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Sample records for kernel canonical correlation

  1. Isointense Infant Brain Segmentation by Stacked Kernel Canonical Correlation Analysis

    PubMed Central

    Wang, Li; Shi, Feng; Gao, Yaozong; Li, Gang; Lin, Weili; Shen, Dinggang

    2016-01-01

    Segmentation of isointense infant brain (at ~6-months-old) MR images is challenging due to the ongoing maturation and myelination process in the first year of life. In particular, signal contrast between white and gray matters inverses around 6 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, thus posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenges based on stacked kernel canonical correlation analysis (KCCA). Our main idea is to utilize the 12-month-old brain image with high tissue contrast to guide the segmentation of 6-month-old brain images with extremely low contrast. Specifically, we use KCCA to learn the common feature representations for both 6-month-old and the subsequent 12-month-old brain images of same subjects to make their features comparable in the common space. Note that the longitudinal 12-month-old brain images are not required in the testing stage, and they are required only in the KCCA based training stage to provide a set of longitudinal 6- and 12-month-old image pairs for training. Moreover, for optimizing the common feature representations, we propose a stacked KCCA mapping, instead of using only the conventional one-step of KCCA mapping. In this way, we can better use the 12-month-old brain images as multiple atlases to guide the segmentation of isointense brain images. Specifically, sparse patch-based multi-atlas labeling is used to propagate tissue labels in the (12-month-old) atlases and segment isointense brain images by measuring patch similarity between testing and atlas images with their learned common features. The proposed method was evaluated on 20 isointense brain images via leave-one-out cross-validation, showing much better performance than the state-of-the-art methods.

  2. Kernel canonical-correlation Granger causality for multiple time series.

    PubMed

    Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

    2011-04-01

    Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

  3. 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.

  4. Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging

    PubMed Central

    Bilenko, Natalia Y.; Gallant, Jack L.

    2016-01-01

    In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model. PMID:27920675

  5. Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

    NASA Astrophysics Data System (ADS)

    Volpi, Michele; Camps-Valls, Gustau; Tuia, Devis

    2015-09-01

    In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to perform model selection is limited, we propose a completely automatic strategy to select the hyperparameters of the system as well as the dimensionality of the transformed (latent) space. The proposed scheme is fully automatic and allows the use of any change detection algorithm in the transformed latent space. A synthetic problem built from real images and a case study involving a real cross-sensor change detection problem illustrate the capabilities of the proposed method. Results show that the proposed system outperforms the linear baseline and provides accuracies close the ones obtained with a fully supervised strategy. We provide a MATLAB implementation of the proposed method as well as the real cross-sensor data we prepared and employed at

  6. Resistant multiple sparse canonical correlation.

    PubMed

    Coleman, Jacob; Replogle, Joseph; Chandler, Gabriel; Hardin, Johanna

    2016-04-01

    Canonical correlation analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to the preceding pair, meaning that new information is gleaned from each pair. By looking at the magnitude of coefficient values, we can find out which variables can be grouped together, thus better understanding multiple interactions that are otherwise difficult to compute or grasp intuitively. CCA appears to have quite powerful applications to high-throughput data, as we can use it to discover, for example, relationships between gene expression and gene copy number variation. One of the biggest problems of CCA is that the number of variables (often upwards of 10,000) makes biological interpretation of linear combinations nearly impossible. To limit variable output, we have employed a method known as sparse canonical correlation analysis (SCCA), while adding estimation which is resistant to extreme observations or other types of deviant data. In this paper, we have demonstrated the success of resistant estimation in variable selection using SCCA. Additionally, we have used SCCA to find multiple canonical pairs for extended knowledge about the datasets at hand. Again, using resistant estimators provided more accurate estimates than standard estimators in the multiple canonical correlation setting. R code is available and documented at https://github.com/hardin47/rmscca.

  7. Functional Multiple-Set Canonical Correlation Analysis

    ERIC Educational Resources Information Center

    Hwang, Heungsun; Jung, Kwanghee; Takane, Yoshio; Woodward, Todd S.

    2012-01-01

    We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the…

  8. The Canon package: a fast kernel for tensor manipulators

    NASA Astrophysics Data System (ADS)

    Manssur, L. R. U.; Portugal, R.

    2004-02-01

    This paper describes the Canon package written in the Maple programming language. Canon's purpose is to work as a kernel for complete Maple tensor packages or any Maple package for manipulating indexed objects obeying generic permutation symmetries and possibly having dummy indices. Canon uses Computational Group Theory algorithms to efficiently simplify or manipulate generic tensor expressions. We describe the main command to access the package, give examples, and estimate typical computation timings. Program summaryTitle of program: Canon Catalogue identifier: ADSP Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADSP Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computers: any machine running Maple versions 6 to 9 Operating systems under which the program has been tested: Microsoft Windows, Linux Programming language used: Maple Memory required to execute with typical data: up to 10 Mb No. of bits in word: 32 or 64 No. of processors used: 1 Has the code been vectorized or parallelized?: No No. of bytes in distributed program, including test data, etc.: 45 910 Distribution format: tar gzip file Nature of physical problem: Manipulation and simplification of tensor expressions (or any expression in terms of indexed objects) in explicit index notation, where the indices obey generic permutation symmetries and there may exist dummy (summed over) indices. Method of solution: Computational Group Theory algorithms have been used, specially algorithms for finding canonical representations of single and double cosets, and algorithms for creating strong generating sets. Restriction on the complexity of the problem: Computer memory. With current equipment, expressions with hundreds of indices have been manipulated successfully. Typical running time: Simplification of expressions with 15 Riemann tensors was done in less than one minute in a personal computer. Unusual features: The use of Computational Group Theory algorithms

  9. A Stepwise Canonical Procedure and the Shrinkage of Canonical Correlations.

    ERIC Educational Resources Information Center

    Rim, Eui-Do

    A stepwise canonical procedure, including two selection indices for variable deletion and a rule for stopping the iterative procedure, was derived as a method of selecting core variables from predictors and criteria. The procedure was applied to simulated data varying in the degree of built in structures in population correlation matrices, number…

  10. Regularized Multiple-Set Canonical Correlation Analysis

    ERIC Educational Resources Information Center

    Takane, Yoshio; Hwang, Heungsun; Abdi, Herve

    2008-01-01

    Multiple-set canonical correlation analysis (Generalized CANO or GCANO for short) is an important technique because it subsumes a number of interesting multivariate data analysis techniques as special cases. More recently, it has also been recognized as an important technique for integrating information from multiple sources. In this paper, we…

  11. Revisiting Interpretation of Canonical Correlation Analysis: A Tutorial and Demonstration of Canonical Commonality Analysis

    ERIC Educational Resources Information Center

    Nimon, Kim; Henson, Robin K.; Gates, Michael S.

    2010-01-01

    In the face of multicollinearity, researchers face challenges interpreting canonical correlation analysis (CCA) results. Although standardized function and structure coefficients provide insight into the canonical variates produced, they fall short when researchers want to fully report canonical effects. This article revisits the interpretation of…

  12. Face hallucination using orthogonal canonical correlation analysis

    NASA Astrophysics Data System (ADS)

    Zhou, Huiling; Lam, Kin-Man

    2016-05-01

    A two-step face-hallucination framework is proposed to reconstruct a high-resolution (HR) version of a face from an input low-resolution (LR) face, based on learning from LR-HR example face pairs using orthogonal canonical correlation analysis (orthogonal CCA) and linear mapping. In the proposed algorithm, face images are first represented using principal component analysis (PCA). Canonical correlation analysis (CCA) with the orthogonality property is then employed, to maximize the correlation between the PCA coefficients of the LR and the HR face pairs to improve the hallucination performance. The original CCA does not own the orthogonality property, which is crucial for information reconstruction. We propose using orthogonal CCA, which is proven by experiments to achieve a better performance in terms of global face reconstruction. In addition, in the residual-compensation process, a linear-mapping method is proposed to include both the inter- and intrainformation about manifolds of different resolutions. Compared with other state-of-the-art approaches, the proposed framework can achieve a comparable, or even better, performance in terms of global face reconstruction and the visual quality of face hallucination. Experiments on images with various parameter settings and blurring distortions show that the proposed approach is robust and has great potential for real-world applications.

  13. Canonical Correlation Analysis: An Explanation with Comments on Correct Practice.

    ERIC Educational Resources Information Center

    Thompson, Bruce

    This paper briefly explains the logic underlying the basic calculations employed in canonical correlation analysis. A small hypothetical data set is employed to illustrate that canonical correlation analysis subsumes both univariate and multivariate parametric methods. Several real data sets are employed to illustrate other themes. Three common…

  14. On Measure Transformed Canonical Correlation Analysis

    DTIC Science & Technology

    2012-01-01

    of permutations M and the significance level α were set to 1000 and 0.01, respectively. REFERENCES [1] H. Hotelling , “Relations between two sets of...kernel-based approach,” Journal of Physics A: Mathematical and General, vol. 39, pp. 10723-10742, 2006. [20] F. H. C. Marriott , A dictionary of

  15. Methods of Assessing Replicability in Canonical Correlation Analysis (CCA).

    ERIC Educational Resources Information Center

    King, Jason E.

    Theoretical hypotheses generated from data analysis of a single sample should not be advanced until the replicability issue is treated. At least one of three questions usually arises when evaluating the invariance of results obtained from a canonical correlation analysis (CCA): (1) "Will an effect occur in subsequent studies?"; (2)…

  16. Interpreting Canonical Correlation Analysis through Biplots of Structure Correlations and Weights.

    ERIC Educational Resources Information Center

    ter Braak, Cajo J. F.

    1990-01-01

    Canonical weights and structure correlations are used to construct low dimensional views of the relationships between two sets of variables. These views, in the form of biplots, display familiar statistics: correlations between pairs of variables, and regression coefficients. (SLD)

  17. Robust visual tracking via adaptive kernelized correlation filter

    NASA Astrophysics Data System (ADS)

    Wang, Bo; Wang, Desheng; Liao, Qingmin

    2016-10-01

    Correlation filter based trackers have proved to be very efficient and robust in object tracking with a notable performance competitive with state-of-art trackers. In this paper, we propose a novel object tracking method named Adaptive Kernelized Correlation Filter (AKCF) via incorporating Kernelized Correlation Filter (KCF) with Structured Output Support Vector Machines (SOSVM) learning method in a collaborative and adaptive way, which can effectively handle severe object appearance changes with low computational cost. AKCF works by dynamically adjusting the learning rate of KCF and reversely verifies the intermediate tracking result by adopting online SOSVM classifier. Meanwhile, we bring Color Names in this formulation to effectively boost the performance owing to its rich feature information encoded. Experimental results on several challenging benchmark datasets reveal that our approach outperforms numerous state-of-art trackers.

  18. A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly

    NASA Technical Reports Server (NTRS)

    Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong

    2001-01-01

    This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

  19. Distortion-invariant kernel correlation filters for general object recognition

    NASA Astrophysics Data System (ADS)

    Patnaik, Rohit

    General object recognition is a specific application of pattern recognition, in which an object in a background must be classified in the presence of several distortions such as aspect-view differences, scale differences, and depression-angle differences. Since the object can be present at different locations in the test input, a classification algorithm must be applied to all possible object locations in the test input. We emphasize one type of classifier, the distortion-invariant filter (DIF), for fast object recognition, since it can be applied to all possible object locations using a fast Fourier transform (FFT) correlation. We refer to distortion-invariant correlation filters simply as DIFs. DIFs all use a combination of training-set images that are representative of the expected distortions in the test set. In this dissertation, we consider a new approach that combines DIFs and the higher-order kernel technique; these form what we refer to as "kernel DIFs." Our objective is to develop higher-order classifiers that can be applied (efficiently and fast) to all possible locations of the object in the test input. All prior kernel DIFs ignored the issue of efficient filter shifts. We detail which kernel DIF formulations are computational realistic to use and why. We discuss the proper way to synthesize DIFs and kernel DIFs for the wide area search case (i.e., when a small filter must be applied to a much larger test input) and the preferable way to perform wide area search with these filters; this is new. We use computer-aided design (CAD) simulated infrared (IR) object imagery and real IR clutter imagery to obtain test results. Our test results on IR data show that a particular kernel DIF, the kernel SDF filter and its new "preprocessed" version, is promising, in terms of both test-set performance and on-line calculations, and is emphasized in this dissertation. We examine the recognition of object variants. We also quantify the effect of different constant

  20. Canonical information analysis

    NASA Astrophysics Data System (ADS)

    Vestergaard, Jacob Schack; Nielsen, Allan Aasbjerg

    2015-03-01

    Canonical correlation analysis is an established multivariate statistical method in which correlation between linear combinations of multivariate sets of variables is maximized. In canonical information analysis introduced here, linear correlation as a measure of association between variables is replaced by the information theoretical, entropy based measure mutual information, which is a much more general measure of association. We make canonical information analysis feasible for large sample problems, including for example multispectral images, due to the use of a fast kernel density estimator for entropy estimation. Canonical information analysis is applied successfully to (1) simple simulated data to illustrate the basic idea and evaluate performance, (2) fusion of weather radar and optical geostationary satellite data in a situation with heavy precipitation, and (3) change detection in optical airborne data. The simulation study shows that canonical information analysis is as accurate as and much faster than algorithms presented in previous work, especially for large sample sizes. URL:

  1. Commonality and the Common Man: Understanding Variance Contributions to Overall Canonical Correlation Effects.

    ERIC Educational Resources Information Center

    Capraro, Robert M.

    Canonical correlation analysis is the most general linear model subsuming all other univariate and multivariate cases (N. Kerlinger & E. Pedhazur, 1973; B. Thompson, 1985, 1991). Because "reality" is a complex place, a multivariate analysis such as canonical correlation analysis is demanded to match the research design. The purpose…

  2. Attenuation of the Squared Canonical Correlation Coefficient under Varying Estimates of Score Reliability

    ERIC Educational Resources Information Center

    Wilson, Celia M.

    2010-01-01

    Research pertaining to the distortion of the squared canonical correlation coefficient has traditionally been limited to the effects of sampling error and associated correction formulas. The purpose of this study was to compare the degree of attenuation of the squared canonical correlation coefficient under varying conditions of score reliability.…

  3. The Basic Concepts of the General Linear Model (GLM): Canonical Correlation Analysis (CCA) as a GLM.

    ERIC Educational Resources Information Center

    Kimbell, Anne-Marie

    This paper illustrates how canonical correlation analysis can be used to implement all the parametric tests that canonical methods subsume as special cases. The point is heuristic: all analyses are correlational, apply weights to measured variables to create synthetic variables, and require the interpretation of both weights and structure…

  4. Association Study between Lead and Zinc Accumulation at Different Physiological Systems of Cattle by Canonical Correlation and Canonical Correspondence Analyses

    SciTech Connect

    Karmakar, Partha; Das, Pradip Kumar; Mondal, Seema Sarkar; Karmakar, Sougata; Mazumdar, Debasis

    2010-10-26

    Pb pollution from automobile exhausts around highways is a persistent problem in India. Pb intoxication in mammalian body is a complex phenomenon which is influence by agonistic and antagonistic interactions of several other heavy metals and micronutrients. An attempt has been made to study the association between Pb and Zn accumulation in different physiological systems of cattles (n = 200) by application of both canonical correlation and canonical correspondence analyses. Pb was estimated from plasma, liver, bone, muscle, kidney, blood and milk where as Zn was measured from all these systems except bone, blood and milk. Both statistical techniques demonstrated that there was a strong association among blood-Pb, liver-Zn, kidney-Zn and muscle-Zn. From observations, it can be assumed that Zn accumulation in cattles' muscle, liver and kidney directs Pb mobilization from those organs which in turn increases Pb pool in blood. It indicates antagonistic activity of Zn to the accumulation of Pb. Although there were some contradictions between the observations obtained from the two different statistical methods, the overall pattern of Pb accumulation in various organs as influenced by Zn were same. It is mainly due to the fact that canonical correlation is actually a special type of canonical correspondence analyses where linear relationship is followed between two groups of variables instead of Gaussian relationship.

  5. Kernel-Correlated Levy Field Driven Forward Rate and Application to Derivative Pricing

    SciTech Connect

    Bo Lijun; Wang Yongjin; Yang Xuewei

    2013-08-01

    We propose a term structure of forward rates driven by a kernel-correlated Levy random field under the HJM framework. The kernel-correlated Levy random field is composed of a kernel-correlated Gaussian random field and a centered Poisson random measure. We shall give a criterion to preclude arbitrage under the risk-neutral pricing measure. As applications, an interest rate derivative with general payoff functional is priced under this pricing measure.

  6. Improved kernel correlation filter tracking with Gaussian scale space

    NASA Astrophysics Data System (ADS)

    Tan, Shukun; Liu, Yunpeng; Li, Yicui

    2016-10-01

    Recently, Kernel Correlation Filter (KCF) has achieved great attention in visual tracking filed, which provide excellent tracking performance and high possessing speed. However, how to handle the scale variation is still an open problem. In this paper, focusing on this issue that a method based on Gaussian scale space is proposed. First, we will use KCF to estimate the location of the target, the context region which includes the target and its surrounding background will be the image to be matched. In order to get the matching image of a Gaussian scale space, image with Gaussian kernel convolution can be gotten. After getting the Gaussian scale space of the image to be matched, then, according to it to estimate target image under different scales. Combine with the scale parameter of scale space, for each corresponding scale image performing bilinear interpolation operation to change the size to simulate target imaging at different scales. Finally, matching the template with different size of images with different scales, use Mean Absolute Difference (MAD) as the match criterion. After getting the optimal matching in the image with the template, we will get the best zoom ratio s, consequently estimate the target size. In the experiments, compare with CSK, KCF etc. demonstrate that the proposed method achieves high improvement in accuracy, is an efficient algorithm.

  7. A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs.

    PubMed

    Kang, Jian; Bowman, F DuBois; Mayberg, Helen; Liu, Han

    2016-11-01

    To establish brain network properties associated with major depressive disorder (MDD) using resting-state functional magnetic resonance imaging (Rs-fMRI) data, we develop a multi-attribute graph model to construct a region-level functional connectivity network that uses all voxel level information. For each region pair, we define the strength of the connectivity as the kernel canonical correlation coefficient between voxels in the two regions; and we develop a permutation test to assess the statistical significance. We also construct a network based classifier for making predictions on the risk of MDD. We apply our method to Rs-fMRI data from 20 MDD patients and 20 healthy control subjects in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Using this method, MDD patients can be distinguished from healthy control subjects based on significant differences in the strength of regional connectivity. We also demonstrate the performance of the proposed method using simulationstudies.

  8. Canonical Correlation Analysis As A Special Case Of A Structural Relations Model.

    PubMed

    Bagozzi, R P; Fornell, C; Larcker, D F

    1981-10-01

    Canonical correlation analysis is commonly considered to be a general model for most parametric bivariate and multivariate statistical methods. Because of its capability for handling multiple criteria and multiple predictors simultaneously, canonical correlation analysis has a great deal of appeal and has also enjoyed increasing application in the behavioral sciences. However, it has also been plagued by several serious shortcomings. In particular, researchers have been unable to determine the statistical significance of individual parameter estimates or to relax assumptions of the canonical model that are inconsistent with theory and/or observed data. As a result, canonical correlation analysis has found more application in exploratory research than in theory testing. This paper illustrates how these problems can be resolved by expressing canonical correlation as a special case of a linear structural relations model.

  9. Creativity and Brain-Functioning in Product Development Engineers: A Canonical Correlation Analysis

    ERIC Educational Resources Information Center

    Travis, Frederick; Lagrosen, Yvonne

    2014-01-01

    This study used canonical correlation analysis to explore the relation among scores on the Torrance test of figural and verbal creativity and demographic, psychological and physiological measures in Swedish product-development engineers. The first canonical variate included figural and verbal flexibility and originality as dependent measures and…

  10. Modeling Travel-Time Correlations Based on Sensitivity Kernels and Correlated Velocity Anomalies

    DTIC Science & Technology

    2008-09-01

    MODELING TRAVEL -TIME CORRELATIONS BASED ON SENSITIVITY KERNELS AND CORRELATED VELOCITY ANOMALIES William L. Rodi1 and Stephen C. Myers2 Massachusetts...05NA266031 and DE-AC52-07NA273442 Proposal No. BAA05-14 ABSTRACT This project concerns the errors in predicted regional and teleseismic travel times...resulting from velocity heterogeneity in the real Earth not represented in the reference Earth model used for travel -time calculation. We are developing

  11. Characterizing nonlinear relationships in functional imaging data using eigenspace maximal information canonical correlation analysis (emiCCA).

    PubMed

    Dong, Li; Zhang, Yangsong; Zhang, Rui; Zhang, Xingxing; Gong, Diankun; Valdes-Sosa, Pedro A; Xu, Peng; Luo, Cheng; Yao, Dezhong

    2015-04-01

    Many important problems in the analysis of neuroimages can be formulated as discovering the relationship between two sets of variables, a task for which linear techniques such as canonical correlation analysis (CCA) have been commonly used. However, to further explore potential nonlinear processes that might co-exist with linear ones in brain function, a more flexible method is required. Here, we propose a new unsupervised and data-driven method, termed the eigenspace maximal information canonical correlation analysis (emiCCA), which is capable of automatically capturing the linear and/or nonlinear relationships between various data sets. A simulation confirmed the superior performance of emiCCA in comparison with linear CCA and kernel CCA (a nonlinear version of CCA). An emiCCA framework for functional magnetic resonance imaging (fMRI) data processing was designed and applied to data from a real motor execution fMRI experiment. This analysis uncovered one linear (in primary motor cortex) and a few nonlinear networks (e.g., in the supplementary motor area, bilateral insula, and cerebellum). This suggests that these various task-related brain areas are part of networks that also contribute to the execution of movements of the hand. These results suggest that emiCCA is a promising technique for exploring various data.

  12. Registration of prone and supine CT colonography scans using correlation optimized warping and canonical correlation analysis

    SciTech Connect

    Wang Shijun; Yao Jianhua; Liu Jiamin; Petrick, Nicholas; Van Uitert, Robert L.; Periaswamy, Senthil; Summers, Ronald M.

    2009-12-15

    Purpose: In computed tomographic colonography (CTC), a patient will be scanned twice--Once supine and once prone--to improve the sensitivity for polyp detection. To assist radiologists in CTC reading, in this paper we propose an automated method for colon registration from supine and prone CTC scans. Methods: We propose a new colon centerline registration method for prone and supine CTC scans using correlation optimized warping (COW) and canonical correlation analysis (CCA) based on the anatomical structure of the colon. Four anatomical salient points on the colon are first automatically distinguished. Then correlation optimized warping is applied to the segments defined by the anatomical landmarks to improve the global registration based on local correlation of segments. The COW method was modified by embedding canonical correlation analysis to allow multiple features along the colon centerline to be used in our implementation. Results: We tested the COW algorithm on a CTC data set of 39 patients with 39 polyps (19 training and 20 test cases) to verify the effectiveness of the proposed COW registration method. Experimental results on the test set show that the COW method significantly reduces the average estimation error in a polyp location between supine and prone scans by 67.6%, from 46.27{+-}52.97 to 14.98 mm{+-}11.41 mm, compared to the normalized distance along the colon centerline algorithm (p<0.01). Conclusions: The proposed COW algorithm is more accurate for the colon centerline registration compared to the normalized distance along the colon centerline method and the dynamic time warping method. Comparison results showed that the feature combination of z-coordinate and curvature achieved lowest registration error compared to the other feature combinations used by COW. The proposed method is tolerant to centerline errors because anatomical landmarks help prevent the propagation of errors across the entire colon centerline.

  13. Nonlinear Canonical Correlation Analysis with k Sets of Variables. Research Report 87-8.

    ERIC Educational Resources Information Center

    van der Burg, Eeke; de Leeuw, Jan

    The multivariate technique OVERALS is introduced as a non-linear generalization of canonical correlation analysis (CCA). First, two sets CCA is introduced. Two sets CCA is a technique that computes linear combinations of sets of variables that correlate in an optimal way. Two sets CCA is then expanded to generalized (or k sets) CCA. The…

  14. A Canonical Correlations Approach to Multiscale Stochastic Realization

    DTIC Science & Technology

    1996-11-01

    match possible between the actual. realize(l covariance P,, and the desired covariance P 0o. Because the desirable model propIerties of low dimeniesion...interest in the geological sciences [17]. The correlation function for this field can be expressed analytically as follows: 1 - 3/2(r/f) + 1/2(r/) 3 0

  15. Drivers and Outcomes of Scenario Planning: A Canonical Correlation Analysis

    ERIC Educational Resources Information Center

    Chermack, Thomas J.; Nimon, Kim

    2013-01-01

    Purpose: The paper's aim is to report a research study on the mediator and outcome variable sets in scenario planning. Design/methodology/approach: This is a cannonical correlation analysis (CCA) Findings Two sets of variables; one as a predictor set that explained a significant amount of variability in the second, or outcome set of variables were…

  16. Generalized Canonical Correlation Analysis of Matrices with Missing Rows: A Simulation Study

    ERIC Educational Resources Information Center

    van de Velden, Michel; Bijmolt, Tammo H. A.

    2006-01-01

    A method is presented for generalized canonical correlation analysis of two or more matrices with missing rows. The method is a combination of Carroll's (1968) method and the missing data approach of the OVERALS technique (Van der Burg, 1988). In a simulation study we assess the performance of the method and compare it to an existing procedure…

  17. The Multivariate Reality of Educational Research: Detecting Interaction Effects Using Canonical Correlation Analysis.

    ERIC Educational Resources Information Center

    Kirby, Peggy C.; Abernathy, Mari W.

    Canonical correlation analysis is the best technique to employ when the research problem has multiple predictor and multiple criterion (outcome) variables, which is usually the case in the "real" world of education. A hypothetical data set is presented to illustrate how this particular multivariate method can be used to detect effects of…

  18. Canonical Correlational Models of Students' Perceptions of Assessment Tasks, Motivational Orientations, and Learning Strategies

    ERIC Educational Resources Information Center

    Alkharusi, Hussain

    2013-01-01

    The present study aims at deriving correlational models of students' perceptions of assessment tasks, motivational orientations, and learning strategies using canonical analyses. Data were collected from 198 Omani tenth grade students. Results showed that high degrees of authenticity and transparency in assessment were associated with positive…

  19. Canonical Correlation Analysis and Structural Equation Modeling: What Do They Have in Common?

    ERIC Educational Resources Information Center

    Fan, Xitao

    1997-01-01

    The relationship between structural equation modeling (SEM) and canonical correlation analysis (CCA) is illustrated. The representation of CCA in SEM may provide interpretive information not available from conventional CCA. Hierarchically, the relationship suggests that SEM is a more general analytic approach. (SLD)

  20. Getting full control of canonical correlation analysis with the AutoBiplot.CCA function

    NASA Astrophysics Data System (ADS)

    Alves, M. Rui

    2016-06-01

    Function AutoBiplot.CCA was built in R language. Given two multivariate data sets, this function carries out a conventional canonical correlation analysis, followed by the automatic production of predictive biplots based on the accuracy of readings as assessed by a mean standard predictive error and a user defined tolerance value. As the user's intervention is mainly restricted to the choice of the magnitude of the t.axis value, common misinterpretations, overestimations and adjustments between outputs and personal beliefs are avoided.

  1. Canonical correlation between LFP network and spike network during working memory task in rat.

    PubMed

    Yi, Hu; Zhang, Xiaofan; Bai, Wenwen; Liu, Tiaotiao; Tian, Xin

    2015-08-01

    Working memory refers to a system to temporary holding and manipulation of information. Previous studies suggested that local field potentials (LFPs) and spikes as well as their coordination provide potential mechanism of working memory. Popular methods for LFP-spike coordination only focus on the two modality signals, isolating each channel from multi-channel data, ignoring the entirety of the networked brain. Therefore, we investigated the coordination between the LFP network and spike network to achieve a better understanding of working memory. Multi-channel LFPs and spikes were simultaneously recorded in rat prefrontal cortex via microelectrode array during a Y-maze working memory task. Functional connectivity in the LFP network and spike network was respectively estimated by the directed transfer function (DTF) and maximum likelihood estimation (MLE). Then the coordination between the two networks was quantified via canonical correlation analysis (CCA). The results show that the canonical correlation (CC) varied during the working memory task. The CC-curve peaked before the choice point, describing the coordination between LFP network and spike network enhanced greatly. The CC value in working memory showed a significant higher level than inter-trial interval. Our results indicate that the enhanced canonical correlation between the LFP network and spike network may provide a potential network integration mechanism for working memory.

  2. Relationship between organisational commitment and burnout syndrome: a canonical correlation approach.

    PubMed

    Enginyurt, Ozgur; Cankaya, Soner; Aksay, Kadir; Tunc, Taner; Koc, Bozkurt; Bas, Orhan; Ozer, Erdal

    2016-04-01

    Objective Burnout syndrome can significantly reduce the performance of health workers. Although many factors have been identified as antecedents of burnout, few studies have investigated the role of organisational commitment in its development. The purpose of the present study was to examine the relationships between subdimensions of burnout syndrome (emotional exhaustion, depersonalisation and personal accomplishment) and subdimensions of organisational commitment (affective commitment, continuance commitment and normative commitment). Methods The present study was a cross-sectional survey of physicians and other healthcare employees working in the Ministry of Health Ordu University Education and Research Hospital. The sample consisted of 486 healthcare workers. Data were collected using the Maslach Burnout Inventory and the Organisation Commitment Scale, and were analysed using the canonical correlation approach. Results The first of three canonical correlation coefficients between pairs of canonical variables (Ui , burnout syndrome and Vi, organisational commitment) was found to be statistically significant. Emotional exhaustion was found to contribute most towards the explanatory capacity of canonical variables estimated from the subdimensions of burnout syndrome, whereas affective commitment provided the largest contribution towards the explanatory capacity of canonical variables estimated from the subdimensions of organisational commitment. Conclusions The results of the present study indicate that affective commitment is the primary determinant of burnout syndrome in healthcare professionals. What is known about the topic? Organisational commitment and burnout syndrome are the most important criteria in predicting health workforce performance. An increasing number of studies in recent years have clearly indicated the field's continued relevance and importance. Conversely, canonical correlation analysis (CCA) is a technique for describing the relationship

  3. Non-linear canonical correlation for joint analysis of MEG signals from two subjects.

    PubMed

    Campi, Cristina; Parkkonen, Lauri; Hari, Riitta; Hyvärinen, Aapo

    2013-01-01

    Traditional stimulus-based analysis methods of magnetoencephalography (MEG) data are often dissatisfactory when applied to naturalistic experiments where two or more subjects are measured either simultaneously or sequentially. To uncover the commonalities in the brain activity of the two subjects, we propose a method that searches for linear transformations that output maximally correlated signals between the two brains. Our method is based on canonical correlation analysis (CCA), which provides linear transformations, one for each subject, such that the temporal correlation between the transformed MEG signals is maximized. Here, we present a non-linear version of CCA which measures the correlation of energies and allows for a variable delay between the time series to accommodate, e.g., leader-follower changes. We test the method with simulations and with MEG data from subjects who received the same naturalistic stimulus sequence. The method may help analyse future experiments where the two subjects are measured simultaneously while engaged in social interaction.

  4. Path analysis and canonical correlations for indirect selection of Jatropha genotypes with higher oil yield.

    PubMed

    Silva, L A; Peixoto, L A; Teodoro, P E; Rodrigues, E V; Laviola, B G; Bhering, L L

    2017-03-22

    Jatropha is a species with great potential for biodiesel production, and the knowledge on how the main agronomic traits are correlated will contribute to its improvement. Therefore, the objectives of this study were to estimate the genetic parameters of the traits: plant height at 12 and 40 months, canopy projection on the row at 12 and 40 months, canopy projection between the row at 12 and 40 months, number of branches at 40 months, grain yield, and oil yield; to verify the existence of phenotypic correlation between these traits; to verify the influence of the morphological traits on oil yield by means of path analysis; and to evaluate the relationship between the productive traits in Jatropha and the morphological traits measured at different ages. Sixty-seven half-sib families were evaluated using a completely randomized block design with two replications and five plants per plot. Analysis of variance was used to estimate the genetic value. Phenotypic correlations were given by the Pearson correlation between traits. For the canonical correlation analysis, two groups of traits were established: group I, consisting of traits of economic importance for the culture, and group II, consisting of morphological traits. Path analysis was carried out considering oil yield as the main dependent variable. Genetic variability was observed among Jatropha families. Productive traits can be indirectly selected via morphological traits due to the correlation between these two groups of traits. Therefore, canonical correlations and path analysis are two strategies that may be useful in Jatropha-breeding program when the objective is to select productive traits via morphological traits.

  5. Canonical correlation analysis of synchronous neural interactions and cognitive deficits in Alzheimer's dementia

    NASA Astrophysics Data System (ADS)

    Karageorgiou, Elissaios; Lewis, Scott M.; Riley McCarten, J.; Leuthold, Arthur C.; Hemmy, Laura S.; McPherson, Susan E.; Rottunda, Susan J.; Rubins, David M.; Georgopoulos, Apostolos P.

    2012-10-01

    In previous work (Georgopoulos et al 2007 J. Neural Eng. 4 349-55) we reported on the use of magnetoencephalographic (MEG) synchronous neural interactions (SNI) as a functional biomarker in Alzheimer's dementia (AD) diagnosis. Here we report on the application of canonical correlation analysis to investigate the relations between SNI and cognitive neuropsychological (NP) domains in AD patients. First, we performed individual correlations between each SNI and each NP, which provided an initial link between SNI and specific cognitive tests. Next, we performed factor analysis on each set, followed by a canonical correlation analysis between the derived SNI and NP factors. This last analysis optimally associated the entire MEG signal with cognitive function. The results revealed that SNI as a whole were mostly associated with memory and language, and, slightly less, executive function, processing speed and visuospatial abilities, thus differentiating functions subserved by the frontoparietal and the temporal cortices. These findings provide a direct interpretation of the information carried by the SNI and set the basis for identifying specific neural disease phenotypes according to cognitive deficits.

  6. Canonical correlation analysis for RNA-seq co-expression networks

    PubMed Central

    Hong, Shengjun; Chen, Xiangning; Jin, Li; Xiong, Momiao

    2013-01-01

    Digital transcriptome analysis by next-generation sequencing discovers substantial mRNA variants. Variation in gene expression underlies many biological processes and holds a key to unravelling mechanism of common diseases. However, the current methods for construction of co-expression networks using overall gene expression are originally designed for microarray expression data, and they overlook a large number of variations in gene expressions. To use information on exon, genomic positional level and allele-specific expressions, we develop novel component-based methods, single and bivariate canonical correlation analysis, for construction of co-expression networks with RNA-seq data. To evaluate the performance of our methods for co-expression network inference with RNA-seq data, they are applied to lung squamous cell cancer expression data from TCGA database and our bipolar disorder and schizophrenia RNA-seq study. The preliminary results demonstrate that the co-expression networks constructed by canonical correlation analysis and RNA-seq data provide rich genetic and molecular information to gain insight into biological processes and disease mechanism. Our new methods substantially outperform the current statistical methods for co-expression network construction with microarray expression data or RNA-seq data based on overall gene expression levels. PMID:23460206

  7. Comparison of JADE and canonical correlation analysis for ECG de-noising.

    PubMed

    Kuzilek, Jakub; Kremen, Vaclav; Lhotska, Lenka

    2014-01-01

    This paper explores differences between two methods for blind source separation within frame of ECG de-noising. First method is joint approximate diagonalization of eigenmatrices, which is based on estimation of fourth order cross-cummulant tensor and its diagonalization. Second one is the statistical method known as canonical correlation analysis, which is based on estimation of correlation matrices between two multidimensional variables. Both methods were used within method, which combines the blind source separation algorithm with decision tree. The evaluation was made on large database of 382 long-term ECG signals and the results were examined. Biggest difference was found in results of 50 Hz power line interference where the CCA algorithm completely failed. Thus main power of CCA lies in estimation of unstructured noise within ECG. JADE algorithm has larger computational complexity thus the CCA perfomed faster when estimating the components.

  8. The Lieb-Oxfourd bound and the exchange-correlation kernel from the strictly-correlated electrons functional

    NASA Astrophysics Data System (ADS)

    Gori-Giorgi, Paola

    I will present some recent results based on the strictly-correlated electrons (SCE) functional: 1) a rigorous method to set lower bounds to the optimal particle-number dependent constant appearing in the Lieb-Oxford bound, and 2) an investigation of exact properties in the time domain, including an analytical expression for the kernel in one-dimension, with an analysis of its behavior for the case of bond-breaking excitations. ERC Consolidator Grant 648932.

  9. Biomarker discovery by sparse canonical correlation analysis of complex clinical phenotypes of tuberculosis and malaria.

    PubMed

    Rousu, Juho; Agranoff, Daniel D; Sodeinde, Olugbemiro; Shawe-Taylor, John; Fernandez-Reyes, Delmiro

    2013-04-01

    Biomarker discovery aims to find small subsets of relevant variables in 'omics data that correlate with the clinical syndromes of interest. Despite the fact that clinical phenotypes are usually characterized by a complex set of clinical parameters, current computational approaches assume univariate targets, e.g. diagnostic classes, against which associations are sought for. We propose an approach based on asymmetrical sparse canonical correlation analysis (SCCA) that finds multivariate correlations between the 'omics measurements and the complex clinical phenotypes. We correlated plasma proteomics data to multivariate overlapping complex clinical phenotypes from tuberculosis and malaria datasets. We discovered relevant 'omic biomarkers that have a high correlation to profiles of clinical measurements and are remarkably sparse, containing 1.5-3% of all 'omic variables. We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. Our approach finds proteomic-biomarkers that correlate with complex combinations of clinical-biomarkers. Using the clinical-biomarkers improves the accuracy of diagnostic class prediction while not requiring the measurement plasma proteomic profiles of each subject. Our approach makes it feasible to use omics' data to build accurate diagnostic algorithms that can be deployed to community health centres lacking the expensive 'omics measurement capabilities.

  10. Non-adiabatic exchange-correlation kernel for the non-equilibrium response of three-dimensional Hubbard model

    NASA Astrophysics Data System (ADS)

    Acharya, Shree Ram; Baral, Nisha; Turkowski, Volodymyr; Rahman, Talat S.

    2015-03-01

    We apply Dynamical Mean-Field Theory (DMFT) to calculate the non-adiabatic (frequency-dependent) exchange-correlation kernel for the three-dimensional Hubbard model. We analyze the dependence of the kernel on the electron doping, local Coulomb repulsion and frequency by using three different impurity solvers: Hubbard-I, Iterative Perturbation Theory (IPT) and Continuous-Time Quantum Monte Carlo (CT-QMC). From the calculated data, we obtain approximate analytical expressions for the kernel. We apply the exact numerical and analytical kernels to study the non-equilibrium response of the system for applied ultrafast laser pulse. We demonstrate that the non-adiabaticity of the kernel plays an important role in the system response; in particular, leading to new excited-states involved in the system dynamics. Work supported in part by DOE Grant No. DOE-DE-FG02-07ER46354.

  11. Personal incentives for exercise and body esteem: a canonical correlation analysis.

    PubMed

    Finkenberg, M E; Dinucci, J M; McCune, S L; McCune, E D

    1994-12-01

    Two hundred twelve women and 93 men enrolled in physical education courses were administered the Personal Incentives for Exercise Questionnaire and the Body Esteem Scale. Canonical correlation was conducted. For women, it was determined that personal incentives for exercise have modest predictive power for the weight concern and physical conditioning dimensions of body esteem and very little predictive power for the sexual attractiveness dimension; the body esteem variates have slight predictive power for the competition and weight management dimensions of investment in exercise. For men, it was determined that personal incentives for exercise have modest predictive power for the physical conditioning dimension of body esteem, slight predictive power for physical attractiveness, and none for upper body strength; the body esteem variate has modest predictive power for the weight management dimension of incentives for exercise and slight predictive power for appearance and affiliation.

  12. Spatial smoothing of canonical correlation analysis for steady state visual evoked potential based brain computer interfaces.

    PubMed

    Ryu, Shingo; Higashi, Hiroshi; Tanaka, Toshihisa; Nakauchi, Shigeki; Minami, Tetsuto

    2016-08-01

    Brain computer interface (BCI) is a system for communication between people and computers via brain activity. Steady-state visual evoked potentials (SSVEPs), a brain response observed in EEG, are evoked by flickering stimuli. SSVEP is one of the promising paradigms for BCI. Canonical correlation analysis (CCA) is widely used for EEG signal processing in SSVEP-based BCIs. However, the classification accuracy of CCA with short signal length is low. In order to solve the problem, we propose a regularization which works in such a way that the CCA spatial filter becomes spatially smooth to give robustness in short signal length condition. The spatial filter is designed in a parameter space spanned by a spatially smooth basis which are given by a graph Fourier transform of three dimensional electrode coordinates. We compared the classification accuracy of the proposed regularized CCA with the standard CCA. The result shows that the proposed CCA outperforms the standard CCA in short signal length condition.

  13. Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis.

    PubMed

    Sun, Liang; Ji, Shuiwang; Ye, Jieping

    2011-01-01

    Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multidimensional variables. It projects both sets of variables onto a lower-dimensional space in which they are maximally correlated. CCA is commonly applied for supervised dimensionality reduction in which the two sets of variables are derived from the data and the class labels, respectively. It is well-known that CCA can be formulated as a least-squares problem in the binary class case. However, the extension to the more general setting remains unclear. In this paper, we show that under a mild condition which tends to hold for high-dimensional data, CCA in the multilabel case can be formulated as a least-squares problem. Based on this equivalence relationship, efficient algorithms for solving least-squares problems can be applied to scale CCA to very large data sets. In addition, we propose several CCA extensions, including the sparse CCA formulation based on the 1-norm regularization. We further extend the least-squares formulation to partial least squares. In addition, we show that the CCA projection for one set of variables is independent of the regularization on the other set of multidimensional variables, providing new insights on the effect of regularization on CCA. We have conducted experiments using benchmark data sets. Experiments on multilabel data sets confirm the established equivalence relationships. Results also demonstrate the effectiveness and efficiency of the proposed CCA extensions.

  14. A Study on the Oral Disfluencies Developmental Traits of EFL Students--A Report Based on Canonical Correlation Analysis

    ERIC Educational Resources Information Center

    Gao, Ying; Du, Wanyi

    2013-01-01

    This paper traces 9 non-English major EFL students and collects their oral productions in 4 successive oral exams in 2 years. The canonical correlation analysis approach of SPSS is adopted to study the disfluencies developmental traits under the influence of language acquisition development. We find that as language acquisition develops, the total…

  15. A Multifunctional Sensor in Ternary Solution Using Canonical Correlations for Variable Links Assessment

    PubMed Central

    Liu, Dan; Wang, Qisong; Liu, Xin; Niu, Ruixin; Zhang, Yan; Sun, Jinwei

    2016-01-01

    Accurately measuring the oil content and salt content of crude oil is very important for both estimating oil reserves and predicting the lifetime of an oil well. There are some problems with the current methods such as high cost, low precision, and difficulties in operation. To solve these problems, we present a multifunctional sensor, which applies, respectively, conductivity method and ultrasound method to measure the contents of oil, water, and salt. Based on cross sensitivity theory, these two transducers are ideally integrated for simplifying the structure. A concentration test of ternary solutions is carried out to testify its effectiveness, and then Canonical Correlation Analysis is applied to evaluate the data. From the perspective of statistics, the sensor inputs, for instance, oil concentration, salt concentration, and temperature, are closely related to its outputs including output voltage and time of flight of ultrasound wave, which further identify the correctness of the sensing theory and the feasibility of the integrated design. Combined with reconstruction algorithms, the sensor can realize the content measurement of the solution precisely. The potential development of the proposed sensor and method in the aspect of online test for crude oil is of important reference and practical value. PMID:27775640

  16. Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging

    PubMed Central

    Rosa, Maria J.; Mehta, Mitul A.; Pich, Emilio M.; Risterucci, Celine; Zelaya, Fernando; Reinders, Antje A. T. S.; Williams, Steve C. R.; Dazzan, Paola; Doyle, Orla M.; Marquand, Andre F.

    2015-01-01

    An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow. PMID:26528117

  17. Discriminative Learning for Alzheimer's Disease Diagnosis via Canonical Correlation Analysis and Multimodal Fusion

    PubMed Central

    Lei, Baiying; Chen, Siping; Ni, Dong; Wang, Tianfu

    2016-01-01

    To address the challenging task of diagnosing neurodegenerative brain disease, such as Alzheimer's disease (AD) and mild cognitive impairment (MCI), we propose a novel method using discriminative feature learning and canonical correlation analysis (CCA) in this paper. Specifically, multimodal features and their CCA projections are concatenated together to represent each subject, and hence both individual and shared information of AD disease are captured. A discriminative learning with multilayer feature hierarchy is designed to further improve performance. Also, hybrid representation is proposed to maximally explore data from multiple modalities. A novel normalization method is devised to tackle the intra- and inter-subject variations from the multimodal data. Based on our extensive experiments, our method achieves an accuracy of 96.93% [AD vs. normal control (NC)], 86.57 % (MCI vs. NC), and 82.75% [MCI converter (MCI-C) vs. MCI non-converter (MCI-NC)], respectively, which outperforms the state-of-the-art methods in the literature. PMID:27242506

  18. Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging.

    PubMed

    Rosa, Maria J; Mehta, Mitul A; Pich, Emilio M; Risterucci, Celine; Zelaya, Fernando; Reinders, Antje A T S; Williams, Steve C R; Dazzan, Paola; Doyle, Orla M; Marquand, Andre F

    2015-01-01

    An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

  19. Canonical correlation analysis of factors involved in the occurrence of peptic ulcers.

    PubMed

    Bayyurt, Nizamettin; Abasiyanik, M Fatih; Sander, Ersan; Salih, Barik A

    2007-01-01

    The impact of risk factors on the development of peptic ulcers has been shown to vary among different populations. We sought to establish a correlation between these factors and their involvement in the occurrence of peptic ulcers for which a canonical correlation analysis was applied. We included 7,014 patient records (48.6% women, 18.4% duodenal ulcer [DU], 4.6% gastric ulcer [GU]) of those underwent upper gastroendoscopy for the last 5 years. The variables measured are endoscopic findings (DU, GU, antral gastritis, erosive gastritis, pangastritis, pyloric deformity, bulbar deformity, bleeding, atrophy, Barret esophagus and gastric polyp) and risk factors (age, gender, Helicobacter pylori infection, smoking, alcohol, and nonsteroidal anti-inflammatory drugs [NSAIDs] and aspirin intake). We found that DU had significant positive correlation with bulbar deformity (P=2.6 x 10(-23)), pyloric deformity (P=2.6 x 10(-23)), gender (P=2.6 x 10(-23)), H. pylori (P=1.4 x 10(-15)), bleeding (P=6.9 x 10(-15)), smoking (P=1.4 x 10(-7)), aspirin use (P=1.1 x 10(-4)), alcohol intake (P=7.7 x 10(-4)), and NSAIDs (P=.01). GU had a significantly positive correlation with pyloric deformity (P=1,6 x 10(-15)), age (P=2.6 x 10(-14)), bleeding (P=3.7 x 10(-8)), gender (P=1.3 x 10(-7)), aspirin use (P=1.1 x 10(-6)), bulbar deformity (P=7.4 x 10(-4)), alcohol intake (P=.03), smoking (P=.04), and Barret esophagus (P=.03). The level of significance was much higher in some variables with DU than with GU and the correlations with GU in spite of being highly significant the majority, were small in magnitude. In conclusion, Turkish patients with the following endoscopic findings bulbar deformity and pyloric deformity are high-risk patients for peptic ulcers with the risk of the occurrence of DU being higher than that of GU. Factors such as H. pylori, smoking, alcohol use, and NSAIDs use (listed in a decreasing manner) are risk factors that have significant impact on the occurrence of DU

  20. A Unified Approach to Functional Principal Component Analysis and Functional Multiple-Set Canonical Correlation.

    PubMed

    Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S

    2016-02-08

    Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

  1. Empirical sensitivity kernels of noise correlations with respect to virtual sources

    NASA Astrophysics Data System (ADS)

    Boué, P.; Stehly, L.; Nakata, N.; Beroza, G. C.

