases the classification function toward a constant function that possibly results in poor generalization. In addition, LR is developed to handle uniformly distributed data (or single-view data), although instances or objects, such as images and videos, are usually represented by multiview features, such as color, shape, and texture. In this paper, we present multiview Hessian regularization (mHR) to address the above two problems in LR-based image annotation. In particular, mHR optimally combines multiple HR, each of which is obtained from a particular view of instances, and steers the classification function that varies linearly along the data manifold. We apply mHR to kernel least squares and support vector machines as two examples for image annotation. Extensive experiments on the PASCAL VOC'07 dataset validate the effectiveness of mHR by comparing it with baseline algorithms, including LR and HR.

  6. EIT image reconstruction with four dimensional regularization.

    Science.gov (United States)

    Dai, Tao; Soleimani, Manuchehr; Adler, Andy

    2008-09-01

    Electrical impedance tomography (EIT) reconstructs internal impedance images of the body from electrical measurements on body surface. The temporal resolution of EIT data can be very high, although the spatial resolution of the images is relatively low. Most EIT reconstruction algorithms calculate images from data frames independently, although data are actually highly correlated especially in high speed EIT systems. This paper proposes a 4-D EIT image reconstruction for functional EIT. The new approach is developed to directly use prior models of the temporal correlations among images and 3-D spatial correlations among image elements. A fast algorithm is also developed to reconstruct the regularized images. Image reconstruction is posed in terms of an augmented image and measurement vector which are concatenated from a specific number of previous and future frames. The reconstruction is then based on an augmented regularization matrix which reflects the a priori constraints on temporal and 3-D spatial correlations of image elements. A temporal factor reflecting the relative strength of the image correlation is objectively calculated from measurement data. Results show that image reconstruction models which account for inter-element correlations, in both space and time, show improved resolution and noise performance, in comparison to simpler image models.

  7. Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression.

    Science.gov (United States)

    Zhen, Xiantong; Yu, Mengyang; Islam, Ali; Bhaduri, Mousumi; Chan, Ian; Li, Shuo

    2017-09-01

    Multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis. However, due to the huge image variability and ambiguity, it is fundamentally challenging to handle the highly complex input-target relationship of multioutput regression, especially with indiscriminate high-dimensional representations. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm for multioutput regression, which can establish discriminative and compact feature representations to improve the multivariate estimation performance. The SDL is formulated as generalized low-rank approximations of matrices with a supervised manifold regularization. The SDL is able to simultaneously extract discriminative features closely related to multivariate targets and remove irrelevant and redundant information by transforming raw features into a new low-dimensional space aligned to targets. The achieved discriminative while compact descriptor largely reduces the variability and ambiguity for multioutput regression, which enables more accurate and efficient multivariate estimation. We conduct extensive evaluation of the proposed SDL on both synthetic data and real-world multioutput regression tasks for both computer vision and medical image analysis. Experimental results have shown that the proposed SDL can achieve high multivariate estimation accuracy on all tasks and largely outperforms the algorithms in the state of the arts. Our method establishes a novel SDL framework for multioutput regression, which can be widely used to boost the performance in different applications.

  8. Accretion onto some well-known regular black holes

    International Nuclear Information System (INIS)

    Jawad, Abdul; Shahzad, M.U.

    2016-01-01

    In this work, we discuss the accretion onto static spherically symmetric regular black holes for specific choices of the equation of state parameter. The underlying regular black holes are charged regular black holes using the Fermi-Dirac distribution, logistic distribution, nonlinear electrodynamics, respectively, and Kehagias-Sftesos asymptotically flat regular black holes. We obtain the critical radius, critical speed, and squared sound speed during the accretion process near the regular black holes. We also study the behavior of radial velocity, energy density, and the rate of change of the mass for each of the regular black holes. (orig.)

  9. Accretion onto some well-known regular black holes

    Energy Technology Data Exchange (ETDEWEB)

    Jawad, Abdul; Shahzad, M.U. [COMSATS Institute of Information Technology, Department of Mathematics, Lahore (Pakistan)

    2016-03-15

    In this work, we discuss the accretion onto static spherically symmetric regular black holes for specific choices of the equation of state parameter. The underlying regular black holes are charged regular black holes using the Fermi-Dirac distribution, logistic distribution, nonlinear electrodynamics, respectively, and Kehagias-Sftesos asymptotically flat regular black holes. We obtain the critical radius, critical speed, and squared sound speed during the accretion process near the regular black holes. We also study the behavior of radial velocity, energy density, and the rate of change of the mass for each of the regular black holes. (orig.)

  10. Accretion onto some well-known regular black holes

    Science.gov (United States)

    Jawad, Abdul; Shahzad, M. Umair

    2016-03-01

    In this work, we discuss the accretion onto static spherically symmetric regular black holes for specific choices of the equation of state parameter. The underlying regular black holes are charged regular black holes using the Fermi-Dirac distribution, logistic distribution, nonlinear electrodynamics, respectively, and Kehagias-Sftesos asymptotically flat regular black holes. We obtain the critical radius, critical speed, and squared sound speed during the accretion process near the regular black holes. We also study the behavior of radial velocity, energy density, and the rate of change of the mass for each of the regular black holes.

  11. System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques

    DEFF Research Database (Denmark)

    Chen, Tianshi; Andersen, Martin Skovgaard; Ljung, Lennart

    2014-01-01

    Model estimation and structure detection with short data records are two issues that receive increasing interests in System Identification. In this paper, a multiple kernel-based regularization method is proposed to handle those issues. Multiple kernels are conic combinations of fixed kernels...

  12. A healthy diet and regular exercise are essential for type-2 diabetes sufferers

    OpenAIRE

    Sutphin, Michael D.

    2007-01-01

    The American Diabetes Association estimates that more than 20.8 million children and adults in the United States have diabetes and many others are at risk for developing the chronic disease. But with proper nutrition and regular physical activity, a diabetic or borderline diabetic can still maintain a healthy lifestyle.

  13. Laplacian embedded regression for scalable manifold regularization.

    Science.gov (United States)

    Chen, Lin; Tsang, Ivor W; Xu, Dong

    2012-06-01

    Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical foundations for a large family of SSL algorithms, such as Laplacian support vector machine (LapSVM) and Laplacian regularized least squares (LapRLS). However, most of these algorithms are limited to small scale problems due to the high computational cost of the matrix inversion operation involved in the optimization problem. In this paper, we propose a novel framework called Laplacian embedded regression by introducing an intermediate decision variable into the manifold regularization framework. By using ∈-insensitive loss, we obtain the Laplacian embedded support vector regression (LapESVR) algorithm, which inherits the sparse solution from SVR. Also, we derive Laplacian embedded RLS (LapERLS) corresponding to RLS under the proposed framework. Both LapESVR and LapERLS possess a simpler form of a transformed kernel, which is the summation of the original kernel and a graph kernel that captures the manifold structure. The benefits of the transformed kernel are two-fold: (1) we can deal with the original kernel matrix and the graph Laplacian matrix in the graph kernel separately and (2) if the graph Laplacian matrix is sparse, we only need to perform the inverse operation for a sparse matrix, which is much more efficient when compared with that for a dense one. Inspired by kernel principal component analysis, we further propose to project the introduced decision variable into a subspace spanned by a few eigenvectors of the graph Laplacian matrix in order to better reflect the data manifold, as well as accelerate the calculation of the graph kernel, allowing our methods to efficiently and effectively cope with large scale SSL problems. Extensive experiments on both toy and real

  14. Correction of engineering servicing regularity of transporttechnological machines in operational process

    Science.gov (United States)

    Makarova, A. N.; Makarov, E. I.; Zakharov, N. S.

    2018-03-01

    In the article, the issue of correcting engineering servicing regularity on the basis of actual dependability data of cars in operation is considered. The purpose of the conducted research is to increase dependability of transport-technological machines by correcting engineering servicing regularity. The subject of the research is the mechanism of engineering servicing regularity influence on reliability measure. On the basis of the analysis of researches carried out before, a method of nonparametric estimation of car failure measure according to actual time-to-failure data was chosen. A possibility of describing the failure measure dependence on engineering servicing regularity by various mathematical models is considered. It is proven that the exponential model is the most appropriate for that purpose. The obtained results can be used as a separate method of engineering servicing regularity correction with certain operational conditions taken into account, as well as for the technical-economical and economical-stochastic methods improvement. Thus, on the basis of the conducted researches, a method of engineering servicing regularity correction of transport-technological machines in the operational process was developed. The use of that method will allow decreasing the number of failures.

  15. Analysis of regularized inversion of data corrupted by white Gaussian noise

    International Nuclear Information System (INIS)

    Kekkonen, Hanne; Lassas, Matti; Siltanen, Samuli

    2014-01-01

    Tikhonov regularization is studied in the case of linear pseudodifferential operator as the forward map and additive white Gaussian noise as the measurement error. The measurement model for an unknown function u(x) is m(x) = Au(x) + δ ε (x), where δ > 0 is the noise magnitude. If ε was an L 2 -function, Tikhonov regularization gives an estimate T α (m) = u∈H r arg min { ||Au-m|| L 2 2 + α||u|| H r 2 } for u where α = α(δ) is the regularization parameter. Here penalization of the Sobolev norm ||u|| H r covers the cases of standard Tikhonov regularization (r = 0) and first derivative penalty (r = 1). Realizations of white Gaussian noise are almost never in L 2 , but do belong to H s with probability one if s < 0 is small enough. A modification of Tikhonov regularization theory is presented, covering the case of white Gaussian measurement noise. Furthermore, the convergence of regularized reconstructions to the correct solution as δ → 0 is proven in appropriate function spaces using microlocal analysis. The convergence of the related finite-dimensional problems to the infinite-dimensional problem is also analysed. (paper)

  16. SU-E-J-67: Evaluation of Breathing Patterns for Respiratory-Gated Radiation Therapy Using Respiration Regularity Index

    International Nuclear Information System (INIS)

    Cheong, K; Lee, M; Kang, S; Yoon, J; Park, S; Hwang, T; Kim, H; Kim, K; Han, T; Bae, H

    2014-01-01

    Purpose: Despite the importance of accurately estimating the respiration regularity of a patient in motion compensation treatment, an effective and simply applicable method has rarely been reported. The authors propose a simple respiration regularity index based on parameters derived from a correspondingly simplified respiration model. Methods: In order to simplify a patient's breathing pattern while preserving the data's intrinsic properties, we defined a respiration model as a power of cosine form with a baseline drift. According to this respiration formula, breathing-pattern fluctuation could be explained using four factors: sample standard deviation of respiration period, sample standard deviation of amplitude and the results of simple regression of the baseline drift (slope and standard deviation of residuals of a respiration signal. Overall irregularity (δ) was defined as a Euclidean norm of newly derived variable using principal component analysis (PCA) for the four fluctuation parameters. Finally, the proposed respiration regularity index was defined as ρ=ln(1+(1/ δ))/2, a higher ρ indicating a more regular breathing pattern. Subsequently, we applied it to simulated and clinical respiration signals from real-time position management (RPM; Varian Medical Systems, Palo Alto, CA) and investigated respiration regularity. Moreover, correlations between the regularity of the first session and the remaining fractions were investigated using Pearson's correlation coefficient. Results: The respiration regularity was determined based on ρ; patients with ρ 0.7 was suitable for respiratory-gated radiation therapy (RGRT). Fluctuations in breathing cycle and amplitude were especially determinative of ρ. If the respiration regularity of a patient's first session was known, it could be estimated through subsequent sessions. Conclusions: Respiration regularity could be objectively determined using a respiration regularity index, ρ. Such single-index testing of

  17. Regularities of radiorace formation in yeasts

    International Nuclear Information System (INIS)

    Korogodin, V.I.; Bliznik, K.M.; Kapul'tsevich, Yu.G.; Petin, V.G.; Akademiya Meditsinskikh Nauk SSSR, Obninsk. Nauchno-Issledovatel'skij Inst. Meditsinskoj Radiologii)

    1977-01-01

    Two strains of diploid yeast, namely, Saccharomyces ellipsoides, Megri 139-B, isolated under natural conditions, and Saccharomyces cerevisiae 5a x 3Bα, heterozygous by genes ade 1 and ade 2, were exposed to γ-quanta of Co 60 . The content of cells-saltants forming colonies with changed morphology, that of the nonviable cells, cells that are respiration mutants, and cells-recombinants by gene ade 1 and ade 2, has been determined. A certain regularity has been revealed in the distribution among the colonies of cells of the four types mentioned above: the higher the content of cells of some one of the types, the higher that of the cells having other hereditary changes

  18. Regularization destriping of remote sensing imagery

    Science.gov (United States)

    Basnayake, Ranil; Bollt, Erik; Tufillaro, Nicholas; Sun, Jie; Gierach, Michelle

    2017-07-01

    We illustrate the utility of variational destriping for ocean color images from both multispectral and hyperspectral sensors. In particular, we examine data from a filter spectrometer, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (NPP) orbiter, and an airborne grating spectrometer, the Jet Population Laboratory's (JPL) hyperspectral Portable Remote Imaging Spectrometer (PRISM) sensor. We solve the destriping problem using a variational regularization method by giving weights spatially to preserve the other features of the image during the destriping process. The target functional penalizes the neighborhood of stripes (strictly, directionally uniform features) while promoting data fidelity, and the functional is minimized by solving the Euler-Lagrange equations with an explicit finite-difference scheme. We show the accuracy of our method from a benchmark data set which represents the sea surface temperature off the coast of Oregon, USA. Technical details, such as how to impose continuity across data gaps using inpainting, are also described.

  19. The Regularity of Optimal Irrigation Patterns

    Science.gov (United States)

    Morel, Jean-Michel; Santambrogio, Filippo

    2010-02-01

    A branched structure is observable in draining and irrigation systems, in electric power supply systems, and in natural objects like blood vessels, the river basins or the trees. Recent approaches of these networks derive their branched structure from an energy functional whose essential feature is to favor wide routes. Given a flow s in a river, a road, a tube or a wire, the transportation cost per unit length is supposed in these models to be proportional to s α with 0 measure is the Lebesgue density on a smooth open set and the irrigating measure is a single source. In that case we prove that all branches of optimal irrigation trees satisfy an elliptic equation and that their curvature is a bounded measure. In consequence all branching points in the network have a tangent cone made of a finite number of segments, and all other points have a tangent. An explicit counterexample disproves these regularity properties for non-Lebesgue irrigated measures.

  20. Singular tachyon kinks from regular profiles

    International Nuclear Information System (INIS)

    Copeland, E.J.; Saffin, P.M.; Steer, D.A.

    2003-01-01

    We demonstrate how Sen's singular kink solution of the Born-Infeld tachyon action can be constructed by taking the appropriate limit of initially regular profiles. It is shown that the order in which different limits are taken plays an important role in determining whether or not such a solution is obtained for a wide class of potentials. Indeed, by introducing a small parameter into the action, we are able circumvent the results of a recent paper which derived two conditions on the asymptotic tachyon potential such that the singular kink could be recovered in the large amplitude limit of periodic solutions. We show that this is explained by the non-commuting nature of two limits, and that Sen's solution is recovered if the order of the limits is chosen appropriately

  1. Two-pass greedy regular expression parsing

    DEFF Research Database (Denmark)

    Grathwohl, Niels Bjørn Bugge; Henglein, Fritz; Nielsen, Lasse

    2013-01-01

    We present new algorithms for producing greedy parses for regular expressions (REs) in a semi-streaming fashion. Our lean-log algorithm executes in time O(mn) for REs of size m and input strings of size n and outputs a compact bit-coded parse tree representation. It improves on previous algorithms...... by: operating in only 2 passes; using only O(m) words of random-access memory (independent of n); requiring only kn bits of sequentially written and read log storage, where k ... and not requiring it to be stored at all. Previous RE parsing algorithms do not scale linearly with input size, or require substantially more log storage and employ 3 passes where the first consists of reversing the input, or do not or are not known to produce a greedy parse. The performance of our unoptimized C...

  2. Discriminative Elastic-Net Regularized Linear Regression.

    Science.gov (United States)

    Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen

    2017-03-01

    In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.

  3. Dimensional regularization and analytical continuation at finite temperature

    International Nuclear Information System (INIS)

    Chen Xiangjun; Liu Lianshou

    1998-01-01

    The relationship between dimensional regularization and analytical continuation of infrared divergent integrals at finite temperature is discussed and a method of regularization of infrared divergent integrals and infrared divergent sums is given

  4. Regular Generalized Star Star closed sets in Bitopological Spaces

    OpenAIRE

    K. Kannan; D. Narasimhan; K. Chandrasekhara Rao; R. Ravikumar

    2011-01-01

    The aim of this paper is to introduce the concepts of τ1τ2-regular generalized star star closed sets , τ1τ2-regular generalized star star open sets and study their basic properties in bitopological spaces.

  5. Exclusion of children with intellectual disabilities from regular ...

    African Journals Online (AJOL)

    Study investigated why teachers exclude children with intellectual disability from the regular classrooms in Nigeria. Participants were, 169 regular teachers randomly selected from Oyo and Ogun states. Questionnaire was used to collect data result revealed that 57.4% regular teachers could not cope with children with ID ...

  6. 39 CFR 6.1 - Regular meetings, annual meeting.

    Science.gov (United States)

    2010-07-01

    ... 39 Postal Service 1 2010-07-01 2010-07-01 false Regular meetings, annual meeting. 6.1 Section 6.1 Postal Service UNITED STATES POSTAL SERVICE THE BOARD OF GOVERNORS OF THE U.S. POSTAL SERVICE MEETINGS (ARTICLE VI) § 6.1 Regular meetings, annual meeting. The Board shall meet regularly on a schedule...

  7. Recognition Memory for Novel Stimuli: The Structural Regularity Hypothesis

    Science.gov (United States)

    Cleary, Anne M.; Morris, Alison L.; Langley, Moses M.

    2007-01-01

    Early studies of human memory suggest that adherence to a known structural regularity (e.g., orthographic regularity) benefits memory for an otherwise novel stimulus (e.g., G. A. Miller, 1958). However, a more recent study suggests that structural regularity can lead to an increase in false-positive responses on recognition memory tests (B. W. A.…

  8. 5 CFR 551.421 - Regular working hours.

    Science.gov (United States)

    2010-01-01

    ... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Regular working hours. 551.421 Section... Activities § 551.421 Regular working hours. (a) Under the Act there is no requirement that a Federal employee... distinction based on whether the activity is performed by an employee during regular working hours or outside...

  9. 20 CFR 226.35 - Deductions from regular annuity rate.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 1 2010-04-01 2010-04-01 false Deductions from regular annuity rate. 226.35... COMPUTING EMPLOYEE, SPOUSE, AND DIVORCED SPOUSE ANNUITIES Computing a Spouse or Divorced Spouse Annuity § 226.35 Deductions from regular annuity rate. The regular annuity rate of the spouse and divorced...

  10. 20 CFR 226.34 - Divorced spouse regular annuity rate.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 1 2010-04-01 2010-04-01 false Divorced spouse regular annuity rate. 226.34... COMPUTING EMPLOYEE, SPOUSE, AND DIVORCED SPOUSE ANNUITIES Computing a Spouse or Divorced Spouse Annuity § 226.34 Divorced spouse regular annuity rate. The regular annuity rate of a divorced spouse is equal to...

  11. 20 CFR 226.14 - Employee regular annuity rate.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 1 2010-04-01 2010-04-01 false Employee regular annuity rate. 226.14 Section... COMPUTING EMPLOYEE, SPOUSE, AND DIVORCED SPOUSE ANNUITIES Computing an Employee Annuity § 226.14 Employee regular annuity rate. The regular annuity rate payable to the employee is the total of the employee tier I...

  12. An Iterative Regularization Method for Identifying the Source Term in a Second Order Differential Equation

    Directory of Open Access Journals (Sweden)

    Fairouz Zouyed

    2015-01-01

    Full Text Available This paper discusses the inverse problem of determining an unknown source in a second order differential equation from measured final data. This problem is ill-posed; that is, the solution (if it exists does not depend continuously on the data. In order to solve the considered problem, an iterative method is proposed. Using this method a regularized solution is constructed and an a priori error estimate between the exact solution and its regularized approximation is obtained. Moreover, numerical results are presented to illustrate the accuracy and efficiency of this method.

  13. A soft double regularization approach to parametric blind image deconvolution.

    Science.gov (United States)

    Chen, Li; Yap, Kim-Hui

    2005-05-01

    This paper proposes a blind image deconvolution scheme based on soft integration of parametric blur structures. Conventional blind image deconvolution methods encounter a difficult dilemma of either imposing stringent and inflexible preconditions on the problem formulation or experiencing poor restoration results due to lack of information. This paper attempts to address this issue by assessing the relevance of parametric blur information, and incorporating the knowledge into the parametric double regularization (PDR) scheme. The PDR method assumes that the actual blur satisfies up to a certain degree of parametric structure, as there are many well-known parametric blurs in practical applications. Further, it can be tailored flexibly to include other blur types if some prior parametric knowledge of the blur is available. A manifold soft parametric modeling technique is proposed to generate the blur manifolds, and estimate the fuzzy blur structure. The PDR scheme involves the development of the meaningful cost function, the estimation of blur support and structure, and the optimization of the cost function. Experimental results show that it is effective in restoring degraded images under different environments.

