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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 canonical-correlation Granger causality for multiple time series

    NASA Astrophysics Data System (ADS)

    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.

  4. 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.

  5. LOCAL KERNEL CANONICAL CORRELATION ANALYSIS WITH APPLICATION TO VIRTUAL DRUG SCREENING

    PubMed Central

    Samarov, Daniel; Marron, J. S.; Liu, Yufeng; Grulke, Christopher; Tropsha, Alexander

    2011-01-01

    Drug discovery is the process of identifying compounds which have potentially meaningful biological activity. A major challenge that arises is that the number of compounds to search over can be quite large, sometimes numbering in the millions, making experimental testing intractable. For this reason computational methods are employed to filter out those compounds which do not exhibit strong biological activity. This filtering step, also called virtual screening reduces the search space, allowing for the remaining compounds to be experimentally tested. In this paper we propose several novel approaches to the problem of virtual screening based on Canonical Correlation Analysis (CCA) and on a kernel-based extension. Spectral learning ideas motivate our proposed new method called Indefinite Kernel CCA (IKCCA). We show the strong performance of this approach both for a toy problem as well as using real world data with dramatic improvements in predictive accuracy of virtual screening over an existing methodology. PMID:22121408

  6. 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

  7. 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

  8. Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis.

    PubMed

    Yamanishi, Y; Vert, J-P; Nakaya, A; Kanehisa, M

    2003-01-01

    A major issue in computational biology is the reconstruction of pathways from several genomic datasets, such as expression data, protein interaction data and phylogenetic profiles. As a first step toward this goal, it is important to investigate the amount of correlation which exists between these data. These methods are successfully tested on their ability to recognize operons in the Escherichia coli genome, from the comparison of three datasets corresponding to functional relationships between genes in metabolic pathways, geometrical relationships along the chromosome, and co-expression relationships as observed by gene expression data.

  9. Canonical Correlation: Terms and Descriptions.

    ERIC Educational Resources Information Center

    Pugh, Richard C.; Hu, Yuehluen

    The use of terms to describe and interpret results from canonical correlation analysis has been inconsistent across research studies. This study assembled the terminology related to the use and interpretation of canonical correlation analysis from research articles, textbooks, and computer manuals. Research articles using canonical correlation…

  10. Regularized Generalized Canonical Correlation Analysis

    ERIC Educational Resources Information Center

    Tenenhaus, Arthur; Tenenhaus, Michel

    2011-01-01

    Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to three or more sets of variables. It constitutes a general framework for many multi-block data analysis methods. It combines the power of multi-block data analysis methods (maximization of well identified criteria) and…

  11. 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.

  12. 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

  13. 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…

  14. 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…

  15. Variable Deletion Strategies in Canonical Correlation Analysis.

    ERIC Educational Resources Information Center

    Tanguma, Jesus

    This paper presents three variable deletion strategies in canonical correlation analysis. All three strategies are illustrated by examples. The first strategy uses the canonical communality (h2) coefficients of the three functions to decide which variable to delete. The second function also uses the canonical communality coefficients, but only…

  16. 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…

  17. A multimodal stress monitoring system with canonical correlation analysis.

    PubMed

    Unsoo Ha; Changhyeon Kim; Yongsu Lee; Hyunki Kim; Taehwan Roh; Hoi-Jun Yoo

    2015-08-01

    The multimodal stress monitoring headband is proposed for mobile stress management system. It is composed of headband and earplugs. Electroencephalography (EEG), hemoencephalography (HEG) and heart-rate variability (HRV) can be achieved simultaneously in the proposed system for user status estimation. With canonical correlation analysis (CCA) and temporal-kernel CCA (tkCCA) algorithm, those different signals can be combined for maximum correlation. Thanks to the proposed combination algorithm, the accuracy of the proposed system increased up to 19 percentage points than unimodal monitoring system in n-back task.

  18. 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…

  19. Local Canonical Correlation Analysis for Nonlinear Common Variables Discovery

    NASA Astrophysics Data System (ADS)

    Yair, Or; Talmon, Ronen

    2017-03-01

    In this paper, we address the problem of hidden common variables discovery from multimodal data sets of nonlinear high-dimensional observations. We present a metric based on local applications of canonical correlation analysis (CCA) and incorporate it in a kernel-based manifold learning technique.We show that this metric discovers the hidden common variables underlying the multimodal observations by estimating the Euclidean distance between them. Our approach can be viewed both as an extension of CCA to a nonlinear setting as well as an extension of manifold learning to multiple data sets. Experimental results show that our method indeed discovers the common variables underlying high-dimensional nonlinear observations without assuming prior rigid model assumptions.

  20. 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…

  1. 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.

  2. 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…

  3. 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

  4. 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)…

  5. Quantum canonical ensemble and correlation femtoscopy at fixed multiplicities

    NASA Astrophysics Data System (ADS)

    Akkelin, S. V.; Sinyukov, Yu. M.

    2016-07-01

    Identical particle correlations at fixed multiplicity are considered by means of quantum canonical ensemble of finite systems. We calculate one-particle momentum spectra and two-particle Bose-Einstein correlation functions in the ideal gas by using a recurrence relation for the partition function. Within such a model we investigate the validity of the thermal Wick's theorem and its applicability for decomposition of the two-particle distribution function. The dependence of the Bose-Einstein correlation parameters on the average momentum of the particle pair is also investigated. Specifically, we present the analytical formulas that allow one to estimate the effect of suppressing the correlation functions in a finite canonical system. The results can be used for the femtoscopy analysis of the A +A and p +p collisions with selected (fixed) multiplicity.

  6. 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)

  7. 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.

  8. The SSVEP topographic scalp maps by canonical correlation analysis.

    PubMed

    Bin, Guangyu; Lin, Zhonglin; Gao, Xiaorong; Hong, Bo; Gao, Shangkai

    2008-01-01

    As the number of electrodes increases, topographic scalp mapping methods for electroencephalogram (EEG) data analysis are becoming important. Canonical correlation analysis (CCA) is a method of extracting similarity between two data sets. This paper presents an EEG topographic scalp mapping -based CCA for the steady-state visual evoked potentials (SSVEP) analysis. Multi-channel EEG data and the sinusoidal reference signal were used as the inputs of CCA. The output linear combination was then employed for mapping. Our experimental results prove the topographic scalp mapping-based CCA can instruct for the improvement of SSVEP-based brain computer interface (BCI) system.

  9. A New Ensemble Canonical Correlation Prediction Scheme for Seasonal Precipitation

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

    Department of Mathematical Sciences, University of Alberta, Edmonton, Canada This paper describes the fundamental theory of the ensemble canonical correlation (ECC) algorithm for the seasonal climate forecasting. The algorithm is a statistical regression sch eme based on maximal correlation between the predictor and predictand. The prediction error is estimated by a spectral method using the basis of empirical orthogonal functions. The ECC algorithm treats the predictors and predictands as continuous fields and is an improvement from the traditional canonical correlation prediction. The improvements include the use of area-factor, estimation of prediction error, and the optimal ensemble of multiple forecasts. The ECC is applied to the seasonal forecasting over various parts of the world. The example presented here is for the North America precipitation. The predictor is the sea surface temperature (SST) from different ocean basins. The Climate Prediction Center's reconstructed SST (1951-1999) is used as the predictor's historical data. The optimally interpolated global monthly precipitation is used as the predictand?s historical data. Our forecast experiments show that the ECC algorithm renders very high skill and the optimal ensemble is very important to the high value.

  10. 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

  11. Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis

    NASA Astrophysics Data System (ADS)

    Fu, Xiao; Huang, Kejun; Hong, Mingyi; Sidiropoulos, Nicholas D.; So, Anthony Man-Cho

    2017-08-01

    Generalized canonical correlation analysis (GCCA) aims at finding latent low-dimensional common structure from multiple views (feature vectors in different domains) of the same entities. Unlike principal component analysis (PCA) that handles a single view, (G)CCA is able to integrate information from different feature spaces. Here we focus on MAX-VAR GCCA, a popular formulation which has recently gained renewed interest in multilingual processing and speech modeling. The classic MAX-VAR GCCA problem can be solved optimally via eigen-decomposition of a matrix that compounds the (whitened) correlation matrices of the views; but this solution has serious scalability issues, and is not directly amenable to incorporating pertinent structural constraints such as non-negativity and sparsity on the canonical components. We posit regularized MAX-VAR GCCA as a non-convex optimization problem and propose an alternating optimization (AO)-based algorithm to handle it. Our algorithm alternates between {\\em inexact} solutions of a regularized least squares subproblem and a manifold-constrained non-convex subproblem, thereby achieving substantial memory and computational savings. An important benefit of our design is that it can easily handle structure-promoting regularization. We show that the algorithm globally converges to a critical point at a sublinear rate, and approaches a global optimal solution at a linear rate when no regularization is considered. Judiciously designed simulations and large-scale word embedding tasks are employed to showcase the effectiveness of the proposed algorithm.

  12. 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.

  13. 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:

  14. 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.

  15. 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.

  16. 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…

  17. 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.…

  18. 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.…

  19. 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…

  20. 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.

  1. Prediction of ENSO episodes using canonical correlation analysis

    SciTech Connect

    Barnston, A.G.; Ropelewski, C.F. )

    1992-11-01

    Canonical correlation analysis (CCA) is explored as a multivariate linear statistical methodology with which to forecast fluctuations of the El Nino/Southern Oscillation (ENSO) in real time. CCA is capable of identifying critical sequences of predictor patterns that tend to evolve into subsequent pattern that can be used to form a forecast. The CCA model is used to forecast the 3-month mean sea surface temperature (SST) in several regions of the tropical Pacific and Indian oceans for projection times of 0 to 4 seasons beyond the immediately forthcoming season. The predictor variables, representing the climate situation in the four consecutive 3-month periods ending at the time of the forecast, are (1) quasi-global seasonal mean sea level pressure (SLP) and (2) SST in the predicted regions themselves. Forecast skill is estimated using cross-validation, and persistence is used as the primary skill control measure. Results indicate that a large region in the eastern equatorial Pacific (120[degrees]-170[degrees] W longitude) has the highest overall predictability, with excellent skill realized for winter forecasts made at the end of summer. CCA outperforms persistence in this region under most conditions, and does noticeably better with the SST included as a predictor in addition to the SLP. It is demonstrated that better forecast performance at the longer lead times would be obtained if some significantly earlier (i.e., up to 4 years) predictor data were included, because the ability to predict the lower-frequency ENSO phase changes would increase. The good performance of the current system at shorter lead times appears to be based largely on the ability to predict ENSO evolution for events already in progress. The forecasting of the eastern tropical Pacific SST using CCA is now done routinely on a monthly basis for a O-, 1-, and 2-season lead at the Climate Analysis Center.

  2. 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.

  3. 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.

  4. 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

  5. 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…

  6. 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…

  7. 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.

  8. 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.

  9. Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

    PubMed Central

    Gutmann, Michael U.; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús

    2014-01-01

    Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation. PMID:24533049

  10. Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images.

    PubMed

    Gutmann, Michael U; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús

    2014-01-01

    Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation.

  11. Enhancing unsupervised canonical correlation analysis-based frequency detection of SSVEPs by incorporating background EEG.

    PubMed

    Nakanishi, Masaki; Wang, Yijun; Wang, Yu-Te; Mitsukura, Yasue; Jung, Tzyy-Ping

    2014-01-01

    Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to provide a fast communication channel between human brain and external devices. In SSVEP-based BCIs, Canonical Correlation Analysis (CCA) has been widely used to detect frequency-coded SSVEPs due to its high efficiency and robustness. However, the detectability of SSVEPs differs among frequencies due to a power-law distribution of the power spectra of spontaneous electroencephalogram (EEG) signals. This study proposed a new method based on the fact that changes of canonical correlation coefficients for SSVEPs and background EEG signals follow the same trend along frequency. The proposed method defined a normalized canonical correlation coefficient, the ratio of the canonical correlation coefficient for SSVEPs to the mean of the canonical correlation coefficients for background EEG signals, to enhance the frequency detection of SSVEPs. An SSVEP dataset from 13 subjects was used for comparing classification performance between the proposed method and the standard CCA method. Classification accuracy and simulated information transfer rates (ITR) suggest that, in an unsupervised way, the proposed method could considerably improve the frequency detection accuracy of SSVEPs with little computational effort.

  12. 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…

  13. 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…

  14. 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…

  15. 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

  16. 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…

  17. 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)

  18. 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…

  19. 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…

  20. 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…

  1. Assessment of long-range-corrected exchange-correlation kernels for solids: Accurate exciton binding energies via an empirically scaled bootstrap kernel

    NASA Astrophysics Data System (ADS)

    Byun, Young-Moo; Ullrich, Carsten A.

    2017-05-01

    In time-dependent density-functional theory, a family of exchange-correlation kernels, known as long-range-corrected (LRC) kernels, have shown promise in the calculation of excitonic effects in solids. We perform a systematic assessment of existing static LRC kernels (empirical LRC, Bootstrap, and jellium-with-a-gap model) for a range of semiconductors and insulators, focusing on optical spectra and exciton binding energies. We find that no LRC kernel is capable of simultaneously producing good optical spectra and quantitatively accurate exciton binding energies for both semiconductors and insulators. We propose a simple and universal, empirically scaled Bootstrap kernel that yields accurate exciton binding energies for all materials under consideration, with low computational cost.

  2. An automatic ocular artifacts removal method based on wavelet-enhanced canonical correlation analysis.

    PubMed

    Zhao, Chunyu; Qiu, Tianshuang

    2011-01-01

    In this paper, a new method for automatic ocular artifacts (OA) removal in EEG recordings is proposed based on wavelet-enhanced canonical correlation analysis (wCCA). Compared to three popular ocular artifacts removal methods, wCCA owns two advantages. First, there is no need to identify the artifact components by subjective visual inspection, because the first canonical components found by CCA for each dataset, also the most common component between the left and right hemisphere, are definitely related to artifacts. Second, quantitative evaluation of the corrected EEG signals demonstrates that wCCA removed the most ocular artifacts with minimal cerebral information loss.

  3. Multiscale causal connectivity analysis by canonical correlation: theory and application to epileptic brain.

    PubMed

    Wu, Guo Rong; Chen, Fuyong; Kang, Dezhi; Zhang, Xiangyang; Marinazzo, Daniele; Chen, Huafu

    2011-11-01

    Multivariate Granger causality is a well-established approach for inferring information flow in complex systems, and it is being increasingly applied to map brain connectivity. Traditional Granger causality is based on vector autoregressive (AR) or mixed autoregressive moving average (ARMA) model, which are potentially affected by errors in parameter estimation and may be contaminated by zero-lag correlation, notably when modeling neuroimaging data. To overcome this issue, we present here an extended canonical correlation approach to measure multivariate Granger causal interactions among time series. The procedure includes a reduced rank step for calculating canonical correlation analysis (CCA), and extends the definition of causality including instantaneous effects, thus avoiding the potential estimation problems of AR (or ARMA) models. We tested this approach on simulated data and confirmed its practical utility by exploring local network connectivity at different scales in the epileptic brain analyzing scalp and depth-EEG data during an interictal period.

  4. Application of semilinear canonical correlation to the measurement of the electroencephalographic effects of volatile anaesthetics.

    PubMed

    Bruhn, J; Rehberg, B; Röpcke, H; Bouillon, T; Hoeft, A

    2002-10-01

    The common parameters of the electroencephalogram quantify a shift of its power spectrum towards lower frequencies with increasing anaesthetic drug concentrations (e.g. spectral-edge frequency 95). These ad hoc parameters are not optimized for the content of information with regard to drug effect. Using semilinear canonical correlation, different frequency ranges (bins) of the power spectrum can be weighted for sensitivity to changes of drug concentration by multiplying their power with iteratively determined coefficients, yielding a new (canonical univariate) electroencephalographic parameter. Electroencephalographic data obtained during application of volatile anaesthetics were used: isoflurane (n = 6), desflurane (7), sevoflurane (7), desflurane during surgical stimulation (12). Volatile anaesthetic end-tidal concentrations varied between 0.5 and 1.6 minimum alveolar concentration (MAC). The canonical univariate parameter and spectral-edge frequency 95 were determined and their correlation with the volatile anaesthetic effect compartment concentration, obtained by simultaneous pharmacokinetic-pharmacodynamic modelling, were compared. The canonical univariate parameter with individually optimized coefficients, but not with mean coefficients, was superior to the spectral-edge frequency 95 as a measure of anaesthetic drug effect. No significant differences of the coefficients were found between the three volatile anaesthetics or between the data with or without surgical stimulus. The coefficients for volatile anaesthetics were similar to the coefficients for opioids, but different from coefficients for propofol and midazolam. The canonical univariate parameter calculated with individually optimized coefficients, but not with mean coefficients, correlates more accurately and consistently with the effect site concentrations of volatile anaesthetics than with spectral-edge frequency 95.

  5. Long-term forecasting of meteorological time series using Nonlinear Canonical Correlation Analysis (NLCCA)

    NASA Astrophysics Data System (ADS)

    Woldesellasse, H. T.; Marpu, P. R.; Ouarda, T.

    2016-12-01

    Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. A number of linear and nonlinear multivariate techniques has been used to predict the stochastic character of wind speed. A wind forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study Nonlinear Canonical Correlation Analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed and wind direction, of Abu Dhabi, UAE. The wind dataset was obtained from six ground stations. The first mode of NLCCA is capable of capturing the nonlinear mode of the climate indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE) and Mean absolute error (MAE). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behaviour of the dataset of variables than linear CCA model in terms of the correlation and root mean square error. Key words: Nonlinear Canonical Correlation Analysis (NLCCA), Canonical Correlation Analysis, Neural Network, Climate Indices, wind speed, wind direction

  6. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.

    PubMed

    Lin, Zhonglin; Zhang, Changshui; Wu, Wei; Gao, Xiaorong

    2006-12-01

    Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method.

  7. 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.

  8. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.

    PubMed

    Lin, Zhonglin; Zhang, Changshui; Wu, Wei; Gao, Xiaorong

    2007-06-01

    Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used FFT (fast Fourier transform)-based spectrum estimation method.

  9. 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.

  10. Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis.

    PubMed

    Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

    2014-06-01

    Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.