    2014-12-01

    Cross-correlation of time-series, or interferometry, applied to the ambient seismic field is an established method to observe the propagation of waves between pairs of sensors without involving transient sources. These reconstructed waves are routinely used to develop high-resolution images of the crust and upper mantle, or in mapping the time-dependent velocity changes associated with tectonic events. Using similar methods, recent work have highlighted more challenging observations, such as higher mode surface wave propagation and body wave reconstruction at various scales of the Earth: from the industrial surveys at the reservoir scale to the global scale. Furthermore, the reconstruction of the correct amplitude information can be used to image the anelastic attenuation of the medium and has led to a new type of ground motion prediction using virtual earthquakes method. The dependability of such amplitude retrieval had been debated and represents a difficult challenge due to uneven source distribution. In this study, we discuss the possibility to use the correlation of ambient noise correlation (similar to C3 method) to map the contribution of different source locations for Rayleigh wave reconstruction between receiver pairs. These maps constructed in terms of traveltime or amplitude perturbations of the Green's function, can be considered as empirical sensitivity kernels with respect to the contribution of each virtual source. We propose for the first time to map these kernels using a dataset of continuous records from a dense array of about 2600 sensors deployed at the local-scale in Long Beach (CA, USA). Finally, these maps are used to analyze the impact of the original ambient noise directivity on the recovered Green's functions and discuss the effects of the velocity lateral heterogeneity within the array. We aim at understanding, and thereby reducing, the bias in ambient field measurements.

  2. A Canonical Correlation Analysis of AIDS Restriction Genes and Metabolic Pathways Identifies Purine Metabolism as a Key Cooperator

    PubMed Central

    Ye, Hanhui; Yuan, Jinjin; Wang, Zhengwu; Huang, Aiqiong; Liu, Xiaolong; Han, Xiao; Chen, Yahong

    2016-01-01

    Human immunodeficiency virus causes a severe disease in humans, referred to as immune deficiency syndrome. Studies on the interaction between host genetic factors and the virus have revealed dozens of genes that impact diverse processes in the AIDS disease. To resolve more genetic factors related to AIDS, a canonical correlation analysis was used to determine the correlation between AIDS restriction and metabolic pathway gene expression. The results show that HIV-1 postentry cellular viral cofactors from AIDS restriction genes are coexpressed in human transcriptome microarray datasets. Further, the purine metabolism pathway comprises novel host factors that are coexpressed with AIDS restriction genes. Using a canonical correlation analysis for expression is a reliable approach to exploring the mechanism underlying AIDS. PMID:27462363

  3. Insights into the spurious long-range nature of local rs-dependent non-local exchange-correlation kernels

    DOE PAGES

    Lu, Deyu

    2016-08-05

    A systematic route to go beyond the exact exchange plus random phase approximation (RPA) is to include a physical exchange-correlation kernel in the adiabatic-connection fluctuation-dissipation theorem. Previously, [D. Lu, J. Chem. Phys. 140, 18A520 (2014)], we found that non-local kernels with a screening length depending on the local Wigner-Seitz radius, rs(r), suffer an error associated with a spurious long-range repulsion in van der Waals bounded systems, which deteriorates the binding energy curve as compared to RPA. Here, we analyze the source of the error and propose to replace rs(r) by a global, average rs in the kernel. Exemplary studies withmore » the Corradini, del Sole, Onida, and Palummo kernel show that while this change does not affect the already outstanding performance in crystalline solids, using an average rs significantly reduces the spurious long-range tail in the exchange-correlation kernel in van der Waals bounded systems. Finally, when this method is combined with further corrections using local dielectric response theory, the binding energy of the Kr dimer is improved three times as compared to RPA.« less

  4. Insights into the spurious long-range nature of local rs-dependent non-local exchange-correlation kernels

    SciTech Connect

    Lu, Deyu

    2016-08-05

    A systematic route to go beyond the exact exchange plus random phase approximation (RPA) is to include a physical exchange-correlation kernel in the adiabatic-connection fluctuation-dissipation theorem. Previously, [D. Lu, J. Chem. Phys. 140, 18A520 (2014)], we found that non-local kernels with a screening length depending on the local Wigner-Seitz radius, rs(r), suffer an error associated with a spurious long-range repulsion in van der Waals bounded systems, which deteriorates the binding energy curve as compared to RPA. Here, we analyze the source of the error and propose to replace rs(r) by a global, average rs in the kernel. Exemplary studies with the Corradini, del Sole, Onida, and Palummo kernel show that while this change does not affect the already outstanding performance in crystalline solids, using an average rs significantly reduces the spurious long-range tail in the exchange-correlation kernel in van der Waals bounded systems. Finally, when this method is combined with further corrections using local dielectric response theory, the binding energy of the Kr dimer is improved three times as compared to RPA.

  5. Performance of Canonical Correlation Analysis (CCA) and Bayesian Hierarchical Modelling (BHM) for European temperature field reconstructions

    NASA Astrophysics Data System (ADS)

    Werner, J. P.; Smerdon, J. E.; Luterbacher, J.

    2011-12-01

    A Pseudoproxy comparison is presented for two statistical methods used to derive annual climate field reconstructions (CFR) for europe. The employed methods use the canonical correlation analysis (CCA) procedure presented by Smerdon et al. (2010, J. Climate) and the Bayesian Hierarchical Model (BHM) based method adopted from Tingley and Huybers (2010a,b, J. Climate). Pseudoproxy experiments are constructed from modelled temperature data sampled from the 1250-year paleo-run of the NCAR CCSM 1.4 model (Ammann et al. 2007, PNAS). The pseudoproxies approximate the distribution of the Mann et al. (1998, Nature) multi-proxy network and use Gaussian white noise to mimic the combined signal and noise properties of real-world proxies. The derived CFRs are tested by comparing the mean temperature bias, the reconstructed temperature variability and two error measures: the cross correlation and the root mean square error. The results show that the BHM method performs much better than the CCA method in areas with good proxy coverage. The BHM method also delivers the added value over the more traditional CCA method by providing objective error estimates. Reconstructions of key years are also analysed. While CCA returns estimates for the full climate field even for areas with sparse data, the more flexible model used in the BHM method returns results that are closer to the target for most of the reconstruction area, albeit with higher uncertainties in data sparse regions. Based on the success of these current BHM results, the algorithm will be extended to make use of proxies with different temporal resolution (cf. Li et al. 2010) in order to reconstruct the temperature and precipitation fields over Europe and the Mediterranean covering much of the common-era period. Ammann, C. et al. (2007), PNAS 104, 3713--3718 Li, B. et al. (2010), J. Am. Stat. Assoc. 105, 883-911 Mann, M. et al. (1998), Nature 392, 779-787 Smerdon, J. et al. (2010), J. Climate 24, 1284-1309 Tingley, M. and

  6. Kernel Feature Cross-Correlation for Unsupervised Quantification of Damage from Windthrow in Forests

    NASA Astrophysics Data System (ADS)

    Pirotti, F.; Travaglini, D.; Giannetti, F.; Kutchartt, E.; Bottalico, F.; Chirici, G.

    2016-06-01

    In this study estimation of tree damage from a windthrow event using feature detection on RGB high resolution imagery is assessed. An accurate quantitative assessment of the damage in terms of volume is important and can be done by ground sampling, which is notably expensive and time-consuming, or by manual interpretation and analyses of aerial images. This latter manual method also requires an expert operator investing time to manually detect damaged trees and apply relation functions between measures and volume which are also error-prone. In the proposed method RGB images with 0.2 m ground sample distance are analysed using an adaptive template matching method. Ten images corresponding to ten separate study areas are tested. A 13x13 pixels kernel with a simplified linear-feature representation of a cylinder is applied at different rotation angles (from 0° to 170° at 10° steps). The higher values of the normalized cross-correlation (NCC) of all angles are recorded for each pixel for each image. Several features are tested: percentiles (75, 80, 85, 90, 95, 99, max) and sum and number of pixels with NCC above 0.55. Three regression methods are tested, multiple regression (mr), support vector machines (svm) with linear kernel and random forests. The first two methods gave the best results. The ground-truth was acquired by ground sampling, and total volumes of damaged trees are estimated for each of the 10 areas. Damaged volumes in the ten areas range from ~1.8 x102 m3 to ~1.2x104 m3. Regression results show that smv regression method over the sum gives an R-squared of 0.92, a mean of absolute errors (MAE) of 255 m3 and a relative absolute error (RAE) of 34% using leave-one-out cross validation from the 10 observations. These initial results are encouraging and support further investigations on more finely tuned kernel template metrics to define an unsupervised image analysis process to automatically assess forest damage from windthrow.

  7. [Improved weighted cross-correlation coefficient with a new kernel and its application in predicting T cell epitopes].

    PubMed

    Huang, Jing; Ma, Jian-hua; Liu, Nan; Qian, Shan-shan

    2010-10-01

    We designed a weighted cross-correlation coefficient considering the "anchor" of the T cell epitopes, and used an evolutionary algorithm to search for an optimal weight vector. A SVM model with this new peptide similarity kernel was evaluated on a T-cell data set. The results demonstrated a good performance of this method.

  8. Frequency Dependence of the Exact Exchange-Correlation Kernel of Time-Dependent Density-Functional Theory

    NASA Astrophysics Data System (ADS)

    Thiele, M.; Kümmel, S.

    2014-02-01

    We present a scheme for numerically reconstructing the exact and the Kohn-Sham time-dependent density-density response functions, and from their inverse the numerical representation of the exact frequency-dependent exchange-correlation kernel fxc of time-dependent Kohn-Sham density-functional theory. We investigate the exact fxc in detail for a system that is known to show strong memory effects. The reconstructed kernel fulfills the appropriate sum rule. Using it in linear response calculations, we show how the exact fxc, due to its frequency dependence, yields the exact excitation energies, including the ones of double excitation character.

  9. Bill colour and correlates of male quality in blackbirds: an analysis using canonical ordination.

    PubMed

    Bright, A; Waas, J R; King, C M; Cuming, P D

    2004-02-27

    Carotenoid-dependent plumage displays are widely assumed to be honest indicators of individual health or quality, which are used as cues during mate choice and/or agonistic signalling. Despite the fact that red, yellow and orange pigmentation of bills is common, and also variable between individuals, comparatively little is known about bill colouration as a condition-dependent trait. Furthermore, many studies of avian colouration are confounded by the lack of objective colour quantification and the use of overly simplistic univariate techniques for analysis of the relationship between the condition-dependent trait and individual quality variables. In this study, we correlated male blackbird bill colour (a likely carotenoid-dependent sexually selected trait) with body/condition variables that reflect male quality. We measured bill colour using photometric techniques, thus ensuring objectivity. The data were analysed using the multivariate statistical techniques of canonical ordination. Analyses based on reflectance spectra of male blackbird bill samples and colour components (i.e. hue, chroma and brightness) derived from the reflectance spectra were very similar. Analysing the entire reflectance spectra of blackbird bill samples with Redundancy Analysis (RDA) allowed examination of individual wavelengths and their specific associations with the body/condition variables. However, hue, chroma and brightness values also provided useful information to explain colour variation, and the two approaches may be complimentary. We did not find any significant associations between male blackbird bill colour and percent incidence of ectoparasites or cloaca size. However, both the colour component and full spectral analyses showed that culmen length explained a significant amount of variation in male blackbird bill colour. Culmen length was positively associated with greater reflectance from the bill samples at longer wavelengths and a higher hue value (i.e. more orange

  10. Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters

    PubMed Central

    Jeong, Soowoong; Kim, Guisik; Lee, Sangkeun

    2017-01-01

    Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved. PMID:28241475

  11. Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer's Disease.

    PubMed

    Hao, Xiaoke; Li, Chanxiu; Du, Lei; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L; Saykin, Andrew J; Shen, Li; Zhang, Daoqiang

    2017-03-14

    Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding.

  12. Correlated-imaging-based chosen plaintext attack on general cryptosystems composed of linear canonical transforms and phase encodings

    NASA Astrophysics Data System (ADS)

    Wu, Jingjing; Liu, Wei; Liu, Zhengjun; Liu, Shutian

    2015-03-01

    We introduce a chosen-plaintext attack scheme on general optical cryptosystems that use linear canonical transform and phase encoding based on correlated imaging. The plaintexts are chosen as Gaussian random real number matrixes, and the corresponding ciphertexts are regarded as prior knowledge of the proposed attack method. To establish the reconstruct of the secret plaintext, correlated imaging is employed using the known resources. Differing from the reported attack methods, there is no need to decipher the distribution of the decryption key. The original secret image can be directly recovered by the attack in the absence of decryption key. In addition, the improved cryptosystems combined with pixel scrambling operations are also vulnerable to the proposed attack method. Necessary mathematical derivations and numerical simulations are carried out to demonstrate the validity of the proposed attack scheme.

  13. Ensemble Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts

    NASA Technical Reports Server (NTRS)

    Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.

    2001-01-01

    This paper presents preliminary results of an ensemble canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.

  14. Using Canonical Correlation Analysis to Identify Environmental Attitude Groups: Considerations for National Forest Planning in the Southwestern U.S.

    NASA Astrophysics Data System (ADS)

    Prera, Alejandro J.; Grimsrud, Kristine M.; Thacher, Jennifer A.; McCollum, Dan W.; Berrens, Robert P.

    2014-10-01

    As public land management agencies pursue region-specific resource management plans, with meaningful consideration of public attitudes and values, there is a need to characterize the complex mix of environmental attitudes in a diverse population. The contribution of this investigation is to make use of a unique household, mail/internet survey data set collected in 2007 in the Southwestern United States (Region 3 of the U.S. Forest Service). With over 5,800 survey responses to a set of 25 Public Land Value statements, canonical correlation analysis is able to identify 7 statistically distinct environmental attitudinal groups. We also examine the effect of expected changes in regional demographics on overall environmental attitudes, which may help guide in the development of socially acceptable long-term forest management policies. Results show significant support for conservationist management policies and passive environmental values, as well as a greater role for stakeholder groups in generating consensus for current and future forest management policies.

  15. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface

    NASA Astrophysics Data System (ADS)

    Chen, Xiaogang; Wang, Yijun; Gao, Shangkai; Jung, Tzyy-Ping; Gao, Xiaorong

    2015-08-01

    Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ˜33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min-1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

  16. Power Series Approximation for the Correlation Kernel Leading to Kohn-Sham Methods Combining Accuracy, Computational Efficiency, and General Applicability

    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.

  17. Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis

    NASA Astrophysics Data System (ADS)

    Somers, Ben; Bertrand, Alexander

    2016-12-01

    Objective. Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. Approach. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. Main results. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Significance. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

  18. Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population

    PubMed Central

    Avants, Brian B.; Libon, David J.; Rascovsky, Katya; Boller, Ashley; McMillan, Corey T.; Massimo, Lauren; Coslett, H. Branch; Chatterjee, Anjan; Gross, Rachel G.; Grossman, Murray

    2014-01-01

    This study establishes that sparse canonical correlation analysis (SCCAN) identifies generalizable, structural MRI-derived cortical networks that relate to five distinct categories of cognition. We obtain multivariate psychometrics from the domain-specific sub-scales of the Philadelphia Brief Assessment of Cognition (PBAC). By using a training and separate testing stage, we find that PBAC-defined cognitive domains of language, visuospatial functioning, episodic memory, executive control, and social functioning correlate with unique and distributed areas of gray matter (GM). In contrast, a parallel univariate framework fails to identify, from the training data, regions that are also significant in the left-out test dataset. The cohort includes164 patients with Alzheimer’s disease, behavioral-variant frontotemporal dementia, semantic variant primary progressive aphasia, nonfluent/agrammatic primary progressive aphasia, or corticobasal syndrome. The analysis is implemented with open-source software for which we provide examples in the text. In conclusion, we show that multivariate techniques identify biologically-plausible brain regions supporting specific cognitive domains. The findings are identified in training data and confirmed in test data. PMID:24096125

  19. Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer.

    PubMed

    Lee, George; Singanamalli, Asha; Wang, Haibo; Feldman, Michael D; Master, Stephen R; Shih, Natalie N C; Spangler, Elaine; Rebbeck, Timothy; Tomaszewski, John E; Madabhushi, Anant

    2015-01-01

    In this work, we present a new methodology to facilitate prediction of recurrent prostate cancer (CaP) following radical prostatectomy (RP) via the integration of quantitative image features and protein expression in the excised prostate. Creating a fused predictor from high-dimensional data streams is challenging because the classifier must 1) account for the "curse of dimensionality" problem, which hinders classifier performance when the number of features exceeds the number of patient studies and 2) balance potential mismatches in the number of features across different channels to avoid classifier bias towards channels with more features. Our new data integration methodology, supervised Multi-view Canonical Correlation Analysis (sMVCCA), aims to integrate infinite views of highdimensional data to provide more amenable data representations for disease classification. Additionally, we demonstrate sMVCCA using Spearman's rank correlation which, unlike Pearson's correlation, can account for nonlinear correlations and outliers. Forty CaP patients with pathological Gleason scores 6-8 were considered for this study. 21 of these men revealed biochemical recurrence (BCR) following RP, while 19 did not. For each patient, 189 quantitative histomorphometric attributes and 650 protein expression levels were extracted from the primary tumor nodule. The fused histomorphometric/proteomic representation via sMVCCA combined with a random forest classifier predicted BCR with a mean AUC of 0.74 and a maximum AUC of 0.9286. We found sMVCCA to perform statistically significantly (p < 0.05) better than comparative state-of-the-art data fusion strategies for predicting BCR. Furthermore, Kaplan-Meier analysis demonstrated improved BCR-free survival prediction for the sMVCCA-fused classifier as compared to histology or proteomic features alone.

  20. An Investigation of the Relationship between the Fear of Receiving Negative Criticism and of Taking Academic Risk through Canonical Correlation Analysis

    ERIC Educational Resources Information Center

    Cetin, Bayram; Ilhan, Mustafa; Yilmaz, Ferat

    2014-01-01

    The aim of this study is to examine the relationship between the fear of receiving negative criticism and taking academic risk through canonical correlation analysis-in which a relational model was used. The participants of the study consisted of 215 university students enrolled in various programs at Dicle University's Ziya Gökalp Faculty of…

  1. Projection of summer precipitation over the Yangtze-Huaihe River basin using multimodel statistical downscaling based on canonical correlation analysis

    NASA Astrophysics Data System (ADS)

    Wu, Dan; Jiang, Zhihong; Ma, Tingting

    2016-12-01

    By using observational daily precipitation data over the Yangtze-Huaihe River basin, ERA-40 data, and the data from eight CMIP5 climate models, statistical downscaling models are constructed based on BP-CCA (combination of empirical orthogonal function and canonical correlation analysis) to project future changes of precipitation. The results show that the absolute values of domain-averaged precipitation relative errors of most models are reduced from 8%-46% to 1%-7% after statistical downscaling. The spatial correlations are all improved from less than 0.40 to more than 0.60. As a result of the statistical downscaling multimodel ensemble (SDMME), the relative error is improved from-15.8% to-1.3%, and the spatial correlation increases significantly from 0.46 to 0.88. These results demonstrate that the simulation skill of SDMME is relatively better than that of the multimodel ensemble (MME) and the downscaling of most individual models. The projections of SDMME reveal that under the RCP (Representative Concentration Pathway) 4.5 scenario, the projected domain-averaged precipitation changes for the early (2016-2035), middle (2046-2065), and late (2081-2100) 21st century are-1.8%, 6.1%, and 9.9%, respectively. For the early period, the increasing trends of precipitation in the western region are relatively weak, while the precipitation in the east shows a decreasing trend. Furthermore, the reliability of the projected changes over the area east of 115 ◦ E is higher than that in the west. The stations with significant increasing trends are primarily located over the western region in both the middle and late periods, with larger magnitude for the latter. Stations with high reliability mainly appear in the region north of 28.5 ◦ N for both periods.

  2. Structured and Sparse Canonical Correlation Analysis as a Brain-Wide Multi-Modal Data Fusion Approach.

    PubMed

    Mohammadi-Nejad, Ali-Reza; Hossein-Zadeh, Gholam-Ali; Soltanian-Zadeh, Hamid

    2017-03-14

    Multi-modal data fusion has recently emerged as a comprehensive neuroimaging analysis approach, which usually uses canonical correlation analysis (CCA). However, the current CCA-based fusion approaches face problems like high-dimensionality, multi-collinearity, unimodal feature selection, asymmetry, and loss of spatial information in reshaping the imaging data into vectors. This paper proposes a structured and sparse CCA (ssCCA) technique as a novel CCA method to overcome the above problems. To investigate the performance of the proposed algorithm, we have compared three data fusion techniques: standard CCA; regularized CCA; and ssCCA and evaluated their ability to detect multi-modal data associations. We have used simulations to compare the performance of these approaches and probe the effects of non-negativity constraint, the dimensionality of features, sample size, and noise power. The results demonstrate that ssCCA outperforms the existing standard and regularized CCA-based fusion approaches. We have also applied the methods to real functional magnetic resonance imaging (fMRI) and structural MRI data of Alzheimer's disease (AD) patients (����= 34) and healthy control (HC) subjects (����= 42) from the ADNI database. The results illustrate that the proposed unsupervised technique differentiates the transition pattern between the subject-course of AD patients and HC subjects with a p-value of less than 1×10 (-6) . Furthermore, we have depicted the brain mapping of functional areas that are most correlated with the anatomical changes in AD patients relative to HC subjects.

  3. Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data.

    PubMed

    Grellmann, Claudia; Bitzer, Sebastian; Neumann, Jane; Westlye, Lars T; Andreassen, Ole A; Villringer, Arno; Horstmann, Annette

    2015-02-15

    The standard analysis approach in neuroimaging genetics studies is the mass-univariate linear modeling (MULM) approach. From a statistical view, however, this approach is disadvantageous, as it is computationally intensive, cannot account for complex multivariate relationships, and has to be corrected for multiple testing. In contrast, multivariate methods offer the opportunity to include combined information from multiple variants to discover meaningful associations between genetic and brain imaging data. We assessed three multivariate techniques, partial least squares correlation (PLSC), sparse canonical correlation analysis (sparse CCA) and Bayesian inter-battery factor analysis (Bayesian IBFA), with respect to their ability to detect multivariate genotype-phenotype associations. Our goal was to systematically compare these three approaches with respect to their performance and to assess their suitability for high-dimensional and multi-collinearly dependent data as is the case in neuroimaging genetics studies. In a series of simulations using both linearly independent and multi-collinear data, we show that sparse CCA and PLSC are suitable even for very high-dimensional collinear imaging data sets. Among those two, the predictive power was higher for sparse CCA when voxel numbers were below 400 times sample size and candidate SNPs were considered. Accordingly, we recommend Sparse CCA for candidate phenotype, candidate SNP studies. When voxel numbers exceeded 500 times sample size, the predictive power was the highest for PLSC. Therefore, PLSC can be considered a promising technique for multivariate modeling of high-dimensional brain-SNP-associations. In contrast, Bayesian IBFA cannot be recommended, since additional post-processing steps were necessary to detect causal relations. To verify the applicability of sparse CCA and PLSC, we applied them to an experimental imaging genetics data set provided for us. Most importantly, application of both methods replicated

  4. Multi-set canonical correlation analysis for the fusion of concurrent single trial ERP and functional MRI.

    PubMed

    Correa, Nicolle M; Eichele, Tom; Adali, Tülay; Li, Yi-Ou; Calhoun, Vince D

    2010-05-01

    Functional magnetic resonance imaging (fMRI) data and electroencephalography (EEG) data provide complementary spatio-temporal information about brain function. Methods to couple the relative strengths of these modalities usually involve two stages: first forming a feature set from each dataset based on one criterion followed by exploration of connections among the features using a second criterion. We propose a data fusion method for simultaneously acquired fMRI and EEG data that combines these steps using a single criterion for finding the cross-modality associations and performing source separation. Using multi-set canonical correlation analysis (M-CCA), we obtain a decomposition of the two modalities, into spatial maps for fMRI data and a corresponding temporal evolution for EEG data, based on trial-to-trial covariation across the two modalities. Additionally, the analysis is performed on data from a group of subjects in order to make group inferences about the covariation across modalities. Being multivariate, the proposed method facilitates the study of brain connectivity along with localization of brain function. M-CCA can be easily extended to incorporate different data types and additional modalities. We demonstrate the promise of the proposed method in finding covarying trial-to-trial amplitude modulations (AMs) in an auditory task involving implicit pattern learning. The results show approximately linear decreasing trends in AMs for both modalities and the corresponding spatial activations occur mainly in motor, frontal, temporal, inferior parietal, and orbito-frontal areas that are linked both to sensory function as well as learning and expectation--all of which match activations related to the presented paradigm.

  5. Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 1. Theory and simple chemometric applications.

    PubMed

    Todeschini, R; Ballabio, D; Consonni, V; Manganaro, A; Mauri, A

    2009-08-19

    So far, similarity/diversity of objects has been widely studied in different research fields and a number of distance measures to estimate diversity between objects have been proposed. However, not much interest has been addressed to analysis of how diverse are configurations of objects in two different multivariate spaces. Since computerisation and automation nowadays lead to a large availability of information, it is apparent that a system could be described in different ways and, consequently, methods for comparison of the different viewpoints are required. These methods, for instance, may be usefully applied to Quantitative Structure-Activity Relationship (QSAR) studies. In this field, several thousands of molecular descriptors have been proposed in the literature and different selections of descriptors define different chemical spaces that need to be compared. Moreover, variable selection techniques such as Genetic Algorithms, Simulated Annealing, and Tabu Search are widely used to process available information in order to select optimal QSAR models. When more than one optimal model results, the problem arising is how to compare these models to find out whether they are really diverse or based on descriptors explaining almost the same information. In this paper, novel indices are proposed to measure similarity/diversity between pairs of data sets by the aid of the variable cross-correlation matrix.

  6. Quasi-local approximation of non-local exchange-correlation kernels in the adiabatic-connection fluctuation-dissipation theorem

    NASA Astrophysics Data System (ADS)

    Lu, Deyu

    The adiabatic-connection fluctuation-dissipation theorem (ACFDT) is a formal theoretical framework to treat van der Waals (vdW) dispersion interactions. Under the random phase approximation (RPA), it yields the correct asymptotic behavior at large distances, but the short-range correlation is overestimated. It has been demonstrated that non-local exchange-correlation kernels can systematically correct the errors of RPA for homogenous electron gas. However, direct extension of non-local kernels derived from the electron gas model to inhomogeneous systems raises several issues. In addition to the high computational expense, the non-local kernels worsen the rare gas dimer binding curve as compared to RPA. In this study, we propose a quasi-local approximation of the non-local kernel in order to address these issues. This research used resources of the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, at Brookhaven National Laboratory under Contract No. DE-SC0012704.

  7. Ab initio-driven nuclear energy density functional method. A proposal for safe/correlated/improvable parametrizations of the off-diagonal EDF kernels

    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.

  8. Multiple collaborative kernel tracking.

    PubMed

    Fan, Zhimin; Yang, Ming; Wu, Ying

    2007-07-01

    Those motion parameters that cannot be recovered from image measurements are unobservable in the visual dynamic system. This paper studies this important issue of singularity in the context of kernel-based tracking and presents a novel approach that is based on a motion field representation which employs redundant but sparsely correlated local motion parameters instead of compact but uncorrelated global ones. This approach makes it easy to design fully observable kernel-based motion estimators. This paper shows that these high-dimensional motion fields can be estimated efficiently by the collaboration among a set of simpler local kernel-based motion estimators, which makes the new approach very practical.

  9. Relativistic four-component calculations of indirect nuclear spin-spin couplings with efficient evaluation of the exchange-correlation response kernel

    SciTech Connect

    Křístková, Anežka; Malkin, Vladimir G.; Komorovsky, Stanislav; Repisky, Michal; Malkina, Olga L.

    2015-03-21

    In this work, we report on the development and implementation of a new scheme for efficient calculation of indirect nuclear spin-spin couplings in the framework of four-component matrix Dirac-Kohn-Sham approach termed matrix Dirac-Kohn-Sham restricted magnetic balance resolution of identity for J and K, which takes advantage of the previous restricted magnetic balance formalism and the density fitting approach for the rapid evaluation of density functional theory exchange-correlation response kernels. The new approach is aimed to speedup the bottleneck in the solution of the coupled perturbed equations: evaluation of the matrix elements of the kernel of the exchange-correlation potential. The performance of the new scheme has been tested on a representative set of indirect nuclear spin-spin couplings. The obtained results have been compared with the corresponding results of the reference method with traditional evaluation of the exchange-correlation kernel, i.e., without employing the fitted electron densities. Overall good agreement between both methods was observed, though the new approach tends to give values by about 4%-5% higher than the reference method. On the average, the solution of the coupled perturbed equations with the new scheme is about 8.5 times faster compared to the reference method.

  10. Relativistic four-component calculations of indirect nuclear spin-spin couplings with efficient evaluation of the exchange-correlation response kernel

    NASA Astrophysics Data System (ADS)

    Křístková, Anežka; Komorovsky, Stanislav; Repisky, Michal; Malkin, Vladimir G.; Malkina, Olga L.

    2015-03-01

    In this work, we report on the development and implementation of a new scheme for efficient calculation of indirect nuclear spin-spin couplings in the framework of four-component matrix Dirac-Kohn-Sham approach termed matrix Dirac-Kohn-Sham restricted magnetic balance resolution of identity for J and K, which takes advantage of the previous restricted magnetic balance formalism and the density fitting approach for the rapid evaluation of density functional theory exchange-correlation response kernels. The new approach is aimed to speedup the bottleneck in the solution of the coupled perturbed equations: evaluation of the matrix elements of the kernel of the exchange-correlation potential. The performance of the new scheme has been tested on a representative set of indirect nuclear spin-spin couplings. The obtained results have been compared with the corresponding results of the reference method with traditional evaluation of the exchange-correlation kernel, i.e., without employing the fitted electron densities. Overall good agreement between both methods was observed, though the new approach tends to give values by about 4%-5% higher than the reference method. On the average, the solution of the coupled perturbed equations with the new scheme is about 8.5 times faster compared to the reference method.

  11. Multiple Kernel Point Set Registration.

    PubMed

    Nguyen, Thanh Minh; Wu, Q M Jonathan

    2015-12-22

    The finite Gaussian mixture model with kernel correlation is a flexible tool that has recently received attention for point set registration. While there are many algorithms for point set registration presented in the literature, an important issue arising from these studies concerns the mapping of data with nonlinear relationships and the ability to select a suitable kernel. Kernel selection is crucial for effective point set registration. We focus here on multiple kernel point set registration. We make several contributions in this paper. First, each observation is modeled using the Student's t-distribution, which is heavily tailed and more robust than the Gaussian distribution. Second, by automatically adjusting the kernel weights, the proposed method allows us to prune the ineffective kernels. This makes the choice of kernels less crucial. After parameter learning, the kernel saliencies of the irrelevant kernels go to zero. Thus, the choice of kernels is less crucial and it is easy to include other kinds of kernels. Finally, we show empirically that our model outperforms state-of-the-art methods recently proposed in the literature.

  12. Multiple Kernel Point Set Registration.

    PubMed

    Nguyen, Thanh Minh; Wu, Q M Jonathan

    2016-06-01

    The finite Gaussian mixture model with kernel correlation is a flexible tool that has recently received attention for point set registration. While there are many algorithms for point set registration presented in the literature, an important issue arising from these studies concerns the mapping of data with nonlinear relationships and the ability to select a suitable kernel. Kernel selection is crucial for effective point set registration. We focus here on multiple kernel point set registration. We make several contributions in this paper. First, each observation is modeled using the Student's t-distribution, which is heavily tailed and more robust than the Gaussian distribution. Second, by automatically adjusting the kernel weights, the proposed method allows us to prune the ineffective kernels. This makes the choice of kernels less crucial. After parameter learning, the kernel saliencies of the irrelevant kernels go to zero. Thus, the choice of kernels is less crucial and it is easy to include other kinds of kernels. Finally, we show empirically that our model outperforms state-of-the-art methods recently proposed in the literature.

  13. Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease

    PubMed Central

    Hao, Xiaoke; Li, Chanxiu; Du, Lei; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L.; Saykin, Andrew J.; Shen, Li; Zhang, Daoqiang; Weiner, Michael W.; Aisen, Paul; Petersen, Ronald; Jack, Clifford R.; Mason, Sara S.; Albers, Colleen S.; Knopman, David; Johnson, Kris; Jagust, William; Trojanowki, John Q.; Toga, Arthur W.; Beckett, Laurel; Green, Robert C.; Farlow, Martin R.; Marie Hake, Ann; Matthews, Brandy R.; Brosch, Jared R.; Herring, Scott; Hunt, Cynthia; Shaw, Leslie M.; Ances, Beau; Morris, John C.; Carroll, Maria; Creech, Mary L.; Franklin, Erin; Mintun, Mark A.; Schneider, Stacy; Oliver, Angela; Kaye, Jeffrey; Quinn, Joseph; Silbert, Lisa; Lind, Betty; Carter, Raina; Dolen, Sara; Schneider, Lon S.; Pawluczyk, Sonia; Beccera, Mauricio; Teodoro, Liberty; Spann, Bryan M.; Brewer, James; Vanderswag, Helen; Fleisher, Adam; Tariot, Pierre; Burke, Anna; Trncic, Nadira; Reeder, Stephanie; Heidebrink, Judith L.; Lord, Joanne L.; Doody, Rachelle S.; Villanueva-Meyer, Javier; Chowdhury, Munir; Rountree, Susan; Dang, Mimi; Stern, Yaakov; Honig, Lawrence S.; Bell, Karen L.; Marson, Daniel; Griffith, Randall; Clark, David; Geldmacher, David; Brockington, John; Roberson, Erik; Love, Marissa Natelson; Grossman, Hillel; Mitsis, Effie; Shah, Raj C.; deToledo-Morrell, Leyla; Duara, Ranjan; Varon, Daniel; Greig, Maria T.; Roberts, Peggy; Albert, Marilyn; Onyike, Chiadi; D’Agostino, Daniel; Kielb, Stephanie; Galvin, James E.; Cerbone, Brittany; Michel, Christina A.; Pogorelec, Dana M.; Rusinek, Henry; de Leon, Mony J.; Glodzik, Lidia; De Santi, Susan; Doraiswamy, P. Murali; Petrella, Jeffrey R.; Borges-Neto, Salvador; Wong, Terence Z.; Coleman, Edward; Smith, Charles D.; Jicha, Greg; Hardy, Peter; Sinha, Partha; Oates, Elizabeth; Conrad, Gary; Porsteinsson, Anton P.; Goldstein, Bonnie S.; Martin, Kim; Makino, Kelly M.; Ismail, M. Saleem; Brand, Connie; Mulnard, Ruth A.; Thai, Gaby; Mc-Adams-Ortiz, Catherine; Womack, Kyle; Mathews, Dana; Quiceno, Mary; Levey, Allan I.; Lah, James J.; Cellar, Janet S.; Burns, Jeffrey M.; Swerdlow, Russell H.; Brooks, William M.; Apostolova, Liana; Tingus, Kathleen; Woo, Ellen; Silverman, Daniel H. S.; Lu, Po H.; Bartzokis, George; Graff-Radford, Neill R.; Parfitt, Francine; Kendall, Tracy; Johnson, Heather; van Dyck, Christopher H.; Carson, Richard E.; MacAvoy, Martha G.; Varma, Pradeep; Chertkow, Howard; Bergman, Howard; Hosein, Chris; Black, Sandra; Stefanovic, Bojana; Caldwell, Curtis; Hsiung, Ging-Yuek Robin; Feldman, Howard; Mudge, Benita; Assaly, Michele; Finger, Elizabeth; Pasternack, Stephen; Rachisky, Irina; Trost, Dick; Kertesz, Andrew; Bernick, Charles; Munic, Donna; Mesulam, Marek-Marsel; Lipowski, Kristine; Weintraub, Sandra; Bonakdarpour, Borna; Kerwin, Diana; Wu, Chuang-Kuo; Johnson, Nancy; Sadowsky, Carl; Villena, Teresa; Turner, Raymond Scott; Johnson, Kathleen; Reynolds, Brigid; Sperling, Reisa A.; Johnson, Keith A.; Marshall, Gad; Yesavage, Jerome; Taylor, Joy L.; Lane, Barton; Rosen, Allyson; Tinklenberg, Jared; Sabbagh, Marwan N.; Belden, Christine M.; Jacobson, Sandra A.; Sirrel, Sherye A.; Kowall, Neil; Killiany, Ronald; Budson, Andrew E.; Norbash, Alexander; Johnson, Patricia Lynn; Obisesan, Thomas O.; Wolday, Saba; Allard, Joanne; Lerner, Alan; Ogrocki, Paula; Tatsuoka, Curtis; Fatica, Parianne; Fletcher, Evan; Maillard, Pauline; Olichney, John; DeCarli, Charles; Carmichael, Owen; Kittur, Smita; Borrie, Michael; Lee, T.-Y.; Bartha, Rob; Johnson, Sterling; Asthana, Sanjay; Carlsson, Cynthia M.; Potkin, Steven G.; Preda, Adrian; Nguyen, Dana; Bates, Vernice; Capote, Horacio; Rainka, Michelle; Scharre, Douglas W.; Kataki, Maria; Adeli, Anahita; Zimmerman, Earl A.; Celmins, Dzintra; Brown, Alice D.; Pearlson, Godfrey D.; Blank, Karen; Anderson, Karen; Flashman, Laura A.; Seltzer, Marc; Hynes, Mary L.; Santulli, Robert B.; Sink, Kaycee M.; Gordineer, Leslie; Williamson, Jeff D.; Garg, Pradeep; Watkins, Franklin; Ott, Brian R.; Querfurth, Henry; Tremont, Geoffrey; Salloway, Stephen; Malloy, Paul; Correia, Stephen; Rosen, Howard J.; Miller, Bruce L.; Perry, David; Mintzer, Jacobo; Spicer, Kenneth; Bachman, David; Pomara, Nunzio; Hernando, Raymundo; Sarrael, Antero; Relkin, Norman; Chaing, Gloria; Lin, Michael; Ravdin, Lisa; Smith, Amanda; Raj, Balebail Ashok; Fargher, Kristin

    2017-01-01

    Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding. PMID:28291242

  14. 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.

  15. Diversity of maize kernels from a breeding program for protein quality: II. Correlatively expressed functional amino acids

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Modern maize breeding and selection for large starchy kernels may have contributed to reduced contents of essential amino acids which represents a serious nutritional problem for humans and animals. The improvement of low levels of essential amino acids, while maintaining high protein content and ha...

  16. An approach for generating trajectory-based dynamics which conserves the canonical distribution in the phase space formulation of quantum mechanics. II. Thermal correlation functions

    SciTech Connect

    Liu Jian; Miller, William H.

    2011-03-14

    We show the exact expression of the quantum mechanical time correlation function in the phase space formulation of quantum mechanics. The trajectory-based dynamics that conserves the quantum canonical distribution-equilibrium Liouville dynamics (ELD) proposed in Paper I is then used to approximately evaluate the exact expression. It gives exact thermal correlation functions (of even nonlinear operators, i.e., nonlinear functions of position or momentum operators) in the classical, high temperature, and harmonic limits. Various methods have been presented for the implementation of ELD. Numerical tests of the ELD approach in the Wigner or Husimi phase space have been made for a harmonic oscillator and two strongly anharmonic model problems, for each potential autocorrelation functions of both linear and nonlinear operators have been calculated. It suggests ELD can be a potentially useful approach for describing quantum effects for complex systems in condense phase.

  17. An approach for generating trajectory-based dynamics which conserves the canonical distribution in the phase space formulation of quantum mechanics. II. Thermal correlation functions

    NASA Astrophysics Data System (ADS)

    Liu, Jian; Miller, William H.

    2011-03-01

    We show the exact expression of the quantum mechanical time correlation function in the phase space formulation of quantum mechanics. The trajectory-based dynamics that conserves the quantum canonical distribution-equilibrium Liouville dynamics (ELD) proposed in Paper I is then used to approximately evaluate the exact expression. It gives exact thermal correlation functions (of even nonlinear operators, i.e., nonlinear functions of position or momentum operators) in the classical, high temperature, and harmonic limits. Various methods have been presented for the implementation of ELD. Numerical tests of the ELD approach in the Wigner or Husimi phase space have been made for a harmonic oscillator and two strongly anharmonic model problems, for each potential autocorrelation functions of both linear and nonlinear operators have been calculated. It suggests ELD can be a potentially useful approach for describing quantum effects for complex systems in condense phase.

  18. Heavy metals relationship with water and size-fractionated sediments in rivers using canonical correlation analysis (CCA) case study, rivers of south western Caspian Sea.

    PubMed

    Vosoogh, Ali; Saeedi, Mohsen; Lak, Raziyeh

    2016-11-01

    Some pollutants can qualitatively affect aquatic freshwater such as rivers, and heavy metals are one of the most important pollutants in aquatic fresh waters. Heavy metals can be found in the form of components dissolved in these waters or in compounds with suspended particles and surface sediments. It can be said that heavy metals are in equilibrium between water and sediment. In this study, the amount of heavy metals is determined in water and different sizes of sediment. To obtain the relationship between heavy metals in water and size-fractionated sediments, a canonical correlation analysis (CCA) was utilized in rivers of the southwestern Caspian Sea. In this research, a case study was carried out on 18 sampling stations in nine rivers. In the first step, the concentrations of heavy metals (Cu, Zn, Cr, Fe, Mn, Pb, Ni, and Cd) were determined in water and size-fractionated sediment samples. Water sampling sites were classified by hierarchical cluster analysis (HCA) utilizing squared Euclidean distance with Ward's method. In addition, for interpreting the obtained results and the relationships between the concentration of heavy metals in the tested river water and sample sediments, canonical correlation analysis (CCA) was utilized. The rivers were grouped into two classes (those having no pollution and those having low pollution) based on the HCA results obtained for river water samples. CCA results found numerous relationships between rivers in Iran's Guilan province and their size-fractionated sediments samples. The heavy metals of sediments with 0.038 to 0.125 mm size in diameter are slightly correlated with those of water samples.

  19. 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.

  20. Combining canonical correlation analysis and infinite reference for frequency recognition of steady-state visual evoked potential recordings: a comparison with periodogram method.

    PubMed

    Tian, Yin; Li, Fali; Xu, Peng; Yuan, Zhen; Zhao, Dechun; Zhang, Haiyong

    2014-01-01

    Steady-state visual evoked potentials (SSVEP) are the visual system responses to a repetitive visual stimulus flickering with the constant frequency and of great importance in the study of brain activity using scalp electroencephalography (EEG) recordings. However, the reference influence for the investigation of SSVEP is generally not considered in previous work. In this study a new approach that combined the canonical correlation analysis with infinite reference (ICCA) was proposed to enhance the accuracy of frequency recognition of SSVEP recordings. Compared with the widely used periodogram method (PM), ICCA is able to achieve higher recognition accuracy when extracts frequency within a short span. Further, the recognition results suggested that ICCA is a very robust tool to study the brain computer interface (BCI) based on SSVEP.

  1. Improving the accuracy of ground-state correlation energies within a plane-wave basis set: The electron-hole exchange kernel

    NASA Astrophysics Data System (ADS)

    Dixit, Anant; Ángyán, János G.; Rocca, Dario

    2016-09-01

    A new formalism was recently proposed to improve random phase approximation (RPA) correlation energies by including approximate exchange effects [B. Mussard et al., J. Chem. Theory Comput. 12, 2191 (2016)]. Within this framework, by keeping only the electron-hole contributions to the exchange kernel, two approximations can be obtained: An adiabatic connection analog of the second order screened exchange (AC-SOSEX) and an approximate electron-hole time-dependent Hartree-Fock (eh-TDHF). Here we show how this formalism is suitable for an efficient implementation within the plane-wave basis set. The response functions involved in the AC-SOSEX and eh-TDHF equations can indeed be compactly represented by an auxiliary basis set obtained from the diagonalization of an approximate dielectric matrix. Additionally, the explicit calculation of unoccupied states can be avoided by using density functional perturbation theory techniques and the matrix elements of dynamical response functions can be efficiently computed by applying the Lanczos algorithm. As shown by several applications to reaction energies and weakly bound dimers, the inclusion of the electron-hole kernel significantly improves the accuracy of ground-state correlation energies with respect to RPA and semi-local functionals.

  2. Discrimination of adulterated milk based on two-dimensional correlation spectroscopy (2D-COS) combined with kernel orthogonal projection to latent structure (K-OPLS).

    PubMed

    Yang, Renjie; Liu, Rong; Xu, Kexin; Yang, Yanrong

    2013-12-01

    A new method for discrimination analysis of adulterated milk and pure milk is proposed by combining two-dimensional correlation spectroscopy (2D-COS) with kernel orthogonal projection to latent structure (K-OPLS). Three adulteration types of milk with urea, melamine, and glucose were prepared, respectively. The synchronous 2D spectra of adulterated milk and pure milk samples were calculated. Based on the characteristics of 2D correlation spectra of adulterated milk and pure milk, a discriminant model of urea-tainted milk, melamine-tainted milk, glucose-tainted milk, and pure milk was built by K-OPLS. The classification accuracy rates of unknown samples were 85.7, 92.3, 100, and 87.5%, respectively. The results show that this method has great potential in the rapid discrimination analysis of adulterated milk and pure milk.

  3. A Technology Integration Education (TIE) Model for Millennial Preservice Teachers: Exploring the Canonical Correlation Relationships among Attitudes, Subjective Norms, Perceived Behavioral Controls, Motivation, and Technological, Pedagogical, and Content Knowledge (TPACK) Competencies

    ERIC Educational Resources Information Center

    Holland, Denise D.; Piper, Randy T.

    2016-01-01

    Intellectual goods can follow the same pattern as physical goods with the product life cycle of birth, growth, maturity, and decline. For the intellectual good of technological, pedagogical, and content knowledge (TPACK), its birth began with Shulman (1986, 1987). Canonical correlation analysis (CCA) was used to test the relationships among five…

  4. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

    PubMed Central

    Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T.; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

    2016-01-01

    Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153689

  5. Weighted Bergman kernels and virtual Bergman kernels

    NASA Astrophysics Data System (ADS)

    Roos, Guy

    2005-12-01

    We introduce the notion of "virtual Bergman kernel" and apply it to the computation of the Bergman kernel of "domains inflated by Hermitian balls", in particular when the base domain is a bounded symmetric domain.

  6. [Canon Busting and Cultural Literacy.

    ERIC Educational Resources Information Center

    National Forum: Phi Kappa Phi Journal, 1989

    1989-01-01

    Articles on literary canon include: "Educational Anomie" (Stephen W. White); "Why Western Civilization?" (William J. Bennett); "Peace Plan for Canon Wars" (Gerald Graff, William E. Cain); "Canons, Cultural Literacy, and Core Curriculum" (Lynne V. Cheney); "Canon Busting: Basic Issues" (Stanley…

  7. The role of the exchange-correlation response kernel and scaling corrections in relativistic density functional nuclear magnetic shielding calculations with the zeroth-order regular approximation

    NASA Astrophysics Data System (ADS)

    Autschbach, Jochen

    2013-09-01

    The relativistic NMR module of the Amsterdam Density Functional (ADF) package, which is frequently utilised in studies of heavy atom NMR chemical shifts, is extended to calculate a hitherto neglected term from the response of the exchange-correlation (XC) potential. The term vanishes in the absence of spin-orbit coupling. Further, corrections to the shielding arising from scaling factors in the zeroth-order regular approximation (zora) relativistic framework are investigated. The XC response markedly improves calculated proton chemical shifts for hydrogen halides. Mercury chemical shifts for mercury dihalides are also noticeably altered. Contributions from density-gradient dependent terms in the response kernel contribute about 30-40%. New fully relativistic density functional theory (DFT) benchmark data are compared with zora and literature reference values. In line with previous work, it is found that absolute shielding constants for Hg are not accurately predicted with zora. However, chemical shifts agree well with fully relativistic calculations. The application of 'scaled-zora' scaling factors deteriorates the shielding constants and is therefore not recommended. The scaling hardly affects chemical shifts. zora calculations are not suitable for absolute shielding of heavy atoms but they can be used safely for chemical shifts in most application scenarios.

  8. Analysis of maize (Zea mays) kernel density and volume using micro-computed tomography and single-kernel near infrared spectroscopy

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Maize kernel density impacts milling quality of the grain due to kernel hardness. Harder kernels are correlated with higher test weight and are more resistant to breakage during harvest and transport. Softer kernels, in addition to being susceptible to mechanical damage, are also prone to pathogen ...

  9. Analysis of maize ( Zea mays ) kernel density and volume using microcomputed tomography and single-kernel near-infrared spectroscopy.

