A method of smoothed particle hydrodynamics using spheroidal kernels
NASA Technical Reports Server (NTRS)
Fulbright, Michael S.; Benz, Willy; Davies, Melvyn B.
1995-01-01
We present a new method of three-dimensional smoothed particle hydrodynamics (SPH) designed to model systems dominated by deformation along a preferential axis. These systems cause severe problems for SPH codes using spherical kernels, which are best suited for modeling systems which retain rough spherical symmetry. Our method allows the smoothing length in the direction of the deformation to evolve independently of the smoothing length in the perpendicular plane, resulting in a kernel with a spheroidal shape. As a result the spatial resolution in the direction of deformation is significantly improved. As a test case we present the one-dimensional homologous collapse of a zero-temperature, uniform-density cloud, which serves to demonstrate the advantages of spheroidal kernels. We also present new results on the problem of the tidal disruption of a star by a massive black hole.
Cen, Guanjun; Yu, Yonghao; Zeng, Xianru; Long, Xiuzhen; Wei, Dewei; Gao, Xuyuan; Zeng, Tao
2015-01-01
In insects, the frequency distribution of the measurements of sclerotized body parts is generally used to classify larval instars and is characterized by a multimodal overlap between instar stages. Nonparametric methods with fixed bandwidths, such as histograms, have significant limitations when used to fit this type of distribution, making it difficult to identify divisions between instars. Fixed bandwidths have also been chosen somewhat subjectively in the past, which is another problem. In this study, we describe an adaptive kernel smoothing method to differentiate instars based on discontinuities in the growth rates of sclerotized insect body parts. From Brooks' rule, we derived a new standard for assessing the quality of instar classification and a bandwidth selector that more accurately reflects the distributed character of specific variables. We used this method to classify the larvae of Austrosimulium tillyardianum (Diptera: Simuliidae) based on five different measurements. Based on head capsule width and head capsule length, the larvae were separated into nine instars. Based on head capsule postoccipital width and mandible length, the larvae were separated into 8 instars and 10 instars, respectively. No reasonable solution was found for antennal segment 3 length. Separation of the larvae into nine instars using head capsule width or head capsule length was most robust and agreed with Crosby's growth rule. By strengthening the distributed character of the separation variable through the use of variable bandwidths, the adaptive kernel smoothing method could identify divisions between instars more effectively and accurately than previous methods.
Cen, Guanjun; Zeng, Xianru; Long, Xiuzhen; Wei, Dewei; Gao, Xuyuan; Zeng, Tao
2015-01-01
In insects, the frequency distribution of the measurements of sclerotized body parts is generally used to classify larval instars and is characterized by a multimodal overlap between instar stages. Nonparametric methods with fixed bandwidths, such as histograms, have significant limitations when used to fit this type of distribution, making it difficult to identify divisions between instars. Fixed bandwidths have also been chosen somewhat subjectively in the past, which is another problem. In this study, we describe an adaptive kernel smoothing method to differentiate instars based on discontinuities in the growth rates of sclerotized insect body parts. From Brooks’ rule, we derived a new standard for assessing the quality of instar classification and a bandwidth selector that more accurately reflects the distributed character of specific variables. We used this method to classify the larvae of Austrosimulium tillyardianum (Diptera: Simuliidae) based on five different measurements. Based on head capsule width and head capsule length, the larvae were separated into nine instars. Based on head capsule postoccipital width and mandible length, the larvae were separated into 8 instars and 10 instars, respectively. No reasonable solution was found for antennal segment 3 length. Separation of the larvae into nine instars using head capsule width or head capsule length was most robust and agreed with Crosby’s growth rule. By strengthening the distributed character of the separation variable through the use of variable bandwidths, the adaptive kernel smoothing method could identify divisions between instars more effectively and accurately than previous methods. PMID:26546689
A high-order fast method for computing convolution integral with smooth kernel
Qiang, Ji
2009-09-28
In this paper we report on a high-order fast method to numerically calculate convolution integral with smooth non-periodic kernel. This method is based on the Newton-Cotes quadrature rule for the integral approximation and an FFT method for discrete summation. The method can have an arbitrarily high-order accuracy in principle depending on the number of points used in the integral approximation and a computational cost of O(Nlog(N)), where N is the number of grid points. For a three-point Simpson rule approximation, the method has an accuracy of O(h{sup 4}), where h is the size of the computational grid. Applications of the Simpson rule based algorithm to the calculation of a one-dimensional continuous Gauss transform and to the calculation of a two-dimensional electric field from a charged beam are also presented.
Heat kernel smoothing using Laplace-Beltrami eigenfunctions.
Seo, Seongho; Chung, Moo K; Vorperian, Houri K
2010-01-01
We present a novel surface smoothing framework using the Laplace-Beltrami eigenfunctions. The Green's function of an isotropic diffusion equation on a manifold is constructed as a linear combination of the Laplace-Beltraimi operator. The Green's function is then used in constructing heat kernel smoothing. Unlike many previous approaches, diffusion is analytically represented as a series expansion avoiding numerical instability and inaccuracy issues. This proposed framework is illustrated with mandible surfaces, and is compared to a widely used iterative kernel smoothing technique in computational anatomy. The MATLAB source code is freely available at http://brainimaging.waisman.wisc.edu/ chung/lb.
Estimating Mixture of Gaussian Processes by Kernel Smoothing
Huang, Mian; Li, Runze; Wang, Hansheng; Yao, Weixin
2014-01-01
When the functional data are not homogeneous, e.g., there exist multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimation procedure for the Mixture of Gaussian Processes, to incorporate both functional and inhomogeneous properties of the data. Our method can be viewed as a natural extension of high-dimensional normal mixtures. However, the key difference is that smoothed structures are imposed for both the mean and covariance functions. The model is shown to be identifiable, and can be estimated efficiently by a combination of the ideas from EM algorithm, kernel regression, and functional principal component analysis. Our methodology is empirically justified by Monte Carlo simulations and illustrated by an analysis of a supermarket dataset. PMID:24976675
Estimating Mixture of Gaussian Processes by Kernel Smoothing.
Huang, Mian; Li, Runze; Wang, Hansheng; Yao, Weixin
2014-01-01
When the functional data are not homogeneous, e.g., there exist multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimation procedure for the Mixture of Gaussian Processes, to incorporate both functional and inhomogeneous properties of the data. Our method can be viewed as a natural extension of high-dimensional normal mixtures. However, the key difference is that smoothed structures are imposed for both the mean and covariance functions. The model is shown to be identifiable, and can be estimated efficiently by a combination of the ideas from EM algorithm, kernel regression, and functional principal component analysis. Our methodology is empirically justified by Monte Carlo simulations and illustrated by an analysis of a supermarket dataset.
Estimating Mixture of Gaussian Processes by Kernel Smoothing.
Huang, Mian; Li, Runze; Wang, Hansheng; Yao, Weixin
2014-01-01
When the functional data are not homogeneous, e.g., there exist multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimation procedure for the Mixture of Gaussian Processes, to incorporate both functional and inhomogeneous properties of the data. Our method can be viewed as a natural extension of high-dimensional normal mixtures. However, the key difference is that smoothed structures are imposed for both the mean and covariance functions. The model is shown to be identifiable, and can be estimated efficiently by a combination of the ideas from EM algorithm, kernel regression, and functional principal component analysis. Our methodology is empirically justified by Monte Carlo simulations and illustrated by an analysis of a supermarket dataset. PMID:24976675
Chung, Moo K.; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K.
2014-01-01
We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface. PMID:25791435
Smoothing Methods for Estimating Test Score Distributions.
ERIC Educational Resources Information Center
Kolen, Michael J.
1991-01-01
Estimation/smoothing methods that are flexible enough to fit a wide variety of test score distributions are reviewed: kernel method, strong true-score model-based method, and method that uses polynomial log-linear models. Applications of these methods include describing/comparing test score distributions, estimating norms, and estimating…
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels.
Jayasumana, Sadeep; Hartley, Richard; Salzmann, Mathieu; Li, Hongdong; Harandi, Mehrtash
2015-12-01
In this paper, we develop an approach to exploiting kernel methods with manifold-valued data. In many computer vision problems, the data can be naturally represented as points on a Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, usual Euclidean computer vision and machine learning algorithms yield inferior results on such data. In this paper, we define Gaussian radial basis function (RBF)-based positive definite kernels on manifolds that permit us to embed a given manifold with a corresponding metric in a high dimensional reproducing kernel Hilbert space. These kernels make it possible to utilize algorithms developed for linear spaces on nonlinear manifold-valued data. Since the Gaussian RBF defined with any given metric is not always positive definite, we present a unified framework for analyzing the positive definiteness of the Gaussian RBF on a generic metric space. We then use the proposed framework to identify positive definite kernels on two specific manifolds commonly encountered in computer vision: the Riemannian manifold of symmetric positive definite matrices and the Grassmann manifold, i.e., the Riemannian manifold of linear subspaces of a Euclidean space. We show that many popular algorithms designed for Euclidean spaces, such as support vector machines, discriminant analysis and principal component analysis can be generalized to Riemannian manifolds with the help of such positive definite Gaussian kernels. PMID:26539851
KERNEL-SMOOTHED CONDITIONAL QUANTILES OF CORRELATED BIVARIATE DISCRETE DATA
De Gooijer, Jan G.; Yuan, Ao
2012-01-01
Socio-economic variables are often measured on a discrete scale or rounded to protect confidentiality. Nevertheless, when exploring the effect of a relevant covariate on the outcome distribution of a discrete response variable, virtually all common quantile regression methods require the distribution of the covariate to be continuous. This paper departs from this basic requirement by presenting an algorithm for nonparametric estimation of conditional quantiles when both the response variable and the covariate are discrete. Moreover, we allow the variables of interest to be pairwise correlated. For computational efficiency, we aggregate the data into smaller subsets by a binning operation, and make inference on the resulting prebinned data. Specifically, we propose two kernel-based binned conditional quantile estimators, one for untransformed discrete response data and one for rank-transformed response data. We establish asymptotic properties of both estimators. A practical procedure for jointly selecting band- and binwidth parameters is also presented. Simulation results show excellent estimation accuracy in terms of bias, mean squared error, and confidence interval coverage. Typically prebinning the data leads to considerable computational savings when large datasets are under study, as compared to direct (un)conditional quantile kernel estimation of multivariate data. With this in mind, we illustrate the proposed methodology with an application to a large dataset concerning US hospital patients with congestive heart failure. PMID:23667297
Hill, Michael R H; Fried, Itzhak; Koch, Christof
2015-02-15
Peristimulus time histograms are a widespread form of visualizing neuronal responses. Kernel convolution methods transform these histograms into a smooth, continuous probability density function. This provides an improved estimate of a neuron's actual response envelope. We here develop a classifier, called the h-coefficient, to determine whether time-locked fluctuations in the firing rate of a neuron should be classified as a response or as random noise. Unlike previous approaches, the h-coefficient takes advantage of the more precise response envelope estimation provided by the kernel convolution method. The h-coefficient quantizes the smoothed response envelope and calculates the probability of a response of a given shape to occur by chance. We tested the efficacy of the h-coefficient in a large data set of Monte Carlo simulated smoothed peristimulus time histograms with varying response amplitudes, response durations, trial numbers, and baseline firing rates. Across all these conditions, the h-coefficient significantly outperformed more classical classifiers, with a mean false alarm rate of 0.004 and a mean hit rate of 0.494. We also tested the h-coefficient's performance in a set of neuronal responses recorded in humans. The algorithm behind the h-coefficient provides various opportunities for further adaptation and the flexibility to target specific parameters in a given data set. Our findings confirm that the h-coefficient can provide a conservative and powerful tool for the analysis of peristimulus time histograms with great potential for future development. PMID:25475352
Jiang, Fei; Ma, Yanyuan; Wang, Yuanjia
2015-01-01
We propose a generalized partially linear functional single index risk score model for repeatedly measured outcomes where the index itself is a function of time. We fuse the nonparametric kernel method and regression spline method, and modify the generalized estimating equation to facilitate estimation and inference. We use local smoothing kernel to estimate the unspecified coefficient functions of time, and use B-splines to estimate the unspecified function of the single index component. The covariance structure is taken into account via a working model, which provides valid estimation and inference procedure whether or not it captures the true covariance. The estimation method is applicable to both continuous and discrete outcomes. We derive large sample properties of the estimation procedure and show different convergence rate of each component of the model. The asymptotic properties when the kernel and regression spline methods are combined in a nested fashion has not been studied prior to this work even in the independent data case. PMID:26283801
NASA Astrophysics Data System (ADS)
García-Senz, Domingo; Cabezón, Rubén M.; Escartín, José A.; Ebinger, Kevin
2014-10-01
Context. The smoothed-particle hydrodynamics (SPH) technique is a numerical method for solving gas-dynamical problems. It has been applied to simulate the evolution of a wide variety of astrophysical systems. The method has a second-order accuracy, with a resolution that is usually much higher in the compressed regions than in the diluted zones of the fluid. Aims: We propose and check a method to balance and equalize the resolution of SPH between high- and low-density regions. This method relies on the versatility of a family of interpolators called sinc kernels, which allows increasing the interpolation quality by varying only a single parameter (the exponent of the sinc function). Methods: The proposed method was checked and validated through a number of numerical tests, from standard one-dimensional Riemann problems in shock tubes, to multidimensional simulations of explosions, hydrodynamic instabilities, and the collapse of a Sun-like polytrope. Results: The analysis of the hydrodynamical simulations suggests that the scheme devised to equalize the accuracy improves the treatment of the post-shock regions and, in general, of the rarefacted zones of fluids while causing no harm to the growth of hydrodynamic instabilities. The method is robust and easy to implement with a low computational overload. It conserves mass, energy, and momentum and reduces to the standard SPH scheme in regions of the fluid that have smooth density gradients.
PET Image Reconstruction Using Kernel Method
Wang, Guobao; Qi, Jinyi
2014-01-01
Image reconstruction from low-count PET projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization (EM) algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4D dynamic PET patient dataset showed promising results. PMID:25095249
NASA Astrophysics Data System (ADS)
Juan-Mian, Lei; Xue-Ying, Peng
2016-02-01
Kernel gradient free-smoothed particle hydrodynamics (KGF-SPH) is a modified smoothed particle hydrodynamics (SPH) method which has higher precision than the conventional SPH. However, the Laplacian in KGF-SPH is approximated by the two-pass model which increases computational cost. A new kind of discretization scheme for the Laplacian is proposed in this paper, then a method with higher precision and better stability, called Improved KGF-SPH, is developed by modifying KGF-SPH with this new Laplacian model. One-dimensional (1D) and two-dimensional (2D) heat conduction problems are used to test the precision and stability of the Improved KGF-SPH. The numerical results demonstrate that the Improved KGF-SPH is more accurate than SPH, and stabler than KGF-SPH. Natural convection in a closed square cavity at different Rayleigh numbers are modeled by the Improved KGF-SPH with shifting particle position, and the Improved KGF-SPH results are presented in comparison with those of SPH and finite volume method (FVM). The numerical results demonstrate that the Improved KGF-SPH is a more accurate method to study and model the heat transfer problems.
ERIC Educational Resources Information Center
Zheng, Yinggan; Gierl, Mark J.; Cui, Ying
2010-01-01
This study combined the kernel smoothing procedure and a nonparametric differential item functioning statistic--Cochran's Z--to statistically test the difference between the kernel-smoothed item response functions for reference and focal groups. Simulation studies were conducted to investigate the Type I error and power of the proposed…
Nonlinear projection trick in kernel methods: an alternative to the kernel trick.
Kwak, Nojun
2013-12-01
In kernel methods such as kernel principal component analysis (PCA) and support vector machines, the so called kernel trick is used to avoid direct calculations in a high (virtually infinite) dimensional kernel space. In this brief, based on the fact that the effective dimensionality of a kernel space is less than the number of training samples, we propose an alternative to the kernel trick that explicitly maps the input data into a reduced dimensional kernel space. This is easily obtained by the eigenvalue decomposition of the kernel matrix. The proposed method is named as the nonlinear projection trick in contrast to the kernel trick. With this technique, the applicability of the kernel methods is widened to arbitrary algorithms that do not use the dot product. The equivalence between the kernel trick and the nonlinear projection trick is shown for several conventional kernel methods. In addition, we extend PCA-L1, which uses L1-norm instead of L2-norm (or dot product), into a kernel version and show the effectiveness of the proposed approach.
Nonlinear projection trick in kernel methods: an alternative to the kernel trick.
Kwak, Nojun
2013-12-01
In kernel methods such as kernel principal component analysis (PCA) and support vector machines, the so called kernel trick is used to avoid direct calculations in a high (virtually infinite) dimensional kernel space. In this brief, based on the fact that the effective dimensionality of a kernel space is less than the number of training samples, we propose an alternative to the kernel trick that explicitly maps the input data into a reduced dimensional kernel space. This is easily obtained by the eigenvalue decomposition of the kernel matrix. The proposed method is named as the nonlinear projection trick in contrast to the kernel trick. With this technique, the applicability of the kernel methods is widened to arbitrary algorithms that do not use the dot product. The equivalence between the kernel trick and the nonlinear projection trick is shown for several conventional kernel methods. In addition, we extend PCA-L1, which uses L1-norm instead of L2-norm (or dot product), into a kernel version and show the effectiveness of the proposed approach. PMID:24805227
ERIC Educational Resources Information Center
Holland, Paul W.; Thayer, Dorothy T.
A new and unified approach to test equating is described that is based on log-linear models for smoothing score distributions and on the kernel method of nonparametric density estimation. The new method contains both linear and standard equipercentile methods as special cases and can handle several important equating data collection designs. An…
Kernel methods for phenotyping complex plant architecture.
Kawamura, Koji; Hibrand-Saint Oyant, Laurence; Foucher, Fabrice; Thouroude, Tatiana; Loustau, Sébastien
2014-02-01
The Quantitative Trait Loci (QTL) mapping of plant architecture is a critical step for understanding the genetic determinism of plant architecture. Previous studies adopted simple measurements, such as plant-height, stem-diameter and branching-intensity for QTL mapping of plant architecture. Many of these quantitative traits were generally correlated to each other, which give rise to statistical problem in the detection of QTL. We aim to test the applicability of kernel methods to phenotyping inflorescence architecture and its QTL mapping. We first test Kernel Principal Component Analysis (KPCA) and Support Vector Machines (SVM) over an artificial dataset of simulated inflorescences with different types of flower distribution, which is coded as a sequence of flower-number per node along a shoot. The ability of discriminating the different inflorescence types by SVM and KPCA is illustrated. We then apply the KPCA representation to the real dataset of rose inflorescence shoots (n=1460) obtained from a 98 F1 hybrid mapping population. We find kernel principal components with high heritability (>0.7), and the QTL analysis identifies a new QTL, which was not detected by a trait-by-trait analysis of simple architectural measurements. The main tools developed in this paper could be use to tackle the general problem of QTL mapping of complex (sequences, 3D structure, graphs) phenotypic traits.
NASA Astrophysics Data System (ADS)
Tian, Yuexin; Liu, Yinghui; Gao, Kun; Shu, Yuwen; Ni, Guoqiang
2014-11-01
A temporal-spatial filtering algorithm based on kernel density estimation structure is presented for background suppression in this paper. The algorithm can be divided into spatial filtering and temporal filtering. Smoothing process is applied to the background of an infrared image sequence by using the kernel density estimation algorithm in spatial filtering. The probability density of the image gray values after spatial filtering is calculated with the kernel density estimation algorithm in temporal filtering. The background residual and blind pixels are picked out based on their gray values, and are further filtered. The algorithm is validated with a real infrared image sequence. The image sequence is processed by using Fuller kernel filter, Uniform kernel filter and high-pass filter. Quantitatively analysis shows that the temporal-spatial filtering algorithm based on the nonparametric method is a satisfactory way to suppress background clutter in infrared images. The SNR is significantly improved as well.
Application of smoothed particle hydrodynamics method in aerodynamics
NASA Astrophysics Data System (ADS)
Cortina, Miguel
2014-11-01
Smoothed Particle Hydrodynamics (SPH) is a meshless Lagrangian method in which the domain is represented by particles. Each particle is assigned properties such as mass, pressure, density, temperature, and velocity. These properties are then evaluated at the particle positions using a smoothing kernel that integrates over the values of the surrounding particles. In the present study the SPH method is first used to obtain numerical solutions for fluid flows over a cylinder and then we are going to apply the same principle over an airfoil obstacle.
Anatomically-aided PET reconstruction using the kernel method
NASA Astrophysics Data System (ADS)
Hutchcroft, Will; Wang, Guobao; Chen, Kevin T.; Catana, Ciprian; Qi, Jinyi
2016-09-01
This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.
Comparison of Kernel Equating and Item Response Theory Equating Methods
ERIC Educational Resources Information Center
Meng, Yu
2012-01-01
The kernel method of test equating is a unified approach to test equating with some advantages over traditional equating methods. Therefore, it is important to evaluate in a comprehensive way the usefulness and appropriateness of the Kernel equating (KE) method, as well as its advantages and disadvantages compared with several popular item…
Introduction to Kernel Methods: Classification of Multivariate Data
NASA Astrophysics Data System (ADS)
Fauvel, M.
2016-05-01
In this chapter, kernel methods are presented for the classification of multivariate data. An introduction example is given to enlighten the main idea of kernel methods. Then emphasis is done on the Support Vector Machine. Structural risk minimization is presented, and linear and non-linear SVM are described. Finally, a full example of SVM classification is given on simulated hyperspectral data.
Protoribosome by quantum kernel energy method.
Huang, Lulu; Krupkin, Miri; Bashan, Anat; Yonath, Ada; Massa, Lou
2013-09-10
Experimental evidence suggests the existence of an RNA molecular prebiotic entity, called by us the "protoribosome," which may have evolved in the RNA world before evolution of the genetic code and proteins. This vestige of the RNA world, which possesses all of the capabilities required for peptide bond formation, seems to be still functioning in the heart of all of the contemporary ribosome. Within the modern ribosome this remnant includes the peptidyl transferase center. Its highly conserved nucleotide sequence is suggestive of its robustness under diverse environmental conditions, and hence on its prebiotic origin. Its twofold pseudosymmetry suggests that this entity could have been a dimer of self-folding RNA units that formed a pocket within which two activated amino acids might be accommodated, similar to the binding mode of modern tRNA molecules that carry amino acids or peptidyl moieties. Using quantum mechanics and crystal coordinates, this work studies the question of whether the putative protoribosome has properties necessary to function as an evolutionary precursor to the modern ribosome. The quantum model used in the calculations is density functional theory--B3LYP/3-21G*, implemented using the kernel energy method to make the computations practical and efficient. It occurs that the necessary conditions that would characterize a practicable protoribosome--namely (i) energetic structural stability and (ii) energetically stable attachment to substrates--are both well satisfied.
Spectrophotometric method for determination of phosphine residues in cashew kernels.
Rangaswamy, J R
1988-01-01
A spectrophotometric method reported for determination of phosphine (PH3) residues in wheat has been extended for determination of these residues in cashew kernels. Unlike the spectrum for wheat, the spectrum of PH3 residue-AgNO3 chromophore from cashew kernels does not show an absorption maximum at 400 nm; nevertheless, reading the absorbance at 400 nm afforded good recoveries of 90-98%. No interference occurred from crop materials, and crop controls showed low absorbance; the method can be applied for determinations as low as 0.01 ppm PH3 residue in cashew kernels.
Intelligent classification methods of grain kernels using computer vision analysis
NASA Astrophysics Data System (ADS)
Lee, Choon Young; Yan, Lei; Wang, Tianfeng; Lee, Sang Ryong; Park, Cheol Woo
2011-06-01
In this paper, a digital image analysis method was developed to classify seven kinds of individual grain kernels (common rice, glutinous rice, rough rice, brown rice, buckwheat, common barley and glutinous barley) widely planted in Korea. A total of 2800 color images of individual grain kernels were acquired as a data set. Seven color and ten morphological features were extracted and processed by linear discriminant analysis to improve the efficiency of the identification process. The output features from linear discriminant analysis were used as input to the four-layer back-propagation network to classify different grain kernel varieties. The data set was divided into three groups: 70% for training, 20% for validation, and 10% for testing the network. The classification experimental results show that the proposed method is able to classify the grain kernel varieties efficiently.
AKSmooth: enhancing low-coverage bisulfite sequencing data via kernel-based smoothing.
Chen, Junfang; Lutsik, Pavlo; Akulenko, Ruslan; Walter, Jörn; Helms, Volkhard
2014-12-01
Whole-genome bisulfite sequencing (WGBS) is an approach of growing importance. It is the only approach that provides a comprehensive picture of the genome-wide DNA methylation profile. However, obtaining a sufficient amount of genome and read coverage typically requires high sequencing costs. Bioinformatics tools can reduce this cost burden by improving the quality of sequencing data. We have developed a statistical method Ajusted Local Kernel Smoother (AKSmooth) that can accurately and efficiently reconstruct the single CpG methylation estimate across the entire methylome using low-coverage bisulfite sequencing (Bi-Seq) data. We demonstrate the AKSmooth performance on the low-coverage (~ 4 ×) DNA methylation profiles of three human colon cancer samples and matched controls. Under the best set of parameters, AKSmooth-curated data showed high concordance with the gold standard high-coverage sample (Pearson 0.90), outperforming the popular analogous method. In addition, AKSmooth showed computational efficiency with runtime benchmark over 4.5 times better than the reference tool. To summarize, AKSmooth is a simple and efficient tool that can provide an accurate human colon methylome estimation profile from low-coverage WGBS data. The proposed method is implemented in R and is available at https://github.com/Junfang/AKSmooth. PMID:25553811
Identification of nonlinear optical systems using adaptive kernel methods
NASA Astrophysics Data System (ADS)
Wang, Xiaodong; Zhang, Changjiang; Zhang, Haoran; Feng, Genliang; Xu, Xiuling
2005-12-01
An identification approach of nonlinear optical dynamic systems, based on adaptive kernel methods which are modified version of least squares support vector machine (LS-SVM), is presented in order to obtain the reference dynamic model for solving real time applications such as adaptive signal processing of the optical systems. The feasibility of this approach is demonstrated with the computer simulation through identifying a Bragg acoustic-optical bistable system. Unlike artificial neural networks, the adaptive kernel methods possess prominent advantages: over fitting is unlikely to occur by employing structural risk minimization criterion, the global optimal solution can be uniquely obtained owing to that its training is performed through the solution of a set of linear equations. Also, the adaptive kernel methods are still effective for the nonlinear optical systems with a variation of the system parameter. This method is robust with respect to noise, and it constitutes another powerful tool for the identification of nonlinear optical systems.
Kernel Methods for Mining Instance Data in Ontologies
NASA Astrophysics Data System (ADS)
Bloehdorn, Stephan; Sure, York
The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.
Multiple predictor smoothing methods for sensitivity analysis.
Helton, Jon Craig; Storlie, Curtis B.
2006-08-01
The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.
Method for producing smooth inner surfaces
Cooper, Charles A.
2016-05-17
The invention provides a method for preparing superconducting cavities, the method comprising causing polishing media to tumble by centrifugal barrel polishing within the cavities for a time sufficient to attain a surface smoothness of less than 15 nm root mean square roughness over approximately a 1 mm.sup.2 scan area. The method also provides for a method for preparing superconducting cavities, the method comprising causing polishing media bound to a carrier to tumble within the cavities. The method also provides for a method for preparing superconducting cavities, the method comprising causing polishing media in a slurry to tumble within the cavities.
An Extended Method of SIRMs Connected Fuzzy Inference Method Using Kernel Method
NASA Astrophysics Data System (ADS)
Seki, Hirosato; Mizuguchi, Fuhito; Watanabe, Satoshi; Ishii, Hiroaki; Mizumoto, Masaharu
The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional-type SIRMs method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs methods can not be applied to XOR (Exclusive OR). In this paper, we propose a “kernel-type SIRMs method” which uses the kernel trick to the SIRMs method, and show that this method can treat XOR. Further, a learning algorithm of the proposed SIRMs method is derived by using the steepest descent method, and compared with the one of conventional SIRMs method and kernel perceptron by applying to identification of nonlinear functions, medical diagnostic system and discriminant analysis of Iris data.
Reproducing kernel particle method for free and forced vibration analysis
NASA Astrophysics Data System (ADS)
Zhou, J. X.; Zhang, H. Y.; Zhang, L.
2005-01-01
A reproducing kernel particle method (RKPM) is presented to analyze the natural frequencies of Euler-Bernoulli beams as well as Kirchhoff plates. In addition, RKPM is also used to predict the forced vibration responses of buried pipelines due to longitudinal travelling waves. Two different approaches, Lagrange multipliers as well as transformation method , are employed to enforce essential boundary conditions. Based on the reproducing kernel approximation, the domain of interest is discretized by a set of particles without the employment of a structured mesh, which constitutes an advantage over the finite element method. Meanwhile, RKPM also exhibits advantages over the classical Rayleigh-Ritz method and its counterparts. Numerical results presented here demonstrate the effectiveness of this novel approach for both free and forced vibration analysis.
Smooth electrode and method of fabricating same
Weaver, Stanton Earl; Kennerly, Stacey Joy; Aimi, Marco Francesco
2012-08-14
A smooth electrode is provided. The smooth electrode includes at least one metal layer having thickness greater than about 1 micron; wherein an average surface roughness of the smooth electrode is less than about 10 nm.
A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine
Zhang, Xueying; Song, Qinbao
2015-01-01
Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896
ERIC Educational Resources Information Center
Ferrando, Pere J.
2004-01-01
This study used kernel-smoothing procedures to estimate the item characteristic functions (ICFs) of a set of continuous personality items. The nonparametric ICFs were compared with the ICFs estimated (a) by the linear model and (b) by Samejima's continuous-response model. The study was based on a conditioned approach and used an error-in-variables…
Kernel methods for large-scale genomic data analysis
Xing, Eric P.; Schaid, Daniel J.
2015-01-01
Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today’s explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion. PMID:25053743
Kernel weights optimization for error diffusion halftoning method
NASA Astrophysics Data System (ADS)
Fedoseev, Victor
2015-02-01
This paper describes a study to find the best error diffusion kernel for digital halftoning under various restrictions on the number of non-zero kernel coefficients and their set of values. As an objective measure of quality, WSNR was used. The problem of multidimensional optimization was solved numerically using several well-known algorithms: Nelder- Mead, BFGS, and others. The study found a kernel function that provides a quality gain of about 5% in comparison with the best of the commonly used kernel introduced by Floyd and Steinberg. Other kernels obtained allow to significantly reduce the computational complexity of the halftoning process without reducing its quality.
MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods
Schmidt, Johannes F. M.; Santelli, Claudio; Kozerke, Sebastian
2016-01-01
An approach to Magnetic Resonance (MR) image reconstruction from undersampled data is proposed. Undersampling artifacts are removed using an iterative thresholding algorithm applied to nonlinearly transformed image block arrays. Each block array is transformed using kernel principal component analysis where the contribution of each image block to the transform depends in a nonlinear fashion on the distance to other image blocks. Elimination of undersampling artifacts is achieved by conventional principal component analysis in the nonlinear transform domain, projection onto the main components and back-mapping into the image domain. Iterative image reconstruction is performed by interleaving the proposed undersampling artifact removal step and gradient updates enforcing consistency with acquired k-space data. The algorithm is evaluated using retrospectively undersampled MR cardiac cine data and compared to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT reconstruction. Evaluation of image quality and root-mean-squared-error (RMSE) reveal improved image reconstruction for up to 8-fold undersampled data with the proposed approach relative to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT. In conclusion, block matching and kernel methods can be used for effective removal of undersampling artifacts in MR image reconstruction and outperform methods using standard compressed sensing and ℓ1-regularized parallel imaging methods. PMID:27116675
Optimal Bandwidth Selection in Observed-Score Kernel Equating
ERIC Educational Resources Information Center
Häggström, Jenny; Wiberg, Marie
2014-01-01
The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent…
Linear and kernel methods for multi- and hypervariate change detection
NASA Astrophysics Data System (ADS)
Nielsen, Allan A.; Canty, Morton J.
2010-10-01
The iteratively re-weighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsuper- vised change detection in multi- and hyperspectral remote sensing imagery as well as for automatic radiometric normalization of multi- or hypervariate multitemporal image sequences. Principal component analysis (PCA) as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (which are nonlinear), may further enhance change signals relative to no-change background. The kernel versions are based on a dual formulation, also termed Q-mode analysis, in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution, also known as the kernel trick, these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of the kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component analysis (PCA), kernel MAF and kernel MNF analyses handle nonlinearities by implicitly transforming data into high (even innite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In image analysis the Gram matrix is often prohibitively large (its size is the number of pixels in the image squared). In this case we may sub-sample the image and carry out the kernel eigenvalue analysis on a set of training data samples only. To obtain a transformed version of the entire image we then project all pixels, which we call the test data, mapped nonlinearly onto the primal eigenvectors. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric normalization and kernel PCA/MAF/MNF transformations have been written
Simulating non-Newtonian flows with the moving particle semi-implicit method with an SPH kernel
NASA Astrophysics Data System (ADS)
Xiang, Hao; Chen, Bin
2015-02-01
The moving particle semi-implicit (MPS) method and smoothed particle hydrodynamics (SPH) are commonly used mesh-free particle methods for free surface flows. The MPS method has superiority in incompressible flow simulation and simple programing. However, the crude kernel function is not accurate enough for the discretization of the divergence of the shear stress tensor by the particle inconsistency when the MPS method is extended to non-Newtonian flows. This paper presents an improved MPS method with an SPH kernel to simulate non-Newtonian flows. To improve the consistency of the partial derivative, the SPH cubic spline kernel and the Taylor series expansion are combined with the MPS method. This approach is suitable for all non-Newtonian fluids that can be described with τ = μ(|γ|) Δ (where τ is the shear stress tensor, μ is the viscosity, |γ| is the shear rate, and Δ is the strain tensor), e.g., the Casson and Cross fluids. Two examples are simulated including the Newtonian Poiseuille flow and container filling process of the Cross fluid. The results of Poiseuille flow are more accurate than the traditional MPS method, and different filling processes are obtained with good agreement with previous results, which verified the validation of the new algorithm. For the Cross fluid, the jet fracture length can be correlated with We0.28Fr0.78 (We is the Weber number, Fr is the Froude number).
Huang, Jessie Y.; Howell, Rebecca M.; Mirkovic, Dragan; Followill, David S.; Kry, Stephen F.; Eklund, David; Childress, Nathan L.
2013-12-15
Purpose: Several simplifications used in clinical implementations of the convolution/superposition (C/S) method, specifically, density scaling of water kernels for heterogeneous media and use of a single polyenergetic kernel, lead to dose calculation inaccuracies. Although these weaknesses of the C/S method are known, it is not well known which of these simplifications has the largest effect on dose calculation accuracy in clinical situations. The purpose of this study was to generate and characterize high-resolution, polyenergetic, and material-specific energy deposition kernels (EDKs), as well as to investigate the dosimetric impact of implementing spatially variant polyenergetic and material-specific kernels in a collapsed cone C/S algorithm.Methods: High-resolution, monoenergetic water EDKs and various material-specific EDKs were simulated using the EGSnrc Monte Carlo code. Polyenergetic kernels, reflecting the primary spectrum of a clinical 6 MV photon beam at different locations in a water phantom, were calculated for different depths, field sizes, and off-axis distances. To investigate the dosimetric impact of implementing spatially variant polyenergetic kernels, depth dose curves in water were calculated using two different implementations of the collapsed cone C/S method. The first method uses a single polyenergetic kernel, while the second method fully takes into account spectral changes in the convolution calculation. To investigate the dosimetric impact of implementing material-specific kernels, depth dose curves were calculated for a simplified titanium implant geometry using both a traditional C/S implementation that performs density scaling of water kernels and a novel implementation using material-specific kernels.Results: For our high-resolution kernels, we found good agreement with the Mackie et al. kernels, with some differences near the interaction site for low photon energies (<500 keV). For our spatially variant polyenergetic kernels, we found
A Comparison of the Kernel Equating Method with Traditional Equating Methods Using SAT[R] Data
ERIC Educational Resources Information Center
Liu, Jinghua; Low, Albert C.
2008-01-01
This study applied kernel equating (KE) in two scenarios: equating to a very similar population and equating to a very different population, referred to as a distant population, using SAT[R] data. The KE results were compared to the results obtained from analogous traditional equating methods in both scenarios. The results indicate that KE results…
Soft and hard classification by reproducing kernel Hilbert space methods.
Wahba, Grace
2002-12-24
Reproducing kernel Hilbert space (RKHS) methods provide a unified context for solving a wide variety of statistical modelling and function estimation problems. We consider two such problems: We are given a training set [yi, ti, i = 1, em leader, n], where yi is the response for the ith subject, and ti is a vector of attributes for this subject. The value of y(i) is a label that indicates which category it came from. For the first problem, we wish to build a model from the training set that assigns to each t in an attribute domain of interest an estimate of the probability pj(t) that a (future) subject with attribute vector t is in category j. The second problem is in some sense less ambitious; it is to build a model that assigns to each t a label, which classifies a future subject with that t into one of the categories or possibly "none of the above." The approach to the first of these two problems discussed here is a special case of what is known as penalized likelihood estimation. The approach to the second problem is known as the support vector machine. We also note some alternate but closely related approaches to the second problem. These approaches are all obtained as solutions to optimization problems in RKHS. Many other problems, in particular the solution of ill-posed inverse problems, can be obtained as solutions to optimization problems in RKHS and are mentioned in passing. We caution the reader that although a large literature exists in all of these topics, in this inaugural article we are selectively highlighting work of the author, former students, and other collaborators.
LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions
Getz, Wayne M.; Fortmann-Roe, Scott; Wilmers, Christopher C.
2007-01-01
Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: “fixed sphere-of-influence,” or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an “adaptive sphere-of-influence,” or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original “fixed-number-of-points,” or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu). PMID:17299587
LoCoH: Non-parameteric kernel methods for constructing home ranges and utilization distributions
Getz, Wayne M.; Fortmann-Roe, Scott; Cross, Paul C.; Lyons, Andrew J.; Ryan, Sadie J.; Wilmers, Christopher C.
2007-01-01
Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: ‘‘fixed sphere-of-influence,’’ or r -LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an ‘‘adaptive sphere-of-influence,’’ or a -LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a ), and compare them to the original ‘‘fixed-number-of-points,’’ or k -LoCoH (all kernels constructed from k -1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a -LoCoH is generally superior to k - and r -LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).
LoCoH: nonparameteric kernel methods for constructing home ranges and utilization distributions.
Getz, Wayne M; Fortmann-Roe, Scott; Cross, Paul C; Lyons, Andrew J; Ryan, Sadie J; Wilmers, Christopher C
2007-02-14
Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: "fixed sphere-of-influence," or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an "adaptive sphere-of-influence," or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original "fixed-number-of-points," or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).
A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature
Tikk, Domonkos; Thomas, Philippe; Palaga, Peter; Hakenberg, Jörg; Leser, Ulf
2010-01-01
The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three
A Non-smooth Newton Method for Multibody Dynamics
Erleben, K.; Ortiz, R.
2008-09-01
In this paper we deal with the simulation of rigid bodies. Rigid body dynamics have become very important for simulating rigid body motion in interactive applications, such as computer games or virtual reality. We present a novel way of computing contact forces using a Newton method. The contact problem is reformulated as a system of non-linear and non-smooth equations, and we solve this system using a non-smooth version of Newton's method. One of the main contribution of this paper is the reformulation of the complementarity problems, used to model impacts, as a system of equations that can be solved using traditional methods.
A Simple Method for Solving the SVM Regularization Path for Semidefinite Kernels.
Sentelle, Christopher G; Anagnostopoulos, Georgios C; Georgiopoulos, Michael
2016-04-01
The support vector machine (SVM) remains a popular classifier for its excellent generalization performance and applicability of kernel methods; however, it still requires tuning of a regularization parameter, C , to achieve optimal performance. Regularization path-following algorithms efficiently solve the solution at all possible values of the regularization parameter relying on the fact that the SVM solution is piece-wise linear in C . The SVMPath originally introduced by Hastie et al., while representing a significant theoretical contribution, does not work with semidefinite kernels. Ong et al. introduce a method improved SVMPath (ISVMP) algorithm, which addresses the semidefinite kernel; however, Singular Value Decomposition or QR factorizations are required, and a linear programming solver is required to find the next C value at each iteration. We introduce a simple implementation of the path-following algorithm that automatically handles semidefinite kernels without requiring a method to detect singular matrices nor requiring specialized factorizations or an external solver. We provide theoretical results showing how this method resolves issues associated with the semidefinite kernel as well as discuss, in detail, the potential sources of degeneracy and cycling and how cycling is resolved. Moreover, we introduce an initialization method for unequal class sizes based upon artificial variables that work within the context of the existing path-following algorithm and do not require an external solver. Experiments compare performance with the ISVMP algorithm introduced by Ong et al. and show that the proposed method is competitive in terms of training time while also maintaining high accuracy. PMID:26011894
Community structure discovery method based on the Gaussian kernel similarity matrix
NASA Astrophysics Data System (ADS)
Guo, Chonghui; Zhao, Haipeng
2012-03-01
Community structure discovery in complex networks is a popular issue, and overlapping community structure discovery in academic research has become one of the hot spots. Based on the Gaussian kernel similarity matrix and spectral bisection, this paper proposes a new community structure discovery method. First, by adjusting the Gaussian kernel parameter to change the scale of similarity, we can find the corresponding non-overlapping community structure when the value of the modularity is the largest relatively. Second, the changes of the Gaussian kernel parameter would lead to the unstable nodes jumping off, so with a slight change in method of non-overlapping community discovery, we can find the overlapping community nodes. Finally, synthetic data, karate club and political books datasets are used to test the proposed method, comparing with some other community discovery methods, to demonstrate the feasibility and effectiveness of this method.
A Fast Multiple-Kernel Method with Applications to Detect Gene-Environment Interaction
Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M.; Worrall, Bradford B.; Williams, Stephen R.; Hsu, Fang-Chi; Tzeng, Jung-Ying
2015-01-01
Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. While most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multi-kernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multi-factor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the EM algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous “fastKM” algorithm for multi-kernel analysis that is based on a low-rank approximation to the nuisance-effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level. PMID:26139508
Likelihood Methods for Adaptive Filtering and Smoothing. Technical Report #455.
ERIC Educational Resources Information Center
Butler, Ronald W.
The dynamic linear model or Kalman filtering model provides a useful methodology for predicting the past, present, and future states of a dynamic system, such as an object in motion or an economic or social indicator that is changing systematically with time. Recursive likelihood methods for adaptive Kalman filtering and smoothing are developed.…
Early discriminant method of infected kernel based on the erosion effects of laser ultrasonics
NASA Astrophysics Data System (ADS)
Fan, Chao
2015-07-01
To discriminate the infected kernel of the wheat as early as possible, a new kind of detection method of hidden insects, especially in their egg and larvae stage, was put forward based on the erosion effect of the laser ultrasonic in this paper. The surface of the grain is exposured by the pulsed laser, the energy of which is absorbed and the ultrasonic is excited, and the infected kernel can be recognized by appropriate signal analyzing. Firstly, the detection principle was given based on the classical wave equation and the platform was established. Then, the detected ultrasonic signal was processed both in the time domain and the frequency domain by using FFT and DCT , and six significant features were selected as the characteristic parameters of the signal by the method of stepwise discriminant analysis. Finally, a BP neural network was designed by using these six parameters as the input to classify the infected kernels from the normal ones. Numerous experiments were performed by using twenty wheat varieties, the results shown that the the infected kernels can be recognized effectively, and the false negative error and the false positive error was 12% and 9% respectively, the discriminant method of the infected kernels based on the erosion effect of laser ultrasonics is feasible.
NASA Astrophysics Data System (ADS)
Moschetti, M. P.; Mueller, C. S.; Boyd, O. S.; Petersen, M. D.
2013-12-01
In anticipation of the update of the Alaska seismic hazard maps (ASHMs) by the U. S. Geological Survey, we report progress on the comparison of smoothed seismicity models developed using fixed and adaptive smoothing algorithms, and investigate the sensitivity of seismic hazard to the models. While fault-based sources, such as those for great earthquakes in the Alaska-Aleutian subduction zone and for the ~10 shallow crustal faults within Alaska, dominate the seismic hazard estimates for locations near to the sources, smoothed seismicity rates make important contributions to seismic hazard away from fault-based sources and where knowledge of recurrence and magnitude is not sufficient for use in hazard studies. Recent developments in adaptive smoothing methods and statistical tests for evaluating and comparing rate models prompt us to investigate the appropriateness of adaptive smoothing for the ASHMs. We develop smoothed seismicity models for Alaska using fixed and adaptive smoothing methods and compare the resulting models by calculating and evaluating the joint likelihood test. We use the earthquake catalog, and associated completeness levels, developed for the 2007 ASHM to produce fixed-bandwidth-smoothed models with smoothing distances varying from 10 to 100 km and adaptively smoothed models. Adaptive smoothing follows the method of Helmstetter et al. and defines a unique smoothing distance for each earthquake epicenter from the distance to the nth nearest neighbor. The consequence of the adaptive smoothing methods is to reduce smoothing distances, causing locally increased seismicity rates, where seismicity rates are high and to increase smoothing distances where seismicity is sparse. We follow guidance from previous studies to optimize the neighbor number (n-value) by comparing model likelihood values, which estimate the likelihood that the observed earthquake epicenters from the recent catalog are derived from the smoothed rate models. We compare likelihood
Moschetti, Morgan P.; Mueller, Charles S.; Boyd, Oliver S.; Petersen, Mark D.
2014-01-01
In anticipation of the update of the Alaska seismic hazard maps (ASHMs) by the U. S. Geological Survey, we report progress on the comparison of smoothed seismicity models developed using fixed and adaptive smoothing algorithms, and investigate the sensitivity of seismic hazard to the models. While fault-based sources, such as those for great earthquakes in the Alaska-Aleutian subduction zone and for the ~10 shallow crustal faults within Alaska, dominate the seismic hazard estimates for locations near to the sources, smoothed seismicity rates make important contributions to seismic hazard away from fault-based sources and where knowledge of recurrence and magnitude is not sufficient for use in hazard studies. Recent developments in adaptive smoothing methods and statistical tests for evaluating and comparing rate models prompt us to investigate the appropriateness of adaptive smoothing for the ASHMs. We develop smoothed seismicity models for Alaska using fixed and adaptive smoothing methods and compare the resulting models by calculating and evaluating the joint likelihood test. We use the earthquake catalog, and associated completeness levels, developed for the 2007 ASHM to produce fixed-bandwidth-smoothed models with smoothing distances varying from 10 to 100 km and adaptively smoothed models. Adaptive smoothing follows the method of Helmstetter et al. and defines a unique smoothing distance for each earthquake epicenter from the distance to the nth nearest neighbor. The consequence of the adaptive smoothing methods is to reduce smoothing distances, causing locally increased seismicity rates, where seismicity rates are high and to increase smoothing distances where seismicity is sparse. We follow guidance from previous studies to optimize the neighbor number (n-value) by comparing model likelihood values, which estimate the likelihood that the observed earthquake epicenters from the recent catalog are derived from the smoothed rate models. We compare likelihood
A Fourier-series-based kernel-independent fast multipole method
Zhang Bo; Huang Jingfang; Pitsianis, Nikos P.; Sun Xiaobai
2011-07-01
We present in this paper a new kernel-independent fast multipole method (FMM), named as FKI-FMM, for pairwise particle interactions with translation-invariant kernel functions. FKI-FMM creates, using numerical techniques, sufficiently accurate and compressive representations of a given kernel function over multi-scale interaction regions in the form of a truncated Fourier series. It provides also economic operators for the multipole-to-multipole, multipole-to-local, and local-to-local translations that are typical and essential in the FMM algorithms. The multipole-to-local translation operator, in particular, is readily diagonal and does not dominate in arithmetic operations. FKI-FMM provides an alternative and competitive option, among other kernel-independent FMM algorithms, for an efficient application of the FMM, especially for applications where the kernel function consists of multi-physics and multi-scale components as those arising in recent studies of biological systems. We present the complexity analysis and demonstrate with experimental results the FKI-FMM performance in accuracy and efficiency.
