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.

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images.

Science.gov (United States)

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-05-22

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures.

Adaptive multiresolution method for MAP reconstruction in electron tomography

Energy Technology Data Exchange (ETDEWEB)

Acar, Erman, E-mail: erman.acar@tut.fi [Department of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere (Finland); BioMediTech, Tampere University of Technology, Biokatu 10, 33520 Tampere (Finland); Peltonen, Sari; Ruotsalainen, Ulla [Department of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere (Finland); BioMediTech, Tampere University of Technology, Biokatu 10, 33520 Tampere (Finland)

2016-11-15

3D image reconstruction with electron tomography holds problems due to the severely limited range of projection angles and low signal to noise ratio of the acquired projection images. The maximum a posteriori (MAP) reconstruction methods have been successful in compensating for the missing information and suppressing noise with their intrinsic regularization techniques. There are two major problems in MAP reconstruction methods: (1) selection of the regularization parameter that controls the balance between the data fidelity and the prior information, and (2) long computation time. One aim of this study is to provide an adaptive solution to the regularization parameter selection problem without having additional knowledge about the imaging environment and the sample. The other aim is to realize the reconstruction using sequences of resolution levels to shorten the computation time. The reconstructions were analyzed in terms of accuracy and computational efficiency using a simulated biological phantom and publically available experimental datasets of electron tomography. The numerical and visual evaluations of the experiments show that the adaptive multiresolution method can provide more accurate results than the weighted back projection (WBP), simultaneous iterative reconstruction technique (SIRT), and sequential MAP expectation maximization (sMAPEM) method. The method is superior to sMAPEM also in terms of computation time and usability since it can reconstruct 3D images significantly faster without requiring any parameter to be set by the user. - Highlights: • An adaptive multiresolution reconstruction method is introduced for electron tomography. • The method provides more accurate results than the conventional reconstruction methods. • The missing wedge and noise problems can be compensated by the method efficiently.

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.

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...

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

Oxidative stress and inflammation: liver responses and adaptations to acute and regular exercise.

Science.gov (United States)

Pillon Barcelos, Rômulo; Freire Royes, Luiz Fernando; Gonzalez-Gallego, Javier; Bresciani, Guilherme

2017-02-01

The liver is remarkably important during exercise outcomes due to its contribution to detoxification, synthesis, and release of biomolecules, and energy supply to the exercising muscles. Recently, liver has been also shown to play an important role in redox status and inflammatory modulation during exercise. However, while several studies have described the adaptations of skeletal muscles to acute and chronic exercise, hepatic changes are still scarcely investigated. Indeed, acute intense exercise challenges the liver with increased reactive oxygen species (ROS) and inflammation onset, whereas regular training induces hepatic antioxidant and anti-inflammatory improvements. Acute and regular exercise protocols in combination with antioxidant and anti-inflammatory supplementation have been also tested to verify hepatic adaptations to exercise. Although positive results have been reported in some acute models, several studies have shown an increased exercise-related stress upon liver. A similar trend has been observed during training: while synergistic effects of training and antioxidant/anti-inflammatory supplementations have been occasionally found, others reported a blunting of relevant adaptations to exercise, following the patterns described in skeletal muscles. This review discusses current data regarding liver responses and adaptation to acute and regular exercise protocols alone or combined with antioxidant and anti-inflammatory supplementation. The understanding of the mechanisms behind these modulations is of interest for both exercise-related health and performance outcomes.

Adaptive Methods for Permeability Estimation and Smart Well Management

Energy Technology Data Exchange (ETDEWEB)

Lien, Martha Oekland

2005-04-01

The main focus of this thesis is on adaptive regularization methods. We consider two different applications, the inverse problem of absolute permeability estimation and the optimal control problem of estimating smart well management. Reliable estimates of absolute permeability are crucial in order to develop a mathematical description of an oil reservoir. Due to the nature of most oil reservoirs, mainly indirect measurements are available. In this work, dynamic production data from wells are considered. More specifically, we have investigated into the resolution power of pressure data for permeability estimation. The inversion of production data into permeability estimates constitutes a severely ill-posed problem. Hence, regularization techniques are required. In this work, deterministic regularization based on adaptive zonation is considered, i.e. a solution approach with adaptive multiscale estimation in conjunction with level set estimation is developed for coarse scale permeability estimation. A good mathematical reservoir model is a valuable tool for future production planning. Recent developments within well technology have given us smart wells, which yield increased flexibility in the reservoir management. In this work, we investigate into the problem of finding the optimal smart well management by means of hierarchical regularization techniques based on multiscale parameterization and refinement indicators. The thesis is divided into two main parts, where Part I gives a theoretical background for a collection of research papers that has been written by the candidate in collaboration with others. These constitutes the most important part of the thesis, and are presented in Part II. A brief outline of the thesis follows below. Numerical aspects concerning calculations of derivatives will also be discussed. Based on the introduction to regularization given in Chapter 2, methods for multiscale zonation, i.e. adaptive multiscale estimation and refinement

PREDICTORS OF SOCIAL AND PSYCHOLOGICAL ADAPTATION OF THE UNEMPLOYED AND PEOPLE WITH REGULAR EMPLOYMENT

Directory of Open Access Journals (Sweden)

Rail M Shamionov

2017-12-01

Full Text Available The article discusses the results of a study on the socio-psychological adaptation predictors of the unemployed in relation to people with regular employment. It is assumed that adaptation of the employed and the unemployed is determined by various socio-psychological phenomena; definition of the phenomena will allow to develop programmes of adaptation for the unemployed with preservation of motivation for self-realization. In total, 362 people (33% of whom were male took part in the study, including 196 unemployed. Standardized methods and scales developed by the authors for assessing the subject position characteristics and adaptive readiness of a person were used. It was found that the unemployed are characterized by lower indicators of socio-psychological adaptation and characteristics that are of paramount importance for adaptation - self-acceptance, acceptance of others, emotional comfort. Socio-demographic characteristics, scales of subjective position, adaptive readiness, subjective well-being and values were consistently introduced to the regression equation. It is shown that adaptive readiness and values are the strongest predictors for the employed, while indicators of subjective well-being and value are more significant for the unemployed. The general predictors of adaptation are the level of education, happiness (positively and negative affect (negatively. In other cases, the predictors are strictly differentiated.

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.

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.)

Improved liver R2* mapping by pixel-wise curve fitting with adaptive neighborhood regularization.

Science.gov (United States)

Wang, Changqing; Zhang, Xinyuan; Liu, Xiaoyun; He, Taigang; Chen, Wufan; Feng, Qianjin; Feng, Yanqiu

2018-08-01

To improve liver R2* mapping by incorporating adaptive neighborhood regularization into pixel-wise curve fitting. Magnetic resonance imaging R2* mapping remains challenging because of the serial images with low signal-to-noise ratio. In this study, we proposed to exploit the neighboring pixels as regularization terms and adaptively determine the regularization parameters according to the interpixel signal similarity. The proposed algorithm, called the pixel-wise curve fitting with adaptive neighborhood regularization (PCANR), was compared with the conventional nonlinear least squares (NLS) and nonlocal means filter-based NLS algorithms on simulated, phantom, and in vivo data. Visually, the PCANR algorithm generates R2* maps with significantly reduced noise and well-preserved tiny structures. Quantitatively, the PCANR algorithm produces R2* maps with lower root mean square errors at varying R2* values and signal-to-noise-ratio levels compared with the NLS and nonlocal means filter-based NLS algorithms. For the high R2* values under low signal-to-noise-ratio levels, the PCANR algorithm outperforms the NLS and nonlocal means filter-based NLS algorithms in the accuracy and precision, in terms of mean and standard deviation of R2* measurements in selected region of interests, respectively. The PCANR algorithm can reduce the effect of noise on liver R2* mapping, and the improved measurement precision will benefit the assessment of hepatic iron in clinical practice. Magn Reson Med 80:792-801, 2018. © 2018 International Society for Magnetic Resonance in Medicine. © 2018 International Society for Magnetic Resonance in Medicine.

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.

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)

An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy

Science.gov (United States)

Hu, Han; Ding, Yulin; Zhu, Qing; Wu, Bo; Lin, Hui; Du, Zhiqiang; Zhang, Yeting; Zhang, Yunsheng

2014-06-01

The filtering of point clouds is a ubiquitous task in the processing of airborne laser scanning (ALS) data; however, such filtering processes are difficult because of the complex configuration of the terrain features. The classical filtering algorithms rely on the cautious tuning of parameters to handle various landforms. To address the challenge posed by the bundling of different terrain features into a single dataset and to surmount the sensitivity of the parameters, in this study, we propose an adaptive surface filter (ASF) for the classification of ALS point clouds. Based on the principle that the threshold should vary in accordance to the terrain smoothness, the ASF embeds bending energy, which quantitatively depicts the local terrain structure to self-adapt the filter threshold automatically. The ASF employs a step factor to control the data pyramid scheme in which the processing window sizes are reduced progressively, and the ASF gradually interpolates thin plate spline surfaces toward the ground with regularization to handle noise. Using the progressive densification strategy, regularization and self-adaption, both performance improvement and resilience to parameter tuning are achieved. When tested against the benchmark datasets provided by ISPRS, the ASF performs the best in comparison with all other filtering methods, yielding an average total error of 2.85% when optimized and 3.67% when using the same parameter set.

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)

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.

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

Use of regularized algebraic methods in tomographic reconstruction

International Nuclear Information System (INIS)

Koulibaly, P.M.; Darcourt, J.; Blanc-Ferraud, L.; Migneco, O.; Barlaud, M.

1997-01-01

The algebraic methods are used in emission tomography to facilitate the compensation of attenuation and of Compton scattering. We have tested on a phantom the use of a regularization (a priori introduction of information), as well as the taking into account of spatial resolution variation with the depth (SRVD). Hence, we have compared the performances of the two methods by back-projection filtering (BPF) and of the two algebraic methods (AM) in terms of FWHM (by means of a point source), of the reduction of background noise (σ/m) on the homogeneous part of Jaszczak's phantom and of reconstruction speed (time unit = BPF). The BPF methods make use of a grade filter (maximal resolution, no noise treatment), single or associated with a Hann's low-pass (f c = 0.4), as well as of an attenuation correction. The AM which embody attenuation and scattering corrections are, on one side, the OS EM (Ordered Subsets, partitioning and rearranging of the projection matrix; Expectation Maximization) without regularization or SRVD correction, and, on the other side, the OS MAP EM (Maximum a posteriori), regularized and embodying the SRVD correction. A table is given containing for each used method (grade, Hann, OS EM and OS MAP EM) the values of FWHM, σ/m and time, respectively. One can observe that the OS MAP EM algebraic method allows ameliorating both the resolution, by taking into account the SRVD in the reconstruction process and noise treatment by regularization. In addition, due to the OS technique the reconstruction times are acceptable

Summation of Divergent Series and Zeldovich's Regularization Method

International Nuclear Information System (INIS)

Mur, V.D.; Pozdnyakov, S.G.; Popruzhenko, S.V.; Popov, V.S.

2005-01-01

A method for summing divergent series, including perturbation-theory series, is considered. This method is an analog of Zeldovich's regularization method in the theory of quasistationary states. It is shown that the method in question is more powerful than the well-known Abel and Borel methods, but that it is compatible with them (that is, it leads to the same value for the sum of a series). The constraints on the parameter domain that arise upon the removal of the regularization of divergent integrals by this method are discussed. The dynamical Stark shifts and widths of loosely bound s states in the field of a circularly polarized electromagnetic wave are calculated at various values of the Keldysh adiabaticity parameter and the multiquantum parameter

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....

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.

Adapting the curriculum of a student in the regular classroom

Directory of Open Access Journals (Sweden)

Cindy Lorena Rodríguez

2015-02-01

Full Text Available This article is based on a research, adapting the curriculum of a student in the regular classroom, based on a multi-skilled inclusive education whose data was collected between 2010 and 2011 from Colegio Real de los Andes. The study was based on the author’s personal experience with student population inside their regular classroom activities. The author was motivated by the desire to know how one could contribute to society’s expectations on an inclusive and integrated education that takes into account the human being as a unique being endowed with different potentials, great expectations, and dreams that nurture him or her into a major player in his or her dignified project of life that will, in turn, contribute towards their full personality growth and hence strengthen their academic skills. Similarly, this will be of great value towards commitment and devotion for inclusion, construed as a paramount import to educational formation. Hence, the dedication of educators in this inclusivity is a fundamental feature not only from the conceptual point of view, but more importantly, as a fundamental element in the essence of an educator, which must be, a human being formed in the richness of values openly projected on a pedagogy without any prejudice and preconceptions during a pedagogical dispensation.

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...

Learning Unknown Structure in CRFs via Adaptive Gradient Projection Method

Directory of Open Access Journals (Sweden)

Wei Xue

2016-08-01

Full Text Available We study the problem of fitting probabilistic graphical models to the given data when the structure is not known. More specifically, we focus on learning unknown structure in conditional random fields, especially learning both the structure and parameters of a conditional random field model simultaneously. To do this, we first formulate the learning problem as a convex minimization problem by adding an l_2-regularization to the node parameters and a group l_1-regularization to the edge parameters, and then a gradient-based projection method is proposed to solve it which combines an adaptive stepsize selection strategy with a nonmonotone line search. Extensive simulation experiments are presented to show the performance of our approach in solving unknown structure learning problems.

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for Lp-Regularization Using the Multiple Sub-Dictionary Representation.

Science.gov (United States)

Li, Yunyi; Zhang, Jie; Fan, Shangang; Yang, Jie; Xiong, Jian; Cheng, Xiefeng; Sari, Hikmet; Adachi, Fumiyuki; Gui, Guan

2017-12-15

Both L 1/2 and L 2/3 are two typical non-convex regularizations of L p (0dictionary sparse transform strategies for the two typical cases p∈{1/2, 2/3} based on an iterative Lp thresholding algorithm and then proposes a sparse adaptive iterative-weighted L p thresholding algorithm (SAITA). Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based L p regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L₁ algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based L p case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work.

Summation of divergent series and Zel'dovich's regularization method

International Nuclear Information System (INIS)

Mur, V.D.; Pozdnyakov, S.G.; Popruzhenko, S.V.; Popov, V.S.

2005-01-01

The method of summation of divergent series, including series of a perturbation theory, which is an analog of the Zel'dovich regularization procedure in the theory of quasistationary states is considered. It is shown that this method is more powerful than the well-known Abel and Borel methods, but compatible with them (i. e., gives the same value for the sum of the series). The restrictions to the range of parameters which appear after removal of the regularization of integrals by this method are discussed. The dynamical Stark shifts and widths of weakly bound s states in a field of circularly polarized electromagnetic wave are calculated at different values of the Keldysh adiabaticity parameter and multiquantum parameter [ru

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.)

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

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.

Adaptive grouping for the higher-order multilevel fast multipole method

DEFF Research Database (Denmark)

Borries, Oscar Peter; Jørgensen, Erik; Meincke, Peter

2014-01-01

An alternative parameter-free adaptive approach for the grouping of the basis function patterns in the multilevel fast multipole method is presented, yielding significant memory savings compared to the traditional Octree grouping for most discretizations, particularly when using higher-order basis...... functions. Results from both a uniformly and nonuniformly meshed scatterer are presented, showing how the technique is worthwhile even for regular meshes, and demonstrating that there is no loss of accuracy in spite of the large reduction in memory requirements and the relatively low computational cost....

A New Method for Determining Optimal Regularization Parameter in Near-Field Acoustic Holography

Directory of Open Access Journals (Sweden)

Yue Xiao

2018-01-01

Full Text Available Tikhonov regularization method is effective in stabilizing reconstruction process of the near-field acoustic holography (NAH based on the equivalent source method (ESM, and the selection of the optimal regularization parameter is a key problem that determines the regularization effect. In this work, a new method for determining the optimal regularization parameter is proposed. The transfer matrix relating the source strengths of the equivalent sources to the measured pressures on the hologram surface is augmented by adding a fictitious point source with zero strength. The minimization of the norm of this fictitious point source strength is as the criterion for choosing the optimal regularization parameter since the reconstructed value should tend to zero. The original inverse problem in calculating the source strengths is converted into a univariate optimization problem which is solved by a one-dimensional search technique. Two numerical simulations with a point driven simply supported plate and a pulsating sphere are investigated to validate the performance of the proposed method by comparison with the L-curve method. The results demonstrate that the proposed method can determine the regularization parameter correctly and effectively for the reconstruction in NAH.

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

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA for L p -Regularization Using the Multiple Sub-Dictionary Representation

Directory of Open Access Journals (Sweden)

Yunyi Li

2017-12-01

Full Text Available Both L 1 / 2 and L 2 / 3 are two typical non-convex regularizations of L p ( 0 < p < 1 , which can be employed to obtain a sparser solution than the L 1 regularization. Recently, the multiple-state sparse transformation strategy has been developed to exploit the sparsity in L 1 regularization for sparse signal recovery, which combines the iterative reweighted algorithms. To further exploit the sparse structure of signal and image, this paper adopts multiple dictionary sparse transform strategies for the two typical cases p ∈ { 1 / 2 ,   2 / 3 } based on an iterative L p thresholding algorithm and then proposes a sparse adaptive iterative-weighted L p thresholding algorithm (SAITA. Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based L p regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L 1 algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based L p case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work.

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.

A convergence analysis of the iteratively regularized Gauss–Newton method under the Lipschitz condition

International Nuclear Information System (INIS)

Jin Qinian

2008-01-01

In this paper we consider the iteratively regularized Gauss–Newton method for solving nonlinear ill-posed inverse problems. Under merely the Lipschitz condition, we prove that this method together with an a posteriori stopping rule defines an order optimal regularization method if the solution is regular in some suitable sense

Quantification of organ motion based on an adaptive image-based scale invariant feature method

Energy Technology Data Exchange (ETDEWEB)

Paganelli, Chiara [Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, piazza L. Da Vinci 32, Milano 20133 (Italy); Peroni, Marta [Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, piazza L. Da Vinci 32, Milano 20133, Italy and Paul Scherrer Institut, Zentrum für Protonentherapie, WMSA/C15, CH-5232 Villigen PSI (Italy); Baroni, Guido; Riboldi, Marco [Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, piazza L. Da Vinci 32, Milano 20133, Italy and Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, strada Campeggi 53, Pavia 27100 (Italy)

2013-11-15

Purpose: The availability of corresponding landmarks in IGRT image series allows quantifying the inter and intrafractional motion of internal organs. In this study, an approach for the automatic localization of anatomical landmarks is presented, with the aim of describing the nonrigid motion of anatomo-pathological structures in radiotherapy treatments according to local image contrast.Methods: An adaptive scale invariant feature transform (SIFT) was developed from the integration of a standard 3D SIFT approach with a local image-based contrast definition. The robustness and invariance of the proposed method to shape-preserving and deformable transforms were analyzed in a CT phantom study. The application of contrast transforms to the phantom images was also tested, in order to verify the variation of the local adaptive measure in relation to the modification of image contrast. The method was also applied to a lung 4D CT dataset, relying on manual feature identification by an expert user as ground truth. The 3D residual distance between matches obtained in adaptive-SIFT was then computed to verify the internal motion quantification with respect to the expert user. Extracted corresponding features in the lungs were used as regularization landmarks in a multistage deformable image registration (DIR) mapping the inhale vs exhale phase. The residual distances between the warped manual landmarks and their reference position in the inhale phase were evaluated, in order to provide a quantitative indication of the registration performed with the three different point sets.Results: The phantom study confirmed the method invariance and robustness properties to shape-preserving and deformable transforms, showing residual matching errors below the voxel dimension. The adapted SIFT algorithm on the 4D CT dataset provided automated and accurate motion detection of peak to peak breathing motion. The proposed method resulted in reduced residual errors with respect to standard SIFT

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…

Global regularization method for planar restricted three-body problem

Directory of Open Access Journals (Sweden)

Sharaf M.A.

2015-01-01

Full Text Available In this paper, global regularization method for planar restricted three-body problem is purposed by using the transformation z = x+iy = ν cos n(u+iv, where i = √−1, 0 < ν ≤ 1 and n is a positive integer. The method is developed analytically and computationally. For the analytical developments, analytical solutions in power series of the pseudotime τ are obtained for positions and velocities (u, v, u', v' and (x, y, x˙, y˙ in both regularized and physical planes respectively, the physical time t is also obtained as power series in τ. Moreover, relations between the coefficients of the power series are obtained for two consequent values of n. Also, we developed analytical solutions in power series form for the inverse problem of finding τ in terms of t. As typical examples, three symbolic expressions for the coefficients of the power series were developed in terms of initial values. As to the computational developments, the global regularized equations of motion are developed together with their initial values in forms suitable for digital computations using any differential equations solver. On the other hand, for numerical evolutions of power series, an efficient method depending on the continued fraction theory is provided.

A parallel direct solver for the self-adaptive hp Finite Element Method

KAUST Repository

Paszyński, Maciej R.; Pardo, David; Torres-Verdí n, Carlos; Demkowicz, Leszek F.; Calo, Victor M.

2010-01-01

measurement simulations problems. We measure the execution time and memory usage of the solver over a large regular mesh with 1.5 million degrees of freedom as well as on the highly non-regular mesh, generated by the self-adaptive h p-FEM, with finite elements

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

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.

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

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...

Regularization of DT-MRI Using 3D Median Filtering Methods

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Soondong Kwon

2014-01-01

Full Text Available DT-MRI (diffusion tensor magnetic resonance imaging tractography is a method to determine the architecture of axonal fibers in the central nervous system by computing the direction of the principal eigenvectors obtained from tensor matrix, which is different from the conventional isotropic MRI. Tractography based on DT-MRI is known to need many computations and is highly sensitive to noise. Hence, adequate regularization methods, such as image processing techniques, are in demand. Among many regularization methods we are interested in the median filtering method. In this paper, we extended two-dimensional median filters already developed to three-dimensional median filters. We compared four median filtering methods which are two-dimensional simple median method (SM2D, two-dimensional successive Fermat method (SF2D, three-dimensional simple median method (SM3D, and three-dimensional successive Fermat method (SF3D. Three kinds of synthetic data with different altitude angles from axial slices and one kind of human data from MR scanner are considered for numerical implementation by the four filtering methods.

Improving thoracic four-dimensional cone-beam CT reconstruction with anatomical-adaptive image regularization (AAIR)

International Nuclear Information System (INIS)

Shieh, Chun-Chien; Kipritidis, John; O'Brien, Ricky T; Cooper, Benjamin J; Keall, Paul J; Kuncic, Zdenka

2015-01-01

Total-variation (TV) minimization reconstructions can significantly reduce noise and streaks in thoracic four-dimensional cone-beam computed tomography (4D CBCT) images compared to the Feldkamp–Davis–Kress (FDK) algorithm currently used in practice. TV minimization reconstructions are, however, prone to over-smoothing anatomical details and are also computationally inefficient. The aim of this study is to demonstrate a proof of concept that these disadvantages can be overcome by incorporating the general knowledge of the thoracic anatomy via anatomy segmentation into the reconstruction. The proposed method, referred as the anatomical-adaptive image regularization (AAIR) method, utilizes the adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS) framework, but introduces an additional anatomy segmentation step in every iteration. The anatomy segmentation information is implemented in the reconstruction using a heuristic approach to adaptively suppress over-smoothing at anatomical structures of interest. The performance of AAIR depends on parameters describing the weighting of the anatomy segmentation prior and segmentation threshold values. A sensitivity study revealed that the reconstruction outcome is not sensitive to these parameters as long as they are chosen within a suitable range. AAIR was validated using a digital phantom and a patient scan and was compared to FDK, ASD-POCS and the prior image constrained compressed sensing (PICCS) method. For the phantom case, AAIR reconstruction was quantitatively shown to be the most accurate as indicated by the mean absolute difference and the structural similarity index. For the patient case, AAIR resulted in the highest signal-to-noise ratio (i.e. the lowest level of noise and streaking) and the highest contrast-to-noise ratios for the tumor and the bony anatomy (i.e. the best visibility of anatomical details). Overall, AAIR was much less prone to over-smoothing anatomical details compared to ASD-POCS and

Single image super-resolution using locally adaptive multiple linear regression.

Science.gov (United States)

Yu, Soohwan; Kang, Wonseok; Ko, Seungyong; Paik, Joonki

2015-12-01

This paper presents a regularized superresolution (SR) reconstruction method using locally adaptive multiple linear regression to overcome the limitation of spatial resolution of digital images. In order to make the SR problem better-posed, the proposed method incorporates the locally adaptive multiple linear regression into the regularization process as a local prior. The local regularization prior assumes that the target high-resolution (HR) pixel is generated by a linear combination of similar pixels in differently scaled patches and optimum weight parameters. In addition, we adapt a modified version of the nonlocal means filter as a smoothness prior to utilize the patch redundancy. Experimental results show that the proposed algorithm better restores HR images than existing state-of-the-art methods in the sense of the most objective measures in the literature.

A regularization method for extrapolation of solar potential magnetic fields

Science.gov (United States)

Gary, G. A.; Musielak, Z. E.

1992-01-01

The mathematical basis of a Tikhonov regularization method for extrapolating the chromospheric-coronal magnetic field using photospheric vector magnetograms is discussed. The basic techniques show that the Cauchy initial value problem can be formulated for potential magnetic fields. The potential field analysis considers a set of linear, elliptic partial differential equations. It is found that, by introducing an appropriate smoothing of the initial data of the Cauchy potential problem, an approximate Fourier integral solution is found, and an upper bound to the error in the solution is derived. This specific regularization technique, which is a function of magnetograph measurement sensitivities, provides a method to extrapolate the potential magnetic field above an active region into the chromosphere and low corona.

A Highly Accurate Regular Domain Collocation Method for Solving Potential Problems in the Irregular Doubly Connected Domains

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Zhao-Qing Wang

2014-01-01

Full Text Available Embedding the irregular doubly connected domain into an annular regular region, the unknown functions can be approximated by the barycentric Lagrange interpolation in the regular region. A highly accurate regular domain collocation method is proposed for solving potential problems on the irregular doubly connected domain in polar coordinate system. The formulations of regular domain collocation method are constructed by using barycentric Lagrange interpolation collocation method on the regular domain in polar coordinate system. The boundary conditions are discretized by barycentric Lagrange interpolation within the regular domain. An additional method is used to impose the boundary conditions. The least square method can be used to solve the overconstrained equations. The function values of points in the irregular doubly connected domain can be calculated by barycentric Lagrange interpolation within the regular domain. Some numerical examples demonstrate the effectiveness and accuracy of the presented method.

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.

Cardiac C-arm computed tomography using a 3D + time ROI reconstruction method with spatial and temporal regularization

Energy Technology Data Exchange (ETDEWEB)

Mory, Cyril, E-mail: cyril.mory@philips.com [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, F-69621 Villeurbanne Cedex (France); Philips Research Medisys, 33 rue de Verdun, 92156 Suresnes (France); Auvray, Vincent; Zhang, Bo [Philips Research Medisys, 33 rue de Verdun, 92156 Suresnes (France); Grass, Michael; Schäfer, Dirk [Philips Research, Röntgenstrasse 24–26, D-22335 Hamburg (Germany); Chen, S. James; Carroll, John D. [Department of Medicine, Division of Cardiology, University of Colorado Denver, 12605 East 16th Avenue, Aurora, Colorado 80045 (United States); Rit, Simon [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1 (France); Centre Léon Bérard, 28 rue Laënnec, F-69373 Lyon (France); Peyrin, Françoise [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, F-69621 Villeurbanne Cedex (France); X-ray Imaging Group, European Synchrotron, Radiation Facility, BP 220, F-38043 Grenoble Cedex (France); Douek, Philippe; Boussel, Loïc [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1 (France); Hospices Civils de Lyon, 28 Avenue du Doyen Jean Lépine, 69500 Bron (France)

2014-02-15

Purpose: Reconstruction of the beating heart in 3D + time in the catheter laboratory using only the available C-arm system would improve diagnosis, guidance, device sizing, and outcome control for intracardiac interventions, e.g., electrophysiology, valvular disease treatment, structural or congenital heart disease. To obtain such a reconstruction, the patient's electrocardiogram (ECG) must be recorded during the acquisition and used in the reconstruction. In this paper, the authors present a 4D reconstruction method aiming to reconstruct the heart from a single sweep 10 s acquisition. Methods: The authors introduce the 4D RecOnstructiOn using Spatial and TEmporal Regularization (short 4D ROOSTER) method, which reconstructs all cardiac phases at once, as a 3D + time volume. The algorithm alternates between a reconstruction step based on conjugate gradient and four regularization steps: enforcing positivity, averaging along time outside a motion mask that contains the heart and vessels, 3D spatial total variation minimization, and 1D temporal total variation minimization. Results: 4D ROOSTER recovers the different temporal representations of a moving Shepp and Logan phantom, and outperforms both ECG-gated simultaneous algebraic reconstruction technique and prior image constrained compressed sensing on a clinical case. It generates 3D + time reconstructions with sharp edges which can be used, for example, to estimate the patient's left ventricular ejection fraction. Conclusions: 4D ROOSTER can be applied for human cardiac C-arm CT, and potentially in other dynamic tomography areas. It can easily be adapted to other problems as regularization is decoupled from projection and back projection.

Cardiac C-arm computed tomography using a 3D + time ROI reconstruction method with spatial and temporal regularization

International Nuclear Information System (INIS)

Mory, Cyril; Auvray, Vincent; Zhang, Bo; Grass, Michael; Schäfer, Dirk; Chen, S. James; Carroll, John D.; Rit, Simon; Peyrin, Françoise; Douek, Philippe; Boussel, Loïc

2014-01-01

Purpose: Reconstruction of the beating heart in 3D + time in the catheter laboratory using only the available C-arm system would improve diagnosis, guidance, device sizing, and outcome control for intracardiac interventions, e.g., electrophysiology, valvular disease treatment, structural or congenital heart disease. To obtain such a reconstruction, the patient's electrocardiogram (ECG) must be recorded during the acquisition and used in the reconstruction. In this paper, the authors present a 4D reconstruction method aiming to reconstruct the heart from a single sweep 10 s acquisition. Methods: The authors introduce the 4D RecOnstructiOn using Spatial and TEmporal Regularization (short 4D ROOSTER) method, which reconstructs all cardiac phases at once, as a 3D + time volume. The algorithm alternates between a reconstruction step based on conjugate gradient and four regularization steps: enforcing positivity, averaging along time outside a motion mask that contains the heart and vessels, 3D spatial total variation minimization, and 1D temporal total variation minimization. Results: 4D ROOSTER recovers the different temporal representations of a moving Shepp and Logan phantom, and outperforms both ECG-gated simultaneous algebraic reconstruction technique and prior image constrained compressed sensing on a clinical case. It generates 3D + time reconstructions with sharp edges which can be used, for example, to estimate the patient's left ventricular ejection fraction. Conclusions: 4D ROOSTER can be applied for human cardiac C-arm CT, and potentially in other dynamic tomography areas. It can easily be adapted to other problems as regularization is decoupled from projection and back projection

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.

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.

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...

A regularized vortex-particle mesh method for large eddy simulation

Science.gov (United States)

Spietz, H. J.; Walther, J. H.; Hejlesen, M. M.