  14. Accreting fluids onto regular black holes via Hamiltonian approach

    Energy Technology Data Exchange (ETDEWEB)

    Jawad, Abdul [COMSATS Institute of Information Technology, Department of Mathematics, Lahore (Pakistan); Shahzad, M.U. [COMSATS Institute of Information Technology, Department of Mathematics, Lahore (Pakistan); University of Central Punjab, CAMS, UCP Business School, Lahore (Pakistan)

    2017-08-15

    We investigate the accretion of test fluids onto regular black holes such as Kehagias-Sfetsos black holes and regular black holes with Dagum distribution function. We analyze the accretion process when different test fluids are falling onto these regular black holes. The accreting fluid is being classified through the equation of state according to the features of regular black holes. The behavior of fluid flow and the existence of sonic points is being checked for these regular black holes. It is noted that the three-velocity depends on critical points and the equation of state parameter on phase space. (orig.)

  15. On the regularized fermionic projector of the vacuum

    Science.gov (United States)

    Finster, Felix

    2008-03-01

    We construct families of fermionic projectors with spherically symmetric regularization, which satisfy the condition of a distributional MP-product. The method is to analyze regularization tails with a power law or logarithmic scaling in composite expressions in the fermionic projector. The resulting regularizations break the Lorentz symmetry and give rise to a multilayer structure of the fermionic projector near the light cone. Furthermore, we construct regularizations which go beyond the distributional MP-product in that they yield additional distributional contributions supported at the origin. The remaining freedom for the regularization parameters and the consequences for the normalization of the fermionic states are discussed.

  16. On the regularized fermionic projector of the vacuum

    International Nuclear Information System (INIS)

    Finster, Felix

    2008-01-01

    We construct families of fermionic projectors with spherically symmetric regularization, which satisfy the condition of a distributional MP-product. The method is to analyze regularization tails with a power law or logarithmic scaling in composite expressions in the fermionic projector. The resulting regularizations break the Lorentz symmetry and give rise to a multilayer structure of the fermionic projector near the light cone. Furthermore, we construct regularizations which go beyond the distributional MP-product in that they yield additional distributional contributions supported at the origin. The remaining freedom for the regularization parameters and the consequences for the normalization of the fermionic states are discussed

  17. MRI reconstruction with joint global regularization and transform learning.

    Science.gov (United States)

    Tanc, A Korhan; Eksioglu, Ender M

    2016-10-01

    Sparsity based regularization has been a popular approach to remedy the measurement scarcity in image reconstruction. Recently, sparsifying transforms learned from image patches have been utilized as an effective regularizer for the Magnetic Resonance Imaging (MRI) reconstruction. Here, we infuse additional global regularization terms to the patch-based transform learning. We develop an algorithm to solve the resulting novel cost function, which includes both patchwise and global regularization terms. Extensive simulation results indicate that the introduced mixed approach has improved MRI reconstruction performance, when compared to the algorithms which use either of the patchwise transform learning or global regularization terms alone. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Efficient rare-event simulation for multiple jump events in regularly varying random walks and compound Poisson processes

    NARCIS (Netherlands)

    B. Chen (Bohan); J. Blanchet; C.H. Rhee (Chang-Han); A.P. Zwart (Bert)

    2017-01-01

    textabstractWe propose a class of strongly efficient rare event simulation estimators for random walks and compound Poisson processes with a regularly varying increment/jump-size distribution in a general large deviations regime. Our estimator is based on an importance sampling strategy that hinges

  19. Manifold Regularized Experimental Design for Active Learning.

    Science.gov (United States)

    Zhang, Lining; Shum, Hubert P H; Shao, Ling

    2016-12-02

    Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

  20. Regularization of the Coulomb scattering problem

    International Nuclear Information System (INIS)

    Baryshevskii, V.G.; Feranchuk, I.D.; Kats, P.B.

    2004-01-01

    The exact solution of the Schroedinger equation for the Coulomb potential is used within the scope of both stationary and time-dependent scattering theories in order to find the parameters which determine the regularization of the Rutherford cross section when the scattering angle tends to zero but the distance r from the center remains finite. The angular distribution of the particles scattered in the Coulomb field is studied on rather a large but finite distance r from the center. It is shown that the standard asymptotic representation of the wave functions is inapplicable in the case when small scattering angles are considered. The unitary property of the scattering matrix is analyzed and the 'optical' theorem for this case is discussed. The total and transport cross sections for scattering the particle by the Coulomb center proved to be finite values and are calculated in the analytical form. It is shown that the effects under consideration can be important for the observed characteristics of the transport processes in semiconductors which are determined by the electron and hole scattering by the field of charged impurity centers

  1. Color correction optimization with hue regularization

    Science.gov (United States)

    Zhang, Heng; Liu, Huaping; Quan, Shuxue

    2011-01-01

    Previous work has suggested that observers are capable of judging the quality of an image without any knowledge of the original scene. When no reference is available, observers can extract the apparent objects in an image and compare them with the typical colors of similar objects recalled from their memories. Some generally agreed upon research results indicate that although perfect colorimetric rendering is not conspicuous and color errors can be well tolerated, the appropriate rendition of certain memory colors such as skin, grass, and sky is an important factor in the overall perceived image quality. These colors are appreciated in a fairly consistent manner and are memorized with slightly different hues and higher color saturation. The aim of color correction for a digital color pipeline is to transform the image data from a device dependent color space to a target color space, usually through a color correction matrix which in its most basic form is optimized through linear regressions between the two sets of data in two color spaces in the sense of minimized Euclidean color error. Unfortunately, this method could result in objectionable distortions if the color error biased certain colors undesirably. In this paper, we propose a color correction optimization method with preferred color reproduction in mind through hue regularization and present some experimental results.

  2. Wave dynamics of regular and chaotic rays

    International Nuclear Information System (INIS)

    McDonald, S.W.

    1983-09-01

    In order to investigate general relationships between waves and rays in chaotic systems, I study the eigenfunctions and spectrum of a simple model, the two-dimensional Helmholtz equation in a stadium boundary, for which the rays are ergodic. Statistical measurements are performed so that the apparent randomness of the stadium modes can be quantitatively contrasted with the familiar regularities observed for the modes in a circular boundary (with integrable rays). The local spatial autocorrelation of the eigenfunctions is constructed in order to indirectly test theoretical predictions for the nature of the Wigner distribution corresponding to chaotic waves. A portion of the large-eigenvalue spectrum is computed and reported in an appendix; the probability distribution of successive level spacings is analyzed and compared with theoretical predictions. The two principal conclusions are: 1) waves associated with chaotic rays may exhibit randomly situated localized regions of high intensity; 2) the Wigner function for these waves may depart significantly from being uniformly distributed over the surface of constant frequency in the ray phase space

  3. Regularities and irregularities in order flow data

    Science.gov (United States)

    Theissen, Martin; Krause, Sebastian M.; Guhr, Thomas

    2017-11-01

    We identify and analyze statistical regularities and irregularities in the recent order flow of different NASDAQ stocks, focusing on the positions where orders are placed in the order book. This includes limit orders being placed outside of the spread, inside the spread and (effective) market orders. Based on the pairwise comparison of the order flow of different stocks, we perform a clustering of stocks into groups with similar behavior. This is useful to assess systemic aspects of stock price dynamics. We find that limit order placement inside the spread is strongly determined by the dynamics of the spread size. Most orders, however, arrive outside of the spread. While for some stocks order placement on or next to the quotes is dominating, deeper price levels are more important for other stocks. As market orders are usually adjusted to the quote volume, the impact of market orders depends on the order book structure, which we find to be quite diverse among the analyzed stocks as a result of the way limit order placement takes place.

  4. Library search with regular reflectance IR spectra

    International Nuclear Information System (INIS)

    Staat, H.; Korte, E.H.; Lampen, P.

    1989-01-01

    Characterisation in situ for coatings and other surface layers is generally favourable, but a prerequisite for precious items such as art objects. In infrared spectroscopy only reflection techniques are applicable here. However for attenuated total reflection (ATR) it is difficult to obtain the necessary optical contact of the crystal with the sample, when the latter is not perfectly plane or flexible. The measurement of diffuse reflectance demands a scattering sample and usually the reflectance is very poor. Therefore in most cases one is left with regular reflectance. Such spectra consist of dispersion-like feature instead of bands impeding their interpretation in the way the analyst is used to. Furthermore for computer search in common spectral libraries compiled from transmittance or absorbance spectra a transformation of the reflectance spectra is needed. The correct conversion is based on the Kramers-Kronig transformation. This somewhat time - consuming procedure can be speeded up by using appropriate approximations. A coarser conversion may be obtained from the first derivative of the reflectance spectrum which resembles the second derivative of a transmittance spectrum. The resulting distorted spectra can still be used successfully for the search in peak table libraries. Experiences with both transformations are presented. (author)

  5. Regularities of praseodymium oxide dissolution in acids

    International Nuclear Information System (INIS)

    Savin, V.D.; Elyutin, A.V.; Mikhajlova, N.P.; Eremenko, Z.V.; Opolchenova, N.L.

    1989-01-01

    The regularities of Pr 2 O 3 , Pr 2 O 5 and Pr(OH) 3 interaction with inorganic acids are studied. pH of the solution and oxidation-reduction potential registrated at 20±1 deg C are the working parameters of studies. It is found that the amount of all oxides dissolved increase in the series of acids - nitric, hydrochloric and sulfuric, in this case for hydrochloric and sulfuric acid it increases in the series of oxides Pr 2 O 3 , Pr 2 O 5 and Pr(OH) 3 . It is noted that Pr 2 O 5 has a high value of oxidation-reduction potential with a positive sign in the whole disslolving range. A low positive value of a redox potential during dissolving belongs to Pr(OH) 3 and in the case of Pr 2 O 3 dissloving redox potential is negative. The schemes of dissolving processes which do not agree with classical assumptions are presented

  6. Regular expressions compiler and some applications

    International Nuclear Information System (INIS)

    Saldana A, H.

    1978-01-01

    We deal with high level programming language of a Regular Expressions Compiler (REC). The first chapter is an introduction in which the history of the REC development and the problems related to its numerous applicatons are described. The syntactic and sematic rules as well as the language features are discussed just after the introduction. Concerning the applicatons as examples, an adaptation is given in order to solve numerical problems and another for the data manipulation. The last chapter is an exposition of ideas and techniques about the compiler construction. Examples of the adaptation to numerical problems show the applications to education, vector analysis, quantum mechanics, physics, mathematics and other sciences. The rudiments of an operating system for a minicomputer are the examples of the adaptation to symbolic data manipulaton. REC is a programming language that could be applied to solve problems in almost any human activity. Handling of computer graphics, control equipment, research on languages, microprocessors and general research are some of the fields in which this programming language can be applied and developed. (author)

  7. Sparsity-regularized HMAX for visual recognition.

    Directory of Open Access Journals (Sweden)

    Xiaolin Hu

    Full Text Available About ten years ago, HMAX was proposed as a simple and biologically feasible model for object recognition, based on how the visual cortex processes information. However, the model does not encompass sparse firing, which is a hallmark of neurons at all stages of the visual pathway. The current paper presents an improved model, called sparse HMAX, which integrates sparse firing. This model is able to learn higher-level features of objects on unlabeled training images. Unlike most other deep learning models that explicitly address global structure of images in every layer, sparse HMAX addresses local to global structure gradually along the hierarchy by applying patch-based learning to the output of the previous layer. As a consequence, the learning method can be standard sparse coding (SSC or independent component analysis (ICA, two techniques deeply rooted in neuroscience. What makes SSC and ICA applicable at higher levels is the introduction of linear higher-order statistical regularities by max pooling. After training, high-level units display sparse, invariant selectivity for particular individuals or for image categories like those observed in human inferior temporal cortex (ITC and medial temporal lobe (MTL. Finally, on an image classification benchmark, sparse HMAX outperforms the original HMAX by a large margin, suggesting its great potential for computer vision.

  8. Quantum implications of a scale invariant regularization

    Science.gov (United States)

    Ghilencea, D. M.

    2018-04-01

    We study scale invariance at the quantum level in a perturbative approach. For a scale-invariant classical theory, the scalar potential is computed at a three-loop level while keeping manifest this symmetry. Spontaneous scale symmetry breaking is transmitted at a quantum level to the visible sector (of ϕ ) by the associated Goldstone mode (dilaton σ ), which enables a scale-invariant regularization and whose vacuum expectation value ⟨σ ⟩ generates the subtraction scale (μ ). While the hidden (σ ) and visible sector (ϕ ) are classically decoupled in d =4 due to an enhanced Poincaré symmetry, they interact through (a series of) evanescent couplings ∝ɛ , dictated by the scale invariance of the action in d =4 -2 ɛ . At the quantum level, these couplings generate new corrections to the potential, as scale-invariant nonpolynomial effective operators ϕ2 n +4/σ2 n. These are comparable in size to "standard" loop corrections and are important for values of ϕ close to ⟨σ ⟩. For n =1 , 2, the beta functions of their coefficient are computed at three loops. In the IR limit, dilaton fluctuations decouple, the effective operators are suppressed by large ⟨σ ⟩, and the effective potential becomes that of a renormalizable theory with explicit scale symmetry breaking by the DR scheme (of μ =constant).

  9. Regularities development of entrepreneurial structures in regions

    Directory of Open Access Journals (Sweden)

    Julia Semenovna Pinkovetskaya

    2012-12-01

    Full Text Available Consider regularities and tendencies for the three types of entrepreneurial structures — small enterprises, medium enterprises and individual entrepreneurs. The aim of the research was to confirm the possibilities of describing indicators of aggregate entrepreneurial structures with the use of normal law distribution functions. Presented proposed by the author the methodological approach and results of construction of the functions of the density distribution for the main indicators for the various objects: the Russian Federation, regions, as well as aggregates ofentrepreneurial structures, specialized in certain forms ofeconomic activity. All the developed functions, as shown by the logical and statistical analysis, are of high quality and well-approximate the original data. In general, the proposed methodological approach is versatile and can be used in further studies of aggregates of entrepreneurial structures. The received results can be applied in solving a wide range of problems justify the need for personnel and financial resources at the federal, regional and municipal levels, as well as the formation of plans and forecasts of development entrepreneurship and improvement of this sector of the economy.

  10. Analysis of the iteratively regularized Gauss-Newton method under a heuristic rule

    Science.gov (United States)

    Jin, Qinian; Wang, Wei

    2018-03-01

    The iteratively regularized Gauss-Newton method is one of the most prominent regularization methods for solving nonlinear ill-posed inverse problems when the data is corrupted by noise. In order to produce a useful approximate solution, this iterative method should be terminated properly. The existing a priori and a posteriori stopping rules require accurate information on the noise level, which may not be available or reliable in practical applications. In this paper we propose a heuristic selection rule for this regularization method, which requires no information on the noise level. By imposing certain conditions on the noise, we derive a posteriori error estimates on the approximate solutions under various source conditions. Furthermore, we establish a convergence result without using any source condition. Numerical results are presented to illustrate the performance of our heuristic selection rule.

  11. Partial regularity of weak solutions to a PDE system with cubic nonlinearity

    Science.gov (United States)

    Liu, Jian-Guo; Xu, Xiangsheng

    2018-04-01

    In this paper we investigate regularity properties of weak solutions to a PDE system that arises in the study of biological transport networks. The system consists of a possibly singular elliptic equation for the scalar pressure of the underlying biological network coupled to a diffusion equation for the conductance vector of the network. There are several different types of nonlinearities in the system. Of particular mathematical interest is a term that is a polynomial function of solutions and their partial derivatives and this polynomial function has degree three. That is, the system contains a cubic nonlinearity. Only weak solutions to the system have been shown to exist. The regularity theory for the system remains fundamentally incomplete. In particular, it is not known whether or not weak solutions develop singularities. In this paper we obtain a partial regularity theorem, which gives an estimate for the parabolic Hausdorff dimension of the set of possible singular points.

  12. Discharge regularity in the turtle posterior crista: comparisons between experiment and theory.

    Science.gov (United States)

    Goldberg, Jay M; Holt, Joseph C

    2013-12-01

    Intra-axonal recordings were made from bouton fibers near their termination in the turtle posterior crista. Spike discharge, miniature excitatory postsynaptic potentials (mEPSPs), and afterhyperpolarizations (AHPs) were monitored during resting activity in both regularly and irregularly discharging units. Quantal size (qsize) and quantal rate (qrate) were estimated by shot-noise theory. Theoretically, the ratio, σV/(dμV/dt), between synaptic noise (σV) and the slope of the mean voltage trajectory (dμV/dt) near threshold crossing should determine discharge regularity. AHPs are deeper and more prolonged in regular units; as a result, dμV/dt is larger, the more regular the discharge. The qsize is larger and qrate smaller in irregular units; these oppositely directed trends lead to little variation in σV with discharge regularity. Of the two variables, dμV/dt is much more influential than the nearly constant σV in determining regularity. Sinusoidal canal-duct indentations at 0.3 Hz led to modulations in spike discharge and synaptic voltage. Gain, the ratio between the amplitudes of the two modulations, and phase leads re indentation of both modulations are larger in irregular units. Gain variations parallel the sensitivity of the postsynaptic spike encoder, the set of conductances that converts synaptic input into spike discharge. Phase variations reflect both synaptic inputs to the encoder and postsynaptic processes. Experimental data were interpreted using a stochastic integrate-and-fire model. Advantages of an irregular discharge include an enhanced encoder gain and the prevention of nonlinear phase locking. Regular and irregular units are more efficient, respectively, in the encoding of low- and high-frequency head rotations, respectively.

  13. TRANSIENT LUNAR PHENOMENA: REGULARITY AND REALITY

    International Nuclear Information System (INIS)

    Crotts, Arlin P. S.

    2009-01-01

    Transient lunar phenomena (TLPs) have been reported for centuries, but their nature is largely unsettled, and even their existence as a coherent phenomenon is controversial. Nonetheless, TLP data show regularities in the observations; a key question is whether this structure is imposed by processes tied to the lunar surface, or by terrestrial atmospheric or human observer effects. I interrogate an extensive catalog of TLPs to gauge how human factors determine the distribution of TLP reports. The sample is grouped according to variables which should produce differing results if determining factors involve humans, and not reflecting phenomena tied to the lunar surface. Features dependent on human factors can then be excluded. Regardless of how the sample is split, the results are similar: ∼50% of reports originate from near Aristarchus, ∼16% from Plato, ∼6% from recent, major impacts (Copernicus, Kepler, Tycho, and Aristarchus), plus several at Grimaldi. Mare Crisium produces a robust signal in some cases (however, Crisium is too large for a 'feature' as defined). TLP count consistency for these features indicates that ∼80% of these may be real. Some commonly reported sites disappear from the robust averages, including Alphonsus, Ross D, and Gassendi. These reports begin almost exclusively after 1955, when TLPs became widely known and many more (and inexperienced) observers searched for TLPs. In a companion paper, we compare the spatial distribution of robust TLP sites to transient outgassing (seen by Apollo and Lunar Prospector instruments). To a high confidence, robust TLP sites and those of lunar outgassing correlate strongly, further arguing for the reality of TLPs.

  14. Elementary Particle Spectroscopy in Regular Solid Rewrite

    International Nuclear Information System (INIS)

    Trell, Erik

    2008-01-01

    The Nilpotent Universal Computer Rewrite System (NUCRS) has operationalized the radical ontological dilemma of Nothing at All versus Anything at All down to the ground recursive syntax and principal mathematical realisation of this categorical dichotomy as such and so governing all its sui generis modalities, leading to fulfilment of their individual terms and compass when the respective choice sequence operations are brought to closure. Focussing on the general grammar, NUCRS by pure logic and its algebraic notations hence bootstraps Quantum Mechanics, aware that it ''is the likely keystone of a fundamental computational foundation'' also for e.g. physics, molecular biology and neuroscience. The present work deals with classical geometry where morphology is the modality, and ventures that the ancient regular solids are its specific rewrite system, in effect extensively anticipating the detailed elementary particle spectroscopy, and further on to essential structures at large both over the inorganic and organic realms. The geodetic antipode to Nothing is extension, with natural eigenvector the endless straight line which when deployed according to the NUCRS as well as Plotelemeian topographic prescriptions forms a real three-dimensional eigenspace with cubical eigenelements where observed quark-skewed quantum-chromodynamical particle events self-generate as an Aristotelean phase transition between the straight and round extremes of absolute endlessness under the symmetry- and gauge-preserving, canonical coset decomposition SO(3)xO(5) of Lie algebra SU(3). The cubical eigen-space and eigen-elements are the parental state and frame, and the other solids are a range of transition matrix elements and portions adapting to the spherical root vector symmetries and so reproducibly reproducing the elementary particle spectroscopy, including a modular, truncated octahedron nano-composition of the Electron which piecemeal enter into molecular structures or compressed to each

  15. 3D first-arrival traveltime tomography with modified total variation regularization

    Science.gov (United States)

    Jiang, Wenbin; Zhang, Jie

    2018-02-01

    Three-dimensional (3D) seismic surveys have become a major tool in the exploration and exploitation of hydrocarbons. 3D seismic first-arrival traveltime tomography is a robust method for near-surface velocity estimation. A common approach for stabilizing the ill-posed inverse problem is to apply Tikhonov regularization to the inversion. However, the Tikhonov regularization method recovers smooth local structures while blurring the sharp features in the model solution. We present a 3D first-arrival traveltime tomography method with modified total variation (MTV) regularization to preserve sharp velocity contrasts and improve the accuracy of velocity inversion. To solve the minimization problem of the new traveltime tomography method, we decouple the original optimization problem into two following subproblems: a standard traveltime tomography problem with the traditional Tikhonov regularization and a L2 total variation problem. We apply the conjugate gradient method and split-Bregman iterative method to solve these two subproblems, respectively. Our synthetic examples show that the new method produces higher resolution models than the conventional traveltime tomography with Tikhonov regularization. We apply the technique to field data. The stacking section shows significant improvements with static corrections from the MTV traveltime tomography.