NASA Astrophysics Data System (ADS)
Yang, Chunwei; Yao, Junping; Sun, Dawei; Wang, Shicheng; Liu, Huaping
2016-05-01
Automatic target recognition in infrared imagery is a challenging problem. In this paper, a kernel sparse coding method for infrared target recognition using covariance descriptor is proposed. First, covariance descriptor combining gray intensity and gradient information of the infrared target is extracted as a feature representation. Then, due to the reason that covariance descriptor lies in non-Euclidean manifold, kernel sparse coding theory is used to solve this problem. We verify the efficacy of the proposed algorithm in terms of the confusion matrices on the real images consisting of seven categories of infrared vehicle targets.
Chemical method for producing smooth surfaces on silicon wafers
Yu, Conrad
2003-01-01
An improved method for producing optically smooth surfaces in silicon wafers during wet chemical etching involves a pre-treatment rinse of the wafers before etching and a post-etching rinse. The pre-treatment with an organic solvent provides a well-wetted surface that ensures uniform mass transfer during etching, which results in optically smooth surfaces. The post-etching treatment with an acetic acid solution stops the etching instantly, preventing any uneven etching that leads to surface roughness. This method can be used to etch silicon surfaces to a depth of 200 .mu.m or more, while the finished surfaces have a surface roughness of only 15-50 .ANG. (RMS).
Standard Errors of the Kernel Equating Methods under the Common-Item Design.
ERIC Educational Resources Information Center
Liou, Michelle; And Others
This research derives simplified formulas for computing the standard error of the frequency estimation method for equating score distributions that are continuized using a uniform or Gaussian kernel function (P. W. Holland, B. F. King, and D. T. Thayer, 1989; Holland and Thayer, 1987). The simplified formulas are applicable to equating both the…
ERIC Educational Resources Information Center
Wang, Tianyou
2008-01-01
Von Davier, Holland, and Thayer (2004) laid out a five-step framework of test equating that can be applied to various data collection designs and equating methods. In the continuization step, they presented an adjusted Gaussian kernel method that preserves the first two moments. This article proposes an alternative continuization method that…
Method for smoothing the surface of a protective coating
Sangeeta, D.; Johnson, Curtis Alan; Nelson, Warren Arthur
2001-01-01
A method for smoothing the surface of a ceramic-based protective coating which exhibits roughness is disclosed. The method includes the steps of applying a ceramic-based slurry or gel coating to the protective coating surface; heating the slurry/gel coating to remove volatile material; and then further heating the slurry/gel coating to cure the coating and bond it to the underlying protective coating. The slurry/gel coating is often based on yttria-stabilized zirconia, and precursors of an oxide matrix. Related articles of manufacture are also described.
A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.
Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying
2015-09-01
Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level. PMID:26139508
A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.
Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying
2015-09-01
Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level.
A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.
Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan
2015-01-01
The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526
The method of tailored sensitivity kernels for GRACE mass change estimates
NASA Astrophysics Data System (ADS)
Groh, Andreas; Horwath, Martin
2016-04-01
To infer mass changes (such as mass changes of an ice sheet) from time series of GRACE spherical harmonic solutions, two basic approaches (with many variants) exist: The regional integration approach (or direct approach) is based on surface mass changes (equivalent water height, EWH) from GRACE and integrates those with specific integration kernels. The forward modeling approach (or mascon approach, or inverse approach) prescribes a finite set of mass change patterns and adjusts the amplitudes of those patterns (in a least squares sense) to the GRACE gravity field changes. The present study reviews the theoretical framework of both approaches. We recall that forward modeling approaches ultimately estimate mass changes by linear functionals of the gravity field changes. Therefore, they implicitly apply sensitivity kernels and may be considered as special realizations of the regional integration approach. We show examples for sensitivity kernels intrinsic to forward modeling approaches. We then propose to directly tailor sensitivity kernels (or in other words: mass change estimators) by a formal optimization procedure that minimizes the sum of propagated GRACE solution errors and leakage errors. This approach involves the incorporation of information on the structure of GRACE errors and the structure of those mass change signals that are most relevant for leakage errors. We discuss the realization of this method, as applied within the ESA "Antarctic Ice Sheet CCI (Climate Change Initiative)" project. Finally, results for the Antarctic Ice Sheet in terms of time series of mass changes of individual drainage basins and time series of gridded EWH changes are presented.
A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System
Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan
2015-01-01
The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526
A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.
Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan
2015-01-01
The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.
Verification and large deformation analysis using the reproducing kernel particle method
Beckwith, Frank
2015-09-01
The reproducing kernel particle method (RKPM) is a meshless method used to solve general boundary value problems using the principle of virtual work. RKPM corrects the kernel approximation by introducing reproducing conditions which force the method to be complete to arbritrary order polynomials selected by the user. Effort in recent years has led to the implementation of RKPM within the Sierra/SM physics software framework. The purpose of this report is to investigate convergence of RKPM for verification and validation purposes as well as to demonstrate the large deformation capability of RKPM in problems where the finite element method is known to experience difficulty. Results from analyses using RKPM are compared against finite element analysis. A host of issues associated with RKPM are identified and a number of potential improvements are discussed for future work.
A kernel method for calculating effective radiative forcing in transient climate simulations
NASA Astrophysics Data System (ADS)
Larson, E. J. L.; Portmann, R. W.
2015-12-01
Effective radiative forcing (ERF) is calculated as the flux change at the top of the atmosphere, after allowing fast adjustments, due to a forcing agent such as greenhouse gasses or volcanic events. Accurate estimates of the ERF are necessary in order to understand the drivers of climate change. ERF cannot be observed directly and is difficult to estimate from indirect observations due to the complexity of climate responses to individual forcing factors. We present a new method of calculating ERF using a kernel populated from a time series of a model variable (e.g. global mean surface temperature) in a CO2 step change experiment. The top of atmosphere (TOA) radiative imbalance has the best noise tolerance for retrieving the ERF of the model variables we tested. We compare the kernel method with the energy balance method for estimating ERF in the CMIP5 models. The energy balance method uses the regression between the TOA imbalance and temperature change in a CO2 step change experiment to estimate the climate feedback parameter. It then assumes the feedback parameter is constant to calculate the forcing time series. This method is sensitive to the number of years chosen for the regression and the nonlinearity in the regression leads to a bias. We quantify the sensitivities and biases of these methods and compare their estimates of forcing. The kernel method is more accurate for models in which a linear fit is a poor approximation for the relationship between temperature change and TOA imbalance.
NASA Astrophysics Data System (ADS)
Wu, Linmei; Shen, Li; Li, Zhipeng
2016-06-01
A kernel-based method for very high spatial resolution remote sensing image classification is proposed in this article. The new kernel method is based on spectral-spatial information and structure information as well, which is acquired from topic model, Latent Dirichlet Allocation model. The final kernel function is defined as K = u1Kspec + u2Kspat + u3Kstru, in which Kspec, Kspat, Kstru are radial basis function (RBF) and u1 + u2 + u3 = 1. In the experiment, comparison with three other kernel methods, including the spectral-based, the spectral- and spatial-based and the spectral- and structure-based method, is provided for a panchromatic QuickBird image of a suburban area with a size of 900 × 900 pixels and spatial resolution of 0.6 m. The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80 %, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based which accuracy respectively is 67 % and 74 %. What's more, the accuracy of the proposed composite kernel method that jointly uses the spectral, spatial, and structure information is highest among the four methods which is increased to 83 %. On the other hand, the result of the experiment also verifies the validity of the expression of structure information about the remote sensing image.
Scalable Kernel Methods and Algorithms for General Sequence Analysis
ERIC Educational Resources Information Center
Kuksa, Pavel
2011-01-01
Analysis of large-scale sequential data has become an important task in machine learning and pattern recognition, inspired in part by numerous scientific and technological applications such as the document and text classification or the analysis of biological sequences. However, current computational methods for sequence comparison still lack…
Single corn kernel aflatoxin B1 extraction and analysis method
Technology Transfer Automated Retrieval System (TEKTRAN)
Aflatoxins are highly carcinogenic compounds produced by the fungus Aspergillus flavus. Aspergillus flavus is a phytopathogenic fungus that commonly infects crops such as cotton, peanuts, and maize. The goal was to design an effective sample preparation method and analysis for the extraction of afla...
Arima model and exponential smoothing method: A comparison
NASA Astrophysics Data System (ADS)
Wan Ahmad, Wan Kamarul Ariffin; Ahmad, Sabri
2013-04-01
This study shows the comparison between Autoregressive Moving Average (ARIMA) model and Exponential Smoothing Method in making a prediction. The comparison is focused on the ability of both methods in making the forecasts with the different number of data sources and the different length of forecasting period. For this purpose, the data from The Price of Crude Palm Oil (RM/tonne), Exchange Rates of Ringgit Malaysia (RM) in comparison to Great Britain Pound (GBP) and also The Price of SMR 20 Rubber Type (cents/kg) with three different time series are used in the comparison process. Then, forecasting accuracy of each model is measured by examinethe prediction error that producedby using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute deviation (MAD). The study shows that the ARIMA model can produce a better prediction for the long-term forecasting with limited data sources, butcannot produce a better prediction for time series with a narrow range of one point to another as in the time series for Exchange Rates. On the contrary, Exponential Smoothing Method can produce a better forecasting for Exchange Rates that has a narrow range of one point to another for its time series, while itcannot produce a better prediction for a longer forecasting period.
Effects of sample size on KERNEL home range estimates
Seaman, D.E.; Millspaugh, J.J.; Kernohan, Brian J.; Brundige, Gary C.; Raedeke, Kenneth J.; Gitzen, Robert A.
1999-01-01
Kernel methods for estimating home range are being used increasingly in wildlife research, but the effect of sample size on their accuracy is not known. We used computer simulations of 10-200 points/home range and compared accuracy of home range estimates produced by fixed and adaptive kernels with the reference (REF) and least-squares cross-validation (LSCV) methods for determining the amount of smoothing. Simulated home ranges varied from simple to complex shapes created by mixing bivariate normal distributions. We used the size of the 95% home range area and the relative mean squared error of the surface fit to assess the accuracy of the kernel home range estimates. For both measures, the bias and variance approached an asymptote at about 50 observations/home range. The fixed kernel with smoothing selected by LSCV provided the least-biased estimates of the 95% home range area. All kernel methods produced similar surface fit for most simulations, but the fixed kernel with LSCV had the lowest frequency and magnitude of very poor estimates. We reviewed 101 papers published in The Journal of Wildlife Management (JWM) between 1980 and 1997 that estimated animal home ranges. A minority of these papers used nonparametric utilization distribution (UD) estimators, and most did not adequately report sample sizes. We recommend that home range studies using kernel estimates use LSCV to determine the amount of smoothing, obtain a minimum of 30 observations per animal (but preferably a?Y50), and report sample sizes in published results.
NASA Astrophysics Data System (ADS)
Jiang, Mingfeng; Zhang, Heng; Zhu, Lingyan; Cao, Li; Wang, Yaming; Xia, Ling; Gong, Yinglan
2015-04-01
Non-invasively reconstructing the cardiac transmembrane potentials (TMPs) from body surface potentials can act as a regression problem. The support vector regression (SVR) method is often used to solve the regression problem, however the computational complexity of the SVR training algorithm is usually intensive. In this paper, another learning algorithm, termed as extreme learning machine (ELM), is proposed to reconstruct the cardiac transmembrane potentials. Moreover, ELM can be extended to single-hidden layer feed forward neural networks with kernel matrix (kernelized ELM), which can achieve a good generalization performance at a fast learning speed. Based on the realistic heart-torso models, a normal and two abnormal ventricular activation cases are applied for training and testing the regression model. The experimental results show that the ELM method can perform a better regression ability than the single SVR method in terms of the TMPs reconstruction accuracy and reconstruction speed. Moreover, compared with the ELM method, the kernelized ELM method features a good approximation and generalization ability when reconstructing the TMPs.
Using nonlinear kernels in seismic tomography: go beyond gradient methods
NASA Astrophysics Data System (ADS)
Wu, R.
2013-05-01
In quasi-linear inversion, a nonlinear problem is typically solved iteratively and at each step the nonlinear problem is linearized through the use of a linear functional derivative, the Fréchet derivative. Higher order terms generally are assumed to be insignificant and neglected. The linearization approach leads to the popular gradient method of seismic inversion. However, for the real Earth, the wave equation (and the real wave propagation) is strongly nonlinear with respect to the medium parameter perturbations. Therefore, the quasi-linear inversion may have a serious convergence problem for strong perturbations. In this presentation I will compare the convergence properties of the Taylor-Fréchet series and the renormalized Fréchet series, the De Wolf approximation, and illustrate the improved convergence property with numerical examples. I'll also discuss the application of nonlinear partial derivative to least-square waveform inversion. References: Bonnans, J., Gilbert, J., Lemarechal, C. and Sagastizabal, C., 2006, Numirical optmization, Springer. Wu, R.S. and Y. Zheng, 2012. Nonlinear Fréchet derivative and its De Wolf approximation, Expanded Abstracts of Society of Exploration Gephysicists, SI 8.1.
Jacquin, Laval; Cao, Tuong-Vi; Ahmadi, Nourollah
2016-01-01
One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named KRMM, which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel “trick” concept, exploited by kernel methods in the context of epistatic genetic architectures, over parametric frameworks used by conventional methods. Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding KRMM package and all scripts have been made publicly available. PMID:27555865
Jacquin, Laval; Cao, Tuong-Vi; Ahmadi, Nourollah
2016-01-01
One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named KRMM, which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel "trick" concept, exploited by kernel methods in the context of epistatic genetic architectures, over parametric frameworks used by conventional methods. Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding KRMM package and all scripts have been made publicly available. PMID:27555865
NASA Astrophysics Data System (ADS)
Huang, Fengzhen; Li, Jingzhen; Cao, Jun
2015-02-01
Temporally and Spatially Modulated Fourier Transform Imaging Spectrometer (TSMFTIS) is a new imaging spectrometer without moving mirrors and slits. As applied in remote sensing, TSMFTIS needs to rely on push-broom of the flying platform to obtain the interferogram of the target detected, and if the moving state of the flying platform changed during the imaging process, the target interferogram picked up from the remote sensing image sequence will deviate from the ideal interferogram, then the target spectrum recovered shall not reflect the real characteristic of the ground target object. Therefore, in order to achieve a high precision spectrum recovery of the target detected, the geometry position of the target point on the TSMFTIS image surface can be calculated in accordance with the sub-pixel image registration method, and the real point interferogram of the target can be obtained with image interpolation method. The core idea of the interpolation methods (nearest, bilinear and cubic etc) are to obtain the grey value of the point to be interpolated by weighting the grey value of the pixel around and with the kernel function constructed by the distance between the pixel around and the point to be interpolated. This paper adopts the gauss-based kernel regression mode, present a kernel function that consists of the grey information making use of the relative deviation and the distance information, then the kernel function is controlled by the deviation degree between the grey value of the pixel around and the means value so as to adjust weights self adaptively. The simulation adopts the partial spectrum data obtained by the pushbroom hyperspectral imager (PHI) as the spectrum of the target, obtains the successively push broomed motion error image in combination with the related parameter of the actual aviation platform; then obtains the interferogram of the target point with the above interpolation method; finally, recovers spectrogram with the nonuniform fast
The methods for the generation of smoothness in dental ceramics.
Yilmaz, Kerem; Ozkan, Pelin
2010-01-01
Occlusal corrections, acidulated phosphate fluoride applications, carbonated beverages, or air-powder abrasion procedures can roughen the surface of restorations. Once deteriorated, porcelain surfaces need to be repolished. These surfaces can be reglazed or polished through various intraoral and extraoral polishing kits. Although clinicians often seek studies on the effect of repeated firings on coherence between metal and ceramic, the color of the porcelain, fluorescence, microstructure, and brightness, only a limited number of studies are available in the literature. This article reviews the available literature and presents methods for generating a smooth surface and assessing surface roughness. It also discusses the differences between glazing and mechanical polishing, highlighting the importance of mechanical polishing of dental ceramics.
Wang, Gang; Zhang, Xiaofeng; Su, Qingtang; Shi, Jie; Caselli, Richard J; Wang, Yalin
2015-05-01
Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the gray matter geometry information from the in vivo brain magnetic resonance (MR) images. First, we construct a tetrahedral mesh that matches the MR images and reflects the inherent geometric characteristics. Second, the harmonic field is computed by the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cortical thickness information between the point on the pial and white matter surfaces. The new method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results on 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm may successfully detect statistically significant difference among patients of AD, MCI and healthy control subjects. Our computational framework is efficient and very general. It has the potential to be used for thickness estimation on any biological structures with clearly defined inner and outer surfaces.
Wang, Gang; Zhang, Xiaofeng; Su, Qingtang; Shi, Jie; Caselli, Richard J.; Wang, Yalin
2015-01-01
Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the grey matter geometry information from the in vivo brain magnetic resonance (MR) images. First, we construct a tetrahedral mesh that matches the MR images and reflects the inherent geometric characteristics. Second, the harmonic field is computed by the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cortical thickness information between the point on the pial and white matter surfaces. The new method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results on 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm may successfully detect statistically significant difference among patients of AD, MCI and healthy control subjects. Our computational framework is efficient and very general. It has the potential to be used for thickness estimation on any biological structures with clearly defined inner and outer surfaces. PMID:25700360
A Kernel-Free Particle-Finite Element Method for Hypervelocity Impact Simulation. Chapter 4
NASA Technical Reports Server (NTRS)
Park, Young-Keun; Fahrenthold, Eric P.
2004-01-01
An improved hybrid particle-finite element method has been developed for the simulation of hypervelocity impact problems. Unlike alternative methods, the revised formulation computes the density without reference to any kernel or interpolation functions, for either the density or the rate of dilatation. This simplifies the state space model and leads to a significant reduction in computational cost. The improved method introduces internal energy variables as generalized coordinates in a new formulation of the thermomechanical Lagrange equations. Example problems show good agreement with exact solutions in one dimension and good agreement with experimental data in a three dimensional simulation.
Kernel bandwidth estimation for nonparametric modeling.
Bors, Adrian G; Nasios, Nikolaos
2009-12-01
Kernel density estimation is a nonparametric procedure for probability density modeling, which has found several applications in various fields. The smoothness and modeling ability of the functional approximation are controlled by the kernel bandwidth. In this paper, we describe a Bayesian estimation method for finding the bandwidth from a given data set. The proposed bandwidth estimation method is applied in three different computational-intelligence methods that rely on kernel density estimation: 1) scale space; 2) mean shift; and 3) quantum clustering. The third method is a novel approach that relies on the principles of quantum mechanics. This method is based on the analogy between data samples and quantum particles and uses the SchrOdinger potential as a cost function. The proposed methodology is used for blind-source separation of modulated signals and for terrain segmentation based on topography information.
Validation tests of an improved kernel density estimation method for identifying disease clusters
Cai, Qiang; Rushton, Gerald; Bhaduri, Budhendra L
2011-01-01
The spatial filter method, which belongs to the class of kernel density estimation methods, has been used to make morbidity and mortality maps in several recent studies. We propose improvements in the method that include a spatial basis of support designed to give a constant standard error for the standardized mortality/morbidity rate; a stair-case weight method for weighting observations to reduce estimation bias; and a method for selecting parameters to control three measures of performance of the method: sensitivity, specificity and false discovery rate. We test the performance of the method using Monte Carlo simulations of hypothetical disease clusters over a test area of four counties in Iowa. The simulations include different types of spatial disease patterns and high resolution population distribution data. Results confirm that the new features of the spatial filter method do substantially improve its performance in realistic situations comparable to those where the method is likely to be used.
Validation tests of an improved kernel density estimation method for identifying disease clusters
NASA Astrophysics Data System (ADS)
Cai, Qiang; Rushton, Gerard; Bhaduri, Budhendra
2012-07-01
The spatial filter method, which belongs to the class of kernel density estimation methods, has been used to make morbidity and mortality maps in several recent studies. We propose improvements in the method to include spatially adaptive filters to achieve constant standard error of the relative risk estimates; a staircase weight method for weighting observations to reduce estimation bias; and a parameter selection tool to enhance disease cluster detection performance, measured by sensitivity, specificity, and false discovery rate. We test the performance of the method using Monte Carlo simulations of hypothetical disease clusters over a test area of four counties in Iowa. The simulations include different types of spatial disease patterns and high-resolution population distribution data. Results confirm that the new features of the spatial filter method do substantially improve its performance in realistic situations comparable to those where the method is likely to be used.
Trox, Jennifer; Vadivel, Vellingiri; Vetter, Walter; Stuetz, Wolfgang; Scherbaum, Veronika; Gola, Ute; Nohr, Donatus; Biesalski, Hans Konrad
2010-05-12
In the present study, the effects of various conventional shelling methods (oil-bath roasting, direct steam roasting, drying, and open pan roasting) as well as a novel "Flores" hand-cracking method on the levels of bioactive compounds of cashew nut kernels were investigated. The raw cashew nut kernels were found to possess appreciable levels of certain bioactive compounds such as beta-carotene (9.57 microg/100 g of DM), lutein (30.29 microg/100 g of DM), zeaxanthin (0.56 microg/100 g of DM), alpha-tocopherol (0.29 mg/100 g of DM), gamma-tocopherol (1.10 mg/100 g of DM), thiamin (1.08 mg/100 g of DM), stearic acid (4.96 g/100 g of DM), oleic acid (21.87 g/100 g of DM), and linoleic acid (5.55 g/100 g of DM). All of the conventional shelling methods including oil-bath roasting, steam roasting, drying, and open pan roasting revealed a significant reduction, whereas the Flores hand-cracking method exhibited similar levels of carotenoids, thiamin, and unsaturated fatty acids in cashew nuts when compared to raw unprocessed samples.
Weighted Wilcoxon-type Smoothly Clipped Absolute Deviation Method
Wang, Lan; Li, Runze
2009-01-01
Summary Shrinkage-type variable selection procedures have recently seen increasing applications in biomedical research. However, their performance can be adversely influenced by outliers in either the response or the covariate space. This paper proposes a weighted Wilcoxon-type smoothly clipped absolute deviation (WW-SCAD) method, which deals with robust variable selection and robust estimation simultaneously. The new procedure can be conveniently implemented with the statistical software R. We establish that the WW-SCAD correctly identifies the set of zero coefficients with probability approaching one and estimates the nonzero coefficients with the rate n−1/2. Moreover, with appropriately chosen weights the WW-SCAD is robust with respect to outliers in both the x and y directions. The important special case with constant weights yields an oracle-type estimator with high efficiency at the presence of heavier-tailed random errors. The robustness of the WW-SCAD is partly justified by its asymptotic performance under local shrinking contamination. We propose a BIC-type tuning parameter selector for the WW-SCAD. The performance of the WW-SCAD is demonstrated via simulations and by an application to a study that investigates the effects of personal characteristics and dietary factors on plasma beta-carotene level. PMID:18647294
A Distributed Learning Method for ℓ1-Regularized Kernel Machine over Wireless Sensor Networks
Ji, Xinrong; Hou, Cuiqin; Hou, Yibin; Gao, Fang; Wang, Shulong
2016-01-01
In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ1 norm regularization (ℓ1-regularized) is investigated, and a novel distributed learning algorithm for the ℓ1-regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost. PMID:27376298
A Distributed Learning Method for ℓ 1 -Regularized Kernel Machine over Wireless Sensor Networks.
Ji, Xinrong; Hou, Cuiqin; Hou, Yibin; Gao, Fang; Wang, Shulong
2016-07-01
In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ 1 norm regularization ( ℓ 1 -regularized) is investigated, and a novel distributed learning algorithm for the ℓ 1 -regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost.
A Distributed Learning Method for ℓ 1 -Regularized Kernel Machine over Wireless Sensor Networks.
Ji, Xinrong; Hou, Cuiqin; Hou, Yibin; Gao, Fang; Wang, Shulong
2016-01-01
In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ 1 norm regularization ( ℓ 1 -regularized) is investigated, and a novel distributed learning algorithm for the ℓ 1 -regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost. PMID:27376298
Approximate kernel competitive learning.
Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang
2015-03-01
Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches.
Approximate kernel competitive learning.
Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang
2015-03-01
Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. PMID:25528318
Multi-class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods.
Lodhi, Huma; Muggleton, Stephen; Sternberg, Mike J E
2010-09-17
Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an Inductive Logic Programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class Inductive Logic Programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms.
Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.
Zhang, Tingting; Kou, S C
2010-01-01
Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.
MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions.
Novosad, Philip; Reader, Andrew J
2016-06-21
Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [(18)F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel
MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions
NASA Astrophysics Data System (ADS)
Novosad, Philip; Reader, Andrew J.
2016-06-01
Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel
Methods and electrolytes for electrodeposition of smooth films
Zhang, Jiguang; Xu, Wu; Graff, Gordon L; Chen, Xilin; Ding, Fei; Shao, Yuyan
2015-03-17
Electrodeposition involving an electrolyte having a surface-smoothing additive can result in self-healing, instead of self-amplification, of initial protuberant tips that give rise to roughness and/or dendrite formation on the substrate and/or film surface. For electrodeposition of a first conductive material (C1) on a substrate from one or more reactants in an electrolyte solution, the electrolyte solution is characterized by a surface-smoothing additive containing cations of a second conductive material (C2), wherein cations of C2 have an effective electrochemical reduction potential in the solution lower than that of the reactants.
Multi-feature-based robust face detection and coarse alignment method via multiple kernel learning
NASA Astrophysics Data System (ADS)
Sun, Bo; Zhang, Di; He, Jun; Yu, Lejun; Wu, Xuewen
2015-10-01
Face detection and alignment are two crucial tasks to face recognition which is a hot topic in the field of defense and security, whatever for the safety of social public, personal property as well as information and communication security. Common approaches toward the treatment of these tasks in recent years are often of three types: template matching-based, knowledge-based and machine learning-based, which are always separate-step, high computation cost or fragile robust. After deep analysis on a great deal of Chinese face images without hats, we propose a novel face detection and coarse alignment method, which is inspired by those three types of methods. It is multi-feature fusion with Simple Multiple Kernel Learning1 (Simple-MKL) algorithm. The proposed method is contrasted with competitive and related algorithms, and demonstrated to achieve promising results.
Impact of beam smoothing method on direct drive target performance for the NIF
Rothenberg, J.E.; Weber, S.V.
1997-01-01
The impact of smoothing method on the performance of a direct drive target is modeled and examined in terms of its 1-mode spectrum. In particular, two classes of smoothing methods are compared, smoothing by spectral dispersion (SSD) and the induced spatial incoherence (ISI) method. It is found that SSD using sinusoidal phase modulation (FM) results in poor smoothing at low 1-modes and therefore inferior target performance at both peak velocity and ignition. This disparity is most notable if the effective imprinting integration time of the target is small. However, using SSD with more generalized phase modulation can result in smoothing at low l-modes which is identical to that obtained with ISI. For either smoothing method, the calculations indicate that at peak velocity the surface perturbations are about 100 times larger than that which leads to nonlinear hydrodynamics. Modeling of the hydrodynamic nonlinearity shows that saturation can reduce the amplified nonuniformities to the level required to achieve ignition for either smoothing method. The low l- mode behavior at ignition is found to be strongly dependent on the induced divergence of the smoothing method. For the NIF parameters the target performance asymptotes for smoothing divergence larger than {approximately}100 {mu}rad.
Suppression of stochastic pulsation in laser-plasma interaction by smoothing methods
NASA Astrophysics Data System (ADS)
Hora, Heinrich; Aydin, Meral
1992-04-01
The control of the very complex behavior of a plasma with laser interaction by smoothing with induced spatial incoherence or other methods was related to improving the lateral uniformity of the irradiation. While this is important, it is shown from numerical hydrodynamic studies that the very strong temporal pulsation (stuttering) will mostly be suppressed by these smoothing methods too.
Suppression of stochastic pulsation in laser-plasma interaction by smoothing methods
Hora, H. ); Aydin, M. )
1992-04-15
The control of the very complex behavior of a plasma with laser interaction by smoothing with induced spatial incoherence or other methods was related to improving the lateral uniformity of the irradiation. While this is important, it is shown from numerical hydrodynamic studies that the very strong temporal pulsation (stuttering) will mostly be suppressed by these smoothing methods too.
A new method of NIR face recognition using kernel projection DCV and neural networks
NASA Astrophysics Data System (ADS)
Qiao, Ya; Lu, Yuan; Feng, Yun-song; Li, Feng; Ling, Yongshun
2013-09-01
A new face recognition system was proposed, which used active near infrared imaging system (ANIRIS) as face images acquisition equipment, used kernel discriminative common vector (KDCV) as the feature extraction algorithm and used neural network as the recognition method. The ANIRIS was established by 40 NIR LEDs which used as active light source and a HWB800-IR-80 near infrared filter which used together with CCD camera to serve as the imaging detector. Its function of reducing the influence of varying illuminations to recognition rate was discussed. The KDCV feature extraction and neural network recognition parts were realized by Matlab programming. The experiments on HITSZ Lab2 face database and self-built face database show that the average recognition rate reached more than 95%, proving the effectiveness of proposed system.
ERIC Educational Resources Information Center
Grant, Mary C.; Zhang, Lilly; Damiano, Michele
2009-01-01
This study investigated kernel equating methods by comparing these methods to operational equatings for two tests in the SAT Subject Tests[TM] program. GENASYS (ETS, 2007) was used for all equating methods and scaled score kernel equating results were compared to Tucker, Levine observed score, chained linear, and chained equipercentile equating…
NASA Astrophysics Data System (ADS)
Li, Heng; Mohan, Radhe; Zhu, X. Ronald
2008-12-01
The clinical applications of kilovoltage x-ray cone-beam computed tomography (CBCT) have been compromised by the limited quality of CBCT images, which typically is due to a substantial scatter component in the projection data. In this paper, we describe an experimental method of deriving the scatter kernel of a CBCT imaging system. The estimated scatter kernel can be used to remove the scatter component from the CBCT projection images, thus improving the quality of the reconstructed image. The scattered radiation was approximated as depth-dependent, pencil-beam kernels, which were derived using an edge-spread function (ESF) method. The ESF geometry was achieved with a half-beam block created by a 3 mm thick lead sheet placed on a stack of slab solid-water phantoms. Measurements for ten water-equivalent thicknesses (WET) ranging from 0 cm to 41 cm were taken with (half-blocked) and without (unblocked) the lead sheet, and corresponding pencil-beam scatter kernels or point-spread functions (PSFs) were then derived without assuming any empirical trial function. The derived scatter kernels were verified with phantom studies. Scatter correction was then incorporated into the reconstruction process to improve image quality. For a 32 cm diameter cylinder phantom, the flatness of the reconstructed image was improved from 22% to 5%. When the method was applied to CBCT images for patients undergoing image-guided therapy of the pelvis and lung, the variation in selected regions of interest (ROIs) was reduced from >300 HU to <100 HU. We conclude that the scatter reduction technique utilizing the scatter kernel effectively suppresses the artifact caused by scatter in CBCT.
A kernel-based method for markerless tumor tracking in kV fluoroscopic images
NASA Astrophysics Data System (ADS)
Zhang, Xiaoyong; Homma, Noriyasu; Ichiji, Kei; Abe, Makoto; Sugita, Norihiro; Takai, Yoshihiro; Narita, Yuichiro; Yoshizawa, Makoto
2014-09-01
Markerless tracking of respiration-induced tumor motion in kilo-voltage (kV) fluoroscopic image sequence is still a challenging task in real time image-guided radiation therapy (IGRT). Most of existing markerless tracking methods are based on a template matching technique or its extensions that are frequently sensitive to non-rigid tumor deformation and involve expensive computation. This paper presents a kernel-based method that is capable of tracking tumor motion in kV fluoroscopic image sequence with robust performance and low computational cost. The proposed tracking system consists of the following three steps. To enhance the contrast of kV fluoroscopic image, we firstly utilize a histogram equalization to transform the intensities of original images to a wider dynamical intensity range. A tumor target in the first frame is then represented by using a histogram-based feature vector. Subsequently, the target tracking is then formulated by maximizing a Bhattacharyya coefficient that measures the similarity between the tumor target and its candidates in the subsequent frames. The numerical solution for maximizing the Bhattacharyya coefficient is performed by a mean-shift algorithm. The proposed method was evaluated by using four clinical kV fluoroscopic image sequences. For comparison, we also implement four conventional template matching-based methods and compare their performance with our proposed method in terms of the tracking accuracy and computational cost. Experimental results demonstrated that the proposed method is superior to conventional template matching-based methods.
A fast object-oriented Matlab implementation of the Reproducing Kernel Particle Method
NASA Astrophysics Data System (ADS)
Barbieri, Ettore; Meo, Michele
2012-05-01
Novel numerical methods, known as Meshless Methods or Meshfree Methods and, in a wider perspective, Partition of Unity Methods, promise to overcome most of disadvantages of the traditional finite element techniques. The absence of a mesh makes meshfree methods very attractive for those problems involving large deformations, moving boundaries and crack propagation. However, meshfree methods still have significant limitations that prevent their acceptance among researchers and engineers, namely the computational costs. This paper presents an in-depth analysis of computational techniques to speed-up the computation of the shape functions in the Reproducing Kernel Particle Method and Moving Least Squares, with particular focus on their bottlenecks, like the neighbour search, the inversion of the moment matrix and the assembly of the stiffness matrix. The paper presents numerous computational solutions aimed at a considerable reduction of the computational times: the use of kd-trees for the neighbour search, sparse indexing of the nodes-points connectivity and, most importantly, the explicit and vectorized inversion of the moment matrix without using loops and numerical routines.
Calculates Thermal Neutron Scattering Kernel.
1989-11-10
Version 00 THRUSH computes the thermal neutron scattering kernel by the phonon expansion method for both coherent and incoherent scattering processes. The calculation of the coherent part is suitable only for calculating the scattering kernel for heavy water.
Technology Transfer Automated Retrieval System (TEKTRAN)
INTRODUCTION Aromatic rice or fragrant rice, (Oryza sativa L.), has a strong popcorn-like aroma due to the presence of a five-membered N-heterocyclic ring compound known as 2-acetyl-1-pyrroline (2-AP). To date, existing methods for detecting this compound in rice require the use of several kernels. ...
An adaptive segment method for smoothing lidar signal based on noise estimation
NASA Astrophysics Data System (ADS)
Wang, Yuzhao; Luo, Pingping
2014-10-01
An adaptive segmentation smoothing method (ASSM) is introduced in the paper to smooth the signal and suppress the noise. In the ASSM, the noise is defined as the 3σ of the background signal. An integer number N is defined for finding the changing positions in the signal curve. If the difference of adjacent two points is greater than 3Nσ, the position is recorded as an end point of the smoothing segment. All the end points detected as above are recorded and the curves between them will be smoothed separately. In the traditional method, the end points of the smoothing windows in the signals are fixed. The ASSM creates changing end points in different signals and the smoothing windows could be set adaptively. The windows are always set as the half of the segmentations and then the average smoothing method will be applied in the segmentations. The Iterative process is required for reducing the end-point aberration effect in the average smoothing method and two or three times are enough. In ASSM, the signals are smoothed in the spacial area nor frequent area, that means the frequent disturbance will be avoided. A lidar echo was simulated in the experimental work. The echo was supposed to be created by a space-born lidar (e.g. CALIOP). And white Gaussian noise was added to the echo to act as the random noise resulted from environment and the detector. The novel method, ASSM, was applied to the noisy echo to filter the noise. In the test, N was set to 3 and the Iteration time is two. The results show that, the signal could be smoothed adaptively by the ASSM, but the N and the Iteration time might be optimized when the ASSM is applied in a different lidar.
Alternative methods to smooth the Earth's gravity field
NASA Technical Reports Server (NTRS)
Jekeli, C.
1981-01-01
Convolutions on the sphere with corresponding convolution theorems are developed for one and two dimensional functions. Some of these results are used in a study of isotropic smoothing operators or filters. Well known filters in Fourier spectral analysis, such as the rectangular, Gaussian, and Hanning filters, are adapted for data on a sphere. The low-pass filter most often used on gravity data is the rectangular (or Pellinen) filter. However, its spectrum has relatively large sidelobes; and therefore, this filter passes a considerable part of the upper end of the gravity spectrum. The spherical adaptations of the Gaussian and Hanning filters are more efficient in suppressing the high-frequency components of the gravity field since their frequency response functions are strongly field since their frequency response functions are strongly tapered at the high frequencies with no, or small, sidelobes. Formulas are given for practical implementation of these new filters.
Volcano clustering determination: Bivariate Gauss vs. Fisher kernels
NASA Astrophysics Data System (ADS)
Cañón-Tapia, Edgardo
2013-05-01
Underlying many studies of volcano clustering is the implicit assumption that vent distribution can be studied by using kernels originally devised for distribution in plane surfaces. Nevertheless, an important change in topology in the volcanic context is related to the distortion that is introduced when attempting to represent features found on the surface of a sphere that are being projected into a plane. This work explores the extent to which different topologies of the kernel used to study the spatial distribution of vents can introduce significant changes in the obtained density functions. To this end, a planar (Gauss) and a spherical (Fisher) kernels are mutually compared. The role of the smoothing factor in these two kernels is also explored with some detail. The results indicate that the topology of the kernel is not extremely influential, and that either type of kernel can be used to characterize a plane or a spherical distribution with exactly the same detail (provided that a suitable smoothing factor is selected in each case). It is also shown that there is a limitation on the resolution of the Fisher kernel relative to the typical separation between data that can be accurately described, because data sets with separations lower than 500 km are considered as a single cluster using this method. In contrast, the Gauss kernel can provide adequate resolutions for vent distributions at a wider range of separations. In addition, this study also shows that the numerical value of the smoothing factor (or bandwidth) of both the Gauss and Fisher kernels has no unique nor direct relationship with the relevant separation among data. In order to establish the relevant distance, it is necessary to take into consideration the value of the respective smoothing factor together with a level of statistical significance at which the contributions to the probability density function will be analyzed. Based on such reference level, it is possible to create a hierarchy of
Smoothing methods comparison for CMB E- and B-mode separation
NASA Astrophysics Data System (ADS)
Wang, Yi-Fan; Wang, Kai; Zhao, Wen
2016-04-01
The anisotropies of the B-mode polarization in the cosmic microwave background radiation play a crucial role in the study of the very early Universe. However, in real observations, a mixture of the E-mode and B-mode can be caused by partial sky surveys, which must be separated before being applied to a cosmological explanation. The separation method developed by Smith (2006) has been widely adopted, where the edge of the top-hat mask should be smoothed to avoid numerical errors. In this paper, we compare three different smoothing methods and investigate leakage residuals of the E-B mixture. We find that, if less information loss is needed and a smaller region is smoothed in the analysis, the sin- and cos-smoothing methods are better. However, if we need a cleanly constructed B-mode map, the larger region around the mask edge should be smoothed. In this case, the Gaussian-smoothing method becomes much better. In addition, we find that the leakage caused by numerical errors in the Gaussian-smoothing method is mostly concentrated in two bands, which is quite easy to reduce for further E-B separations.
A numerical study of the Regge calculus and smooth lattice methods on a Kasner cosmology
NASA Astrophysics Data System (ADS)
Brewin, Leo
2015-10-01
Two lattice based methods for numerical relativity, the Regge calculus and the smooth lattice relativity, will be compared with respect to accuracy and computational speed in a full 3+1 evolution of initial data representing a standard Kasner cosmology. It will be shown that both methods provide convergent approximations to the exact Kasner cosmology. It will also be shown that the Regge calculus is of the order of 110 times slower than the smooth lattice method.
Mizutani, Shohei; Takada, Yoshihisa; Kohno, Ryosuke; Hotta, Kenji; Tansho, Ryohei; Akimoto, Tetsuo
2016-01-01
Full Monte Carlo (FMC) calculation of dose distribution has been recognized to have superior accuracy, compared with the pencil beam algorithm (PBA). However, since the FMC methods require long calculation time, it is difficult to apply them to routine treatment planning at present. In order to improve the situation, a simplified Monte Carlo (SMC) method has been introduced to the dose kernel calculation applicable to dose optimization procedure for the proton pencil beam scanning. We have evaluated accuracy of the SMC calculation by comparing a result of the dose kernel calculation using the SMC method with that using the FMC method in an inhomogeneous phantom. The dose distribution obtained by the SMC method was in good agreement with that obtained by the FMC method. To assess the usefulness of SMC calculation in clinical situations, we have compared results of the dose calculation using the SMC with those using the PBA method for three clinical cases of tumor treatment. The dose distributions calculated with the PBA dose kernels appear to be homogeneous in the planning target volumes (PTVs). In practice, the dose distributions calculated with the SMC dose kernels with the spot weights optimized with the PBA method show largely inhomogeneous dose distributions in the PTVs, while those with the spot weights optimized with the SMC method have moderately homogeneous distributions in the PTVs. Calculation using the SMC method is faster than that using the GEANT4 by three orders of magnitude. In addition, the graphic processing unit (GPU) boosts the calculation speed by 13 times for the treatment planning using the SMC method. Thence, the SMC method will be applicable to routine clinical treatment planning for reproduction of the complex dose distribution more accurately than the PBA method in a reasonably short time by use of the GPU-based calculation engine. PMID:27074456
Effect of spatial smoothing on the performance of MUSIC and the minimum-norm method
NASA Astrophysics Data System (ADS)
Rao, B. D.; Hari, K. V. S.
1990-12-01
The effect of using a spatially smoothed forward-backward covariance matrix on the statistical performance of two eigendecomposition-based methods, MUSIC and the minimum-norm method, is analyzed. It is shown that, in general, for all subspace methods the forward-backward smoothing approach is preferable to the forward smoothing approach. The error in the signal zeros obtained using MUSIC is shown to follow a different trend compared to the error in the direction of arrival (DOA) estimates, leading to difficulty in interpreting the spatial spectrum. On the other hand, it is shown that for the minimum-norm method the errors in the signal zeros exhibit the same trend as the DOA estimates so that no such problem is created. It is also shown that proper spatial smoothing enables the performance of the minimum-norm method to be made comparable to that of MUSIC.
Lin, Wan-Yu; Yi, Nengjun; Lou, Xiang-Yang; Zhi, Degui; Zhang, Kui; Gao, Guimin; Tiwari, Hemant K.; Liu, Nianjun
2014-01-01
For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome-wide association studies is minor. Although the so-called ‘rare variants’ (minor allele frequency [MAF] < 1%) have attracted increasing attention, they are unlikely to account for much of the ‘missing heritability’ because very few people may carry these rare variants. The genetic variants that are likely to fill in the ‘missing heritability’ include uncommon causal variants (MAF < 5%), which are generally untyped in association studies using tagging single-nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome-wide or exome-wide next-generation sequencing. In this work, we propose a haplotype kernel association test (HKAT) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of HKAT to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, HKAT is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants. PMID:23740760
A spatial-temporal contextual Markovian kernel method for multi-temporal land cover mapping
NASA Astrophysics Data System (ADS)
Wehmann, Adam; Liu, Desheng
2015-09-01
Due to a lack of spatial-temporal consistency, the current generation of multi-temporal land cover products is subject to significant error propagation in change detection results. To address the evolving needs of land change science, the next generation of land cover products must be derived from new classification methods that are designed specifically for multi-temporal land cover mapping. In this paper, a next generation classifier is proposed that fully exploits contextual information by combining results born from the machine learning paradigm in remote sensing with domain knowledge from multi-temporal land cover mapping. This classifier, the Spatial-Temporal Markovian Support Vector Classifier, exhibits an entirely new level of accuracy of change detection when evaluated for the classification of seven Landsat images from an Appalachian Ohio study area. It exceeds previous leading techniques employing machine learning kernel methods and Markov Random Field models of image context on all accuracy metrics for the creation of a spatial-temporally consistent land cover product. It owes its performance to the greatly improved decision-making about contextual information afforded by the extension and integration of these previous techniques. With such a classifier, substantially more accurate and spatial-temporally consistent multi-temporal land cover products are possible that are suitable for the detailed study of land cover change.
Kernel regression image processing method for optical readout MEMS based uncooled IRFPA
NASA Astrophysics Data System (ADS)
Dong, Liquan; Liu, Xiaohua; Zhao, Yuejin; Hui, Mei; Zhou, Xiaoxiao
2009-11-01
Almost two years after the investors in Sarcon Microsystems pulled the plug, the micro-cantilever array based uncooled IR detector technology is again attracting more and more attention because of its low cost and high credibility. An uncooled thermal detector array with low NETD is designed and fabricated using MEMS bimaterial microcantilever structures that bend in response to thermal change. The IR images of objects obtained by these FPAs are readout by an optical method. For the IR images, one of the most problems of fixed pattern noise (FPN) is complicated by the fact that the response of each FPA detector changes due to a variety of factors. We adapt and expand kernel regression ideas for use in image denoising. The processed image quality is improved obviously. Great compute and analysis have been realized by using the discussed algorithm to the simulated data and in applications on real data. The experimental results demonstrate, better RMSE and highest Peak Signal-to- Noise Ratio (PSNR) compared with traditional methods can be obtained. At last we discuss the factors that determine the ultimate performance of the FPA. And we indicated that one of the unique advantages of the present approach is the scalability to larger imaging arrays.
NASA Astrophysics Data System (ADS)
Jiang, Li; Shi, Tielin; Xuan, Jianping
2012-05-01
Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.
ERIC Educational Resources Information Center
Choi, Sae Il
2009-01-01
This study used simulation (a) to compare the kernel equating method to traditional equipercentile equating methods under the equivalent-groups (EG) design and the nonequivalent-groups with anchor test (NEAT) design and (b) to apply the parametric bootstrap method for estimating standard errors of equating. A two-parameter logistic item response…
Numerical Convergence In Smoothed Particle Hydrodynamics
NASA Astrophysics Data System (ADS)
Zhu, Qirong; Hernquist, Lars; Li, Yuexing
2015-02-01
We study the convergence properties of smoothed particle hydrodynamics (SPH) using numerical tests and simple analytic considerations. Our analysis shows that formal numerical convergence is possible in SPH only in the joint limit N → ∞, h → 0, and Nnb → ∞, where N is the total number of particles, h is the smoothing length, and Nnb is the number of neighbor particles within the smoothing volume used to compute smoothed estimates. Previous work has generally assumed that the conditions N → ∞ and h → 0 are sufficient to achieve convergence, while holding Nnb fixed. We demonstrate that if Nnb is held fixed as the resolution is increased, there will be a residual source of error that does not vanish as N → ∞ and h → 0. Formal numerical convergence in SPH is possible only if Nnb is increased systematically as the resolution is improved. Using analytic arguments, we derive an optimal compromise scaling for Nnb by requiring that this source of error balance that present in the smoothing procedure. For typical choices of the smoothing kernel, we find Nnb vpropN 0.5. This means that if SPH is to be used as a numerically convergent method, the required computational cost does not scale with particle number as O(N), but rather as O(N 1 + δ), where δ ≈ 0.5, with a weak dependence on the form of the smoothing kernel.
New method for fast detection of railway track smoothness by fiber optic gyro
NASA Astrophysics Data System (ADS)
Wang, Lixin; Liang, Lei; Hu, Wenbin
2000-05-01
In this article, the conducting schemes for fiber optic gyro (FOG) used int he fast detecting of the smoothness of rail track has been proposed from the practical use point of view. The relevant approximate method of calculating has been given. The experiments in lab have been carried out, and the factors to influence the detecting precision of the smoothness of rail track such as the precision of FOG have been analyzed.
Study on preparation method of Zanthoxylum bungeanum seeds kernel oil with zero trans-fatty acids.