2017-11-01

We present recent developments of the remeshed vortex particle-mesh method for simulating incompressible fluid flow. The presented method relies on a parallel higher-order FFT based solver for the Poisson equation. Arbitrary high order is achieved through regularization of singular Green's function solutions to the Poisson equation and recently we have derived novel high order solutions for a mixture of open and periodic domains. With this approach the simulated variables may formally be viewed as the approximate solution to the filtered Navier Stokes equations, hence we use the method for Large Eddy Simulation by including a dynamic subfilter-scale model based on test-filters compatible with the aforementioned regularization functions. Further the subfilter-scale model uses Lagrangian averaging, which is a natural candidate in light of the Lagrangian nature of vortex particle methods. A multiresolution variation of the method is applied to simulate the benchmark problem of the flow past a square cylinder at Re = 22000 and the obtained results are compared to results from the literature.

Regularization of the double period method for experimental data processing

Science.gov (United States)

Belov, A. A.; Kalitkin, N. N.

2017-11-01

In physical and technical applications, an important task is to process experimental curves measured with large errors. Such problems are solved by applying regularization methods, in which success depends on the mathematician's intuition. We propose an approximation based on the double period method developed for smooth nonperiodic functions. Tikhonov's stabilizer with a squared second derivative is used for regularization. As a result, the spurious oscillations are suppressed and the shape of an experimental curve is accurately represented. This approach offers a universal strategy for solving a broad class of problems. The method is illustrated by approximating cross sections of nuclear reactions important for controlled thermonuclear fusion. Tables recommended as reference data are obtained. These results are used to calculate the reaction rates, which are approximated in a way convenient for gasdynamic codes. These approximations are superior to previously known formulas in the covered temperature range and accuracy.

Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation

Directory of Open Access Journals (Sweden)

Jinping Tang

2017-01-01

Full Text Available Optical tomography is an emerging and important molecular imaging modality. The aim of optical tomography is to reconstruct optical properties of human tissues. In this paper, we focus on reconstructing the absorption coefficient based on the radiative transfer equation (RTE. It is an ill-posed parameter identification problem. Regularization methods have been broadly applied to reconstruct the optical coefficients, such as the total variation (TV regularization and the L1 regularization. In order to better reconstruct the piecewise constant and sparse coefficient distributions, TV and L1 norms are combined as the regularization. The forward problem is discretized with the discontinuous Galerkin method on the spatial space and the finite element method on the angular space. The minimization problem is solved by a Jacobian-based Levenberg-Marquardt type method which is equipped with a split Bregman algorithms for the L1 regularization. We use the adjoint method to compute the Jacobian matrix which dramatically improves the computation efficiency. By comparing with the other imaging reconstruction methods based on TV and L1 regularizations, the simulation results show the validity and efficiency of the proposed method.

The Validity of Dimensional Regularization Method on Fractal Spacetime

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Yong Tao

2013-01-01

Full Text Available Svozil developed a regularization method for quantum field theory on fractal spacetime (1987. Such a method can be applied to the low-order perturbative renormalization of quantum electrodynamics but will depend on a conjectural integral formula on non-integer-dimensional topological spaces. The main purpose of this paper is to construct a fractal measure so as to guarantee the validity of the conjectural integral formula.

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

Integrating adaptive governance and participatory multicriteria methods: a framework for climate adaptation governance

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Stefania Munaretto

2014-06-01

Full Text Available Climate adaptation is a dynamic social and institutional process where the governance dimension is receiving growing attention. Adaptive governance is an approach that promises to reduce uncertainty by improving the knowledge base for decision making. As uncertainty is an inherent feature of climate adaptation, adaptive governance seems to be a promising approach for improving climate adaptation governance. However, the adaptive governance literature has so far paid little attention to decision-making tools and methods, and the literature on the governance of adaptation is in its infancy in this regard. We argue that climate adaptation governance would benefit from systematic and yet flexible decision-making tools and methods such as participatory multicriteria methods for the evaluation of adaptation options, and that these methods can be linked to key adaptive governance principles. Moving from these premises, we propose a framework that integrates key adaptive governance features into participatory multicriteria methods for the governance of climate adaptation.

Contribution to regularizing iterative method development for attenuation correction in gamma emission tomography

International Nuclear Information System (INIS)

Cao, A.

1981-07-01

This study is concerned with the transverse axial gamma emission tomography. The problem of self-attenuation of radiations in biologic tissues is raised. The regularizing iterative method is developed, as a reconstruction method of 3 dimensional images. The different steps from acquisition to results, necessary to its application, are described. Organigrams relative to each step are explained. Comparison notion between two reconstruction methods is introduced. Some methods used for the comparison or to bring about the characteristics of a reconstruction technique are defined. The studies realized to test the regularizing iterative method are presented and results are analyzed [fr

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

A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise

Directory of Open Access Journals (Sweden)

Han Pan

2017-01-01

Full Text Available The removal of mixed Gaussian-impulse noise plays an important role in many areas, such as remote sensing. However, traditional methods may be unaware of promoting the degree of the sparsity adaptively after decomposing into low rank component and sparse component. In this paper, a new problem formulation with regular spectral k-support norm and regular k-support l1 norm is proposed. A unified framework is developed to capture the intrinsic sparsity structure of all two components. To address the resulting problem, an efficient minimization scheme within the framework of accelerated proximal gradient is proposed. This scheme is achieved by alternating regular k-shrinkage thresholding operator. Experimental comparison with the other state-of-the-art methods demonstrates the efficacy of the proposed method.

METHOD OF ADAPTIVE MAGNETOTHERAPY

OpenAIRE

Rudyk, Valentine Yu.; Tereshchenko, Mykola F.; Rudyk, Tatiana A.

2016-01-01

Practical realization of adaptive control in magnetotherapy apparatus acquires an actual importance on the modern stage of development of magnetotherapy.The structural scheme of method of adaptive impulsive magnetotherapy and algorithm of adaptive control of feed-back signal during procedure of magnetotherapy is represented.A feed-back in magnetotherapy complex will be realized with control of magnetic induction and analysis of man's physiological indexes (temperature, pulse, blood prassure, ...

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

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.

Application of L1/2 regularization logistic method in heart disease diagnosis.

Science.gov (United States)

Zhang, Bowen; Chai, Hua; Yang, Ziyi; Liang, Yong; Chu, Gejin; Liu, Xiaoying

2014-01-01

Heart disease has become the number one killer of human health, and its diagnosis depends on many features, such as age, blood pressure, heart rate and other dozens of physiological indicators. Although there are so many risk factors, doctors usually diagnose the disease depending on their intuition and experience, which requires a lot of knowledge and experience for correct determination. To find the hidden medical information in the existing clinical data is a noticeable and powerful approach in the study of heart disease diagnosis. In this paper, sparse logistic regression method is introduced to detect the key risk factors using L(1/2) regularization on the real heart disease data. Experimental results show that the sparse logistic L(1/2) regularization method achieves fewer but informative key features than Lasso, SCAD, MCP and Elastic net regularization approaches. Simultaneously, the proposed method can cut down the computational complexity, save cost and time to undergo medical tests and checkups, reduce the number of attributes needed to be taken from patients.

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.

A convergent blind deconvolution method for post-adaptive-optics astronomical imaging

International Nuclear Information System (INIS)

Prato, M; Camera, A La; Bertero, M; Bonettini, S

2013-01-01

In this paper, we propose a blind deconvolution method which applies to data perturbed by Poisson noise. The objective function is a generalized Kullback–Leibler (KL) divergence, depending on both the unknown object and unknown point spread function (PSF), without the addition of regularization terms; constrained minimization, with suitable convex constraints on both unknowns, is considered. The problem is non-convex and we propose to solve it by means of an inexact alternating minimization method, whose global convergence to stationary points of the objective function has been recently proved in a general setting. The method is iterative and each iteration, also called outer iteration, consists of alternating an update of the object and the PSF by means of a fixed number of iterations, also called inner iterations, of the scaled gradient projection (SGP) method. Therefore, the method is similar to other proposed methods based on the Richardson–Lucy (RL) algorithm, with SGP replacing RL. The use of SGP has two advantages: first, it allows one to prove global convergence of the blind method; secondly, it allows the introduction of different constraints on the object and the PSF. The specific constraint on the PSF, besides non-negativity and normalization, is an upper bound derived from the so-called Strehl ratio (SR), which is the ratio between the peak value of an aberrated versus a perfect wavefront. Therefore, a typical application, but not a unique one, is to the imaging of modern telescopes equipped with adaptive optics systems for the partial correction of the aberrations due to atmospheric turbulence. In the paper, we describe in detail the algorithm and we recall the results leading to its convergence. Moreover, we illustrate its effectiveness by means of numerical experiments whose results indicate that the method, pushed to convergence, is very promising in the reconstruction of non-dense stellar clusters. The case of more complex astronomical targets

Optimized star sensors laboratory calibration method using a regularization neural network.

Science.gov (United States)

Zhang, Chengfen; Niu, Yanxiong; Zhang, Hao; Lu, Jiazhen

2018-02-10

High-precision ground calibration is essential to ensure the performance of star sensors. However, the complex distortion and multi-error coupling have brought great difficulties to traditional calibration methods, especially for large field of view (FOV) star sensors. Although increasing the complexity of models is an effective way to improve the calibration accuracy, it significantly increases the demand for calibration data. In order to achieve high-precision calibration of star sensors with large FOV, a novel laboratory calibration method based on a regularization neural network is proposed. A multi-layer structure neural network is designed to represent the mapping of the star vector and the corresponding star point coordinate directly. To ensure the generalization performance of the network, regularization strategies are incorporated into the net structure and the training algorithm. Simulation and experiment results demonstrate that the proposed method can achieve high precision with less calibration data and without any other priori information. Compared with traditional methods, the calibration error of the star sensor decreased by about 30%. The proposed method can satisfy the precision requirement for large FOV star sensors.

Adaptive scalarization methods in multiobjective optimization

CERN Document Server

Eichfelder, Gabriele

2008-01-01

This book presents adaptive solution methods for multiobjective optimization problems based on parameter dependent scalarization approaches. Readers will benefit from the new adaptive methods and ideas for solving multiobjective optimization.

Robust Optimal Adaptive Control Method with Large Adaptive Gain

Science.gov (United States)

Nguyen, Nhan T.

2009-01-01

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.

Use of regularization method in the determination of ring parameters and orbit correction

International Nuclear Information System (INIS)

Tang, Y.N.; Krinsky, S.

1993-01-01

We discuss applying the regularization method of Tikhonov to the solution of inverse problems arising in accelerator operations. This approach has been successfully used for orbit correction on the NSLS storage rings, and is presently being applied to the determination of betatron functions and phases from the measured response matrix. The inverse problem of differential equation often leads to a set of integral equations of the first kind which are ill-conditioned. The regularization method is used to combat the ill-posedness

Phase reconstruction by a multilevel iteratively regularized Gauss–Newton method

International Nuclear Information System (INIS)

Langemann, Dirk; Tasche, Manfred

2008-01-01

In this paper we consider the numerical solution of a phase retrieval problem for a compactly supported, linear spline f : R → C with the Fourier transform f-circumflex, where values of |f| and |f-circumflex| at finitely many equispaced nodes are given. The unknown phases of complex spline coefficients fulfil a well-structured system of nonlinear equations. Thus the phase reconstruction leads to a nonlinear inverse problem, which is solved by a multilevel strategy and iterative Tikhonov regularization. The multilevel strategy concentrates the main effort of the solution of the phase retrieval problem in the coarse, less expensive levels and provides convenient initial guesses at the next finer level. On each level, the corresponding nonlinear system is solved by an iteratively regularized Gauss–Newton method. The multilevel strategy is motivated by convergence results of IRGN. This method is applicable to a wide range of examples as shown in several numerical tests for noiseless and noisy data

Regularization methods for ill-posed problems in multiple Hilbert scales

International Nuclear Information System (INIS)

Mazzieri, Gisela L; Spies, Ruben D

2012-01-01

Several convergence results in Hilbert scales under different source conditions are proved and orders of convergence and optimal orders of convergence are derived. Also, relations between those source conditions are proved. The concept of a multiple Hilbert scale on a product space is introduced, and regularization methods on these scales are defined, both for the case of a single observation and for the case of multiple observations. In the latter case, it is shown how vector-valued regularization functions in these multiple Hilbert scales can be used. In all cases, convergence is proved and orders and optimal orders of convergence are shown. Finally, some potential applications and open problems are discussed. (paper)

L{sub 1/2} regularization based numerical method for effective reconstruction of bioluminescence tomography

Energy Technology Data Exchange (ETDEWEB)

Chen, Xueli, E-mail: xlchen@xidian.edu.cn, E-mail: jimleung@mail.xidian.edu.cn; Yang, Defu; Zhang, Qitan; Liang, Jimin, E-mail: xlchen@xidian.edu.cn, E-mail: jimleung@mail.xidian.edu.cn [School of Life Science and Technology, Xidian University, Xi' an 710071 (China); Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education (China)

2014-05-14

Even though bioluminescence tomography (BLT) exhibits significant potential and wide applications in macroscopic imaging of small animals in vivo, the inverse reconstruction is still a tough problem that has plagued researchers in a related area. The ill-posedness of inverse reconstruction arises from insufficient measurements and modeling errors, so that the inverse reconstruction cannot be solved directly. In this study, an l{sub 1/2} regularization based numerical method was developed for effective reconstruction of BLT. In the method, the inverse reconstruction of BLT was constrained into an l{sub 1/2} regularization problem, and then the weighted interior-point algorithm (WIPA) was applied to solve the problem through transforming it into obtaining the solution of a series of l{sub 1} regularizers. The feasibility and effectiveness of the proposed method were demonstrated with numerical simulations on a digital mouse. Stability verification experiments further illustrated the robustness of the proposed method for different levels of Gaussian noise.

A wavelet-MRA-based adaptive semi-Lagrangian method for the relativistic Vlasov-Maxwell system

International Nuclear Information System (INIS)

Besse, Nicolas; Latu, Guillaume; Ghizzo, Alain; Sonnendruecker, Eric; Bertrand, Pierre

2008-01-01

In this paper we present a new method for the numerical solution of the relativistic Vlasov-Maxwell system on a phase-space grid using an adaptive semi-Lagrangian method. The adaptivity is performed through a wavelet multiresolution analysis, which gives a powerful and natural refinement criterion based on the local measurement of the approximation error and regularity of the distribution function. Therefore, the multiscale expansion of the distribution function allows to get a sparse representation of the data and thus save memory space and CPU time. We apply this numerical scheme to reduced Vlasov-Maxwell systems arising in laser-plasma physics. Interaction of relativistically strong laser pulses with overdense plasma slabs is investigated. These Vlasov simulations revealed a rich variety of phenomena associated with the fast particle dynamics induced by electromagnetic waves as electron trapping, particle acceleration, and electron plasma wavebreaking. However, the wavelet based adaptive method that we developed here, does not yield significant improvements compared to Vlasov solvers on a uniform mesh due to the substantial overhead that the method introduces. Nonetheless they might be a first step towards more efficient adaptive solvers based on different ideas for the grid refinement or on a more efficient implementation. Here the Vlasov simulations are performed in a two-dimensional phase-space where the development of thin filaments, strongly amplified by relativistic effects requires an important increase of the total number of points of the phase-space grid as they get finer as time goes on. The adaptive method could be more useful in cases where these thin filaments that need to be resolved are a very small fraction of the hyper-volume, which arises in higher dimensions because of the surface-to-volume scaling and the essentially one-dimensional structure of the filaments. Moreover, the main way to improve the efficiency of the adaptive method is to

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...

Heat adaptation from regular hot water immersion decreases proinflammatory responses, HSP70 expression, and physical heat stress.

Science.gov (United States)

Yang, Fwu-Lin; Lee, Chia-Chi; Subeq, Yi-Maun; Lee, Chung-Jen; Ke, Chun-Yen; Lee, Ru-Ping

2017-10-01

Hot-water immersion (HWI) is a type of thermal therapy for treating various diseases. In our study, the physiological responses to occasional and regular HWI have been explored. The rats were divided into a control group, occasional group (1D), and regular group (7D). The 1D and 7D groups received 42°C during 15mins HWI for 1 and 7 days, respectively. The blood samples were collected for proinflammatory cytokines examinations, the heart, liver and kidney were excised for subsequent IHC analysis to measure the level of heat shock protein 70 (HSP70). The results revealed that the body temperature increased significantly during HWI on Day 3 and significantly declined on Days 6 and 7. For the 7D group, body weight, heart rate, hematocrit, platelet, osmolarity, and lactate level were lower than those in the 1D group. Furthermore, the levels of granulocyte counts, tumor necrosis factor-α, and interleukin-6 were lower in the 7D group than in the 1D group. The induction of HSP70 in the 1D group was higher than in the other groups. Physiological responses to occasional HWI are disadvantageous because of heat stress. However, adaptation to heat from regular HWI resulted in decreased proinflammatory responses and physical heat stress. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive method of lines

CERN Document Server

Saucez, Ph

2001-01-01

The general Method of Lines (MOL) procedure provides a flexible format for the solution of all the major classes of partial differential equations (PDEs) and is particularly well suited to evolutionary, nonlinear wave PDEs. Despite its utility, however, there are relatively few texts that explore it at a more advanced level and reflect the method''s current state of development.Written by distinguished researchers in the field, Adaptive Method of Lines reflects the diversity of techniques and applications related to the MOL. Most of its chapters focus on a particular application but also provide a discussion of underlying philosophy and technique. Particular attention is paid to the concept of both temporal and spatial adaptivity in solving time-dependent PDEs. Many important ideas and methods are introduced, including moving grids and grid refinement, static and dynamic gridding, the equidistribution principle and the concept of a monitor function, the minimization of a functional, and the moving finite elem...

Quantitative SPECT reconstruction for brain distribution with a non-uniform attenuation using a regularizing method

International Nuclear Information System (INIS)

Soussaline, F.; Bidaut, L.; Raynaud, C.; Le Coq, G.

1983-06-01

An analytical solution to the SPECT reconstruction problem, where the actual attenuation effect can be included, was developped using a regularizing iterative method (RIM). The potential of this approach in quantitative brain studies when using a tracer for cerebrovascular disorders is now under evaluation. Mathematical simulations for a distributed activity in the brain surrounded by the skull and physical phantom studies were performed, using a rotating camera based SPECT system, allowing the calibration of the system and the evaluation of the adapted method to be used. On the simulation studies, the contrast obtained along a profile, was less than 5%, the standard deviation 8% and the quantitative accuracy 13%, for a uniform emission distribution of mean = 100 per pixel and a double attenuation coefficient of μ = 0.115 cm -1 and 0.5 cm -1 . Clinical data obtained after injection of 123 I (AMPI) were reconstructed using the RIM without and with cerebrovascular diseases or lesion defects. Contour finding techniques were used for the delineation of the brain and the skull, and measured attenuation coefficients were assumed within these two regions. Using volumes of interest, selected on homogeneous regions on an hemisphere and reported symetrically, the statistical uncertainty for 300 K events in the tomogram was found to be 12%, the index of symetry was of 4% for normal distribution. These results suggest that quantitative SPECT reconstruction for brain distribution is feasible, and that combined with an adapted tracer and an adequate model physiopathological parameters could be extracted

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

Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering

Directory of Open Access Journals (Sweden)

Ahmed Elazab

2015-01-01

Full Text Available An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

Online Adaptive Replanning Method for Prostate Radiotherapy

International Nuclear Information System (INIS)

Ahunbay, Ergun E.; Peng Cheng; Holmes, Shannon; Godley, Andrew; Lawton, Colleen; Li, X. Allen

2010-01-01

Purpose: To report the application of an adaptive replanning technique for prostate cancer radiotherapy (RT), consisting of two steps: (1) segment aperture morphing (SAM), and (2) segment weight optimization (SWO), to account for interfraction variations. Methods and Materials: The new 'SAM+SWO' scheme was retroactively applied to the daily CT images acquired for 10 prostate cancer patients on a linear accelerator and CT-on-Rails combination during the course of RT. Doses generated by the SAM+SWO scheme based on the daily CT images were compared with doses generated after patient repositioning using the current planning target volume (PTV) margin (5 mm, 3 mm toward rectum) and a reduced margin (2 mm), along with full reoptimization scans based on the daily CT images to evaluate dosimetry benefits. Results: For all cases studied, the online replanning method provided significantly better target coverage when compared with repositioning with reduced PTV (13% increase in minimum prostate dose) and improved organ sparing when compared with repositioning with regular PTV (13% decrease in the generalized equivalent uniform dose of rectum). The time required to complete the online replanning process was 6 ± 2 minutes. Conclusion: The proposed online replanning method can be used to account for interfraction variations for prostate RT with a practically acceptable time frame (5-10 min) and with significant dosimetric benefits. On the basis of this study, the developed online replanning scheme is being implemented in the clinic for prostate RT.

Adaptive Method Using Controlled Grid Deformation

Directory of Open Access Journals (Sweden)

Florin FRUNZULICA

2011-09-01

Full Text Available The paper presents an adaptive method using the controlled grid deformation over an elastic, isotropic and continuous domain. The adaptive process is controlled with the principal strains and principal strain directions and uses the finite elements method. Numerical results are presented for several test cases.

An extended L-curve method for choosing a regularization parameter in electrical resistance tomography

International Nuclear Information System (INIS)

Xu, Yanbin; Pei, Yang; Dong, Feng

2016-01-01

The L-curve method is a popular regularization parameter choice method for the ill-posed inverse problem of electrical resistance tomography (ERT). However the method cannot always determine a proper parameter for all situations. An investigation into those situations where the L-curve method failed show that a new corner point appears on the L-curve and the parameter corresponding to the new corner point can obtain a satisfactory reconstructed solution. Thus an extended L-curve method, which determines the regularization parameter associated with either global corner or the new corner, is proposed. Furthermore, two strategies are provided to determine the new corner–one is based on the second-order differential of L-curve, and the other is based on the curvature of L-curve. The proposed method is examined by both numerical simulations and experimental tests. And the results indicate that the extended method can handle the parameter choice problem even in the case where the typical L-curve method fails. Finally, in order to reduce the running time of the method, the extended method is combined with a projection method based on the Krylov subspace, which was able to boost the extended L-curve method. The results verify that the speed of the extended L-curve method is distinctly improved. The proposed method extends the application of the L-curve in the field of choosing regularization parameter with an acceptable running time and can also be used in other kinds of tomography. (paper)

3D CSEM inversion based on goal-oriented adaptive finite element method

Science.gov (United States)

Zhang, Y.; Key, K.

2016-12-01

We present a parallel 3D frequency domain controlled-source electromagnetic inversion code name MARE3DEM. Non-linear inversion of observed data is performed with the Occam variant of regularized Gauss-Newton optimization. The forward operator is based on the goal-oriented finite element method that efficiently calculates the responses and sensitivity kernels in parallel using a data decomposition scheme where independent modeling tasks contain different frequencies and subsets of the transmitters and receivers. To accommodate complex 3D conductivity variation with high flexibility and precision, we adopt the dual-grid approach where the forward mesh conforms to the inversion parameter grid and is adaptively refined until the forward solution converges to the desired accuracy. This dual-grid approach is memory efficient, since the inverse parameter grid remains independent from fine meshing generated around the transmitter and receivers by the adaptive finite element method. Besides, the unstructured inverse mesh efficiently handles multiple scale structures and allows for fine-scale model parameters within the region of interest. Our mesh generation engine keeps track of the refinement hierarchy so that the map of conductivity and sensitivity kernel between the forward and inverse mesh is retained. We employ the adjoint-reciprocity method to calculate the sensitivity kernels which establish a linear relationship between changes in the conductivity model and changes in the modeled responses. Our code uses a direcy solver for the linear systems, so the adjoint problem is efficiently computed by re-using the factorization from the primary problem. Further computational efficiency and scalability is obtained in the regularized Gauss-Newton portion of the inversion using parallel dense matrix-matrix multiplication and matrix factorization routines implemented with the ScaLAPACK library. We show the scalability, reliability and the potential of the algorithm to deal with

A Frequency-Domain Adaptive Filter (FDAF) Prediction Error Method (PEM) Framework for Double-Talk-Robust Acoustic Echo Cancellation

DEFF Research Database (Denmark)

Gil-Cacho, Jose M.; van Waterschoot, Toon; Moonen, Marc

2014-01-01

to the FDAF-PEM-AFROW algorithm. We show that FDAF-PEM-AFROW is by construction related to the best linear unbiased estimate (BLUE) of the echo path. We depart from this framework to show an improvement in performance with respect to other adaptive filters minimizing the BLUE criterion, namely the PEM......In this paper, we propose a new framework to tackle the double-talk (DT) problem in acoustic echo cancellation (AEC). It is based on a frequency-domain adaptive filter (FDAF) implementation of the so-called prediction error method adaptive filtering using row operations (PEM-AFROW) leading...... regularization (VR) algorithms. The FDAF-PEM-AFROW versions significantly outperform the original versions in every simulation. In terms of computational complexity, the FDAF-PEM-AFROW versions are themselves about two orders of magnitude cheaper than the original versions....

An adaptive pruning algorithm for the discrete L-curve criterion

DEFF Research Database (Denmark)

Hansen, Per Christian; Jensen, Toke Koldborg; Rodriguez, Giuseppe

2007-01-01

We describe a robust and adaptive implementation of the L-curve criterion, i.e., for locating the corner of a discrete L-curve consisting of a log-log plot of corresponding residual and solution norms of regularized solutions from a method with a discrete regularization parameter (such as truncated...

A regularized vortex-particle mesh method for large eddy simulation

DEFF Research Database (Denmark)

Spietz, Henrik Juul; Walther, Jens Honore; Hejlesen, Mads Mølholm

We present recent developments of the remeshed vortex particle-mesh method for simulating incompressible fluid flow. The presented method relies on a parallel higher-order FFT based solver for the Poisson equation. Arbitrary high order is achieved through regularization of singular Green’s function...... solutions to the Poisson equation and recently we have derived novel high order solutions for a mixture of open and periodic domains. With this approach the simulated variables may formally be viewed as the approximate solution to the filtered Navier Stokes equations, hence we use the method for Large Eddy...

Goal oriented adaptivity in the IRGNM for parameter identification in PDEs: I. reduced formulation

International Nuclear Information System (INIS)

Kaltenbacher, B; Kirchner, A; Veljović, S

2014-01-01

In this paper we study adaptive discretization of the iteratively regularized Gauss–Newton method (IRGNM) with an a posteriori (discrepancy principle) choice of the regularization parameter in each Newton step and of the stopping index. We first of all prove convergence and convergence rates under some accuracy requirements formulated in terms of four quantities of interest. Then computation of error estimators for these quantities based on a weighted dual residual method is discussed, which results in an algorithm for adaptive refinement. Finally we extend the results from the Hilbert space setting with quadratic penalty to Banach spaces and general Tikhonov functionals for the regularization of each Newton step. (paper)

An algorithmic framework for Mumford–Shah regularization of inverse problems in imaging

International Nuclear Information System (INIS)

Hohm, Kilian; Weinmann, Andreas; Storath, Martin

2015-01-01

The Mumford–Shah model is a very powerful variational approach for edge preserving regularization of image reconstruction processes. However, it is algorithmically challenging because one has to deal with a non-smooth and non-convex functional. In this paper, we propose a new efficient algorithmic framework for Mumford–Shah regularization of inverse problems in imaging. It is based on a splitting into specific subproblems that can be solved exactly. We derive fast solvers for the subproblems which are key for an efficient overall algorithm. Our method neither requires a priori knowledge of the gray or color levels nor of the shape of the discontinuity set. We demonstrate the wide applicability of the method for different modalities. In particular, we consider the reconstruction from Radon data, inpainting, and deconvolution. Our method can be easily adapted to many further imaging setups. The relevant condition is that the proximal mapping of the data fidelity can be evaluated a within reasonable time. In other words, it can be used whenever classical Tikhonov regularization is possible. (paper)

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.

Limited angle CT reconstruction by simultaneous spatial and Radon domain regularization based on TV and data-driven tight frame

Science.gov (United States)

Zhang, Wenkun; Zhang, Hanming; Wang, Linyuan; Cai, Ailong; Li, Lei; Yan, Bin

2018-02-01

Limited angle computed tomography (CT) reconstruction is widely performed in medical diagnosis and industrial testing because of the size of objects, engine/armor inspection requirements, and limited scan flexibility. Limited angle reconstruction necessitates usage of optimization-based methods that utilize additional sparse priors. However, most of conventional methods solely exploit sparsity priors of spatial domains. When CT projection suffers from serious data deficiency or various noises, obtaining reconstruction images that meet the requirement of quality becomes difficult and challenging. To solve this problem, this paper developed an adaptive reconstruction method for limited angle CT problem. The proposed method simultaneously uses spatial and Radon domain regularization model based on total variation (TV) and data-driven tight frame. Data-driven tight frame being derived from wavelet transformation aims at exploiting sparsity priors of sinogram in Radon domain. Unlike existing works that utilize pre-constructed sparse transformation, the framelets of the data-driven regularization model can be adaptively learned from the latest projection data in the process of iterative reconstruction to provide optimal sparse approximations for given sinogram. At the same time, an effective alternating direction method is designed to solve the simultaneous spatial and Radon domain regularization model. The experiments for both simulation and real data demonstrate that the proposed algorithm shows better performance in artifacts depression and details preservation than the algorithms solely using regularization model of spatial domain. Quantitative evaluations for the results also indicate that the proposed algorithm applying learning strategy performs better than the dual domains algorithms without learning regularization model

Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization

KAUST Repository

Wang, Jim Jing-Yan; AbdulJabbar, Mustafa Abdulmajeed

2012-01-01

Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of non-negative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation, which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.

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.

An adaptive method for γ spectra smoothing

International Nuclear Information System (INIS)

Xiao Gang; Zhou Chunlin; Li Tiantuo; Han Feng; Di Yuming

2001-01-01

Adaptive wavelet method and multinomial fitting gliding method are used for smoothing γ spectra, respectively, and then FWHM of 1332 keV peak of 60 Co and activities of 238 U standard specimen are calculated. Calculated results show that adaptive wavelet method is better than the other

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.

Construction of a Mean Square Error Adaptive Euler–Maruyama Method With Applications in Multilevel Monte Carlo

KAUST Repository

Hoel, Hakon

2016-06-13

A formal mean square error expansion (MSE) is derived for Euler-Maruyama numerical solutions of stochastic differential equations (SDE). The error expansion is used to construct a pathwise, a posteriori, adaptive time-stepping Euler-Maruyama algorithm for numerical solutions of SDE, and the resulting algorithm is incorporated into a multilevel Monte Carlo (MLMC) algorithm for weak approximations of SDE. This gives an efficient MSE adaptive MLMC algorithm for handling a number of low-regularity approximation problems. In low-regularity numerical example problems, the developed adaptive MLMC algorithm is shown to outperform the uniform time-stepping MLMC algorithm by orders of magnitude, producing output whose error with high probability is bounded by TOL > 0 at the near-optimal MLMC cost rate б(TOL log(TOL)) that is achieved when the cost of sample generation is б(1).

