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Sample records for canonical discriminant analysis

  1. Optimal Class Separation in Hyperspectral Image Data: Iterated Canonical Discriminant Analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Müller, Andreas

    This paper describes canonical discriminant analysis and sketches an iterative version which is then applied to obtain optimal separation between a region, here examplified by either “water” or “wood/trees” and the rest of a HyMap image. We show that the iterative version greatly enhances...

  2. Combined approach based on principal component analysis and canonical discriminant analysis for investigating hyperspectral plant response

    Directory of Open Access Journals (Sweden)

    Anna Maria Stellacci

    2012-07-01

    Full Text Available Hyperspectral (HS data represents an extremely powerful means for rapidly detecting crop stress and then aiding in the rational management of natural resources in agriculture. However, large volume of data poses a challenge for data processing and extracting crucial information. Multivariate statistical techniques can play a key role in the analysis of HS data, as they may allow to both eliminate redundant information and identify synthetic indices which maximize differences among levels of stress. In this paper we propose an integrated approach, based on the combined use of Principal Component Analysis (PCA and Canonical Discriminant Analysis (CDA, to investigate HS plant response and discriminate plant status. The approach was preliminary evaluated on a data set collected on durum wheat plants grown under different nitrogen (N stress levels. Hyperspectral measurements were performed at anthesis through a high resolution field spectroradiometer, ASD FieldSpec HandHeld, covering the 325-1075 nm region. Reflectance data were first restricted to the interval 510-1000 nm and then divided into five bands of the electromagnetic spectrum [green: 510-580 nm; yellow: 581-630 nm; red: 631-690 nm; red-edge: 705-770 nm; near-infrared (NIR: 771-1000 nm]. PCA was applied to each spectral interval. CDA was performed on the extracted components to identify the factors maximizing the differences among plants fertilised with increasing N rates. Within the intervals of green, yellow and red only the first principal component (PC had an eigenvalue greater than 1 and explained more than 95% of total variance; within the ranges of red-edge and NIR, the first two PCs had an eigenvalue higher than 1. Two canonical variables explained cumulatively more than 81% of total variance and the first was able to discriminate wheat plants differently fertilised, as confirmed also by the significant correlation with aboveground biomass and grain yield parameters. The combined

  3. Application of canonical discriminant analysis in differentiation of natural populations of Pinus nigra in Serbia based on terpene composition

    OpenAIRE

    Šarac, Z.; Bojović, S.; Nikolić, B.; Zlatković, B.; Marin, P.

    2014-01-01

    The canonical discriminant analysis (CDA) was performed in order to check the hypothesis of chemical separation infraspecific taxa of Pinus nigra J.F. Arnold (ssp. nigra, var. gocensis, ssp. pallasiana, and var. banatica) in Serbia based on variability of the needle terpenes. The CDA, which maximizes variations between a priori groups, showed division of seven native P. nigra populations into three groups, which belong to three taxonomically recognized taxa (ssp. nigra, ssp. pallasiana, and v...

  4. Canonical Information Analysis

    DEFF Research Database (Denmark)

    Vestergaard, Jacob Schack; Nielsen, Allan Aasbjerg

    2015-01-01

    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 ...

  5. Regularized Generalized Canonical Correlation Analysis

    Science.gov (United States)

    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…

  6. Generalized canonical correlation analysis with missing values

    NARCIS (Netherlands)

    M. van de Velden (Michel); Y. Takane

    2012-01-01

    textabstractGeneralized canonical correlation analysis is a versatile technique that allows the joint analysis of several sets of data matrices. The generalized canonical correlation analysis solution can be obtained through an eigenequation and distributional assumptions are not required. When

  7. Canonical analysis based on mutual information

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

    2015-01-01

    combinations with the information theoretical measure mutual information (MI). We term this type of analysis canonical information analysis (CIA). MI allows for the actual joint distribution of the variables involved and not just second order statistics. While CCA is ideal for Gaussian data, CIA facilitates...

  8. Generalized canonical correlation analysis with missing values

    NARCIS (Netherlands)

    M. van de Velden (Michel); Y. Takane

    2009-01-01

    textabstractTwo new methods for dealing with missing values in generalized canonical correlation analysis are introduced. The first approach, which does not require iterations, is a generalization of the Test Equating method available for principal component analysis. In the second approach,

  9. Convergence analysis of canonical genetic algorithms.

    Science.gov (United States)

    Rudolph, G

    1994-01-01

    This paper analyzes the convergence properties of the canonical genetic algorithm (CGA) with mutation, crossover and proportional reproduction applied to static optimization problems. It is proved by means of homogeneous finite Markov chain analysis that a CGA will never converge to the global optimum regardless of the initialization, crossover, operator and objective function. But variants of CGA's that always maintain the best solution in the population, either before or after selection, are shown to converge to the global optimum due to the irreducibility property of the underlying original nonconvergent CGA. These results are discussed with respect to the schema theorem.

  10. Face hallucination using orthogonal canonical correlation analysis

    Science.gov (United States)

    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.

  11. Sparse canonical correlation analysis: new formulation and algorithm.

    Science.gov (United States)

    Chu, Delin; Liao, Li-Zhi; Ng, Michael K; Zhang, Xiaowei

    2013-12-01

    In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms.

  12. Unseemly acts of canonized monarchs: an experience of theological analysis

    Directory of Open Access Journals (Sweden)

    Nikolsky Evgeny Vladimirovich

    2015-10-01

    Full Text Available The article discuses an "unpopular" aspect of hagiology-crimes and other nefarious acts of canonized monarchs. The analysis solves a theological dilemma: how can atrocities unite with Holiness. It is noted that if real or alleged crimes are attributed to the monarch who completed his life in martyrdom, the question of his canonization automatically disappears because the martyrdom cleans all the sins. Examples of such case are Saint Michael of Chernigov, Andrei Bogolyubsky, Nicholas II.

  13. Group sparse canonical correlation analysis for genomic data integration.

    Science.gov (United States)

    Lin, Dongdong; Zhang, Jigang; Li, Jingyao; Calhoun, Vince D; Deng, Hong-Wen; Wang, Yu-Ping

    2013-08-12

    The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group). We propose a new group sparse CCA method (CCA-sparse group) along with an effective numerical algorithm to study the mutual relationship between two different types of genomic data (i.e., SNP and gene expression). We then extend the model to a more general formulation that can include the existing sCCA models. We apply the model to feature/variable selection from two data sets and compare our group sparse CCA method with existing sCCA methods on both simulation and two real datasets (human gliomas data and NCI60 data). We use a graphical representation of the samples with a pair of canonical variates to demonstrate the discriminating characteristic of the selected features. Pathway analysis is further performed for biological interpretation of those features. The CCA-sparse group method incorporates group effects of features into the correlation analysis while performs individual feature

  14. Canonical correlation analysis of course and teacher evaluation

    DEFF Research Database (Denmark)

    Sliusarenko, Tamara; Ersbøll, Bjarne Kjær

    2010-01-01

    At the Technical University of Denmark course evaluations are performed by the students on a questionnaire. On one form the students are asked specific questions regarding the course. On a second form they are asked specific questions about the teacher. This study investigates the extent to which...... information obtained from the course evaluation form overlaps with information obtained from the teacher evaluation form. Employing canonical correlation analysis it was found that course and teacher evaluations are correlated. However, the structure of the canonical correlation is subject to change...... with changes in teaching methods from one year to another....

  15. Testing the significance of canonical axes in redundancy analysis

    NARCIS (Netherlands)

    Legendre, P.; Oksanen, J.; Braak, ter C.J.F.

    2011-01-01

    1. Tests of significance of the individual canonical axes in redundancy analysis allow researchers to determine which of the axes represent variation that can be distinguished from random. Variation along the significant axes can be mapped, used to draw biplots or interpreted through subsequent

  16. Nonlinear canonical correlation analysis with k sets of variables

    NARCIS (Netherlands)

    van der Burg, Eeke; de Leeuw, Jan

    1987-01-01

    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

  17. Canonical correspondence analysis and related multivariate methods in aquatic ecology

    NARCIS (Netherlands)

    Braak, Ter Cajo J.F.; Verdonschot, Piet F.M.

    1995-01-01

    Canonical correspondence analysis (CCA) is a multivariate method to elucidate the relationships between biological assemblages of species and their environment. The method is designed to extract synthetic environmental gradients from ecological data-sets. The gradients are the basis for succinctly

  18. Linear discriminant analysis of structure within African eggplant 'Shum'

    African Journals Online (AJOL)

    A MANOVA preceded linear discriminant analysis, to model each of 61 variables, as predicted by clusters and experiment to filter out non-significant traits. Four distinct clusters emerged, with a cophenetic relation coefficient of 0.87 (P<0.01). Canonical variates that best predicted the observed clusters include petiole length, ...

  19. Insight into dynamic genome imaging: Canonical framework identification and high-throughput analysis.

    Science.gov (United States)

    Ronquist, Scott; Meixner, Walter; Rajapakse, Indika; Snyder, John

    2017-07-01

    The human genome is dynamic in structure, complicating researcher's attempts at fully understanding it. Time series "Fluorescent in situ Hybridization" (FISH) imaging has increased our ability to observe genome structure, but due to cell type and experimental variability this data is often noisy and difficult to analyze. Furthermore, computational analysis techniques are needed for homolog discrimination and canonical framework detection, in the case of time-series images. In this paper we introduce novel ideas for nucleus imaging analysis, present findings extracted using dynamic genome imaging, and propose an objective algorithm for high-throughput, time-series FISH imaging. While a canonical framework could not be detected beyond statistical significance in the analyzed dataset, a mathematical framework for detection has been outlined with extension to 3D image analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Manifold Partition Discriminant Analysis.

    Science.gov (United States)

    Yang Zhou; Shiliang Sun

    2017-04-01

    We propose a novel algorithm for supervised dimensionality reduction named manifold partition discriminant analysis (MPDA). It aims to find a linear embedding space where the within-class similarity is achieved along the direction that is consistent with the local variation of the data manifold, while nearby data belonging to different classes are well separated. By partitioning the data manifold into a number of linear subspaces and utilizing the first-order Taylor expansion, MPDA explicitly parameterizes the connections of tangent spaces and represents the data manifold in a piecewise manner. While graph Laplacian methods capture only the pairwise interaction between data points, our method captures both pairwise and higher order interactions (using regional consistency) between data points. This manifold representation can help to improve the measure of within-class similarity, which further leads to improved performance of dimensionality reduction. Experimental results on multiple real-world data sets demonstrate the effectiveness of the proposed method.

  1. Hierarchical Discriminant Analysis.

    Science.gov (United States)

    Lu, Di; Ding, Chuntao; Xu, Jinliang; Wang, Shangguang

    2018-01-18

    The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA). It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

  2. Hierarchical Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Di Lu

    2018-01-01

    Full Text Available The Internet of Things (IoT generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA. It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

  3. Bitcoin Market Volatility Analysis Using Grand Canonical Minority Game

    Directory of Open Access Journals (Sweden)

    Matteo Ortisi

    2016-12-01

    Full Text Available In this paper we propose to use the Grand Canonical Minority Game (GCMG, a highly simplified financial market model as a model of bitcoin market to show how the lack of an income for “miners”, similar to yield earned by bond holders, could be a structural reason for high volatility of bitcoin price in a reference currency. Coherently with present analysis, the introduction of future contracts on bitcoin would have the effect of reducing the overall market volatility.

  4. Climate Prediction Center(CPC)Ensemble Canonical Correlation Analysis Forecast of Temperature

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) temperature forecast is a 90-day (seasonal) outlook of US surface temperature anomalies. The ECCA uses Canonical...

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

    Science.gov (United States)

    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.

  6. Decoding the auditory brain with canonical component analysis

    DEFF Research Database (Denmark)

    de Cheveigné, Alain; Wong, Daniel D E; Di Liberto, Giovanni M

    2018-01-01

    The relation between a stimulus and the evoked brain response can shed light on perceptual processes within the brain. Signals derived from this relation can also be harnessed to control external devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP......) is appropriate for isolated stimuli, more sophisticated "decoding" strategies are needed to address continuous stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both the stimulus...

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

    Science.gov (United States)

    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.

  8. DISCRIMINANT ANALYSIS OF BANK PROFITABILITY LEVELS

    Directory of Open Access Journals (Sweden)

    Ante Rozga

    2013-02-01

    Full Text Available Discriminant analysis has been employed in this paper in order to identify and explain key features of bank profitability levels. Bank profitability is set up in the form of two categorical variables: profit or loss recorded and above or below average return on equity. Predictor variables are selected from various groups of financial indicators usually included in the empirical work on microeconomic determinants of bank profitability. The data from the Croatian banking sector is analyzed using the Enter method. General recommendations for a more profitable business of banking found in the bank management literature and existing empirical framework such as rationalization of overhead costs, asset growth, increase of non-interest income by expanding scale and scope of financial products proved to be important for classification of banks in different profitability levels. A higher market share may bring additional advantages. Classification results, canonical correlation and Wilks’ Lambda test confirm statistical significance of research results. Altogether, discriminant analysis turns out to be a suitable statistical method for solving presented research problem and moving forward from the bankruptcy, credit rating or default issues in finance.

  9. Canonical analysis of sentinel-1 radar and sentinel-2 optical data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Larsen, Rasmus

    2017-01-01

    This paper gives results from joint analyses of dual polarimety synthetic aperture radar data from the Sentinel-1 mission and optical data from the Sentinel-2 mission. The analyses are carried out by means of traditional canonical correlation analysis (CCA) and canonical information analysis (CIA...

  10. Distribution-free discriminant analysis

    Energy Technology Data Exchange (ETDEWEB)

    Burr, T.; Doak, J.

    1997-05-01

    This report describes our experience in implementing a non-parametric (distribution-free) discriminant analysis module for use in a wide range of pattern recognition problems. Issues discussed include performance results on both real and simulated data sets, comparisons to other methods, and the computational environment. In some cases, this module performs better than other existing methods. Nearly all cases can benefit from the application of multiple methods.

  11. Generalized canonical analysis based on optimizing matrix correlations and a relation with IDIOSCAL

    NARCIS (Netherlands)

    Kiers, Henk A.L.; Cléroux, R.; Ten Berge, Jos M.F.

    1994-01-01

    Carroll's method for generalized canonical analysis of two or more sets of variables is shown to optimize the sum of squared inner-product matrix correlations between a consensus matrix and matrices with canonical variates for each set of variables. In addition, the method that analogously optimizes

  12. Canonical correlation analysis for gene-based pleiotropy discovery.

    Directory of Open Access Journals (Sweden)

    Jose A Seoane

    2014-10-01

    Full Text Available Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy and for testing multiple variants for association with a single phenotype (gene-based association tests. Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women's Heart and Health Study, we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1 with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.