Liu, Tong; Yao, Shi-Yong; Yin, Zhong-Yi; Zheng, Xu-Xu; Shen, Yu
2016-04-01
The seed of Zanthoxylum bungeanum (Z. bungeanum) is a by-product of pepper production and rich in unsaturated fatty acid, cellulose, and protein. The seed oil obtained from traditional producing process by squeezing or extracting would be bad quality and could not be used as edible oil. In this paper, a new preparation method of Z. bungeanum seed kernel oil (ZSKO) was developed by comparing the advantages and disadvantages of alkali saponification-cold squeezing, alkali saponification-solvent extraction, and alkali saponification-supercritical fluid extraction with carbon dioxide (SFE-CO2). The results showed that the alkali saponification-cold squeezing could be the optimal preparation method of ZSKO, which contained the following steps: Z. bungeanum seed was pretreated by alkali saponification under the conditions of adding 10 %NaOH (w/w), solution temperature was 80 °C, and saponification reaction time was 45 min, and pretreated seed was separated by filtering, water washing, and overnight drying at 50 °C, then repeated squeezing was taken until no oil generated at 60 °C with 15 % moisture content, and ZSKO was attained finally using centrifuge. The produced ZSKO contained more than 90 % unsaturated fatty acids and no trans-fatty acids and be testified as a good edible oil with low-value level of acid and peroxide. It was demonstrated that the alkali saponification-cold squeezing process could be scaled up and applied to industrialized production of ZSKO.
Smooth connection method of segment test data in road surface profile measurement
NASA Astrophysics Data System (ADS)
Duan, Hu-Ming; Ma, Ying; Shi, Feng; Zhang, Kai-Bin; Xie, Fei
2012-01-01
It's reviewed that the measurement system of road surface profile and the calculation method of segment road test data have been introduced. Because of there are sudden vertical steps at the connection points of segment data which will influence the application of road surface data in automotive engineering. So a new smooth connection method of segment test data is proposed which revised the sudden vertical steps connection by the Signal Local Baseline Adjustment (SLBA) method. Besides, there is an actual example which mentioned the detailed process of the smooth connection of segment test data by the SLBA method and the adjusting results at these connection points. The application and calculation results show that the SLBA method is simple and has achieved obvious effect in smooth connection of the segment road test data. The method of SLBA can be widely applied to segment road surface data processing or the long period vibration signal processing.
Smooth connection method of segment test data in road surface profile measurement
NASA Astrophysics Data System (ADS)
Duan, Hu-Ming; Ma, Ying; Shi, Feng; Zhang, Kai-Bin; Xie, Fei
2011-12-01
It's reviewed that the measurement system of road surface profile and the calculation method of segment road test data have been introduced. Because of there are sudden vertical steps at the connection points of segment data which will influence the application of road surface data in automotive engineering. So a new smooth connection method of segment test data is proposed which revised the sudden vertical steps connection by the Signal Local Baseline Adjustment (SLBA) method. Besides, there is an actual example which mentioned the detailed process of the smooth connection of segment test data by the SLBA method and the adjusting results at these connection points. The application and calculation results show that the SLBA method is simple and has achieved obvious effect in smooth connection of the segment road test data. The method of SLBA can be widely applied to segment road surface data processing or the long period vibration signal processing.
NASA Technical Reports Server (NTRS)
Lan, C. E.; Lamar, J. E.
1977-01-01
A logarithmic-singularity correction factor is derived for use in kernel function methods associated with Multhopp's subsonic lifting-surface theory. Because of the form of the factor, a relation was formulated between the numbers of chordwise and spanwise control points needed for good accuracy. This formulation is developed and discussed. Numerical results are given to show the improvement of the computation with the new correction factor.
Melnikov Method for a Class of Planar Hybrid Piecewise-Smooth Systems
NASA Astrophysics Data System (ADS)
Li, Shuangbao; Ma, Wensai; Zhang, Wei; Hao, Yuxin
In this paper, we extend the well-known Melnikov method for smooth systems to a class of periodic perturbed planar hybrid piecewise-smooth systems. In this class, the switching manifold is a straight line which divides the plane into two zones, and the dynamics in each zone is governed by a smooth system. When a trajectory reaches the separation line, then a reset map is applied instantaneously before entering the trajectory in the other zone. We assume that the unperturbed system is a piecewise Hamiltonian system which possesses a piecewise-smooth homoclinic solution transversally crossing the switching manifold. Then, we study the persistence of the homoclinic orbit under a nonautonomous periodic perturbation and the reset map. To achieve this objective, we obtain the Melnikov function to measure the distance of the perturbed stable and unstable manifolds and present the theorem for homoclinic bifurcations for the class of planar hybrid piecewise-smooth systems. Furthermore, we employ the obtained Melnikov function to detect the chaotic boundaries for a concrete planar hybrid piecewise-smooth system.
NASA Technical Reports Server (NTRS)
Desmarais, R. N.
1982-01-01
The method is capable of generating approximations of arbitrary accuracy. It is based on approximating the algebraic part of the nonelementary integrals in the kernel by exponential functions and then integrating termwise. The exponent spacing in the approximation is a geometric sequence. The coefficients and exponent multiplier of the exponential approximation are computed by least squares so the method is completely automated. Exponential approximates generated in this manner are two orders of magnitude more accurate than the exponential approximation that is currently most often used for this purpose. The method can be used to generate approximations to attain any desired trade-off between accuracy and computing cost.
MC Kernel: Broadband Waveform Sensitivity Kernels for Seismic Tomography
NASA Astrophysics Data System (ADS)
Stähler, Simon C.; van Driel, Martin; Auer, Ludwig; Hosseini, Kasra; Sigloch, Karin; Nissen-Meyer, Tarje
2016-04-01
We present MC Kernel, a software implementation to calculate seismic sensitivity kernels on arbitrary tetrahedral or hexahedral grids across the whole observable seismic frequency band. Seismic sensitivity kernels are the basis for seismic tomography, since they map measurements to model perturbations. Their calculation over the whole frequency range was so far only possible with approximative methods (Dahlen et al. 2000). Fully numerical methods were restricted to the lower frequency range (usually below 0.05 Hz, Tromp et al. 2005). With our implementation, it's possible to compute accurate sensitivity kernels for global tomography across the observable seismic frequency band. These kernels rely on wavefield databases computed via AxiSEM (www.axisem.info), and thus on spherically symmetric models. The advantage is that frequencies up to 0.2 Hz and higher can be accessed. Since the usage of irregular, adapted grids is an integral part of regularisation in seismic tomography, MC Kernel works in a inversion-grid-centred fashion: A Monte-Carlo integration method is used to project the kernel onto each basis function, which allows to control the desired precision of the kernel estimation. Also, it means that the code concentrates calculation effort on regions of interest without prior assumptions on the kernel shape. The code makes extensive use of redundancies in calculating kernels for different receivers or frequency-pass-bands for one earthquake, to facilitate its usage in large-scale global seismic tomography.
A relaxation method for minimizing a smooth function on a generalized spherical segment
NASA Astrophysics Data System (ADS)
Dulliev, A. M.
2014-02-01
The minimization of a smooth functional on a generalized spherical segment of a finite-dimensional Euclidean space is examined. A relaxation method that involves successive projections of the antigradient onto auxiliary sets of a simpler structure is proposed. It is shown that, under certain natural assumptions, this method converges to a stationary point.
Linearized Kernel Dictionary Learning
NASA Astrophysics Data System (ADS)
Golts, Alona; Elad, Michael
2016-06-01
In this paper we present a new approach of incorporating kernels into dictionary learning. The kernel K-SVD algorithm (KKSVD), which has been introduced recently, shows an improvement in classification performance, with relation to its linear counterpart K-SVD. However, this algorithm requires the storage and handling of a very large kernel matrix, which leads to high computational cost, while also limiting its use to setups with small number of training examples. We address these problems by combining two ideas: first we approximate the kernel matrix using a cleverly sampled subset of its columns using the Nystr\\"{o}m method; secondly, as we wish to avoid using this matrix altogether, we decompose it by SVD to form new "virtual samples," on which any linear dictionary learning can be employed. Our method, termed "Linearized Kernel Dictionary Learning" (LKDL) can be seamlessly applied as a pre-processing stage on top of any efficient off-the-shelf dictionary learning scheme, effectively "kernelizing" it. We demonstrate the effectiveness of our method on several tasks of both supervised and unsupervised classification and show the efficiency of the proposed scheme, its easy integration and performance boosting properties.
A simple method for computing the relativistic Compton scattering kernel for radiative transfer
NASA Technical Reports Server (NTRS)
Prasad, M. K.; Kershaw, D. S.; Beason, J. D.
1986-01-01
Correct computation of the Compton scattering kernel (CSK), defined to be the Klein-Nishina differential cross section averaged over a relativistic Maxwellian electron distribution, is reported. The CSK is analytically reduced to a single integral, which can then be rapidly evaluated using a power series expansion, asymptotic series, and rational approximation for sigma(s). The CSK calculation has application to production codes that aim at understanding certain astrophysical, laser fusion, and nuclear weapons effects phenomena.
A Meshfree Cell-based Smoothed Point Interpolation Method for Solid Mechanics Problems
Zhang Guiyong; Liu Guirong
2010-05-21
In the framework of a weakened weak (W{sup 2}) formulation using a generalized gradient smoothing operation, this paper introduces a novel meshfree cell-based smoothed point interpolation method (CS-PIM) for solid mechanics problems. The W{sup 2} formulation seeks solutions from a normed G space which includes both continuous and discontinuous functions and allows the use of much more types of methods to create shape functions for numerical methods. When PIM shape functions are used, the functions constructed are in general not continuous over the entire problem domain and hence are not compatible. Such an interpolation is not in a traditional H{sup 1} space, but in a G{sup 1} space. By introducing the generalized gradient smoothing operation properly, the requirement on function is now further weakened upon the already weakened requirement for functions in a H{sup 1} space and G{sup 1} space can be viewed as a space of functions with weakened weak (W{sup 2}) requirement on continuity. The cell-based smoothed point interpolation method (CS-PIM) is formulated based on the W{sup 2} formulation, in which displacement field is approximated using the PIM shape functions, which possess the Kronecker delta property facilitating the enforcement of essential boundary conditions [3]. The gradient (strain) field is constructed by the generalized gradient smoothing operation within the cell-based smoothing domains, which are exactly the triangular background cells. A W{sup 2} formulation of generalized smoothed Galerkin (GS-Galerkin) weak form is used to derive the discretized system equations. It was found that the CS-PIM possesses the following attractive properties: (1) It is very easy to implement and works well with the simplest linear triangular mesh without introducing additional degrees of freedom; (2) it is at least linearly conforming; (3) this method is temporally stable and works well for dynamic analysis; (4) it possesses a close-to-exact stiffness, which is much
Bayesian Kernel Mixtures for Counts
Canale, Antonio; Dunson, David B.
2011-01-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online. PMID:22523437
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online. PMID:22523437
NASA Astrophysics Data System (ADS)
Erhard, Jannis; Bleiziffer, Patrick; Görling, Andreas
2016-09-01
A power series approximation for the correlation kernel of time-dependent density-functional theory is presented. Using this approximation in the adiabatic-connection fluctuation-dissipation (ACFD) theorem leads to a new family of Kohn-Sham methods. The new methods yield reaction energies and barriers of unprecedented accuracy and enable a treatment of static (strong) correlation with an accuracy of high-level multireference configuration interaction methods but are single-reference methods allowing for a black-box-like handling of static correlation. The new methods exhibit a better scaling of the computational effort with the system size than rivaling wave-function-based electronic structure methods. Moreover, the new methods do not suffer from the problem of singularities in response functions plaguing previous ACFD methods and therefore are applicable to any type of electronic system.
A Monte Carlo method for variance estimation for estimators based on induced smoothing
Jin, Zhezhen; Shao, Yongzhao; Ying, Zhiliang
2015-01-01
An important issue in statistical inference for semiparametric models is how to provide reliable and consistent variance estimation. Brown and Wang (2005. Standard errors and covariance matrices for smoothed rank estimators. Biometrika 92, 732–746) proposed a variance estimation procedure based on an induced smoothing for non-smooth estimating functions. Herein a Monte Carlo version is developed that does not require any explicit form for the estimating function itself, as long as numerical evaluation can be carried out. A general convergence theory is established, showing that any one-step iteration leads to a consistent variance estimator and continuation of the iterations converges at an exponential rate. The method is demonstrated through the Buckley–James estimator and the weighted log-rank estimators for censored linear regression, and rank estimation for multiple event times data. PMID:24812418
On the accuracy of analytical methods for turbulent flows near smooth walls
NASA Astrophysics Data System (ADS)
Absi, Rafik; Di Nucci, Carmine
2012-09-01
This Note presents two methods for mean streamwise velocity profiles of fully-developed turbulent pipe and channel flows near smooth walls. The first is the classical approach where the mean streamwise velocity is obtained by solving the momentum equation with an eddy viscosity formulation [R. Absi, A simple eddy viscosity formulation for turbulent boundary layers near smooth walls, C. R. Mecanique 337 (2009) 158-165]. The second approach presents a formulation of the velocity profile based on an analogy with an electric field distribution [C. Di Nucci, E. Fiorucci, Mean velocity profiles of fully-developed turbulent flows near smooth walls, C. R. Mecanique 339 (2011) 388-395] and a formulation for the turbulent shear stress. However, this formulation for the turbulent shear stress shows a weakness. A corrected formulation is presented. Comparisons with DNS data show that the classical approach with the eddy viscosity formulation provides more accurate profiles for both turbulent shear stress and velocity gradient.
Yan, Qi; Weeks, Daniel E; Celedón, Juan C; Tiwari, Hemant K; Li, Bingshan; Wang, Xiaojing; Lin, Wan-Yu; Lou, Xiang-Yang; Gao, Guimin; Chen, Wei; Liu, Nianjun
2015-12-01
The recent development of sequencing technology allows identification of association between the whole spectrum of genetic variants and complex diseases. Over the past few years, a number of association tests for rare variants have been developed. Jointly testing for association between genetic variants and multiple correlated phenotypes may increase the power to detect causal genes in family-based studies, but familial correlation needs to be appropriately handled to avoid an inflated type I error rate. Here we propose a novel approach for multivariate family data using kernel machine regression (denoted as MF-KM) that is based on a linear mixed-model framework and can be applied to a large range of studies with different types of traits. In our simulation studies, the usual kernel machine test has inflated type I error rates when applied directly to familial data, while our proposed MF-KM method preserves the expected type I error rates. Moreover, the MF-KM method has increased power compared to methods that either analyze each phenotype separately while considering family structure or use only unrelated founders from the families. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.
NASA Astrophysics Data System (ADS)
Danilewicz, Andrzej; Sikora, Zbigniew
2015-02-01
A theoretical base of SPH method, including the governing equations, discussion of importance of the smoothing function length, contact formulation, boundary treatment and finally utilization in hydrocode simulations are presented. An application of SPH to a real case of large penetrations (crater creating) into the soil caused by falling mass in Dynamic Replacement Method is discussed. An influence of particles spacing on method accuracy is presented. An example calculated by LS-DYNA software is discussed. Chronological development of Smooth Particle Hydrodynamics is presented. Theoretical basics of SPH method stability and consistency in SPH formulation, artificial viscosity and boundary treatment are discussed. Time integration techniques with stability conditions, SPH+FEM coupling, constitutive equation and equation of state (EOS) are presented as well.
Local Observed-Score Kernel Equating
ERIC Educational Resources Information Center
Wiberg, Marie; van der Linden, Wim J.; von Davier, Alina A.
2014-01-01
Three local observed-score kernel equating methods that integrate methods from the local equating and kernel equating frameworks are proposed. The new methods were compared with their earlier counterparts with respect to such measures as bias--as defined by Lord's criterion of equity--and percent relative error. The local kernel item response…
A method for smoothing segmented lung boundary in chest CT images
NASA Astrophysics Data System (ADS)
Yim, Yeny; Hong, Helen
2007-03-01
To segment low density lung regions in chest CT images, most of methods use the difference in gray-level value of pixels. However, radiodense pulmonary vessels and pleural nodules that contact with the surrounding anatomy are often excluded from the segmentation result. To smooth lung boundary segmented by gray-level processing in chest CT images, we propose a new method using scan line search. Our method consists of three main steps. First, lung boundary is extracted by our automatic segmentation method. Second, segmented lung contour is smoothed in each axial CT slice. We propose a scan line search to track the points on lung contour and find rapidly changing curvature efficiently. Finally, to provide consistent appearance between lung contours in adjacent axial slices, 2D closing in coronal plane is applied within pre-defined subvolume. Our method has been applied for performance evaluation with the aspects of visual inspection, accuracy and processing time. The results of our method show that the smoothness of lung contour was considerably increased by compensating for pulmonary vessels and pleural nodules.
Testing local anisotropy using the method of smoothed residuals I — methodology
Appleby, Stephen; Shafieloo, Arman E-mail: arman@apctp.org
2014-03-01
We discuss some details regarding the method of smoothed residuals, which has recently been used to search for anisotropic signals in low-redshift distance measurements (Supernovae). In this short note we focus on some details regarding the implementation of the method, particularly the issue of effectively detecting signals in data that are inhomogeneously distributed on the sky. Using simulated data, we argue that the original method proposed in Colin et al. [1] will not detect spurious signals due to incomplete sky coverage, and that introducing additional Gaussian weighting to the statistic as in [2] can hinder its ability to detect a signal. Issues related to the width of the Gaussian smoothing are also discussed.
Learning With Jensen-Tsallis Kernels.
Ghoshdastidar, Debarghya; Adsul, Ajay P; Dukkipati, Ambedkar
2016-10-01
Jensen-type [Jensen-Shannon (JS) and Jensen-Tsallis] kernels were first proposed by Martins et al. (2009). These kernels are based on JS divergences that originated in the information theory. In this paper, we extend the Jensen-type kernels on probability measures to define positive-definite kernels on Euclidean space. We show that the special cases of these kernels include dot-product kernels. Since Jensen-type divergences are multidistribution divergences, we propose their multipoint variants, and study spectral clustering and kernel methods based on these. We also provide experimental studies on benchmark image database and gene expression database that show the benefits of the proposed kernels compared with the existing kernels. The experiments on clustering also demonstrate the use of constructing multipoint similarities.
Simple parameterized coordinate transformation method for deep- and smooth-profile gratings.
Xu, Xihong; Li, Lifeng
2014-12-01
A simple variable transformation that consists of two joined straight-line segments per grating period is proposed for the parameterized coordinate transformation method (the C method). With this bilinear parameterization, the C method can produce convergent numerical results for gratings of deep and smooth profiles with a groove depth-to-period ratio as high as 10, which to date has been far out of reach of the C method. The danger of getting divergent results due to inadvertently using an overly large truncation number is also practically eliminated.
NASA Astrophysics Data System (ADS)
Priyatikanto, R.; Arifyanto, M. I.
2015-01-01
Stellar membership determination of an open cluster is an important process to do before further analysis. Basically, there are two classes of membership determination method: parametric and non-parametric. In this study, an alternative of non-parametric method based on Binned Kernel Density Estimation that accounts measurements errors (simply called BKDE- e) is proposed. This method is applied upon proper motions data to determine cluster's membership kinematically and estimate the average proper motions of the cluster. Monte Carlo simulations show that the average proper motions determination using this proposed method is statistically more accurate than ordinary Kernel Density Estimator (KDE). By including measurement errors in the calculation, the mode location from the resulting density estimate is less sensitive to non-physical or stochastic fluctuation as compared to ordinary KDE that excludes measurement errors. For the typical mean measurement error of 7 mas/yr, BKDE- e suppresses the potential of miscalculation by a factor of two compared to KDE. With median accuracy of about 93 %, BKDE- e method has comparable accuracy with respect to parametric method (modified Sanders algorithm). Application to real data from The Fourth USNO CCD Astrograph Catalog (UCAC4), especially to NGC 2682 is also performed. The mode of member stars distribution on Vector Point Diagram is located at μ α cos δ=-9.94±0.85 mas/yr and μ δ =-4.92±0.88 mas/yr. Although the BKDE- e performance does not overtake parametric approach, it serves a new view of doing membership analysis, expandable to astrometric and photometric data or even in binary cluster search.
NASA Astrophysics Data System (ADS)
Morency, C.; Tromp, J.
2008-12-01
successfully performed. We present finite-frequency sensitivity kernels for wave propagation in porous media based upon adjoint methods. We first show that the adjoint equations in porous media are similar to the regular Biot equations upon defining an appropriate adjoint source. Then we present finite-frequency kernels for seismic phases in porous media (e.g., fast P, slow P, and S). These kernels illustrate the sensitivity of seismic observables to structural parameters and form the basis of tomographic inversions. Finally, we show an application of this imaging technique related to the detection of buried landmines and unexploded ordnance (UXO) in porous environments.
Xiao, Zhu; Havyarimana, Vincent; Li, Tong; Wang, Dong
2016-01-01
In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student's t-distribution is adopted in order to compute the probability distribution function (PDF) related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF) is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods. PMID:27187405
Xiao, Zhu; Havyarimana, Vincent; Li, Tong; Wang, Dong
2016-01-01
In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student’s t-distribution is adopted in order to compute the probability distribution function (PDF) related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF) is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods. PMID:27187405
Methods for Least Squares Data Smoothing by Adjustment of Divided Differences
NASA Astrophysics Data System (ADS)
Demetriou, I. C.
2008-09-01
A brief survey is presented for the main methods that are used in least squares data smoothing by adjusting the signs of divided differences of the smoothed values. The most distinctive feature of the smoothing approach is that it provides automatically a piecewise monotonic or a piecewise convex/concave fit to the data. The data are measured values of a function of one variable that contain random errors. As a consequence of the errors, the number of sign alterations in the sequence of mth divided differences is usually unacceptably large, where m is a prescribed positive integer. Therefore, we make the least sum of squares change to the measurements by requiring the sequence of the divided differences of order m to have at most k-1 sign changes, for some positive integer k. Although, it is a combinatorial problem, whose solution can require about O(nk) quadratic programming calculations in n variables and n-m constraints, where n is the number of data, very efficient algorithms have been developed for the cases when m = 1 or m = 2 and k is arbitrary, as well as when m>2 for small values of k. Attention is paid to the purpose of each method instead of to its details. Some software packages make the methods publicly accessible through library systems.
NASA Astrophysics Data System (ADS)
Baraldi, Piero; Di Maio, Francesco; Turati, Pietro; Zio, Enrico
2015-08-01
In this work, we propose a modification of the traditional Auto Associative Kernel Regression (AAKR) method which enhances the signal reconstruction robustness, i.e., the capability of reconstructing abnormal signals to the values expected in normal conditions. The modification is based on the definition of a new procedure for the computation of the similarity between the present measurements and the historical patterns used to perform the signal reconstructions. The underlying conjecture for this is that malfunctions causing variations of a small number of signals are more frequent than those causing variations of a large number of signals. The proposed method has been applied to real normal condition data collected in an industrial plant for energy production. Its performance has been verified considering synthetic and real malfunctioning. The obtained results show an improvement in the early detection of abnormal conditions and the correct identification of the signals responsible of triggering the detection.
Source Region Identification Using Kernel Smoothing
As described in this paper, Nonparametric Wind Regression is a source-to-receptor source apportionment model that can be used to identify and quantify the impact of possible source regions of pollutants as defined by wind direction sectors. It is described in detail with an exam...
Kernel Phase and Kernel Amplitude in Fizeau Imaging
NASA Astrophysics Data System (ADS)
Pope, Benjamin J. S.
2016-09-01
Kernel phase interferometry is an approach to high angular resolution imaging which enhances the performance of speckle imaging with adaptive optics. Kernel phases are self-calibrating observables that generalize the idea of closure phases from non-redundant arrays to telescopes with arbitrarily shaped pupils, by considering a matrix-based approximation to the diffraction problem. In this paper I discuss the recent fhistory of kernel phase, in particular in the matrix-based study of sparse arrays, and propose an analogous generalization of the closure amplitude to kernel amplitudes. This new approach can self-calibrate throughput and scintillation errors in optical imaging, which extends the power of kernel phase-like methods to symmetric targets where amplitude and not phase calibration can be a significant limitation, and will enable further developments in high angular resolution astronomy.
Bramble, J. H.; Pasciak, J. E.; Sammon, P. H.; Thomee, V.
1989-04-01
Backward difference methods for the discretization of parabolic boundary value problems are considered in this paper. In particular, we analyze the case when the backward difference equations are only solved 'approximately' by a preconditioned iteration. We provide an analysis which shows that these methods remain stable and accurate if a suitable number of iterations (often independent of the spatial discretization and time step size) are used. Results are provided for the smooth as well as nonsmooth initial data cases. Finally, the results of numerical experiments illustrating the algorithms' performance on model problems are given.
NASA Astrophysics Data System (ADS)
Preza, Chrysanthe; Miller, Michael I.; Conchello, Jose-Angel
1993-07-01
We have shown that the linear least-squares (LLS) estimate of the intensities of a 3-D object obtained from a set of optical sections is unstable due to the inversion of small and zero-valued eigenvalues of the point-spread function (PSF) operator. The LLS solution was regularized by constraining it to lie in a subspace spanned by the eigenvectors corresponding to a selected number of the largest eigenvalues. In this paper we extend the regularized LLS solution to a maximum a posteriori (MAP) solution induced by a prior formed from a 'Good's like' smoothness penalty. This approach also yields a regularized linear estimator which reduces noise as well as edge artifacts in the reconstruction. The advantage of the linear MAP (LMAP) estimate over the current regularized LLS (RLLS) is its ability to regularize the inverse problem by smoothly penalizing components in the image associated with small eigenvalues. Computer simulations were performed using a theoretical PSF and a simple phantom to compare the two regularization techniques. It is shown that the reconstructions using the smoothness prior, give superior variance and bias results compared to the RLLS reconstructions. Encouraging reconstructions obtained with the LMAP method from real microscopical images of a 10 micrometers fluorescent bead, and a four-cell Volvox embryo are shown.
The multiscale restriction smoothed basis method for fractured porous media (F-MsRSB)
NASA Astrophysics Data System (ADS)
Shah, Swej; Møyner, Olav; Tene, Matei; Lie, Knut-Andreas; Hajibeygi, Hadi
2016-08-01
A novel multiscale method for multiphase flow in heterogeneous fractured porous media is devised. The discrete fine-scale system is described using an embedded fracture modeling approach, in which the heterogeneous rock (matrix) and highly-conductive fractures are represented on independent grids. Given this fine-scale discrete system, the method first partitions the fine-scale volumetric grid representing the matrix and the lower-dimensional grids representing fractures into independent coarse grids. Then, basis functions for matrix and fractures are constructed by restricted smoothing, which gives a flexible and robust treatment of complex geometrical features and heterogeneous coefficients. From the basis functions one constructs a prolongation operator that maps between the coarse- and fine-scale systems. The resulting method allows for general coupling of matrix and fracture basis functions, giving efficient treatment of a large variety of fracture conductivities. In addition, basis functions can be adaptively updated using efficient global smoothing strategies to account for multiphase flow effects. The method is conservative and because it is described and implemented in algebraic form, it is straightforward to employ it to both rectilinear and unstructured grids. Through a series of challenging test cases for single and multiphase flow, in which synthetic and realistic fracture maps are combined with heterogeneous petrophysical matrix properties, we validate the method and conclude that it is an efficient and accurate approach for simulating flow in complex, large-scale, fractured media.
Sasipriya, Gopalakrishnan; Siddhuraju, Perumal
2012-08-01
The present study is proposed to determine the antioxidant activity of raw and processed samples of underutilized legumes, Entada scandens seed kernel and Canavalia gladiata seeds. The indigenous processing methods like dry heating, autoclaving and soaking followed by autoclaving in different solutions (plain water, ash, sugar and sodium bicarbonate) were adopted to seed samples. All other processing methods than dry heat showed significant reduction in phenolics (2.9-63%), tannins (26-100%) and flavonoids (14-67%). However, in processed samples of E. scandens, the hydroxyl radical scavenging activity and β-carotene bleaching inhibition activity were increased, whereas, 2,2-azinobis (3-ethyl benzothiazoline-6-sulfonic acid) diammonium salt (ABTS·(+)), ferric reducing antioxidant power (FRAP), metal chelating and superoxide anion scavenging activity were similar to unprocessed ones. In contrary, except dry heating in C. gladiata, all other processing methods significantly (P<0.05) reduced the 2,2'-diphenyl-1-picryl-hydrazyl (DPPH·) (20-35%), ABTS·(+) (22-75%), FRAP (34-74%), metal chelating (30-41%), superoxide anion radical scavenging (8-80%), hydroxyl radical scavenging (20-40%) and β-carotene bleaching inhibition activity (15-69%). In addition, the sample extracts of raw and dry heated samples protected DNA damage at 10 μg. All processing methods in E. scandens and dry heating in C. gladiata would be a suitable method for adopting in domestic or industrial processing. PMID:22683485
Deng, Zhaohong; Choi, Kup-Sze; Jiang, Yizhang; Wang, Shitong
2014-12-01
Inductive transfer learning has attracted increasing attention for the training of effective model in the target domain by leveraging the information in the source domain. However, most transfer learning methods are developed for a specific model, such as the commonly used support vector machine, which makes the methods applicable only to the adopted models. In this regard, the generalized hidden-mapping ridge regression (GHRR) method is introduced in order to train various types of classical intelligence models, including neural networks, fuzzy logical systems and kernel methods. Furthermore, the knowledge-leverage based transfer learning mechanism is integrated with GHRR to realize the inductive transfer learning method called transfer GHRR (TGHRR). Since the information from the induced knowledge is much clearer and more concise than that from the data in the source domain, it is more convenient to control and balance the similarity and difference of data distributions between the source and target domains. The proposed GHRR and TGHRR algorithms have been evaluated experimentally by performing regression and classification on synthetic and real world datasets. The results demonstrate that the performance of TGHRR is competitive with or even superior to existing state-of-the-art inductive transfer learning algorithms.
Smoothed Particle Inference: A Kilo-Parametric Method for X-ray Galaxy Cluster Modeling
Peterson, John R.; Marshall, P.J.; Andersson, K.; /Stockholm U. /SLAC
2005-08-05
We propose an ambitious new method that models the intracluster medium in clusters of galaxies as a set of X-ray emitting smoothed particles of plasma. Each smoothed particle is described by a handful of parameters including temperature, location, size, and elemental abundances. Hundreds to thousands of these particles are used to construct a model cluster of galaxies, with the appropriate complexity estimated from the data quality. This model is then compared iteratively with X-ray data in the form of adaptively binned photon lists via a two-sample likelihood statistic and iterated via Markov Chain Monte Carlo. The complex cluster model is propagated through the X-ray instrument response using direct sampling Monte Carlo methods. Using this approach the method can reproduce many of the features observed in the X-ray emission in a less assumption-dependent way that traditional analyses, and it allows for a more detailed characterization of the density, temperature, and metal abundance structure of clusters. Multi-instrument X-ray analyses and simultaneous X-ray, Sunyaev-Zeldovich (SZ), and lensing analyses are a straight-forward extension of this methodology. Significant challenges still exist in understanding the degeneracy in these models and the statistical noise induced by the complexity of the models.
NASA Technical Reports Server (NTRS)
Verger, Aleixandre; Baret, F.; Weiss, M.; Kandasamy, S.; Vermote, E.
2013-01-01
Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981-2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods-the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT-revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50-60% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction.
NASA Astrophysics Data System (ADS)
Li, Fang; Wang, Shoudong; Chen, Xiaohong; Liu, Guochang; Zheng, Qiang
2014-04-01
Deconvolution is an important part of seismic processing tool for improving the resolution. One of the key assumptions made in most deconvolutional methods is that the seismic data is stationary. However, due to the anelastic absorption, the seismic data is usually nonstationary. In this paper, a novel nonstationary deconvolution approach is proposed based on spectral modeling and variable-step sampling (VSS) hyperbolic smoothing. To facilitate our method, firstly, we apply the Gabor transform to perform a time-frequency decomposition of the nonstationary seismic trace. Secondly, we estimate the source wavelet amplitude spectrum by spectral modeling. Thirdly, smoothing the Gabor magnitude spectrum of seismic data along hyperbolic paths with VSS can obtain the magnitude of the attenuation function, and can also eliminate the effect of source wavelet. Fourthly, by assuming that the source wavelet and attenuation function are minimum phase, their phases can be determined by Hilbert transform. Finally, the estimated two factors are removed by dividing them into the Gabor spectrum of the trace to estimate the Gabor spectrum of the reflectivity. An inverse Gabor transform gives the time-domain reflectivity estimate. Tests on synthetic and field data show that the presented method is an effective tool that not only has the advantages of stationary deconvolution, but also can compensate for the energy absorption, without knowing or estimating the quality factor Q.
Ge, Tian; Nichols, Thomas E; Ghosh, Debashis; Mormino, Elizabeth C; Smoller, Jordan W; Sabuncu, Mert R
2015-04-01
Measurements derived from neuroimaging data can serve as markers of disease and/or healthy development, are largely heritable, and have been increasingly utilized as (intermediate) phenotypes in genetic association studies. To date, imaging genetic studies have mostly focused on discovering isolated genetic effects, typically ignoring potential interactions with non-genetic variables such as disease risk factors, environmental exposures, and epigenetic markers. However, identifying significant interaction effects is critical for revealing the true relationship between genetic and phenotypic variables, and shedding light on disease mechanisms. In this paper, we present a general kernel machine based method for detecting effects of the interaction between multidimensional variable sets. This method can model the joint and epistatic effect of a collection of single nucleotide polymorphisms (SNPs), accommodate multiple factors that potentially moderate genetic influences, and test for nonlinear interactions between sets of variables in a flexible framework. As a demonstration of application, we applied the method to the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to detect the effects of the interactions between candidate Alzheimer's disease (AD) risk genes and a collection of cardiovascular disease (CVD) risk factors, on hippocampal volume measurements derived from structural brain magnetic resonance imaging (MRI) scans. Our method identified that two genes, CR1 and EPHA1, demonstrate significant interactions with CVD risk factors on hippocampal volume, suggesting that CR1 and EPHA1 may play a role in influencing AD-related neurodegeneration in the presence of CVD risks. PMID:25600633
Method of adiabatic modes in studying problems of smoothly irregular open waveguide structures
Sevastianov, L. A.; Egorov, A. A.; Sevastyanov, A. L.
2013-02-15
Basic steps in developing an original method of adiabatic modes that makes it possible to solve the direct and inverse problems of simulating and designing three-dimensional multilayered smoothly irregular open waveguide structures are described. A new element in the method is that an approximate solution of Maxwell's equations is made to obey 'inclined' boundary conditions at the interfaces between themedia being considered. These boundary conditions take into account the obliqueness of planes tangent to nonplanar boundaries between the media and lead to new equations for coupled vector quasiwaveguide hybrid adiabatic modes. Solutions of these equations describe the phenomenon of 'entanglement' of two linear polarizations of an irregular multilayered waveguide, the appearance of a new mode in an entangled state, and the effect of rotation of the polarization plane of quasiwaveguide modes. The efficiency of the method is demonstrated by considering the example of numerically simulating a thin-film generalized waveguide Lueneburg lens.
Immersed smoothed finite element method for fluid-structure interaction simulation of aortic valves
NASA Astrophysics Data System (ADS)
Yao, Jianyao; Liu, G. R.; Narmoneva, Daria A.; Hinton, Robert B.; Zhang, Zhi-Qian
2012-12-01
This paper presents a novel numerical method for simulating the fluid-structure interaction (FSI) problems when blood flows over aortic valves. The method uses the immersed boundary/element method and the smoothed finite element method and hence it is termed as IS-FEM. The IS-FEM is a partitioned approach and does not need a body-fitted mesh for FSI simulations. It consists of three main modules: the fluid solver, the solid solver and the FSI force solver. In this work, the blood is modeled as incompressible viscous flow and solved using the characteristic-based-split scheme with FEM for spacial discretization. The leaflets of the aortic valve are modeled as Mooney-Rivlin hyperelastic materials and solved using smoothed finite element method (or S-FEM). The FSI force is calculated on the Lagrangian fictitious fluid mesh that is identical to the moving solid mesh. The octree search and neighbor-to-neighbor schemes are used to detect efficiently the FSI pairs of fluid and solid cells. As an example, a 3D idealized model of aortic valve is modeled, and the opening process of the valve is simulated using the proposed IS-FEM. Numerical results indicate that the IS-FEM can serve as an efficient tool in the study of aortic valve dynamics to reveal the details of stresses in the aortic valves, the flow velocities in the blood, and the shear forces on the interfaces. This tool can also be applied to animal models studying disease processes and may ultimately translate to a new adaptive methods working with magnetic resonance images, leading to improvements on diagnostic and prognostic paradigms, as well as surgical planning, in the care of patients.
A method for the accurate and smooth approximation of standard thermodynamic functions
NASA Astrophysics Data System (ADS)
Coufal, O.
2013-01-01
A method is proposed for the calculation of approximations of standard thermodynamic functions. The method is consistent with the physical properties of standard thermodynamic functions. This means that the approximation functions are, in contrast to the hitherto used approximations, continuous and smooth in every temperature interval in which no phase transformations take place. The calculation algorithm was implemented by the SmoothSTF program in the C++ language which is part of this paper. Program summaryProgram title:SmoothSTF Catalogue identifier: AENH_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AENH_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 3807 No. of bytes in distributed program, including test data, etc.: 131965 Distribution format: tar.gz Programming language: C++. Computer: Any computer with gcc version 4.3.2 compiler. Operating system: Debian GNU Linux 6.0. The program can be run in operating systems in which the gcc compiler can be installed, see http://gcc.gnu.org/install/specific.html. RAM: 256 MB are sufficient for the table of standard thermodynamic functions with 500 lines Classification: 4.9. Nature of problem: Standard thermodynamic functions (STF) of individual substances are given by thermal capacity at constant pressure, entropy and enthalpy. STF are continuous and smooth in every temperature interval in which no phase transformations take place. The temperature dependence of STF as expressed by the table of its values is for further application approximated by temperature functions. In the paper, a method is proposed for calculating approximation functions which, in contrast to the hitherto used approximations, are continuous and smooth in every temperature interval. Solution method: The approximation functions are
Sogi, Dalbir Singh; Siddiq, Muhammad; Greiby, Ibrahim; Dolan, Kirk D
2013-12-01
Mango processing produces significant amount of waste (peels and kernels) that can be utilized for the production of value-added ingredients for various food applications. Mango peel and kernel were dried using different techniques, such as freeze drying, hot air, vacuum and infrared. Freeze dried mango waste had higher antioxidant properties than those from other techniques. The ORAC values of peel and kernel varied from 418-776 and 1547-1819 μmol TE/g db. The solubility of freeze dried peel and kernel powder was the highest. The water and oil absorption index of mango waste powders ranged between 1.83-6.05 and 1.66-3.10, respectively. Freeze dried powders had the lowest bulk density values among different techniques tried. The cabinet dried waste powders can be potentially used in food products to enhance their nutritional and antioxidant properties. PMID:23871007
NASA Technical Reports Server (NTRS)
Zeng, S.; Wesseling, P.
1993-01-01
The performance of a linear multigrid method using four smoothing methods, called SCGS (Symmetrical Coupled GauBeta-Seidel), CLGS (Collective Line GauBeta-Seidel), SILU (Scalar ILU), and CILU (Collective ILU), is investigated for the incompressible Navier-Stokes equations in general coordinates, in association with Galerkin coarse grid approximation. Robustness and efficiency are measured and compared by application to test problems. The numerical results show that CILU is the most robust, SILU the least, with CLGS and SCGS in between. CLGS is the best in efficiency, SCGS and CILU follow, and SILU is the worst.
NASA Astrophysics Data System (ADS)
Sugio, Tetsuya; Yamamoto, Masayoshi; Funabiki, Shigeyuki
The use of an SMES (Superconducting Magnetic Energy Storage) for smoothing power fluctuations in a railway substation has been discussed. This paper proposes a smoothing control method based on fuzzy reasoning for reducing the SMES capacity at substations along high-speed railways. The proposed smoothing control method comprises three countermeasures for reduction of the SMES capacity. The first countermeasure involves modification of rule 1 for smoothing out the fluctuating electric power to its average value. The other countermeasures involve the modification of the central value of the stored energy control in the SMES and revision of the membership function in rule 2 for reduction of the SMES capacity. The SMES capacity in the proposed smoothing control method is reduced by 49.5% when compared to that in the nonrevised control method. It is confirmed by computer simulations that the proposed control method is suitable for smoothing out power fluctuations in substations along high-speed railways and for reducing the SMES capacity.
Forecasting Smoothed Non-Stationary Time Series Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Norouzzadeh, P.; Rahmani, B.; Norouzzadeh, M. S.
We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time series and deriving a functional form of Tehran price index (TEPIX) that best approximates the time variability of it. The final model is mainly dominated by a linear relationship with the most recent past value, while contributions from nonlinear terms to the total forecasting performance are rather small.
An incompressible smoothed particle hydrodynamics method for the motion of rigid bodies in fluids
NASA Astrophysics Data System (ADS)
Tofighi, N.; Ozbulut, M.; Rahmat, A.; Feng, J. J.; Yildiz, M.
2015-09-01
A two-dimensional incompressible smoothed particle hydrodynamics scheme is presented for simulation of rigid bodies moving through Newtonian fluids. The scheme relies on combined usage of the rigidity constraints and the viscous penalty method to simulate rigid body motion. Different viscosity ratios and interpolation schemes are tested by simulating a rigid disc descending in quiescent medium. A viscosity ratio of 100 coupled with weighted harmonic averaging scheme has been found to provide satisfactory results. The performance of the resulting scheme is systematically tested for cases with linear motion, rotational motion and their combination. The test cases include sedimentation of a single and a pair of circular discs, sedimentation of an elliptic disc and migration and rotation of a circular disc in linear shear flow. Comparison with previous results at various Reynolds numbers indicates that the proposed method captures the motion of rigid bodies driven by flow or external body forces accurately.
NASA Astrophysics Data System (ADS)
Yang, G.; Han, X.; Hu, D. A.
2015-11-01
Modified cylindrical smoothed particle hydrodynamics (MCSPH) approximation equations are derived for hydrodynamics with material strength in axisymmetric cylindrical coordinates. The momentum equation and internal energy equation are represented to be in the axisymmetric form. The MCSPH approximation equations are applied to simulate the process of explosively driven metallic tubes, which includes strong shock waves, large deformations and large inhomogeneities, etc. The meshless and Lagrangian character of the MCSPH method offers the advantages in treating the difficulties embodied in these physical phenomena. Two test cases, the cylinder test and the metallic tube driven by two head-on colliding detonation waves, are presented. Numerical simulation results show that the new form of the MCSPH method can predict the detonation process of high explosives and the expansion process of metallic tubes accurately and robustly.
NASA Astrophysics Data System (ADS)
Mozdgir, A.; Mahdavi, Iraj; Seyyedi, I.; Shiraqei, M. E.
2011-06-01
An assembly line is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The assembly line balancing problem arises and has to be solved when an assembly line has to be configured or redesigned. The so-called simple assembly line balancing problem (SALBP), a basic version of the general problem, has attracted attention of researchers and practitioners of operations research for almost half a century. There are four types of objective functions which are considered to this kind of problem. The versions of SALBP may be complemented by a secondary objective which consists of smoothing station loads. Many heuristics have been proposed for the assembly line balancing problem due to its computational complexity and difficulty in identifying an optimal solution and so many heuristic solutions are supposed to solve this problem. In this paper a differential evolution algorithm is developed to minimize workload smoothness index in SALBP-2 and the algorithm parameters are optimized using Taguchi method.
NASA Astrophysics Data System (ADS)
Khoei, A. R.; Samimi, M.; Azami, A. R.
2007-02-01
In this paper, an application of the reproducing kernel particle method (RKPM) is presented in plasticity behavior of pressure-sensitive material. The RKPM technique is implemented in large deformation analysis of powder compaction process. The RKPM shape function and its derivatives are constructed by imposing the consistency conditions. The essential boundary conditions are enforced by the use of the penalty approach. The support of the RKPM shape function covers the same set of particles during powder compaction, hence no instability is encountered in the large deformation computation. A double-surface plasticity model is developed in numerical simulation of pressure-sensitive material. The plasticity model includes a failure surface and an elliptical cap, which closes the open space between the failure surface and hydrostatic axis. The moving cap expands in the stress space according to a specified hardening rule. The cap model is presented within the framework of large deformation RKPM analysis in order to predict the non-uniform relative density distribution during powder die pressing. Numerical computations are performed to demonstrate the applicability of the algorithm in modeling of powder forming processes and the results are compared to those obtained from finite element simulation to demonstrate the accuracy of the proposed model.
Coupling of Smoothed Particle Hydrodynamics with Finite Volume method for free-surface flows
NASA Astrophysics Data System (ADS)
Marrone, S.; Di Mascio, A.; Le Touzé, D.
2016-04-01
A new algorithm for the solution of free surface flows with large front deformation and fragmentation is presented. The algorithm is obtained by coupling a classical Finite Volume (FV) approach, that discretizes the Navier-Stokes equations on a block structured Eulerian grid, with an approach based on the Smoothed Particle Hydrodynamics (SPH) method, implemented in a Lagrangian framework. The coupling procedure is formulated in such a way that each solver is applied in the region where its intrinsic characteristics can be exploited in the most efficient and accurate way: the FV solver is used to resolve the bulk flow and the wall regions, whereas the SPH solver is implemented in the free surface region to capture details of the front evolution. The reported results clearly prove that the combined use of the two solvers is convenient from the point of view of both accuracy and computing time.
Wu, Wei; Fan, Qinwei; Zurada, Jacek M; Wang, Jian; Yang, Dakun; Liu, Yan
2014-02-01
The aim of this paper is to develop a novel method to prune feedforward neural networks by introducing an L1/2 regularization term into the error function. This procedure forces weights to become smaller during the training and can eventually removed after the training. The usual L1/2 regularization term involves absolute values and is not differentiable at the origin, which typically causes oscillation of the gradient of the error function during the training. A key point of this paper is to modify the usual L1/2 regularization term by smoothing it at the origin. This approach offers the following three advantages: First, it removes the oscillation of the gradient value. Secondly, it gives better pruning, namely the final weights to be removed are smaller than those produced through the usual L1/2 regularization. Thirdly, it makes it possible to prove the convergence of the training. Supporting numerical examples are also provided.
A DAFT DL_POLY distributed memory adaptation of the Smoothed Particle Mesh Ewald method
NASA Astrophysics Data System (ADS)
Bush, I. J.; Todorov, I. T.; Smith, W.
2006-09-01
The Smoothed Particle Mesh Ewald method [U. Essmann, L. Perera, M.L. Berkowtz, T. Darden, H. Lee, L.G. Pedersen, J. Chem. Phys. 103 (1995) 8577] for calculating long ranged forces in molecular simulation has been adapted for the parallel molecular dynamics code DL_POLY_3 [I.T. Todorov, W. Smith, Philos. Trans. Roy. Soc. London 362 (2004) 1835], making use of a novel 3D Fast Fourier Transform (DAFT) [I.J. Bush, The Daresbury Advanced Fourier transform, Daresbury Laboratory, 1999] that perfectly matches the Domain Decomposition (DD) parallelisation strategy [W. Smith, Comput. Phys. Comm. 62 (1991) 229; M.R.S. Pinches, D. Tildesley, W. Smith, Mol. Sim. 6 (1991) 51; D. Rapaport, Comput. Phys. Comm. 62 (1991) 217] of the DL_POLY_3 code. In this article we describe software adaptations undertaken to import this functionality and provide a review of its performance.