An interior-point method for total variation regularized positron emission tomography image reconstruction

Science.gov (United States)

Bai, Bing

2012-03-01

There has been a lot of work on total variation (TV) regularized tomographic image reconstruction recently. Many of them use gradient-based optimization algorithms with a differentiable approximation of the TV functional. In this paper we apply TV regularization in Positron Emission Tomography (PET) image reconstruction. We reconstruct the PET image in a Bayesian framework, using Poisson noise model and TV prior functional. The original optimization problem is transformed to an equivalent problem with inequality constraints by adding auxiliary variables. Then we use an interior point method with logarithmic barrier functions to solve the constrained optimization problem. In this method, a series of points approaching the solution from inside the feasible region are found by solving a sequence of subproblems characterized by an increasing positive parameter. We use preconditioned conjugate gradient (PCG) algorithm to solve the subproblems directly. The nonnegativity constraint is enforced by bend line search. The exact expression of the TV functional is used in our calculations. Simulation results show that the algorithm converges fast and the convergence is insensitive to the values of the regularization and reconstruction parameters.

Data mining methods application in reflexive adaptation realization in e-learning systems

Directory of Open Access Journals (Sweden)

A. S. Bozhday

2017-01-01

Full Text Available In recent years, e-learning technologies are rapidly gaining momentum in their evolution. In this regard, issues related to improving the quality of software for virtual educational systems are becoming topical: increasing the period of exploitation of programs, increasing their reliability and flexibility. The above characteristics directly depend on the ability of the software system to adapt to changes in the domain, environment and user characteristics. In some cases, this ability is reduced to the timely optimization of the program’s own interfaces and data structure. At present, several approaches to creating mechanisms for self-optimization of software systems are known, but all of them have an insufficient degree of formalization and, as a consequence, weak universality. The purpose of this work is to develop the basics of the technology of self-optimization of software systems in the structure of e-learning. The proposed technology is based on the formulated and formalized principle of reflexive adaptation of software, applicable to a wide class of software systems and based on the discovery of new knowledge in the behavioral products of the system.To solve this problem, methods of data mining were applied. Data mining allows finding regularities in the functioning of software systems, which may not be obvious at the stage of their development. Finding such regularities and their subsequent analysis will make it possible to reorganize the structure of the system in a more optimal way and without human intervention, which will prolong the life cycle of the software and reduce the costs of its maintenance. Achieving this effect is important for e-learning systems, since they are quite expensive.The main results of the work include: the proposed classification of software adaptation mechanisms, taking into account the latest trends in the IT field in general and in the field of e-learning in particular; Formulation and formalization of

Output regularization of SVM seizure predictors: Kalman Filter versus the "Firing Power" method.

Science.gov (United States)

Teixeira, Cesar; Direito, Bruno; Bandarabadi, Mojtaba; Dourado, António

2012-01-01

Two methods for output regularization of support vector machines (SVMs) classifiers were applied for seizure prediction in 10 patients with long-term annotated data. The output of the classifiers were regularized by two methods: one based on the Kalman Filter (KF) and other based on a measure called the "Firing Power" (FP). The FP is a quantification of the rate of the classification in the preictal class in a past time window. In order to enable the application of the KF, the classification problem was subdivided in a two two-class problem, and the real-valued output of SVMs was considered. The results point that the FP method raise less false alarms than the KF approach. However, the KF approach presents an higher sensitivity, but the high number of false alarms turns their applicability negligible in some situations.

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

Designing adaptive intensive interventions using methods from engineering.

Science.gov (United States)

Lagoa, Constantino M; Bekiroglu, Korkut; Lanza, Stephanie T; Murphy, Susan A

2014-10-01

Adaptive intensive interventions are introduced, and new methods from the field of control engineering for use in their design are illustrated. A detailed step-by-step explanation of how control engineering methods can be used with intensive longitudinal data to design an adaptive intensive intervention is provided. The methods are evaluated via simulation. Simulation results illustrate how the designed adaptive intensive intervention can result in improved outcomes with less treatment by providing treatment only when it is needed. Furthermore, the methods are robust to model misspecification as well as the influence of unobserved causes. These new methods can be used to design adaptive interventions that are effective yet reduce participant burden. PsycINFO Database Record (c) 2014 APA, all rights reserved.

A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms.

Science.gov (United States)

Caldas, Rafael; Mundt, Marion; Potthast, Wolfgang; Buarque de Lima Neto, Fernando; Markert, Bernd

2017-09-01

The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects. Copyright © 2017 Elsevier B.V. All rights reserved.

New method for minimizing regular functions with constraints on parameter region

International Nuclear Information System (INIS)

Kurbatov, V.S.; Silin, I.N.

1993-01-01

The new method of function minimization is developed. Its main features are considered. It is possible minimization of regular function with the arbitrary structure. For χ 2 -like function the usage of simplified second derivatives is possible with the control of correctness. The constraints of arbitrary structure can be used. The means for fast movement along multidimensional valleys are used. The method is tested on real data of K π2 decay of the experiment on rare K - -decays. 6 refs

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

Technical Note: Regularization performances with the error consistency method in the case of retrieved atmospheric profiles

Directory of Open Access Journals (Sweden)

S. Ceccherini

2007-01-01

Full Text Available The retrieval of concentration vertical profiles of atmospheric constituents from spectroscopic measurements is often an ill-conditioned problem and regularization methods are frequently used to improve its stability. Recently a new method, that provides a good compromise between precision and vertical resolution, was proposed to determine analytically the value of the regularization parameter. This method is applied for the first time to real measurements with its implementation in the operational retrieval code of the satellite limb-emission measurements of the MIPAS instrument and its performances are quantitatively analyzed. The adopted regularization improves the stability of the retrieval providing smooth profiles without major degradation of the vertical resolution. In the analyzed measurements the retrieval procedure provides a vertical resolution that, in the troposphere and low stratosphere, is smaller than the vertical field of view of the instrument.

Iterative Method of Regularization with Application of Advanced Technique for Detection of Contours

International Nuclear Information System (INIS)

Niedziela, T.; Stankiewicz, A.

2000-01-01

This paper proposes a novel iterative method of regularization with application of an advanced technique for detection of contours. To eliminate noises, the properties of convolution of functions are utilized. The method can be accomplished in a simple neural cellular network, which creates the possibility of extraction of contours by automatic image recognition equipment. (author)

Traction cytometry: regularization in the Fourier approach and comparisons with finite element method.

Science.gov (United States)

Kulkarni, Ankur H; Ghosh, Prasenjit; Seetharaman, Ashwin; Kondaiah, Paturu; Gundiah, Namrata

2018-05-09

Traction forces exerted by adherent cells are quantified using displacements of embedded markers on polyacrylamide substrates due to cell contractility. Fourier Transform Traction Cytometry (FTTC) is widely used to calculate tractions but has inherent limitations due to errors in the displacement fields; these are mitigated through a regularization parameter (γ) in the Reg-FTTC method. An alternate finite element (FE) approach computes tractions on a domain using known boundary conditions. Robust verification and recovery studies are lacking but essential in assessing the accuracy and noise sensitivity of the traction solutions from the different methods. We implemented the L2 regularization method and defined a maximum curvature point in the traction with γ plot as the optimal regularization parameter (γ*) in the Reg-FTTC approach. Traction reconstructions using γ* yield accurate values of low and maximum tractions (Tmax) in the presence of up to 5% noise. Reg-FTTC is hence a clear improvement over the FTTC method but is inadequate to reconstruct low stresses such as those at nascent focal adhesions. FE, implemented using a node-by-node comparison, showed an intermediate reconstruction compared to Reg-FTTC. We performed experiments using mouse embryonic fibroblast (MEF) and compared results between these approaches. Tractions from FTTC and FE showed differences of ∼92% and 22% as compared to Reg-FTTC. Selection of an optimum value of γ for each cell reduced variability in the computed tractions as compared to using a single value of γ for all the MEF cells in this study.

Sound Attenuation in Elliptic Mufflers Using a Regular Perturbation Method

OpenAIRE

Banerjee, Subhabrata; Jacobi, Anthony M.

2012-01-01

The study of sound attenuation in an elliptical chamber involves the solution of the Helmholtz equation in elliptic coordinate systems. The Eigen solutions for such problems involve the Mathieu and the modified Mathieu functions. The computation of such functions poses considerable challenge. An alternative method to solve such problems had been proposed in this paper. The elliptical cross-section of the muffler has been treated as a perturbed circle, enabling the use of a regular perturbatio...

Differential regularization and renormalization: a new method of calculation in quantum field theory

International Nuclear Information System (INIS)

Freedman, D.Z.; Johnson, K.; Latorre, J.I.

1992-01-01

Most primitively divergent Feynman diagrams are well defined in x-space but too singular at short distances for transformation to p-space. A new method of regularization is developed in which singular functions are written as derivatives of less singular functions which contain a logarithmic mass scale. The Fourier transform is then defined by formal integration by parts. The procedure is extended to graphs with divergent subgraphs. No explicit cutoff or counterterms are required, and the method automatically delivers renormalized amplitudes which satisfy Callan-Symanzik equations. These features are thoroughly explored in massless φ 4 theory through 3-loop order, and the method yields explicit functional forms for all amplitudes with less difficulty than conventional methods which use dimensional regularization in p-space. The procedure also appears to be compatible with gauge invariance and the chiral structure of the standard model. This aspect is tested in extensive 1-loop calculations which include the Ward identity in quantum electrodynamics, the chiral anomaly, and the background field algorithm in non-abelian gauge theories. (orig.)

Adaptive tight frame based medical image reconstruction: a proof-of-concept study for computed tomography

International Nuclear Information System (INIS)

Zhou, Weifeng; Cai, Jian-Feng; Gao, Hao

2013-01-01

A popular approach for medical image reconstruction has been through the sparsity regularization, assuming the targeted image can be well approximated by sparse coefficients under some properly designed system. The wavelet tight frame is such a widely used system due to its capability for sparsely approximating piecewise-smooth functions, such as medical images. However, using a fixed system may not always be optimal for reconstructing a variety of diversified images. Recently, the method based on the adaptive over-complete dictionary that is specific to structures of the targeted images has demonstrated its superiority for image processing. This work is to develop the adaptive wavelet tight frame method image reconstruction. The proposed scheme first constructs the adaptive wavelet tight frame that is task specific, and then reconstructs the image of interest by solving an l 1 -regularized minimization problem using the constructed adaptive tight frame system. The proof-of-concept study is performed for computed tomography (CT), and the simulation results suggest that the adaptive tight frame method improves the reconstructed CT image quality from the traditional tight frame method. (paper)

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.

A regularization method for solving the Poisson equation for mixed unbounded-periodic domains

DEFF Research Database (Denmark)

Spietz, Henrik Juul; Mølholm Hejlesen, Mads; Walther, Jens Honoré

2018-01-01

the regularized unbounded-periodic Green's functions can be implemented in an FFT-based Poisson solver to obtain a convergence rate corresponding to the regularization order of the Green's function. The high order is achieved without any additional computational cost from the conventional FFT-based Poisson solver...... and enables the calculation of the derivative of the solution to the same high order by direct spectral differentiation. We illustrate an application of the FFT-based Poisson solver by using it with a vortex particle mesh method for the approximation of incompressible flow for a problem with a single periodic...

Adaptive finite element methods for differential equations

CERN Document Server

Bangerth, Wolfgang

2003-01-01

These Lecture Notes discuss concepts of `self-adaptivity' in the numerical solution of differential equations, with emphasis on Galerkin finite element methods. The key issues are a posteriori error estimation and it automatic mesh adaptation. Besides the traditional approach of energy-norm error control, a new duality-based technique, the Dual Weighted Residual method for goal-oriented error estimation, is discussed in detail. This method aims at economical computation of arbitrary quantities of physical interest by properly adapting the computational mesh. This is typically required in the design cycles of technical applications. For example, the drag coefficient of a body immersed in a viscous flow is computed, then it is minimized by varying certain control parameters, and finally the stability of the resulting flow is investigated by solving an eigenvalue problem. `Goal-oriented' adaptivity is designed to achieve these tasks with minimal cost. At the end of each chapter some exercises are posed in order ...

A parallel direct solver for the self-adaptive hp Finite Element Method

KAUST Repository

Paszyński, Maciej R.

2010-03-01

In this paper we present a new parallel multi-frontal direct solver, dedicated for the hp Finite Element Method (hp-FEM). The self-adaptive hp-FEM generates in a fully automatic mode, a sequence of hp-meshes delivering exponential convergence of the error with respect to the number of degrees of freedom (d.o.f.) as well as the CPU time, by performing a sequence of hp refinements starting from an arbitrary initial mesh. The solver constructs an initial elimination tree for an arbitrary initial mesh, and expands the elimination tree each time the mesh is refined. This allows us to keep track of the order of elimination for the solver. The solver also minimizes the memory usage, by de-allocating partial LU factorizations computed during the elimination stage of the solver, and recomputes them for the backward substitution stage, by utilizing only about 10% of the computational time necessary for the original computations. The solver has been tested on 3D Direct Current (DC) borehole resistivity measurement simulations problems. We measure the execution time and memory usage of the solver over a large regular mesh with 1.5 million degrees of freedom as well as on the highly non-regular mesh, generated by the self-adaptive h p-FEM, with finite elements of various sizes and polynomial orders of approximation varying from p = 1 to p = 9. From the presented experiments it follows that the parallel solver scales well up to the maximum number of utilized processors. The limit for the solver scalability is the maximum sequential part of the algorithm: the computations of the partial LU factorizations over the longest path, coming from the root of the elimination tree down to the deepest leaf. © 2009 Elsevier Inc. All rights reserved.

Adaptive and non-adaptive data hiding methods for grayscale images based on modulus function

Directory of Open Access Journals (Sweden)

Najme Maleki

2014-07-01

Full Text Available This paper presents two adaptive and non-adaptive data hiding methods for grayscale images based on modulus function. Our adaptive scheme is based on the concept of human vision sensitivity, so the pixels in edge areas than to smooth areas can tolerate much more changes without making visible distortion for human eyes. In our adaptive scheme, the average differencing value of four neighborhood pixels into a block via a threshold secret key determines whether current block is located in edge or smooth area. Pixels in the edge areas are embedded by Q-bit of secret data with a larger value of Q than that of pixels placed in smooth areas. Also in this scholar, we represent one non-adaptive data hiding algorithm. Our non-adaptive scheme, via an error reduction procedure, produces a high visual quality for stego-image. The proposed schemes present several advantages. 1-of aspects the embedding capacity and visual quality of stego-image are scalable. In other words, the embedding rate as well as the image quality can be scaled for practical applications 2-the high embedding capacity with minimal visual distortion can be achieved, 3-our methods require little memory space for secret data embedding and extracting phases, 4-secret keys have used to protect of the embedded secret data. Thus, level of security is high, 5-the problem of overflow or underflow does not occur. Experimental results indicated that the proposed adaptive scheme significantly is superior to the currently existing scheme, in terms of stego-image visual quality, embedding capacity and level of security and also our non-adaptive method is better than other non-adaptive methods, in view of stego-image quality. Results show which our adaptive algorithm can resist against the RS steganalysis attack.

Generalized Bregman distances and convergence rates for non-convex regularization methods

International Nuclear Information System (INIS)

Grasmair, Markus

2010-01-01

We generalize the notion of Bregman distance using concepts from abstract convexity in order to derive convergence rates for Tikhonov regularization with non-convex regularization terms. In particular, we study the non-convex regularization of linear operator equations on Hilbert spaces, showing that the conditions required for the application of the convergence rates results are strongly related to the standard range conditions from the convex case. Moreover, we consider the setting of sparse regularization, where we show that a rate of order δ 1/p holds, if the regularization term has a slightly faster growth at zero than |t| p

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.

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

The Method of Adaptive Comparative Judgement

Science.gov (United States)

Pollitt, Alastair

2012-01-01

Adaptive Comparative Judgement (ACJ) is a modification of Thurstone's method of comparative judgement that exploits the power of adaptivity, but in scoring rather than testing. Professional judgement by teachers replaces the marking of tests; a judge is asked to compare the work of two students and simply to decide which of them is the better.…

Linearized Alternating Direction Method of Multipliers for Constrained Nonconvex Regularized Optimization

Science.gov (United States)

2016-11-22

structure of the graph, we replace the ℓ1- norm by the nonconvex Capped -ℓ1 norm , and obtain the Generalized Capped -ℓ1 regularized logistic regression...X. M. Yuan. Linearized augmented lagrangian and alternating direction methods for nuclear norm minimization. Mathematics of Computation, 82(281):301...better approximations of ℓ0- norm theoretically and computationally beyond ℓ1- norm , for example, the compressive sensing (Xiao et al., 2011). The

Application of Littlewood-Paley decomposition to the regularity of Boltzmann type kinetic equations

International Nuclear Information System (INIS)

EL Safadi, M.

2007-03-01

We study the regularity of kinetic equations of Boltzmann type.We use essentially Littlewood-Paley method from harmonic analysis, consisting mainly in working with dyadics annulus. We shall mainly concern with the homogeneous case, where the solution f(t,x,v) depends only on the time t and on the velocities v, while working with realistic and singular cross-sections (non cutoff). In the first part, we study the particular case of Maxwellian molecules. Under this hypothesis, the structure of the Boltzmann operator and his Fourier transform write in a simple form. We show a global C ∞ regularity. Then, we deal with the case of general cross-sections with 'hard potential'. We are interested in the Landau equation which is limit equation to the Boltzmann equation, taking in account grazing collisions. We prove that any weak solution belongs to Schwartz space S. We demonstrate also a similar regularity for the case of Boltzmann equation. Let us note that our method applies directly for all dimensions, and proofs are often simpler compared to other previous ones. Finally, we finish with Boltzmann-Dirac equation. In particular, we adapt the result of regularity obtained in Alexandre, Desvillettes, Wennberg and Villani work, using the dissipation rate connected with Boltzmann-Dirac equation. (author)

An Adaptive Reordered Method for Computing PageRank

Directory of Open Access Journals (Sweden)

Yi-Ming Bu

2013-01-01

Full Text Available We propose an adaptive reordered method to deal with the PageRank problem. It has been shown that one can reorder the hyperlink matrix of PageRank problem to calculate a reduced system and get the full PageRank vector through forward substitutions. This method can provide a speedup for calculating the PageRank vector. We observe that in the existing reordered method, the cost of the recursively reordering procedure could offset the computational reduction brought by minimizing the dimension of linear system. With this observation, we introduce an adaptive reordered method to accelerate the total calculation, in which we terminate the reordering procedure appropriately instead of reordering to the end. Numerical experiments show the effectiveness of this adaptive reordered method.

Subcortical processing of speech regularities underlies reading and music aptitude in children

Science.gov (United States)

2011-01-01

Background Neural sensitivity to acoustic regularities supports fundamental human behaviors such as hearing in noise and reading. Although the failure to encode acoustic regularities in ongoing speech has been associated with language and literacy deficits, how auditory expertise, such as the expertise that is associated with musical skill, relates to the brainstem processing of speech regularities is unknown. An association between musical skill and neural sensitivity to acoustic regularities would not be surprising given the importance of repetition and regularity in music. Here, we aimed to define relationships between the subcortical processing of speech regularities, music aptitude, and reading abilities in children with and without reading impairment. We hypothesized that, in combination with auditory cognitive abilities, neural sensitivity to regularities in ongoing speech provides a common biological mechanism underlying the development of music and reading abilities. Methods We assessed auditory working memory and attention, music aptitude, reading ability, and neural sensitivity to acoustic regularities in 42 school-aged children with a wide range of reading ability. Neural sensitivity to acoustic regularities was assessed by recording brainstem responses to the same speech sound presented in predictable and variable speech streams. Results Through correlation analyses and structural equation modeling, we reveal that music aptitude and literacy both relate to the extent of subcortical adaptation to regularities in ongoing speech as well as with auditory working memory and attention. Relationships between music and speech processing are specifically driven by performance on a musical rhythm task, underscoring the importance of rhythmic regularity for both language and music. Conclusions These data indicate common brain mechanisms underlying reading and music abilities that relate to how the nervous system responds to regularities in auditory input

Subcortical processing of speech regularities underlies reading and music aptitude in children

Directory of Open Access Journals (Sweden)

Strait Dana L

2011-10-01

Full Text Available Abstract Background Neural sensitivity to acoustic regularities supports fundamental human behaviors such as hearing in noise and reading. Although the failure to encode acoustic regularities in ongoing speech has been associated with language and literacy deficits, how auditory expertise, such as the expertise that is associated with musical skill, relates to the brainstem processing of speech regularities is unknown. An association between musical skill and neural sensitivity to acoustic regularities would not be surprising given the importance of repetition and regularity in music. Here, we aimed to define relationships between the subcortical processing of speech regularities, music aptitude, and reading abilities in children with and without reading impairment. We hypothesized that, in combination with auditory cognitive abilities, neural sensitivity to regularities in ongoing speech provides a common biological mechanism underlying the development of music and reading abilities. Methods We assessed auditory working memory and attention, music aptitude, reading ability, and neural sensitivity to acoustic regularities in 42 school-aged children with a wide range of reading ability. Neural sensitivity to acoustic regularities was assessed by recording brainstem responses to the same speech sound presented in predictable and variable speech streams. Results Through correlation analyses and structural equation modeling, we reveal that music aptitude and literacy both relate to the extent of subcortical adaptation to regularities in ongoing speech as well as with auditory working memory and attention. Relationships between music and speech processing are specifically driven by performance on a musical rhythm task, underscoring the importance of rhythmic regularity for both language and music. Conclusions These data indicate common brain mechanisms underlying reading and music abilities that relate to how the nervous system responds to

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.

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.

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.

Track and vertex reconstruction: From classical to adaptive methods

International Nuclear Information System (INIS)

Strandlie, Are; Fruehwirth, Rudolf

2010-01-01

This paper reviews classical and adaptive methods of track and vertex reconstruction in particle physics experiments. Adaptive methods have been developed to meet the experimental challenges at high-energy colliders, in particular, the CERN Large Hadron Collider. They can be characterized by the obliteration of the traditional boundaries between pattern recognition and statistical estimation, by the competition between different hypotheses about what constitutes a track or a vertex, and by a high level of flexibility and robustness achieved with a minimum of assumptions about the data. The theoretical background of some of the adaptive methods is described, and it is shown that there is a close connection between the two main branches of adaptive methods: neural networks and deformable templates, on the one hand, and robust stochastic filters with annealing, on the other hand. As both classical and adaptive methods of track and vertex reconstruction presuppose precise knowledge of the positions of the sensitive detector elements, the paper includes an overview of detector alignment methods and a survey of the alignment strategies employed by past and current experiments.

Adaptation of an Agile Information System Development Method

NARCIS (Netherlands)

Aydin, M.N.; Harmsen, A.F.; van Hillegersberg, Jos; Stegwee, R.A.; Siau, K.

2007-01-01

Little specific research has been conducted to date on the adaptation of agile information systems development (ISD) methods. This chapter presents the work practice in dealing with the adaptation of such a method in the ISD department of one of the leading financial institutes in Europe. The

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.

Adaptive [theta]-methods for pricing American options

Science.gov (United States)

Khaliq, Abdul Q. M.; Voss, David A.; Kazmi, Kamran

2008-12-01

We develop adaptive [theta]-methods for solving the Black-Scholes PDE for American options. By adding a small, continuous term, the Black-Scholes PDE becomes an advection-diffusion-reaction equation on a fixed spatial domain. Standard implementation of [theta]-methods would require a Newton-type iterative procedure at each time step thereby increasing the computational complexity of the methods. Our linearly implicit approach avoids such complications. We establish a general framework under which [theta]-methods satisfy a discrete version of the positivity constraint characteristic of American options, and numerically demonstrate the sensitivity of the constraint. The positivity results are established for the single-asset and independent two-asset models. In addition, we have incorporated and analyzed an adaptive time-step control strategy to increase the computational efficiency. Numerical experiments are presented for one- and two-asset American options, using adaptive exponential splitting for two-asset problems. The approach is compared with an iterative solution of the two-asset problem in terms of computational efficiency.

Catalytic micromotor generating self-propelled regular motion through random fluctuation

Science.gov (United States)

Yamamoto, Daigo; Mukai, Atsushi; Okita, Naoaki; Yoshikawa, Kenichi; Shioi, Akihisa

2013-07-01

Most of the current studies on nano/microscale motors to generate regular motion have adapted the strategy to fabricate a composite with different materials. In this paper, we report that a simple object solely made of platinum generates regular motion driven by a catalytic chemical reaction with hydrogen peroxide. Depending on the morphological symmetry of the catalytic particles, a rich variety of random and regular motions are observed. The experimental trend is well reproduced by a simple theoretical model by taking into account of the anisotropic viscous effect on the self-propelled active Brownian fluctuation.

Recursive regularization step for high-order lattice Boltzmann methods

Science.gov (United States)

Coreixas, Christophe; Wissocq, Gauthier; Puigt, Guillaume; Boussuge, Jean-François; Sagaut, Pierre

2017-09-01

A lattice Boltzmann method (LBM) with enhanced stability and accuracy is presented for various Hermite tensor-based lattice structures. The collision operator relies on a regularization step, which is here improved through a recursive computation of nonequilibrium Hermite polynomial coefficients. In addition to the reduced computational cost of this procedure with respect to the standard one, the recursive step allows to considerably enhance the stability and accuracy of the numerical scheme by properly filtering out second- (and higher-) order nonhydrodynamic contributions in under-resolved conditions. This is first shown in the isothermal case where the simulation of the doubly periodic shear layer is performed with a Reynolds number ranging from 104 to 106, and where a thorough analysis of the case at Re=3 ×104 is conducted. In the latter, results obtained using both regularization steps are compared against the Bhatnagar-Gross-Krook LBM for standard (D2Q9) and high-order (D2V17 and D2V37) lattice structures, confirming the tremendous increase of stability range of the proposed approach. Further comparisons on thermal and fully compressible flows, using the general extension of this procedure, are then conducted through the numerical simulation of Sod shock tubes with the D2V37 lattice. They confirm the stability increase induced by the recursive approach as compared with the standard one.

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.

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)

Methods used in adaptation of health-related guidelines: A systematic survey.

Science.gov (United States)

Abdul-Khalek, Rima A; Darzi, Andrea J; Godah, Mohammad W; Kilzar, Lama; Lakis, Chantal; Agarwal, Arnav; Abou-Jaoude, Elias; Meerpohl, Joerg J; Wiercioch, Wojtek; Santesso, Nancy; Brax, Hneine; Schünemann, Holger; Akl, Elie A

2017-12-01

Adaptation refers to the systematic approach for considering the endorsement or modification of recommendations produced in one setting for application in another as an alternative to de novo development. To describe and assess the methods used for adapting health-related guidelines published in peer-reviewed journals, and to assess the quality of the resulting adapted guidelines. We searched Medline and Embase up to June 2015. We assessed the method of adaptation, and the quality of included guidelines. Seventy-two papers were eligible. Most adapted guidelines and their source guidelines were published by professional societies (71% and 68% respectively), and in high-income countries (83% and 85% respectively). Of the 57 adapted guidelines that reported any detail about adaptation method, 34 (60%) did not use a published adaptation method. The number (and percentage) of adapted guidelines fulfilling each of the ADAPTE steps ranged between 2 (4%) and 57 (100%). The quality of adapted guidelines was highest for the "scope and purpose" domain and lowest for the "editorial independence" domain (respective mean percentages of the maximum possible scores were 93% and 43%). The mean score for "rigor of development" was 57%. Most adapted guidelines published in peer-reviewed journals do not report using a published adaptation method, and their adaptation quality was variable.

A sparsity-regularized Born iterative method for reconstruction of two-dimensional piecewise continuous inhomogeneous domains

KAUST Repository

Sandhu, Ali Imran; Desmal, Abdulla; Bagci, Hakan

2016-01-01

A sparsity-regularized Born iterative method (BIM) is proposed for efficiently reconstructing two-dimensional piecewise-continuous inhomogeneous dielectric profiles. Such profiles are typically not spatially sparse, which reduces the efficiency of the sparsity-promoting regularization. To overcome this problem, scattered fields are represented in terms of the spatial derivative of the dielectric profile and reconstruction is carried out over samples of the dielectric profile's derivative. Then, like the conventional BIM, the nonlinear problem is iteratively converted into a sequence of linear problems (in derivative samples) and sparsity constraint is enforced on each linear problem using the thresholded Landweber iterations. Numerical results, which demonstrate the efficiency and accuracy of the proposed method in reconstructing piecewise-continuous dielectric profiles, are presented.

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

Science.gov (United States)

Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

2017-12-20

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

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

Image super-resolution reconstruction based on regularization technique and guided filter

Science.gov (United States)

Huang, De-tian; Huang, Wei-qin; Gu, Pei-ting; Liu, Pei-zhong; Luo, Yan-min

2017-06-01

In order to improve the accuracy of sparse representation coefficients and the quality of reconstructed images, an improved image super-resolution algorithm based on sparse representation is presented. In the sparse coding stage, the autoregressive (AR) regularization and the non-local (NL) similarity regularization are introduced to improve the sparse coding objective function. A group of AR models which describe the image local structures are pre-learned from the training samples, and one or several suitable AR models can be adaptively selected for each image patch to regularize the solution space. Then, the image non-local redundancy is obtained by the NL similarity regularization to preserve edges. In the process of computing the sparse representation coefficients, the feature-sign search algorithm is utilized instead of the conventional orthogonal matching pursuit algorithm to improve the accuracy of the sparse coefficients. To restore image details further, a global error compensation model based on weighted guided filter is proposed to realize error compensation for the reconstructed images. Experimental results demonstrate that compared with Bicubic, L1SR, SISR, GR, ANR, NE + LS, NE + NNLS, NE + LLE and A + (16 atoms) methods, the proposed approach has remarkable improvement in peak signal-to-noise ratio, structural similarity and subjective visual perception.

Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids

KAUST Repository

Buse, Gerrit; Pflü ger, Dirk; Jacob, Riko

2014-01-01

In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated

Towards Adaptive Grids for Atmospheric Boundary-Layer Simulations

Science.gov (United States)

van Hooft, J. Antoon; Popinet, Stéphane; van Heerwaarden, Chiel C.; van der Linden, Steven J. A.; de Roode, Stephan R.; van de Wiel, Bas J. H.

2018-02-01

We present a proof-of-concept for the adaptive mesh refinement method applied to atmospheric boundary-layer simulations. Such a method may form an attractive alternative to static grids for studies on atmospheric flows that have a high degree of scale separation in space and/or time. Examples include the diurnal cycle and a convective boundary layer capped by a strong inversion. For such cases, large-eddy simulations using regular grids often have to rely on a subgrid-scale closure for the most challenging regions in the spatial and/or temporal domain. Here we analyze a flow configuration that describes the growth and subsequent decay of a convective boundary layer using direct numerical simulation (DNS). We validate the obtained results and benchmark the performance of the adaptive solver against two runs using fixed regular grids. It appears that the adaptive-mesh algorithm is able to coarsen and refine the grid dynamically whilst maintaining an accurate solution. In particular, during the initial growth of the convective boundary layer a high resolution is required compared to the subsequent stage of decaying turbulence. More specifically, the number of grid cells varies by two orders of magnitude over the course of the simulation. For this specific DNS case, the adaptive solver was not yet more efficient than the more traditional solver that is dedicated to these types of flows. However, the overall analysis shows that the method has a clear potential for numerical investigations of the most challenging atmospheric cases.