  16. Inverse problems with Poisson data: statistical regularization theory, applications and algorithms

    International Nuclear Information System (INIS)

    Hohage, Thorsten; Werner, Frank

    2016-01-01

    Inverse problems with Poisson data arise in many photonic imaging modalities in medicine, engineering and astronomy. The design of regularization methods and estimators for such problems has been studied intensively over the last two decades. In this review we give an overview of statistical regularization theory for such problems, the most important applications, and the most widely used algorithms. The focus is on variational regularization methods in the form of penalized maximum likelihood estimators, which can be analyzed in a general setup. Complementing a number of recent convergence rate results we will establish consistency results. Moreover, we discuss estimators based on a wavelet-vaguelette decomposition of the (necessarily linear) forward operator. As most prominent applications we briefly introduce Positron emission tomography, inverse problems in fluorescence microscopy, and phase retrieval problems. The computation of a penalized maximum likelihood estimator involves the solution of a (typically convex) minimization problem. We also review several efficient algorithms which have been proposed for such problems over the last five years. (topical review)

  17. Regularization of plurisubharmonic functions with a net of good points

    OpenAIRE

    Li, Long

    2017-01-01

    The purpose of this article is to present a new regularization technique of quasi-plurisubharmoinc functions on a compact Kaehler manifold. The idea is to regularize the function on local coordinate balls first, and then glue each piece together. Therefore, all the higher order terms in the complex Hessian of this regularization vanish at the center of each coordinate ball, and all the centers build a delta-net of the manifold eventually.

  18. Dimensional regularization in position space and a forest formula for regularized Epstein-Glaser renormalization

    International Nuclear Information System (INIS)

    Keller, Kai Johannes

    2010-04-01

    The present work contains a consistent formulation of the methods of dimensional regularization (DimReg) and minimal subtraction (MS) in Minkowski position space. The methods are implemented into the framework of perturbative Algebraic Quantum Field Theory (pAQFT). The developed methods are used to solve the Epstein-Glaser recursion for the construction of time-ordered products in all orders of causal perturbation theory. A solution is given in terms of a forest formula in the sense of Zimmermann. A relation to the alternative approach to renormalization theory using Hopf algebras is established. (orig.)

  19. Dimensional regularization in position space and a forest formula for regularized Epstein-Glaser renormalization

    Energy Technology Data Exchange (ETDEWEB)

    Keller, Kai Johannes

    2010-04-15

    The present work contains a consistent formulation of the methods of dimensional regularization (DimReg) and minimal subtraction (MS) in Minkowski position space. The methods are implemented into the framework of perturbative Algebraic Quantum Field Theory (pAQFT). The developed methods are used to solve the Epstein-Glaser recursion for the construction of time-ordered products in all orders of causal perturbation theory. A solution is given in terms of a forest formula in the sense of Zimmermann. A relation to the alternative approach to renormalization theory using Hopf algebras is established. (orig.)

  20. Estimation of Jump Tails

    DEFF Research Database (Denmark)

    Bollerslev, Tim; Todorov, Victor

    We propose a new and flexible non-parametric framework for estimating the jump tails of Itô semimartingale processes. The approach is based on a relatively simple-to-implement set of estimating equations associated with the compensator for the jump measure, or its "intensity", that only utilizes...... the weak assumption of regular variation in the jump tails, along with in-fill asymptotic arguments for uniquely identifying the "large" jumps from the data. The estimation allows for very general dynamic dependencies in the jump tails, and does not restrict the continuous part of the process...... and the temporal variation in the stochastic volatility. On implementing the new estimation procedure with actual high-frequency data for the S&P 500 aggregate market portfolio, we find strong evidence for richer and more complex dynamic dependencies in the jump tails than hitherto entertained in the literature....

  1. Higher order total variation regularization for EIT reconstruction.

    Science.gov (United States)

    Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Zhang, Fan; Mueller-Lisse, Ullrich; Moeller, Knut

    2018-01-08

    Electrical impedance tomography (EIT) attempts to reveal the conductivity distribution of a domain based on the electrical boundary condition. This is an ill-posed inverse problem; its solution is very unstable. Total variation (TV) regularization is one of the techniques commonly employed to stabilize reconstructions. However, it is well known that TV regularization induces staircase effects, which are not realistic in clinical applications. To reduce such artifacts, modified TV regularization terms considering a higher order differential operator were developed in several previous studies. One of them is called total generalized variation (TGV) regularization. TGV regularization has been successively applied in image processing in a regular grid context. In this study, we adapted TGV regularization to the finite element model (FEM) framework for EIT reconstruction. Reconstructions using simulation and clinical data were performed. First results indicate that, in comparison to TV regularization, TGV regularization promotes more realistic images. Graphical abstract Reconstructed conductivity changes located on selected vertical lines. For each of the reconstructed images as well as the ground truth image, conductivity changes located along the selected left and right vertical lines are plotted. In these plots, the notation GT in the legend stands for ground truth, TV stands for total variation method, and TGV stands for total generalized variation method. Reconstructed conductivity distributions from the GREIT algorithm are also demonstrated.

  2. Decay property of regularity-loss type for solutions in elastic solids with voids

    KAUST Repository

    Djouamai, Leila; Said-Houari, Belkacem

    2014-01-01

    In this paper, we consider the Cauchy problem for a system of elastic solids with voids. First, we show that a linear porous dissipation leads to decay rates of regularity-loss type of the solution. We show some decay estimates for initial data in Hs(R)∩L1(R). Furthermore, we prove that by restricting the initial data to be in Hs(R)∩L1,γ(R) and γ. ∈. [0, 1], we can derive faster decay estimates of the solution. Second, we show that by adding a viscoelastic damping term, then we gain the regularity of the solution and obtain the optimal decay rate. © 2013 Elsevier Ltd.

  3. From Matched Spatial Filtering towards the Fused Statistical Descriptive Regularization Method for Enhanced Radar Imaging

    Directory of Open Access Journals (Sweden)

    Shkvarko Yuriy

    2006-01-01

    Full Text Available We address a new approach to solve the ill-posed nonlinear inverse problem of high-resolution numerical reconstruction of the spatial spectrum pattern (SSP of the backscattered wavefield sources distributed over the remotely sensed scene. An array or synthesized array radar (SAR that employs digital data signal processing is considered. By exploiting the idea of combining the statistical minimum risk estimation paradigm with numerical descriptive regularization techniques, we address a new fused statistical descriptive regularization (SDR strategy for enhanced radar imaging. Pursuing such an approach, we establish a family of the SDR-related SSP estimators, that encompass a manifold of existing beamforming techniques ranging from traditional matched filter to robust and adaptive spatial filtering, and minimum variance methods.

  4. Regular-soda intake independent of weight status is associated with asthma among US high school students.

    Science.gov (United States)

    Park, Sohyun; Blanck, Heidi M; Sherry, Bettylou; Jones, Sherry Everett; Pan, Liping

    2013-01-01

    Limited research shows an inconclusive association between soda intake and asthma, potentially attributable to certain preservatives in sodas. This cross-sectional study examined the association between regular (nondiet)-soda intake and current asthma among a nationally representative sample of high school students. Analysis was based on the 2009 national Youth Risk Behavior Survey and included 15,960 students (grades 9 through 12) with data for both regular-soda intake and current asthma status. The outcome measure was current asthma (ie, told by doctor/nurse that they had asthma and still have asthma). The main exposure variable was regular-soda intake (ie, drank a can/bottle/glass of soda during the 7 days before the survey). Multivariable logistic regression was used to estimate the adjusted odds ratios for regular-soda intake with current asthma after controlling for age, sex, race/ethnicity, weight status, and current cigarette use. Overall, 10.8% of students had current asthma. In addition, 9.7% of students who did not drink regular soda had current asthma, and 14.7% of students who drank regular soda three or more times per day had current asthma. Compared with those who did not drink regular soda, odds of having current asthma were higher among students who drank regular soda two times per day (adjusted odds ratio=1.28; 95% CI 1.02 to 1.62) and three or more times per day (adjusted odds ratio=1.64; 95% CI 1.25 to 2.16). The association between high regular-soda intake and current asthma suggests efforts to reduce regular-soda intake among youth might have benefits beyond improving diet quality. However, this association needs additional research, such as a longitudinal examination. Published by Elsevier Inc.

  5. Utilitarian cycling in Belgium: a cross-sectional study in a sample of regular cyclists.

    OpenAIRE

    de Geus, B.; Degraeuwe, B.; Vandenbulcke, G.; INT PANIS, Luc; Thomas, I.; Aertsens, Joris; De Weerdt, Y.; Torfs, R.; Meeusen, R.

    2014-01-01

    Background: For an accurate estimation of health benefits and hazards of utilitarian cycling, a prospective collection of bicycle usage data (exposure) is fundamental. Individual and environmental correlates are necessary to guide health promotion and traffic safety issues. Firstly, this study aims to report on utilitarian bicycle usage in Belgium, using a prospective data collection in regular adult commuter cyclists. Secondly, the association is explored between the individual variation in ...

  6. Influence of the surface layer characteristics on the regularities of the cutting process

    Directory of Open Access Journals (Sweden)

    Krainev Dmitriy V.

    2017-01-01

    Full Text Available The article considers the influence of the surface layer characteristics on the regularities of the cutting process and the formation of the quality of the surface machined. This effect has been confirmed by the study results of the combined cutting method with advanced plastic deformation (APD. The work estimates the impact of the change in the surface layer properties on the forces and temperature of cutting, stability of the chip formation and quality parameters of the surface machined.

  7. Regular Breakfast and Blood Lead Levels among Preschool Children

    Directory of Open Access Journals (Sweden)

    Needleman Herbert

    2011-04-01

    Full Text Available Abstract Background Previous studies have shown that fasting increases lead absorption in the gastrointestinal tract of adults. Regular meals/snacks are recommended as a nutritional intervention for lead poisoning in children, but epidemiological evidence of links between fasting and blood lead levels (B-Pb is rare. The purpose of this study was to examine the association between eating a regular breakfast and B-Pb among children using data from the China Jintan Child Cohort Study. Methods Parents completed a questionnaire regarding children's breakfast-eating habit (regular or not, demographics, and food frequency. Whole blood samples were collected from 1,344 children for the measurements of B-Pb and micronutrients (iron, copper, zinc, calcium, and magnesium. B-Pb and other measures were compared between children with and without regular breakfast. Linear regression modeling was used to evaluate the association between regular breakfast and log-transformed B-Pb. The association between regular breakfast and risk of lead poisoning (B-Pb≥10 μg/dL was examined using logistic regression modeling. Results Median B-Pb among children who ate breakfast regularly and those who did not eat breakfast regularly were 6.1 μg/dL and 7.2 μg/dL, respectively. Eating breakfast was also associated with greater zinc blood levels. Adjusting for other relevant factors, the linear regression model revealed that eating breakfast regularly was significantly associated with lower B-Pb (beta = -0.10 units of log-transformed B-Pb compared with children who did not eat breakfast regularly, p = 0.02. Conclusion The present study provides some initial human data supporting the notion that eating a regular breakfast might reduce B-Pb in young children. To our knowledge, this is the first human study exploring the association between breakfast frequency and B-Pb in young children.

  8. Two-way regularization for MEG source reconstruction via multilevel coordinate descent

    KAUST Repository

    Siva Tian, Tian

    2013-12-01

    Magnetoencephalography (MEG) source reconstruction refers to the inverse problem of recovering the neural activity from the MEG time course measurements. A spatiotemporal two-way regularization (TWR) method was recently proposed by Tian et al. to solve this inverse problem and was shown to outperform several one-way regularization methods and spatiotemporal methods. This TWR method is a two-stage procedure that first obtains a raw estimate of the source signals and then refines the raw estimate to ensure spatial focality and temporal smoothness using spatiotemporal regularized matrix decomposition. Although proven to be effective, the performance of two-stage TWR depends on the quality of the raw estimate. In this paper we directly solve the MEG source reconstruction problem using a multivariate penalized regression where the number of variables is much larger than the number of cases. A special feature of this regression is that the regression coefficient matrix has a spatiotemporal two-way structure that naturally invites a two-way penalty. Making use of this structure, we develop a computationally efficient multilevel coordinate descent algorithm to implement the method. This new one-stage TWR method has shown its superiority to the two-stage TWR method in three simulation studies with different levels of complexity and a real-world MEG data analysis. © 2013 Wiley Periodicals, Inc., A Wiley Company.

  9. Chimeric mitochondrial peptides from contiguous regular and swinger RNA.

    Science.gov (United States)

    Seligmann, Hervé

    2016-01-01

    Previous mass spectrometry analyses described human mitochondrial peptides entirely translated from swinger RNAs, RNAs where polymerization systematically exchanged nucleotides. Exchanges follow one among 23 bijective transformation rules, nine symmetric exchanges (X ↔ Y, e.g. A ↔ C) and fourteen asymmetric exchanges (X → Y → Z → X, e.g. A → C → G → A), multiplying by 24 DNA's protein coding potential. Abrupt switches from regular to swinger polymerization produce chimeric RNAs. Here, human mitochondrial proteomic analyses assuming abrupt switches between regular and swinger transcriptions, detect chimeric peptides, encoded by part regular, part swinger RNA. Contiguous regular- and swinger-encoded residues within single peptides are stronger evidence for translation of swinger RNA than previously detected, entirely swinger-encoded peptides: regular parts are positive controls matched with contiguous swinger parts, increasing confidence in results. Chimeric peptides are 200 × rarer than swinger peptides (3/100,000 versus 6/1000). Among 186 peptides with > 8 residues for each regular and swinger parts, regular parts of eleven chimeric peptides correspond to six among the thirteen recognized, mitochondrial protein-coding genes. Chimeric peptides matching partly regular proteins are rarer and less expressed than chimeric peptides matching non-coding sequences, suggesting targeted degradation of misfolded proteins. Present results strengthen hypotheses that the short mitogenome encodes far more proteins than hitherto assumed. Entirely swinger-encoded proteins could exist.

  10. Tur\\'an type inequalities for regular Coulomb wave functions

    OpenAIRE

    Baricz, Árpád

    2015-01-01

    Tur\\'an, Mitrinovi\\'c-Adamovi\\'c and Wilker type inequalities are deduced for regular Coulomb wave functions. The proofs are based on a Mittag-Leffler expansion for the regular Coulomb wave function, which may be of independent interest. Moreover, some complete monotonicity results concerning the Coulomb zeta functions and some interlacing properties of the zeros of Coulomb wave functions are given.

  11. Regularization and Complexity Control in Feed-forward Networks

    OpenAIRE

    Bishop, C. M.

    1995-01-01

    In this paper we consider four alternative approaches to complexity control in feed-forward networks based respectively on architecture selection, regularization, early stopping, and training with noise. We show that there are close similarities between these approaches and we argue that, for most practical applications, the technique of regularization should be the method of choice.

  12. Optimal Embeddings of Distance Regular Graphs into Euclidean Spaces

    NARCIS (Netherlands)

    F. Vallentin (Frank)

    2008-01-01

    htmlabstractIn this paper we give a lower bound for the least distortion embedding of a distance regular graph into Euclidean space. We use the lower bound for finding the least distortion for Hamming graphs, Johnson graphs, and all strongly regular graphs. Our technique involves semidefinite

  13. Degree-regular triangulations of torus and Klein bottle

    Indian Academy of Sciences (India)

    Home; Journals; Proceedings – Mathematical Sciences; Volume 115; Issue 3 ... A triangulation of a connected closed surface is called degree-regular if each of its vertices have the same degree. ... In [5], Datta and Nilakantan have classified all the degree-regular triangulations of closed surfaces on at most 11 vertices.

  14. Adaptive Regularization of Neural Networks Using Conjugate Gradient

    DEFF Research Database (Denmark)

    Goutte, Cyril; Larsen, Jan

    1998-01-01

    Andersen et al. (1997) and Larsen et al. (1996, 1997) suggested a regularization scheme which iteratively adapts regularization parameters by minimizing validation error using simple gradient descent. In this contribution we present an improved algorithm based on the conjugate gradient technique........ Numerical experiments with feedforward neural networks successfully demonstrate improved generalization ability and lower computational cost...

  15. Strictly-regular number system and data structures

    DEFF Research Database (Denmark)

    Elmasry, Amr Ahmed Abd Elmoneim; Jensen, Claus; Katajainen, Jyrki

    2010-01-01

    We introduce a new number system that we call the strictly-regular system, which efficiently supports the operations: digit-increment, digit-decrement, cut, concatenate, and add. Compared to other number systems, the strictly-regular system has distinguishable properties. It is superior to the re...

  16. Inclusion Professional Development Model and Regular Middle School Educators

    Science.gov (United States)

    Royster, Otelia; Reglin, Gary L.; Losike-Sedimo, Nonofo

    2014-01-01

    The purpose of this study was to determine the impact of a professional development model on regular education middle school teachers' knowledge of best practices for teaching inclusive classes and attitudes toward teaching these classes. There were 19 regular education teachers who taught the core subjects. Findings for Research Question 1…

  17. The equivalence problem for LL- and LR-regular grammars

    NARCIS (Netherlands)

    Nijholt, Antinus; Gecsec, F.

    It will be shown that the equivalence problem for LL-regular grammars is decidable. Apart from extending the known result for LL(k) grammar equivalence to LLregular grammar equivalence, we obtain an alternative proof of the decidability of LL(k) equivalence. The equivalence prob]em for LL-regular

  18. The Effects of Regular Exercise on the Physical Fitness Levels

    Science.gov (United States)

    Kirandi, Ozlem

    2016-01-01

    The purpose of the present research is investigating the effects of regular exercise on the physical fitness levels among sedentary individuals. The total of 65 sedentary male individuals between the ages of 19-45, who had never exercises regularly in their lives, participated in the present research. Of these participants, 35 wanted to be…

  19. Regular perturbations in a vector space with indefinite metric

    International Nuclear Information System (INIS)

    Chiang, C.C.

    1975-08-01

    The Klein space is discussed in connection with practical applications. Some lemmas are presented which are to be used for the discussion of regular self-adjoint operators. The criteria for the regularity of perturbed operators are given. (U.S.)

  20. Pairing renormalization and regularization within the local density approximation

    International Nuclear Information System (INIS)

    Borycki, P.J.; Dobaczewski, J.; Nazarewicz, W.; Stoitsov, M.V.

    2006-01-01

    We discuss methods used in mean-field theories to treat pairing correlations within the local density approximation. Pairing renormalization and regularization procedures are compared in spherical and deformed nuclei. Both prescriptions give fairly similar results, although the theoretical motivation, simplicity, and stability of the regularization procedure make it a method of choice for future applications

  1. Cognitive Aspects of Regularity Exhibit When Neighborhood Disappears

    Science.gov (United States)

    Chen, Sau-Chin; Hu, Jon-Fan

    2015-01-01

    Although regularity refers to the compatibility between pronunciation of character and sound of phonetic component, it has been suggested as being part of consistency, which is defined by neighborhood characteristics. Two experiments demonstrate how regularity effect is amplified or reduced by neighborhood characteristics and reveals the…

  2. Regularity conditions of the field on a toroidal magnetic surface

    International Nuclear Information System (INIS)

    Bouligand, M.

    1985-06-01

    We show that a field B vector which is derived from an analytic canonical potential on an ordinary toroidal surface is regular on this surface when the potential satisfies an elliptic equation (owing to the conservative field) subject to certain conditions of regularity of its coefficients [fr

  3. 47 CFR 76.614 - Cable television system regular monitoring.

    Science.gov (United States)

    2010-10-01

    ...-137 and 225-400 MHz shall provide for a program of regular monitoring for signal leakage by... in these bands of 20 uV/m or greater at a distance of 3 meters. During regular monitoring, any leakage source which produces a field strength of 20 uV/m or greater at a distance of 3 meters in the...