  13. Finding Efficient Nonlinear Visual Operators using Canonical Correlation Analysis

    OpenAIRE

    Borga, Magnus; Knutsson, Hans

    2000-01-01

    This paper presents a general strategy for designing efficient visual operators. The approach is highly task oriented and what constitutes the relevant information is defined by a set of examples. The examples are pairs of images displaying a strong dependence in the chosen feature but are otherwise independent. Particularly important concepts in the work are mutual information and canonical correlation. Visual operators learned from examples are presented, e.g. local shift invariant orientat...

  14. Climate Prediction Center (CPC)Ensemble Canonical Correlation Analysis 90-Day Seasonal Forecast of Precipitation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) precipitation forecast is a 90-day (seasonal) outlook of US surface precipitation anomalies. The ECCA uses...

  15. Influence and canonical supremacy: an analysis of how George Herbert Mead demoted Charles Horton Cooley in the sociological canon.

    Science.gov (United States)

    Jacobs, Glenn

    2009-01-01

    This analysis assesses the factors underlying Charles Horton Cooley's place in the sociological canon as they relate to George Herbert Mead's puzzling diatribe-echoed in secondary accounts-against Cooley's social psychology and view of the self published scarcely a year after his death. The illocutionary act of publishing his critique stands as an effort to project the image of Mead's intellectual self and enhance his standing among sociologists within and outside the orbit of the University of Chicago. It expressed Mead's ambivalence toward his precursor Cooley, whose influence he never fully acknowledged. In addition, it typifies the contending fractal distinctions of the scientifically discursive versus literary styles of Mead and Cooley, who both founded the interpretive sociological tradition. The contrasting styles and attitudes toward writing of the two figures are discussed, and their implications for the problems of scale that have stymied the symbolic interactionist tradition are explored.

  16. Canonical discrimination of the effect of a new broiler production facility on soil chemical profiles as related to current management practices.

    Science.gov (United States)

    Sheffield, Cynthia L; Crippen, Tawni L; Byrd, J Allen; Beier, Ross C; Yeater, Kathleen

    2015-01-01

    The effect dirt-floored broiler houses have on the underlying native soil, and the potential for contamination of the ground water by leaching under the foundation, is an understudied area. This study examines alterations in fifteen quantitative soil parameters (Ca, Cu, electrical conductivity, Fe, K, Mg, Mn, Na, NO3, organic matter, P, pH, S, soil moisture and Zn) in the underlayment of a newly constructed dirt-floored broiler house over the first two years of production (Native through Flock 11). The experiment was conducted near NW Robertson County, Texas, where the native soil is a fine, smectitic thermic Udertic Paleustalfs and the slopes range from zero to three percent. Multiple samples were collected from under each of three water and three feed lines the length of the house, in a longitudinal study during February 2008 through August 2010. To better define the relationship between the soil parameters and sampling times, a canonical discriminant analysis approach was used. The soil profiles assembled into five distinctive clusters corresponding to time and management practices. Results of this work revealed that the majority of parameters increased over time. The management practices of partial and total house clean-outs markedly altered soil profiles the house underlayment, thus reducing the risk of infiltration into the ground water near the farm. This is important as most broiler farms consist of several houses within a small area, so the cumulative ecological impact could be substantial if not properly managed.

  17. Canonical discrimination of the effect of a new broiler production facility on soil chemical profiles as related to current management practices.

    Directory of Open Access Journals (Sweden)

    Cynthia L Sheffield

    Full Text Available The effect dirt-floored broiler houses have on the underlying native soil, and the potential for contamination of the ground water by leaching under the foundation, is an understudied area. This study examines alterations in fifteen quantitative soil parameters (Ca, Cu, electrical conductivity, Fe, K, Mg, Mn, Na, NO3, organic matter, P, pH, S, soil moisture and Zn in the underlayment of a newly constructed dirt-floored broiler house over the first two years of production (Native through Flock 11. The experiment was conducted near NW Robertson County, Texas, where the native soil is a fine, smectitic thermic Udertic Paleustalfs and the slopes range from zero to three percent. Multiple samples were collected from under each of three water and three feed lines the length of the house, in a longitudinal study during February 2008 through August 2010. To better define the relationship between the soil parameters and sampling times, a canonical discriminant analysis approach was used. The soil profiles assembled into five distinctive clusters corresponding to time and management practices. Results of this work revealed that the majority of parameters increased over time. The management practices of partial and total house clean-outs markedly altered soil profiles the house underlayment, thus reducing the risk of infiltration into the ground water near the farm. This is important as most broiler farms consist of several houses within a small area, so the cumulative ecological impact could be substantial if not properly managed.

  18. Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

    Directory of Open Access Journals (Sweden)

    Zwinderman Aeilko H

    2009-09-01

    Full Text Available Abstract Background We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes. Results We performed a small simulation to illustrate the model's capability to identify new candidate genes to incorporate in the pathway: in our simulations it appeared that a gene was correctly identified if the correlation with the pathway genes was 0.3 or more. We applied the methods to a gene-expression microarray data set of 12, 209 genes measured in 45 patients with glioblastoma, and we considered genes to incorporate in the glioma-pathway: we identified more than 25 genes that correlated > 0.9 with canonical variates of the pathway genes. Conclusion We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.

  19. Canonical correlation analysis of the career attitudes and strategies inventory and the adult career concerns inventory

    Directory of Open Access Journals (Sweden)

    Charlene C Lew

    2006-04-01

    Full Text Available This study investigated the relationships between the scales of the Adult Career Concerns Inventory (ACCI and those of the Career Attitudes and Strategies Inventory (CASI. The scores of 202 South African adults for the two inventories were subjected to a canonical correlation analysis. Two canonical variates made statistically significant contributions to the explanation of the relationships between the two sets of variables. Inspection of the correlations of the original variables with the first canonical variate suggested that a high level of career concerns in general, as measured by the ACCI, is associated with high levels of career worries, more geographical barriers, a low risk-taking style and a non-dominant interpersonal style, as measured by the CASI. The second canonical variate suggested that concerns with career exploration and advancement of one’s career is associated with low job satisfaction, low family commitment, high work involvement, and a dominant style at work.

  20. A framework for analyzing seasonal prediction through canonical event analysis

    Science.gov (United States)

    Wood, Eric; Roundy, Joshua; Yuan, Xing

    2014-05-01

    Hydrologic extremes in the form of wet and dry periods (flood and drought prone periods) have large impacts on society. The ability to predict such periods allows for preparations that can reduce the severity of these events on society. Such preparations require predictions at a seasonal timescales. While seasonal predictions from global climate models can provide forecasts at such timescales; their skill varies seasonally and spatially, which severely limits their practical use. A better understanding of when and where climate models are skillful is assessed through hindcasts, which are usually limited to less than 30 years and are therefore prone to randomness and uncertainty. Until now, most hindcast analyses used to assess seasonal forecast skill have focused on a single temporal or spatial resolution ? often the model resolution ? even though it?s recognized that the fidelity of forecast skill deceases with lead time. In this work, we analyze ?canonical? forecast events, which are defined as space-time averaged forecasts that range from 2-week forecasts at leads 0 to 7 months to single 8 month seasonal average forecast (52 events), and spatially from forecasts for a single grid to an average forecast for an 8x8 grid area (9 events). Together this results in 468 canonical events per forecast, and 5612 events per year. To better understand the seasonal space-time skill, a probabilistic predictability metric based on model skill was developed across temporal and spatial scales; i.e. for the canonical events. This probabilistic predictability metric is demonstrated using the 28 year hindcast data set from NCEP?s Climate Forecast System version 2 for forecast of precipitation and daily maximum and minimum temperature. Additionally, the attribution of this skill to the El-Nino Southern Oscillation (ENSO) over the contiguous United States is also explored. The results show clear seasonal and spatial patterns of predictability that vary with each forecast variable and

  1. Canonical integration and analysis of periodic maps using non-standard analysis and life methods

    Energy Technology Data Exchange (ETDEWEB)

    Forest, E.; Berz, M.

    1988-06-01

    We describe a method and a way of thinking which is ideally suited for the study of systems represented by canonical integrators. Starting with the continuous description provided by the Hamiltonians, we replace it by a succession of preferably canonical maps. The power series representation of these maps can be extracted with a computer implementation of the tools of Non-Standard Analysis and analyzed by the same tools. For a nearly integrable system, we can define a Floquet ring in a way consistent with our needs. Using the finite time maps, the Floquet ring is defined only at the locations s/sub i/ where one perturbs or observes the phase space. At most the total number of locations is equal to the total number of steps of our integrator. We can also produce pseudo-Hamiltonians which describe the motion induced by these maps. 15 refs., 1 fig.

  2. Fluid Dynamic Models for Bhattacharyya-Based Discriminant Analysis.

    Science.gov (United States)

    Noh, Yung-Kyun; Hamm, Jihun; Park, Frank Chongwoo; Zhang, Byoung-Tak; Lee, Daniel D

    2018-01-01

    Classical discriminant analysis attempts to discover a low-dimensional subspace where class label information is maximally preserved under projection. Canonical methods for estimating the subspace optimize an information-theoretic criterion that measures the separation between the class-conditional distributions. Unfortunately, direct optimization of the information-theoretic criteria is generally non-convex and intractable in high-dimensional spaces. In this work, we propose a novel, tractable algorithm for discriminant analysis that considers the class-conditional densities as interacting fluids in the high-dimensional embedding space. We use the Bhattacharyya criterion as a potential function that generates forces between the interacting fluids, and derive a computationally tractable method for finding the low-dimensional subspace that optimally constrains the resulting fluid flow. We show that this model properly reduces to the optimal solution for homoscedastic data as well as for heteroscedastic Gaussian distributions with equal means. We also extend this model to discover optimal filters for discriminating Gaussian processes and provide experimental results and comparisons on a number of datasets.

  3. Multiset Canonical Correlations Analysis and Multispectral, Truly Multitemporal Remote Sensing Data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2002-01-01

    This paper describes two- and multiset canonical correlations analysis (CCA) for data fusion, multi-source, multiset or multi-temporal exploratory data analysis. These techniques transform multivariate multiset data into new orthogonal variables called canonical variates (CVs) which when applied....... This difference is ascribed to the noise structure in the data. The CCA methods are related to partial least squares (PLS) methods. The paper very briefly describes multiset CCA based multiset PLS. Also, the CCA methods can be applied as multivariate extensions to empirical orthogonal functions (EOF) techniques....... (Multiset) CCA is well suited for inclusion in geographical information systems, GIS....

  4. Change detection in bi-temporal data by canonical information analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

    2015-01-01

    combinations with the information theoretical measure mutual information (MI). We term this type of analysis canonical information analysis (CIA). MI allows for the actual joint distribution of the variables involved and not just second order statistics. Where CCA is ideal for Gaussian data, CIA facilitates...

  5. USING DISCRIMINANT ANALYSIS IN RELATIONSHIP MARKETING

    OpenAIRE

    Iacob Catoiu; Mihai Èšichindelean; Simona Vinerean

    2013-01-01

    The purpose of the present paper is to describe and apply discriminant analysis withina relationship marketing context. The paper is structured into two parts; the first part contains aliterature review regarding the value chain concept and the dimensions it is built on, while thesecond part includes the results of applying discriminant analysis on several value chaindimensions. The authors have considered the client-company relationships of the gas-station marketas proper for studying the di...

  6. Personality Traits as Predictors of Shopping Motivations and Behaviors: A Canonical Correlation Analysis

    OpenAIRE

    Ali Gohary; Kambiz Heidarzadeh Hanzaee

    2014-01-01

    This study examines the relationship between Big Five personality traits with shopping motivation variables consisting of compulsive and impulsive buying, hedonic and utilitarian shopping values. Two hundred forty seven college students were recruited to participate in this research. Bivariate correlation demonstrates an overlap between personality traits; consequently, canonical correlation was performed to prevent this phenomenon. The results of multiple regression analysis suggested consci...

  7. Generalized canonical correlation analysis of matrices with missing rows : A simulation study

    NARCIS (Netherlands)

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

    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

  8. Functional connectivity analysis of fMRI data based on regularized multiset canonical correlation analysis.

    Science.gov (United States)

    Deleus, Filip; Van Hulle, Marc M

    2011-04-15

    In this paper we describe a method for functional connectivity analysis of fMRI data between given brain regions-of-interest (ROIs). The method relies on nonnegativity constrained- and spatially regularized multiset canonical correlation analysis (CCA), and assigns weights to the fMRI signals of the ROIs so that their representative signals become simultaneously maximally correlated. The different pairwise correlations between the representative signals of the ROIs are combined using the maxvar approach for multiset CCA, which has been shown to be equivalent to the generalized eigenvector formulation of CCA. The eigenvector in the maxvar approach gives an indication of the relative importance of each ROI in obtaining a maximal overall correlation, and hence, can be interpreted as a functional connectivity pattern of the ROIs. The successive canonical correlations define subsequent functional connectivity patterns, in decreasing order of importance. We apply our method on synthetic data and real fMRI data and show its advantages compared to unconstrained CCA and to PCA. Furthermore, since the representative signals for the ROIs are optimized for maximal correlation they are also ideally suited for further effective connectivity analyses, to assess the information flows between the ROIs in the brain. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. Canonical and symplectic analysis for three dimensional gravity without dynamics

    International Nuclear Information System (INIS)

    Escalante, Alberto; Osmart Ochoa-Gutiérrez, H.

    2017-01-01

    In this paper a detailed Hamiltonian analysis of three-dimensional gravity without dynamics proposed by V. Hussain is performed. We report the complete structure of the constraints and the Dirac brackets are explicitly computed. In addition, the Faddeev–Jackiw symplectic approach is developed; we report the complete set of Faddeev–Jackiw constraints and the generalized brackets, then we show that the Dirac and the generalized Faddeev–Jackiw brackets coincide to each other. Finally, the similarities and advantages between Faddeev–Jackiw and Dirac’s formalism are briefly discussed. - Highlights: • We report the symplectic analysis for three dimensional gravity without dynamics. • We report the Faddeev–Jackiw constraints. • A pure Dirac’s analysis is performed. • The complete structure of Dirac’s constraints is reported. • We show that symplectic and Dirac’s brackets coincide to each other.

  10. Canonical and symplectic analysis for three dimensional gravity without dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Escalante, Alberto, E-mail: aescalan@ifuap.buap.mx [Instituto de Física, Benemérita Universidad Autónoma de Puebla, Apartado Postal J-48 72570, Puebla, Pue. (Mexico); Osmart Ochoa-Gutiérrez, H. [Facultad de Ciencias Físico Matemáticas, Benemérita Universidad Autónoma de Puebla, Apartado postal 1152, 72001 Puebla, Pue. (Mexico)

    2017-03-15

    In this paper a detailed Hamiltonian analysis of three-dimensional gravity without dynamics proposed by V. Hussain is performed. We report the complete structure of the constraints and the Dirac brackets are explicitly computed. In addition, the Faddeev–Jackiw symplectic approach is developed; we report the complete set of Faddeev–Jackiw constraints and the generalized brackets, then we show that the Dirac and the generalized Faddeev–Jackiw brackets coincide to each other. Finally, the similarities and advantages between Faddeev–Jackiw and Dirac’s formalism are briefly discussed. - Highlights: • We report the symplectic analysis for three dimensional gravity without dynamics. • We report the Faddeev–Jackiw constraints. • A pure Dirac’s analysis is performed. • The complete structure of Dirac’s constraints is reported. • We show that symplectic and Dirac’s brackets coincide to each other.