    PubMed

    Gustin, Jeffery L; Jackson, Sean; Williams, Chekeria; Patel, Anokhee; Armstrong, Paul; Peter, Gary F; Settles, A Mark

    2013-11-20

    Maize kernel density affects milling quality of the grain. Kernel density of bulk samples can be predicted by near-infrared reflectance (NIR) spectroscopy, but no accurate method to measure individual kernel density has been reported. This study demonstrates that individual kernel density and volume are accurately measured using X-ray microcomputed tomography (μCT). Kernel density was significantly correlated with kernel volume, air space within the kernel, and protein content. Embryo density and volume did not influence overall kernel density. Partial least-squares (PLS) regression of μCT traits with single-kernel NIR spectra gave stable predictive models for kernel density (R(2) = 0.78, SEP = 0.034 g/cm(3)) and volume (R(2) = 0.86, SEP = 2.88 cm(3)). Density and volume predictions were accurate for data collected over 10 months based on kernel weights calculated from predicted density and volume (R(2) = 0.83, SEP = 24.78 mg). Kernel density was significantly correlated with bulk test weight (r = 0.80), suggesting that selection of dense kernels can translate to improved agronomic performance.

  10. Preliminary Results of Autotuning GEMM Kernels for the NVIDIA Kepler Architecture- GeForce GTX 680

    SciTech Connect

    Kurzak, Jakub; Luszczek, Pitor; Tomov, Stanimire; Dongarra, Jack

    2012-04-01

    Kepler is the newest GPU architecture from NVIDIA, and the GTX 680 is the first commercially available graphics card based on that architecture. Matrix multiplication is a canonical computational kernel, and often the main target of initial optimization efforts for a new chip. This article presents preliminary results of automatically tuning matrix multiplication kernels for the Kepler architecture using the GTX 680 card.

  11. Relationship between cyanogenic compounds in kernels, leaves, and roots of sweet and bitter kernelled almonds.

    PubMed

    Dicenta, F; Martínez-Gómez, P; Grané, N; Martín, M L; León, A; Cánovas, J A; Berenguer, V

    2002-03-27

    The relationship between the levels of cyanogenic compounds (amygdalin and prunasin) in kernels, leaves, and roots of 5 sweet-, 5 slightly bitter-, and 5 bitter-kernelled almond trees was determined. Variability was observed among the genotypes for these compounds. Prunasin was found only in the vegetative part (roots and leaves) for all genotypes tested. Amygdalin was detected only in the kernels, mainly in bitter genotypes. In general, bitter-kernelled genotypes had higher levels of prunasin in their roots than nonbitter ones, but the correlation between cyanogenic compounds in the different parts of plants was not high. While prunasin seems to be present in most almond roots (with a variable concentration) only bitter-kernelled genotypes are able to transform it into amygdalin in the kernel. Breeding for prunasin-based resistance to the buprestid beetle Capnodis tenebrionis L. is discussed.

  12. Semisupervised kernel matrix learning by kernel propagation.

    PubMed

    Hu, Enliang; Chen, Songcan; Zhang, Daoqiang; Yin, Xuesong

    2010-11-01

    The goal of semisupervised kernel matrix learning (SS-KML) is to learn a kernel matrix on all the given samples on which just a little supervised information, such as class label or pairwise constraint, is provided. Despite extensive research, the performance of SS-KML still leaves some space for improvement in terms of effectiveness and efficiency. For example, a recent pairwise constraints propagation (PCP) algorithm has formulated SS-KML into a semidefinite programming (SDP) problem, but its computation is very expensive, which undoubtedly restricts PCPs scalability in practice. In this paper, a novel algorithm, called kernel propagation (KP), is proposed to improve the comprehensive performance in SS-KML. The main idea of KP is first to learn a small-sized sub-kernel matrix (named seed-kernel matrix) and then propagate it into a larger-sized full-kernel matrix. Specifically, the implementation of KP consists of three stages: 1) separate the supervised sample (sub)set X(l) from the full sample set X; 2) learn a seed-kernel matrix on X(l) through solving a small-scale SDP problem; and 3) propagate the learnt seed-kernel matrix into a full-kernel matrix on X . Furthermore, following the idea in KP, we naturally develop two conveniently realizable out-of-sample extensions for KML: one is batch-style extension, and the other is online-style extension. The experiments demonstrate that KP is encouraging in both effectiveness and efficiency compared with three state-of-the-art algorithms and its related out-of-sample extensions are promising too.

  13. Approximate kernel competitive learning.

    PubMed

    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.

  14. Relations between canonical and non-canonical inflation

    SciTech Connect

    Gwyn, Rhiannon; Rummel, Markus; Westphal, Alexander E-mail: markus.rummel@physics.ox.ac.uk

    2013-12-01

    We look for potential observational degeneracies between canonical and non-canonical models of inflation of a single field φ. Non-canonical inflationary models are characterized by higher than linear powers of the standard kinetic term X in the effective Lagrangian p(X,φ) and arise for instance in the context of the Dirac-Born-Infeld (DBI) action in string theory. An on-shell transformation is introduced that transforms non-canonical inflationary theories to theories with a canonical kinetic term. The 2-point function observables of the original non-canonical theory and its canonical transform are found to match in the case of DBI inflation.

  15. Optimized Kernel Entropy Components.

    PubMed

    Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau

    2016-02-25

    This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

  16. Giant spin-orbit effects on (1)H and (13)C NMR shifts for uranium(vi) complexes revisited: role of the exchange-correlation response kernel, bonding analyses, and new predictions.

    PubMed

    Greif, Anja H; Hrobárik, Peter; Autschbach, Jochen; Kaupp, Martin

    2016-11-09

    Previous relativistic quantum-chemical predictions of unusually large (1)H and (13)C NMR chemical shifts for ligand atoms directly bonded to a diamagnetic uranium(vi) center (P. Hrobárik, V. Hrobáriková, A. H. Greif and M. Kaupp, Angew. Chem., Int. Ed., 2012, 51, 10884) have been revisited by two- and four-component relativistic density functional methods. In particular, the effect of the exchange-correlation response kernel, which had been missing in the previously used two-component version of the Amsterdam Density Functional program, has been examined. Kernel contributions are large for cases with large spin-orbit (SO) contributions to the NMR shifts and may amount to up to ∼30% of the total shifts, which means more than a 50 ppm difference for the metal-bonded carbon shifts in some extreme cases. Previous calculations with a PBE-40HF functional had provided overall reasonable predictions, due to cancellation of errors between the missing kernel contributions and the enhanced exact-exchange (EXX) admixture of 40%. In the presence of an exchange-correlation kernel, functionals with lower EXX admixtures give already good agreement with experiments, and the PBE0 functional provides reasonable predictive quality. Most importantly, the revised approach still predicts unprecedented giant (1)H NMR shifts between +30 ppm and more than +200 ppm for uranium(vi) hydride species. We also predict uranium-bonded (13)C NMR shifts for some synthetically known organometallic U(vi) complexes, for which no corresponding signals have been detected to date. In several cases, the experimental lack of these signals may be attributed to unexpected spectral regions in which some of the (13)C NMR shifts can appear, sometimes beyond the usual measurement area. An extremely large uranium-bonded (13)C shift above 550 ppm, near the upper end of the diamagnetic (13)C shift range, is predicted for a known pincer carbene complex. Bonding analyses allow in particular the magnitude of the SO

  17. Iterative software kernels

    SciTech Connect

    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`.

  18. Learning with Box Kernels.

    PubMed

    Melacci, Stefano; Gori, Marco

    2013-04-12

    Supervised examples and prior knowledge on regions of the input space have been profitably integrated in kernel machines to improve the performance of classifiers in different real-world contexts. The proposed solutions, which rely on the unified supervision of points and sets, have been mostly based on specific optimization schemes in which, as usual, the kernel function operates on points only. In this paper, arguments from variational calculus are used to support the choice of a special class of kernels, referred to as box kernels, which emerges directly from the choice of the kernel function associated with a regularization operator. It is proven that there is no need to search for kernels to incorporate the structure deriving from the supervision of regions of the input space, since the optimal kernel arises as a consequence of the chosen regularization operator. Although most of the given results hold for sets, we focus attention on boxes, whose labeling is associated with their propositional description. Based on different assumptions, some representer theorems are given which dictate the structure of the solution in terms of box kernel expansion. Successful results are given for problems of medical diagnosis, image, and text categorization.

  19. Learning with box kernels.

    PubMed

    Melacci, Stefano; Gori, Marco

    2013-11-01

    Supervised examples and prior knowledge on regions of the input space have been profitably integrated in kernel machines to improve the performance of classifiers in different real-world contexts. The proposed solutions, which rely on the unified supervision of points and sets, have been mostly based on specific optimization schemes in which, as usual, the kernel function operates on points only. In this paper, arguments from variational calculus are used to support the choice of a special class of kernels, referred to as box kernels, which emerges directly from the choice of the kernel function associated with a regularization operator. It is proven that there is no need to search for kernels to incorporate the structure deriving from the supervision of regions of the input space, because the optimal kernel arises as a consequence of the chosen regularization operator. Although most of the given results hold for sets, we focus attention on boxes, whose labeling is associated with their propositional description. Based on different assumptions, some representer theorems are given that dictate the structure of the solution in terms of box kernel expansion. Successful results are given for problems of medical diagnosis, image, and text categorization.

  20. Influence of wheat kernel physical properties on the pulverizing process.

    PubMed

    Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula

    2014-10-01

    The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p < 0.05) were found between wheat kernel physical properties and pulverizing process of wheat kernel, especially wheat kernel hardness index (obtained on the basis of Single Kernel Characterization System) and vitreousness significantly and positively correlated with the grinding energy indices and the mass fraction of coarse particles (> 0.5 mm). Among the kernel mechanical properties determined on the basis of uniaxial compression test only the rapture force was correlated with the impact grinding results. The results showed also positive and significant relationships between kernel ash content and grinding energy requirements. On the basis of wheat physical properties the multiple linear regression was proposed for predicting the average particle size of pulverized kernel.

  1. Kernel Affine Projection Algorithms

    NASA Astrophysics Data System (ADS)

    Liu, Weifeng; Príncipe, José C.

    2008-12-01

    The combination of the famed kernel trick and affine projection algorithms (APAs) yields powerful nonlinear extensions, named collectively here, KAPA. This paper is a follow-up study of the recently introduced kernel least-mean-square algorithm (KLMS). KAPA inherits the simplicity and online nature of KLMS while reducing its gradient noise, boosting performance. More interestingly, it provides a unifying model for several neural network techniques, including kernel least-mean-square algorithms, kernel adaline, sliding-window kernel recursive-least squares (KRLS), and regularization networks. Therefore, many insights can be gained into the basic relations among them and the tradeoff between computation complexity and performance. Several simulations illustrate its wide applicability.

  2. Canonical and Non-canonical Reelin Signaling

    PubMed Central

    Bock, Hans H.; May, Petra

    2016-01-01

    Reelin is a large secreted glycoprotein that is essential for correct neuronal positioning during neurodevelopment and is important for synaptic plasticity in the mature brain. Moreover, Reelin is expressed in many extraneuronal tissues; yet the roles of peripheral Reelin are largely unknown. In the brain, many of Reelin’s functions are mediated by a molecular signaling cascade that involves two lipoprotein receptors, apolipoprotein E receptor-2 (Apoer2) and very low density-lipoprotein receptor (Vldlr), the neuronal phosphoprotein Disabled-1 (Dab1), and members of the Src family of protein tyrosine kinases as crucial elements. This core signaling pathway in turn modulates the activity of adaptor proteins and downstream protein kinase cascades, many of which target the neuronal cytoskeleton. However, additional Reelin-binding receptors have been postulated or described, either as coreceptors that are essential for the activation of the “canonical” Reelin signaling cascade involving Apoer2/Vldlr and Dab1, or as receptors that activate alternative or additional signaling pathways. Here we will give an overview of canonical and alternative Reelin signaling pathways, molecular mechanisms involved, and their potential physiological roles in the context of different biological settings. PMID:27445693

  3. Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

    PubMed

    Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong

    2014-01-01

    Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

  4. Canonical fluid thermodynamics

    NASA Technical Reports Server (NTRS)

    Schmid, L. A.

    1972-01-01

    The space-time integral of the thermodynamic pressure plays the role of the thermodynamic potential for compressible, adiabatic flow in the sense that the pressure integral for stable flow is less than for all slightly different flows. This stability criterion can be converted into a variational minimum principle by requiring the molar free-enthalpy and the temperature, which are the arguments of the pressure function, to be generalized velocities, that is, the proper-time derivatives of scalar spare-time functions which are generalized coordinates in the canonical formalism. In a fluid context, proper-time differentiation must be expressed in terms of three independent quantities that specify the fluid velocity. This can be done in several ways, all of which lead to different variants (canonical transformations) of the same constraint-free action integral whose Euler-Lagrange equations are just the well-known equations of motion for adiabatic compressible flow.

  5. Canonical gravity with fermions

    SciTech Connect

    Bojowald, Martin; Das, Rupam

    2008-09-15

    Canonical gravity in real Ashtekar-Barbero variables is generalized to allow for fermionic matter. The resulting torsion changes several expressions in Holst's original vacuum analysis, which are summarized here. This in turn requires adaptations to the known loop quantization of gravity coupled to fermions, which is discussed on the basis of the classical analysis. As a result, parity invariance is not manifestly realized in loop quantum gravity.

  6. Canonical Transformations of Kepler Trajectories

    ERIC Educational Resources Information Center

    Mostowski, Jan

    2010-01-01

    In this paper, canonical transformations generated by constants of motion in the case of the Kepler problem are discussed. It is shown that canonical transformations generated by angular momentum are rotations of the trajectory. Particular attention is paid to canonical transformations generated by the Runge-Lenz vector. It is shown that these…

  7. [Canon Busting and Cultural Literacy.

    ERIC Educational Resources Information Center

    National Forum: Phi Kappa Phi Journal, 1989

    1989-01-01

    Articles on the literary canon include: "Contingencies of Value" (Barbara Herrnstein Smith); "Canon Fodder, the Cultural Hustle, and the Minotaur" (R. T. Smith); "Curriculum Battles and Global Politics" (Betty Jean Craige); "The Feminist Challenge to the Canon" (Elizabeth Fox-Genovese); and "Education…

  8. Robotic Intelligence Kernel: Communications

    SciTech Connect

    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.

  9. Robotic Intelligence Kernel: Driver

    SciTech Connect

    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.

  10. Matrix product purifications for canonical ensembles and quantum number distributions

    NASA Astrophysics Data System (ADS)

    Barthel, Thomas

    2016-09-01

    Matrix product purifications (MPPs) are a very efficient tool for the simulation of strongly correlated quantum many-body systems at finite temperatures. When a system features symmetries, these can be used to reduce computation costs substantially. It is straightforward to compute an MPP of a grand-canonical ensemble, also when symmetries are exploited. This paper provides and demonstrates methods for the efficient computation of MPPs of canonical ensembles under utilization of symmetries. Furthermore, we present a scheme for the evaluation of global quantum number distributions using matrix product density operators (MPDOs). We provide exact matrix product representations for canonical infinite-temperature states, and discuss how they can be constructed alternatively by applying matrix product operators to vacuum-type states or by using entangler Hamiltonians. A demonstration of the techniques for Heisenberg spin-1 /2 chains explains why the difference in the energy densities of canonical and grand-canonical ensembles decays as 1 /L .

  11. UNICOS Kernel Internals Application Development

    NASA Technical Reports Server (NTRS)

    Caredo, Nicholas; Craw, James M. (Technical Monitor)

    1995-01-01

    Having an understanding of UNICOS Kernel Internals is valuable information. However, having the knowledge is only half the value. The second half comes with knowing how to use this information and apply it to the development of tools. The kernel contains vast amounts of useful information that can be utilized. This paper discusses the intricacies of developing utilities that utilize kernel information. In addition, algorithms, logic, and code will be discussed for accessing kernel information. Code segments will be provided that demonstrate how to locate and read kernel structures. Types of applications that can utilize kernel information will also be discussed.

  12. Kernel mucking in top

    SciTech Connect

    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.

  13. Kernel Manifold Alignment for Domain Adaptation.

    PubMed

    Tuia, Devis; Camps-Valls, Gustau

    2016-01-01

    The wealth of sensory data coming from different modalities has opened numerous opportunities for data analysis. The data are of increasing volume, complexity and dimensionality, thus calling for new methodological innovations towards multimodal data processing. However, multimodal architectures must rely on models able to adapt to changes in the data distribution. Differences in the density functions can be due to changes in acquisition conditions (pose, illumination), sensors characteristics (number of channels, resolution) or different views (e.g. street level vs. aerial views of a same building). We call these different acquisition modes domains, and refer to the adaptation problem as domain adaptation. In this paper, instead of adapting the trained models themselves, we alternatively focus on finding mappings of the data sources into a common, semantically meaningful, representation domain. This field of manifold alignment extends traditional techniques in statistics such as canonical correlation analysis (CCA) to deal with nonlinear adaptation and possibly non-corresponding data pairs between the domains. We introduce a kernel method for manifold alignment (KEMA) that can match an arbitrary number of data sources without needing corresponding pairs, just few labeled examples in all domains. KEMA has interesting properties: 1) it generalizes other manifold alignment methods, 2) it can align manifolds of very different complexities, performing a discriminative alignment preserving each manifold inner structure, 3) it can define a domain-specific metric to cope with multimodal specificities, 4) it can align data spaces of different dimensionality, 5) it is robust to strong nonlinear feature deformations, and 6) it is closed-form invertible, which allows transfer across-domains and data synthesis. To authors' knowledge this is the first method addressing all these important issues at once. We also present a reduced-rank version of KEMA for computational

  14. Kernel Manifold Alignment for Domain Adaptation

    PubMed Central

    Tuia, Devis; Camps-Valls, Gustau

    2016-01-01

    The wealth of sensory data coming from different modalities has opened numerous opportunities for data analysis. The data are of increasing volume, complexity and dimensionality, thus calling for new methodological innovations towards multimodal data processing. However, multimodal architectures must rely on models able to adapt to changes in the data distribution. Differences in the density functions can be due to changes in acquisition conditions (pose, illumination), sensors characteristics (number of channels, resolution) or different views (e.g. street level vs. aerial views of a same building). We call these different acquisition modes domains, and refer to the adaptation problem as domain adaptation. In this paper, instead of adapting the trained models themselves, we alternatively focus on finding mappings of the data sources into a common, semantically meaningful, representation domain. This field of manifold alignment extends traditional techniques in statistics such as canonical correlation analysis (CCA) to deal with nonlinear adaptation and possibly non-corresponding data pairs between the domains. We introduce a kernel method for manifold alignment (KEMA) that can match an arbitrary number of data sources without needing corresponding pairs, just few labeled examples in all domains. KEMA has interesting properties: 1) it generalizes other manifold alignment methods, 2) it can align manifolds of very different complexities, performing a discriminative alignment preserving each manifold inner structure, 3) it can define a domain-specific metric to cope with multimodal specificities, 4) it can align data spaces of different dimensionality, 5) it is robust to strong nonlinear feature deformations, and 6) it is closed-form invertible, which allows transfer across-domains and data synthesis. To authors’ knowledge this is the first method addressing all these important issues at once. We also present a reduced-rank version of KEMA for computational

  15. Canonical Granger causality between regions of interest.

    PubMed

    Ashrafulla, Syed; Haldar, Justin P; Joshi, Anand A; Leahy, Richard M

    2013-12-01

    Estimating and modeling functional connectivity in the brain is a challenging problem with potential applications in the understanding of brain organization and various neurological and neuropsychological conditions. An important objective in connectivity analysis is to determine the connections between regions of interest in the brain. However, traditional functional connectivity analyses have frequently focused on modeling interactions between time series recordings at individual sensors, voxels, or vertices despite the fact that a single region of interest will often include multiple such recordings. In this paper, we present a novel measure of interaction between regions of interest rather than individual signals. The proposed measure, termed canonical Granger causality, combines ideas from canonical correlation and Granger causality analysis to yield a measure that reflects directed causality between two regions of interest. In particular, canonical Granger causality uses optimized linear combinations of signals from each region of interest to enable accurate causality measurements from substantially less data compared to alternative multivariate methods that have previously been proposed for this scenario. The optimized linear combinations are obtained using a variation of a technique developed for optimization on the Stiefel manifold. We demonstrate the advantages of canonical Granger causality in comparison to alternative causality measures for a range of different simulated datasets. We also apply the proposed measure to local field potential data recorded in a macaque brain during a visuomotor task. Results demonstrate that canonical Granger causality can be used to identify causal relationships between striate and prestriate cortexes in cases where standard Granger causality is unable to identify statistically significant interactions.

  16. Robotic Intelligence Kernel: Architecture

    SciTech Connect

    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.

  17. Robotic Intelligence Kernel: Visualization

    SciTech Connect

    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.

  18. Canonical Bose gas simulations with stochastic gauges.

    PubMed

    Drummond, P D; Deuar, P; Kheruntsyan, K V

    2004-01-30

    A technique to simulate the grand canonical ensembles of interacting Bose gases is presented. Results are generated for many temperatures by averaging over energy-weighted stochastic paths, each corresponding to a solution of coupled Gross-Pitaevskii equations with phase noise. The stochastic gauge method used relies on an off-diagonal coherent-state expansion, thus taking into account all quantum correlations. As an example, the second-order spatial correlation function and momentum distribution for an interacting 1D Bose gas are calculated.

  19. Kernel Optimization in Discriminant Analysis

    PubMed Central

    You, Di; Hamsici, Onur C.; Martinez, Aleix M.

    2011-01-01

    Kernel mapping is one of the most used approaches to intrinsically derive nonlinear classifiers. The idea is to use a kernel function which maps the original nonlinearly separable problem to a space of intrinsically larger dimensionality where the classes are linearly separable. A major problem in the design of kernel methods is to find the kernel parameters that make the problem linear in the mapped representation. This paper derives the first criterion that specifically aims to find a kernel representation where the Bayes classifier becomes linear. We illustrate how this result can be successfully applied in several kernel discriminant analysis algorithms. Experimental results using a large number of databases and classifiers demonstrate the utility of the proposed approach. The paper also shows (theoretically and experimentally) that a kernel version of Subclass Discriminant Analysis yields the highest recognition rates. PMID:20820072

  20. Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

    PubMed

    Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan

    2016-11-01

    In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects.

  1. Uncertainty relations, zero point energy and the linear canonical group

    NASA Technical Reports Server (NTRS)

    Sudarshan, E. C. G.

    1993-01-01

    The close relationship between the zero point energy, the uncertainty relations, coherent states, squeezed states, and correlated states for one mode is investigated. This group-theoretic perspective enables the parametrization and identification of their multimode generalization. In particular the generalized Schroedinger-Robertson uncertainty relations are analyzed. An elementary method of determining the canonical structure of the generalized correlated states is presented.

  2. Canonical nucleosome organization at promoters forms during genome activation.

    PubMed

    Zhang, Yong; Vastenhouw, Nadine L; Feng, Jianxing; Fu, Kai; Wang, Chenfei; Ge, Ying; Pauli, Andrea; van Hummelen, Paul; Schier, Alexander F; Liu, X Shirley

    2014-02-01

    The organization of nucleosomes influences transcriptional activity by controlling accessibility of DNA binding proteins to the genome. Genome-wide nucleosome binding profiles have identified a canonical nucleosome organization at gene promoters, where arrays of well-positioned nucleosomes emanate from nucleosome-depleted regions. The mechanisms of formation and the function of canonical promoter nucleosome organization remain unclear. Here we analyze the genome-wide location of nucleosomes during zebrafish embryogenesis and show that well-positioned nucleosome arrays appear on thousands of promoters during the activation of the zygotic genome. The formation of canonical promoter nucleosome organization is independent of DNA sequence preference, transcriptional elongation, and robust RNA polymerase II (Pol II) binding. Instead, canonical promoter nucleosome organization correlates with the presence of histone H3 lysine 4 trimethylation (H3K4me3) and affects future transcriptional activation. These findings reveal that genome activation is central to the organization of nucleosome arrays during early embryogenesis.

  3. Kernel machine SNP-set testing under multiple candidate kernels.

    PubMed

    Wu, Michael C; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M; Harmon, Quaker E; Lin, Xinyi; Engel, Stephanie M; Molldrem, Jeffrey J; Armistead, Paul M

    2013-04-01

    Joint testing for the cumulative effect of multiple single-nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large-scale genetic association studies. The kernel machine (KM)-testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori because this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest P-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power vs. using the best candidate kernel.

  4. 7 CFR 51.1415 - Inedible kernels.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Inedible kernels. 51.1415 Section 51.1415 Agriculture... Standards for Grades of Pecans in the Shell 1 Definitions § 51.1415 Inedible kernels. Inedible kernels means that the kernel or pieces of kernels are rancid, moldy, decayed, injured by insects or...

  5. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture... Administrative Rules and Regulations § 981.408 Inedible kernel. Pursuant to § 981.8, the definition of inedible kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored...

  6. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.8 Section 981.8 Agriculture... Regulating Handling Definitions § 981.8 Inedible kernel. Inedible kernel means a kernel, piece, or particle of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel,...

  7. Approximate but accurate quantum dynamics from the Mori formalism. II. Equilibrium time correlation functions.

    PubMed

    Montoya-Castillo, Andrés; Reichman, David R

    2017-02-28

    The ability to efficiently and accurately calculate equilibrium time correlation functions of many-body condensed phase quantum systems is one of the outstanding problems in theoretical chemistry. The Nakajima-Zwanzig-Mori formalism coupled to the self-consistent solution of the memory kernel has recently proven to be highly successful for the computation of nonequilibrium dynamical averages. Here, we extend this formalism to treat symmetrized equilibrium time correlation functions for the spin-boson model. Following the first paper in this series [A. Montoya-Castillo and D. R. Reichman, J. Chem. Phys. 144, 184104 (2016)], we use a Dyson-type expansion of the projected propagator to obtain a self-consistent solution for the memory kernel that requires only the calculation of normally evolved auxiliary kernels. We employ the approximate mean-field Ehrenfest method to demonstrate the feasibility of this approach. Via comparison with numerically exact results for the correlation function Czz(t)=Re⟨σz(0)σz(t)⟩, we show that the current scheme affords remarkable boosts in accuracy and efficiency over bare Ehrenfest dynamics. We further explore the sensitivity of the resulting dynamics to the choice of kernel closures and the accuracy of the initial canonical density operator.

  8. Canonical form of Hamiltonian matrices

    NASA Astrophysics Data System (ADS)

    Zuker, A. P.; Waha Ndeuna, L.; Nowacki, F.; Caurier, E.

    2001-08-01

    On the basis of shell model simulations, it is conjectured that the Lanczos construction at fixed quantum numbers defines-within fluctuations and behavior very near the origin-smooth canonical matrices whose forms depend on the rank of the Hamiltonian, dimensionality of the vector space, and second and third moments. A framework emerges that amounts to a general Anderson model capable of dealing with ground state properties and strength functions. The smooth forms imply binomial level densities. A simplified approach to canonical thermodynamics is proposed.

  9. Kernel phase and kernel amplitude in Fizeau imaging

    NASA Astrophysics Data System (ADS)

    Pope, Benjamin J. S.

    2016-12-01

    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 history 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.

  10. The Adaptive Kernel Neural Network

    DTIC Science & Technology

    1989-10-01

    A neural network architecture for clustering and classification is described. The Adaptive Kernel Neural Network (AKNN) is a density estimation...classification layer. The AKNN retains the inherent parallelism common in neural network models. Its relationship to the kernel estimator allows the network to

  11. Canonical Views of Dynamic Scenes

    ERIC Educational Resources Information Center

    Garsoffky, Barbel; Schwan, Stephan; Huff, Markus

    2009-01-01

    The visual recognition of dynamic scenes was examined. The authors hypothesized that the notion of canonical views, which has received strong empirical support for static objects, also holds for dynamic scenes. In Experiment 1, viewpoints orthogonal to the main axis of movement in the scene were preferred over other viewpoints, whereas viewpoints…

  12. Romanticism, Sexuality, and the Canon.

    ERIC Educational Resources Information Center

    Rowe, Kathleen K.

    1990-01-01

    Traces the Romanticism in the work and persona of film director Jean-Luc Godard. Examines the contradictions posed by Godard's politics and representations of sexuality. Asserts, that by bringing an ironic distance to the works of such canonized directors, viewers can take pleasure in those works despite their contradictions. (MM)

  13. Robotic intelligence kernel

    DOEpatents

    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.

  14. Flexible Kernel Memory

    PubMed Central

    Nowicki, Dimitri; Siegelmann, Hava

    2010-01-01

    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. PMID:20552013

  15. The transport of relative canonical helicity

    SciTech Connect

    You, S.

    2012-09-15

    The evolution of relative canonical helicity is examined in the two-fluid magnetohydrodynamic formalism. Canonical helicity is defined here as the helicity of the plasma species' canonical momentum. The species' canonical helicity are coupled together and can be converted from one into the other while the total gauge-invariant relative canonical helicity remains globally invariant. The conversion is driven by enthalpy differences at a surface common to ion and electron canonical flux tubes. The model provides an explanation for why the threshold for bifurcation in counter-helicity merging depends on the size parameter. The size parameter determines whether magnetic helicity annihilation channels enthalpy into the magnetic flux tube or into the vorticity flow tube components of the canonical flux tube. The transport of relative canonical helicity constrains the interaction between plasma flows and magnetic fields, and provides a more general framework for driving flows and currents from enthalpy or inductive boundary conditions.

  16. Kernel methods for phenotyping complex plant architecture.

    PubMed

    Kawamura, Koji; Hibrand-Saint Oyant, Laurence; Foucher, Fabrice; Thouroude, Tatiana; Loustau, Sébastien

    2014-02-07

    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.

  17. Canonical microcircuits for predictive coding

    PubMed Central

    Bastos, Andre M.; Usrey, W. Martin; Adams, Rick A.; Mangun, George R.; Fries, Pascal; Friston, Karl J.

    2013-01-01

    Summary This review considers the influential notion of a canonical (cortical) microcircuit in light of recent theories about neuronal processing. Specifically, we conciliate quantitative studies of microcircuitry and the functional logic of neuronal computations. We revisit the established idea that message passing among hierarchical cortical areas implements a form of Bayesian inference – paying careful attention to the implications for intrinsic connections among neuronal populations. By deriving canonical forms for these computations, one can associate specific neuronal populations with specific computational roles. This analysis discloses a remarkable correspondence between the microcircuitry of the cortical column and the connectivity implied by predictive coding. Furthermore, it provides some intuitive insights into the functional asymmetries between feedforward and feedback connections and the characteristic frequencies over which they operate. PMID:23177956

  18. Modern Canonical Quantum General Relativity

    NASA Astrophysics Data System (ADS)

    Thiemann, Thomas

    2007-09-01

    Preface; Notation and conventions; Introduction; Part I. Classical Foundations, Interpretation and the Canonical Quantisation Programme: 1. Classical Hamiltonian formulation of general relativity; 2. The problem of time, locality and the interpretation of quantum mechanics; 3. The programme of canonical quantisation; 4. The new canonical variables of Ashtekar for general relativity; Part II. Foundations of Modern Canonical Quantum General Relativity: 5. Introduction; 6. Step I: the holonomy-flux algebra [P]; 7. Step II: quantum-algebra; 8. Step III: representation theory of [A]; 9. Step IV: 1. Implementation and solution of the kinematical constraints; 10. Step V: 2. Implementation and solution of the Hamiltonian constraint; 11. Step VI: semiclassical analysis; Part III. Physical Applications: 12. Extension to standard matter; 13. Kinematical geometrical operators; 14. Spin foam models; 15. Quantum black hole physics; 16. Applications to particle physics and quantum cosmology; 17. Loop quantum gravity phenomenology; Part IV. Mathematical Tools and their Connection to Physics: 18. Tools from general topology; 19. Differential, Riemannian, symplectic and complex geometry; 20. Semianalytical category; 21. Elements of fibre bundle theory; 22. Holonomies on non-trivial fibre bundles; 23. Geometric quantisation; 24. The Dirac algorithm for field theories with constraints; 25. Tools from measure theory; 26. Elementary introduction to Gel'fand theory for Abelean C* algebras; 27. Bohr compactification of the real line; 28. Operatir -algebras and spectral theorem; 29. Refined algebraic quantisation (RAQ) and direct integral decomposition (DID); 30. Basics of harmonic analysis on compact Lie groups; 31. Spin network functions for SU(2); 32. + Functional analytical description of classical connection dynamics; Bibliography; Index.

  19. Modern Canonical Quantum General Relativity

    NASA Astrophysics Data System (ADS)

    Thiemann, Thomas

    2008-11-01

    Preface; Notation and conventions; Introduction; Part I. Classical Foundations, Interpretation and the Canonical Quantisation Programme: 1. Classical Hamiltonian formulation of general relativity; 2. The problem of time, locality and the interpretation of quantum mechanics; 3. The programme of canonical quantisation; 4. The new canonical variables of Ashtekar for general relativity; Part II. Foundations of Modern Canonical Quantum General Relativity: 5. Introduction; 6. Step I: the holonomy-flux algebra [P]; 7. Step II: quantum-algebra; 8. Step III: representation theory of [A]; 9. Step IV: 1. Implementation and solution of the kinematical constraints; 10. Step V: 2. Implementation and solution of the Hamiltonian constraint; 11. Step VI: semiclassical analysis; Part III. Physical Applications: 12. Extension to standard matter; 13. Kinematical geometrical operators; 14. Spin foam models; 15. Quantum black hole physics; 16. Applications to particle physics and quantum cosmology; 17. Loop quantum gravity phenomenology; Part IV. Mathematical Tools and their Connection to Physics: 18. Tools from general topology; 19. Differential, Riemannian, symplectic and complex geometry; 20. Semianalytical category; 21. Elements of fibre bundle theory; 22. Holonomies on non-trivial fibre bundles; 23. Geometric quantisation; 24. The Dirac algorithm for field theories with constraints; 25. Tools from measure theory; 26. Elementary introduction to Gel'fand theory for Abelean C* algebras; 27. Bohr compactification of the real line; 28. Operatir -algebras and spectral theorem; 29. Refined algebraic quantisation (RAQ) and direct integral decomposition (DID); 30. Basics of harmonic analysis on compact Lie groups; 31. Spin network functions for SU(2); 32. + Functional analytical description of classical connection dynamics; Bibliography; Index.

  20. Nonlinear stochastic system identification of skin using volterra kernels.

    PubMed

    Chen, Yi; Hunter, Ian W

    2013-04-01

    Volterra kernel stochastic system identification is a technique that can be used to capture and model nonlinear dynamics in biological systems, including the nonlinear properties of skin during indentation. A high bandwidth and high stroke Lorentz force linear actuator system was developed and used to test the mechanical properties of bulk skin and underlying tissue in vivo using a non-white input force and measuring an output position. These short tests (5 s) were conducted in an indentation configuration normal to the skin surface and in an extension configuration tangent to the skin surface. Volterra kernel solution methods were used including a fast least squares procedure and an orthogonalization solution method. The practical modifications, such as frequency domain filtering, necessary for working with low-pass filtered inputs are also described. A simple linear stochastic system identification technique had a variance accounted for (VAF) of less than 75%. Representations using the first and second Volterra kernels had a much higher VAF (90-97%) as well as a lower Akaike information criteria (AICc) indicating that the Volterra kernel models were more efficient. The experimental second Volterra kernel matches well with results from a dynamic-parameter nonlinearity model with fixed mass as a function of depth as well as stiffness and damping that increase with depth into the skin. A study with 16 subjects showed that the kernel peak values have mean coefficients of variation (CV) that ranged from 3 to 8% and showed that the kernel principal components were correlated with location on the body, subject mass, body mass index (BMI), and gender. These fast and robust methods for Volterra kernel stochastic system identification can be applied to the characterization of biological tissues, diagnosis of skin diseases, and determination of consumer product efficacy.

  1. 7 CFR 51.2295 - Half kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Half kernel. 51.2295 Section 51.2295 Agriculture... Standards for Shelled English Walnuts (Juglans Regia) Definitions § 51.2295 Half kernel. Half kernel means the separated half of a kernel with not more than one-eighth broken off....

  2. 7 CFR 981.9 - Kernel weight.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Kernel weight. 981.9 Section 981.9 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels,...

  3. 7 CFR 981.7 - Edible kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle...

  4. Neutron scattering kernel for solid deuterium

    NASA Astrophysics Data System (ADS)

    Granada, J. R.

    2009-06-01

    A new scattering kernel to describe the interaction of slow neutrons with solid deuterium was developed. The main characteristics of that system are contained in the formalism, including the lattice's density of states, the Young-Koppel quantum treatment of the rotations, and the internal molecular vibrations. The elastic processes involving coherent and incoherent contributions are fully described, as well as the spin-correlation effects. The results from the new model are compared with the best available experimental data, showing very good agreement.

  5. Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization.

    PubMed

    Han, Yina; Yang, Kunde; Yang, Yixin; Ma, Yuanliang

    2016-12-20

    Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features with regard to their discriminative power for each individual sample. However, the learning of numerous local solutions may not scale well even for a moderately sized training set, and the independently learned local models may suffer from overfitting. Hence, in existing local methods, the distributed samples are typically assumed to share the same weights, and various unsupervised clustering methods are applied as preprocessing. In this paper, to enable the learner to discover and benefit from the underlying local coherence and diversity of the samples, we incorporate the clustering procedure into the canonical support vector machine-based LMKL framework. Then, to explore the relatedness among different samples, which has been ignored in a vector ℓp-norm analysis, we organize the cluster-specific kernel weights into a matrix and introduce a matrix-based extension of the ℓp-norm for constraint enforcement. By casting the joint optimization problem as a problem of alternating optimization, we show how the cluster structure is gradually revealed and how the matrix-regularized kernel weights are obtained. A theoretical analysis of such a regularizer is performed using a Rademacher complexity bound, and complementary empirical experiments on real-world data sets demonstrate the effectiveness of our technique.

  6. An Approximate Approach to Automatic Kernel Selection.

    PubMed

    Ding, Lizhong; Liao, Shizhong

    2016-02-02

    Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.

  7. Canonical and alternative MAPK signaling.

    PubMed

    Pimienta, Genaro; Pascual, Jaime

    2007-11-01

    The archetype of MAPK cascade activation is somewhat challenged by the most recent discovery of unexpected phosphorylation patterns, alternative activation mechanisms and sub-cellular localization, in various members of this protein kinase family. In particular, activation by autophosphorylation pathways has now been described for the three best understood MAPK subgroups: ERK1/2; JNK1/2 and p38 alpha/beta. Also, a form of dosage compensation between homologs has been shown to occur in the case of ERK1/2 and JNK1/2. In this paper we summarize the MAPK activation pathway, with an emphasis on non-canonical examples. We use this information to propose a model for MAPK signal transduction that considers a cross-talk between MAPKs with different activation loop sequence motifs and unique C-terminal extensions. We highlight the occurrence of non-canonical substrate specificity during MAPK auto-activation, in strong connection with MAPK homo- and hetero-dimerization events.

  8. Generalized Langevin equation with tempered memory kernel

    NASA Astrophysics Data System (ADS)

    Liemert, André; Sandev, Trifce; Kantz, Holger

    2017-01-01

    We study a generalized Langevin equation for a free particle in presence of a truncated power-law and Mittag-Leffler memory kernel. It is shown that in presence of truncation, the particle from subdiffusive behavior in the short time limit, turns to normal diffusion in the long time limit. The case of harmonic oscillator is considered as well, and the relaxation functions and the normalized displacement correlation function are represented in an exact form. By considering external time-dependent periodic force we obtain resonant behavior even in case of a free particle due to the influence of the environment on the particle movement. Additionally, the double-peak phenomenon in the imaginary part of the complex susceptibility is observed. It is obtained that the truncation parameter has a huge influence on the behavior of these quantities, and it is shown how the truncation parameter changes the critical frequencies. The normalized displacement correlation function for a fractional generalized Langevin equation is investigated as well. All the results are exact and given in terms of the three parameter Mittag-Leffler function and the Prabhakar generalized integral operator, which in the kernel contains a three parameter Mittag-Leffler function. Such kind of truncated Langevin equation motion can be of high relevance for the description of lateral diffusion of lipids and proteins in cell membranes.

  9. 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.

  10. 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.

  11. The canonical and other mechanisms of elementary chemical reactions.

    PubMed

    Aldegunde, Jesús; Aoiz, F Javier; Sáez-Rábanos, Vicente; Kendrick, Brian K; de Miranda, Marcelo P

    2007-11-21

    This article introduces a definition of the concept of elementary reaction mechanism that, while conforming to the traditional view of reaction mechanisms as dynamical processes whereby reagents are transformed into products, sharpens it by requiring reagent and product states to be completely specified and fully correlated. This leads to well-defined mathematical requirements for classification of a dynamical process as a reaction mechanism and also to a straightforward mathematical procedure for the determination of a special class of independent collision mechanisms that are dubbed "canonical". Canonical mechanisms result from an exact decomposition of the differential cross section of the reaction and form a complete orthogonal basis in terms of which all reaction mechanisms can be described. Examples involving the benchmark F + H2 and D + H2 reactions at energies ranging from ultralow to hyperthermal illustrate how canonical and other reaction mechanisms can be visualised and also how analysis of a reaction in terms of its canonical mechanisms can provide insight into its dynamics.

  12. 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…

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. Travel-Time and Amplitude Sensitivity Kernels

    DTIC Science & Technology

    2011-09-01

    amplitude sensitivity kernels shown in the lower panels concentrate about the corresponding eigenrays . Each 3D kernel exhibits a broad negative...in 2 and 3 dimensions have similar 11 shapes to corresponding travel-time sensitivity kernels (TSKs), centered about the respective eigenrays

  18. Adaptive wiener image restoration kernel

    SciTech Connect

    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.

  19. 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.

  20. The Current Canon in British Romantics Studies.

    ERIC Educational Resources Information Center

    Linkin, Harriet Kramer

    1991-01-01

    Describes and reports on a survey of 164 U.S. universities to ascertain what is taught as the current canon of British Romantic literature. Asserts that the canon may now include Mary Shelley with the former standard six major male Romantic poets, indicating a significant emergence of a feminist perspective on British Romanticism in the classroom.…

  1. Multics Security Kernel Certification Plan

    DTIC Science & Technology

    1976-07-01

    security level. A process may only invoke those instantiations of funtions that have been assigned the security level of the process. A system is...In the canonical form, the grammar of SPECIAL is modified and augmented as follows. An <expression> in the body of a function specification

  2. Correlators with sℓ2 Yangian symmetry

    NASA Astrophysics Data System (ADS)

    Fuksa, J.; Kirschner, R.

    2017-01-01

    Correlators based on sℓ2 Yangian symmetry and its quantum deformation are studied. Symmetric integral operators can be defined with such correlators as kernels. Yang-Baxter operators can be represented in this way. Particular Yangian symmetric correlators are related to the kernels of QCD parton evolution. The solution of the eigenvalue problem of Yangian symmetric operators is described.

  3. 17 CFR 200.52 - Copies of the Canons.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... AND ETHICS; AND INFORMATION AND REQUESTS Canons of Ethics § 200.52 Copies of the Canons. The Canons have been distributed to employees of the Commission. In addition, executive and professional...

  4. The canonical forms of a lattice rule

    SciTech Connect

    Lyness, J.N.

    1992-12-31

    Much of the elementary theory of lattice rules may, be presented as an elegant application of classical results. These include Kronecker group representation theorem and the Hermite and Smith normal forms of integer matrices. The theory of the canonical form is a case in point. In this paper, some of this theory is treated in a constructive rather than abstract manner. A step-by-step approach that parallels the group theory is described, leading to an algorithm to obtain a canonical form of a rule of prime power order. The number of possible distinct canonical forms is derived, and this is used to determine the number of integration lattices having specified invariants.

  5. The canonical forms of a lattice rule

    SciTech Connect

    Lyness, J.N.

    1992-01-01

    Much of the elementary theory of lattice rules may, be presented as an elegant application of classical results. These include Kronecker group representation theorem and the Hermite and Smith normal forms of integer matrices. The theory of the canonical form is a case in point. In this paper, some of this theory is treated in a constructive rather than abstract manner. A step-by-step approach that parallels the group theory is described, leading to an algorithm to obtain a canonical form of a rule of prime power order. The number of possible distinct canonical forms is derived, and this is used to determine the number of integration lattices having specified invariants.

  6. Antioxidant capacity and phenolics content of apricot (Prunus armeniaca L.) kernel as a function of genotype.

    PubMed

    Korekar, Girish; Stobdan, Tsering; Arora, Richa; Yadav, Ashish; Singh, Shashi Bala

    2011-11-01

    Fourteen apricot genotypes grown under similar cultural practices in Trans-Himalayan Ladakh region were studied to find out the influence of genotype on antioxidant capacity and total phenolic content (TPC) of apricot kernel. The kernels were found to be rich in TPC ranging from 92.2 to 162.1 mg gallic acid equivalent/100 g. The free radical-scavenging activity in terms of inhibitory concentration (IC(50)) ranged from 43.8 to 123.4 mg/ml and ferric reducing antioxidant potential (FRAP) from 154.1 to 243.6 FeSO(4).7H(2)O μg/ml. A variation of 1-1.7 fold in total phenolic content, 1-2.8 fold in IC(50) by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay and 1-1.6 fold in ferric reducing antioxidant potential among the examined kernels underlines the important role played by genetic background for determining the phenolic content and antioxidant potential of apricot kernel. A positive significant correlation between TPC and FRAP (r=0.671) was found. No significant correlation was found between TPC and IC(50); FRAP and IC(50); TPC and physical properties of kernel. Principal component analysis demonstrated that genotypic effect is more pronounced towards TPC and total antioxidant capacity (TAC) content in apricot kernel while the contribution of seed and kernel physical properties are not highly significant.

  7. Relationships of Single Kernel Characterization System Variables and Milling Quality and Flour Protein Content in Soft White Winter Wheats

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Single Kernel Characterization System (SKCS) was used to analyze 222 soft winter wheat samples harvested in 2005 and 2006 in Oregon. Among SKCS characteristics, kernel diameter had significant correlations with flour yield (FY, r=0.675, P<0.001), and flour protein content (FPC, r=-0.565, P<0.001). ...

  8. 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.

  9. Nonlinear Deep Kernel Learning for Image Annotation.

    PubMed

    Jiu, Mingyuan; Sahbi, Hichem

    2017-02-08

    Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

  10. Phenolic compounds and antioxidant activity of kernels and shells of Mexican pecan (Carya illinoinensis).

    PubMed

    de la Rosa, Laura A; Alvarez-Parrilla, Emilio; Shahidi, Fereidoon

    2011-01-12

    The phenolic composition and antioxidant activity of pecan kernels and shells cultivated in three regions of the state of Chihuahua, Mexico, were analyzed. High concentrations of total extractable phenolics, flavonoids, and proanthocyanidins were found in kernels, and 5-20-fold higher concentrations were found in shells. Their concentrations were significantly affected by the growing region. Antioxidant activity was evaluated by ORAC, DPPH•, HO•, and ABTS•-- scavenging (TAC) methods. Antioxidant activity was strongly correlated with the concentrations of phenolic compounds. A strong correlation existed among the results obtained using these four methods. Five individual phenolic compounds were positively identified and quantified in kernels: ellagic, gallic, protocatechuic, and p-hydroxybenzoic acids and catechin. Only ellagic and gallic acids could be identified in shells. Seven phenolic compounds were tentatively identified in kernels by means of MS and UV spectral comparison, namely, protocatechuic aldehyde, (epi)gallocatechin, one gallic acid-glucose conjugate, three ellagic acid derivatives, and valoneic acid dilactone.

  11. Canonical Force Distributions in Pairwise Interatomic Interactions from the Perspective of the Hellmann-Feynman Theorem.

    PubMed

    Walton, Jay R; Rivera-Rivera, Luis A; Lucchese, Robert R; Bevan, John W

    2016-05-26

    Force-based canonical approaches have recently given a unified but different viewpoint on the nature of bonding in pairwise interatomic interactions. Differing molecular categories (covalent, ionic, van der Waals, hydrogen, and halogen bonding) of representative interatomic interactions with binding energies ranging from 1.01 to 1072.03 kJ/mol have been modeled canonically giving a rigorous semiempirical verification to high accuracy. However, the fundamental physical basis expected to provide the inherent characteristics of these canonical transformations has not yet been elucidated. Subsequently, it was shown through direct numerical differentiation of these potentials that their associated force curves have canonical shapes. However, this approach to analyzing force results in inherent loss of accuracy coming from numerical differentiation of the potentials. We now show that this serious obstruction can be avoided by directly demonstrating the canonical nature of force distributions from the perspective of the Hellmann-Feynman theorem. This requires only differentiation of explicitly known Coulombic potentials, and we discuss how this approach to canonical forces can be used to further explain the nature of chemical bonding in pairwise interatomic interactions. All parameter values used in the canonical transformation are determined through explicit physical based algorithms, and it does not require direct consideration of electron correlation effects.