  4. Analysis of regularized Navier-Stokes equations, 2

    Science.gov (United States)

    Ou, Yuh-Roung; Sritharan, S. S.

    1989-01-01

    A practically important regularization of the Navier-Stokes equations was analyzed. As a continuation of the previous work, the structure of the attractors characterizing the solutins was studied. Local as well as global invariant manifolds were found. Regularity properties of these manifolds are analyzed.

  5. 20 CFR 226.33 - Spouse regular annuity rate.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 1 2010-04-01 2010-04-01 false Spouse regular annuity rate. 226.33 Section... COMPUTING EMPLOYEE, SPOUSE, AND DIVORCED SPOUSE ANNUITIES Computing a Spouse or Divorced Spouse Annuity § 226.33 Spouse regular annuity rate. The final tier I and tier II rates, from §§ 226.30 and 226.32, are...

  6. Optimal behaviour can violate the principle of regularity.

    Science.gov (United States)

    Trimmer, Pete C

    2013-07-22

    Understanding decisions is a fundamental aim of behavioural ecology, psychology and economics. The regularity axiom of utility theory holds that a preference between options should be maintained when other options are made available. Empirical studies have shown that animals violate regularity but this has not been understood from a theoretical perspective, such decisions have therefore been labelled as irrational. Here, I use models of state-dependent behaviour to demonstrate that choices can violate regularity even when behavioural strategies are optimal. I also show that the range of conditions over which regularity should be violated can be larger when options do not always persist into the future. Consequently, utility theory--based on axioms, including transitivity, regularity and the independence of irrelevant alternatives--is undermined, because even alternatives that are never chosen by an animal (in its current state) can be relevant to a decision.

  7. Laplacian manifold regularization method for fluorescence molecular tomography

    Science.gov (United States)

    He, Xuelei; Wang, Xiaodong; Yi, Huangjian; Chen, Yanrong; Zhang, Xu; Yu, Jingjing; He, Xiaowei

    2017-04-01

    Sparse regularization methods have been widely used in fluorescence molecular tomography (FMT) for stable three-dimensional reconstruction. Generally, ℓ1-regularization-based methods allow for utilizing the sparsity nature of the target distribution. However, in addition to sparsity, the spatial structure information should be exploited as well. A joint ℓ1 and Laplacian manifold regularization model is proposed to improve the reconstruction performance, and two algorithms (with and without Barzilai-Borwein strategy) are presented to solve the regularization model. Numerical studies and in vivo experiment demonstrate that the proposed Gradient projection-resolved Laplacian manifold regularization method for the joint model performed better than the comparative algorithm for ℓ1 minimization method in both spatial aggregation and location accuracy.

  8. SU-E-J-67: Evaluation of Breathing Patterns for Respiratory-Gated Radiation Therapy Using Respiration Regularity Index

    Energy Technology Data Exchange (ETDEWEB)

    Cheong, K; Lee, M; Kang, S; Yoon, J; Park, S; Hwang, T; Kim, H; Kim, K; Han, T; Bae, H [Hallym University College of Medicine, Anyang (Korea, Republic of)

    2014-06-01

    Purpose: Despite the importance of accurately estimating the respiration regularity of a patient in motion compensation treatment, an effective and simply applicable method has rarely been reported. The authors propose a simple respiration regularity index based on parameters derived from a correspondingly simplified respiration model. Methods: In order to simplify a patient's breathing pattern while preserving the data's intrinsic properties, we defined a respiration model as a power of cosine form with a baseline drift. According to this respiration formula, breathing-pattern fluctuation could be explained using four factors: sample standard deviation of respiration period, sample standard deviation of amplitude and the results of simple regression of the baseline drift (slope and standard deviation of residuals of a respiration signal. Overall irregularity (δ) was defined as a Euclidean norm of newly derived variable using principal component analysis (PCA) for the four fluctuation parameters. Finally, the proposed respiration regularity index was defined as ρ=ln(1+(1/ δ))/2, a higher ρ indicating a more regular breathing pattern. Subsequently, we applied it to simulated and clinical respiration signals from real-time position management (RPM; Varian Medical Systems, Palo Alto, CA) and investigated respiration regularity. Moreover, correlations between the regularity of the first session and the remaining fractions were investigated using Pearson's correlation coefficient. Results: The respiration regularity was determined based on ρ; patients with ρ<0.3 showed worse regularity than the others, whereas ρ>0.7 was suitable for respiratory-gated radiation therapy (RGRT). Fluctuations in breathing cycle and amplitude were especially determinative of ρ. If the respiration regularity of a patient's first session was known, it could be estimated through subsequent sessions. Conclusions: Respiration regularity could be objectively determined

  9. Prospective regularization design in prior-image-based reconstruction

    International Nuclear Information System (INIS)

    Dang, Hao; Siewerdsen, Jeffrey H; Stayman, J Webster

    2015-01-01

    Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in

  10. Regularization Techniques for ECG Imaging during Atrial Fibrillation: a Computational Study

    Directory of Open Access Journals (Sweden)

    Carlos Figuera

    2016-10-01

    Full Text Available The inverse problem of electrocardiography is usually analyzed during stationary rhythms. However, the performance of the regularization methods under fibrillatory conditions has not been fully studied. In this work, we assessed different regularization techniques during atrial fibrillation (AF for estimating four target parameters, namely, epicardial potentials, dominant frequency (DF, phase maps, and singularity point (SP location. We use a realistic mathematical model of atria and torso anatomy with three different electrical activity patterns (i.e. sinus rhythm, simple AF and complex AF. Body surface potentials (BSP were simulated using Boundary Element Method and corrupted with white Gaussian noise of different powers. Noisy BSPs were used to obtain the epicardial potentials on the atrial surface, using fourteen different regularization techniques. DF, phase maps and SP location were computed from estimated epicardial potentials. Inverse solutions were evaluated using a set of performance metrics adapted to each clinical target. For the case of SP location, an assessment methodology based on the spatial mass function of the SP location and four spatial error metrics was proposed. The role of the regularization parameter for Tikhonov-based methods, and the effect of noise level and imperfections in the knowledge of the transfer matrix were also addressed. Results showed that the Bayes maximum-a-posteriori method clearly outperforms the rest of the techniques but requires a priori information about the epicardial potentials. Among the purely non-invasive techniques, Tikhonov-based methods performed as well as more complex techniques in realistic fibrillatory conditions, with a slight gain between 0.02 and 0.2 in terms of the correlation coefficient. Also, the use of a constant regularization parameter may be advisable since the performance was similar to that obtained with a variable parameter (indeed there was no difference for the zero

  11. Salt-body Inversion with Minimum Gradient Support and Sobolev Space Norm Regularizations

    KAUST Repository

    Kazei, Vladimir

    2017-05-26

    Full-waveform inversion (FWI) is a technique which solves the ill-posed seismic inversion problem of fitting our model data to the measured ones from the field. FWI is capable of providing high-resolution estimates of the model, and of handling wave propagation of arbitrary complexity (visco-elastic, anisotropic); yet, it often fails to retrieve high-contrast geological structures, such as salt. One of the reasons for the FWI failure is that the updates at earlier iterations are too smooth to capture the sharp edges of the salt boundary. We compare several regularization approaches, which promote sharpness of the edges. Minimum gradient support (MGS) regularization focuses the inversion on blocky models, even more than the total variation (TV) does. However, both approaches try to invert undesirable high wavenumbers in the model too early for a model of complex structure. Therefore, we apply the Sobolev space norm as a regularizing term in order to maintain a balance between sharp and smooth updates in FWI. We demonstrate the application of these regularizations on a Marmousi model, enriched by a chunk of salt. The model turns out to be too complex in some parts to retrieve its full velocity distribution, yet the salt shape and contrast are retrieved.

  12. On the regularity of the covariance matrix of a discretized scalar field on the sphere

    Energy Technology Data Exchange (ETDEWEB)

    Bilbao-Ahedo, J.D. [Departamento de Física Moderna, Universidad de Cantabria, Av. los Castros s/n, 39005 Santander (Spain); Barreiro, R.B.; Herranz, D.; Vielva, P.; Martínez-González, E., E-mail: bilbao@ifca.unican.es, E-mail: barreiro@ifca.unican.es, E-mail: herranz@ifca.unican.es, E-mail: vielva@ifca.unican.es, E-mail: martinez@ifca.unican.es [Instituto de Física de Cantabria (CSIC-UC), Av. los Castros s/n, 39005 Santander (Spain)

    2017-02-01

    We present a comprehensive study of the regularity of the covariance matrix of a discretized field on the sphere. In a particular situation, the rank of the matrix depends on the number of pixels, the number of spherical harmonics, the symmetries of the pixelization scheme and the presence of a mask. Taking into account the above mentioned components, we provide analytical expressions that constrain the rank of the matrix. They are obtained by expanding the determinant of the covariance matrix as a sum of determinants of matrices made up of spherical harmonics. We investigate these constraints for five different pixelizations that have been used in the context of Cosmic Microwave Background (CMB) data analysis: Cube, Icosahedron, Igloo, GLESP and HEALPix, finding that, at least in the considered cases, the HEALPix pixelization tends to provide a covariance matrix with a rank closer to the maximum expected theoretical value than the other pixelizations. The effect of the propagation of numerical errors in the regularity of the covariance matrix is also studied for different computational precisions, as well as the effect of adding a certain level of noise in order to regularize the matrix. In addition, we investigate the application of the previous results to a particular example that requires the inversion of the covariance matrix: the estimation of the CMB temperature power spectrum through the Quadratic Maximum Likelihood algorithm. Finally, some general considerations in order to achieve a regular covariance matrix are also presented.

  13. Does regular practice of physical activity reduce the risk of dysphonia?

    Science.gov (United States)

    Assunção, Ada Avila; de Medeiros, Adriane Mesquita; Barreto, Sandhi Maria; Gama, Ana Cristina Cortes

    2009-12-01

    The purpose of this study was to investigate the association between regular physical activity and the prevalence of dysphonia. A cross-sectional study was conducted with 3142 teachers from 129 municipal public schools in the city of Belo Horizonte, Brazil. The dependent variable, dysphonia, was classified (absent or present) according to reported symptoms (fatigue when speaking and loss of voice quality), their frequency (occasionally and daily), and duration (past 15 days). The independent variable was regular physical activity. The degree of association was estimated based on the prevalence ratio and a 95% confidence interval obtained by the Poisson regression adapted for cross-sectional studies. In the study sample, the prevalence of dysphonia in teachers was 15.63%. Nearly half (47.52%) of the teachers reported no regular practice of physical exercises. The remaining teachers (52.48%) walked and did physical exercises, sports, and other activities; 31.25% undertook these activities once or twice a week, and 21.23% exercised three or more times a week. Teachers who did not practice physical activity were more likely to present dysphonia compared to those that exercised three or more times a week. Regular physical activity was associated positively with the prevalence of dysphonia.

  14. A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging

    International Nuclear Information System (INIS)

    Chaari, L.; Pesquet, J.Ch.; Chaari, L.; Ciuciu, Ph.; Benazza-Benyahia, A.

    2011-01-01

    To reduce scanning time and/or improve spatial/temporal resolution in some Magnetic Resonance Imaging (MRI) applications, parallel MRI acquisition techniques with multiple coils acquisition have emerged since the early 1990's as powerful imaging methods that allow a faster acquisition process. In these techniques, the full FOV image has to be reconstructed from the resulting acquired under sampled k-space data. To this end, several reconstruction techniques have been proposed such as the widely-used Sensitivity Encoding (SENSE) method. However, the reconstructed image generally presents artifacts when perturbations occur in both the measured data and the estimated coil sensitivity profiles. In this paper, we aim at achieving accurate image reconstruction under degraded experimental conditions (low magnetic field and high reduction factor), in which neither the SENSE method nor the Tikhonov regularization in the image domain give convincing results. To this end, we present a novel method for SENSE-based reconstruction which proceeds with regularization in the complex wavelet domain by promoting sparsity. The proposed approach relies on a fast algorithm that enables the minimization of regularized non-differentiable criteria including more general penalties than a classical l 1 term. To further enhance the reconstructed image quality, local convex constraints are added to the regularization process. In vivo human brain experiments carried out on Gradient-Echo (GRE) anatomical and Echo Planar Imaging (EPI) functional MRI data at 1.5 T indicate that our algorithm provides reconstructed images with reduced artifacts for high reduction factors. (authors)

  15. Total variation regularization for a backward time-fractional diffusion problem

    International Nuclear Information System (INIS)

    Wang, Liyan; Liu, Jijun

    2013-01-01

    Consider a two-dimensional backward problem for a time-fractional diffusion process, which can be considered as image de-blurring where the blurring process is assumed to be slow diffusion. In order to avoid the over-smoothing effect for object image with edges and to construct a fast reconstruction scheme, the total variation regularizing term and the data residual error in the frequency domain are coupled to construct the cost functional. The well posedness of this optimization problem is studied. The minimizer is sought approximately using the iteration process for a series of optimization problems with Bregman distance as a penalty term. This iteration reconstruction scheme is essentially a new regularizing scheme with coupling parameter in the cost functional and the iteration stopping times as two regularizing parameters. We give the choice strategy for the regularizing parameters in terms of the noise level of measurement data, which yields the optimal error estimate on the iterative solution. The series optimization problems are solved by alternative iteration with explicit exact solution and therefore the amount of computation is much weakened. Numerical implementations are given to support our theoretical analysis on the convergence rate and to show the significant reconstruction improvements. (paper)

  16. Three regularities of recognition memory: the role of bias.

    Science.gov (United States)

    Hilford, Andrew; Maloney, Laurence T; Glanzer, Murray; Kim, Kisok

    2015-12-01

    A basic assumption of Signal Detection Theory is that decisions are made on the basis of likelihood ratios. In a preceding paper, Glanzer, Hilford, and Maloney (Psychonomic Bulletin & Review, 16, 431-455, 2009) showed that the likelihood ratio assumption implies that three regularities will occur in recognition memory: (1) the Mirror Effect, (2) the Variance Effect, (3) the normalized Receiver Operating Characteristic (z-ROC) Length Effect. The paper offered formal proofs and computational demonstrations that decisions based on likelihood ratios produce the three regularities. A survey of data based on group ROCs from 36 studies validated the likelihood ratio assumption by showing that its three implied regularities are ubiquitous. The study noted, however, that bias, another basic factor in Signal Detection Theory, can obscure the Mirror Effect. In this paper we examine how bias affects the regularities at the theoretical level. The theoretical analysis shows: (1) how bias obscures the Mirror Effect, not the other two regularities, and (2) four ways to counter that obscuring. We then report the results of five experiments that support the theoretical analysis. The analyses and the experimental results also demonstrate: (1) that the three regularities govern individual, as well as group, performance, (2) alternative explanations of the regularities are ruled out, and (3) that Signal Detection Theory, correctly applied, gives a simple and unified explanation of recognition memory data.

  17. Learning regularization parameters for general-form Tikhonov

    International Nuclear Information System (INIS)

    Chung, Julianne; Español, Malena I

    2017-01-01

    Computing regularization parameters for general-form Tikhonov regularization can be an expensive and difficult task, especially if multiple parameters or many solutions need to be computed in real time. In this work, we assume training data is available and describe an efficient learning approach for computing regularization parameters that can be used for a large set of problems. We consider an empirical Bayes risk minimization framework for finding regularization parameters that minimize average errors for the training data. We first extend methods from Chung et al (2011 SIAM J. Sci. Comput. 33 3132–52) to the general-form Tikhonov problem. Then we develop a learning approach for multi-parameter Tikhonov problems, for the case where all involved matrices are simultaneously diagonalizable. For problems where this is not the case, we describe an approach to compute near-optimal regularization parameters by using operator approximations for the original problem. Finally, we propose a new class of regularizing filters, where solutions correspond to multi-parameter Tikhonov solutions, that requires less data than previously proposed optimal error filters, avoids the generalized SVD, and allows flexibility and novelty in the choice of regularization matrices. Numerical results for 1D and 2D examples using different norms on the errors show the effectiveness of our methods. (paper)

  18. Regularized Biot–Savart Laws for Modeling Magnetic Flux Ropes

    Science.gov (United States)

    Titov, Viacheslav S.; Downs, Cooper; Mikić, Zoran; Török, Tibor; Linker, Jon A.; Caplan, Ronald M.

    2018-01-01

    Many existing models assume that magnetic flux ropes play a key role in solar flares and coronal mass ejections (CMEs). It is therefore important to develop efficient methods for constructing flux-rope configurations constrained by observed magnetic data and the morphology of the pre-eruptive source region. For this purpose, we have derived and implemented a compact analytical form that represents the magnetic field of a thin flux rope with an axis of arbitrary shape and circular cross-sections. This form implies that the flux rope carries axial current I and axial flux F, so that the respective magnetic field is the curl of the sum of axial and azimuthal vector potentials proportional to I and F, respectively. We expressed the vector potentials in terms of modified Biot–Savart laws, whose kernels are regularized at the axis in such a way that, when the axis is straight, these laws define a cylindrical force-free flux rope with a parabolic profile for the axial current density. For the cases we have studied so far, we determined the shape of the rope axis by following the polarity inversion line of the eruptions’ source region, using observed magnetograms. The height variation along the axis and other flux-rope parameters are estimated by means of potential-field extrapolations. Using this heuristic approach, we were able to construct pre-eruption configurations for the 2009 February 13 and 2011 October 1 CME events. These applications demonstrate the flexibility and efficiency of our new method for energizing pre-eruptive configurations in simulations of CMEs.

  19. Closedness type regularity conditions in convex optimization and beyond

    Directory of Open Access Journals (Sweden)

    Sorin-Mihai Grad

    2016-09-01

    Full Text Available The closedness type regularity conditions have proven during the last decade to be viable alternatives to their more restrictive interiority type counterparts, in both convex optimization and different areas where it was successfully applied. In this review article we de- and reconstruct some closedness type regularity conditions formulated by means of epigraphs and subdifferentials, respectively, for general optimization problems in order to stress that they arise naturally when dealing with such problems. The results are then specialized for constrained and unconstrained convex optimization problems. We also hint towards other classes of optimization problems where closedness type regularity conditions were successfully employed and discuss other possible applications of them.

  20. Capped Lp approximations for the composite L0 regularization problem

    OpenAIRE

    Li, Qia; Zhang, Na

    2017-01-01

    The composite L0 function serves as a sparse regularizer in many applications. The algorithmic difficulty caused by the composite L0 regularization (the L0 norm composed with a linear mapping) is usually bypassed through approximating the L0 norm. We consider in this paper capped Lp approximations with $p>0$ for the composite L0 regularization problem. For each $p>0$, the capped Lp function converges to the L0 norm pointwisely as the approximation parameter tends to infinity. We point out tha...

  1. Generalization Performance of Regularized Ranking With Multiscale Kernels.

    Science.gov (United States)

    Zhou, Yicong; Chen, Hong; Lan, Rushi; Pan, Zhibin

    2016-05-01

    The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.

  2. Likelihood ratio decisions in memory: three implied regularities.

    Science.gov (United States)

    Glanzer, Murray; Hilford, Andrew; Maloney, Laurence T

    2009-06-01

    We analyze four general signal detection models for recognition memory that differ in their distributional assumptions. Our analyses show that a basic assumption of signal detection theory, the likelihood ratio decision axis, implies three regularities in recognition memory: (1) the mirror effect, (2) the variance effect, and (3) the z-ROC length effect. For each model, we present the equations that produce the three regularities and show, in computed examples, how they do so. We then show that the regularities appear in data from a range of recognition studies. The analyses and data in our study support the following generalization: Individuals make efficient recognition decisions on the basis of likelihood ratios.

  3. Fluctuations of quantum fields via zeta function regularization

    International Nuclear Information System (INIS)

    Cognola, Guido; Zerbini, Sergio; Elizalde, Emilio

    2002-01-01

    Explicit expressions for the expectation values and the variances of some observables, which are bilinear quantities in the quantum fields on a D-dimensional manifold, are derived making use of zeta function regularization. It is found that the variance, related to the second functional variation of the effective action, requires a further regularization and that the relative regularized variance turns out to be 2/N, where N is the number of the fields, thus being independent of the dimension D. Some illustrating examples are worked through. The issue of the stress tensor is also briefly addressed

  4. Low-Rank Matrix Factorization With Adaptive Graph Regularizer.

    Science.gov (United States)

    Lu, Gui-Fu; Wang, Yong; Zou, Jian

    2016-05-01

    In this paper, we present a novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR). We extend the recently proposed low-rank matrix with manifold regularization (MMF) method with an adaptive regularizer. Different from MMF, which constructs an affinity graph in advance, LMFAGR can simultaneously seek graph weight matrix and low-dimensional representations of data. That is, graph construction and low-rank matrix factorization are incorporated into a unified framework, which results in an automatically updated graph rather than a predefined one. The experimental results on some data sets demonstrate that the proposed algorithm outperforms the state-of-the-art low-rank matrix factorization methods.