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

    Science.gov (United States)

    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…

  12. Frontotemporal Dysfunction in Amyotrophic Lateral Sclerosis: A Discriminant Function Analysis.

    Science.gov (United States)

    Nidos, Andreas; Kasselimis, Dimitrios S; Simos, Panagiotis G; Rentzos, Michael; Alexakis, Theodoros; Zalonis, Ioannis; Zouvelou, Vassiliki; Potagas, Constantin; Evdokimidis, Ioannis; Woolley, Susan C

    2016-01-01

    There is growing evidence for extramotor dysfunction (EMd) in amyotrophic lateral sclerosis (ALS), with a reported prevalence of up to 52%. In the present study, we explore the clinical utility of a brief neuropsychological battery for the investigation of cognitive, behavioral, and language deficits in patients with ALS. Thirty-four consecutive ALS patients aged 44-89 years were tested with a brief neuropsychological battery, including executive, behavioral, and language measures. Patients were initially classified as EMd or non-EMd based on their scores on the frontal assessment battery (FAB). Between-group comparisons revealed significant differences in all measures (p < 0.01). Discriminant analysis resulted in a single canonical function, with all tests serving as significant predictors. This function agreed with the FAB in 13 of 17 patients screened as EMd and identified extramotor deficits in 2 additional patients. Overall sensitivity and specificity estimates against FAB were 88.2%. We stress the importance of discriminant function analysis in clinical neuropsychological assessment and argue that the proposed neuropsychological battery may be of clinical value, especially when the option of extensive and comprehensive neuropsychological testing is limited. The psychometric validity of an ALS-frontotemporal dementia diagnosis using neuropsychological tests is also discussed. © 2015 S. Karger AG, Basel.

  13. Linear and Nonlinear Multiset Canonical Correlation Analysis (invited talk)

    DEFF Research Database (Denmark)

    Hilger, Klaus Baggesen; Nielsen, Allan Aasbjerg; Larsen, Rasmus

    2002-01-01

    This paper deals with decompositioning of multiset data. Friedman's alternating conditional expectations (ACE) algorithm is extended to handle multiple sets of variables of different mixtures. The new algorithm finds estimates of the optimal transformations of the involved variables that maximize...... the sum of the pair-wise correlations over all sets. The new algorithm is termed multi-set ACE (MACE) and can find multiple orthogonal eigensolutions. MACE is a generalization of the linear multiset correlations analysis (MCCA). It handles multivariate multisets of arbitrary mixtures of both continuous...

  14. Avicenna and cataracts: a new analysis of contributions to diagnosis and treatment from the canon.

    Science.gov (United States)

    Nejabat, M; Maleki, B; Nimrouzi, M; Mahbodi, A; Salehi, A

    2012-05-01

    Physicians in ancient Persia played an important role in the development of medicine in the medieval era. One of the most influential figures of this era was Abu Ali Sina or Ibn Sina, known as Avicenna in the western world. The author of more than 200 books on medicine and philosophy, Avicenna followed and further expanded on the tradition of western philosophy and medicine introduced by Aristotle, Hippocrates and Galen. Few researchers have looked into the different medical issues in his best known work, the Canon of Medicine, particularly with regard to ophthalmology. In this analysis, Avicenna's views on and contributions to the diagnosis and treatment of cataracts in his Canon were elucidated. We first reviewed an electronic copy of the Canon and then reviewed other important sources in traditional medicine including the Kamel-al-Sanaeh, Al-Havi (Continents) and Zakhireh-kharazmshahi, available in the Avicenna Special Traditional Medicine Library of Shiraz University of Medical Sciences. We also searched Medline, Embase, Scopus, Iranmedex and Science Iranian Database (SID) with these keywords: "traditional medicine," "Avicenna," "cataract", "Canon", "history", "ophthalmology" and "eye disorders". According to the Canon, nozul-al-maa or cataract is an obstructive disease in which external moisture accumulates between the aqueous humor and the corneal membrane and prevents images from entering the eye. Avicenna classified cataracts on the basis of size, density and color. According to size, he identified two types of cataracts including complete and partial obstruction. According to the Canon, surgical intervention was necessary only for certain indications. Avicenna believed that opacity in the initial stages of cataract could be diminished by medicines and foods, and described several medicines for cataracts. He believed that surgery should be postponed until the liquid accumulation stopped, and the cataract reached its mature state. After surgery, according to

  15. Morphological Discrimination of Greek Honey Bee Populations Based on Geometric Morphometrics Analysis of Wing Shape

    Directory of Open Access Journals (Sweden)

    Charistos Leonidas

    2014-06-01

    Full Text Available Honey bees collected from 32 different localities in Greece were studied based on the geometric morphometrics approach using the coordinates of 19 landmarks located at wing vein intersections. Procrustes analysis, principal component analysis, and Canonical variate analysis (CVA detected population variability among the studied samples. According to the Principal component analysis (PCA of pooled data from each locality, the most differentiated populations were the populations from the Aegean island localities Astypalaia, Chios, and Kythira. However, the populations with the most distant according to the canonical variate analysis performed on all measurements were the populations from Heraklion and Chania (both from Crete island. These results can be used as a starting point for the use of geometric morphometrics in the discrimination of honey bee populations in Greece and the establishment of conservation areas for local honey bee populations.

  16. Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis.

    Science.gov (United States)

    Chen, Jun; Bushman, Frederic D; Lewis, James D; Wu, Gary D; Li, Hongzhe

    2013-04-01

    Motivated by studying the association between nutrient intake and human gut microbiome composition, we developed a method for structure-constrained sparse canonical correlation analysis (ssCCA) in a high-dimensional setting. ssCCA takes into account the phylogenetic relationships among bacteria, which provides important prior knowledge on evolutionary relationships among bacterial taxa. Our ssCCA formulation utilizes a phylogenetic structure-constrained penalty function to impose certain smoothness on the linear coefficients according to the phylogenetic relationships among the taxa. An efficient coordinate descent algorithm is developed for optimization. A human gut microbiome data set is used to illustrate this method. Both simulations and real data applications show that ssCCA performs better than the standard sparse CCA in identifying meaningful variables when there are structures in the data.

  17. Calibration model transfer for near-infrared spectra based on canonical correlation analysis.

    Science.gov (United States)

    Fan, Wei; Liang, Yizeng; Yuan, Dalin; Wang, Jiajun

    2008-08-08

    In order to solve the calibration transformation problem in near-infrared (NIR) spectroscopy, a method based on canonical correlation analysis (CCA) for calibration model transfer is developed in this work. Two real NIR data sets were tested. A comparative study between the proposed method and piecewise direct standardization (PDS) was conducted. It is shown that the transfer results obtained with the proposed method based on CCA were better than those obtained by PDS when the subset had sufficient samples.

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

    Science.gov (United States)

    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

  19. Sparse Canonical Correlation Analysis via Truncated ℓ1-norm with Application to Brain Imaging Genetics.

    Science.gov (United States)

    Du, Lei; Zhang, Tuo; Liu, Kefei; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L; Guo, Lei; Saykin, Andrew J; Shen, Li

    2016-01-01

    Discovering bi-multivariate associations between genetic markers and neuroimaging quantitative traits is a major task in brain imaging genetics. Sparse Canonical Correlation Analysis (SCCA) is a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ 1 -norm or its variants. The ℓ 0 -norm is more desirable, which however remains unexplored since the ℓ 0 -norm minimization is NP-hard. In this paper, we impose the truncated ℓ 1 -norm to improve the performance of the ℓ 1 -norm based SCCA methods. Besides, we propose two efficient optimization algorithms and prove their convergence. The experimental results, compared with two benchmark methods, show that our method identifies better and meaningful canonical loading patterns in both simulated and real imaging genetic analyse.

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

    Science.gov (United States)

    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.

  1. Pre-steady state kinetic analysis of HIV-1 reverse transcriptase for non-canonical ribonucleoside triphosphate incorporation and DNA synthesis from ribonucleoside-containing DNA template.

    Science.gov (United States)

    Nguyen, Laura A; Domaoal, Robert A; Kennedy, Edward M; Kim, Dong-Hyun; Schinazi, Raymond F; Kim, Baek

    2015-03-01

    Non-dividing macrophages maintain extremely low cellular deoxyribonucleotide triphosphate (dNTP) levels, but high ribonucleotide triphosphate (rNTP) concentrations. The disparate nucleotide pools kinetically forces Human Immunodeficiency Virus 1 (HIV-1) reverse transcriptase (RT) to incorporate non-canonical rNTPs during reverse transcription. HIV-1 RT pauses near ribonucleoside monophosphates (rNMPs) embedded in the template DNA, which has previously been shown to enhance mismatch extension. Here, pre-steady state kinetic analysis shows rNTP binding affinity (Kd) of HIV-1 RT for non-canonical rNTPs was 1.4- to 43-fold lower, and the rNTP rate of incorporation (kpol) was 15- to 1551-fold slower than for dNTPs. This suggests that RT is more selective for incorporation of dNTPs rather than rNTPs. HIV-1 RT selectivity for dNTP versus rNTP is the lowest for ATP, implying that HIV-1 RT preferentially incorporates ATP when dATP concentration is limited. We observed that incorporation of a dNTP occurring one nucleotide before an embedded rNMP in the template had a 29-fold greater Kd and a 20-fold slower kpol as compared to the same template containing dNMP. This reduced the overall dNTP incorporation efficiency of HIV-1 RT by 581-fold. Finally, the RT mutant Y115F displayed lower discrimination against rNTPs due to its increase in binding affinity for non-canonical rNTPs. Overall, these kinetic results demonstrate that HIV-1 RT utilizes both substrate binding and a conformational change during: (1) enzymatic discrimination of non-canonical rNTPs from dNTPs and (2) during dNTP primer extension with DNA templates containing embedded rNMP. Copyright © 2014 Elsevier B.V. All rights reserved.

  2. Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method.

    Science.gov (United States)

    Du, Lei; Huang, Heng; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon L; Inlow, Mark; Moore, Jason H; Saykin, Andrew J; Shen, Li

    2016-05-15

    Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated. We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations. The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/angscca/ shenli@iu.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  3. Regularized Discriminant Analysis: A Large Dimensional Study

    KAUST Repository

    Yang, Xiaoke

    2018-04-28

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

  4. A Computational Discriminability Analysis on Twin Fingerprints

    Science.gov (United States)

    Liu, Yu; Srihari, Sargur N.

    Sharing similar genetic traits makes the investigation of twins an important study in forensics and biometrics. Fingerprints are one of the most commonly found types of forensic evidence. The similarity between twins’ prints is critical establish to the reliability of fingerprint identification. We present a quantitative analysis of the discriminability of twin fingerprints on a new data set (227 pairs of identical twins and fraternal twins) recently collected from a twin population using both level 1 and level 2 features. Although the patterns of minutiae among twins are more similar than in the general population, the similarity of fingerprints of twins is significantly different from that between genuine prints of the same finger. Twins fingerprints are discriminable with a 1.5%~1.7% higher EER than non-twins. And identical twins can be distinguished by examine fingerprint with a slightly higher error rate than fraternal twins.

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

    Directory of Open Access Journals (Sweden)

    Cristina eCampi

    2013-06-01

    Full Text Available We consider the problem of analysing magnetoencephalography (MEG data measured from two persons undergoing the same experiment, and we propose a method that searches for sources with maximally correlated energies. Our method is based on canonical correlation analysis (CCA, which provides linear transformations, one for each subject, such that the correlation between the transformed MEG signals is maximized. Here, we present a nonlinear version of CCA which measures the correlation of energies. Furthermore, we introduce a delay parameter in the modelto analyse, e.g., leader-follower changes in experiments where the two subjects are engaged in social interaction.

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

    Science.gov (United States)

    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.

  7. Noether analysis of the twisted Hopf symmetries of canonical noncommutative spacetimes

    International Nuclear Information System (INIS)

    Amelino-Camelia, Giovanni; Gubitosi, Giulia; Marciano, Antonino; Martinetti, Pierre; Mercati, Flavio; Briscese, Fabio

    2008-01-01

    We study the twisted Hopf-algebra symmetries of observer-independent canonical spacetime noncommutativity, for which the commutators of the spacetime coordinates take the form [x^ μ ,x^ ν ]=iθ μν with observer-independent (and coordinate-independent) θ μν . We find that it is necessary to introduce nontrivial commutators between transformation parameters and spacetime coordinates, and that the form of these commutators implies that all symmetry transformations must include a translation component. We show that with our noncommutative transformation parameters the Noether analysis of the symmetries is straightforward, and we compare our canonical-noncommutativity results with the structure of the conserved charges and the ''no-pure-boost'' requirement derived in a previous study of κ-Minkowski noncommutativity. We also verify that, while at intermediate stages of the analysis we do find terms that depend on the ordering convention adopted in setting up the Weyl map, the final result for the conserved charges is reassuringly independent of the choice of Weyl map and (the corresponding choice of) star product.

  8. Discrimination by means of components that are orthogonal in the data space

    NARCIS (Netherlands)

    Kiers, Henk A.L.

    1997-01-01

    Krzanowski (J. Chemometrics, 9, 509 (1995)) proposed a method for obtaining so-called orthogonal canonical variates (henceforth called components) for discrimination purposes. In contrast with ordinary discriminant analysis, this method employs components that are orthogonal in the original data

  9. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin

    2015-04-03

    © 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

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

    Science.gov (United States)

    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.

  11. Personality Traits as Predictors of Shopping Motivations and Behaviors: A Canonical Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Ali Gohary

    2014-10-01

    Full Text Available This study examines the relationship between Big Five personality traits with shopping motivation variables consisting of compulsive and impulsive buying, hedonic and utilitarian shopping values. Two hundred forty seven college students were recruited to participate in this research. Bivariate correlation demonstrates an overlap between personality traits; consequently, canonical correlation was performed to prevent this phenomenon. The results of multiple regression analysis suggested conscientiousness, neuroticism and openness as predictors of compulsive buying, impulsive buying and utilitarian shopping values. In addition, the results showed significant differences between males and females on conscientiousness, neuroticism, openness, compulsive buying and hedonic shopping value. Besides, using hierarchical regression analysis, we examined sex as moderator between Big Five personality traits and shopping variables, but we didn’t find sufficient evidence to prove it.

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

    Science.gov (United States)

    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.