  12. Nonlinear projection trick in kernel methods: an alternative to the kernel trick.

    PubMed

    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.

  13. Refining inflation using non-canonical scalars

    SciTech Connect

    Unnikrishnan, Sanil; Sahni, Varun; Toporensky, Aleksey E-mail: varun@iucaa.ernet.in

    2012-08-01

    This paper revisits the Inflationary scenario within the framework of scalar field models possessing a non-canonical kinetic term. We obtain closed form solutions for all essential quantities associated with chaotic inflation including slow roll parameters, scalar and tensor power spectra, spectral indices, the tensor-to-scalar ratio, etc. We also examine the Hamilton-Jacobi equation and demonstrate the existence of an inflationary attractor. Our results highlight the fact that non-canonical scalars can significantly improve the viability of inflationary models. They accomplish this by decreasing the tensor-to-scalar ratio while simultaneously increasing the value of the scalar spectral index, thereby redeeming models which are incompatible with the cosmic microwave background (CMB) in their canonical version. For instance, the non-canonical version of the chaotic inflationary potential, V(φ) ∼ λφ{sup 4}, is found to agree with observations for values of λ as large as unity! The exponential potential can also provide a reasonable fit to CMB observations. A central result of this paper is that steep potentials (such as V∝φ{sup −n}) usually associated with dark energy, can drive inflation in the non-canonical setting. Interestingly, non-canonical scalars violate the consistency relation r = −8n{sub T}, which emerges as a smoking gun test for this class of models.

  14. Investigating the Dynamics of Canonical Flux Tubes

    NASA Astrophysics Data System (ADS)

    von der Linden, Jens; Sears, Jason; Intrator, Thomas; You, Setthivoine

    2016-10-01

    Canonical flux tubes are flux tubes of the circulation of a species' canonical momentum. They provide a convenient generalization of magnetic flux tubes to regimes beyond magnetohydrodynamics (MHD). We hypothesize that hierarchies of instabilities which couple disparate scales could transfer magnetic pitch into helical flows and vice versa while conserving the total canonical helicity. This work first explores the possibility of a sausage instability existing on top of a kink as mechanism for coupling scales, then presents the evolution of canonical helicity in a gyrating kinked flux rope. Analytical and numerical stability spaces derived for magnetic flux tubes with core and skin currents indicate that, as a flux tube lengthens and collimates, it may become kink unstable with a sausage instability developing on top of the kink. A new analysis of 3D magnetic field and ion flow data on gyrating kinked magnetic flux ropes from the Reconnection Scaling Experiment tracks the evolution of canonical flux tubes and their helicity. These results and methodology are being developed as part of the Mochi experiment specifically designed to observe the dynamics of canonical flux tubes. This work is supported by DOE Grant DE-SC0010340 and the DOE Office of Science Graduate Student Research Program and prepared in part by LLNL under Contract DE-AC52-07NA27344. LLNL-ABS-697161.

  15. Diffusion Map Kernel Analysis for Target Classification

    DTIC Science & Technology

    2010-06-01

    Gaussian and Polynomial kernels are most familiar from support vector machines. The Laplacian and Rayleigh were introduced previously in [7]. IV ...Cancer • Clev. Heart: Heart Disease Data Set, Cleveland • Wisc . BC: Wisconsin Breast Cancer Original • Sonar2: Shallow Water Acoustic Toolset [9...the Rayleigh kernel captures the embedding with an average PC of 77.3% and a slightly higher PFA than the Gaussian kernel. For the Wisc . BC

  16. A locally adaptive kernel regression method for facies delineation

    NASA Astrophysics Data System (ADS)

    Fernàndez-Garcia, D.; Barahona-Palomo, M.; Henri, C. V.; Sanchez-Vila, X.

    2015-12-01

    Facies delineation is defined as the separation of geological units with distinct intrinsic characteristics (grain size, hydraulic conductivity, mineralogical composition). A major challenge in this area stems from the fact that only a few scattered pieces of hydrogeological information are available to delineate geological facies. Several methods to delineate facies are available in the literature, ranging from those based only on existing hard data, to those including secondary data or external knowledge about sedimentological patterns. This paper describes a methodology to use kernel regression methods as an effective tool for facies delineation. The method uses both the spatial and the actual sampled values to produce, for each individual hard data point, a locally adaptive steering kernel function, self-adjusting the principal directions of the local anisotropic kernels to the direction of highest local spatial correlation. The method is shown to outperform the nearest neighbor classification method in a number of synthetic aquifers whenever the available number of hard data is small and randomly distributed in space. In the case of exhaustive sampling, the steering kernel regression method converges to the true solution. Simulations ran in a suite of synthetic examples are used to explore the selection of kernel parameters in typical field settings. It is shown that, in practice, a rule of thumb can be used to obtain suboptimal results. The performance of the method is demonstrated to significantly improve when external information regarding facies proportions is incorporated. Remarkably, the method allows for a reasonable reconstruction of the facies connectivity patterns, shown in terms of breakthrough curves performance.

  17. Molecular Hydrodynamics from Memory Kernels

    NASA Astrophysics Data System (ADS)

    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.

  18. MBARI CANON Experiment Visualization and Analysis

    NASA Astrophysics Data System (ADS)

    Fatland, R.; Oscar, N.; Ryan, J. P.; Bellingham, J. G.

    2013-12-01

    We describe the task of understanding a marine drift experiment conducted by MBARI in Fall 2012 ('CANON'). Datasets were aggregated from a drifting ADCP, from the MBARI Environmental Sample Processor, from Long Range Autonomous Underwater Vehicles (LRAUVs), from other in situ sensors, from NASA and NOAA remote sensing platforms, from moorings, from shipboard CTD casts and from post-experiment metagenomic analysis. We seek to combine existing approaches to data synthesis -- visual inspection, cross correlation and co.-- with three new ideas. This approach has the purpose of differentiating biological signals into three causal categories: Microcurrent advection, physical factors and microbe metabolism. Respective examples are aberrance from Lagrangian frame drift due to windage, changes in solar flux over several days, and microbial population responses to shifts in nitrate concentration. The three ideas we implemented are as follows: First, we advect LRAUV data to look for patterns in time series data for conserved quanitities such as salinity. We investigate whether such patterns can be used to support or undermine the premise of Lagrangian motion of the experiment ensemble. Second we built a set of configurable filters that enable us to visually isolate segments of data: By type, value, time, anomaly and location. Third we associated data hypotheses with a Bayesian inferrence engine for the purpose of model validation, again across sections taken from within the complete data complex. The end result is towards a free-form exploration of experimental data with low latency: from question to view, from hypothesis to test (albeit with considerable preparatory effort.) Preliminary results show the three causal categories shifting in relative influence.

  19. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume...

  20. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ..., CERTIFICATION, AND STANDARDS) United States Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than...

  1. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ..., CERTIFICATION, AND STANDARDS) United States Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than...

  2. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume...

  3. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume...

  4. Bergman Kernel from Path Integral

    NASA Astrophysics Data System (ADS)

    Douglas, Michael R.; Klevtsov, Semyon

    2010-01-01

    We rederive the expansion of the Bergman kernel on Kähler manifolds developed by Tian, Yau, Zelditch, Lu and Catlin, using path integral and perturbation theory, and generalize it to supersymmetric quantum mechanics. One physics interpretation of this result is as an expansion of the projector of wave functions on the lowest Landau level, in the special case that the magnetic field is proportional to the Kähler form. This is relevant for the quantum Hall effect in curved space, and for its higher dimensional generalizations. Other applications include the theory of coherent states, the study of balanced metrics, noncommutative field theory, and a conjecture on metrics in black hole backgrounds discussed in [24]. We give a short overview of these various topics. From a conceptual point of view, this expansion is noteworthy as it is a geometric expansion, somewhat similar to the DeWitt-Seeley-Gilkey et al short time expansion for the heat kernel, but in this case describing the long time limit, without depending on supersymmetry.

  5. Kernel current source density method.

    PubMed

    Potworowski, Jan; Jakuczun, Wit; Lȩski, Szymon; Wójcik, Daniel

    2012-02-01

    Local field potentials (LFP), the low-frequency part of extracellular electrical recordings, are a measure of the neural activity reflecting dendritic processing of synaptic inputs to neuronal populations. To localize synaptic dynamics, it is convenient, whenever possible, to estimate the density of transmembrane current sources (CSD) generating the LFP. In this work, we propose a new framework, the kernel current source density method (kCSD), for nonparametric estimation of CSD from LFP recorded from arbitrarily distributed electrodes using kernel methods. We test specific implementations of this framework on model data measured with one-, two-, and three-dimensional multielectrode setups. We compare these methods with the traditional approach through numerical approximation of the Laplacian and with the recently developed inverse current source density methods (iCSD). We show that iCSD is a special case of kCSD. The proposed method opens up new experimental possibilities for CSD analysis from existing or new recordings on arbitrarily distributed electrodes (not necessarily on a grid), which can be obtained in extracellular recordings of single unit activity with multiple electrodes.

  6. KERNEL PHASE IN FIZEAU INTERFEROMETRY

    SciTech Connect

    Martinache, Frantz

    2010-11-20

    The detection of high contrast companions at small angular separation appears feasible in conventional direct images using the self-calibration properties of interferometric observable quantities. The friendly notion of closure phase, which is key to the recent observational successes of non-redundant aperture masking interferometry used with adaptive optics, appears to be one example of a wide family of observable quantities that are not contaminated by phase noise. In the high-Strehl regime, soon to be available thanks to the coming generation of extreme adaptive optics systems on ground-based telescopes, and already available from space, closure phase like information can be extracted from any direct image, even taken with a redundant aperture. These new phase-noise immune observable quantities, called kernel phases, are determined a priori from the knowledge of the geometry of the pupil only. Re-analysis of archive data acquired with the Hubble Space Telescope NICMOS instrument using this new kernel-phase algorithm demonstrates the power of the method as it clearly detects and locates with milliarcsecond precision a known companion to a star at angular separation less than the diffraction limit.

  7. Multivariate phenotype association analysis by marker-set kernel machine regression.

    PubMed

    Maity, Arnab; Sullivan, Patrick F; Tzeng, Jun-Ying

    2012-11-01

    Genetic studies of complex diseases often collect multiple phenotypes relevant to the disorders. As these phenotypes can be correlated and share common genetic mechanisms, jointly analyzing these traits may bring more power to detect genes influencing individual or multiple phenotypes. Given the advancement brought by the multivariate phenotype approaches and the multimarker kernel machine regression, we construct a multivariate regression based on kernel machine to facilitate the joint evaluation of multimarker effects on multiple phenotypes. The kernel machine serves as a powerful dimension-reduction tool to capture complex effects among markers. The multivariate framework incorporates the potentially correlated multidimensional phenotypic information and accommodates common or different environmental covariates for each trait. We derive the multivariate kernel machine test based on a score-like statistic, and conduct simulations to evaluate the validity and efficacy of the method. We also study the performance of the commonly adapted strategies for kernel machine analysis on multiple phenotypes, including the multiple univariate kernel machine tests with original phenotypes or with their principal components. Our results suggest that none of these approaches has the uniformly best power, and the optimal test depends on the magnitude of the phenotype correlation and the effect patterns. However, the multivariate test retains to be a reasonable approach when the multiple phenotypes have none or mild correlations, and gives the best power once the correlation becomes stronger or when there exist genes that affect more than one phenotype. We illustrate the utility of the multivariate kernel machine method through the Clinical Antipsychotic Trails of Intervention Effectiveness antibody study.

  8. Ranking Support Vector Machine with Kernel Approximation

    PubMed Central

    Dou, Yong

    2017-01-01

    Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms. PMID:28293256

  9. Ranking Support Vector Machine with Kernel Approximation.

    PubMed

    Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

    2017-01-01

    Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

  10. 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…

  11. Lessons from non-canonical splicing

    PubMed Central

    Ule, Jernej

    2016-01-01

    Recent improvements in experimental and computational techniques used to study the transcriptome have enabled an unprecedented view of RNA processing, revealing many previously unknown non-canonical splicing events. This includes cryptic events located far from the currently annotated exons, and unconventional splicing mechanisms that have important roles in regulating gene expression. These non-canonical splicing events are a major source of newly emerging transcripts during evolution, especially when they involve sequences derived from transposable elements. They are therefore under precise regulation and quality control, which minimises their potential to disrupt gene expression. While non-canonical splicing can lead to aberrant transcripts that cause many diseases, we also explain how it can be exploited for new therapeutic strategies. PMID:27240813

  12. Grand and Semigrand Canonical Basin-Hopping

    PubMed Central

    2015-01-01

    We introduce grand and semigrand canonical global optimization approaches using basin-hopping with an acceptance criterion based on the local contribution of each potential energy minimum to the (semi)grand potential. The method is tested using local harmonic vibrational densities of states for atomic clusters as a function of temperature and chemical potential. The predicted global minima switch from dissociated states to clusters for larger values of the chemical potential and lower temperatures, in agreement with the predictions of a model fitted to heat capacity data for selected clusters. Semigrand canonical optimization allows us to identify particularly stable compositions in multicomponent nanoalloys as a function of increasing temperature, whereas the grand canonical potential can produce a useful survey of favorable structures as a byproduct of the global optimization search. PMID:26669731

  13. Canonical Correlations with Respect to a Complex Structure

    DTIC Science & Technology

    1978-07-01

    Bourbaki , N. (1959). Elements de Mathematique, Algebra, Chapitre 9, Hermann, Paris. [4] Hotelling, H. (1936). Relations between two sets of...distribution of E is the Wishart distribution on the set p(E*) r of positive definite forms on E* with N degrees of freedom and parameter l \\’ N...the complex numbers under the definition zx* = xi: o :Z = (x -+ x*(zx) ; x E E) , x’’tE E*,z E IC • The set Pa:;(E*)r = {E E p(E*)riE(zx*, y*) = L:(x

  14. The context-tree kernel for strings.

    PubMed

    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.

  15. Kernel method for corrections to scaling.

    PubMed

    Harada, Kenji

    2015-07-01

    Scaling analysis, in which one infers scaling exponents and a scaling function in a scaling law from given data, is a powerful tool for determining universal properties of critical phenomena in many fields of science. However, there are corrections to scaling in many cases, and then the inference problem becomes ill-posed by an uncontrollable irrelevant scaling variable. We propose a new kernel method based on Gaussian process regression to fix this problem generally. We test the performance of the new kernel method for some example cases. In all cases, when the precision of the example data increases, inference results of the new kernel method correctly converge. Because there is no limitation in the new kernel method for the scaling function even with corrections to scaling, unlike in the conventional method, the new kernel method can be widely applied to real data in critical phenomena.

  16. Dispersion Operators Algebra and Linear Canonical Transformations

    NASA Astrophysics Data System (ADS)

    Andriambololona, Raoelina; Ranaivoson, Ravo Tokiniaina; Hasimbola Damo Emile, Randriamisy; Rakotoson, Hanitriarivo

    2017-04-01

    This work intends to present a study on relations between a Lie algebra called dispersion operators algebra, linear canonical transformation and a phase space representation of quantum mechanics that we have introduced and studied in previous works. The paper begins with a brief recall of our previous works followed by the description of the dispersion operators algebra which is performed in the framework of the phase space representation. Then, linear canonical transformations are introduced and linked with this algebra. A multidimensional generalization of the obtained results is given.

  17. Dispersion Operators Algebra and Linear Canonical Transformations

    NASA Astrophysics Data System (ADS)

    Andriambololona, Raoelina; Ranaivoson, Ravo Tokiniaina; Hasimbola Damo Emile, Randriamisy; Rakotoson, Hanitriarivo

    2017-02-01

    This work intends to present a study on relations between a Lie algebra called dispersion operators algebra, linear canonical transformation and a phase space representation of quantum mechanics that we have introduced and studied in previous works. The paper begins with a brief recall of our previous works followed by the description of the dispersion operators algebra which is performed in the framework of the phase space representation. Then, linear canonical transformations are introduced and linked with this algebra. A multidimensional generalization of the obtained results is given.

  18. Canonical transformations and Hamiltonian evolutionary systems

    SciTech Connect

    Al-Ashhab, Samer

    2012-06-15

    In many Lagrangian field theories, one has a Poisson bracket defined on the space of local functionals. We find necessary and sufficient conditions for a transformation on the space of local functionals to be canonical in three different cases. These three cases depend on the specific dimensions of the vector bundle of the theory and the associated Hamiltonian differential operator. We also show how a canonical transformation transforms a Hamiltonian evolutionary system and its conservation laws. Finally, we illustrate these ideas with three examples.

  19. Neuronal model with distributed delay: analysis and simulation study for gamma distribution memory kernel.

    PubMed

    Karmeshu; Gupta, Varun; Kadambari, K V

    2011-06-01

    A single neuronal model incorporating distributed delay (memory)is proposed. The stochastic model has been formulated as a Stochastic Integro-Differential Equation (SIDE) which results in the underlying process being non-Markovian. A detailed analysis of the model when the distributed delay kernel has exponential form (weak delay) has been carried out. The selection of exponential kernel has enabled the transformation of the non-Markovian model to a Markovian model in an extended state space. For the study of First Passage Time (FPT) with exponential delay kernel, the model has been transformed to a system of coupled Stochastic Differential Equations (SDEs) in two-dimensional state space. Simulation studies of the SDEs provide insight into the effect of weak delay kernel on the Inter-Spike Interval(ISI) distribution. A measure based on Jensen-Shannon divergence is proposed which can be used to make a choice between two competing models viz. distributed delay model vis-á-vis LIF model. An interesting feature of the model is that the behavior of (CV(t))((ISI)) (Coefficient of Variation) of the ISI distribution with respect to memory kernel time constant parameter η reveals that neuron can switch from a bursting state to non-bursting state as the noise intensity parameter changes. The membrane potential exhibits decaying auto-correlation structure with or without damped oscillatory behavior depending on the choice of parameters. This behavior is in agreement with empirically observed pattern of spike count in a fixed time window. The power spectral density derived from the auto-correlation function is found to exhibit single and double peaks. The model is also examined for the case of strong delay with memory kernel having the form of Gamma distribution. In contrast to fast decay of damped oscillations of the ISI distribution for the model with weak delay kernel, the decay of damped oscillations is found to be slower for the model with strong delay kernel.

  20. Pollen source effects on growth of kernel structures and embryo chemical compounds in maize

    PubMed Central

    Tanaka, W.; Mantese, A. I.; Maddonni, G. A.

    2009-01-01

    Background and Aims Previous studies have reported effects of pollen source on the oil concentration of maize (Zea mays) kernels through modifications to both the embryo/kernel ratio and embryo oil concentration. The present study expands upon previous analyses by addressing pollen source effects on the growth of kernel structures (i.e. pericarp, endosperm and embryo), allocation of embryo chemical constituents (i.e. oil, protein, starch and soluble sugars), and the anatomy and histology of the embryos. Methods Maize kernels with different oil concentration were obtained from pollinations with two parental genotypes of contrasting oil concentration. The dynamics of the growth of kernel structures and allocation of embryo chemical constituents were analysed during the post-flowering period. Mature kernels were dissected to study the anatomy (embryonic axis and scutellum) and histology [cell number and cell size of the scutellums, presence of sub-cellular structures in scutellum tissue (starch granules, oil and protein bodies)] of the embryos. Key Results Plants of all crosses exhibited a similar kernel number and kernel weight. Pollen source modified neither the growth period of kernel structures, nor pericarp growth rate. By contrast, pollen source determined a trade-off between embryo and endosperm growth rates, which impacted on the embryo/kernel ratio of mature kernels. Modifications to the embryo size were mediated by scutellum cell number. Pollen source also affected (P < 0·01) allocation of embryo chemical compounds. Negative correlations among embryo oil concentration and those of starch (r = 0·98, P < 0·01) and soluble sugars (r = 0·95, P < 0·05) were found. Coincidently, embryos with low oil concentration had an increased (P < 0·05–0·10) scutellum cell area occupied by starch granules and fewer oil bodies. Conclusions The effects of pollen source on both embryo/kernel ratio and allocation of embryo chemicals seems to be related to the early

  1. Bayesian Kernel Mixtures for Counts.

    PubMed

    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.

  2. MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES

    PubMed Central

    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

  3. Steady shear flow thermodynamics based on a canonical distribution approach.

    PubMed

    Taniguchi, Tooru; Morriss, Gary P

    2004-11-01

    A nonequilibrium steady-state thermodynamics to describe shear flow is developed using a canonical distribution approach. We construct a canonical distribution for shear flow based on the energy in the moving frame using the Lagrangian formalism of the classical mechanics. From this distribution, we derive the Evans-Hanley shear flow thermodynamics, which is characterized by the first law of thermodynamics dE=TdS-Qdgamma relating infinitesimal changes in energy E, entropy S, and shear rate gamma with kinetic temperature T. Our central result is that the coefficient Q is given by Helfand's moment for viscosity. This approach leads to thermodynamic stability conditions for shear flow, one of which is equivalent to the positivity of the correlation function for Q. We show the consistency of this approach with the Kawasaki distribution function for shear flow, from which a response formula for viscosity is derived in the form of a correlation function for the time-derivative of Q. We emphasize the role of the external work required to sustain the steady shear flow in this approach, and show theoretically that the ensemble average of its power W must be non-negative. A nonequilibrium entropy, increasing in time, is introduced, so that the amount of heat based on this entropy is equal to the average of W. Numerical results from nonequilibrium molecular-dynamics simulation of two-dimensional many-particle systems with soft-core interactions are presented which support our interpretation.

  4. Infants' Recognition of Objects Using Canonical Color

    ERIC Educational Resources Information Center

    Kimura, Atsushi; Wada, Yuji; Yang, Jiale; Otsuka, Yumiko; Dan, Ippeita; Masuda, Tomohiro; Kanazawa, So; Yamaguchi, Masami K.

    2010-01-01

    We explored infants' ability to recognize the canonical colors of daily objects, including two color-specific objects (human face and fruit) and a non-color-specific object (flower), by using a preferential looking technique. A total of 58 infants between 5 and 8 months of age were tested with a stimulus composed of two color pictures of an object…

  5. Development of Canonical Transformations from Hamilton's Principle.

    ERIC Educational Resources Information Center

    Quade, C. Richard

    1979-01-01

    The theory of canonical transformations and its development are discussed with regard to its application to Hutton's principle. Included are the derivation of the equations of motion and a lack of symmetry in the formulaion with respect to Lagrangian and the fundamental commutator relations of quantum mechanics. (Author/SA)

  6. Canonical duties, liabilities of trustees and administrators.

    PubMed

    Morrisey, F G

    1985-06-01

    The new Code of Canon Law outlines a number of duties of those who have responsibility for administering the Church's temporal goods. Before assuming office, administrators must pledge to be efficient and faithful, and they must prepare an inventory of goods belonging to the juridic person they serve. Among their duties, administrators must: Ensure that adequate insurance is provided; Use civilly valid methods to protect canonical ownership of the goods; Observe civil and canon law prescriptions as well as donors' intentions; Collect and safeguard revenues, repay debts, and invest funds securely; Maintain accurate records, keep documents secure, and prepare an annual budget; Prepare an annual report and present it to the Ordinary where prescribed; Observe civil law concerning labor and social policy, and pay employees a just and decent wage. Administrators who carry out acts that are invalid canonically are liable for such acts. The juridic person is not liable, unless it derived benefit from the transaction. Liability is especially high when the sale of property is involved or when a contract is entered into without proper cannonical consent. Although Church law is relatively powerless to punish those who have been negligent, stewards, administrators, and trustees must do all they can to be truthful to the responsibility with which they have been entrusted.

  7. 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%.

  8. Perturbed kernel approximation on homogeneous manifolds

    NASA Astrophysics Data System (ADS)

    Levesley, J.; Sun, X.

    2007-02-01

    Current methods for interpolation and approximation within a native space rely heavily on the strict positive-definiteness of the underlying kernels. If the domains of approximation are the unit spheres in euclidean spaces, then zonal kernels (kernels that are invariant under the orthogonal group action) are strongly favored. In the implementation of these methods to handle real world problems, however, some or all of the symmetries and positive-definiteness may be lost in digitalization due to small random errors that occur unpredictably during various stages of the execution. Perturbation analysis is therefore needed to address the stability problem encountered. In this paper we study two kinds of perturbations of positive-definite kernels: small random perturbations and perturbations by Dunkl's intertwining operators [C. Dunkl, Y. Xu, Orthogonal polynomials of several variables, Encyclopedia of Mathematics and Its Applications, vol. 81, Cambridge University Press, Cambridge, 2001]. We show that with some reasonable assumptions, a small random perturbation of a strictly positive-definite kernel can still provide vehicles for interpolation and enjoy the same error estimates. We examine the actions of the Dunkl intertwining operators on zonal (strictly) positive-definite kernels on spheres. We show that the resulted kernels are (strictly) positive-definite on spheres of lower dimensions.

  9. Abiotic stress growth conditions induce different responses in kernel iron concentration across genotypically distinct maize inbred varieties.

    PubMed

    Kandianis, Catherine B; Michenfelder, Abigail S; Simmons, Susan J; Grusak, Michael A; Stapleton, Ann E

    2013-01-01

    The improvement of grain nutrient profiles for essential minerals and vitamins through breeding strategies is a target important for agricultural regions where nutrient poor crops like maize contribute a large proportion of the daily caloric intake. Kernel iron concentration in maize exhibits a broad range. However, the magnitude of genotype by environment (GxE) effects on this trait reduces the efficacy and predictability of selection programs, particularly when challenged with abiotic stress such as water and nitrogen limitations. Selection has also been limited by an inverse correlation between kernel iron concentration and the yield component of kernel size in target environments. Using 25 maize inbred lines for which extensive genome sequence data is publicly available, we evaluated the response of kernel iron density and kernel mass to water and nitrogen limitation in a managed field stress experiment using a factorial design. To further understand GxE interactions we used partition analysis to characterize response of kernel iron and weight to abiotic stressors among all genotypes, and observed two patterns: one characterized by higher kernel iron concentrations in control over stress conditions, and another with higher kernel iron concentration under drought and combined stress conditions. Breeding efforts for this nutritional trait could exploit these complementary responses through combinations of favorable allelic variation from these already well-characterized genetic stocks.

  10. A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images.

    PubMed

    Miller, Nathan D; Haase, Nicholas J; Lee, Jonghyun; Kaeppler, Shawn M; de Leon, Natalia; Spalding, Edgar P

    2017-01-01

    Grain yield of the maize plant depends on the sizes, shapes, and numbers of ears and the kernels they bear. An automated pipeline that can measure these components of yield from easily-obtained digital images is needed to advance our understanding of this globally important crop. Here we present three custom algorithms designed to compute such yield components automatically from digital images acquired by a low-cost platform. One algorithm determines the average space each kernel occupies along the cob axis using a sliding-window Fourier transform analysis of image intensity features. A second counts individual kernels removed from ears, including those in clusters. A third measures each kernel's major and minor axis after a Bayesian analysis of contour points identifies the kernel tip. Dimensionless ear and kernel shape traits that may interrelate yield components are measured by principal components analysis of contour point sets. Increased objectivity and speed compared to typical manual methods are achieved without loss of accuracy as evidenced by high correlations with ground truth measurements and simulated data. Millimeter-scale differences among ear, cob, and kernel traits that ranged more than 2.5-fold across a diverse group of inbred maize lines were resolved. This system for measuring maize ear, cob, and kernel attributes is being used by multiple research groups as an automated Web service running on community high-throughput computing and distributed data storage infrastructure. Users may create their own workflow using the source code that is staged for download on a public repository.

  11. Abiotic stress growth conditions induce different responses in kernel iron concentration across genotypically distinct maize inbred varieties

    PubMed Central

    Kandianis, Catherine B.; Michenfelder, Abigail S.; Simmons, Susan J.; Grusak, Michael A.; Stapleton, Ann E.

    2013-01-01

    The improvement of grain nutrient profiles for essential minerals and vitamins through breeding strategies is a target important for agricultural regions where nutrient poor crops like maize contribute a large proportion of the daily caloric intake. Kernel iron concentration in maize exhibits a broad range. However, the magnitude of genotype by environment (GxE) effects on this trait reduces the efficacy and predictability of selection programs, particularly when challenged with abiotic stress such as water and nitrogen limitations. Selection has also been limited by an inverse correlation between kernel iron concentration and the yield component of kernel size in target environments. Using 25 maize inbred lines for which extensive genome sequence data is publicly available, we evaluated the response of kernel iron density and kernel mass to water and nitrogen limitation in a managed field stress experiment using a factorial design. To further understand GxE interactions we used partition analysis to characterize response of kernel iron and weight to abiotic stressors among all genotypes, and observed two patterns: one characterized by higher kernel iron concentrations in control over stress conditions, and another with higher kernel iron concentration under drought and combined stress conditions. Breeding efforts for this nutritional trait could exploit these complementary responses through combinations of favorable allelic variation from these already well-characterized genetic stocks. PMID:24363659

  12. Data mining graphene: correlative analysis of structure and electronic degrees of freedom in graphenic monolayers with defects

    NASA Astrophysics Data System (ADS)

    Ziatdinov, Maxim; Fujii, Shintaro; Kiguchi, Manabu; Enoki, Toshiaki; Jesse, Stephen; Kalinin, Sergei V.

    2016-12-01

    The link between changes in the material crystal structure and its mechanical, electronic, magnetic and optical functionalities—known as the structure-property relationship—is the cornerstone of modern materials science research. The recent advances in scanning transmission electron and scanning probe microscopies (STEM and SPM) have opened an unprecedented path towards examining the structure-property relationships of materials at the single-impurity and atomic-configuration levels. However, there are no statistics-based approaches for cross-correlation of structure and property variables obtained from the different information channels of STEM and SPM experiments. Here we have designed an approach based on a combination of sliding window fast Fourier transform, Pearson correlation matrix and linear and kernel canonical correlation methods to study the relationship between lattice distortions and electron scattering from SPM data on graphene with defects. Our analysis revealed that the strength of coupling to strain is altered between different scattering channels, which can explain the coexistence of several quasiparticle interference patterns in nanoscale regions of interest. In addition, the application of kernel functions allowed us to extract a non-linear component of the relationship between the lattice strain and scattering intensity in graphene. The outlined approach can be further used to analyze correlations in various multi-modal imaging techniques where the information of interest is spatially distributed and generally has a complex multi-dimensional nature.

  13. Data mining graphene: Correlative analysis of structure and electronic degrees of freedom in graphenic monolayers with defects

    DOE PAGES

    Ziatdinov, Maxim A.; Fujii, Shintaro; Kiguchi, Manabu; ...

    2016-11-09

    The link between changes in the material crystal structure and its mechanical, electronic, magnetic, and optical functionalities known as the structure-property relationship is the cornerstone of the contemporary materials science research. The recent advances in scanning transmission electron and scanning probe microscopies (STEM and SPM) have opened an unprecedented path towards examining the materials structure property relationships on the single-impurity and atomic-configuration levels. Lacking, however, are the statistics-based approaches for cross-correlation of structure and property variables obtained in different information channels of the STEM and SPM experiments. Here we have designed an approach based on a combination of sliding windowmore » Fast Fourier Transform, Pearson correlation matrix, linear and kernel canonical correlation, to study a relationship between lattice distortions and electron scattering from the SPM data on graphene with defects. Our analysis revealed that the strength of coupling to strain is altered between different scattering channels which can explain coexistence of several quasiparticle interference patterns in the nanoscale regions of interest. In addition, the application of the kernel functions allowed us extracting a non-linear component of the relationship between the lattice strain and scattering intensity in graphene. Lastly, the outlined approach can be further utilized to analyzing correlations in various multi-modal imaging techniques where the information of interest is spatially distributed and has usually a complex multidimensional nature.« less

  14. Data mining graphene: Correlative analysis of structure and electronic degrees of freedom in graphenic monolayers with defects

    SciTech Connect

    Ziatdinov, Maxim A.; Fujii, Shintaro; Kiguchi, Manabu; Enoki, Toshiaki; Jesse, Stephen; Kalinin, Sergei V.

    2016-11-09

    The link between changes in the material crystal structure and its mechanical, electronic, magnetic, and optical functionalities known as the structure-property relationship is the cornerstone of the contemporary materials science research. The recent advances in scanning transmission electron and scanning probe microscopies (STEM and SPM) have opened an unprecedented path towards examining the materials structure property relationships on the single-impurity and atomic-configuration levels. Lacking, however, are the statistics-based approaches for cross-correlation of structure and property variables obtained in different information channels of the STEM and SPM experiments. Here we have designed an approach based on a combination of sliding window Fast Fourier Transform, Pearson correlation matrix, linear and kernel canonical correlation, to study a relationship between lattice distortions and electron scattering from the SPM data on graphene with defects. Our analysis revealed that the strength of coupling to strain is altered between different scattering channels which can explain coexistence of several quasiparticle interference patterns in the nanoscale regions of interest. In addition, the application of the kernel functions allowed us extracting a non-linear component of the relationship between the lattice strain and scattering intensity in graphene. Lastly, the outlined approach can be further utilized to analyzing correlations in various multi-modal imaging techniques where the information of interest is spatially distributed and has usually a complex multidimensional nature.

  15. Src promotes castration-recurrent prostate cancer through androgen receptor-dependent canonical and non-canonical transcriptional signatures.

    PubMed

    Chattopadhyay, Indranil; Wang, Jianmin; Qin, Maochun; Gao, Lingqiu; Holtz, Renae; Vessella, Robert L; Leach, Robert W; Gelman, Irwin H

    2017-02-07

    Progression of prostate cancer (PC) to castration-recurrent growth (CRPC) remains dependent on sustained expression and transcriptional activity of the androgen receptor (AR). A major mechanism contributing to CRPC progression is through the direct phosphorylation and activation of AR by Src-family (SFK) and ACK1 tyrosine kinases. However, the AR-dependent transcriptional networks activated by Src during CRPC progression have not been elucidated. Here, we show that activated Src (Src527F) induces androgen-independent growth in human LNCaP cells, concomitant with its ability to induce proliferation/survival genes normally induced by dihydrotestosterone (DHT) in androgen-dependent LNCaP and VCaP cells. Src induces additional gene signatures unique to CRPC cell lines, LNCaP-C4-2 and CWR22Rv1, and to CRPC LuCaP35.1 xenografts. By comparing the Src-induced AR-cistrome and/or transcriptome in LNCaP to those in CRPC and LuCaP35.1 tumors, we identified an 11-gene Src-regulated CRPC signature consisting of AR-dependent, AR binding site (ARBS)-associated genes whose expression is altered by DHT in LNCaP[Src527F] but not in LNCaP cells. The differential expression of a subset (DPP4, BCAT1, CNTNAP4, CDH3) correlates with earlier PC metastasis onset and poorer survival, with the expression of BCAT1 required for Src-induced androgen-independent proliferation. Lastly, Src enhances AR binding to non-canonical ARBS enriched for FOXO1, TOP2B and ZNF217 binding motifs; cooperative AR/TOP2B binding to a non-canonical ARBS was both Src- and DHT-sensitive and correlated with increased levels of Src-induced phosphotyrosyl-TOP2B. These data suggest that CRPC progression is facilitated via Src-induced sensitization of AR to intracrine androgen levels, resulting in the engagement of canonical and non-canonical ARBS-dependent gene signatures.

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 7 CFR 51.2296 - Three-fourths half kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Three-fourths half kernel. 51.2296 Section 51.2296 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards...-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more...

  2. 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 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will...

  3. Canonical Analysis of the WISC and ITPA: A Reanalysis of the Wakefield and Carlson Data

    ERIC Educational Resources Information Center

    Pielstick, N. L.; Thorndike, Robert M.

    1976-01-01

    Reanalysis of Wakefield and Carlson's data confirmed canonical correlations of .84 and .69, but analysis of redundancies revealed that only 34 percent of the total WISC subtest variance is redundant with the ITPA and 39 percent of the ITPA subtest variance is redundant with the WISC. (Author)

  4. Invariance on Multivariate Results: A Monte Carlo Study of Canonical Coefficients.

    ERIC Educational Resources Information Center

    Thompson, Bruce

    In the present study Monte Carlo methods were employed to evaluate the degree to which canonical function and structure coefficients may be differentially sensitive to sampling error. Sampling error influences were investigated across variations in variable and sample (n) sizes, and across variations in average within-set correlation sizes and in…

  5. Structural Equation Modeling versus Ordinary Least Squares Canonical Analysis: Some Heuristic Comparisons.

    ERIC Educational Resources Information Center

    Dawson, Thomas E.

    This paper describes structural equation modeling (SEM) in comparison with another overarching analysis within the general linear model (GLM) analytic family: canonical correlation analysis. The uninitiated reader can gain an understanding of SEM's basic tenets and applications. Latent constructs discovered via a measurement model are explored and…

  6. Considering causal genes in the genetic dissection of kernel traits in common wheat.

    PubMed

    Mohler, Volker; Albrecht, Theresa; Castell, Adelheid; Diethelm, Manuela; Schweizer, Günther; Hartl, Lorenz

    2016-11-01

    Genetic factors controlling thousand-kernel weight (TKW) were characterized for their association with other seed traits, including kernel width, kernel length, ratio of kernel width to kernel length (KW/KL), kernel area, and spike number per m(2) (SN). For this purpose, a genetic map was established utilizing a doubled haploid population derived from a cross between German winter wheat cultivars Pamier and Format. Association studies in a diversity panel of elite cultivars supplemented genetic analysis of kernel traits. In both populations, genomic signatures of 13 candidate genes for TKW and kernel size were analyzed. Major quantitative trait loci (QTL) for TKW were identified on chromosomes 1B, 2A, 2D, and 4D, and their locations coincided with major QTL for kernel size traits, supporting the common belief that TKW is a function of other kernel traits. The QTL on chromosome 2A was associated with TKW candidate gene TaCwi-A1 and the QTL on chromosome 4D was associated with dwarfing gene Rht-D1. A minor QTL for TKW on chromosome 6B coincided with TaGW2-6B. The QTL for kernel dimensions that did not affect TKW were detected on eight chromosomes. A major QTL for KW/KL located at the distal tip of chromosome arm 5AS is being reported for the first time. TaSus1-7A and TaSAP-A1, closely linked to each other on chromosome 7A, could be related to a minor QTL for KW/KL. Genetic analysis of SN confirmed its negative correlation with TKW in this cross. In the diversity panel, TaSus1-7A was associated with TKW. Compared to the Pamier/Format bi-parental population where TaCwi-A1a was associated with higher TKW, the same allele reduced grain yield in the diversity panel, suggesting opposite effects of TaCwi-A1 on these two traits.

  7. Kernel-Based Equiprobabilistic Topographic Map Formation.

    PubMed

    Van Hulle MM

    1998-09-15

    We introduce a new unsupervised competitive learning rule, the kernel-based maximum entropy learning rule (kMER), which performs equiprobabilistic topographic map formation in regular, fixed-topology lattices, for use with nonparametric density estimation as well as nonparametric regression analysis. The receptive fields of the formal neurons are overlapping radially symmetric kernels, compatible with radial basis functions (RBFs); but unlike other learning schemes, the radii of these kernels do not have to be chosen in an ad hoc manner: the radii are adapted to the local input density, together with the weight vectors that define the kernel centers, so as to produce maps of which the neurons have an equal probability to be active (equiprobabilistic maps). Both an "online" and a "batch" version of the learning rule are introduced, which are applied to nonparametric density estimation and regression, respectively. The application envisaged is blind source separation (BSS) from nonlinear, noisy mixtures.

  8. Bergman kernel from the lowest Landau level

    NASA Astrophysics Data System (ADS)

    Klevtsov, S.

    2009-07-01

    We use path integral representation for the density matrix, projected on the lowest Landau level, to generalize the expansion of the Bergman kernel on Kähler manifold to the case of arbitrary magnetic field.

  9. Quantum kernel applications in medicinal chemistry.

    PubMed

    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.

  10. KITTEN Lightweight Kernel 0.1 Beta

    SciTech Connect

    Pedretti, Kevin; Levenhagen, Michael; Kelly, Suzanne; VanDyke, John; Hudson, Trammell

    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 provides 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.

  11. TICK: Transparent Incremental Checkpointing at Kernel Level

    SciTech Connect

    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

  12. Evaluating the Gradient of the Thin Wire Kernel

    NASA Technical Reports Server (NTRS)

    Wilton, Donald R.; Champagne, Nathan J.

    2008-01-01

    Recently, a formulation for evaluating the thin wire kernel was developed that employed a change of variable to smooth the kernel integrand, canceling the singularity in the integrand. Hence, the typical expansion of the wire kernel in a series for use in the potential integrals is avoided. The new expression for the kernel is exact and may be used directly to determine the gradient of the wire kernel, which consists of components that are parallel and radial to the wire axis.

  13. 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 for x,y near z, where φ(x,y) is an almost-analytic extension of &\\phi(x)=φ(x,x) and similarly for ψ. Further, . If in addition Ω is of finite type, φ,ψ behave reasonably at the boundary, and - log φ, - log ψ are strictly plurisubharmonic on Ω, we obtain also an analogous asymptotic expansion for the Berezin transform and give applications to the Berezin quantization. Finally, for Ω smoothly bounded and strictly pseudoconvex and φ a smooth strictly plurisubharmonic defining function for Ω, we also obtain results on the Berezin-Toeplitz quantization.

  14. Canonical acoustic thin-shell wormholes

    NASA Astrophysics Data System (ADS)

    Jusufi, Kimet; Övgün, Ali

    2017-03-01

    In this paper, we model a canonical acoustic thin-shell wormhole (CATSW) in the framework of analogue gravity systems. In this model, we apply cut and paste technique to join together two spherically symmetric, analogue canonical acoustic solutions, and compute the analogue surface density/surface pressure of the fluid using the Darmois-Israel formalism. We study the stability analyses by using a linear barotropic fluid (LBF), Chaplygin fluid (CF), logarithmic fluid (LogF), polytropic fluid (PF) and finally Van der Waals Quintessence (VDWQ). We show that a kind of analog acoustic fluid with negative energy is required at the throat to keep the wormhole stable. It is argued that CATSW can be a stabile thin-shell wormhole if we choose a suitable parameter values.

  15. Non-canonical modulators of nuclear receptors.

    PubMed

    Tice, Colin M; Zheng, Ya-Jun

    2016-09-01

    Like G protein-coupled receptors (GPCRs) and protein kinases, nuclear receptors (NRs) are a rich source of pharmaceutical targets. Over 80 NR-targeting drugs have been approved for 18 NRs. The focus of drug discovery in NRs has hitherto been on identifying ligands that bind to the canonical ligand binding pockets of the C-terminal ligand binding domains (LBDs). Due to the development of drug resistance and selectivity concerns, there has been considerable interest in exploring other, non-canonical ligand binding sites. Unfortunately, the potencies of compounds binding at other sites have generally not been sufficient for clinical development. However, the situation has changed dramatically over the last 3years, as compounds with sufficient potency have been reported for several NR targets. Here we review recent developments in this area from a medicinal chemistry point of view in the hope of stimulating further interest in this area of research.

  16. RKF-PCA: robust kernel fuzzy PCA.

    PubMed

    Heo, Gyeongyong; Gader, Paul; Frigui, Hichem

    2009-01-01

    Principal component analysis (PCA) is a mathematical method that reduces the dimensionality of the data while retaining most of the variation in the data. Although PCA has been applied in many areas successfully, it suffers from sensitivity to noise and is limited to linear principal components. The noise sensitivity problem comes from the least-squares measure used in PCA and the limitation to linear components originates from the fact that PCA uses an affine transform defined by eigenvectors of the covariance matrix and the mean of the data. In this paper, a robust kernel PCA method that extends the kernel PCA and uses fuzzy memberships is introduced to tackle the two problems simultaneously. We first introduce an iterative method to find robust principal components, called Robust Fuzzy PCA (RF-PCA), which has a connection with robust statistics and entropy regularization. The RF-PCA method is then extended to a non-linear one, Robust Kernel Fuzzy PCA (RKF-PCA), using kernels. The modified kernel used in the RKF-PCA satisfies the Mercer's condition, which means that the derivation of the K-PCA is also valid for the RKF-PCA. Formal analyses and experimental results suggest that the RKF-PCA is an efficient non-linear dimension reduction method and is more noise-robust than the original kernel PCA.

  17. Eukaryotic evolution: early origin of canonical introns.

    PubMed

    Simpson, Alastair G B; MacQuarrie, Erin K; Roger, Andrew J

    2002-09-19

    Spliceosomal introns, one of the hallmarks of eukaryotic genomes, were thought to have originated late in evolution and were assumed not to exist in eukaryotes that diverged early -- until the discovery of a single intron with an aberrant splice boundary in the primitive 'protozoan' Giardia. Here we describe introns from a close relative of Giardia, Carpediemonas membranifera, that have boundary sequences of the normal eukaryotic type, indicating that canonical introns are likely to have arisen very early in eukaryotic evolution.

  18. Canonical approach to Ginsparg-Wilson fermions

    SciTech Connect

    Matsui, Kosuke; Okamoto, Tomohito; Fujiwara, Takanori

    2005-06-01

    Based upon the lattice Dirac operator satisfying the Ginsparg-Wilson relation, we investigate canonical formulation of massless fermion on the spatial lattice. For free fermion system exact chiral symmetry can be implemented without species doubling. In the presence of gauge couplings the chiral symmetry is violated. We show that the divergence of the axial vector current is related to the chiral anomaly in the classical continuum limit.

  19. Canonical Analysis as a Generalized Regression Technique for Multivariate Analysis.

    ERIC Educational Resources Information Center

    Williams, John D.

    The use of characteristic coding (dummy coding) is made in showing solutions to four multivariate problems using canonical analysis. The canonical variates can be themselves analyzed by the use of multiple linear regression. When the canonical variates are used as criteria in a multiple linear regression, the R2 values are equal to 0, where 0 is…

  20. A divergent canonical WNT-signaling pathway regulates microtubule dynamics

    PubMed Central

    Ciani, Lorenza; Krylova, Olga; Smalley, Matthew J.; Dale, Trevor C.; Salinas, Patricia C.

    2004-01-01

    Dishevelled (DVL) is associated with axonal microtubules and regulates microtubule stability through the inhibition of the serine/threonine kinase, glycogen synthase kinase 3β (GSK-3β). In the canonical WNT pathway, the negative regulator Axin forms a complex with β-catenin and GSK-3β, resulting in β-catenin degradation. Inhibition of GSK-3β by DVL increases β-catenin stability and TCF transcriptional activation. Here, we show that Axin associates with microtubules and unexpectedly stabilizes microtubules through DVL. In turn, DVL stabilizes microtubules by inhibiting GSK-3β through a transcription- and β-catenin–independent pathway. More importantly, axonal microtubules are stabilized after DVL localizes to axons. Increased microtubule stability is correlated with a decrease in GSK-3β–mediated phosphorylation of MAP-1B. We propose a model in which Axin, through DVL, stabilizes microtubules by inhibiting a pool of GSK-3β, resulting in local changes in the phosphorylation of cellular targets. Our data indicate a bifurcation in the so-called canonical WNT-signaling pathway to regulate microtubule stability. PMID:14734535

  1. Canonical energy is quantum Fisher information

    NASA Astrophysics Data System (ADS)

    Lashkari, Nima; Van Raamsdonk, Mark

    2016-04-01

    In quantum information theory, Fisher Information is a natural metric on the space of perturbations to a density matrix, defined by calculating the relative entropy with the unperturbed state at quadratic order in perturbations. In gravitational physics, Canonical Energy defines a natural metric on the space of perturbations to spacetimes with a Killing horizon. In this paper, we show that the Fisher information metric for perturbations to the vacuum density matrix of a ball-shaped region B in a holographic CFT is dual to the canonical energy metric for perturbations to a corresponding Rindler wedge R B of Anti-de-Sitter space. Positivity of relative entropy at second order implies that the Fisher information metric is positive definite. Thus, for physical perturbations to anti-de-Sitter spacetime, the canonical energy associated to any Rindler wedge must be positive. This second-order constraint on the metric extends the first order result from relative entropy positivity that physical perturbations must satisfy the linearized Einstein's equations.