  5. Regularization theory for ill-posed problems selected topics

    CERN Document Server

    Lu, Shuai

    2013-01-01

    Thismonograph is a valuable contribution to thehighly topical and extremly productive field ofregularisationmethods for inverse and ill-posed problems. The author is an internationally outstanding and acceptedmathematicianin this field. In his book he offers a well-balanced mixtureof basic and innovative aspects.He demonstrates new,differentiatedviewpoints, and important examples for applications. The bookdemontrates thecurrent developments inthe field of regularization theory,such as multiparameter regularization and regularization in learning theory. The book is written for graduate and PhDs

  6. Short-time regularity assessment of fibrillatory waves from the surface ECG in atrial fibrillation

    International Nuclear Information System (INIS)

    Alcaraz, Raúl; Martínez, Arturo; Hornero, Fernando; Rieta, José J

    2012-01-01

    This paper proposes the first non-invasive method for direct and short-time regularity quantification of atrial fibrillatory (f) waves from the surface ECG in atrial fibrillation (AF). Regularity is estimated by computing individual morphological variations among f waves, which are delineated and extracted from the atrial activity (AA) signal, making use of an adaptive signed correlation index. The algorithm was tested on real AF surface recordings in order to discriminate atrial signals with different organization degrees, providing a notably higher global accuracy (90.3%) than the two non-invasive AF organization estimates defined to date: the dominant atrial frequency (70.5%) and sample entropy (76.1%). Furthermore, due to its ability to assess AA regularity wave to wave, the proposed method is also able to pursue AF organization time course more precisely than the aforementioned indices. As a consequence, this work opens a new perspective in the non-invasive analysis of AF, such as the individualized study of each f wave, that could improve the understanding of AF mechanisms and become useful for its clinical treatment. (paper)

  7. On the theory of drainage area for regular and non-regular points

    Science.gov (United States)

    Bonetti, S.; Bragg, A. D.; Porporato, A.

    2018-03-01

    The drainage area is an important, non-local property of a landscape, which controls surface and subsurface hydrological fluxes. Its role in numerous ecohydrological and geomorphological applications has given rise to several numerical methods for its computation. However, its theoretical analysis has lagged behind. Only recently, an analytical definition for the specific catchment area was proposed (Gallant & Hutchinson. 2011 Water Resour. Res. 47, W05535. (doi:10.1029/2009WR008540)), with the derivation of a differential equation whose validity is limited to regular points of the watershed. Here, we show that such a differential equation can be derived from a continuity equation (Chen et al. 2014 Geomorphology 219, 68-86. (doi:10.1016/j.geomorph.2014.04.037)) and extend the theory to critical and singular points both by applying Gauss's theorem and by means of a dynamical systems approach to define basins of attraction of local surface minima. Simple analytical examples as well as applications to more complex topographic surfaces are examined. The theoretical description of topographic features and properties, such as the drainage area, channel lines and watershed divides, can be broadly adopted to develop and test the numerical algorithms currently used in digital terrain analysis for the computation of the drainage area, as well as for the theoretical analysis of landscape evolution and stability.

  8. Automatic Constraint Detection for 2D Layout Regularization.

    Science.gov (United States)

    Jiang, Haiyong; Nan, Liangliang; Yan, Dong-Ming; Dong, Weiming; Zhang, Xiaopeng; Wonka, Peter

    2016-08-01

    In this paper, we address the problem of constraint detection for layout regularization. The layout we consider is a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important in digitizing plans or images, such as floor plans and facade images, and in the improvement of user-created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm that automatically detects constraints. We evaluate the proposed framework using a variety of input layouts from different applications. Our results demonstrate that our method has superior performance to the state of the art.

  9. A Regularized Algorithm for the Proximal Split Feasibility Problem

    Directory of Open Access Journals (Sweden)

    Zhangsong Yao

    2014-01-01

    Full Text Available The proximal split feasibility problem has been studied. A regularized method has been presented for solving the proximal split feasibility problem. Strong convergence theorem is given.

  10. Anaemia in Patients with Diabetes Mellitus attending regular ...

    African Journals Online (AJOL)

    Anaemia in Patients with Diabetes Mellitus attending regular Diabetic ... Nigerian Journal of Health and Biomedical Sciences ... some patients may omit important food items in their daily diet for fear of increasing their blood sugar level.

  11. Automatic Constraint Detection for 2D Layout Regularization

    KAUST Repository

    Jiang, Haiyong

    2015-09-18

    In this paper, we address the problem of constraint detection for layout regularization. As layout we consider a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important for digitizing plans or images, such as floor plans and facade images, and for the improvement of user created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate the layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm to automatically detect constraints. In our results, we evaluate the proposed framework on a variety of input layouts from different applications, which demonstrates our method has superior performance to the state of the art.

  12. Body composition, disordered eating and menstrual regularity in a ...

    African Journals Online (AJOL)

    Body composition, disordered eating and menstrual regularity in a group of South African ... e between body composition and disordered eating in irregular vs normal menstruating athletes. ... measured by air displacement plethysmography.

  13. A new approach to nonlinear constrained Tikhonov regularization

    KAUST Repository

    Ito, Kazufumi; Jin, Bangti

    2011-01-01

    operator. The approach is exploited to derive convergence rate results for a priori as well as a posteriori choice rules, e.g., discrepancy principle and balancing principle, for selecting the regularization parameter. The idea is further illustrated on a

  14. Supporting primary school teachers in differentiating in the regular classroom

    NARCIS (Netherlands)

    Eysink, Tessa H.S.; Hulsbeek, Manon; Gijlers, Hannie

    Many primary school teachers experience difficulties in effectively differentiating in the regular classroom. This study investigated the effect of the STIP-approach on teachers' differentiation activities and self-efficacy, and children's learning outcomes and instructional value. Teachers using

  15. Regularized plane-wave least-squares Kirchhoff migration

    KAUST Repository

    Wang, Xin; Dai, Wei; Schuster, Gerard T.

    2013-01-01

    A Kirchhoff least-squares migration (LSM) is developed in the prestack plane-wave domain to increase the quality of migration images. A regularization term is included that accounts for mispositioning of reflectors due to errors in the velocity

  16. Automatic Constraint Detection for 2D Layout Regularization

    KAUST Repository

    Jiang, Haiyong; Nan, Liangliang; Yan, Dongming; Dong, Weiming; Zhang, Xiaopeng; Wonka, Peter

    2015-01-01

    plans or images, such as floor plans and facade images, and for the improvement of user created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing

  17. Regularized Estimation of Structural Instability in Factor Models: The US Macroeconomy and the Great Moderation

    DEFF Research Database (Denmark)

    Callot, Laurent; Kristensen, Johannes Tang

    This paper shows that the parsimoniously time-varying methodology of Callot and Kristensen (2015) can be applied to factor models.We apply this method to study macroeconomic instability in the US from 1959:1 to 2006:4 with a particular focus on the Great Moderation. Models with parsimoniously time...... that the parameters of both models exhibit a higher degree of instability in the period from 1970:1 to 1984:4 relative to the following 15 years. In our setting the Great Moderation appears as the gradual ending of a period of high structural instability that took place in the 1970s and early 1980s....

  18. Estimate of the regularly gridded 3D vector flow field from a set of tomographic maps

    Czech Academy of Sciences Publication Activity Database

    Švanda, Michal; Kozoň, M.

    2017-01-01

    Roč. 600, April (2017), A117/1-A117/12 E-ISSN 1432-0746 R&D Projects: GA ČR(CZ) GA14-04338S Institutional support: RVO:67985815 Keywords : Sun * helioseismology * miscellaneous Subject RIV: BN - Astronomy , Celestial Mechanics, Astrophysics OBOR OECD: Astronomy (including astrophysics,space science) Impact factor: 5.014, year: 2016

  19. Regularization method for solving the inverse scattering problem

    International Nuclear Information System (INIS)

    Denisov, A.M.; Krylov, A.S.

    1985-01-01

    The inverse scattering problem for the Schroedinger radial equation consisting in determining the potential according to the scattering phase is considered. The problem of potential restoration according to the phase specified with fixed error in a finite range is solved by the regularization method based on minimization of the Tikhonov's smoothing functional. The regularization method is used for solving the problem of neutron-proton potential restoration according to the scattering phases. The determined potentials are given in the table

  20. Viscous Regularization of the Euler Equations and Entropy Principles

    KAUST Repository

    Guermond, Jean-Luc

    2014-03-11

    This paper investigates a general class of viscous regularizations of the compressible Euler equations. A unique regularization is identified that is compatible with all the generalized entropies, à la [Harten et al., SIAM J. Numer. Anal., 35 (1998), pp. 2117-2127], and satisfies the minimum entropy principle. A connection with a recently proposed phenomenological model by [H. Brenner, Phys. A, 370 (2006), pp. 190-224] is made. © 2014 Society for Industrial and Applied Mathematics.

  1. Dimensional versus lattice regularization within Luescher's Yang Mills theory

    International Nuclear Information System (INIS)

    Diekmann, B.; Langer, M.; Schuette, D.

    1993-01-01

    It is pointed out that the coefficients of Luescher's effective model space Hamiltonian, which is based upon dimensional regularization techniques, can be reproduced by applying folded diagram perturbation theory to the Kogut Susskind Hamiltonian and by performing a lattice continuum limit (keeping the volume fixed). Alternative cutoff regularizations of the Hamiltonian are in general inconsistent, the critical point beeing the correct prediction for Luescher's tadpole coefficient which is formally quadratically divergent and which has to become a well defined (negative) number. (orig.)

  2. Left regular bands of groups of left quotients

    International Nuclear Information System (INIS)

    El-Qallali, A.

    1988-10-01

    A semigroup S which has a left regular band of groups as a semigroup of left quotients is shown to be the semigroup which is a left regular band of right reversible cancellative semigroups. An alternative characterization is provided by using spinned products. These results are applied to the case where S is a superabundant whose set of idempotents forms a left normal band. (author). 13 refs

  3. Human visual system automatically encodes sequential regularities of discrete events.

    Science.gov (United States)

    Kimura, Motohiro; Schröger, Erich; Czigler, István; Ohira, Hideki

    2010-06-01

    For our adaptive behavior in a dynamically changing environment, an essential task of the brain is to automatically encode sequential regularities inherent in the environment into a memory representation. Recent studies in neuroscience have suggested that sequential regularities embedded in discrete sensory events are automatically encoded into a memory representation at the level of the sensory system. This notion is largely supported by evidence from investigations using auditory mismatch negativity (auditory MMN), an event-related brain potential (ERP) correlate of an automatic memory-mismatch process in the auditory sensory system. However, it is still largely unclear whether or not this notion can be generalized to other sensory modalities. The purpose of the present study was to investigate the contribution of the visual sensory system to the automatic encoding of sequential regularities using visual mismatch negativity (visual MMN), an ERP correlate of an automatic memory-mismatch process in the visual sensory system. To this end, we conducted a sequential analysis of visual MMN in an oddball sequence consisting of infrequent deviant and frequent standard stimuli, and tested whether the underlying memory representation of visual MMN generation contains only a sensory memory trace of standard stimuli (trace-mismatch hypothesis) or whether it also contains sequential regularities extracted from the repetitive standard sequence (regularity-violation hypothesis). The results showed that visual MMN was elicited by first deviant (deviant stimuli following at least one standard stimulus), second deviant (deviant stimuli immediately following first deviant), and first standard (standard stimuli immediately following first deviant), but not by second standard (standard stimuli immediately following first standard). These results are consistent with the regularity-violation hypothesis, suggesting that the visual sensory system automatically encodes sequential

  4. Regularization of the quantum field theory of charges and monopoles

    International Nuclear Information System (INIS)

    Panagiotakopoulos, C.

    1981-09-01

    A gauge invariant regularization procedure for quantum field theories of electric and magnetic charges based on Zwanziger's local formulation is proposed. The bare regularized full Green's functions of gauge invariant operators are shown to be Lorentz invariant. This would have as a consequence the Lorentz invariance of the finite Green's functions that might result after any reasonable subtraction if such a subtraction can be found. (author)

  5. The Impact of Computerization on Regular Employment (Japanese)

    OpenAIRE

    SUNADA Mitsuru; HIGUCHI Yoshio; ABE Masahiro

    2004-01-01

    This paper uses micro data from the Basic Survey of Japanese Business Structure and Activity to analyze the effects of companies' introduction of information and telecommunications technology on employment structures, especially regular versus non-regular employment. Firstly, examination of trends in the ratio of part-time workers recorded in the Basic Survey shows that part-time worker ratios in manufacturing firms are rising slightly, but that companies with a high proportion of part-timers...

  6. Analytic regularization of the Yukawa model at finite temperature

    International Nuclear Information System (INIS)

    Malbouisson, A.P.C.; Svaiter, N.F.; Svaiter, B.F.

    1996-07-01

    It is analysed the one-loop fermionic contribution for the scalar effective potential in the temperature dependent Yukawa model. Ir order to regularize the model a mix between dimensional and analytic regularization procedures is used. It is found a general expression for the fermionic contribution in arbitrary spacetime dimension. It is also found that in D = 3 this contribution is finite. (author). 19 refs

  7. Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data

    Directory of Open Access Journals (Sweden)

    Jaewook Jung

    2017-03-01

    Full Text Available With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL combined with Hypothesize and Test (HAT. The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International

  8. Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data.

    Science.gov (United States)

    Jung, Jaewook; Jwa, Yoonseok; Sohn, Gunho

    2017-03-19

    With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS) data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP) technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International Society for

  9. A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video

    Directory of Open Access Journals (Sweden)

    Zhang Liangpei

    2007-01-01

    Full Text Available Super-resolution (SR reconstruction technique is capable of producing a high-resolution image from a sequence of low-resolution images. In this paper, we study an efficient SR algorithm for digital video. To effectively deal with the intractable problems in SR video reconstruction, such as inevitable motion estimation errors, noise, blurring, missing regions, and compression artifacts, the total variation (TV regularization is employed in the reconstruction model. We use the fixed-point iteration method and preconditioning techniques to efficiently solve the associated nonlinear Euler-Lagrange equations of the corresponding variational problem in SR. The proposed algorithm has been tested in several cases of motion and degradation. It is also compared with the Laplacian regularization-based SR algorithm and other TV-based SR algorithms. Experimental results are presented to illustrate the effectiveness of the proposed algorithm.

  10. Information operator approach and iterative regularization methods for atmospheric remote sensing

    International Nuclear Information System (INIS)

    Doicu, A.; Hilgers, S.; Bargen, A. von; Rozanov, A.; Eichmann, K.-U.; Savigny, C. von; Burrows, J.P.

    2007-01-01

    In this study, we present the main features of the information operator approach for solving linear inverse problems arising in atmospheric remote sensing. This method is superior to the stochastic version of the Tikhonov regularization (or the optimal estimation method) due to its capability to filter out the noise-dominated components of the solution generated by an inappropriate choice of the regularization parameter. We extend this approach to iterative methods for nonlinear ill-posed problems and derive the truncated versions of the Gauss-Newton and Levenberg-Marquardt methods. Although the paper mostly focuses on discussing the mathematical details of the inverse method, retrieval results have been provided, which exemplify the performances of the methods. These results correspond to the NO 2 retrieval from SCIAMACHY limb scatter measurements and have been obtained by using the retrieval processors developed at the German Aerospace Center Oberpfaffenhofen and Institute of Environmental Physics of the University of Bremen

  11. The relationship between lifestyle regularity and subjective sleep quality

    Science.gov (United States)

    Monk, Timothy H.; Reynolds, Charles F 3rd; Buysse, Daniel J.; DeGrazia, Jean M.; Kupfer, David J.

    2003-01-01

    In previous work we have developed a diary instrument-the Social Rhythm Metric (SRM), which allows the assessment of lifestyle regularity-and a questionnaire instrument--the Pittsburgh Sleep Quality Index (PSQI), which allows the assessment of subjective sleep quality. The aim of the present study was to explore the relationship between lifestyle regularity and subjective sleep quality. Lifestyle regularity was assessed by both standard (SRM-17) and shortened (SRM-5) metrics; subjective sleep quality was assessed by the PSQI. We hypothesized that high lifestyle regularity would be conducive to better sleep. Both instruments were given to a sample of 100 healthy subjects who were studied as part of a variety of different experiments spanning a 9-yr time frame. Ages ranged from 19 to 49 yr (mean age: 31.2 yr, s.d.: 7.8 yr); there were 48 women and 52 men. SRM scores were derived from a two-week diary. The hypothesis was confirmed. There was a significant (rho = -0.4, p subjects with higher levels of lifestyle regularity reported fewer sleep problems. This relationship was also supported by a categorical analysis, where the proportion of "poor sleepers" was doubled in the "irregular types" group as compared with the "non-irregular types" group. Thus, there appears to be an association between lifestyle regularity and good sleep, though the direction of causality remains to be tested.

  12. Regularity criteria for incompressible magnetohydrodynamics equations in three dimensions

    International Nuclear Information System (INIS)

    Lin, Hongxia; Du, Lili

    2013-01-01

    In this paper, we give some new global regularity criteria for three-dimensional incompressible magnetohydrodynamics (MHD) equations. More precisely, we provide some sufficient conditions in terms of the derivatives of the velocity or pressure, for the global regularity of strong solutions to 3D incompressible MHD equations in the whole space, as well as for periodic boundary conditions. Moreover, the regularity criterion involving three of the nine components of the velocity gradient tensor is also obtained. The main results generalize the recent work by Cao and Wu (2010 Two regularity criteria for the 3D MHD equations J. Diff. Eqns 248 2263–74) and the analysis in part is based on the works by Cao C and Titi E (2008 Regularity criteria for the three-dimensional Navier–Stokes equations Indiana Univ. Math. J. 57 2643–61; 2011 Gobal regularity criterion for the 3D Navier–Stokes equations involving one entry of the velocity gradient tensor Arch. Rational Mech. Anal. 202 919–32) for 3D incompressible Navier–Stokes equations. (paper)

  13. Geostatistical regularization operators for geophysical inverse problems on irregular meshes

    Science.gov (United States)

    Jordi, C.; Doetsch, J.; Günther, T.; Schmelzbach, C.; Robertsson, J. OA

    2018-05-01

    Irregular meshes allow to include complicated subsurface structures into geophysical modelling and inverse problems. The non-uniqueness of these inverse problems requires appropriate regularization that can incorporate a priori information. However, defining regularization operators for irregular discretizations is not trivial. Different schemes for calculating smoothness operators on irregular meshes have been proposed. In contrast to classical regularization constraints that are only defined using the nearest neighbours of a cell, geostatistical operators include a larger neighbourhood around a particular cell. A correlation model defines the extent of the neighbourhood and allows to incorporate information about geological structures. We propose an approach to calculate geostatistical operators for inverse problems on irregular meshes by eigendecomposition of a covariance matrix that contains the a priori geological information. Using our approach, the calculation of the operator matrix becomes tractable for 3-D inverse problems on irregular meshes. We tested the performance of the geostatistical regularization operators and compared them against the results of anisotropic smoothing in inversions of 2-D surface synthetic electrical resistivity tomography (ERT) data as well as in the inversion of a realistic 3-D cross-well synthetic ERT scenario. The inversions of 2-D ERT and seismic traveltime field data with geostatistical regularization provide results that are in good accordance with the expected geology and thus facilitate their interpretation. In particular, for layered structures the geostatistical regularization provides geologically more plausible results compared to the anisotropic smoothness constraints.

  14. Near-Regular Structure Discovery Using Linear Programming

    KAUST Repository

    Huang, Qixing

    2014-06-02

    Near-regular structures are common in manmade and natural objects. Algorithmic detection of such regularity greatly facilitates our understanding of shape structures, leads to compact encoding of input geometries, and enables efficient generation and manipulation of complex patterns on both acquired and synthesized objects. Such regularity manifests itself both in the repetition of certain geometric elements, as well as in the structured arrangement of the elements. We cast the regularity detection problem as an optimization and efficiently solve it using linear programming techniques. Our optimization has a discrete aspect, that is, the connectivity relationships among the elements, as well as a continuous aspect, namely the locations of the elements of interest. Both these aspects are captured by our near-regular structure extraction framework, which alternates between discrete and continuous optimizations. We demonstrate the effectiveness of our framework on a variety of problems including near-regular structure extraction, structure-preserving pattern manipulation, and markerless correspondence detection. Robustness results with respect to geometric and topological noise are presented on synthesized, real-world, and also benchmark datasets. © 2014 ACM.