  13. Calibration transfer of near-infrared spectroscopy by canonical correlation analysis coupled with wavelet transform.

    Science.gov (United States)

    Bin, Jun; Li, Xin; Fan, Wei; Zhou, Ji-Heng; Wang, Cheng-Wei

    2017-06-21

    Calibration model transfer has played a prominent role in the practical application of NIR spectral analysis. The change of instruments and sample physical states may lead to variation of the NIR spectrum, which results in the applicability of the model in judicatory practice being unsatisfactory. Therefore, a transfer for the calibration model considering both the variation of instruments and sample states is a necessity to ensure its availability. In this paper, a novel approach, namely canonical correlation analysis coupled with wavelet transform (WTCCA), was proposed for calibration transfer between two near infrared spectrometers (a portable and a laboratory instrument), and simultaneously, among three physical states (tobacco powder, tobacco filament and intact leaf) to determine the content of total sugars, reducing sugars, and nicotine in tobacco leaf samples, respectively. Wavelet transform (WT) is introduced to reduce noise and deduct background shifts from the spectra by compression, and then, calibration transfer by canonical correlation analysis (CTCCA) extracts the compressed spectral similarities using canonical scores for spectra correction. Three similar standardization algorithms, including piecewise direct standardization (PDS), piecewise direct standardization with wavelet transform (WTPDS), and CTCCA were compared with WTCCA to evaluate its relative performance. The obtained results showed that the employment of WTCCA yielded the lowest root mean standard error of prediction (RMSEP) on the three analytes in three physical states. For the tobacco powder dataset, the RMSEP values had a reduction of 25.83%, 13.96%, and 14.22% compared with the values of direct prediction without spectra transfer, respectively. For the tobacco filament dataset, the corresponding values were decreased by 18.06%, 14.90%, and 13.61% and for the intact leaf dataset, the values had dropped by 10.70%, 18.21%, and 28.21%, respectively. In summary, the comprehensive

  14. The integrated model of sport confidence: a canonical correlation and mediational analysis.

    Science.gov (United States)

    Koehn, Stefan; Pearce, Alan J; Morris, Tony

    2013-12-01

    The main purpose of the study was to examine crucial parts of Vealey's (2001) integrated framework hypothesizing that sport confidence is a mediating variable between sources of sport confidence (including achievement, self-regulation, and social climate) and athletes' affect in competition. The sample consisted of 386 athletes, who completed the Sources of Sport Confidence Questionnaire, Trait Sport Confidence Inventory, and Dispositional Flow Scale-2. Canonical correlation analysis revealed a confidence-achievement dimension underlying flow. Bias-corrected bootstrap confidence intervals in AMOS 20.0 were used in examining mediation effects between source domains and dispositional flow. Results showed that sport confidence partially mediated the relationship between achievement and self-regulation domains and flow, whereas no significant mediation was found for social climate. On a subscale level, full mediation models emerged for achievement and flow dimensions of challenge-skills balance, clear goals, and concentration on the task at hand.

  15. Data-driven fault detection for industrial processes canonical correlation analysis and projection based methods

    CERN Document Server

    Chen, Zhiwen

    2017-01-01

    Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed. Contents A New Index for Performance Evaluation of FD Methods CCA-based FD Method for the Monitoring of Stationary Processes Projection-based FD Method for the Monitoring of Dynamic Processes Benchmark Study and Real-Time Implementat...

  16. A canonical correlation analysis based EMG classification algorithm for eliminating electrode shift effect.

    Science.gov (United States)

    Zhe Fan; Zhong Wang; Guanglin Li; Ruomei Wang

    2016-08-01

    Motion classification system based on surface Electromyography (sEMG) pattern recognition has achieved good results in experimental condition. But it is still a challenge for clinical implement and practical application. Many factors contribute to the difficulty of clinical use of the EMG based dexterous control. The most obvious and important is the noise in the EMG signal caused by electrode shift, muscle fatigue, motion artifact, inherent instability of signal and biological signals such as Electrocardiogram. In this paper, a novel method based on Canonical Correlation Analysis (CCA) was developed to eliminate the reduction of classification accuracy caused by electrode shift. The average classification accuracy of our method were above 95% for the healthy subjects. In the process, we validated the influence of electrode shift on motion classification accuracy and discovered the strong correlation with correlation coefficient of >0.9 between shift position data and normal position data.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  19. Adaptive Kernel Canonical Correlation Analysis Algorithms for Nonparametric Identification of Wiener and Hammerstein Systems

    Directory of Open Access Journals (Sweden)

    Ignacio Santamaría

    2008-04-01

    Full Text Available This paper treats the identification of nonlinear systems that consist of a cascade of a linear channel and a nonlinearity, such as the well-known Wiener and Hammerstein systems. In particular, we follow a supervised identification approach that simultaneously identifies both parts of the nonlinear system. Given the correct restrictions on the identification problem, we show how kernel canonical correlation analysis (KCCA emerges as the logical solution to this problem. We then extend the proposed identification algorithm to an adaptive version allowing to deal with time-varying systems. In order to avoid overfitting problems, we discuss and compare three possible regularization techniques for both the batch and the adaptive versions of the proposed algorithm. Simulations are included to demonstrate the effectiveness of the presented algorithm.

  20. Canonical Least-Squares Monte Carlo Valuation of American Options: Convergence and Empirical Pricing Analysis

    Directory of Open Access Journals (Sweden)

    Xisheng Yu

    2014-01-01

    Full Text Available The paper by Liu (2010 introduces a method termed the canonical least-squares Monte Carlo (CLM which combines a martingale-constrained entropy model and a least-squares Monte Carlo algorithm to price American options. In this paper, we first provide the convergence results of CLM and numerically examine the convergence properties. Then, the comparative analysis is empirically conducted using a large sample of the S&P 100 Index (OEX puts and IBM puts. The results on the convergence show that choosing the shifted Legendre polynomials with four regressors is more appropriate considering the pricing accuracy and the computational cost. With this choice, CLM method is empirically demonstrated to be superior to the benchmark methods of binominal tree and finite difference with historical volatilities.

  1. Coherence analysis using canonical coordinate decomposition with applications to sparse processing and optimal array deployment

    Science.gov (United States)

    Azimi-Sadjadi, Mahmood R.; Pezeshki, Ali; Wade, Robert L.

    2004-09-01

    Sparse array processing methods are typically used to improve the spatial resolution of sensor arrays for the estimation of direction of arrival (DOA). The fundamental assumption behind these methods is that signals that are received by the sparse sensors (or a group of sensors) are coherent. However, coherence may vary significantly with the changes in environmental, terrain, and, operating conditions. In this paper canonical correlation analysis is used to study the variations in coherence between pairs of sub-arrays in a sparse array problem. The data set for this study is a subset of an acoustic signature data set, acquired from the US Army TACOM-ARDEC, Picatinny Arsenal, NJ. This data set is collected using three wagon-wheel type arrays with five microphones. The results show that in nominal operating conditions, i.e. no extreme wind noise or masking effects by trees, building, etc., the signals collected at different sensor arrays are indeed coherent even at distant node separation.

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

    International Nuclear Information System (INIS)

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

    2009-01-01

    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.

  3. Personality disorders in substance abusers: Validation of the DIP-Q through principal components factor analysis and canonical correlation analysis

    Directory of Open Access Journals (Sweden)

    Hesse Morten

    2005-05-01

    Full Text Available Abstract Background Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. Methods The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133, and (b a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. Results It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. Conclusion The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers.

  4. Finite canonization

    OpenAIRE

    Shelah, Saharon

    1995-01-01

    The canonization theorem says that for given m,n for some m^* (the first one is called ER(n;m)) we have: for every function f with domain [{1, ...,m^*}]^n, for some A in [{1, ...,m^*}]^m, the question of when the equality f({i_1, ...,i_n})=f({j_1, ...,j_n}) (where i_1< ...

  5. Using discriminant analysis for credit decision

    Directory of Open Access Journals (Sweden)

    Gheorghiţa DINCĂ

    2015-12-01

    Full Text Available This paper follows to highlight the link between the results obtained applying discriminant analysis and lending decision. For this purpose, we have carried out the research on a sample of 24 Romanian private companies, pertaining to 12 different economic sectors, from I and II categories of Bucharest Stock Exchange, for the period 2010-2012. Our study works with two popular bankruptcy risk’s prediction models, the Altman model and the Anghel model. We have double-checked and confirmed the results of our research by comparing the results from applying the two fore-mentioned models as well as by checking existing debt commitments of each analyzed company to credit institutions during the 2010-2012 period. The aim of this paper was the classification of studied companies into potential bankrupt and non-bankrupt, to assist credit institutions in their decision to grant credit, understanding the approval or rejection algorithm of loan applications and even help potential investors in these ompanies.

  6. Side Channel Authenticity Discriminant Analysis for Device Class Identification

    Science.gov (United States)

    2016-03-31

    Side Channel Authenticity Discriminant Analysis for Device Class Identification Eric Koziel, Kate...include additional identification components. We instead propose Side Channel Authenticity Discriminant Analysis (SICADA) to leverage physical phenomena...manifesting from device operation to match suspect parts to a class of authentic parts. This paper examines the extent that power dissipation

  7. Combination of canonical correlation analysis and empirical mode decomposition applied to denoising the labor electrohysterogram.

    Science.gov (United States)

    Hassan, Mahmoud; Boudaoud, Sofiane; Terrien, Jérémy; Karlsson, Brynjar; Marque, Catherine

    2011-09-01

    The electrohysterogram (EHG) is often corrupted by electronic and electromagnetic noise as well as movement artifacts, skeletal electromyogram, and ECGs from both mother and fetus. The interfering signals are sporadic and/or have spectra overlapping the spectra of the signals of interest rendering classical filtering ineffective. In the absence of efficient methods for denoising the monopolar EHG signal, bipolar methods are usually used. In this paper, we propose a novel combination of blind source separation using canonical correlation analysis (BSS_CCA) and empirical mode decomposition (EMD) methods to denoise monopolar EHG. We first extract the uterine bursts by using BSS_CCA then the biggest part of any residual noise is removed from the bursts by EMD. Our algorithm, called CCA_EMD, was compared with wavelet filtering and independent component analysis. We also compared CCA_EMD with the corresponding bipolar signals to demonstrate that the new method gives signals that have not been degraded by the new method. The proposed method successfully removed artifacts from the signal without altering the underlying uterine activity as observed by bipolar methods. The CCA_EMD algorithm performed considerably better than the comparison methods.

  8. Prediction of East African Seasonal Rainfall Using Simplex Canonical Correlation Analysis.

    Science.gov (United States)

    Ntale, Henry K.; Yew Gan, Thian; Mwale, Davison

    2003-06-01

    A linear statistical model, canonical correlation analysis (CCA), was driven by the Nelder-Mead simplex optimization algorithm (called CCA-NMS) to predict the standardized seasonal rainfall totals of East Africa at 3-month lead time using SLP and SST anomaly fields of the Indian and Atlantic Oceans combined together by 24 simplex optimized weights, and then `reduced' by the principal component analysis. Applying the optimized weights to the predictor fields produced better March-April-May (MAM) and September-October-November (SON) seasonal rain forecasts than a direct application of the same, unweighted predictor fields to CCA at both calibration and validation stages. Northeastern Tanzania and south-central Kenya had the best SON prediction results with both validation correlation and Hanssen-Kuipers skill scores exceeding +0.3. The MAM season was better predicted in the western parts of East Africa. The CCA correlation maps showed that low SON rainfall in East Africa is associated with cold SSTs off the Somali coast and the Benguela (Angola) coast, and low MAM rainfall is associated with a buildup of low SSTs in the Indian Ocean adjacent to East Africa and the Gulf of Guinea.

  9. Group Study of Simulated Driving fMRI Data by Multiset Canonical Correlation Analysis.

    Science.gov (United States)

    Li, Yi-Ou; Adalı, Tulay; Calhoun, Vince D

    2012-07-01

    In this work, we apply a novel statistical method, multiset canonical correlation analysis (M-CCA), to study a group of functional magnetic resonance imaging (fMRI) datasets acquired during simulated driving task. The M-CCA method jointly decomposes fMRI datasets from different subjects/sessions into brain activation maps and their associated time courses, such that the correlation in each group of estimated activation maps across datasets is maximized. Therefore, the functional activations across all datasets are extracted in the order of consistency across different dataset. On the other hand, M-CCA preserves the uniqueness of the functional maps estimated from each dataset by avoiding concatenation of different datasets in the analysis. Hence, the cross-dataset variation of the functional activations can be used to test the hypothesis of functional-behavioral association. In this work, we study 120 simulated driving fMRI datasets and identify parietal-occipital regions and frontal lobe as the most consistently engaged areas across all the subjects and sessions during simulated driving. The functional-behavioral association study indicates that all the estimated brain activations are significantly correlated with the steering operation during the driving task. M-CCA thus provides a new approach to investigate the complex relationship between the brain functions and multiple behavioral variables, especially in naturalistic tasks as demonstrated by the simulated driving study.

  10. Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

    Directory of Open Access Journals (Sweden)

    Chin-Teng Lin

    2018-01-01

    Full Text Available Electroencephalogram (EEG signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA, feature extraction, and the Gaussian mixture model (GMM to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

  11. Contributions to sensitivity analysis and generalized discriminant analysis

    International Nuclear Information System (INIS)

    Jacques, J.

    2005-12-01

    Two topics are studied in this thesis: sensitivity analysis and generalized discriminant analysis. Global sensitivity analysis of a mathematical model studies how the output variables of this last react to variations of its inputs. The methods based on the study of the variance quantify the part of variance of the response of the model due to each input variable and each subset of input variables. The first subject of this thesis is the impact of a model uncertainty on results of a sensitivity analysis. Two particular forms of uncertainty are studied: that due to a change of the model of reference, and that due to the use of a simplified model with the place of the model of reference. A second problem was studied during this thesis, that of models with correlated inputs. Indeed, classical sensitivity indices not having significance (from an interpretation point of view) in the presence of correlation of the inputs, we propose a multidimensional approach consisting in expressing the sensitivity of the output of the model to groups of correlated variables. Applications in the field of nuclear engineering illustrate this work. Generalized discriminant analysis consists in classifying the individuals of a test sample in groups, by using information contained in a training sample, when these two samples do not come from the same population. This work extends existing methods in a Gaussian context to the case of binary data. An application in public health illustrates the utility of generalized discrimination models thus defined. (author)

  12. A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval.

    Science.gov (United States)

    Cai, Jia; Tang, Yi

    2018-02-01

    Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. [Investigation and canonical correspondence analysis of salinity contents in secondary salinization greenhouse soils in Shanghai suburb].