  2. One- to four-dimensional kernels for virtual screening and the prediction of physical, chemical, and biological properties.

    PubMed

    Azencott, Chloé-Agathe; Ksikes, Alexandre; Swamidass, S Joshua; Chen, Jonathan H; Ralaivola, Liva; Baldi, Pierre

    2007-01-01

    Many chemoinformatics applications, including high-throughput virtual screening, benefit from being able to rapidly predict the physical, chemical, and biological properties of small molecules to screen large repositories and identify suitable candidates. When training sets are available, machine learning methods provide an effective alternative to ab initio methods for these predictions. Here, we leverage rich molecular representations including 1D SMILES strings, 2D graphs of bonds, and 3D coordinates to derive efficient machine learning kernels to address regression problems. We further expand the library of available spectral kernels for small molecules developed for classification problems to include 2.5D surface and 3D kernels using Delaunay tetrahedrization and other techniques from computational geometry, 3D pharmacophore kernels, and 3.5D or 4D kernels capable of taking into account multiple molecular configurations, such as conformers. The kernels are comprehensively tested using cross-validation and redundancy-reduction methods on regression problems using several available data sets to predict boiling points, melting points, aqueous solubility, octanol/water partition coefficients, and biological activity with state-of-the art results. When sufficient training data are available, 2D spectral kernels in general tend to yield the best and most robust results, better than state-of-the art. On data sets containing thousands of molecules, the kernels achieve a squared correlation coefficient of 0.91 for aqueous solubility prediction and 0.94 for octanol/water partition coefficient prediction. Averaging over conformations improves the performance of kernels based on the three-dimensional structure of molecules, especially on challenging data sets. Kernel predictors for aqueous solubility (kSOL), LogP (kLOGP), and melting point (kMELT) are available over the Web through: http://cdb.ics.uci.edu.

  3. Canonical Wnt signalling regulates epithelial patterning by modulating levels of laminins in zebrafish appendages.

    PubMed

    Nagendran, Monica; Arora, Prateek; Gori, Payal; Mulay, Aditya; Ray, Shinjini; Jacob, Tressa; Sonawane, Mahendra

    2015-01-15

    The patterning and morphogenesis of body appendages - such as limbs and fins - is orchestrated by the activities of several developmental pathways. Wnt signalling is essential for the induction of limbs. However, it is unclear whether a canonical Wnt signalling gradient exists and regulates the patterning of epithelium in vertebrate appendages. Using an evolutionarily old appendage - the median fin in zebrafish - as a model, we show that the fin epithelium exhibits graded changes in cellular morphology along the proximo-distal axis. This epithelial pattern is strictly correlated with the gradient of canonical Wnt signalling activity. By combining genetic analyses with cellular imaging, we show that canonical Wnt signalling regulates epithelial cell morphology by modulating the levels of laminins, which are extracellular matrix components. We have unravelled a hitherto unknown mechanism involved in epithelial patterning, which is also conserved in the pectoral fins - evolutionarily recent appendages that are homologous to tetrapod limbs.

  4. Multireference quantum chemistry through a joint density matrix renormalization group and canonical transformation theory

    NASA Astrophysics Data System (ADS)

    Yanai, Takeshi; Kurashige, Yuki; Neuscamman, Eric; Chan, Garnet Kin-Lic

    2010-01-01

    We describe the joint application of the density matrix renormalization group and canonical transformation theory to multireference quantum chemistry. The density matrix renormalization group provides the ability to describe static correlation in large active spaces, while the canonical transformation theory provides a high-order description of the dynamic correlation effects. We demonstrate the joint theory in two benchmark systems designed to test the dynamic and static correlation capabilities of the methods, namely, (i) total correlation energies in long polyenes and (ii) the isomerization curve of the [Cu2O2]2+ core. The largest complete active spaces and atomic orbital basis sets treated by the joint DMRG-CT theory in these systems correspond to a (24e,24o) active space and 268 atomic orbitals in the polyenes and a (28e,32o) active space and 278 atomic orbitals in [Cu2O2]2+.

  5. Ciliary IFT80 balances canonical versus non-canonical hedgehog signalling for osteoblast differentiation.

    PubMed

    Yuan, Xue; Cao, Jay; He, Xiaoning; Serra, Rosa; Qu, Jun; Cao, Xu; Yang, Shuying

    2016-03-21

    Intraflagellar transport proteins (IFT) are required for hedgehog (Hh) signalling transduction that is essential for bone development, however, how IFT proteins regulate Hh signalling in osteoblasts (OBs) remains unclear. Here we show that deletion of ciliary IFT80 in OB precursor cells (OPC) in mice results in growth retardation and markedly decreased bone mass with impaired OB differentiation. Loss of IFT80 blocks canonical Hh-Gli signalling via disrupting Smo ciliary localization, but elevates non-canonical Hh-Gαi-RhoA-stress fibre signalling by increasing Smo and Gαi binding. Inhibition of RhoA and ROCK activity partially restores osteogenic differentiation of IFT80-deficient OPCs by inhibiting non-canonical Hh-RhoA-Cofilin/MLC2 signalling. Cytochalasin D, an actin destabilizer, dramatically restores OB differentiation of IFT80-deficient OPCs by disrupting actin stress fibres and promoting cilia formation and Hh-Gli signalling. These findings reveal that IFT80 is required for OB differentiation by balancing between canonical Hh-Gli and non-canonical Hh-Gαi-RhoA pathways and highlight IFT80 as a therapeutic target for craniofacial and skeletal abnormalities.

  6. Ciliary IFT80 balances canonical versus non-canonical hedgehog signalling for osteoblast differentiation

    PubMed Central

    Yuan, Xue; Cao, Jay; He, Xiaoning; Serra, Rosa; Qu, Jun; Cao, Xu; Yang, Shuying

    2016-01-01

    Intraflagellar transport proteins (IFT) are required for hedgehog (Hh) signalling transduction that is essential for bone development, however, how IFT proteins regulate Hh signalling in osteoblasts (OBs) remains unclear. Here we show that deletion of ciliary IFT80 in OB precursor cells (OPC) in mice results in growth retardation and markedly decreased bone mass with impaired OB differentiation. Loss of IFT80 blocks canonical Hh–Gli signalling via disrupting Smo ciliary localization, but elevates non-canonical Hh–Gαi–RhoA–stress fibre signalling by increasing Smo and Gαi binding. Inhibition of RhoA and ROCK activity partially restores osteogenic differentiation of IFT80-deficient OPCs by inhibiting non-canonical Hh–RhoA–Cofilin/MLC2 signalling. Cytochalasin D, an actin destabilizer, dramatically restores OB differentiation of IFT80-deficient OPCs by disrupting actin stress fibres and promoting cilia formation and Hh–Gli signalling. These findings reveal that IFT80 is required for OB differentiation by balancing between canonical Hh–Gli and non-canonical Hh–Gαi–RhoA pathways and highlight IFT80 as a therapeutic target for craniofacial and skeletal abnormalities. PMID:26996322

  7. Kernel-Based Reconstruction of Graph Signals

    NASA Astrophysics Data System (ADS)

    Romero, Daniel; Ma, Meng; Giannakis, Georgios B.

    2017-02-01

    A number of applications in engineering, social sciences, physics, and biology involve inference over networks. In this context, graph signals are widely encountered as descriptors of vertex attributes or features in graph-structured data. Estimating such signals in all vertices given noisy observations of their values on a subset of vertices has been extensively analyzed in the literature of signal processing on graphs (SPoG). This paper advocates kernel regression as a framework generalizing popular SPoG modeling and reconstruction and expanding their capabilities. Formulating signal reconstruction as a regression task on reproducing kernel Hilbert spaces of graph signals permeates benefits from statistical learning, offers fresh insights, and allows for estimators to leverage richer forms of prior information than existing alternatives. A number of SPoG notions such as bandlimitedness, graph filters, and the graph Fourier transform are naturally accommodated in the kernel framework. Additionally, this paper capitalizes on the so-called representer theorem to devise simpler versions of existing Thikhonov regularized estimators, and offers a novel probabilistic interpretation of kernel methods on graphs based on graphical models. Motivated by the challenges of selecting the bandwidth parameter in SPoG estimators or the kernel map in kernel-based methods, the present paper further proposes two multi-kernel approaches with complementary strengths. Whereas the first enables estimation of the unknown bandwidth of bandlimited signals, the second allows for efficient graph filter selection. Numerical tests with synthetic as well as real data demonstrate the merits of the proposed methods relative to state-of-the-art alternatives.

  8. Oecophylla longinoda (Hymenoptera: Formicidae) Lead to Increased Cashew Kernel Size and Kernel Quality.

    PubMed

    Anato, F M; Sinzogan, A A C; Offenberg, J; Adandonon, A; Wargui, R B; Deguenon, J M; Ayelo, P M; Vayssières, J-F; Kossou, D K

    2017-03-03

    Weaver ants, Oecophylla spp., are known to positively affect cashew, Anacardium occidentale L., raw nut yield, but their effects on the kernels have not been reported. We compared nut size and the proportion of marketable kernels between raw nuts collected from trees with and without ants. Raw nuts collected from trees with weaver ants were 2.9% larger than nuts from control trees (i.e., without weaver ants), leading to 14% higher proportion of marketable kernels. On trees with ants, the kernel: raw nut ratio from nuts damaged by formic acid was 4.8% lower compared with nondamaged nuts from the same trees. Weaver ants provided three benefits to cashew production by increasing yields, yielding larger nuts, and by producing greater proportions of marketable kernel mass.

  9. A new Mercer sigmoid kernel for clinical data classification.

    PubMed

    Carrington, André M; Fieguth, Paul W; Chen, Helen H

    2014-01-01

    In classification with Support Vector Machines, only Mercer kernels, i.e. valid kernels, such as the Gaussian RBF kernel, are widely accepted and thus suitable for clinical data. Practitioners would also like to use the sigmoid kernel, a non-Mercer kernel, but its range of validity is difficult to determine, and even within range its validity is in dispute. Despite these shortcomings the sigmoid kernel is used by some, and two kernels in the literature attempt to emulate and improve upon it. We propose the first Mercer sigmoid kernel, that is therefore trustworthy for the classification of clinical data. We show the similarity between the Mercer sigmoid kernel and the sigmoid kernel and, in the process, identify a normalization technique that improves the classification accuracy of the latter. The Mercer sigmoid kernel achieves the best mean accuracy on three clinical data sets, detecting melanoma in skin lesions better than the most popular kernels; while with non-clinical data sets it has no significant difference in median accuracy as compared with the Gaussian RBF kernel. It consistently classifies some points correctly that the Gaussian RBF kernel does not and vice versa.

  10. Temperature fluctuations in canonical systems: Insights from molecular dynamics simulations

    NASA Astrophysics Data System (ADS)

    Hickman, J.; Mishin, Y.

    2016-11-01

    Molecular dynamics simulations of a quasiharmonic solid are conducted to elucidate the meaning of temperature fluctuations in canonical systems and validate a well-known but frequently contested equation predicting the mean square of such fluctuations. The simulations implement two virtual and one physical (natural) thermostat and examine the kinetic, potential, and total energy correlation functions in the time and frequency domains. The results clearly demonstrate the existence of quasiequilibrium states in which the system can be characterized by a well-defined temperature that follows the mentioned fluctuation equation. The emergence of such states is due to the wide separation of time scales between thermal relaxation by phonon scattering and slow energy exchanges with the thermostat. The quasiequilibrium states exist between these two time scales when the system behaves as virtually isolated and equilibrium.

  11. Estimating the Kernel Mass Ratio in Peanuts Nondestructively Using a Low-Cost Impedance Meter

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Earlier, we investigated the possibility of estimating the mass of the kernels in a given volume of unshelled peanuts using a commercial impedance meter. Measurements of impedance and phase angles of peanut samples were made from 1 to 10 MHz at intervals of 1 MHz. The measured values were correlate...

  12. Kernel bandwidth optimization in spike rate estimation.

    PubMed

    Shimazaki, Hideaki; Shinomoto, Shigeru

    2010-08-01

    Kernel smoother and a time-histogram are classical tools for estimating an instantaneous rate of spike occurrences. We recently established a method for selecting the bin width of the time-histogram, based on the principle of minimizing the mean integrated square error (MISE) between the estimated rate and unknown underlying rate. Here we apply the same optimization principle to the kernel density estimation in selecting the width or "bandwidth" of the kernel, and further extend the algorithm to allow a variable bandwidth, in conformity with data. The variable kernel has the potential to accurately grasp non-stationary phenomena, such as abrupt changes in the firing rate, which we often encounter in neuroscience. In order to avoid possible overfitting that may take place due to excessive freedom, we introduced a stiffness constant for bandwidth variability. Our method automatically adjusts the stiffness constant, thereby adapting to the entire set of spike data. It is revealed that the classical kernel smoother may exhibit goodness-of-fit comparable to, or even better than, that of modern sophisticated rate estimation methods, provided that the bandwidth is selected properly for a given set of spike data, according to the optimization methods presented here.

  13. 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.

  14. Online Sequential Extreme Learning Machine With Kernels.

    PubMed

    Scardapane, Simone; Comminiello, Danilo; Scarpiniti, Michele; Uncini, Aurelio

    2015-09-01

    The extreme learning machine (ELM) was recently proposed as a unifying framework for different families of learning algorithms. The classical ELM model consists of a linear combination of a fixed number of nonlinear expansions of the input vector. Learning in ELM is hence equivalent to finding the optimal weights that minimize the error on a dataset. The update works in batch mode, either with explicit feature mappings or with implicit mappings defined by kernels. Although an online version has been proposed for the former, no work has been done up to this point for the latter, and whether an efficient learning algorithm for online kernel-based ELM exists remains an open problem. By explicating some connections between nonlinear adaptive filtering and ELM theory, in this brief, we present an algorithm for this task. In particular, we propose a straightforward extension of the well-known kernel recursive least-squares, belonging to the kernel adaptive filtering (KAF) family, to the ELM framework. We call the resulting algorithm the kernel online sequential ELM (KOS-ELM). Moreover, we consider two different criteria used in the KAF field to obtain sparse filters and extend them to our context. We show that KOS-ELM, with their integration, can result in a highly efficient algorithm, both in terms of obtained generalization error and training time. Empirical evaluations demonstrate interesting results on some benchmarking datasets.

  15. The connection between regularization operators and support vector kernels.

    PubMed

    Smola, Alex J.; Schölkopf, Bernhard; Müller, Klaus Robert

    1998-06-01

    In this paper a correspondence is derived between regularization operators used in regularization networks and support vector kernels. We prove that the Green's Functions associated with regularization operators are suitable support vector kernels with equivalent regularization properties. Moreover, the paper provides an analysis of currently used support vector kernels in the view of regularization theory and corresponding operators associated with the classes of both polynomial kernels and translation invariant kernels. The latter are also analyzed on periodical domains. As a by-product we show that a large number of radial basis functions, namely conditionally positive definite functions, may be used as support vector kernels.

  16. A Canonical Biomechanical Vocal Fold Model

    PubMed Central

    Bhattacharya, Pinaki; Siegmund, Thomas H.

    2012-01-01

    Summary The present article aimed at constructing a canonical geometry of the human vocal fold (VF) from subject-specific image slice data. A computer-aided design approach automated the model construction. A subject-specific geometry available in literature, three abstractions (which successively diminished in geometric detail) derived from it, and a widely used quasi two-dimensional VF model geometry were used to create computational models. The first three natural frequencies of the models were used to characterize their mechanical response. These frequencies were determined for a representative range of tissue biomechanical properties, accounting for underlying VF histology. Compared with the subject-specific geometry model (baseline), a higher degree of abstraction was found to always correspond to a larger deviation in model frequency (up to 50% in the relevant range of tissue biomechanical properties). The model we deemed canonical was optimally abstracted, in that it significantly simplified the VF geometry compared with the baseline geometry but can be recalibrated in a consistent manner to match the baseline response. Models providing only a marginally higher degree of abstraction were found to have significant deviation in predicted frequency response. The quasi two-dimensional model presented an extreme situation: it could not be recalibrated for its frequency response to match the subject-specific model. This deficiency was attributed to complex support conditions at anterior-posterior extremities of the VFs, accentuated by further issues introduced through the tissue biomechanical properties. In creating canonical models by leveraging advances in clinical imaging techniques, the automated design procedure makes VF modeling based on subject-specific geometry more realizable. PMID:22209063

  17. Kato expansion in quantum canonical perturbation theory

    NASA Astrophysics Data System (ADS)

    Nikolaev, Andrey

    2016-06-01

    This work establishes a connection between canonical perturbation series in quantum mechanics and a Kato expansion for the resolvent of the Liouville superoperator. Our approach leads to an explicit expression for a generator of a block-diagonalizing Dyson's ordered exponential in arbitrary perturbation order. Unitary intertwining of perturbed and unperturbed averaging superprojectors allows for a description of ambiguities in the generator and block-diagonalized Hamiltonian. We compare the efficiency of the corresponding computational algorithm with the efficiencies of the Van Vleck and Magnus methods for high perturbative orders.

  18. Canonical formalism for coupled beam optics

    SciTech Connect

    Kheifets, S.A.

    1989-09-01

    Beam optics of a lattice with an inter-plane coupling is treated using canonical Hamiltonian formalism. The method developed is equally applicable both to a circular (periodic) machine and to an open transport line. A solution of the equation of a particle motion (and correspondingly transfer matrix between two arbitrary points of the lattice) are described in terms of two amplitude functions (and their derivatives and corresponding phases of oscillations) and four coupling functions, defined by a solution of the system of the first-order nonlinear differential equations derived in the paper. Thus total number of independent parameters is equal to ten. 8 refs.

  19. Canonical energy and linear stability of Schwarzschild

    NASA Astrophysics Data System (ADS)

    Prabhu, Kartik; Wald, Robert

    2017-01-01

    Consider linearised perturbations of the Schwarzschild black hole in 4 dimensions. Using the linearised Newman-Penrose curvature component, which satisfies the Teukolsky equation, as a Hertz potential we generate a `new' metric perturbation satisfying the linearised Einstein equation. We show that the canonical energy, given by Hollands and Wald, of the `new' metric perturbation is the conserved Regge-Wheeler-like energy used by Dafermos, Holzegel and Rodnianski to prove linear stability and decay of perturbations of Schwarzschild. We comment on a generalisation of this strategy to prove the linear stability of the Kerr black hole.

  20. Canonical forms of unconditionally convergent multipliers☆

    PubMed Central

    Stoeva, D.T.; Balazs, P.

    2013-01-01

    Multipliers are operators that combine (frame-like) analysis, a multiplication with a fixed sequence, called the symbol, and synthesis. They are very interesting mathematical objects that also have a lot of applications for example in acoustical signal processing. It is known that bounded symbols and Bessel sequences guarantee unconditional convergence. In this paper we investigate necessary and equivalent conditions for the unconditional convergence of multipliers. In particular, we show that, under mild conditions, unconditionally convergent multipliers can be transformed by shifting weights between symbol and sequence, into multipliers with symbol (1) and Bessel sequences (called multipliers in canonical form). PMID:23564973

  1. Canonical forms of unconditionally convergent multipliers.

    PubMed

    Stoeva, D T; Balazs, P

    2013-03-01

    Multipliers are operators that combine (frame-like) analysis, a multiplication with a fixed sequence, called the symbol, and synthesis. They are very interesting mathematical objects that also have a lot of applications for example in acoustical signal processing. It is known that bounded symbols and Bessel sequences guarantee unconditional convergence. In this paper we investigate necessary and equivalent conditions for the unconditional convergence of multipliers. In particular, we show that, under mild conditions, unconditionally convergent multipliers can be transformed by shifting weights between symbol and sequence, into multipliers with symbol (1) and Bessel sequences (called multipliers in canonical form).

  2. A rainfall spatial interpolation algorithm based on inhomogeneous kernels

    NASA Astrophysics Data System (ADS)

    Campo, Lorenzo; Fiori, Elisabetta; Molini, Luca

    2015-04-01

    Rainfall fields constitute the main input of hydrological distributed models, both for long period water balance and for short period flood forecast and monitoring. The importance of an accurate reconstruction of the spatial pattern of rainfall is, thus, well recognized in several fields of application: agricultural planning, water balance at watershed scale, water management, flood monitoring. The latter case is particularly critical, due to the strong effect of the combination of the soil moisture pattern and of the rainfall pattern on the intensity peak of the flood. Despite the importance of the spatial characterization of the rainfall height, this variable still presents several difficulties when an interpolation is required. Rainfall fields present spatial and temporal alternance of large zero-values areas (no-rainfall) and complex pattern of non zero heights (rainfall events). Furthermore, the spatial patterns strongly depend on the type and the origin of rain event (convective, stratiform, orographic) and on the spatial scale. Different kind of rainfall measures and estimates (rainfall gauges, satellite estimates, meteo radar) are available, as well as large amount of literature for the spatial interpolation: from Thiessen polygons to Inverse Distance Weight (IDW) to different variants of kriging, neural network and other deterministic or geostatistic methods. In this work a kernel-based method for interpolation of point measures (raingauges) is proposed, in which spatially inhomogeneous kernel are used. For each gauge a particular kernel is fitted following the particular correlation structures between the rainfall time series of the given gauge and those of its neighbors. In this way the local features of the field are considered following the observed dependence spatial pattern. The kernel are assumed to be Gaussian, whose covariance matrices are fitted basing on the values of the correlation of the time series and the location. A similar approach is

  3. Nonparametric entropy estimation using kernel densities.

    PubMed

    Lake, Douglas E

    2009-01-01

    The entropy of experimental data from the biological and medical sciences provides additional information over summary statistics. Calculating entropy involves estimates of probability density functions, which can be effectively accomplished using kernel density methods. Kernel density estimation has been widely studied and a univariate implementation is readily available in MATLAB. The traditional definition of Shannon entropy is part of a larger family of statistics, called Renyi entropy, which are useful in applications that require a measure of the Gaussianity of data. Of particular note is the quadratic entropy which is related to the Friedman-Tukey (FT) index, a widely used measure in the statistical community. One application where quadratic entropy is very useful is the detection of abnormal cardiac rhythms, such as atrial fibrillation (AF). Asymptotic and exact small-sample results for optimal bandwidth and kernel selection to estimate the FT index are presented and lead to improved methods for entropy estimation.

  4. Fast generation of sparse random kernel graphs

    SciTech Connect

    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.

  5. Fast generation of sparse random kernel graphs

    DOE PAGES

    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

  6. Phenolic constituents of shea (Vitellaria paradoxa) kernels.

    PubMed

    Maranz, Steven; Wiesman, Zeev; Garti, Nissim

    2003-10-08

    Analysis of the phenolic constituents of shea (Vitellaria paradoxa) kernels by LC-MS revealed eight catechin compounds-gallic acid, catechin, epicatechin, epicatechin gallate, gallocatechin, epigallocatechin, gallocatechin gallate, and epigallocatechin gallate-as well as quercetin and trans-cinnamic acid. The mean kernel content of the eight catechin compounds was 4000 ppm (0.4% of kernel dry weight), with a 2100-9500 ppm range. Comparison of the profiles of the six major catechins from 40 Vitellaria provenances from 10 African countries showed that the relative proportions of these compounds varied from region to region. Gallic acid was the major phenolic compound, comprising an average of 27% of the measured total phenols and exceeding 70% in some populations. Colorimetric analysis (101 samples) of total polyphenols extracted from shea butter into hexane gave an average of 97 ppm, with the values for different provenances varying between 62 and 135 ppm of total polyphenols.

  7. Tile-Compressed FITS Kernel for IRAF

    NASA Astrophysics Data System (ADS)

    Seaman, R.

    2011-07-01

    The Flexible Image Transport System (FITS) is a ubiquitously supported standard of the astronomical community. Similarly, the Image Reduction and Analysis Facility (IRAF), developed by the National Optical Astronomy Observatory, is a widely used astronomical data reduction package. IRAF supplies compatibility with FITS format data through numerous tools and interfaces. The most integrated of these is IRAF's FITS image kernel that provides access to FITS from any IRAF task that uses the basic IMIO interface. The original FITS kernel is a complex interface of purpose-built procedures that presents growing maintenance issues and lacks recent FITS innovations. A new FITS kernel is being developed at NOAO that is layered on the CFITSIO library from the NASA Goddard Space Flight Center. The simplified interface will minimize maintenance headaches as well as add important new features such as support for the FITS tile-compressed (fpack) format.

  8. Fractal Weyl law for Linux Kernel architecture

    NASA Astrophysics Data System (ADS)

    Ermann, L.; Chepelianskii, A. D.; Shepelyansky, D. L.

    2011-01-01

    We study the properties of spectrum and eigenstates of the Google matrix of a directed network formed by the procedure calls in the Linux Kernel. Our results obtained for various versions of the Linux Kernel show that the spectrum is characterized by the fractal Weyl law established recently for systems of quantum chaotic scattering and the Perron-Frobenius operators of dynamical maps. The fractal Weyl exponent is found to be ν ≈ 0.65 that corresponds to the fractal dimension of the network d ≈ 1.3. An independent computation of the fractal dimension by the cluster growing method, generalized for directed networks, gives a close value d ≈ 1.4. The eigenmodes of the Google matrix of Linux Kernel are localized on certain principal nodes. We argue that the fractal Weyl law should be generic for directed networks with the fractal dimension d < 2.

  9. A kernel-based approach for biomedical named entity recognition.

    PubMed

    Patra, Rakesh; Saha, Sujan Kumar

    2013-01-01

    Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.

  10. Experimental study of turbulent flame kernel propagation

    SciTech Connect

    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)

  11. A dynamic kernel modifier for linux

    SciTech Connect

    Minnich, R. G.

    2002-09-03

    Dynamic Kernel Modifier, or DKM, is a kernel module for Linux that allows user-mode programs to modify the execution of functions in the kernel without recompiling or modifying the kernel source in any way. Functions may be traced, either function entry only or function entry and exit; nullified; or replaced with some other function. For the tracing case, function execution results in the activation of a watchpoint. When the watchpoint is activated, the address of the function is logged in a FIFO buffer that is readable by external applications. The watchpoints are time-stamped with the resolution of the processor high resolution timers, which on most modem processors are accurate to a single processor tick. DKM is very similar to earlier systems such as the SunOS trace device or Linux TT. Unlike these two systems, and other similar systems, DKM requires no kernel modifications. DKM allows users to do initial probing of the kernel to look for performance problems, or even to resolve potential problems by turning functions off or replacing them. DKM watchpoints are not without cost: it takes about 200 nanoseconds to make a log entry on an 800 Mhz Pentium-Ill. The overhead numbers are actually competitive with other hardware-based trace systems, although it has less 'Los Alamos National Laboratory is operated by the University of California for the National Nuclear Security Administration of the United States Department of Energy under contract W-7405-ENG-36. accuracy than an In-Circuit Emulator such as the American Arium. Once the user has zeroed in on a problem, other mechanisms with a higher degree of accuracy can be used.

  12. Kernel abortion in maize. II. Distribution of /sup 14/C among kernel carboydrates

    SciTech Connect

    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.

  13. QTL Mapping of Kernel Number-Related Traits and Validation of One Major QTL for Ear Length in Maize.

    PubMed

    Huo, Dongao; Ning, Qiang; Shen, Xiaomeng; Liu, Lei; Zhang, Zuxin

    2016-01-01

    The kernel number is a grain yield component and an important maize breeding goal. Ear length, kernel number per row and ear row number are highly correlated with the kernel number per ear, which eventually determines the ear weight and grain yield. In this study, two sets of F2:3 families developed from two bi-parental crosses sharing one inbred line were used to identify quantitative trait loci (QTL) for four kernel number-related traits: ear length, kernel number per row, ear row number and ear weight. A total of 39 QTLs for the four traits were identified in the two populations. The phenotypic variance explained by a single QTL ranged from 0.4% to 29.5%. Additionally, 14 overlapping QTLs formed 5 QTL clusters on chromosomes 1, 4, 5, 7, and 10. Intriguingly, six QTLs for ear length and kernel number per row overlapped in a region on chromosome 1. This region was designated qEL1.10 and was validated as being simultaneously responsible for ear length, kernel number per row and ear weight in a near isogenic line-derived population, suggesting that qEL1.10 was a pleiotropic QTL with large effects. Furthermore, the performance of hybrids generated by crossing 6 elite inbred lines with two near isogenic lines at qEL1.10 showed the breeding value of qEL1.10 for the improvement of the kernel number and grain yield of maize hybrids. This study provides a basis for further fine mapping, molecular marker-aided breeding and functional studies of kernel number-related traits in maize.

  14. QTL Mapping of Kernel Number-Related Traits and Validation of One Major QTL for Ear Length in Maize

    PubMed Central

    Huo, Dongao; Ning, Qiang; Shen, Xiaomeng; Liu, Lei; Zhang, Zuxin

    2016-01-01

    The kernel number is a grain yield component and an important maize breeding goal. Ear length, kernel number per row and ear row number are highly correlated with the kernel number per ear, which eventually determines the ear weight and grain yield. In this study, two sets of F2:3 families developed from two bi-parental crosses sharing one inbred line were used to identify quantitative trait loci (QTL) for four kernel number-related traits: ear length, kernel number per row, ear row number and ear weight. A total of 39 QTLs for the four traits were identified in the two populations. The phenotypic variance explained by a single QTL ranged from 0.4% to 29.5%. Additionally, 14 overlapping QTLs formed 5 QTL clusters on chromosomes 1, 4, 5, 7, and 10. Intriguingly, six QTLs for ear length and kernel number per row overlapped in a region on chromosome 1. This region was designated qEL1.10 and was validated as being simultaneously responsible for ear length, kernel number per row and ear weight in a near isogenic line-derived population, suggesting that qEL1.10 was a pleiotropic QTL with large effects. Furthermore, the performance of hybrids generated by crossing 6 elite inbred lines with two near isogenic lines at qEL1.10 showed the breeding value of qEL1.10 for the improvement of the kernel number and grain yield of maize hybrids. This study provides a basis for further fine mapping, molecular marker-aided breeding and functional studies of kernel number-related traits in maize. PMID:27176215

  15. Reduced multiple empirical kernel learning machine.

    PubMed

    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

  16. Full Waveform Inversion Using Waveform Sensitivity Kernels

    NASA Astrophysics Data System (ADS)

    Schumacher, Florian; Friederich, Wolfgang

    2013-04-01

    We present a full waveform inversion concept for applications ranging from seismological to enineering contexts, in which the steps of forward simulation, computation of sensitivity kernels, and the actual inversion are kept separate of each other. We derive waveform sensitivity kernels from Born scattering theory, which for unit material perturbations are identical to the Born integrand for the considered path between source and receiver. The evaluation of such a kernel requires the calculation of Green functions and their strains for single forces at the receiver position, as well as displacement fields and strains originating at the seismic source. We compute these quantities in the frequency domain using the 3D spectral element code SPECFEM3D (Tromp, Komatitsch and Liu, 2008) and the 1D semi-analytical code GEMINI (Friederich and Dalkolmo, 1995) in both, Cartesian and spherical framework. We developed and implemented the modularized software package ASKI (Analysis of Sensitivity and Kernel Inversion) to compute waveform sensitivity kernels from wavefields generated by any of the above methods (support for more methods is planned), where some examples will be shown. As the kernels can be computed independently from any data values, this approach allows to do a sensitivity and resolution analysis first without inverting any data. In the context of active seismic experiments, this property may be used to investigate optimal acquisition geometry and expectable resolution before actually collecting any data, assuming the background model is known sufficiently well. The actual inversion step then, can be repeated at relatively low costs with different (sub)sets of data, adding different smoothing conditions. Using the sensitivity kernels, we expect the waveform inversion to have better convergence properties compared with strategies that use gradients of a misfit function. Also the propagation of the forward wavefield and the backward propagation from the receiver

  17. Canon Fodder: Young Adult Literature as a Tool for Critiquing Canonicity

    ERIC Educational Resources Information Center

    Hateley, Erica

    2013-01-01

    Young adult literature is a tool of socialisation and acculturation for young readers. This extends to endowing "reading" with particular significance in terms of what literature should be read and why. This paper considers some recent young adult fiction with an eye to its engagement with canonical literature and its representations of…

  18. Canonical and non-canonical VEGF pathways: New developments in biology and signal transduction

    PubMed Central

    Domigan, Courtney K.; Ziyad, Safiyyah; Iruela-Arispe, M. Luisa

    2014-01-01

    The last five years have witnessed a significant expansion in our understanding of VEGF signaling. In particular, the process of canonical activation of VEGFR tyrosine kinases by homodimeric VEGF molecules have now been broadened by the realization that heterodimeric ligands and receptors are also active participants in the signaling process. While heterodimer receptors were described two decades ago, their impact, along with the effect of additional cell surface partners and novel autocrine VEGF signaling pathways, are only now starting to be clarified. Furthermore, ligand-independent signaling (non-canonical) has been identified which occurs through galectin and gremlin binding, and upon rise of intracellular levels of reactive oxygen species. Activation of the VEGF receptors in the absence of ligand holds immediate implications for therapeutic approaches that exclusively target VEGF. The present review provides a concise summary of the recent developments in both canonical and non-canonical VEGF signaling and places these findings in perspective to their potential clinical and biological ramifications. PMID:25278287

  19. Regularization techniques for PSF-matching kernels - I. Choice of kernel basis

    NASA Astrophysics Data System (ADS)

    Becker, A. C.; Homrighausen, D.; Connolly, A. J.; Genovese, C. R.; Owen, R.; Bickerton, S. J.; Lupton, R. H.

    2012-09-01

    We review current methods for building point spread function (PSF)-matching kernels for the purposes of image subtraction or co-addition. Such methods use a linear decomposition of the kernel on a series of basis functions. The correct choice of these basis functions is fundamental to the efficiency and effectiveness of the matching - the chosen bases should represent the underlying signal using a reasonably small number of shapes, and/or have a minimum number of user-adjustable tuning parameters. We examine methods whose bases comprise multiple Gauss-Hermite polynomials, as well as a form-free basis composed of delta-functions. Kernels derived from delta-functions are unsurprisingly shown to be more expressive; they are able to take more general shapes and perform better in situations where sum-of-Gaussian methods are known to fail. However, due to its many degrees of freedom (the maximum number allowed by the kernel size) this basis tends to overfit the problem and yields noisy kernels having large variance. We introduce a new technique to regularize these delta-function kernel solutions, which bridges the gap between the generality of delta-function kernels and the compactness of sum-of-Gaussian kernels. Through this regularization we are able to create general kernel solutions that represent the intrinsic shape of the PSF-matching kernel with only one degree of freedom, the strength of the regularization λ. The role of λ is effectively to exchange variance in the resulting difference image with variance in the kernel itself. We examine considerations in choosing the value of λ, including statistical risk estimators and the ability of the solution to predict solutions for adjacent areas. Both of these suggest moderate strengths of λ between 0.1 and 1.0, although this optimization is likely data set dependent. This model allows for flexible representations of the convolution kernel that have significant predictive ability and will prove useful in implementing

  20. Identifying Odd/Even-Order Binary Kernel Slices for a Nonlinear System Using Inverse Repeat m-Sequences

    PubMed Central

    Hu, Jin-yan; Yan, Gang

    2015-01-01

    The study of various living complex systems by system identification method is important, and the identification of the problem is even more challenging when dealing with a dynamic nonlinear system of discrete time. A well-established model based on kernel functions for input of the maximum length sequence (m-sequence) can be used to estimate nonlinear binary kernel slices using cross-correlation method. In this study, we examine the relevant mathematical properties of kernel slices, particularly their shift-and-product property and overlap distortion problem caused by the irregular shifting of the estimated kernel slices in the cross-correlation function between the input m-sequence and the system output. We then derive the properties of the inverse repeat (IR) m-sequence and propose a method of using IR m-sequence as an input to separately estimate odd- and even-order kernel slices to reduce the chance of kernel-slice overlapping. An instance of third-order Wiener nonlinear model is simulated to justify the proposed method. PMID:25873988

  1. Identifying odd/even-order binary kernel slices for a nonlinear system using inverse repeat m-sequences.

    PubMed

    Hu, Jin-Yan; Yan, Gang; Wang, Tao

    2015-01-01

    The study of various living complex systems by system identification method is important, and the identification of the problem is even more challenging when dealing with a dynamic nonlinear system of discrete time. A well-established model based on kernel functions for input of the maximum length sequence (m-sequence) can be used to estimate nonlinear binary kernel slices using cross-correlation method. In this study, we examine the relevant mathematical properties of kernel slices, particularly their shift-and-product property and overlap distortion problem caused by the irregular shifting of the estimated kernel slices in the cross-correlation function between the input m-sequence and the system output. We then derive the properties of the inverse repeat (IR) m-sequence and propose a method of using IR m-sequence as an input to separately estimate odd- and even-order kernel slices to reduce the chance of kernel-slice overlapping. An instance of third-order Wiener nonlinear model is simulated to justify the proposed method.

  2. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    PubMed Central

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo

    2016-01-01

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970

  3. Accuracy of Reduced and Extended Thin-Wire Kernels

    SciTech Connect

    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.

  4. [Huang Yizhou's study on Nei jing (Inner Canon)].

    PubMed

    Hu, Benxiang; Huang, Youmei; Yu, Chengfen

    2002-01-01

    Being a great classical scholar of the late Qing dynasty, Huang Yizhou collated Nei jing (Inner Canon) by textual criticism. But most of his works were missing. By reviewing historical documents and literature, it has been found that his collated books include Huang di nei jing su wen jiao ben (Collated Edition of Huangdi's Inner Canon Plain Questions), Huang di nei jing su wen chong jiao zheng (Recollated Huangdi's Inner Canon Plain Questions), Nei jing zhen ci (Acupuncture in Inner Canon), Huang di nei jing jiu juan ji zhu (Variorum of Nine Volumes of Huangdi's Inner Canon), Huang di nei jing ming tang (Acupuncture Chart of Huangdi's Inner Canon), and Jiu chao tai su jiao ben (Old Extremely Plain Question Recension). Many of his disciples became famous scholars in the Republican period.

  5. Fabrication of Uranium Oxycarbide Kernels for HTR Fuel

    SciTech Connect

    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.

  6. 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.

  7. Grand canonical Monte Carlo simulation of liquid argon

    NASA Astrophysics Data System (ADS)

    Ruff, Imre; Baranyai, András; Pálinkás, Gábor; Heinzinger, Karl

    1986-08-01

    A grand canonical Monte Carlo procedure with fixed values of the chemical potential μ, volume V, and temperature T, is described which is suitable to simulate simple fluids with only a minor increase in computer time in comparison with canonical (N,V,T) simulations and considerably faster than (N,p,T) ones. The method is rapidly convergent for rather dense systems with a reduced density of about ρσ3=0.88. The rapid convergence is attained by decreasing the vain attempts in the regime when new particles are added. The chance to find a place for an additional particle is increased by locating the cavities suitable to house a particle with the aid of the Dirichlet-Voronoi polyhedra. As an example, liquid argon is simulated with Lennard-Jones potentials at T=86.3 K and μ=-73.4 J/mol. The simulated density has been found to be 1.468 g/cm3 which is to be compared with the experimental value of 1.425 g/cm3. The same density was obtained by starting the procedure with both 216 and 250 particles in the simulation box of length 2.1895 nm. The pair correlation function is also in very good agreement with both earlier (N,V,T) simulations and diffraction experiments. The configurations obtained are analyzed by the second- and third-order invariants of the even-l spherical harmonics as order parameters characterizing the nearest neighbors of argon atoms. These results as well as some other statistics on the geometry of the coordination sphere indicate that the prevailing cluster geometry in liquid argon is a distorted hexagonal close packed arrangement which is nevertheless distinguishable from face centered cubic or icosahedral clusters distorted to the same degree or more. The surroundings of vacancies, however, are completely random with no characteristic symmetry properties.

  8. Canonical terminal patterning is an evolutionary novelty.

    PubMed

    Duncan, Elizabeth J; Benton, Matthew A; Dearden, Peter K

    2013-05-01

    Patterning of the terminal regions of the Drosophila embryo is achieved by an exquisitely regulated signal that passes between the follicle cells of the ovary, and the developing embryo. This pathway, however, is missing or modified in other insects. Here we trace the evolution of this pathway by examining the origins and expression of its components. The three core components of this pathway: trunk, torso and torso-like have different evolutionary histories and have been assembled step-wise to form the canonical terminal patterning pathway of Drosophila and Tribolium. Trunk, torso and a gene unrelated to terminal patterning, prothoraciotrophic hormone (PTTH), show an intimately linked evolutionary history, with every holometabolous insect, except the honeybee, possessing both PTTH and torso genes. Trunk is more restricted in its phylogenetic distribution, present only in the Diptera and Tribolium and, surprisingly, in the chelicerate Ixodes scapularis, raising the possibility that trunk and torso evolved earlier than previously thought. In Drosophila torso-like restricts the activation of the terminal patterning pathway to the poles of the embryo. Torso-like evolved in the pan-crustacean lineage, but based on expression of components of the canonical terminal patterning system in the hemimetabolous insect Acyrthosiphon pisum and the holometabolous insect Apis mellifera, we find that the canonical terminal-patterning system is not active in these insects. We therefore propose that the ancestral function of torso-like is unrelated to terminal patterning and that torso-like has become co-opted into terminal patterning in the lineage leading to Coleoptera and Diptera. We also show that this co-option has not resulted in changes to the molecular function of this protein. Torso-like from the pea aphid, honeybee and Drosophila, despite being expressed in different patterns, are functionally equivalent. We propose that co-option of torso-like into restricting the activity

  9. 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...

  10. 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...

  11. 7 CFR 981.60 - Determination of kernel weight.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Determination of kernel weight. 981.60 Section 981.60... Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which...

  12. Multiple spectral kernel learning and a gaussian complexity computation.

    PubMed

    Reyhani, Nima

    2013-07-01

    Multiple kernel learning (MKL) partially solves the kernel selection problem in support vector machines and similar classifiers by minimizing the empirical risk over a subset of the linear combination of given kernel matrices. For large sample sets, the size of the kernel matrices becomes a numerical issue. In many cases, the kernel matrix is of low-efficient rank. However, the low-rank property is not efficiently utilized in MKL algorithms. Here, we suggest multiple spectral kernel learning that efficiently uses the low-rank property by finding a kernel matrix from a set of Gram matrices of a few eigenvectors from all given kernel matrices, called a spectral kernel set. We provide a new bound for the gaussian complexity of the proposed kernel set, which depends on both the geometry of the kernel set and the number of Gram matrices. This characterization of the complexity implies that in an MKL setting, adding more kernels may not monotonically increase the complexity, while previous bounds show otherwise.

  13. 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...

  14. 7 CFR 981.61 - Redetermination of kernel weight.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Redetermination of kernel weight. 981.61 Section 981... GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.61 Redetermination of kernel weight. The Board, on the basis of reports by handlers, shall redetermine the kernel weight of...

  15. Thermomechanical property of rice kernels studied by DMA

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The thermomechanical property of the rice kernels was investigated using a dynamic mechanical analyzer (DMA). The length change of rice kernel with a loaded constant force along the major axis direction was detected during temperature scanning. The thermomechanical transition occurred in rice kernel...

  16. NIRS method for precise identification of Fusarium damaged wheat kernels

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Development of scab resistant wheat varieties may be enhanced by non-destructive evaluation of kernels for Fusarium damaged kernels (FDKs) and deoxynivalenol (DON) levels. Fusarium infection generally affects kernel appearance, but insect damage and other fungi can cause similar symptoms. Also, some...

  17. 7 CFR 981.401 - Adjusted kernel weight.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... shall mean the actual gross weight of any lot of almonds: Less weight of containers; less moisture of... material, 350 grams, and moisture content of kernels, seven percent. Excess moisture is two percent. The...: Edible kernels, 840 grams; inedible kernels, 120 grams; foreign material, 40 grams; and moisture...

  18. 7 CFR 981.401 - Adjusted kernel weight.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... shall mean the actual gross weight of any lot of almonds: Less weight of containers; less moisture of... material, 350 grams, and moisture content of kernels, seven percent. Excess moisture is two percent. The...: Edible kernels, 840 grams; inedible kernels, 120 grams; foreign material, 40 grams; and moisture...

  19. 7 CFR 981.401 - Adjusted kernel weight.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... shall mean the actual gross weight of any lot of almonds: Less weight of containers; less moisture of... material, 350 grams, and moisture content of kernels, seven percent. Excess moisture is two percent. The...: Edible kernels, 840 grams; inedible kernels, 120 grams; foreign material, 40 grams; and moisture...

  20. 7 CFR 981.401 - Adjusted kernel weight.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... shall mean the actual gross weight of any lot of almonds: Less weight of containers; less moisture of... material, 350 grams, and moisture content of kernels, seven percent. Excess moisture is two percent. The...: Edible kernels, 840 grams; inedible kernels, 120 grams; foreign material, 40 grams; and moisture...

  1. 7 CFR 981.401 - Adjusted kernel weight.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... shall mean the actual gross weight of any lot of almonds: Less weight of containers; less moisture of... material, 350 grams, and moisture content of kernels, seven percent. Excess moisture is two percent. The...: Edible kernels, 840 grams; inedible kernels, 120 grams; foreign material, 40 grams; and moisture...

  2. The Topology of Canonical Flux Tubes in Flared Jet Geometry

    NASA Astrophysics Data System (ADS)

    Sander Lavine, Eric; You, Setthivoine

    2017-01-01

    Magnetized plasma jets are generally modeled as magnetic flux tubes filled with flowing plasma governed by magnetohydrodynamics (MHD). We outline here a more fundamental approach based on flux tubes of canonical vorticity, where canonical vorticity is defined as the circulation of the species’ canonical momentum. This approach extends the concept of magnetic flux tube evolution to include the effects of finite particle momentum and enables visualization of the topology of plasma jets in regimes beyond MHD. A flared, current-carrying magnetic flux tube in an ion-electron plasma with finite ion momentum is thus equivalent to either a pair of electron and ion flow flux tubes, a pair of electron and ion canonical momentum flux tubes, or a pair of electron and ion canonical vorticity flux tubes. We examine the morphology of all these flux tubes for increasing electrical currents, different radial current profiles, different electron Mach numbers, and a fixed, flared, axisymmetric magnetic geometry. Calculations of gauge-invariant relative canonical helicities track the evolution of magnetic, cross, and kinetic helicities in the system, and show that ion flow fields can unwind to compensate for an increasing magnetic twist. The results demonstrate that including a species’ finite momentum can result in a very long collimated canonical vorticity flux tube even if the magnetic flux tube is flared. With finite momentum, particle density gradients must be normal to canonical vorticities, not to magnetic fields, so observations of collimated astrophysical jets could be images of canonical vorticity flux tubes instead of magnetic flux tubes.

  3. 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...

  4. 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...

  5. 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...

  6. 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. Protein Structure Prediction Using String Kernels

    DTIC Science & Technology

    2006-03-03

    Prediction using String Kernels 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER...consists of 4352 sequences from SCOP version 1.53 extracted from the Astral database, grouped into families and superfamilies. The dataset is processed

  8. Kernel Temporal Differences for Neural Decoding

    PubMed Central

    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

  9. Convolution kernels for multi-wavelength imaging

    NASA Astrophysics Data System (ADS)

    Boucaud, A.; Bocchio, M.; Abergel, A.; Orieux, F.; Dole, H.; Hadj-Youcef, M. A.

    2016-12-01

    Astrophysical images issued from different instruments and/or spectral bands often require to be processed together, either for fitting or comparison purposes. However each image is affected by an instrumental response, also known as point-spread function (PSF), that depends on the characteristics of the instrument as well as the wavelength and the observing strategy. Given the knowledge of the PSF in each band, a straightforward way of processing images is to homogenise them all to a target PSF using convolution kernels, so that they appear as if they had been acquired by the same instrument. We propose an algorithm that generates such PSF-matching kernels, based on Wiener filtering with a tunable regularisation parameter. This method ensures all anisotropic features in the PSFs to be taken into account. We compare our method to existing procedures using measured Herschel/PACS and SPIRE PSFs and simulated JWST/MIRI PSFs. Significant gains up to two orders of magnitude are obtained with respect to the use of kernels computed assuming Gaussian or circularised PSFs. A software to compute these kernels is available at https://github.com/aboucaud/pypher

  10. Non-canonical actions of mismatch repair

    PubMed Central

    Crouse, Gray F.