NASA Astrophysics Data System (ADS)
Møyner, Olav; Lie, Knut-Andreas
2016-01-01
A wide variety of multiscale methods have been proposed in the literature to reduce runtime and provide better scaling for the solution of Poisson-type equations modeling flow in porous media. We present a new multiscale restricted-smoothed basis (MsRSB) method that is designed to be applicable to both rectilinear grids and unstructured grids. Like many other multiscale methods, MsRSB relies on a coarse partition of the underlying fine grid and a set of local prolongation operators (multiscale basis functions) that map unknowns associated with the fine grid cells to unknowns associated with blocks in the coarse partition. These mappings are constructed by restricted smoothing: Starting from a constant, a localized iterative scheme is applied directly to the fine-scale discretization to compute prolongation operators that are consistent with the local properties of the differential operators. The resulting method has three main advantages: First of all, both the coarse and the fine grid can have general polyhedral geometry and unstructured topology. This means that partitions and good prolongation operators can easily be constructed for complex models involving high media contrasts and unstructured cell connections introduced by faults, pinch-outs, erosion, local grid refinement, etc. In particular, the coarse partition can be adapted to geological or flow-field properties represented on cells or faces to improve accuracy. Secondly, the method is accurate and robust when compared to existing multiscale methods and does not need expensive recomputation of local basis functions to account for transient behavior: Dynamic mobility changes are incorporated by continuing to iterate a few extra steps on existing basis functions. This way, the cost of updating the prolongation operators becomes proportional to the amount of change in fluid mobility and one reduces the need for expensive, tolerance-based updates. Finally, since the MsRSB method is formulated on top of a cell
Workshop on advances in smooth particle hydrodynamics
Wingate, C.A.; Miller, W.A.
1993-12-31
This proceedings contains viewgraphs presented at the 1993 workshop held at Los Alamos National Laboratory. Discussed topics include: negative stress, reactive flow calculations, interface problems, boundaries and interfaces, energy conservation in viscous flows, linked penetration calculations, stability and consistency of the SPH method, instabilities, wall heating and conservative smoothing, tensors, tidal disruption of stars, breaking the 10,000,000 particle limit, modelling relativistic collapse, SPH without H, relativistic KSPH avoidance of velocity based kernels, tidal compression and disruption of stars near a supermassive rotation black hole, and finally relativistic SPH viscosity and energy.
NASA Astrophysics Data System (ADS)
Wang, Liang; Chen, Dong; Cheng, Tinghai; He, Pu; Lu, Xiaohui; Zhao, Hongwei
2016-08-01
The smooth impact drive mechanism (SIDM) is a type of piezoelectric actuator that has been developed for several decades. As a kind of driving method for the SIDM, the traditional sawtooth (TS) wave is always employed. The kinetic friction force during the rapid contraction stage usually results in the generation of a backward motion. A friction regulation hybrid (FRH) driving method realized by a composite waveform for the backward motion restraint of the SIDM is proposed in this paper. The composite waveform is composed of a sawtooth driving (SD) wave and a sinusoidal friction regulation (SFR) wave which is applied to the rapid deformation stage of the SD wave. A prototype of the SIDM was fabricated and its output performance under the excitation of the FRH driving method and the TS wave driving method was tested. The results indicate that the backward motion can be restrained obviously using the FRH driving method. Compared with the driving effect of the TS wave, the backward rates of the prototype in forward and reverse motions are decreased by 83% and 85%, respectively.
Guo, Yi; Gao, Junbin; Kwan, Paul W
2008-08-01
In most existing dimensionality reduction algorithms, the main objective is to preserve relational structure among objects of the input space in a low dimensional embedding space. This is achieved by minimizing the inconsistency between two similarity/dissimilarity measures, one for the input data and the other for the embedded data, via a separate matching objective function. Based on this idea, a new dimensionality reduction method called Twin Kernel Embedding (TKE) is proposed. TKE addresses the problem of visualizing non-vectorial data that is difficult for conventional methods in practice due to the lack of efficient vectorial representation. TKE solves this problem by minimizing the inconsistency between the similarity measures captured respectively by their kernel Gram matrices in the two spaces. In the implementation, by optimizing a nonlinear objective function using the gradient descent algorithm, a local minimum can be reached. The results obtained include both the optimal similarity preserving embedding and the appropriate values for the hyperparameters of the kernel. Experimental evaluation on real non-vectorial datasets confirmed the effectiveness of TKE. TKE can be applied to other types of data beyond those mentioned in this paper whenever suitable measures of similarity/dissimilarity can be defined on the input data. PMID:18566501
NASA Astrophysics Data System (ADS)
Duguet, T.; Bender, M.; Ebran, J.-P.; Lesinski, T.; Somà, V.
2015-12-01
This programmatic paper lays down the possibility to reconcile the necessity to resum many-body correlations into the energy kernel with the fact that safe multi-reference energy density functional (EDF) calculations cannot be achieved whenever the Pauli principle is not enforced, as is for example the case when many-body correlations are parametrized under the form of empirical density dependencies. Our proposal is to exploit a newly developed ab initio many-body formalism to guide the construction of safe, explicitly correlated and systematically improvable parametrizations of the off-diagonal energy and norm kernels that lie at the heart of the nuclear EDF method. The many-body formalism of interest relies on the concepts of symmetry breaking and restoration that have made the fortune of the nuclear EDF method and is, as such, amenable to this guidance. After elaborating on our proposal, we briefly outline the project we plan to execute in the years to come.
NASA Astrophysics Data System (ADS)
Gaudeua de Gerlicz, C.; Golding, J. G.; Bobola, Ph.; Moutarde, C.; Naji, S.
2008-06-01
The spaceflight under microgravity cause basically biological and physiological imbalance in human being. Lot of study has been yet release on this topic especially about sleep disturbances and on the circadian rhythms (alternation vigilance-sleep, body, temperature...). Factors like space motion sickness, noise, or excitement can cause severe sleep disturbances. For a stay of longer than four months in space, gradual increases in the planned duration of sleep were reported. [1] The average sleep in orbit was more than 1.5 hours shorter than the during control periods on earth, where sleep averaged 7.9 hours. [2] Alertness and calmness were unregistered yield clear circadian pattern of 24h but with a phase delay of 4h.The calmness showed a biphasic component (12h) mean sleep duration was 6.4 structured by 3-5 non REM/REM cycles. Modelisations of neurophysiologic mechanisms of stress and interactions between various physiological and psychological variables of rhythms have can be yet release with the COSINOR method. [3
Crespo, Alejandro C.; Dominguez, Jose M.; Barreiro, Anxo; Gómez-Gesteira, Moncho; Rogers, Benedict D.
2011-01-01
Smoothed Particle Hydrodynamics (SPH) is a numerical method commonly used in Computational Fluid Dynamics (CFD) to simulate complex free-surface flows. Simulations with this mesh-free particle method far exceed the capacity of a single processor. In this paper, as part of a dual-functioning code for either central processing units (CPUs) or Graphics Processor Units (GPUs), a parallelisation using GPUs is presented. The GPU parallelisation technique uses the Compute Unified Device Architecture (CUDA) of nVidia devices. Simulations with more than one million particles on a single GPU card exhibit speedups of up to two orders of magnitude over using a single-core CPU. It is demonstrated that the code achieves different speedups with different CUDA-enabled GPUs. The numerical behaviour of the SPH code is validated with a standard benchmark test case of dam break flow impacting on an obstacle where good agreement with the experimental results is observed. Both the achieved speed-ups and the quantitative agreement with experiments suggest that CUDA-based GPU programming can be used in SPH methods with efficiency and reliability. PMID:21695185
ERIC Educational Resources Information Center
Gardner, Don E.
The merits of double exponential smoothing are discussed relative to other types of pattern-based enrollment forecasting methods. The difficulties associated with selecting an appropriate weight factor are discussed, and their potential effects on prediction results are illustrated. Two methods for objectively selecting the "best" weight factor…
NASA Astrophysics Data System (ADS)
Rahbaralam, Maryam; Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier
2015-12-01
Random walk particle tracking methods are a computationally efficient family of methods to solve reactive transport problems. While the number of particles in most realistic applications is in the order of 106-109, the number of reactive molecules even in diluted systems might be in the order of fractions of the Avogadro number. Thus, each particle actually represents a group of potentially reactive molecules. The use of a low number of particles may result not only in loss of accuracy, but also may lead to an improper reproduction of the mixing process, limited by diffusion. Recent works have used this effect as a proxy to model incomplete mixing in porous media. In this work, we propose using a Kernel Density Estimation (KDE) of the concentrations that allows getting the expected results for a well-mixed solution with a limited number of particles. The idea consists of treating each particle as a sample drawn from the pool of molecules that it represents; this way, the actual location of a tracked particle is seen as a sample drawn from the density function of the location of molecules represented by that given particle, rigorously represented by a kernel density function. The probability of reaction can be obtained by combining the kernels associated to two potentially reactive particles. We demonstrate that the observed deviation in the reaction vs time curves in numerical experiments reported in the literature could be attributed to the statistical method used to reconstruct concentrations (fixed particle support) from discrete particle distributions, and not to the occurrence of true incomplete mixing. We further explore the evolution of the kernel size with time, linking it to the diffusion process. Our results show that KDEs are powerful tools to improve computational efficiency and robustness in reactive transport simulations, and indicates that incomplete mixing in diluted systems should be modeled based on alternative mechanistic models and not on a
ERIC Educational Resources Information Center
Chen, Haiwen; Holland, Paul
2010-01-01
In this paper, we develop a new curvilinear equating for the nonequivalent groups with anchor test (NEAT) design under the assumption of the classical test theory model, that we name curvilinear Levine observed score equating. In fact, by applying both the kernel equating framework and the mean preserving linear transformation of…
NASA Astrophysics Data System (ADS)
Dyachkov, S. A.; Parshikov, A. N.; Zhakhovsky, V. V.
2015-11-01
Experimental methods of observation of early stage of shock-induced ejecta from metal surface with micrometer-sized perturbations are still limited in terms of following a complete sequence of processes having microscale dimensions and nanoscale times. Therefore, simulations by the smoothed particle hydrodynamics (SPH) and molecular dynamics (MD) methods can shed of light on details of micro-jet evolution. The size of simulated sample is too restricted in MD, but the simulations with large enough number of atoms can be scaled well to the sizes of realistic samples. To validate such scaling the comparative MD and SPH simulations of tin samples are performed. SPH simulation takes the realistic experimental sizes, while MD uses the proportionally scaled sizes of samples. It is shown that the velocity and mass distributions along the jets simulated by MD and SPH are in a good agreement. The observed difference in velocity of spikes between MD and experiments can be partially explained by a profound effect of surface tension on jets ejected from the small-scale samples.
MASS TRANSFER IN BINARY STARS USING SMOOTHED PARTICLE HYDRODYNAMICS. I. NUMERICAL METHOD
Lajoie, Charles-Philippe; Sills, Alison E-mail: asills@mcmaster.ca
2011-01-10
Close interactions and mass transfer in binary stars can lead to the formation of many different exotic stellar populations, but detailed modeling of mass transfer is a computationally challenging problem. Here, we present an alternate smoothed particle hydrodynamics approach to the modeling of mass transfer in binary systems that allows a better resolution of the flow of matter between main-sequence stars. Our approach consists of modeling only the outermost layers of the stars using appropriate boundary conditions and ghost particles. We arbitrarily set the radius of the boundary and find that our boundary treatment behaves physically and conserves energy well. In particular, when used with our binary relaxation procedure, our treatment of boundary conditions is also shown to evolve circular binaries properly for many orbits. The results of our first simulation of mass transfer are also discussed and used to assess the strengths and limitations of our method. We conclude that it is well suited for the modeling of interacting binary stars. The method presented here represents a convenient alternative to previous hydrodynamical techniques aimed at modeling mass transfer in binary systems since it can be used to model both the donor and the accretor while maintaining the density profiles taken from realistic stellar models.
Predicting Protein Function Using Multiple Kernels.
Yu, Guoxian; Rangwala, Huzefa; Domeniconi, Carlotta; Zhang, Guoji; Zhang, Zili
2015-01-01
High-throughput experimental techniques provide a wide variety of heterogeneous proteomic data sources. To exploit the information spread across multiple sources for protein function prediction, these data sources are transformed into kernels and then integrated into a composite kernel. Several methods first optimize the weights on these kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these approaches result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some approaches optimize the loss of binary classifiers and learn weights for the different kernels iteratively. For multi-class or multi-label data, these methods have to solve the problem of optimizing weights on these kernels for each of the labels, which are computationally expensive and ignore the correlation among labels. In this paper, we propose a method called Predicting Protein Function using Multiple Kernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reduces the empirical loss of multi-label classifier for each of the labels simultaneously. ProMK can integrate kernels selectively and downgrade the weights on noisy kernels. We investigate the performance of ProMK on several publicly available protein function prediction benchmarks and synthetic datasets. We show that the proposed approach performs better than previously proposed protein function prediction approaches that integrate multiple data sources and multi-label multiple kernel learning methods. The codes of our proposed method are available at https://sites.google.com/site/guoxian85/promk.
Kernel earth mover's distance for EEG classification.
Daliri, Mohammad Reza
2013-07-01
Here, we propose a new kernel approach based on the earth mover's distance (EMD) for electroencephalography (EEG) signal classification. The EEG time series are first transformed into histograms in this approach. The distance between these histograms is then computed using the EMD in a pair-wise manner. We bring the distances into a kernel form called kernel EMD. The support vector classifier can then be used for the classification of EEG signals. The experimental results on the real EEG data show that the new kernel method is very effective, and can classify the data with higher accuracy than traditional methods.
Method reduces computer time for smoothing functions and derivatives through ninth order polynomials
NASA Technical Reports Server (NTRS)
Glauz, R. D.; Wilgus, C. A.
1969-01-01
Analysis presented is an efficient technique to adjust previously calculated orthogonal polynomial coefficients for an odd number of equally spaced data points. The adjusting technique derivation is for a ninth order polynomial. It reduces computer time for smoothing functions.
Kernel Partial Least Squares for Nonlinear Regression and Discrimination
NASA Technical Reports Server (NTRS)
Rosipal, Roman; Clancy, Daniel (Technical Monitor)
2002-01-01
This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.
NASA Technical Reports Server (NTRS)
Pinson, Robin M.; Schmitt, Terri L.; Hanson, John M.
2008-01-01
Six degree-of-freedom (DOF) launch vehicle trajectories are designed to follow an optimized 3-DOF reference trajectory. A vehicle has a finite amount of control power that it can allocate to performing maneuvers. Therefore, the 3-DOF trajectory must be designed to refrain from using 100% of the allowable control capability to perform maneuvers, saving control power for handling off-nominal conditions, wind gusts and other perturbations. During the Ares I trajectory analysis, two maneuvers were found to be hard for the control system to implement; a roll maneuver prior to the gravity turn and an angle of attack maneuver immediately after the J-2X engine start-up. It was decided to develop an approach for creating smooth maneuvers in the optimized reference trajectories that accounts for the thrust available from the engines. A feature of this method is that no additional angular velocity in the direction of the maneuver has been added to the vehicle after the maneuver completion. This paper discusses the equations behind these new maneuvers and their implementation into the Ares I trajectory design cycle. Also discussed is a possible extension to adjusting closed-loop guidance.
Stem kernels for RNA sequence analyses.
Sakakibara, Yasubumi; Popendorf, Kris; Ogawa, Nana; Asai, Kiyoshi; Sato, Kengo
2007-10-01
Several computational methods based on stochastic context-free grammars have been developed for modeling and analyzing functional RNA sequences. These grammatical methods have succeeded in modeling typical secondary structures of RNA, and are used for structural alignment of RNA sequences. However, such stochastic models cannot sufficiently discriminate member sequences of an RNA family from nonmembers and hence detect noncoding RNA regions from genome sequences. A novel kernel function, stem kernel, for the discrimination and detection of functional RNA sequences using support vector machines (SVMs) is proposed. The stem kernel is a natural extension of the string kernel, specifically the all-subsequences kernel, and is tailored to measure the similarity of two RNA sequences from the viewpoint of secondary structures. The stem kernel examines all possible common base pairs and stem structures of arbitrary lengths, including pseudoknots between two RNA sequences, and calculates the inner product of common stem structure counts. An efficient algorithm is developed to calculate the stem kernels based on dynamic programming. The stem kernels are then applied to discriminate members of an RNA family from nonmembers using SVMs. The study indicates that the discrimination ability of the stem kernel is strong compared with conventional methods. Furthermore, the potential application of the stem kernel is demonstrated by the detection of remotely homologous RNA families in terms of secondary structures. This is because the string kernel is proven to work for the remote homology detection of protein sequences. These experimental results have convinced us to apply the stem kernel in order to find novel RNA families from genome sequences. PMID:17933013
Improving the Bandwidth Selection in Kernel Equating
ERIC Educational Resources Information Center
Andersson, Björn; von Davier, Alina A.
2014-01-01
We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners…
NASA Astrophysics Data System (ADS)
Zouch, Wassim; Slima, Mohamed Ben; Feki, Imed; Derambure, Philippe; Taleb-Ahmed, Abdelmalik; Hamida, Ahmed Ben
2010-12-01
A new nonparametric method, based on the smooth weighted-minimum-norm (WMN) focal underdetermined-system solver (FOCUSS), for electrical cerebral activity localization using electroencephalography measurements is proposed. This method iteratively adjusts the spatial sources by reducing the size of the lead-field and the weighting matrix. Thus, an enhancement of source localization is obtained, as well as a reduction of the computational complexity. The performance of the proposed method, in terms of localization errors, robustness, and computation time, is compared with the WMN-FOCUSS and nonshrinking smooth WMN-FOCUSS methods as well as with standard generalized inverse methods (unweighted minimum norm, WMN, and FOCUSS). Simulation results for single-source localization confirm the effectiveness and robustness of the proposed method with respect to the reconstruction accuracy of a simulated single dipole.
Mazza, G; Roßmanith, E; Lang-Olip, I; Pfeiffer, D
2016-08-01
Even though umbilical cord arteries are a common source of vascular smooth muscle cells, the lack of reliable marker profiles have not facilitated the isolation of human umbilical artery smooth muscle cells (HUASMC). For accurate characterization of HUASMC and cells in their environment, the expression of smooth muscle and mesenchymal markers was analyzed in umbilical cord tissue sections. The resulting marker profile was then used to evaluate the quality of HUASMC isolation and culture methods. HUASMC and perivascular-Wharton's jelly stromal cells (pv-WJSC) showed positive staining for α-smooth muscle actin (α-SMA), smooth muscle myosin heavy chain (SM-MHC), desmin, vimentin and CD90. Anti-CD10 stained only pv-WJSC. Consequently, HUASMC could be characterized as α-SMA+ , SM-MHC+ , CD10- cells, which are additionally negative for endothelial markers (CD31 and CD34). Enzymatic isolation provided primary HUASMC batches with 90-99 % purity, yet, under standard culture conditions, contaminant CD10+ cells rapidly constituted more than 80 % of the total cell population. Contamination was mainly due to the poor adhesion of HUASMC to cell culture plates, regardless of the different protein coatings (fibronectin, collagen I or gelatin). HUASMC showed strong attachment and long-term viability only in 3D matrices. The explant isolation method achieved cultures with only 13-40 % purity with considerable contamination by CD10+ cells. CD10+ cells showed spindle-like morphology and up-regulated expression of α-SMA and SM-MHC upon culture in smooth muscle differentiation medium. Considering the high contamination risk of HUASMC cultures by CD10+ neighboring cells and their phenotypic similarities, precise characterization is mandatory to avoid misleading results.
Methods and energy storage devices utilizing electrolytes having surface-smoothing additives
Xu, Wu; Zhang, Jiguang; Graff, Gordon L; Chen, Xilin; Ding, Fei
2015-11-12
Electrodeposition and energy storage devices utilizing an electrolyte having a surface-smoothing additive can result in self-healing, instead of self-amplification, of initial protuberant tips that give rise to roughness and/or dendrite formation on the substrate and anode surface. For electrodeposition of a first metal (M1) on a substrate or anode from one or more cations of M1 in an electrolyte solution, the electrolyte solution is characterized by a surface-smoothing additive containing cations of a second metal (M2), wherein cations of M2 have an effective electrochemical reduction potential in the solution lower than that of the cations of M1.
Induced Pluripotent Stem Cell-derived Vascular Smooth Muscle Cells: Methods and Application
Dash, Biraja C.; Jiang, Zhengxin; Suh, Carol; Qyang, Yibing
2015-01-01
Vascular smooth muscle cells (VSMCs) play a major role in the pathophysiology of cardiovascular diseases. The advent of induced pluripotent stem cell (iPSC) technology and their capability to differentiation into virtually every cell type in the human body make this field a ray of hope for vascular regenerative therapy and for understanding disease mechanism. In this review, we first discuss the recent iPSC technology and vascular smooth muscle development from embryo and then examine different methodology to derive VSMCs from iPSCs and their applications in regenerative therapy and disease modeling. PMID:25559088
The context-tree kernel for strings.
Cuturi, Marco; Vert, Jean-Philippe
2005-10-01
We propose a new kernel for strings which borrows ideas and techniques from information theory and data compression. This kernel can be used in combination with any kernel method, in particular Support Vector Machines for string classification, with notable applications in proteomics. By using a Bayesian averaging framework with conjugate priors on a class of Markovian models known as probabilistic suffix trees or context-trees, we compute the value of this kernel in linear time and space while only using the information contained in the spectrum of the considered strings. This is ensured through an adaptation of a compression method known as the context-tree weighting algorithm. Encouraging classification results are reported on a standard protein homology detection experiment, showing that the context-tree kernel performs well with respect to other state-of-the-art methods while using no biological prior knowledge.
Density Estimation with Mercer Kernels
NASA Technical Reports Server (NTRS)
Macready, William G.
2003-01-01
We present a new method for density estimation based on Mercer kernels. The density estimate can be understood as the density induced on a data manifold by a mixture of Gaussians fit in a feature space. As is usual, the feature space and data manifold are defined with any suitable positive-definite kernel function. We modify the standard EM algorithm for mixtures of Gaussians to infer the parameters of the density. One benefit of the approach is it's conceptual simplicity, and uniform applicability over many different types of data. Preliminary results are presented for a number of simple problems.
Tan, Stéphanie; Soulez, Gilles; Diez Martinez, Patricia; Larrivée, Sandra; Stevens, Louis-Mathieu; Goussard, Yves; Mansour, Samer; Chartrand-Lefebvre, Carl
2016-01-01
Purpose Metallic artifacts can result in an artificial thickening of the coronary stent wall which can significantly impair computed tomography (CT) imaging in patients with coronary stents. The objective of this study is to assess in vivo visualization of coronary stent wall and lumen with an edge-enhancing CT reconstruction kernel, as compared to a standard kernel. Methods This is a prospective cross-sectional study involving the assessment of 71 coronary stents (24 patients), with blinded observers. After 256-slice CT angiography, image reconstruction was done with medium-smooth and edge-enhancing kernels. Stent wall thickness was measured with both orthogonal and circumference methods, averaging thickness from diameter and circumference measurements, respectively. Image quality was assessed quantitatively using objective parameters (noise, signal to noise (SNR) and contrast to noise (CNR) ratios), as well as visually using a 5-point Likert scale. Results Stent wall thickness was decreased with the edge-enhancing kernel in comparison to the standard kernel, either with the orthogonal (0.97 ± 0.02 versus 1.09 ± 0.03 mm, respectively; p<0.001) or the circumference method (1.13 ± 0.02 versus 1.21 ± 0.02 mm, respectively; p = 0.001). The edge-enhancing kernel generated less overestimation from nominal thickness compared to the standard kernel, both with the orthogonal (0.89 ± 0.19 versus 1.00 ± 0.26 mm, respectively; p<0.001) and the circumference (1.06 ± 0.26 versus 1.13 ± 0.31 mm, respectively; p = 0.005) methods. The edge-enhancing kernel was associated with lower SNR and CNR, as well as higher background noise (all p < 0.001), in comparison to the medium-smooth kernel. Stent visual scores were higher with the edge-enhancing kernel (p<0.001). Conclusion In vivo 256-slice CT assessment of coronary stents shows that the edge-enhancing CT reconstruction kernel generates thinner stent walls, less overestimation from nominal thickness, and better image quality
Adaptive wiener image restoration kernel
Yuan, Ding
2007-06-05
A method and device for restoration of electro-optical image data using an adaptive Wiener filter begins with constructing imaging system Optical Transfer Function, and the Fourier Transformations of the noise and the image. A spatial representation of the imaged object is restored by spatial convolution of the image using a Wiener restoration kernel.
NASA Astrophysics Data System (ADS)
Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; Qin, Jian; Karpeev, Dmitry; Hernandez-Ortiz, Juan; de Pablo, Juan J.; Heinonen, Olle
2016-08-01
Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O(N2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Method (FMM) to evaluate the integrals in O(N) operations, with O(N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. The results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.
NASA Astrophysics Data System (ADS)
Szczesna, Dorota H.; Kulas, Zbigniew; Kasprzak, Henryk T.; Stenevi, Ulf
2009-11-01
A lateral shearing interferometer was used to examine the smoothness of the tear film. The information about the distribution and stability of the precorneal tear film is carried out by the wavefront reflected from the surface of tears and coded in interference fringes. Smooth and regular fringes indicate a smooth tear film surface. On corneae after laser in situ keratomileusis (LASIK) or radial keratotomy (RK) surgery, the interference fringes are seldom regular. The fringes are bent on bright lines, which are interpreted as tear film breakups. The high-intensity pattern seems to appear in similar location on the corneal surface after refractive surgery. Our purpose was to extract information about the pattern existing under the interference fringes and calculate its shape reproducibility over time and following eye blinks. A low-pass filter was applied and correlation coefficient was calculated to compare a selected fragment of the template image to each of the following frames in the recorded sequence. High values of the correlation coefficient suggest that irregularities of the corneal epithelium might influence tear film instability and that tear film breakup may be associated with local irregularities of the corneal topography created after the LASIK and RK surgeries.
Bleiziffer, Patrick Krug, Marcel; Görling, Andreas
2015-06-28
A self-consistent Kohn-Sham method based on the adiabatic-connection fluctuation-dissipation (ACFD) theorem, employing the frequency-dependent exact exchange kernel f{sub x} is presented. The resulting SC-exact-exchange-only (EXX)-ACFD method leads to even more accurate correlation potentials than those obtained within the direct random phase approximation (dRPA). In contrast to dRPA methods, not only the Coulomb kernel but also the exact exchange kernel f{sub x} is taken into account in the EXX-ACFD correlation which results in a method that, unlike dRPA methods, is free of self-correlations, i.e., a method that treats exactly all one-electron systems, like, e.g., the hydrogen atom. The self-consistent evaluation of EXX-ACFD total energies improves the accuracy compared to EXX-ACFD total energies evaluated non-self-consistently with EXX or dRPA orbitals and eigenvalues. Reaction energies of a set of small molecules, for which highly accurate experimental reference data are available, are calculated and compared to quantum chemistry methods like Møller-Plesset perturbation theory of second order (MP2) or coupled cluster methods [CCSD, coupled cluster singles, doubles, and perturbative triples (CCSD(T))]. Moreover, we compare our methods to other ACFD variants like dRPA combined with perturbative corrections such as the second order screened exchange corrections or a renormalized singles correction. Similarly, the performance of our EXX-ACFD methods is investigated for the non-covalently bonded dimers of the S22 reference set and for potential energy curves of noble gas, water, and benzene dimers. The computational effort of the SC-EXX-ACFD method exhibits the same scaling of N{sup 5} with respect to the system size N as the non-self-consistent evaluation of only the EXX-ACFD correlation energy; however, the prefactor increases significantly. Reaction energies from the SC-EXX-ACFD method deviate quite little from EXX-ACFD energies obtained non
On Quantile Regression in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint
Zhang, Chong; Liu, Yufeng; Wu, Yichao
2015-01-01
For spline regressions, it is well known that the choice of knots is crucial for the performance of the estimator. As a general learning framework covering the smoothing splines, learning in a Reproducing Kernel Hilbert Space (RKHS) has a similar issue. However, the selection of training data points for kernel functions in the RKHS representation has not been carefully studied in the literature. In this paper we study quantile regression as an example of learning in a RKHS. In this case, the regular squared norm penalty does not perform training data selection. We propose a data sparsity constraint that imposes thresholding on the kernel function coefficients to achieve a sparse kernel function representation. We demonstrate that the proposed data sparsity method can have competitive prediction performance for certain situations, and have comparable performance in other cases compared to that of the traditional squared norm penalty. Therefore, the data sparsity method can serve as a competitive alternative to the squared norm penalty method. Some theoretical properties of our proposed method using the data sparsity constraint are obtained. Both simulated and real data sets are used to demonstrate the usefulness of our data sparsity constraint. PMID:27134575
Kalita, Dhruba Jyoti; Gupta, Ashish K
2010-10-01
A study of the multiphoton dissociation of H(2)(+) in intense laser field using the smooth exterior scaling method to calculate resonance states is presented. This method is very attractive as it does not disturb the interaction region. The wave functions calculated with this method provide indisputable proof in support of the mechanisms of the different phenomena happening during photodissociation. Wave functions corresponding to the "vibrationally trapped" (bond-hardening) states are found. A unequivocal mechanism for "bond-softening" is provided. It is observed that with an increase in intensity, the lifetime of low vibrational level increases. The mechanism for this novel phenomenon is also explained.
Modeling the propagation of volcanic debris avalanches by a Smoothed Particle Hydrodynamics method
NASA Astrophysics Data System (ADS)
Sosio, Rosanna; Battista Crosta, Giovanni
2010-05-01
Hazard from collapses of volcanic edifices threatens million of people which currently live on top of volcanic deposits or around volcanoes prone to fail. Nevertheless, no much effort has been dedicated for the evaluation of the hazard posed by volcanic debris avalanches (e.g. emergency plans, hazard zoning maps). This work focuses at evaluating the exceptional mobility of volcanic debris avalanches for hazard analyses purposes by providing a set of calibrated cases. We model the propagation of eight debris avalanche selected among the best known historical events originated from sector collapses of volcanic edifices. The events have large volumes (ranging from 0.01-0.02 km3 to 25 km3) and are well preserved so that their main features are recognizable from satellite images. The events developed in a variety of settings and condition and they vary with respect to their morphological constrains, materials, styles of failure. The modeling has been performed using a Lagragian numerical method adapted from Smoothed Particle Hydrodynamics to solve the depth averaged quasi-3D equation for motion (McDougall and Hungr 2004). This code has been designed and satisfactorily used to simulate rock and debris avalanches in non-volcanic settings (McDougall and Hungr, 2004). Its use is here extended to model volcanic debris avalanches which may differ from non-volcanic ones by dimensions, water content and by possible thermodynamic effects or degassing caused by active volcanic processes. The resolution of the topographic data is generally low for remote areas like the ones considered in this study, while the pre event topographies are more often not available. The effect of the poor topographic resolution on the final results has been evaluated by replicating the modeling on satellite-derived topographical grids with varying cell size (from 22 m to 90 m). The event reconstructions and the back analyses are based on the observations available from the literature. We test the
Simulation of wave mitigation by coastal vegetation using smoothed particle hydrodynamics method
NASA Astrophysics Data System (ADS)
Iryanto; Gunawan, P. H.
2016-02-01
Vegetation in coastal area lead to wave mitigation has been studied by some researchers recently. The effect of vegetation forest in coastal area is minimizing the negative impact of wave propagation. In order to describe the effect of vegetation resistance into the water flow, the modified model of framework smoothed hydrodynamics particle has been constructed. In the Lagrangian framework, the Darcy, Manning, and laminar viscosity resistances are added. The effect of each resistances is given in some results of numerical simulations. Simulation of wave mitigation on sloping beach is also given.
Airway mechanics and methods used to visualize smooth muscle dynamics in vitro.
Cooper, P R; McParland, B E; Mitchell, H W; Noble, P B; Politi, A Z; Ressmeyer, A R; West, A R
2009-10-01
Contraction of airway smooth muscle (ASM) is regulated by the physiological, structural and mechanical environment in the lung. We review two in vitro techniques, lung slices and airway segment preparations, that enable in situ ASM contraction and airway narrowing to be visualized. Lung slices and airway segment approaches bridge a gap between cell culture and isolated ASM, and whole animal studies. Imaging techniques enable key upstream events involved in airway narrowing, such as ASM cell signalling and structural and mechanical events impinging on ASM, to be investigated.
Ning, Yong; Zhu, Shan'an; Zhao, Yuming
2015-02-01
A new method based on convolution kernel compensation (CKC) for decomposing multi-channel surface electromyogram (sEMG) signals is proposed in this paper. Unsupervised learning and clustering function of self-organizing map (SOM) neural network are employed in this method. An initial innervations pulse train (IPT) is firstly estimated, some time instants corresponding to the highest peaks from the initial IPT are clustered by SOM neural network. Then the final IPT can be obtained from the observations corresponding to these time instants. In this paper, the proposed method was tested on the simulated signal, the influence of signal to noise ratio (SNR), the number of groups clustered by SOM and the number of highest peaks selected from the initial pulse train on the number of reconstructed sources and the pulse accuracy were studied, and the results show that the proposed approach is effective in decomposing multi-channel sEMG signals. PMID:25997257
A smooth dissipative particle dynamics method for domains with arbitrary-geometry solid boundaries
NASA Astrophysics Data System (ADS)
Gatsonis, Nikolaos A.; Potami, Raffaele; Yang, Jun
2014-01-01
A smooth dissipative particle dynamics method with dynamic virtual particle allocation (SDPD-DV) for modeling and simulation of mesoscopic fluids in wall-bounded domains is presented. The physical domain in SDPD-DV may contain external and internal solid boundaries of arbitrary geometries, periodic inlets and outlets, and the fluid region. The SDPD-DV method is realized with fluid particles, boundary particles, and dynamically allocated virtual particles. The internal or external solid boundaries of the domain can be of arbitrary geometry and are discretized with a surface grid. These boundaries are represented by boundary particles with assigned properties. The fluid domain is discretized with fluid particles of constant mass and variable volume. Conservative and dissipative force models due to virtual particles exerted on a fluid particle in the proximity of a solid boundary supplement the original SDPD formulation. The dynamic virtual particle allocation approach provides the density and the forces due to virtual particles. The integration of the SDPD equations is accomplished with a velocity-Verlet algorithm for the momentum and a Runge-Kutta for the entropy equation. The velocity integrator is supplemented by a bounce-forward algorithm in cases where the virtual particle force model is not able to prevent particle penetration. For the incompressible isothermal systems considered in this work, the pressure of a fluid particle is obtained by an artificial compressibility formulation for liquids and the ideal gas law for gases. The self-diffusion coefficient is obtained by an implementation of the generalized Einstein and the Green-Kubo relations. Field properties are obtained by sampling SDPD-DV outputs on a post-processing grid that allows harnessing the particle information on desired spatiotemporal scales. The SDPD-DV method is verified and validated with simulations in bounded and periodic domains that cover the hydrodynamic and mesoscopic regimes for
A smooth dissipative particle dynamics method for domains with arbitrary-geometry solid boundaries
NASA Astrophysics Data System (ADS)
Gatsonis, Nikolaos A.; Potami, Raffaele; Yang, Jun
2014-01-01
A smooth dissipative particle dynamics method with dynamic virtual particle allocation (SDPD-DV) for modeling and simulation of mesoscopic fluids in wall-bounded domains is presented. The physical domain in SDPD-DV may contain external and internal solid boundaries of arbitrary geometries, periodic inlets and outlets, and the fluid region. The SDPD-DV method is realized with fluid particles, boundary particles, and dynamically allocated virtual particles. The internal or external solid boundaries of the domain can be of arbitrary geometry and are discretized with a surface grid. These boundaries are represented by boundary particles with assigned properties. The fluid domain is discretized with fluid particles of constant mass and variable volume. Conservative and dissipative force models due to virtual particles exerted on a fluid particle in the proximity of a solid boundary supplement the original SDPD formulation. The dynamic virtual particle allocation approach provides the density and the forces due to virtual particles. The integration of the SDPD equations is accomplished with a velocity-Verlet algorithm for the momentum and a Runge-Kutta for the entropy equation. The velocity integrator is supplemented by a bounce-forward algorithm in cases where the virtual particle force model is not able to prevent particle penetration. For the incompressible isothermal systems considered in this work, the pressure of a fluid particle is obtained by an artificial compressibility formulation for liquids and the ideal gas law for gases. The self-diffusion coefficient is obtained by an implementation of the generalized Einstein and the Green-Kubo relations. Field properties are obtained by sampling SDPD-DV outputs on a post-processing grid that allows harnessing the particle information on desired spatiotemporal scales. The SDPD-DV method is verified and validated with simulations in bounded and periodic domains that cover the hydrodynamic and mesoscopic regimes for
Adaptively smoothed background seismicity rates in the Intermountain West, United States
NASA Astrophysics Data System (ADS)
Moschetti, M. P.
2013-05-01
Spatially smoothed seismicity rates are an important seismic source for seismic hazard calculations across much of the Intermountain West (IMW). The U.S. national seismic hazard maps have historically used smoothed seismicity rate models generated with fixed-bandwidth smoothing methods (Frankel, 1996; Petersen et al., 2008); however, recent tests using the California earthquake catalog indicate that adapting the smoothing bandwidth to the local seismicity density (e.g., Helmstetter et al., 2007; Werner et al., 2011) produces improved seismic source models relative to models with fixed smoothing bandwidths (Schorlemmer et al., 2010). To test the ability of adaptively smoothed seismicity models to match epicenter locations from later parts of the IMW earthquake catalog, I generate time-independent maps of smoothed seismicity rates by spatially smoothing the seismicity rates of M4+ earthquake epicenters using fixed-radius and adaptive smoothing methods. I evaluate the 'forecast' smoothed seismicity models generated from the early part of the earthquake catalog by comparing the locations of earthquakes that occur in the later times of the catalog with the forecast seismicity rates. Forecasts are generated from a de-clustered catalog (Gardner and Knopoff, 1974) with completeness levels ranging from M4-6. The forecasts assume that the Gutenberg-Richter relation describes the magnitude-frequency distribution and that the locations of smaller earthquakes (M4+) can identify the locations of future large, and damaging, earthquakes. Spatially smoothed seismicity rate models are generated with isotropic Gaussian and power-law smoothing kernels using fixed and adaptive bandwidths; the adaptive smoothing bandwidths are calculated with the method of Helmstetter et al. (2007). To identify optimal smoothing methods for long-term earthquake rates, I calculate likelihood values for all smoothed seismicity models by using a Poisson distribution for earthquake occurrence and select the
Weighted Bergman Kernels and Quantization}
NASA Astrophysics Data System (ADS)
Engliš, Miroslav
Let Ω be a bounded pseudoconvex domain in CN, φ, ψ two positive functions on Ω such that - log ψ, - log φ are plurisubharmonic, and z∈Ω a point at which - log φ is smooth and strictly plurisubharmonic. We show that as k-->∞, the Bergman kernels with respect to the weights φkψ have an asymptotic expansion
NASA Astrophysics Data System (ADS)
Fessant, Françoise; Pierret, Catherine; Lantos, Pierre
1996-10-01
In this paper we propose a comparison between two methods for the problem of long-term prediction of the smoothed sunspot index. These two methods are first the classical method of McNish and Lincoln (as improved by Stewart and Ostrow), and second a neural network method. The results of these two methods are compared in two periods, during the ascending and the declining phases of the current cycle 22 (1986 1996). The predictions with neural networks are much better than with the McNish and Lincoln method for the atypical ascending phase of cycle 22. During the second period the predictions are very similar, and in agreement with observations, when the McNish and Lincoln method is based on the data of declining phases of the cycles.
Putting Priors in Mixture Density Mercer Kernels
NASA Technical Reports Server (NTRS)
Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd
2004-01-01
This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.
Duff, I.
1994-12-31
This workshop focuses on kernels for iterative software packages. Specifically, the three speakers discuss various aspects of sparse BLAS kernels. Their topics are: `Current status of user lever sparse BLAS`; Current status of the sparse BLAS toolkit`; and `Adding matrix-matrix and matrix-matrix-matrix multiply to the sparse BLAS toolkit`.
An analysis of smoothed particle hydrodynamics
Swegle, J.W.; Attaway, S.W.; Heinstein, M.W.; Mello, F.J.; Hicks, D.L.
1994-03-01
SPH (Smoothed Particle Hydrodynamics) is a gridless Lagrangian technique which is appealing as a possible alternative to numerical techniques currently used to analyze high deformation impulsive loading events. In the present study, the SPH algorithm has been subjected to detailed testing and analysis to determine its applicability in the field of solid dynamics. An important result of the work is a rigorous von Neumann stability analysis which provides a simple criterion for the stability or instability of the method in terms of the stress state and the second derivative of the kernel function. Instability, which typically occurs only for solids in tension, results not from the numerical time integration algorithm, but because the SPH algorithm creates an effective stress with a negative modulus. The analysis provides insight into possible methods for removing the instability. Also, SPH has been coupled into the transient dynamics finite element code PRONTO, and a weighted residual derivation of the SPH equations has been obtained.
Asymptotic expansion of the trace of the heat kernel associated to the Dirichlet-to-Neumann operator
NASA Astrophysics Data System (ADS)
Liu, Genqian
2015-10-01
For a given bounded domain Ω with smooth boundary in a smooth Riemannian manifold (M, g), by decomposing the Dirichlet-to-Neumann operator into a sum of the square root of the Laplacian and a pseudodifferential operator, and by applying Grubb's method of symbolic calculus for the corresponding pseudodifferential heat kernel operators, we establish a procedure to calculate all the coefficients of the asymptotic expansion of the trace of the heat kernel associated to Dirichlet-to-Neumann operator as t →0+. In particular, we explicitly give the first four coefficients of this asymptotic expansion. These coefficients provide precise information regarding the area and curvatures of the boundary of the domain in terms of the spectrum of the Steklov problem.
Kernel PLS Estimation of Single-trial Event-related Potentials
NASA Technical Reports Server (NTRS)
Rosipal, Roman; Trejo, Leonard J.
2004-01-01
Nonlinear kernel partial least squaes (KPLS) regressior, is a novel smoothing approach to nonparametric regression curve fitting. We have developed a KPLS approach to the estimation of single-trial event related potentials (ERPs). For improved accuracy of estimation, we also developed a local KPLS method for situations in which there exists prior knowledge about the approximate latency of individual ERP components. To assess the utility of the KPLS approach, we compared non-local KPLS and local KPLS smoothing with other nonparametric signal processing and smoothing methods. In particular, we examined wavelet denoising, smoothing splines, and localized smoothing splines. We applied these methods to the estimation of simulated mixtures of human ERPs and ongoing electroencephalogram (EEG) activity using a dipole simulator (BESA). In this scenario we considered ongoing EEG to represent spatially and temporally correlated noise added to the ERPs. This simulation provided a reasonable but simplified model of real-world ERP measurements. For estimation of the simulated single-trial ERPs, local KPLS provided a level of accuracy that was comparable with or better than the other methods. We also applied the local KPLS method to the estimation of human ERPs recorded in an experiment on co,onitive fatigue. For these data, the local KPLS method provided a clear improvement in visualization of single-trial ERPs as well as their averages. The local KPLS method may serve as a new alternative to the estimation of single-trial ERPs and improvement of ERP averages.
Locally-Based Kernal PLS Smoothing to Non-Parametric Regression Curve Fitting
NASA Technical Reports Server (NTRS)
Rosipal, Roman; Trejo, Leonard J.; Wheeler, Kevin; Korsmeyer, David (Technical Monitor)
2002-01-01
We present a novel smoothing approach to non-parametric regression curve fitting. This is based on kernel partial least squares (PLS) regression in reproducing kernel Hilbert space. It is our concern to apply the methodology for smoothing experimental data where some level of knowledge about the approximate shape, local inhomogeneities or points where the desired function changes its curvature is known a priori or can be derived based on the observed noisy data. We propose locally-based kernel PLS regression that extends the previous kernel PLS methodology by incorporating this knowledge. We compare our approach with existing smoothing splines, hybrid adaptive splines and wavelet shrinkage techniques on two generated data sets.
Chen, Tai-Been; Chen, Jyh-Cheng; Lu, Henry Horng-Shing
2012-01-01
Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method. PMID:22948355
NASA Astrophysics Data System (ADS)
Kuboyama, Tetsuji; Hirata, Kouichi; Kashima, Hisashi; F. Aoki-Kinoshita, Kiyoko; Yasuda, Hiroshi
Learning from tree-structured data has received increasing interest with the rapid growth of tree-encodable data in the World Wide Web, in biology, and in other areas. Our kernel function measures the similarity between two trees by counting the number of shared sub-patterns called tree q-grams, and runs, in effect, in linear time with respect to the number of tree nodes. We apply our kernel function with a support vector machine (SVM) to classify biological data, the glycans of several blood components. The experimental results show that our kernel function performs as well as one exclusively tailored to glycan properties.
Barceló, M Antònia; Saez, Marc; Cano-Serral, Gemma; Martínez-Beneito, Miguel Angel; Martínez, José Miguel; Borrell, Carme; Ocaña-Riola, Ricardo; Montoya, Imanol; Calvo, Montse; López-Abente, Gonzalo; Rodríguez-Sanz, Maica; Toro, Silvia; Alcalá, José Tomás; Saurina, Carme; Sánchez-Villegas, Pablo; Figueiras, Adolfo
2008-01-01
Although there is some experience in the study of mortality inequalities in Spanish cities, there are large urban centers that have not yet been investigated using the census tract as the unit of territorial analysis. The coordinated project
Modelling shear flows with smoothed particle hydrodynamics and grid-based methods
NASA Astrophysics Data System (ADS)
Junk, Veronika; Walch, Stefanie; Heitsch, Fabian; Burkert, Andreas; Wetzstein, Markus; Schartmann, Marc; Price, Daniel
2010-09-01
Given the importance of shear flows for astrophysical gas dynamics, we study the evolution of the Kelvin-Helmholtz instability (KHI) analytically and numerically. We derive the dispersion relation for the two-dimensional KHI including viscous dissipation. The resulting expression for the growth rate is then used to estimate the intrinsic viscosity of four numerical schemes depending on code-specific as well as on physical parameters. Our set of numerical schemes includes the Tree-SPH code VINE, an alternative smoothed particle hydrodynamics (SPH) formulation developed by Price and the finite-volume grid codes FLASH and PLUTO. In the first part, we explicitly demonstrate the effect of dissipation-inhibiting mechanisms such as the Balsara viscosity on the evolution of the KHI. With VINE, increasing density contrasts lead to a continuously increasing suppression of the KHI (with complete suppression from a contrast of 6:1 or higher). The alternative SPH formulation including an artificial thermal conductivity reproduces the analytically expected growth rates up to a density contrast of 10:1. The second part addresses the shear flow evolution with FLASH and PLUTO. Both codes result in a consistent non-viscous evolution (in the equal as well as in the different density case) in agreement with the analytical prediction. The viscous evolution studied with FLASH shows minor deviations from the analytical prediction.
Convex-relaxed kernel mapping for image segmentation.
Ben Salah, Mohamed; Ben Ayed, Ismail; Jing Yuan; Hong Zhang
2014-03-01
This paper investigates a convex-relaxed kernel mapping formulation of image segmentation. We optimize, under some partition constraints, a functional containing two characteristic terms: 1) a data term, which maps the observation space to a higher (possibly infinite) dimensional feature space via a kernel function, thereby evaluating nonlinear distances between the observations and segments parameters and 2) a total-variation term, which favors smooth segment surfaces (or boundaries). The algorithm iterates two steps: 1) a convex-relaxation optimization with respect to the segments by solving an equivalent constrained problem via the augmented Lagrange multiplier method and 2) a convergent fixed-point optimization with respect to the segments parameters. The proposed algorithm can bear with a variety of image types without the need for complex and application-specific statistical modeling, while having the computational benefits of convex relaxation. Our solution is amenable to parallelized implementations on graphics processing units (GPUs) and extends easily to high dimensions. We evaluated the proposed algorithm with several sets of comprehensive experiments and comparisons, including: 1) computational evaluations over 3D medical-imaging examples and high-resolution large-size color photographs, which demonstrate that a parallelized implementation of the proposed method run on a GPU can bring a significant speed-up and 2) accuracy evaluations against five state-of-the-art methods over the Berkeley color-image database and a multimodel synthetic data set, which demonstrates competitive performances of the algorithm. PMID:24723519
Multiple kernel learning for sparse representation-based classification.