  11. 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

  12. Correspondence between fMRI and SNP data by group sparse canonical correlation analysis

    PubMed Central

    Lin, Dongdong; Calhoun, Vince D.; Wang, Yu-Ping

    2013-01-01

    Both genetic variants and brain region abnormalities are recognized as important factors for complex diseases (e.g., schizophrenia). In this paper, we investigated the correspondence between single nucleotide polymorphism (SNP) and brain activity measured by functional magnetic resonance imaging (fMRI) to understand how genetic variation influences the brain activity. A group sparse canonical correlation analysis method (group sparse CCA) was developed to explore the correlation between these two datasets which are high dimensional-the number of SNPs/voxels is far greater than the number of samples. Different from the existing sparse CCA methods (sCCA), our approach can exploit structural information in the correlation analysis by introducing group constraints. A simulation study demonstrates that it outperforms the existing sCCA. We applied this method to the real data analysis and identified two pairs of significant canonical variates with average correlations of 0.4527 and 0.4292 respectively, which were used to identify genes and voxels associated with schizophrenia. The selected genes are mostly from 5 schizophrenia (SZ)-related signalling pathways. The brain mappings of the selected voxles also indicate the abnormal brain regions susceptible to schizophrenia. A gene and brain region of interest (ROI) correlation analysis was further performed to confirm the significant correlations between genes and ROIs. PMID:24247004

  13. Optimization of Correlation Kernel Size for Accurate Estimation of Myocardial Contraction and Relaxation

    NASA Astrophysics Data System (ADS)

    Honjo, Yasunori; Hasegawa, Hideyuki; Kanai, Hiroshi

    2012-07-01

    For noninvasive and quantitative measurements of global two-dimensional (2D) heart wall motion, speckle tracking methods have been developed and applied. In these conventional methods, the frame rate is limited to about 200 Hz, corresponding to the sampling period of 5 ms. However, myocardial function during short periods, as obtained by these conventional speckle tracking methods, remains unclear owing to low temporal and spatial resolutions of these methods. Moreover, an important parameter, the optimal kernel size, has not been thoroughly investigated. In our previous study, the optimal kernel size was determined in a phantom experiment under a high signal-to-noise ratio (SNR), and the determined optimal kernel size was applied to the in vivo measurement of 2D displacements of the heart wall by block matching using normalized cross-correlation between RF echoes at a high frame rate of 860 Hz, corresponding to a temporal resolution of 1.1 ms. However, estimations under low SNRs and the effects of the difference in echo characteristics, i.e., specular reflection and speckle-like echoes, have not been considered, and the evaluation of accuracy in the estimation of the strain rate is still insufficient. In this study, the optimal kernel sizes were determined in a phantom experiment under several SNRs and, then, the myocardial strain rate was estimated such that the myocardial function can be measured at a high frame rate. In a basic experiment, the optimal kernel sizes at depths of 20, 40, 60, and 80 mm yielded similar results: in particular, SNR was more than 15 dB. Moreover, it was found that the kernel size at the boundary must be set larger than that at the inside. The optimal sizes of the correlation kernel were seven times and four times the size of the point spread function around the boundary and inside the silicone rubber, respectively. To compare the optimal kernel sizes, which was determined in a phantom experiment, with other sizes, the radial strain

  14. 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.

  15. An extension of the Canonical Correlation Analysis to the case of multiple observations of two groups of variables.

    PubMed

    Phlypo, Ronald; Congedo, Marco

    2010-01-01

    In this contribution we present a method that extends the Canonical Correlation Analysis for two groups of variables to the case of multiple conditions. Contrary to the extensions in literature based on augmenting the number of variable groups, the addition of conditions allows for a more robust estimate of the canonical correlation structure inherently present in the data. Algorithms to solve the estimation problem are based on joint approximate diagonalization algorithms for matrix sets. Simulations show the performance of the proposed method under two different scenarios: the calculation of a latent canonical structure and the estimation of a bilinear mixture model.

  16. Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram.

    PubMed

    De Clercq, Wim; Vergult, Anneleen; Vanrumste, Bart; Van Paesschen, Wim; Van Huffel, Sabine

    2006-12-01

    The electroencephalogram (EEG) is often contaminated by muscle artifacts. In this paper, a new method for muscle artifact removal in EEG is presented, based on canonical correlation analysis (CCA) as a blind source separation (BSS) technique. This method is demonstrated on a synthetic data set. The method outperformed a low-pass filter with different cutoff frequencies and an independent component analysis (ICA)-based technique for muscle artifact removal. In addition, the method is applied on a real ictal EEG recording contaminated with muscle artifacts. The proposed method removed successfully the muscle artifact without altering the recorded underlying ictal activity.

  17. 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.

  18. 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.

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

    PubMed

    Karageorgiou, Elissaios; Lewis, Scott M; McCarten, J Riley; 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.

  20. L1-regularized Multiway canonical correlation analysis for SSVEP-based BCI.

    PubMed

    Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Minjue; Wang, Xingyu; Cichocki, Andrzej

    2013-11-01

    Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the reference signals of sine- cosine waves without subject-specific and inter-trial information can hardly give the optimal recognition accuracy, due to possible overfitting, especially within a short time window length. This paper introduces an L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further. A multiway extension of the CCA, called MCCA, is first presented, in which collaborative CCAs are exploited to optimize the reference signals in correlation analysis for SSVEP recognition alternatingly from the channel-way and trial-way arrays of constructed EEG tensor. L1-regularization is subsequently imposed on the trial-way array optimization in the MCCA, and hence results in the more powerful L1-MCCA with function of effective trial selection. Both the proposed MCCA and L1-MCCA methods are validated for SSVEP recognition with EEG data from 10 healthy subjects, and compared to the ordinary CCA without reference signal optimization. Experimental results show that the MCCA significantly outperforms the CCA for SSVEP recognition. The L1-MCCA further improves the recognition accuracy which is significantly higher than that of the MCCA.

  1. Multiple time-lag canonical correlation analysis for removing muscular artifacts in EEG.

    PubMed

    Shen, Kaiquan; Yu, Ke; Bandla, Aishwarya; Sun, Yu; Thakor, Nitish; Li, Xiaoping

    2013-01-01

    In this work, a new approach for joint blind source separation (BSS) of datasets at multiple time lags using canonical correlation analysis (CCA) is developed for removing muscular artifacts from electroencephalogram (EEG) recordings. The proposed approach jointly extracts sources from each dataset in a decreasing order of between-set source correlations. Muscular artifact sources that typically have lowest between-set correlations can then be removed. It is shown theoretically that the proposed use of CCA on multiple datasets at multiple time lags achieves better BSS under a more relaxed condition and hence offers better performance in removing muscular artifacts than the conventional CCA. This is further demonstrated by experiments on real EEG data.

  2. 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.

  3. 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

  4. Application of Canonical Correlation Analysis for Detecting Risk Factors Leading to Recurrence of Breast Cancer.

    PubMed

    Sadoughi, Farahnaz; Lotfnezhad Afshar, Hadi; Olfatbakhsh, Asiie; Mehrdad, Neda

    2016-03-01

    Advances in treatment options of breast cancer and development of cancer research centers have necessitated the collection of many variables about breast cancer patients. Detection of important variables as predictors and outcomes among them, without applying an appropriate statistical method is a very challenging task. Because of recurrent nature of breast cancer occurring in different time intervals, there are usually more than one variable in the outcome set. For the prevention of this problem that causes multicollinearity, a statistical method named canonical correlation analysis (CCA) is a good solution. The purpose of this study was to analyze the data related to breast cancer recurrence of Iranian females using the CCA method to determine important risk factors. In this cross-sectional study, data of 584 female patients (mean age of 45.9 years) referred to Breast Cancer Research Center (Tehran, Iran) were analyzed anonymously. SPSS and NORM softwares (2.03) were used for data transformation, running and interpretation of CCA and replacing missing values, respectively. Data were obtained from Breast Cancer Research Center, Tehran, Iran. Analysis showed seven important predictors resulting in breast cancer recurrence in different time periods. Family history and loco-regional recurrence more than 5 years after diagnosis were the most important variables among predictors and outcomes sets, respectively. Canonical correlation analysis can be used as a useful tool for management and preparing of medical data for discovering of knowledge hidden in them.

  5. 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.

  6. A learning algorithm for adaptive canonical correlation analysis of several data sets.

    PubMed

    Vía, Javier; Santamaría, Ignacio; Pérez, Jesús

    2007-01-01

    Canonical correlation analysis (CCA) is a classical tool in statistical analysis to find the projections that maximize the correlation between two data sets. In this work we propose a generalization of CCA to several data sets, which is shown to be equivalent to the classical maximum variance (MAXVAR) generalization proposed by Kettenring. The reformulation of this generalization as a set of coupled least squares regression problems is exploited to develop a neural structure for CCA. In particular, the proposed CCA model is a two layer feedforward neural network with lateral connections in the output layer to achieve the simultaneous extraction of all the CCA eigenvectors through deflation. The CCA neural model is trained using a recursive least squares (RLS) algorithm. Finally, the convergence of the proposed learning rule is proved by means of stochastic approximation techniques and their performance is analyzed through simulations.

  7. 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.

  8. Producing data-based sensitivity kernels from convolution and correlation in exploration geophysics.

    NASA Astrophysics Data System (ADS)

    Chmiel, M. J.; Roux, P.; Herrmann, P.; Rondeleux, B.

    2016-12-01

    Many studies have shown that seismic interferometry can be used to estimate surface wave arrivals by correlation of seismic signals recorded at a pair of locations. In the case of ambient noise sources, the convergence towards the surface wave Green's functions is obtained with the criterion of equipartitioned energy. However, seismic acquisition with active, controlled sources gives more possibilities when it comes to interferometry. The use of controlled sources makes it possible to recover the surface wave Green's function between two points using either correlation or convolution. We investigate the convolutional and correlational approaches using land active-seismic data from exploration geophysics. The data were recorded on 10,710 vertical receivers using 51,808 sources (seismic vibrator trucks). The sources spacing is the same in both X and Y directions (30 m) which is known as a "carpet shooting". The receivers are placed in parallel lines with a spacing 150 m in the X direction and 30 m in the Y direction. Invoking spatial reciprocity between sources and receivers, correlation and convolution functions can thus be constructed between either pairs of receivers or pairs of sources. Benefiting from the dense acquisition, we extract sensitivity kernels from correlation and convolution measurements of the seismic data. These sensitivity kernels are subsequently used to produce phase-velocity dispersion curves between two points and to separate the higher mode from the fundamental mode for surface waves. Potential application to surface wave cancellation is also envisaged.

  9. Semilinear canonical correlation applied to the measurement of the electroencephalographic effects of midazolam and flumazenil reversal.

    PubMed

    Schnider, T W; Minto, C F; Fiset, P; Gregg, K M; Shafer, S L

    1996-03-01

    The electroencephalographic (EEG) effect of benzodiazepines, and midazolam in particular, has been described using simple measures such as total power in the beta band, waves.s(-1) in the beta band and total power from aperiodic analysis. All these parameters failed to consistently describe the EEG effect of midazolam in a study in which large doses of midazolam were infused, and the effect subsequently reversed with flumazenil. Using a technique called semilinear correlation it is possible to extract a parameter from the EEG that is statistically optimally correlated with the apparent concentration of the benzodiazepine in the effect site. This method has been used to develop new univariate measures of the effects of opioids on the EEG but has not previously been applied to the EEG effects of benzodiazepines. Data from ten subjects who received an infusion of midazolam were analyzed. The data were divided into "learning" and "test" sets. The learning set consisted of ten studies in which the volunteers received an infusion of 2.5 mg.min(-1) midazolam. Semilinear canonical correlation was used to extract an univariate descriptor of the EEG effect by weighting the different frequency bands of the EEG power spectrum. The test set comprised the same subjects on subsequent visits, in which the subjects received a continuous infusion of midazolam to maintain 20% or 80% of the peak drug effect for 3h. Twenty minutes after start of the midazolam infusion, the patient received an infusion of flumazenil to acutely reverse the benzodiazepine drug effect. The weights obtained from the learning set were tested prospectively in the test set, based on the coefficient of multiple determination, R(2), obtained by fitting the EEG effect to a sigmoid Emax model. The canonical univariate parameter of benzodiazepine drug effect on the EEG, when applied to the test set receiving the midazolam infusion with flumazenil reversal, yielded a median R(2) of 0.78. The median R(2) of six

  10. 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.

  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. Decoding of responses to mixed frequency and phase coded visual stimuli using multiset canonical correlation analysis.

    PubMed

    Suefusa, Kaori; Tanaka, Toshihisa

    2016-08-01

    Brain-computer interfacing (BCI) based on steady-state visual evoked potentials (SSVEPs) is one of the most practical BCIs because of its high recognition accuracies and little training of a user. Mixed frequency and phase coding which can implement a number of commands and achieve a high information transfer rate (ITR) has recently been gaining much attention. In order to implement mixed-coded SSVEP-BCI as a reliable interface, it is important to detect commands fast and accurately. This paper presents a novel method to recognize mixed-coded SSVEPs which achieves high performance. The method employs multiset canonical correlation analysis to obtain spatial filters which enhance SSVEP components. An experiment with a mixed-coded SSVEP-BCI was conducted to evaluate performance of the proposed method compared with the previous work. The experimental results showed that the proposed method achieved significantly higher command recognition accuracy and ITR than the state-of-the-art.

  14. 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.

  15. Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis.

    PubMed

    Gao, Junfeng; Zheng, Chongxun; Wang, Pei

    2010-01-01

    The electroencephalogram (EEG) is often contaminated by electromyography (EMG). In this paper, a novel and robust technique is presented to eliminate EMG artifacts from EEG signals in real-time. First, the canonical correlation analysis (CCA) method is applied on the simulated EEG data contaminated by EMG and electrooculography (EOG) artifacts for separating EMG artifacts from EEG signals. The components responsible for EMG artifacts are distinguished from those responsible for brain activity based on the relative low autocorrelation. We demonstrate that the CCA method is more suitable to reconstruct the EMG-free EEG data than independent component analysis (ICA) methods. In addition, by applying CCA to analyze a number of EEG data contaminated by EMG artifacts, a correlation threshold is determined using an unbiased procedure. Hence, CCA can be used to remove EMG artifacts automatically. Finally, an example is given to verify that, after EMG artifacts were removed successfully from the EEG data contaminated by EMG and EOG simultaneously, not only the underlying brain activity signals but the EOG artifacts are preserved with little distortion.

  16. The correlation of chemical and physical corn kernel traits with growth performance and carcass characteristics in pigs.

    PubMed

    Moore, S M; Stalder, K J; Beitz, D C; Stahl, C H; Fithian, W A; Bregendahl, K

    2008-03-01

    Corn kernel composition may affect its nutritive value and, thus, pig growth performance and carcass characteristics. The objective of this study was to determine the influence of the chemical and physical traits of corn kernels from different hybrids on the growth performance and carcass characteristics of pigs. A total of 288 crossbred pigs were grown in a 3-phase program from 21 kg of BW until slaughter at 113 kg of BW with 12 pens (4 pigs/pen) per dietary treatment. Target BW for each phase were 20 to 40 kg (grower 1), 40 to 80 kg (grower 2), and 80 to 120 kg (finisher). In each phase, diets were formulated to be marginally deficient in Lys, TSAA, Ca, Na, and nonphytate P to improve the likelihood of detecting differences in performance due to corn hybrid. Each of 6 corn hybrids represented a wide range of kernel chemical and physical traits and was substituted for corn in a common diet formulation on an equal weight basis to make the 6 dietary treatments. Physical and chemical composition of the kernels were analyzed and correlated with performance measures by multivariate ANOVA. Kernel density was correlated with i.m. fat (IMF) content in LM (r = -.35, P < 0.05). Stenvert grinding time was correlated (P < 0.05) with ADG during the grower 1 phase (r = 0.26), ADFI during the grower 2 phase (r = 0.27), final BW (r = 0.27), and IMF (r = -0.36). The amylose content of the cornstarch was correlated (P < 0.05) with ADG during the grower 2 phase (r = -0.28) and with BW at the end of the grower 2 phase (r = -0.27). The NDF content of the kernels was correlated (P < 0.05) with ADG during the finisher phase (r = -0.30), final BW (r = -0.33), and number of days to market (r = 0.31). The ADF content of the kernels was correlated (P < 0.05) with ADG during the grower 1 phase (r = -0.26), final BW (r = -0.26), and IMF (r = 0.31). The correlations of performance measure variation with individual kernel hybrid physical and chemical traits were statistically significant yet

  17. Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter

    NASA Astrophysics Data System (ADS)

    Qian, Kun; Zhou, Huixin; Rong, Shenghui; Wang, Bingjian; Cheng, Kuanhong

    2017-05-01

    Infrared small target tracking plays an important role in applications including military reconnaissance, early warning and terminal guidance. In this paper, an effective algorithm based on the Singular Value Decomposition (SVD) and the improved Kernelized Correlation Filter (KCF) is presented for infrared small target tracking. Firstly, the super performance of the SVD-based algorithm is that it takes advantage of the target's global information and obtains a background estimation of an infrared image. A dim target is enhanced by subtracting the corresponding estimated background with update from the original image. Secondly, the KCF algorithm is combined with Gaussian Curvature Filter (GCF) to eliminate the excursion problem. The GCF technology is adopted to preserve the edge and eliminate the noise of the base sample in the KCF algorithm, helping to calculate the classifier parameter for a small target. At last, the target position is estimated with a response map, which is obtained via the kernelized classifier. Experimental results demonstrate that the presented algorithm performs favorably in terms of efficiency and accuracy, compared with several state-of-the-art algorithms.

  18. 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…

  19. 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.

  20. Expression of Genes for Si Uptake, Accumulation, and Correlation of Si with Other Elements in Ionome of Maize Kernel.

    PubMed

    Bokor, Boris; Ondoš, Slavomír; Vaculík, Marek; Bokorová, Silvia; Weidinger, Marieluise; Lichtscheidl, Irene; Turňa, Ján; Lux, Alexander

    2017-01-01

    The mineral composition of cells, tissues, and organs is decisive for the functioning of the organisms, and is at the same time an indicator for understanding of physiological processes. We measured the composition of the ionome in the different tissues of maize kernels by element microanalysis, with special emphasis on silicon (Si). We therefore also measured the expression levels of the Si transporter genes ZmLsi1, ZmLsi2 and ZmLsi6, responsible for Si uptake and accumulation. Two weeks after pollination ZmLsi1 and ZmLsi6 genes were expressed, and expression continued until the final developmental stage of the kernels, while ZmLsi2 was not expressed. These results suggest that exclusively ZmLsi1 and ZmLsi6 are responsible for Si transport in various stages of kernel development. Expression level of ZmLsi genes was consistent with Si accumulation within kernel tissues. Silicon was mainly accumulated in pericarp and embryo proper and the lowest Si content was detected in soft endosperm and the scutellum. Correlation linkages between the distribution of Si and some other elements (macroelements Mg, P, S, N, P, and Ca and microelements Cl, Zn, and Fe) were found. The relation of Si with Mg was detected in all kernel tissues. The Si linkage with other elements was rather specific and found only in certain kernel tissues of maize. These relations may have effect on nutrient uptake and accumulation.