    2015-01-01

    At the heart of the mismatch repair (MMR) system are proteins that recognize mismatches in DNA. Such mismatches can be mispairs involving normal or damaged bases or insertion/deletion loops due to strand misalignment. When such mispairs are generated during replication or recombination, MMR will direct removal of an incorrectly paired base or block recombination between nonidentical sequences. However, when mispairs are recognized outside the context of replication, proper strand discrimination between old and new DNA is lost, and MMR can act randomly and mutagenically on mispaired DNA. Such non-canonical actions of MMR are important in somatic hypermutation and class switch recombination, expansion of triplet repeats, and potentially in mutations arising in nondividing cells. MMR involvement in damage recognition and signaling is complex, with the end result likely dependent on the amount of DNA damage in a cell. PMID:26698648

  11. Using Kernel Principal Components for Color Image Segmentation

    NASA Astrophysics Data System (ADS)

    Wesolkowski, Slawo

    2002-11-01

    Distinguishing objects on the basis of color is fundamental to humans. In this paper, a clustering approach is used to segment color images. Clustering is usually done using a single point or vector as a cluster prototype. The data can be clustered in the input or feature space where the feature space is some nonlinear transformation of the input space. The idea of kernel principal component analysis (KPCA) was introduced to align data along principal components in the kernel or feature space. KPCA is a nonlinear transformation of the input data that finds the eigenvectors along which this data has maximum information content (or variation). The principal components resulting from KPCA are nonlinear in the input space and represent principal curves. This is a necessary step as colors in RGB are not linearly correlated especially considering illumination effects such as shading or highlights. The performance of the k-means (Euclidean distance-based) and Mixture of Principal Components (vector angle-based) algorithms are analyzed in the context of the input space and the feature space obtained using KPCA. Results are presented on a color image segmentation task. The results are discussed and further extensions are suggested.

  12. Kernel energy method applied to vesicular stomatitis virus nucleoprotein

    PubMed Central

    Huang, Lulu; Massa, Lou; Karle, Jerome

    2009-01-01

    The kernel energy method (KEM) is applied to the vesicular stomatitis virus (VSV) nucleoprotein (PDB ID code 2QVJ). The calculations employ atomic coordinates from the crystal structure at 2.8-Å resolution, except for the hydrogen atoms, whose positions were modeled by using the computer program HYPERCHEM. The calculated KEM ab initio limited basis Hartree-Fock energy for the full 33,175 atom molecule (including hydrogen atoms) is obtained. In the KEM, a full biological molecule is represented by smaller “kernels” of atoms, greatly simplifying the calculations. Collections of kernels are well suited for parallel computation. VSV consists of five similar chains, and we obtain the energy of each chain. Interchain hydrogen bonds contribute to the interaction energy between the chains. These hydrogen bond energies are calculated in Hartree-Fock (HF) and Møller-Plesset perturbation theory to second order (MP2) approximations by using 6–31G** basis orbitals. The correlation energy, included in MP2, is a significant factor in the interchain hydrogen bond energies. PMID:19188588

  13. Canonical and non-canonical Hedgehog signalling and the control of metabolism

    PubMed Central

    Teperino, Raffaele; Aberger, Fritz; Esterbauer, Harald; Riobo, Natalia; Pospisilik, John Andrew

    2014-01-01

    Obesity and diabetes represent key healthcare challenges of our day, affecting upwards of one billion people worldwide. These individuals are at higher risk for cancer, stroke, blindness, heart and cardiovascular disease, and to date, have no effective long-term treatment options available. Recent and accumulating evidence has implicated the developmental morphogen Hedgehog and its downstream signalling in metabolic control. Generally thought to be quiescent in adults, Hedgehog is associated with several human cancers, and as such, has already emerged as a therapeutic target in oncology. Here, we attempt to give a comprehensive overview of the key signalling events associated with both canonical and non-canonical Hedgehog signalling, and highlight the increasingly complex regulatory modalities that appear to link Hedgehog and control metabolism. We highlight these key findings and discuss their impact for therapeutic development, cancer and metabolic disease. PMID:24862854

  14. 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.

  15. Generalization Performance of Regularized Ranking With Multiscale Kernels.

    PubMed

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

    2016-05-01

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

  16. Difference image analysis: automatic kernel design using information criteria

    NASA Astrophysics Data System (ADS)

    Bramich, D. M.; Horne, Keith; Alsubai, K. A.; Bachelet, E.; Mislis, D.; Parley, N.

    2016-03-01

    We present a selection of methods for automatically constructing an optimal kernel model for difference image analysis which require very few external parameters to control the kernel design. Each method consists of two components; namely, a kernel design algorithm to generate a set of candidate kernel models, and a model selection criterion to select the simplest kernel model from the candidate models that provides a sufficiently good fit to the target image. We restricted our attention to the case of solving for a spatially invariant convolution kernel composed of delta basis functions, and we considered 19 different kernel solution methods including six employing kernel regularization. We tested these kernel solution methods by performing a comprehensive set of image simulations and investigating how their performance in terms of model error, fit quality, and photometric accuracy depends on the properties of the reference and target images. We find that the irregular kernel design algorithm employing unregularized delta basis functions, combined with either the Akaike or Takeuchi information criterion, is the best kernel solution method in terms of photometric accuracy. Our results are validated by tests performed on two independent sets of real data. Finally, we provide some important recommendations for software implementations of difference image analysis.

  17. Efficient $\\chi ^{2}$ Kernel Linearization via Random Feature Maps.

    PubMed

    Yuan, Xiao-Tong; Wang, Zhenzhen; Deng, Jiankang; Liu, Qingshan

    2016-11-01

    Explicit feature mapping is an appealing way to linearize additive kernels, such as χ(2) kernel for training large-scale support vector machines (SVMs). Although accurate in approximation, feature mapping could pose computational challenges in high-dimensional settings as it expands the original features to a higher dimensional space. To handle this issue in the context of χ(2) kernel SVMs learning, we introduce a simple yet efficient method to approximately linearize χ(2) kernel through random feature maps. The main idea is to use sparse random projection to reduce the dimensionality of feature maps while preserving their approximation capability to the original kernel. We provide approximation error bound for the proposed method. Furthermore, we extend our method to χ(2) multiple kernel SVMs learning. Extensive experiments on large-scale image classification tasks confirm that the proposed approach is able to significantly speed up the training process of the χ(2) kernel SVMs at almost no cost of testing accuracy.

  18. Effect of dietary palm kernel oil and biotin on the fatty liver and kidney syndrome in broiler chicken.

    PubMed

    Oloyo, R A; Ogunmodede, B K

    1989-01-01

    The effect of feeding biotin and palm kernel oil to broiler chicks on the appearance of Fatty Liver and Kidney Syndrome (FLKS) was investigated. A total of 480 broiler chicks was divided into two equal batches each of which was divided into 6 groups of 40 chicks per group. Each group was further subdivided into equal units of 20 chicks. Six dietary levels of biotin (40, 80, 120, 160, 200, and 240 mcg/kg feed) were given to the first batch of chicks, while the second batch had 2% palm kernel oil added to the six dietarybiotin levels. These two basic rations were supplemented with biotin in order to obtain six levels of the vitamin in the rations. The results showed that the 2% palm kernel oil forage affected FLKS mortality and the minimum biotin requirement. FLKS mortality was significantly reduced in case of palm kernel oil supplement. A lower amount of biotin (120 mcg/kg feed) was needed in case of palm kernel oil supplement-as compared with the necessary biotin (160 mcg/kg feed)-in order to prevent FLKS mortality when palm kernel oil was not contained in the rations. The biochemical analysis of the liver and kidney syndrome-coupled with the correlation and regression analysis of the data collected-showed that a minimum of 120 mcg/kg feed was needed by broiler chicks for the prevention of FLKS.

  19. Geothermal resource assessment of Canon City, Colorado Area

    SciTech Connect

    Zacharakis, Ted G.; Pearl, Richard Howard

    1982-01-01

    In 1979 a program was initiated to fully define the geothermal conditions of an area east of Canon City, bounded by the mountains on the north and west, the Arkansas River on the south and Colorado Highway 115 on the east. Within this area are a number of thermal springs and wells in two distinct groups. The eastern group consists of 5 thermal artesian wells located within one mile of Colorado Highway 115 from Penrose on the north to the Arkansas river on the south. The western group, located in and adjacent to Canon City, consists of one thermal spring on the south bank of the Arkansas River on the west side of Canon City, a thermal well in the northeast corner of Canon City, another well along the banks of Four Mile Creek east of Canon City and a well north of Canon City on Four Mile Creek. All the thermal waters in the Canon City Embayment, of which the study area is part of, are found in the study area. The thermal waters unlike the cold ground waters of the Canon City Embayment, are a calcium-bicarbonate type and range in temperature from 79 F (26 C) to a high of 108 F (42 C). The total combined surface discharge o fall the thermal water in the study area is in excess of 532 acre feet (A.F.) per year.

  20. The Asian American Fakeness Canon, 1972-2002

    ERIC Educational Resources Information Center

    Oishi, Eve

    2007-01-01

    The year 1972 can be seen to inaugurate not a tradition of Asian American New York theater, but the rich and multigenre collection of writing that the author has called "the Asian American fakeness canon." The fakeness canon refers to a collection of writings that take as one of their central points of reference the question of cultural…

  1. The Western Canon: The Books and School of the Ages.

    ERIC Educational Resources Information Center

    Bloom, Harold

    This book argues against the politicization of literature and presents a guide to the great works and essential writers of the ages, the "Western Canon." The book studies 26 writers and seeks to isolate the qualities that made these authors canonical, that is, authoritative in Western culture. Noting that although originally the…

  2. Structuring Catholic Schools: Creative Imagination Meets Canon Law

    ERIC Educational Resources Information Center

    Brown, Phillip J.

    2010-01-01

    This paper will explore the underlying requirements of canon law for establishing and administering Catholic schools, with a view toward helping to arrive at creative solutions to the question of how best to structure these schools civilly and canonically in order to ensure their temporal, spiritual, and religious well-being, and to assure that…

  3. The Canonical Passive Construction: Theory and Practice. CLCS Occasional Paper.

    ERIC Educational Resources Information Center

    El-Marzouk, Ghiath

    This paper examines problems with description of the canonical passive construction, noting how new terminology facilitates consideration of a particular approach to frequency asymmetry. It compares the canonical passive construction in Arabic and English as examples of genetically unrelated languages, referring to other languages where…

  4. Critical Literature Pedagogy: Teaching Canonical Literature for Critical Literacy

    ERIC Educational Resources Information Center

    Borsheim-Black, Carlin; Macaluso, Michael; Petrone, Robert

    2014-01-01

    This article introduces Critical Literature Pedagogy (CLP), a pedagogical framework for applying goals of critical literacy within the context of teaching canonical literature. Critical literacies encompass skills and dispositions to understand, question, and critique ideological messages of texts; because canonical literature is often…

  5. A Novel Framework for Learning Geometry-Aware Kernels.

    PubMed

    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.

  6. Kernel Density Estimation, Kernel Methods, and Fast Learning in Large Data Sets.

    PubMed

    Wang, Shitong; Wang, Jun; Chung, Fu-lai

    2014-01-01

    Kernel methods such as the standard support vector machine and support vector regression trainings take O(N(3)) time and O(N(2)) space complexities in their naïve implementations, where N is the training set size. It is thus computationally infeasible in applying them to large data sets, and a replacement of the naive method for finding the quadratic programming (QP) solutions is highly desirable. By observing that many kernel methods can be linked up with kernel density estimate (KDE) which can be efficiently implemented by some approximation techniques, a new learning method called fast KDE (FastKDE) is proposed to scale up kernel methods. It is based on establishing a connection between KDE and the QP problems formulated for kernel methods using an entropy-based integrated-squared-error criterion. As a result, FastKDE approximation methods can be applied to solve these QP problems. In this paper, the latest advance in fast data reduction via KDE is exploited. With just a simple sampling strategy, the resulted FastKDE method can be used to scale up various kernel methods with a theoretical guarantee that their performance does not degrade a lot. It has a time complexity of O(m(3)) where m is the number of the data points sampled from the training set. Experiments on different benchmarking data sets demonstrate that the proposed method has comparable performance with the state-of-art method and it is effective for a wide range of kernel methods to achieve fast learning in large data sets.

  7. Wilson Dslash Kernel From Lattice QCD Optimization

    SciTech Connect

    Joo, Balint; Smelyanskiy, Mikhail; Kalamkar, Dhiraj D.; Vaidyanathan, Karthikeyan

    2015-07-01

    Lattice Quantum Chromodynamics (LQCD) is a numerical technique used for calculations in Theoretical Nuclear and High Energy Physics. LQCD is traditionally one of the first applications ported to many new high performance computing architectures and indeed LQCD practitioners have been known to design and build custom LQCD computers. Lattice QCD kernels are frequently used as benchmarks (e.g. 168.wupwise in the SPEC suite) and are generally well understood, and as such are ideal to illustrate several optimization techniques. In this chapter we will detail our work in optimizing the Wilson-Dslash kernels for Intel Xeon Phi, however, as we will show the technique gives excellent performance on regular Xeon Architecture as well.

  8. El Escritor y las Normas del Canon Literario (The Writer and the Norms of the Literary Canon).

    ERIC Educational Resources Information Center

    Policarpo, Alcibiades

    This paper speculates about whether a literary canon exists in contemporary Latin American literature, particularly in the prose genre. The paper points to Carlos Fuentes, Gabriel Garcia Marquez, and Mario Vargas Llosa as the three authors who might form this traditional and liberal canon with their works "La Muerte de Artemio Cruz"…

  9. Bergman kernel and complex singularity exponent

    NASA Astrophysics Data System (ADS)

    Chen, Boyong; Lee, Hanjin

    2009-12-01

    We give a precise estimate of the Bergman kernel for the model domain defined by $\\Omega_F=\\{(z,w)\\in \\mathbb{C}^{n+1}:{\\rm Im}w-|F(z)|^2>0\\},$ where $F=(f_1,...,f_m)$ is a holomorphic map from $\\mathbb{C}^n$ to $\\mathbb{C}^m$, in terms of the complex singularity exponent of $F$.

  10. Advanced Development of Certified OS Kernels

    DTIC Science & Technology

    2015-06-01

    and Coq Ltac libraries. 15. SUBJECT TERMS Certified Software; Certified OS Kernels; Certified Compilers; Abstraction Layers; Modularity; Deep ...module should only need to be done once (to show that it implements its deep functional specification [14]). Global properties should be derived from the...building certified abstraction layers with deep specifications. A certified layer is a new language-based module construct that consists of a triple pL1,M

  11. Comparative Evaluation of Pavement Crack Detection Using Kernel-Based Techniques in Asphalt Road Surfaces

    NASA Astrophysics Data System (ADS)

    Miraliakbari, A.; Sok, S.; Ouma, Y. O.; Hahn, M.

    2016-06-01

    With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.

  12. p120-catenin in canonical Wnt signaling.

    PubMed

    Duñach, Mireia; Del Valle-Pérez, Beatriz; García de Herreros, Antonio

    2017-03-03

    Canonical Wnt signaling controls β-catenin protein stabilization, its translocation to the nucleus and the activation of β-catenin/Tcf-4-dependent transcription. In this review, we revise and discuss the recent results describing actions of p120-catenin in different phases of this pathway. More specifically, we comment its involvement in four different steps: (i) the very early activation of CK1ɛ, essential for Dvl-2 binding to the Wnt receptor complex; (ii) the internalization of GSK3 and Axin into multivesicular bodies, necessary for a complete stabilization of β-catenin; (iii) the activation of Rac1 small GTPase, required for β-catenin translocation to the nucleus; and (iv) the release of the inhibitory action caused by Kaiso transcriptional repressor. We integrate these new results with the previously known action of other elements in this pathway, giving a particular relevance to the responses of the Wnt pathway not required for β-catenin stabilization but for β-catenin transcriptional activity. Moreover, we discuss the possible future implications, suggesting that the two cellular compartments where β-catenin is localized, thus, the adherens junction complex and the Wnt signalosome, are more physically connected that previously thought.

  13. Shannon Entropy of the Canonical Genetic Code

    NASA Astrophysics Data System (ADS)

    Nemzer, Louis

    The probability that a non-synonymous point mutation in DNA will adversely affect the functionality of the resultant protein is greatly reduced if the substitution is conservative. In that case, the amino acid coded by the mutated codon has similar physico-chemical properties to the original. Many simplified alphabets, which group the 20 common amino acids into families, have been proposed. To evaluate these schema objectively, we introduce a novel, quantitative method based on the inherent redundancy in the canonical genetic code. By calculating the Shannon information entropy carried by 1- or 2-bit messages, groupings that best leverage the robustness of the code are identified. The relative importance of properties related to protein folding - like hydropathy and size - and function, including side-chain acidity, can also be estimated. In addition, this approach allows us to quantify the average information value of nucleotide codon positions, and explore the physiological basis for distinguishing between transition and transversion mutations. Supported by NSU PFRDG Grant #335347.

  14. Finite canonical measure for nonsingular cosmologies

    SciTech Connect

    Page, Don N.

    2011-06-01

    The total canonical (Liouville-Henneaux-Gibbons-Hawking-Stewart) measure is finite for completely nonsingular Friedmann-Lemaître-Robertson-Walker classical universes with a minimally coupled massive scalar field and a positive cosmological constant. For a cosmological constant very small in units of the square of the scalar field mass, most of the measure is for nearly de Sitter solutions with no inflation at a much more rapid rate. However, if one restricts to solutions in which the scalar field energy density is ever more than twice the equivalent energy density of the cosmological constant, then the number of e-folds of rapid inflation must be large, and the fraction of the measure is low in which the spatial curvature is comparable to the cosmological constant at the time when it is comparable to the energy density of the scalar field. The measure for such classical FLRWΛ-φ models with both a big bang and a big crunch is also finite. Only the solutions with a big bang that expand forever, or the time-reversed ones that contract from infinity to a big crunch, have infinite measure.

  15. The Palomar kernel-phase experiment: testing kernel phase interferometry for ground-based astronomical observations

    NASA Astrophysics Data System (ADS)

    Pope, Benjamin; Tuthill, Peter; Hinkley, Sasha; Ireland, Michael J.; Greenbaum, Alexandra; Latyshev, Alexey; Monnier, John D.; Martinache, Frantz

    2016-01-01

    At present, the principal limitation on the resolution and contrast of astronomical imaging instruments comes from aberrations in the optical path, which may be imposed by the Earth's turbulent atmosphere or by variations in the alignment and shape of the telescope optics. These errors can be corrected physically, with active and adaptive optics, and in post-processing of the resulting image. A recently developed adaptive optics post-processing technique, called kernel-phase interferometry, uses linear combinations of phases that are self-calibrating with respect to small errors, with the goal of constructing observables that are robust against the residual optical aberrations in otherwise well-corrected imaging systems. Here, we present a direct comparison between kernel phase and the more established competing techniques, aperture masking interferometry, point spread function (PSF) fitting and bispectral analysis. We resolve the α Ophiuchi binary system near periastron, using the Palomar 200-Inch Telescope. This is the first case in which kernel phase has been used with a full aperture to resolve a system close to the diffraction limit with ground-based extreme adaptive optics observations. Excellent agreement in astrometric quantities is found between kernel phase and masking, and kernel phase significantly outperforms PSF fitting and bispectral analysis, demonstrating its viability as an alternative to conventional non-redundant masking under appropriate conditions.

  16. A Fast Reduced Kernel Extreme Learning Machine.

    PubMed

    Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

    2016-04-01

    In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred.

  17. Kernel Non-Rigid Structure from Motion

    PubMed Central

    Gotardo, Paulo F. U.; Martinez, Aleix M.

    2013-01-01

    Non-rigid structure from motion (NRSFM) is a difficult, underconstrained problem in computer vision. The standard approach in NRSFM constrains 3D shape deformation using a linear combination of K basis shapes; the solution is then obtained as the low-rank factorization of an input observation matrix. An important but overlooked problem with this approach is that non-linear deformations are often observed; these deformations lead to a weakened low-rank constraint due to the need to use additional basis shapes to linearly model points that move along curves. Here, we demonstrate how the kernel trick can be applied in standard NRSFM. As a result, we model complex, deformable 3D shapes as the outputs of a non-linear mapping whose inputs are points within a low-dimensional shape space. This approach is flexible and can use different kernels to build different non-linear models. Using the kernel trick, our model complements the low-rank constraint by capturing non-linear relationships in the shape coefficients of the linear model. The net effect can be seen as using non-linear dimensionality reduction to further compress the (shape) space of possible solutions. PMID:24002226

  18. Balancing continuous covariates based on Kernel densities.

    PubMed

    Ma, Zhenjun; Hu, Feifang

    2013-03-01

    The balance of important baseline covariates is essential for convincing treatment comparisons. Stratified permuted block design and minimization are the two most commonly used balancing strategies, both of which require the covariates to be discrete. Continuous covariates are typically discretized in order to be included in the randomization scheme. But breakdown of continuous covariates into subcategories often changes the nature of the covariates and makes distributional balance unattainable. In this article, we propose to balance continuous covariates based on Kernel density estimations, which keeps the continuity of the covariates. Simulation studies show that the proposed Kernel-Minimization can achieve distributional balance of both continuous and categorical covariates, while also keeping the group size well balanced. It is also shown that the Kernel-Minimization is less predictable than stratified permuted block design and minimization. Finally, we apply the proposed method to redesign the NINDS trial, which has been a source of controversy due to imbalance of continuous baseline covariates. Simulation shows that imbalances such as those observed in the NINDS trial can be generally avoided through the implementation of the new method.

  19. BOOK REVIEW: Modern Canonical Quantum General Relativity

    NASA Astrophysics Data System (ADS)

    Kiefer, Claus

    2008-06-01

    The open problem of constructing a consistent and experimentally tested quantum theory of the gravitational field has its place at the heart of fundamental physics. The main approaches can be roughly divided into two classes: either one seeks a unified quantum framework of all interactions or one starts with a direct quantization of general relativity. In the first class, string theory (M-theory) is the only known example. In the second class, one can make an additional methodological distinction: while covariant approaches such as path-integral quantization use the four-dimensional metric as an essential ingredient of their formalism, canonical approaches start with a foliation of spacetime into spacelike hypersurfaces in order to arrive at a Hamiltonian formulation. The present book is devoted to one of the canonical approaches—loop quantum gravity. It is named modern canonical quantum general relativity by the author because it uses connections and holonomies as central variables, which are analogous to the variables used in Yang Mills theories. In fact, the canonically conjugate variables are a holonomy of a connection and the flux of a non-Abelian electric field. This has to be contrasted with the older geometrodynamical approach in which the metric of three-dimensional space and the second fundamental form are the fundamental entities, an approach which is still actively being pursued. It is the author's ambition to present loop quantum gravity in a way in which every step is formulated in a mathematically rigorous form. In his own words: 'loop quantum gravity is an attempt to construct a mathematically rigorous, background-independent, non-perturbative quantum field theory of Lorentzian general relativity and all known matter in four spacetime dimensions, not more and not less'. The formal Leitmotiv of loop quantum gravity is background independence. Non-gravitational theories are usually quantized on a given non-dynamical background. In contrast, due to

  20. Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

    PubMed

    Kwak, Nojun

    2016-05-20

    Recently, the nonlinear projection trick (NPT) was introduced enabling direct computation of coordinates of samples in a reproducing kernel Hilbert space. With NPT, any machine learning algorithm can be extended to a kernel version without relying on the so called kernel trick. However, NPT is inherently difficult to be implemented incrementally because an ever increasing kernel matrix should be treated as additional training samples are introduced. In this paper, an incremental version of the NPT (INPT) is proposed based on the observation that the centerization step in NPT is unnecessary. Because the proposed INPT does not change the coordinates of the old data, the coordinates obtained by INPT can directly be used in any incremental methods to implement a kernel version of the incremental methods. The effectiveness of the INPT is shown by applying it to implement incremental versions of kernel methods such as, kernel singular value decomposition, kernel principal component analysis, and kernel discriminant analysis which are utilized for problems of kernel matrix reconstruction, letter classification, and face image retrieval, respectively.

  1. Independent genetic control of maize (Zea mays L.) kernel weight determination and its phenotypic plasticity.

    PubMed

    Alvarez Prado, Santiago; Sadras, Víctor O; Borrás, Lucas

    2014-08-01

    Maize kernel weight (KW) is associated with the duration of the grain-filling period (GFD) and the rate of kernel biomass accumulation (KGR). It is also related to the dynamics of water and hence is physiologically linked to the maximum kernel water content (MWC), kernel desiccation rate (KDR), and moisture concentration at physiological maturity (MCPM). This work proposed that principles of phenotypic plasticity can help to consolidated the understanding of the environmental modulation and genetic control of these traits. For that purpose, a maize population of 245 recombinant inbred lines (RILs) was grown under different environmental conditions. Trait plasticity was calculated as the ratio of the variance of each RIL to the overall phenotypic variance of the population of RILs. This work found a hierarchy of plasticities: KDR ≈ GFD > MCPM > KGR > KW > MWC. There was no phenotypic and genetic correlation between traits per se and trait plasticities. MWC, the trait with the lowest plasticity, was the exception because common quantitative trait loci were found for the trait and its plasticity. Independent genetic control of a trait per se and genetic control of its plasticity is a condition for the independent evolution of traits and their plasticities. This allows breeders potentially to select for high or low plasticity in combination with high or low values of economically relevant traits.

  2. The shape of the spatial kernel and its implications for biological invasions in patchy environments.

    PubMed

    Lindström, Tom; Håkansson, Nina; Wennergren, Uno

    2011-05-22

    Ecological and epidemiological invasions occur in a spatial context. We investigated how these processes correlate to the distance dependence of spread or dispersal between spatial entities such as habitat patches or epidemiological units. Distance dependence is described by a spatial kernel, characterized by its shape (kurtosis) and width (variance). We also developed a novel method to analyse and generate point-pattern landscapes based on spectral representation. This involves two measures: continuity, which is related to autocorrelation and contrast, which refers to variation in patch density. We also analysed some empirical data where our results are expected to have implications, namely distributions of trees (Quercus and Ulmus) and farms in Sweden. Through a simulation study, we found that kernel shape was not important for predicting the invasion speed in randomly distributed patches. However, the shape may be essential when the distribution of patches deviates from randomness, particularly when the contrast is high. We conclude that the speed of invasions depends on the spatial context and the effect of the spatial kernel is intertwined with the spatial structure. This implies substantial demands on the empirical data, because it requires knowledge of shape and width of the spatial kernel, and spatial structure.

  3. Analysis of Heterosis and Quantitative Trait Loci for Kernel Shape Related Traits Using Triple Testcross Population in Maize

    PubMed Central

    Jiang, Lu; Ge, Min; Zhao, Han; Zhang, Tifu

    2015-01-01

    Kernel shape related traits (KSRTs) have been shown to have important influences on grain yield. The previous studies that emphasize kernel length (KL) and kernel width (KW) lack a comprehensive evaluation of characters affecting kernel shape. In this study, materials of the basic generations (B73, Mo17, and B73 × Mo17), 82 intermated B73 × Mo17 (IBM) individuals, and the corresponding triple testcross (TTC) populations were used to evaluate heterosis, investigate correlations, and characterize the quantitative trait loci (QTL) for six KSRTs: KL, KW, length to width ratio (LWR), perimeter length (PL), kernel area (KA), and circularity (CS). The results showed that the mid-parent heterosis (MPH) for most of the KSRTs was moderate. The performance of KL, KW, PL, and KA exhibited significant positive correlation with heterozygosity but their Pearson’s R values were low. Among KSRTs, the strongest significant correlation was found between PL and KA with R values was up to 0.964. In addition, KW, PL, KA, and CS were shown to be significant positive correlation with 100-kernel weight (HKW). 28 QTLs were detected for KSRTs in which nine were augmented additive, 13 were augmented dominant, and six were dominance × additive epistatic. The contribution of a single QTL to total phenotypic variation ranged from 2.1% to 32.9%. Furthermore, 19 additive × additive digenic epistatic interactions were detected for all KSRTs with the highest total R2 for KW (78.8%), and nine dominance × dominance digenic epistatic interactions detected for KL, LWR, and CS with the highest total R2 (55.3%). Among significant digenic interactions, most occurred between genomic regions not mapped with main-effect QTLs. These findings display the complexity of the genetic basis for KSRTs and enhance our understanding on heterosis of KSRTs from the quantitative genetic perspective. PMID:25919458

  4. Analysis of heterosis and quantitative trait loci for kernel shape related traits using triple testcross population in maize.

    PubMed

    Jiang, Lu; Ge, Min; Zhao, Han; Zhang, Tifu

    2015-01-01

    Kernel shape related traits (KSRTs) have been shown to have important influences on grain yield. The previous studies that emphasize kernel length (KL) and kernel width (KW) lack a comprehensive evaluation of characters affecting kernel shape. In this study, materials of the basic generations (B73, Mo17, and B73 × Mo17), 82 intermated B73 × Mo17 (IBM) individuals, and the corresponding triple testcross (TTC) populations were used to evaluate heterosis, investigate correlations, and characterize the quantitative trait loci (QTL) for six KSRTs: KL, KW, length to width ratio (LWR), perimeter length (PL), kernel area (KA), and circularity (CS). The results showed that the mid-parent heterosis (MPH) for most of the KSRTs was moderate. The performance of KL, KW, PL, and KA exhibited significant positive correlation with heterozygosity but their Pearson's R values were low. Among KSRTs, the strongest significant correlation was found between PL and KA with R values was up to 0.964. In addition, KW, PL, KA, and CS were shown to be significant positive correlation with 100-kernel weight (HKW). 28 QTLs were detected for KSRTs in which nine were augmented additive, 13 were augmented dominant, and six were dominance × additive epistatic. The contribution of a single QTL to total phenotypic variation ranged from 2.1% to 32.9%. Furthermore, 19 additive × additive digenic epistatic interactions were detected for all KSRTs with the highest total R2 for KW (78.8%), and nine dominance × dominance digenic epistatic interactions detected for KL, LWR, and CS with the highest total R2 (55.3%). Among significant digenic interactions, most occurred between genomic regions not mapped with main-effect QTLs. These findings display the complexity of the genetic basis for KSRTs and enhance our understanding on heterosis of KSRTs from the quantitative genetic perspective.

  5. Non-canonical WNT signalling in the lung.

    PubMed

    Li, Changgong; Bellusci, Saverio; Borok, Zea; Minoo, Parviz

    2015-11-01

    The role of WNT signalling in metazoan organogenesis has been a topic of widespread interest. In the lung, while the role of canonical WNT signalling has been examined in some detail by multiple studies, the non-canonical WNT signalling has received limited attention. Reliable evidence shows that this important signalling mechanism constitutes a major regulatory pathway in lung development. In addition, accumulating evidence has also shown that the non-canonical WNT pathway is critical for maintaining lung homeostasis and that aberrant activation of this pathway may underlie several debilitating lung diseases. Functional analyses have further revealed that the non-canonical WNT pathway regulates multiple cellular activities in the lung that are dependent on the specific cellular context. In most cell types, non-canonical WNT signalling regulates canonical WNT activity, which is also critical for many aspects of lung biology. This review will summarize what is currently known about the role of non-canonical WNT signalling in lung development, homeostasis and pathogenesis of disease.

  6. Non-canonical WNT signalling in the lung

    PubMed Central

    Li, Changgong; Bellusci, Saverio; Borok, Zea; Minoo, Parviz

    2015-01-01

    The role of WNT signalling in metazoan organogenesis has been a topic of widespread interest. In the lung, while the role of canonical WNT signalling has been examined in some detail by multiple studies, the non-canonical WNT signalling has received limited attention. Reliable evidence shows that this important signalling mechanism constitutes a major regulatory pathway in lung development. In addition, accumulating evidence has also shown that the non-canonical WNT pathway is critical for maintaining lung homeostasis and that aberrant activation of this pathway may underlie several debilitating lung diseases. Functional analyses have further revealed that the non-canonical WNT pathway regulates multiple cellular activities in the lung that are dependent on the specific cellular context. In most cell types, non-canonical WNT signalling regulates canonical WNT activity, which is also critical for many aspects of lung biology. This review will summarize what is currently known about the role of non-canonical WNT signalling in lung development, homeostasis and pathogenesis of disease. PMID:26261051

  7. Evaluation of quality changes in walnut kernels (Juglans regia L.) by Vis/NIR spectroscopy.

    PubMed

    Jensen, P N; Sørensen, G; Engelsen, S B; Bertelsen, G

    2001-12-01

    Storage of walnut kernels in light and at room temperature, as is common practice, is detrimental to their sensory quality and shelf life. This study demonstrates that Vis/NIR spectroscopy, in combination with multivariate data analysis (chemometrics), is a most capable rapid method for monitoring the overall quality deterioration of walnut kernels. Spectral predictions of the sensory attributes nutty and rancid tastes by partial least-squares regression (PLSR) resulted in correlations (r(2)) of 0.77 and 0.86, respectively, whereas with PLSR prediction of the chemical parameter hexanal content a correlation (r(2)) of 0.72 was obtained. The study further establishes that storage in light results in pronounced oxidative changes, especially in walnuts stored at 21 degrees C, whereas dark storage at 5 degrees C results in walnuts without any trace of rancid taste during 25 weeks of storage at accelerated storage conditions (50% oxygen).

  8. Schwinger-Keldysh canonical formalism for electronic Raman scattering

    NASA Astrophysics Data System (ADS)

    Su, Yuehua

    2016-03-01

    Inelastic low-energy Raman and high-energy X-ray scatterings have made great progress in instrumentation to investigate the strong electronic correlations in matter. However, theoretical study of the relevant scattering spectrum is still a challenge. In this paper, we present a Schwinger-Keldysh canonical perturbation formalism for the electronic Raman scattering, where all the resonant, non-resonant and mixed responses are considered uniformly. We show how to use this formalism to evaluate the cross section of the electronic Raman scattering off an one-band superconductor. All the two-photon scattering processes from electrons, the non-resonant charge density response, the elastic Rayleigh scattering, the fluorescence, the intrinsic energy-shift Raman scattering and the mixed response, are included. In the mean-field superconducting state, Cooper pairs contribute only to the non-resonant response. All the other responses are dominated by the single-particle excitations and are strongly suppressed due to the opening of the superconducting gap. Our formalism for the electronic Raman scattering can be easily extended to study the high-energy resonant inelastic X-ray scattering.

  9. Statistical mechanics of neocortical interactions: Canonical momenta indicatorsof electroencephalography

    NASA Astrophysics Data System (ADS)

    Ingber, Lester

    1997-04-01

    A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electrical-chemical properties of synaptic interactions. While not useful to yield insights at the single neuron level, SMNI has demonstrated its capability in describing large-scale properties of short-term memory and electroencephalographic (EEG) systematics. The necessity of including nonlinear and stochastic structures in this development has been stressed. Sets of EEG and evoked potential data were fit, collected to investigate genetic predispositions to alcoholism and to extract brain ``signatures'' of short-term memory. Adaptive simulated annealing (ASA), a global optimization algorithm, was used to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta indicators (CMI) are thereby derived for an individual's EEG data. The CMI give better signal recognition than the raw data, and can be used to advantage as correlates of behavioral states. These results give strong quantitative support for an accurate intuitive picture, portraying neocortical interactions as having common algebraic or physics mechanisms that scale across quite disparate spatial scales and functional or behavioral phenomena, i.e., describing interactions among neurons, columns of neurons, and regional masses of neurons.

  10. Comparing Alternative Kernels for the Kernel Method of Test Equating: Gaussian, Logistic, and Uniform Kernels. Research Report. ETS RR-08-12

    ERIC Educational Resources Information Center

    Lee, Yi-Hsuan; von Davier, Alina A.

    2008-01-01

    The kernel equating method (von Davier, Holland, & Thayer, 2004) is based on a flexible family of equipercentile-like equating functions that use a Gaussian kernel to continuize the discrete score distributions. While the classical equipercentile, or percentile-rank, equating method carries out the continuization step by linear interpolation,…

  11. Critical adsorption and critical Casimir forces in the canonical ensemble

    NASA Astrophysics Data System (ADS)

    Gross, Markus; Vasilyev, Oleg; Gambassi, Andrea; Dietrich, S.

    2016-08-01

    Critical properties of a liquid film between two planar walls are investigated in the canonical ensemble, within which the total number of fluid particles, rather than their chemical potential, is kept constant. The effect of this constraint is analyzed within mean-field theory (MFT) based on a Ginzburg-Landau free-energy functional as well as via Monte Carlo simulations of the three-dimensional Ising model with fixed total magnetization. Within MFT and for finite adsorption strengths at the walls, the thermodynamic properties of the film in the canonical ensemble can be mapped exactly onto a grand canonical ensemble in which the corresponding chemical potential plays the role of the Lagrange multiplier associated with the constraint. However, due to a nonintegrable divergence of the mean-field order parameter profile near a wall, the limit of infinitely strong adsorption turns out to be not well-defined within MFT, because it would necessarily violate the constraint. The critical Casimir force (CCF) acting on the two planar walls of the film is generally found to behave differently in the canonical and grand canonical ensembles. For instance, the canonical CCF in the presence of equal preferential adsorption at the two walls is found to have the opposite sign and a slower decay behavior as a function of the film thickness compared to its grand canonical counterpart. We derive the stress tensor in the canonical ensemble and find that it has the same expression as in the grand canonical case, but with the chemical potential playing the role of the Lagrange multiplier associated with the constraint. The different behavior of the CCF in the two ensembles is rationalized within MFT by showing that, for a prescribed value of the thermodynamic control parameter of the film, i.e., density or chemical potential, the film pressures are identical in the two ensembles, while the corresponding bulk pressures are not.

  12. Interpreting medium ring canonical conformers by a triangular plane tessellation of the macrocycle

    NASA Astrophysics Data System (ADS)

    Khalili, Pegah; Barnett, Christopher B.; Naidoo, Kevin J.

    2013-05-01

    Cyclic conformational coordinates are essential for the distinction of molecular ring conformers as the use of Cremer-Pople coordinates have illustrated for five- and six-membered rings. Here, by tessellating medium rings into triangular planes and using the relative angles made between triangular planes we are able to assign macrocyclic pucker conformations into canonical pucker conformers such as chairs, boats, etc. We show that the definition is straightforward compared with other methods popularly used for small rings and that it is computationally simple to implement for complex macrocyclic rings. These cyclic conformational coordinates directly couple to the motion of individual nodes of a ring. Therefore, they are useful for correlating the physical properties of macrocycles with their ring pucker and measuring the dynamic ring conformational behavior. We illustrate the triangular tessellation, assignment, and pucker analysis on 7- and 8-membered rings. Sets of canonical states are given for cycloheptane and cyclooctane that have been previously experimentally analysed.

  13. Chondrules: The canonical and noncanonical views

    NASA Astrophysics Data System (ADS)

    Connolly, Harold C.; Jones, Rhian H.

    2016-10-01

    Millimeter-scale rock particles called chondrules are the principal components of the most common meteorites, chondrites. Hence, chondrules were arguably the most abundant components of the early solar system at the time of planetesimal accretion. Despite their fundamental importance, the existence of chondrules would not be predicted from current observations and models of young planetary systems. There are many different models for chondrule formation, but no single model satisfies the many constraints determined from their mineralogical and chemical properties and from chondrule analog experiments. Significant recent progress has shown that several models can satisfy first-order constraints and successfully reproduce chondrule thermal histories. However, second- and third-order constraints such as chondrule size ranges, open system behavior, oxidation states, reheating, and chemical diversity have not generally been addressed. Chondrule formation models include those based on processes that are known to occur in protoplanetary disk environments, including interactions with the early active Sun, impacts and collisions between planetary bodies, and radiative heating. Other models for chondrule heating mechanisms are based on hypothetical processes that are possible but have not been observed, like shock waves, planetesimal bow shocks, and lightning. We examine the evidence for the canonical view of chondrule formation, in which chondrules were free-floating particles in the protoplanetary disk, and the noncanonical view, in which chondrules were the by-products of planetesimal formation. The fundamental difference between these approaches has a bearing on the importance of chondrules during planet formation and the relevance of chondrules to interpreting the evolution of protoplanetary disks and planetary systems.

  14. Small convolution kernels for high-fidelity image restoration

    NASA Technical Reports Server (NTRS)

    Reichenbach, Stephen E.; Park, Stephen K.

    1991-01-01

    An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

  15. Canonical and Non-Canonical Barriers Facing AntimiR Cancer Therapeutics

    PubMed Central

    Cheng, Christopher J.; Saltzman, W. Mark; Slack, Frank J.

    2013-01-01

    Once considered genetic “oddities”, microRNAs (miRNAs) are now recognized as key epigenetic regulators of numerous biological processes, including some with a causal link to the pathogenesis, maintenance, and treatment of cancer. The crux of small RNA-based therapeutics lies in the antagonism of potent cellular targets; the main shortcoming of the field in general, lies in ineffective delivery. Inhibition of oncogenic miRNAs is a relatively nascent therapeutic concept, but as with predecessor RNA-based therapies, success hinges on delivery efficacy. This review will describe the canonical (e.g. pharmacokinetics and clearance, cellular uptake, endosome escape, etc.) and non-canonical (e.g. spatial localization and accessibility of miRNA, technical limitations of miRNA inhibition, off-target impacts, etc.) challenges to the delivery of antisense-based anti-miRNA therapeutics (i.e. antimiRs) for the treatment of cancer. Emphasis will be placed on how the current leading antimiR platforms—ranging from naked chemically modified oligonucleotides to nanoscale delivery vehicles—are affected by and overcome these barriers. The perplexity of antimiR delivery presents both engineering and biological hurdles that must be overcome in order to capitalize on the extensive pharmacological benefits of antagonizing tumor-associated miRNAs PMID:23745563

  16. The constraint on the integral kernels of density functional theories which results from insisting that there be a unique solution for the density function

    NASA Astrophysics Data System (ADS)

    Lovett, Ronald

    1988-06-01

    All predictive theories for the spatial variation of the density in an inhomogeneous system can be constructed by approximating exact, nonlinear integral equations which relate the density and pair correlation functions of the system. It is shown that the set of correct kernels in the exact integral equations for the density is on the boundary between the set of kernels for which the integral equations have no solution for the density and the set for which the integral equations have a multiplicity of solutions. Thus arbitrarily small deviations from the correct kernel can make these integral equations insoluble. A heuristic model equation is used to illustrate how the density functional problem can be so sensitive to the approximation made to the correlation function kernel and it is then shown explicitly that this behavior is realized in the relation between the density and the direct correlation function and in the lowest order BGYB equation. Functional equations are identified for the kernels in these equations which are satisified by the correct kernels, which guarantee a unique solution to the integral equations, and which provide a natural constraint on approximations which can be used in density functional theory. It is also shown that this sensitive behavior is a general property of density functional problems and that the methodology for constructing the constraints is equally general. A variety of applications of density functional theory are reviewed to illustrate practical consequences of this sensitivity.

  17. Geometric tree kernels: classification of COPD from airway tree geometry.

    PubMed

    Feragen, Aasa; Petersen, Jens; Grimm, Dominik; Dirksen, Asger; Pedersen, Jesper Holst; Borgwardt, Karsten; de Bruijne, Marleen

    2013-01-01

    Methodological contributions: This paper introduces a family of kernels for analyzing (anatomical) trees endowed with vector valued measurements made along the tree. While state-of-the-art graph and tree kernels use combinatorial tree/graph structure with discrete node and edge labels, the kernels presented in this paper can include geometric information such as branch shape, branch radius or other vector valued properties. In addition to being flexible in their ability to model different types of attributes, the presented kernels are computationally efficient and some of them can easily be computed for large datasets (N - 10.000) of trees with 30 - 600 branches. Combining the kernels with standard machine learning tools enables us to analyze the relation between disease and anatomical tree structure and geometry. Experimental results: The kernels are used to compare airway trees segmented from low-dose CT, endowed with branch shape descriptors and airway wall area percentage measurements made along the tree. Using kernelized hypothesis testing we show that the geometric airway trees are significantly differently distributed in patients with Chronic Obstructive Pulmonary Disease (COPD) than in healthy individuals. The geometric tree kernels also give a significant increase in the classification accuracy of COPD from geometric tree structure endowed with airway wall thickness measurements in comparison with state-of-the-art methods, giving further insight into the relationship between airway wall thickness and COPD. Software: Software for computing kernels and statistical tests is available at http://image.diku.dk/aasa/software.php.

  18. 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.

  19. Canonical distributions on Riemannian homogeneous k-symmetric spaces

    NASA Astrophysics Data System (ADS)

    Balashchenko, Vitaly V.

    2015-01-01

    It is known that distributions generated by almost product structures are applicable, in particular, to some problems in the theory of Monge-Ampère equations. In this paper, we characterize canonical distributions defined by canonical almost product structures on Riemannian homogeneous k-symmetric spaces in the sense of types AF (anti-foliation), F (foliation), TGF (totally geodesic foliation). Algebraic criteria for all these types on k-symmetric spaces of orders k = 4, 5, 6 were obtained. Note that canonical distributions on homogeneous k-symmetric spaces are closely related to special canonical almost complex structures and f-structures, which were recently applied by I. Khemar to studying elliptic integrable systems.

  20. Kernel-based machine learning techniques for infrasound signal classification

    NASA Astrophysics Data System (ADS)

    Tuma, Matthias; Igel, Christian; Mialle, Pierrick

    2014-05-01

    Infrasound monitoring is one of four remote sensing technologies continuously employed by the CTBTO Preparatory Commission. The CTBTO's infrasound network is designed to monitor the Earth for potential evidence of atmospheric or shallow underground nuclear explosions. Upon completion, it will comprise 60 infrasound array stations distributed around the globe, of which 47 were certified in January 2014. Three stages can be identified in CTBTO infrasound data processing: automated processing at the level of single array stations, automated processing at the level of the overall global network, and interactive review by human analysts. At station level, the cross correlation-based PMCC algorithm is used for initial detection of coherent wavefronts. It produces estimates for trace velocity and azimuth of incoming wavefronts, as well as other descriptive features characterizing a signal. Detected arrivals are then categorized into potentially treaty-relevant versus noise-type signals by a rule-based expert system. This corresponds to a binary classification task at the level of station processing. In addition, incoming signals may be grouped according to their travel path in the atmosphere. The present work investigates automatic classification of infrasound arrivals by kernel-based pattern recognition methods. It aims to explore the potential of state-of-the-art machine learning methods vis-a-vis the current rule-based and task-tailored expert system. To this purpose, we first address the compilation of a representative, labeled reference benchmark dataset as a prerequisite for both classifier training and evaluation. Data representation is based on features extracted by the CTBTO's PMCC algorithm. As classifiers, we employ support vector machines (SVMs) in a supervised learning setting. Different SVM kernel functions are used and adapted through different hyperparameter optimization routines. The resulting performance is compared to several baseline classifiers. All

  1. Model-based online learning with kernels.

    PubMed

    Li, Guoqi; Wen, Changyun; Li, Zheng Guo; Zhang, Aimin; Yang, Feng; Mao, Kezhi

    2013-03-01

    New optimization models and algorithms for online learning with Kernels (OLK) in classification, regression, and novelty detection are proposed in a reproducing Kernel Hilbert space. Unlike the stochastic gradient descent algorithm, called the naive online Reg minimization algorithm (NORMA), OLK algorithms are obtained by solving a constrained optimization problem based on the proposed models. By exploiting the techniques of the Lagrange dual problem like Vapnik's support vector machine (SVM), the solution of the optimization problem can be obtained iteratively and the iteration process is similar to that of the NORMA. This further strengthens the foundation of OLK and enriches the research area of SVM. We also apply the obtained OLK algorithms to problems in classification, regression, and novelty detection, including real time background substraction, to show their effectiveness. It is illustrated that, based on the experimental results of both classification and regression, the accuracy of OLK algorithms is comparable with traditional SVM-based algorithms, such as SVM and least square SVM (LS-SVM), and with the state-of-the-art algorithms, such as Kernel recursive least square (KRLS) method and projectron method, while it is slightly higher than that of NORMA. On the other hand, the computational cost of the OLK algorithm is comparable with or slightly lower than existing online methods, such as above mentioned NORMA, KRLS, and projectron methods, but much lower than that of SVM-based algorithms. In addition, different from SVM and LS-SVM, it is possible for OLK algorithms to be applied to non-stationary problems. Also, the applicability of OLK in novelty detection is illustrated by simulation results.