    Science.gov (United States)

    Tang, Dong; Mao, Liang; Zhi, Yue-e; Zhang, Jin-Zhong; Zhou, Pei; Chai, Xiao-Tong

    2014-12-01

    The salinity characteristics of greenhouse soils with cropping obstacles in Shanghai suburb were investigated and analyzed. The salinity contents of the salinization greenhouse soils showed a trend of first increasing and then decreasing with the increasing cropping duration. The salinized soils mainly included slightly salted, mildly salted and salted soils, which accounted for 17.39%, 56.52% and 13.04%, respectively. Among them, the degree of salinity in greenhouse soil planted with asparagus in Chongming County was the highest. Among the salt ions in greenhouse soils, the cations were mainly Ca2+ and Na+, while the anions were mainly NO3- and SO4(2-). The degree of salinity was mainly influenced by fertilization mode, cropping duration, crop type and management level, which led to the great variation in the salinity contents and salt ions. Canonical correspondence analysis found that the contents of Ca2+, Mg2+ and NO3- in greenhouse soils were greatly affected by cropping duration, and the degree of salinity would be enhanced and attenuated with long-term application of single fertilizer and mixed application of chemical fertilizer and organic manure, respectively. The greenhouse soils in Shanghai suburb could be classified as four patterns influenced by the relationship between salinity ions and samples, and the most soils were influenced by Ca2+, Mg2+, NO3- and Cl-, which required to be primarily controlled.

  14. Sparse and smooth canonical correlation analysis through rank-1 matrix approximation

    Science.gov (United States)

    Aïssa-El-Bey, Abdeldjalil; Seghouane, Abd-Krim

    2017-12-01

    Canonical correlation analysis (CCA) is a well-known technique used to characterize the relationship between two sets of multidimensional variables by finding linear combinations of variables with maximal correlation. Sparse CCA and smooth or regularized CCA are two widely used variants of CCA because of the improved interpretability of the former and the better performance of the later. So far, the cross-matrix product of the two sets of multidimensional variables has been widely used for the derivation of these variants. In this paper, two new algorithms for sparse CCA and smooth CCA are proposed. These algorithms differ from the existing ones in their derivation which is based on penalized rank-1 matrix approximation and the orthogonal projectors onto the space spanned by the two sets of multidimensional variables instead of the simple cross-matrix product. The performance and effectiveness of the proposed algorithms are tested on simulated experiments. On these results, it can be observed that they outperform the state of the art sparse CCA algorithms.

  15. Study on soil water characteristics of tobacco fields based on canonical correlation analysis

    Directory of Open Access Journals (Sweden)

    Xiao-hou Shao

    2009-06-01

    Full Text Available In order to identify the principal factors influencing soil water characteristics (SWC and evaluate SWC effectively, the multivariate-statistical canonical correlation analysis (CCA method was used to study and analyze the correlation between SWC and soil physical and chemical properties. Twenty-two soil samples were taken from 11 main tobacco-growing areas in Guizhou Province in China and the soil water characteristic curves (SWCC and basic physical and chemical properties of the soil samples were determined. The results show that: (1 The soil bulk density, soil total porosity and soil capillary porosity have significant effects on SWC of tobacco fiels. Bulk density and total porosity are positively correlated with soil water retention characteristics (SWRC, and soil capillary porosity is positively correlated with soil water supply characteristics (SWSC. (2 Soil samples from different soil layers at the same soil sampling point show similarity or consistency in SWC. Inadequate soil water supply capability and imbalance between SWRC and SWSC are problems of tobacco soil. (3 The SWC of loamy clay are generally superior to those of silty clay loam.

  16. Canonical Correlation Analysis Between Supply Chain Quality Management And Competitive Advantages

    Directory of Open Access Journals (Sweden)

    Chaghooshi Ahmad Jafarnejad

    2015-06-01

    Full Text Available Competitive environment of today’s organizations, more than ever, is extensive, and the major concern for managers is to preserve and promote the sustainable competitive advantage. Companies have an obligation to improve their product quality and have extensive and close cooperation with other companies involved in the supply chain of products. Supply chain quality management (SCQM is a systematic approach to improve the performance that integrates supply chain partners and uses the opportunity in the best way, establish linkages between upstream and downstream flows, and investigate on creating value and satisfaction of intermediaries and final customers. Furthermore, achieving competitive advantages enables an organization to create a remarkable position in market and differentiate itself from competitors. This paper aims to understand the relationships between SCQM and competitive advantage. Sixty-eight experts of 25 companies in Sahami Alyaf (SA supply chain has been participated in this research. The research method used for this article is descriptive correlation. To assess the relationships between the criteria, canonical correlation analysis was used. The result shows that the SCQM and competitive advantages have a meaningful relationship. It also shows that most important variable in the linear combination of SCQM and competitive advantages are “customer focus and quality,” respectively.

  17. Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal.

    Science.gov (United States)

    Roy, Vandana; Shukla, Shailja; Shukla, Piyush Kumar; Rawat, Paresh

    2017-01-01

    The motion generated at the capturing time of electro-encephalography (EEG) signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA) for removing artifacts along with ensemble empirical mode decomposition (EEMD) and wavelet transform (WT). A new approach is proposed to further analyse and improve the filtering performance and reduce the filter computation time under highly noisy environment. This new approach of CCA is based on Gaussian elimination method which is used for calculating the correlation coefficients using backslash operation and is designed for EEG signal motion artifact removal. Gaussian elimination is used for solving linear equation to calculate Eigen values which reduces the computation cost of the CCA method. This novel proposed method is tested against currently available artifact removal techniques using EEMD-CCA and wavelet transform. The performance is tested on synthetic and real EEG signal data. The proposed artifact removal technique is evaluated using efficiency matrices such as del signal to noise ratio (DSNR), lambda ( λ ), root mean square error (RMSE), elapsed time, and ROC parameters. The results indicate suitablity of the proposed algorithm for use as a supplement to algorithms currently in use.

  18. Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal

    Directory of Open Access Journals (Sweden)

    Vandana Roy

    2017-01-01

    Full Text Available The motion generated at the capturing time of electro-encephalography (EEG signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA for removing artifacts along with ensemble empirical mode decomposition (EEMD and wavelet transform (WT. A new approach is proposed to further analyse and improve the filtering performance and reduce the filter computation time under highly noisy environment. This new approach of CCA is based on Gaussian elimination method which is used for calculating the correlation coefficients using backslash operation and is designed for EEG signal motion artifact removal. Gaussian elimination is used for solving linear equation to calculate Eigen values which reduces the computation cost of the CCA method. This novel proposed method is tested against currently available artifact removal techniques using EEMD-CCA and wavelet transform. The performance is tested on synthetic and real EEG signal data. The proposed artifact removal technique is evaluated using efficiency matrices such as del signal to noise ratio (DSNR, lambda (λ, root mean square error (RMSE, elapsed time, and ROC parameters. The results indicate suitablity of the proposed algorithm for use as a supplement to algorithms currently in use.

  19. 3D spatially-adaptive canonical correlation analysis: Local and global methods.

    Science.gov (United States)

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

    2018-04-01

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

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

    Science.gov (United States)

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

    2017-06-01

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

  1. PRICE DISCRIMINATION AND MARKET POWER: A THEORETICAL ANALYSIS

    Directory of Open Access Journals (Sweden)

    Olga Smirnova

    2015-07-01

    Full Text Available This paper analyzes the contemporary theoretical and empirical research in the field of impact assessment of market power and conclusions about the possibilities of the company to implement price discrimination in different market structures. The results of the analysis allow to evaluate current approaches to antitrust regulation of price discrimination.

  2. Discrete Discriminant analysis based on tree-structured graphical models

    DEFF Research Database (Denmark)

    Perez de la Cruz, Gonzalo; Eslava, Guillermina

    The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant...

  3. Rainfall prediction of Cimanuk watershed regions with canonical correlation analysis (CCA)

    Science.gov (United States)

    Rustiana, Shailla; Nurani Ruchjana, Budi; Setiawan Abdullah, Atje; Hermawan, Eddy; Berliana Sipayung, Sinta; Gede Nyoman Mindra Jaya, I.; Krismianto

    2017-10-01

    Rainfall prediction in Indonesia is very influential on various development sectors, such as agriculture, fisheries, water resources, industry, and other sectors. The inaccurate predictions can lead to negative effects. Cimanuk watershed is one of the main pillar of water resources in West Java. This watersheds divided into three parts, which is a headwater of Cimanuk sub-watershed, Middle of Cimanuk sub-watershed and downstream of Cimanuk sub- watershed. The flow of this watershed will flow through the Jatigede reservoir and will supply water to the north-coast area in the next few years. So, the reliable model of rainfall prediction is very needed in this watershed. Rainfall prediction conducted with Canonical Correlation Analysis (CCA) method using Climate Predictability Tool (CPT) software. The prediction is every 3months on 2016 (after January) based on Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over West Java. Predictors used in CPT were the monthly data index of Nino3.4, Dipole Mode (DMI), and Monsoon Index (AUSMI-ISMI-WNPMI-WYMI) with initial condition January. The initial condition is chosen by the last data update. While, the predictant were monthly rainfall data CHIRPS region of West Java. The results of prediction rainfall showed by skill map from Pearson Correlation. High correlation of skill map are on MAM (Mar-Apr-May), AMJ (Apr-May-Jun), and JJA (Jun-Jul-Aug) which means the model is reliable to forecast rainfall distribution over Cimanuk watersheds region (over West Java) on those seasons. CCA score over those season prediction mostly over 0.7. The accuracy of the model CPT also indicated by the Relative Operating Characteristic (ROC) curve of the results of Pearson correlation 3 representative point of sub-watershed (Sumedang, Majalengka, and Cirebon), were mostly located in the top line of non-skill, and evidenced by the same of rainfall patterns between observation and forecast. So, the model of CPT with CCA method

  4. Analysis of input variables of an artificial neural network using bivariate correlation and canonical correlation

    International Nuclear Information System (INIS)

    Costa, Valter Magalhaes; Pereira, Iraci Martinez

    2011-01-01

    The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because a previous diagnosis allows the correction of the fault and, like this, to prevent the production stopped, improving operator's security and it's not provoking economics losses. The objective of this work is to build a set, using bivariate correlation and canonical correlation, which will be the set of input variables of an artificial neural network to monitor the greater number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. Initially, for the input set of neural network we selected the variables: nuclear power, primary circuit flow rate, control/safety rod position and difference in pressure in the core of the reactor, because almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The nuclear power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures; the primary circuit flow rate has the function of energy transport by removing the nucleus heat. An artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables. (author)

  5. Analysis of input variables of an artificial neural network using bivariate correlation and canonical correlation

    Energy Technology Data Exchange (ETDEWEB)

    Costa, Valter Magalhaes; Pereira, Iraci Martinez, E-mail: valter.costa@usp.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2011-07-01

    The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because a previous diagnosis allows the correction of the fault and, like this, to prevent the production stopped, improving operator's security and it's not provoking economics losses. The objective of this work is to build a set, using bivariate correlation and canonical correlation, which will be the set of input variables of an artificial neural network to monitor the greater number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. Initially, for the input set of neural network we selected the variables: nuclear power, primary circuit flow rate, control/safety rod position and difference in pressure in the core of the reactor, because almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The nuclear power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures; the primary circuit flow rate has the function of energy transport by removing the nucleus heat. An artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables. (author)

  6. School Literary Canon and Teaching of Literature in Middle School: A Critical Analysis of High School Programs in El Salvador

    OpenAIRE

    Aguilar-Ciciliano, Mauricio

    2013-01-01

    This article analyzes the pedagogical-didactic model for the teaching of Literature in Middle School in the Salvadoran Educational system. This is part of a larger work towards a PhD project. The main goal of this project is to characterize the historical process in the construction of this model through a critical analysis of canonization sources. The findings suggest that the teaching of Literature is performed based on a historicist, pro-European, male-based approach. Among the consequence...

  7. Dimensional Analysis with space discrimination applied to Fickian difussion phenomena

    International Nuclear Information System (INIS)

    Diaz Sanchidrian, C.; Castans, M.

    1989-01-01

    Dimensional Analysis with space discrimination is applied to Fickian difussion phenomena in order to transform its partial differen-tial equations into ordinary ones, and also to obtain in a dimensionl-ess fom the Ficks second law. (Author)

  8. Stable locality sensitive discriminant analysis for image recognition.

    Science.gov (United States)

    Gao, Quanxue; Liu, Jingjing; Cui, Kai; Zhang, Hailin; Wang, Xiaogang

    2014-06-01

    Locality Sensitive Discriminant Analysis (LSDA) is one of the prevalent discriminant approaches based on manifold learning for dimensionality reduction. However, LSDA ignores the intra-class variation that characterizes the diversity of data, resulting in unstableness of the intra-class geometrical structure representation and not good enough performance of the algorithm. In this paper, a novel approach is proposed, namely stable locality sensitive discriminant analysis (SLSDA), for dimensionality reduction. SLSDA constructs an adjacency graph to model the diversity of data and then integrates it in the objective function of LSDA. Experimental results in five databases show the effectiveness of the proposed approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Relationships between sensory and physicochemical measurements in meat of rabbit from three different breeding systems using canonical correlation analysis.

    Science.gov (United States)

    Combes, Sylvie; González, Ignacio; Déjean, Sébastien; Baccini, Alain; Jehl, Nathalie; Juin, Hervé; Cauquil, Laurent; Gabinaud, Béatrice; Lebas, François; Larzul, Catherine

    2008-11-01

    Meat from rabbits reared either according to a standard (STAND) or a high quality norm (LABEL) or a low growth breeding (RUSSE) system were submitted to a sensory evaluation and to a large set of physicochemical measurements (weight of retail cuts, colour parameters, ultimate pH, femur flexure test, Warner-Bratzler shear test, water holding capacities and cooking losses). STAND rabbit meat exhibited the most juicy meat in back and in leg (pLABEL>RUSSE. Canonical correlation analysis showed strong correlations between physicochemical and sensory variables (R(2)=0.73 and 0.68 between the two first pairs of canonical variates). Especially, sensory tenderness and WB shear test variables assessed on raw longissimus muscle (LL) were correlated. Fibrous attribute in back was correlated with cooking loss in LL. When analysed separately only RUSSE rabbits exhibited the same relations between variables as those calculated in whole dataset.

  10. The use of the discriminant analysis method for e π μ separation in BES

    International Nuclear Information System (INIS)

    Jiang Zhijin; Wang Taijie; Xie Yigang; Huang Tao

    1994-01-01

    We use the discriminant analysis method in multivariate statistical theory to handle the e π μ separation in BES, describing the principle of the discriminant analysis method, deriving the unstandardized discriminant functions (responsible for particle separation), giving the discriminant efficiency for e π μ and comparing the results from the discriminant analysis method with those obtained in a conventional way. ((orig.))