Shrivastava, Ashish; Patel, Vishal M; Chellappa, Rama
2014-07-01
In this paper, we propose a multiple kernel learning (MKL) algorithm that is based on the sparse representation-based classification (SRC) method. Taking advantage of the nonlinear kernel SRC in efficiently representing the nonlinearities in the high-dimensional feature space, we propose an MKL method based on the kernel alignment criteria. Our method uses a two step training method to learn the kernel weights and sparse codes. At each iteration, the sparse codes are updated first while fixing the kernel mixing coefficients, and then the kernel mixing coefficients are updated while fixing the sparse codes. These two steps are repeated until a stopping criteria is met. The effectiveness of the proposed method is demonstrated using several publicly available image classification databases and it is shown that this method can perform significantly better than many competitive image classification algorithms. PMID:24835226
Jalali-Heravi, Mehdi; Kyani, Anahita
2007-05-01
This paper introduces the genetic algorithm-kernel partial least square (GA-KPLS), as a novel nonlinear feature selection method. This technique combines genetic algorithms (GAs) as powerful optimization methods with KPLS as a robust nonlinear statistical method for variable selection. This feature selection method is combined with artificial neural network to develop a nonlinear QSAR model for predicting activities of a series of substituted aromatic sulfonamides as carbonic anhydrase II (CA II) inhibitors. Eight simple one- and two-dimensional descriptors were selected by GA-KPLS and considered as inputs for developing artificial neural networks (ANNs). These parameters represent the role of acceptor-donor pair, hydrogen bonding, hydrosolubility and lipophilicity of the active sites and also the size of the inhibitors on inhibitor-isozyme interaction. The accuracy of 8-4-1 networks was illustrated by validation techniques of leave-one-out (LOO) and leave-multiple-out (LMO) cross-validations and Y-randomization. Superiority of this method (GA-KPLS-ANN) over the linear one (MLR) in a previous work and also the GA-PLS-ANN in which a linear feature selection method has been used indicates that the GA-KPLS approach is a powerful method for the variable selection in nonlinear systems. PMID:17316919
NASA Astrophysics Data System (ADS)
Kandasamy, S.; Baret, F.; Verger, A.; Neveux, P.; Weiss, M.
2013-06-01
Moderate resolution satellite sensors including MODIS (Moderate Resolution Imaging Spectroradiometer) already provide more than 10 yr of observations well suited to describe and understand the dynamics of earth's surface. However, these time series are associated with significant uncertainties and incomplete because of cloud cover. This study compares eight methods designed to improve the continuity by filling gaps and consistency by smoothing the time course. It includes methods exploiting the time series as a whole (iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of a few weeks to few months (adaptive Savitzky-Golay filter (SGF), temporal smoothing and gap filling (TSGF), and asymmetric Gaussian function (AGF)), in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to the MODIS leaf area index product for the period 2000-2008 and over 25 sites showed a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that the EMD, LPF and AGF methods were failing because of a significant fraction of gaps (more than 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (Clim) was able to fill more than 50% of the gaps for sites with more than 60% (80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances that are significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large
NASA Astrophysics Data System (ADS)
Kandasamy, S.; Baret, F.; Verger, A.; Neveux, P.; Weiss, M.
2012-12-01
Moderate resolution satellite sensors including MODIS already provide more than 10 yr of observations well suited to describe and understand the dynamics of the Earth surface. However, these time series are incomplete because of cloud cover and associated with significant uncertainties. This study compares eight methods designed to improve the continuity by filling gaps and the consistency by smoothing the time course. It includes methods exploiting the time series as a whole (Iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of few weeks to few months (Adaptive Savitzky-Golay filter (SGF), temporal smoothing and gap filling (TSGF) and asymmetric Gaussian function (AGF)) in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to MODIS leaf area index product for the period 2000-2008 over 25 sites showing a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that EMD, LPF and AGF methods were failing in case of significant fraction of gaps (%Gap > 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (respectively Clim) was able to fill more than 50% of the gaps for sites with more than 60% (resp. 80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the several methods, with a decrease
Gabor-based kernel PCA with fractional power polynomial models for face recognition.
Liu, Chengjun
2004-05-01
This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power
NASA Astrophysics Data System (ADS)
Sohn, Dongwoo; Im, Seyoung
2013-06-01
In this paper, novel finite elements that include an arbitrary number of additional nodes on each edge of a quadrilateral element are proposed to achieve compatible connection of neighboring nonmatching meshes in plate and shell analyses. The elements, termed variable-node plate elements, are based on two-dimensional variable-node elements with point interpolation and on the Mindlin-Reissner plate theory. Subsequently the flat shell elements, termed variable-node shell elements, are formulated by further extending the plate elements. To eliminate a transverse shear locking phenomenon, the assumed natural strain method is used for plate and shell analyses. Since the variable-node plate and shell elements allow an arbitrary number of additional nodes and overcome locking problems, they make it possible to connect two nonmatching meshes and to provide accurate solutions in local mesh refinement. In addition, the curvature and strain smoothing methods through smoothed integration are adopted to improve the element performance. Several numerical examples are presented to demonstrate the effectiveness of the elements in terms of the accuracy and efficiency of the analyses.
NASA Astrophysics Data System (ADS)
Nassauer, Benjamin; Liedke, Thomas; Kuna, Meinhard
2016-03-01
In the present paper, the direct coupling of a discrete element method (DEM) with polyhedral particles and smoothed particle hydrodynamics (SPH) is presented. The two simulation techniques are fully coupled in both ways through interaction forces between the solid DEM particles and the fluid SPH particles. Thus this simulation method provides the possibility to simulate the individual movement of polyhedral, sharp-edged particles as well as the flow field around these particles in fluid-saturated granular matter which occurs in many technical processes e.g. wire sawing, grinding or lapping. The coupled method is exemplified and validated by the simulation of a particle in a shear flow, which shows good agreement with analytical solutions.
Critical Parameters of the In Vitro Method of Vascular Smooth Muscle Cell Calcification
Hortells, Luis; Sosa, Cecilia; Millán, Ángel; Sorribas, Víctor
2015-01-01
Background Vascular calcification (VC) is primarily studied using cultures of vascular smooth muscle cells. However, the use of very different protocols and extreme conditions can provide findings unrelated to VC. In this work we aimed to determine the critical experimental parameters that affect calcification in vitro and to determine the relevance to calcification in vivo. Experimental Procedures and Results Rat VSMC calcification in vitro was studied using different concentrations of fetal calf serum, calcium, and phosphate, in different types of culture media, and using various volumes and rates of change. The bicarbonate content of the media critically affected pH and resulted in supersaturation, depending on the concentration of Ca2+ and Pi. Such supersaturation is a consequence of the high dependence of bicarbonate buffers on CO2 vapor pressure and bicarbonate concentration at pHs above 7.40. Such buffer systems cause considerable pH variations as a result of minor experimental changes. The variations are more critical for DMEM and are negligible when the bicarbonate concentration is reduced to ¼. Particle nucleation and growth were observed by dynamic light scattering and electron microscopy. Using 2mM Pi, particles of ~200nm were observed at 24 hours in MEM and at 1 hour in DMEM. These nuclei grew over time, were deposited in the cells, and caused osteogene expression or cell death, depending on the precipitation rate. TEM observations showed that the initial precipitate was amorphous calcium phosphate (ACP), which converts into hydroxyapatite over time. In blood, the scenario is different, because supersaturation is avoided by a tightly controlled pH of 7.4, which prevents the formation of PO43--containing ACP. Conclusions The precipitation of ACP in vitro is unrelated to VC in vivo. The model needs to be refined through controlled pH and the use of additional procalcifying agents other than Pi in order to reproduce calcium phosphate deposition in vivo
Robotic Intelligence Kernel: Communications
Walton, Mike C.
2009-09-16
The INL Robotic Intelligence Kernel-Comms is the communication server that transmits information between one or more robots using the RIK and one or more user interfaces. It supports event handling and multiple hardware communication protocols.
Community detection using Kernel Spectral Clustering with memory
NASA Astrophysics Data System (ADS)
Langone, Rocco; Suykens, Johan A. K.
2013-02-01
This work is related to the problem of community detection in dynamic scenarios, which for instance arises in the segmentation of moving objects, clustering of telephone traffic data, time-series micro-array data etc. A desirable feature of a clustering model which has to capture the evolution of communities over time is the temporal smoothness between clusters in successive time-steps. In this way the model is able to track the long-term trend and in the same time it smooths out short-term variation due to noise. We use the Kernel Spectral Clustering with Memory effect (MKSC) which allows to predict cluster memberships of new nodes via out-of-sample extension and has a proper model selection scheme. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness as a valid prior knowledge. The latter, in fact, allows the model to cluster the current data well and to be consistent with the recent history. Here we propose a generalization of the MKSC model with an arbitrary memory, not only one time-step in the past. The experiments conducted on toy problems confirm our expectations: the more memory we add to the model, the smoother over time are the clustering results. We also compare with the Evolutionary Spectral Clustering (ESC) algorithm which is a state-of-the art method, and we obtain comparable or better results.
Improving convergence in smoothed particle hydrodynamics simulations without pairing instability
NASA Astrophysics Data System (ADS)
Dehnen, Walter; Aly, Hossam
2012-09-01
The numerical convergence of smoothed particle hydrodynamics (SPH) can be severely restricted by random force errors induced by particle disorder, especially in shear flows, which are ubiquitous in astrophysics. The increase in the number NH of neighbours when switching to more extended smoothing kernels at fixed resolution (using an appropriate definition for the SPH resolution scale) is insufficient to combat these errors. Consequently, trading resolution for better convergence is necessary, but for traditional smoothing kernels this option is limited by the pairing (or clumping) instability. Therefore, we investigate the suitability of the Wendland functions as smoothing kernels and compare them with the traditional B-splines. Linear stability analysis in three dimensions and test simulations demonstrate that the Wendland kernels avoid the pairing instability for all NH, despite having vanishing derivative at the origin (disproving traditional ideas about the origin of this instability; instead, we uncover a relation with the kernel Fourier transform and give an explanation in terms of the SPH density estimator). The Wendland kernels are computationally more convenient than the higher order B-splines, allowing large NH and hence better numerical convergence (note that computational costs rise sublinear with NH). Our analysis also shows that at low NH the quartic spline kernel with NH ≈ 60 obtains much better convergence than the standard cubic spline.
Robotic Intelligence Kernel: Driver
2009-09-16
The INL Robotic Intelligence Kernel-Driver is built on top of the RIK-A and implements a dynamic autonomy structure. The RIK-D is used to orchestrate hardware for sensing and action as well as software components for perception, communication, behavior and world modeling into a single cognitive behavior kernel that provides intrinsic intelligence for a wide variety of unmanned ground vehicle systems.
Quantum kernel applications in medicinal chemistry.
Huang, Lulu; Massa, Lou
2012-07-01
Progress in the quantum mechanics of biological molecules is being driven by computational advances. The notion of quantum kernels can be introduced to simplify the formalism of quantum mechanics, making it especially suitable for parallel computation of very large biological molecules. The essential idea is to mathematically break large biological molecules into smaller kernels that are calculationally tractable, and then to represent the full molecule by a summation over the kernels. The accuracy of the kernel energy method (KEM) is shown by systematic application to a great variety of molecular types found in biology. These include peptides, proteins, DNA and RNA. Examples are given that explore the KEM across a variety of chemical models, and to the outer limits of energy accuracy and molecular size. KEM represents an advance in quantum biology applicable to problems in medicine and drug design. PMID:22857535
A Novel Framework for Learning Geometry-Aware Kernels.
Pan, Binbin; Chen, Wen-Sheng; Xu, Chen; Chen, Bo
2016-05-01
The data from real world usually have nonlinear geometric structure, which are often assumed to lie on or close to a low-dimensional manifold in a high-dimensional space. How to detect this nonlinear geometric structure of the data is important for the learning algorithms. Recently, there has been a surge of interest in utilizing kernels to exploit the manifold structure of the data. Such kernels are called geometry-aware kernels and are widely used in the machine learning algorithms. The performance of these algorithms critically relies on the choice of the geometry-aware kernels. Intuitively, a good geometry-aware kernel should utilize additional information other than the geometric information. In many applications, it is required to compute the out-of-sample data directly. However, most of the geometry-aware kernel methods are restricted to the available data given beforehand, with no straightforward extension for out-of-sample data. In this paper, we propose a framework for more general geometry-aware kernel learning. The proposed framework integrates multiple sources of information and enables us to develop flexible and effective kernel matrices. Then, we theoretically show how the learned kernel matrices are extended to the corresponding kernel functions, in which the out-of-sample data can be computed directly. Under our framework, a novel family of geometry-aware kernels is developed. Especially, some existing geometry-aware kernels can be viewed as instances of our framework. The performance of the kernels is evaluated on dimensionality reduction, classification, and clustering tasks. The empirical results show that our kernels significantly improve the performance.
A kernel autoassociator approach to pattern classification.
Zhang, Haihong; Huang, Weimin; Huang, Zhiyong; Zhang, Bailing
2005-06-01
Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains. PMID:15971928
A kernel autoassociator approach to pattern classification.
Zhang, Haihong; Huang, Weimin; Huang, Zhiyong; Zhang, Bailing
2005-06-01
Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.
Federico, Alejandro; Kaufmann, Guillermo H
2003-12-10
We evaluate the use of a smoothed space-frequency distribution (SSFD) to retrieve optical phase maps in digital speckle pattern interferometry (DSPI). The performance of this method is tested by use of computer-simulated DSPI fringes. Phase gradients are found along a pixel path from a single DSPI image, and the phase map is finally determined by integration. This technique does not need the application of a phase unwrapping algorithm or the introduction of carrier fringes in the interferometer. It is shown that a Wigner-Ville distribution with a smoothing Gaussian kernel gives more-accurate results than methods based on the continuous wavelet transform. We also discuss the influence of filtering on smoothing of the DSPI fringes and some additional limitations that emerge when this technique is applied. The performance of the SSFD method for processing experimental data is then illustrated.
Owusu-Edusei, Kwame; Owens, Chantelle J
2009-01-01
Background Chlamydia continues to be the most prevalent disease in the United States. Effective spatial monitoring of chlamydia incidence is important for successful implementation of control and prevention programs. The objective of this study is to apply Bayesian smoothing and exploratory spatial data analysis (ESDA) methods to monitor Texas county-level chlamydia incidence rates by examining spatiotemporal patterns. We used county-level data on chlamydia incidence (for all ages, gender and races) from the National Electronic Telecommunications System for Surveillance (NETSS) for 2004 and 2005. Results Bayesian-smoothed chlamydia incidence rates were spatially dependent both in levels and in relative changes. Erath county had significantly (p < 0.05) higher smoothed rates (> 300 cases per 100,000 residents) than its contiguous neighbors (195 or less) in both years. Gaines county experienced the highest relative increase in smoothed rates (173% – 139 to 379). The relative change in smoothed chlamydia rates in Newton county was significantly (p < 0.05) higher than its contiguous neighbors. Conclusion Bayesian smoothing and ESDA methods can assist programs in using chlamydia surveillance data to identify outliers, as well as relevant changes in chlamydia incidence in specific geographic units. Secondly, it may also indirectly help in assessing existing differences and changes in chlamydia surveillance systems over time. PMID:19245686
LeFebvre, W.
1994-08-01
For many years, the popular program top has aided system administrations in examination of process resource usage on their machines. Yet few are familiar with the techniques involved in obtaining this information. Most of what is displayed by top is available only in the dark recesses of kernel memory. Extracting this information requires familiarity not only with how bytes are read from the kernel, but also what data needs to be read. The wide variety of systems and variants of the Unix operating system in today`s marketplace makes writing such a program very challenging. This paper explores the tremendous diversity in kernel information across the many platforms and the solutions employed by top to achieve and maintain ease of portability in the presence of such divergent systems.
ERIC Educational Resources Information Center
Price, Beverley; Pincott, Maxine; Rebman, Ashley; Northcutt, Jen; Barsanti, Amy; Silkunas, Betty; Brighton, Susan K.; Reitz, David; Winkler, Maureen
1999-01-01
Presents discipline tips from several teachers to keep classrooms running smoothly all year. Some of the suggestions include the following: a bear-cave warning system, peer mediation, a motivational mystery, problem students acting as the teacher's assistant, a positive-behavior-reward chain, a hallway scavenger hunt (to ensure quiet passage…
Joshi, Anand A.; Shattuck, David W.; Thompson, Paul M.; Leahy, Richard M.
2009-01-01
Neuroimaging data, such as 3-D maps of cortical thickness or neural activation, can often be analyzed more informatively with respect to the cortical surface rather than the entire volume of the brain. Any cortical surface-based analysis should be carried out using computations in the intrinsic geometry of the surface rather than using the metric of the ambient 3-D space. We present parameterization-based numerical methods for performing isotropic and anisotropic filtering on triangulated surface geometries. In contrast to existing FEM-based methods for triangulated geometries, our approach accounts for the metric of the surface. In order to discretize and numerically compute the isotropic and anisotropic geometric operators, we first parameterize the surface using a p-harmonic mapping. We then use this parameterization as our computational domain and account for the surface metric while carrying out isotropic and anisotropic filtering. To validate our method, we compare our numerical results to the analytical expression for isotropic diffusion on a spherical surface. We apply these methods to smoothing of mean curvature maps on the cortical surface, a step commonly required for analysis of gyrification or for registering surface-based maps across subjects. PMID:19423447
Adaptive particle refinement and derefinement applied to the smoothed particle hydrodynamics method
NASA Astrophysics Data System (ADS)
Barcarolo, D. A.; Le Touzé, D.; Oger, G.; de Vuyst, F.
2014-09-01
SPH simulations are usually performed with a uniform particle distribution. New techniques have been recently proposed to enable the use of spatially varying particle distributions, which encouraged the development of automatic adaptivity and particle refinement/derefinement algorithms. All these efforts resulted in very interesting and promising procedures leading to more efficient and faster SPH simulations. In this article, a family of particle refinement techniques is reviewed and a new derefinement technique is proposed and validated through several test cases involving both free-surface and viscous flows. Besides, this new procedure allows higher resolutions in the regions requiring increased accuracy. Moreover, several levels of refinement can be used with this new technique, as often encountered in adaptive mesh refinement techniques in mesh-based methods.
Adaptive kernels for multi-fiber reconstruction.
Barmpoutis, Angelos; Jian, Bing; Vemuri, Baba C
2009-01-01
In this paper we present a novel method for multi-fiber reconstruction given a diffusion-weighted MRI dataset. There are several existing methods that employ various spherical deconvolution kernels for achieving this task. However the kernels in all of the existing methods rely on certain assumptions regarding the properties of the underlying fibers, which introduce inaccuracies and unnatural limitations in them. Our model is a non trivial generalization of the spherical deconvolution model, which unlike the existing methods does not make use of a fix-shaped kernel. Instead, the shape of the kernel is estimated simultaneously with the rest of the unknown parameters by employing a general adaptive model that can theoretically approximate any spherical deconvolution kernel. The performance of our model is demonstrated using simulated and real diffusion-weighed MR datasets and compared quantitatively with several existing techniques in literature. The results obtained indicate that our model has superior performance that is close to the theoretic limit of the best possible achievable result.
Analog forecasting with dynamics-adapted kernels
NASA Astrophysics Data System (ADS)
Zhao, Zhizhen; Giannakis, Dimitrios
2016-09-01
Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens’ delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nyström method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.
Robust visual tracking via speedup multiple kernel ridge regression
NASA Astrophysics Data System (ADS)
Qian, Cheng; Breckon, Toby P.; Li, Hui
2015-09-01
Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize the nonlinear structure well. We propose an appearance model based on kernel ridge regression for visual tracking. Dense sampling is fulfilled around the target image patches to collect the training samples. In order to obtain a kernel space in favor of describing the target appearance, multiple kernel learning is introduced into the selection of kernels. Under the framework, instead of a single kernel, a linear combination of kernels is learned from the training samples to create a kernel space. Resorting to the circulant property of a kernel matrix, a fast interpolate iterative algorithm is developed to seek coefficients that are assigned to these kernels so as to give an optimal combination. After the regression function is learned, all candidate image patches gathered are taken as the input of the function, and the candidate with the maximal response is regarded as the object image patch. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art tracking methods.
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps.
Rasmussen, Peter Mondrup; Madsen, Kristoffer Hougaard; Lund, Torben Ellegaard; Hansen, Lars Kai
2011-04-01
There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification models. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher discriminant, and the SVM, and conclude that the sensitivity map is a versatile and computationally efficient tool for visualization of nonlinear kernel models in neuroimaging.
Robotic Intelligence Kernel: Visualization
2009-09-16
The INL Robotic Intelligence Kernel-Visualization is the software that supports the user interface. It uses the RIK-C software to communicate information to and from the robot. The RIK-V illustrates the data in a 3D display and provides an operating picture wherein the user can task the robot.
Robotic Intelligence Kernel: Architecture
2009-09-16
The INL Robotic Intelligence Kernel Architecture (RIK-A) is a multi-level architecture that supports a dynamic autonomy structure. The RIK-A is used to coalesce hardware for sensing and action as well as software components for perception, communication, behavior and world modeling into a framework that can be used to create behaviors for humans to interact with the robot.
A Kernel-based Account of Bibliometric Measures
NASA Astrophysics Data System (ADS)
Ito, Takahiko; Shimbo, Masashi; Kudo, Taku; Matsumoto, Yuji
The application of kernel methods to citation analysis is explored. We show that a family of kernels on graphs provides a unified perspective on the three bibliometric measures that have been discussed independently: relatedness between documents, global importance of individual documents, and importance of documents relative to one or more (root) documents (relative importance). The framework provided by the kernels establishes relative importance as an intermediate between relatedness and global importance, in which the degree of `relativity,' or the bias between relatedness and importance, is naturally controlled by a parameter characterizing individual kernels in the family.
An Investigation of Methods for Improving Estimation of Test Score Distributions.
ERIC Educational Resources Information Center
Hanson, Bradley A.
Three methods of estimating test score distributions that may improve on using the observed frequencies (OBFs) as estimates of a population test score distribution are considered: the kernel method (KM); the polynomial method (PM); and the four-parameter beta binomial method (FPBBM). The assumption each method makes about the smoothness of the…
Li, Bin; Sang, Jizhang; Zhang, Zhongping
2016-01-01
A critical requirement to achieve high efficiency of debris laser tracking is to have sufficiently accurate orbit predictions (OP) in both the pointing direction (better than 20 arc seconds) and distance from the tracking station to the debris objects, with the former more important than the latter because of the narrow laser beam. When the two line element (TLE) is used to provide the orbit predictions, the resultant pointing errors are usually on the order of tens to hundreds of arc seconds. In practice, therefore, angular observations of debris objects are first collected using an optical tracking sensor, and then used to guide the laser beam pointing to the objects. The manual guidance may cause interrupts to the laser tracking, and consequently loss of valuable laser tracking data. This paper presents a real-time orbit determination (OD) and prediction method to realize smooth and efficient debris laser tracking. The method uses TLE-computed positions and angles over a short-arc of less than 2 min as observations in an OD process where simplified force models are considered. After the OD convergence, the OP is performed from the last observation epoch to the end of the tracking pass. Simulation and real tracking data processing results show that the pointing prediction errors are usually less than 10″, and the distance errors less than 100 m, therefore, the prediction accuracy is sufficient for the blind laser tracking. PMID:27347958
NASA Astrophysics Data System (ADS)
Suwa, T.; Imamura, F.; Sugawara, D.; Ogasawara, K.; Watanabe, M.; Hirahara, T.
2014-12-01
A tsunami simulator integrating a 3-D fluid simulation technology that runs on large-scale parallel computers using smoothed-particle hydrodynamics (SPH) method has been developed together with a 2-D tsunami propagation simulation technique using a nonlinear shallow water wave model. We use the 2-D simulation to calculate tsunami propagation of scale of about 1000km from epicenter to near shore. The 3-D SPH method can be used to calculate the water surface and hydraulic force that a tsunami can exert on a building, and to simulate flooding patterns at urban area of at most km scale. With our simulator we can also see three dimensional fluid feature such as complex changes a tsunami undergoes as it interacts with coastal topography or structures. As a result it is hoped that, e.g. , effect of the structures to dissipate waves energy passing over it can be elucidated. The authors utilize the simulator in the third of five fields of the Strategic Programs for Innovative Research, "Advanced Prediction Researches for Natural Disaster Prevention and Reduction," or the theme "Improvement of the tsunami forecasting system on the HPCI computer." The results of tsunami simulation using the K computer will be reported. We are going to apply it to a real problem of the disaster prevention in future.
Du, Hui; He, Jianyu; Wang, Sicen; He, Langchong
2010-07-01
The dissociation equilibrium constant (K(D)) is an important affinity parameter for studying drug-receptor interactions. A vascular smooth muscle (VSM) cell membrane chromatography (CMC) method was developed for determination of the K(D) values for calcium antagonist-L-type calcium channel (L-CC) interactions. VSM cells, by means of primary culture with rat thoracic aortas, were used for preparation of the cell membrane stationary phase in the VSM/CMC model. All measurements were performed with spectrophotometric detection (237 nm) at 37 degrees C. The K(D) values obtained using frontal analysis were 3.36 x 10(-6) M for nifedipine, 1.34 x 10(-6) M for nimodipine, 6.83 x 10(-7) M for nitrendipine, 1.23 x 10(-7) M for nicardipine, 1.09 x 10(-7) M for amlodipine, and 8.51 x 10(-8) M for verapamil. This affinity rank order obtained from the VSM/CMC method had a strong positive correlation with that obtained from radioligand binding assay. The location of the binding region was examined by displacement experiments using nitrendipine as a mobile-phase additive. It was found that verapamil occupied a class of binding sites on L-CCs different from those occupied by nitrendipine. In addition, nicardipine, amlodipine, and nitrendipine had direct competition at a single common binding site. The studies showed that CMC can be applied to the investigation of drug-receptor interactions.
Li, Bin; Sang, Jizhang; Zhang, Zhongping
2016-01-01
A critical requirement to achieve high efficiency of debris laser tracking is to have sufficiently accurate orbit predictions (OP) in both the pointing direction (better than 20 arc seconds) and distance from the tracking station to the debris objects, with the former more important than the latter because of the narrow laser beam. When the two line element (TLE) is used to provide the orbit predictions, the resultant pointing errors are usually on the order of tens to hundreds of arc seconds. In practice, therefore, angular observations of debris objects are first collected using an optical tracking sensor, and then used to guide the laser beam pointing to the objects. The manual guidance may cause interrupts to the laser tracking, and consequently loss of valuable laser tracking data. This paper presents a real-time orbit determination (OD) and prediction method to realize smooth and efficient debris laser tracking. The method uses TLE-computed positions and angles over a short-arc of less than 2 min as observations in an OD process where simplified force models are considered. After the OD convergence, the OP is performed from the last observation epoch to the end of the tracking pass. Simulation and real tracking data processing results show that the pointing prediction errors are usually less than 10″, and the distance errors less than 100 m, therefore, the prediction accuracy is sufficient for the blind laser tracking. PMID:27347958
Anthraquinones isolated from the browned Chinese chestnut kernels (Castanea mollissima blume)
NASA Astrophysics Data System (ADS)
Zhang, Y. L.; Qi, J. H.; Qin, L.; Wang, F.; Pang, M. X.
2016-08-01
Anthraquinones (AQS) represent a group of secondary metallic products in plants. AQS are often naturally occurring in plants and microorganisms. In a previous study, we found that AQS were produced by enzymatic browning reaction in Chinese chestnut kernels. To find out whether non-enzymatic browning reaction in the kernels could produce AQS too, AQS were extracted from three groups of chestnut kernels: fresh kernels, non-enzymatic browned kernels, and browned kernels, and the contents of AQS were determined. High performance liquid chromatography (HPLC) and nuclear magnetic resonance (NMR) methods were used to identify two compounds of AQS, rehein(1) and emodin(2). AQS were barely exists in the fresh kernels, while both browned kernel groups sample contained a high amount of AQS. Thus, we comfirmed that AQS could be produced during both enzymatic and non-enzymatic browning process. Rhein and emodin were the main components of AQS in the browned kernels.
Bobodzhanov, A A; Safonov, V F
2013-07-31
The paper deals with extending the Lomov regularization method to classes of singularly perturbed Fredholm-type integro-differential systems, which have not so far been studied. In these the limiting operator is discretely noninvertible. Such systems are commonly known as problems with unstable spectrum. Separating out the essential singularities in the solutions to these problems presents great difficulties. The principal one is to give an adequate description of the singularities induced by 'instability points' of the spectrum. A methodology for separating singularities by using normal forms is developed. It is applied to the above type of systems and is substantiated in these systems. Bibliography: 10 titles.
Liu, Jin; Guo, Ting-ting; Li, Hao-chuan; Jia, Shi-qiang; Yan, Yan-lu; An, Dong; Zhang, Yao; Chen, Shao-jiang
2015-11-01
Doubled haploid (DH) lines are routinely applied in the hybrid maize breeding programs of many institutes and companies for their advantages of complete homozygosity and short breeding cycle length. A key issue in this approach is an efficient screening system to identify haploid kernels from the hybrid kernels crossed with the inducer. At present, haploid kernel selection is carried out manually using the"red-crown" kernel trait (the haploid kernel has a non-pigmented embryo and pigmented endosperm) controlled by the R1-nj gene. Manual selection is time-consuming and unreliable. Furthermore, the color of the kernel embryo is concealed by the pericarp. Here, we establish a novel approach for identifying maize haploid kernels based on visible (Vis) spectroscopy and support vector machine (SVM) pattern recognition technology. The diffuse transmittance spectra of individual kernels (141 haploid kernels and 141 hybrid kernels from 9 genotypes) were collected using a portable UV-Vis spectrometer and integrating sphere. The raw spectral data were preprocessed using smoothing and vector normalization methods. The desired feature wavelengths were selected based on the results of the Kolmogorov-Smirnov test. The wavelengths with p values above 0. 05 were eliminated because the distributions of absorbance data in these wavelengths show no significant difference between haploid and hybrid kernels. Principal component analysis was then performed to reduce the number of variables. The SVM model was evaluated by 9-fold cross-validation. In each round, samples of one genotype were used as the testing set, while those of other genotypes were used as the training set. The mean rate of correct discrimination was 92.06%. This result demonstrates the feasibility of using Vis spectroscopy to identify haploid maize kernels. The method would help develop a rapid and accurate automated screening-system for haploid kernels. PMID:26978947
Technology Transfer Automated Retrieval System (TEKTRAN)
The current US corn grading system accounts for the portion of damaged kernels, which is measured by time-consuming and inaccurate visual inspection. Near infrared spectroscopy (NIRS), a non-destructive and fast analytical method, was tested as a tool for discriminating corn kernels with heat and f...
Gaussian kernel width optimization for sparse Bayesian learning.
Mohsenzadeh, Yalda; Sheikhzadeh, Hamid
2015-04-01
Sparse kernel methods have been widely used in regression and classification applications. The performance and the sparsity of these methods are dependent on the appropriate choice of the corresponding kernel functions and their parameters. Typically, the kernel parameters are selected using a cross-validation approach. In this paper, a learning method that is an extension of the relevance vector machine (RVM) is presented. The proposed method can find the optimal values of the kernel parameters during the training procedure. This algorithm uses an expectation-maximization approach for updating kernel parameters as well as other model parameters; therefore, the speed of convergence and computational complexity of the proposed method are the same as the standard RVM. To control the convergence of this fully parameterized model, the optimization with respect to the kernel parameters is performed using a constraint on these parameters. The proposed method is compared with the typical RVM and other competing methods to analyze the performance. The experimental results on the commonly used synthetic data, as well as benchmark data sets, demonstrate the effectiveness of the proposed method in reducing the performance dependency on the initial choice of the kernel parameters. PMID:25794377
Huang, Jian; Yuen, Pong C; Chen, Wen-Sheng; Lai, Jian Huang
2007-08-01
This paper addresses the problem of automatically tuning multiple kernel parameters for the kernel-based linear discriminant analysis (LDA) method. The kernel approach has been proposed to solve face recognition problems under complex distribution by mapping the input space to a high-dimensional feature space. Some recognition algorithms such as the kernel principal components analysis, kernel Fisher discriminant, generalized discriminant analysis, and kernel direct LDA have been developed in the last five years. The experimental results show that the kernel-based method is a good and feasible approach to tackle the pose and illumination variations. One of the crucial factors in the kernel approach is the selection of kernel parameters, which highly affects the generalization capability and stability of the kernel-based learning methods. In view of this, we propose an eigenvalue-stability-bounded margin maximization (ESBMM) algorithm to automatically tune the multiple parameters of the Gaussian radial basis function kernel for the kernel subspace LDA (KSLDA) method, which is developed based on our previously developed subspace LDA method. The ESBMM algorithm improves the generalization capability of the kernel-based LDA method by maximizing the margin maximization criterion while maintaining the eigenvalue stability of the kernel-based LDA method. An in-depth investigation on the generalization performance on pose and illumination dimensions is performed using the YaleB and CMU PIE databases. The FERET database is also used for benchmark evaluation. Compared with the existing PCA-based and LDA-based methods, our proposed KSLDA method, with the ESBMM kernel parameter estimation algorithm, gives superior performance.
Increasing accuracy of dispersal kernels in grid-based population models
Slone, D.H.
2011-01-01
Dispersal kernels in grid-based population models specify the proportion, distance and direction of movements within the model landscape. Spatial errors in dispersal kernels can have large compounding effects on model accuracy. Circular Gaussian and Laplacian dispersal kernels at a range of spatial resolutions were investigated, and methods for minimizing errors caused by the discretizing process were explored. Kernels of progressively smaller sizes relative to the landscape grid size were calculated using cell-integration and cell-center methods. These kernels were convolved repeatedly, and the final distribution was compared with a reference analytical solution. For large Gaussian kernels (σ > 10 cells), the total kernel error was <10 &sup-11; compared to analytical results. Using an invasion model that tracked the time a population took to reach a defined goal, the discrete model results were comparable to the analytical reference. With Gaussian kernels that had σ ≤ 0.12 using the cell integration method, or σ ≤ 0.22 using the cell center method, the kernel error was greater than 10%, which resulted in invasion times that were orders of magnitude different than theoretical results. A goal-seeking routine was developed to adjust the kernels to minimize overall error. With this, corrections for small kernels were found that decreased overall kernel error to <10-11 and invasion time error to <5%.
Estimation of Smoothing Error in SBUV Profile and Total Ozone Retrieval
NASA Technical Reports Server (NTRS)
Kramarova, N. A.; Bhartia, P. K.; Frith, S. M.; Fisher, B. L.; McPeters, R. D.; Taylor, S.; Labow, G. J.
2011-01-01
Data from the Nimbus-4, Nimbus-7 Solar Backscatter Ultra Violet (SBUV) and seven of the NOAA series of SBUV/2 instruments spanning 41 years are being reprocessed using V8.6 algorithm. The data are scheduled to be released by the end of August 2011. An important focus of the new algorithm is to estimate various sources of errors in the SBUV profiles and total ozone retrievals. We discuss here the smoothing errors that describe the components of the profile variability that the SBUV observing system can not measure. The SBUV(/2) instruments have a vertical resolution of 5 km in the middle stratosphere, decreasing to 8 to 10 km below the ozone peak and above 0.5 hPa. To estimate the smoothing effect of the SBUV algorithm, the actual statistics of the fine vertical structure of ozone profiles must be known. The covariance matrix of the ensemble of measured ozone profiles with the high vertical resolution would be a formal representation of the actual ozone variability. We merged the MLS (version 3) and sonde ozone profiles to calculate the covariance matrix, which in general case, for single profile retrieval, might be a function of the latitude and month. Using the averaging kernels of the SBUV(/2) measurements and calculated total covariance matrix one can estimate the smoothing errors for the SBUV ozone profiles. A method to estimate the smoothing effect of the SBUV algorithm is described and the covariance matrixes and averaging kernels are provided along with the SBUV(/2) ozone profiles. The magnitude of the smoothing error varies with altitude, latitude, season and solar zenith angle. The analysis of the smoothing errors, based on the SBUV(/2) monthly zonal mean time series, shows that the largest smoothing errors were detected in the troposphere and might be as large as 15-20% and rapidly decrease with the altitude. In the stratosphere above 40 hPa the smoothing errors are less than 5% and between 10 and 1 hPa the smoothing errors are on the order of 1%. We
Numerical solution of the nonlinear Schrödinger equation using smoothed-particle hydrodynamics
NASA Astrophysics Data System (ADS)
Mocz, Philip; Succi, Sauro
2015-05-01
We formulate a smoothed-particle hydrodynamics numerical method, traditionally used for the Euler equations for fluid dynamics in the context of astrophysical simulations, to solve the nonlinear Schrödinger equation in the Madelung formulation. The probability density of the wave function is discretized into moving particles, whose properties are smoothed by a kernel function. The traditional fluid pressure is replaced by a quantum pressure tensor, for which a robust discretization is found. We demonstrate our numerical method on a variety of numerical test problems involving the simple harmonic oscillator, soliton-soliton collision, Bose-Einstein condensates, collapsing singularities, and dark matter halos governed by the Gross-Pitaevskii-Poisson equation. Our method is conservative, applicable to unbounded domains, and is automatically adaptive in its resolution, making it well suited to study problems with collapsing solutions.
Sebastian Schunert; Yousry Y. Azmy
2011-05-01
The quantification of the discretization error associated with the spatial discretization of the Discrete Ordinate(DO) equations in multidimensional Cartesian geometries is the central problem in error estimation of spatial discretization schemes for transport theory as well as computer code verification. Traditionally fine mesh solutions are employed as reference, because analytical solutions only exist in the absence of scattering. This approach, however, is inadequate when the discretization error associated with the reference solution is not small compared to the discretization error associated with the mesh under scrutiny. Typically this situation occurs if the mesh of interest is only a couple of refinement levels away from the reference solution or if the order of accuracy of the numerical method (and hence the reference as well) is lower than expected. In this work we present a Method of Manufactured Solutions (MMS) benchmark suite with variable order of smoothness of the underlying exact solution for two-dimensional Cartesian geometries which provides analytical solutions aver- aged over arbitrary orthogonal meshes for scattering and non-scattering media. It should be emphasized that the developed MMS benchmark suite first eliminates the aforementioned limitation of fine mesh reference solutions since it secures knowledge of the underlying true solution and second that it allows for an arbitrary order of smoothness of the underlying ex- act solution. The latter is of importance because even for smooth parameters and boundary conditions the DO equations can feature exact solution with limited smoothness. Moreover, the degree of smoothness is crucial for both the order of accuracy and the magnitude of the discretization error for any spatial discretization scheme.
Dieudonné, Arnaud; Hobbs, Robert F.; Lebtahi, Rachida; Maurel, Fabien; Baechler, Sébastien; Wahl, Richard L.; Boubaker, Ariane; Le Guludec, Dominique; Sgouros, Georges; Gardin, Isabelle
2014-01-01
Dose kernel convolution (DK) methods have been proposed to speed up absorbed dose calculations in molecular radionuclide therapy. Our aim was to evaluate the impact of tissue density heterogeneities (TDH) on dosimetry when using a DK method and to propose a simple density-correction method. Methods This study has been conducted on 3 clinical cases: case 1, non-Hodgkin lymphoma treated with 131I-tositumomab; case 2, a neuroendocrine tumor treatment simulated with 177Lu-peptides; and case 3, hepatocellular carcinoma treated with 90Y-microspheres. Absorbed dose calculations were performed using a direct Monte Carlo approach accounting for TDH (3D-RD), and a DK approach (VoxelDose, or VD). For each individual voxel, the VD absorbed dose, DVD, calculated assuming uniform density, was corrected for density, giving DVDd. The average 3D-RD absorbed dose values, D3DRD, were compared with DVD and DVDd, using the relative difference ΔVD/3DRD. At the voxel level, density-binned ΔVD/3DRD and ΔVDd/3DRD were plotted against ρ and fitted with a linear regression. Results The DVD calculations showed a good agreement with D3DRD. ΔVD/3DRD was less than 3.5%, except for the tumor of case 1 (5.9%) and the renal cortex of case 2 (5.6%). At the voxel level, the ΔVD/3DRD range was 0%–14% for cases 1 and 2, and −3% to 7% for case 3. All 3 cases showed a linear relationship between voxel bin-averaged ΔVD/3DRD and density, ρ: case 1 (Δ = −0.56ρ + 0.62, R2 = 0.93), case 2 (Δ = −0.91ρ + 0.96, R2 = 0.99), and case 3 (Δ = −0.69ρ + 0.72, R2 = 0.91). The density correction improved the agreement of the DK method with the Monte Carlo approach (ΔVDd/3DRD < 1.1%), but with a lesser extent for the tumor of case 1 (3.1%). At the voxel level, the ΔVDd/3DRD range decreased for the 3 clinical cases (case 1, −1% to 4%; case 2, −0.5% to 1.5%, and −1.5% to 2%). No more linear regression existed for cases 2 and 3, contrary to case 1 (Δ = 0.41ρ − 0.38, R2 = 0.88) although
Estimating the Bias of Local Polynomial Approximations Using the Peano Kernel
Blair, J., and Machorro, E.
2012-03-22
These presentation visuals define local polynomial approximations, give formulas for bias and random components of the error, and express bias error in terms of the Peano kernel. They further derive constants that give figures of merit, and show the figures of merit for 3 common weighting functions. The Peano kernel theorem yields estimates for the bias error for local-polynomial-approximation smoothing that are superior in several ways to the error estimates in the current literature.
Optimal heat kernel estimates for schrödinger operators with magnetic fields in two dimensions
NASA Astrophysics Data System (ADS)
Loss, Michael; Thaller, Bernd
1997-06-01
Sharp smoothing estimates are proven for magnetic Schrödinger semigroups in two dimensions under the assumption that the magnetic field is bounded below by some positive constant B 0. As a consequence the L∞ norm of the associated integral kernel is bounded by the L∞ norm of the Mehler kernel of the Schrödinger semigroup with the constant magnetic field B 0.
Kernel approximation for solving few-body integral equations
NASA Astrophysics Data System (ADS)
Christie, I.; Eyre, D.
1986-06-01
This paper investigates an approximate method for solving integral equations that arise in few-body problems. The method is to replace the kernel by a degenerate kernel defined on a finite dimensional subspace of piecewise Lagrange polynomials. Numerical accuracy of the method is tested by solving the two-body Lippmann-Schwinger equation with non-separable potentials, and the three-body Amado-Lovelace equation with separable two-body potentials.
MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES
Dunson, David B.
2013-01-01
Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitates sparse nonparametric modeling of multivariate random effects distributions, this article proposes a kernel partition process (KPP) in which the cluster allocation varies for different parameters. The KPP is shown to be the driving measure for a multivariate ordered Chinese restaurant process that induces a highly-flexible dependence structure in local clustering. This structure allows the relative locations of the random effects to inform the clustering process, with spatially-proximal random effects likely to be assigned the same cluster index. An exact block Gibbs sampler is developed for posterior computation, avoiding truncation of the infinite measure. The methods are applied to hormone curve data, and a dependent KPP is proposed for classification from functional predictors. PMID:24478563
Yuan, Ying; Wang, Wei; Chu, Xuan; Xi, Ming-jie
2016-01-01
The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy with spectral range between 833 and 2 500 nm to detect the moldy corn kernels with different levels of mildew was verified in this paper. Firstly, to avoid the influence of noise, moving average smoothing was used for spectral data preprocessing after four common pretreatment methods were compared. Then to improve the prediction performance of the model, SPXY (sample set partitioning based on joint x-y distance) was selected and used for sample set partition. Furthermore, in order to reduce the dimensions of the original spectral data, successive projection algorithm (SPA) was adopted and ultimately 7 characteristic wavelengths were extracted, the characteristic wave-lengths were 833, 927, 1 208, 1 337, 1 454, 1 861, 2 280 nm. The experimental results showed when the spectrum data of the 7 characteristic wavelengths were taken as the input of SVM, the radial basic function (RBF) used as the kernel function, and kernel parameter C = 7 760 469, γ = 0.017 003, the classification accuracies of the established SVM model were 97.78% and 93.33% for the training and testing sets respectively. In addition, the independent validation set was selected in the same standard, and used to verify the model. At last, the classification accuracy of 91.11% for the independent validation set was achieved. The result indicated that it is feasible to identify and classify different degree of moldy corn grain kernels using SPA and SVM, and characteristic wavelengths selected by SPA in this paper also lay a foundation for the online NIR detection of mildew corn kernels. PMID:27228772
Yuan, Ying; Wang, Wei; Chu, Xuan; Xi, Ming-jie
2016-01-01
The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy with spectral range between 833 and 2 500 nm to detect the moldy corn kernels with different levels of mildew was verified in this paper. Firstly, to avoid the influence of noise, moving average smoothing was used for spectral data preprocessing after four common pretreatment methods were compared. Then to improve the prediction performance of the model, SPXY (sample set partitioning based on joint x-y distance) was selected and used for sample set partition. Furthermore, in order to reduce the dimensions of the original spectral data, successive projection algorithm (SPA) was adopted and ultimately 7 characteristic wavelengths were extracted, the characteristic wave-lengths were 833, 927, 1 208, 1 337, 1 454, 1 861, 2 280 nm. The experimental results showed when the spectrum data of the 7 characteristic wavelengths were taken as the input of SVM, the radial basic function (RBF) used as the kernel function, and kernel parameter C = 7 760 469, γ = 0.017 003, the classification accuracies of the established SVM model were 97.78% and 93.33% for the training and testing sets respectively. In addition, the independent validation set was selected in the same standard, and used to verify the model. At last, the classification accuracy of 91.11% for the independent validation set was achieved. The result indicated that it is feasible to identify and classify different degree of moldy corn grain kernels using SPA and SVM, and characteristic wavelengths selected by SPA in this paper also lay a foundation for the online NIR detection of mildew corn kernels.
Prediction of kernel density of corn using single-kernel near infrared spectroscopy
Technology Transfer Automated Retrieval System (TEKTRAN)
Corn hardness as is an important property for dry and wet-millers, food processors and corn breeders developing hybrids for specific markets. Of the several methods used to measure hardness, kernel density measurements are one of the more repeatable methods to quantify hardness. Near infrared spec...
Ohkubo, Masaki; Wada, Shinichi; Kayugawa, Akihiro; Matsumoto, Toru; Murao, Kohei
2011-07-15
Purpose: While the acquisition of projection data in a computed tomography (CT) scanner is generally carried out once, the projection data is often removed from the system, making further reconstruction with a different reconstruction filter impossible. The reconstruction kernel is one of the most important parameters. To have access to all the reconstructions, either prior reconstructions with multiple kernels must be performed or the projection data must be stored. Each of these requirements would increase the burden on data archiving. This study aimed to design an effective method to achieve similar image quality using an image filtering technique in the image space, instead of a reconstruction filter in the projection space for CT imaging. The authors evaluated the clinical feasibility of the proposed method in lung cancer screening. Methods: The proposed technique is essentially the same as common image filtering, which performs processing in the spatial-frequency domain with a filter function. However, the filter function was determined based on the quantitative analysis of the point spread functions (PSFs) measured in the system. The modulation transfer functions (MTFs) were derived from the PSFs, and the ratio of the MTFs was used as the filter function. Therefore, using an image reconstructed with a kernel, an image reconstructed with a different kernel was obtained by filtering, which used the ratio of the MTFs obtained for the two kernels. The performance of the method was evaluated by using routine clinical images obtained from CT screening for lung cancer in five subjects. Results: Filtered images for all combinations of three types of reconstruction kernels (''smooth,''''standard,'' and ''sharp'' kernels) showed good agreement with original reconstructed images regarded as the gold standard. On the filtered images, abnormal shadows suspected as being lung cancers were identical to those on the reconstructed images. The standard deviations (SDs) for
Diffusion tensor smoothing through weighted Karcher means.