  2. 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.

  3. Canonical straight field line magnetic flux coordinates for tokamaks

    NASA Astrophysics Data System (ADS)

    Li, Meng; Breizman, Boris N.; Zheng, Linjin

    2016-12-01

    New global straight field line coordinates are introduced for a toroidal plasma configuration. The new coordinate system provides a canonical description of particle guiding center motion while maintaining the straight field line feature. These coordinates are convenient for combining MHD calculations with kinetic modeling of energetic particles. We demonstrate how the new coordinate system can be constructed by transforming the poloidal and toroidal angles. Numerical examples show comparison of the new coordinates with various non-canonical coordinates for the same equilibrium configuration.

  4. Barbero-Immirzi field in canonical formalism of pure gravity

    NASA Astrophysics Data System (ADS)

    Calcagni, Gianluca; Mercuri, Simone

    2009-04-01

    The Barbero-Immirzi (BI) parameter is promoted to a field and a canonical analysis is performed when it is coupled with a Nieh-Yan topological invariant. It is shown that, in the effective theory, the BI field is a canonical pseudoscalar minimally coupled with gravity. This framework is argued to be more natural than the one of the usual Holst action. Potential consequences in relation with inflation and the quantum theory are briefly discussed.

  5. Canonical algorithms for numerical integration of charged particle motion equations

    NASA Astrophysics Data System (ADS)

    Efimov, I. N.; Morozov, E. A.; Morozova, A. R.

    2017-02-01

    A technique for numerically integrating the equation of charged particle motion in a magnetic field is considered. It is based on the canonical transformations of the phase space in Hamiltonian mechanics. The canonical transformations make the integration process stable against counting error accumulation. The integration algorithms contain a minimum possible amount of arithmetics and can be used to design accelerators and devices of electron and ion optics.

  6. Determine the Role of Canonical Wnt Signaling in Ovarian Tumorigenesis

    DTIC Science & Technology

    2012-10-01

    Goldstein M, Sellers WR, Yaron Y , et al. Multiple genes in human 20q13 chromosomal region are involved in an advanced prostate cancer xenograft...Months 1-12) Specifically, we will determine whether inhibition of canonical Wnt signaling induces the expression of markers of senescence in human...cells treated with FJ9 demonstrated features of senescence such as a large flat cell morphology (Figure 1B). However, examination of markers of canonical

  7. 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...

  8. Oil point pressure of Indian almond kernels

    NASA Astrophysics Data System (ADS)

    Aregbesola, O.; Olatunde, G.; Esuola, S.; Owolarafe, O.

    2012-07-01

    The effect of preprocessing conditions such as moisture content, heating temperature, heating time and particle size on oil point pressure of Indian almond kernel was investigated. Results showed that oil point pressure was significantly (P < 0.05) affected by above mentioned parameters. It was also observed that oil point pressure reduced with increase in heating temperature and heating time for both coarse and fine particles. Furthermore, an increase in moisture content resulted in increased oil point pressure for coarse particles while there was a reduction in oil point pressure with increase in moisture content for fine particles.

  9. Verification of Chare-kernel programs

    SciTech Connect

    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.

  10. Enhancement of canonical sampling by virtual-state transitions.

    PubMed

    Higo, Junichi; Kasahara, Kota; Dasgupta, Bhaskar; Nakamura, Haruki

    2017-01-28

    A novel method was developed to enhance canonical sampling. A system is divided into virtually introduced sub-states, called "virtual states," which does not exist in reality. The configuration sampling is achieved by a standard canonical sampling method, the Metropolis Monte Carlo method, and confined in a virtual state for a while. In contrast, inter-virtual state motions are controlled by transition probabilities, which can be set arbitrarily. A simple recursive equation was introduced to determine the inter-virtual state transition probabilities, by which the sampling is enhanced considerably. We named this method "virtual-system coupled canonical Monte Carlo (VcMC) sampling." A simple method was proposed to reconstruct a canonical distribution function at a certain temperature from the resultant VcMC sampling data. Two systems, a one-dimensional double-well potential and a three-dimensional ligand-receptor binding/unbinding model, were examined. VcMC produced an accurate canonical distribution much more quickly than a conventional canonical Monte Carlo simulation does.

  11. Enhancement of canonical sampling by virtual-state transitions

    NASA Astrophysics Data System (ADS)

    Higo, Junichi; Kasahara, Kota; Dasgupta, Bhaskar; Nakamura, Haruki

    2017-01-01

    A novel method was developed to enhance canonical sampling. A system is divided into virtually introduced sub-states, called "virtual states," which does not exist in reality. The configuration sampling is achieved by a standard canonical sampling method, the Metropolis Monte Carlo method, and confined in a virtual state for a while. In contrast, inter-virtual state motions are controlled by transition probabilities, which can be set arbitrarily. A simple recursive equation was introduced to determine the inter-virtual state transition probabilities, by which the sampling is enhanced considerably. We named this method "virtual-system coupled canonical Monte Carlo (VcMC) sampling." A simple method was proposed to reconstruct a canonical distribution function at a certain temperature from the resultant VcMC sampling data. Two systems, a one-dimensional double-well potential and a three-dimensional ligand-receptor binding/unbinding model, were examined. VcMC produced an accurate canonical distribution much more quickly than a conventional canonical Monte Carlo simulation does.

  12. Accretion of the Moon from non-canonical discs.

    PubMed

    Salmon, J; Canup, R M

    2014-09-13

    Impacts that leave the Earth-Moon system with a large excess in angular momentum have recently been advocated as a means of generating a protolunar disc with a composition that is nearly identical to that of the Earth's mantle. We here investigate the accretion of the Moon from discs generated by such 'non-canonical' impacts, which are typically more compact than discs produced by canonical impacts and have a higher fraction of their mass initially located inside the Roche limit. Our model predicts a similar overall accretional history for both canonical and non-canonical discs, with the Moon forming in three consecutive steps over hundreds of years. However, we find that, to yield a lunar-mass Moon, the more compact non-canonical discs must initially be more massive than implied by prior estimates, and only a few of the discs produced by impact simulations to date appear to meet this condition. Non-canonical impacts require that capture of the Moon into the evection resonance with the Sun reduced the Earth-Moon angular momentum by a factor of 2 or more. We find that the Moon's semi-major axis at the end of its accretion is approximately 7R⊕, which is comparable to the location of the evection resonance for a post-impact Earth with a 2.5 h rotation period in the absence of a disc. Thus, the dynamics of the Moon's assembly may directly affect its ability to be captured into the resonance.

  13. The role of the Wnt canonical signaling in neurodegenerative diseases.

    PubMed

    Libro, Rosaliana; Bramanti, Placido; Mazzon, Emanuela

    2016-08-01

    The Wnt/β-catenin or Wnt canonical pathway controls multiple biological processes throughout development and adult life. Growing evidences have suggested that deregulation of the Wnt canonical pathway could be involved in the pathogenesis of neurodegenerative diseases. The Wnt canonical signaling is a pathway tightly regulated, which activation results in the inhibition of the Glycogen Synthase Kinase 3β (GSK-3β) function and in increased β-catenin activity, that migrates into the nucleus, activating the transcription of the Wnt target genes. Conversely, when the Wnt canonical pathway is turned off, increased levels of GSK-3β promote β-catenin degradation. Hence, GSK-3β could be considered as a key regulator of the Wnt canonical pathway. Of note, GSK-3β has also been involved in the modulation of inflammation and apoptosis, determining the delicate balance between immune tolerance/inflammation and neuronal survival/neurodegeneration. In this review, we have summarized the current acknowledgements about the role of the Wnt canonical pathway in the pathogenesis of some neurodegenerative diseases including Alzheimer's disease, cerebral ischemia, Parkinson's disease, Huntington's disease, multiple sclerosis and amyotrophic lateral sclerosis, with particular regard to the main in vitro and in vivo studies in this field, by reviewing 85 research articles about.

  14. The Topology of Canonical Flux Tubes in Flared Jet Geometry

    NASA Astrophysics Data System (ADS)

    Lavine, Eric Sander; You, Setthivoine

    2016-10-01

    Magnetized plasma jets are generally modeled as magnetic flux tubes filled with flowing plasma governed by MHD. We outline here a more fundamental approach based on flux tubes of canonical vorticity. This approach extends the concept of magnetic flux tube evolution to include the effects of finite particle momentum and enables visualization of the topology of plasma jets in regimes beyond MHD. We examine the morphology of these canonical flux tubes for increasing electrical currents, different radial current profiles, different electron Mach numbers, and a fixed, flared, dipole magnetic field. Calculations of gauge-invariant relative canonical helicity track the evolution of magnetic, cross, and kinetic helicities in the system and show that ion flow fields can unwind to compensate for increasing magnetic twist. The results demonstrate that including a species' finite momentum can result in long, collimated canonical vorticity flux tubes even when the magnetic flux tube is flared. With finite momentum, particle density gradients must be normal to canonical vorticities not to magnetic fields, so observations of collimated astrophysical jets could be images of canonical vorticity flux tubes instead of magnetic flux tubes. This work is supported by DOE Grant DE-SC0010340.

  15. Distinguishing k-defects from their canonical twins

    NASA Astrophysics Data System (ADS)

    Andrews, Melinda; Lewandowski, Matt; Trodden, Mark; Wesley, Daniel

    2010-11-01

    We study k-defects—topological defects in theories with more than two derivatives and second-order equations of motion—and describe some striking ways in which these defects both resemble and differ from their analogues in canonical scalar field theories. We show that, for some models, the homotopy structure of the vacuum manifold is insufficient to establish the existence of k-defects, in contrast to the canonical case. These results also constrain certain families of Dirac-Born-Infeld instanton solutions in the 4-dimensional effective theory. We then describe a class of k-defect solutions, which we dub “doppelgängers,” that precisely match the field profile and energy density of their canonical scalar field theory counterparts. We give a complete characterization of Lagrangians which admit doppelgänger domain walls. By numerically computing the fluctuation eigenmodes about domain wall solutions, we find different spectra for doppelgängers and canonical walls, allowing us to distinguish between k-defects and the canonical walls they mimic. We search for doppelgängers for cosmic strings by numerically constructing solutions of Dirac-Born-Infeld and canonical scalar field theories. Despite investigating several examples, we are unable to find doppelgänger cosmic strings, hence the existence of doppelgängers for defects with codimension >1 remains an open question.

  16. L-Kuramoto-Sivashinsky SPDEs in one-to-three dimensions: L-KS kernel, sharp Hölder regularity, and Swift-Hohenberg law equivalence

    NASA Astrophysics Data System (ADS)

    Allouba, Hassan

    2015-12-01

    Generalizing the L-Kuramoto-Sivashinsky (L-KS) kernel from our earlier work, we give a novel explicit-kernel formulation useful for a large class of fourth order deterministic, stochastic, linear, and nonlinear PDEs in multispatial dimensions. These include pattern formation equations like the Swift-Hohenberg and many other prominent and new PDEs. We first establish existence, uniqueness, and sharp dimension-dependent spatio-temporal Hölder regularity for the canonical (zero drift) L-KS SPDE, driven by white noise on R+ ×Rd d = 1 3 . The spatio-temporal Hölder exponents are exactly the same as the striking ones we proved for our recently introduced Brownian-time Brownian motion (BTBM) stochastic integral equation, associated with time-fractional PDEs. The challenge here is that, unlike the positive BTBM density, the L-KS kernel is the Gaussian average of a modified, highly oscillatory, and complex Schrödinger propagator. We use a combination of harmonic and delicate analysis to get the necessary estimates. Second, attaching order parameters ε1 to the L-KS spatial operator and ε2 to the noise term, we show that the dimension-dependent critical ratio ε2 /ε1d/8 controls the limiting behavior of the L-KS SPDE, as ε1 ,ε2 ↘ 0; and we compare this behavior to that of the less regular second order heat SPDEs. Finally, we give a change-of-measure equivalence between the canonical L-KS SPDE and nonlinear L-KS SPDEs. In particular, we prove uniqueness in law for the Swift-Hohenberg and the law equivalence-and hence the same Hölder regularity-of the Swift-Hohenberg SPDE and the canonical L-KS SPDE on compacts in one-to-three dimensions.

  17. Kernel learning at the first level of inference.

    PubMed

    Cawley, Gavin C; Talbot, Nicola L C

    2014-05-01

    Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense.

  18. Canonical Wnt signaling transiently stimulates proliferation and enhances neurogenesis in neonatal neural progenitor cultures

    SciTech Connect

    Hirsch, Cordula; Campano, Louise M.; Woehrle, Simon; Hecht, Andreas . E-mail: andreas.hecht@mol-med.uni-freiburg.de

    2007-02-01

    Canonical Wnt signaling triggers the formation of heterodimeric transcription factor complexes consisting of {beta}-catenin and T cell factors, and thereby controls the execution of specific genetic programs. During the expansion and neurogenic phases of embryonic neural development canonical Wnt signaling initially controls proliferation of neural progenitor cells, and later neuronal differentiation. Whether Wnt growth factors affect neural progenitor cells postnatally is not known. Therefore, we have analyzed the impact of Wnt signaling on neural progenitors isolated from cerebral cortices of newborn mice. Expression profiling of pathway components revealed that these cells are fully equipped to respond to Wnt signals. However, Wnt pathway activation affected only a subset of neonatal progenitors and elicited a limited increase in proliferation and neuronal differentiation in distinct subsets of cells. Moreover, Wnt pathway activation only transiently stimulated S-phase entry but did not support long-term proliferation of progenitor cultures. The dampened nature of the Wnt response correlates with the predominant expression of inhibitory pathway components and the rapid actuation of negative feedback mechanisms. Interestingly, in differentiating cell cultures activation of canonical Wnt signaling reduced Hes1 and Hes5 expression suggesting that during postnatal neural development, Wnt/{beta}-catenin signaling enhances neurogenesis from progenitor cells by interfering with Notch pathway activity.

  19. Delimiting Areas of Endemism through Kernel Interpolation

    PubMed Central

    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

  20. Bergman kernel, balanced metrics and black holes

    NASA Astrophysics Data System (ADS)

    Klevtsov, Semyon

    In this thesis we explore the connections between the Kahler geometry and Landau levels on compact manifolds. We rederive the expansion of the Bergman kernel on Kahler manifolds developed by Tian, Yau, Zelditch, Lu and Catlin, using path integral and perturbation theory. The physics interpretation of this result is as an expansion of the projector of wavefunctions on the lowest Landau level, in the special case that the magnetic field is proportional to the Kahler form. This is a geometric expansion, somewhat similar to the DeWitt-Seeley-Gilkey short time expansion for the heat kernel, but in this case describing the long time limit, without depending on supersymmetry. We also generalize this expansion to supersymmetric quantum mechanics and more general magnetic fields, and explore its applications. These include the quantum Hall effect in curved space, the balanced metrics and Kahler gravity. In particular, we conjecture that for a probe in a BPS black hole in type II strings compactified on Calabi-Yau manifolds, the moduli space metric is the balanced metric.

  1. Scientific Computing Kernels on the Cell Processor

    SciTech Connect

    Williams, Samuel W.; Shalf, John; Oliker, Leonid; Kamil, Shoaib; Husbands, Parry; Yelick, Katherine

    2007-04-04

    The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. As a result, the high performance computing community is examining alternative architectures that address the limitations of modern cache-based designs. In this work, we examine the potential of using the recently-released STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. First, we introduce a performance model for Cell and apply it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs. The difficulty of programming Cell, which requires assembly level intrinsics for the best performance, makes this model useful as an initial step in algorithm design and evaluation. Next, we validate the accuracy of our model by comparing results against published hardware results, as well as our own implementations on a 3.2GHz Cell blade. Additionally, we compare Cell performance to benchmarks run on leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1E) architectures. Our work also explores several different mappings of the kernels and demonstrates a simple and effective programming model for Cell's unique architecture. Finally, we propose modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations. Overall results demonstrate the tremendous potential of the Cell architecture for scientific computations in terms of both raw performance and power efficiency.

  2. Transcriptome analysis of Ginkgo biloba kernels

    PubMed Central

    He, Bing; Gu, Yincong; Xu, Meng; Wang, Jianwen; Cao, Fuliang; Xu, Li-an

    2015-01-01

    Ginkgo biloba is a dioecious species native to China with medicinally and phylogenetically important characteristics; however, genomic resources for this species are limited. In this study, we performed the first transcriptome sequencing for Ginkgo kernels at five time points using Illumina paired-end sequencing. Approximately 25.08-Gb clean reads were obtained, and 68,547 unigenes with an average length of 870 bp were generated by de novo assembly. Of these unigenes, 29,987 (43.74%) were annotated in publicly available plant protein database. A total of 3,869 genes were identified as significantly differentially expressed, and enrichment analysis was conducted at different time points. Furthermore, metabolic pathway analysis revealed that 66 unigenes were responsible for terpenoid backbone biosynthesis, with up to 12 up-regulated unigenes involved in the biosynthesis of ginkgolide and bilobalide. Differential gene expression analysis together with real-time PCR experiments indicated that the synthesis of bilobalide may have interfered with the ginkgolide synthesis process in the kernel. These data can remarkably expand the existing transcriptome resources of Ginkgo, and provide a valuable platform to reveal more on developmental and metabolic mechanisms of this species. PMID:26500663

  3. Aligning Biomolecular Networks Using Modular Graph Kernels

    NASA Astrophysics Data System (ADS)

    Towfic, Fadi; Greenlee, M. Heather West; Honavar, Vasant

    Comparative analysis of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. We explore a class of algorithms for aligning large biomolecular networks by breaking down such networks into subgraphs and computing the alignment of the networks based on the alignment of their subgraphs. The resulting subnetworks are compared using graph kernels as scoring functions. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit. Our experiments using Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository of protein-protein interaction data demonstrate that the performance of the proposed algorithms (as measured by % GO term enrichment of subnetworks identified by the alignment) is competitive with some of the state-of-the-art algorithms for pair-wise alignment of large protein-protein interaction networks. Our results also show that the inter-species similarity scores computed based on graph kernels can be used to cluster the species into a species tree that is consistent with the known phylogenetic relationships among the species.

  4. Infrared microspectroscopic imaging of plant tissues: spectral visualization of Triticum aestivum kernel and Arabidopsis leaf microstructure

    PubMed Central

    Warren, Frederick J; Perston, Benjamin B; Galindez-Najera, Silvia P; Edwards, Cathrina H; Powell, Prudence O; Mandalari, Giusy; Campbell, Grant M; Butterworth, Peter J; Ellis, Peter R

    2015-01-01

    Infrared microspectroscopy is a tool with potential for studies of the microstructure, chemical composition and functionality of plants at a subcellular level. Here we present the use of high-resolution bench top-based infrared microspectroscopy to investigate the microstructure of Triticum aestivum L. (wheat) kernels and Arabidopsis leaves. Images of isolated wheat kernel tissues and whole wheat kernels following hydrothermal processing and simulated gastric and duodenal digestion were generated, as well as images of Arabidopsis leaves at different points during a diurnal cycle. Individual cells and cell walls were resolved, and large structures within cells, such as starch granules and protein bodies, were clearly identified. Contrast was provided by converting the hyperspectral image cubes into false-colour images using either principal component analysis (PCA) overlays or by correlation analysis. The unsupervised PCA approach provided a clear view of the sample microstructure, whereas the correlation analysis was used to confirm the identity of different anatomical structures using the spectra from isolated components. It was then demonstrated that gelatinized and native starch within cells could be distinguished, and that the loss of starch during wheat digestion could be observed, as well as the accumulation of starch in leaves during a diurnal period. PMID:26400058

  5. Infrared microspectroscopic imaging of plant tissues: spectral visualization of Triticum aestivum kernel and Arabidopsis leaf microstructure.

    PubMed

    Warren, Frederick J; Perston, Benjamin B; Galindez-Najera, Silvia P; Edwards, Cathrina H; Powell, Prudence O; Mandalari, Giusy; Campbell, Grant M; Butterworth, Peter J; Ellis, Peter R

    2015-11-01

    Infrared microspectroscopy is a tool with potential for studies of the microstructure, chemical composition and functionality of plants at a subcellular level. Here we present the use of high-resolution bench top-based infrared microspectroscopy to investigate the microstructure of Triticum aestivum L. (wheat) kernels and Arabidopsis leaves. Images of isolated wheat kernel tissues and whole wheat kernels following hydrothermal processing and simulated gastric and duodenal digestion were generated, as well as images of Arabidopsis leaves at different points during a diurnal cycle. Individual cells and cell walls were resolved, and large structures within cells, such as starch granules and protein bodies, were clearly identified. Contrast was provided by converting the hyperspectral image cubes into false-colour images using either principal component analysis (PCA) overlays or by correlation analysis. The unsupervised PCA approach provided a clear view of the sample microstructure, whereas the correlation analysis was used to confirm the identity of different anatomical structures using the spectra from isolated components. It was then demonstrated that gelatinized and native starch within cells could be distinguished, and that the loss of starch during wheat digestion could be observed, as well as the accumulation of starch in leaves during a diurnal period.

  6. Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis

    PubMed Central

    Zhu, Xiaofeng; Suk, Heung-Il

    2016-01-01

    Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract features independently from each modality, and then simply concatenate them into a long vector for classification, without appropriate consideration of the correlation among modalities. In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation, by canonical correlation analysis. We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. In our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we use Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer’s disease diagnosis. The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods. PMID:26254746

  7. Sugar uptake into kernels of tunicate tassel-seed maize

    SciTech Connect

    Thomas, P.A.; Felker, F.C.; Crawford, C.G. )

    1990-05-01

    A maize (Zea mays L.) strain expressing both the tassel-seed (Ts-5) and tunicate (Tu) characters was developed which produces glume-covered kernels on the tassel, often born on 7-10 mm pedicels. Vigorous plants produce up to 100 such kernels interspersed with additional sessile kernels. This floral unit provides a potentially valuable experimental system for studying sugar uptake into developing maize seeds. When detached kernels (with glumes and pedicel intact) are placed in incubation solution, fluid flows up the pedicel and into the glumes, entering the pedicel apoplast near the kernel base. The unusual anatomical features of this maize strain permit experimental access to the pedicel apoplast with much less possibility of kernel base tissue damage than with kernels excised from the cob. ({sup 14}C)Fructose incorporation into soluble and insoluble fractions of endosperm increased for 8 days. Endosperm uptake of sucrose, fructose, and D-glucose was significantly greater than that of L-glucose. Fructose uptake was significantly inhibited by CCCP, DNP, and PCMBS. These results suggest the presence of an active, non-diffusion component of sugar transport in maize kernels.

  8. Integral Transform Methods: A Critical Review of Various Kernels

    NASA Astrophysics Data System (ADS)

    Orlandini, Giuseppina; Turro, Francesco

    2017-03-01

    Some general remarks about integral transform approaches to response functions are made. Their advantage for calculating cross sections at energies in the continuum is stressed. In particular we discuss the class of kernels that allow calculations of the transform by matrix diagonalization. A particular set of such kernels, namely the wavelets, is tested in a model study.

  9. Evidence-Based Kernels: Fundamental Units of Behavioral Influence

    ERIC Educational Resources Information Center

    Embry, Dennis D.; Biglan, Anthony

    2008-01-01

    This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior-influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of…

  10. 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…

  11. Integrating the Gradient of the Thin Wire Kernel

    NASA Technical Reports Server (NTRS)

    Champagne, Nathan J.; Wilton, Donald R.

    2008-01-01

    A formulation for integrating the gradient of the thin wire kernel is presented. This approach employs a new expression for the gradient of the thin wire kernel derived from a recent technique for numerically evaluating the exact thin wire kernel. This approach should provide essentially arbitrary accuracy and may be used with higher-order elements and basis functions using the procedure described in [4].When the source and observation points are close, the potential integrals over wire segments involving the wire kernel are split into parts to handle the singular behavior of the integrand [1]. The singularity characteristics of the gradient of the wire kernel are different than those of the wire kernel, and the axial and radial components have different singularities. The characteristics of the gradient of the wire kernel are discussed in [2]. To evaluate the near electric and magnetic fields of a wire, the integration of the gradient of the wire kernel needs to be calculated over the source wire. Since the vector bases for current have constant direction on linear wire segments, these integrals reduce to integrals of the form

  12. 7 CFR 981.60 - Determination of kernel weight.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... AGREEMENTS AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which...

  13. 7 CFR 981.60 - Determination of kernel weight.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... Agreements and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which...

  14. 7 CFR 981.60 - Determination of kernel weight.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... AGREEMENTS AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which...

  15. 7 CFR 981.60 - Determination of kernel weight.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... Agreements and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which...

  16. High speed sorting of Fusarium-damaged wheat kernels

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Recent studies have found that resistance to Fusarium fungal infection can be inherited in wheat from one generation to another. However, there is not yet available a cost effective method to separate Fusarium-damaged wheat kernels from undamaged kernels so that wheat breeders can take advantage of...

  17. End-use quality of soft kernel durum wheat

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Kernel texture is a major determinant of end-use quality of wheat. Durum wheat is known for its very hard texture, which influences how it is milled and for what products it is well suited. We developed soft kernel durum wheat lines via Ph1b-mediated homoeologous recombination with Dr. Leonard Joppa...

  18. 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…

  19. Parametric kernel-driven active contours for image segmentation

    NASA Astrophysics Data System (ADS)

    Wu, Qiongzhi; Fang, Jiangxiong

    2012-10-01

    We investigated a parametric kernel-driven active contour (PKAC) model, which implicitly transfers kernel mapping and piecewise constant to modeling the image data via kernel function. The proposed model consists of curve evolution functional with three terms: global kernel-driven and local kernel-driven terms, which evaluate the deviation of the mapped image data within each region from the piecewise constant model, and a regularization term expressed as the length of the evolution curves. In the local kernel-driven term, the proposed model can effectively segment images with intensity inhomogeneity by incorporating the local image information. By balancing the weight between the global kernel-driven term and the local kernel-driven term, the proposed model can segment the images with either intensity homogeneity or intensity inhomogeneity. To ensure the smoothness of the level set function and reduce the computational cost, the distance regularizing term is applied to penalize the deviation of the level set function and eliminate the requirement of re-initialization. Compared with the local image fitting model and local binary fitting model, experimental results show the advantages of the proposed method in terms of computational efficiency and accuracy.

  20. Evidence-based Kernels: Fundamental Units of Behavioral Influence

    PubMed Central

    Biglan, Anthony

    2008-01-01

    This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior. PMID:18712600

  1. Computing the roots of complex orthogonal and kernel polynomials

    SciTech Connect

    Saylor, P.E.; Smolarski, D.C.

    1988-01-01

    A method is presented to compute the roots of complex orthogonal and kernel polynomials. An important application of complex kernel polynomials is the acceleration of iterative methods for the solution of nonsymmetric linear equations. In the real case, the roots of orthogonal polynomials coincide with the eigenvalues of the Jacobi matrix, a symmetric tridiagonal matrix obtained from the defining three-term recurrence relationship for the orthogonal polynomials. In the real case kernel polynomials are orthogonal. The Stieltjes procedure is an algorithm to compute the roots of orthogonal and kernel polynomials bases on these facts. In the complex case, the Jacobi matrix generalizes to a Hessenberg matrix, the eigenvalues of which are roots of either orthogonal or kernel polynomials. The resulting algorithm generalizes the Stieljes procedure. It may not be defined in the case of kernel polynomials, a consequence of the fact that they are orthogonal with respect to a nonpositive bilinear form. (Another consequence is that kernel polynomials need not be of exact degree.) A second algorithm that is always defined is presented for kernel polynomials. Numerical examples are described.

  2. A new method for evaluation of the resistance to rice kernel cracking based on moisture absorption in brown rice under controlled conditions

    PubMed Central

    Hayashi, Takeshi; Kobayashi, Asako; Tomita, Katsura; Shimizu, Toyohiro

    2015-01-01

    We developed and evaluated the effectiveness of a new method to detect differences among rice cultivars in their resistance to kernel cracking. The method induces kernel cracking under laboratory controlled condition by moisture absorption to brown rice. The optimal moisture absorption conditions were determined using two japonica cultivars, ‘Nipponbare’ as a cracking-resistant cultivar and ‘Yamahikari’ as a cracking-susceptible cultivar: 12% initial moisture content of the brown rice, a temperature of 25°C, a duration of 5 h, and only a single absorption treatment. We then evaluated the effectiveness of these conditions using 12 japonica cultivars. The proportion of cracked kernels was significantly correlated with the mean 10-day maximum temperature after heading. In addition, the correlation between the proportions of cracked kernels in the 2 years of the study was higher than that for values obtained using the traditional late harvest method. The new moisture absorption method could stably evaluate the resistance to kernel cracking, and will help breeders to develop future cultivars with less cracking of the kernels. PMID:26719740

  3. OSKI: A Library of Automatically Tuned Sparse Matrix Kernels

    SciTech Connect

    Vuduc, R; Demmel, J W; Yelick, K A

    2005-07-19

    The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives that provide automatically tuned computational kernels on sparse matrices, for use by solver libraries and applications. These kernels include sparse matrix-vector multiply and sparse triangular solve, among others. The primary aim of this interface is to hide the complex decision-making process needed to tune the performance of a kernel implementation for a particular user's sparse matrix and machine, while also exposing the steps and potentially non-trivial costs of tuning at run-time. This paper provides an overview of OSKI, which is based on our research on automatically tuned sparse kernels for modern cache-based superscalar machines.

  4. Direct Measurement of Wave Kernels in Time-Distance Helioseismology

    NASA Technical Reports Server (NTRS)

    Duvall, T. L., Jr.

    2006-01-01

    Solar f-mode waves are surface-gravity waves which propagate horizontally in a thin layer near the photosphere with a dispersion relation approximately that of deep water waves. At the power maximum near 3 mHz, the wavelength of 5 Mm is large enough for various wave scattering properties to be observable. Gizon and Birch (2002,ApJ,571,966)h ave calculated kernels, in the Born approximation, for the sensitivity of wave travel times to local changes in damping rate and source strength. In this work, using isolated small magnetic features as approximate point-sourc'e scatterers, such a kernel has been measured. The observed kernel contains similar features to a theoretical damping kernel but not for a source kernel. A full understanding of the effect of small magnetic features on the waves will require more detailed modeling.

  5. 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.

  6. A novel extended kernel recursive least squares algorithm.

    PubMed

    Zhu, Pingping; Chen, Badong; Príncipe, José C

    2012-08-01

    In this paper, a novel extended kernel recursive least squares algorithm is proposed combining the kernel recursive least squares algorithm and the Kalman filter or its extensions to estimate or predict signals. Unlike the extended kernel recursive least squares (Ex-KRLS) algorithm proposed by Liu, the state model of our algorithm is still constructed in the original state space and the hidden state is estimated using the Kalman filter. The measurement model used in hidden state estimation is learned by the kernel recursive least squares algorithm (KRLS) in reproducing kernel Hilbert space (RKHS). The novel algorithm has more flexible state and noise models. We apply this algorithm to vehicle tracking and the nonlinear Rayleigh fading channel tracking, and compare the tracking performances with other existing algorithms.

  7. Reaction Kernel Structure of a Slot Jet Diffusion Flame in Microgravity

    NASA Technical Reports Server (NTRS)

    Takahashi, F.; Katta, V. R.

    2001-01-01

    Diffusion flame stabilization in normal earth gravity (1 g) has long been a fundamental research subject in combustion. Local flame-flow phenomena, including heat and species transport and chemical reactions, around the flame base in the vicinity of condensed surfaces control flame stabilization and fire spreading processes. Therefore, gravity plays an important role in the subject topic because buoyancy induces flow in the flame zone, thus increasing the convective (and diffusive) oxygen transport into the flame zone and, in turn, reaction rates. Recent computations show that a peak reactivity (heat-release or oxygen-consumption rate) spot, or reaction kernel, is formed in the flame base by back-diffusion and reactions of radical species in the incoming oxygen-abundant flow at relatively low temperatures (about 1550 K). Quasi-linear correlations were found between the peak heat-release or oxygen-consumption rate and the velocity at the reaction kernel for cases including both jet and flat-plate diffusion flames in airflow. The reaction kernel provides a stationary ignition source to incoming reactants, sustains combustion, and thus stabilizes the trailing diffusion flame. In a quiescent microgravity environment, no buoyancy-induced flow exits and thus purely diffusive transport controls the reaction rates. Flame stabilization mechanisms in such purely diffusion-controlled regime remain largely unstudied. Therefore, it will be a rigorous test for the reaction kernel correlation if it can be extended toward zero velocity conditions in the purely diffusion-controlled regime. The objectives of this study are to reveal the structure of the flame-stabilizing region of a two-dimensional (2D) laminar jet diffusion flame in microgravity and develop a unified diffusion flame stabilization mechanism. This paper reports the recent progress in the computation and experiment performed in microgravity.

  8. Image quality of mixed convolution kernel in thoracic computed tomography.

    PubMed

    Neubauer, Jakob; Spira, Eva Maria; Strube, Juliane; Langer, Mathias; Voss, Christian; Kotter, Elmar

    2016-11-01

    The mixed convolution kernel alters his properties geographically according to the depicted organ structure, especially for the lung. Therefore, we compared the image quality of the mixed convolution kernel to standard soft and hard kernel reconstructions for different organ structures in thoracic computed tomography (CT) images.Our Ethics Committee approved this prospective study. In total, 31 patients who underwent contrast-enhanced thoracic CT studies were included after informed consent. Axial reconstructions were performed with hard, soft, and mixed convolution kernel. Three independent and blinded observers rated the image quality according to the European Guidelines for Quality Criteria of Thoracic CT for 13 organ structures. The observers rated the depiction of the structures in all reconstructions on a 5-point Likert scale. Statistical analysis was performed with the Friedman Test and post hoc analysis with the Wilcoxon rank-sum test.Compared to the soft convolution kernel, the mixed convolution kernel was rated with a higher image quality for lung parenchyma, segmental bronchi, and the border between the pleura and the thoracic wall (P < 0.03). Compared to the hard convolution kernel, the mixed convolution kernel was rated with a higher image quality for aorta, anterior mediastinal structures, paratracheal soft tissue, hilar lymph nodes, esophagus, pleuromediastinal border, large and medium sized pulmonary vessels and abdomen (P < 0.004) but a lower image quality for trachea, segmental bronchi, lung parenchyma, and skeleton (P < 0.001).The mixed convolution kernel cannot fully substitute the standard CT reconstructions. Hard and soft convolution kernel reconstructions still seem to be mandatory for thoracic CT.

  9. A visualization tool for the kernel-driven model with improved ability in data analysis and kernel assessment

    NASA Astrophysics Data System (ADS)

    Dong, Yadong; Jiao, Ziti; Zhang, Hu; Bai, Dongni; Zhang, Xiaoning; Li, Yang; He, Dandan

    2016-10-01

    The semi-empirical, kernel-driven Bidirectional Reflectance Distribution Function (BRDF) model has been widely used for many aspects of remote sensing. With the development of the kernel-driven model, there is a need to further assess the performance of newly developed kernels. The use of visualization tools can facilitate the analysis of model results and the assessment of newly developed kernels. However, the current version of the kernel-driven model does not contain a visualization function. In this study, a user-friendly visualization tool, named MaKeMAT, was developed specifically for the kernel-driven model. The POLDER-3 and CAR BRDF datasets were used to demonstrate the applicability of MaKeMAT. The visualization of inputted multi-angle measurements enhances understanding of multi-angle measurements and allows the choice of measurements with good representativeness. The visualization of modeling results facilitates the assessment of newly developed kernels. The study shows that the visualization tool MaKeMAT can promote the widespread application of the kernel-driven model.

  10. Canonical Wnt Signaling Regulates Atrioventricular Junction Programming and Electrophysiological Properties

    PubMed Central

    Gillers, Benjamin S; Chiplunkar, Aditi; Aly, Haytham; Valenta, Tomas; Basler, Konrad; Christoffels, Vincent M.; Efimov, Igor R; Boukens, Bastiaan J; Rentschler, Stacey

    2014-01-01

    Rationale Proper patterning of the atrioventricular canal (AVC) is essential for delay of electrical impulses between atria and ventricles, and defects in AVC maturation can result in congenital heart disease. Objective To determine the role of canonical Wnt signaling in the myocardium during AVC development. Methods and Results We utilized a novel allele of β-catenin that preserves β-catenin’s cell adhesive functions but disrupts canonical Wnt signaling, allowing us to probe the effects of Wnt loss of function independently. We show that loss of canonical Wnt signaling in the myocardium results in tricuspid atresia with hypoplastic right ventricle associated with loss of AVC myocardium. In contrast, ectopic activation of Wnt signaling was sufficient to induce formation of ectopic AV junction-like tissue as assessed by morphology, gene expression, and electrophysiologic criteria. Aberrant AVC development can lead to ventricular preexcitation, a characteristic feature of Wolff-Parkinson-White syndrome. We demonstrate that postnatal activation of Notch signaling downregulates canonical Wnt targets within the AV junction. Stabilization of β-catenin protein levels can rescue Notch-mediated ventricular preexcitation and dysregulated ion channel gene expression. Conclusions Our data demonstrate that myocardial canonical Wnt signaling is an important regulator of AVC maturation and electrical programming upstream of Tbx3. Our data further suggests that ventricular preexcitation may require both morphologic patterning defects, as well as myocardial lineage reprogramming, to allow robust conduction across accessory pathway tissue. PMID:25599332

  11. Interference Mitigation Based on Intelligent Location Selection in a Canonical Communication Network

    NASA Astrophysics Data System (ADS)

    Qu, Junyue; Cai, Yueming; Zheng, Jianchao; Yang, Wendong; Yang, Weiwei; Hu, Yajie

    2016-01-01

    In this letter, the interference mitigation in a canonical communication network is discussed from the perspective of intelligent location selection. A potential game model is constructed and a location-selection algorithm is designed combining no-regret procedure. With the proposed algorithm, all nodes can update their strategies with limited information exchange. Specifically, our proposed algorithm can converge to a set of correlated equilibria which are the globally or locally optimal solution to the problem of interference minimization. Moreover, our proposed algorithm can achieve distributed implementation without a central node. Simulation results demonstrate that the total interference can be mitigated efficiently with our proposed algorithm. And the proposed algorithm can converge fast.

  12. On the Kernelization Complexity of Colorful Motifs

    NASA Astrophysics Data System (ADS)

    Ambalath, Abhimanyu M.; Balasundaram, Radheshyam; Rao H., Chintan; Koppula, Venkata; Misra, Neeldhara; Philip, Geevarghese; Ramanujan, M. S.

    The Colorful Motif problem asks if, given a vertex-colored graph G, there exists a subset S of vertices of G such that the graph induced by G on S is connected and contains every color in the graph exactly once. The problem is motivated by applications in computational biology and is also well-studied from the theoretical point of view. In particular, it is known to be NP-complete even on trees of maximum degree three [Fellows et al, ICALP 2007]. In their pioneering paper that introduced the color-coding technique, Alon et al. [STOC 1995] show, inter alia, that the problem is FPT on general graphs. More recently, Cygan et al. [WG 2010] showed that Colorful Motif is NP-complete on comb graphs, a special subclass of the set of trees of maximum degree three. They also showed that the problem is not likely to admit polynomial kernels on forests.

  13. Kernel density estimation using graphical processing unit

    NASA Astrophysics Data System (ADS)

    Sunarko, Su'ud, Zaki

    2015-09-01

    Kernel density estimation for particles distributed over a 2-dimensional space is calculated using a single graphical processing unit (GTX 660Ti GPU) and CUDA-C language. Parallel calculations are done for particles having bivariate normal distribution and by assigning calculations for equally-spaced node points to each scalar processor in the GPU. The number of particles, blocks and threads are varied to identify favorable configuration. Comparisons are obtained by performing the same calculation using 1, 2 and 4 processors on a 3.0 GHz CPU using MPICH 2.0 routines. Speedups attained with the GPU are in the range of 88 to 349 times compared the multiprocessor CPU. Blocks of 128 threads are found to be the optimum configuration for this case.

  14. Privacy preserving RBF kernel support vector machine.

    PubMed

    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.

  15. Learning molecular energies using localized graph kernels

    NASA Astrophysics Data System (ADS)

    Ferré, Grégoire; Haut, Terry; Barros, Kipton

    2017-03-01

    Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

  16. The flare kernel in the impulsive phase

    NASA Technical Reports Server (NTRS)

    Dejager, C.

    1986-01-01

    The impulsive phase of a flare is characterized by impulsive bursts of X-ray and microwave radiation, related to impulsive footpoint heating up to 50 or 60 MK, by upward gas velocities (150 to 400 km/sec) and by a gradual increase of the flare's thermal energy content. These phenomena, as well as non-thermal effects, are all related to the impulsive energy injection into the flare. The available observations are also quantitatively consistent with a model in which energy is injected into the flare by beams of energetic electrons, causing ablation of chromospheric gas, followed by convective rise of gas. Thus, a hole is burned into the chromosphere; at the end of impulsive phase of an average flare the lower part of that hole is situated about 1800 km above the photosphere. H alpha and other optical and UV line emission is radiated by a thin layer (approx. 20 km) at the bottom of the flare kernel. The upward rising and outward streaming gas cools down by conduction in about 45 s. The non-thermal effects in the initial phase are due to curtailing of the energy distribution function by escape of energetic electrons. The single flux tube model of a flare does not fit with these observations; instead we propose the spaghetti-bundle model. Microwave and gamma-ray observations suggest the occurrence of dense flare knots of approx. 800 km diameter, and of high temperature. Future observations should concentrate on locating the microwave/gamma-ray sources, and on determining the kernel's fine structure and the related multi-loop structure of the flaring area.

  17. Labeled Graph Kernel for Behavior Analysis.

    PubMed

    Zhao, Ruiqi; Martinez, Aleix M

    2016-08-01

    Automatic behavior analysis from video is a major topic in many areas of research, including computer vision, multimedia, robotics, biology, cognitive science, social psychology, psychiatry, and linguistics. Two major problems are of interest when analyzing behavior. First, we wish to automatically categorize observed behaviors into a discrete set of classes (i.e., classification). For example, to determine word production from video sequences in sign language. Second, we wish to understand the relevance of each behavioral feature in achieving this classification (i.e., decoding). For instance, to know which behavior variables are used to discriminate between the words apple and onion in American Sign Language (ASL). The present paper proposes to model behavior using a labeled graph, where the nodes define behavioral features and the edges are labels specifying their order (e.g., before, overlaps, start). In this approach, classification reduces to a simple labeled graph matching. Unfortunately, the complexity of labeled graph matching grows exponentially with the number of categories we wish to represent. Here, we derive a graph kernel to quickly and accurately compute this graph similarity. This approach is very general and can be plugged into any kernel-based classifier. Specifically, we derive a Labeled Graph Support Vector Machine (LGSVM) and a Labeled Graph Logistic Regressor (LGLR) that can be readily employed to discriminate between many actions (e.g., sign language concepts). The derived approach can be readily used for decoding too, yielding invaluable information for the understanding of a problem (e.g., to know how to teach a sign language). The derived algorithms allow us to achieve higher accuracy results than those of state-of-the-art algorithms in a fraction of the time. We show experimental results on a variety of problems and datasets, including multimodal data.

  18. Labeled Graph Kernel for Behavior Analysis

    PubMed Central

    Zhao, Ruiqi; Martinez, Aleix M.

    2016-01-01

    Automatic behavior analysis from video is a major topic in many areas of research, including computer vision, multimedia, robotics, biology, cognitive science, social psychology, psychiatry, and linguistics. Two major problems are of interest when analyzing behavior. First, we wish to automatically categorize observed behaviors into a discrete set of classes (i.e., classification). For example, to determine word production from video sequences in sign language. Second, we wish to understand the relevance of each behavioral feature in achieving this classification (i.e., decoding). For instance, to know which behavior variables are used to discriminate between the words apple and onion in American Sign Language (ASL). The present paper proposes to model behavior using a labeled graph, where the nodes define behavioral features and the edges are labels specifying their order (e.g., before, overlaps, start). In this approach, classification reduces to a simple labeled graph matching. Unfortunately, the complexity of labeled graph matching grows exponentially with the number of categories we wish to represent. Here, we derive a graph kernel to quickly and accurately compute this graph similarity. This approach is very general and can be plugged into any kernel-based classifier. Specifically, we derive a Labeled Graph Support Vector Machine (LGSVM) and a Labeled Graph Logistic Regressor (LGLR) that can be readily employed to discriminate between many actions (e.g., sign language concepts). The derived approach can be readily used for decoding too, yielding invaluable information for the understanding of a problem (e.g., to know how to teach a sign language). The derived algorithms allow us to achieve higher accuracy results than those of state-of-the-art algorithms in a fraction of the time. We show experimental results on a variety of problems and datasets, including multimodal data. PMID:26415154

  19. Spectrum-based kernel length estimation for Gaussian process classification.

    PubMed

    Wang, Liang; Li, Chuan

    2014-06-01

    Recent studies have shown that Gaussian process (GP) classification, a discriminative supervised learning approach, has achieved competitive performance in real applications compared with most state-of-the-art supervised learning methods. However, the problem of automatic model selection in GP classification, involving the kernel function form and the corresponding parameter values (which are unknown in advance), remains a challenge. To make GP classification a more practical tool, this paper presents a novel spectrum analysis-based approach for model selection by refining the GP kernel function to match the given input data. Specifically, we target the problem of GP kernel length scale estimation. Spectrums are first calculated analytically from the kernel function itself using the autocorrelation theorem as well as being estimated numerically from the training data themselves. Then, the kernel length scale is automatically estimated by equating the two spectrum values, i.e., the kernel function spectrum equals to the estimated training data spectrum. Compared with the classical Bayesian method for kernel length scale estimation via maximizing the marginal likelihood (which is time consuming and could suffer from multiple local optima), extensive experimental results on various data sets show that our proposed method is both efficient and accurate.

  20. Training Lp norm multiple kernel learning in the primal.

    PubMed

    Liang, Zhizheng; Xia, Shixiong; Zhou, Yong; Zhang, Lei

    2013-10-01

    Some multiple kernel learning (MKL) models are usually solved by utilizing the alternating optimization method where one alternately solves SVMs in the dual and updates kernel weights. Since the dual and primal optimization can achieve the same aim, it is valuable in exploring how to perform Lp norm MKL in the primal. In this paper, we propose an Lp norm multiple kernel learning algorithm in the primal where we resort to the alternating optimization method: one cycle for solving SVMs in the primal by using the preconditioned conjugate gradient method and other cycle for learning the kernel weights. It is interesting to note that the kernel weights in our method can obtain analytical solutions. Most importantly, the proposed method is well suited for the manifold regularization framework in the primal since solving LapSVMs in the primal is much more effective than solving LapSVMs in the dual. In addition, we also carry out theoretical analysis for multiple kernel learning in the primal in terms of the empirical Rademacher complexity. It is found that optimizing the empirical Rademacher complexity may obtain a type of kernel weights. The experiments on some datasets are carried out to demonstrate the feasibility and effectiveness of the proposed method.

  1. Gaussian kernel width optimization for sparse Bayesian learning.

    PubMed

    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.

  2. Relaxation and diffusion models with non-singular kernels

    NASA Astrophysics Data System (ADS)

    Sun, HongGuang; Hao, Xiaoxiao; Zhang, Yong; Baleanu, Dumitru

    2017-02-01

    Anomalous relaxation and diffusion processes have been widely quantified by fractional derivative models, where the definition of the fractional-order derivative remains a historical debate due to its limitation in describing different kinds of non-exponential decays (e.g. stretched exponential decay). Meanwhile, many efforts by mathematicians and engineers have been made to overcome the singularity of power function kernel in its definition. This study first explores physical properties of relaxation and diffusion models where the temporal derivative was defined recently using an exponential kernel. Analytical analysis shows that the Caputo type derivative model with an exponential kernel cannot characterize non-exponential dynamics well-documented in anomalous relaxation and diffusion. A legitimate extension of the previous derivative is then proposed by replacing the exponential kernel with a stretched exponential kernel. Numerical tests show that the Caputo type derivative model with the stretched exponential kernel can describe a much wider range of anomalous diffusion than the exponential kernel, implying the potential applicability of the new derivative in quantifying real-world, anomalous relaxation and diffusion processes.

  3. Effects of sample size on KERNEL home range estimates

    USGS Publications Warehouse

    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.