  11. Gender Differences in and the Relationships Between Social Anxiety and Problematic Internet Use: Canonical Analysis.

    Science.gov (United States)

    Baloğlu, Mustafa; Özteke Kozan, Hatice İrem; Kesici, Şahin

    2018-01-24

    The cognitive-behavioral model of problematic Internet use (PIU) proposes that psychological well-being is associated with specific thoughts and behaviors on the Internet. Hence, there is growing concern that PIU is associated with psychological impairments. Given the proposal of gender schema theory and social role theory, men and women are predisposed to experience social anxiety and engage in Internet use differently. Thus, an investigation of gender differences in these areas is warranted. According to the cognitive-behavioral model of PIU, social anxiety is associated with specific cognitions and behaviors on the Internet. Thus, an investigation of the association between social anxiety and PIU is essential. In addition, research that takes into account the multidimensional nature of social anxiety and PIU is lacking. Therefore, this study aimed to explore multivariate gender differences in and the relationships between social anxiety and PIU. Participants included 505 college students, of whom 241 (47.7%) were women and 264 (52.3%) were men. Participants' ages ranged from 18 to 22 years, with a mean age of 20.34 (SD=1.16). The Social Anxiety Scale and Problematic Internet Use Scale were used in data collection. Multivariate analysis of variance (MANOVA) and canonical correlation analysis were used. Mean differences between men and women were not statistically significant in social anxiety (λ=.02, F3,501=2.47, P=.06). In all three PIU dimensions, men scored higher than women, and MANOVA shows that multivariate difference was statistically significant (λ=.94, F3,501=10.69, Psocial anxiety levels between men and women. We found that men showed more difficulties than women in terms of running away from personal problems (ie, social benefit), used the Internet more excessively, and experienced more interpersonal problems with significant others due to Internet use. We conclude that men are under a greater risk of social impairments due to PIU. Our overall

  12. A Large Dimensional Analysis of Regularized Discriminant Analysis Classifiers

    KAUST Repository

    Elkhalil, Khalil

    2017-11-01

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

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

    Science.gov (United States)

    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

  14. Analysis of input variables of an artificial neural network using bivariate correlation and canonical correlation

    International Nuclear Information System (INIS)

    Costa, Valter Magalhaes

    2011-01-01

    The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because an early diagnosis allows the correction of the fault and, like this, do not cause the production interruption, improving operator's security and it's not provoking economics losses. The objective of this work is, in the whole of all variables monitor of a nuclear power plant, to build a set, not necessary minimum, which will be the set of input variables of an artificial neural network and, like way, to monitor the biggest number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. For this, the variables Power, Rate of flow of primary circuit, Rod of control/security and Difference in pressure in the core of the reactor ( Δ P) was grouped, because, for hypothesis, almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The Power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the Rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures and the Rate of flow of primary circuit has function of the transport of energy by removing of heat of the nucleus Like this, labeling B= {Power, Rate of flow of Primary Circuit, Rod of Control/Security and Δ P} was computed the correlation between B and all another variables monitoring (coefficient of multiple correlation), that is, by the computer of the multiple correlation, that is tool of Theory of Canonical Correlations, was possible to computer how much the set B can predict each variable. Due the impossibility of a satisfactory approximation by B in the prediction of some variables, it was included one or more variables that have high correlation with this variable to improve the quality of prediction. In this work an artificial neural network

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  17. Discrimination of Xihulongjing tea grade using an electronic tongue ...

    African Journals Online (AJOL)

    Five grades of Xihulongjing tea (grade: AAA, AA, A, B and C, from the same region and processed with the same processing method) were discriminated using -Astree II electronic tongue (e-tongue) coupled with pattern recognition methods including principal component analysis (PCA), canonical discriminant analysis ...

  18. Canonical profiles in tokamaks

    International Nuclear Information System (INIS)

    Dnestrovskij, Yu.N.

    2002-01-01

    We consider the problem of the canonical profiles for tokamak plasma with arbitrary cross-section, taking into account two principles: 1) the free plasma energy minimum with the constraint of total current conservation and 2) the profile consistency. We deduce the Euler differential equation for the canonical profile of μ=1/q with two types of the boundary conditions: soft and stiff. The soft conditions correspond to the Kadomtsev solution for the circular cylinder. The stiff conditions describe a fast response of the plasma over the whole cross-section on the edge impact. Using the canonical profile of the current density, we calculate the critical gradients for the temperature, and create the transport model for the electron and ion temperatures and density. We show that, when the aspect ratio is diminished, or when the elongation increases, the canonical profiles become flatten. The similar tendency for the real profiles of the electron temperature was found in analysis of JET and START experiments. The obtained critical gradients were used to analysis of the experiments in tokamaks with moderate and tight aspect ratios. (author)

  19. Discriminant analysis for repeated measures data: a review

    Directory of Open Access Journals (Sweden)

    Lisa Lix

    2010-09-01

    Full Text Available Discriminant analysis (DA encompasses procedures for classifying observations into groups (i.e., predictive discriminative analysis and describing the relative importance of variables for distinguishing amongst groups (i.e., descriptive discriminative analysis. In recent years, a number of developments have occurred in DA procedures for the analysis of data from repeated measures designs. Specifically, DA procedures have been developed for repeated measures data characterized by missing observations and/or unbalanced measurement occasions, as well as high-dimensional data in which measurements are collected repeatedly on two or more variables. This paper reviews the literature on DA procedures for univariate and multivariate repeated measures data, focusing on covariance pattern and linear mixed-effects models. A numeric example illustrates their implementation using SAS software.

  20. Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

    Science.gov (United States)

    Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

    2016-01-01

    Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

  1. Discriminant Function Analysis as a Proof for Sexual Dimorphism ...

    African Journals Online (AJOL)

    Background: Forensic scientists study human skeleton in legal setting. Discriminant function analysis has become important in forensic anthropology. The aim of this study was to determine the sex of adolescent Yoruba ethnic group of Nigeria using iscriminant function analysis. Methodology: One thousand (500 males and ...

  2. Canonical variate regression.

    Science.gov (United States)

    Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun

    2016-07-01

    In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

    Directory of Open Access Journals (Sweden)

    Cheng-Yuan Shih

    2010-01-01

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

  4. Discriminant analysis of functional optical topography for schizophrenia diagnosis

    Science.gov (United States)

    Chuang, Ching-Cheng; Nakagome, Kazuyuki; Pu, Shenghong; Lan, Tsuo-Hung; Lee, Chia-Yen; Sun, Chia-Wei

    2014-01-01

    Abnormal prefrontal function plays a central role in the cognition deficits of schizophrenic patients; however, the character of the relationship between discriminant analysis and prefrontal activation remains undetermined. Recently, evidence of low prefrontal cortex (PFC) activation in individuals with schizophrenia has also been found during verbal fluency tests (VFT) and other cognitive tests with several neuroimaging methods. The purpose of this study is to assess the hemodynamic changes of the PFC and discriminant analysis between schizophrenia patients and healthy controls during VFT task by utilizing functional optical topography. A total of 99 subjects including 53 schizophrenic patients and 46 age- and gender-matched healthy controls were studied. The results showed that the healthy group had larger activation in the right and left PFC than in the middle PFC. Besides, the schizophrenic group showed weaker task performance and lower activation in the whole PFC than the healthy group. The result of the discriminant analysis showed a significant difference with P value diagnosis.

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

    Science.gov (United States)

    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.

  6. Performance of Multi Model Canonical Correlation Analysis (MMCCA) for prediction of Indian summer monsoon rainfall using GCMs output

    Science.gov (United States)

    Singh, Ankita; Acharya, Nachiketa; Mohanty, Uma Charan; Mishra, Gopbandhu

    2013-02-01

    The emerging advances in the field of dynamical prediction of monsoon using state-of-the-art General Circulation Models (GCMs) have led to the development of various multi model ensemble techniques (MMEs). In the present study, the concept of Canonical Correlation Analysis is used for making MME (referred as Multi Model Canonical Correlation Analysis or MMCCA) for the prediction of Indian summer monsoon rainfall (ISMR) during June-July-August-September (JJAS). This method has been employed on the rainfall outputs of six different GCMs for the period 1982 to 2008. The prediction skill of ISMR by MMCCA is compared with the simple composite method (SCM) (i.e. arithmetic mean of all GCMs), which is taken as a benchmark. After a rigorous analysis through different skill metrics such as correlation coefficient and index of agreement, the superiority of MMCCA over SCM is illustrated. Performance of both models is also evaluated during six typical monsoon years and the results indicate the potential of MMCCA over SCM in capturing the spatial pattern during extreme years.

  7. Pharmacokinetic-Pharmacodynamic (PKPD) Analysis with Drug Discrimination.

    Science.gov (United States)

    Negus, S Stevens; Banks, Matthew L

    2016-08-30

    Discriminative stimulus and other drug effects are determined by the concentration of drug at its target receptor and by the pharmacodynamic consequences of drug-receptor interaction. For in vivo procedures such as drug discrimination, drug concentration at receptors in a given anatomical location (e.g., the brain) is determined both by the dose of drug administered and by pharmacokinetic processes of absorption, distribution, metabolism, and excretion that deliver drug to and from that anatomical location. Drug discrimination data are often analyzed by strategies of dose-effect analysis to determine parameters such as potency and efficacy. Pharmacokinetic-Pharmacodynamic (PKPD) analysis is an alternative to conventional dose-effect analysis, and it relates drug effects to a measure of drug concentration in a body compartment (e.g., venous blood) rather than to drug dose. PKPD analysis can yield insights on pharmacokinetic and pharmacodynamic determinants of drug action. PKPD analysis can also facilitate translational research by identifying species differences in pharmacokinetics and providing a basis for integrating these differences into interpretation of drug effects. Examples are discussed here to illustrate the application of PKPD analysis to the evaluation of drug effects in rhesus monkeys trained to discriminate cocaine from saline.

  8. Optimal Feature Selection in High-Dimensional Discriminant Analysis.

    Science.gov (United States)

    Kolar, Mladen; Liu, Han

    2015-02-01

    We consider the high-dimensional discriminant analysis problem. For this problem, different methods have been proposed and justified by establishing exact convergence rates for the classification risk, as well as the ℓ 2 convergence results to the discriminative rule. However, sharp theoretical analysis for the variable selection performance of these procedures have not been established, even though model interpretation is of fundamental importance in scientific data analysis. This paper bridges the gap by providing sharp sufficient conditions for consistent variable selection using the sparse discriminant analysis (Mai et al., 2012). Through careful analysis, we establish rates of convergence that are significantly faster than the best known results and admit an optimal scaling of the sample size n , dimensionality p , and sparsity level s in the high-dimensional setting. Sufficient conditions are complemented by the necessary information theoretic limits on the variable selection problem in the context of high-dimensional discriminant analysis. Exploiting a numerical equivalence result, our method also establish the optimal results for the ROAD estimator (Fan et al., 2012) and the sparse optimal scaling estimator (Clemmensen et al., 2011). Furthermore, we analyze an exhaustive search procedure, whose performance serves as a benchmark, and show that it is variable selection consistent under weaker conditions. Extensive simulations demonstrating the sharpness of the bounds are also provided.

  9. Enamel surface topography analysis for diet discrimination. A methodology to enhance and select discriminative parameters

    Science.gov (United States)

    Francisco, Arthur; Blondel, Cécile; Brunetière, Noël; Ramdarshan, Anusha; Merceron, Gildas

    2018-03-01

    Tooth wear and, more specifically, dental microwear texture is a dietary proxy that has been used for years in vertebrate paleoecology and ecology. DMTA, dental microwear texture analysis, relies on a few parameters related to the surface complexity, anisotropy and heterogeneity of the enamel facets at the micrometric scale. Working with few but physically meaningful parameters helps in comparing published results and in defining levels for classification purposes. Other dental microwear approaches are based on ISO parameters and coupled with statistical tests to find the more relevant ones. The present study roughly utilizes most of the aforementioned parameters in their more or less modified form. But more than parameters, we here propose a new approach: instead of a single parameter characterizing the whole surface, we sample the surface and thus generate 9 derived parameters in order to broaden the parameter set. The identification of the most discriminative parameters is performed with an automated procedure which is an extended and refined version of the workflows encountered in some studies. The procedure in its initial form includes the most common tools, like the ANOVA and the correlation analysis, along with the required mathematical tests. The discrimination results show that a simplified form of the procedure is able to more efficiently identify the desired number of discriminative parameters. Also highlighted are some trends like the relevance of working with both height and spatial parameters, as well as the potential benefits of dimensionless surfaces. On a set of 45 surfaces issued from 45 specimens of three modern ruminants with differences in feeding preferences (grazing, leaf-browsing and fruit-eating), it is clearly shown that the level of wear discrimination is improved with the new methodology compared to the other ones.

  10. A canonical correlation analysis on the relationship between functional fitness and health-related quality of life in older adults.

    Science.gov (United States)

    Chung, Pak-Kwong; Zhao, Yanan; Liu, Jing-Dong; Quach, Binh

    This study aimed to explore the relationship between the functional fitness (FF) and health-related quality of life (HRQoL) in older adults, and to identify the key subdimensions of FF and HRQoL influencing their overall relationship. This cross-sectional study was performed among 851 independent community members (65-84 years; men=402). The Senior Fitness Test and the Short Form 36 Health Survey were used to measure FF and HRQoL, respectively. A canonical correlation analysis was conducted using seven fitness variables as predictors of eight HRQoL variables to examine the relationship between FF and HRQoL. The overall FF was positively correlated with the overall HRQoL in both men (canonical correlation=0.350) and women (canonical correlation=0.456). The up-and-go and 2-min step contributed the most to FF, and physical functioning contributed the most to HRQOL among men. Conversely, the up-and-go and 30-s chair stand contributed the most to FF, and physical functioning contributed the most to HRQoL in women. There were positive and moderate relationships between overall FF and overall HRQOL in older adults. The FF has a significant influence on HRQoL, particularly physical functioning. The main FF components influencing the relationship between FF and HRQoL in men are balance and agility and aerobic endurance, whereas in women they are balance and agility and lower extremity muscle strength. Results from this study facilitate comprehensively understanding the relationship between FF and HRQoL, and generating critical insight into HRQoL improvement from the perspective of FF enhancement. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  11. Canonical correlation analysis of infant's size at birth and maternal factors: a study in rural northwest Bangladesh.

    Directory of Open Access Journals (Sweden)

    Alamgir Kabir

    Full Text Available This analysis was conducted to explore the association between 5 birth size measurements (weight, length and head, chest and mid-upper arm [MUAC] circumferences as dependent variables and 10 maternal factors as independent variables using canonical correlation analysis (CCA. CCA considers simultaneously sets of dependent and independent variables and, thus, generates a substantially reduced type 1 error. Data were from women delivering a singleton live birth (n = 14,506 while participating in a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural Bangladesh. The first canonical correlation was 0.42 (P<0.001, demonstrating a moderate positive correlation mainly between the 5 birth size measurements and 5 maternal factors (preterm delivery, early pregnancy MUAC, infant sex, age and parity. A significant interaction between infant sex and preterm delivery on birth size was also revealed from the score plot. Thirteen percent of birth size variability was explained by the composite score of the maternal factors (Redundancy, RY/X = 0.131. Given an ability to accommodate numerous relationships and reduce complexities of multiple comparisons, CCA identified the 5 maternal variables able to predict birth size in this rural Bangladesh setting. CCA may offer an efficient, practical and inclusive approach to assessing the association between two sets of variables, addressing the innate complexity of interactions.