The Method of Lines Solution of the Regularized Long-Wave Equation Using Runge-Kutta Time Discretization Method

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H. O. Bakodah

2013-01-01

Full Text Available A method of lines approach to the numerical solution of nonlinear wave equations typified by the regularized long wave (RLW is presented. The method developed uses a finite differences discretization to the space. Solution of the resulting system was obtained by applying fourth Runge-Kutta time discretization method. Using Von Neumann stability analysis, it is shown that the proposed method is marginally stable. To test the accuracy of the method some numerical experiments on test problems are presented. Test problems including solitary wave motion, two-solitary wave interaction, and the temporal evaluation of a Maxwellian initial pulse are studied. The accuracy of the present method is tested with and error norms and the conservation properties of mass, energy, and momentum under the RLW equation.

A sparsity-regularized Born iterative method for reconstruction of two-dimensional piecewise continuous inhomogeneous domains

KAUST Repository

Sandhu, Ali Imran

2016-04-10

A sparsity-regularized Born iterative method (BIM) is proposed for efficiently reconstructing two-dimensional piecewise-continuous inhomogeneous dielectric profiles. Such profiles are typically not spatially sparse, which reduces the efficiency of the sparsity-promoting regularization. To overcome this problem, scattered fields are represented in terms of the spatial derivative of the dielectric profile and reconstruction is carried out over samples of the dielectric profile\\'s derivative. Then, like the conventional BIM, the nonlinear problem is iteratively converted into a sequence of linear problems (in derivative samples) and sparsity constraint is enforced on each linear problem using the thresholded Landweber iterations. Numerical results, which demonstrate the efficiency and accuracy of the proposed method in reconstructing piecewise-continuous dielectric profiles, are presented.

New adaptive sampling method in particle image velocimetry

International Nuclear Information System (INIS)

Yu, Kaikai; Xu, Jinglei; Tang, Lan; Mo, Jianwei

2015-01-01

This study proposes a new adaptive method to enable the number of interrogation windows and their positions in a particle image velocimetry (PIV) image interrogation algorithm to become self-adapted according to the seeding density. The proposed method can relax the constraint of uniform sampling rate and uniform window size commonly adopted in the traditional PIV algorithm. In addition, the positions of the sampling points are redistributed on the basis of the spring force generated by the sampling points. The advantages include control of the number of interrogation windows according to the local seeding density and smoother distribution of sampling points. The reliability of the adaptive sampling method is illustrated by processing synthetic and experimental images. The synthetic example attests to the advantages of the sampling method. Compared with that of the uniform interrogation technique in the experimental application, the spatial resolution is locally enhanced when using the proposed sampling method. (technical design note)

Adaptive design methods in clinical trials – a review

Directory of Open Access Journals (Sweden)

Chang Mark

2008-05-01

Full Text Available Abstract In recent years, the use of adaptive design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, adaptive designs can be classified into three categories: prospective, concurrent (ad hoc, and retrospective adaptive designs. An adaptive design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered adaptive designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments, challenges in by design (prospective adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of adaptive design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given. Practical issues that are commonly encountered when implementing adaptive design methods in clinical trials are also discussed.

On the evaluation of X-ray diffraction experiments by the regularization method

Energy Technology Data Exchange (ETDEWEB)

Trubin, V.A.; Szasz, A. (Lab. of Surface and Interface Physics, Eoetvoes Univ., Budapest (Hungary))

1991-05-16

The characteristic property of diffractometers as the presence of occasional and systematic errors in measured patterns requires such an evaluation which is as informative as possible. This circumstance gives rise to the problem of optimal planning of the experiment. The X-ray diffraction optimization problem with application of the regularization method is studied. The proposal permits to determine more accurately the unknown true characteristics of the X-ray diffraction experiment. (orig.).

On the evaluation of X-ray diffraction experiments by the regularization method

International Nuclear Information System (INIS)

Trubin, V.A.; Szasz, A.

1991-01-01

The characteristic property of diffractometers as the presence of occasional and systematic errors in measured patterns requires such an evaluation which is as informative as possible. This circumstance gives rise to the problem of optimal planning of the experiment. The X-ray diffraction optimization problem with application of the regularization method is studied. The proposal permits to determine more accurately the unknown true characteristics of the X-ray diffraction experiment. (orig.)

Implementing Adaptive Educational Methods with IMS Learning Design

NARCIS (Netherlands)

Specht, Marcus; Burgos, Daniel

2006-01-01

Please, cite this publication as: Specht, M. & Burgos, D. (2006). Implementing Adaptive Educational Methods with IMS Learning Design. Proceedings of Adaptive Hypermedia. June, Dublin, Ireland. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

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.

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

Shape-constrained regularization by statistical multiresolution for inverse problems: asymptotic analysis

International Nuclear Information System (INIS)

Frick, Klaus; Marnitz, Philipp; Munk, Axel

2012-01-01

This paper is concerned with a novel regularization technique for solving linear ill-posed operator equations in Hilbert spaces from data that are corrupted by white noise. We combine convex penalty functionals with extreme-value statistics of projections of the residuals on a given set of sub-spaces in the image space of the operator. We prove general consistency and convergence rate results in the framework of Bregman divergences which allows for a vast range of penalty functionals. Various examples that indicate the applicability of our approach will be discussed. We will illustrate in the context of signal and image processing that the presented method constitutes a locally adaptive reconstruction method. (paper)

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.

On Landweber–Kaczmarz methods for regularizing systems of ill-posed equations in Banach spaces

International Nuclear Information System (INIS)

Leitão, A; Alves, M Marques

2012-01-01

In this paper, iterative regularization methods of Landweber–Kaczmarz type are considered for solving systems of ill-posed equations modeled (finitely many) by operators acting between Banach spaces. Using assumptions of uniform convexity and smoothness on the parameter space, we are able to prove a monotony result for the proposed method, as well as to establish convergence (for exact data) and stability results (in the noisy data case). (paper)

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.

Adaptive Control Methods for Soft Robots

Data.gov (United States)

National Aeronautics and Space Administration — I propose to develop methods for soft and inflatable robots that will allow the control system to adapt and change control parameters based on changing conditions...

Numerical simulation of the regularized long wave equation by He's homotopy perturbation method

International Nuclear Information System (INIS)

Inc, Mustafa; Ugurlu, Yavuz

2007-01-01

In this Letter, we present the homotopy perturbation method (shortly HPM) for obtaining the numerical solution of the RLW equation. We obtain the exact and numerical solutions of the Regularized Long Wave (RLW) equation for certain initial condition. The initial approximation can be freely chosen with possible unknown constants which can be determined by imposing the boundary and initial conditions. Comparison of the results with those of other methods have led us to significant consequences. The numerical solutions are compared with the known analytical solutions

Regularized variable metric method versus the conjugate gradient method in solution of radiative boundary design problem

International Nuclear Information System (INIS)

Kowsary, F.; Pooladvand, K.; Pourshaghaghy, A.

2007-01-01

In this paper, an appropriate distribution of the heating elements' strengths in a radiation furnace is estimated using inverse methods so that a pre-specified temperature and heat flux distribution is attained on the design surface. Minimization of the sum of the squares of the error function is performed using the variable metric method (VMM), and the results are compared with those obtained by the conjugate gradient method (CGM) established previously in the literature. It is shown via test cases and a well-founded validation procedure that the VMM, when using a 'regularized' estimator, is more accurate and is able to reach at a higher quality final solution as compared to the CGM. The test cases used in this study were two-dimensional furnaces filled with an absorbing, emitting, and scattering gas

TEMPERAMENTAL ADAPTABILITY, PERSISTENCE, AND REGULARITY: PARENTAL RATINGS OF NORWEGIAN INFANTS AGED 6 TO 12 MONTHS, WITH SOME IMPLICATIONS FOR PREVENTIVE PRACTICE.

Science.gov (United States)

Olafsen, Kåre S; Ulvund, Stein Erik; Torgersen, Anne Mari; Wentzel-Larsen, Tore; Smith, Lars; Moe, Vibeke

2018-03-01

There is a need for standardized measures of infant temperament to strengthen current practices in prevention and early intervention. The present study provides Norwegian data on the Cameron-Rice Infant Temperament Questionnaire (CRITQ; J.R. Cameron & D.C. Rice, 1986a), which comprises 46 items and is used within a U.S. health maintenance organization. The CRITQ was filled out by mothers and fathers at 6 and again at 12 months as part of a longitudinal study of mental health during the first years of life (the "Little in Norway" study, N = 1,041 families enrolled; V. Moe & L. Smith, 2010). Results showed that internal consistencies were comparable with U.S. The temperament dimensions of persistence, adaptability, and regularity had acceptable or close-to-acceptable reliabilities in the U.S. study as well as in this study, and also were unifactorial in confirmatory factor analysis. These dimensions are the focus in this article. Findings concerning parents' differential ratings of their infants on the three dimensions are reported, as is the stability of parents' ratings of temperament from 6 to 12 months. In addition, results on the relation between temperament and parenting stress are presented. The study suggests that temperamental adaptability, persistence, and regularity may be relevant when assessing infant behavior, and may be applied in systematic prevention trials for families with infants. The inclusion of concepts related to individual differences in response tendencies and regulatory efforts may broaden the understanding of parent-infant transactions, and thus enrich prevention and sensitizing interventions with the aim of assisting infants' development. © 2018 Michigan Association for Infant Mental Health.

An Adaptive B-Spline Method for Low-order Image Reconstruction Problems - Final Report - 09/24/1997 - 09/24/2000

Energy Technology Data Exchange (ETDEWEB)

Li, Xin; Miller, Eric L.; Rappaport, Carey; Silevich, Michael

2000-04-11

A common problem in signal processing is to estimate the structure of an object from noisy measurements linearly related to the desired image. These problems are broadly known as inverse problems. A key feature which complicates the solution to such problems is their ill-posedness. That is, small perturbations in the data arising e.g. from noise can and do lead to severe, non-physical artifacts in the recovered image. The process of stabilizing these problems is known as regularization of which Tikhonov regularization is one of the most common. While this approach leads to a simple linear least squares problem to solve for generating the reconstruction, it has the unfortunate side effect of producing smooth images thereby obscuring important features such as edges. Therefore, over the past decade there has been much work in the development of edge-preserving regularizers. This technique leads to image estimates in which the important features are retained, but computationally the y require the solution of a nonlinear least squares problem, a daunting task in many practical multi-dimensional applications. In this thesis we explore low-order models for reducing the complexity of the re-construction process. Specifically, B-Splines are used to approximate the object. If a ''proper'' collection B-Splines are chosen that the object can be efficiently represented using a few basis functions, the dimensionality of the underlying problem will be significantly decreased. Consequently, an optimum distribution of splines needs to be determined. Here, an adaptive refining and pruning algorithm is developed to solve the problem. The refining part is based on curvature information, in which the intuition is that a relatively dense set of fine scale basis elements should cluster near regions of high curvature while a spares collection of basis vectors are required to adequately represent the object over spatially smooth areas. The pruning part is a greedy

The fast multipole method and Fourier convolution for the solution of acoustic scattering on regular volumetric grids

Science.gov (United States)

Hesford, Andrew J.; Waag, Robert C.

2010-10-01

The fast multipole method (FMM) is applied to the solution of large-scale, three-dimensional acoustic scattering problems involving inhomogeneous objects defined on a regular grid. The grid arrangement is especially well suited to applications in which the scattering geometry is not known a priori and is reconstructed on a regular grid using iterative inverse scattering algorithms or other imaging techniques. The regular structure of unknown scattering elements facilitates a dramatic reduction in the amount of storage and computation required for the FMM, both of which scale linearly with the number of scattering elements. In particular, the use of fast Fourier transforms to compute Green's function convolutions required for neighboring interactions lowers the often-significant cost of finest-level FMM computations and helps mitigate the dependence of FMM cost on finest-level box size. Numerical results demonstrate the efficiency of the composite method as the number of scattering elements in each finest-level box is increased.

Visuomotor adaptability in older adults with mild cognitive decline.

Science.gov (United States)

Schaffert, Jeffrey; Lee, Chi-Mei; Neill, Rebecca; Bo, Jin

2017-02-01

The current study examined the augmentation of error feedback on visuomotor adaptability in older adults with varying degrees of cognitive decline (assessed by the Montreal Cognitive Assessment; MoCA). Twenty-three participants performed a center-out computerized visuomotor adaptation task when the visual feedback of their hand movement error was presented in a regular (ratio=1:1) or enhanced (ratio=1:2) error feedback schedule. Results showed that older adults with lower scores on the MoCA had less adaptability than those with higher MoCA scores during the regular feedback schedule. However, participants demonstrated similar adaptability during the enhanced feedback schedule, regardless of their cognitive ability. Furthermore, individuals with lower MoCA scores showed larger after-effects in spatial control during the enhanced schedule compared to the regular schedule, whereas individuals with higher MoCA scores displayed the opposite pattern. Additional neuro-cognitive assessments revealed that spatial working memory and processing speed were positively related to motor adaptability during the regular scheduled but negatively related to adaptability during the enhanced schedule. We argue that individuals with mild cognitive decline employed different adaptation strategies when encountering enhanced visual feedback, suggesting older adults with mild cognitive impairment (MCI) may benefit from enhanced visual error feedback during sensorimotor adaptation. Copyright © 2016 Elsevier B.V. All rights reserved.

REGULAR METHOD FOR SYNTHESIS OF BASIC BENT-SQUARES OF RANDOM ORDER

Directory of Open Access Journals (Sweden)

A. V. Sokolov

2016-01-01

Full Text Available The paper is devoted to the class construction of the most non-linear Boolean bent-functions of any length N = 2k (k = 2, 4, 6…, on the basis of their spectral representation – Agievich bent squares. These perfect algebraic constructions are used as a basis to build many new cryptographic primitives, such as generators of pseudo-random key sequences, crypto graphic S-boxes, etc. Bent-functions also find their application in the construction of C-codes in the systems with code division multiple access (CDMA to provide the lowest possible value of Peak-to-Average Power Ratio (PAPR k = 1, as well as for the construction of error-correcting codes and systems of orthogonal biphasic signals. All the numerous applications of bent-functions relate to the theory of their synthesis. However, regular methods for complete class synthesis of bent-functions of any length N = 2k are currently unknown. The paper proposes a regular synthesis method for the basic Agievich bent squares of any order n, based on a regular operator of dyadic shift. Classification for a complete set of spectral vectors of lengths (l = 8, 16, … based on a criterion of the maximum absolute value and set of absolute values of spectral components has been carried out in the paper. It has been shown that any spectral vector can be a basis for building bent squares. Results of the synthesis for the Agievich bent squares of order n = 8 have been generalized and it has been revealed that there are only 3 basic bent squares for this order, while the other 5 can be obtained with help the operation of step-cyclic shift. All the basic bent squares of order n = 16 have been synthesized that allows to construct the bent-functions of length N = 256. The obtained basic bent squares can be used either for direct synthesis of bent-functions and their practical application or for further research in order to synthesize new structures of bent squares of orders n = 16, 32, 64, …

Generating Models of Infinite-State Communication Protocols Using Regular Inference with Abstraction

Science.gov (United States)

Aarts, Fides; Jonsson, Bengt; Uijen, Johan

In order to facilitate model-based verification and validation, effort is underway to develop techniques for generating models of communication system components from observations of their external behavior. Most previous such work has employed regular inference techniques which generate modest-size finite-state models. They typically suppress parameters of messages, although these have a significant impact on control flow in many communication protocols. We present a framework, which adapts regular inference to include data parameters in messages and states for generating components with large or infinite message alphabets. A main idea is to adapt the framework of predicate abstraction, successfully used in formal verification. Since we are in a black-box setting, the abstraction must be supplied externally, using information about how the component manages data parameters. We have implemented our techniques by connecting the LearnLib tool for regular inference with the protocol simulator ns-2, and generated a model of the SIP component as implemented in ns-2.

Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method

Directory of Open Access Journals (Sweden)

Lijuan Zhang

2014-01-01

Full Text Available To improve the effect of adaptive optics images’ restoration, we put forward a deconvolution algorithm improved by the EM algorithm which joints multiframe adaptive optics images based on expectation-maximization theory. Firstly, we need to make a mathematical model for the degenerate multiframe adaptive optics images. The function model is deduced for the points that spread with time based on phase error. The AO images are denoised using the image power spectral density and support constraint. Secondly, the EM algorithm is improved by combining the AO imaging system parameters and regularization technique. A cost function for the joint-deconvolution multiframe AO images is given, and the optimization model for their parameter estimations is built. Lastly, the image-restoration experiments on both analog images and the real AO are performed to verify the recovery effect of our algorithm. The experimental results show that comparing with the Wiener-IBD or RL-IBD algorithm, our iterations decrease 14.3% and well improve the estimation accuracy. The model distinguishes the PSF of the AO images and recovers the observed target images clearly.

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

Wavelet methods in multi-conjugate adaptive optics

International Nuclear Information System (INIS)

Helin, T; Yudytskiy, M

2013-01-01

The next generation ground-based telescopes rely heavily on adaptive optics for overcoming the limitation of atmospheric turbulence. In the future adaptive optics modalities, like multi-conjugate adaptive optics (MCAO), atmospheric tomography is the major mathematical and computational challenge. In this severely ill-posed problem, a fast and stable reconstruction algorithm is needed that can take into account many real-life phenomena of telescope imaging. We introduce a novel reconstruction method for the atmospheric tomography problem and demonstrate its performance and flexibility in the context of MCAO. Our method is based on using locality properties of compactly supported wavelets, both in the spatial and frequency domains. The reconstruction in the atmospheric tomography problem is obtained by solving the Bayesian MAP estimator with a conjugate-gradient-based algorithm. An accelerated algorithm with preconditioning is also introduced. Numerical performance is demonstrated on the official end-to-end simulation tool OCTOPUS of European Southern Observatory. (paper)

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.

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.

Adaptative mixed methods to axisymmetric shells

International Nuclear Information System (INIS)

Malta, S.M.C.; Loula, A.F.D.; Garcia, E.L.M.

1989-09-01

The mixed Petrov-Galerkin method is applied to axisymmetric shells with uniform and non uniform meshes. Numerical experiments with a cylindrical shell showed a significant improvement in convergence and accuracy with adaptive meshes. (A.C.A.S.) [pt

Incompressible Navier-Stokes inverse design method based on adaptive unstructured meshes

International Nuclear Information System (INIS)

Rahmati, M.T.; Charlesworth, D.; Zangeneh, M.

2005-01-01

An inverse method for blade design based on Navier-Stokes equations on adaptive unstructured meshes has been developed. In the method, unlike the method based on inviscid equations, the effect of viscosity is directly taken into account. In the method, the pressure (or pressure loading) is prescribed. The design method then computes the blade shape that would accomplish the target prescribed pressure distribution. The method is implemented using a cell-centered finite volume method, which solves the incompressible Navier-Stokes equations on unstructured meshes. An adaptive unstructured mesh method based on grid subdivision and local adaptive mesh method is utilized for increasing the accuracy. (author)

Cultural adaptation and translation of measures: an integrated method.

Science.gov (United States)

Sidani, Souraya; Guruge, Sepali; Miranda, Joyal; Ford-Gilboe, Marilyn; Varcoe, Colleen

2010-04-01

Differences in the conceptualization and operationalization of health-related concepts may exist across cultures. Such differences underscore the importance of examining conceptual equivalence when adapting and translating instruments. In this article, we describe an integrated method for exploring conceptual equivalence within the process of adapting and translating measures. The integrated method involves five phases including selection of instruments for cultural adaptation and translation; assessment of conceptual equivalence, leading to the generation of a set of items deemed to be culturally and linguistically appropriate to assess the concept of interest in the target community; forward translation; back translation (optional); and pre-testing of the set of items. Strengths and limitations of the proposed integrated method are discussed. (c) 2010 Wiley Periodicals, Inc.

Manifold Adaptive Label Propagation for Face Clustering.

Science.gov (United States)

Pei, Xiaobing; Lyu, Zehua; Chen, Changqing; Chen, Chuanbo

2015-08-01

In this paper, a novel label propagation (LP) method is presented, called the manifold adaptive label propagation (MALP) method, which is to extend original LP by integrating sparse representation constraint into regularization framework of LP method. Similar to most LP, first of all, MALP also finds graph edges from given data and gives weights to the graph edges. Our goal is to find graph weights matrix adaptively. The key advantage of our approach is that MALP simultaneously finds graph weights matrix and predicts the label of unlabeled data. This paper also derives efficient algorithm to solve the proposed problem. Extensions of our MALP in kernel space and robust version are presented. The proposed method has been applied to the problem of semi-supervised face clustering using the well-known ORL, Yale, extended YaleB, and PIE datasets. Our experimental evaluations show the effectiveness of our method.

Prediction Reweighting for Domain Adaptation.

Science.gov (United States)

Shuang Li; Shiji Song; Gao Huang

2017-07-01

There are plenty of classification methods that perform well when training and testing data are drawn from the same distribution. However, in real applications, this condition may be violated, which causes degradation of classification accuracy. Domain adaptation is an effective approach to address this problem. In this paper, we propose a general domain adaptation framework from the perspective of prediction reweighting, from which a novel approach is derived. Different from the major domain adaptation methods, our idea is to reweight predictions of the training classifier on testing data according to their signed distance to the domain separator, which is a classifier that distinguishes training data (from source domain) and testing data (from target domain). We then propagate the labels of target instances with larger weights to ones with smaller weights by introducing a manifold regularization method. It can be proved that our reweighting scheme effectively brings the source and target domains closer to each other in an appropriate sense, such that classification in target domain becomes easier. The proposed method can be implemented efficiently by a simple two-stage algorithm, and the target classifier has a closed-form solution. The effectiveness of our approach is verified by the experiments on artificial datasets and two standard benchmarks, a visual object recognition task and a cross-domain sentiment analysis of text. Experimental results demonstrate that our method is competitive with the state-of-the-art domain adaptation algorithms.

Regular pipeline maintenance of gas pipeline using technical operational diagnostics methods

Energy Technology Data Exchange (ETDEWEB)

Volentic, J [Gas Transportation Department, Slovensky plynarensky priemysel, Slovak Gas Industry, Bratislava (Slovakia)

1998-12-31

Slovensky plynarensky priemysel (SPP) has operated 17 487 km of gas pipelines in 1995. The length of the long-line pipelines reached 5 191 km, distribution network was 12 296 km. The international transit system of long-line gas pipelines ranged 1 939 km of pipelines of various dimensions. The described scale of transport and distribution system represents a multibillion investments stored in the ground, which are exposed to the environmental influences and to pipeline operational stresses. In spite of all technical and maintenance arrangements, which have to be performed upon operating gas pipelines, the gradual ageing takes place anyway, expressed in degradation process both in steel tube, as well as in the anti-corrosion coating. Within a certain time horizon, a consistent and regular application of methods and means of in-service technical diagnostics and rehabilitation of existing pipeline systems make it possible to save substantial investment funds, postponing the need in funds for a complex or partial reconstruction or a new construction of a specific gas section. The purpose of this presentation is to report on the implementation of the programme of in-service technical diagnostics of gas pipelines within the framework of regular maintenance of SPP s.p. Bratislava high pressure gas pipelines. (orig.) 6 refs.

Regular pipeline maintenance of gas pipeline using technical operational diagnostics methods

Energy Technology Data Exchange (ETDEWEB)

Volentic, J. [Gas Transportation Department, Slovensky plynarensky priemysel, Slovak Gas Industry, Bratislava (Slovakia)

1997-12-31

Slovensky plynarensky priemysel (SPP) has operated 17 487 km of gas pipelines in 1995. The length of the long-line pipelines reached 5 191 km, distribution network was 12 296 km. The international transit system of long-line gas pipelines ranged 1 939 km of pipelines of various dimensions. The described scale of transport and distribution system represents a multibillion investments stored in the ground, which are exposed to the environmental influences and to pipeline operational stresses. In spite of all technical and maintenance arrangements, which have to be performed upon operating gas pipelines, the gradual ageing takes place anyway, expressed in degradation process both in steel tube, as well as in the anti-corrosion coating. Within a certain time horizon, a consistent and regular application of methods and means of in-service technical diagnostics and rehabilitation of existing pipeline systems make it possible to save substantial investment funds, postponing the need in funds for a complex or partial reconstruction or a new construction of a specific gas section. The purpose of this presentation is to report on the implementation of the programme of in-service technical diagnostics of gas pipelines within the framework of regular maintenance of SPP s.p. Bratislava high pressure gas pipelines. (orig.) 6 refs.

Convergence acceleration of Navier-Stokes equation using adaptive wavelet method

International Nuclear Information System (INIS)

Kang, Hyung Min; Ghafoor, Imran; Lee, Do Hyung

2010-01-01

An efficient adaptive wavelet method is proposed for the enhancement of computational efficiency of the Navier-Stokes equations. The method is based on sparse point representation (SPR), which uses the wavelet decomposition and thresholding to obtain a sparsely distributed dataset. The threshold mechanism is modified in order to maintain the spatial accuracy of a conventional Navier-Stokes solver by adapting the threshold value to the order of spatial truncation error. The computational grid can be dynamically adapted to a transient solution to reflect local changes in the solution. The flux evaluation is then carried out only at the points of the adapted dataset, which reduces the computational effort and memory requirements. A stabilization technique is also implemented to avoid the additional numerical errors introduced by the threshold procedure. The numerical results of the adaptive wavelet method are compared with a conventional solver to validate the enhancement in computational efficiency of Navier-Stokes equations without the degeneration of the numerical accuracy of a conventional solver

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 ...

Adaptive finite element method for shape optimization

KAUST Repository

Morin, Pedro; Nochetto, Ricardo H.; Pauletti, Miguel S.; Verani, Marco

2012-01-01

We examine shape optimization problems in the context of inexact sequential quadratic programming. Inexactness is a consequence of using adaptive finite element methods (AFEM) to approximate the state and adjoint equations (via the dual weighted residual method), update the boundary, and compute the geometric functional. We present a novel algorithm that equidistributes the errors due to shape optimization and discretization, thereby leading to coarse resolution in the early stages and fine resolution upon convergence, and thus optimizing the computational effort. We discuss the ability of the algorithm to detect whether or not geometric singularities such as corners are genuine to the problem or simply due to lack of resolution - a new paradigm in adaptivity. © EDP Sciences, SMAI, 2012.

Adaptive finite element method for shape optimization

KAUST Repository

Morin, Pedro

2012-01-16

We examine shape optimization problems in the context of inexact sequential quadratic programming. Inexactness is a consequence of using adaptive finite element methods (AFEM) to approximate the state and adjoint equations (via the dual weighted residual method), update the boundary, and compute the geometric functional. We present a novel algorithm that equidistributes the errors due to shape optimization and discretization, thereby leading to coarse resolution in the early stages and fine resolution upon convergence, and thus optimizing the computational effort. We discuss the ability of the algorithm to detect whether or not geometric singularities such as corners are genuine to the problem or simply due to lack of resolution - a new paradigm in adaptivity. © EDP Sciences, SMAI, 2012.

A Galerkin Finite Element Method for Numerical Solutions of the Modified Regularized Long Wave Equation

Directory of Open Access Journals (Sweden)

Liquan Mei

2014-01-01

Full Text Available A Galerkin method for a modified regularized long wave equation is studied using finite elements in space, the Crank-Nicolson scheme, and the Runge-Kutta scheme in time. In addition, an extrapolation technique is used to transform a nonlinear system into a linear system in order to improve the time accuracy of this method. A Fourier stability analysis for the method is shown to be marginally stable. Three invariants of motion are investigated. Numerical experiments are presented to check the theoretical study of this method.

SU-E-I-93: Improved Imaging Quality for Multislice Helical CT Via Sparsity Regularized Iterative Image Reconstruction Method Based On Tensor Framelet

International Nuclear Information System (INIS)

Nam, H; Guo, M; Lee, K; Li, R; Xing, L; Gao, H

2014-01-01

Purpose: Inspired by compressive sensing, sparsity regularized iterative reconstruction method has been extensively studied. However, its utility pertinent to multislice helical 4D CT for radiotherapy with respect to imaging quality, dose, and time has not been thoroughly addressed. As the beginning of such an investigation, this work carries out the initial comparison of reconstructed imaging quality between sparsity regularized iterative method and analytic method through static phantom studies using a state-of-art 128-channel multi-slice Siemens helical CT scanner. Methods: In our iterative method, tensor framelet (TF) is chosen as the regularization method for its superior performance from total variation regularization in terms of reduced piecewise-constant artifacts and improved imaging quality that has been demonstrated in our prior work. On the other hand, X-ray transforms and its adjoints are computed on-the-fly through GPU implementation using our previous developed fast parallel algorithms with O(1) complexity per computing thread. For comparison, both FDK (approximate analytic method) and Katsevich algorithm (exact analytic method) are used for multislice helical CT image reconstruction. Results: The phantom experimental data with different imaging doses were acquired using a state-of-art 128-channel multi-slice Siemens helical CT scanner. The reconstructed image quality was compared between TF-based iterative method, FDK and Katsevich algorithm with the quantitative analysis for characterizing signal-to-noise ratio, image contrast, and spatial resolution of high-contrast and low-contrast objects. Conclusion: The experimental results suggest that our tensor framelet regularized iterative reconstruction algorithm improves the helical CT imaging quality from FDK and Katsevich algorithm for static experimental phantom studies that have been performed

Adaptive sampling method in deep-penetration particle transport problem

International Nuclear Information System (INIS)

Wang Ruihong; Ji Zhicheng; Pei Lucheng

2012-01-01

Deep-penetration problem has been one of the difficult problems in shielding calculation with Monte Carlo method for several decades. In this paper, a kind of particle transport random walking system under the emission point as a sampling station is built. Then, an adaptive sampling scheme is derived for better solution with the achieved information. The main advantage of the adaptive scheme is to choose the most suitable sampling number from the emission point station to obtain the minimum value of the total cost in the process of the random walk. Further, the related importance sampling method is introduced. Its main principle is to define the importance function due to the particle state and to ensure the sampling number of the emission particle is proportional to the importance function. The numerical results show that the adaptive scheme under the emission point as a station could overcome the difficulty of underestimation of the result in some degree, and the adaptive importance sampling method gets satisfied results as well. (authors)

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.

On the Adaptation of an Agile Information Systems Development Method

NARCIS (Netherlands)

Aydin, M.N.; Harmsen, F.; van Slooten, C.; Stegwee, R.A.