  1. Expression of Genes for Si Uptake, Accumulation, and Correlation of Si with Other Elements in Ionome of Maize Kernel

    PubMed Central

    Bokor, Boris; Ondoš, Slavomír; Vaculík, Marek; Bokorová, Silvia; Weidinger, Marieluise; Lichtscheidl, Irene; Turňa, Ján; Lux, Alexander

    2017-01-01

    The mineral composition of cells, tissues, and organs is decisive for the functioning of the organisms, and is at the same time an indicator for understanding of physiological processes. We measured the composition of the ionome in the different tissues of maize kernels by element microanalysis, with special emphasis on silicon (Si). We therefore also measured the expression levels of the Si transporter genes ZmLsi1, ZmLsi2 and ZmLsi6, responsible for Si uptake and accumulation. Two weeks after pollination ZmLsi1 and ZmLsi6 genes were expressed, and expression continued until the final developmental stage of the kernels, while ZmLsi2 was not expressed. These results suggest that exclusively ZmLsi1 and ZmLsi6 are responsible for Si transport in various stages of kernel development. Expression level of ZmLsi genes was consistent with Si accumulation within kernel tissues. Silicon was mainly accumulated in pericarp and embryo proper and the lowest Si content was detected in soft endosperm and the scutellum. Correlation linkages between the distribution of Si and some other elements (macroelements Mg, P, S, N, P, and Ca and microelements Cl, Zn, and Fe) were found. The relation of Si with Mg was detected in all kernel tissues. The Si linkage with other elements was rather specific and found only in certain kernel tissues of maize. These relations may have effect on nutrient uptake and accumulation. PMID:28674553

  2. 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

  3. 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

  4. Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

    PubMed

    Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

    2012-01-01

    Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

  5. Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

    PubMed Central

    Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

    2012-01-01

    Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

  6. Removal of the ballistocardiographic artifact from EEG-fMRI data: a canonical correlation approach

    NASA Astrophysics Data System (ADS)

    Assecondi, Sara; Hallez, Hans; Staelens, Steven; Bianchi, Anna M; Huiskamp, Geertjan M; Lemahieu, Ignace

    2009-03-01

    The simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) can give new insights into how the brain functions. However, the strong electromagnetic field of the MR scanner generates artifacts that obscure the EEG and diminish its readability. Among them, the ballistocardiographic artifact (BCGa) that appears on the EEG is believed to be related to blood flow in scalp arteries leading to electrode movements. Average artifact subtraction (AAS) techniques, used to remove the BCGa, assume a deterministic nature of the artifact. This assumption may be too strong, considering the blood flow related nature of the phenomenon. In this work we propose a new method, based on canonical correlation analysis (CCA) and blind source separation (BSS) techniques, to reduce the BCGa from simultaneously recorded EEG-fMRI. We optimized the method to reduce the user's interaction to a minimum. When tested on six subjects, recorded in 1.5 T or 3 T, the average artifact extracted with BSS-CCA and AAS did not show significant differences, proving the absence of systematic errors. On the other hand, when compared on the basis of intra-subject variability, we found significant differences and better performance of the proposed method with respect to AAS. We demonstrated that our method deals with the intrinsic subject variability specific to the artifact that may cause averaging techniques to fail.

  7. Using static and dynamic canonical correlation coefficients as quantitative EEG markers for Alzheimer's disease severity.

    PubMed

    Waser, M; Garn, H; Deistler, M; Benke, T; Dal-Bianco, P; Ransmayr, G; Schmidt, H; Sanin, G; Santer, P; Caravias, G; Seiler, S; Grossegger, D; Fruehwirt, W; Schmidt, R

    2014-01-01

    We analyzed the relation between Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores and quantitative electroencephalographic (qEEG) markers that were derived from canonical correlation analysis. This allowed an investigation of EEG synchrony between groups of EEG channels. In this study, we applied the data from 79 participants in the multi-centric cohort study PRODEM-Austria with probable AD. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. A quadratic regression model was used to describe the relation between MMSE and the qEEG synchrony markers. This relation was most significant in the δ and θ frequency bands in resting state, and between left-hemispheric central, temporal and parietal channel groups during the cognitive task. Here, the MMSE explained up to 40% of the qEEG marker's variation. QEEG markers showed an ambiguous trend, i.e. an increase of EEG synchrony in the initial stage of AD (MMSE>20) and a decrease in later stages. This effect could be caused by compensatory brain mechanisms. We conclude that the proposed qEEG markers are closely related to AD severity. Despite the ambiguous trend and the resulting diagnostic ambiguity, the qEEG markers could provide aid in the diagnostics of early-stage AD.

  8. Removal of the ballistocardiographic artifact from EEG-fMRI data: a canonical correlation approach.

    PubMed

    Assecondi, Sara; Hallez, Hans; Staelens, Steven; Bianchi, Anna M; Huiskamp, Geertjan M; Lemahieu, Ignace

    2009-03-21

    The simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) can give new insights into how the brain functions. However, the strong electromagnetic field of the MR scanner generates artifacts that obscure the EEG and diminish its readability. Among them, the ballistocardiographic artifact (BCGa) that appears on the EEG is believed to be related to blood flow in scalp arteries leading to electrode movements. Average artifact subtraction (AAS) techniques, used to remove the BCGa, assume a deterministic nature of the artifact. This assumption may be too strong, considering the blood flow related nature of the phenomenon. In this work we propose a new method, based on canonical correlation analysis (CCA) and blind source separation (BSS) techniques, to reduce the BCGa from simultaneously recorded EEG-fMRI. We optimized the method to reduce the user's interaction to a minimum. When tested on six subjects, recorded in 1.5 T or 3 T, the average artifact extracted with BSS-CCA and AAS did not show significant differences, proving the absence of systematic errors. On the other hand, when compared on the basis of intra-subject variability, we found significant differences and better performance of the proposed method with respect to AAS. We demonstrated that our method deals with the intrinsic subject variability specific to the artifact that may cause averaging techniques to fail.

  9. 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

  10. 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.

  11. 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

  12. Integrative analysis of gene expression and copy number alterations using canonical correlation analysis

    PubMed Central

    2010-01-01

    Background With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia. Results Using the correlation-maximizing methods, regularized dual CCA and PCA+CCA, we show that without pre-selection of known disease-relevant genes, and without using information about clinical class membership, an exploratory analysis singles out two patient groups, corresponding to well-known leukemia subtypes. Furthermore, the variables showing the highest relevance to the extracted features agree with previous biological knowledge concerning copy number alterations and gene expression changes in these subtypes. Finally, the correlation-maximizing methods are shown to yield results which are more

  13. 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.

  14. The correlation of chemical and physical corn kernel traits with production performance in broiler chickens and laying hens.

    PubMed

    Moore, S M; Stalder, K J; Beitz, D C; Stahl, C H; Fithian, W A; Bregendahl, K

    2008-04-01

    A study was conducted to determine the influence on broiler chicken growth and laying hen performance of chemical and physical traits of corn kernels from different hybrids. A total of 720 male 1-d-old Ross-308 broiler chicks were allotted to floor pens in 2 replicated experiments with a randomized complete block design. A total of 240 fifty-two-week-old Hy-Line W-36 laying hens were allotted to cages in a randomized complete block design. Corn-soybean meal diets were formulated for 3 broiler growth phases and one 14-wk-long laying hen phase to be marginally deficient in Lys and TSAA to allow for the detection of differences or correlations attributable to corn kernel chemical or physical traits. The broiler chicken diets were also marginally deficient in Ca and nonphytate P. Within a phase, corn- and soybean-based diets containing equal amounts of 1 of 6 different corn hybrids were formulated. The corn hybrids were selected to vary widely in chemical and physical traits. Feed consumption and BW were recorded for broiler chickens every 2 wk from 0 to 6 wk of age. Egg production was recorded daily, and feed consumption and egg weights were recorded weekly for laying hens between 53 and 67 wk of age. Physical and chemical composition of kernels was correlated with performance measures by multivariate ANOVA. Chemical and physical kernel traits were weakly correlated with performance in broiler chickens from 0 to 2 wk of age (P<0.05, | r |<0.42). However, from 4 to 6 wk of age and 0 to 6 wk of age, only kernel chemical traits were correlated with broiler chicken performance (P<0.05, | r |<0.29). From 53 to 67 wk of age, correlations were observed between both kernel physical and chemical traits and laying hen performance (P<0.05, | r |<0.34). In both experiments, the correlations of performance measures with individual kernel chemical and physical traits for any single kernel trait were not large enough to base corn hybrid selection on for feeding poultry.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. [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.

  20. 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.

  1. 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

  2. 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

  3. Supervised multi-view canonical correlation analysis: fused multimodal prediction of disease diagnosis and prognosis

    NASA Astrophysics Data System (ADS)

    Singanamalli, Asha; Wang, Haibo; Lee, George; Shih, Natalie; Rosen, Mark; Master, Stephen; Tomaszewski, John; Feldman, Michael; Madabhushi, Anant

    2014-03-01

    While the plethora of information from multiple imaging and non-imaging data streams presents an opportunity for discovery of fused multimodal, multiscale biomarkers, they also introduce multiple independent sources of noise that hinder their collective utility. The goal of this work is to create fused predictors of disease diagnosis and prognosis by combining multiple data streams, which we hypothesize will provide improved performance as compared to predictors from individual data streams. To achieve this goal, we introduce supervised multiview canonical correlation analysis (sMVCCA), a novel data fusion method that attempts to find a common representation for multiscale, multimodal data where class separation is maximized while noise is minimized. In doing so, sMVCCA assumes that the different sources of information are complementary and thereby act synergistically when combined. Although this method can be applied to any number of modalities and to any disease domain, we demonstrate its utility using three datasets. We fuse (i) 1.5 Tesla (T) magnetic resonance imaging (MRI) features with cerbrospinal fluid (CSF) proteomic measurements for early diagnosis of Alzheimer's disease (n = 30), (ii) 3T Dynamic Contrast Enhanced (DCE) MRI and T2w MRI for in vivo prediction of prostate cancer grade on a per slice basis (n = 33) and (iii) quantitative histomorphometric features of glands and proteomic measurements from mass spectrometry for prediction of 5 year biochemical recurrence postradical prostatectomy (n = 40). Random Forest classifier applied to the sMVCCA fused subspace, as compared to that of MVCCA, PCA and LDA, yielded the highest classification AUC of 0.82 +/- 0.05, 0.76 +/- 0.01, 0.70 +/- 0.07, respectively for the aforementioned datasets. In addition, sMVCCA fused subspace provided 13.6%, 7.6% and 15.3% increase in AUC as compared with that of the best performing individual view in each of the three datasets, respectively. For the biochemical recurrence

  4. 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

  5. Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations.

    PubMed

    Pustina, Dorian; Avants, Brian; Faseyitan, Olufunsho K; Medaglia, John D; Coslett, H Branch

    2017-09-05

    Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain-behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass-univariate approach. Several authors have shown that VLSM suffers from limitations that may decrease the accuracy and reliability of the findings, and have proposed the use of multivariate methods to overcome these limitations. In this study, we propose a multivariate optimization technique known as sparse canonical correlation analysis for neuroimaging (SCCAN) for lesion to symptom mapping. To validate the method and compare it with mass-univariate results, we used data from 131 patients with chronic stroke lesions in the territory of the middle cerebral artery, and created synthetic behavioral scores based on the lesion load of 93 brain regions (putative functional units). LSM analyses were performed with univariate VLSM or SCCAN, and the accuracy of the two methods was compared in terms of both overlap and displacement from the simulated functional areas. Overall, SCCAN produced more accurate results - higher dice overlap and smaller average displacement - compared to VLSM. This advantage persisted at different sample sizes (N = 20-131) and different multiple comparison corrections (false discovery rate, FDR; Bonferroni; permutation-based family wise error rate, FWER). These findings were replicated with a fully automated SCCAN routine that relied on cross-validated predictive accuracy to find the optimal sparseness value. Simulations of one, two, and three brain regions showed a systematic advantage of SCCAN over VLSM; under no circumstance could VLSM exceed the accuracy obtained with SCCAN. When considering functional units composed of multiple brain areas VLSM identified fewer areas than SCCAN. The investigation of real scores of aphasia severity (aphasia quotient and picture naming) showed that SCCAN could accurately

  6. Contributions of facial morphology, age, and gender to EMG activity under biting and resting conditions: a canonical correlation analysis.

    PubMed

    Fogle, L L; Glaros, A G

    1995-08-01

    Theoretical studies suggest that facial morphology may confer a mechanical advantage to particular individuals during force production, but not during rest. However, prior studies on the relationship between facial morphology and EMG suffer from various methodological limitations. We examined the hypothesis that facial morphology variables contribute significantly and meaningfully to the variance in masticatory muscle EMG when subjects produce specific levels of interocclusal force, but not when subjects are at rest. Measures of facial morphology included gonial angle, ramus height, and maxillary height, as determined from lateral cephalograms. EMG data were obtained from surface electrodes placed on masseter and temporalis sites. Subjects (N = 96) sat in a darkened, sound-attenuated room while they watched a seven-minute segment of a movie. EMG activity obtained during the last two minutes was used as a baseline period. Using the central incisors, subjects then provided five different force levels ranging from 6.5 to 48 lb in random order on a bite-force device while EMG data were collected. A canonical correlation analysis, performed on the set of predictor variables (age, gender, and facial morphology measurements) and the set of criterion variables (EMG data), showed a significant canonical correlation between the two variable sets while biting, but not at rest. Age, but not the facial morphology variables, was highly related to the canonical variate.(ABSTRACT TRUNCATED AT 250 WORDS)

  7. The Relationship Between Procrastination, Learning Strategies and Statistics Anxiety Among Iranian College Students: A Canonical Correlation Analysis

    PubMed Central

    Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

    2012-01-01

    Objective: Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. Methods: A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Results: Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. Conclusion: These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction. PMID:24644468

  8. The relationship between procrastination, learning strategies and statistics anxiety among Iranian college students: a canonical correlation analysis.

    PubMed

    Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

    2012-01-01

    Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction.

  9. Life skills and subjective well-being of people with disabilities: a canonical correlation analysis.

    PubMed

    da Silva Cardoso, Elizabeth; Blalock, Kacie; Allen, Chase A; Chan, Fong; Rubin, Stanford E

    2004-12-01

    This study examined the canonical relationships between a set of life skill variables and a set of subjective well-being variables among a national sample of vocational rehabilitation clients in the USA. Self-direction, work tolerance, general employability, and self-care were related to physical, family and social, and financial well-being. This analysis also found that communication skill is related to family and social well-being, while psychological well-being is not related to any life skills in the set. The results showed that vocational rehabilitation services aimed to improve life functioning will lead to an improvement in subjective quality of life.

  10. Social inequality, lifestyles and health - a non-linear canonical correlation analysis based on the approach of Pierre Bourdieu.

    PubMed

    Grosse Frie, Kirstin; Janssen, Christian

    2009-01-01

    Based on the theoretical and empirical approach of Pierre Bourdieu, a multivariate non-linear method is introduced as an alternative way to analyse the complex relationships between social determinants and health. The analysis is based on face-to-face interviews with 695 randomly selected respondents aged 30 to 59. Variables regarding socio-economic status, life circumstances, lifestyles, health-related behaviour and health were chosen for the analysis. In order to determine whether the respondents can be differentiated and described based on these variables, a non-linear canonical correlation analysis (OVERALS) was performed. The results can be described on three dimensions; Eigenvalues add up to the fit of 1.444, which can be interpreted as approximately 50 % of explained variance. The three-dimensional space illustrates correspondences between variables and provides a framework for interpretation based on latent dimensions, which can be described by age, education, income and gender. Using non-linear canonical correlation analysis, health characteristics can be analysed in conjunction with socio-economic conditions and lifestyles. Based on Bourdieus theoretical approach, the complex correlations between these variables can be more substantially interpreted and presented.

  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. 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.

  15. Using canonical correlation analysis to identify environmental attitude groups: considerations for national forest planning in the southwestern U.S.

    PubMed

    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.

  16. 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.

  17. Spatial filtering based on canonical correlation analysis for classification of evoked or event-related potentials in EEG data.

    PubMed

    Spüler, Martin; Walter, Armin; Rosenstiel, Wolfgang; Bogdan, Martin

    2014-11-01

    Classification of evoked or event-related potentials is an important prerequisite for many types of brain-computer interfaces (BCIs). To increase classification accuracy, spatial filters are used to improve the signal-to-noise ratio of the brain signals and thereby facilitate the detection and classification of evoked or event-related potentials. While canonical correlation analysis (CCA) has previously been used to construct spatial filters that increase classification accuracy for BCIs based on visual evoked potentials, we show in this paper, how CCA can also be used for spatial filtering of event-related potentials like P300. We also evaluate the use of CCA for spatial filtering on other data with evoked and event-related potentials and show that CCA performs consistently better than other standard spatial filtering methods.

  18. 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.

  19. 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.

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

    PubMed

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

    2015-08-01

    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. 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. 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). 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.

  1. A new approach for SSVEP detection using PARAFAC and canonical correlation analysis.

    PubMed

    Tello, Richard; Pouryazdian, Saeed; Ferreira, Andre; Beheshti, Soosan; Krishnan, Sridhar; Bastos, Teodiano

    2015-01-01

    This paper presents a new way for automatic detection of SSVEPs through correlation analysis between tensor models. 3-way EEG tensor of channel × frequency × time is decomposed into constituting factor matrices using PARAFAC model. PARAFAC analysis of EEG tensor enables us to decompose multichannel EEG into constituting temporal, spectral and spatial signatures. SSVEPs characterized with localized spectral and spatial signatures are then detected exploiting a correlation analysis between extracted signatures of the EEG tensor and the corresponding simulated signatures of all target SSVEP signals. The SSVEP that has the highest correlation is selected as the intended target. Two flickers blinking at 8 and 13 Hz were used as visual stimuli and the detection was performed based on data packets of 1 second without overlapping. Five subjects participated in the experiments and the highest classification rate of 83.34% was achieved, leading to the Information Transfer Rate (ITR) of 21.01 bits/min.

  2. Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests.

    PubMed

    Anastasiadou, Maria N; Christodoulakis, Manolis; Papathanasiou, Eleftherios S; Papacostas, Savvas S; Mitsis, Georgios D

    2017-09-01

    This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert

  3. 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.

  4. 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

    2017-07-01

    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 (n = 34) and healthy control (HC) subjects (n = 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.

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

    PubMed

    Somers, Ben; Bertrand, Alexander

    2016-12-01

    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. 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. 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. 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.

  6. 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

  7. 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.

  8. 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…

  9. 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.

  10. A multivariate method for estimating cross-frequency neuronal interactions and correcting linear mixing in MEG data, using canonical correlations.