  15. Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization

    Energy Technology Data Exchange (ETDEWEB)

    Zeng, Dong; Zhang, Xinyu; Bian, Zhaoying, E-mail: zybian@smu.edu.cn, E-mail: jhma@smu.edu.cn; Huang, Jing; Zhang, Hua; Lu, Lijun; Lyu, Wenbing; Feng, Qianjin; Chen, Wufan; Ma, Jianhua, E-mail: zybian@smu.edu.cn, E-mail: jhma@smu.edu.cn [Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515 (China); Zhang, Jing [Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052 (China)

    2016-05-15

    Purpose: Cerebral perfusion computed tomography (PCT) imaging as an accurate and fast acute ischemic stroke examination has been widely used in clinic. Meanwhile, a major drawback of PCT imaging is the high radiation dose due to its dynamic scan protocol. The purpose of this work is to develop a robust perfusion deconvolution approach via structure tensor total variation (STV) regularization (PD-STV) for estimating an accurate residue function in PCT imaging with the low-milliampere-seconds (low-mAs) data acquisition. Methods: Besides modeling the spatio-temporal structure information of PCT data, the STV regularization of the present PD-STV approach can utilize the higher order derivatives of the residue function to enhance denoising performance. To minimize the objective function, the authors propose an effective iterative algorithm with a shrinkage/thresholding scheme. A simulation study on a digital brain perfusion phantom and a clinical study on an old infarction patient were conducted to validate and evaluate the performance of the present PD-STV approach. Results: In the digital phantom study, visual inspection and quantitative metrics (i.e., the normalized mean square error, the peak signal-to-noise ratio, and the universal quality index) assessments demonstrated that the PD-STV approach outperformed other existing approaches in terms of the performance of noise-induced artifacts reduction and accurate perfusion hemodynamic maps (PHM) estimation. In the patient data study, the present PD-STV approach could yield accurate PHM estimation with several noticeable gains over other existing approaches in terms of visual inspection and correlation analysis. Conclusions: This study demonstrated the feasibility and efficacy of the present PD-STV approach in utilizing STV regularization to improve the accuracy of residue function estimation of cerebral PCT imaging in the case of low-mAs.

  16. The neural substrates of impaired finger tapping regularity after stroke.

    Science.gov (United States)

    Calautti, Cinzia; Jones, P Simon; Guincestre, Jean-Yves; Naccarato, Marcello; Sharma, Nikhil; Day, Diana J; Carpenter, T Adrian; Warburton, Elizabeth A; Baron, Jean-Claude

    2010-03-01

    Not only finger tapping speed, but also tapping regularity can be impaired after stroke, contributing to reduced dexterity. The neural substrates of impaired tapping regularity after stroke are unknown. Previous work suggests damage to the dorsal premotor cortex (PMd) and prefrontal cortex (PFCx) affects externally-cued hand movement. We tested the hypothesis that these two areas are involved in impaired post-stroke tapping regularity. In 19 right-handed patients (15 men/4 women; age 45-80 years; purely subcortical in 16) partially to fully recovered from hemiparetic stroke, tri-axial accelerometric quantitative assessment of tapping regularity and BOLD fMRI were obtained during fixed-rate auditory-cued index-thumb tapping, in a single session 10-230 days after stroke. A strong random-effect correlation between tapping regularity index and fMRI signal was found in contralesional PMd such that the worse the regularity the stronger the activation. A significant correlation in the opposite direction was also present within contralesional PFCx. Both correlations were maintained if maximal index tapping speed, degree of paresis and time since stroke were added as potential confounds. Thus, the contralesional PMd and PFCx appear to be involved in the impaired ability of stroke patients to fingertap in pace with external cues. The findings for PMd are consistent with repetitive TMS investigations in stroke suggesting a role for this area in affected-hand movement timing. The inverse relationship with tapping regularity observed for the PFCx and the PMd suggests these two anatomically-connected areas negatively co-operate. These findings have implications for understanding the disruption and reorganization of the motor systems after stroke. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  17. Regularity and mass conservation for discrete coagulation–fragmentation equations with diffusion

    KAUST Repository

    Cañizo, J.A.

    2010-03-01

    We present a new a priori estimate for discrete coagulation-fragmentation systems with size-dependent diffusion within a bounded, regular domain confined by homogeneous Neumann boundary conditions. Following from a duality argument, this a priori estimate provides a global L2 bound on the mass density and was previously used, for instance, in the context of reaction-diffusion equations. In this paper we demonstrate two lines of applications for such an estimate: On the one hand, it enables to simplify parts of the known existence theory and allows to show existence of solutions for generalised models involving collision-induced, quadratic fragmentation terms for which the previous existence theory seems difficult to apply. On the other hand and most prominently, it proves mass conservation (and thus the absence of gelation) for almost all the coagulation coefficients for which mass conservation is known to hold true in the space homogeneous case. © 2009 Elsevier Masson SAS. All rights reserved.

  18. Parameter Estimation

    DEFF Research Database (Denmark)

    Sales-Cruz, Mauricio; Heitzig, Martina; Cameron, Ian

    2011-01-01

    of optimisation techniques coupled with dynamic solution of the underlying model. Linear and nonlinear approaches to parameter estimation are investigated. There is also the application of maximum likelihood principles in the estimation of parameters, as well as the use of orthogonal collocation to generate a set......In this chapter the importance of parameter estimation in model development is illustrated through various applications related to reaction systems. In particular, rate constants in a reaction system are obtained through parameter estimation methods. These approaches often require the application...... of algebraic equations as the basis for parameter estimation.These approaches are illustrated using estimations of kinetic constants from reaction system models....

  19. Rotating Hayward’s regular black hole as particle accelerator

    International Nuclear Information System (INIS)

    Amir, Muhammed; Ghosh, Sushant G.

    2015-01-01

    Recently, Bañados, Silk and West (BSW) demonstrated that the extremal Kerr black hole can act as a particle accelerator with arbitrarily high center-of-mass energy (E CM ) when the collision takes place near the horizon. The rotating Hayward’s regular black hole, apart from Mass (M) and angular momentum (a), has a new parameter g (g>0 is a constant) that provides a deviation from the Kerr black hole. We demonstrate that for each g, with M=1, there exist critical a E and r H E , which corresponds to a regular extremal black hole with degenerate horizons, and a E decreases whereas r H E increases with increase in g. While aregular non-extremal black hole with outer and inner horizons. We apply the BSW process to the rotating Hayward’s regular black hole, for different g, and demonstrate numerically that the E CM diverges in the vicinity of the horizon for the extremal cases thereby suggesting that a rotating regular black hole can also act as a particle accelerator and thus in turn provide a suitable framework for Plank-scale physics. For a non-extremal case, there always exist a finite upper bound for the E CM , which increases with the deviation parameter g.

  20. Method of transferring regular shaped vessel into cell

    International Nuclear Information System (INIS)

    Murai, Tsunehiko.

    1997-01-01

    The present invention concerns a method of transferring regular shaped vessels from a non-contaminated area to a contaminated cell. A passage hole for allowing the regular shaped vessels to pass in the longitudinal direction is formed to a partitioning wall at the bottom of the contaminated cell. A plurality of regular shaped vessel are stacked in multiple stages in a vertical direction from the non-contaminated area present below the passage hole, allowed to pass while being urged and transferred successively into the contaminated cell. As a result, since they are transferred while substantially closing the passage hole by the regular shaped vessels, radiation rays or contaminated materials are prevented from discharging from the contaminated cell to the non-contaminated area. Since there is no requirement to open/close an isolation door frequently, the workability upon transfer can be improved remarkably. In addition, the sealing member for sealing the gap between the regular shaped vessel passing through the passage hole and the partitioning wall of the bottom is disposed to the passage hole, the contaminated materials in the contaminated cells can be prevented from discharging from the gap to the non-contaminated area. (N.H.)

  1. X-ray computed tomography using curvelet sparse regularization.

    Science.gov (United States)

    Wieczorek, Matthias; Frikel, Jürgen; Vogel, Jakob; Eggl, Elena; Kopp, Felix; Noël, Peter B; Pfeiffer, Franz; Demaret, Laurent; Lasser, Tobias

    2015-04-01

    Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

  2. On the MSE Performance and Optimization of Regularized Problems

    KAUST Repository

    Alrashdi, Ayed

    2016-11-01

    The amount of data that has been measured, transmitted/received, and stored in the recent years has dramatically increased. So, today, we are in the world of big data. Fortunately, in many applications, we can take advantages of possible structures and patterns in the data to overcome the curse of dimensionality. The most well known structures include sparsity, low-rankness, block sparsity. This includes a wide range of applications such as machine learning, medical imaging, signal processing, social networks and computer vision. This also led to a specific interest in recovering signals from noisy compressed measurements (Compressed Sensing (CS) problem). Such problems are generally ill-posed unless the signal is structured. The structure can be captured by a regularizer function. This gives rise to a potential interest in regularized inverse problems, where the process of reconstructing the structured signal can be modeled as a regularized problem. This thesis particularly focuses on finding the optimal regularization parameter for such problems, such as ridge regression, LASSO, square-root LASSO and low-rank Generalized LASSO. Our goal is to optimally tune the regularizer to minimize the mean-squared error (MSE) of the solution when the noise variance or structure parameters are unknown. The analysis is based on the framework of the Convex Gaussian Min-max Theorem (CGMT) that has been used recently to precisely predict performance errors.

  3. Two-Stage Regularized Linear Discriminant Analysis for 2-D Data.

    Science.gov (United States)

    Zhao, Jianhua; Shi, Lei; Zhu, Ji

    2015-08-01

    Fisher linear discriminant analysis (LDA) involves within-class and between-class covariance matrices. For 2-D data such as images, regularized LDA (RLDA) can improve LDA due to the regularized eigenvalues of the estimated within-class matrix. However, it fails to consider the eigenvectors and the estimated between-class matrix. To improve these two matrices simultaneously, we propose in this paper a new two-stage method for 2-D data, namely a bidirectional LDA (BLDA) in the first stage and the RLDA in the second stage, where both BLDA and RLDA are based on the Fisher criterion that tackles correlation. BLDA performs the LDA under special separable covariance constraints that incorporate the row and column correlations inherent in 2-D data. The main novelty is that we propose a simple but effective statistical test to determine the subspace dimensionality in the first stage. As a result, the first stage reduces the dimensionality substantially while keeping the significant discriminant information in the data. This enables the second stage to perform RLDA in a much lower dimensional subspace, and thus improves the two estimated matrices simultaneously. Experiments on a number of 2-D synthetic and real-world data sets show that BLDA+RLDA outperforms several closely related competitors.

  4. Travel time tomography with local image regularization by sparsity constrained dictionary learning

    Science.gov (United States)

    Bianco, M.; Gerstoft, P.

    2017-12-01

    We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

  5. The relationship between synchronization and percolation for regular networks

    Science.gov (United States)

    Li, Zhe; Ren, Tao; Xu, Yanjie; Jin, Jianyu

    2018-02-01

    Synchronization and percolation are two essential phenomena in complex dynamical networks. They have been studied widely, but previously treated as unrelated. In this paper, the relationship between synchronization and percolation are revealed for regular networks. Firstly, we discovered a bridge between synchronization and percolation by using the eigenvalues of the Laplacian matrix to describe the synchronizability and using the eigenvalues of the adjacency matrix to describe the percolation threshold. Then, we proposed a method to find the relationship for regular networks based on the topology of networks. Particularly, if the degree distribution of the network is subject to delta function, we show that only the eigenvalues of the adjacency matrix need to be calculated. Finally, several examples are provided to demonstrate how to apply our proposed method to discover the relationship between synchronization and percolation for regular networks.

  6. Variational analysis of regular mappings theory and applications

    CERN Document Server

    Ioffe, Alexander D

    2017-01-01

    This monograph offers the first systematic account of (metric) regularity theory in variational analysis. It presents new developments alongside classical results and demonstrates the power of the theory through applications to various problems in analysis and optimization theory. The origins of metric regularity theory can be traced back to a series of fundamental ideas and results of nonlinear functional analysis and global analysis centered around problems of existence and stability of solutions of nonlinear equations. In variational analysis, regularity theory goes far beyond the classical setting and is also concerned with non-differentiable and multi-valued operators. The present volume explores all basic aspects of the theory, from the most general problems for mappings between metric spaces to those connected with fairly concrete and important classes of operators acting in Banach and finite dimensional spaces. Written by a leading expert in the field, the book covers new and powerful techniques, whic...

  7. Variational regularization of 3D data experiments with Matlab

    CERN Document Server

    Montegranario, Hebert

    2014-01-01

    Variational Regularization of 3D Data provides an introduction to variational methods for data modelling and its application in computer vision. In this book, the authors identify interpolation as an inverse problem that can be solved by Tikhonov regularization. The proposed solutions are generalizations of one-dimensional splines, applicable to n-dimensional data and the central idea is that these splines can be obtained by regularization theory using a trade-off between the fidelity of the data and smoothness properties.As a foundation, the authors present a comprehensive guide to the necessary fundamentals of functional analysis and variational calculus, as well as splines. The implementation and numerical experiments are illustrated using MATLAB®. The book also includes the necessary theoretical background for approximation methods and some details of the computer implementation of the algorithms. A working knowledge of multivariable calculus and basic vector and matrix methods should serve as an adequat...

  8. Wavelet domain image restoration with adaptive edge-preserving regularization.

    Science.gov (United States)

    Belge, M; Kilmer, M E; Miller, E L

    2000-01-01

    In this paper, we consider a wavelet based edge-preserving regularization scheme for use in linear image restoration problems. Our efforts build on a collection of mathematical results indicating that wavelets are especially useful for representing functions that contain discontinuities (i.e., edges in two dimensions or jumps in one dimension). We interpret the resulting theory in a statistical signal processing framework and obtain a highly flexible framework for adapting the degree of regularization to the local structure of the underlying image. In particular, we are able to adapt quite easily to scale-varying and orientation-varying features in the image while simultaneously retaining the edge preservation properties of the regularizer. We demonstrate a half-quadratic algorithm for obtaining the restorations from observed data.

  9. Breast ultrasound tomography with total-variation regularization

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Lianjie [Los Alamos National Laboratory; Li, Cuiping [KARMANOS CANCER INSTIT.; Duric, Neb [KARMANOS CANCER INSTIT

    2009-01-01

    Breast ultrasound tomography is a rapidly developing imaging modality that has the potential to impact breast cancer screening and diagnosis. A new ultrasound breast imaging device (CURE) with a ring array of transducers has been designed and built at Karmanos Cancer Institute, which acquires both reflection and transmission ultrasound signals. To extract the sound-speed information from the breast data acquired by CURE, we have developed an iterative sound-speed image reconstruction algorithm for breast ultrasound transmission tomography based on total-variation (TV) minimization. We investigate applicability of the TV tomography algorithm using in vivo ultrasound breast data from 61 patients, and compare the results with those obtained using the Tikhonov regularization method. We demonstrate that, compared to the Tikhonov regularization scheme, the TV regularization method significantly improves image quality, resulting in sound-speed tomography images with sharp (preserved) edges of abnormalities and few artifacts.

  10. Manufacture of Regularly Shaped Sol-Gel Pellets

    Science.gov (United States)

    Leventis, Nicholas; Johnston, James C.; Kinder, James D.

    2006-01-01

    An extrusion batch process for manufacturing regularly shaped sol-gel pellets has been devised as an improved alternative to a spray process that yields irregularly shaped pellets. The aspect ratio of regularly shaped pellets can be controlled more easily, while regularly shaped pellets pack more efficiently. In the extrusion process, a wet gel is pushed out of a mold and chopped repetitively into short, cylindrical pieces as it emerges from the mold. The pieces are collected and can be either (1) dried at ambient pressure to xerogel, (2) solvent exchanged and dried under ambient pressure to ambigels, or (3) supercritically dried to aerogel. Advantageously, the extruded pellets can be dropped directly in a cross-linking bath, where they develop a conformal polymer coating around the skeletal framework of the wet gel via reaction with the cross linker. These pellets can be dried to mechanically robust X-Aerogel.

  11. Gamma regularization based reconstruction for low dose CT

    International Nuclear Information System (INIS)

    Zhang, Junfeng; Chen, Yang; Hu, Yining; Luo, Limin; Shu, Huazhong; Li, Bicao; Liu, Jin; Coatrieux, Jean-Louis

    2015-01-01

    Reducing the radiation in computerized tomography is today a major concern in radiology. Low dose computerized tomography (LDCT) offers a sound way to deal with this problem. However, more severe noise in the reconstructed CT images is observed under low dose scan protocols (e.g. lowered tube current or voltage values). In this paper we propose a Gamma regularization based algorithm for LDCT image reconstruction. This solution is flexible and provides a good balance between the regularizations based on l 0 -norm and l 1 -norm. We evaluate the proposed approach using the projection data from simulated phantoms and scanned Catphan phantoms. Qualitative and quantitative results show that the Gamma regularization based reconstruction can perform better in both edge-preserving and noise suppression when compared with other norms. (paper)

  12. Further investigation on "A multiplicative regularization for force reconstruction"

    Science.gov (United States)

    Aucejo, M.; De Smet, O.

    2018-05-01

    We have recently proposed a multiplicative regularization to reconstruct mechanical forces acting on a structure from vibration measurements. This method does not require any selection procedure for choosing the regularization parameter, since the amount of regularization is automatically adjusted throughout an iterative resolution process. The proposed iterative algorithm has been developed with performance and efficiency in mind, but it is actually a simplified version of a full iterative procedure not described in the original paper. The present paper aims at introducing the full resolution algorithm and comparing it with its simplified version in terms of computational efficiency and solution accuracy. In particular, it is shown that both algorithms lead to very similar identified solutions.

  13. Structural characterization of the packings of granular regular polygons.

    Science.gov (United States)

    Wang, Chuncheng; Dong, Kejun; Yu, Aibing

    2015-12-01

    By using a recently developed method for discrete modeling of nonspherical particles, we simulate the random packings of granular regular polygons with three to 11 edges under gravity. The effects of shape and friction on the packing structures are investigated by various structural parameters, including packing fraction, the radial distribution function, coordination number, Voronoi tessellation, and bond-orientational order. We find that packing fraction is generally higher for geometrically nonfrustrated regular polygons, and can be increased by the increase of edge number and decrease of friction. The changes of packing fraction are linked with those of the microstructures, such as the variations of the translational and orientational orders and local configurations. In particular, the free areas of Voronoi tessellations (which are related to local packing fractions) can be described by log-normal distributions for all polygons. The quantitative analyses establish a clearer picture for the packings of regular polygons.

  14. Manifestly scale-invariant regularization and quantum effective operators

    CERN Document Server

    Ghilencea, D.M.

    2016-01-01

    Scale invariant theories are often used to address the hierarchy problem, however the regularization of their quantum corrections introduces a dimensionful coupling (dimensional regularization) or scale (Pauli-Villars, etc) which break this symmetry explicitly. We show how to avoid this problem and study the implications of a manifestly scale invariant regularization in (classical) scale invariant theories. We use a dilaton-dependent subtraction function $\\mu(\\sigma)$ which after spontaneous breaking of scale symmetry generates the usual DR subtraction scale $\\mu(\\langle\\sigma\\rangle)$. One consequence is that "evanescent" interactions generated by scale invariance of the action in $d=4-2\\epsilon$ (but vanishing in $d=4$), give rise to new, finite quantum corrections. We find a (finite) correction $\\Delta U(\\phi,\\sigma)$ to the one-loop scalar potential for $\\phi$ and $\\sigma$, beyond the Coleman-Weinberg term. $\\Delta U$ is due to an evanescent correction ($\\propto\\epsilon$) to the field-dependent masses (of...

  15. Low-rank matrix approximation with manifold regularization.

    Science.gov (United States)

    Zhang, Zhenyue; Zhao, Keke

    2013-07-01

    This paper proposes a new model of low-rank matrix factorization that incorporates manifold regularization to the matrix factorization. Superior to the graph-regularized nonnegative matrix factorization, this new regularization model has globally optimal and closed-form solutions. A direct algorithm (for data with small number of points) and an alternate iterative algorithm with inexact inner iteration (for large scale data) are proposed to solve the new model. A convergence analysis establishes the global convergence of the iterative algorithm. The efficiency and precision of the algorithm are demonstrated numerically through applications to six real-world datasets on clustering and classification. Performance comparison with existing algorithms shows the effectiveness of the proposed method for low-rank factorization in general.

  16. Selecting protein families for environmental features based on manifold regularization.

    Science.gov (United States)

    Jiang, Xingpeng; Xu, Weiwei; Park, E K; Li, Guangrong

    2014-06-01

    Recently, statistics and machine learning have been developed to identify functional or taxonomic features of environmental features or physiological status. Important proteins (or other functional and taxonomic entities) to environmental features can be potentially used as biosensors. A major challenge is how the distribution of protein and gene functions embodies the adaption of microbial communities across environments and host habitats. In this paper, we propose a novel regularization method for linear regression to adapt the challenge. The approach is inspired by local linear embedding (LLE) and we call it a manifold-constrained regularization for linear regression (McRe). The novel regularization procedure also has potential to be used in solving other linear systems. We demonstrate the efficiency and the performance of the approach in both simulation and real data.

  17. Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network.