  4. Impact of laminitis on the canonical Wnt signaling pathway in basal epithelial cells of the equine digital laminae.

    PubMed

    Wang, Le; Pawlak, Erica A; Johnson, Philip J; Belknap, James K; Eades, Susan; Stack, Sharon; Cousin, Helene; Black, Samuel J

    2013-01-01

    The digital laminae is a two layer tissue that attaches the distal phalanx to the inner hoof wall, thus suspending the horse's axial skeleton in the hoof capsule. This tissue fails at the epidermal:dermal junction in laminitic horses, causing crippling disease. Basal epithelial cells line the laminar epidermal:dermal junction, undergo physiological change in laminitic horses, and lose versican gene expression. Versican gene expression is purportedly under control of the canonical Wnt signaling pathway and is a trigger for mesenchymal-to-epithelial transition; thus, its repression in laminar epithelial cells of laminitic horses may be associated with suppression of the canonical Wnt signaling pathway and loss of the epithelial cell phenotype. In support of the former contention, we show, using laminae from healthy horses and horses with carbohydrate overload-induced laminitis, quantitative real-time polymerase chain reaction, Western blotting after sodium dodecylsulfate polyacrylamide gel electrophoresis, and immunofluorescent tissue staining, that positive and negative regulatory components of the canonical Wnt signaling pathway are expressed in laminar basal epithelial cells of healthy horses. Furthermore, expression of positive regulators is suppressed and negative regulators elevated in laminae of laminitic compared to healthy horses. We also show that versican gene expression in the epithelial cells correlates positively with that of β-catenin and T-cell Factor 4, consistent with regulation by the canonical Wnt signaling pathway. In addition, gene and protein expression of β-catenin correlates positively with that of integrin β4 and both are strongly suppressed in laminar basal epithelial cells of laminitic horses, which remain E-cadherin(+)/vimentin(-), excluding mesenchymal transition as contributing to loss of the adherens junction and hemidesmosome components. We propose that suppression of the canonical Wnt signaling pathway, and accompanying reduced

  5. Increased susceptibility and reduced phytoalexin accumulation in drought-stressed peanut kernels challenged with Aspergillus flavus.

    PubMed Central

    Wotton, H R; Strange, R N

    1987-01-01

    Three genotypes of peanut (Arachis hypogaea L.), with ICG numbers 221, 1104, and 1326, were grown in three replicate plots and drought stressed during the last 58 days before harvest by withholding irrigation water. Within each plot there were eight levels of stress ranging from 1.1 to 25.9 cm of water. Kernels harvested from the plots were hydrated to 20% moisture and challenged with Aspergillus flavus. Fungal colonization, aflatoxin content, and phytoalexin accumulation were measured. Fungal colonization of non-drought-stressed kernels virtually ceased by 3 days after inoculation, when the phytoalexin concentration exceeded 50 micrograms/g (fresh weight) of kernels, but the aflatoxin concentration continued to rise exponentially for an additional day. When fungal colonization, aflatoxin production, and phytoalexin accumulation were measured 3 days after drought-stressed material was challenged, the following relationships were apparent. Fungal colonization was inversely related to water supply (r varied from -0.848 to -0.904, according to genotype), as was aflatoxin production (r varied from -0.876 to -0.912, according to genotype); the phytoalexin concentration was correlated with water supply when this exceeded 11 cm (r varied from 0.696 to 0.917, according to genotype). The results are discussed in terms of the critical role played by drought stress in predisposing peanuts to infection by A. flavus and the role of the impaired phytoalexin response in mediating this increased susceptibility. PMID:3105455

  6. A fast small-sample kernel independence test for microbiome community-level association analysis.

    PubMed

    Zhan, Xiang; Plantinga, Anna; Zhao, Ni; Wu, Michael C

    2017-03-10

    To fully understand the role of microbiome in human health and diseases, researchers are increasingly interested in assessing the relationship between microbiome composition and host genomic data. The dimensionality of the data as well as complex relationships between microbiota and host genomics pose considerable challenges for analysis. In this article, we apply a kernel RV coefficient (KRV) test to evaluate the overall association between host gene expression and microbiome composition. The KRV statistic can capture nonlinear correlations and complex relationships among the individual data types and between gene expression and microbiome composition through measuring general dependency. Testing proceeds via a similar route as existing tests of the generalized RV coefficients and allows for rapid p-value calculation. Strategies to allow adjustment for confounding effects, which is crucial for avoiding misleading results, and to alleviate the problem of selecting the most favorable kernel are considered. Simulation studies show that KRV is useful in testing statistical independence with finite samples given the kernels are appropriately chosen, and can powerfully identify existing associations between microbiome composition and host genomic data while protecting type I error. We apply the KRV to a microbiome study examining the relationship between host transcriptome and microbiome composition within the context of inflammatory bowel disease and are able to derive new biological insights and provide formal inference on prior qualitative observations.

  7. Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

    PubMed

    Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

    2016-01-01

    Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

  8. Right ventricle segmentation with probability product kernel constraints.

    PubMed

    Nambakhsh, Cyrus M S; Peters, Terry M; Islam, Ali; Ayed, Ismail Ben

    2013-01-01

    We propose a fast algorithm for 3D segmentation of the right ventricle (RV) in MRI using shape and appearance constraints based on probability product kernels (PPK). The proposed constraints remove the need for large, manually-segmented training sets and costly pose estimation (or registration) procedures, as is the case of the existing algorithms. We report comprehensive experiments, which demonstrate that the proposed algorithm (i) requires only a single subject for training; and (ii) yields a performance that is not significantly affected by the choice of the training data. Our PPK constraints are non-linear (high-order) functionals, which are not directly amenable to standard optimizers. We split the problem into several surrogate-functional optimizations, each solved via an efficient convex relaxation that is amenable to parallel implementations. We further introduce a scale variable that we optimize with fast fixed-point computations, thereby achieving pose invariance in real-time. Our parallelized implementation on a graphics processing unit (GPU) demonstrates that the proposed algorithm can yield a real-time solution for typical cardiac MRI volumes, with a speed-up of more than 20 times compared to the CPU version. We report a comprehensive experimental validations over 400 volumes acquired from 20 subjects, and demonstrate that the obtained 3D surfaces correlate with independent manual delineations.

  9. Adiabatic-connection fluctuation-dissipation DFT for the structural properties of solids—The renormalized ALDA and electron gas kernels

    SciTech Connect

    Patrick, Christopher E. Thygesen, Kristian S.

    2015-09-14

    We present calculations of the correlation energies of crystalline solids and isolated systems within the adiabatic-connection fluctuation-dissipation formulation of density-functional theory. We perform a quantitative comparison of a set of model exchange-correlation kernels originally derived for the homogeneous electron gas (HEG), including the recently introduced renormalized adiabatic local-density approximation (rALDA) and also kernels which (a) satisfy known exact limits of the HEG, (b) carry a frequency dependence, or (c) display a 1/k{sup 2} divergence for small wavevectors. After generalizing the kernels to inhomogeneous systems through a reciprocal-space averaging procedure, we calculate the lattice constants and bulk moduli of a test set of 10 solids consisting of tetrahedrally bonded semiconductors (C, Si, SiC), ionic compounds (MgO, LiCl, LiF), and metals (Al, Na, Cu, Pd). We also consider the atomization energy of the H{sub 2} molecule. We compare the results calculated with different kernels to those obtained from the random-phase approximation (RPA) and to experimental measurements. We demonstrate that the model kernels correct the RPA’s tendency to overestimate the magnitude of the correlation energy whilst maintaining a high-accuracy description of structural properties.

  10. Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT).

    PubMed

    Urrutia, Eugene; Lee, Seunggeun; Maity, Arnab; Zhao, Ni; Shen, Judong; Li, Yun; Wu, Michael C

    Analysis of rare genetic variants has focused on region-based analysis wherein a subset of the variants within a genomic region is tested for association with a complex trait. Two important practical challenges have emerged. First, it is difficult to choose which test to use. Second, it is unclear which group of variants within a region should be tested. Both depend on the unknown true state of nature. Therefore, we develop the Multi-Kernel SKAT (MK-SKAT) which tests across a range of rare variant tests and groupings. Specifically, we demonstrate that several popular rare variant tests are special cases of the sequence kernel association test which compares pair-wise similarity in trait value to similarity in the rare variant genotypes between subjects as measured through a kernel function. Choosing a particular test is equivalent to choosing a kernel. Similarly, choosing which group of variants to test also reduces to choosing a kernel. Thus, MK-SKAT uses perturbation to test across a range of kernels. Simulations and real data analyses show that our framework controls type I error while maintaining high power across settings: MK-SKAT loses power when compared to the kernel for a particular scenario but has much greater power than poor choices.

  11. Survey of Salmonella contamination of edible nut kernels on retail sale in the UK.

    PubMed

    Little, C L; Rawal, N; de Pinna, E; McLauchlin, J

    2010-02-01

    Consumption of nut kernels has shown an upward trend due to people's increasing tendency to eat healthy snacks. The purpose of this survey was to establish the microbiological safety of retail edible nut kernel samples of different varieties. Overall Salmonella spp. and Escherichia coli were detected from 0.1% and 0.8% of 2886 edible nut kernels, respectively. S. Senftenberg and S. Tennessee were detected from two pre-packed samples of Brazil nuts (0.4%) and S. Anatum from a pre-packed mixed nuts sample (0.9%; mix: almonds, Brazils, cashews, peanuts, walnuts) indicating a risk to health. The levels of Salmonella ranged from <0.01 to 0.23/g. E. coli at unsatisfactory levels (150/g) was present in another pre-packed Brazils nuts sample (0.2%). E. coli was additionally found at lower levels (range: 3.6-43/g) in Brazils (1.9%), macadamia (1.5%), pistachios (1.1%), walnuts (0.7%), peanuts (0.7%), hazels (0.5%), cashews (0.4%), and almonds (0.3%). Levels of E. coli did not correlate with the presence of Salmonella. The batches contaminated with Salmonella were recalled and Food Standards Agency food alerts were issued to advise against the consumption of the affected products. The presence of Salmonella is unacceptable in ready-to-eat foods and follows that the need for applying good agricultural and hygiene practices and effective decontamination procedures during the production of edible kernels cannot be overemphasized.

  12. Temporal Evolution and Spatial Distribution of White-light Flare Kernels in a Solar Flare

    NASA Astrophysics Data System (ADS)

    Kawate, T.; Ishii, T. T.; Nakatani, Y.; Ichimoto, K.; Asai, A.; Morita, S.; Masuda, S.

    2016-12-01

    On 2011 September 6, we observed an X2.1-class flare in continuum and Hα with a frame rate of about 30 Hz. After processing images of the event by using a speckle-masking image reconstruction, we identified white-light (WL) flare ribbons on opposite sides of the magnetic neutral line. We derive the light curve decay times of the WL flare kernels at each resolution element by assuming that the kernels consist of one or two components that decay exponentially, starting from the peak time. As a result, 42% of the pixels have two decay-time components with average decay times of 15.6 and 587 s, whereas the average decay time is 254 s for WL kernels with only one decay-time component. The peak intensities of the shorter decay-time component exhibit good spatial correlation with the WL intensity, whereas the peak intensities of the long decay-time components tend to be larger in the early phase of the flare at the inner part of the flare ribbons, close to the magnetic neutral line. The average intensity of the longer decay-time components is 1.78 times higher than that of the shorter decay-time components. If the shorter decay time is determined by either the chromospheric cooling time or the nonthermal ionization timescale and the longer decay time is attributed to the coronal cooling time, this result suggests that WL sources from both regions appear in 42% of the WL kernels and that WL emission of the coronal origin is sometimes stronger than that of chromospheric origin.

  13. Dickkopf-1 induced apoptosis in human placental choriocarcinoma is independent of canonical Wnt signaling

    SciTech Connect

    Peng Sha; Miao Chenglin; Li Jing; Fan Xiujun; Cao Yujing; Duan Enkui . E-mail: duane@ioz.ac.cn

    2006-11-24

    Placental choriocarcinoma, a reproductive system carcinoma in women, has about 0.81% occurrence frequency in China, which leads to over 90% lethality due to indistinct pathogenesis and the absence of efficient therapeutic treatment. In the present study, using immunostaining and reverse transcription PCR, we reported that Dickkopf-1 (Dkk-1) is prominently expressed in human cytotrophoblast (CTB) cell, but absent in the human placental choriocarcinoma cell line JAR and JEG3, implicating an unknown correlation between Dkk-1 and carcinogenesis of placental choriocarcinoma. Further, through exogenous introduction of Dkk-1, we found repressed proliferation in JAR and JEG3, induced apoptosis in JAR, and discovered significant tumor suppression effects of Dkk-1 in placental choriocarcinoma. Moreover we found that this function of Dkk-1 is achieved through c-Jun N-terminal kinase (JNK), whereas the canonical Wnt pathway may not have a great role. This discovery is not symphonic to previous functional understanding of Dkk-1, a canonical Wnt signaling antagonist. Together, our data indicate the possible correlation between Dkk-1 and human placental choriocarcinoma and suggest potential applications of Dkk-1 in treatment of human placental choriocarcinomas.

  14. Canonical structure of higher derivative gravity in 3D

    SciTech Connect

    Guellue, Ibrahim; Sisman, Tahsin Cagri; Tekin, Bayram

    2010-05-15

    We give an explicitly gauge-invariant canonical analysis of linearized quadratic gravity theories in three dimensions for both flat and de Sitter backgrounds. In flat backgrounds, we also study the effects of the gravitational Chern-Simons term, include the sources, and compute the weak field limit as well as scattering between spinning massive particles.

  15. The Problematics of Postmodernism: The Double-Voiced Honors Canon.

    ERIC Educational Resources Information Center

    McCracken, Tim

    Honors education is not immune from the current controversy concerning the role of the literary canon. Indeed, the problem seems especially crucial for honors programs, for their curriculums are often multi-disciplinary in their approaches to culture and history. The solution may lie in what Linda Hutcheon calls the "poetics of the…

  16. Canonical structure of the E10 model and supersymmetry

    NASA Astrophysics Data System (ADS)

    Kleinschmidt, Axel; Nicolai, Hermann; Chidambaram, Nitin K.

    2015-04-01

    A coset model based on the hyperbolic Kac-Moody algebra E10 has been conjectured to underlie 11-dimensional supergravity and M theory. In this note we study the canonical structure of the bosonic model for finite- and infinite-dimensional groups. In the case of finite-dimensional groups like G L (n ) we exhibit a convenient set of variables with Borel-type canonical brackets. The generalization to the Kac-Moody case requires a proper treatment of the imaginary roots that remains elusive. As a second result, we show that the supersymmetry constraint of D =11 supergravity can be rewritten in a suggestive way using E10 algebra data. Combined with the canonical structure, this rewriting explains the previously observed association of the canonical constraints with null roots of E10. We also exhibit a basic incompatibility between local supersymmetry and the K (E10) "R symmetry" that can be traced back to the presence of imaginary roots and to the unfaithfulness of the spinor representations occurring in the present formulation of the E10 worldline model, and that may require a novel type of bosonization/fermionization for its resolution. This appears to be a key challenge for future progress with E10.

  17. Intermediate inflation from a non-canonical scalar field

    SciTech Connect

    Rezazadeh, K.; Karami, K.; Karimi, P. E-mail: KKarami@uok.ac.ir

    2015-09-01

    We study the intermediate inflation in a non-canonical scalar field framework with a power-like Lagrangian. We show that in contrast with the standard canonical intermediate inflation, our non-canonical model is compatible with the observational results of Planck 2015. Also, we estimate the equilateral non-Gaussianity parameter which is in well agreement with the prediction of Planck 2015. Then, we obtain an approximation for the energy scale at the initial time of inflation and show that it can be of order of the Planck energy scale, i.e. M{sub P} ∼ 10{sup 18}GeV. We will see that after a short period of time, inflation enters in the slow-roll regime that its energy scale is of order M{sub P}/100 ∼ 10{sup 16}GeV and the horizon exit takes place in this energy scale. We also examine an idea in our non-canonical model to overcome the central drawback of intermediate inflation which is the fact that inflation never ends. We solve this problem without disturbing significantly the nature of the intermediate inflation until the time of horizon exit.

  18. Non-Canonical Replication Initiation: You're Fired!

    PubMed

    Ravoitytė, Bazilė; Wellinger, Ralf Erik

    2017-01-27

    The division of prokaryotic and eukaryotic cells produces two cells that inherit a perfect copy of the genetic material originally derived from the mother cell. The initiation of canonical DNA replication must be coordinated to the cell cycle to ensure the accuracy of genome duplication. Controlled replication initiation depends on a complex interplay of cis-acting DNA sequences, the so-called origins of replication (ori), with trans-acting factors involved in the onset of DNA synthesis. The interplay of cis-acting elements and trans-acting factors ensures that cells initiate replication at sequence-specific sites only once, and in a timely order, to avoid chromosomal endoreplication. However, chromosome breakage and excessive RNA:DNA hybrid formation can cause breakinduced (BIR) or transcription-initiated replication (TIR), respectively. These non-canonical replication events are expected to affect eukaryotic genome function and maintenance, and could be important for genome evolution and disease development. In this review, we describe the difference between canonical and non-canonical DNA replication, and focus on mechanistic differences and common features between BIR and TIR. Finally, we discuss open issues on the factors and molecular mechanisms involved in TIR.

  19. "Where Is Vietnam?" Antiwar Poetry and the Canon.

    ERIC Educational Resources Information Center

    Bibby, Michael

    1993-01-01

    Argues for the pervasive intervention of the Vietnam War in the cultural production of U.S. poetry. Questions the academic canon of post-World War II poetry and criticizes anthologies for ignoring Vietnam War poetry. Suggests why Vietnam War poetry has remained such an avoided subject. Lists anthologies including such poetry. (HB)

  20. A Problem-Centered Approach to Canonical Matrix Forms

    ERIC Educational Resources Information Center

    Sylvestre, Jeremy

    2014-01-01

    This article outlines a problem-centered approach to the topic of canonical matrix forms in a second linear algebra course. In this approach, abstract theory, including such topics as eigenvalues, generalized eigenspaces, invariant subspaces, independent subspaces, nilpotency, and cyclic spaces, is developed in response to the patterns discovered…

  1. Imagined Victorians, Real Victorians, and the Literary Canon.

    ERIC Educational Resources Information Center

    Fenstermaker, John J.

    1989-01-01

    Considers the issue of literary canons, raised in the context of a week-long series of lectures and discussions on "the Victorians" in an Elderhostel program, with participants for whom these texts were the product of their parents' generation and of their own childhood reading. Raises substantive questions about the meaning of a…

  2. A Canonical Trace Associated with Certain Spectral Triples

    NASA Astrophysics Data System (ADS)

    Paycha, Sylvie

    2010-09-01

    In the abstract pseudodifferential setup of Connes and Moscovici, we prove a general formula for the discrepancies of zeta-regularised traces associated with certain spectral triples, and we introduce a canonical trace on operators, whose order lies outside (minus) the dimension spectrum of the spectral triple.

  3. Connecting the Canon to Current Young Adult Literature

    ERIC Educational Resources Information Center

    Rybakova, Katie; Roccanti, Rikki

    2016-01-01

    In this article we discuss the respective roles of young adult literature and literary texts in the secondary level English Language Arts classroom and explore the connections that can be made between popular young adult books and the traditional canon. We provide examples showing how young adult literature bestsellers such as "The Book…

  4. Courtroom Access: Clarification and Recommendation for Canon 35.

    ERIC Educational Resources Information Center

    Forston, Robert F.; Forston, Anne L.

    Canon 35, concerning improper publicizing of court proceedings, is one of the professional codes of the American Bar Association. First adopted in 1937, it has twice been amended and is widely observed by most courts throughout the United States. Reasons for barring radio or television coverage of trials are based on concerns that broadcasting…

  5. Non-Canonical Replication Initiation: You’re Fired!

    PubMed Central

    Ravoitytė, Bazilė; Wellinger, Ralf Erik

    2017-01-01

    The division of prokaryotic and eukaryotic cells produces two cells that inherit a perfect copy of the genetic material originally derived from the mother cell. The initiation of canonical DNA replication must be coordinated to the cell cycle to ensure the accuracy of genome duplication. Controlled replication initiation depends on a complex interplay of cis-acting DNA sequences, the so-called origins of replication (ori), with trans-acting factors involved in the onset of DNA synthesis. The interplay of cis-acting elements and trans-acting factors ensures that cells initiate replication at sequence-specific sites only once, and in a timely order, to avoid chromosomal endoreplication. However, chromosome breakage and excessive RNA:DNA hybrid formation can cause break-induced (BIR) or transcription-initiated replication (TIR), respectively. These non-canonical replication events are expected to affect eukaryotic genome function and maintenance, and could be important for genome evolution and disease development. In this review, we describe the difference between canonical and non-canonical DNA replication, and focus on mechanistic differences and common features between BIR and TIR. Finally, we discuss open issues on the factors and molecular mechanisms involved in TIR. PMID:28134821

  6. Reputation, Canon-Formation, Pedagogy: George Orwell in the Classroom.

    ERIC Educational Resources Information Center

    Rodden, John

    1991-01-01

    Investigates the process by which books become canonized in British and U.S. schools and universities. Uses the case of George Orwell to examine the institutional and historical factors which condition the inclusion and exclusion of writer's work in Anglo-American classrooms. (SR)

  7. Canonical transformations for hyperhamiltonian dynamics in Euclidean spaces

    NASA Astrophysics Data System (ADS)

    Gaeta, G.; Rodríguez, M. A.

    2017-03-01

    We prove that in hyperhamiltonian dynamics, any local one-parameter group of canonical transformation is realized as the flow of a vector field related to the underlying hyperkahler structure, similarly to the case of standard Hamiltonian dynamics and the underlying symplectic structure. In this case the relevant class of vector fields is that of Dirac vector fields for the hyperkahler structure.

  8. Parallel canonical Monte Carlo simulations through sequential updating of particles

    NASA Astrophysics Data System (ADS)

    O'Keeffe, C. J.; Orkoulas, G.

    2009-04-01

    In canonical Monte Carlo simulations, sequential updating of particles is equivalent to random updating due to particle indistinguishability. In contrast, in grand canonical Monte Carlo simulations, sequential implementation of the particle transfer steps in a dense grid of distinct points in space improves both the serial and the parallel efficiency of the simulation. The main advantage of sequential updating in parallel canonical Monte Carlo simulations is the reduction in interprocessor communication, which is usually a slow process. In this work, we propose a parallelization method for canonical Monte Carlo simulations via domain decomposition techniques and sequential updating of particles. Each domain is further divided into a middle and two outer sections. Information exchange is required after the completion of the updating of the outer regions. During the updating of the middle section, communication does not occur unless a particle moves out of this section. Results on two- and three-dimensional Lennard-Jones fluids indicate a nearly perfect improvement in parallel efficiency for large systems.

  9. Parallel canonical Monte Carlo simulations through sequential updating of particles.

    PubMed

    O'Keeffe, C J; Orkoulas, G

    2009-04-07

    In canonical Monte Carlo simulations, sequential updating of particles is equivalent to random updating due to particle indistinguishability. In contrast, in grand canonical Monte Carlo simulations, sequential implementation of the particle transfer steps in a dense grid of distinct points in space improves both the serial and the parallel efficiency of the simulation. The main advantage of sequential updating in parallel canonical Monte Carlo simulations is the reduction in interprocessor communication, which is usually a slow process. In this work, we propose a parallelization method for canonical Monte Carlo simulations via domain decomposition techniques and sequential updating of particles. Each domain is further divided into a middle and two outer sections. Information exchange is required after the completion of the updating of the outer regions. During the updating of the middle section, communication does not occur unless a particle moves out of this section. Results on two- and three-dimensional Lennard-Jones fluids indicate a nearly perfect improvement in parallel efficiency for large systems.

  10. Intermediate inflation from a non-canonical scalar field

    NASA Astrophysics Data System (ADS)

    Rezazadeh, K.; Karami, K.; Karimi, P.

    2015-09-01

    We study the intermediate inflation in a non-canonical scalar field framework with a power-like Lagrangian. We show that in contrast with the standard canonical intermediate inflation, our non-canonical model is compatible with the observational results of Planck 2015. Also, we estimate the equilateral non-Gaussianity parameter which is in well agreement with the prediction of Planck 2015. Then, we obtain an approximation for the energy scale at the initial time of inflation and show that it can be of order of the Planck energy scale, i.e. MP ~ 1018GeV. We will see that after a short period of time, inflation enters in the slow-roll regime that its energy scale is of order MP/100 ~ 1016GeV and the horizon exit takes place in this energy scale. We also examine an idea in our non-canonical model to overcome the central drawback of intermediate inflation which is the fact that inflation never ends. We solve this problem without disturbing significantly the nature of the intermediate inflation until the time of horizon exit.

  11. Catechistic Teaching, National Canons, and the Regimentation of Students' Voice

    ERIC Educational Resources Information Center

    Kroon, Sjaak

    2013-01-01

    Drawing on key incident analysis of classroom transcripts from Bashkortostan, France, North Korea, and Suriname, this article discusses the relationship between an increasingly canonical content of education and the discursive organization of teaching processes at the expense of both teachers' and students' voice. It argues that canonical…

  12. Bilinear analysis for kernel selection and nonlinear feature extraction.

    PubMed

    Yang, Shu; Yan, Shuicheng; Zhang, Chao; Tang, Xiaoou

    2007-09-01

    This paper presents a unified criterion, Fisher + kernel criterion (FKC), for feature extraction and recognition. This new criterion is intended to extract the most discriminant features in different nonlinear spaces, and then, fuse these features under a unified measurement. Thus, FKC can simultaneously achieve nonlinear discriminant analysis and kernel selection. In addition, we present an efficient algorithm Fisher + kernel analysis (FKA), which utilizes the bilinear analysis, to optimize the new criterion. This FKA algorithm can alleviate the ill-posed problem existed in traditional kernel discriminant analysis (KDA), and usually, has no singularity problem. The effectiveness of our proposed algorithm is validated by a series of face-recognition experiments on several different databases.

  13. Inheritance of Kernel Color in Corn: Explanations and Investigations.

    ERIC Educational Resources Information Center

    Ford, Rosemary H.

    2000-01-01

    Offers a new perspective on traditional problems in genetics on kernel color in corn, including information about genetic regulation, metabolic pathways, and evolution of genes. (Contains 15 references.) (ASK)

  14. 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.

  15. Nonlinear hyperspectral unmixing based on constrained multiple kernel NMF

    NASA Astrophysics Data System (ADS)

    Cui, Jiantao; Li, Xiaorun; Zhao, Liaoying

    2014-05-01

    Nonlinear spectral unmixing constitutes an important field of research for hyperspectral imagery. An unsupervised nonlinear spectral unmixing algorithm, namely multiple kernel constrained nonnegative matrix factorization (MKCNMF) is proposed by coupling multiple-kernel selection with kernel NMF. Additionally, a minimum endmemberwise distance constraint and an abundance smoothness constraint are introduced to alleviate the uniqueness problem of NMF in the algorithm. In the MKCNMF, two problems of optimizing matrices and selecting the proper kernel are jointly solved. The performance of the proposed unmixing algorithm is evaluated via experiments based on synthetic and real hyperspectral data sets. The experimental results demonstrate that the proposed method outperforms some existing unmixing algorithms in terms of spectral angle distance (SAD) and abundance fractions.

  16. Hash subgraph pairwise kernel for protein-protein interaction extraction.

    PubMed

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng

    2012-01-01

    Extracting protein-protein interaction (PPI) from biomedical literature is an important task in biomedical text mining (BioTM). In this paper, we propose a hash subgraph pairwise (HSP) kernel-based approach for this task. The key to the novel kernel is to use the hierarchical hash labels to express the structural information of subgraphs in a linear time. We apply the graph kernel to compute dependency graphs representing the sentence structure for protein-protein interaction extraction task, which can efficiently make use of full graph structural information, and particularly capture the contiguous topological and label information ignored before. We evaluate the proposed approach on five publicly available PPI corpora. The experimental results show that our approach significantly outperforms all-path kernel approach on all five corpora and achieves state-of-the-art performance.

  17. On the asymptotic expansion of the Bergman kernel

    NASA Astrophysics Data System (ADS)

    Seto, Shoo

    Let (L, h) → (M, o) be a polarized Kahler manifold. We define the Bergman kernel for H0(M, Lk), holomorphic sections of the high tensor powers of the line bundle L. In this thesis, we will study the asymptotic expansion of the Bergman kernel. We will consider the on-diagonal, near-diagonal and far off-diagonal, using L2 estimates to show the existence of the asymptotic expansion and computation of the coefficients for the on and near-diagonal case, and a heat kernel approach to show the exponential decay of the off-diagonal of the Bergman kernel for noncompact manifolds assuming only a lower bound on Ricci curvature and C2 regularity of the metric.

  18. Kernel-based Linux emulation for Plan 9.

    SciTech Connect

    Minnich, Ronald G.

    2010-09-01

    CNKemu is a kernel-based system for the 9k variant of the Plan 9 kernel. It is designed to provide transparent binary support for programs compiled for IBM's Compute Node Kernel (CNK) on the Blue Gene series of supercomputers. This support allows users to build applications with the standard Blue Gene toolchain, including C++ and Fortran compilers. While the CNK is not Linux, IBM designed the CNK so that the user interface has much in common with the Linux 2.0 system call interface. The Plan 9 CNK emulator hence provides the foundation of kernel-based Linux system call support on Plan 9. In this paper we discuss cnkemu's implementation and some of its more interesting features, such as the ability to easily intermix Plan 9 and Linux system calls.

  19. Canonical Wnt signaling is necessary for object recognition memory consolidation.

    PubMed

    Fortress, Ashley M; Schram, Sarah L; Tuscher, Jennifer J; Frick, Karyn M

    2013-07-31

    Wnt signaling has emerged as a potent regulator of hippocampal synaptic function, although no evidence yet supports a critical role for Wnt signaling in hippocampal memory. Here, we sought to determine whether canonical β-catenin-dependent Wnt signaling is necessary for hippocampal memory consolidation. Immediately after training in a hippocampal-dependent object recognition task, mice received a dorsal hippocampal (DH) infusion of vehicle or the canonical Wnt antagonist Dickkopf-1 (Dkk-1; 50, 100, or 200 ng/hemisphere). Twenty-four hours later, mice receiving vehicle remembered the familiar object explored during training. However, mice receiving Dkk-1 exhibited no memory for the training object, indicating that object recognition memory consolidation is dependent on canonical Wnt signaling. To determine how Dkk-1 affects canonical Wnt signaling, mice were infused with vehicle or 50 ng/hemisphere Dkk-1 and protein levels of Wnt-related proteins (Dkk-1, GSK3β, β-catenin, TCF1, LEF1, Cyclin D1, c-myc, Wnt7a, Wnt1, and PSD95) were measured in the dorsal hippocampus 5 min or 4 h later. Dkk-1 produced a rapid increase in Dkk-1 protein levels and a decrease in phosphorylated GSK3β levels, followed by a decrease in β-catenin, TCF1, LEF1, Cyclin D1, c-myc, Wnt7a, and PSD95 protein levels 4 h later. These data suggest that alterations in Wnt/GSK3β/β-catenin signaling may underlie the memory impairments induced by Dkk-1. In a subsequent experiment, object training alone rapidly increased DH GSK3β phosphorylation and levels of β-catenin and Cyclin D1. These data suggest that canonical Wnt signaling is regulated by object learning and is necessary for hippocampal memory consolidation.

  20. Accretion of the Moon from non-canonical discs

    PubMed Central

    Salmon, J.; Canup, R. M

    2014-01-01

    Impacts that leave the Earth–Moon system with a large excess in angular momentum have recently been advocated as a means of generating a protolunar disc with a composition that is nearly identical to that of the Earth's mantle. We here investigate the accretion of the Moon from discs generated by such ‘non-canonical’ impacts, which are typically more compact than discs produced by canonical impacts and have a higher fraction of their mass initially located inside the Roche limit. Our model predicts a similar overall accretional history for both canonical and non-canonical discs, with the Moon forming in three consecutive steps over hundreds of years. However, we find that, to yield a lunar-mass Moon, the more compact non-canonical discs must initially be more massive than implied by prior estimates, and only a few of the discs produced by impact simulations to date appear to meet this condition. Non-canonical impacts require that capture of the Moon into the evection resonance with the Sun reduced the Earth–Moon angular momentum by a factor of 2 or more. We find that the Moon's semi-major axis at the end of its accretion is approximately 7R⊕, which is comparable to the location of the evection resonance for a post-impact Earth with a 2.5 h rotation period in the absence of a disc. Thus, the dynamics of the Moon's assembly may directly affect its ability to be captured into the resonance. PMID:25114307

  1. Resummed memory kernels in generalized system-bath master equations.

    PubMed

    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.

  2. Landslide: Systematic Dynamic Race Detection in Kernel Space

    DTIC Science & Technology

    2012-05-01

    the general challenges of kernel-level concurrency, and we evaluate its effectiveness and usability as a debugging aid. We show that our techniques make...effectiveness and usability as a de- bugging aid. We show that our techniques make systematic testing in kernel-space feasible and that Landslide is a useful...Binary Instrumentation and Applications, WBIA ’09, pages 62–71, New York, NY, USA, 2009. ACM. [SKM+11] Eunsoo Seo , Mohammad Maifi Hasan Khan, Prasant

  3. The Weighted Super Bergman Kernels Over the Supermatrix Spaces

    NASA Astrophysics Data System (ADS)

    Feng, Zhiming

    2015-12-01

    The purpose of this paper is threefold. Firstly, using Howe duality for , we obtain integral formulas of the super Schur functions with respect to the super standard Gaussian distributions. Secondly, we give explicit expressions of the super Szegö kernels and the weighted super Bergman kernels for the Cartan superdomains of type I. Thirdly, combining these results, we obtain duality relations of integrals over the unitary groups and the Cartan superdomains, and the marginal distributions of the weighted measure.

  4. Kernel generalized neighbor discriminant embedding for SAR automatic target recognition

    NASA Astrophysics Data System (ADS)

    Huang, Yulin; Pei, Jifang; Yang, Jianyu; Wang, Tao; Yang, Haiguang; Wang, Bing

    2014-12-01

    In this paper, we propose a new supervised feature extraction algorithm in synthetic aperture radar automatic target recognition (SAR ATR), called generalized neighbor discriminant embedding (GNDE). Based on manifold learning, GNDE integrates class and neighborhood information to enhance discriminative power of extracted feature. Besides, the kernelized counterpart of this algorithm is also proposed, called kernel-GNDE (KGNDE). The experiment in this paper shows that the proposed algorithms have better recognition performance than PCA and KPCA.

  5. Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

    DTIC Science & Technology

    2016-01-05

    events (and subsequently, their likelihood of occurrence) based on historical evidence of the counts of previous event occurrences. The novel Bayesian...Aug-2014 22-May-2015 Approved for Public Release; Distribution Unlimited Final Report: Sparse Event Modeling with Hierarchical Bayesian Kernel Methods...Sparse Event Modeling with Hierarchical Bayesian Kernel Methods Report Title The research objective of this proposal was to develop a predictive Bayesian

  6. Resummed memory kernels in generalized system-bath master equations

    NASA Astrophysics Data System (ADS)

    Mavros, Michael G.; Van Voorhis, Troy

    2014-08-01

    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.

  7. Resummed memory kernels in generalized system-bath master equations

    SciTech Connect

    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.

  8. Protoribosome by quantum kernel energy method.

    PubMed

    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.

  9. Enhanced FMAM based on empirical kernel map.

    PubMed

    Wang, Min; Chen, Songcan

    2005-05-01

    The existing morphological auto-associative memory models based on the morphological operations, typically including morphological auto-associative memories (auto-MAM) proposed by Ritter et al. and our fuzzy morphological auto-associative memories (auto-FMAM), have many attractive advantages such as unlimited storage capacity, one-shot recall speed and good noise-tolerance to single erosive or dilative noise. However, they suffer from the extreme vulnerability to noise of mixing erosion and dilation, resulting in great degradation on recall performance. To overcome this shortcoming, we focus on FMAM and propose an enhanced FMAM (EFMAM) based on the empirical kernel map. Although it is simple, EFMAM can significantly improve the auto-FMAM with respect to the recognition accuracy under hybrid-noise and computational effort. Experiments conducted on the thumbnail-sized faces (28 x 23 and 14 x 11) scaled from the ORL database show the average accuracies of 92%, 90%, and 88% with 40 classes under 10%, 20%, and 30% randomly generated hybrid-noises, respectively, which are far higher than the auto-FMAM (67%, 46%, 31%) under the same noise levels.

  10. Generalized Bergman kernels and geometric quantization

    NASA Astrophysics Data System (ADS)

    Tuynman, G. M.

    1987-03-01

    In geometric quantization it is well known that, if f is an observable and F a polarization on a symplectic manifold (M,ω), then the condition ``Xf leaves F invariant'' (where Xf denotes the Hamiltonian vector field associated to f ) is sufficient to guarantee that one does not have to compute the BKS kernel explicitly in order to know the corresponding quantum operator. It is shown in this paper that this condition on f can be weakened to ``Xf leaves F+F° invariant''and the corresponding quantum operator is then given implicitly by formula (4.8); in particular when F is a (positive) Kähler polarization, all observables can be quantized ``directly'' and moreover, an ``explicit'' formula for the corresponding quantum operator is derived (Theorem 5.8). Applying this to the phase space R2n one obtains a quantization prescription which ressembles the normal ordering of operators in quantum field theory. When we translate this prescription to the usual position representation of quantum mechanics, the result is (a.o) that the operator associated to a classical potential is multiplication by a function which is essentially the convolution of the potential function with a Gaussian function of width ℏ, instead of multiplication by the potential itself.

  11. 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.

  12. The Dynamic Kernel Scheduler-Part 1

    NASA Astrophysics Data System (ADS)

    Adelmann, Andreas; Locans, Uldis; Suter, Andreas

    2016-10-01

    Emerging processor architectures such as GPUs and Intel MICs provide a huge performance potential for high performance computing. However developing software that uses these hardware accelerators introduces additional challenges for the developer. These challenges may include exposing increased parallelism, handling different hardware designs, and using multiple development frameworks in order to utilise devices from different vendors. The Dynamic Kernel Scheduler (DKS) is being developed in order to provide a software layer between the host application and different hardware accelerators. DKS handles the communication between the host and the device, schedules task execution, and provides a library of built-in algorithms. Algorithms available in the DKS library will be written in CUDA, OpenCL, and OpenMP. Depending on the available hardware, the DKS can select the appropriate implementation of the algorithm. The first DKS version was created using CUDA for the Nvidia GPUs and OpenMP for Intel MIC. DKS was further integrated into OPAL (Object-oriented Parallel Accelerator Library) in order to speed up a parallel FFT based Poisson solver and Monte Carlo simulations for particle-matter interaction used for proton therapy degrader modelling. DKS was also used together with Minuit2 for parameter fitting, where χ2 and max-log-likelihood functions were offloaded to the hardware accelerator. The concepts of the DKS, first results, and plans for the future will be shown in this paper.

  13. Stability Performance of Inductively Coupled Plasma Mass Spectrometry-Phenotyped Kernel Minerals Concentration and Grain Yield in Maize in Different Agro-Climatic Zones

    PubMed Central

    Mallikarjuna, Mallana Gowdra; Thirunavukkarasu, Nepolean; Hossain, Firoz; Bhat, Jayant S.; Jha, Shailendra K.; Rathore, Abhishek; Agrawal, Pawan Kumar; Pattanayak, Arunava; Reddy, Sokka S.; Gularia, Satish Kumar; Singh, Anju Mahendru; Manjaiah, Kanchikeri Math; Gupta, Hari Shanker

    2015-01-01

    Deficiency of iron and zinc causes micronutrient malnutrition or hidden hunger, which severely affects ~25% of global population. Genetic biofortification of maize has emerged as cost effective and sustainable approach in addressing malnourishment of iron and zinc deficiency. Therefore, understanding the genetic variation and stability of kernel micronutrients and grain yield of the maize inbreds is a prerequisite in breeding micronutrient-rich high yielding hybrids to alleviate micronutrient malnutrition. We report here, the genetic variability and stability of the kernel micronutrients concentration and grain yield in a set of 50 maize inbred panel selected from the national and the international centres that were raised at six different maize growing regions of India. Phenotyping of kernels using inductively coupled plasma mass spectrometry (ICP-MS) revealed considerable variability for kernel minerals concentration (iron: 18.88 to 47.65 mg kg–1; zinc: 5.41 to 30.85 mg kg–1; manganese: 3.30 to17.73 mg kg–1; copper: 0.53 to 5.48 mg kg–1) and grain yield (826.6 to 5413 kg ha–1). Significant positive correlation was observed between kernel iron and zinc within (r = 0.37 to r = 0.52, p < 0.05) and across locations (r = 0.44, p < 0.01). Variance components of the additive main effects and multiplicative interactions (AMMI) model showed significant genotype and genotype × environment interaction for kernel minerals concentration and grain yield. Most of the variation was contributed by genotype main effect for kernel iron (39.6%), manganese (41.34%) and copper (41.12%), and environment main effects for both kernel zinc (40.5%) and grain yield (37.0%). Genotype main effect plus genotype-by-environment interaction (GGE) biplot identified several mega environments for kernel minerals and grain yield. Comparison of stability parameters revealed AMMI stability value (ASV) as the better representative of the AMMI stability parameters. Dynamic stability parameter

  14. Stability Performance of Inductively Coupled Plasma Mass Spectrometry-Phenotyped Kernel Minerals Concentration and Grain Yield in Maize in Different Agro-Climatic Zones.

    PubMed

    Mallikarjuna, Mallana Gowdra; Thirunavukkarasu, Nepolean; Hossain, Firoz; Bhat, Jayant S; Jha, Shailendra K; Rathore, Abhishek; Agrawal, Pawan Kumar; Pattanayak, Arunava; Reddy, Sokka S; Gularia, Satish Kumar; Singh, Anju Mahendru; Manjaiah, Kanchikeri Math; Gupta, Hari Shanker

    2015-01-01

    Deficiency of iron and zinc causes micronutrient malnutrition or hidden hunger, which severely affects ~25% of global population. Genetic biofortification of maize has emerged as cost effective and sustainable approach in addressing malnourishment of iron and zinc deficiency. Therefore, understanding the genetic variation and stability of kernel micronutrients and grain yield of the maize inbreds is a prerequisite in breeding micronutrient-rich high yielding hybrids to alleviate micronutrient malnutrition. We report here, the genetic variability and stability of the kernel micronutrients concentration and grain yield in a set of 50 maize inbred panel selected from the national and the international centres that were raised at six different maize growing regions of India. Phenotyping of kernels using inductively coupled plasma mass spectrometry (ICP-MS) revealed considerable variability for kernel minerals concentration (iron: 18.88 to 47.65 mg kg(-1); zinc: 5.41 to 30.85 mg kg(-1); manganese: 3.30 to 17.73 mg kg(-1); copper: 0.53 to 5.48 mg kg(-1)) and grain yield (826.6 to 5413 kg ha(-1)). Significant positive correlation was observed between kernel iron and zinc within (r = 0.37 to r = 0.52, p < 0.05) and across locations (r = 0.44, p < 0.01). Variance components of the additive main effects and multiplicative interactions (AMMI) model showed significant genotype and genotype × environment interaction for kernel minerals concentration and grain yield. Most of the variation was contributed by genotype main effect for kernel iron (39.6%), manganese (41.34%) and copper (41.12%), and environment main effects for both kernel zinc (40.5%) and grain yield (37.0%). Genotype main effect plus genotype-by-environment interaction (GGE) biplot identified several mega environments for kernel minerals and grain yield. Comparison of stability parameters revealed AMMI stability value (ASV) as the better representative of the AMMI stability parameters. Dynamic stability parameter

  15. Canonical and Non-Canonical Activation of NLRP3 Inflammasome at the Crossroad between Immune Tolerance and Intestinal Inflammation

    PubMed Central

    Pellegrini, Carolina; Antonioli, Luca; Lopez-Castejon, Gloria; Blandizzi, Corrado; Fornai, Matteo

    2017-01-01

    Several lines of evidence point out the relevance of nucleotide-binding oligomerization domain leucine rich repeat and pyrin domain-containing protein 3 (NLRP3) inflammasome as a pivotal player in regulating the integrity of intestinal homeostasis and shaping innate immune responses during bowel inflammation. Intensive research efforts are being made to achieve an integrated view about the protective/detrimental role of canonical and non-canonical NLRP3 inflammasome activation in the maintenance of intestinal microenvironment integrity. Evidence is also emerging that the pharmacological modulation of NLRP3 inflammasome could represent a promising molecular target for the therapeutic management of inflammatory immune-mediated gut diseases. The present review has been intended to provide a critical appraisal of the available knowledge about the role of canonical and non-canonical NLRP3 inflammasome activation in the dynamic interplay between microbiota, intestinal epithelium, and innate immune system, taken together as a whole integrated network regulating the maintenance/breakdown of intestinal homeostasis. Moreover, special attention has been paid to the pharmacological modulation of NLRP3 inflammasome, emphasizing the concept that this multiprotein complex could represent a suitable target for the management of inflammatory bowel diseases. PMID:28179906

  16. Searching for efficient Markov chain Monte Carlo proposal kernels.

    PubMed

    Yang, Ziheng; Rodríguez, Carlos E

    2013-11-26

    Markov chain Monte Carlo (MCMC) or the Metropolis-Hastings algorithm is a simulation algorithm that has made modern Bayesian statistical inference possible. Nevertheless, the efficiency of different Metropolis-Hastings proposal kernels has rarely been studied except for the Gaussian proposal. Here we propose a unique class of Bactrian kernels, which avoid proposing values that are very close to the current value, and compare their efficiency with a number of proposals for simulating different target distributions, with efficiency measured by the asymptotic variance of a parameter estimate. The uniform kernel is found to be more efficient than the Gaussian kernel, whereas the Bactrian kernel is even better. When optimal scales are used for both, the Bactrian kernel is at least 50% more efficient than the Gaussian. Implementation in a Bayesian program for molecular clock dating confirms the general applicability of our results to generic MCMC algorithms. Our results refute a previous claim that all proposals had nearly identical performance and will prompt further research into efficient MCMC proposals.

  17. Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery.

    PubMed

    Feng, Yunlong; Lv, Shao-Gao; Hang, Hanyuan; Suykens, Johan A K

    2016-03-01

    Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic net regularization (Zou & Hastie, 2005). The kernel in KENReg is not required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens (2014) showed that KENReg has some nice properties including stability, sparseness, and generalization. In this letter, we continue our study on KENReg by conducting a refined learning theory analysis. This letter makes the following three main contributions. First, we present refined error analysis on the generalization performance of KENReg. The main difficulty of analyzing the generalization error of KENReg lies in characterizing the population version of its empirical target function. We overcome this by introducing a weighted Banach space associated with the elastic net regularization. We are then able to conduct elaborated learning theory analysis and obtain fast convergence rates under proper complexity and regularity assumptions. Second, we study the sparse recovery problem in KENReg with fixed design and show that the kernelization may improve the sparse recovery ability compared to the classical elastic net regularization. Finally, we discuss the interplay among different properties of KENReg that include sparseness, stability, and generalization. We show that the stability of KENReg leads to generalization, and its sparseness confidence can be derived from generalization. Moreover, KENReg is stable and can be simultaneously sparse, which makes it attractive theoretically and practically.

  18. 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.

  19. Multiple Kernel Learning for Visual Object Recognition: A Review.

    PubMed

    Bucak, Serhat S; Rong Jin; Jain, Anil K

    2014-07-01

    Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels for a given recognition task. A number of studies have shown that MKL is a useful tool for object recognition, where each image is represented by multiple sets of features and MKL is applied to combine different feature sets. We review the state-of-the-art for MKL, including different formulations and algorithms for solving the related optimization problems, with the focus on their applications to object recognition. One dilemma faced by practitioners interested in using MKL for object recognition is that different studies often provide conflicting results about the effectiveness and efficiency of MKL. To resolve this, we conduct extensive experiments on standard datasets to evaluate various approaches to MKL for object recognition. We argue that the seemingly contradictory conclusions offered by studies are due to different experimental setups. The conclusions of our study are: (i) given a sufficient number of training examples and feature/kernel types, MKL is more effective for object recognition than simple kernel combination (e.g., choosing the best performing kernel or average of kernels); and (ii) among the various approaches proposed for MKL, the sequential minimal optimization, semi-infinite programming, and level method based ones are computationally most efficient.

  20. Out-of-Sample Extensions for Non-Parametric Kernel Methods.

    PubMed

    Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang

    2017-02-01

    Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.