    PubMed

    Soto, Juan L P; Lachaux, Jean-Philippe; Baillet, Sylvain; Jerbi, Karim

    2016-09-15

    Cross-frequency interactions between distinct brain areas have been observed in connection with a variety of cognitive tasks. With electro- and magnetoencephalography (EEG/MEG) data, typical connectivity measures between two brain regions analyze a single quantity from each region within a specific frequency band; given the wideband nature of EEG/MEG signals, many statistical tests may be required to identify true coupling. Furthermore, because of the poor spatial resolution of activity reconstructed from EEG/MEG, some interactions may actually be due to the linear mixing of brain sources. In the present work, a method for the detection of cross-frequency functional connectivity in MEG data using canonical correlation analysis (CCA) is described. We demonstrate that CCA identifies correlated signals and also the frequencies that cause the correlation. We also implement a procedure to deal with linear mixing based on symmetry properties of cross-covariance matrices. Our tests with both simulated and real MEG data demonstrate that CCA is able to detect interacting locations and the frequencies that cause them, while accurately discarding spurious coupling. Recent techniques look at time delays in the activity between two locations to discard spurious interactions, while we propose a linear mixing model and demonstrate its relationship with symmetry aspects of cross-covariance matrices. Our tests indicate the benefits of the CCA approach in connectivity studies, as it allows the simultaneous evaluation of several possible combinations of cross-frequency interactions in a single statistical test. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study.

    PubMed

    Al-Shargie, Fares; Tang, Tong Boon; Kiguchi, Masashi

    2017-05-01

    This paper presents an investigation about the effects of mental stress on prefrontal cortex (PFC) subregions using simultaneous measurement of functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) signals. The aim is to explore canonical correlation analysis (CCA) technique to study the relationship among the bi-modality signals in mental stress assessment, and how we could fuse the signals for better accuracy in stress detection. Twenty-five male healthy subjects participated in the study while performing mental arithmetic task under control and stress (under time pressure with negative feedback) conditions. The fusion of brain signals acquired by fNIRS-EEG was performed at feature-level using CCA by maximizing the inter-subject covariance across modalities. The CCA result discovered the associations across the modalities and estimated the components responsible for these associations. The experiment results showed that mental stress experienced by this cohort of subjects is subregion specific and localized to the right ventrolateral PFC subregion. These suggest the right ventrolateral PFC as a suitable candidate region to extract biomarkers as performance indicators of neurofeedback training in stress coping.

  12. A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials.

    PubMed

    Nakanishi, Masaki; Wang, Yijun; Wang, Yu-Te; Jung, Tzyy-Ping

    2015-01-01

    Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibration. However, the detection performance can be deteriorated by the interference from the spontaneous EEG activities. Recently, various extended methods have been developed to incorporate individual EEG calibration data in CCA to improve the detection performance. Although advantages of the extended CCA methods have been demonstrated in separate studies, a comprehensive comparison between these methods is still missing. This study performed a comparison of the existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment. Classification accuracy and information transfer rate (ITR) were used for performance evaluation. The results suggest that individual calibration data can significantly improve the detection performance. Furthermore, the results showed that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance.

  13. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method.

    PubMed

    Bin, Guangyu; Gao, Xiaorong; Yan, Zheng; Hong, Bo; Gao, Shangkai

    2009-08-01

    In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain-computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP. The key parameters, channel location, window length and the number of harmonics, are investigated using offline data, and the result used to guide the design of the online system. An SSVEP-based BCI system with six targets, which use nine channel locations in the occipital and parietal lobes, a window length of 2 s and the first harmonic, is used for online testing on 12 subjects. The results show that the proposed BCI system has a high performance, achieving an average accuracy of 95.3% and an information transfer rate of 58 +/- 9.6 bit min(-1). The positive characteristics of the proposed system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.

  14. The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique.

    PubMed

    Sweeney, Kevin T; McLoone, Seán F; Ward, Tomás E

    2013-01-01

    Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results.

  15. Extracting drug mechanism and pharmacodynamic information from clinical electroencephalographic data using generalised semi-linear canonical correlation analysis.

    PubMed

    Brain, P; Strimenopoulou, F; Diukova, A; Berry, E; Jolly, A; Hall, J E; Wise, R G; Ivarsson, M; Wilson, F J

    2014-12-01

    Conventional analysis of clinical resting electroencephalography (EEG) recordings typically involves assessment of spectral power in pre-defined frequency bands at specific electrodes. EEG is a potentially useful technique in drug development for measuring the pharmacodynamic (PD) effects of a centrally acting compound and hence to assess the likelihood of success of a novel drug based on pharmacokinetic-pharmacodynamic (PK-PD) principles. However, the need to define the electrodes and spectral bands to be analysed a priori is limiting where the nature of the drug-induced EEG effects is initially not known. We describe the extension to human EEG data of a generalised semi-linear canonical correlation analysis (GSLCCA), developed for small animal data. GSLCCA uses data from the whole spectrum, the entire recording duration and multiple electrodes. It provides interpretable information on the mechanism of drug action and a PD measure suitable for use in PK-PD modelling. Data from a study with low (analgesic) doses of the μ-opioid agonist, remifentanil, in 12 healthy subjects were analysed using conventional spectral edge analysis and GSLCCA. At this low dose, the conventional analysis was unsuccessful but plausible results consistent with previous observations were obtained using GSLCCA, confirming that GSLCCA can be successfully applied to clinical EEG data.

  16. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method

    NASA Astrophysics Data System (ADS)

    Bin, Guangyu; Gao, Xiaorong; Yan, Zheng; Hong, Bo; Gao, Shangkai

    2009-08-01

    In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain-computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP. The key parameters, channel location, window length and the number of harmonics, are investigated using offline data, and the result used to guide the design of the online system. An SSVEP-based BCI system with six targets, which use nine channel locations in the occipital and parietal lobes, a window length of 2 s and the first harmonic, is used for online testing on 12 subjects. The results show that the proposed BCI system has a high performance, achieving an average accuracy of 95.3% and an information transfer rate of 58 ± 9.6 bit min-1. The positive characteristics of the proposed system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.

  17. Sequence detection analysis based on canonical correlation for steady-state visual evoked potential brain computer interfaces.

    PubMed

    Cao, Lei; Ju, Zhengyu; Li, Jie; Jian, Rongjun; Jiang, Changjun

    2015-09-30

    Steady-state visual evoked potential (SSVEP) has been widely applied to develop brain computer interface (BCI) systems. The essence of SSVEP recognition is to recognize the frequency component of target stimulus focused by a subject significantly present in EEG spectrum. In this paper, a novel statistical approach based on sequence detection (SD) is proposed for improving the performance of SSVEP recognition. This method uses canonical correlation analysis (CCA) coefficients to observe SSVEP signal sequence. And then, a threshold strategy is utilized for SSVEP recognition. The result showed the classification performance with the longer duration of time window achieved the higher accuracy for most subjects. And the average time costing per trial was lower than the predefined recognition time. It was implicated that our approach could improve the speed of BCI system in contrast to other methods. Comparison with existing method(s): In comparison with other resultful algorithms, experimental accuracy of SD approach was better than those using a widely used CCA-based method and two newly proposed algorithms, least absolute shrinkage and selection operator (LASSO) recognition model as well as multivariate synchronization index (MSI) method. Furthermore, the information transfer rate (ITR) obtained by SD approach was higher than those using other three methods for most participants. These conclusions demonstrated that our proposed method was promising for a high-speed online BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. 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

  19. 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. 2010 Elsevier Inc. All rights reserved.

  20. 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.

  1. 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.

  2. SU-F-P-64: The Impact of Plan Complexity Parameters On the Plan Quality and Deliverability of Volumetric Modulated Arc Therapy with Canonical Correlation Analysis

    SciTech Connect

    Jin, X; Yi, J; Xie, C

    2016-06-15

    Purpose: To evaluate the impact of complexity indices on the plan quality and deliverability of volumetric modulated arc therapy (VMAT), and to determine the most significant parameters in the generation of an ideal VMAT plan. Methods: A multi-dimensional exploratory statistical method, canonical correlation analysis (CCA) was adopted to study the correlations between VMAT parameters of complexity, quality and deliverability, as well as their contribution weights with 32 two-arc VMAT nasopharyngeal cancer (NPC) patients and 31 one-arc VMAT prostate cancer patients. Results: The MU per arc (MU/Arc) and MU per control point (MU/CP) of NPC were 337.8±25.2 and 3.7±0.3, respectively, which were significantly lower than those of prostate cancer patients (MU/Arc : 506.9±95.4, MU/CP : 5.6±1.1). The plan complexity indices indicated that two-arc VMAT plans were more complex than one-arc VMAT plans. Plan quality comparison confirmed that one-arc VMAT plans had a high quality than two-arc VMAT plans. CCA results implied that plan complexity parameters were highly correlated with plan quality with the first two canonical correlations of 0.96, 0.88 (both p<0.001) and significantly correlated with deliverability with the first canonical correlation of 0.79 (p<0.001), plan quality and deliverability was also correlated with the first canonical correlation of 0.71 (p=0.02). Complexity parameters of MU/CP, segment area (SA) per CP, percent of MU/CP less 3 and planning target volume (PTV) were weighted heavily in correlation with plan quality and deliveability . Similar results obtained from individual NPC and prostate CCA analysis. Conclusion: Relationship between complexity, quality, and deliverability parameters were investigated with CCA. MU, SA related parameters and PTV volume were found to have strong effect on the plan quality and deliverability. The presented correlation among different quantified parameters could be used to improve the plan quality and the efficiency

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. A mobile SSVEP-based brain-computer interface for freely moving humans: the robustness of canonical correlation analysis to motion artifacts.

    PubMed

    Lin, Yuan-Pin; Wang, Yijun; Jung, Tzyy-Ping

    2013-01-01

    Recently, translating a steady-state visual-evoked potential (SSVEP)-based brain-computer interface (BCI) from laboratory settings to real-life applications has gained increasing attention. This study systematically tests the signal quality of SSVEP acquired by a mobile electroencephalogram (EEG) system, which features dry electrodes and wireless telemetry, under challenging (e.g. walking) recording conditions. Empirical results of this study demonstrated the robustness of canonical correlation analysis (CCA) to movement artifacts for SSVEP detection. This demonstration considerably improves the practicality of real-life applications of mobile and wireless BCI systems for users actively behaving in and interacting with their environments.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  10. 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

  11. 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.

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

    PubMed

    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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis.

    PubMed

    Pan, Jie; Gao, Xiaorong; Duan, Fang; Yan, Zheng; Gao, Shangkai

    2011-06-01

    In this study, a novel method of phase constrained canonical correlation analysis (p-CCA) is presented for classifying steady-state visual evoked potentials (SSVEPs) using multichannel electroencephalography (EEG) signals. p-CCA is employed to improve the performance of the SSVEP-based brain-computer interface (BCI) system using standard CCA. SSVEP response phases are estimated based on the physiologically meaningful apparent latency and are added as a reliable constraint into standard CCA. The results of EEG experiments involving 10 subjects demonstrate that p-CCA consistently outperforms standard CCA in classification accuracy. The improvement is up to 6.8% using 1-4 s data segments. The results indicate that the reliable measurement of phase information is of importance in SSVEP-based BCIs.

  18. Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis

    NASA Astrophysics Data System (ADS)

    Pan, Jie; Gao, Xiaorong; Duan, Fang; Yan, Zheng; Gao, Shangkai

    2011-06-01

    In this study, a novel method of phase constrained canonical correlation analysis (p-CCA) is presented for classifying steady-state visual evoked potentials (SSVEPs) using multichannel electroencephalography (EEG) signals. p-CCA is employed to improve the performance of the SSVEP-based brain-computer interface (BCI) system using standard CCA. SSVEP response phases are estimated based on the physiologically meaningful apparent latency and are added as a reliable constraint into standard CCA. The results of EEG experiments involving 10 subjects demonstrate that p-CCA consistently outperforms standard CCA in classification accuracy. The improvement is up to 6.8% using 1-4 s data segments. The results indicate that the reliable measurement of phase information is of importance in SSVEP-based BCIs.

  19. 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.

  20. 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.

  1. Pair natural orbital and canonical coupled cluster reaction enthalpies involving light to heavy alkali and alkaline earth metals: the importance of sub-valence correlation.

    PubMed

    Minenkov, Yury; Bistoni, Giovanni; Riplinger, Christoph; Auer, Alexander A; Neese, Frank; Cavallo, Luigi

    2017-04-05

    In this work, we tested canonical and domain based pair natural orbital coupled cluster methods (CCSD(T) and DLPNO-CCSD(T), respectively) for a set of 32 ligand exchange and association/dissociation reaction enthalpies involving ionic complexes of Li, Be, Na, Mg, Ca, Sr, Ba and Pb(ii). Two strategies were investigated: in the former, only valence electrons were included in the correlation treatment, giving rise to the computationally very efficient FC (frozen core) approach; in the latter, all non-ECP electrons were included in the correlation treatment, giving rise to the AE (all electron) approach. Apart from reactions involving Li and Be, the FC approach resulted in non-homogeneous performance. The FC approach leads to very small errors (<2 kcal mol(-1)) for some reactions of Na, Mg, Ca, Sr, Ba and Pb, while for a few reactions of Ca and Ba deviations up to 40 kcal mol(-1) have been obtained. Large errors are both due to artificial mixing of the core (sub-valence) orbitals of metals and the valence orbitals of oxygen and halogens in the molecular orbitals treated as core, and due to neglecting core-core and core-valence correlation effects. These large errors are reduced to a few kcal mol(-1) if the AE approach is used or the sub-valence orbitals of metals are included in the correlation treatment. On the technical side, the CCSD(T) and DLPNO-CCSD(T) results differ by a fraction of kcal mol(-1), indicating the latter method as the perfect choice when the CPU efficiency is essential. For completely black-box applications, as requested in catalysis or thermochemical calculations, we recommend the DLPNO-CCSD(T) method with all electrons that are not covered by effective core potentials included in the correlation treatment and correlation-consistent polarized core valence basis sets of cc-pwCVQZ(-PP) quality.

  2. The Bargmann transform and canonical transformations

    NASA Astrophysics Data System (ADS)

    Villegas-Blas, Carlos

    2002-05-01

    This paper concerns a relationship between the kernel of the Bargmann transform and the corresponding canonical transformation. We study this fact for a Bargmann transform introduced by Thomas and Wassell [J. Math. Phys. 36, 5480-5505 (1995)]—when the configuration space is the two-sphere S2 and for a Bargmann transform that we introduce for the three-sphere S3. It is shown that the kernel of the Bargmann transform is a power series in a function which is a generating function of the corresponding canonical transformation (a classical analog of the Bargmann transform). We show in each case that our canonical transformation is a composition of two other canonical transformations involving the complex null quadric in C3 or C4. We also describe quantizations of those two other canonical transformations by dealing with spaces of holomorphic functions on the aforementioned null quadrics. Some of these quantizations have been studied by Bargmann and Todorov [J. Math. Phys. 18, 1141-1148 (1977)] and the other quantizations are related to the work of Guillemin [Integ. Eq. Operator Theory 7, 145-205 (1984)]. Since suitable infinite linear combinations of powers of the generating functions are coherent states for L2(S2) or L2(S3), we show finally that the studied Bargmann transforms are actually coherent states transforms.

  3. Defining the Relationship of Student Achievement Between STEM Subjects Through Canonical Correlation Analysis of 2011 Trends in International Mathematics and Science Study (TIMSS) Data

    NASA Astrophysics Data System (ADS)

    O'Neal, Melissa Jean

    Canonical correlation analysis was used to analyze data from Trends in International Mathematics and Science Study (TIMSS) 2011 achievement databases encompassing information from fourth/eighth grades. Student achievement in life science/biology was correlated with achievement in mathematics and other sciences across three analytical areas: mathematics and science student performance, achievement in cognitive domains, and achievement in content domains. Strong correlations between student achievement in life science/biology with achievement in mathematics and overall science occurred for both high- and low-performing education systems. Hence, partial emphases on the inter-subject connections did not always lead to a better student learning outcome in STEM education. In addition, student achievement in life science/biology was positively correlated with achievement in mathematics and science cognitive domains; these patterns held true for correlations of life science/biology with mathematics as well as other sciences. The importance of linking student learning experiences between and within STEM domains to support high performance on TIMSS assessments was indicated by correlations of moderate strength (57 TIMSS assessments was indicated by correlations of moderate strength (57 < r < 85) stronger correlations (73 < r < 97) between life science/biology and other science domains. Results demonstrated the foundational nature of STEM knowledge at the fourth grade level, and established the importance of strong interconnections among life science/biology, mathematics, and other sciences. At the eighth grade level, students who built increasing levels of cognitive complexity upon firm foundations were prepared for successful learning throughout their educational careers. The results from this investigation promote a holistic design of school learning opportunities to improve student achievement in life science/biology and other science, technology, engineering, and

  4. 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…

  5. 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…

  6. 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

  7. A unified canonical correlation analysis-based framework for removing gradient artifact in concurrent EEG/fMRI recording and motion artifact in walking recording from EEG signal.

    PubMed

    Li, Junhua; Chen, Yu; Taya, Fumihiko; Lim, Julian; Wong, Kianfoong; Sun, Yu; Bezerianos, Anastasios

    2017-02-09

    Artifacts cause distortion and fuzziness in electroencephalographic (EEG) signal and hamper EEG analysis, so it is necessary to remove them prior to the analysis. Particularly, artifact removal becomes a critical issue in experimental protocols with significant inherent recording noise, such as mobile EEG recordings and concurrent EEG-fMRI acquisitions. In this paper, we proposed a unified framework based on canonical correlation analysis for artifact removal. Raw signals were reorganized to construct a pair of matrices, based on which sources were sought through maximizing autocorrelation. Those sources related to artifacts were then removed by setting them as zeros, and the remaining sources were used to reconstruct artifact-free EEG. Both simulated and real recorded data were utilized to assess the proposed framework. Qualitative and quantitative results showed that the proposed framework was effective to remove artifacts from EEG signal. Specifically, the proposed method outperformed independent component analysis method for mitigating motion-related artifacts and had advantages for removing gradient artifact compared to the classical method (average artifacts subtraction) and the state-of-the-art method (optimal basis set) in terms of the combination of performance and computational complexity.

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

    PubMed

    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

    2016-07-01

    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. 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. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  9. 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.

  10. Covariant canonical superstrings

    SciTech Connect

    Aratyn, H.; Ingermanson, R.; Niemi, A.J.

    1987-12-01

    A covariant canonical formulation of generic superstrings is presented. The (super)geometry emerges dynamically and supergravity transformations are identified with particular canonical transformations. By construction these transformations are off-shell closed, and the necessary auxiliary fields can be identified with canonical momenta.