    Science.gov (United States)

    Wan, Mengting; Ouyang, Yunbo; Kaplan, Lance; Han, Jiawei

    2015-01-01

    A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because network based information for unlabeled objects is usually limited to make precise estimations. In this paper, we consider a graph regularized meta-path based transductive regression model ( Grempt ), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks. The computation of our method is time and space efficient and the precision of our model can be verified by numerical experiments.

  18. Fibonacci-regularization method for solving Cauchy integral equations of the first kind

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Fariborzi Araghi

    2017-09-01

    Full Text Available In this paper, a novel scheme is proposed to solve the first kind Cauchy integral equation over a finite interval. For this purpose, the regularization method is considered. Then, the collocation method with Fibonacci base function is applied to solve the obtained second kind singular integral equation. Also, the error estimate of the proposed scheme is discussed. Finally, some sample Cauchy integral equations stem from the theory of airfoils in fluid mechanics are presented and solved to illustrate the importance and applicability of the given algorithm. The tables in the examples show the efficiency of the method.

  19. Processing SPARQL queries with regular expressions in RDF databases

    Science.gov (United States)

    2011-01-01

    Background As the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf.org), SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users’ requests for extracting information from the RDF data as well as the lack of users’ knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph. Results In this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1) We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2) We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3) We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique. Conclusions Experiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns. PMID:21489225

  20. Processing SPARQL queries with regular expressions in RDF databases

    Directory of Open Access Journals (Sweden)

    Cho Hune

    2011-03-01

    Full Text Available Abstract Background As the Resource Description Framework (RDF data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf or Bio2RDF (bio2rdf.org, SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users’ requests for extracting information from the RDF data as well as the lack of users’ knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph. Results In this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1 We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2 We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3 We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique. Conclusions Experiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns.

  1. Processing SPARQL queries with regular expressions in RDF databases.

    Science.gov (United States)

    Lee, Jinsoo; Pham, Minh-Duc; Lee, Jihwan; Han, Wook-Shin; Cho, Hune; Yu, Hwanjo; Lee, Jeong-Hoon

    2011-03-29

    As the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf.org), SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users' requests for extracting information from the RDF data as well as the lack of users' knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph. In this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1) We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2) We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3) We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique. Experiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns.

  2. Substructural Regularization With Data-Sensitive Granularity for Sequence Transfer Learning.

    Science.gov (United States)

    Sun, Shichang; Liu, Hongbo; Meng, Jiana; Chen, C L Philip; Yang, Yu

    2018-06-01

    Sequence transfer learning is of interest in both academia and industry with the emergence of numerous new text domains from Twitter and other social media tools. In this paper, we put forward the data-sensitive granularity for transfer learning, and then, a novel substructural regularization transfer learning model (STLM) is proposed to preserve target domain features at substructural granularity in the light of the condition of labeled data set size. Our model is underpinned by hidden Markov model and regularization theory, where the substructural representation can be integrated as a penalty after measuring the dissimilarity of substructures between target domain and STLM with relative entropy. STLM can achieve the competing goals of preserving the target domain substructure and utilizing the observations from both the target and source domains simultaneously. The estimation of STLM is very efficient since an analytical solution can be derived as a necessary and sufficient condition. The relative usability of substructures to act as regularization parameters and the time complexity of STLM are also analyzed and discussed. Comprehensive experiments of part-of-speech tagging with both Brown and Twitter corpora fully justify that our model can make improvements on all the combinations of source and target domains.

  3. The Effect of Regular Physical Education in the Transformation Motor Development of Children with Special Needs

    Directory of Open Access Journals (Sweden)

    Danilo Bojanić

    2016-02-01

    Full Text Available The aim of the research is to determine the level of quantitative changes of motor abilities of pupils with special needs under the influence of kinetic activity regular physical education teaching. The survey was conducted on students of the Centre for children and youth with special needs in Mostar, the city of Los Rosales in Mostar and day care facilities for children with special needs in Niksic. The sample was composed of boys of 46 subjects, who were involved in regular physical education for a period of one school year. The level of quantitative and qualitative changes in motor skills, written under the influence of kinesiology operators within regular school physical education classes, was estimated by applying appropriate tests of motor skills, selected in accordance with the degree of mental ability and biological age. Manifest variables applied in this experiment were processed using standard descriptive methods in order to determine their distribution function and basic function parameters. Comparisons of results of measures of central dispersion parameters initial and final measurement, it is evident that the applied program of physical education and sport contribute to changing the distribution of central and dispersion parameters, and that the same distribution of the final measurement closer to the normal distribution of results.

  4. R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization.

    Science.gov (United States)

    Dazard, Jean-Eudes; Xu, Hua; Rao, J Sunil

    2011-01-01

    We present an implementation in the R language for statistical computing of our recent non-parametric joint adaptive mean-variance regularization and variance stabilization procedure. The method is specifically suited for handling difficult problems posed by high-dimensional multivariate datasets ( p ≫ n paradigm), such as in 'omics'-type data, among which are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. The implementation offers a complete set of features including: (i) normalization and/or variance stabilization function, (ii) computation of mean-variance-regularized t and F statistics, (iii) generation of diverse diagnostic plots, (iv) synthetic and real 'omics' test datasets, (v) computationally efficient implementation, using C interfacing, and an option for parallel computing, (vi) manual and documentation on how to setup a cluster. To make each feature as user-friendly as possible, only one subroutine per functionality is to be handled by the end-user. It is available as an R package, called MVR ('Mean-Variance Regularization'), downloadable from the CRAN.

  5. A Projection free method for Generalized Eigenvalue Problem with a nonsmooth Regularizer.

    Science.gov (United States)

    Hwang, Seong Jae; Collins, Maxwell D; Ravi, Sathya N; Ithapu, Vamsi K; Adluru, Nagesh; Johnson, Sterling C; Singh, Vikas

    2015-12-01

    Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation. Few other linear algebra problems have a more mature set of numerical routines available and many computer vision libraries leverage such tools extensively. However, the ability to call the underlying solver only as a "black box" can often become restrictive. Many 'human in the loop' settings in vision frequently exploit supervision from an expert, to the extent that the user can be considered a subroutine in the overall system. In other cases, there is additional domain knowledge, side or even partial information that one may want to incorporate within the formulation. In general, regularizing a (generalized) eigenvalue problem with such side information remains difficult. Motivated by these needs, this paper presents an optimization scheme to solve generalized eigenvalue problems (GEP) involving a (nonsmooth) regularizer. We start from an alternative formulation of GEP where the feasibility set of the model involves the Stiefel manifold. The core of this paper presents an end to end stochastic optimization scheme for the resultant problem. We show how this general algorithm enables improved statistical analysis of brain imaging data where the regularizer is derived from other 'views' of the disease pathology, involving clinical measurements and other image-derived representations.

  6. Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal.

    Science.gov (United States)

    Ge, Qi; Jing, Xiao-Yuan; Wu, Fei; Wei, Zhi-Hui; Xiao, Liang; Shao, Wen-Ze; Yue, Dong; Li, Hai-Bo

    2017-07-01

    Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms.

  7. REGULARIZED D-BAR METHOD FOR THE INVERSE CONDUCTIVITY PROBLEM

    DEFF Research Database (Denmark)

    Knudsen, Kim; Lassas, Matti; Mueller, Jennifer

    2009-01-01

    A strategy for regularizing the inversion procedure for the two-dimensional D-bar reconstruction algorithm based on the global uniqueness proof of Nachman [Ann. Math. 143 (1996)] for the ill-posed inverse conductivity problem is presented. The strategy utilizes truncation of the boundary integral...... the convergence of the reconstructed conductivity to the true conductivity as the noise level tends to zero. The results provide a link between two traditions of inverse problems research: theory of regularization and inversion methods based on complex geometrical optics. Also, the procedure is a novel...

  8. Arithmetic properties of $\\ell$-regular overpartition pairs

    OpenAIRE

    NAIKA, MEGADAHALLI SIDDA MAHADEVA; SHIVASHANKAR, CHANDRAPPA

    2017-01-01

    In this paper, we investigate the arithmetic properties of $\\ell$-regular overpartition pairs. Let $\\overline{B}_{\\ell}(n)$ denote the number of $\\ell$-regular overpartition pairs of $n$. We will prove the number of Ramanujan-like congruences and infinite families of congruences modulo 3, 8, 16, 36, 48, 96 for $\\overline{B}_3(n)$ and modulo 3, 16, 64, 96 for $\\overline{B}_4(n)$. For example, we find that for all nonnegative integers $\\alpha$ and $n$, $\\overline{B}_{3}(3^{\\alpha}(3n+2))\\equiv ...

  9. Adiabatic regularization of power spectra in nonminimally coupled chaotic inflation

    Energy Technology Data Exchange (ETDEWEB)

    Alinea, Allan L., E-mail: alinea@het.phys.sci.osaka-u.ac.jp [Department of Physics, Osaka University, Toyonaka, Osaka 560-0043 (Japan)

    2016-10-01

    We investigate the effect of adiabatic regularization on both the tensor- and scalar-perturbation power spectra in nonminimally coupled chaotic inflation. Similar to that of the minimally coupled general single-field inflation, we find that the subtraction term is suppressed by an exponentially decaying factor involving the number of e -folds. By following the subtraction term long enough beyond horizon crossing, the regularized power spectrum tends to the ''bare'' power spectrum. This study justifies the use of the unregularized (''bare'') power spectrum in standard calculations.

  10. Lifshitz anomalies, Ward identities and split dimensional regularization

    Energy Technology Data Exchange (ETDEWEB)

    Arav, Igal; Oz, Yaron; Raviv-Moshe, Avia [Raymond and Beverly Sackler School of Physics and Astronomy, Tel-Aviv University,55 Haim Levanon street, Tel-Aviv, 69978 (Israel)

    2017-03-16

    We analyze the structure of the stress-energy tensor correlation functions in Lifshitz field theories and construct the corresponding anomalous Ward identities. We develop a framework for calculating the anomaly coefficients that employs a split dimensional regularization and the pole residues. We demonstrate the procedure by calculating the free scalar Lifshitz scale anomalies in 2+1 spacetime dimensions. We find that the analysis of the regularization dependent trivial terms requires a curved spacetime description without a foliation structure. We discuss potential ambiguities in Lifshitz scale anomaly definitions.

  11. Stability of negative ionization fronts: Regularization by electric screening?

    International Nuclear Information System (INIS)

    Arrayas, Manuel; Ebert, Ute

    2004-01-01

    We recently have proposed that a reduced interfacial model for streamer propagation is able to explain spontaneous branching. Such models require regularization. In the present paper we investigate how transversal Fourier modes of a planar ionization front are regularized by the electric screening length. For a fixed value of the electric field ahead of the front we calculate the dispersion relation numerically. These results guide the derivation of analytical asymptotes for arbitrary fields: for small wave-vector k, the growth rate s(k) grows linearly with k, for large k, it saturates at some positive plateau value. We give a physical interpretation of these results

  12. Spanning k-ended trees of 3-regular connected graphs

    Directory of Open Access Journals (Sweden)

    Hamed Ghasemian Zoeram

    2017-10-01

    Full Text Available A vertex of degree one is called an end-vertex and the set of end-vertices of G is denoted by End(G. For a positive integer k, a tree T be called k-ended tree if $|End(T| \\leq k$. In this paper, we obtain sufficient conditions for spanning k-trees of 3-regular connected graphs. We give a construction sequence of graphs satisfying the condition. At the end, we present a conjecture about spanning k-ended trees of 3-regular connected graphs.

  13. Mathematical Modeling the Geometric Regularity in Proteus Mirabilis Colonies

    Science.gov (United States)

    Zhang, Bin; Jiang, Yi; Minsu Kim Collaboration

    Proteus Mirabilis colony exhibits striking spatiotemporal regularity, with concentric ring patterns with alternative high and low bacteria density in space, and periodicity for repetition process of growth and swarm in time. We present a simple mathematical model to explain the spatiotemporal regularity of P. Mirabilis colonies. We study a one-dimensional system. Using a reaction-diffusion model with thresholds in cell density and nutrient concentration, we recreated periodic growth and spread patterns, suggesting that the nutrient constraint and cell density regulation might be sufficient to explain the spatiotemporal periodicity in P. Mirabilis colonies. We further verify this result using a cell based model.

  14. Parameter optimization in the regularized kernel minimum noise fraction transformation

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

    2012-01-01

    Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF...... analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given....

  15. Lifshitz anomalies, Ward identities and split dimensional regularization

    International Nuclear Information System (INIS)

    Arav, Igal; Oz, Yaron; Raviv-Moshe, Avia

    2017-01-01

    We analyze the structure of the stress-energy tensor correlation functions in Lifshitz field theories and construct the corresponding anomalous Ward identities. We develop a framework for calculating the anomaly coefficients that employs a split dimensional regularization and the pole residues. We demonstrate the procedure by calculating the free scalar Lifshitz scale anomalies in 2+1 spacetime dimensions. We find that the analysis of the regularization dependent trivial terms requires a curved spacetime description without a foliation structure. We discuss potential ambiguities in Lifshitz scale anomaly definitions.

  16. Experimental investigations on frictional resistance and velocity distribution of rough wall with regularly distributed triangular ribs

    International Nuclear Information System (INIS)

    Motozawa, Masaaki; Ito, Takahiro; Iwamoto, Kaoru; Kawashima, Hideki; Ando, Hirotomo; Senda, Tetsuya; Tsuji, Yoshiyuki; Kawaguchi, Yasuo

    2013-01-01

    Highlights: • Flow over the regularly distributed triangular ribs was investigated. • Simultaneous measurement of flow resistance and velocity profile was performed. • Flow resistance was measured directly and velocity profile was measured by LDV. • Flow resistance was estimated by the information of the velocity field. • Estimated flow resistance has good agreement with the measured flow resistance. -- Abstract: The relationship between the flow resistance of a turbulent flow over triangular ribs regularly distributed on a wall surface and the velocity distribution around the ribs was investigated experimentally. A concentric cylinder device composed of an inner test cylinder and an outer cylinder was employed to measure the flow resistance using the torque of the shaft of the inner cylinder and the velocity distribution of the flow around a rib by laser Doppler velocimetry (LDV) simultaneously. We prepared four inner test cylinders having 4, 8, 12 and 16 triangular ribs on the surface with the same interval between them. Each rib had an isosceles right triangle V-shape and a height of 2 mm. To investigate the relationship between flow resistance and velocity distribution, we estimated the frictional drag and pressure drag acting on the surface of the ribs separately using the velocity distribution. Therefore, we could also estimate the total flow resistance using the velocity distribution. As a result of the experiment, the flow resistance and the attachment point downstream of the rib were shown to depend on the distance between ribs. Moreover, the flow resistance estimated using the velocity distribution had good agreement with the flow resistance measured using the torque of the inner cylinder

  17. A practicable γ5-scheme in dimensional regularization

    International Nuclear Information System (INIS)

    Koerner, J.G.; Kreimer, D.; Schilcher, K.

    1991-08-01

    We present a new simple Υ 5 regularization scheme. We discuss its use in the standard radiative correction calculations including the anomaly contributions. The new scheme features an anticommuting Υ 5 which leads to great simplifications in practical calculations. We carefully discuss the underlying mathematics of our Υ 5 -scheme which is formulated in terms of simple projection operations. (orig.)

  18. The regularized monotonicity method: detecting irregular indefinite inclusions

    DEFF Research Database (Denmark)

    Garde, Henrik; Staboulis, Stratos

    2018-01-01

    inclusions, where the conductivity distribution has both more and less conductive parts relative to the background conductivity; one such method is the monotonicity method of Harrach, Seo, and Ullrich. We formulate the method for irregular indefinite inclusions, meaning that we make no regularity assumptions...

  19. Fast regularizing sequential subspace optimization in Banach spaces

    International Nuclear Information System (INIS)

    Schöpfer, F; Schuster, T

    2009-01-01

    We are concerned with fast computations of regularized solutions of linear operator equations in Banach spaces in case only noisy data are available. To this end we modify recently developed sequential subspace optimization methods in such a way that the therein employed Bregman projections onto hyperplanes are replaced by Bregman projections onto stripes whose width is in the order of the noise level

  20. Statistical regularities in art: Relations with visual coding and perception.

    Science.gov (United States)

    Graham, Daniel J; Redies, Christoph

    2010-07-21

    Since at least 1935, vision researchers have used art stimuli to test human response to complex scenes. This is sensible given the "inherent interestingness" of art and its relation to the natural visual world. The use of art stimuli has remained popular, especially in eye tracking studies. Moreover, stimuli in common use by vision scientists are inspired by the work of famous artists (e.g., Mondrians). Artworks are also popular in vision science as illustrations of a host of visual phenomena, such as depth cues and surface properties. However, until recently, there has been scant consideration of the spatial, luminance, and color statistics of artwork, and even less study of ways that regularities in such statistics could affect visual processing. Furthermore, the relationship between regularities in art images and those in natural scenes has received little or no attention. In the past few years, there has been a concerted effort to study statistical regularities in art as they relate to neural coding and visual perception, and art stimuli have begun to be studied in rigorous ways, as natural scenes have been. In this minireview, we summarize quantitative studies of links between regular statistics in artwork and processing in the visual stream. The results of these studies suggest that art is especially germane to understanding human visual coding and perception, and it therefore warrants wider study. Copyright 2010 Elsevier Ltd. All rights reserved.

  1. Cost Effectiveness of Premium Versus Regular Gasoline in MCPS Buses.

    Science.gov (United States)

    Baacke, Clifford M.; Frankel, Steven M.

    The primary question posed in this study is whether premium or regular gasoline is more cost effective for the Montgomery County Public School (MCPS) bus fleet, as a whole, when miles-per-gallon, cost-per-gallon, and repair costs associated with mileage are considered. On average, both miles-per-gallon, and repair costs-per-mile favor premium…

  2. A dynamical system approach to texel identification in regular textures

    NARCIS (Netherlands)

    Grigorescu, S.E.; Petkov, N.; Loncaric, S; Neri, A; Babic, H

    2003-01-01

    We propose a texture analysis method based on Rényi’s entropies. The method aims at identifying texels in regular textures by searching for the smallest window through which the minimum number of different visual patterns is observed when moving the window over a given texture. The experimental

  3. Patterns and Regularities in the European Marketing Academic Community

    DEFF Research Database (Denmark)

    Christensen, Katrine; Krystallis Krontalis, Athanasios; Ormrod, Robert P.

    2011-01-01

    ) and to examine the appearance of patterns and regularities in the way EMAC members choose collaboration partners. The work is based on a Social Network Analysis of the co-authored publications presented at the 2000-2010 EMAC conferences. Results show that the main selection criteria for choosing collaboration...

  4. Analytic stochastic regularization in QCD and its supersymmetric extension

    International Nuclear Information System (INIS)

    Abdalla, E.; Vianna, R.L.

    1987-08-01

    We outline some features of stochastic quantization and regularization of fermionic fields with applications to spinor QCD, showing the appearence of a non-gauge invariant counterterm. We also show that non-invariant terms cancel in supersymmetric multiplets. (Author) [pt

  5. On the number of spanning trees in random regular graphs

    DEFF Research Database (Denmark)

    Greenhill, Catherine; Kwan, Matthew; Wind, David Kofoed

    2014-01-01

    Let d >= 3 be a fixed integer. We give an asympotic formula for the expected number of spanning trees in a uniformly random d-regular graph with n vertices. (The asymptotics are as n -> infinity, restricted to even n if d is odd.) We also obtain the asymptotic distribution of the number of spanning...

  6. BER analysis of regularized least squares for BPSK recovery

    KAUST Repository

    Ben Atitallah, Ismail; Thrampoulidis, Christos; Kammoun, Abla; Al-Naffouri, Tareq Y.; Hassibi, Babak; Alouini, Mohamed-Slim

    2017-01-01

    This paper investigates the problem of recovering an n-dimensional BPSK signal x0 ∈ {−1, 1}n from m-dimensional measurement vector y = Ax+z, where A and z are assumed to be Gaussian with iid entries. We consider two variants of decoders based on the regularized least squares followed by hard-thresholding: the case where the convex relaxation is from {−1, 1}n to ℝn and the box constrained case where the relaxation is to [−1, 1]n. For both cases, we derive an exact expression of the bit error probability when n and m grow simultaneously large at a fixed ratio. For the box constrained case, we show that there exists a critical value of the SNR, above which the optimal regularizer is zero. On the other side, the regularization can further improve the performance of the box relaxation at low to moderate SNR regimes. We also prove that the optimal regularizer in the bit error rate sense for the unboxed case is nothing but the MMSE detector.

  7. The Student with Albinism in the Regular Classroom.

    Science.gov (United States)

    Ashley, Julia Robertson

    This booklet, intended for regular education teachers who have children with albinism in their classes, begins with an explanation of albinism, then discusses the special needs of the student with albinism in the classroom, and presents information about adaptations and other methods for responding to these needs. Special social and emotional…

  8. Noise Representation in Residuals of LSQR, LSMR, and CRAIG Regularization

    Czech Academy of Sciences Publication Activity Database

    Hnětynková, I.; Kubínová, Marie; Plešinger, M.