Carmichael, Owen; Chen, Jun; Paul, Debashis; Peng, Jie
2013-01-01
Diffusion tensor magnetic resonance imaging (MRI) quantifies the spatial distribution of water Diffusion at each voxel on a regular grid of locations in a biological specimen by Diffusion tensors- 3 × 3 positive definite matrices. Removal of noise from DTI is an important problem due to the high scientific relevance of DTI and relatively low signal to noise ratio it provides. Leading approaches to this problem amount to estimation of weighted Karcher means of Diffusion tensors within spatial neighborhoods, under various metrics imposed on the space of tensors. However, it is unclear how the behavior of these estimators varies with the magnitude of DTI sensor noise (the noise resulting from the thermal e!ects of MRI scanning) as well as the geometric structure of the underlying Diffusion tensor neighborhoods. In this paper, we combine theoretical analysis, empirical analysis of simulated DTI data, and empirical analysis of real DTI scans to compare the noise removal performance of three kernel-based DTI smoothers that are based on Euclidean, log-Euclidean, and affine-invariant metrics. The results suggest, contrary to conventional wisdom, that imposing a simplistic Euclidean metric may in fact provide comparable or superior noise removal, especially in relatively unstructured regions and/or in the presence of moderate to high levels of sensor noise. On the contrary, log-Euclidean and affine-invariant metrics may lead to better noise removal in highly structured anatomical regions, especially when the sensor noise is of low magnitude. These findings emphasize the importance of considering the interplay of sensor noise magnitude and tensor field geometric structure when assessing Diffusion tensor smoothing options. They also point to the necessity for continued development of smoothing methods that perform well across a large range of scenarios. PMID:25419264
Bruemmer, David J.
2009-11-17
A robot platform includes perceptors, locomotors, and a system controller. The system controller executes a robot intelligence kernel (RIK) that includes a multi-level architecture and a dynamic autonomy structure. The multi-level architecture includes a robot behavior level for defining robot behaviors, that incorporate robot attributes and a cognitive level for defining conduct modules that blend an adaptive interaction between predefined decision functions and the robot behaviors. The dynamic autonomy structure is configured for modifying a transaction capacity between an operator intervention and a robot initiative and may include multiple levels with at least a teleoperation mode configured to maximize the operator intervention and minimize the robot initiative and an autonomous mode configured to minimize the operator intervention and maximize the robot initiative. Within the RIK at least the cognitive level includes the dynamic autonomy structure.
Nowicki, Dimitri; Siegelmann, Hava
2010-06-11
This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces.
Bantroch, S; Bühler, T; Lam, J S
1994-01-01
Smooth, rough, and neutral forms of lipopolysaccharide (LPS) from Pseudomonas aeruginosa were used to assess the appropriate conditions for effective enzyme-linked immunosorbent assay (ELISA) of LPS. Each of these forms of well-defined LPS was tested for the efficiency of antigen coating by various methods as well as to identify an appropriate type of microtiter plate to use. For smooth LPS, the standard carbonate-bicarbonate buffer method was as efficient as the other sensitivity-enhancing plate-coating methods compared. The rough LPS, which has an overall hydrophobic characteristic, was shown to adhere effectively, regardless of the coating method used, to only one type of microtiter plate, CovaLink. This type of plate has secondary amine groups attached on its polystyrene surface by carbon chain spacers, which likely favors hydrophobic interactions between the rough LPS and the well surfaces. Dehydration methods were effective for coating microtiter plates with the neutral LPS examined, which is composed predominantly of a D-rhamnan. For the two dehydration procedures, LPS suspended in water or the organic solvent chloroform-ethanol was added directly to the wells, and the solvent was allowed to dehydrate or evaporate overnight. Precoating of plates with either polymyxin or poly-L-lysine did not give any major improvement in coating with the various forms of LPS. The possibility of using proteinase K- and sodium dodecyl sulfate-treated LPS preparations for ELISAs was also investigated. Smooth LPS prepared by this method was as effective in ELISA as LPS prepared by the hot water-phenol method, while the rough and neutral LPSs prepared this way were not satisfactory for ELISA. PMID:7496923
Seismic hazard assessment in Central Asia using smoothed seismicity approaches
NASA Astrophysics Data System (ADS)
Ullah, Shahid; Bindi, Dino; Zuccolo, Elisa; Mikhailova, Natalia; Danciu, Laurentiu; Parolai, Stefano
2014-05-01
Central Asia has a long history of large to moderate frequent seismicity and is therefore considered one of the most seismically active regions with a high hazard level in the world. In the hazard map produced at global scale by GSHAP project in 1999( Giardini, 1999), Central Asia is characterized by peak ground accelerations with return period of 475 years as high as 4.8 m/s2. Therefore Central Asia was selected as a target area for EMCA project (Earthquake Model Central Asia), a regional project of GEM (Global Earthquake Model) for this area. In the framework of EMCA, a new generation of seismic hazard maps are foreseen in terms of macro-seismic intensity, in turn to be used to obtain seismic risk maps for the region. Therefore Intensity Prediction Equation (IPE) had been developed for the region based on the distribution of intensity data for different earthquakes occurred in Central Asia since the end of 19th century (Bindi et al. 2011). The same observed intensity distribution had been used to assess the seismic hazard following the site approach (Bindi et al. 2012). In this study, we present the probabilistic seismic hazard assessment of Central Asia in terms of MSK-64 based on two kernel estimation methods. We consider the smoothed seismicity approaches of Frankel (1995), modified for considering the adaptive kernel proposed by Stock and Smith (2002), and of Woo (1996), modified for considering a grid of sites and estimating a separate bandwidth for each site. The activity rate maps are shown from Frankel approach showing the effects of fixed and adaptive kernel. The hazard is estimated for rock site condition based on 10% probability of exceedance in 50 years. Maximum intensity of about 9 is observed in the Hindukush region.
NASA Astrophysics Data System (ADS)
Bauer, Daniela
2005-03-01
A light scratch with a needle induces histamine and neuropetide release on the line of stroke and in the surrounding tissue. Histamine and neuropeptides are vasodilaters. They create vasodilation by changing the contraction state of the vascular smooth muscles and hence vessel compliance. Smooth muscle contraction state is very difficult to measure. We propose an identification procedure that determines change in compliance. The procedure is based on numerical and experimental results. Blood flow is measured by Laser Doppler Velocimetry. Numerical data is obtained by a continuous model of hierarchically arranged porous media of the vascular network [1]. We show that compliance increases after the stroke in the entire tissue. Then, compliance decreases in the surrounding tissue, while it keeps increasing on the line of stroke. Hence, blood is transported from the surrounding tissue to the line of stroke. Thus, higher blood volume on the line of stroke is obtained. [1] Bauer, D., Grebe, R. Ehrlacher, A., 2004. A three layer continuous model of porous media to describe the first phase of skin irritation. J. Theoret. Bio. in press
Conservative smoothing versus artificial viscosity
Guenther, C.; Hicks, D.L.; Swegle, J.W.
1994-08-01
This report was stimulated by some recent investigations of S.P.H. (Smoothed Particle Hydrodynamics method). Solid dynamics computations with S.P.H. show symptoms of instabilities which are not eliminated by artificial viscosities. Both analysis and experiment indicate that conservative smoothing eliminates the instabilities in S.P.H. computations which artificial viscosities cannot. Questions were raised as to whether conservative smoothing might smear solutions more than artificial viscosity. Conservative smoothing, properly used, can produce more accurate solutions than the von Neumann-Richtmyer-Landshoff artificial viscosity which has been the standard for many years. The authors illustrate this using the vNR scheme on a test problem with known exact solution involving a shock collision in an ideal gas. They show that the norms of the errors with conservative smoothing are significantly smaller than the norms of the errors with artificial viscosity.
An Ensemble Approach to Building Mercer Kernels with Prior Information
NASA Technical Reports Server (NTRS)
Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd
2005-01-01
This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.
Progress in smooth particle hydrodynamics
Wingate, C.A.; Dilts, G.A.; Mandell, D.A.; Crotzer, L.A.; Knapp, C.E.
1998-07-01
Smooth Particle Hydrodynamics (SPH) is a meshless, Lagrangian numerical method for hydrodynamics calculations where calculational elements are fuzzy particles which move according to the hydrodynamic equations of motion. Each particle carries local values of density, temperature, pressure and other hydrodynamic parameters. A major advantage of SPH is that it is meshless, thus large deformation calculations can be easily done with no connectivity complications. Interface positions are known and there are no problems with advecting quantities through a mesh that typical Eulerian codes have. These underlying SPH features make fracture physics easy and natural and in fact, much of the applications work revolves around simulating fracture. Debris particles from impacts can be easily transported across large voids with SPH. While SPH has considerable promise, there are some problems inherent in the technique that have so far limited its usefulness. The most serious problem is the well known instability in tension leading to particle clumping and numerical fracture. Another problem is that the SPH interpolation is only correct when particles are uniformly spaced a half particle apart leading to incorrect strain rates, accelerations and other quantities for general particle distributions. SPH calculations are also sensitive to particle locations. The standard artificial viscosity treatment in SPH leads to spurious viscosity in shear flows. This paper will demonstrate solutions for these problems that they and others have been developing. The most promising is to replace the SPH interpolant with the moving least squares (MLS) interpolant invented by Lancaster and Salkauskas in 1981. SPH and MLS are closely related with MLS being essentially SPH with corrected particle volumes. When formulated correctly, JLS is conservative, stable in both compression and tension, does not have the SPH boundary problems and is not sensitive to particle placement. The other approach to
Smoothed Standardization Assessment of Testlet Level DIF on a Math Free-Response Item Type.
ERIC Educational Resources Information Center
Lyu, C. Felicia; And Others
A smoothed version of standardization, which merges kernel smoothing with the traditional standardization differential item functioning (DIF) approach, was used to examine DIF for student-produced response (SPR) items on the Scholastic Assessment Test (SAT) I mathematics test at both the item and testlet levels. This nonparametric technique avoids…
Variational Dirichlet Blur Kernel Estimation.
Zhou, Xu; Mateos, Javier; Zhou, Fugen; Molina, Rafael; Katsaggelos, Aggelos K
2015-12-01
Blind image deconvolution involves two key objectives: 1) latent image and 2) blur estimation. For latent image estimation, we propose a fast deconvolution algorithm, which uses an image prior of nondimensional Gaussianity measure to enforce sparsity and an undetermined boundary condition methodology to reduce boundary artifacts. For blur estimation, a linear inverse problem with normalization and nonnegative constraints must be solved. However, the normalization constraint is ignored in many blind image deblurring methods, mainly because it makes the problem less tractable. In this paper, we show that the normalization constraint can be very naturally incorporated into the estimation process by using a Dirichlet distribution to approximate the posterior distribution of the blur. Making use of variational Dirichlet approximation, we provide a blur posterior approximation that considers the uncertainty of the estimate and removes noise in the estimated kernel. Experiments with synthetic and real data demonstrate that the proposed method is very competitive to the state-of-the-art blind image restoration methods. PMID:26390458
Predictor-corrector schemes for visualization of smoothed particle hydrodynamics data.
Schindler, Benjamin; Fuchs, Raphael; Biddiscombe, John; Peikert, Ronald
2009-01-01
In this paper we present a method for vortex core line extraction which operates directly on the smoothed particle hydrodynamics (SPH) representation and, by this, generates smoother and more (spatially and temporally) coherent results in an efficient way. The underlying predictor-corrector scheme is general enough to be applied to other line-type features and it is extendable to the extraction of surfaces such as isosurfaces or Lagrangian coherent structures. The proposed method exploits temporal coherence to speed up computation for subsequent time steps. We show how the predictor-corrector formulation can be specialized for several variants of vortex core line definitions including two recent unsteady extensions, and we contribute a theoretical and practical comparison of these. In particular, we reveal a close relation between unsteady extensions of Fuchs et al. and Weinkauf et al. and we give a proof of the Galilean invariance of the latter. When visualizing SPH data, there is the possibility to use the same interpolation method for visualization as has been used for the simulation. This is different from the case of finite volume simulation results, where it is not possible to recover from the results the spatial interpolation that was used during the simulation. Such data are typically interpolated using the basic trilinear interpolant, and if smoothness is required, some artificial processing is added. In SPH data, however, the smoothing kernels are specified from the simulation, and they provide an exact and smooth interpolation of data or gradients at arbitrary points in the domain.
NASA Astrophysics Data System (ADS)
Kajzer, A.; Pozorski, J.; Szewc, K.
2014-08-01
In the paper we present Large-eddy simulation (LES) results of 3D Taylor- Green vortex obtained by the three different computational approaches: Smoothed Particle Hydrodynamics (SPH), Lattice Boltzmann Method (LBM) and Finite Volume Method (FVM). The Smagorinsky model was chosen as a subgrid-scale closure in LES for all considered methods and a selection of spatial resolutions have been investigated. The SPH and LBM computations have been carried out with the use of the in-house codes executed on GPU and compared, for validation purposes, with the FVM results obtained using the open-source CFD software OpenFOAM. A comparative study in terms of one-point statistics and turbulent energy spectra shows a good agreement of LES results for all methods. An analysis of the GPU code efficiency and implementation difficulties has been made. It is shown that both SPH and LBM may offer a significant advantage over mesh-based CFD methods.
Kar, Arindam; Bhattacharjee, Debotosh; Basu, Dipak Kumar; Nasipuri, Mita; Kundu, Mahantapas
2012-01-01
In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low-energized discriminating feature vectors to obtain the real component of a complex quantity for face recognition. In order to derive positive kernel discriminative vectors, we apply only those kernel discriminative eigenvectors that are associated with nonzero eigenvalues. The feasibility of the low-energized Gabor-block-based generalized KDCV method with cosine kernel function models has been successfully tested for classification using the L1, L2 distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach. PMID:23365559
Kernel spectral clustering with memory effect
NASA Astrophysics Data System (ADS)
Langone, Rocco; Alzate, Carlos; Suykens, Johan A. K.
2013-05-01
Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.
Kernel-aligned multi-view canonical correlation analysis for image recognition
NASA Astrophysics Data System (ADS)
Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao
2016-09-01
Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.
A kernel adaptive algorithm for quaternion-valued inputs.
Paul, Thomas K; Ogunfunmi, Tokunbo
2015-10-01
The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations. PMID:25594982
Martín-Merino, Manuel; Blanco, Ángela; De Las Rivas, Javier
2009-01-01
DNA microarrays provide rich profiles that are used in cancer prediction considering the gene expression levels across a collection of related samples. Support Vector Machines (SVM) have been applied to the classification of cancer samples with encouraging results. However, they rely on Euclidean distances that fail to reflect accurately the proximities among sample profiles. Then, non-Euclidean dissimilarities provide additional information that should be considered to reduce the misclassification errors. In this paper, we incorporate in the ν-SVM algorithm a linear combination of non-Euclidean dissimilarities. The weights of the combination are learnt in a (Hyper Reproducing Kernel Hilbert Space) HRKHS using a Semidefinite Programming algorithm. This approach allows us to incorporate a smoothing term that penalizes the complexity of the family of distances and avoids overfitting. The experimental results suggest that the method proposed helps to reduce the misclassification errors in several human cancer problems. PMID:19584909
ERIC Educational Resources Information Center
Moses, Tim; Liu, Jinghua
2011-01-01
In equating research and practice, equating functions that are smooth are typically assumed to be more accurate than equating functions with irregularities. This assumption presumes that population test score distributions are relatively smooth. In this study, two examples were used to reconsider common beliefs about smoothing and equating. The…
ERIC Educational Resources Information Center
Moses, Tim; Miao, Jing; Dorans, Neil
2010-01-01
This study compared the accuracies of four differential item functioning (DIF) estimation methods, where each method makes use of only one of the following: raw data, logistic regression, loglinear models, or kernel smoothing. The major focus was on the estimation strategies' potential for estimating score-level, conditional DIF. A secondary focus…
Semi-Supervised Kernel Mean Shift Clustering.
Anand, Saket; Mittal, Sushil; Tuzel, Oncel; Meer, Peter
2014-06-01
Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original distance metric fails to capture the underlying cluster structure. Despite recent advances in semi-supervised clustering methods, there has been little effort towards incorporating supervision into mean shift. We propose a semi-supervised framework for kernel mean shift clustering (SKMS) that uses only pairwise constraints to guide the clustering procedure. The points are first mapped to a high-dimensional kernel space where the constraints are imposed by a linear transformation of the mapped points. This is achieved by modifying the initial kernel matrix by minimizing a log det divergence-based objective function. We show the advantages of SKMS by evaluating its performance on various synthetic and real datasets while comparing with state-of-the-art semi-supervised clustering algorithms. PMID:26353281
Resummed memory kernels in generalized system-bath master equations
Mavros, Michael G.; Van Voorhis, Troy
2014-08-07
Generalized master equations provide a concise formalism for studying reduced population dynamics. Usually, these master equations require a perturbative expansion of the memory kernels governing the dynamics; in order to prevent divergences, these expansions must be resummed. Resummation techniques of perturbation series are ubiquitous in physics, but they have not been readily studied for the time-dependent memory kernels used in generalized master equations. In this paper, we present a comparison of different resummation techniques for such memory kernels up to fourth order. We study specifically the spin-boson Hamiltonian as a model system bath Hamiltonian, treating the diabatic coupling between the two states as a perturbation. A novel derivation of the fourth-order memory kernel for the spin-boson problem is presented; then, the second- and fourth-order kernels are evaluated numerically for a variety of spin-boson parameter regimes. We find that resumming the kernels through fourth order using a Padé approximant results in divergent populations in the strong electronic coupling regime due to a singularity introduced by the nature of the resummation, and thus recommend a non-divergent exponential resummation (the “Landau-Zener resummation” of previous work). The inclusion of fourth-order effects in a Landau-Zener-resummed kernel is shown to improve both the dephasing rate and the obedience of detailed balance over simpler prescriptions like the non-interacting blip approximation, showing a relatively quick convergence on the exact answer. The results suggest that including higher-order contributions to the memory kernel of a generalized master equation and performing an appropriate resummation can provide a numerically-exact solution to system-bath dynamics for a general spectral density, opening the way to a new class of methods for treating system-bath dynamics.
Protein fold recognition using geometric kernel data fusion
Zakeri, Pooya; Jeuris, Ben; Vandebril, Raf; Moreau, Yves
2014-01-01
Motivation: Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. Results: We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. Availability and implementation: The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/ Contact: pooyapaydar@gmail.com or yves
NASA Astrophysics Data System (ADS)
Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin
2015-10-01
The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.
M Ali, M. K. E-mail: eutoco@gmail.com; Ruslan, M. H. E-mail: eutoco@gmail.com; Muthuvalu, M. S. E-mail: jumat@ums.edu.my; Wong, J. E-mail: jumat@ums.edu.my; Sulaiman, J. E-mail: hafidzruslan@eng.ukm.my; Yasir, S. Md. E-mail: hafidzruslan@eng.ukm.my
2014-06-19
The solar drying experiment of seaweed using Green V-Roof Hybrid Solar Drier (GVRHSD) was conducted in Semporna, Sabah under the metrological condition in Malaysia. Drying of sample seaweed in GVRHSD reduced the moisture content from about 93.4% to 8.2% in 4 days at average solar radiation of about 600W/m{sup 2} and mass flow rate about 0.5 kg/s. Generally the plots of drying rate need more smoothing compared moisture content data. Special cares is needed at low drying rates and moisture contents. It is shown the cubic spline (CS) have been found to be effective for moisture-time curves. The idea of this method consists of an approximation of data by a CS regression having first and second derivatives. The analytical differentiation of the spline regression permits the determination of instantaneous rate. The method of minimization of the functional of average risk was used successfully to solve the problem. This method permits to obtain the instantaneous rate to be obtained directly from the experimental data. The drying kinetics was fitted with six published exponential thin layer drying models. The models were fitted using the coefficient of determination (R{sup 2}), and root mean square error (RMSE). The modeling of models using raw data tested with the possible of exponential drying method. The result showed that the model from Two Term was found to be the best models describe the drying behavior. Besides that, the drying rate smoothed using CS shows to be effective method for moisture-time curves good estimators as well as for the missing moisture content data of seaweed Kappaphycus Striatum Variety Durian in Solar Dryer under the condition tested.
NASA Astrophysics Data System (ADS)
M Ali, M. K.; Ruslan, M. H.; Muthuvalu, M. S.; Wong, J.; Sulaiman, J.; Yasir, S. Md.
2014-06-01
The solar drying experiment of seaweed using Green V-Roof Hybrid Solar Drier (GVRHSD) was conducted in Semporna, Sabah under the metrological condition in Malaysia. Drying of sample seaweed in GVRHSD reduced the moisture content from about 93.4% to 8.2% in 4 days at average solar radiation of about 600W/m2 and mass flow rate about 0.5 kg/s. Generally the plots of drying rate need more smoothing compared moisture content data. Special cares is needed at low drying rates and moisture contents. It is shown the cubic spline (CS) have been found to be effective for moisture-time curves. The idea of this method consists of an approximation of data by a CS regression having first and second derivatives. The analytical differentiation of the spline regression permits the determination of instantaneous rate. The method of minimization of the functional of average risk was used successfully to solve the problem. This method permits to obtain the instantaneous rate to be obtained directly from the experimental data. The drying kinetics was fitted with six published exponential thin layer drying models. The models were fitted using the coefficient of determination (R2), and root mean square error (RMSE). The modeling of models using raw data tested with the possible of exponential drying method. The result showed that the model from Two Term was found to be the best models describe the drying behavior. Besides that, the drying rate smoothed using CS shows to be effective method for moisture-time curves good estimators as well as for the missing moisture content data of seaweed Kappaphycus Striatum Variety Durian in Solar Dryer under the condition tested.
NASA Astrophysics Data System (ADS)
Zhu, Fengle; Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Brown, Robert; Bhatnagar, Deepak; Cleveland, Thomas
2015-05-01
Aflatoxins are secondary metabolites produced by certain fungal species of the Aspergillus genus. Aflatoxin contamination remains a problem in agricultural products due to its toxic and carcinogenic properties. Conventional chemical methods for aflatoxin detection are time-consuming and destructive. This study employed fluorescence and reflectance visible near-infrared (VNIR) hyperspectral images to classify aflatoxin contaminated corn kernels rapidly and non-destructively. Corn ears were artificially inoculated in the field with toxigenic A. flavus spores at the early dough stage of kernel development. After harvest, a total of 300 kernels were collected from the inoculated ears. Fluorescence hyperspectral imagery with UV excitation and reflectance hyperspectral imagery with halogen illumination were acquired on both endosperm and germ sides of kernels. All kernels were then subjected to chemical analysis individually to determine aflatoxin concentrations. A region of interest (ROI) was created for each kernel to extract averaged spectra. Compared with healthy kernels, fluorescence spectral peaks for contaminated kernels shifted to longer wavelengths with lower intensity, and reflectance values for contaminated kernels were lower with a different spectral shape in 700-800 nm region. Principal component analysis was applied for data compression before classifying kernels into contaminated and healthy based on a 20 ppb threshold utilizing the K-nearest neighbors algorithm. The best overall accuracy achieved was 92.67% for germ side in the fluorescence data analysis. The germ side generally performed better than endosperm side. Fluorescence and reflectance image data achieved similar accuracy.
General-form 3-3-3 interpolation kernel and its simplified frequency-response derivation
NASA Astrophysics Data System (ADS)
Deng, Tian-Bo
2016-11-01
An interpolation kernel is required in a wide variety of signal processing applications such as image interpolation and timing adjustment in digital communications. This article presents a general-form interpolation kernel called 3-3-3 interpolation kernel and derives its frequency response in a closed-form by using a simple derivation method. This closed-form formula is preliminary to designing various 3-3-3 interpolation kernels subject to a set of design constraints. The 3-3-3 interpolation kernel is formed through utilising the third-degree piecewise polynomials, and it is an even-symmetric function. Thus, it will suffice to consider only its right-hand side when deriving its frequency response. Since the right-hand side of the interpolation kernel contains three piecewise polynomials of the third degree, i.e. the degrees of the three piecewise polynomials are (3,3,3), we call it the 3-3-3 interpolation kernel. Once the general-form frequency-response formula is derived, we can systematically formulate the design of various 3-3-3 interpolation kernels subject to a set of design constraints, which are targeted for different interpolation applications. Therefore, the closed-form frequency-response expression is preliminary to the optimal design of various 3-3-3 interpolation kernels. We will use an example to show the optimal design of a 3-3-3 interpolation kernel based on the closed-form frequency-response expression.
RTOS kernel in portable electrocardiograph
NASA Astrophysics Data System (ADS)
Centeno, C. A.; Voos, J. A.; Riva, G. G.; Zerbini, C.; Gonzalez, E. A.
2011-12-01
This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.
Antioxidant and antimicrobial activities of bitter and sweet apricot (Prunus armeniaca L.) kernels.
Yiğit, D; Yiğit, N; Mavi, A
2009-04-01
The present study describes the in vitro antimicrobial and antioxidant activity of methanol and water extracts of sweet and bitter apricot (Prunus armeniaca L.) kernels. The antioxidant properties of apricot kernels were evaluated by determining radical scavenging power, lipid peroxidation inhibition activity and total phenol content measured with a DPPH test, the thiocyanate method and the Folin method, respectively. In contrast to extracts of the bitter kernels, both the water and methanol extracts of sweet kernels have antioxidant potential. The highest percent inhibition of lipid peroxidation (69%) and total phenolic content (7.9 +/- 0.2 microg/mL) were detected in the methanol extract of sweet kernels (Hasanbey) and in the water extract of the same cultivar, respectively. The antimicrobial activities of the above extracts were also tested against human pathogenic microorganisms using a disc-diffusion method, and the minimal inhibitory concentration (MIC) values of each active extract were determined. The most effective antibacterial activity was observed in the methanol and water extracts of bitter kernels and in the methanol extract of sweet kernels against the Gram-positive bacteria Staphylococcus aureus. Additionally, the methanol extracts of the bitter kernels were very potent against the Gram-negative bacteria Escherichia coli (0.312 mg/mL MIC value). Significant anti-candida activity was also observed with the methanol extract of bitter apricot kernels against Candida albicans, consisting of a 14 mm in diameter of inhibition zone and a 0.625 mg/mL MIC value.
Removing blur kernel noise via a hybrid ℓp norm
NASA Astrophysics Data System (ADS)
Yu, Xin; Zhang, Shunli; Zhao, Xiaolin; Zhang, Li
2015-01-01
When estimating a sharp image from a blurred one, blur kernel noise often leads to inaccurate recovery. We develop an effective method to estimate a blur kernel which is able to remove kernel noise and prevent the production of an overly sparse kernel. Our method is based on an iterative framework which alternatingly recovers the sharp image and estimates the blur kernel. In the image recovery step, we utilize the total variation (TV) regularization to recover latent images. In solving TV regularization, we propose a new criterion which adaptively terminates the iterations before convergence. While improving the efficiency, the quality of the final results is not degraded. In the kernel estimation step, we develop a metric to measure the usefulness of image edges, by which we can reduce the ambiguity of kernel estimation caused by small-scale edges. We also propose a hybrid ℓp norm, which is composed of ℓ2 norm and ℓp norm with 0.7≤p<1, to construct a sparsity constraint. Using the hybrid ℓp norm, we reduce a wider range of kernel noise and recover a more accurate blur kernel. The experiments show that the proposed method achieves promising results on both synthetic and real images.
Design of a multiple kernel learning algorithm for LS-SVM by convex programming.
Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou
2011-06-01
As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed.
Smooth halos in the cosmic web
NASA Astrophysics Data System (ADS)
Gaite, José
2015-04-01
Dark matter halos can be defined as smooth distributions of dark matter placed in a non-smooth cosmic web structure. This definition of halos demands a precise definition of smoothness and a characterization of the manner in which the transition from smooth halos to the cosmic web takes place. We introduce entropic measures of smoothness, related to measures of inequality previously used in economy and with the advantage of being connected with standard methods of multifractal analysis already used for characterizing the cosmic web structure in cold dark matter N-body simulations. These entropic measures provide us with a quantitative description of the transition from the small scales portrayed as a distribution of halos to the larger scales portrayed as a cosmic web and, therefore, allow us to assign definite sizes to halos. However, these ``smoothness sizes'' have no direct relation to the virial radii. Finally, we discuss the influence of N-body discreteness parameters on smoothness.
Smooth halos in the cosmic web
Gaite, José
2015-04-01
Dark matter halos can be defined as smooth distributions of dark matter placed in a non-smooth cosmic web structure. This definition of halos demands a precise definition of smoothness and a characterization of the manner in which the transition from smooth halos to the cosmic web takes place. We introduce entropic measures of smoothness, related to measures of inequality previously used in economy and with the advantage of being connected with standard methods of multifractal analysis already used for characterizing the cosmic web structure in cold dark matter N-body simulations. These entropic measures provide us with a quantitative description of the transition from the small scales portrayed as a distribution of halos to the larger scales portrayed as a cosmic web and, therefore, allow us to assign definite sizes to halos. However, these ''smoothness sizes'' have no direct relation to the virial radii. Finally, we discuss the influence of N-body discreteness parameters on smoothness.
NASA Astrophysics Data System (ADS)
Li, Zhe; Leduc, Julien; Nunez-Ramirez, Jorge; Combescure, Alain; Marongiu, Jean-Christophe
2015-04-01
We propose a non-intrusive numerical coupling method for transient fluid-structure interaction (FSI) problems simulated by means of different discretization methods: smoothed particle hydrodynamics (SPH) and finite element (FE) methods for the fluid and the solid sub-domains, respectively. As a partitioned coupling method, the present algorithm can ensure a zero interface energy during the whole period of numerical simulation, even in the presence of large interface motion. In other words, the time integrations of the two sub-domains (second order Runge-Kutta scheme for fluid and Newmark integrator for solid) are synchronized. Thanks to this energy-conserving feature, one can preserve the minimal order of accuracy in time and the numerical stability of the FSI simulations, which are validated with a 1D and a 2D trivial numerical test cases. Additionally, some other 2D FSI simulations involving large interface motion have also been carried out with the proposed SPH-FE coupling method. Finally, an example of aquaplaning problem is given in order to show the feasibility of such coupling method in multi-dimensional applications with complicated structural geometries.
The NAS kernel benchmark program
NASA Technical Reports Server (NTRS)
Bailey, D. H.; Barton, J. T.
1985-01-01
A collection of benchmark test kernels that measure supercomputer performance has been developed for the use of the NAS (Numerical Aerodynamic Simulation) program at the NASA Ames Research Center. This benchmark program is described in detail and the specific ground rules are given for running the program as a performance test.
Weighted Feature Gaussian Kernel SVM for Emotion Recognition
Jia, Qingxuan
2016-01-01
Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods. PMID:27807443
Robust kernel collaborative representation for face recognition
NASA Astrophysics Data System (ADS)
Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong
2015-05-01
One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.
NASA Astrophysics Data System (ADS)
Tune, D. D.; Stolz, B. W.; Pfohl, M.; Flavel, B. S.
2016-02-01
We show that the application of lateral shear force on a randomly oriented thin film of carbon nanotubes, in the dry state, causes significant reordering of the nanotubes at the film surface. This new technique of dry shear aligning is applicable to carbon nanotube thin films produced by many of the established methods.We show that the application of lateral shear force on a randomly oriented thin film of carbon nanotubes, in the dry state, causes significant reordering of the nanotubes at the film surface. This new technique of dry shear aligning is applicable to carbon nanotube thin films produced by many of the established methods. Electronic supplementary information (ESI) available: Detailed experimental methods, table of nanotube details, absorption spectra, further SEM data, plots of sheet resistance, DC to optical conductivity, and 2D order parameter as a function of transmittance. See DOI: 10.1039/c5nr08784h
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2014 CFR
2014-01-01
... AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2013 CFR
2013-01-01
... AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2011 CFR
2011-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2012 CFR
2012-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2010 CFR
2010-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
Verification of Chare-kernel programs
Bhansali, S.; Kale, L.V. )
1989-01-01
Experience with concurrent programming has shown that concurrent programs can conceal bugs even after extensive testing. Thus, there is a need for practical techniques which can establish the correctness of parallel programs. This paper proposes a method for showing how to prove the partial correctness of programs written in the Chare-kernel language, which is a language designed to support the parallel execution of computation with irregular structures. The proof is based on the lattice proof technique and is divided into two parts. The first part is concerned with the program behavior within a single chare instance, whereas the second part captures the inter-chare interaction.
Delimiting Areas of Endemism through Kernel Interpolation
Oliveira, Ubirajara; Brescovit, Antonio D.; Santos, Adalberto J.
2015-01-01
We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE), based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units. PMID:25611971
Thermal-to-visible face recognition using multiple kernel learning
NASA Astrophysics Data System (ADS)
Hu, Shuowen; Gurram, Prudhvi; Kwon, Heesung; Chan, Alex L.
2014-06-01
Recognizing faces acquired in the thermal spectrum from a gallery of visible face images is a desired capability for the military and homeland security, especially for nighttime surveillance and intelligence gathering. However, thermal-tovisible face recognition is a highly challenging problem, due to the large modality gap between thermal and visible imaging. In this paper, we propose a thermal-to-visible face recognition approach based on multiple kernel learning (MKL) with support vector machines (SVMs). We first subdivide the face into non-overlapping spatial regions or blocks using a method based on coalitional game theory. For comparison purposes, we also investigate uniform spatial subdivisions. Following this subdivision, histogram of oriented gradients (HOG) features are extracted from each block and utilized to compute a kernel for each region. We apply sparse multiple kernel learning (SMKL), which is a MKLbased approach that learns a set of sparse kernel weights, as well as the decision function of a one-vs-all SVM classifier for each of the subjects in the gallery. We also apply equal kernel weights (non-sparse) and obtain one-vs-all SVM models for the same subjects in the gallery. Only visible images of each subject are used for MKL training, while thermal images are used as probe images during testing. With subdivision generated by game theory, we achieved Rank-1 identification rate of 50.7% for SMKL and 93.6% for equal kernel weighting using a multimodal dataset of 65 subjects. With uniform subdivisions, we achieved a Rank-1 identification rate of 88.3% for SMKL, but 92.7% for equal kernel weighting.
Sliding Window Generalized Kernel Affine Projection Algorithm Using Projection Mappings
NASA Astrophysics Data System (ADS)
Slavakis, Konstantinos; Theodoridis, Sergios
2008-12-01
Very recently, a solution to the kernel-based online classification problem has been given by the adaptive projected subgradient method (APSM). The developed algorithm can be considered as a generalization of a kernel affine projection algorithm (APA) and the kernel normalized least mean squares (NLMS). Furthermore, sparsification of the resulting kernel series expansion was achieved by imposing a closed ball (convex set) constraint on the norm of the classifiers. This paper presents another sparsification method for the APSM approach to the online classification task by generating a sequence of linear subspaces in a reproducing kernel Hilbert space (RKHS). To cope with the inherent memory limitations of online systems and to embed tracking capabilities to the design, an upper bound on the dimension of the linear subspaces is imposed. The underlying principle of the design is the notion of projection mappings. Classification is performed by metric projection mappings, sparsification is achieved by orthogonal projections, while the online system's memory requirements and tracking are attained by oblique projections. The resulting sparsification scheme shows strong similarities with the classical sliding window adaptive schemes. The proposed design is validated by the adaptive equalization problem of a nonlinear communication channel, and is compared with classical and recent stochastic gradient descent techniques, as well as with the APSM's solution where sparsification is performed by a closed ball constraint on the norm of the classifiers.
Hua, Wen-Yu; Ghosh, Debashis
2015-09-01
Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression framework that models covariate effects parametrically and genetic markers non-parametrically, while KDC represents a class of methods that include distance covariance (DC) and Hilbert-Schmidt independence criterion (HSIC), which are nonparametric tests of independence. We show that the equivalence between the score test of KMR and the KDC statistic under certain conditions can lead to a novel generalization of the KDC test that incorporates covariates. Our contributions are 3-fold: (1) establishing the equivalence between KMR and KDC; (2) showing that the principles of KMR can be applied to the interpretation of KDC; (3) the development of a broader class of KDC statistics, where the class members are statistics corresponding to different kernel combinations. Finally, we perform simulation studies and an analysis of real data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The ADNI study suggest that SNPs of FLJ16124 exhibit pairwise interaction effects that are strongly correlated to the changes of brain region volumes. PMID:25939365
Hua, Wen-Yu; Ghosh, Debashis
2015-09-01
Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression framework that models covariate effects parametrically and genetic markers non-parametrically, while KDC represents a class of methods that include distance covariance (DC) and Hilbert-Schmidt independence criterion (HSIC), which are nonparametric tests of independence. We show that the equivalence between the score test of KMR and the KDC statistic under certain conditions can lead to a novel generalization of the KDC test that incorporates covariates. Our contributions are 3-fold: (1) establishing the equivalence between KMR and KDC; (2) showing that the principles of KMR can be applied to the interpretation of KDC; (3) the development of a broader class of KDC statistics, where the class members are statistics corresponding to different kernel combinations. Finally, we perform simulation studies and an analysis of real data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The ADNI study suggest that SNPs of FLJ16124 exhibit pairwise interaction effects that are strongly correlated to the changes of brain region volumes.
Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing
Li, Shuang; Liu, Bing; Zhang, Chen
2016-01-01
Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios. PMID:27247562
Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.
Li, Shuang; Liu, Bing; Zhang, Chen
2016-01-01
Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.
Fast image search with locality-sensitive hashing and homogeneous kernels map.
Li, Jun-yi; Li, Jian-hua
2015-01-01
Fast image search with efficient additive kernels and kernel locality-sensitive hashing has been proposed. As to hold the kernel functions, recent work has probed methods to create locality-sensitive hashing, which guarantee our approach's linear time; however existing methods still do not solve the problem of locality-sensitive hashing (LSH) algorithm and indirectly sacrifice the loss in accuracy of search results in order to allow fast queries. To improve the search accuracy, we show how to apply explicit feature maps into the homogeneous kernels, which help in feature transformation and combine it with kernel locality-sensitive hashing. We prove our method on several large datasets and illustrate that it improves the accuracy relative to commonly used methods and make the task of object classification and, content-based retrieval more fast and accurate.
Wigner functions defined with Laplace transform kernels.
Oh, Se Baek; Petruccelli, Jonathan C; Tian, Lei; Barbastathis, George
2011-10-24
We propose a new Wigner-type phase-space function using Laplace transform kernels--Laplace kernel Wigner function. Whereas momentum variables are real in the traditional Wigner function, the Laplace kernel Wigner function may have complex momentum variables. Due to the property of the Laplace transform, a broader range of signals can be represented in complex phase-space. We show that the Laplace kernel Wigner function exhibits similar properties in the marginals as the traditional Wigner function. As an example, we use the Laplace kernel Wigner function to analyze evanescent waves supported by surface plasmon polariton.
NASA Astrophysics Data System (ADS)
Hendrikse, Anne; Veldhuis, Raymond; Spreeuwers, Luuk
2013-12-01
Second-order statistics play an important role in data modeling. Nowadays, there is a tendency toward measuring more signals with higher resolution (e.g., high-resolution video), causing a rapid increase of dimensionality of the measured samples, while the number of samples remains more or less the same. As a result the eigenvalue estimates are significantly biased as described by the Marčenko Pastur equation for the limit of both the number of samples and their dimensionality going to infinity. By introducing a smoothness factor, we show that the Marčenko Pastur equation can be used in practical situations where both the number of samples and their dimensionality remain finite. Based on this result we derive methods, one already known and one new to our knowledge, to estimate the sample eigenvalues when the population eigenvalues are known. However, usually the sample eigenvalues are known and the population eigenvalues are required. We therefore applied one of the these methods in a feedback loop, resulting in an eigenvalue bias correction method. We compare this eigenvalue correction method with the state-of-the-art methods and show that our method outperforms other methods particularly in real-life situations often encountered in biometrics: underdetermined configurations, high-dimensional configurations, and configurations where the eigenvalues are exponentially distributed.
ibr: Iterative bias reduction multivariate smoothing
Hengartner, Nicholas W; Cornillon, Pierre-andre; Matzner - Lober, Eric
2009-01-01
Regression is a fundamental data analysis tool for relating a univariate response variable Y to a multivariate predictor X {element_of} E R{sup d} from the observations (X{sub i}, Y{sub i}), i = 1,...,n. Traditional nonparametric regression use the assumption that the regression function varies smoothly in the independent variable x to locally estimate the conditional expectation m(x) = E[Y|X = x]. The resulting vector of predicted values {cflx Y}{sub i} at the observed covariates X{sub i} is called a regression smoother, or simply a smoother, because the predicted values {cflx Y}{sub i} are less variable than the original observations Y{sub i}. Linear smoothers are linear in the response variable Y and are operationally written as {cflx m} = X{sub {lambda}}Y, where S{sub {lambda}} is a n x n smoothing matrix. The smoothing matrix S{sub {lambda}} typically depends on a tuning parameter which we denote by {lambda}, and that governs the tradeoff between the smoothness of the estimate and the goodness-of-fit of the smoother to the data by controlling the effective size of the local neighborhood over which the responses are averaged. We parameterize the smoothing matrix such that large values of {lambda} are associated to smoothers that averages over larger neighborhood and produce very smooth curves, while small {lambda} are associated to smoothers that average over smaller neighborhood to produce a more wiggly curve that wants to interpolate the data. The parameter {lambda} is the bandwidth for kernel smoother, the span size for running-mean smoother, bin smoother, and the penalty factor {lambda} for spline smoother.
Morota, Gota; Boddhireddy, Prashanth; Vukasinovic, Natascha; Gianola, Daniel; DeNise, Sue
2014-01-01
Prediction of complex trait phenotypes in the presence of unknown gene action is an ongoing challenge in animals, plants, and humans. Development of flexible predictive models that perform well irrespective of genetic and environmental architectures is desirable. Methods that can address non-additive variation in a non-explicit manner are gaining attention for this purpose and, in particular, semi-parametric kernel-based methods have been applied to diverse datasets, mostly providing encouraging results. On the other hand, the gains obtained from these methods have been smaller when smoothed values such as estimated breeding value (EBV) have been used as response variables. However, less emphasis has been placed on the choice of phenotypes to be used in kernel-based whole-genome prediction. This study aimed to evaluate differences between semi-parametric and parametric approaches using two types of response variables and molecular markers as inputs. Pre-corrected phenotypes (PCP) and EBV obtained for dairy cow health traits were used for this comparison. We observed that non-additive genetic variances were major contributors to total genetic variances in PCP, whereas additivity was the largest contributor to variability of EBV, as expected. Within the kernels evaluated, non-parametric methods yielded slightly better predictive performance across traits relative to their additive counterparts regardless of the type of response variable used. This reinforces the view that non-parametric kernels aiming to capture non-linear relationships between a panel of SNPs and phenotypes are appealing for complex trait prediction. However, like past studies, the gain in predictive correlation was not large for either PCP or EBV. We conclude that capturing non-additive genetic variation, especially epistatic variation, in a cross-validation framework remains a significant challenge even when it is important, as seems to be the case for health traits in dairy cows. PMID:24715901
Evaluating Equating Results: Percent Relative Error for Chained Kernel Equating
ERIC Educational Resources Information Center
Jiang, Yanlin; von Davier, Alina A.; Chen, Haiwen
2012-01-01
This article presents a method for evaluating equating results. Within the kernel equating framework, the percent relative error (PRE) for chained equipercentile equating was computed under the nonequivalent groups with anchor test (NEAT) design. The method was applied to two data sets to obtain the PRE, which can be used to measure equating…
NASA Astrophysics Data System (ADS)
Iga, Shin-ichi
2015-09-01
A generation method for smooth, seamless, and structured triangular grids on a sphere with flexibility in resolution distribution is proposed. This method is applicable to many fields that deal with a sphere on which the required resolution is not uniform. The grids were generated using the spring dynamics method, and adjustments were made using analytical functions. The mesh topology determined its resolution distribution, derived from a combination of conformal mapping factors: polar stereographic projection (PSP), Lambert conformal conic projection (LCCP), and Mercator projection (MP). Their combination generated, for example, a tropically fine grid that had a nearly constant high-resolution belt around the equator, with a gradual decrease in resolution distribution outside of the belt. This grid can be applied to boundary-less simulations of tropical meteorology. The other example involves a regionally fine grid with a nearly constant high-resolution circular region and a gradually decreasing resolution distribution outside of the region. This is applicable to regional atmospheric simulations without grid nesting. The proposed grids are compatible with computer architecture because they possess a structured form. Each triangle of the proposed grids was highly regular, implying a high local isotropy in resolution. Finally, the proposed grids were examined by advection and shallow water simulations.
Travel-time sensitivity kernels in long-range propagation.
Skarsoulis, E K; Cornuelle, B D; Dzieciuch, M A
2009-11-01
Wave-theoretic travel-time sensitivity kernels (TSKs) are calculated in two-dimensional (2D) and three-dimensional (3D) environments and their behavior with increasing propagation range is studied and compared to that of ray-theoretic TSKs and corresponding Fresnel-volumes. The differences between the 2D and 3D TSKs average out when horizontal or cross-range marginals are considered, which indicates that they are not important in the case of range-independent sound-speed perturbations or perturbations of large scale compared to the lateral TSK extent. With increasing range, the wave-theoretic TSKs expand in the horizontal cross-range direction, their cross-range extent being comparable to that of the corresponding free-space Fresnel zone, whereas they remain bounded in the vertical. Vertical travel-time sensitivity kernels (VTSKs)-one-dimensional kernels describing the effect of horizontally uniform sound-speed changes on travel-times-are calculated analytically using a perturbation approach, and also numerically, as horizontal marginals of the corresponding TSKs. Good agreement between analytical and numerical VTSKs, as well as between 2D and 3D VTSKs, is found. As an alternative method to obtain wave-theoretic sensitivity kernels, the parabolic approximation is used; the resulting TSKs and VTSKs are in good agreement with normal-mode results. With increasing range, the wave-theoretic VTSKs approach the corresponding ray-theoretic sensitivity kernels.
[Utilizable value of wild economic plant resource--acron kernel].
He, R; Wang, K; Wang, Y; Xiong, T
2000-04-01
Peking whites breeding hens were selected. Using true metabolizable energy method (TME) to evaluate the available nutritive value of acorn kernel, while maize and rice were used as control. The results showed that the contents of gross energy (GE), apparent metabolizable energy (AME), true metabolizable energy (TME) and crude protein (CP) in the acorn kernel were 16.53 mg/kg-1, 11.13 mg.kg-1, 11.66 mg.kg-1 and 10.63%, respectively. The apparent availability and true availability of crude protein were 45.55% and 49.83%. The gross content of 17 amino acids, essential amino acids and semiessential amino acids were 9.23% and 4.84%. The true availability of amino acid and the content of true available amino acid were 60.85% and 6.09%. The contents of tannin and hydrocyanic acid were 4.55% and 0.98% in acorn kernel. The available nutritive value of acorn kernel is similar to maize or slightly lower, but slightly higher than that of rice. Acorn kernel is a wild economic plant resource to exploit and utilize but it contains higher tannin and hydrocyanic acid. PMID:11767593
NASA Astrophysics Data System (ADS)
Baker, M. P.; King, J. C.; Gorman, B. P.; Braley, J. C.
2015-03-01
Current methods of TRISO fuel kernel production in the United States use a sol-gel process with trichloroethylene (TCE) as the forming fluid. After contact with radioactive materials, the spent TCE becomes a mixed hazardous waste, and high costs are associated with its recycling or disposal. Reducing or eliminating this mixed waste stream would not only benefit the environment, but would also enhance the economics of kernel production. Previous research yielded three candidates for testing as alternatives to TCE: 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane. This study considers the production of yttria-stabilized zirconia (YSZ) kernels in silicone oil and the three chosen alternative formation fluids, with subsequent characterization of the produced kernels and used forming fluid. Kernels formed in silicone oil and bromotetradecane were comparable to those produced by previous kernel production efforts, while those produced in chlorooctadecane and iodododecane experienced gelation issues leading to poor kernel formation and geometry.