  11. Generalized Canonical Time Warping.

    PubMed

    Zhou, Feng; De la Torre, Fernando

    2016-02-01

    Temporal alignment of human motion has been of recent interest due to its applications in animation, tele-rehabilitation and activity recognition. This paper presents generalized canonical time warping (GCTW), an extension of dynamic time warping (DTW) and canonical correlation analysis (CCA) for temporally aligning multi-modal sequences from multiple subjects performing similar activities. GCTW extends previous work on DTW and CCA in several ways: (1) it combines CCA with DTW to align multi-modal data (e.g., video and motion capture data); (2) it extends DTW by using a linear combination of monotonic functions to represent the warping path, providing a more flexible temporal warp. Unlike exact DTW, which has quadratic complexity, we propose a linear time algorithm to minimize GCTW. (3) GCTW allows simultaneous alignment of multiple sequences. Experimental results on aligning multi-modal data, facial expressions, motion capture data and video illustrate the benefits of GCTW. The code is available at http://humansensing.cs.cmu.edu/ctw.

  12. Application of Adjusted Canonical Correlation Analysis (ACCA) to study the association between mathematics in Level 1 and Level 2 and performance of engineering disciplines in Level 2

    NASA Astrophysics Data System (ADS)

    Peiris, T. S. G.; Nanayakkara, K. A. D. S. A.

    2017-09-01

    Mathematics plays a key role in engineering sciences as it assists to develop the intellectual maturity and analytical thinking of engineering students and exploring the student academic performance has received great attention recently. The lack of control over covariates motivates the need for their adjustment when measuring the degree of association between two sets of variables in Canonical Correlation Analysis (CCA). Thus to examine the individual effects of mathematics in Level 1 and Level 2 on engineering performance in Level 2, two adjusted analyses in CCA: Part CCA and Partial CCA were applied for the raw marks of engineering undergraduates for three different disciplines, at the Faculty of Engineering, University of Moratuwa, Sri Lanka. The joint influence of mathematics in Level 1 and Level 2 is significant on engineering performance in Level 2 irrespective of the engineering disciplines. The individual effect of mathematics in Level 2 is significantly higher compared to the individual effect of mathematics in Level 1 on engineering performance in Level 2. Furthermore, the individual effect of mathematics in Level 1 can be negligible. But, there would be a notable indirect effect of mathematics in Level 1 on engineering performance in Level 2. It can be concluded that the joint effect of mathematics in both Level 1 and Level 2 is immensely beneficial to improve the overall academic performance at the end of Level 2 of the engineering students. Furthermore, it was found that the impact mathematics varies among engineering disciplines. As partial CCA and partial CCA are not widely explored in applied work, it is recommended to use these techniques for various applications.

  13. Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia.

    PubMed

    Correa, Nicolle M; Li, Yi-Ou; Adalı, Tülay; Calhoun, Vince D

    2008-12-01

    Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. However, fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. We propose a data fusion scheme at the feature level using canonical correlation analysis (CCA) to determine inter-subject covariations across modalities. As we show both with simulation results and application to real data, multimodal CCA (mCCA) proves to be a flexible and powerful method for discovering associations among various data types. We demonstrate the versatility of the method with application to two datasets, an fMRI and EEG, and an fMRI and sMRI dataset, both collected from patients diagnosed with schizophrenia and healthy controls. CCA results for fMRI and EEG data collected for an auditory oddball task reveal associations of the temporal and motor areas with the N2 and P3 peaks. For the application to fMRI and sMRI data collected for an auditory sensorimotor task, CCA results show an interesting joint relationship between fMRI and gray matter, with patients with schizophrenia showing more functional activity in motor areas and less activity in temporal areas associated with less gray matter as compared to healthy controls. Additionally, we compare our scheme with an independent component analysis based fusion method, joint-ICA that has proven useful for such a study and note that the two methods provide complementary perspectives on data fusion.

  14. [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…

  15. [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…

  16. 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.

  17. Canonical and non-canonical Notch ligands

    PubMed Central

    D’SOUZA, BRENDAN; MELOTY-KAPELLA, LAURENCE; WEINMASTER, GERRY

    2015-01-01

    Notch signaling induced by canonical Notch ligands is critical for normal embryonic development and tissue homeostasis through the regulation of a variety of cell fate decisions and cellular processes. Activation of Notch signaling is normally tightly controlled by direct interactions with ligand-expressing cells and dysregulated Notch signaling is associated with developmental abnormalities and cancer. While canonical Notch ligands are responsible for the majority of Notch signaling, a diverse group of structurally unrelated non-canonical ligands has also been identified that activate Notch and likely contribute to the pleiotropic effects of Notch signaling. Soluble forms of both canonical and non-canonical ligands have been isolated, some of which block Notch signaling and could serve as natural inhibitors of this pathway. Ligand activity can also be indirectly regulated by other signaling pathways at the level of ligand expression, serving to spatio-temporally compartmentalize Notch signaling activity and integrate Notch signaling into a molecular network that orchestrates developmental events. Here, we review the molecular mechanisms underlying the dual role of Notch ligands as activators and inhibitors of Notch signaling. Additionally, evidence that Notch ligands function independent of Notch are presented. We also discuss how ligand post-translational modification, endocytosis, proteolysis and spatio-temporal expression regulate their signaling activity. PMID:20816393

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

    USDA-ARS?s Scientific Manuscript database

    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 ...

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Superselection from canonical constraints

    NASA Astrophysics Data System (ADS)

    Hall, Michael J. W.

    2004-08-01

    The evolution of both quantum and classical ensembles may be described via the probability density P on configuration space, its canonical conjugate S, and an ensemble Hamiltonian \\skew3\\tilde{H}[P,S] . For quantum ensembles this evolution is, of course, equivalent to the Schrödinger equation for the wavefunction, which is linear. However, quite simple constraints on the canonical fields P and S correspond to nonlinear constraints on the wavefunction. Such constraints act to prevent certain superpositions of wavefunctions from being realized, leading to superselection-type rules. Examples leading to superselection for energy, spin direction and 'classicality' are given. The canonical formulation of the equations of motion, in terms of a probability density and its conjugate, provides a universal language for describing classical and quantum ensembles on both continuous and discrete configuration spaces, and is briefly reviewed in an appendix.

  7. 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`.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Macrophages Mediate a Switch between Canonical and Non-Canonical Wnt Pathways in Canine Mammary Tumors

    PubMed Central

    Król, Magdalena; Mucha, Joanna; Majchrzak, Kinga; Homa, Agata; Bulkowska, Małgorzata; Majewska, Alicja; Gajewska, Małgorzata; Pietrzak, Marta; Perszko, Mikołaj; Romanowska, Karolina; Pawłowski, Karol; Manuali, Elisabetta; Hellmen, Eva; Motyl, Tomasz

    2014-01-01

    Objective According to the current hypothesis, tumor-associated macrophages (TAMs) are “corrupted” by cancer cells and subsequently facilitate, rather than inhibit, tumor metastasis. Because the molecular mechanisms of cancer cell–TAM interactions are complicated and controversial we aimed to better define this phenomenon. Methods and Results Using microRNA microarrays, Real-time qPCR and Western blot we showed that co-culture of canine mammary tumor cells with TAMs or treatment with macrophage-conditioned medium inhibited the canonical Wnt pathway and activated the non-canonical Wnt pathway in tumor cells. We also showed that co-culture of TAMs with tumor cells increased expression of canonical Wnt inhibitors in TAMs. Subsequently, we demonstrated macrophage-induced invasive growth patterns and epithelial–mesenchymal transition of tumor cells. Validation of these results in canine mammary carcinoma tissues (n = 50) and xenograft tumors indicated the activation of non-canonical and canonical Wnt pathways in metastatic tumors and non-metastatic malignancies, respectively. Activation of non-canonical Wnt pathway correlated with number of TAMs. Conclusions We demonstrated that TAMs mediate a “switch” between canonical and non-canonical Wnt signaling pathways in canine mammary tumors, leading to increased tumor invasion and metastasis. Interestingly, similar changes in neoplastic cells were observed in the presence of macrophage-conditioned medium or live macrophages. These observations indicate that rather than being “corrupted” by cancer cells, TAMs constitutively secrete canonical Wnt inhibitors that decrease tumor proliferation and development, but as a side effect, they induce the non-canonical Wnt pathway, which leads to tumor metastasis. These data challenge the conventional understanding of TAM–cancer cell interactions. PMID:24404146

  14. 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

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. [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…

  1. 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…

  2. [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…

  3. 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…

  4. Canonical Transformations in Quantum Mechanics

    NASA Astrophysics Data System (ADS)

    Anderson, A.

    1994-06-01

    Quantum canonical transformations are defined algebraically outside of a Hilbert space context. This generalizes the quantum canonical transformations of Weyl and Dirac to include non-unitary transformations. The importance of non-unitary transformations for constructing solutions of the Schrödinger equation is discussed. Three elementary canonical transformations are shown both to have quantum implementations as finite transformations and to generate, classically and infinitesimally, the full canonical algebra. A general canonical transformation can be realized quantum mechanically as a product of these transformations. Each transformation corresponds to a familiar tool used in solving differential equations, and the procedure of solving a differential equation is systematized by the use of the canonical transformations. Several examples are done to illustrate the use of the canonical transformations.

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. 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.

  11. 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 .

  12. 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

  13. Canonical Granger Causality between Regions of Interest

    PubMed Central

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

    2014-01-01

    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 Steifel 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 cortex in cases where standard Granger causality is unable to identify statistically significant interactions. PMID:23811410

  14. 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.

  15. Quantum canonical ensemble: A projection operator approach

    NASA Astrophysics Data System (ADS)

    Magnus, Wim; Lemmens, Lucien; Brosens, Fons

    2017-09-01

    Knowing the exact number of particles N, and taking this knowledge into account, the quantum canonical ensemble imposes a constraint on the occupation number operators. The constraint particularly hampers the systematic calculation of the partition function and any relevant thermodynamic expectation value for arbitrary but fixed N. On the other hand, fixing only the average number of particles, one may remove the above constraint and simply factorize the traces in Fock space into traces over single-particle states. As is well known, that would be the strategy of the grand-canonical ensemble which, however, comes with an additional Lagrange multiplier to impose the average number of particles. The appearance of this multiplier can be avoided by invoking a projection operator that enables a constraint-free computation of the partition function and its derived quantities in the canonical ensemble, at the price of an angular or contour integration. Introduced in the recent past to handle various issues related to particle-number projected statistics, the projection operator approach proves beneficial to a wide variety of problems in condensed matter physics for which the canonical ensemble offers a natural and appropriate environment. In this light, we present a systematic treatment of the canonical ensemble that embeds the projection operator into the formalism of second quantization while explicitly fixing N, the very number of particles rather than the average. Being applicable to both bosonic and fermionic systems in arbitrary dimensions, transparent integral representations are provided for the partition function ZN and the Helmholtz free energy FN as well as for two- and four-point correlation functions. The chemical potential is not a Lagrange multiplier regulating the average particle number but can be extracted from FN+1 -FN, as illustrated for a two-dimensional fermion gas.

  16. Mother canonical tensor model

    NASA Astrophysics Data System (ADS)

    Narain, Gaurav; Sasakura, Naoki

    2017-07-01

    The canonical tensor model (CTM) is a tensor model formulated in the Hamilton formalism as a totally constrained system with first class constraints, the algebraic structure of which is very similar to that of the ADM formalism of general relativity. It has recently been shown that a formal continuum limit of the classical equation of motion of CTM in a derivative expansion of the tensor up to the fourth derivatives agrees with that of a coupled system of general relativity and a scalar field in the Hamilton-Jacobi formalism. This suggests the existence of a ‘mother’ tensor model which derives CTM through the Hamilton-Jacobi procedure, and we have successfully found such a ‘mother’ CTM (mCTM) in this paper. The quantization of the mCTM is as straightforward as the CTM. However, we have not been able to identify all the secondary constraints, and therefore the full structure of the model has been left for future study. Nonetheless, we have found some exact physical wave functions and classical phase spaces, which can be shown to solve the primary and all the (possibly infinite) secondary constraints in the quantum and classical cases, respectively, and have thereby proven the non-triviality of the model. It has also been shown that the mCTM has more interesting dynamics than the CTM from the perspective of randomly connected tensor networks.

  17. Education in optics in Canon

    NASA Astrophysics Data System (ADS)

    Tanaka, Kazuo

    1995-10-01

    The HRD philosophy in R&D in Canon is based on the three-self spirit (self-motivation, self-awareness and self-management). The Canon's R&D engineers are required a positive attitude, creativity and courage to research and to develop new products. Educational measures in optics being in effect in Canon consist of the in-house training courses, studying abroad, publication and others. Several matters which should be noted in education in optics are also discussed; for example, the relationship among geometrical, physical and quantum optics.

  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. 3-D sensitivity kernels of the Rayleigh wave ellipticity

    NASA Astrophysics Data System (ADS)

    Maupin, Valérie

    2017-10-01

    The ellipticity of the Rayleigh wave at the surface depends on the seismic structure beneath and in the vicinity of the seismological station where it is measured. We derive here the expression and compute the 3-D kernels that describe this dependence with respect to S-wave velocity, P-wave velocity and density. Near-field terms as well as coupling to Love waves are included in the expressions. We show that the ellipticity kernels are the difference between the amplitude kernels of the radial and vertical components of motion. They show maximum values close to the station, but with a complex pattern, even when smoothing in a finite-frequency range is used to remove the oscillatory pattern present in mono-frequency kernels. In order to follow the usual data processing flow, we also compute and analyse the kernels of the ellipticity averaged over incoming wave backazimuth. The kernel with respect to P-wave velocity has the simplest lateral variation and is in good agreement with commonly used 1-D kernels. The kernels with respect to S-wave velocity and density are more complex and we have not been able to find a good correlation between the 3-D and 1-D kernels. Although it is clear that the ellipticity is mostly sensitive to the structure within half-a-wavelength of the station, the complexity of the kernels within this zone prevents simple approximations like a depth dependence times a lateral variation to be useful in the inversion of the ellipticity.

  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. 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.

  2. Canonical Chern-Simons gravity

    NASA Astrophysics Data System (ADS)

    Sarkar, Souvik; Vaz, Cenalo

    2017-07-01

    We study the canonical description of the axisymmetric vacuum in 2 +1 -dimensional gravity, treating Einstein's gravity as a Chern-Simons gauge theory on a manifold with the restriction that the dreibein is invertible. Our treatment is in the spirit of Kuchař's description of the Schwarzschild black hole in 3 +1 dimensions, where the mass and angular momentum are expressed in terms of the canonical variables and a series of canonical transformations that turn the curvature coordinates and their conjugate momenta into new canonical variables is performed. In their final form, the constraints are seen to require that the momenta conjugate to the Killing time and curvature radius vanish, and what remains is the mass, the angular momentum, and their conjugate momenta, which we derive. The Wheeler-DeWitt equation is trivial and describes time independent systems with wave functions described only by the total mass and total angular momentum.

  3. 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...

  4. Widely Linear Complex-Valued Kernel Methods for Regression

    NASA Astrophysics Data System (ADS)

    Boloix-Tortosa, Rafael; Murillo-Fuentes, Juan Jose; Santos, Irene; Perez-Cruz, Fernando

    2017-10-01

    Usually, complex-valued RKHS are presented as an straightforward application of the real-valued case. In this paper we prove that this procedure yields a limited solution for regression. We show that another kernel, here denoted as pseudo kernel, is needed to learn any function in complex-valued fields. Accordingly, we derive a novel RKHS to include it, the widely RKHS (WRKHS). When the pseudo-kernel cancels, WRKHS reduces to complex-valued RKHS of previous approaches. We address the kernel and pseudo-kernel design, paying attention to the kernel and the pseudo-kernel being complex-valued. In the experiments included we report remarkable improvements in simple scenarios where real a imaginary parts have different similitude relations for given inputs or cases where real and imaginary parts are correlated. In the context of these novel results we revisit the problem of non-linear channel equalization, to show that the WRKHS helps to design more efficient solutions.

  5. 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.

  6. 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.

  7. 7 CFR 981.9 - Kernel weight.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels, including pieces and particles, regardless of whether edible or inedible, contained in any lot of almonds...

  8. 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.

  9. 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

  10. On the implementation of the canonical quantum simplicity constraint

    NASA Astrophysics Data System (ADS)

    Bodendorfer, N.; Thiemann, T.; Thurn, A.

    2013-02-01

    In this paper, we discuss several approaches to solve the quadratic and linear simplicity constraints in the context of the canonical formulations of higher dimensional general relativity and supergravity developed in our companion papers. Since the canonical quadratic simplicity constraint operators have been shown to be anomalous in any dimension D ⩾ 3 in Class. Quantum Grav. 30 045003, non-standard methods have to be employed to avoid inconsistencies in the quantum theory. We show that one can choose a subset of quadratic simplicity constraint operators which are non-anomalous among themselves and allow for a natural unitary map of the spin networks in the kernel of these simplicity constraint operators to the SU(2)-based Ashtekar-Lewandowski Hilbert space in D = 3. The linear constraint operators on the other hand are non-anomalous by themselves; however, their solution space is shown to differ in D = 3 from the expected Ashtekar-Lewandowski Hilbert space. We comment on possible strategies to make a connection to the quadratic theory. Also, we comment on the relation of our proposals to the existing work in the spin foam literature and how these works could be used in the canonical theory. We emphasize that many ideas developed in this paper are certainly incomplete and should be considered as suggestions for possible starting points for more satisfactory treatments in the future.

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

    NASA Astrophysics Data System (ADS)

    Montoya-Castillo, Andrés; Reichman, David R.

    2017-02-01

    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 Cz z(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.

  12. 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.

  13. Robotic intelligence kernel

    DOEpatents

    Bruemmer, David J [Idaho Falls, ID

    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. Spherically symmetric canonical quantum gravity

    NASA Astrophysics Data System (ADS)

    Brahma, Suddhasattwa

    2015-06-01

    Canonical quantization of spherically symmetric space-times is carried out, using real-valued densitized triads and extrinsic curvature components, with specific factor-ordering choices ensuring in an anomaly free quantum constraint algebra. Comparison with previous work [Nucl. Phys. B399, 211 (1993)] reveals that the resulting physical Hilbert space has the same form, although the basic canonical variables are different in the two approaches. As an extension, holonomy modifications from loop quantum gravity are shown to deform the Dirac space-time algebra, while going beyond "effective" calculations.

  16. 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.

  17. [Proportion of aflatoxin B1 contaminated kernels and its concentration in imported peanut samples].

    PubMed

    Hirano, S; Shima, T; Shimada, T

    2001-08-01

    Moldy and split peanut kernels were separated from peanuts exported from Brazil, Sudan, India and Taiwan by visual inspection. The remaining peanuts from Brazil, Sudan and India were roasted lightly and the skins were removed. Stained peanuts were separated from the others. Aflatoxin was detected in moldy and stained peanuts. There was a positive correlation between % of aflatoxin-contaminated peanut kernels and aflatoxin B1 concentration in whole samples. Aflatoxin concentration of moldy peanuts was higher than that of stained peanut kernels.