  12. Canonical Analysis Technique as an Approach to Determine Optimal Conditions for Lactic Acid Production by Lactobacillus helveticus ATCC 15009

    Directory of Open Access Journals (Sweden)

    Marcelo Teixeira Leite

    2012-01-01

    Full Text Available The response surface methodology and canonical analysis were employed to find the most suitable conditions for Lactobacillus helveticus to produce lactic acid from cheese whey in batch fermentation. The analyzed variables were temperature, pH, and the concentrations of lactose and yeast extract. The experiments were carried out according to a central composite design with three center points. An empiric equation that correlated the concentration of lactic acid with the independent variables was proposed. The optimal conditions determined by the canonical analysis of the fitted model were 40°C, pH 6.8, 82 g/L of lactose, and 23.36 g/L of yeast extract. At this point, the lactic acid concentration reached 59.38 g/L. A subsequent fermentation, carried out under optimal conditions, confirmed the product concentration predicted by the adjusted model. This concentration of lactic acid is the highest ever reported for Lactobacillus helveticus ATCC 15009 in batch process using cheese whey as substrate.

  13. Discrimination and numerical analysis of human pathogenic ...

    African Journals Online (AJOL)

    SERVER

    2008-02-19

    Feb 19, 2008 ... ... 21 Candida albicans strains were investigated using the commercial kit API 20C. AUX system and the numerical analysis of whole-cell protein profiles. The results of the commercial kit confirmed that the all the strains belonged to C. albicans species. However, the research indicated that. SDS-PAGE of ...

  14. [Discrimination of Rice Syrup Adulterant of Acacia Honey Based Using Near-Infrared Spectroscopy].

    Science.gov (United States)

    Zhang, Yan-nan; Chen, Lan-zhen; Xue, Xiao-feng; Wu, Li-ming; Li, Yi; Yang, Juan

    2015-09-01

    At present, the rice syrup as a low price of the sweeteners was often adulterated into acacia honey and the adulterated honeys were sold in honey markets, while there is no suitable and fast method to identify honey adulterated with rice syrup. In this study, Near infrared spectroscopy (NIR) combined with chemometric methods were used to discriminate authenticity of honey. 20 unprocessed acacia honey samples from the different honey producing areas, mixed? with different proportion of rice syrup, were prepared of seven different concentration gradient? including 121 samples. The near infrared spectrum (NIR) instrument and spectrum processing software have been applied in the? spectrum? scanning and data conversion on adulterant samples, respectively. Then it was analyzed by Principal component analysis (PCA) and canonical discriminant analysis methods in order to discriminating adulterated honey. The results showed that after principal components analysis, the first two principal components accounted for 97.23% of total variation, but the regionalism of the score plot of the first two PCs was not obvious, so the canonical discriminant analysis was used to make the further discrimination, all samples had been discriminated correctly, the first two discriminant functions accounted for 91.6% among the six canonical discriminant functions, Then the different concentration of adulterant samples can be discriminated correctly, it illustrate that canonical discriminant analysis method combined with NIR spectroscopy is not only feasible but also practical for rapid and effective discriminate of the rice syrup adulterant of acacia honey.

  15. Discrimination of trace nitroaromatics using linear discriminant analysis on aerosol jet printed fluorescent sensor arrays

    Science.gov (United States)

    Bolse, N.; Eckstein, R.; Schend, M.; Habermehl, A.; Hernandez-Sosa, G.; Eschenbaum, C.; Lemmer, U.

    2017-05-01

    In this work, we report on fluorescent sensor arrays fabricated by aerosol jet printing on glass substrates to detect explosives-related nitroaromatic species. The printed sensor arrays consist of six different fluorescent polymers responding to nitroaromatic vapors through a photo-induced electron transfer. This results in a quenched fluorescence proportional to the vapor concentration. Distinct fluorescence quenching patterns are detected for nitroaromatic species including nitrobenzene, 1,3-dinitrobenzene and 2,4-dinitrotoluene. The detected fingerprints are evaluated at low concentrations of only 1, 3 and 10 parts-per-billion in air. Linear discriminant analysis is used to train each sensor array enabling the discrimination of the target analyte vapors. To investigate the reproducibility of multiple sensor arrays on a single substrate, the measured fluorescence quenching patterns are used to benchmark the linear discriminant models. For this purpose, the target analytes and vapor concentrations are predicted for each sensor array. On average, we report low and reproducible misclassification rates of about 4 % indicating excellent discriminatory abilities at low concentrations close to the detection limits. We conclude that digital printing of fluorescent polymers offers the potential to realize low-cost sensor arrays for a reliable detection of trace explosives.

  16. Discrimination analysis of ononis repens and ononis spinosa of the ...

    African Journals Online (AJOL)

    Discrimination analysis of ononis repens and ononis spinosa of the British Isles. CE Stephens. Abstract. No Abstract. Journal of the Ghana Association Vol. 2 (3) 1999: pp.88-94. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT · http://dx.doi.org/10.4314/jgsa.v2i3.17997.

  17. Quadratic versus Linear Rules in Predictive Discriminant Analysis.

    Science.gov (United States)

    Young, Brian

    Either linear or quadratic rules may be used to derive classification equations in discriminant analysis for the purpose of predicting group membership. Generally, the decision about which rule to use is governed by the degree to which the separate group covariance matrices are unequal. An example is presented that supports the superior internal…

  18. Classification of Bladder Cancer Patients via Penalized Linear Discriminant Analysis

    Science.gov (United States)

    Raeisi Shahraki, Hadi; Bemani, Peyman; Jalali, Maryam

    2017-05-01

    Objectives: In order to identify genes with the greatest contribution to bladder cancer, we proposed a sparse model making the best discrimination from other patients. Methods: In a cross-sectional study, 22 genes with a key role in most cancers were considered in 21 bladder cancer patients and 14 participants of the same age (± 3 years) without bladder cancer in Shiraz city, Southern Iran. Real time-PCR was carried out using SYBR Green and for each of the 22 target genes 2-Δct as a quantitative index of gene expression was reported. We determined the most affective genes for the discriminant vector by applying penalized linear discriminant analysis using LASSO penalties. All the analyses were performed using SPSS version 18 and the penalized LDA package in R.3.1.3 software. Results: Using penalized linear discriminant analysis led to elimination of 13 less important genes. Considering the simultaneous effects of 22 genes with important influence on many cancers, it was found that TGFβ, IL12A, Her2, MDM2, CTLA-4 and IL-23 genes had the greatest contribution in classifying bladder cancer patients with the penalized linear discriminant vector. The receiver operating characteristic (ROC) curve revealed that the proposed vector had good performance with minimal (only 3) mis- classification. The area under the curve (AUC) of our proposed test was 96% (95% CI: 83%- 100%) and sensitivity, specificity, positive and negative predictive values were 90.5%, 85.7%, 90.5% and 85.7%, respectively. Conclusions: The penalized discriminant method can be considered as appropriate for classifying bladder cancer cases and searching for important biomarkers. Creative Commons Attribution License

  19. The use of discriminant analysis method to particle separation in BES

    International Nuclear Information System (INIS)

    Jiang Zhijin; Wang Taijie; Xie Yigang; Huang Tao

    1994-01-01

    The discriminant analysis method in Multivariate Statistical Theory is used to handle e, π, μ separation in BES. The principle of the discriminant analysis method is described, the unstandardized discriminant functions (responsible for particle separation) are derived, the discriminant efficiencies for e, π, μ are given and the results are compared with those obtained from conventional way

  20. A Critical Analysis of Anti-Discrimination Law and Microaggressions in Academia

    Science.gov (United States)

    Lukes, Robin; Bangs, Joann

    2014-01-01

    This article provides a critical analysis of microaggressions and anti-discrimination law in academia. There are many challenges for faculty claiming discrimination under current civil rights laws. Examples of microaggressions that fall outside of anti-discrimination law will be provided. Traditional legal analysis of discrimination will not end…

  1. The Use of Canonical Correlation Analysis to Assess the Relationship Between Executive Functioning and Verbal Memory in Older Adults

    Directory of Open Access Journals (Sweden)

    Pedro Silva Moreira MSc

    2015-08-01

    Full Text Available Executive functioning (EF, which is considered to govern complex cognition, and verbal memory (VM are constructs assumed to be related. However, it is not known the magnitude of the association between EF and VM, and how sociodemographic and psychological factors may affect this relationship, including in normal aging. In this study, we assessed different EF and VM parameters, via a battery of neurocognitive/psychological tests, and performed a Canonical Correlation Analysis (CCA to explore the connection between these constructs, in a sample of middle-aged and older healthy individuals without cognitive impairment ( N = 563, 50+ years of age. The analysis revealed a positive and moderate association between EF and VM independently of gender, age, education, global cognitive performance level, and mood. These results confirm that EF presents a significant association with VM performance.

  2. Fish otoliths analysis by PIXE: application to stock discrimination

    International Nuclear Information System (INIS)

    Arai, Nobuaki; Takai, Noriyuki; Sakamoto, Wataru; Yoshida, Koji; Maeda, Kuniko.

    1996-01-01

    Fish otoliths are continuously deposited from fish birth to its death along with encoding environmental information. In order to decode the information, PIXE was adopted as trace elemental analysis of the otoliths. Strontium to calcium concentration ratios of red sea bream otoliths varied among rearing stations. The Sr/Ca ratios of Lake Biwa catfishes also varied between male and female and among fishing grounds. The PIXE analysis was applied to the fish stock discrimination. (author)

  3. Canonical Authors in Consumption Theory

    DEFF Research Database (Denmark)

    Canonical Authors in Consumption Theory is the first work to compile the contributions of the greatest social thinkers in the global conversation about consumption and consumer culture. A prestigious reference work, it offers original chapters by the world's most prominent thought leaders....... This book provides a solid framework for understanding the relevance of these canonical authors in social theory to facilitate analysis of consumer culture, and to act as a comprehensive reference point for consumer researchers, doctoral students and practitioners....

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  7. On discriminant analysis techniques and correlation structures in high dimensions

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder

    the methods in two: Those who assume independence between the variables and thus use a diagonal estimate of the within-class covariance matrix, and those who assume dependence between the variables and thus use an estimate of the within-class covariance matrix, which also estimates the correlations between......This paper compares several recently proposed techniques for performing discriminant analysis in high dimensions, and illustrates that the various sparse methods dier in prediction abilities depending on their underlying assumptions about the correlation structures in the data. The techniques...... generally focus on two things: Obtaining sparsity (variable selection) and regularizing the estimate of the within-class covariance matrix. For high-dimensional data, this gives rise to increased interpretability and generalization ability over standard linear discriminant analysis. Here, we group...

  8. Quark/gluon jet discrimination: a reproducible analysis using R

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    The power to discriminate between light-quark jets and gluon jets would have a huge impact on many searches for new physics at CERN and beyond. This talk will present a walk-through of the development of a prototype machine learning classifier for differentiating between quark and gluon jets at experiments like those at the Large Hadron Collider at CERN. A new fast feature selection method that combines information theory and graph analytics will be outlined. This method has found new variables that promise significant improvements in discrimination power. The prototype jet tagger is simple, interpretable, parsimonious, and computationally extremely cheap, and therefore might be suitable for use in trigger systems for real-time data processing. Nested stratified k-fold cross validation was used to generate robust estimates of model performance. The data analysis was performed entirely in the R statistical programming language, and is fully reproducible. The entire analysis workflow is data-driven, automated a...

  9. Robust L1-norm two-dimensional linear discriminant analysis.

    Science.gov (United States)

    Li, Chun-Na; Shao, Yuan-Hai; Deng, Nai-Yang

    2015-05-01

    In this paper, we propose an L1-norm two-dimensional linear discriminant analysis (L1-2DLDA) with robust performance. Different from the conventional two-dimensional linear discriminant analysis with L2-norm (L2-2DLDA), where the optimization problem is transferred to a generalized eigenvalue problem, the optimization problem in our L1-2DLDA is solved by a simple justifiable iterative technique, and its convergence is guaranteed. Compared with L2-2DLDA, our L1-2DLDA is more robust to outliers and noises since the L1-norm is used. This is supported by our preliminary experiments on toy example and face datasets, which show the improvement of our L1-2DLDA over L2-2DLDA. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Rotational Linear Discriminant Analysis Using Bayes Rule for Dimensionality Reduction

    OpenAIRE

    Alok Sharma; Kuldip K. Paliwal

    2006-01-01

    Linear discriminant analysis (LDA) finds an orientation that projects high dimensional feature vectors to reduced dimensional feature space in such a way that the overlapping between the classes in this feature space is minimum. This overlapping is usually finite and produces finite classification error which is further minimized by rotational LDA technique. This rotational LDA technique rotates the classes individually in the original feature space in a manner that enables further reduction ...

  11. Identification of Clay Minerals by Infrared Spectroscopy and Discriminant Analysis

    Czech Academy of Sciences Publication Activity Database

    Ritz, M.; Vaculíková, Lenka; Plevová, Eva

    2010-01-01

    Roč. 64, č. 12 (2010), s. 1379-1387 ISSN 0003-7028 R&D Projects: GA ČR GA105/08/1398; GA ČR GP105/07/P416 Institutional research plan: CEZ:AV0Z30860518 Keywords : clay minerals * infrared spectroscopy * discriminant analysis Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 1.729, year: 2010 http://www.ingentaconnect.com/content/sas/sas/2010/00000064/00000012/art00017

  12. A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data.

    Science.gov (United States)

    Lin, Nan; Zhu, Yun; Fan, Ruzong; Xiong, Momiao

    2017-10-01

    Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the ten other statistics.

  13. Quantum discriminant analysis for dimensionality reduction and classification

    Science.gov (United States)

    Cong, Iris; Duan, Luming

    2016-07-01

    We present quantum algorithms to efficiently perform discriminant analysis for dimensionality reduction and classification over an exponentially large input data set. Compared with the best-known classical algorithms, the quantum algorithms show an exponential speedup in both the number of training vectors M and the feature space dimension N. We generalize the previous quantum algorithm for solving systems of linear equations (2009 Phys. Rev. Lett. 103 150502) to efficiently implement a Hermitian chain product of k trace-normalized N ×N Hermitian positive-semidefinite matrices with time complexity of O({log}(N)). Using this result, we perform linear as well as nonlinear Fisher discriminant analysis for dimensionality reduction over M vectors, each in an N-dimensional feature space, in time O(p {polylog}({MN})/{ε }3), where ɛ denotes the tolerance error, and p is the number of principal projection directions desired. We also present a quantum discriminant analysis algorithm for data classification with time complexity O({log}({MN})/{ε }3).

  14. Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia

    Directory of Open Access Journals (Sweden)

    Eloísa Urrechaga

    2013-01-01

    Full Text Available Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA to the differential diagnosis of microcytic anemia. Methods. The training group was composed of 200 β-thalassemia carriers, 65 α-thalassemia carriers, 170 iron deficiency anemia (IDA, and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC, hemoglobin (Hb, mean cell volume (MCV, mean cell hemoglobin (MCH, and RBC distribution width (RDW. The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia. Results. For classifying patients into two groups (genetic anemia and acquired anemia, only one function was needed; 87.9% β-thalassemia carriers, and 83.3% α-thalassemia carriers, and 72.1% in the mixed group were correctly classified. Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia.