2005-01-01

Little specific research has been conducted to date on the adaptation of agile information systems development (ISD) methods. This article presents the work practice in dealing with the adaptation of such a method in the ISD department of one of the leading financial institutes in Europe. Two forms

The adaptive collision source method for discrete ordinates radiation transport

International Nuclear Information System (INIS)

Walters, William J.; Haghighat, Alireza

2017-01-01

Highlights: • A new adaptive quadrature method to solve the discrete ordinates transport equation. • The adaptive collision source (ACS) method splits the flux into n’th collided components. • Uncollided flux requires high quadrature; this is lowered with number of collisions. • ACS automatically applies appropriate quadrature order each collided component. • The adaptive quadrature is 1.5–4 times more efficient than uniform quadrature. - Abstract: A novel collision source method has been developed to solve the Linear Boltzmann Equation (LBE) more efficiently by adaptation of the angular quadrature order. The angular adaptation method is unique in that the flux from each scattering source iteration is obtained, with potentially a different quadrature order used for each. Traditionally, the flux from every iteration is combined, with the same quadrature applied to the combined flux. Since the scattering process tends to distribute the radiation more evenly over angles (i.e., make it more isotropic), the quadrature requirements generally decrease with each iteration. This method allows for an optimal use of processing power, by using a high order quadrature for the first iterations that need it, before shifting to lower order quadratures for the remaining iterations. This is essentially an extension of the first collision source method, and is referred to as the adaptive collision source (ACS) method. The ACS methodology has been implemented in the 3-D, parallel, multigroup discrete ordinates code TITAN. This code was tested on a several simple and complex fixed-source problems. The ACS implementation in TITAN has shown a reduction in computation time by a factor of 1.5–4 on the fixed-source test problems, for the same desired level of accuracy, as compared to the standard TITAN code.

Tikhonov regularization method for the numerical inversion of Mellin transforms using splines

International Nuclear Information System (INIS)

Iqbal, M.

2005-01-01

Mellin transform is an ill-posed problem. These problems arise in many branches of science and engineering. In the typical situation one is interested in recovering the original function, given a finite number of noisy measurements of data. In this paper, we shall convert Mellin transform to Laplace transform and then an integral equation of the first kind of convolution type. We solve the integral equation using Tikhonov regularization with splines as basis function. The method is applied to various test examples in the literature and results are shown in the table

Regularization of the Fourier series of discontinuous functions by various summation methods

Energy Technology Data Exchange (ETDEWEB)

Ahmad, S.S.; Beghi, L. (Padua Univ. (Italy). Seminario Matematico)

1983-07-01

In this paper the regularization by various summation methods of the Fourier series of functions containing discontinuities of the first and second kind are studied and the results of the numerical analyses referring to some typical periodic functions are presented. In addition to the Cesaro and Lanczos weightings, a new (i.e. cosine) weighting for accelerating the convergence rate is proposed. A comparison with the results obtained by Garibotti and Massaro with the punctual Pade approximants (PPA) technique in case of a periodic step function is also carried out.

Strategies in edge plasma simulation using adaptive dynamic nodalization techniques

International Nuclear Information System (INIS)

Kainz, A.; Weimann, G.; Kamelander, G.

2003-01-01

A wide span of steady-state and transient edge plasma processes simulation problems require accurate discretization techniques and can then be treated with Finite Element (FE) and Finite Volume (FV) methods. The software used here to meet these meshing requirements is a 2D finite element grid generator. It allows to produce adaptive unstructured grids taking into consideration the flux surface characteristics. To comply with the common mesh handling features of FE/FV packages, some options have been added to the basic generation tool. These enhancements include quadrilateral meshes without non-regular transition elements obtained by substituting them by transition constructions consisting of regular quadrilateral elements. Furthermore triangular grids can be created with one edge parallel to the magnetic field and modified by the basic adaptation/realignment techniques. Enhanced code operation properties and processing capabilities are expected. (author)

Application of Tikhonov regularization method to wind retrieval from scatterometer data II: cyclone wind retrieval with consideration of rain

International Nuclear Information System (INIS)

Zhong Jian; Huang Si-Xun; Fei Jian-Fang; Du Hua-Dong; Zhang Liang

2011-01-01

According to the conclusion of the simulation experiments in paper I, the Tikhonov regularization method is applied to cyclone wind retrieval with a rain-effect-considering geophysical model function (called GMF+Rain). The GMF+Rain model which is based on the NASA scatterometer-2 (NSCAT2) GMF is presented to compensate for the effects of rain on cyclone wind retrieval. With the multiple solution scheme (MSS), the noise of wind retrieval is effectively suppressed, but the influence of the background increases. It will cause a large wind direction error in ambiguity removal when the background error is large. However, this can be mitigated by the new ambiguity removal method of Tikhonov regularization as proved in the simulation experiments. A case study on an extratropical cyclone of hurricane observed with SeaWinds at 25-km resolution shows that the retrieved wind speed for areas with rain is in better agreement with that derived from the best track analysis for the GMF+Rain model, but the wind direction obtained with the two-dimensional variational (2DVAR) ambiguity removal is incorrect. The new method of Tikhonov regularization effectively improves the performance of wind direction ambiguity removal through choosing appropriate regularization parameters and the retrieved wind speed is almost the same as that obtained from the 2DVAR. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

A new method for the regularization of a class of divergent Feynman integrals in covariant and axial gauges

International Nuclear Information System (INIS)

Lee, H.C.; Milgram, M.S.

1984-07-01

A hybrid of dimensional and analytic regularization is used to regulate and uncover a Meijer's G-function representation for a class of massless, divergent Feynman integrals in an axial gauge. Integrals in the covariant gauge belong to a subclass and those in the light-cone gauge are reached by analytic continuation. The method decouples the physical ultraviolet and infrared singularities from the spurious axial gauge singularity but regulates all three simultaneously. For the axial gauge singularity, the new analytic method is more powerful and elegant than the old principal value prescription, but the two methods yield identical infinite as well as regular parts. It is shown that dimensional and analytic regularization can be made equivalent, implying that the former method is free from spurious γ5-anomalies and the latter preserves gauge invariance. The hybrid method permits the evaluation of integrals containing arbritrary integer powers of logarithms in the integrand by differentiation with respect to exponents. Such 'exponent derivatives' generate the same set of 'polylogs' as that generated in multi-loop integrals in perturbation theories and may be useful for solving equations in nonperturbation theories. The close relation between the method of exponent derivatives and the prescription of 't Hooft and Veltman for treating overlapping divergencies is pointed out. It is demonstrated that both methods generate functions that are free from unrecognizable logarithmic infinite parts. Nonperturbation theories expressed in terms of exponent derivatives are thus renormalizable. Some intriguing connections between nonperturbation theories and nonintegral exponents are pointed out

Regularization based on steering parameterized Gaussian filters and a Bhattacharyya distance functional

Science.gov (United States)

Lopes, Emerson P.

2001-08-01

Template regularization embeds the problem of class separability. In the machine vision perspective, this problem is critical when a textural classification procedure is applied to non-stationary pattern mosaic images. These applications often present low accuracy performance due to disturbance of the classifiers produced by exogenous or endogenous signal regularity perturbations. Natural scene imaging, where the images present certain degree of homogeneity in terms of texture element size or shape (primitives) shows a variety of behaviors, especially varying the preferential spatial directionality. The space-time image pattern characterization is only solved if classification procedures are designed considering the most robust tools within a parallel and hardware perspective. The results to be compared in this paper are obtained using a framework based on multi-resolution, frame and hypothesis approach. Two strategies for the bank of Gabor filters applications are considered: adaptive strategy using the KL transform and fix configuration strategy. The regularization under discussion is accomplished in the pyramid building system instance. The filterings are steering Gaussians controlled by free parameters which are adjusted in accordance with a feedback process driven by hints obtained from sequence of frames interaction functionals pos-processed in the training process and including classification of training set samples as examples. Besides these adjustments there is continuous input data sensitive adaptiveness. The experimental result assessments are focused on two basic issues: Bhattacharyya distance as pattern characterization feature and the combination of KL transform as feature selection and adaptive criterion with the regularization of the pattern Bhattacharyya distance functional (BDF) behavior, using the BDF state separability and symmetry as the main indicators of an optimum framework parameter configuration.

Hermite regularization of the lattice Boltzmann method for open source computational aeroacoustics.

Science.gov (United States)

Brogi, F; Malaspinas, O; Chopard, B; Bonadonna, C

2017-10-01

The lattice Boltzmann method (LBM) is emerging as a powerful engineering tool for aeroacoustic computations. However, the LBM has been shown to present accuracy and stability issues in the medium-low Mach number range, which is of interest for aeroacoustic applications. Several solutions have been proposed but are often too computationally expensive, do not retain the simplicity and the advantages typical of the LBM, or are not described well enough to be usable by the community due to proprietary software policies. An original regularized collision operator is proposed, based on the expansion of Hermite polynomials, that greatly improves the accuracy and stability of the LBM without significantly altering its algorithm. The regularized LBM can be easily coupled with both non-reflective boundary conditions and a multi-level grid strategy, essential ingredients for aeroacoustic simulations. Excellent agreement was found between this approach and both experimental and numerical data on two different benchmarks: the laminar, unsteady flow past a 2D cylinder and the 3D turbulent jet. Finally, most of the aeroacoustic computations with LBM have been done with commercial software, while here the entire theoretical framework is implemented using an open source library (palabos).

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)

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)

Application of the Tikhonov regularization method to wind retrieval from scatterometer data I. Sensitivity analysis and simulation experiments

International Nuclear Information System (INIS)

Zhong Jian; Huang Si-Xun; Du Hua-Dong; Zhang Liang

2011-01-01

Scatterometer is an instrument which provides all-day and large-scale wind field information, and its application especially to wind retrieval always attracts meteorologists. Certain reasons cause large direction error, so it is important to find where the error mainly comes. Does it mainly result from the background field, the normalized radar cross-section (NRCS) or the method of wind retrieval? It is valuable to research. First, depending on SDP2.0, the simulated ‘true’ NRCS is calculated from the simulated ‘true’ wind through the geophysical model function NSCAT2. The simulated background field is configured by adding a noise to the simulated ‘true’ wind with the non-divergence constraint. Also, the simulated ‘measured’ NRCS is formed by adding a noise to the simulated ‘true’ NRCS. Then, the sensitivity experiments are taken, and the new method of regularization is used to improve the ambiguity removal with simulation experiments. The results show that the accuracy of wind retrieval is more sensitive to the noise in the background than in the measured NRCS; compared with the two-dimensional variational (2DVAR) ambiguity removal method, the accuracy of wind retrieval can be improved with the new method of Tikhonov regularization through choosing an appropriate regularization parameter, especially for the case of large error in the background. The work will provide important information and a new method for the wind retrieval with real data. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

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.

TU-CD-BRA-12: Coupling PET Image Restoration and Segmentation Using Variational Method with Multiple Regularizations

Energy Technology Data Exchange (ETDEWEB)

Li, L; Tan, S [Huazhong University of Science and Technology, Wuhan, Hubei (China); Lu, W [University of Maryland School of Medicine, Baltimore, MD (United States)

2015-06-15

Purpose: To propose a new variational method which couples image restoration with tumor segmentation for PET images using multiple regularizations. Methods: Partial volume effect (PVE) is a major degrading factor impacting tumor segmentation accuracy in PET imaging. The existing segmentation methods usually need to take prior calibrations to compensate PVE and they are highly system-dependent. Taking into account that image restoration and segmentation can promote each other and they are tightly coupled, we proposed a variational method to solve the two problems together. Our method integrated total variation (TV) semi-blind deconvolution and Mumford-Shah (MS) segmentation. The TV norm was used on edges to protect the edge information, and the L{sub 2} norm was used to avoid staircase effect in the no-edge area. The blur kernel was constrained to the Gaussian model parameterized by its variance and we assumed that the variances in the X-Y and Z directions are different. The energy functional was iteratively optimized by an alternate minimization algorithm. Segmentation performance was tested on eleven patients with non-Hodgkin’s lymphoma, and evaluated by Dice similarity index (DSI) and classification error (CE). For comparison, seven other widely used methods were also tested and evaluated. Results: The combination of TV and L{sub 2} regularizations effectively improved the segmentation accuracy. The average DSI increased by around 0.1 than using either the TV or the L{sub 2} norm. The proposed method was obviously superior to other tested methods. It has an average DSI and CE of 0.80 and 0.41, while the FCM method — the second best one — has only an average DSI and CE of 0.66 and 0.64. Conclusion: Coupling image restoration and segmentation can handle PVE and thus improves tumor segmentation accuracy in PET. Alternate use of TV and L2 regularizations can further improve the performance of the algorithm. This work was supported in part by National Natural

An h-adaptive finite element method for turbulent heat transfer

Energy Technology Data Exchange (ETDEWEB)

Carriington, David B [Los Alamos National Laboratory

2009-01-01

A two-equation turbulence closure model (k-{omega}) using an h-adaptive grid technique and finite element method (FEM) has been developed to simulate low Mach flow and heat transfer. These flows are applicable to many flows in engineering and environmental sciences. Of particular interest in the engineering modeling areas are: combustion, solidification, and heat exchanger design. Flows for indoor air quality modeling and atmospheric pollution transport are typical types of environmental flows modeled with this method. The numerical method is based on a hybrid finite element model using an equal-order projection process. The model includes thermal and species transport, localized mesh refinement (h-adaptive) and Petrov-Galerkin weighting for the stabilizing the advection. This work develops the continuum model of a two-equation turbulence closure method. The fractional step solution method is stated along with the h-adaptive grid method (Carrington and Pepper, 2002). Solutions are presented for 2d flow over a backward-facing step.

The Translation and Adaptation of Agile Methods

DEFF Research Database (Denmark)

Pries-Heje, Jan; Baskerville, Richard

2017-01-01

Purpose The purpose of this paper is to use translation theory to develop a framework (called FTRA) that explains how companies adopt agile methods in a discourse of fragmentation and articulation. Design/methodology/approach A qualitative multiple case study of six firms using the Scrum agile...... (Scrum). This limits the confidence that the framework is suitable for other kinds of methodologies. Practical implications The FTRA framework and the technological rules are promising for use in practice as a prescriptive or even normative frame for governing methodology adaptation. Social implications....../value The use of translation theory and the FTRA framework to explain how agile adaptation (in particular Scrum) emerges continuously in a process where method fragments are articulated and re-articulated to momentarily suit the local setting. Complete agility that rapidly and elegantly changes its own...

Adaptive BDDC Deluxe Methods for H(curl)

KAUST Repository

Zampini, Stefano

2017-01-01

The work presents numerical results using adaptive BDDC deluxe methods for preconditioning the linear systems arising from finite element discretizations of the time-domain, quasi-static approximation of the Maxwell’s equations. The provided results

Capillary Electrophoresis Sensitivity Enhancement Based on Adaptive Moving Average Method.

Science.gov (United States)

Drevinskas, Tomas; Telksnys, Laimutis; Maruška, Audrius; Gorbatsova, Jelena; Kaljurand, Mihkel

2018-06-05

In the present work, we demonstrate a novel approach to improve the sensitivity of the "out of lab" portable capillary electrophoretic measurements. Nowadays, many signal enhancement methods are (i) underused (nonoptimal), (ii) overused (distorts the data), or (iii) inapplicable in field-portable instrumentation because of a lack of computational power. The described innovative migration velocity-adaptive moving average method uses an optimal averaging window size and can be easily implemented with a microcontroller. The contactless conductivity detection was used as a model for the development of a signal processing method and the demonstration of its impact on the sensitivity. The frequency characteristics of the recorded electropherograms and peaks were clarified. Higher electrophoretic mobility analytes exhibit higher-frequency peaks, whereas lower electrophoretic mobility analytes exhibit lower-frequency peaks. On the basis of the obtained data, a migration velocity-adaptive moving average algorithm was created, adapted, and programmed into capillary electrophoresis data-processing software. Employing the developed algorithm, each data point is processed depending on a certain migration time of the analyte. Because of the implemented migration velocity-adaptive moving average method, the signal-to-noise ratio improved up to 11 times for sampling frequency of 4.6 Hz and up to 22 times for sampling frequency of 25 Hz. This paper could potentially be used as a methodological guideline for the development of new smoothing algorithms that require adaptive conditions in capillary electrophoresis and other separation methods.

An Analysis of Dynamic Instability on TC-Like Vortex Using the Regularization-Based Eigenmode Linear Superposition Method

Directory of Open Access Journals (Sweden)

Shuang Liu

2018-01-01

Full Text Available In this paper, the eigenmode linear superposition (ELS method based on the regularization is used to discuss the distributions of all eigenmodes and the role of their instability to the intensity and structure change in TC-like vortex. Results show that the regularization approach can overcome the ill-posed problem occurring in solving mode weight coefficients as the ELS method are applied to analyze the impacts of dynamic instability on the intensity and structure change of TC-like vortex. The Generalized Cross-validation (GCV method and the L curve method are used to determine the regularization parameters, and the results of the two approaches are compared. It is found that the results based on the GCV method are closer to the given initial condition in the solution of the inverse problem of the vortex system. Then, the instability characteristic of the hollow vortex as the basic state are examined based on the linear barotropic shallow water equations. It is shown that the wavenumber distribution of system instability obtained from the ELS method is well consistent with that of the numerical analysis based on the norm mode. On the other hand, the evolution of the hollow vortex are discussed using the product of each eigenmode and its corresponding weight coefficient. Results show that the intensity and structure change of the system are mainly affected by the dynamic instability in the early stage of disturbance development, and the most unstable mode has a dominant role in the growth rate and the horizontal distribution of intense disturbance in the near-core region. Moreover, the wave structure of the most unstable mode possesses typical characteristics of mixed vortex Rossby-inertio-gravity waves (VRIGWs.

An Adaptively Accelerated Bayesian Deblurring Method with Entropy Prior

Directory of Open Access Journals (Sweden)

Yong-Hoon Kim

2008-05-01

Full Text Available The development of an efficient adaptively accelerated iterative deblurring algorithm based on Bayesian statistical concept has been reported. Entropy of an image has been used as a “prior” distribution and instead of additive form, used in conventional acceleration methods an exponent form of relaxation constant has been used for acceleration. Thus the proposed method is called hereafter as adaptively accelerated maximum a posteriori with entropy prior (AAMAPE. Based on empirical observations in different experiments, the exponent is computed adaptively using first-order derivatives of the deblurred image from previous two iterations. This exponent improves speed of the AAMAPE method in early stages and ensures stability at later stages of iteration. In AAMAPE method, we also consider the constraint of the nonnegativity and flux conservation. The paper discusses the fundamental idea of the Bayesian image deblurring with the use of entropy as prior, and the analytical analysis of superresolution and the noise amplification characteristics of the proposed method. The experimental results show that the proposed AAMAPE method gives lower RMSE and higher SNR in 44% lesser iterations as compared to nonaccelerated maximum a posteriori with entropy prior (MAPE method. Moreover, AAMAPE followed by wavelet wiener filtering gives better result than the state-of-the-art methods.

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)

Use of dynamic grid adaption in the ASWR-method

International Nuclear Information System (INIS)

Graf, U.; Romstedt, P.; Werner, W.

1985-01-01

A dynamic grid adaption method has been developed for use with the ASWR-method. The method automatically adapts the number and position of the spatial meshpoints as the solution of hyperbolic or parabolic vector partial differential equations progresses in time. The mesh selection algorithm is based on the minimization of the L 2 -norm of the spatial discretization error. The method permits accurate calculation of the evolution of inhomogenities like wave fronts, shock layers and other sharp transitions, while generally using a coarse computational grid. The number of required mesh points is significantly reduced, relative to a fixed Eulerian grid. Since the mesh selection algorithm is computationally inexpensive, a corresponding reduction of computing time results

A cellular automaton - finite volume method for the simulation of dendritic and eutectic growth in binary alloys using an adaptive mesh refinement

Science.gov (United States)

Dobravec, Tadej; Mavrič, Boštjan; Šarler, Božidar

2017-11-01

A two-dimensional model to simulate the dendritic and eutectic growth in binary alloys is developed. A cellular automaton method is adopted to track the movement of the solid-liquid interface. The diffusion equation is solved in the solid and liquid phases by using an explicit finite volume method. The computational domain is divided into square cells that can be hierarchically refined or coarsened using an adaptive mesh based on the quadtree algorithm. Such a mesh refines the regions of the domain near the solid-liquid interface, where the highest concentration gradients are observed. In the regions where the lowest concentration gradients are observed the cells are coarsened. The originality of the work is in the novel, adaptive approach to the efficient and accurate solution of the posed multiscale problem. The model is verified and assessed by comparison with the analytical results of the Lipton-Glicksman-Kurz model for the steady growth of a dendrite tip and the Jackson-Hunt model for regular eutectic growth. Several examples of typical microstructures are simulated and the features of the method as well as further developments are discussed.

An Adaptive Robot Game

DEFF Research Database (Denmark)

Hansen, Søren Tranberg; Svenstrup, Mikael; Dalgaard, Lars

2010-01-01

The goal of this paper is to describe an adaptive robot game, which motivates elderly people to do a regular amount of physical exercise while playing. One of the advantages of robot based games is that the initiative to play can be taken autonomously by the robot. In this case, the goal is to im......The goal of this paper is to describe an adaptive robot game, which motivates elderly people to do a regular amount of physical exercise while playing. One of the advantages of robot based games is that the initiative to play can be taken autonomously by the robot. In this case, the goal...... is to improve the mental and physical state of the user by playing a physical game with the robot. Ideally, a robot game should be simple to learn but difficult to master, providing an appropriate degree of challenge for players with different skills. In order to achieve that, the robot should be able to adapt...

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

Final Report: Symposium on Adaptive Methods for Partial Differential Equations

Energy Technology Data Exchange (ETDEWEB)

Pernice, M.; Johnson, C.R.; Smith, P.J.; Fogelson, A.

1998-12-10

OAK-B135 Final Report: Symposium on Adaptive Methods for Partial Differential Equations. Complex physical phenomena often include features that span a wide range of spatial and temporal scales. Accurate simulation of such phenomena can be difficult to obtain, and computations that are under-resolved can even exhibit spurious features. While it is possible to resolve small scale features by increasing the number of grid points, global grid refinement can quickly lead to problems that are intractable, even on the largest available computing facilities. These constraints are particularly severe for three dimensional problems that involve complex physics. One way to achieve the needed resolution is to refine the computational mesh locally, in only those regions where enhanced resolution is required. Adaptive solution methods concentrate computational effort in regions where it is most needed. These methods have been successfully applied to a wide variety of problems in computational science and engineering. Adaptive methods can be difficult to implement, prompting the development of tools and environments to facilitate their use. To ensure that the results of their efforts are useful, algorithm and tool developers must maintain close communication with application specialists. Conversely it remains difficult for application specialists who are unfamiliar with the methods to evaluate the trade-offs between the benefits of enhanced local resolution and the effort needed to implement an adaptive solution method.

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction.

Science.gov (United States)

Lu, Hongyang; Wei, Jingbo; Liu, Qiegen; Wang, Yuhao; Deng, Xiaohua

2016-01-01

Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

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...

Adaptive SLICE method: an enhanced method to determine nonlinear dynamic respiratory system mechanics

International Nuclear Information System (INIS)

Zhao, Zhanqi; Möller, Knut; Guttmann, Josef

2012-01-01

The objective of this paper is to introduce and evaluate the adaptive SLICE method (ASM) for continuous determination of intratidal nonlinear dynamic compliance and resistance. The tidal volume is subdivided into a series of volume intervals called slices. For each slice, one compliance and one resistance are calculated by applying a least-squares-fit method. The volume window (width) covered by each slice is determined based on the confidence interval of the parameter estimation. The method was compared to the original SLICE method and evaluated using simulation and animal data. The ASM was also challenged with separate analysis of dynamic compliance during inspiration. If the signal-to-noise ratio (SNR) in the respiratory data decreased from +∞ to 10 dB, the relative errors of compliance increased from 0.1% to 22% for the ASM and from 0.2% to 227% for the SLICE method. Fewer differences were found in resistance. When the SNR was larger than 40 dB, the ASM delivered over 40 parameter estimates (42.2 ± 1.3). When analyzing the compliance during inspiration separately, the estimates calculated with the ASM were more stable. The adaptive determination of slice bounds results in consistent and reliable parameter values. Online analysis of nonlinear respiratory mechanics will profit from such an adaptive selection of interval size. (paper)

Numerical simulation of the regularized long wave equation by He's homotopy perturbation method

Energy Technology Data Exchange (ETDEWEB)

Inc, Mustafa [Department of Mathematics, Firat University, 23119 Elazig (Turkey)], E-mail: minc@firat.edu.tr; Ugurlu, Yavuz [Department of Mathematics, Firat University, 23119 Elazig (Turkey)

2007-09-17

In this Letter, we present the homotopy perturbation method (shortly HPM) for obtaining the numerical solution of the RLW equation. We obtain the exact and numerical solutions of the Regularized Long Wave (RLW) equation for certain initial condition. The initial approximation can be freely chosen with possible unknown constants which can be determined by imposing the boundary and initial conditions. Comparison of the results with those of other methods have led us to significant consequences. The numerical solutions are compared with the known analytical solutions.

Error Argumentation Enhance Adaptability in Adults With Low Motor Ability.

Science.gov (United States)

Lee, Chi-Mei; Bo, Jin

2016-01-01

The authors focused on young adults with varying degrees of motor difficulties and examined their adaptability in a visuomotor adaptation task where the visual feedback of participants' movement error was presented with either 1:1 ratio (i.e., regular feedback schedule) or 1:2 ratio (i.e., enhanced feedback schedule). Within-subject design was used with two feedback schedules counter-balanced and separated for 10 days. Results revealed that participants with greater motor difficulties showed less adaptability than those with normal motor abilities in the regular feedback schedule; however, all participants demonstrated similar level of adaptability in the enhanced feedback schedule. The results suggest that error argumentation enhances adaptability in adults with low motor ability.

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

Directory of Open Access Journals (Sweden)

Mittelmann Hans D

2010-01-01

Full Text Available Abstract Background The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC. Results We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides. Conclusions The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at http://bordnerlab.org/RTA/.

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.

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

Evaluation framework based on fuzzy measured method in adaptive learning systems

OpenAIRE

Houda Zouari Ounaies, ,; Yassine Jamoussi; Henda Hajjami Ben Ghezala

2008-01-01

Currently, e-learning systems are mainly web-based applications and tackle a wide range of users all over the world. Fitting learners’ needs is considered as a key issue to guaranty the success of these systems. Many researches work on providing adaptive systems. Nevertheless, evaluation of the adaptivity is still in an exploratory phase. Adaptation methods are a basic factor to guaranty an effective adaptation. This issue is referred as meta-adaptation in numerous researches. In our research...

On the limitations of fixed-step-size adaptive methods with response confidence.

Science.gov (United States)

Hsu, Yung-Fong; Chin, Ching-Lan

2014-05-01

The family of (non-parametric, fixed-step-size) adaptive methods, also known as 'up-down' or 'staircase' methods, has been used extensively in psychophysical studies for threshold estimation. Extensions of adaptive methods to non-binary responses have also been proposed. An example is the three-category weighted up-down (WUD) method (Kaernbach, 2001) and its four-category extension (Klein, 2001). Such an extension, however, is somewhat restricted, and in this paper we discuss its limitations. To facilitate the discussion, we characterize the extension of WUD by an algorithm that incorporates response confidence into a family of adaptive methods. This algorithm can also be applied to two other adaptive methods, namely Derman's up-down method and the biased-coin design, which are suitable for estimating any threshold quantiles. We then discuss via simulations of the above three methods the limitations of the algorithm. To illustrate, we conduct a small scale of experiment using the extended WUD under different response confidence formats to evaluate the consistency of threshold estimation. © 2013 The British Psychological Society.

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.

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.

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

The adaptation method in the Monte Carlo simulation for computed tomography

Energy Technology Data Exchange (ETDEWEB)

Lee, Hyoung Gun; Yoon, Chang Yeon; Lee, Won Ho [Dept. of Bio-convergence Engineering, Korea University, Seoul (Korea, Republic of); Cho, Seung Ryong [Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of); Park, Sung Ho [Dept. of Neurosurgery, Ulsan University Hospital, Ulsan (Korea, Republic of)

2015-06-15

The patient dose incurred from diagnostic procedures during advanced radiotherapy has become an important issue. Many researchers in medical physics are using computational simulations to calculate complex parameters in experiments. However, extended computation times make it difficult for personal computers to run the conventional Monte Carlo method to simulate radiological images with high-flux photons such as images produced by computed tomography (CT). To minimize the computation time without degrading imaging quality, we applied a deterministic adaptation to the Monte Carlo calculation and verified its effectiveness by simulating CT image reconstruction for an image evaluation phantom (Catphan; Phantom Laboratory, New York NY, USA) and a human-like voxel phantom (KTMAN-2) (Los Alamos National Laboratory, Los Alamos, NM, USA). For the deterministic adaptation, the relationship between iteration numbers and the simulations was estimated and the option to simulate scattered radiation was evaluated. The processing times of simulations using the adaptive method were at least 500 times faster than those using a conventional statistical process. In addition, compared with the conventional statistical method, the adaptive method provided images that were more similar to the experimental images, which proved that the adaptive method was highly effective for a simulation that requires a large number of iterations-assuming no radiation scattering in the vicinity of detectors minimized artifacts in the reconstructed image.

An adaptive EFG-FE coupling method for elasto-plastic contact of rough surfaces

International Nuclear Information System (INIS)

Liu Lan; Liu Geng; Tong Ruiting; Jin Saiying

2010-01-01

Differing from Finite Element Method, the meshless method does not need any mesh information and can arrange nodes freely which is perfectly suitable for adaptive analysis. In order to simulate the contact condition factually and improve computational efficiency, an adaptive procedure for Element-free Galerkin-Finite Element (EFG-FE) coupling contact model is established and developed to investigate the elastoplastic contact performance for engineering rough surfaces. The local adaptive refinement strategy combined with the strain energy gradient-based error estimation model is employed. The schemes, including principle explanation, arithmetic analysis and programming realization, are introduced and discussed. Furthermore, some related parameters on adaptive convergence criterion are researched emphatically, including adaptation-stop criterion, refinement or coarsening criterion which are guided by the relative error in total strain energy with two adjacent stages. Based on pioneering works of the EFG-FE coupling method for contact problems, an adaptive EFG-FE model for asperity contact is studied. Compared with the solutions obtained from the uniform refinement model, the adaptation results indicate that the adaptive method presented in this paper is capable of solving asperity contact problems with excellent calculation accuracy and computational efficiency.

The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2007-01-01

This paper describes new extensions to the previously published multivariate alteration detection (MAD) method for change detection in bi-temporal, multi- and hypervariate data such as remote sensing imagery. Much like boosting methods often applied in data mining work, the iteratively reweighted...... to observations that show little change, i.e., for which the sum of squared, standardized MAD variates is small, and small weights are assigned to observations for which the sum is large. Like the original MAD method, the iterative extension is invariant to linear (affine) transformations of the original...... an agricultural region in Kenya, and hyperspectral airborne HyMap data from a small rural area in southeastern Germany are given. The latter case demonstrates the need for regularization....

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

A multiresolution method for solving the Poisson equation using high order regularization

DEFF Research Database (Denmark)

Hejlesen, Mads Mølholm; Walther, Jens Honore

2016-01-01

We present a novel high order multiresolution Poisson solver based on regularized Green's function solutions to obtain exact free-space boundary conditions while using fast Fourier transforms for computational efficiency. Multiresolution is a achieved through local refinement patches and regulari......We present a novel high order multiresolution Poisson solver based on regularized Green's function solutions to obtain exact free-space boundary conditions while using fast Fourier transforms for computational efficiency. Multiresolution is a achieved through local refinement patches...... and regularized Green's functions corresponding to the difference in the spatial resolution between the patches. The full solution is obtained utilizing the linearity of the Poisson equation enabling super-position of solutions. We show that the multiresolution Poisson solver produces convergence rates...

Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

KAUST Repository

Wang, Jim Jing-Yan; Huang, Jianhua Z.; Sun, Yijun; Gao, Xin

2014-01-01

by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant

Adaptation Method for Overall and Local Performances of Gas Turbine Engine Model

Science.gov (United States)

Kim, Sangjo; Kim, Kuisoon; Son, Changmin

2018-04-01

An adaptation method was proposed to improve the modeling accuracy of overall and local performances of gas turbine engine. The adaptation method was divided into two steps. First, the overall performance parameters such as engine thrust, thermal efficiency, and pressure ratio were adapted by calibrating compressor maps, and second, the local performance parameters such as temperature of component intersection and shaft speed were adjusted by additional adaptation factors. An optimization technique was used to find the correlation equation of adaptation factors for compressor performance maps. The multi-island genetic algorithm (MIGA) was employed in the present optimization. The correlations of local adaptation factors were generated based on the difference between the first adapted engine model and performance test data. The proposed adaptation method applied to a low-bypass ratio turbofan engine of 12,000 lb thrust. The gas turbine engine model was generated and validated based on the performance test data in the sea-level static condition. In flight condition at 20,000 ft and 0.9 Mach number, the result of adapted engine model showed improved prediction in engine thrust (overall performance parameter) by reducing the difference from 14.5 to 3.3%. Moreover, there was further improvement in the comparison of low-pressure turbine exit temperature (local performance parameter) as the difference is reduced from 3.2 to 0.4%.

r-Adaptive mesh generation for shell finite element analysis

International Nuclear Information System (INIS)

Cho, Maenghyo; Jun, Seongki

2004-01-01

An r-adaptive method or moving grid technique relocates a grid so that it becomes concentrated in the desired region. This concentration improves the accuracy and efficiency of finite element solutions. We apply the r-adaptive method to computational mesh of shell surfaces, which is initially regular and uniform. The r-adaptive method, given by Liao and Anderson [Appl. Anal. 44 (1992) 285], aggregate the grid in the region with a relatively high weight function without any grid-tangling. The stress error estimator is calculated in the initial uniform mesh for a weight function. However, since the r-adaptive method is a method that moves the grid, shell surface geometry error such as curvature error and mesh distortion error will increase. Therefore, to represent the exact geometry of a shell surface and to prevent surface geometric errors, we use the Naghdi's shell theory and express the shell surface by a B-spline patch. In addition, using a nine-node element, which is relatively less sensitive to mesh distortion, we try to diminish mesh distortion error in the application of an r-adaptive method. In the numerical examples, it is shown that the values of the error estimator for a cylinder, hemisphere, and torus in the overall domain can be reduced effectively by using the mesh generated by the r-adaptive method. Also, the reductions of the estimated relative errors are demonstrated in the numerical examples. In particular, a new functional is proposed to construct an adjusted mesh configuration by considering a mesh distortion measure as well as the stress error function. The proposed weight function provides a reliable mesh adaptation method after a parameter value in the weight function is properly chosen

Regularized spherical polar fourier diffusion MRI with optimal dictionary learning.

Science.gov (United States)

Cheng, Jian; Jiang, Tianzi; Deriche, Rachid; Shen, Dinggang; Yap, Pew-Thian

2013-01-01

Compressed Sensing (CS) takes advantage of signal sparsity or compressibility and allows superb signal reconstruction from relatively few measurements. Based on CS theory, a suitable dictionary for sparse representation of the signal is required. In diffusion MRI (dMRI), CS methods proposed for reconstruction of diffusion-weighted signal and the Ensemble Average Propagator (EAP) utilize two kinds of Dictionary Learning (DL) methods: 1) Discrete Representation DL (DR-DL), and 2) Continuous Representation DL (CR-DL). DR-DL is susceptible to numerical inaccuracy owing to interpolation and regridding errors in a discretized q-space. In this paper, we propose a novel CR-DL approach, called Dictionary Learning - Spherical Polar Fourier Imaging (DL-SPFI) for effective compressed-sensing reconstruction of the q-space diffusion-weighted signal and the EAP. In DL-SPFI, a dictionary that sparsifies the signal is learned from the space of continuous Gaussian diffusion signals. The learned dictionary is then adaptively applied to different voxels using a weighted LASSO framework for robust signal reconstruction. Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., respectively, our work offers the following advantages. First, the learned dictionary is proved to be optimal for Gaussian diffusion signals. Second, to our knowledge, this is the first work to learn a voxel-adaptive dictionary. The importance of the adaptive dictionary in EAP reconstruction will be demonstrated theoretically and empirically. Third, optimization in DL-SPFI is only performed in a small subspace resided by the SPF coefficients, as opposed to the q-space approach utilized by Merlet et al. We experimentally evaluated DL-SPFI with respect to L1-norm regularized SPFI (L1-SPFI), which uses the original SPF basis, and the DR-DL method proposed by Bilgic et al. The experiment results on synthetic and real data indicate that the learned dictionary produces

Convergence of a Scholtes-type regularization method for cardinality-constrained optimization problems with an application in sparse robust portfolio optimization

Czech Academy of Sciences Publication Activity Database

Branda, Martin; Bucher, M.; Červinka, Michal; Schwartz, A.

2018-01-01

Roč. 70, č. 2 (2018), s. 503-530 ISSN 0926-6003 R&D Projects: GA ČR GA15-00735S Institutional support: RVO:67985556 Keywords : Cardinality constraints * Regularization method * Scholtes regularization * Strong stationarity * Sparse portfolio optimization * Robust portfolio optimization Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 1.520, year: 2016 http://library.utia.cas.cz/separaty/2018/MTR/branda-0489264.pdf

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

A two-dimensional adaptive numerical grids generation method and its realization

International Nuclear Information System (INIS)

Xu Tao; Shui Hongshou

1998-12-01

A two-dimensional adaptive numerical grids generation method and its particular realization is discussed. This method is effective and easy to realize if the control functions are given continuously, and the grids for some regions is showed in this case. For Computational Fluid Dynamics, because the control values of adaptive grids-numerical solution is given in dispersed form, it is needed to interpolate these values to get the continuous control functions. These interpolation techniques are discussed, and some efficient adaptive grids are given. A two-dimensional fluid dynamics example was also given

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

Directory of Open Access Journals (Sweden)

Hongyang Lu

2016-01-01

Full Text Available Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV approach and adaptive dictionary learning (DL. In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

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

High Resolution DNS of Turbulent Flows using an Adaptive, Finite Volume Method

Science.gov (United States)

Trebotich, David

2014-11-01

We present a new computational capability for high resolution simulation of incompressible viscous flows. Our approach is based on cut cell methods where an irregular geometry such as a bluff body is intersected with a rectangular Cartesian grid resulting in cut cells near the boundary. In the cut cells we use a conservative discretization based on a discrete form of the divergence theorem to approximate fluxes for elliptic and hyperbolic terms in the Navier-Stokes equations. Away from the boundary the method reduces to a finite difference method. The algorithm is implemented in the Chombo software framework which supports adaptive mesh refinement and massively parallel computations. The code is scalable to 200,000 + processor cores on DOE supercomputers, resulting in DNS studies at unprecedented scale and resolution. For flow past a cylinder in transition (Re = 300) we observe a number of secondary structures in the far wake in 2D where the wake is over 120 cylinder diameters in length. These are compared with the more regularized wake structures in 3D at the same scale. For flow past a sphere (Re = 600) we resolve an arrowhead structure in the velocity in the near wake. The effectiveness of AMR is further highlighted in a simulation of turbulent flow (Re = 6000) in the contraction of an oil well blowout preventer. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program under Contract Number DE-AC02-05-CH11231.

Regularity in an environment produces an internal torque pattern for biped balance control.

Science.gov (United States)

Ito, Satoshi; Kawasaki, Haruhisa

2005-04-01

In this paper, we present a control method for achieving biped static balance under unknown periodic external forces whose periods are only known. In order to maintain static balance adaptively in an uncertain environment, it is essential to have information on the ground reaction forces. However, when the biped is exposed to a steady environment that provides an external force periodically, uncertain factors on the regularity with respect to a steady environment are gradually clarified using learning process, and finally a torque pattern for balancing motion is acquired. Consequently, static balance is maintained without feedback from ground reaction forces and achieved in a feedforward manner.

Adaptive enhanced sampling by force-biasing using neural networks

Science.gov (United States)

Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

2018-04-01

A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

Use of regularized principal component analysis to model anatomical changes during head and neck radiation therapy for treatment adaptation and response assessment

International Nuclear Information System (INIS)

Chetvertkov, Mikhail A.; Siddiqui, Farzan; Chetty, Indrin; Kumarasiri, Akila; Liu, Chang; Gordon, J. James; Kim, Jinkoo

2016-01-01

Purpose: To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (H&N) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment. Methods: Planning CT images of ten H&N patients were artificially deformed to create “digital phantom” images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients’ actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment. Results: Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA’s ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion. Conclusions: Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during H&N radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more

Use of regularized principal component analysis to model anatomical changes during head and neck radiation therapy for treatment adaptation and response assessment

Energy Technology Data Exchange (ETDEWEB)

Chetvertkov, Mikhail A., E-mail: chetvertkov@wayne.edu [Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, Michigan 48201 and Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202 (United States); Siddiqui, Farzan; Chetty, Indrin; Kumarasiri, Akila; Liu, Chang; Gordon, J. James [Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202 (United States); Kim, Jinkoo [Department of Radiation Oncology, Stony Brook University Hospital, Stony Brook, New York 11794 (United States)

2016-10-15

Purpose: To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (H&N) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment. Methods: Planning CT images of ten H&N patients were artificially deformed to create “digital phantom” images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients’ actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment. Results: Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA’s ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion. Conclusions: Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during H&N radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more

Parallel 3D Mortar Element Method for Adaptive Nonconforming Meshes

Science.gov (United States)

Feng, Huiyu; Mavriplis, Catherine; VanderWijngaart, Rob; Biswas, Rupak

2004-01-01

High order methods are frequently used in computational simulation for their high accuracy. An efficient way to avoid unnecessary computation in smooth regions of the solution is to use adaptive meshes which employ fine grids only in areas where they are needed. Nonconforming spectral elements allow the grid to be flexibly adjusted to satisfy the computational accuracy requirements. The method is suitable for computational simulations of unsteady problems with very disparate length scales or unsteady moving features, such as heat transfer, fluid dynamics or flame combustion. In this work, we select the Mark Element Method (MEM) to handle the non-conforming interfaces between elements. A new technique is introduced to efficiently implement MEM in 3-D nonconforming meshes. By introducing an "intermediate mortar", the proposed method decomposes the projection between 3-D elements and mortars into two steps. In each step, projection matrices derived in 2-D are used. The two-step method avoids explicitly forming/deriving large projection matrices for 3-D meshes, and also helps to simplify the implementation. This new technique can be used for both h- and p-type adaptation. This method is applied to an unsteady 3-D moving heat source problem. With our new MEM implementation, mesh adaptation is able to efficiently refine the grid near the heat source and coarsen the grid once the heat source passes. The savings in computational work resulting from the dynamic mesh adaptation is demonstrated by the reduction of the the number of elements used and CPU time spent. MEM and mesh adaptation, respectively, bring irregularity and dynamics to the computer memory access pattern. Hence, they provide a good way to gauge the performance of computer systems when running scientific applications whose memory access patterns are irregular and unpredictable. We select a 3-D moving heat source problem as the Unstructured Adaptive (UA) grid benchmark, a new component of the NAS Parallel

Solving delay differential equations in S-ADAPT by method of steps.

Science.gov (United States)

Bauer, Robert J; Mo, Gary; Krzyzanski, Wojciech

2013-09-01

S-ADAPT is a version of the ADAPT program that contains additional simulation and optimization abilities such as parametric population analysis. S-ADAPT utilizes LSODA to solve ordinary differential equations (ODEs), an algorithm designed for large dimension non-stiff and stiff problems. However, S-ADAPT does not have a solver for delay differential equations (DDEs). Our objective was to implement in S-ADAPT a DDE solver using the methods of steps. The method of steps allows one to solve virtually any DDE system by transforming it to an ODE system. The solver was validated for scalar linear DDEs with one delay and bolus and infusion inputs for which explicit analytic solutions were derived. Solutions of nonlinear DDE problems coded in S-ADAPT were validated by comparing them with ones obtained by the MATLAB DDE solver dde23. The estimation of parameters was tested on the MATLB simulated population pharmacodynamics data. The comparison of S-ADAPT generated solutions for DDE problems with the explicit solutions as well as MATLAB produced solutions which agreed to at least 7 significant digits. The population parameter estimates from using importance sampling expectation-maximization in S-ADAPT agreed with ones used to generate the data. Published by Elsevier Ireland Ltd.

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).

Adaptive Subband Filtering Method for MEMS Accelerometer Noise Reduction

Directory of Open Access Journals (Sweden)

Piotr PIETRZAK

2008-12-01

Full Text Available Silicon microaccelerometers can be considered as an alternative to high-priced piezoelectric sensors. Unfortunately, relatively high noise floor of commercially available MEMS (Micro-Electro-Mechanical Systems sensors limits the possibility of their usage in condition monitoring systems of rotating machines. The solution of this problem is the method of signal filtering described in the paper. It is based on adaptive subband filtering employing Adaptive Line Enhancer. For filter weights adaptation, two novel algorithms have been developed. They are based on the NLMS algorithm. Both of them significantly simplify its software and hardware implementation and accelerate the adaptation process. The paper also presents the software (Matlab and hardware (FPGA implementation of the proposed noise filter. In addition, the results of the performed tests are reported. They confirm high efficiency of the solution.

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.

SPET reconstruction with a non-uniform attenuation coefficient using an analytical regularizing iterative method

International Nuclear Information System (INIS)

Soussaline, F.; LeCoq, C.; Raynaud, C.; Kellershohn

1982-01-01

The potential of the Regularizing Iterative Method (RIM), when used in brain studies, is evaluated. RIM is designed to provide fast and accurate reconstruction of tomographic images when non-uniform attenuation is to be accounted for. As indicated by phantom studies, this method improves the contrast and the signal-to-noise ratio as compared to those obtained with Filtered Back Projection (FBP) technique. Preliminary results obtained in brain studies using isopropil-amphetamine I-123 (AMPI-123) are very encouraging in terms of quantitative regional cellular activity. However, the clinical usefulness of this mathematically accurate reconstruction procedure is going to be demonstrated, in comparing quantitative data in heart or liver studies where control values can be obtained

Methods for high-resolution anisotropic finite element modeling of the human head: automatic MR white matter anisotropy-adaptive mesh generation.

Science.gov (United States)

Lee, Won Hee; Kim, Tae-Seong

2012-01-01

This study proposes an advanced finite element (FE) head modeling technique through which high-resolution FE meshes adaptive to the degree of tissue anisotropy can be generated. Our adaptive meshing scheme (called wMesh) uses MRI structural information and fractional anisotropy maps derived from diffusion tensors in the FE mesh generation process, optimally reflecting electrical properties of the human brain. We examined the characteristics of the wMeshes through various qualitative and quantitative comparisons to the conventional FE regular-sized meshes that are non-adaptive to the degree of white matter anisotropy. We investigated numerical differences in the FE forward solutions that include the electrical potential and current density generated by current sources in the brain. The quantitative difference was calculated by two statistical measures of relative difference measure (RDM) and magnification factor (MAG). The results show that the wMeshes are adaptive to the anisotropic density of the WM anisotropy, and they better reflect the density and directionality of tissue conductivity anisotropy. Our comparison results between various anisotropic regular mesh and wMesh models show that there are substantial differences in the EEG forward solutions in the brain (up to RDM=0.48 and MAG=0.63 in the electrical potential, and RDM=0.65 and MAG=0.52 in the current density). Our analysis results indicate that the wMeshes produce different forward solutions that are different from the conventional regular meshes. We present some results that the wMesh head modeling approach enhances the sensitivity and accuracy of the FE solutions at the interfaces or in the regions where the anisotropic conductivities change sharply or their directional changes are complex. The fully automatic wMesh generation technique should be useful for modeling an individual-specific and high-resolution anisotropic FE head model incorporating realistic anisotropic conductivity distributions

An entropy regularization method applied to the identification of wave distribution function for an ELF hiss event

Science.gov (United States)

Prot, Olivier; SantolíK, OndřEj; Trotignon, Jean-Gabriel; Deferaudy, Hervé

2006-06-01

An entropy regularization algorithm (ERA) has been developed to compute the wave-energy density from electromagnetic field measurements. It is based on the wave distribution function (WDF) concept. To assess its suitability and efficiency, the algorithm is applied to experimental data that has already been analyzed using other inversion techniques. The FREJA satellite data that is used consists of six spectral matrices corresponding to six time-frequency points of an ELF hiss-event spectrogram. The WDF analysis is performed on these six points and the results are compared with those obtained previously. A statistical stability analysis confirms the stability of the solutions. The WDF computation is fast and without any prespecified parameters. The regularization parameter has been chosen in accordance with the Morozov's discrepancy principle. The Generalized Cross Validation and L-curve criterions are then tentatively used to provide a fully data-driven method. However, these criterions fail to determine a suitable value of the regularization parameter. Although the entropy regularization leads to solutions that agree fairly well with those already published, some differences are observed, and these are discussed in detail. The main advantage of the ERA is to return the WDF that exhibits the largest entropy and to avoid the use of a priori models, which sometimes seem to be more accurate but without any justification.

Extended generalized Lagrangian multipliers for magnetohydrodynamics using adaptive multiresolution methods

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Domingues M. O.

2013-12-01

Full Text Available We present a new adaptive multiresoltion method for the numerical simulation of ideal magnetohydrodynamics. The governing equations, i.e., the compressible Euler equations coupled with the Maxwell equations are discretized using a finite volume scheme on a two-dimensional Cartesian mesh. Adaptivity in space is obtained via Harten’s cell average multiresolution analysis, which allows the reliable introduction of a locally refined mesh while controlling the error. The explicit time discretization uses a compact Runge–Kutta method for local time stepping and an embedded Runge-Kutta scheme for automatic time step control. An extended generalized Lagrangian multiplier approach with the mixed hyperbolic-parabolic correction type is used to control the incompressibility of the magnetic field. Applications to a two-dimensional problem illustrate the properties of the method. Memory savings and numerical divergences of magnetic field are reported and the accuracy of the adaptive computations is assessed by comparing with the available exact solution.

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.

An adaptive sampling and windowing interrogation method in PIV

Science.gov (United States)

Theunissen, R.; Scarano, F.; Riethmuller, M. L.

2007-01-01

This study proposes a cross-correlation based PIV image interrogation algorithm that adapts the number of interrogation windows and their size to the image properties and to the flow conditions. The proposed methodology releases the constraint of uniform sampling rate (Cartesian mesh) and spatial resolution (uniform window size) commonly adopted in PIV interrogation. Especially in non-optimal experimental conditions where the flow seeding is inhomogeneous, this leads either to loss of robustness (too few particles per window) or measurement precision (too large or coarsely spaced interrogation windows). Two criteria are investigated, namely adaptation to the local signal content in the image and adaptation to local flow conditions. The implementation of the adaptive criteria within a recursive interrogation method is described. The location and size of the interrogation windows are locally adapted to the image signal (i.e., seeding density). Also the local window spacing (commonly set by the overlap factor) is put in relation with the spatial variation of the velocity field. The viability of the method is illustrated over two experimental cases where the limitation of a uniform interrogation approach appears clearly: a shock-wave-boundary layer interaction and an aircraft vortex wake. The examples show that the spatial sampling rate can be adapted to the actual flow features and that the interrogation window size can be arranged so as to follow the spatial distribution of seeding particle images and flow velocity fluctuations. In comparison with the uniform interrogation technique, the spatial resolution is locally enhanced while in poorly seeded regions the level of robustness of the analysis (signal-to-noise ratio) is kept almost constant.

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.

Adaptive Second-Order Total Variation: An Approach Aware of Slope Discontinuities

KAUST Repository

Lenzen, Frank; Becker, Florian; Lellmann, Jan

2013-01-01

Total variation (TV) regularization, originally introduced by Rudin, Osher and Fatemi in the context of image denoising, has become widely used in the field of inverse problems. Two major directions of modifications of the original approach were proposed later on. The first concerns adaptive variants of TV regularization, the second focuses on higher-order TV models. In the present paper, we combine the ideas of both directions by proposing adaptive second-order TV models, including one anisotropic model. Experiments demonstrate that introducing adaptivity results in an improvement of the reconstruction error. © 2013 Springer-Verlag.

Performance Comparisons of Improved Regular Repeat Accumulate (RA and Irregular Repeat Accumulate (IRA Turbo Decoding

Directory of Open Access Journals (Sweden)

Ahmed Abdulkadhim Hamad

2017-08-01

Full Text Available In this paper, different techniques are used to improve the turbo decoding of regular repeat accumulate (RA and irregular repeat accumulate (IRA codes. The adaptive scaling of a-posteriori information produced by Soft-output Viterbi decoder (SOVA is proposed. The encoded pilots are another scheme that applied for short length RA codes. This work also suggests a simple and a fast method to generate a random interleaver having a free 4 cycle Tanner graph. Progressive edge growth algorithm (PEG is also studied and simulated to create the Tanner graphs which have a great girth.

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

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.

Channel Bonding in Linux Ethernet Environment using Regular Switching Hub

Directory of Open Access Journals (Sweden)

Chih-wen Hsueh

2004-06-01

Full Text Available Bandwidth plays an important role for quality of service in most network systems. There are many technologies developed to increase host bandwidth in a LAN environment. Most of them need special hardware support, such as switching hub that supports IEEE Link Aggregation standard. In this paper, we propose a Linux solution to increase the bandwidth between hosts with multiple network adapters connected to a regular switching hub. The approach is implemented as two Linux kernel modules in a LAN environment without modification to the hardware and operating systems on host machines. Packets are dispatched to bonding network adapters for transmission. The proposed approach is backward compatible, flexible and transparent to users and only one IP address is needed for multiple bonding network adapters. Evaluation experiments in TCP and UDP transmission are shown with bandwidth gain proportionally to the number of network adapters. It is suitable for large-scale LAN systems with high bandwidth requirement, such as clustering systems.

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.

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

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.

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.

A geometric buckling expression for regular polygons: II. Analyses based on the multiple reciprocity boundary element method

International Nuclear Information System (INIS)

Itagaki, Masafumi; Miyoshi, Yoshinori; Hirose, Hideyuki

1993-01-01

A procedure is presented for the determination of geometric buckling for regular polygons. A new computation technique, the multiple reciprocity boundary element method (MRBEM), has been applied to solve the one-group neutron diffusion equation. The main difficulty in applying the ordinary boundary element method (BEM) to neutron diffusion problems has been the need to compute a domain integral, resulting from the fission source. The MRBEM has been developed for transforming this type of domain integral into an equivalent boundary integral. The basic idea of the MRBEM is to apply repeatedly the reciprocity theorem (Green's second formula) using a sequence of higher order fundamental solutions. The MRBEM requires discretization of the boundary only rather than of the domain. This advantage is useful for extensive survey analyses of buckling for complex geometries. The results of survey analyses have indicated that the general form of geometric buckling is B g 2 = (a n /R c ) 2 , where R c represents the radius of the circumscribed circle of the regular polygon under consideration. The geometric constant A n depends on the type of regular polygon and takes the value of π for a square and 2.405 for a circle, an extreme case that has an infinite number of sides. Values of a n for a triangle, pentagon, hexagon, and octagon have been calculated as 4.190, 2.281, 2.675, and 2.547, respectively

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

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

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.

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

A blind deconvolution method based on L1/L2 regularization prior in the gradient space

Science.gov (United States)

Cai, Ying; Shi, Yu; Hua, Xia

2018-02-01

In the process of image restoration, the result of image restoration is very different from the real image because of the existence of noise, in order to solve the ill posed problem in image restoration, a blind deconvolution method based on L1/L2 regularization prior to gradient domain is proposed. The method presented in this paper first adds a function to the prior knowledge, which is the ratio of the L1 norm to the L2 norm, and takes the function as the penalty term in the high frequency domain of the image. Then, the function is iteratively updated, and the iterative shrinkage threshold algorithm is applied to solve the high frequency image. In this paper, it is considered that the information in the gradient domain is better for the estimation of blur kernel, so the blur kernel is estimated in the gradient domain. This problem can be quickly implemented in the frequency domain by fast Fast Fourier Transform. In addition, in order to improve the effectiveness of the algorithm, we have added a multi-scale iterative optimization method. This paper proposes the blind deconvolution method based on L1/L2 regularization priors in the gradient space can obtain the unique and stable solution in the process of image restoration, which not only keeps the edges and details of the image, but also ensures the accuracy of the results.

Subcortical processing of speech regularities underlies reading and music aptitude in children.

Science.gov (United States)

Strait, Dana L; Hornickel, Jane; Kraus, Nina

2011-10-17

Neural sensitivity to acoustic regularities supports fundamental human behaviors such as hearing in noise and reading. Although the failure to encode acoustic regularities in ongoing speech has been associated with language and literacy deficits, how auditory expertise, such as the expertise that is associated with musical skill, relates to the brainstem processing of speech regularities is unknown. An association between musical skill and neural sensitivity to acoustic regularities would not be surprising given the importance of repetition and regularity in music. Here, we aimed to define relationships between the subcortical processing of speech regularities, music aptitude, and reading abilities in children with and without reading impairment. We hypothesized that, in combination with auditory cognitive abilities, neural sensitivity to regularities in ongoing speech provides a common biological mechanism underlying the development of music and reading abilities. We assessed auditory working memory and attention, music aptitude, reading ability, and neural sensitivity to acoustic regularities in 42 school-aged children with a wide range of reading ability. Neural sensitivity to acoustic regularities was assessed by recording brainstem responses to the same speech sound presented in predictable and variable speech streams. Through correlation analyses and structural equation modeling, we reveal that music aptitude and literacy both relate to the extent of subcortical adaptation to regularities in ongoing speech as well as with auditory working memory and attention. Relationships between music and speech processing are specifically driven by performance on a musical rhythm task, underscoring the importance of rhythmic regularity for both language and music. These data indicate common brain mechanisms underlying reading and music abilities that relate to how the nervous system responds to regularities in auditory input. Definition of common biological underpinnings

A Dynamic and Adaptive Selection Radar Tracking Method Based on Information Entropy

Directory of Open Access Journals (Sweden)

Ge Jianjun

2017-12-01

Full Text Available Nowadays, the battlefield environment has become much more complex and variable. This paper presents a quantitative method and lower bound for the amount of target information acquired from multiple radar observations to adaptively and dynamically organize the detection of battlefield resources based on the principle of information entropy. Furthermore, for minimizing the given information entropy’s lower bound for target measurement at every moment, a method to dynamically and adaptively select radars with a high amount of information for target tracking is proposed. The simulation results indicate that the proposed method has higher tracking accuracy than that of tracking without adaptive radar selection based on entropy.

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.

Robust design optimization using the price of robustness, robust least squares and regularization methods

Science.gov (United States)

Bukhari, Hassan J.

2017-12-01

In this paper a framework for robust optimization of mechanical design problems and process systems that have parametric uncertainty is presented using three different approaches. Robust optimization problems are formulated so that the optimal solution is robust which means it is minimally sensitive to any perturbations in parameters. The first method uses the price of robustness approach which assumes the uncertain parameters to be symmetric and bounded. The robustness for the design can be controlled by limiting the parameters that can perturb.The second method uses the robust least squares method to determine the optimal parameters when data itself is subjected to perturbations instead of the parameters. The last method manages uncertainty by restricting the perturbation on parameters to improve sensitivity similar to Tikhonov regularization. The methods are implemented on two sets of problems; one linear and the other non-linear. This methodology will be compared with a prior method using multiple Monte Carlo simulation runs which shows that the approach being presented in this paper results in better performance.

Final Report: Symposium on Adaptive Methods for Partial Differential Equations

Energy Technology Data Exchange (ETDEWEB)

Pernice, Michael; Johnson, Christopher R.; Smith, Philip J.; Fogelson, Aaron

1998-12-08

Complex physical phenomena often include features that span a wide range of spatial and temporal scales. Accurate simulation of such phenomena can be difficult to obtain, and computations that are under-resolved can even exhibit spurious features. While it is possible to resolve small scale features by increasing the number of grid points, global grid refinement can quickly lead to problems that are intractable, even on the largest available computing facilities. These constraints are particularly severe for three dimensional problems that involve complex physics. One way to achieve the needed resolution is to refine the computational mesh locally, in only those regions where enhanced resolution is required. Adaptive solution methods concentrate computational effort in regions where it is most needed. These methods have been successfully applied to a wide variety of problems in computational science and engineering. Adaptive methods can be difficult to implement, prompting the development of tools and environments to facilitate their use. To ensure that the results of their efforts are useful, algorithm and tool developers must maintain close communication with application specialists. Conversely it remains difficult for application specialists who are unfamiliar with the methods to evaluate the trade-offs between the benefits of enhanced local resolution and the effort needed to implement an adaptive solution method.

An h-adaptive mesh method for Boltzmann-BGK/hydrodynamics coupling

International Nuclear Information System (INIS)

Cai Zhenning; Li Ruo

2010-01-01

We introduce a coupled method for hydrodynamic and kinetic equations on 2-dimensional h-adaptive meshes. We adopt the Euler equations with a fast kinetic solver in the region near thermodynamical equilibrium, while use the Boltzmann-BGK equation in kinetic regions where fluids are far from equilibrium. A buffer zone is created around the kinetic regions, on which a gradually varying numerical flux is adopted. Based on the property of a continuously discretized cut-off function which describes how the flux varies, the coupling will be conservative. In order for the conservative 2-dimensional specularly reflective boundary condition to be implemented conveniently, the discrete Maxwellian is approximated by a high order continuous formula with improved accuracy on a disc instead of on a square domain. The h-adaptive method can work smoothly with a time-split numerical scheme. Through h-adaptation, the cell number is greatly reduced. This method is particularly suitable for problems with hydrodynamics breakdown on only a small part of the whole domain, so that the total efficiency of the algorithm can be greatly improved. Three numerical examples are presented to validate the proposed method and demonstrate its efficiency.

Adaptive BDDC Deluxe Methods for H(curl)

KAUST Repository

Zampini, Stefano

2017-03-17

The work presents numerical results using adaptive BDDC deluxe methods for preconditioning the linear systems arising from finite element discretizations of the time-domain, quasi-static approximation of the Maxwell’s equations. The provided results, obtained using the BDDC implementation of the PETSc library, show that these methods are poly-logarithmic in the polynomial degree of the Nédélec elements of first and second kind, and robust with respect to arbitrary distributions of the magnetic permeability and the conductivity of the medium.

ECG-derived respiration methods: adapted ICA and PCA.