  18. Non-canonical generalizations of slow-roll inflation models

    NASA Astrophysics Data System (ADS)

    Tzirakis, Konstantinos; Kinney, William H.

    2009-01-01

    We consider non-canonical generalizations of two classes of single-field inflation models. First, we study the non-canonical version of ''ultra-slow roll'' inflation, which is a class of inflation models for which quantum modes do not freeze at horizon crossing, but instead evolve rapidly on superhorizon scales. Second, we consider the non-canonical generalization of the simplest ''chaotic'' inflation scenario, with a potential dominated by a quadratic (mass) term for the inflaton. We find a class of related non-canonical solutions with polynomial potentials, but with varying speed of sound. These solutions are characterized by a constant field velocity, and we dub such models isokinetic inflation. As in the canonical limit, isokinetic inflation has a slightly red-tilted power spectrum, consistent with current data. Unlike the canonical case, however, these models can have an arbitrarily small tensor/scalar ratio. Of particular interest is that isokinetic inflation is marked by a correlation between the tensor/scalar ratio and the amplitude of non-Gaussianity such that parameter regimes with small tensor/scalar ratio have large associated non-Gaussianity, which is a distinct observational signature.

  19. 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)

  20. 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)

  1. 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…

  2. Symmetric minimally entangled typical thermal states for canonical and grand-canonical ensembles

    NASA Astrophysics Data System (ADS)

    Binder, Moritz; Barthel, Thomas

    2017-05-01

    Based on the density matrix renormalization group (DMRG), strongly correlated quantum many-body systems at finite temperatures can be simulated by sampling over a certain class of pure matrix product states (MPS) called minimally entangled typical thermal states (METTS). When a system features symmetries, these can be utilized to substantially reduce MPS computation costs. It is conceptually straightforward to simulate canonical ensembles using symmetric METTS. In practice, it is important to alternate between different symmetric collapse bases to decrease autocorrelations in the Markov chain of METTS. To this purpose, we introduce symmetric Fourier and Haar-random block bases that are efficiently mixing. We also show how grand-canonical ensembles can be simulated efficiently with symmetric METTS. We demonstrate these approaches for spin-1 /2 X X Z chains and discuss how the choice of the collapse bases influences autocorrelations as well as the distribution of measurement values and, hence, convergence speeds.

  3. 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.

  4. 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...

  5. 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....

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. A canonical form for nonlinear systems

    NASA Technical Reports Server (NTRS)

    Su, R.; Hunt, L. R.

    1985-01-01

    The conceptions of transformation and canonical form have been much used to analyze the structure of linear systems. A coordinate system and a corresponding canonical form are developed for general nonlinear control systems. Their usefulness is demonstrated by showing that every feedback linearizable system becomes a system with only feedback paths in the canonical form.

  14. 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.

  15. A Kernel-based Lagrangian method for imperfectly-mixed chemical reactions

    NASA Astrophysics Data System (ADS)

    Schmidt, Michael J.; Pankavich, Stephen; Benson, David A.

    2017-05-01

    Current Lagrangian (particle-tracking) algorithms used to simulate diffusion-reaction equations must employ a certain number of particles to properly emulate the system dynamics-particularly for imperfectly-mixed systems. The number of particles is tied to the statistics of the initial concentration fields of the system at hand. Systems with shorter-range correlation and/or smaller concentration variance require more particles, potentially limiting the computational feasibility of the method. For the well-known problem of bimolecular reaction, we show that using kernel-based, rather than Dirac delta, particles can significantly reduce the required number of particles. We derive the fixed width of a Gaussian kernel for a given reduced number of particles that analytically eliminates the error between kernel and Dirac solutions at any specified time. We also show how to solve for the fixed kernel size by minimizing the squared differences between solutions over any given time interval. Numerical results show that the width of the kernel should be kept below about 12% of the domain size, and that the analytic equations used to derive kernel width suffer significantly from the neglect of higher-order moments. The simulations with a kernel width given by least squares minimization perform better than those made to match at one specific time. A heuristic time-variable kernel size, based on the previous results, performs on par with the least squares fixed kernel size.

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

    PubMed

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

    2009-08-01

    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. 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. 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. The effects of pollen source on both embryo/kernel ratio and allocation of embryo chemicals seems to be related to the early established sink strength (i.e. sink size and sink activity) of the

  17. 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.

  18. 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.

  19. 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.

  20. Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies.

    PubMed

    Hua, Wen-Yu; Ghosh, Debashis

    2015-09-01

    Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression framework that models covariate effects parametrically and genetic markers non-parametrically, while KDC represents a class of methods that include distance covariance (DC) and Hilbert-Schmidt independence criterion (HSIC), which are nonparametric tests of independence. We show that the equivalence between the score test of KMR and the KDC statistic under certain conditions can lead to a novel generalization of the KDC test that incorporates covariates. Our contributions are 3-fold: (1) establishing the equivalence between KMR and KDC; (2) showing that the principles of KMR can be applied to the interpretation of KDC; (3) the development of a broader class of KDC statistics, where the class members are statistics corresponding to different kernel combinations. Finally, we perform simulation studies and an analysis of real data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The ADNI study suggest that SNPs of FLJ16124 exhibit pairwise interaction effects that are strongly correlated to the changes of brain region volumes. © 2015, The International Biometric Society.

  1. 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.

  2. Canonical trivialization of gravitational gradients

    NASA Astrophysics Data System (ADS)

    Niedermaier, Max

    2017-06-01

    A one-parameter family of canonical transformations is constructed that reduces the Hamiltonian form of the Einstein-Hilbert action to its strong coupling limit where dynamical spatial gradients are absent. The parameter can alternatively be viewed as the overall scale of the spatial metric or as a fractional inverse power of Newton’s constant. The generating function of the canonical transformation is constructed iteratively as a powerseries in the parameter to all orders. The algorithm draws on Lie-Deprit transformation theory and defines a ‘trivialization map’ with several bonus properties: (i) Trivialization of the Hamiltonian constraint implies that of the action while the diffeomorphism constraint is automatically co-transformed. (ii) Only a set of ordinary differential equations needs to be solved to drive the iteration via a homological equation where no gauge fixing is required. (iii) In contrast to (the classical limit of) a Lagrangian trivialization map the algorithm also produces series solutions of the field equations. (iv) In the strong coupling theory temporal gauge variations are abelian, nevertheless the map intertwines with the respective gauge symmetries on the action, the field equations, and their solutions.

  3. 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

  4. 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…

  5. 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...

  6. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...

  7. 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.

  8. Polar lipids from oat kernels

    USDA-ARS?s Scientific Manuscript database

    Oat (Avena sativa L.) kernels appear to contain much higher polar lipid concentrations than other plant tissues. We have extracted, identified, and quantified polar lipids from 18 oat genotypes grown in replicated plots in three environments in order to determine genotypic or environmental variation...

  9. Adaptive wiener image restoration kernel

    DOEpatents

    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.

  10. Diffusion Kernels on Statistical Manifolds

    DTIC Science & Technology

    2004-01-16

    International Press, 1994. Michael Spivak . Differential Geometry, volume 1. Publish or Perish, 1979. 36 Chengxiang Zhai and John Lafferty. A study of smoothing...construction of information diffusion kernels, since these concepts are not widely used in machine learning. We refer to Spivak (1979) for details and further

  11. A canonical approach to forces in molecules

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

    In previous studies, we introduced a generalized formulation for canonical transformations and spectra to investigate the concept of canonical potentials strictly within the Born-Oppenheimer approximation. Data for the most accurate available ground electronic state pairwise intramolecular potentials in H2+, H2, HeH+, and LiH were used to rigorously establish such conclusions. Now, a canonical transformation is derived for the molecular force, F(R), with H2+ as molecular reference. These transformations are demonstrated to be inherently canonical to high accuracy but distinctly different from those corresponding to the respective potentials of H2, HeH+, and LiH. In this paper, we establish the canonical nature of the molecular force which is key to fundamental generalization of canonical approaches to molecular bonding. As further examples Mg2, benzene dimer and to water dimer are also considered within the radial limit as applications of the current methodology.

  12. The canonical Wnt signaling pathway in autism.

    PubMed

    Zhang, Yinghua; Yuan, Xiangshan; Wang, Zhongping; Li, Ruixi

    2014-01-01

    Mounting attention is being focused on the canonical Wnt signaling pathway which has been implicated in the pathogenesis of autism in some our and other recent studies. The canonical Wnt pathway is involved in cell proliferation, differentiation and migration, especially during nervous system development. Given its various functions, dysfunction of the canonical Wnt pathway may exert adverse effects on neurodevelopment and therefore leads to the pathogenesis of autism. Here, we review human and animal studies that implicate the canonical Wnt signal transduction pathway in the pathogenesis of autism. We also describe the crosstalk between the canonical Wnt pathway and the Notch signaling pathway in several types of autism spectrum disorders, including Asperger syndrome and Fragile X. Further research on the crosstalk between the canonical Wnt signaling pathway and other signaling cascades in autism may be an efficient avenue to understand the etiology of autism and ultimately lead to alternative medications for autism-like phenotypes.

  13. A canonical form for nonlinear systems

    NASA Technical Reports Server (NTRS)

    Su, R.; Hunt, L. R.

    1986-01-01

    The concepts of transformation and canonical form have been used in analyzing linear systems. These ideas are extended to nonlinear systems. A coordinate system and a corresponding canonical form are developed for general nonlinear control systems. Their usefulness is demonstrated by showing that every feedback linearizable system becomes a system with only feedback paths in the canonical form. For control design involving a nonlinear system, one approach is to put the system in its canonical form and approximate by that part having only feedback paths.

  14. 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.

  15. 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

  16. Calculations and Canonical Elements: Part I.

    ERIC Educational Resources Information Center

    Stewart, Ian; Tall, David

    1979-01-01

    The author argues that the idea of canonical elements provides a coherent relationship between equivalence relations, the basis of modern approaches to too many mathematical topics, and the traditional aspect of computation. Examples of equivalent relations, canonical elements, and their calculations are given. (MK)

  17. 37 CFR 10.100 - Canon 8.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Canon 8. 10.100 Section 10.100 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF COMMERCE REPRESENTATION OF OTHERS BEFORE THE PATENT AND TRADEMARK OFFICE Patent and Trademark Office Code of Professional Responsibility § 10.100 Canon 8....

  18. 37 CFR 10.83 - Canon 7.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Canon 7. 10.83 Section 10.83 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF COMMERCE... Responsibility § 10.83 Canon 7. A practitioner should represent a client zealously within the bounds of the law. ...

  19. 37 CFR 10.83 - Canon 7.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 37 Patents, Trademarks, and Copyrights 1 2012-07-01 2012-07-01 false Canon 7. 10.83 Section 10.83 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF COMMERCE... Responsibility § 10.83 Canon 7. A practitioner should represent a client zealously within the bounds of the law. ...

  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. 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.…

  2. 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.

  3. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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). ...

  5. 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.

  6. 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.

  7. QTL mapping of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum L.).

    PubMed

    Ramya, P; Chaubal, A; Kulkarni, K; Gupta, L; Kadoo, N; Dhaliwal, H S; Chhuneja, P; Lagu, M; Gupta, V

    2010-01-01

    Kernel size and morphology influence the market value and milling yield of bread wheat (Triticum aestivum L.). The objective of this study was to identify quantitative trait loci (QTLs) controlling kernel traits in hexaploid wheat. We recorded 1000-kernel weight, kernel length, and kernel width for 185 recombinant inbred lines from the cross Rye Selection 111 × Chinese Spring grown in 2 agro-climatic regions in India for many years. Composite interval mapping (CIM) was employed for QTL detection using a linkage map with 169 simple sequence repeat (SSR) markers. For 1000-kernel weight, 10 QTLs were identified on wheat chromosomes 1A, 1D, 2B, 2D, 4B, 5B, and 6B, whereas 6 QTLs for kernel length were detected on 1A, 2B, 2D, 5A, 5B and 5D. Chromosomes 1D, 2B, 2D, 4B, 5B and 5D had 9 QTLs for kernel width. Chromosomal regions with QTLs detected consistently for multiple year-location combinations were identified for each trait. Pleiotropic QTLs were found on chromosomes 2B, 2D, 4B, and 5B. The identified genomic regions controlling wheat kernel size and shape can be targeted during further studies for their genetic dissection.

  8. 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.

  9. Filters, reproducing kernel, and adaptive meshfree method

    NASA Astrophysics Data System (ADS)

    You, Y.; Chen, J.-S.; Lu, H.

    Reproducing kernel, with its intrinsic feature of moving averaging, can be utilized as a low-pass filter with scale decomposition capability. The discrete convolution of two nth order reproducing kernels with arbitrary support size in each kernel results in a filtered reproducing kernel function that has the same reproducing order. This property is utilized to separate the numerical solution into an unfiltered lower order portion and a filtered higher order portion. As such, the corresponding high-pass filter of this reproducing kernel filter can be used to identify the locations of high gradient, and consequently serves as an operator for error indication in meshfree analysis. In conjunction with the naturally conforming property of the reproducing kernel approximation, a meshfree adaptivity method is also proposed.

  10. Image texture analysis of crushed wheat kernels

    NASA Astrophysics Data System (ADS)

    Zayas, Inna Y.; Martin, C. R.; Steele, James L.; Dempster, Richard E.

    1992-03-01

    The development of new approaches for wheat hardness assessment may impact the grain industry in marketing, milling, and breeding. This study used image texture features for wheat hardness evaluation. Application of digital imaging to grain for grading purposes is principally based on morphometrical (shape and size) characteristics of the kernels. A composite sample of 320 kernels for 17 wheat varieties were collected after testing and crushing with a single kernel hardness characterization meter. Six wheat classes where represented: HRW, HRS, SRW, SWW, Durum, and Club. In this study, parameters which characterize texture or spatial distribution of gray levels of an image were determined and used to classify images of crushed wheat kernels. The texture parameters of crushed wheat kernel images were different depending on class, hardness and variety of the wheat. Image texture analysis of crushed wheat kernels showed promise for use in class, hardness, milling quality, and variety discrimination.

  11. 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...

  12. Several new kernel estimators for population abundance

    NASA Astrophysics Data System (ADS)

    Albadareen, Baker; Ismail, Noriszura

    2017-04-01

    The parameter f(0) is crucial in line transect sampling which is regularly used for computing population abundance in wildlife. The usual kernel estimator of f(0) has a high negative bias. Our study proposes several new estimators which are shown to be more efficient than the usual kernel estimator. A simulation technique is adopted to compare the performance of the proposed estimators with the classical kernel estimator. An application of the new estimators on real data set is discussed.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. Kernel earth mover's distance for EEG classification.

    PubMed

    Daliri, Mohammad Reza

    2013-07-01

    Here, we propose a new kernel approach based on the earth mover's distance (EMD) for electroencephalography (EEG) signal classification. The EEG time series are first transformed into histograms in this approach. The distance between these histograms is then computed using the EMD in a pair-wise manner. We bring the distances into a kernel form called kernel EMD. The support vector classifier can then be used for the classification of EEG signals. The experimental results on the real EEG data show that the new kernel method is very effective, and can classify the data with higher accuracy than traditional methods.

  18. Modeling an Operating System Kernel

    NASA Astrophysics Data System (ADS)

    Börger, Egon; Craig, Iain

    We define a high-level model of an operating system (OS) kernel which can be refined to concrete systems in various ways, reflecting alternative design decisions. We aim at an exposition practitioners and lecturers can use effectively to communicate (document and teach) design ideas for operating system functionality at a conceptual level. The operational and rigorous nature of our definition provides a basis for the practitioner to validate and verify precisely stated system properties of interest, thus helping to make OS code reliable. As a by-product we introduce a novel combination of parallel and interruptable sequential Abstract State Machine steps.

  19. 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.

  20. 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.

  1. 37 CFR 10.56 - Canon 4.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... REPRESENTATION OF OTHERS BEFORE THE PATENT AND TRADEMARK OFFICE Patent and Trademark Office Code of Professional Responsibility § 10.56 Canon 4. A practitioner should preserve the confidences and secrets of a client. ...

  2. 37 CFR 10.46 - Canon 3.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... REPRESENTATION OF OTHERS BEFORE THE PATENT AND TRADEMARK OFFICE Patent and Trademark Office Code of Professional Responsibility § 10.46 Canon 3. A practitioner should assist in preventing the unauthorized practice of law. ...

  3. 37 CFR 10.21 - Canon 1.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... REPRESENTATION OF OTHERS BEFORE THE PATENT AND TRADEMARK OFFICE Patent and Trademark Office Code of Professional Responsibility § 10.21 Canon 1. A practitioner should assist in maintaining the integrity and competence of the...

  4. 37 CFR 10.61 - Canon 5.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... REPRESENTATION OF OTHERS BEFORE THE PATENT AND TRADEMARK OFFICE Patent and Trademark Office Code of Professional Responsibility § 10.61 Canon 5. A practitioner should exercise independent professional judgment on behalf of a...

  5. 37 CFR 10.76 - Canon 6.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... REPRESENTATION OF OTHERS BEFORE THE PATENT AND TRADEMARK OFFICE Patent and Trademark Office Code of Professional Responsibility § 10.76 Canon 6. A practitioner should represent a client competently. ...

  6. 37 CFR 10.30 - Canon 2.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... REPRESENTATION OF OTHERS BEFORE THE PATENT AND TRADEMARK OFFICE Patent and Trademark Office Code of Professional Responsibility § 10.30 Canon 2. A practitioner should assist the legal profession in fulfilling its duty to make...

  7. 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.

  8. 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.

  9. 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.

  10. 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...

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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.

  16. 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.

  17. 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.

  18. Zebrafish Naked 1 and Naked 2 antagonize both canonical and non-canonical Wnt signaling

    PubMed Central

    Van Raay, Terence J.; Coffey, Robert J.; Solnica-Krezel, Lilianna

    2007-01-01

    Wnt signaling controls a wide range of developmental processes and its aberrant regulation can lead to disease. To better understand the regulation of this pathway, we identified zebrafish homologues of Naked Cuticle (Nkd), Nkd1 and Nkd2, which have previously been shown to inhibit canonical Wnt/β-catenin signaling. Zebrafish nkd1 expression increases substantially after the mid-blastula transition in a pattern mirroring that of activated canonical Wnt/β-catenin signaling, being expressed in both the ventrolateral blastoderm margin and also in the axial mesendoderm. In contrast, zebrafish nkd2 is maternally and ubiquitously expressed. Overexpression of Nkd1 or Nkd2a suppressed canonical Wnt/β-catenin signaling at multiple stages of early zebrafish development and also exacerbated the cyclopia and axial mesendoderm convergence and extension (C&E) defect in the non-canonical Wnt/PCP mutant silberblick (slb/wnt11). Thus, Nkds are sufficient to antagonize both canonical and non-canonical Wnt signaling. Reducing Nkd function using antisense morpholino oligonucleotides resulted in increased expression of canonical Wnt/β-catenin target genes. Finally, reducing Nkd1 function in slb mutants suppressed the axial mesendoderm C&E defect. These data indicate that zebrafish Nkd1 and Nkd2 function to limit both canonical and non-canonical Wnt signaling. PMID:17689523

  19. 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…

  20. 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

  1. 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.