    2017-01-01

    Roč. 533, č. 15 (2017), s. 357-379 ISSN 0024-3795 Grant - others:GA ČR(CZ) GC17-04150J Institutional support: RVO:67985807 Keywords : Ill-posed problems * regularization * Golub–Kahan iterative bidiagonalization * LSQR * LSMR * CRAIG Subject RIV: BA - General Mathematics OBOR OECD: Applied mathematics Impact factor: 0.973, year: 2016

  9. Spectral Regularization Algorithms for Learning Large Incomplete Matrices.

    Science.gov (United States)

    Mazumder, Rahul; Hastie, Trevor; Tibshirani, Robert

    2010-03-01

    We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and very efficient convex algorithm for minimizing the reconstruction error subject to a bound on the nuclear norm. Our algorithm Soft-Impute iteratively replaces the missing elements with those obtained from a soft-thresholded SVD. With warm starts this allows us to efficiently compute an entire regularization path of solutions on a grid of values of the regularization parameter. The computationally intensive part of our algorithm is in computing a low-rank SVD of a dense matrix. Exploiting the problem structure, we show that the task can be performed with a complexity linear in the matrix dimensions. Our semidefinite-programming algorithm is readily scalable to large matrices: for example it can obtain a rank-80 approximation of a 10(6) × 10(6) incomplete matrix with 10(5) observed entries in 2.5 hours, and can fit a rank 40 approximation to the full Netflix training set in 6.6 hours. Our methods show very good performance both in training and test error when compared to other competitive state-of-the art techniques.

  10. Surface-based prostate registration with biomechanical regularization

    Science.gov (United States)

    van de Ven, Wendy J. M.; Hu, Yipeng; Barentsz, Jelle O.; Karssemeijer, Nico; Barratt, Dean; Huisman, Henkjan J.

    2013-03-01

    Adding MR-derived information to standard transrectal ultrasound (TRUS) images for guiding prostate biopsy is of substantial clinical interest. A tumor visible on MR images can be projected on ultrasound by using MRUS registration. A common approach is to use surface-based registration. We hypothesize that biomechanical modeling will better control deformation inside the prostate than a regular surface-based registration method. We developed a novel method by extending a surface-based registration with finite element (FE) simulation to better predict internal deformation of the prostate. For each of six patients, a tetrahedral mesh was constructed from the manual prostate segmentation. Next, the internal prostate deformation was simulated using the derived radial surface displacement as boundary condition. The deformation field within the gland was calculated using the predicted FE node displacements and thin-plate spline interpolation. We tested our method on MR guided MR biopsy imaging data, as landmarks can easily be identified on MR images. For evaluation of the registration accuracy we used 45 anatomical landmarks located in all regions of the prostate. Our results show that the median target registration error of a surface-based registration with biomechanical regularization is 1.88 mm, which is significantly different from 2.61 mm without biomechanical regularization. We can conclude that biomechanical FE modeling has the potential to improve the accuracy of multimodal prostate registration when comparing it to regular surface-based registration.

  11. Optimal Tikhonov Regularization in Finite-Frequency Tomography

    Science.gov (United States)

    Fang, Y.; Yao, Z.; Zhou, Y.

    2017-12-01

    The last decade has witnessed a progressive transition in seismic tomography from ray theory to finite-frequency theory which overcomes the resolution limit of the high-frequency approximation in ray theory. In addition to approximations in wave propagation physics, a main difference between ray-theoretical tomography and finite-frequency tomography is the sparseness of the associated sensitivity matrix. It is well known that seismic tomographic problems are ill-posed and regularizations such as damping and smoothing are often applied to analyze the tradeoff between data misfit and model uncertainty. The regularizations depend on the structure of the matrix as well as noise level of the data. Cross-validation has been used to constrain data uncertainties in body-wave finite-frequency inversions when measurements at multiple frequencies are available to invert for a common structure. In this study, we explore an optimal Tikhonov regularization in surface-wave phase-velocity tomography based on minimization of an empirical Bayes risk function using theoretical training datasets. We exploit the structure of the sensitivity matrix in the framework of singular value decomposition (SVD) which also allows for the calculation of complete resolution matrix. We compare the optimal Tikhonov regularization in finite-frequency tomography with traditional tradeo-off analysis using surface wave dispersion measurements from global as well as regional studies.

  12. Regularizing Unpredictable Variation: Evidence from a Natural Language Setting

    Science.gov (United States)

    Hendricks, Alison Eisel; Miller, Karen; Jackson, Carrie N.

    2018-01-01

    While previous sociolinguistic research has demonstrated that children faithfully acquire probabilistic input constrained by sociolinguistic and linguistic factors (e.g., gender and socioeconomic status), research suggests children regularize inconsistent input-probabilistic input that is not sociolinguistically constrained (e.g., Hudson Kam &…

  13. Regular graph construction for semi-supervised learning

    International Nuclear Information System (INIS)

    Vega-Oliveros, Didier A; Berton, Lilian; Eberle, Andre Mantini; Lopes, Alneu de Andrade; Zhao, Liang

    2014-01-01

    Semi-supervised learning (SSL) stands out for using a small amount of labeled points for data clustering and classification. In this scenario graph-based methods allow the analysis of local and global characteristics of the available data by identifying classes or groups regardless data distribution and representing submanifold in Euclidean space. Most of methods used in literature for SSL classification do not worry about graph construction. However, regular graphs can obtain better classification accuracy compared to traditional methods such as k-nearest neighbor (kNN), since kNN benefits the generation of hubs and it is not appropriate for high-dimensionality data. Nevertheless, methods commonly used for generating regular graphs have high computational cost. We tackle this problem introducing an alternative method for generation of regular graphs with better runtime performance compared to methods usually find in the area. Our technique is based on the preferential selection of vertices according some topological measures, like closeness, generating at the end of the process a regular graph. Experiments using the global and local consistency method for label propagation show that our method provides better or equal classification rate in comparison with kNN

  14. Human visual system automatically represents large-scale sequential regularities.

    Science.gov (United States)

    Kimura, Motohiro; Widmann, Andreas; Schröger, Erich

    2010-03-04

    Our brain recordings reveal that large-scale sequential regularities defined across non-adjacent stimuli can be automatically represented in visual sensory memory. To show that, we adopted an auditory paradigm developed by Sussman, E., Ritter, W., and Vaughan, H. G. Jr. (1998). Predictability of stimulus deviance and the mismatch negativity. NeuroReport, 9, 4167-4170, Sussman, E., and Gumenyuk, V. (2005). Organization of sequential sounds in auditory memory. NeuroReport, 16, 1519-1523 to the visual domain by presenting task-irrelevant infrequent luminance-deviant stimuli (D, 20%) inserted among task-irrelevant frequent stimuli being of standard luminance (S, 80%) in randomized (randomized condition, SSSDSSSSSDSSSSD...) and fixed manners (fixed condition, SSSSDSSSSDSSSSD...). Comparing the visual mismatch negativity (visual MMN), an event-related brain potential (ERP) index of memory-mismatch processes in human visual sensory system, revealed that visual MMN elicited by deviant stimuli was reduced in the fixed compared to the randomized condition. Thus, the large-scale sequential regularity being present in the fixed condition (SSSSD) must have been represented in visual sensory memory. Interestingly, this effect did not occur in conditions with stimulus-onset asynchronies (SOAs) of 480 and 800 ms but was confined to the 160-ms SOA condition supporting the hypothesis that large-scale regularity extraction was based on perceptual grouping of the five successive stimuli defining the regularity. 2010 Elsevier B.V. All rights reserved.

  15. 76 FR 74831 - Regular Board of Directors Meeting; Sunshine Act

    Science.gov (United States)

    2011-12-01

    ..., DC 20005. STATUS: Open. CONTACT PERSON FOR MORE INFORMATION: Erica Hall, Assistant Corporate... the Regular Board of Directors Meeting Minutes IV. Approval of the Finance, Budget and Program Committee Meeting Minutes V. Approval of the Corporate Administrative Committee Meeting Minutes VI. Approval...

  16. SparseBeads data: benchmarking sparsity-regularized computed tomography

    DEFF Research Database (Denmark)

    Jørgensen, Jakob Sauer; Coban, Sophia B.; Lionheart, William R. B.

    2017-01-01

    -regularized reconstruction. A collection of 48 x-ray CT datasets called SparseBeads was designed for benchmarking SR reconstruction algorithms. Beadpacks comprising glass beads of five different sizes as well as mixtures were scanned in a micro-CT scanner to provide structured datasets with variable image sparsity levels...

  17. BER analysis of regularized least squares for BPSK recovery

    KAUST Repository

    Ben Atitallah, Ismail

    2017-06-20

    This paper investigates the problem of recovering an n-dimensional BPSK signal x0 ∈ {−1, 1}n from m-dimensional measurement vector y = Ax+z, where A and z are assumed to be Gaussian with iid entries. We consider two variants of decoders based on the regularized least squares followed by hard-thresholding: the case where the convex relaxation is from {−1, 1}n to ℝn and the box constrained case where the relaxation is to [−1, 1]n. For both cases, we derive an exact expression of the bit error probability when n and m grow simultaneously large at a fixed ratio. For the box constrained case, we show that there exists a critical value of the SNR, above which the optimal regularizer is zero. On the other side, the regularization can further improve the performance of the box relaxation at low to moderate SNR regimes. We also prove that the optimal regularizer in the bit error rate sense for the unboxed case is nothing but the MMSE detector.

  18. Regular and stochastic particle motion in plasma dynamics

    International Nuclear Information System (INIS)

    Kaufman, A.N.

    1979-08-01

    A Hamiltonian formalism is presented for the study of charged-particle trajectories in the self-consistent field of the particles. The intention is to develop a general approach to plasma dynamics. Transformations of phase-space variables are used to separate out the regular, adiabatic motion from the irregular, stochastic trajectories. Several new techniques are included in this presentation

  19. Cospectral Graphs and Regular Orthogonal Matrices of Level 2

    NARCIS (Netherlands)

    Abiad Monge, A.; Haemers, W.H.

    2012-01-01

    Abstract: For a graph Γ with adjacency matrix A, we consider a switching operation that takes Γ into a graph Γ' with adjacency matrix A', defined by A' = QtAQ, where Q is a regular orthogonal matrix of level 2 (that is, QtQ = I, Q1 = 1, 2Q is integral, and Q is not a permutation matrix). If such an

  20. Robust regularized singular value decomposition with application to mortality data

    KAUST Repository

    Zhang, Lingsong; Shen, Haipeng; Huang, Jianhua Z.

    2013-01-01

    We develop a robust regularized singular value decomposition (RobRSVD) method for analyzing two-way functional data. The research is motivated by the application of modeling human mortality as a smooth two-way function of age group and year. The Rob

  1. Regularized Adaptive Notch Filters for Acoustic Howling Suppression

    DEFF Research Database (Denmark)

    Gil-Cacho, Pepe; van Waterschoot, Toon; Moonen, Marc

    2009-01-01

    In this paper, a method for the suppression of acoustic howling is developed, based on adaptive notch filters (ANF) with regularization (RANF). The method features three RANFs working in parallel to achieve frequency tracking, howling detection and suppression. The ANF-based approach to howling...

  2. dental services and attitudes towards its regular utilization among

    African Journals Online (AJOL)

    DENTAL SERVICES AND ATTITUDES TOWARDS ITS REGULAR UTILIZATION. AMONG CIVIL SERVANTS IN IBADAN, NIGERIA. M.E. Osuh1, G.A. Oke1 and M.C. Asuzu2. 1Department of Periodontology and Community Dentistry, Faculty of Dentistry, University of Ibadan. 2Department of Preventive Medicine and Primary ...

  3. Infants Learn Phonotactic Regularities from Brief Auditory Experience.

    Science.gov (United States)

    Chambers, Kyle E.; Onishi, Kristine H.; Fisher, Cynthia

    2003-01-01

    Two experiments investigated whether novel phonotactic regularities, not present in English, could be acquired by 16.5-month-olds from brief auditory experience. Subjects listened to consonant-vowel-consonant syllables in which particular consonants were artificially restricted to either initial or final position. Findings in a subsequent…

  4. Regularizing properties of Complex Monge-Amp\\`ere flows

    OpenAIRE

    Tô, Tat Dat

    2016-01-01

    We study the regularizing properties of complex Monge-Amp\\`ere flows on a K\\"ahler manifold $(X,\\omega)$ when the initial data are $\\omega$-psh functions with zero Lelong number at all points. We prove that the general Monge-Amp\\`ere flow has a solution which is immediately smooth. We also prove the uniqueness and stability of solution.

  5. Periodontal Disease, Regular Dental Care Use, and Incident Ischemic Stroke.

    Science.gov (United States)

    Sen, Souvik; Giamberardino, Lauren D; Moss, Kevin; Morelli, Thiago; Rosamond, Wayne D; Gottesman, Rebecca F; Beck, James; Offenbacher, Steven

    2018-02-01

    Periodontal disease is independently associated with cardiovascular disease. Identification of periodontal disease as a risk factor for incident ischemic stroke raises the possibility that regular dental care utilization may reduce the stroke risk. In the ARIC (Atherosclerosis Risk in Communities) study, pattern of dental visits were classified as regular or episodic dental care users. In the ancillary dental ARIC study, selected subjects from ARIC underwent fullmouth periodontal measurements collected at 6 sites per tooth and classified into 7 periodontal profile classes (PPCs). In the ARIC study 10 362 stroke-free participants, 584 participants had incident ischemic strokes over a 15-year period. In the dental ARIC study, 6736 dentate subjects were assessed for periodontal disease status using PPC with a total of 299 incident ischemic strokes over the 15-year period. The 7 levels of PPC showed a trend toward an increased stroke risk (χ 2 trend P periodontal disease). Periodontal disease was significantly associated with cardioembolic (hazard ratio, 2.6; 95% confidence interval, 1.2-5.6) and thrombotic (hazard ratio, 2.2; 95% confidence interval, 1.3-3.8) stroke subtypes. Regular dental care utilization was associated with lower adjusted stroke risk (hazard ratio, 0.77; 95% confidence interval, 0.63-0.94). We confirm an independent association between periodontal disease and incident stroke risk, particularly cardioembolic and thrombotic stroke subtype. Further, we report that regular dental care utilization may lower this risk for stroke. © 2018 American Heart Association, Inc.

  6. Dental Services and Attitudes towards its regular Utilization ... - Ibadan

    African Journals Online (AJOL)

    Background: Regular utilization of dental services is key to the attainment of optimal oral health state, an integral component of general health and well being needed for effective productivity by working personnel. Objective: This study assessed the rate and pattern of dental service utilization among civil servants and their ...

  7. Comparative study of casual and regular workers' job satisfaction ...

    African Journals Online (AJOL)

    This study investigated the comparative study of regular and casual workers' job satisfaction and commitment in two selected banks in Lagos State, A descriptive survey research design was adopted for the study. A total of 145 respondents were selected for the study using proportionate stratified sampling technique.

  8. Regularity theorem for functions that are extremal to Paley inequality ...

    African Journals Online (AJOL)

    Regularity theorem for functions that are extremal to Paley inequality. Seid Mohammed. Abstract. In this paper we study the asymptotic behavior of functions that are extremal to the inequality introduced by Paley (1932) via a normal family of subharmonic functions. SINET: Ethiopian Journal of Science Volume 24, No.

  9. Shakeout: A New Approach to Regularized Deep Neural Network Training.

    Science.gov (United States)

    Kang, Guoliang; Li, Jun; Tao, Dacheng

    2018-05-01

    Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines , and regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.

  10. Linear operator inequalities for strongly stable weakly regular linear systems

    NARCIS (Netherlands)

    Curtain, RF

    2001-01-01

    We consider the question of the existence of solutions to certain linear operator inequalities (Lur'e equations) for strongly stable, weakly regular linear systems with generating operators A, B, C, 0. These operator inequalities are related to the spectral factorization of an associated Popov

  11. Effects of Irregular Bridge Columns and Feasibility of Seismic Regularity

    Science.gov (United States)

    Thomas, Abey E.

    2018-05-01

    Bridges with unequal column height is one of the main irregularities in bridge design particularly while negotiating steep valleys, making the bridges vulnerable to seismic action. The desirable behaviour of bridge columns towards seismic loading is that, they should perform in a regular fashion, i.e. the capacity of each column should be utilized evenly. But, this type of behaviour is often missing when the column heights are unequal along the length of the bridge, allowing short columns to bear the maximum lateral load. In the present study, the effects of unequal column height on the global seismic performance of bridges are studied using pushover analysis. Codes such as CalTrans (Engineering service center, earthquake engineering branch, 2013) and EC-8 (EN 1998-2: design of structures for earthquake resistance. Part 2: bridges, European Committee for Standardization, Brussels, 2005) suggests seismic regularity criterion for achieving regular seismic performance level at all the bridge columns. The feasibility of adopting these seismic regularity criterions along with those mentioned in literatures will be assessed for bridges designed as per the Indian Standards in the present study.

  12. Psychological Benefits of Regular Physical Activity: Evidence from Emerging Adults

    Science.gov (United States)

    Cekin, Resul

    2015-01-01

    Emerging adulthood is a transitional stage between late adolescence and young adulthood in life-span development that requires significant changes in people's lives. Therefore, identifying protective factors for this population is crucial. This study investigated the effects of regular physical activity on self-esteem, optimism, and happiness in…

  13. 75 FR 23218 - Information Collection; Direct Loan Servicing-Regular

    Science.gov (United States)

    2010-05-03

    ... collections established in the regulation are necessary for FSA to monitor and account for loan security... DEPARTMENT OF AGRICULTURE Farm Service Agency Information Collection; Direct Loan Servicing... collection that supports Direct Loan Servicing-Regular programs. The information is used to determine...

  14. On certain inequalities for some regular functions in |z|<1

    Directory of Open Access Journals (Sweden)

    Milutin Obradovic

    1985-01-01

    Full Text Available In this paper we give some inequalities for regular functions f(z=z+a2z2+… in |z|<1 especially for starlike and convex functions of order α, 0≤α<1, To some extent those inequalities are the generalisations and improvements of the previous results given by Bernardi [1]. Some interesting consequences are given, too.

  15. Regular classroom assessment as a means of enhancing Teacher ...

    African Journals Online (AJOL)

    This study was an action research which employed regular classroom tests to help students learn and understand some concepts in electricity and magnetism. The participants of the study were 35 Level 200 B.Ed. (Basic Education, JSS Option) pre-service science teachers of the University of Education, Winneba in the ...

  16. Boosting Maintenance in Working Memory with Temporal Regularities

    Science.gov (United States)

    Plancher, Gaën; Lévêque, Yohana; Fanuel, Lison; Piquandet, Gaëlle; Tillmann, Barbara

    2018-01-01

    Music cognition research has provided evidence for the benefit of temporally regular structures guiding attention over time. The present study investigated whether maintenance in working memory can benefit from an isochronous rhythm. Participants were asked to remember series of 6 letters for serial recall. In the rhythm condition of Experiment…

  17. Rhythmic regularity revisited : Is beat induction indeed pre-attentive?

    NARCIS (Netherlands)

    Bouwer, F.; Honing, H.; Cambouropoulos, E.; Tsougras, C.; Mavromatis, P.; Pastiadis, K.

    2012-01-01

    When listening to musical rhythm, regularity in time is often perceived in the form of a beat or pulse. External rhythmic events can give rise to the perception of a beat, through a process known as beat induction. In addition, internal processes, like long-term memory, working memory and automatic

  18. Manifold regularization for sparse unmixing of hyperspectral images.

    Science.gov (United States)

    Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin

    2016-01-01

    Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

  19. Regularization of the Boundary-Saddle-Node Bifurcation

    Directory of Open Access Journals (Sweden)

    Xia Liu

    2018-01-01

    Full Text Available In this paper we treat a particular class of planar Filippov systems which consist of two smooth systems that are separated by a discontinuity boundary. In such systems one vector field undergoes a saddle-node bifurcation while the other vector field is transversal to the boundary. The boundary-saddle-node (BSN bifurcation occurs at a critical value when the saddle-node point is located on the discontinuity boundary. We derive a local topological normal form for the BSN bifurcation and study its local dynamics by applying the classical Filippov’s convex method and a novel regularization approach. In fact, by the regularization approach a given Filippov system is approximated by a piecewise-smooth continuous system. Moreover, the regularization process produces a singular perturbation problem where the original discontinuous set becomes a center manifold. Thus, the regularization enables us to make use of the established theories for continuous systems and slow-fast systems to study the local behavior around the BSN bifurcation.

  20. Continuous Flattening of a Regular Tetrahedron with Explicit Mappings

    Directory of Open Access Journals (Sweden)

    Jin-ichi Itoh

    2012-01-01

    Full Text Available We proved in [10] that each Platonic polyhedron P can be folded into a flat multilayered face of P by a continuous folding process of polyhedra. In this paper, we give explicit formulas of continuous functions for such a continuous flattening process in R³ for a regular tetrahedron.The article is published in the author’s wording.