Protein Analysis Meets Visual Word Recognition: A Case for String Kernels in the Brain
ERIC Educational Resources Information Center
Hannagan, Thomas; Grainger, Jonathan
2012-01-01
It has been recently argued that some machine learning techniques known as Kernel methods could be relevant for capturing cognitive and neural mechanisms (Jakel, Scholkopf, & Wichmann, 2009). We point out that "String kernels," initially designed for protein function prediction and spam detection, are virtually identical to one contending proposal…
Resistant-starch Formation in High-amylose Maize Starch During Kernel Development
Technology Transfer Automated Retrieval System (TEKTRAN)
The objective of this study was to understand the resistant-starch (RS) formation during the kernel development of high-amylose maize, GEMS-0067 line. RS content of the starch, determined using AOAC Method 991.43 for total dietary fiber, increased with kernel maturation and the increase in amylose/...
DFT calculations of molecular excited states using an orbital-dependent nonadiabatic exchange kernel
Ipatov, A. N.
2010-02-15
A density functional method for computing molecular excitation spectra is presented that uses a frequency-dependent kernel and takes into account the nonlocality of exchange interaction. Owing to its high numerical stability and the use of a nonadiabatic (frequency-dependent) exchange kernel, the proposed approach provides a qualitatively correct description of the asymptotic behavior of charge-transfer excitation energies.
A Comparison of Methods for Nonparametric Estimation of Item Characteristic Curves for Binary Items
ERIC Educational Resources Information Center
Lee, Young-Sun
2007-01-01
This study compares the performance of three nonparametric item characteristic curve (ICC) estimation procedures: isotonic regression, smoothed isotonic regression, and kernel smoothing. Smoothed isotonic regression, employed along with an appropriate kernel function, provides better estimates and also satisfies the assumption of strict…
Glosup, J.
1992-07-23
The class of gene linear models is extended to develop a class of nonparametric regression models known as generalized smooth models. The technique of local scoring is used to estimate a generalized smooth model and the estimation procedure based on locally weighted regression is shown to produce local likelihood estimates. The asymptotically correct distribution of the deviance difference is derived and its use in comparing the fits of generalized linear models and generalized smooth models is illustrated. The relationship between generalized smooth models and generalized additive models is discussed, also.
Kernel Near Principal Component Analysis
MARTIN, SHAWN B.
2002-07-01
We propose a novel algorithm based on Principal Component Analysis (PCA). First, we present an interesting approximation of PCA using Gram-Schmidt orthonormalization. Next, we combine our approximation with the kernel functions from Support Vector Machines (SVMs) to provide a nonlinear generalization of PCA. After benchmarking our algorithm in the linear case, we explore its use in both the linear and nonlinear cases. We include applications to face data analysis, handwritten digit recognition, and fluid flow.
Iris Image Blur Detection with Multiple Kernel Learning
NASA Astrophysics Data System (ADS)
Pan, Lili; Xie, Mei; Mao, Ling
In this letter, we analyze the influence of motion and out-of-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets.
Polarization smoothing for the National Ignition Facility
Rothenberg, J F
1998-08-13
Polarization smoothing (PS) is the illumination of the target with two distinct and orthogonally polarized speckle patterns. Since these two polarizations do not interfere, the intensity patterns add incoherently and thus the contrast of the intensity nonuniformity can be reduced by a factor of {radical}2 in addition to any reduction achieved by temporal smoothing techniques. Smoothing by PS is completely effective on an instantaneous basis and is therefore of particular interest for the suppression of laser plasma instabilities, which have a very rapid response time. The various implementations of PS are considered and their impact, in conjunction with temporal smoothing methods, on the spatial spectrum of the target illumination is analyzed.
Broadband Waveform Sensitivity Kernels for Large-Scale Seismic Tomography
NASA Astrophysics Data System (ADS)
Nissen-Meyer, T.; Stähler, S. C.; van Driel, M.; Hosseini, K.; Auer, L.; Sigloch, K.
2015-12-01
Seismic sensitivity kernels, i.e. the basis for mapping misfit functionals to structural parameters in seismic inversions, have received much attention in recent years. Their computation has been conducted via ray-theory based approaches (Dahlen et al., 2000) or fully numerical solutions based on the adjoint-state formulation (e.g. Tromp et al., 2005). The core problem is the exuberant computational cost due to the large number of source-receiver pairs, each of which require solutions to the forward problem. This is exacerbated in the high-frequency regime where numerical solutions become prohibitively expensive. We present a methodology to compute accurate sensitivity kernels for global tomography across the observable seismic frequency band. These kernels rely on wavefield databases computed via AxiSEM (abstract ID# 77891, www.axisem.info), and thus on spherically symmetric models. As a consequence of this method's numerical efficiency even in high-frequency regimes, kernels can be computed in a time- and frequency-dependent manner, thus providing the full generic mapping from perturbed waveform to perturbed structure. Such waveform kernels can then be used for a variety of misfit functions, structural parameters and refiltered into bandpasses without recomputing any wavefields. A core component of the kernel method presented here is the mapping from numerical wavefields to inversion meshes. This is achieved by a Monte-Carlo approach, allowing for convergent and controllable accuracy on arbitrarily shaped tetrahedral and hexahedral meshes. We test and validate this accuracy by comparing to reference traveltimes, show the projection onto various locally adaptive inversion meshes and discuss computational efficiency for ongoing tomographic applications in the range of millions of observed body-wave data between periods of 2-30s.
Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen
2014-09-01
For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. PMID:24929345
Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen
2014-09-01
For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance.
On the equivalence between kernel self-organising maps and self-organising mixture density networks.
Yin, Hujun
2006-01-01
The kernel method has become a useful trick and has been widely applied to various learning models to extend their nonlinear approximation and classification capabilities. Such extensions have also recently occurred to the Self-Organising Map (SOM). In this paper, two recently proposed kernel SOMs are reviewed, together with their link to an energy function. The Self-Organising Mixture Network is an extension of the SOM for mixture density modelling. This paper shows that with an isotropic, density-type kernel function, the kernel SOM is equivalent to a homoscedastic Self-Organising Mixture Network, an entropy-based density estimator. This revelation on the one hand explains that kernelising SOM can improve classification performance by acquiring better probability models of the data; but on the other hand it also explains that the SOM already naturally approximates the kernel method.
Technology Transfer Automated Retrieval System (TEKTRAN)
Vascular smooth muscle cells (SMCs) originate from multiple types of progenitor cells. In the embryo, the most well-studied SMC progenitor is the cardiac neural crest stem cell. Smooth muscle differentiation in the neural crest lineage is controlled by a combination of cell intrinsic factors, includ...
Physics Integration KErnels (PIKE)
2014-07-31
Pike is a software library for coupling and solving multiphysics applications. It provides basic interfaces and utilities for performing code-to-code coupling. It provides simple black-box Picard iteration methods for solving the coupled system of equations including Jacobi and Gauss-Seidel solvers. Pike was developed originally to couple neutronics and thermal fluids codes to simulate a light water nuclear reactor for the Consortium for Simulation of Light-water Reactors (CASL) DOE Energy Innovation Hub. The Pike library containsmore » no physics and just provides interfaces and utilities for coupling codes. It will be released open source under a BSD license as part of the Trilinos solver framework (trilinos.org) which is also BSD. This code provides capabilities similar to other open source multiphysics coupling libraries such as LIME, AMP, and MOOSE.« less
Physics Integration KErnels (PIKE)
Pawlowski, Roger
2014-07-31
Pike is a software library for coupling and solving multiphysics applications. It provides basic interfaces and utilities for performing code-to-code coupling. It provides simple black-box Picard iteration methods for solving the coupled system of equations including Jacobi and Gauss-Seidel solvers. Pike was developed originally to couple neutronics and thermal fluids codes to simulate a light water nuclear reactor for the Consortium for Simulation of Light-water Reactors (CASL) DOE Energy Innovation Hub. The Pike library contains no physics and just provides interfaces and utilities for coupling codes. It will be released open source under a BSD license as part of the Trilinos solver framework (trilinos.org) which is also BSD. This code provides capabilities similar to other open source multiphysics coupling libraries such as LIME, AMP, and MOOSE.
ERIC Educational Resources Information Center
von Davier, Alina A.; Fournier-Zajac, Stephanie; Holland, Paul W.
2007-01-01
In the nonequivalent groups with anchor test (NEAT) design, there are several ways to use the information provided by the anchor in the equating process. One of the NEAT-design equating methods is the linear observed-score Levine method (Kolen & Brennan, 2004). It is based on a classical test theory model of the true scores on the test forms…
Pareto-path multitask multiple kernel learning.
Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2015-01-01
A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches. PMID:25532155
Pareto-path multitask multiple kernel learning.
Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2015-01-01
A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.
Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali
2014-05-01
Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities. PMID:24338099
NASA Technical Reports Server (NTRS)
Voronov, Oleg
2007-01-01
Diamond smoothing tools have been proposed for use in conjunction with diamond cutting tools that are used in many finish-machining operations. Diamond machining (including finishing) is often used, for example, in fabrication of precise metal mirrors. A diamond smoothing tool according to the proposal would have a smooth spherical surface. For a given finish machining operation, the smoothing tool would be mounted next to the cutting tool. The smoothing tool would slide on the machined surface left behind by the cutting tool, plastically deforming the surface material and thereby reducing the roughness of the surface, closing microcracks and otherwise generally reducing or eliminating microscopic surface and subsurface defects, and increasing the microhardness of the surface layer. It has been estimated that if smoothing tools of this type were used in conjunction with cutting tools on sufficiently precise lathes, it would be possible to reduce the roughness of machined surfaces to as little as 3 nm. A tool according to the proposal would consist of a smoothing insert in a metal holder. The smoothing insert would be made from a diamond/metal functionally graded composite rod preform, which, in turn, would be made by sintering together a bulk single-crystal or polycrystalline diamond, a diamond powder, and a metallic alloy at high pressure. To form the spherical smoothing tip, the diamond end of the preform would be subjected to flat grinding, conical grinding, spherical grinding using diamond wheels, and finally spherical polishing and/or buffing using diamond powders. If the diamond were a single crystal, then it would be crystallographically oriented, relative to the machining motion, to minimize its wear and maximize its hardness. Spherically polished diamonds could also be useful for purposes other than smoothing in finish machining: They would likely also be suitable for use as heat-resistant, wear-resistant, unlubricated sliding-fit bearing inserts.
Metabolite identification through multiple kernel learning on fragmentation trees
Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho
2014-01-01
Motivation: Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Results: Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. Contact: huibin.shen@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24931979
Popping the Kernel Modeling the States of Matter
ERIC Educational Resources Information Center
Hitt, Austin; White, Orvil; Hanson, Debbie
2005-01-01
This article discusses how to use popcorn to engage students in model building and to teach them about the nature of matter. Popping kernels is a simple and effective method to connect the concepts of heat, motion, and volume with the different phases of matter. Before proceeding with the activity the class should discuss the nature of scientific…
The Stokes problem for the ellipsoid using ellipsoidal kernels
NASA Technical Reports Server (NTRS)
Zhu, Z.
1981-01-01
A brief review of Stokes' problem for the ellipsoid as a reference surface is given. Another solution of the problem using an ellipsoidal kernel, which represents an iterative form of Stokes' integral, is suggested with a relative error of the order of the flattening. On studying of Rapp's method in detail the procedures of improving its convergence are discussed.
Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image
NASA Astrophysics Data System (ADS)
Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Ononye, Ambrose; Brown, Robert L.; Cleveland, Thomas E.
2010-04-01
Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.
FABRICATION PROCESS AND PRODUCT QUALITY IMPROVEMENTS IN ADVANCED GAS REACTOR UCO KERNELS
Charles M Barnes
2008-09-01
A major element of the Advanced Gas Reactor (AGR) program is developing fuel fabrication processes to produce high quality uranium-containing kernels, TRISO-coated particles and fuel compacts needed for planned irradiation tests. The goals of the AGR program also include developing the fabrication technology to mass produce this fuel at low cost. Kernels for the first AGR test (“AGR-1) consisted of uranium oxycarbide (UCO) microspheres that werre produced by an internal gelation process followed by high temperature steps tot convert the UO3 + C “green” microspheres to first UO2 + C and then UO2 + UCx. The high temperature steps also densified the kernels. Babcock and Wilcox (B&W) fabricated UCO kernels for the AGR-1 irradiation experiment, which went into the Advance Test Reactor (ATR) at Idaho National Laboratory in December 2006. An evaluation of the kernel process following AGR-1 kernel production led to several recommendations to improve the fabrication process. These recommendations included testing alternative methods of dispersing carbon during broth preparation, evaluating the method of broth mixing, optimizing the broth chemistry, optimizing sintering conditions, and demonstrating fabrication of larger diameter UCO kernels needed for the second AGR irradiation test. Based on these recommendations and requirements, a test program was defined and performed. Certain portions of the test program were performed by Oak Ridge National Laboratory (ORNL), while tests at larger scale were performed by B&W. The tests at B&W have demonstrated improvements in both kernel properties and process operation. Changes in the form of carbon black used and the method of mixing the carbon prior to forming kernels led to improvements in the phase distribution in the sintered kernels, greater consistency in kernel properties, a reduction in forming run time, and simplifications to the forming process. Process parameter variation tests in both forming and sintering steps led
Privacy preserving RBF kernel support vector machine.
Li, Haoran; Xiong, Li; Ohno-Machado, Lucila; Jiang, Xiaoqian
2014-01-01
Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data. PMID:25013805
Smoothed-particle-hydrodynamics modeling of dissipation mechanisms in gravity waves.
Colagrossi, Andrea; Souto-Iglesias, Antonio; Antuono, Matteo; Marrone, Salvatore
2013-02-01
The smoothed-particle-hydrodynamics (SPH) method has been used to study the evolution of free-surface Newtonian viscous flows specifically focusing on dissipation mechanisms in gravity waves. The numerical results have been compared with an analytical solution of the linearized Navier-Stokes equations for Reynolds numbers in the range 50-5000. We found that a correct choice of the number of neighboring particles is of fundamental importance in order to obtain convergence towards the analytical solution. This number has to increase with higher Reynolds numbers in order to prevent the onset of spurious vorticity inside the bulk of the fluid, leading to an unphysical overdamping of the wave amplitude. This generation of spurious vorticity strongly depends on the specific kernel function used in the SPH model.
Molecular Hydrodynamics from Memory Kernels.
Lesnicki, Dominika; Vuilleumier, Rodolphe; Carof, Antoine; Rotenberg, Benjamin
2016-04-01
The memory kernel for a tagged particle in a fluid, computed from molecular dynamics simulations, decays algebraically as t^{-3/2}. We show how the hydrodynamic Basset-Boussinesq force naturally emerges from this long-time tail and generalize the concept of hydrodynamic added mass. This mass term is negative in the present case of a molecular solute, which is at odds with incompressible hydrodynamics predictions. Lastly, we discuss the various contributions to the friction, the associated time scales, and the crossover between the molecular and hydrodynamic regimes upon increasing the solute radius. PMID:27104730
Facemyer, K C; Cremo, C R
1992-01-01
We present a new method to specifically and stably label proteins by attaching extrinsic probes to amino acids that are thiophosphorylated by protein kinases and ATP gamma S. The method was demonstrated for labeling of a thiophosphorylatable serine of the isolated regulatory light chain of smooth muscle myosin. We stoichiometrically blocked the single thiol (Cys-108) either by forming a reversible intermolecular disulfide bond or by reacting with iodoacetic acid. The protein was stoichiometrically thiophosphorylated at Ser-19 by myosin light chain kinase and ATP gamma S. The nucleophilic sulfur of the protein phosphorothioate was coupled at pH 7.9 and 25 degrees C to the fluorescent haloacetate [3H]-5-[[2-[(iodoacetyl)-amino]ethyl]amino]naphthalene-1- sulfonic acid ([3H]IAEDANS) by displacement of the iodide. Typical labeling efficiencies were 70-100%. The labeling was specific for the thiophosphorylated Ser-19, as determined from the sequences of two labeled peptides isolated from a tryptic digest of the labeled protein. [3H]IAEDANS attached to the thiophosphorylated Ser-19 was stable at pH 3-10 at 25 degrees C, and to boiling in high concentrations of reductant. The labeled light chains were efficiently exchanged for unlabeled regulatory light chains of the whole myosin molecule. The resulting labeled myosin had normal ATPase activities in the absence of actin, indicating that the modification of Ser-19 and the exchange of the labeled light chain into myosin did not significantly disrupt the protein. The labeled myosin partially retained the elevated actin-activated Mg(2+)-ATPase activity which is characteristic of thiophosphorylated myosin. This indicates that labeling of the thiophosphate group with [3H]IAEDANS did not completely disrupt the functional properties of the thiophosphorylated protein in the presence of actin.
Parmar, Nina; Ahmadi, Raheleh
2015-01-01
Muscle degeneration is a prevalent disease, particularly in aging societies where it has a huge impact on quality of life and incurs colossal health costs. Suitable donor sources of smooth muscle cells are limited and minimally invasive therapeutic approaches are sought that will augment muscle volume by delivering cells to damaged or degenerated areas of muscle. For the first time, we report the use of highly porous microcarriers produced using thermally induced phase separation (TIPS) to expand and differentiate adipose-derived mesenchymal stem cells (AdMSCs) into smooth muscle-like cells in a format that requires minimal manipulation before clinical delivery. AdMSCs readily attached to the surface of TIPS microcarriers and proliferated while maintained in suspension culture for 12 days. Switching the incubation medium to a differentiation medium containing 2 ng/mL transforming growth factor beta-1 resulted in a significant increase in both the mRNA and protein expression of cell contractile apparatus components caldesmon, calponin, and myosin heavy chains, indicative of a smooth muscle cell-like phenotype. Growth of smooth muscle cells on the surface of the microcarriers caused no change to the integrity of the polymer microspheres making them suitable for a cell-delivery vehicle. Our results indicate that TIPS microspheres provide an ideal substrate for the expansion and differentiation of AdMSCs into smooth muscle-like cells as well as a microcarrier delivery vehicle for the attached cells ready for therapeutic applications. PMID:25205072
Kernel-based least squares policy iteration for reinforcement learning.
Xu, Xin; Hu, Dewen; Lu, Xicheng
2007-07-01
In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating
NASA Astrophysics Data System (ADS)
Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Ji, Jin-Chao
2016-04-01
In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.
NASA Astrophysics Data System (ADS)
Kujawa, Sebastian; Weres, Jerzy; Olek, Wiesław
2016-07-01
Uncertainties in mathematical modelling of water transport in cereal grain kernels during drying and storage are mainly due to implementing unreliable values of the water diffusion coefficient and simplifying the geometry of kernels. In the present study an attempt was made to reduce the uncertainties by developing a method for computer-aided identification of the water diffusion coefficient and more accurate 3D geometry modelling for individual kernels using original inverse finite element algorithms. The approach was exemplified by identifying the water diffusion coefficient for maize kernels subjected to drying. On the basis of the developed method, values of the water diffusion coefficient were estimated, 3D geometry of a maize kernel was represented by isoparametric finite elements, and the moisture content inside maize kernels dried in a thin layer was predicted. Validation of the results against experimental data showed significantly lower error values than in the case of results obtained for the water diffusion coefficient values available in the literature.
Discrete square root smoothing.
NASA Technical Reports Server (NTRS)
Kaminski, P. G.; Bryson, A. E., Jr.
1972-01-01
The basic techniques applied in the square root least squares and square root filtering solutions are applied to the smoothing problem. Both conventional and square root solutions are obtained by computing the filtered solutions, then modifying the results to include the effect of all measurements. A comparison of computation requirements indicates that the square root information smoother (SRIS) is more efficient than conventional solutions in a large class of fixed interval smoothing problems.
NASA Astrophysics Data System (ADS)
Song, Bongyong; Park, Justin C.; Song, William Y.
2014-11-01
The Barzilai-Borwein (BB) 2-point step size gradient method is receiving attention for accelerating Total Variation (TV) based CBCT reconstructions. In order to become truly viable for clinical applications, however, its convergence property needs to be properly addressed. We propose a novel fast converging gradient projection BB method that requires ‘at most one function evaluation’ in each iterative step. This Selective Function Evaluation method, referred to as GPBB-SFE in this paper, exhibits the desired convergence property when it is combined with a ‘smoothed TV’ or any other differentiable prior. This way, the proposed GPBB-SFE algorithm offers fast and guaranteed convergence to the desired 3DCBCT image with minimal computational complexity. We first applied this algorithm to a Shepp-Logan numerical phantom. We then applied to a CatPhan 600 physical phantom (The Phantom Laboratory, Salem, NY) and a clinically-treated head-and-neck patient, both acquired from the TrueBeam™ system (Varian Medical Systems, Palo Alto, CA). Furthermore, we accelerated the reconstruction by implementing the algorithm on NVIDIA GTX 480 GPU card. We first compared GPBB-SFE with three recently proposed BB-based CBCT reconstruction methods available in the literature using Shepp-Logan numerical phantom with 40 projections. It is found that GPBB-SFE shows either faster convergence speed/time or superior convergence property compared to existing BB-based algorithms. With the CatPhan 600 physical phantom, the GPBB-SFE algorithm requires only 3 function evaluations in 30 iterations and reconstructs the standard, 364-projection FDK reconstruction quality image using only 60 projections. We then applied the algorithm to a clinically-treated head-and-neck patient. It was observed that the GPBB-SFE algorithm requires only 18 function evaluations in 30 iterations. Compared with the FDK algorithm with 364 projections, the GPBB-SFE algorithm produces visibly equivalent quality CBCT
A bound for the smoothing parameter in certain well-known nonparametric density estimators
NASA Technical Reports Server (NTRS)
Terrell, G. R.
1980-01-01
Two classes of nonparametric density estimators, the histogram and the kernel estimator, both require a choice of smoothing parameter, or 'window width'. The optimum choice of this parameter is in general very difficult. An upper bound to the choices that depends only on the standard deviation of the distribution is described.
Heat kernel asymptotic expansions for the Heisenberg sub-Laplacian and the Grushin operator
Chang, Der-Chen; Li, Yutian
2015-01-01
The sub-Laplacian on the Heisenberg group and the Grushin operator are typical examples of sub-elliptic operators. Their heat kernels are both given in the form of Laplace-type integrals. By using Laplace's method, the method of stationary phase and the method of steepest descent, we derive the small-time asymptotic expansions for these heat kernels, which are related to the geodesic structure of the induced geometries. PMID:25792966
NASA Astrophysics Data System (ADS)
Rojas-Lima, J. E.; Domínguez-Pacheco, A.; Hernández-Aguilar, C.; Cruz-Orea, A.
2016-09-01
Considering the necessity of photothermal alternative approaches for characterizing nonhomogeneous materials like maize seeds, the objective of this research work was to analyze statistically the amplitude variations of photopyroelectric signals, by means of nonparametric techniques such as the histogram and the kernel density estimator, and the probability density function of the amplitude variations of two genotypes of maize seeds with different pigmentations and structural components: crystalline and floury. To determine if the probability density function had a known parametric form, the histogram was determined which did not present a known parametric form, so the kernel density estimator using the Gaussian kernel, with an efficiency of 95 % in density estimation, was used to obtain the probability density function. The results obtained indicated that maize seeds could be differentiated in terms of the statistical values for floury and crystalline seeds such as the mean (93.11, 159.21), variance (1.64× 103, 1.48× 103), and standard deviation (40.54, 38.47) obtained from the amplitude variations of photopyroelectric signals in the case of the histogram approach. For the case of the kernel density estimator, seeds can be differentiated in terms of kernel bandwidth or smoothing constant h of 9.85 and 6.09 for floury and crystalline seeds, respectively.
Point-Kernel Shielding Code System.
1982-02-17
Version 00 QAD-BSA is a three-dimensional, point-kernel shielding code system based upon the CCC-48/QAD series. It is designed to calculate photon dose rates and heating rates using exponential attenuation and infinite medium buildup factors. Calculational provisions include estimates of fast neutron penetration using data computed by the moments method. Included geometry routines can describe complicated source and shield geometries. An internal library contains data for many frequently used structural and shielding materials, enabling the codemore » to solve most problems with only source strengths and problem geometry required as input. This code system adapts especially well to problems requiring multiple sources and sources with asymmetrical geometry. In addition to being edited separately, the total interaction rates from many sources may be edited at each detector point. Calculated photon interaction rates agree closely with those obtained using QAD-P5A.« less
Excitons in solids with time-dependent density-functional theory: the bootstrap kernel and beyond
NASA Astrophysics Data System (ADS)
Byun, Young-Moo; Yang, Zeng-Hui; Ullrich, Carsten
Time-dependent density-functional theory (TDDFT) is an efficient method to describe the optical properties of solids. Lately, a series of bootstrap-type exchange-correlation (xc) kernels have been reported to produce accurate excitons in solids, but different bootstrap-type kernels exist in the literature, with mixed results. In this presentation, we reveal the origin of the confusion and show a new empirical TDDFT xc kernel to compute excitonic properties of semiconductors and insulators efficiently and accurately. Our method can be used for high-throughput screening calculations and large unit cell calculations. Work supported by NSF Grant DMR-1408904.
Kernel maximum likelihood scaled locally linear embedding for night vision images
NASA Astrophysics Data System (ADS)
Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lian-fa
2014-03-01
This paper proposes a robust method to analyze night vision data. A new kernel manifold algorithm is designed to match an ideal distribution with a complex one in natural data. First, an outlier-probability based on similarity metric is derived by solving the maximum likelihood in kernel space, which is corresponding with classification property for considering the statistical information on manifold. Then a robust nonlinear mapping is completed by scaling the embedding process of kernel LLE with the outlier-probability. In the simulations of artificial manifolds, real low-light-level (LLL) and infrared image sets, the proposed method show remarkable performances in dimension reduction and classification.
Single infrared image super-resolution combining non-local means with kernel regression
NASA Astrophysics Data System (ADS)
Yu, Hui; Chen, Fu-sheng; Zhang, Zhi-jie; Wang, Chen-sheng
2013-11-01
In many infrared imaging systems, the focal plane array is not sufficient dense to adequately sample the scene with the desired field of view. Therefore, there are not enough high frequency details in the infrared image generally. Super-resolution (SR) technology can be used to increase the resolution of low-resolution (LR) infrared image. In this paper, a novel super-resolution algorithm is proposed based on non-local means (NLM) and steering kernel regression (SKR). Based on that there are a large number of similar patches within an infrared image, NLM method can abstract the non-local similarity information and then the value of high-resolution (HR) pixel can be estimated. SKR method is derived based on the local smoothness of the natural images. In this paper the SKR is used to give the regularization term which can restrict the image noise and protect image edges. The estimated SR image is obtained by minimizing a cost function. In the experiments the proposed algorithm is compared with state-of-the-art algorithms. The comparison results show that the proposed method is robust to the noise and it can restore higher quality image both in quantitative term and visual effect.
Smoothed particle hydrodynamics with smoothed pseudo-density
NASA Astrophysics Data System (ADS)
Yamamoto, Satoko; Saitoh, Takayuki R.; Makino, Junichiro
2015-06-01
In this paper, we present a new formulation of smoothed particle hydrodynamics (SPH), which, unlike the standard SPH (SSPH), is well behaved at the contact discontinuity. The SSPH scheme cannot handle discontinuities in density (e.g., the contact discontinuity and the free surface), because it requires that the density of fluid is positive and continuous everywhere. Thus there is inconsistency in the formulation of the SSPH scheme at discontinuities of the fluid density. To solve this problem, we introduce a new quantity associated with particles and the "density" of that quantity. This "density" evolves through the usual continuity equation with an additional artificial diffusion term, in order to guarantee the continuity of the "density." We use this "density," or pseudo-density, instead of the mass density, to formulate our SPH scheme. We call our new method SPH with smoothed pseudo-density, and we show that it is physically consistent and can handle discontinuities quite well.
Improved scatter correction using adaptive scatter kernel superposition
NASA Astrophysics Data System (ADS)
Sun, M.; Star-Lack, J. M.
2010-11-01
Accurate scatter correction is required to produce high-quality reconstructions of x-ray cone-beam computed tomography (CBCT) scans. This paper describes new scatter kernel superposition (SKS) algorithms for deconvolving scatter from projection data. The algorithms are designed to improve upon the conventional approach whose accuracy is limited by the use of symmetric kernels that characterize the scatter properties of uniform slabs. To model scatter transport in more realistic objects, nonstationary kernels, whose shapes adapt to local thickness variations in the projection data, are proposed. Two methods are introduced: (1) adaptive scatter kernel superposition (ASKS) requiring spatial domain convolutions and (2) fast adaptive scatter kernel superposition (fASKS) where, through a linearity approximation, convolution is efficiently performed in Fourier space. The conventional SKS algorithm, ASKS, and fASKS, were tested with Monte Carlo simulations and with phantom data acquired on a table-top CBCT system matching the Varian On-Board Imager (OBI). All three models accounted for scatter point-spread broadening due to object thickening, object edge effects, detector scatter properties and an anti-scatter grid. Hounsfield unit (HU) errors in reconstructions of a large pelvis phantom with a measured maximum scatter-to-primary ratio over 200% were reduced from -90 ± 58 HU (mean ± standard deviation) with no scatter correction to 53 ± 82 HU with SKS, to 19 ± 25 HU with fASKS and to 13 ± 21 HU with ASKS. HU accuracies and measured contrast were similarly improved in reconstructions of a body-sized elliptical Catphan phantom. The results show that the adaptive SKS methods offer significant advantages over the conventional scatter deconvolution technique.
Ong, L G A; Abd-Aziz, S; Noraini, S; Karim, M I A; Hassan, M A
2004-01-01
The oil palm sector is one of the major plantation industries in Malaysia. Palm kernel cake is a byproduct of extracted palm kernel oil. Mostly palm kernel cake is wasted or is mixed with other nutrients and used as animal feed, especially for ruminant animals. Recently, palm kernel cake has been identified as an important ingredient for the formulation of animal feed, and it is also exported especially to Europe, South Korea, and Japan. It can barely be consumed by nonruminant (monogastric) animals owing to the high percentages of hemicellulose and cellulose contents. Palm kernel cake must undergo suitable pretreatment in order to decrease the percentage of hemicellulose and cellulose. One of the methods employed in this study is fermentation with microorganisms, particularly fungi, to partially degrade the hemicellulose and cellulose content. This work focused on the production of enzymes by Aspergillus niger and profiling using palm kernel cake as carbon source.
Effective face recognition using bag of features with additive kernels
NASA Astrophysics Data System (ADS)
Yang, Shicai; Bebis, George; Chu, Yongjie; Zhao, Lindu
2016-01-01
In past decades, many techniques have been used to improve face recognition performance. The most common and well-studied ways are to use the whole face image to build a subspace based on the reduction of dimensionality. Differing from methods above, we consider face recognition as an image classification problem. The face images of the same person are considered to fall into the same category. Each category and each face image could be both represented by a simple pyramid histogram. Spatial dense scale-invariant feature transform features and bag of features method are used to build categories and face representations. In an effort to make the method more efficient, a linear support vector machine solver, Pegasos, is used for the classification in the kernel space with additive kernels instead of nonlinear SVMs. Our experimental results demonstrate that the proposed method can achieve very high recognition accuracy on the ORL, YALE, and FERET databases.
Undersampled dynamic magnetic resonance imaging using kernel principal component analysis.
Wang, Yanhua; Ying, Leslie
2014-01-01
Compressed sensing (CS) is a promising approach to accelerate dynamic magnetic resonance imaging (MRI). Most existing CS methods employ linear sparsifying transforms. The recent developments in non-linear or kernel-based sparse representations have been shown to outperform the linear transforms. In this paper, we present an iterative non-linear CS dynamic MRI reconstruction framework that uses the kernel principal component analysis (KPCA) to exploit the sparseness of the dynamic image sequence in the feature space. Specifically, we apply KPCA to represent the temporal profiles of each spatial location and reconstruct the images through a modified pre-image problem. The underlying optimization algorithm is based on variable splitting and fixed-point iteration method. Simulation results show that the proposed method outperforms conventional CS method in terms of aliasing artifact reduction and kinetic information preservation. PMID:25570262
Improved beam smoothing with SSD using generalized phase modulation
Rothenberg, J.E.
1997-01-01
The smoothing of the spatial illumination of an inertial confinement fusion target is examined by its spatial frequency content. It is found that the smoothing by spectral dispersion method, although efficient for glass lasers, can yield poor smoothing at low spatial frequency. The dependence of the smoothed spatial spectrum on the characteristics of phase modulation and dispersion is examined for both sinusoidal and more general phase modulation. It is shown that smoothing with non-sinusoidal phase modulation can result in spatial spectra which are substantially identical to that obtained with the induced spatial incoherence or similar method where random phase plates are present in both methods and identical beam divergence is assumed.
Reproducing kernel hilbert space based single infrared image super resolution
NASA Astrophysics Data System (ADS)
Chen, Liangliang; Deng, Liangjian; Shen, Wei; Xi, Ning; Zhou, Zhanxin; Song, Bo; Yang, Yongliang; Cheng, Yu; Dong, Lixin
2016-07-01
The spatial resolution of Infrared (IR) images is limited by lens optical diffraction, sensor array pitch size and pixel dimension. In this work, a robust model is proposed to reconstruct high resolution infrared image via a single low resolution sampling, where the image features are discussed and classified as reflective, cooled emissive and uncooled emissive based on infrared irradiation source. A spline based reproducing kernel hilbert space and approximative heaviside function are deployed to model smooth part and edge component of image respectively. By adjusting the parameters of heaviside function, the proposed model can enhance distinct part of images. The experimental results show that the model is applicable on both reflective and emissive low resolution infrared images to improve thermal contrast. The overall outcome produces a high resolution IR image, which makes IR camera better measurement accuracy and observes more details at long distance.
Fast metabolite identification with Input Output Kernel Regression
Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho
2016-01-01
Motivation: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. Results: We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods. Availability and implementation: Contact: celine.brouard@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307628
PyPHER: Python-based PSF Homogenization kERnels
NASA Astrophysics Data System (ADS)
Boucaud, Alexandre; Bocchio, Marco; Abergel, Alain; Orieux, François; Dole, Hervé; Amine Hadj-Youcef, Mohamed
2016-09-01
PyPHER (Python-based PSF Homogenization kERnels) computes an homogenization kernel between two PSFs; the code is well-suited for PSF matching applications in both an astronomical or microscopy context. It can warp (rotation + resampling) the PSF images (if necessary), filter images in Fourier space using a regularized Wiener filter, and produce a homogenization kernel. PyPHER requires the pixel scale information to be present in the FITS files, which can if necessary be added by using the provided ADDPIXSCL method.
A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm
NASA Astrophysics Data System (ADS)
Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina
The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.
Ideal regularization for learning kernels from labels.
Pan, Binbin; Lai, Jianhuang; Shen, Lixin
2014-08-01
In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. The proposed regularization, referred to as ideal regularization, is a linear function of the kernel matrix to be learned. The ideal regularization allows us to develop efficient algorithms to exploit labels. Three applications of the ideal regularization are considered. Firstly, we use the ideal regularization to incorporate the labels into a standard kernel, making the resulting kernel more appropriate for learning tasks. Next, we employ the ideal regularization to learn a data-dependent kernel matrix from an initial kernel matrix (which contains prior similarity information, geometric structures, and labels of the data). Finally, we incorporate the ideal regularization to some state-of-the-art kernel learning problems. With this regularization, these learning problems can be formulated as simpler ones which permit more efficient solvers. Empirical results show that the ideal regularization exploits the labels effectively and efficiently.
Kernel score statistic for dependent data.
Malzahn, Dörthe; Friedrichs, Stefanie; Rosenberger, Albert; Bickeböller, Heike
2014-01-01
The kernel score statistic is a global covariance component test over a set of genetic markers. It provides a flexible modeling framework and does not collapse marker information. We generalize the kernel score statistic to allow for familial dependencies and to adjust for random confounder effects. With this extension, we adjust our analysis of real and simulated baseline systolic blood pressure for polygenic familial background. We find that the kernel score test gains appreciably in power through the use of sequencing compared to tag-single-nucleotide polymorphisms for very rare single nucleotide polymorphisms with <1% minor allele frequency.
Predicting activity approach based on new atoms similarity kernel function.
Abu El-Atta, Ahmed H; Moussa, M I; Hassanien, Aboul Ella
2015-07-01
Drug design is a high cost and long term process. To reduce time and costs for drugs discoveries, new techniques are needed. Chemoinformatics field implements the informational techniques and computer science like machine learning and graph theory to discover the chemical compounds properties, such as toxicity or biological activity. This is done through analyzing their molecular structure (molecular graph). To overcome this problem there is an increasing need for algorithms to analyze and classify graph data to predict the activity of molecules. Kernels methods provide a powerful framework which combines machine learning with graph theory techniques. These kernels methods have led to impressive performance results in many several chemoinformatics problems like biological activity prediction. This paper presents a new approach based on kernel functions to solve activity prediction problem for chemical compounds. First we encode all atoms depending on their neighbors then we use these codes to find a relationship between those atoms each other. Then we use relation between different atoms to find similarity between chemical compounds. The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods.
Kernels for longitudinal data with variable sequence length and sampling intervals.
Lu, Zhengdong; Leen, Todd K; Kaye, Jeffrey
2011-09-01
We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers. The hierarchical generative model allows us to handle variations in sequence length and sampling interval gracefully. We also give nonparametric kernels not based on generative models, but rather on the reproducing kernel Hilbert space. We apply the methods to detecting cognitive decline from longitudinal clinical data on motor and neuropsychological tests. The likelihood ratio classifiers based on the neuropsychological tests perform better than than classifiers based on the motor behavior. Discriminant classifiers performed better than likelihood ratio classifiers for the motor behavior tests.
Reduction of Aflatoxins in Apricot Kernels by Electronic and Manual Color Sorting.
Zivoli, Rosanna; Gambacorta, Lucia; Piemontese, Luca; Solfrizzo, Michele
2016-01-19
The efficacy of color sorting on reducing aflatoxin levels in shelled apricot kernels was assessed. Naturally-contaminated kernels were submitted to an electronic optical sorter or blanched, peeled, and manually sorted to visually identify and sort discolored kernels (dark and spotted) from healthy ones. The samples obtained from the two sorting approaches were ground, homogenized, and analysed by HPLC-FLD for their aflatoxin content. A mass balance approach was used to measure the distribution of aflatoxins in the collected fractions. Aflatoxin B₁ and B₂ were identified and quantitated in all collected fractions at levels ranging from 1.7 to 22,451.5 µg/kg of AFB₁ + AFB₂, whereas AFG₁ and AFG₂ were not detected. Excellent results were obtained by manual sorting of peeled kernels since the removal of discolored kernels (2.6%-19.9% of total peeled kernels) removed 97.3%-99.5% of total aflatoxins. The combination of peeling and visual/manual separation of discolored kernels is a feasible strategy to remove 97%-99% of aflatoxins accumulated in naturally-contaminated samples. Electronic optical sorter gave highly variable results since the amount of AFB₁ + AFB₂ measured in rejected fractions (15%-18% of total kernels) ranged from 13% to 59% of total aflatoxins. An improved immunoaffinity-based HPLC-FLD method having low limits of detection for the four aflatoxins (0.01-0.05 µg/kg) was developed and used to monitor the occurrence of aflatoxins in 47 commercial products containing apricot kernels and/or almonds commercialized in Italy. Low aflatoxin levels were found in 38% of the tested samples and ranged from 0.06 to 1.50 μg/kg for AFB₁ and from 0.06 to 1.79 μg/kg for total aflatoxins.
Reduction of Aflatoxins in Apricot Kernels by Electronic and Manual Color Sorting
Zivoli, Rosanna; Gambacorta, Lucia; Piemontese, Luca; Solfrizzo, Michele
2016-01-01
The efficacy of color sorting on reducing aflatoxin levels in shelled apricot kernels was assessed. Naturally-contaminated kernels were submitted to an electronic optical sorter or blanched, peeled, and manually sorted to visually identify and sort discolored kernels (dark and spotted) from healthy ones. The samples obtained from the two sorting approaches were ground, homogenized, and analysed by HPLC-FLD for their aflatoxin content. A mass balance approach was used to measure the distribution of aflatoxins in the collected fractions. Aflatoxin B1 and B2 were identified and quantitated in all collected fractions at levels ranging from 1.7 to 22,451.5 µg/kg of AFB1 + AFB2, whereas AFG1 and AFG2 were not detected. Excellent results were obtained by manual sorting of peeled kernels since the removal of discolored kernels (2.6%–19.9% of total peeled kernels) removed 97.3%–99.5% of total aflatoxins. The combination of peeling and visual/manual separation of discolored kernels is a feasible strategy to remove 97%–99% of aflatoxins accumulated in naturally-contaminated samples. Electronic optical sorter gave highly variable results since the amount of AFB1 + AFB2 measured in rejected fractions (15%–18% of total kernels) ranged from 13% to 59% of total aflatoxins. An improved immunoaffinity-based HPLC-FLD method having low limits of detection for the four aflatoxins (0.01–0.05 µg/kg) was developed and used to monitor the occurrence of aflatoxins in 47 commercial products containing apricot kernels and/or almonds commercialized in Italy. Low aflatoxin levels were found in 38% of the tested samples and ranged from 0.06 to 1.50 μg/kg for AFB1 and from 0.06 to 1.79 μg/kg for total aflatoxins. PMID:26797635
Reduction of Aflatoxins in Apricot Kernels by Electronic and Manual Color Sorting.
Zivoli, Rosanna; Gambacorta, Lucia; Piemontese, Luca; Solfrizzo, Michele
2016-01-01
The efficacy of color sorting on reducing aflatoxin levels in shelled apricot kernels was assessed. Naturally-contaminated kernels were submitted to an electronic optical sorter or blanched, peeled, and manually sorted to visually identify and sort discolored kernels (dark and spotted) from healthy ones. The samples obtained from the two sorting approaches were ground, homogenized, and analysed by HPLC-FLD for their aflatoxin content. A mass balance approach was used to measure the distribution of aflatoxins in the collected fractions. Aflatoxin B₁ and B₂ were identified and quantitated in all collected fractions at levels ranging from 1.7 to 22,451.5 µg/kg of AFB₁ + AFB₂, whereas AFG₁ and AFG₂ were not detected. Excellent results were obtained by manual sorting of peeled kernels since the removal of discolored kernels (2.6%-19.9% of total peeled kernels) removed 97.3%-99.5% of total aflatoxins. The combination of peeling and visual/manual separation of discolored kernels is a feasible strategy to remove 97%-99% of aflatoxins accumulated in naturally-contaminated samples. Electronic optical sorter gave highly variable results since the amount of AFB₁ + AFB₂ measured in rejected fractions (15%-18% of total kernels) ranged from 13% to 59% of total aflatoxins. An improved immunoaffinity-based HPLC-FLD method having low limits of detection for the four aflatoxins (0.01-0.05 µg/kg) was developed and used to monitor the occurrence of aflatoxins in 47 commercial products containing apricot kernels and/or almonds commercialized in Italy. Low aflatoxin levels were found in 38% of the tested samples and ranged from 0.06 to 1.50 μg/kg for AFB₁ and from 0.06 to 1.79 μg/kg for total aflatoxins. PMID:26797635
Relationship between processing score and kernel-fraction particle size in whole-plant corn silage.
Dias Junior, G S; Ferraretto, L F; Salvati, G G S; de Resende, L C; Hoffman, P C; Pereira, M N; Shaver, R D
2016-04-01
Kernel processing increases starch digestibility in whole-plant corn silage (WPCS). Corn silage processing score (CSPS), the percentage of starch passing through a 4.75-mm sieve, is widely used to assess degree of kernel breakage in WPCS. However, the geometric mean particle size (GMPS) of the kernel-fraction that passes through the 4.75-mm sieve has not been well described. Therefore, the objectives of this study were (1) to evaluate particle size distribution and digestibility of kernels cut in varied particle sizes; (2) to propose a method to measure GMPS in WPCS kernels; and (3) to evaluate the relationship between CSPS and GMPS of the kernel fraction in WPCS. Composite samples of unfermented, dried kernels from 110 corn hybrids commonly used for silage production were kept whole (WH) or manually cut in 2, 4, 8, 16, 32 or 64 pieces (2P, 4P, 8P, 16P, 32P, and 64P, respectively). Dry sieving to determine GMPS, surface area, and particle size distribution using 9 sieves with nominal square apertures of 9.50, 6.70, 4.75, 3.35, 2.36, 1.70, 1.18, and 0.59 mm and pan, as well as ruminal in situ dry matter (DM) digestibilities were performed for each kernel particle number treatment. Incubation times were 0, 3, 6, 12, and 24 h. The ruminal in situ DM disappearance of unfermented kernels increased with the reduction in particle size of corn kernels. Kernels kept whole had the lowest ruminal DM disappearance for all time points with maximum DM disappearance of 6.9% at 24 h and the greatest disappearance was observed for 64P, followed by 32P and 16P. Samples of WPCS (n=80) from 3 studies representing varied theoretical length of cut settings and processor types and settings were also evaluated. Each WPCS sample was divided in 2 and then dried at 60 °C for 48 h. The CSPS was determined in duplicate on 1 of the split samples, whereas on the other split sample the kernel and stover fractions were separated using a hydrodynamic separation procedure. After separation, the
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2014 CFR
2014-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2014-01-01 2014-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2012 CFR
2012-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2012-01-01 2012-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2013 CFR
2013-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2013-01-01 2013-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2011 CFR
2011-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2011-01-01 2011-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2010 CFR
2010-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2010-01-01 2010-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 7 Agriculture 2 2010-01-01 2010-01-01 false Split or broken kernels. 51.2125 Section 51.2125... STANDARDS) United States Standards for Grades of Shelled Almonds Definitions § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 7 Agriculture 2 2011-01-01 2011-01-01 false Split or broken kernels. 51.2125 Section 51.2125... STANDARDS) United States Standards for Grades of Shelled Almonds Definitions § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 7 Agriculture 2 2012-01-01 2012-01-01 false Split or broken kernels. 51.2125 Section 51.2125... STANDARDS) United States Standards for Grades of Shelled Almonds Definitions § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will...
7 CFR 51.1403 - Kernel color classification.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 7 Agriculture 2 2012-01-01 2012-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the...
7 CFR 51.1403 - Kernel color classification.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 7 Agriculture 2 2010-01-01 2010-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the...
7 CFR 51.1403 - Kernel color classification.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 7 Agriculture 2 2014-01-01 2014-01-01 false Kernel color classification. 51.1403 Section 51.1403... Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color classifications provided in this section. When the color of kernels in a...
7 CFR 51.1403 - Kernel color classification.
Code of Federal Regulations, 2013 CFR
2013-01-01
... 7 Agriculture 2 2013-01-01 2013-01-01 false Kernel color classification. 51.1403 Section 51.1403... Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color classifications provided in this section. When the color of kernels in a...
7 CFR 51.1403 - Kernel color classification.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 7 Agriculture 2 2011-01-01 2011-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the...
Finite-frequency sensitivity kernels of seismic waves to fault zone structures
NASA Astrophysics Data System (ADS)
Allam, A. A.; Tape, C.; Ben-Zion, Y.