  2. 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…

  3. 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. © 2012 Wiley Periodicals, Inc.

  4. 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.

  5. 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.

  6. Fruit position within the canopy affects kernel lipid composition of hazelnuts.

    PubMed

    Pannico, Antonio; Cirillo, Chiara; Giaccone, Matteo; Scognamiglio, Pasquale; Romano, Raffaele; Caporaso, Nicola; Sacchi, Raffaele; Basile, Boris

    2017-04-04

    The aim of this research was to study the variability in kernel composition within the canopy of hazelnut trees. Kernel fresh and dry weight increased linearly with fruit height above the ground. Fat content decreased, while protein and ash content increased, from the bottom to the top layers of the canopy. The level of unsaturation of fatty acids decreased from the bottom to the top of the canopy. Thus, the kernels located in the bottom layers of the canopy appear to be more interesting from a nutritional point of view, but their lipids may be more exposed to oxidation. The content of different phytosterols increased progressively from bottom to top canopy layers. Most of these effects correlated with the pattern in light distribution inside the canopy. The results of this study indicate that fruit position within the canopy is an important factor in determining hazelnut kernel growth and composition. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

  7. 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.

  8. Mapping quantitative trait loci for kernel composition in almond

    PubMed Central

    2012-01-01

    Background Almond breeding is increasingly taking into account kernel quality as a breeding objective. Information on the parameters to be considered in evaluating almond quality, such as protein and oil content, as well as oleic acid and tocopherol concentration, has been recently compiled. The genetic control of these traits has not yet been studied in almond, although this information would improve the efficiency of almond breeding programs. Results A map with 56 simple sequence repeat or microsatellite (SSR) markers was constructed for an almond population showing a wide range of variability for the chemical components of the almond kernel. A total of 12 putative quantitative trait loci (QTL) controlling these chemical traits have been detected in this analysis, corresponding to seven genomic regions of the eight almond linkage groups (LG). Some QTL were clustered in the same region or shared the same molecular markers, according to the correlations already found between the chemical traits. The logarithm of the odds (LOD) values for any given trait ranged from 2.12 to 4.87, explaining from 11.0 to 33.1 % of the phenotypic variance of the trait. Conclusions The results produced in the study offer the opportunity to include the new genetic information in almond breeding programs. Increases in the positive traits of kernel quality may be looked for simultaneously whenever they are genetically independent, even if they are negatively correlated. We have provided the first genetic framework for the chemical components of the almond kernel, with twelve QTL in agreement with the large number of genes controlling their metabolism. PMID:22720975

  9. Mapping quantitative trait loci for kernel composition in almond.

    PubMed

    Font i Forcada, Carolina; Fernández i Martí, Angel; Socias i Company, Rafel

    2012-06-21

    Almond breeding is increasingly taking into account kernel quality as a breeding objective. Information on the parameters to be considered in evaluating almond quality, such as protein and oil content, as well as oleic acid and tocopherol concentration, has been recently compiled. The genetic control of these traits has not yet been studied in almond, although this information would improve the efficiency of almond breeding programs. A map with 56 simple sequence repeat or microsatellite (SSR) markers was constructed for an almond population showing a wide range of variability for the chemical components of the almond kernel. A total of 12 putative quantitative trait loci (QTL) controlling these chemical traits have been detected in this analysis, corresponding to seven genomic regions of the eight almond linkage groups (LG). Some QTL were clustered in the same region or shared the same molecular markers, according to the correlations already found between the chemical traits. The logarithm of the odds (LOD) values for any given trait ranged from 2.12 to 4.87, explaining from 11.0 to 33.1 % of the phenotypic variance of the trait. The results produced in the study offer the opportunity to include the new genetic information in almond breeding programs. Increases in the positive traits of kernel quality may be looked for simultaneously whenever they are genetically independent, even if they are negatively correlated. We have provided the first genetic framework for the chemical components of the almond kernel, with twelve QTL in agreement with the large number of genes controlling their metabolism.

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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%.

  16. 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

  17. 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

  18. Canonical forms of multidimensional steady inviscid flows

    NASA Technical Reports Server (NTRS)

    Taasan, Shlomo

    1993-01-01

    Canonical forms and canonical variables for inviscid flow problems are derived. In these forms the components of the system governed by different types of operators (elliptic and hyperbolic) are separated. Both the incompressible and compressible cases are analyzed, and their similarities and differences are discussed. The canonical forms obtained are block upper triangular operator form in which the elliptic and non-elliptic parts reside in different blocks. The full nonlinear equations are treated without using any linearization process. This form enables a better analysis of the equations as well as better numerical treatment. These forms are the analog of the decomposition of the one dimensional Euler equations into characteristic directions and Riemann invariants.

  19. Approximating W projection as a separable kernel

    NASA Astrophysics Data System (ADS)

    Merry, Bruce

    2016-02-01

    W projection is a commonly used approach to allow interferometric imaging to be accelerated by fast Fourier transforms, but it can require a huge amount of storage for convolution kernels. The kernels are not separable, but we show that they can be closely approximated by separable kernels. The error scales with the fourth power of the field of view, and so is small enough to be ignored at mid- to high frequencies. We also show that hybrid imaging algorithms combining W projection with either faceting, snapshotting, or W stacking allow the error to be made arbitrarily small, making the approximation suitable even for high-resolution wide-field instruments.

  20. Invariance kernel of biological regulatory networks.

    PubMed

    Ahmad, Jamil; Roux, Olivier

    2010-01-01

    The analysis of Biological Regulatory Network (BRN) leads to the computing of the set of the possible behaviours of the biological components. These behaviours are seen as trajectories and we are specifically interested in cyclic trajectories since they stand for stability. The set of cycles is given by the so-called invariance kernel of a BRN. This paper presents a method for deriving symbolic formulae for the length, volume and diameter of a cylindrical invariance kernel. These formulae are expressed in terms of delay parameters expressions and give the existence of an invariance kernel and a hint of the number of cyclic trajectories.

  1. The Kernel Energy Method: Construction of 3 & 4 tuple Kernels from a List of Double Kernel Interactions

    PubMed Central

    Huang, Lulu; Massa, Lou

    2010-01-01

    The Kernel Energy Method (KEM) provides a way to calculate the ab-initio energy of very large biological molecules. The results are accurate, and the computational time reduced. However, by use of a list of double kernel interactions a significant additional reduction of computational effort may be achieved, still retaining ab-initio accuracy. A numerical comparison of the indices that name the known double interactions in question, allow one to list higher order interactions having the property of topological continuity within the full molecule of interest. When, that list of interactions is unpacked, as a kernel expansion, which weights the relative importance of each kernel in an expression for the total molecular energy, high accuracy, and a further significant reduction in computational effort results. A KEM molecular energy calculation based upon the HF/STO3G chemical model, is applied to the protein insulin, as an illustration. PMID:21243065

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. Kernel map compression for speeding the execution of kernel-based methods.

    PubMed

    Arif, Omar; Vela, Patricio A

    2011-06-01

    The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machines. Unfortunately, after learning, the computational complexity of execution through a kernel is of the order of the size of the training set, which is quite large for many applications. This paper proposes a two-step procedure for arriving at a compact and computationally efficient execution procedure. After learning in the kernel space, the proposed extension exploits the universal approximation capabilities of generalized radial basis function neural networks to efficiently approximate and replace the projections onto the empirical kernel map used during execution. Sample applications demonstrate significant compression of the kernel representation with graceful performance loss.

  9. Canonical Correlations between Dimensions of Acculturation and Psychological Adjustment.

    ERIC Educational Resources Information Center

    Orozco, Sergio; Freidrich, Katherine R.

    The relationship between factors underlying a measure of acculturation, the Acculturation Rating Scale for Mexican Americans (ARSMA), and the 566-item Minnesota Multiphasic Personality Inventory (MMPI) was studied. The ARSMA consists of 20 questions that are scored on a 5-point Likert scale ranging from Mexican oriented (1) to Anglo oriented (5).…

  10. 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

  11. 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...

  12. 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...

  13. 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 lot...

  14. 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 color...

  15. 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 color...

  16. 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 lot...

  17. Generalization between canonical and non-canonical views in object recognition

    PubMed Central

    Ghose, Tandra; Liu, Zili

    2013-01-01

    Viewpoint generalization in object recognition is the process that allows recognition of a given 3D object from many different viewpoints despite variations in its 2D projections. We used the canonical view effects as a foundation to empirically test the validity of a major theory in object recognition, the view-approximation model (Poggio & Edelman, 1990). This model predicts that generalization should be better when an object is first seen from a non-canonical view and then a canonical view than when seen in the reversed order. We also manipulated object similarity to study the degree to which this view generalization was constrained by shape details and task instructions (object vs. image recognition). Old-new recognition performance for basic and subordinate level objects was measured in separate blocks. We found that for object recognition, view generalization between canonical and non-canonical views was comparable for basic level objects. For subordinate level objects, recognition performance was more accurate from non-canonical to canonical views than the other way around. When the task was changed from object recognition to image recognition, the pattern of the results reversed. Interestingly, participants responded “old” to “new” images of “old” objects with a substantially higher rate than to “new” objects, despite instructions to the contrary, thereby indicating involuntary view generalization. Our empirical findings are incompatible with the prediction of the view-approximation theory, and argue against the hypothesis that views are stored independently. PMID:23283692

  18. Differential expression of canonical and non-canonical Wnt ligands in ameloblastoma.

    PubMed

    Siar, Chong Huat; Nagatsuka, Hitoshi; Han, Phuu Pwint; Buery, Rosario Rivera; Tsujigiwa, Hidetsugu; Nakano, Keisuke; Ng, Kok Han; Kawakami, Toshiyuki

    2012-04-01

    Canonical and non-canonical Wnt signaling pathways modulate diverse cellular processes during embryogenesis and post-natally. Their deregulations have been implicated in cancer development and progression. Wnt signaling is essential for odontogenesis. The ameloblastoma is an odontogenic epithelial neoplasm of enamel organ origin. Altered expressions of Wnts-1, -2, -5a, and -10a are detected in this tumor. The activity of other Wnt members remains unclarified. Canonical (Wnts-1, -2, -3, -8a, -8b, -10a, and -10b), non-canonical (Wnts-4, -5a, -5b, -6, 7a, -7b, and -11), and indeterminate groups (Wnts-2b and -9b) were examined immunohistochemically in 72 cases of ameloblastoma (19 unicystic [UA], 35 solid/multicystic [SMA], eight desmoplastic [DA], and 10 recurrent [RA]). Canonical Wnt proteins (except Wnt-10b) were heterogeneously expressed in ameloblastoma. Their distribution patterns were distinctive with some overlap. Protein localization was mainly membranous and/or cytoplasmic. Overexpression of Wnt-1 in most subsets (UA = 19/19; SMA = 35/35; DA = 5/8; RA = 7/10) (P < 0.05), Wnt-3 in granular cell variant (n = 3/3), and Wnt-8b in DA (n = 8/8) was key observations. Wnts-8a and -10a demonstrated enhanced expression in tumoral buddings and acanthomatous areas. Non-canonical and indeterminate Wnts were absent except for limited Wnt-7b immunoreactivity in UA (n = 1/19) and SMA (n = 1/35). Stromal components expressed variable Wnt positivity. Differential expression of Wnt ligands in different ameloblastoma subtypes suggests that the canonical and non-canonical Wnt pathways are selectively activated or repressed depending on the tumor cell differentiation status. Canonical Wnt pathway is most likely the main transduction pathway while Wnt-1 might be the key signaling molecule involved in ameloblastoma tumorigenesis. © 2011 John Wiley & Sons A/S.

  19. Discriminant Kernel Assignment for Image Coding.

    PubMed

    Deng, Yue; Zhao, Yanyu; Ren, Zhiquan; Kong, Youyong; Bao, Feng; Dai, Qionghai

    2017-06-01

    This paper proposes discriminant kernel assignment (DKA) in the bag-of-features framework for image representation. DKA slightly modifies existing kernel assignment to learn width-variant Gaussian kernel functions to perform discriminant local feature assignment. When directly applying gradient-descent method to solve DKA, the optimization may contain multiple time-consuming reassignment implementations in iterations. Accordingly, we introduce a more practical way to locally linearize the DKA objective and the difficult task is cast as a sequence of easier ones. Since DKA only focuses on the feature assignment part, it seamlessly collaborates with other discriminative learning approaches, e.g., discriminant dictionary learning or multiple kernel learning, for even better performances. Experimental evaluations on multiple benchmark datasets verify that DKA outperforms other image assignment approaches and exhibits significant efficiency in feature coding.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  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. 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…

  7. Letting Off that Loose Italian Canon Again?

    ERIC Educational Resources Information Center

    Bressan, Dino

    2001-01-01

    Discusses the decline in the use of literature in foreign language instruction at the secondary level, despite the role literature plays in college level instruction. Describes a project in an Italian studies program that introduced units based on a number of canonical texts. (Author/VWL)

  8. Canonical Transformation to the Free Particle

    ERIC Educational Resources Information Center

    Glass, E. N.; Scanio, Joseph J. G.

    1977-01-01

    Demonstrates how to find some canonical transformations without solving the Hamilton-Jacobi equation. Constructs the transformations from the harmonic oscillator to the free particle and uses these as examples of transformations that cannot be maintained when going from classical to quantum systems. (MLH)

  9. Reflections on a Democratically Constructed Canon

    ERIC Educational Resources Information Center

    Shafer, Gregory

    2003-01-01

    American schools have debated the merits of a national canon since the inception of English as a subject a century ago. In earlier years, the mission of the language arts was much more elitist and hierarchical. English was a subject that taught the great works, so that aspiring students could be familiar with the standard pantheon of authors and…

  10. 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.

  11. 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…

  12. William Blake and the Literary Canon.

    ERIC Educational Resources Information Center

    Brogan, Howard O.

    1990-01-01

    Concludes through an examination of recent criticism of William Blake's works that the literary canon is subject to change over time. Suggests that this is true because of both new critical developments and accumulations of new information through research. Argues that even critical theory is affected by such research. (SG)

  13. Investigating the Dynamics of Canonical Flux Tubes

    NASA Astrophysics Data System (ADS)

    von der Linden, Jens; Carroll, Evan; Kamikawa, Yu; Lavine, Eric; Vereen, Keon; You, Setthivoine

    2013-10-01

    Canonical flux tubes are defined by tracing areas of constant magnetic and fluid vorticity flux. This poster will present the theory for canonical flux tubes and current progress in the construction of an experiment designed to observe their evolution. In the zero flow limit, canonical flux tubes are magnetic flux tubes, but in full form, present the distinct advantage of reconciling two-fluid plasma dynamics with familiar concepts of helicity, twists and linkages. The experiment and the DCON code will be used to investigate a new MHD stability criterion for sausage and kink modes in screw pinches that has been generalized to magnetic flux tubes with skin and core currents. Camera images and a 3D array of ˙ B probes will measure tube aspect-ratio and ratio of current-to-magnetic flux, respectively, to trace these flux tube parameters in a stability space. The experiment's triple electrode planar gun is designed to generate azimuthal and axial flows. These diagnostics together with a 3D vector tomographic reconstruction of ion Doppler spectroscopy will be used to verify the theory of canonical helicity transport. This work was sponsored in part by the US DOE Grant DE-SC0010340.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Hierarchical computation in the canonical auditory cortical circuit

    PubMed Central

    Atencio, Craig A.; Sharpee, Tatyana O.; Schreiner, Christoph E.

    2009-01-01

    Sensory cortical anatomy has identified a canonical microcircuit underlying computations between and within layers. This feed-forward circuit processes information serially from granular to supragranular and to infragranular layers. How this substrate correlates with an auditory cortical processing hierarchy is unclear. We recorded simultaneously from all layers in cat primary auditory cortex (AI) and estimated spectrotemporal receptive fields (STRFs) and associated nonlinearities. Spike-triggered averaged STRFs revealed that temporal precision, spectrotemporal separability, and feature selectivity varied with layer according to a hierarchical processing model. STRFs from maximally informative dimension (MID) analysis confirmed hierarchical processing. Of two cooperative MIDs identified for each neuron, the first comprised the majority of stimulus information in granular layers. Second MID contributions and nonlinear cooperativity increased in supragranular and infragranular layers. The AI microcircuit provides a valid template for three independent hierarchical computation principles. Increases in processing complexity, STRF cooperativity, and nonlinearity correlate with the synaptic distance from granular layers. PMID:19918079

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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…

  8. 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…

  9. 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)

  10. 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.

  11. A class of kernel based real-time elastography algorithms.

    PubMed

    Kibria, Md Golam; Hasan, Md Kamrul

    2015-08-01

    In this paper, a novel real-time kernel-based and gradient-based Phase Root Seeking (PRS) algorithm for ultrasound elastography is proposed. The signal-to-noise ratio of the strain image resulting from this method is improved by minimizing the cross-correlation discrepancy between the pre- and post-compression radio frequency signals with an adaptive temporal stretching method and employing built-in smoothing through an exponentially weighted neighborhood kernel in the displacement calculation. Unlike conventional PRS algorithms, displacement due to tissue compression is estimated from the root of the weighted average of the zero-lag cross-correlation phases of the pair of corresponding analytic pre- and post-compression windows in the neighborhood kernel. In addition to the proposed one, the other time- and frequency-domain elastography algorithms (Ara et al., 2013; Hussain et al., 2012; Hasan et al., 2012) proposed by our group are also implemented in real-time using Java where the computations are serially executed or parallely executed in multiple processors with efficient memory management. Simulation results using finite element modeling simulation phantom show that the proposed method significantly improves the strain image quality in terms of elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe) and mean structural similarity (MSSIM) for strains as high as 4% as compared to other reported techniques in the literature. Strain images obtained for the experimental phantom as well as in vivo breast data of malignant or benign masses also show the efficacy of our proposed method over the other reported techniques in the literature. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Complex phylogenetic distribution of a non-canonical genetic code in green algae

    PubMed Central

    2010-01-01

    reassignment of both stop codons to glutamine. Instead the standard code was retained by the disappearance of the ambiguously decoding tRNAs from the genome. We correlate the emergence of a non-canonical genetic code in the Ulvophyceae to their multinucleate nature. PMID:20977766

  13. 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.