Science.gov (United States)

Tiinanen, Suvi; Noponen, Kai; Tulppo, Mikko; Kiviniemi, Antti; Seppänen, Tapio

2015-05-01

Respiration is an important signal in early diagnostics, prediction, and treatment of several diseases. Moreover, a growing trend toward ambulatory measurements outside laboratory environments encourages developing indirect measurement methods such as ECG derived respiration (EDR). Recently, decomposition techniques like principal component analysis (PCA), and its nonlinear version, kernel PCA (KPCA), have been used to derive a surrogate respiration signal from single-channel ECG. In this paper, we propose an adapted independent component analysis (AICA) algorithm to obtain EDR signal, and extend the normal linear PCA technique based on the best principal component (PC) selection (APCA, adapted PCA) to improve its performance further. We also demonstrate that the usage of smoothing spline resampling and bandpass-filtering improve the performance of all EDR methods. Compared with other recent EDR methods using correlation coefficient and magnitude squared coherence, the proposed AICA and APCA yield a statistically significant improvement with correlations 0.84, 0.82, 0.76 and coherences 0.90, 0.91, 0.85 between reference respiration and AICA, APCA and KPCA, respectively. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality.

Science.gov (United States)

Gosling, Simon N; Hondula, David M; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

2017-08-16

Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to "adaptation uncertainty" (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. This study had three aims: a ) Compare the range in projected impacts that arises from using different adaptation modeling methods; b ) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c ) recommend modeling method(s) to use in future impact assessments. We estimated impacts for 2070-2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634.

A constrained Delaunay discretization method for adaptively meshing highly discontinuous geological media

Science.gov (United States)

Wang, Yang; Ma, Guowei; Ren, Feng; Li, Tuo

2017-12-01

A constrained Delaunay discretization method is developed to generate high-quality doubly adaptive meshes of highly discontinuous geological media. Complex features such as three-dimensional discrete fracture networks (DFNs), tunnels, shafts, slopes, boreholes, water curtains, and drainage systems are taken into account in the mesh generation. The constrained Delaunay triangulation method is used to create adaptive triangular elements on planar fractures. Persson's algorithm (Persson, 2005), based on an analogy between triangular elements and spring networks, is enriched to automatically discretize a planar fracture into mesh points with varying density and smooth-quality gradient. The triangulated planar fractures are treated as planar straight-line graphs (PSLGs) to construct piecewise-linear complex (PLC) for constrained Delaunay tetrahedralization. This guarantees the doubly adaptive characteristic of the resulted mesh: the mesh is adaptive not only along fractures but also in space. The quality of elements is compared with the results from an existing method. It is verified that the present method can generate smoother elements and a better distribution of element aspect ratios. Two numerical simulations are implemented to demonstrate that the present method can be applied to various simulations of complex geological media that contain a large number of discontinuities.

Adaptive aerostructures : The first decade of flight on uninhabited aerial vehicles

NARCIS (Netherlands)

Barrett, R.

2004-01-01

Although many subscale aircraft regularly fly with adaptive materials in sensors and small components in secondary subsystems, only a handful have flown with adaptive aerostructures as flight critical, enabling components. This paper reviews several families of adaptive aerostructures which have

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.

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.

A multilevel correction adaptive finite element method for Kohn-Sham equation

Science.gov (United States)

Hu, Guanghui; Xie, Hehu; Xu, Fei

2018-02-01

In this paper, an adaptive finite element method is proposed for solving Kohn-Sham equation with the multilevel correction technique. In the method, the Kohn-Sham equation is solved on a fixed and appropriately coarse mesh with the finite element method in which the finite element space is kept improving by solving the derived boundary value problems on a series of adaptively and successively refined meshes. A main feature of the method is that solving large scale Kohn-Sham system is avoided effectively, and solving the derived boundary value problems can be handled efficiently by classical methods such as the multigrid method. Hence, the significant acceleration can be obtained on solving Kohn-Sham equation with the proposed multilevel correction technique. The performance of the method is examined by a variety of numerical experiments.

Followee recommendation in microblog using matrix factorization model with structural regularization.

Science.gov (United States)

Yu, Yan; Qiu, Robin G

2014-01-01

Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.

Optimal and adaptive methods of processing hydroacoustic signals (review)

Science.gov (United States)

Malyshkin, G. S.; Sidel'nikov, G. B.

2014-09-01

Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.

HAM-Based Adaptive Multiscale Meshless Method for Burgers Equation

Directory of Open Access Journals (Sweden)

Shu-Li Mei

2013-01-01

Full Text Available Based on the multilevel interpolation theory, we constructed a meshless adaptive multiscale interpolation operator (MAMIO with the radial basis function. Using this operator, any nonlinear partial differential equations such as Burgers equation can be discretized adaptively in physical spaces as a nonlinear matrix ordinary differential equation. In order to obtain the analytical solution of the system of ODEs, the homotopy analysis method (HAM proposed by Shijun Liao was developed to solve the system of ODEs by combining the precise integration method (PIM which can be employed to get the analytical solution of linear system of ODEs. The numerical experiences show that HAM is not sensitive to the time step, and so the arithmetic error is mainly derived from the discrete in physical space.

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.

Adaptive laboratory evolution – principles and applications for biotechnology

Science.gov (United States)

2013-01-01

Adaptive laboratory evolution is a frequent method in biological studies to gain insights into the basic mechanisms of molecular evolution and adaptive changes that accumulate in microbial populations during long term selection under specified growth conditions. Although regularly performed for more than 25 years, the advent of transcript and cheap next-generation sequencing technologies has resulted in many recent studies, which successfully applied this technique in order to engineer microbial cells for biotechnological applications. Adaptive laboratory evolution has some major benefits as compared with classical genetic engineering but also some inherent limitations. However, recent studies show how some of the limitations may be overcome in order to successfully incorporate adaptive laboratory evolution in microbial cell factory design. Over the last two decades important insights into nutrient and stress metabolism of relevant model species were acquired, whereas some other aspects such as niche-specific differences of non-conventional cell factories are not completely understood. Altogether the current status and its future perspectives highlight the importance and potential of adaptive laboratory evolution as approach in biotechnological engineering. PMID:23815749

Adaptive Mesh Iteration Method for Trajectory Optimization Based on Hermite-Pseudospectral Direct Transcription

Directory of Open Access Journals (Sweden)

Humin Lei

2017-01-01

Full Text Available An adaptive mesh iteration method based on Hermite-Pseudospectral is described for trajectory optimization. The method uses the Legendre-Gauss-Lobatto points as interpolation points; then the state equations are approximated by Hermite interpolating polynomials. The method allows for changes in both number of mesh points and the number of mesh intervals and produces significantly smaller mesh sizes with a higher accuracy tolerance solution. The derived relative error estimate is then used to trade the number of mesh points with the number of mesh intervals. The adaptive mesh iteration method is applied successfully to the examples of trajectory optimization of Maneuverable Reentry Research Vehicle, and the simulation experiment results show that the adaptive mesh iteration method has many advantages.

Discrete linear canonical transform computation by adaptive method.

Science.gov (United States)

Zhang, Feng; Tao, Ran; Wang, Yue

2013-07-29

The linear canonical transform (LCT) describes the effect of quadratic phase systems on a wavefield and generalizes many optical transforms. In this paper, the computation method for the discrete LCT using the adaptive least-mean-square (LMS) algorithm is presented. The computation approaches of the block-based discrete LCT and the stream-based discrete LCT using the LMS algorithm are derived, and the implementation structures of these approaches by the adaptive filter system are considered. The proposed computation approaches have the inherent parallel structures which make them suitable for efficient VLSI implementations, and are robust to the propagation of possible errors in the computation process.

Models, methods and software tools for building complex adaptive traffic systems

International Nuclear Information System (INIS)

Alyushin, S.A.

2011-01-01

The paper studies the modern methods and tools to simulate the behavior of complex adaptive systems (CAS), the existing systems of traffic modeling in simulators and their characteristics; proposes requirements for assessing the suitability of the system to simulate the CAS behavior in simulators. The author has developed a model of adaptive agent representation and its functioning environment to meet certain requirements set above, and has presented methods of agents' interactions and methods of conflict resolution in simulated traffic situations. A simulation system realizing computer modeling for simulating the behavior of CAS in traffic situations has been created [ru

Adaptive variational mode decomposition method for signal processing based on mode characteristic

Science.gov (United States)

Lian, Jijian; Liu, Zhuo; Wang, Haijun; Dong, Xiaofeng

2018-07-01

Variational mode decomposition is a completely non-recursive decomposition model, where all the modes are extracted concurrently. However, the model requires a preset mode number, which limits the adaptability of the method since a large deviation in the number of mode set will cause the discard or mixing of the mode. Hence, a method called Adaptive Variational Mode Decomposition (AVMD) was proposed to automatically determine the mode number based on the characteristic of intrinsic mode function. The method was used to analyze the simulation signals and the measured signals in the hydropower plant. Comparisons have also been conducted to evaluate the performance by using VMD, EMD and EWT. It is indicated that the proposed method has strong adaptability and is robust to noise. It can determine the mode number appropriately without modulation even when the signal frequencies are relatively close.

A method for the deliberate and deliberative selection of policy instrument mixes for climate change adaptation

Directory of Open Access Journals (Sweden)

Heleen L. P. Mees

2014-06-01

Full Text Available Policy instruments can help put climate adaptation plans into action. Here, we propose a method for the systematic assessment and selection of policy instruments for stimulating adaptation action. The multi-disciplinary set of six assessment criteria is derived from economics, policy, and legal studies. These criteria are specified for the purpose of climate adaptation by taking into account four challenges to the governance of climate adaptation: uncertainty, spatial diversity, controversy, and social complexity. The six criteria and four challenges are integrated into a step-wise method that enables the selection of instruments starting from a generic assessment and ending with a specific assessment of policy instrument mixes for the stimulation of a specific adaptation measure. We then apply the method to three examples of adaptation measures. The method's merits lie in enabling deliberate choices through a holistic and comprehensive set of adaptation specific criteria, as well as deliberative choices by offering a stepwise method that structures an informed dialog on instrument selection. Although the method was created and applied by scientific experts, policy-makers can also use the method.

The persistence of the attentional bias to regularities in a changing environment.

Science.gov (United States)

Yu, Ru Qi; Zhao, Jiaying

2015-10-01

The environment often is stable, but some aspects may change over time. The challenge for the visual system is to discover and flexibly adapt to the changes. We examined how attention is shifted in the presence of changes in the underlying structure of the environment. In six experiments, observers viewed four simultaneous streams of objects while performing a visual search task. In the first half of each experiment, the stream in the structured location contained regularities, the shapes in the random location were randomized, and gray squares appeared in two neutral locations. In the second half, the stream in the structured or the random location may change. In the first half of all experiments, visual search was facilitated in the structured location, suggesting that attention was consistently biased toward regularities. In the second half, this bias persisted in the structured location when no change occurred (Experiment 1), when the regularities were removed (Experiment 2), or when new regularities embedded in the original or novel stimuli emerged in the previously random location (Experiments 3 and 6). However, visual search was numerically but no longer reliably faster in the structured location when the initial regularities were removed and new regularities were introduced in the previously random location (Experiment 4), or when novel random stimuli appeared in the random location (Experiment 5). This suggests that the attentional bias was weakened. Overall, the results demonstrate that the attentional bias to regularities was persistent but also sensitive to changes in the environment.

Novel Harmonic Regularization Approach for Variable Selection in Cox’s Proportional Hazards Model

Directory of Open Access Journals (Sweden)

Ge-Jin Chu

2014-01-01

Full Text Available Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex Lq  (1/2regularizations, to select key risk factors in the Cox’s proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL, the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods.

Adaptive wavelet tight frame construction for accelerating MRI reconstruction

Directory of Open Access Journals (Sweden)

Genjiao Zhou

2017-09-01

Full Text Available The sparsity regularization approach, which assumes that the image of interest is likely to have sparse representation in some transform domain, has been an active research area in image processing and medical image reconstruction. Although various sparsifying transforms have been used in medical image reconstruction such as wavelet, contourlet, and total variation (TV etc., the efficiency of these transforms typically rely on the special structure of the underlying image. A better way to address this issue is to develop an overcomplete dictionary from the input data in order to get a better sparsifying transform for the underlying image. However, the general overcomplete dictionaries do not satisfy the so-called perfect reconstruction property which ensures that the given signal can be perfectly represented by its canonical coefficients in a manner similar to orthonormal bases, resulting in time consuming in the iterative image reconstruction. This work is to develop an adaptive wavelet tight frame method for magnetic resonance image reconstruction. The proposed scheme incorporates the adaptive wavelet tight frame approach into the magnetic resonance image reconstruction by solving a l0-regularized minimization problem. Numerical results show that the proposed approach provides significant time savings as compared to the over-complete dictionary based methods with comparable performance in terms of both peak signal-to-noise ratio and subjective visual quality.

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.

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.

Adaptive Finite Volume Method for the Shallow Water Equations on Triangular Grids

Directory of Open Access Journals (Sweden)

Sudi Mungkasi

2016-01-01

Full Text Available This paper presents a numerical entropy production (NEP scheme for two-dimensional shallow water equations on unstructured triangular grids. We implement NEP as the error indicator for adaptive mesh refinement or coarsening in solving the shallow water equations using a finite volume method. Numerical simulations show that NEP is successful to be a refinement/coarsening indicator in the adaptive mesh finite volume method, as the method refines the mesh or grids around nonsmooth regions and coarsens them around smooth regions.

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,

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

A constrained regularization method for inverting data represented by linear algebraic or integral equations

Science.gov (United States)

Provencher, Stephen W.

1982-09-01

CONTIN is a portable Fortran IV package for inverting noisy linear operator equations. These problems occur in the analysis of data from a wide variety experiments. They are generally ill-posed problems, which means that errors in an unregularized inversion are unbounded. Instead, CONTIN seeks the optimal solution by incorporating parsimony and any statistical prior knowledge into the regularizor and absolute prior knowledge into equallity and inequality constraints. This can be greatly increase the resolution and accuracyh of the solution. CONTIN is very flexible, consisting of a core of about 50 subprograms plus 13 small "USER" subprograms, which the user can easily modify to specify special-purpose constraints, regularizors, operator equations, simulations, statistical weighting, etc. Specjial collections of USER subprograms are available for photon correlation spectroscopy, multicomponent spectra, and Fourier-Bessel, Fourier and Laplace transforms. Numerically stable algorithms are used throughout CONTIN. A fairly precise definition of information content in terms of degrees of freedom is given. The regularization parameter can be automatically chosen on the basis of an F-test and confidence region. The interpretation of the latter and of error estimates based on the covariance matrix of the constrained regularized solution are discussed. The strategies, methods and options in CONTIN are outlined. The program itself is described in the following paper.

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�

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

Characterizing Reinforcement Learning Methods through Parameterized Learning Problems

Science.gov (United States)

2011-06-03

extraneous. The agent could potentially adapt these representational aspects by applying methods from feature selection ( Kolter and Ng, 2009; Petrik et al...611–616. AAAI Press. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature selection in least-squares temporal difference learning. In A. P

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.

On Self-Adaptive Method for General Mixed Variational Inequalities

Directory of Open Access Journals (Sweden)

Abdellah Bnouhachem

2008-01-01

Full Text Available We suggest and analyze a new self-adaptive method for solving general mixed variational inequalities, which can be viewed as an improvement of the method of (Noor 2003. Global convergence of the new method is proved under the same assumptions as Noor's method. Some preliminary computational results are given to illustrate the efficiency of the proposed method. Since the general mixed variational inequalities include general variational inequalities, quasivariational inequalities, and nonlinear (implicit complementarity problems as special cases, results proved in this paper continue to hold for these problems.

Applying Maxi-adjustment to Adaptive Information Filtering Agents

OpenAIRE

Lau, Raymond; ter Hofstede, Arthur H. M.; Bruza, Peter D.

2000-01-01

Learning and adaptation is a fundamental property of intelligent agents. In the context of adaptive information filtering, a filtering agent's beliefs about a user's information needs have to be revised regularly with reference to the user's most current information preferences. This learning and adaptation process is essential for maintaining the agent's filtering performance. The AGM belief revision paradigm provides a rigorous foundation for modelling rational and minimal changes to an age...

Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA).

Science.gov (United States)

Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A

2015-02-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms.

An adaptive multi-element probabilistic collocation method for statistical EMC/EMI characterization

KAUST Repository

Yücel, Abdulkadir C.

2013-12-01

An adaptive multi-element probabilistic collocation (ME-PC) method for quantifying uncertainties in electromagnetic compatibility and interference phenomena involving electrically large, multi-scale, and complex platforms is presented. The method permits the efficient and accurate statistical characterization of observables (i.e., quantities of interest such as coupled voltages) that potentially vary rapidly and/or are discontinuous in the random variables (i.e., parameters that characterize uncertainty in a system\\'s geometry, configuration, or excitation). The method achieves its efficiency and accuracy by recursively and adaptively dividing the domain of the random variables into subdomains using as a guide the decay rate of relative error in a polynomial chaos expansion of the observables. While constructing local polynomial expansions on each subdomain, a fast integral-equation-based deterministic field-cable-circuit simulator is used to compute the observable values at the collocation/integration points determined by the adaptive ME-PC scheme. The adaptive ME-PC scheme requires far fewer (computationally costly) deterministic simulations than traditional polynomial chaos collocation and Monte Carlo methods for computing averages, standard deviations, and probability density functions of rapidly varying observables. The efficiency and accuracy of the method are demonstrated via its applications to the statistical characterization of voltages in shielded/unshielded microwave amplifiers and magnetic fields induced on car tire pressure sensors. © 2013 IEEE.

Stabilized Conservative Level Set Method with Adaptive Wavelet-based Mesh Refinement

Science.gov (United States)

Shervani-Tabar, Navid; Vasilyev, Oleg V.

2016-11-01

This paper addresses one of the main challenges of the conservative level set method, namely the ill-conditioned behavior of the normal vector away from the interface. An alternative formulation for reconstruction of the interface is proposed. Unlike the commonly used methods which rely on the unit normal vector, Stabilized Conservative Level Set (SCLS) uses a modified renormalization vector with diminishing magnitude away from the interface. With the new formulation, in the vicinity of the interface the reinitialization procedure utilizes compressive flux and diffusive terms only in the normal direction to the interface, thus, preserving the conservative level set properties, while away from the interfaces the directional diffusion mechanism automatically switches to homogeneous diffusion. The proposed formulation is robust and general. It is especially well suited for use with adaptive mesh refinement (AMR) approaches due to need for a finer resolution in the vicinity of the interface in comparison with the rest of the domain. All of the results were obtained using the Adaptive Wavelet Collocation Method, a general AMR-type method, which utilizes wavelet decomposition to adapt on steep gradients in the solution while retaining a predetermined order of accuracy.

Adaptive Kernel in Meshsize Boosting Algorithm in KDE ...

African Journals Online (AJOL)

This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...

Successful adaptation of a research methods course in South America.

Science.gov (United States)

Tamariz, Leonardo; Vasquez, Diego; Loor, Cecilia; Palacio, Ana

2017-01-01

South America has low research productivity. The lack of a structured research curriculum is one of the barriers to conducting research. To report our experience adapting an active learning-based research methods curriculum to improve research productivity at a university in Ecuador. We used a mixed-method approach to test the adaptation of the research curriculum at Universidad Catolica Santiago de Guayaquil. The curriculum uses a flipped classroom and active learning approach to teach research methods. When adapted, it was longitudinal and had 16-hour programme of in-person teaching and a six-month follow-up online component. Learners were organized in theme groups according to interest, and each group had a faculty leader. Our primary outcome was research productivity, which was measured by the succesful presentation of the research project at a national meeting, or publication in a peer-review journal. Our secondary outcomes were knowledge and perceived competence before and after course completion. We conducted qualitative interviews of faculty members and students to evaluate themes related to participation in research. Fifty university students and 10 faculty members attended the course. We had a total of 15 groups. Both knowledge and perceived competence increased by 17 and 18 percentage points, respectively. The presentation or publication rate for the entire group was 50%. The qualitative analysis showed that a lack of research culture and curriculum were common barriers to research. A US-based curriculum can be successfully adapted in low-middle income countries. A research curriculum aids in achieving pre-determined milestones. UCSG: Universidad Catolica Santiago de Guayaquil; UM: University of Miami.

Adaptive control of a PWR core power using neural networks

International Nuclear Information System (INIS)

Arab-Alibeik, H.; Setayeshi, S.

2005-01-01

Reactor power control is important because of safety concerns and the call for regular and appropriate operation of nuclear power plants. It seems that the load-follow operation of these plants will be unavoidable in the future. Discrepancies between the real plant and the model used in controller design for load-follow operation encourage one to use auto-tuning and (or) adaptive techniques. Neural network technology shows great promise for addressing many problems in non-model-based adaptive control methods. Also, there has been a great attention to inverse control especially in the neural and fuzzy control context. Fortunately, online adaptation eliminates some limitations of inverse control and its shortcomings for real world applications. We use a neural adaptive inverse controller to control the power of a PWR reactor. The stability of the system and convergence of the controller parameters are guaranteed during online adaptation phase provided the controller is near the plant's real inverse after offline training period. The performance of the controller is verified using nonlinear simulations in diverse operating conditions

Adaptation in CRISPR-Cas Systems.

Science.gov (United States)

Sternberg, Samuel H; Richter, Hagen; Charpentier, Emmanuelle; Qimron, Udi

2016-03-17

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute an adaptive immune system in prokaryotes. The system preserves memories of prior infections by integrating short segments of foreign DNA, termed spacers, into the CRISPR array in a process termed adaptation. During the past 3 years, significant progress has been made on the genetic requirements and molecular mechanisms of adaptation. Here we review these recent advances, with a focus on the experimental approaches that have been developed, the insights they generated, and a proposed mechanism for self- versus non-self-discrimination during the process of spacer selection. We further describe the regulation of adaptation and the protein players involved in this fascinating process that allows bacteria and archaea to harbor adaptive immunity. Copyright © 2016 Elsevier Inc. All rights reserved.

[Comparative adaptation of crowns of selective laser melting and wax-lost-casting method].

Science.gov (United States)

Li, Guo-qiang; Shen, Qing-yi; Gao, Jian-hua; Wu, Xue-ying; Chen, Li; Dai, Wen-an

2012-07-01

To investigate the marginal adaptation of crowns fabricated by selective laser melting (SLM) and wax-lost-casting method, so as to provide an experimental basis for clinic. Co-Cr alloy full crown were fabricated by SLM and wax-lost-casting for 24 samples in each group. All crowns were cemented with zinc phosphate cement and cut along longitudinal axis by line cutting machine. The gap between crown tissue surface and die was measured by 6-point measuring method with scanning electron microscope (SEM). The marginal adaptation of crowns fabricated by SLM and wax-lost-casting were compared statistically. The gap between SLM crowns were (36.51 ± 2.94), (49.36 ± 3.31), (56.48 ± 3.35), (42.20 ± 3.60) µm, and wax-lost-casting crowns were (68.86 ± 5.41), (58.86 ± 6.10), (70.62 ± 5.79), (69.90 ± 6.00) µm. There were significant difference between two groups (P casting method and SLM method provide acceptable marginal adaptation in clinic, and the marginal adaptation of SLM is better than that of wax-lost-casting method.

Regularization of Hamilton-Lagrangian guiding center theories

International Nuclear Information System (INIS)

Correa-Restrepo, D.; Wimmel, H.K.

1985-04-01

The Hamilton-Lagrangian guiding-center (G.C.) theories of Littlejohn, Wimmel, and Pfirsch show a singularity for B-fields with non-vanishing parallel curl at a critical value of vsub(parallel), which complicates applications. The singularity is related to a sudden breakdown, at a critical vsub(parallel), of gyration in the exact particle mechanics. While the latter is a real effect, the G.C. singularity can be removed. To this end a regularization method is defined that preserves the Hamilton-Lagrangian structure and the conservation theorems. For demonstration this method is applied to the standard G.C. theory (without polarization drift). Liouville's theorem and G.C. kinetic equations are also derived in regularized form. The method could equally well be applied to the case with polarization drift and to relativistic G.C. theory. (orig.)

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.)

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.)

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.

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...

Combining kernel matrix optimization and regularization to improve particle size distribution retrieval

Science.gov (United States)

Ma, Qian; Xia, Houping; Xu, Qiang; Zhao, Lei

2018-05-01

A new method combining Tikhonov regularization and kernel matrix optimization by multi-wavelength incidence is proposed for retrieving particle size distribution (PSD) in an independent model with improved accuracy and stability. In comparison to individual regularization or multi-wavelength least squares, the proposed method exhibited better anti-noise capability, higher accuracy and stability. While standard regularization typically makes use of the unit matrix, it is not universal for different PSDs, particularly for Junge distributions. Thus, a suitable regularization matrix was chosen by numerical simulation, with the second-order differential matrix found to be appropriate for most PSD types.

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

Science.gov (United States)

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-01-01

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG Pattern Recognition

Directory of Open Access Journals (Sweden)

Qi Huang

2017-06-01

Full Text Available Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC, by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC. We compared PAC performance with incremental support vector classifier (ISVC and non-adapting SVC (NSVC in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05 and ISVC (13.38% ± 2.62%, p = 0.001, and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle.

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.

Total variation regularization for fMRI-based prediction of behavior

Science.gov (United States)

Michel, Vincent; Gramfort, Alexandre; Varoquaux, Gaël; Eger, Evelyn; Thirion, Bertrand

2011-01-01

While medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional MRI (fMRI) data, that provide an indirect measure of task-related or spontaneous neuronal activity, are classically analyzed in a mass-univariate procedure yielding statistical parametric maps. This analysis framework disregards some important principles of brain organization: population coding, distributed and overlapping representations. Multivariate pattern analysis, i.e., the prediction of behavioural variables from brain activation patterns better captures this structure. To cope with the high dimensionality of the data, the learning method has to be regularized. However, the spatial structure of the image is not taken into account in standard regularization methods, so that the extracted features are often hard to interpret. More informative and interpretable results can be obtained with the ℓ1 norm of the image gradient, a.k.a. its Total Variation (TV), as regularization. We apply for the first time this method to fMRI data, and show that TV regularization is well suited to the purpose of brain mapping while being a powerful tool for brain decoding. Moreover, this article presents the first use of TV regularization for classification. PMID:21317080

Feed back Petrov-Galerkin methods for convection dominated problems

International Nuclear Information System (INIS)

Carmo, E.G.D. do; Galeao, A.C.

1988-09-01

The Petrov-Galerkin method is adaptively applied to convection dominated problems. To this end a feedback function is created which increases the control of derivatives in the direction of he gradient of the approximate solution. This leads to a method with good stability properties close to boundary layers and high accuracy in those regions where regular solutions do occur. (author) [pt

Solving point reactor kinetic equations by time step-size adaptable numerical methods

International Nuclear Information System (INIS)

Liao Chaqing

2007-01-01

Based on the analysis of effects of time step-size on numerical solutions, this paper showed the necessity of step-size adaptation. Based on the relationship between error and step-size, two-step adaptation methods for solving initial value problems (IVPs) were introduced. They are Two-Step Method and Embedded Runge-Kutta Method. PRKEs were solved by implicit Euler method with step-sizes optimized by using Two-Step Method. It was observed that the control error has important influence on the step-size and the accuracy of solutions. With suitable control errors, the solutions of PRKEs computed by the above mentioned method are accurate reasonably. The accuracy and usage of MATLAB built-in ODE solvers ode23 and ode45, both of which adopt Runge-Kutta-Fehlberg method, were also studied and discussed. (authors)

Adaptive and dynamic meshing methods for numerical simulations

Science.gov (United States)

Acikgoz, Nazmiye

-hoc application of the simulated annealing technique, which improves the likelihood of removing poor elements from the grid. Moreover, a local implementation of the simulated annealing is proposed to reduce the computational cost. Many challenging multi-physics and multi-field problems that are unsteady in nature are characterized by moving boundaries and/or interfaces. When the boundary displacements are large, which typically occurs when implicit time marching procedures are used, degenerate elements are easily formed in the grid such that frequent remeshing is required. To deal with this problem, in the second part of this work, we propose a new r-adaptation methodology. The new technique is valid for both simplicial (e.g., triangular, tet) and non-simplicial (e.g., quadrilateral, hex) deforming grids that undergo large imposed displacements at their boundaries. A two- or three-dimensional grid is deformed using a network of linear springs composed of edge springs and a set of virtual springs. The virtual springs are constructed in such a way as to oppose element collapsing. This is accomplished by confining each vertex to its ball through springs that are attached to the vertex and its projection on the ball entities. The resulting linear problem is solved using a preconditioned conjugate gradient method. The new method is compared with the classical spring analogy technique in two- and three-dimensional examples, highlighting the performance improvements achieved by the new method. Meshes are an important part of numerical simulations. Depending on the geometry and flow conditions, the most suitable mesh for each particular problem is different. Meshes are usually generated by either using a suitable software package or solving a PDE. In both cases, engineering intuition plays a significant role in deciding where clusterings should take place. In addition, for unsteady problems, the gradients vary for each time step, which requires frequent remeshing during simulations

Adaptive implicit method for thermal compositional reservoir simulation

Energy Technology Data Exchange (ETDEWEB)

Agarwal, A.; Tchelepi, H.A. [Society of Petroleum Engineers, Richardson, TX (United States)]|[Stanford Univ., Palo Alto (United States)

2008-10-15

As the global demand for oil increases, thermal enhanced oil recovery techniques are becoming increasingly important. Numerical reservoir simulation of thermal methods such as steam assisted gravity drainage (SAGD) is complex and requires a solution of nonlinear mass and energy conservation equations on a fine reservoir grid. The most currently used technique for solving these equations is the fully IMplicit (FIM) method which is unconditionally stable, allowing for large timesteps in simulation. However, it is computationally expensive. On the other hand, the method known as IMplicit pressure explicit saturations, temperature and compositions (IMPEST) is computationally inexpensive, but it is only conditionally stable and restricts the timestep size. To improve the balance between the timestep size and computational cost, the thermal adaptive IMplicit (TAIM) method uses stability criteria and a switching algorithm, where some simulation variables such as pressure, saturations, temperature, compositions are treated implicitly while others are treated with explicit schemes. This presentation described ongoing research on TAIM with particular reference to thermal displacement processes such as the stability criteria that dictate the maximum allowed timestep size for simulation based on the von Neumann linear stability analysis method; the switching algorithm that adapts labeling of reservoir variables as implicit or explicit as a function of space and time; and, complex physical behaviors such as heat and fluid convection, thermal conduction and compressibility. Key numerical results obtained by enhancing Stanford's General Purpose Research Simulator (GPRS) were also presented along with a list of research challenges. 14 refs., 2 tabs., 11 figs., 1 appendix.

Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction.

Science.gov (United States)

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2018-06-01

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively. Graphical abstract ᅟ.

Adaptive decoupled power control method for inverter connected DG

DEFF Research Database (Denmark)

Sun, Xiaofeng; Tian, Yanjun; Chen, Zhe

2014-01-01

an adaptive droop control method based on online evaluation of power decouple matrix for inverter connected distributed generations in distribution system. Traditional decoupled power control is simply based on line impedance parameter, but the load characteristics also cause the power coupling, and alter...

Ensemble Kalman filter regularization using leave-one-out data cross-validation

KAUST Repository

Rayo Schiappacasse, Lautaro Jerónimo

2012-09-19

In this work, the classical leave-one-out cross-validation method for selecting a regularization parameter for the Tikhonov problem is implemented within the EnKF framework. Following the original concept, the regularization parameter is selected such that it minimizes the predictive error. Some ideas about the implementation, suitability and conceptual interest of the method are discussed. Finally, what will be called the data cross-validation regularized EnKF (dCVr-EnKF) is implemented in a 2D 2-phase synthetic oil reservoir experiment and the results analyzed.