2015-12-01
We analyse the volumetric sensitivity of fault zone seismic head and trapped waves by constructing finite-frequency sensitivity (Fréchet) kernels for these phases using a suite of idealized and tomographically derived velocity models of fault zones. We first validate numerical calculations by waveform comparisons with analytical results for two simple fault zone models: a vertical bimaterial interface separating two solids of differing elastic properties, and a `vertical sandwich' with a vertical low velocity zone surrounded on both sides by higher velocity media. Establishing numerical accuracy up to 12 Hz, we compute sensitivity kernels for various phases that arise in these and more realistic models. In contrast to direct P body waves, which have little or no sensitivity to the internal fault zone structure, the sensitivity kernels for head waves have sharp peaks with high values near the fault in the faster medium. Surface wave kernels show the broadest spatial distribution of sensitivity, while trapped wave kernels are extremely narrow with sensitivity focused entirely inside the low-velocity fault zone layer. Trapped waves are shown to exhibit sensitivity patterns similar to Love waves, with decreasing width as a function of frequency and multiple Fresnel zones of alternating polarity. In models that include smoothing of the boundaries of the low velocity zone, there is little effect on the trapped wave kernels, which are focused in the central core of the low velocity zone. When the source is located outside a shallow fault zone layer, trapped waves propagate through the surrounding medium with body wave sensitivity before becoming confined. The results provide building blocks for full waveform tomography of fault zone regions combining high-frequency head, trapped, body, and surface waves. Such an imaging approach can constrain fault zone structure across a larger range of scales than has previously been possible.
Kernel MAD Algorithm for Relative Radiometric Normalization
NASA Astrophysics Data System (ADS)
Bai, Yang; Tang, Ping; Hu, Changmiao
2016-06-01
The multivariate alteration detection (MAD) algorithm is commonly used in relative radiometric normalization. This algorithm is based on linear canonical correlation analysis (CCA) which can analyze only linear relationships among bands. Therefore, we first introduce a new version of MAD in this study based on the established method known as kernel canonical correlation analysis (KCCA). The proposed method effectively extracts the non-linear and complex relationships among variables. We then conduct relative radiometric normalization experiments on both the linear CCA and KCCA version of the MAD algorithm with the use of Landsat-8 data of Beijing, China, and Gaofen-1(GF-1) data derived from South China. Finally, we analyze the difference between the two methods. Results show that the KCCA-based MAD can be satisfactorily applied to relative radiometric normalization, this algorithm can well describe the nonlinear relationship between multi-temporal images. This work is the first attempt to apply a KCCA-based MAD algorithm to relative radiometric normalization.
KITTEN Lightweight Kernel 0.1 Beta
2007-12-12
The Kitten Lightweight Kernel is a simplified OS (operating system) kernel that is intended to manage a compute node's hardware resources. It provides a set of mechanisms to user-level applications for utilizing hardware resources (e.g., allocating memory, creating processes, accessing the network). Kitten is much simpler than general-purpose OS kernels, such as Linux or Windows, but includes all of the esssential functionality needed to support HPC (high-performance computing) MPI, PGAS and OpenMP applications. Kitten providesmore » unique capabilities such as physically contiguous application memory, transparent large page support, and noise-free tick-less operation, which enable HPC applications to obtain greater efficiency and scalability than with general purpose OS kernels.« less
Nonequilibrium flows with smooth particle applied mechanics
Kum, O.
1995-07-01
Smooth particle methods are relatively new methods for simulating solid and fluid flows through they have a 20-year history of solving complex hydrodynamic problems in astrophysics, such as colliding planets and stars, for which correct answers are unknown. The results presented in this thesis evaluate the adaptability or fitness of the method for typical hydrocode production problems. For finite hydrodynamic systems, boundary conditions are important. A reflective boundary condition with image particles is a good way to prevent a density anomaly at the boundary and to keep the fluxes continuous there. Boundary values of temperature and velocity can be separately controlled. The gradient algorithm, based on differentiating the smooth particle expression for (u{rho}) and (T{rho}), does not show numerical instabilities for the stress tensor and heat flux vector quantities which require second derivatives in space when Fourier`s heat-flow law and Newton`s viscous force law are used. Smooth particle methods show an interesting parallel linking to them to molecular dynamics. For the inviscid Euler equation, with an isentropic ideal gas equation of state, the smooth particle algorithm generates trajectories isomorphic to those generated by molecular dynamics. The shear moduli were evaluated based on molecular dynamics calculations for the three weighting functions, B spline, Lucy, and Cusp functions. The accuracy and applicability of the methods were estimated by comparing a set of smooth particle Rayleigh-Benard problems, all in the laminar regime, to corresponding highly-accurate grid-based numerical solutions of continuum equations. Both transient and stationary smooth particle solutions reproduce the grid-based data with velocity errors on the order of 5%. The smooth particle method still provides robust solutions at high Rayleigh number where grid-based methods fails.
Origin-Destination Flow Data Smoothing and Mapping.
Guo, Diansheng; Zhu, Xi
2014-12-01
This paper presents a new approach to flow mapping that extracts inherent patterns from massive geographic mobility data and constructs effective visual representations of the data for the understanding of complex flow trends. This approach involves a new method for origin-destination flow density estimation and a new method for flow map generalization, which together can remove spurious data variance, normalize flows with control population, and detect high-level patterns that are not discernable with existing approaches. The approach achieves three main objectives in addressing the challenges for analyzing and mapping massive flow data. First, it removes the effect of size differences among spatial units via kernel-based density estimation, which produces a measurement of flow volume between each pair of origin and destination. Second, it extracts major flow patterns in massive flow data through a new flow sampling method, which filters out duplicate information in the smoothed flows. Third, it enables effective flow mapping and allows intuitive perception of flow patterns among origins and destinations without bundling or altering flow paths. The approach can work with both point-based flow data (such as taxi trips with GPS locations) and area-based flow data (such as county-to-county migration). Moreover, the approach can be used to detect and compare flow patterns at different scales or in relatively sparse flow datasets, such as migration for each age group. We evaluate and demonstrate the new approach with case studies of U.S. migration data and experiments with synthetic data. PMID:26356918
Three-body-continuum Coulomb problem using a compact-kernel-integral-equation approach
NASA Astrophysics Data System (ADS)
Silenou Mengoue, M.
2013-02-01
We present an approach associated with the Jacobi matrix method to calculate a three-body wave function that describes the double continuum of an atomic two-electron system. In this approach, a symmetrized product of two Coulomb waves is used to describe the asymptotic wave function, while a smooth cutoff function is introduced to the dielectronic potential that enters its integral part in order to have a compact kernel of the corresponding Lippmann-Schwinger-type equation to be solved. As an application, the integral equation for the (e-,e-,He2+) system is solved numerically; the fully fivefold differential cross sections (FDCSs) for (e,3e) processes in helium are presented within the first-order Born approximation. The calculation is performed for a coplanar geometry in which the incident electron is fast (˜6 keV) and for a symmetric energy sharing between both slow ejected electrons at excess energy of 20 eV. The experimental and theoretical FDCSs agree satisfactorily both in shape and in magnitude. Full convergence in terms of the basis size is reached and presented.
Knowledge Driven Image Mining with Mixture Density Mercer Kernels
NASA Technical Reports Server (NTRS)
Srivastava, Ashok N.; Oza, Nikunj
2004-01-01
This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels; which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.
Dimensionality reduction of hyperspectral images using kernel ICA
NASA Astrophysics Data System (ADS)
Khan, Asif; Kim, Intaek; Kong, Seong G.
2009-05-01
Computational burden due to high dimensionality of Hyperspectral images is an obstacle in efficient analysis and processing of Hyperspectral images. In this paper, we use Kernel Independent Component Analysis (KICA) for dimensionality reduction of Hyperspectraql images based on band selection. Commonly used ICA and PCA based dimensionality reduction methods do not consider non linear transformations and assumes that data has non-gaussian distribution. When the relation of source signals (pure materials) and observed Hyperspectral images is nonlinear then these methods drop a lot of information during dimensionality reduction process. Recent research shows that kernel-based methods are effective in nonlinear transformations. KICA is robust technique of blind source separation and can even work on near-gaussina data. We use Kernel Independent Component Analysis (KICA) for the selection of minimum number of bands that contain maximum information for detection in Hyperspectral images. The reduction of bands is basd on the evaluation of weight matrix generated by KICA. From the selected lower number of bands, we generate a new spectral image with reduced dimension and use it for hyperspectral image analysis. We use this technique as preprocessing step in detection and classification of poultry skin tumors. The hyperspectral iamge samples of chicken tumors used contain 65 spectral bands of fluorescence in the visible region of the spectrum. Experimental results show that KICA based band selection has high accuracy than that of fastICA based band selection for dimensionality reduction and analysis for Hyperspectral images.
TICK: Transparent Incremental Checkpointing at Kernel Level
Petrini, Fabrizio; Gioiosa, Roberto
2004-10-25
TICK is a software package implemented in Linux 2.6 that allows the save and restore of user processes, without any change to the user code or binary. With TICK a process can be suspended by the Linux kernel upon receiving an interrupt and saved in a file. This file can be later thawed in another computer running Linux (potentially the same computer). TICK is implemented as a Linux kernel module, in the Linux version 2.6.5
Backward smoothing for precise GNSS applications
NASA Astrophysics Data System (ADS)
Vaclavovic, Pavel; Dousa, Jan
2015-10-01
The Extended Kalman filter is widely used for its robustness and simple implementation. Parameters estimated for solving dynamical systems usually require certain time to converge and need to be smoothed by a dedicated algorithms. The purpose of our study was to implement smoothing algorithms for processing both code and carrier phase observations with Precise Point Positioning method. We implemented and used the well known Rauch-Tung-Striebel smoother (RTS). It has been found out that the RTS suffer from significant numerical instability in smoothed state covariance matrix determination. We improved the processing with algorithms based on Singular Value Decomposition, which was more robust. Observations from many permanent stations have been processed with final orbits and clocks provided by the International GNSS service (IGS), and the smoothing improved stability and precision in every cases. Moreover, (re)convergence of the parameters were always successfully eliminated.
Disease gene identification by using graph kernels and Markov random fields.
Chen, BoLin; Li, Min; Wang, JianXin; Wu, Fang-Xiang
2014-11-01
Genes associated with similar diseases are often functionally related. This principle is largely supported by many biological data sources, such as disease phenotype similarities, protein complexes, protein-protein interactions, pathways and gene expression profiles. Integrating multiple types of biological data is an effective method to identify disease genes for many genetic diseases. To capture the gene-disease associations based on biological networks, a kernel-based MRF method is proposed by combining graph kernels and the Markov random field (MRF) method. In the proposed method, three kinds of kernels are employed to describe the overall relationships of vertices in five biological networks, respectively, and a novel weighted MRF method is developed to integrate those data. In addition, an improved Gibbs sampling procedure and a novel parameter estimation method are proposed to generate predictions from the kernel-based MRF method. Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. The proposed kernel-based MRF method is evaluated by the leave-one-out cross validation paradigm, achieving an AUC score of 0.771 when integrating all those biological data in our experiments, which indicates that our proposed method is very promising compared with many existing methods.
Learning rates of lq coefficient regularization learning with gaussian kernel.
Lin, Shaobo; Zeng, Jinshan; Fang, Jian; Xu, Zongben
2014-10-01
Regularization is a well-recognized powerful strategy to improve the performance of a learning machine and l(q) regularization schemes with 0 < q < ∞ are central in use. It is known that different q leads to different properties of the deduced estimators, say, l(2) regularization leads to a smooth estimator, while l(1) regularization leads to a sparse estimator. Then how the generalization capability of l(q) regularization learning varies with q is worthy of investigation. In this letter, we study this problem in the framework of statistical learning theory. Our main results show that implementing l(q) coefficient regularization schemes in the sample-dependent hypothesis space associated with a gaussian kernel can attain the same almost optimal learning rates for all 0 < q < ∞. That is, the upper and lower bounds of learning rates for l(q) regularization learning are asymptotically identical for all 0 < q < ∞. Our finding tentatively reveals that in some modeling contexts, the choice of q might not have a strong impact on the generalization capability. From this perspective, q can be arbitrarily specified, or specified merely by other nongeneralization criteria like smoothness, computational complexity or sparsity.
Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System.
Liu, Chunmei; Wang, Yirui; Gao, Shangce
2016-01-01
This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour. PMID:27379165
Nawaz, Malik A; Gaiani, Claire; Fukai, Shu; Bhandari, Bhesh
2016-12-01
The objectives of this study were to evaluate the ability of X-ray photoelectron spectroscopy (XPS) to differentiate rice macromolecules and to calculate the surface composition of rice kernels and flours. The uncooked kernels and flours surface composition of the two selected rice varieties, Thadokkham-11 (TDK11) and Doongara (DG) demonstrated an over-expression of lipids and proteins and an under-expression of starch compared to the bulk composition. The results of the study showed that XPS was able to differentiate rice polysaccharides (mainly starch), proteins and lipids in uncooked rice kernels and flours. Nevertheless, it was unable to distinguish components in cooked rice samples possibly due to complex interactions between gelatinized starch, denatured proteins and lipids. High resolution imaging methods (Scanning Electron Microscopy and Confocal Laser Scanning Microscopy) were employed to obtain complementary information about the properties and location of starch, proteins and lipids in rice kernels and flours. PMID:27374542
Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System
2016-01-01
This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour. PMID:27379165
NASA Astrophysics Data System (ADS)
Bao, Yuanxun; Kaye, Jason; Peskin, Charles S.
2016-07-01
The immersed boundary (IB) method is a general mathematical framework for studying problems involving fluid-structure interactions in which an elastic structure is immersed in a viscous incompressible fluid. In the IB formulation, the fluid described by Eulerian variables is coupled with the immersed structure described by Lagrangian variables via the use of the Dirac delta function. From a numerical standpoint, the Lagrangian force spreading and the Eulerian velocity interpolation are carried out by a regularized, compactly supported discrete delta function, which is assumed to be a tensor product of a single-variable immersed-boundary kernel. IB kernels are derived from a set of postulates designed to achieve approximate grid translational invariance, interpolation accuracy and computational efficiency. In this note, we present a new 6-point immersed-boundary kernel that is C3 and yields a substantially improved translational invariance compared to other common IB kernels.
Reduced-size kernel models for nonlinear hybrid system identification.
Le, Van Luong; Bloch, Grard; Lauer, Fabien
2011-12-01
This brief paper focuses on the identification of nonlinear hybrid dynamical systems, i.e., systems switching between multiple nonlinear dynamical behaviors. Thus the aim is to learn an ensemble of submodels from a single set of input-output data in a regression setting with no prior knowledge on the grouping of the data points into similar behaviors. To be able to approximate arbitrary nonlinearities, kernel submodels are considered. However, in order to maintain efficiency when applying the method to large data sets, a preprocessing step is required in order to fix the submodel sizes and limit the number of optimization variables. This brief paper proposes four approaches, respectively inspired by the fixed-size least-squares support vector machines, the feature vector selection method, the kernel principal component regression and a modification of the latter, in order to deal with this issue and build sparse kernel submodels. These are compared in numerical experiments, which show that the proposed approach achieves the simultaneous classification of data points and approximation of the nonlinear behaviors in an efficient and accurate manner.
... medlineplus.gov/ency/article/003531.htm Anti-smooth muscle antibody To use the sharing features on this page, please enable JavaScript. Anti-smooth muscle antibody is a blood test that detects the ...
Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology
Poon, Art F.Y.
2015-01-01
The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this “kernel-ABC” method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods. PMID:26006189
Modeling non-stationarity of kernel weights for k-space reconstruction in partially parallel imaging
Miao, Jun; Wong, Wilbur C. K.; Narayan, Sreenath; Huo, Donglai; Wilson, David L.
2011-01-01
Purpose: In partially parallel imaging, most k-space-based reconstruction algorithms such as GRAPPA adopt a single finite-size kernel to approximate the true relationship between sampled and nonsampled signals. However, the estimation of this kernel based on k-space signals is imperfect, and the authors are investigating methods dealing with local variation of k-space signals. Methods: To model nonstationarity of kernel weights, similar to performing a spatially adaptive regularization, the authors fit a set of linear functions using concepts from geographically weighted regression, a methodology used in geophysical analysis. Instead of a reconstruction with a single set of kernel weights, the authors use multiple sets. A missing signal is reconstructed with its kernel weights set determined by k-space clustering. Simulated and acquired MR data with several different image content and acquisition schemes, including MR tagging, were tested. A perceptual difference model (Case-PDM) was used to quantitatively evaluate the quality of over 1000 test images, and to optimize the parameters of our algorithm. Results: A MOdeling Non-stationarity of KErnel wEightS (“MONKEES”) reconstruction with two sets of kernel weights gave reconstructions with significantly better image quality than the original GRAPPA in all test images. Using more sets produced improved image quality but with diminishing returns. As a rule of thumb, at least two sets of kernel weights, one from low- and the other from high frequency k-space, should be used. Conclusions: The authors conclude that the MONKEES can significantly and robustly improve the image quality in parallel MR imaging, particularly, cardiac imaging. PMID:21928649
Beam-smoothing investigation on Heaven I
NASA Astrophysics Data System (ADS)
Xiang, Yi-huai; Gao, Zhi-xing; Tong, Xiao-hui; Dai, Hui; Tang, Xiu-zhang; Shan, Yu-sheng
2007-01-01
Directly driven targets for inertial confinement fusion (ICF) require laser beams with extremely smooth irradiance profiles to prevent hydrodynamic instabilities that destroy the spherical symmetry of the target during implosion. Such instabilities can break up and mix together the target's wall and fuel material, preventing it from reaching the density and temperature required for fusion ignition. 1,2 Measurements in the equation of state (EOS) experiments require laser beams with flat-roofed profiles to generate uniform shockwave 3. Some method for beam smooth, is thus needed. A technique called echelon-free induced spatial incoherence (EFISI) is proposed for producing smooth target beam profiles with large KrF lasers. The idea is basically an image projection technique that projects the desired time-averaged spatial profile onto the target via the laser system, using partially coherent broadband lighe. Utilize the technique, we developing beam- smoothing investigation on "Heaven I". At China Institute of Atomic Energy , a new angular multiplexing providing with beam-smoothing function has been developed, the total energy is 158J, the stability of energy is 4%, the pulse duration is 25ns, the effective diameter of focusing spot is 400um, and the ununiformity is about 1.6%, the power density on the target is about 3.7×10 12W/cm2. At present, the system have provided steady and smooth laser irradiation for EOS experiments.
Local Kernel for Brains Classification in Schizophrenia
NASA Astrophysics Data System (ADS)
Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.
In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.
Noise Level Estimation for Model Selection in Kernel PCA Denoising.
Varon, Carolina; Alzate, Carlos; Suykens, Johan A K
2015-11-01
One of the main challenges in unsupervised learning is to find suitable values for the model parameters. In kernel principal component analysis (kPCA), for example, these are the number of components, the kernel, and its parameters. This paper presents a model selection criterion based on distance distributions (MDDs). This criterion can be used to find the number of components and the σ(2) parameter of radial basis function kernels by means of spectral comparison between information and noise. The noise content is estimated from the statistical moments of the distribution of distances in the original dataset. This allows for a type of randomization of the dataset, without actually having to permute the data points or generate artificial datasets. After comparing the eigenvalues computed from the estimated noise with the ones from the input dataset, information is retained and maximized by a set of model parameters. In addition to the model selection criterion, this paper proposes a modification to the fixed-size method and uses the incomplete Cholesky factorization, both of which are used to solve kPCA in large-scale applications. These two approaches, together with the model selection MDD, were tested in toy examples and real life applications, and it is shown that they outperform other known algorithms. PMID:25608316
Hyperspectral anomaly detection using sparse kernel-based ensemble learning
NASA Astrophysics Data System (ADS)
Gurram, Prudhvi; Han, Timothy; Kwon, Heesung
2011-06-01
In this paper, sparse kernel-based ensemble learning for hyperspectral anomaly detection is proposed. The proposed technique is aimed to optimize an ensemble of kernel-based one class classifiers, such as Support Vector Data Description (SVDD) classifiers, by estimating optimal sparse weights. In this method, hyperspectral signatures are first randomly sub-sampled into a large number of spectral feature subspaces. An enclosing hypersphere that defines the support of spectral data, corresponding to the normalcy/background data, in the Reproducing Kernel Hilbert Space (RKHS) of each respective feature subspace is then estimated using regular SVDD. The enclosing hypersphere basically represents the spectral characteristics of the background data in the respective feature subspace. The joint hypersphere is learned by optimally combining the hyperspheres from the individual RKHS, while imposing the l1 constraint on the combining weights. The joint hypersphere representing the most optimal compact support of the local hyperspectral data in the joint feature subspaces is then used to test each pixel in hyperspectral image data to determine if it belongs to the local background data or not. The outliers are considered to be targets. The performance comparison between the proposed technique and the regular SVDD is provided using the HYDICE hyperspectral images.
Open-cluster density profiles derived using a kernel estimator
NASA Astrophysics Data System (ADS)
Seleznev, Anton F.
2016-03-01
Surface and spatial radial density profiles in open clusters are derived using a kernel estimator method. Formulae are obtained for the contribution of every star into the spatial density profile. The evaluation of spatial density profiles is tested against open-cluster models from N-body experiments with N = 500. Surface density profiles are derived for seven open clusters (NGC 1502, 1960, 2287, 2516, 2682, 6819 and 6939) using Two-Micron All-Sky Survey data and for different limiting magnitudes. The selection of an optimal kernel half-width is discussed. It is shown that open-cluster radius estimates hardly depend on the kernel half-width. Hints of stellar mass segregation and structural features indicating cluster non-stationarity in the regular force field are found. A comparison with other investigations shows that the data on open-cluster sizes are often underestimated. The existence of an extended corona around the open cluster NGC 6939 was confirmed. A combined function composed of the King density profile for the cluster core and the uniform sphere for the cluster corona is shown to be a better approximation of the surface radial density profile.The King function alone does not reproduce surface density profiles of sample clusters properly. The number of stars, the cluster masses and the tidal radii in the Galactic gravitational field for the sample clusters are estimated. It is shown that NGC 6819 and 6939 are extended beyond their tidal surfaces.
Classification of EEG signals using a multiple kernel learning support vector machine.
Li, Xiaoou; Chen, Xun; Yan, Yuning; Wei, Wenshi; Wang, Z Jane
2014-07-17
In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates.
Dusty gas with one fluid in smoothed particle hydrodynamics
NASA Astrophysics Data System (ADS)
Laibe, Guillaume; Price, Daniel J.
2014-05-01
In a companion paper we have shown how the equations describing gas and dust as two fluids coupled by a drag term can be re-formulated to describe the system as a single-fluid mixture. Here, we present a numerical implementation of the one-fluid dusty gas algorithm using smoothed particle hydrodynamics (SPH). The algorithm preserves the conservation properties of the SPH formalism. In particular, the total gas and dust mass, momentum, angular momentum and energy are all exactly conserved. Shock viscosity and conductivity terms are generalized to handle the two-phase mixture accordingly. The algorithm is benchmarked against a comprehensive suit of problems: DUSTYBOX, DUSTYWAVE, DUSTYSHOCK and DUSTYOSCILL, each of them addressing different properties of the method. We compare the performance of the one-fluid algorithm to the standard two-fluid approach. The one-fluid algorithm is found to solve both of the fundamental limitations of the two-fluid algorithm: it is no longer possible to concentrate dust below the resolution of the gas (they have the same resolution by definition), and the spatial resolution criterion h < csts, required in two-fluid codes to avoid over-damping of kinetic energy, is unnecessary. Implicit time-stepping is straightforward. As a result, the algorithm is up to ten billion times more efficient for 3D simulations of small grains. Additional benefits include the use of half as many particles, a single kernel and fewer SPH interpolations. The only limitation is that it does not capture multi-streaming of dust in the limit of zero coupling, suggesting that in this case a hybrid approach may be required.
Asymmetric scatter kernels for software-based scatter correction of gridless mammography
NASA Astrophysics Data System (ADS)
Wang, Adam; Shapiro, Edward; Yoon, Sungwon; Ganguly, Arundhuti; Proano, Cesar; Colbeth, Rick; Lehto, Erkki; Star-Lack, Josh
2015-03-01
Scattered radiation remains one of the primary challenges for digital mammography, resulting in decreased image contrast and visualization of key features. While anti-scatter grids are commonly used to reduce scattered radiation in digital mammography, they are an incomplete solution that can add radiation dose, cost, and complexity. Instead, a software-based scatter correction method utilizing asymmetric scatter kernels is developed and evaluated in this work, which improves upon conventional symmetric kernels by adapting to local variations in object thickness and attenuation that result from the heterogeneous nature of breast tissue. This fast adaptive scatter kernel superposition (fASKS) method was applied to mammography by generating scatter kernels specific to the object size, x-ray energy, and system geometry of the projection data. The method was first validated with Monte Carlo simulation of a statistically-defined digital breast phantom, which was followed by initial validation on phantom studies conducted on a clinical mammography system. Results from the Monte Carlo simulation demonstrate excellent agreement between the estimated and true scatter signal, resulting in accurate scatter correction and recovery of 87% of the image contrast originally lost to scatter. Additionally, the asymmetric kernel provided more accurate scatter correction than the conventional symmetric kernel, especially at the edge of the breast. Results from the phantom studies on a clinical system further validate the ability of the asymmetric kernel correction method to accurately subtract the scatter signal and improve image quality. In conclusion, software-based scatter correction for mammography is a promising alternative to hardware-based approaches such as anti-scatter grids.
Fast generation of sparse random kernel graphs
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.
Fast generation of sparse random kernel graphs
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less
Experimental study of turbulent flame kernel propagation
Mansour, Mohy; Peters, Norbert; Schrader, Lars-Uve
2008-07-15
Flame kernels in spark ignited combustion systems dominate the flame propagation and combustion stability and performance. They are likely controlled by the spark energy, flow field and mixing field. The aim of the present work is to experimentally investigate the structure and propagation of the flame kernel in turbulent premixed methane flow using advanced laser-based techniques. The spark is generated using pulsed Nd:YAG laser with 20 mJ pulse energy in order to avoid the effect of the electrodes on the flame kernel structure and the variation of spark energy from shot-to-shot. Four flames have been investigated at equivalence ratios, {phi}{sub j}, of 0.8 and 1.0 and jet velocities, U{sub j}, of 6 and 12 m/s. A combined two-dimensional Rayleigh and LIPF-OH technique has been applied. The flame kernel structure has been collected at several time intervals from the laser ignition between 10 {mu}s and 2 ms. The data show that the flame kernel structure starts with spherical shape and changes gradually to peanut-like, then to mushroom-like and finally disturbed by the turbulence. The mushroom-like structure lasts longer in the stoichiometric and slower jet velocity. The growth rate of the average flame kernel radius is divided into two linear relations; the first one during the first 100 {mu}s is almost three times faster than that at the later stage between 100 and 2000 {mu}s. The flame propagation is slightly faster in leaner flames. The trends of the flame propagation, flame radius, flame cross-sectional area and mean flame temperature are related to the jet velocity and equivalence ratio. The relations obtained in the present work allow the prediction of any of these parameters at different conditions. (author)
Anytime query-tuned kernel machine classifiers via Cholesky factorization
NASA Technical Reports Server (NTRS)
DeCoste, D.
2002-01-01
We recently demonstrated 2 to 64-fold query-time speedups of Support Vector Machine and Kernel Fisher classifiers via a new computational geometry method for anytime output bounds (DeCoste,2002). This new paper refines our approach in two key ways. First, we introduce a simple linear algebra formulation based on Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on benchmark datasets.
Improved metabolite profile smoothing for flux estimation.
Dromms, Robert A; Styczynski, Mark P
2015-09-01
As genome-scale metabolic models become more sophisticated and dynamic, one significant challenge in using these models is to effectively integrate increasingly prevalent systems-scale metabolite profiling data into them. One common data processing step when integrating metabolite data is to smooth experimental time course measurements: the smoothed profiles can be used to estimate metabolite accumulation (derivatives), and thus the flux distribution of the metabolic model. However, this smoothing step is susceptible to the (often significant) noise in experimental measurements, limiting the accuracy of downstream model predictions. Here, we present several improvements to current approaches for smoothing metabolite time course data using defined functions. First, we use a biologically-inspired mathematical model function taken from transcriptional profiling and clustering literature that captures the dynamics of many biologically relevant transient processes. We demonstrate that it is competitive with, and often superior to, previously described fitting schemas, and may serve as an effective single option for data smoothing in metabolic flux applications. We also implement a resampling-based approach to buffer out sensitivity to specific data sets and allow for more accurate fitting of noisy data. We found that this method, as well as the addition of parameter space constraints, yielded improved estimates of concentrations and derivatives (fluxes) in previously described fitting functions. These methods have the potential to improve the accuracy of existing and future dynamic metabolic models by allowing for the more effective integration of metabolite profiling data.
Volatile compound formation during argan kernel roasting.
El Monfalouti, Hanae; Charrouf, Zoubida; Giordano, Manuela; Guillaume, Dominique; Kartah, Badreddine; Harhar, Hicham; Gharby, Saïd; Denhez, Clément; Zeppa, Giuseppe
2013-01-01
Virgin edible argan oil is prepared by cold-pressing argan kernels previously roasted at 110 degrees C for up to 25 minutes. The concentration of 40 volatile compounds in virgin edible argan oil was determined as a function of argan kernel roasting time. Most of the volatile compounds begin to be formed after 15 to 25 minutes of roasting. This suggests that a strictly controlled roasting time should allow the modulation of argan oil taste and thus satisfy different types of consumers. This could be of major importance considering the present booming use of edible argan oil.
Reduced multiple empirical kernel learning machine.
Wang, Zhe; Lu, MingZhe; Gao, Daqi
2015-02-01
Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3
Utilizing Kernelized Advection Schemes in Ocean Models
NASA Astrophysics Data System (ADS)
Zadeh, N.; Balaji, V.
2008-12-01
There has been a recent effort in the ocean model community to use a set of generic FORTRAN library routines for advection of scalar tracers in the ocean. In a collaborative project called Hybrid Ocean Model Environement (HOME), vastly different advection schemes (space-differencing schemes for advection equation) become available to modelers in the form of subroutine calls (kernels). In this talk we explore the possibility of utilizing ESMF data structures in wrapping these kernels so that they can be readily used in ESMF gridded components.
Kernel abortion in maize. II. Distribution of /sup 14/C among kernel carboydrates
Hanft, J.M.; Jones, R.J.
1986-06-01
This study was designed to compare the uptake and distribution of /sup 14/C among fructose, glucose, sucrose, and starch in the cob, pedicel, and endosperm tissues of maize (Zea mays L.) kernels induced to abort by high temperature with those that develop normally. Kernels cultured in vitro at 309 and 35/sup 0/C were transferred to (/sup 14/C)sucrose media 10 days after pollination. Kernels cultured at 35/sup 0/C aborted prior to the onset of linear dry matter accumulation. Significant uptake into the cob, pedicel, and endosperm of radioactivity associated with the soluble and starch fractions of the tissues was detected after 24 hours in culture on atlageled media. After 8 days in culture on (/sup 14/C)sucrose media, 48 and 40% of the radioactivity associated with the cob carbohydrates was found in the reducing sugars at 30 and 35/sup 0/C, respectively. Of the total carbohydrates, a higher percentage of label was associated with sucrose and lower percentage with fructose and glucose in pedicel tissue of kernels cultured at 35/sup 0/C compared to kernels cultured at 30/sup 0/C. These results indicate that sucrose was not cleaved to fructose and glucose as rapidly during the unloading process in the pedicel of kernels induced to abort by high temperature. Kernels cultured at 35/sup 0/C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30/sup 0/C (89%). Kernels cultured at 35/sup 0/C had a correspondingly higher proportion of /sup 14/C in endosperm fructose, glucose, and sucrose.
Approximate l-fold cross-validation with Least Squares SVM and Kernel Ridge Regression
Edwards, Richard E; Zhang, Hao; Parker, Lynne Edwards; New, Joshua Ryan
2013-01-01
Kernel methods have difficulties scaling to large modern data sets. The scalability issues are based on computational and memory requirements for working with a large matrix. These requirements have been addressed over the years by using low-rank kernel approximations or by improving the solvers scalability. However, Least Squares Support VectorMachines (LS-SVM), a popular SVM variant, and Kernel Ridge Regression still have several scalability issues. In particular, the O(n^3) computational complexity for solving a single model, and the overall computational complexity associated with tuning hyperparameters are still major problems. We address these problems by introducing an O(n log n) approximate l-fold cross-validation method that uses a multi-level circulant matrix to approximate the kernel. In addition, we prove our algorithm s computational complexity and present empirical runtimes on data sets with approximately 1 million data points. We also validate our approximate method s effectiveness at selecting hyperparameters on real world and standard benchmark data sets. Lastly, we provide experimental results on using a multi-level circulant kernel approximation to solve LS-SVM problems with hyperparameters selected using our method.
Cross-domain question classification in community question answering via kernel mapping
NASA Astrophysics Data System (ADS)
Su, Lei; Hu, Zuoliang; Yang, Bin; Li, Yiyang; Chen, Jun
2015-10-01
An increasingly popular method for retrieving information is via the community question answering (CQA) systems such as Yahoo! Answers and Baidu Knows. In CQA, question classification plays an important role to find the answers. However, the labeled training examples for statistical question classifier are fairly expensive to obtain, as they require the experienced human efforts. Meanwhile, unlabeled data are readily available. This paper employs the method of domain adaptation via kernel mapping to solve this problem. In detail, the kernel approach is utilized to map the target-domain data and the source-domain data into a common space, where the question classifiers are trained under the closer conditional probabilities. The kernel mapping function is constructed by domain knowledge. Therefore, domain knowledge could be transferred from the labeled examples in the source domain to the unlabeled ones in the targeted domain. The statistical training model can be improved by using a large number of unlabeled data. Meanwhile, the Hadoop Platform is used to construct the mapping mechanism to reduce the time complexity. Map/Reduce enable kernel mapping for domain adaptation in parallel in the Hadoop Platform. Experimental results show that the accuracy of question classification could be improved by the method of kernel mapping. Furthermore, the parallel method in the Hadoop Platform could effective schedule the computing resources to reduce the running time.
Accuracy of Reduced and Extended Thin-Wire Kernels
Burke, G J
2008-11-24
Some results are presented comparing the accuracy of the reduced thin-wire kernel and an extended kernel with exact integration of the 1/R term of the Green's function and results are shown for simple wire structures.
Further results on the L1 analysis of sampled-data systems via kernel approximation approach
NASA Astrophysics Data System (ADS)
Kim, Jung Hoon; Hagiwara, Tomomichi
2016-08-01
This paper gives two methods for the L1 analysis of sampled-data systems, by which we mean computing the L∞-induced norm of sampled-data systems. This is achieved by developing what we call the kernel approximation approach in the setting of sampled-data systems. We first consider the lifting treatment of sampled-data systems and give an operator theoretic representation of their input/output relation. We further apply the fast-lifting technique by which the sampling interval [0, h) is divided into M subintervals with an equal width, and provide methods for computing the L∞-induced norm. In contrast to a similar approach developed earlier called the input approximation approach, we use an idea of kernel approximation, in which the kernel function of an input operator and the hold function of an output operator are approximated by piecewise constant or piecewise linear functions. Furthermore, it is shown that the approximation errors in the piecewise constant approximation or piecewise linear approximation scheme converge to 0 at the rate of 1/M or 1/M2, respectively. In comparison with the existing input approximation approach, in which the input function (rather than the kernel function) of the input operator is approximated by piecewise constant or piecewise linear functions, we show that the kernel approximation approach gives improved computation results. More precisely, even though the convergence rates in the kernel approximation approach remain qualitatively the same as those in the input approximation approach, the newly developed former approach could lead to quantitatively improved approximation errors than the latter approach particularly when the piecewise linear approximation scheme is taken. Finally, a numerical example is given to demonstrate the effectiveness of the kernel approximation approach with this scheme.
Learning a peptide-protein binding affinity predictor with kernel ridge regression
2013-01-01
Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting
SIMS: computation of a smooth invariant molecular surface.
Vorobjev, Y N; Hermans, J
1997-01-01
SIMS, a new method of calculating a smooth invariant molecular dot surface, is presented. The SIMS method generates the smooth molecular surface by rolling two probe spheres. A solvent probe sphere is rolled over the molecule and produces a Richards-Connolly molecular surface (MS), which envelops the solvent-excluded volume of the molecule. In deep crevices, Connolly's method of calculating the MS has two deficiencies. First, it produces self-intersecting parts of the molecular surface, which must be removed to obtain the correct MS. Second, the correct MS is not smooth, i.e., the direction of the normal vector of the MS is not continuous, and some points of the MS are singular. We present an exact method for removing self-intersecting parts and smoothing the singular regions of the MS. The singular MS is smoothed by rolling a smoothing probe sphere over the inward side of the singular MS. The MS in the vicinity of singularities is replaced with the reentrant surface of the smoothing probe sphere. The smoothing method does not disturb the topology of a singular MS, and the smooth MS is a better approximation of the dielectric border between high dielectric solvent and the low dielectric molecular interior. The SIMS method generates a smooth molecular dot surface, which has a quasi-uniform dot distribution in two orthogonal directions on the molecular surface, which is invariant with molecular rotation and stable under changes in the molecular conformation, and which can be used in a variety of implicit methods of modeling solvent effects. The SIMS program is faster than the Connolly MS program, and in a matter of seconds generates a smooth dot MS of a 200-residue protein. The program is available from the authors on request (see http:@femto.med.unc.edu/SIMS). PMID:9251789
Fabrication of Uranium Oxycarbide Kernels for HTR Fuel
Charles Barnes; CLay Richardson; Scott Nagley; John Hunn; Eric Shaber
2010-10-01
Babcock and Wilcox (B&W) has been producing high quality uranium oxycarbide (UCO) kernels for Advanced Gas Reactor (AGR) fuel tests at the Idaho National Laboratory. In 2005, 350-µm, 19.7% 235U-enriched UCO kernels were produced for the AGR-1 test fuel. Following coating of these kernels and forming the coated-particles into compacts, this fuel was irradiated in the Advanced Test Reactor (ATR) from December 2006 until November 2009. B&W produced 425-µm, 14% enriched UCO kernels in 2008, and these kernels were used to produce fuel for the AGR-2 experiment that was inserted in ATR in 2010. B&W also produced 500-µm, 9.6% enriched UO2 kernels for the AGR-2 experiments. Kernels of the same size and enrichment as AGR-1 were also produced for the AGR-3/4 experiment. In addition to fabricating enriched UCO and UO2 kernels, B&W has produced more than 100 kg of natural uranium UCO kernels which are being used in coating development tests. Successive lots of kernels have demonstrated consistent high quality and also allowed for fabrication process improvements. Improvements in kernel forming were made subsequent to AGR-1 kernel production. Following fabrication of AGR-2 kernels, incremental increases in sintering furnace charge size have been demonstrated. Recently small scale sintering tests using a small development furnace equipped with a residual gas analyzer (RGA) has increased understanding of how kernel sintering parameters affect sintered kernel properties. The steps taken to increase throughput and process knowledge have reduced kernel production costs. Studies have been performed of additional modifications toward the goal of increasing capacity of the current fabrication line to use for production of first core fuel for the Next Generation Nuclear Plant (NGNP) and providing a basis for the design of a full scale fuel fabrication facility.
Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.
Yuan, Shasha; Zhou, Weidong; Wu, Qi; Zhang, Yanli
2016-05-01
Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.
Kernel-based whole-genome prediction of complex traits: a review
Morota, Gota; Gianola, Daniel
2014-01-01
Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics. PMID:25360145
Filtering with State-Observation Examples via Kernel Monte Carlo Filter.
Kanagawa, Motonobu; Nishiyama, Yu; Gretton, Arthur; Fukumizu, Kenji
2016-02-01
This letter addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e., the observation model) is given explicitly or at least parametrically. We consider a setting where this assumption is not satisfied; we assume that the knowledge of the observation model is provided only by examples of state-observation pairs. This setting is important and appears when state variables are defined as quantities that are very different from the observations. We propose kernel Monte Carlo filter, a novel filtering method that is focused on this setting. Our approach is based on the framework of kernel mean embeddings, which enables nonparametric posterior inference using the state-observation examples. The proposed method represents state distributions as weighted samples, propagates these samples by sampling, estimates the state posteriors by kernel Bayes' rule, and resamples by kernel herding. In particular, the sampling and resampling procedures are novel in being expressed using kernel mean embeddings, so we theoretically analyze their behaviors. We reveal the following properties, which are similar to those of corresponding procedures in particle methods: the performance of sampling can degrade if the effective sample size of a weighted sample is small, and resampling improves the sampling performance by increasing the effective sample size. We first demonstrate these theoretical findings by synthetic experiments. Then we show the effectiveness of the proposed filter by artificial and real data experiments, which include vision-based mobile robot localization. PMID:26654205
Code of Federal Regulations, 2010 CFR
2010-01-01
... Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards, Inspections, Marketing Practices), DEPARTMENT OF AGRICULTURE REGULATIONS AND STANDARDS UNDER THE AGRICULTURAL MARKETING ACT OF 1946... the separated half of a kernel with not more than one-eighth broken off....
Kernel Temporal Differences for Neural Decoding
Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.
2015-01-01
We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2013 CFR
2013-01-01
... AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2011 CFR
2011-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2010 CFR
2010-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2012 CFR
2012-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2014 CFR
2014-01-01
... AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
NASA Astrophysics Data System (ADS)
Ling, Jianguo; Liu, Erqi; Liang, Haiyan; Yang, Jie
2007-06-01
An infrared target tracking framework is presented that consists of three main parts: mean shift tracking, its tracking performance evaluation, and position correction. The mean shift tracking algorithm, which is a widely used kernel-based method, has been developed for the initial tracking for its efficiency and effectiveness. A performance evaluation module is applied for the online evaluation of its tracking performance with a kernel- based metric to unify the tracking and performance metric within a kernel-based tracking framework. Then the tracking performance evaluation result is input into a controller in which a decision is made whether to trigger a position correction process. The position correction module employs a matching method with a new eigenvalue-based similarity measure computed from a local complexity degree weighted covariance matrix. Experimental results on real-life infrared image sequences are presented to demonstrate the efficacy of the proposed method.
Correlation-based smoothing model for optical polishing.
Shu, Yong; Kim, Dae Wook; Martin, Hubert M; Burge, James H
2013-11-18
A generalized model is developed to quantitatively describe the smoothing effects from different polishing tools used for optical surfaces. The smoothing effect naturally corrects mid-to-high spatial frequency errors that have features small compared to the size of the polishing lap. The original parametric smoothing model provided a convenient way to compare smoothing efficiency of different polishing tools for the case of sinusoidal surface irregularity, providing the ratio of surface improvement via smoothing to the bulk material removal. A new correlation-based smoothing model expands the capability to quantify smoothing using general surface data with complex irregularity. For this case, we define smoothing as a band-limited correlated component of the change in the surface and original surface. Various concepts and methods, such as correlation screening, have been developed and verified to manipulate the data for the calculation of smoothing factor. Data from two actual polishing runs from the Giant Magellan Telescope off-axis segment and the Large Synoptic Survey Telescope monolithic primary-tertiary mirror were processed, and a quantitative evaluation for the smoothing efficiency of a large pitch lap and a conformal lap with polishing pads is provided.
Graphlet kernels for prediction of functional residues in protein structures.
Vacic, Vladimir; Iakoucheva, Lilia M; Lonardi, Stefano; Radivojac, Predrag
2010-01-01
We introduce a novel graph-based kernel method for annotating functional residues in protein structures. A structure is first modeled as a protein contact graph, where nodes correspond to residues and edges connect spatially neighboring residues. Each vertex in the graph is then represented as a vector of counts of labeled non-isomorphic subgraphs (graphlets), centered on the vertex of interest. A similarity measure between two vertices is expressed as the inner product of their respective count vectors and is used in a supervised learning framework to classify protein residues. We evaluated our method on two function prediction problems: identification of catalytic residues in proteins, which is a well-studied problem suitable for benchmarking, and a much less explored problem of predicting phosphorylation sites in protein structures. The performance of the graphlet kernel approach was then compared against two alternative methods, a sequence-based predictor and our implementation of the FEATURE framework. On both tasks, the graphlet kernel performed favorably; however, the margin of difference was considerably higher on the problem of phosphorylation site prediction. While there is data that phosphorylation sites are preferentially positioned in intrinsically disordered regions, we provide evidence that for the sites that are located in structured regions, neither the surface accessibility alone nor the averaged measures calculated from the residue microenvironments utilized by FEATURE were sufficient to achieve high accuracy. The key benefit of the graphlet representation is its ability to capture neighborhood similarities in protein structures via enumerating the patterns of local connectivity in the corresponding labeled graphs.
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2011 CFR
2011-04-01
... 21 Food and Drugs 3 2011-04-01 2011-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2012 CFR
2012-04-01
... 21 Food and Drugs 3 2012-04-01 2012-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2013 CFR
2013-04-01
... 21 Food and Drugs 3 2013-04-01 2013-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2014 CFR
2014-04-01
... 21 Food and Drugs 3 2014-04-01 2014-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a..., packaging, transporting, or holding food, subject to the provisions of this section. (a) Tamarind...
7 CFR 868.254 - Broken kernels determination.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
7 CFR 868.304 - Broken kernels determination.
Code of Federal Regulations, 2013 CFR
2013-01-01
... 7 Agriculture 7 2013-01-01 2013-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
7 CFR 868.304 - Broken kernels determination.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 7 Agriculture 7 2012-01-01 2012-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
7 CFR 868.254 - Broken kernels determination.
Code of Federal Regulations, 2013 CFR
2013-01-01
... 7 Agriculture 7 2013-01-01 2013-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
7 CFR 868.304 - Broken kernels determination.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 7 Agriculture 7 2014-01-01 2014-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
7 CFR 868.254 - Broken kernels determination.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 7 Agriculture 7 2012-01-01 2012-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
7 CFR 868.254 - Broken kernels determination.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 7 Agriculture 7 2011-01-01 2011-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 7 Agriculture 2 2014-01-01 2014-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of...
7 CFR 868.304 - Broken kernels determination.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 7 Agriculture 7 2011-01-01 2011-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2013 CFR
2013-01-01
... 7 Agriculture 2 2013-01-01 2013-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of...
7 CFR 868.254 - Broken kernels determination.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 7 Agriculture 7 2014-01-01 2014-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
7 CFR 868.304 - Broken kernels determination.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
Smooth PARAFAC Decomposition for Tensor Completion
NASA Astrophysics Data System (ADS)
Yokota, Tatsuya; Zhao, Qibin; Cichocki, Andrzej
2016-10-01
In recent years, low-rank based tensor completion, which is a higher-order extension of matrix completion, has received considerable attention. However, the low-rank assumption is not sufficient for the recovery of visual data, such as color and 3D images, where the ratio of missing data is extremely high. In this paper, we consider "smoothness" constraints as well as low-rank approximations, and propose an efficient algorithm for performing tensor completion that is particularly powerful regarding visual data. The proposed method admits significant advantages, owing to the integration of smooth PARAFAC decomposition for incomplete tensors and the efficient selection of models in order to minimize the tensor rank. Thus, our proposed method is termed as "smooth PARAFAC tensor completion (SPC)." In order to impose the smoothness constraints, we employ two strategies, total variation (SPC-TV) and quadratic variation (SPC-QV), and invoke the corresponding algorithms for model learning. Extensive experimental evaluations on both synthetic and real-world visual data illustrate the significant improvements of our method, in terms of both prediction performance and efficiency, compared with many state-of-the-art tensor completion methods.
Nonlinear Knowledge in Kernel-Based Multiple Criteria Programming Classifier
NASA Astrophysics Data System (ADS)
Zhang, Dongling; Tian, Yingjie; Shi, Yong
Kernel-based Multiple Criteria Linear Programming (KMCLP) model is used as classification methods, which can learn from training examples. Whereas, in traditional machine learning area, data sets are classified only by prior knowledge. Some works combine the above two classification principle to overcome the defaults of each approach. In this paper, we propose a model to incorporate the nonlinear knowledge into KMCLP in order to solve the problem when input consists of not only training example, but also nonlinear prior knowledge. In dealing with real world case breast cancer diagnosis, the model shows its better performance than the model solely based on training data.
Kernel PLS-SVC for Linear and Nonlinear Discrimination
NASA Technical Reports Server (NTRS)
Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan
2003-01-01
A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.