  14. Spin foam model from canonical quantization

    SciTech Connect

    Alexandrov, Sergei

    2008-01-15

    We suggest a modification of the Barrett-Crane spin foam model of four-dimensional Lorentzian general relativity motivated by the canonical quantization. The starting point is Lorentz covariant loop quantum gravity. Its kinematical Hilbert space is found as a space of the so-called projected spin networks. These spin networks are identified with the boundary states of a spin foam model and provide a generalization of the unique Barrett-Crane intertwiner. We propose a way to modify the Barrett-Crane quantization procedure to arrive at this generalization: the B field (bivectors) should be promoted not to generators of the gauge algebra, but to their certain projection. The modification is also justified by the canonical analysis of the Plebanski formulation. Finally, we compare our construction with other proposals to modify the Barrett-Crane model.

  15. 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.

  16. 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.

  17. 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.

  18. Are Young Children's Drawings Canonically Biased?

    ERIC Educational Resources Information Center

    Picard, Delphine; Durand, Karine

    2005-01-01

    In a between-subjects design, 4-to 6-year-olds were asked to draw from three-dimensional (3D) models, two-and-a-half-dimensional (212D) models with or without depth cues, or two-dimensional (2D) models of a familiar object (a saucepan) in noncanonical orientations (handle at the back or at the front). Results showed that canonical errors were…

  19. Canonical quantization of gravitation with higher derivatives

    SciTech Connect

    Dukhbinder, I.L.; Lyakhovich, S.L.

    1986-06-01

    The authors construct a Hamiltonian formulation, canonically quantize it, and find the local measure for a theory of gravitation with Langrangian. The approach is based on the general method of ''Hamiltonianization'' of theories with higher derivatives containing coupling, in a form specially suited for work with gauge theories. An expression is obtained for the generating functional for Green's functions in the form of a continuum over the metric, with a nontrivial measure different from that of Einsteinian gravitation.

  20. 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…

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. The gauge-invariant canonical energy-momentum tensor

    NASA Astrophysics Data System (ADS)

    Lorcé, Cédric

    2016-03-01

    The canonical energy-momentum tensor is often considered as a purely academic object because of its gauge dependence. However, it has recently been realized that canonical quantities can in fact be defined in a gauge-invariant way provided that strict locality is abandoned, the non-local aspect being dictacted in high-energy physics by the factorization theorems. Using the general techniques for the parametrization of non-local parton correlators, we provide for the first time a complete parametrization of the energy-momentum tensor (generalizing the purely local parametrizations of Ji and Bakker-Leader-Trueman used for the kinetic energy-momentum tensor) and identify explicitly the parts accessible from measurable two-parton distribution functions (TMDs and GPDs). As by-products, we confirm the absence of model-independent relations between TMDs and parton orbital angular momentum, recover in a much simpler way the Burkardt sum rule and derive three similar new sum rules expressing the conservation of transverse momentum.

  7. 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.

  8. Fusion and kernel type selection in adaptive image retrieval

    NASA Astrophysics Data System (ADS)

    Doloc-Mihu, Anca; Raghavan, Vijay V.

    2007-04-01

    In this work we investigate the relationships between features representing images, fusion schemes for these features and kernel types used in an Web-based Adaptive Image Retrieval System. Using the Kernel Rocchio learning method, several kernels having polynomial and Gaussian forms are applied to general images represented by annotations and by color histograms in RGB and HSV color spaces. We propose different fusion schemes, which incorporate kernel selector component(s). We perform experiments to study the relationships between a concatenated vector and several kernel types. Experimental results show that an appropriate kernel could significantly improve the performance of the retrieval system.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  10. Convolution-controlled rotation and scale invariance in optical correlation

    NASA Technical Reports Server (NTRS)

    Juday, Richard D.; Bourgeois, Brian

    1988-01-01

    A method is presented for evoking a controlled, continuously-variable degree of rotation- and scale-invariance in optical correlation; the method is suitable for off-line computation of filters, though not for real-time computation. While a closed-form solution for the blur kernels has thus far evaded solution, a digital approximation method has been presented. A simulated correlation run with real, frame-grabbed imagery has indicated the method's desired performance. These Gaussian blur kernels can be replaced with box-car kernels or other blur kernels suitable for the given correlation-task.

  11. 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.

  12. Robust C-Loss Kernel Classifiers.

    PubMed

    Xu, Guibiao; Hu, Bao-Gang; Principe, Jose C

    2016-12-29

    The correntropy-induced loss (C-loss) function has the nice property of being robust to outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization term, which is used to avoid overfitting. After using the half-quadratic optimization algorithm, which converges much faster than the gradient optimization algorithm, we find out that the resulting C-loss kernel classifier is equivalent to an iterative weighted least square support vector machine (LS-SVM). This relationship helps explain the robustness of iterative weighted LS-SVM from the correntropy and density estimation perspectives. On the large-scale data sets which have low-rank Gram matrices, we suggest to use incomplete Cholesky decomposition to speed up the training process. Moreover, we use the representer theorem to improve the sparseness of the resulting C-loss kernel classifier. Experimental results confirm that our methods are more robust to outliers than the existing common classifiers.

  13. 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.

  14. 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.

  15. 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

  16. Kernel bandwidth estimation for nonparametric modeling.

    PubMed

    Bors, Adrian G; Nasios, Nikolaos

    2009-12-01

    Kernel density estimation is a nonparametric procedure for probability density modeling, which has found several applications in various fields. The smoothness and modeling ability of the functional approximation are controlled by the kernel bandwidth. In this paper, we describe a Bayesian estimation method for finding the bandwidth from a given data set. The proposed bandwidth estimation method is applied in three different computational-intelligence methods that rely on kernel density estimation: 1) scale space; 2) mean shift; and 3) quantum clustering. The third method is a novel approach that relies on the principles of quantum mechanics. This method is based on the analogy between data samples and quantum particles and uses the SchrOdinger potential as a cost function. The proposed methodology is used for blind-source separation of modulated signals and for terrain segmentation based on topography information.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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+.

  4. 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.

  5. 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)

  6. 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

  7. 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.

  8. 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

  9. Volatile compound formation during argan kernel roasting.

    PubMed

    El Monfalouti, Hanae; Charrouf, Zoubida; Giordano, Manuela; Guillaume, Dominique; Kartah, Badreddine; Harhar, Hicham; Gharby, Saïd; Denhez, Clément; Zeppa, Giuseppe

    2013-01-01

    Virgin edible argan oil is prepared by cold-pressing argan kernels previously roasted at 110 degrees C for up to 25 minutes. The concentration of 40 volatile compounds in virgin edible argan oil was determined as a function of argan kernel roasting time. Most of the volatile compounds begin to be formed after 15 to 25 minutes of roasting. This suggests that a strictly controlled roasting time should allow the modulation of argan oil taste and thus satisfy different types of consumers. This could be of major importance considering the present booming use of edible argan oil.

  10. 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

  11. 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

  12. 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

  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. The periodic Cauchy kernel, the periodic Bochner kernel, discrete pseudo-differential operators

    NASA Astrophysics Data System (ADS)

    Vasilyev, Vladimir

    2017-07-01

    We introduce a discrete pseudo-differential operator in an appropriate discrete functional space and study the invertibility properties for such simplest operators in certain canonical domains of an Euclidean space. We construct special projectors for studying these operators according to a type of canonical domain and show how these operators are related to special boundary value problems for holomorphic functions of several variables.

  15. 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.

  16. 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.

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

    PubMed

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

    2017-01-05

    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 [Formula: see text] 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 [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] 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 [Formula: see text] 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 [Formula: see text]. Copyright © 2017 Cuevas et al.

  18. 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

  19. 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.

  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. Evaluation of visco-elastic properties of conditioned wheat kernels and their doughs using a compression test under small strain.

    PubMed

    Ponce-García, Néstor; Ramírez-Wong, Benjamín; Torres-Chávez, Patricia I; Figueroa-Cárdenas, Juan de Dios; Serna-Saldívar, Sergio O; Cortez-Rocha, Mario O; Escalante-Aburto, Anayansi

    2017-03-01

    The aim of this research was to evaluate the visco-elastic properties of conditioned wheat kernels and their doughs by applying the compression test under a small strain. Conditioned wheat kernels and their doughs, from soft and hard wheat classes were evaluated for total work (Wt ), elastic work (We ) and plastic work (Wp ). Soft wheat kernels showed lower We than Wp , while the hard wheat kernels had a We that was higher than Wp . Regarding dough visco-elasticity, cultivars from soft and hard wheat showed higher Wp than We . The degree of elasticity (DE%) of the conditioned wheat kernel related to its dough decreased ∼46% in both wheat classes. The Wt , We and Wp from the soft wheat kernel and dough correlated with physico-chemical and farinographic flour tests. The Wt , Wp and the maximum compression force (Fmax ) of the dough from hard wheat class presented highly significant negative correlations with wet gluten. The visco-elasticity parameters from compression test presented significant differences among conditioned wheat classes and their doughs. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

  2. 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.

  3. Kernel maximum autocorrelation factor and minimum noise fraction transformations.

    PubMed

    Nielsen, Allan Aasbjerg

    2011-03-01

    This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual formulation also termed Q-mode analysis in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version, the inner products of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution also known as the kernel trick these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component analysis (PCA), kernel MAF, and kernel MNF analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. Three examples show the very successful application of kernel MAF/MNF analysis to: 1) change detection in DLR 3K camera data recorded 0.7 s apart over a busy motorway, 2) change detection in hyperspectral HyMap scanner data covering a small agricultural area, and 3) maize kernel inspection. In the cases shown, the kernel MAF/MNF transformation performs better than its linear counterpart as well as linear and kernel PCA. The leading kernel MAF/MNF variates seem to possess the ability to adapt to even abruptly varying multi and hypervariate backgrounds and focus on extreme observations.

  4. 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.

  5. 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

  6. 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

  7. 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).

  8. 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.

  9. 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.

  10. Kato expansion in quantum canonical perturbation theory

    SciTech Connect

    Nikolaev, Andrey

    2016-06-15

    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.

  11. 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.

  12. End-use quality of soft kernel durum wheat

    USDA-ARS?s Scientific Manuscript database

    Kernel texture is a major determinant of end-use quality of wheat. Durum wheat has very hard kernels. We developed soft kernel durum wheat via Ph1b-mediated homoeologous recombination. The Hardness locus was transferred from Chinese Spring to Svevo durum wheat via back-crossing. ‘Soft Svevo’ had SKC...

  13. Reduction of complex signaling networks to a representative kernel.

    PubMed

    Kim, Jeong-Rae; Kim, Junil; Kwon, Yung-Keun; Lee, Hwang-Yeol; Heslop-Harrison, Pat; Cho, Kwang-Hyun

    2011-05-31

    The network of biomolecular interactions that occurs within cells is large and complex. When such a network is analyzed, it can be helpful to reduce the complexity of the network to a "kernel" that maintains the essential regulatory functions for the output under consideration. We developed an algorithm to identify such a kernel and showed that the resultant kernel preserves the network dynamics. Using an integrated network of all of the human signaling pathways retrieved from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, we identified this network's kernel and compared the properties of the kernel to those of the original network. We found that the percentage of essential genes to the genes encoding nodes outside of the kernel was about 10%, whereas ~32% of the genes encoding nodes within the kernel were essential. In addition, we found that 95% of the kernel nodes corresponded to Mendelian disease genes and that 93% of synthetic lethal pairs associated with the network were contained in the kernel. Genes corresponding to nodes in the kernel had low evolutionary rates, were ubiquitously expressed in various tissues, and were well conserved between species. Furthermore, kernel genes included many drug targets, suggesting that other kernel nodes may be potential drug targets. Owing to the simplification of the entire network, the efficient modeling of a large-scale signaling network and an understanding of the core structure within a complex framework become possible.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  15. Thermomechanical property of rice kernels studied by DMA

    USDA-ARS?s Scientific Manuscript database

    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. 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...

  17. 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...

  18. 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.

  19. 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 producing...

  20. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... 21 Food and Drugs 3 2014-04-01 2014-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a..., packaging, transporting, or holding food, subject to the provisions of this section. (a) Tamarind seed...

  1. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... 21 Food and Drugs 3 2012-04-01 2012-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

  2. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 21 Food and Drugs 3 2011-04-01 2011-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

  3. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... 21 Food and Drugs 3 2013-04-01 2013-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

  4. QTLs and candidate genes for desiccation and abscisic acid content in maize kernels

    PubMed Central

    2010-01-01

    and not correlated with ABA content in either tissue. Conclusions A high resolution QTL map for kernel desiccation and ABA content in embryo and endosperm showed several precise colocations between desiccation and ABA traits. Five new members of the maize NCED gene family and another maize ZEP gene were identified and mapped. Among all the identified candidates, aquaporins and members of the Responsive to ABA gene family appeared better candidates than NCEDs and ZEPs. PMID:20047666

  5. 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

  6. Arbitrary-resolution global sensitivity kernels

    NASA Astrophysics Data System (ADS)

    Nissen-Meyer, T.; Fournier, A.; Dahlen, F.

    2007-12-01

    Extracting observables out of any part of a seismogram (e.g. including diffracted phases such as Pdiff) necessitates the knowledge of 3-D time-space wavefields for the Green functions that form the backbone of Fréchet sensitivity kernels. While known for a while, this idea is still computationally intractable in 3-D, facing major simulation and storage issues when high-frequency wavefields are considered at the global scale. We recently developed a new "collapsed-dimension" spectral-element method that solves the 3-D system of elastodynamic equations in a 2-D space, based on exploring symmetry considerations of the seismic-wave radiation patterns. We will present the technical background on the computation of waveform kernels, various examples of time- and frequency-dependent sensitivity kernels and subsequently extracted time-window kernels (e.g. banana- doughnuts). Given the computationally light-weighted 2-D nature, we will explore some crucial parameters such as excitation type, source time functions, frequency, azimuth, discontinuity locations, and phase type, i.e. an a priori view into how, when, and where seismograms carry 3-D Earth signature. A once-and-for-all database of 2-D waveforms for various source depths shall then serve as a complete set of global time-space sensitivity for a given spherically symmetric background model, thereby allowing for tomographic inversions with arbitrary frequencies, observables, and phases.

  7. 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 Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... washing: Provided, That the presence of web or frass shall not be considered serious damage for the...

  8. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... 7 Agriculture 8 2014-01-01 2014-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (MARKETING AGREEMENTS... washing: Provided, That the presence of web or frass shall not be considered serious damage for the...

  9. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 7 Agriculture 8 2011-01-01 2011-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... washing: Provided, That the presence of web or frass shall not be considered serious damage for the...

  10. 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

  11. Kernel temporal differences for neural decoding.

    PubMed

    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.

  12. 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...

  13. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...

  14. 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...

  15. 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...

  16. 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...

  17. Symbol recognition with kernel density matching.

    PubMed

    Zhang, Wan; Wenyin, Liu; Zhang, Kun

    2006-12-01

    We propose a novel approach to similarity assessment for graphic symbols. Symbols are represented as 2D kernel densities and their similarity is measured by the Kullback-Leibler divergence. Symbol orientation is found by gradient-based angle searching or independent component analysis. Experimental results show the outstanding performance of this approach in various situations.

  18. Spark Ignited Turbulent Flame Kernel Growth

    SciTech Connect

    Santavicca, D.A.

    1995-06-01

    An experimental study of the effects of spark power and of incomplete fuel-air mixing on spark-ignited flame kernel growth was conducted in turbulent propane-air mixtures at 1 atm, 300K conditions. The results showed that increased spark power resulted in an increased growth rate, where the effect of short duration breakdown sparks was found to persist for times of the order of milliseconds. The effectiveness of increased spark power was found to be less at high turbulence and high dilution conditions. Increased spark power had a greater effect on the 0-5 mm burn time than on the 5-13 mm burn time, in part because of the effect of breakdown energy on the initial size of the flame kernel. And finally, when spark power was increased by shortening the spark duration while keeping the effective energy the same there was a significant increase in the misfire rate, however when the spark power was further increased by increasing the breakdown energy the misfire rate dropped to zero. The results also showed that fluctuations in local mixture strength due to incomplete fuel-air mixing cause the flame kernel surface to become wrinkled and distorted; and that the amount of wrinkling increases as the degree of incomplete fuel-air mixing increases. Incomplete fuel-air mixing was also found to result in a significant increase in cyclic variations in the flame kernel growth. The average flame kernel growth rates for the premixed and the incompletely mixed cases were found to be within the experimental uncertainty except for the 33%-RMS-fluctuation case where the growth rate was significantly lower. The premixed and 6%-RMS-fluctuation cases had a 0% misfire rate. The misfire rates were 1% and 2% for the 13%-RMS-fluctuation and 24%-RMS-fluctuation cases, respectively; however, it drastically increased to 23% in the 33%-RMS-fluctuation case.

  19. Turbulent transport across invariant canonical flux surfaces

    SciTech Connect

    Hollenberg, J.B.; Callen, J.D.

    1994-07-01

    Net transport due to a combination of Coulomb collisions and turbulence effects in a plasma is investigated using a fluid moment description that allows for kinetic and nonlinear effects via closure relations. The model considered allows for ``ideal`` turbulent fluctuations that distort but preserve the topology of species-dependent canonical flux surfaces {psi}{sub {number_sign},s} {triple_bond} {integral} dF {center_dot} B{sub {number_sign},s} {triple_bond} {gradient} {times} [A + (m{sub s}/q{sub s})u{sub s}] in which u{sub s} is the flow velocity of the fluid species. Equations for the net transport relative to these surfaces due to ``nonideal`` dissipative processes are found for the total number of particles and total entropy enclosed by a moving canonical flux surface. The corresponding particle transport flux is calculated using a toroidal axisymmetry approximation of the ideal surfaces. The resulting Lagrangian transport flux includes classical, neoclassical-like, and anomalous contributions and shows for the first time how these various contributions should be summed to obtain the total particle transport flux.

  20. Finite thermal reservoirs and the canonical distribution

    NASA Astrophysics Data System (ADS)

    Griffin, William; Matty, Michael; Swendsen, Robert H.

    2017-10-01

    The microcanonical ensemble has long been a starting point for the development of thermodynamics from statistical mechanics. However, this approach presents two problems. First, it predicts that the entropy is only defined on a discrete set of energies for finite, quantum systems, while thermodynamics requires the entropy to be a continuous function of the energy. Second, it fails to satisfy the stability condition (ΔS / ΔU < 0) for first-order transitions with both classical and quantum systems. Swendsen has recently shown that the source of these problems lies in the microcanonical ensemble itself, which contains only energy eigenstates and excludes their linear combinations. To the contrary, if the system of interest has ever been in thermal contact with another system, it will be described by a probability distribution over many eigenstates that is equivalent to the canonical ensemble for sufficiently large systems. Novotny et al. have recently supported this picture by dynamical numerical calculations for a quantum mechanical model, in which they showed the approach to a canonical distribution for up to 40 quantum spins. By simplifying the problem to calculate only the equilibrium properties, we are able to extend the demonstration to more than a million particles.