Control of beam halo-chaos using neural network self-adaptation method

International Nuclear Information System (INIS)

Fang Jinqing; Huang Guoxian; Luo Xiaoshu

2004-11-01

Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

An adaptive phase space method with application to reflection traveltime tomography

International Nuclear Information System (INIS)

Chung, Eric; Qian, Jianliang; Uhlmann, Gunther; Zhao, Hongkai

2011-01-01

In this work, an adaptive strategy for the phase space method for traveltime tomography (Chung et al 2007 Inverse Problems 23 309–29) is developed. The method first uses those geodesics/rays that produce smaller mismatch with the measurements and continues on in the spirit of layer stripping without defining the layers explicitly. The adaptive approach improves stability, efficiency and accuracy. We then extend our method to reflection traveltime tomography by incorporating broken geodesics/rays for which a jump condition has to be imposed at the broken point for the geodesic flow. In particular, we show that our method can distinguish non-broken and broken geodesics in the measurement and utilize them accordingly in reflection traveltime tomography. We demonstrate that our method can recover the convex hull (with respect to the underlying metric) of unknown obstacles as well as the metric outside the convex hull. (paper)

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

Noninvasive technique for measurement of heartbeat regularity in zebrafish (Danio rerio embryos

Directory of Open Access Journals (Sweden)

Cheng Shuk

2009-02-01

Full Text Available Abstract Background Zebrafish (Danio rerio, due to its optical accessibility and similarity to human, has emerged as model organism for cardiac research. Although various methods have been developed to assess cardiac functions in zebrafish embryos, there lacks a method to assess heartbeat regularity in blood vessels. Heartbeat regularity is an important parameter for cardiac function and is associated with cardiotoxicity in human being. Using stereomicroscope and digital video camera, we have developed a simple, noninvasive method to measure the heart rate and heartbeat regularity in peripheral blood vessels. Anesthetized embryos were mounted laterally in agarose on a slide and the caudal blood circulation of zebrafish embryo was video-recorded under stereomicroscope and the data was analyzed by custom-made software. The heart rate was determined by digital motion analysis and power spectral analysis through extraction of frequency characteristics of the cardiac rhythm. The heartbeat regularity, defined as the rhythmicity index, was determined by short-time Fourier Transform analysis. Results The heart rate measured by this noninvasive method in zebrafish embryos at 52 hour post-fertilization was similar to that determined by direct visual counting of ventricle beating (p > 0.05. In addition, the method was validated by a known cardiotoxic drug, terfenadine, which affects heartbeat regularity in humans and induces bradycardia and atrioventricular blockage in zebrafish. A significant decrease in heart rate was found by our method in treated embryos (p p Conclusion The data support and validate this rapid, simple, noninvasive method, which includes video image analysis and frequency analysis. This method is capable of measuring the heart rate and heartbeat regularity simultaneously via the analysis of caudal blood flow in zebrafish embryos. With the advantages of rapid sample preparation procedures, automatic image analysis and data analysis, this

An adaptive angle-doppler compensation method for airborne bistatic radar based on PAST

Science.gov (United States)

Hang, Xu; Jun, Zhao

2018-05-01

Adaptive angle-Doppler compensation method extract the requisite information based on the data itself adaptively, thus avoiding the problem of performance degradation caused by inertia system error. However, this method requires estimation and egiendecomposition of sample covariance matrix, which has a high computational complexity and limits its real-time application. In this paper, an adaptive angle Doppler compensation method based on projection approximation subspace tracking (PAST) is studied. The method uses cyclic iterative processing to quickly estimate the positions of the spectral center of the maximum eigenvector of each range cell, and the computational burden of matrix estimation and eigen-decompositon is avoided, and then the spectral centers of all range cells is overlapped by two dimensional compensation. Simulation results show the proposed method can effectively reduce the no homogeneity of airborne bistatic radar, and its performance is similar to that of egien-decomposition algorithms, but the computation load is obviously reduced and easy to be realized.

Panchromatic cooperative hyperspectral adaptive wide band deletion repair method

Science.gov (United States)

Jiang, Bitao; Shi, Chunyu

2018-02-01

In the hyperspectral data, the phenomenon of stripe deletion often occurs, which seriously affects the efficiency and accuracy of data analysis and application. Narrow band deletion can be directly repaired by interpolation, and this method is not ideal for wide band deletion repair. In this paper, an adaptive spectral wide band missing restoration method based on panchromatic information is proposed, and the effectiveness of the algorithm is verified by experiments.

A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability

International Nuclear Information System (INIS)

Wen, Zhixun; Pei, Haiqing; Liu, Hai; Yue, Zhufeng

2016-01-01

The sequential Kriging reliability analysis (SKRA) method has been developed in recent years for nonlinear implicit response functions which are expensive to evaluate. This type of method includes EGRA: the efficient reliability analysis method, and AK-MCS: the active learning reliability method combining Kriging model and Monte Carlo simulation. The purpose of this paper is to improve SKRA by adaptive sampling regions and parallelizability. The adaptive sampling regions strategy is proposed to avoid selecting samples in regions where the probability density is so low that the accuracy of these regions has negligible effects on the results. The size of the sampling regions is adapted according to the failure probability calculated by last iteration. Two parallel strategies are introduced and compared, aimed at selecting multiple sample points at a time. The improvement is verified through several troublesome examples. - Highlights: • The ISKRA method improves the efficiency of SKRA. • Adaptive sampling regions strategy reduces the number of needed samples. • The two parallel strategies reduce the number of needed iterations. • The accuracy of the optimal value impacts the number of samples significantly.

Sparse Pseudo Spectral Projection Methods with Directional Adaptation for Uncertainty Quantification

KAUST Repository

Winokur, J.; Kim, D.; Bisetti, Fabrizio; Le Maî tre, O. P.; Knio, Omar

2015-01-01

We investigate two methods to build a polynomial approximation of a model output depending on some parameters. The two approaches are based on pseudo-spectral projection (PSP) methods on adaptively constructed sparse grids, and aim at providing a

Interference by clustered regularly interspaced short palindromic repeat (CRISPR) RNA is governed by a seed sequence

NARCIS (Netherlands)

Semenova, E.V.; Jore, M.M.; Westra, E.R.; Oost, van der J.; Brouns, S.J.J.

2011-01-01

Prokaryotic clustered regularly interspaced short palindromic repeat (CRISPR)/Cas (CRISPR-associated sequences) systems provide adaptive immunity against viruses when a spacer sequence of small CRISPR RNA (crRNA) matches a protospacer sequence in the viral genome. Viruses that escape CRISPR/Cas

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.

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.

Use of a dynamic grid adaptation in the asymmetric weighted residual method

International Nuclear Information System (INIS)

Graf, V.; Romstedt, P.; Werner, W.

1986-01-01

A dynamic grid adaptive method has been developed for use with the asymmetric weighted residual method. The method automatically adapts the number and position of the spatial mesh points as the solution of hyperbolic or parabolic vector partial differential equations progresses in time. The mesh selection algorithm is based on the minimization of the L 2 norm of the spatial discretization error. The method permits the accurate calculation of the evolution of inhomogeneities, like wave fronts, shock layers, and other sharp transitions, while generally using a coarse computational grid. The number of required mesh points is significantly reduced, relative to a fixed Eulerian grid. Since the mesh selection algorithm is computationally inexpensive, a corresponding reduction of computing time results

AP-Cloud: Adaptive Particle-in-Cloud method for optimal solutions to Vlasov–Poisson equation

International Nuclear Information System (INIS)

Wang, Xingyu; Samulyak, Roman; Jiao, Xiangmin; Yu, Kwangmin

2016-01-01

We propose a new adaptive Particle-in-Cloud (AP-Cloud) method for obtaining optimal numerical solutions to the Vlasov–Poisson equation. Unlike the traditional particle-in-cell (PIC) method, which is commonly used for solving this problem, the AP-Cloud adaptively selects computational nodes or particles to deliver higher accuracy and efficiency when the particle distribution is highly non-uniform. Unlike other adaptive techniques for PIC, our method balances the errors in PDE discretization and Monte Carlo integration, and discretizes the differential operators using a generalized finite difference (GFD) method based on a weighted least square formulation. As a result, AP-Cloud is independent of the geometric shapes of computational domains and is free of artificial parameters. Efficient and robust implementation is achieved through an octree data structure with 2:1 balance. We analyze the accuracy and convergence order of AP-Cloud theoretically, and verify the method using an electrostatic problem of a particle beam with halo. Simulation results show that the AP-Cloud method is substantially more accurate and faster than the traditional PIC, and it is free of artificial forces that are typical for some adaptive PIC techniques.

Regularization dependence on phase diagram in Nambu–Jona-Lasinio model

International Nuclear Information System (INIS)

Kohyama, H.; Kimura, D.; Inagaki, T.

2015-01-01

We study the regularization dependence on meson properties and the phase diagram of quark matter by using the two flavor Nambu–Jona-Lasinio model. The model also has the parameter dependence in each regularization, so we explicitly give the model parameters for some sets of the input observables, then investigate its effect on the phase diagram. We find that the location or the existence of the critical end point highly depends on the regularization methods and the model parameters. Then we think that regularization and parameters are carefully considered when one investigates the QCD critical end point in the effective model studies

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.

Enhancing Low-Rank Subspace Clustering by Manifold Regularization.

Science.gov (United States)

Liu, Junmin; Chen, Yijun; Zhang, JiangShe; Xu, Zongben

2014-07-25

Recently, low-rank representation (LRR) method has achieved great success in subspace clustering (SC), which aims to cluster the data points that lie in a union of low-dimensional subspace. Given a set of data points, LRR seeks the lowest rank representation among the many possible linear combinations of the bases in a given dictionary or in terms of the data itself. However, LRR only considers the global Euclidean structure, while the local manifold structure, which is often important for many real applications, is ignored. In this paper, to exploit the local manifold structure of the data, a manifold regularization characterized by a Laplacian graph has been incorporated into LRR, leading to our proposed Laplacian regularized LRR (LapLRR). An efficient optimization procedure, which is based on alternating direction method of multipliers (ADMM), is developed for LapLRR. Experimental results on synthetic and real data sets are presented to demonstrate that the performance of LRR has been enhanced by using the manifold regularization.

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.

Adaptive-mesh zoning by the equipotential method

Energy Technology Data Exchange (ETDEWEB)

Winslow, A.M.

1981-04-01

An adaptive mesh method is proposed for the numerical solution of differential equations which causes the mesh lines to move closer together in regions where higher resolution in some physical quantity T is desired. A coefficient D > 0 is introduced into the equipotential zoning equations, where D depends on the gradient of T . The equations are inverted, leading to nonlinear elliptic equations for the mesh coordinates with source terms which depend on the gradient of D. A functional form of D is proposed.

A novel adaptive force control method for IPMC manipulation

International Nuclear Information System (INIS)

Hao, Lina; Sun, Zhiyong; Su, Yunquan; Gao, Jianchao; Li, Zhi

2012-01-01

IPMC is a type of electro-active polymer material, also called artificial muscle, which can generate a relatively large deformation under a relatively low input voltage (generally speaking, less than 5 V), and can be implemented in a water environment. Due to these advantages, IPMC can be used in many fields such as biomimetics, service robots, bio-manipulation, etc. Until now, most existing methods for IPMC manipulation are displacement control not directly force control, however, under most conditions, the success rate of manipulations for tiny fragile objects is limited by the contact force, such as using an IPMC gripper to fix cells. Like most EAPs, a creep phenomenon exists in IPMC, of which the generated force will change with time and the creep model will be influenced by the change of the water content or other environmental factors, so a proper force control method is urgently needed. This paper presents a novel adaptive force control method (AIPOF control—adaptive integral periodic output feedback control), based on employing a creep model of which parameters are obtained by using the FRLS on-line identification method. The AIPOF control method can achieve an arbitrary pole configuration as long as the plant is controllable and observable. This paper also designs the POF and IPOF controller to compare their test results. Simulation and experiments of micro-force-tracking tests are carried out, with results confirming that the proposed control method is viable. (paper)

Adaptive Kernel In The Bootstrap Boosting Algorithm In KDE ...

African Journals Online (AJOL)

This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...

Spatially adaptive hp refinement approach for PN neutron transport equation using spectral element method

International Nuclear Information System (INIS)

Nahavandi, N.; Minuchehr, A.; Zolfaghari, A.; Abbasi, M.

2015-01-01

Highlights: • Powerful hp-SEM refinement approach for P N neutron transport equation has been presented. • The method provides great geometrical flexibility and lower computational cost. • There is a capability of using arbitrary high order and non uniform meshes. • Both posteriori and priori local error estimation approaches have been employed. • High accurate results are compared against other common adaptive and uniform grids. - Abstract: In this work we presented the adaptive hp-SEM approach which is obtained from the incorporation of Spectral Element Method (SEM) and adaptive hp refinement. The SEM nodal discretization and hp adaptive grid-refinement for even-parity Boltzmann neutron transport equation creates powerful grid refinement approach with high accuracy solutions. In this regard a computer code has been developed to solve multi-group neutron transport equation in one-dimensional geometry using even-parity transport theory. The spatial dependence of flux has been developed via SEM method with Lobatto orthogonal polynomial. Two commonly error estimation approaches, the posteriori and the priori has been implemented. The incorporation of SEM nodal discretization method and adaptive hp grid refinement leads to high accurate solutions. Coarser meshes efficiency and significant reduction of computer program runtime in comparison with other common refining methods and uniform meshing approaches is tested along several well-known transport benchmarks

Adaptive finite element analysis of incompressible viscous flow using posteriori error estimation and control of node density distribution

International Nuclear Information System (INIS)

Yashiki, Taturou; Yagawa, Genki; Okuda, Hiroshi

1995-01-01

The adaptive finite element method based on an 'a posteriori error estimation' is known to be a powerful technique for analyzing the engineering practical problems, since it excludes the instinctive aspect of the mesh subdivision and gives high accuracy with relatively low computational cost. In the adaptive procedure, both the error estimation and the mesh generation according to the error estimator are essential. In this paper, the adaptive procedure is realized by the automatic mesh generation based on the control of node density distribution, which is decided according to the error estimator. The global percentage error, CPU time, the degrees of freedom and the accuracy of the solution of the adaptive procedure are compared with those of the conventional method using regular meshes. Such numerical examples as the driven cavity flows of various Reynolds numbers and the flows around a cylinder have shown the very high performance of the proposed adaptive procedure. (author)

Adapting a perinatal empathic training method from South Africa to Germany.

Science.gov (United States)

Knapp, Caprice; Honikman, Simone; Wirsching, Michael; Husni-Pascha, Gidah; Hänselmann, Eva

2018-01-01

Maternal mental health conditions are prevalent across the world. For women, the perinatal period is associated with increased rates of depression and anxiety. At the same time, there is widespread documentation of disrespectful care for women by maternity health staff. Improving the empathic engagement skills of maternity healthcare workers may enable them to respond to the mental health needs of their clients more effectively. In South Africa, a participatory empathic training method, the "Secret History" has been used as part of a national Department of Health training program with maternity staff and has showed promising results. For this paper, we aimed to describe an adaptation of the Secret History empathic training method from the South African to the German setting and to evaluate the adapted training. The pilot study occurred in an academic medical center in Germany. A focus group ( n  = 8) was used to adapt the training by describing the local context and changing the materials to be relevant to Germany. After adapting the materials, the pilot training was conducted with a mixed group of professionals ( n  = 15), many of whom were trainers themselves. A pre-post survey assessed the participants' empathy levels and attitudes towards the training method. In adapting the materials, the focus group discussion generated several experiences that were considered to be typical interpersonal and structural challenges facing healthcare workers in maternal care in Germany. These experiences were crafted into case scenarios that then formed the basis of the activities used in the Secret History empathic training pilot. Evaluation of the pilot training showed that although the participants had high levels of empathy in the pre-phase (100% estimated their empathic ability as high or very high), 69% became more aware of their own emotional experiences with patients and the need for self-care after the training. A majority, or 85%, indicated that the training

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.

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.

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.

Finite element method for solving Kohn-Sham equations based on self-adaptive tetrahedral mesh

International Nuclear Information System (INIS)

Zhang Dier; Shen Lihua; Zhou Aihui; Gong Xingao

2008-01-01

A finite element (FE) method with self-adaptive mesh-refinement technique is developed for solving the density functional Kohn-Sham equations. The FE method adopts local piecewise polynomials basis functions, which produces sparsely structured matrices of Hamiltonian. The method is well suitable for parallel implementation without using Fourier transform. In addition, the self-adaptive mesh-refinement technique can control the computational accuracy and efficiency with optimal mesh density in different regions

Method and system for training dynamic nonlinear adaptive filters which have embedded memory

Science.gov (United States)

Rabinowitz, Matthew (Inventor)

2002-01-01

Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.

Ensemble Kalman filter regularization using leave-one-out data cross-validation

KAUST Repository

Rayo Schiappacasse, Lautaro Jeró nimo; Hoteit, Ibrahim

2012-01-01

In this work, the classical leave-one-out cross-validation method for selecting a regularization parameter for the Tikhonov problem is implemented within the EnKF framework. Following the original concept, the regularization parameter is selected

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

Functional reach and lateral reach tests adapted for aquatic physical therapy

Directory of Open Access Journals (Sweden)

Ana Angélica Ribeiro de Lima

Full Text Available Abstract Introduction: Functional reach (FR and lateral reach (LR tests are widely used in scientific research and clinical practice. Assessment tools are useful in assessing subjects with greater accuracy and are usually adapted according to the limitations of each condition. Objective: To adapt FR and LR tests for use in an aquatic environment and assess the performance of healthy young adults. Methods: We collected anthropometric data and information on whether the participant exercised regularly or not. The FR and LR tests were adapted for use in an aquatic environment and administered to 47 healthy subjects aged 20-30 years. Each test was repeated three times. Results: Forty-one females and six males were assessed. The mean FR test score for men was 24.06 cm, whereas the mean value for right lateral reach (RLR was 10.94 cm and for left lateral reach (LLR was 9.78 cm. For females, the mean FR score was 17.57 cm, while the mean values for RLR was 8.84cm and for LLR was 7.76 cm. Men performed better in the FR (p < 0.001 and RLR tests than women (p = 0.037. Individuals who exercised regularly showed no differences in performance level when compared with their counterparts. Conclusion: The FR and LR tests were adapted for use in an aquatic environment. Males performed better on the FR and RLR tests, when compared to females. There was no correlation between the FR and LR tests and weight, height, Body Mass Index (BMI, foot length or length of the dominant upper limb.

3D spatially-adaptive canonical correlation analysis: Local and global methods.

Science.gov (United States)

Yang, Zhengshi; Zhuang, Xiaowei; Sreenivasan, Karthik; Mishra, Virendra; Curran, Tim; Byrd, Richard; Nandy, Rajesh; Cordes, Dietmar

2018-04-01

Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

SPATIAL MODELING OF SOLID-STATE REGULAR POLYHEDRA (SOLIDS OF PLATON IN AUTOCAD SYSTEM

Directory of Open Access Journals (Sweden)

P. V. Bezditko

2009-03-01

Full Text Available This article describes the technology of modeling regular polyhedra by graphic methods. The authors came to the conclusion that in order to create solid models of regular polyhedra the method of extrusion is best to use.

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

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...

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

An Adaptive Laboratory Evolution Method to Accelerate Autotrophic Metabolism

DEFF Research Database (Denmark)

Zhang, Tian; Tremblay, Pier-Luc

2018-01-01

Adaptive laboratory evolution (ALE) is an approach enabling the development of novel characteristics in microbial strains via the application of a constant selection pressure. This method is also an efficient tool to acquire insights on molecular mechanisms responsible for specific phenotypes. ALE...... autotrophically and reducing CO2 into acetate more efficiently. Strains developed via this ALE method were also used to gain knowledge on the autotrophic metabolism of S. ovata as well as other acetogenic bacteria....

Method and system for environmentally adaptive fault tolerant computing

Science.gov (United States)

Copenhaver, Jason L. (Inventor); Jeremy, Ramos (Inventor); Wolfe, Jeffrey M. (Inventor); Brenner, Dean (Inventor)

2010-01-01

A method and system for adapting fault tolerant computing. The method includes the steps of measuring an environmental condition representative of an environment. An on-board processing system's sensitivity to the measured environmental condition is measured. It is determined whether to reconfigure a fault tolerance of the on-board processing system based in part on the measured environmental condition. The fault tolerance of the on-board processing system may be reconfigured based in part on the measured environmental condition.

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.

On convergence rates for iteratively regularized procedures with linear penalty terms

International Nuclear Information System (INIS)

Smirnova, Alexandra

2012-01-01

The impact of this paper is twofold. First, we study convergence rates of the iteratively regularized Gauss–Newton (IRGN) algorithm with a linear penalty term under a generalized source assumption and show how the regularizing properties of new iterations depend on the solution smoothness. Secondly, we introduce an adaptive IRGN procedure, which is investigated under a relaxed smoothness condition. The introduction and analysis of a more general penalty term are of great importance since, apart from bringing stability to the numerical scheme designed for solving a large class of applied inverse problems, it allows us to incorporate various types of a priori information available on the model. Both a priori and a posteriori stopping rules are investigated. For the a priori stopping rule, optimal convergence rates are derived. A numerical example illustrating convergence rates is considered. (paper)

An adaptive proper orthogonal decomposition method for model order reduction of multi-disc rotor system

Science.gov (United States)

Jin, Yulin; Lu, Kuan; Hou, Lei; Chen, Yushu

2017-12-01

The proper orthogonal decomposition (POD) method is a main and efficient tool for order reduction of high-dimensional complex systems in many research fields. However, the robustness problem of this method is always unsolved, although there are some modified POD methods which were proposed to solve this problem. In this paper, a new adaptive POD method called the interpolation Grassmann manifold (IGM) method is proposed to address the weakness of local property of the interpolation tangent-space of Grassmann manifold (ITGM) method in a wider parametric region. This method is demonstrated here by a nonlinear rotor system of 33-degrees of freedom (DOFs) with a pair of liquid-film bearings and a pedestal looseness fault. The motion region of the rotor system is divided into two parts: simple motion region and complex motion region. The adaptive POD method is compared with the ITGM method for the large and small spans of parameter in the two parametric regions to present the advantage of this method and disadvantage of the ITGM method. The comparisons of the responses are applied to verify the accuracy and robustness of the adaptive POD method, as well as the computational efficiency is also analyzed. As a result, the new adaptive POD method has a strong robustness and high computational efficiency and accuracy in a wide scope of parameter.

Information-theoretic semi-supervised metric learning via entropy regularization.

Science.gov (United States)

Niu, Gang; Dai, Bo; Yamada, Makoto; Sugiyama, Masashi

2014-08-01

We propose a general information-theoretic approach to semi-supervised metric learning called SERAPH (SEmi-supervised metRic leArning Paradigm with Hypersparsity) that does not rely on the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize its entropy on labeled data and minimize its entropy on unlabeled data following entropy regularization. For metric learning, entropy regularization improves manifold regularization by considering the dissimilarity information of unlabeled data in the unsupervised part, and hence it allows the supervised and unsupervised parts to be integrated in a natural and meaningful way. Moreover, we regularize SERAPH by trace-norm regularization to encourage low-dimensional projections associated with the distance metric. The nonconvex optimization problem of SERAPH could be solved efficiently and stably by either a gradient projection algorithm or an EM-like iterative algorithm whose M-step is convex. Experiments demonstrate that SERAPH compares favorably with many well-known metric learning methods, and the learned Mahalanobis distance possesses high discriminability even under noisy environments.

Hybrid Adaptive Flight Control with Model Inversion Adaptation

Science.gov (United States)

Nguyen, Nhan

2011-01-01

This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.

Sparse Pseudo Spectral Projection Methods with Directional Adaptation for Uncertainty Quantification

KAUST Repository

Winokur, J.

2015-12-19

We investigate two methods to build a polynomial approximation of a model output depending on some parameters. The two approaches are based on pseudo-spectral projection (PSP) methods on adaptively constructed sparse grids, and aim at providing a finer control of the resolution along two distinct subsets of model parameters. The control of the error along different subsets of parameters may be needed for instance in the case of a model depending on uncertain parameters and deterministic design variables. We first consider a nested approach where an independent adaptive sparse grid PSP is performed along the first set of directions only, and at each point a sparse grid is constructed adaptively in the second set of directions. We then consider the application of aPSP in the space of all parameters, and introduce directional refinement criteria to provide a tighter control of the projection error along individual dimensions. Specifically, we use a Sobol decomposition of the projection surpluses to tune the sparse grid adaptation. The behavior and performance of the two approaches are compared for a simple two-dimensional test problem and for a shock-tube ignition model involving 22 uncertain parameters and 3 design parameters. The numerical experiments indicate that whereas both methods provide effective means for tuning the quality of the representation along distinct subsets of parameters, PSP in the global parameter space generally requires fewer model evaluations than the nested approach to achieve similar projection error. In addition, the global approach is better suited for generalization to more than two subsets of directions.

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...

Adaptive wavelet method for pricing two-asset Asian options with floating strike

Science.gov (United States)

Černá, Dana

2017-12-01

Asian options are path-dependent option contracts which payoff depends on the average value of the asset price over some period of time. We focus on pricing of Asian options on two assets. The model for pricing these options is represented by a parabolic equation with time variable and three state variables, but using substitution it can be reduced to the equation with only two state variables. For time discretization we use the θ-scheme. We propose a wavelet basis that is adapted to boundary conditions and use an adaptive scheme with this basis for discretization on the given time level. The main advantage of this scheme is small number of degrees of freedom. We present numerical experiments for the Asian put option with floating strike and compare the results for the proposed adaptive method and the Galerkin method.

How little data is enough? Phase-diagram analysis of sparsity-regularized X-ray computed tomography.

Science.gov (United States)

Jørgensen, J S; Sidky, E Y

2015-06-13

We introduce phase-diagram analysis, a standard tool in compressed sensing (CS), to the X-ray computed tomography (CT) community as a systematic method for determining how few projections suffice for accurate sparsity-regularized reconstruction. In CS, a phase diagram is a convenient way to study and express certain theoretical relations between sparsity and sufficient sampling. We adapt phase-diagram analysis for empirical use in X-ray CT for which the same theoretical results do not hold. We demonstrate in three case studies the potential of phase-diagram analysis for providing quantitative answers to questions of undersampling. First, we demonstrate that there are cases where X-ray CT empirically performs comparably with a near-optimal CS strategy, namely taking measurements with Gaussian sensing matrices. Second, we show that, in contrast to what might have been anticipated, taking randomized CT measurements does not lead to improved performance compared with standard structured sampling patterns. Finally, we show preliminary results of how well phase-diagram analysis can predict the sufficient number of projections for accurately reconstructing a large-scale image of a given sparsity by means of total-variation regularization.

The adaptive problems of female teenage refugees and their behavioral adjustment methods for coping

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Mhaidat F

2016-04-01

Full Text Available Fatin Mhaidat Department of Educational Psychology, Faculty of Educational Sciences, The Hashemite University, Zarqa, Jordan Abstract: This study aimed at identifying the levels of adaptive problems among teenage female refugees in the government schools and explored the behavioral methods that were used to cope with the problems. The sample was composed of 220 Syrian female students (seventh to first secondary grades enrolled at government schools within the Zarqa Directorate and who came to Jordan due to the war conditions in their home country. The study used the scale of adaptive problems that consists of four dimensions (depression, anger and hostility, low self-esteem, and feeling insecure and a questionnaire of the behavioral adjustment methods for dealing with the problem of asylum. The results indicated that the Syrian teenage female refugees suffer a moderate degree of adaptation problems, and the positive adjustment methods they have used are more than the negatives. Keywords: adaptive problems, female teenage refugees, behavioral adjustment

Regular Single Valued Neutrosophic Hypergraphs

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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.

Optimal Design of Large Dimensional Adaptive Subspace Detectors

KAUST Repository

Ben Atitallah, Ismail; Kammoun, Abla; Alouini, Mohamed-Slim; Alnaffouri, Tareq Y.

2016-01-01

This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.

Optimal Design of Large Dimensional Adaptive Subspace Detectors

KAUST Repository

Ben Atitallah, Ismail

2016-05-27

This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.

The Temporal Dynamics of Regularity Extraction in Non-Human Primates

Science.gov (United States)

Minier, Laure; Fagot, Joël; Rey, Arnaud

2016-01-01

Extracting the regularities of our environment is one of our core cognitive abilities. To study the fine-grained dynamics of the extraction of embedded regularities, a method combining the advantages of the artificial language paradigm (Saffran, Aslin, & Newport, [Saffran, J. R., 1996]) and the serial response time task (Nissen & Bullemer,…

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.

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

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He, Tongdi; Che, Zongxi

2018-01-01

This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

Reducing errors in the GRACE gravity solutions using regularization

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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

An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning

Directory of Open Access Journals (Sweden)

Jingsha He

2017-03-01

Full Text Available In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people’s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users’ privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL, to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.

A class of discontinuous Petrov–Galerkin methods. Part III: Adaptivity

KAUST Repository

Demkowicz, Leszek

2012-04-01

We continue our theoretical and numerical study on the Discontinuous Petrov-Galerkin method with optimal test functions in context of 1D and 2D convection-dominated diffusion problems and hp-adaptivity. With a proper choice of the norm for the test space, we prove robustness (uniform stability with respect to the diffusion parameter) and mesh-independence of the energy norm of the FE error for the 1D problem. With hp-adaptivity and a proper scaling of the norms for the test functions, we establish new limits for solving convection-dominated diffusion problems numerically: ε=10 -11 for 1D and ε=10 -7 for 2D problems. The adaptive process is fully automatic and starts with a mesh consisting of few elements only. © 2011 IMACS. Published by Elsevier B.V. All rights reserved.

Hessian regularization based symmetric nonnegative matrix factorization for clustering gene expression and microbiome data.

Science.gov (United States)

Ma, Yuanyuan; Hu, Xiaohua; He, Tingting; Jiang, Xingpeng

2016-12-01

Nonnegative matrix factorization (NMF) has received considerable attention due to its interpretation of observed samples as combinations of different components, and has been successfully used as a clustering method. As an extension of NMF, Symmetric NMF (SNMF) inherits the advantages of NMF. Unlike NMF, however, SNMF takes a nonnegative similarity matrix as an input, and two lower rank nonnegative matrices (H, H T ) are computed as an output to approximate the original similarity matrix. Laplacian regularization has improved the clustering performance of NMF and SNMF. However, Laplacian regularization (LR), as a classic manifold regularization method, suffers some problems because of its weak extrapolating ability. In this paper, we propose a novel variant of SNMF, called Hessian regularization based symmetric nonnegative matrix factorization (HSNMF), for this purpose. In contrast to Laplacian regularization, Hessian regularization fits the data perfectly and extrapolates nicely to unseen data. We conduct extensive experiments on several datasets including text data, gene expression data and HMP (Human Microbiome Project) data. The results show that the proposed method outperforms other methods, which suggests the potential application of HSNMF in biological data clustering. Copyright © 2016. Published by Elsevier Inc.