  15. Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis.

    Science.gov (United States)

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

    2017-07-15

    Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers. The proposed TGSCCA method is able to capture the temporal changes in brain from longitudinal phenotypes by incorporating the fused penalty, which requires that the differences between two consecutive canonical weight vectors from adjacent time-points should be small. A new efficient optimization algorithm is designed to solve the objective function. Furthermore, we demonstrate the effectiveness of our algorithm on both synthetic and real data (i.e., the Alzheimer's Disease Neuroimaging Initiative cohort, including progressive mild cognitive impairment, stable MCI and Normal Control participants). In comparison with conventional SCCA, our proposed method can achieve strong associations and discover phenotypic biomarkers across multiple time-points to guide disease-progressive interpretation. The Matlab code is available at https://sourceforge.net/projects/ibrain-cn/files/ . dqzhang@nuaa.edu.cn or shenli@iu.edu. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  16. DISCRIMINATIVE ANALYSIS OF MORPHOLOGIC AND MOTORIC PARAMETER TO JUDO AND KARATE SPORTIEST BOYS

    Directory of Open Access Journals (Sweden)

    Lulzim Ibri

    2013-07-01

    Full Text Available In sample from 160 boys from secondary schools of Prizren 16-17 age, separated in two groups were implicated 18 tests, from them 10 test for valuation morphologic characteristic and 8 test, for valuation motoric abilities. Group (A is component from 80 judo athletes’ boys and group (B from 80 karate athletes’ boys. Purpose of this investigation is to verify changes between judo and karate athletes’ boys in morphologic characteristic and motoric abilities. The problem of investigation was to investigate if there are changes between judo and karate athletes’ boys in morphologic characteristic that represent longitudinal dimensionality, body measure and adipose tissue, and in motoric abilities (used is eurofit battery tests. For global analysis of dimension to some changes and variable system (which contribute in changes between judo and karate athletes’ boys were implicated t-test for small independent sample and, canonic discriminative analysis. The results of this study show that judo and karate athletes significantly differ among themselves in motoric abilities, judo athletes are better in the tests: long jump from place (LOJU, squeeze palm (SQPA and support the knuckle (SUKN, while the karate athletes are better in the tests: taping for hands (TAHE, reach sitting down position (RSDP and run there-hire 10x5 meters (R10x5M, but these changes were not noticed and morphological variables.

  17. Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods

    Science.gov (United States)

    Ding, Hao; Cao, Ming; DuPont, Andrew W.; Scott, Larry D.; Guha, Sushovan; Singhal, Shashideep; Younes, Mamoun; Pence, Isaac; Herline, Alan; Schwartz, David; Xu, Hua; Mahadevan-Jansen, Anita; Bi, Xiaohong

    2016-03-01

    Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.

  18. Use of discriminant analysis to identify propensity for purchasing properties

    Directory of Open Access Journals (Sweden)

    Ricardo Floriani

    2015-03-01

    Full Text Available Properties usually represent a milestone for people and families due to the high added-value when compared with family income. The objective of this study is the proposition of a discrimination model, by a discriminant analysis of people with characteristics (according to independent variables classified as potential buyers of properties, as well as to identify the interest in the use of such property, if it will be assigned to housing or leisure activities such as a cottage or beach house, and/or for investment. Thus, the following research question is proposed: What are the characteristics that better describe the profile of people which intend to acquire properties? The study justifies itself by its economic relevance in the real estate industry, as well as to the players of the real estate Market that may develop products based on the profile of potential customers. As a statistical technique, discriminant analysis was applied to the data gathered by questionnaire, which was sent via e-mail. Three hundred and thirty four responses were gathered. Based on this study, it was observed that it is possible to identify the intention for acquired properties, as well the purpose for acquiring it, for housing or investments.

  19. Computer vision inspection of rice seed quality with discriminant analysis

    Science.gov (United States)

    Cheng, Fang; Ying, Yibin

    2004-10-01

    This study was undertaken to develop computer vision-based rice seeds inspection technology for quality control. Color image classification using a discriminant analysis algorithm identifying germinated rice seed was successfully implemented. The hybrid rice seed cultivars involved were Jinyou402, Shanyou10, Zhongyou207 and Jiayou99. Sixteen morphological features and six color features were extracted from sample images belong to training sets. The color feature of 'Huebmean' shows the strongest classification ability among all the features. Computed as the area of seed region divided by area of the smallest convex polygon that can contain the seed region, the feature of 'Solidity' is prior to the other morphological features in germinated seeds recognition. Combined with the two features of 'Huebmean' and 'Solidity', discriminant analysis was used to classify normal rice seeds and seeds germinated on panicle. Results show that the algorithm achieved an overall average accuracy of 98.4% for both of normal seeds and germinated seeds in all cultivars. The combination of 'Huebmean' and 'Solidity' was proved to be a good indicator for germinated seeds. The simple discriminant algorithm using just two features shows high accuracy and good adaptability.

  20. Sexual dimorphism in Hucul horses using discriminant analysis.

    Science.gov (United States)

    Purzyc, H; Kobryńczuk, F; Bojarski, J

    2011-02-01

    The purpose of this study has been to evaluate the applicability of discriminant function analysis to determine gender dimorphism in Hucul horses, based on morphological indices obtained in different stages of life. A total of 243 horses, divided into six age groups, have been examined in its course. For each horse we have measured 12 metric traits, which were then used to calculate 13 biometric indices commonly used in horse breeding in Poland. These have become the basis for defining functions classifying the animals by gender in each of the six age groups. This study answers the question of what parameters play the greatest role in the course of shaping of body proportions of male and female horses in post-foetal development. The following indices have been found to significantly contribute in discriminant models: boniness, smaller trunk length, height at the croup, pelvis width and width of chest.

  1. Otolith shape analysis for stock discrimination of two Collichthys genus croaker (Pieces: Sciaenidae,) from the northern Chinese coast

    Science.gov (United States)

    Zhao, Bo; Liu, Jinhu; Song, Junjie; Cao, Liang; Dou, Shuozeng

    2017-08-01

    The otolith morphology of two croaker species (Collichthys lucidus and Collichthys niveatus) from three areas (Liaodong Bay, LD; Huanghe (Yellow) River estuary, HRE; Jiaozhou Bay, JZ) along the northern Chinese coast were investigated for species identification and stock discrimination. The otolith contour shape described by elliptic Fourier coefficients (EFC) were analysed using principal components analysis (PCA) and stepwise canonical discriminant analysis (CDA) to identify species and stocks. The two species were well differentiated, with an overall classification success rate of 97.8%. And variations in the otolith shapes were significant enough to discriminate among the three geographical samples of C. lucidus (67.7%) or C. niveatus (65.2%). Relatively high mis-assignment occurred between the geographically adjacent LD and HRE samples, which implied that individual mixing may exist between the two samples. This study yielded information complementary to that derived from genetic studies and provided information for assessing the stock structure of C. lucidus and C. niveatus in the Bohai Sea and the Yellow Sea.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

    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. (paper)

  3. Discriminant analysis in Polish manufacturing sector performance assessment

    Directory of Open Access Journals (Sweden)

    Józef Dziechciarz

    2004-01-01

    Full Text Available This is a presentation of the preliminary results of a larger project on the determination of the attractiveness of manufacturing branches. Results of the performance assessment of Polish manufacturing branches in 2000 (section D „Manufacturing” – based on NACE – Nomenclatures des Activites de Communite Europeene are shown. In the research, the classical (Fisher’s linear discriminant analysis technique was used for the analysis of the profit generation ability by the firms belonging to a certain production branch. For estimation, the data describing group level was used – for cross-validation, the classes data.

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

    Science.gov (United States)

    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…

  5. Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Xun Chen

    2014-01-01

    Full Text Available Electroencephalogram (EEG recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD with multiset canonical correlation analysis (MCCA. We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications.

  6. Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

    Directory of Open Access Journals (Sweden)

    Ting-quan Deng

    2014-01-01

    Full Text Available In the visual tracking scenarios, if there are multiple objects, due to the interference of similar objects, tracking may fail in the progress of occlusion to separation. To address this problem, this paper proposed a visual tracking algorithm with discrimination through multimanifold learning. Color-gradient-based feature tensor was used to describe object appearance for accommodation of partial occlusion. A prior multimanifold tensor dataset is established through the template matching tracking algorithm. For the purpose of discrimination, tensor distance was defined to determine the intramanifold and intermanifold neighborhood relationship in multimanifold space. Then multimanifold discriminate analysis was employed to construct multilinear projection matrices of submanifolds. Finally, object states were obtained by combining with sequence inference. Meanwhile, the multimanifold dataset and manifold learning embedded projection should be updated online. Experiments were conducted on two real visual surveillance sequences to evaluate the proposed algorithm with three state-of-the-art tracking methods qualitatively and quantitatively. Experimental results show that the proposed algorithm can achieve effective and robust effect in multi-similar-object mutual occlusion scenarios.

  7. Isokinetic evaluation of knee muscles in soccer players: discriminant analysis

    Directory of Open Access Journals (Sweden)

    Bruno Fles Mazuquin

    2015-10-01

    Full Text Available ABSTRACTIntroduction:Muscle activity in soccer players can be measured by isokinetic dynamometer, which is a reliable tool for assessing human performance.Objectives:To perform isokinetic analyses and to determine which variables differentiate the under-17 (U17 soccer category from the professional (PRO.Methods:Thirty four players were assessed (n=17 for each category. The isokinetic variables used for the knee extension-flexion analysis were: peak torque (Nm, total work (J, average power (W, angle of peak torque (deg., agonist/ antagonist ratio (%, measured for three velocities (60°/s, 120°/s and 300°/s, with each series containing five repetitions. Three Wilks' Lambda discriminant analyses were performed, to identify which variables were more significant for the definition of each of the categories.Results:The discriminative variables at 60°/s in the PRO category were: extension peak torque, flexion total work, extension average power and agonist/antagonist ratio; and for the U17s were: extension total work, flexion peak torque and flexion average power. At 120°/s for the PRO category the discriminant variables were: flexion peak torque and extension average power; for the U17s they were: extension total work and flexion average power. Finally at 300°/s, the variables found in the PRO and U17 categories respectively were: extension average power and extension total work.Conclusion:Isokinetic variables for flexion and extension knee muscles were able to significantly discriminate between PRO and U17 soccer players.

  8. Time as a Quantum Observable, Canonically Conjugated to Energy, and Foundations of Self-Consistent Time Analysis of Quantum Processes

    Directory of Open Access Journals (Sweden)

    V. S. Olkhovsky

    2009-01-01

    Full Text Available Recent developments are reviewed and some new results are presented in the study of time in quantum mechanics and quantum electrodynamics as an observable, canonically conjugate to energy. This paper deals with the maximal Hermitian (but nonself-adjoint operator for time which appears in nonrelativistic quantum mechanics and in quantum electrodynamics for systems with continuous energy spectra and also, briefly, with the four-momentum and four-position operators, for relativistic spin-zero particles. Two measures of averaging over time and connection between them are analyzed. The results of the study of time as a quantum observable in the cases of the discrete energy spectra are also presented, and in this case the quasi-self-adjoint time operator appears. Then, the general foundations of time analysis of quantum processes (collisions and decays are developed on the base of time operator with the proper measures of averaging over time. Finally, some applications of time analysis of quantum processes (concretely, tunneling phenomena and nuclear processes are reviewed.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  11. A new kernel discriminant analysis framework for electronic nose recognition

    International Nuclear Information System (INIS)

    Zhang, Lei; Tian, Feng-Chun

    2014-01-01

    Graphical abstract: - Highlights: • This paper proposes a new discriminant analysis framework for feature extraction and recognition. • The principle of the proposed NDA is derived mathematically. • The NDA framework is coupled with kernel PCA for classification. • The proposed KNDA is compared with state of the art e-Nose recognition methods. • The proposed KNDA shows the best performance in e-Nose experiments. - Abstract: Electronic nose (e-Nose) technology based on metal oxide semiconductor gas sensor array is widely studied for detection of gas components. This paper proposes a new discriminant analysis framework (NDA) for dimension reduction and e-Nose recognition. In a NDA, the between-class and the within-class Laplacian scatter matrix are designed from sample to sample, respectively, to characterize the between-class separability and the within-class compactness by seeking for discriminant matrix to simultaneously maximize the between-class Laplacian scatter and minimize the within-class Laplacian scatter. In terms of the linear separability in high dimensional kernel mapping space and the dimension reduction of principal component analysis (PCA), an effective kernel PCA plus NDA method (KNDA) is proposed for rapid detection of gas mixture components by an e-Nose. The NDA framework is derived in this paper as well as the specific implementations of the proposed KNDA method in training and recognition process. The KNDA is examined on the e-Nose datasets of six kinds of gas components, and compared with state of the art e-Nose classification methods. Experimental results demonstrate that the proposed KNDA method shows the best performance with average recognition rate and total recognition rate as 94.14% and 95.06% which leads to a promising feature extraction and multi-class recognition in e-Nose

  12. Discriminant learning through multiple principal angles for visual recognition.

    Science.gov (United States)

    Su, Ya; Fu, Yun; Gao, Xinbo; Tian, Qi

    2012-03-01

    Canonical correlation has been prevalent for multiset-based pairwise subspace analysis. As an extension, discriminant canonical correlations (DCCs) have been developed for classification purpose by learning a global subspace based on Fisher discriminant modeling of pairwise subspaces. However, the discriminative power of DCCs is not optimal as it only measures the "local" canonical correlations within subspace pairs, which lacks the "global" measurement among all the subspaces. In this paper, we propose a multiset discriminant canonical correlation method, i.e., multiple principal angle (MPA). It jointly considers both "local" and "global" canonical correlations by iteratively learning multiple subspaces (one for each set) as well as a global discriminative subspace, on which the angle among multiple subspaces of the same class is minimized while that of different classes is maximized. The proposed computational solution is guaranteed to be convergent with much faster converging speed than DCC. Extensive experiments on pattern recognition applications demonstrate the superior performance of MPA compared to existing subspace learning methods.

  13. Canonical correlation analysis in education: associations between student evaluations of courses and instructors

    DEFF Research Database (Denmark)

    Sliusarenko, Tamara; Clemmensen, Line Katrine Harder

    correlation analysis (CCA) to investigate the association between how students evaluate the course and how students evaluate the teacher and to reveal the structure of this association. Student’s evaluation data is characterized by high correlation between the variables (questions) and insufficient sample...

  14. Multi-set multi-temporal canonical analysis of psoriasis images

    DEFF Research Database (Denmark)

    Gomez, David Delgado; Maletti, Gabriela Mariel; Nielsen, Allan Aasbjerg

    2004-01-01

    to detect where changes occur. An experiment with 5 different time series collected from psoriasis patients during 4 different sessions is conducted. The analysis of the obtained results points out some patterns that can be used both to interpret and summarize the evolution of the lesion and to achieve...

  15. DNA pattern recognition using canonical correlation algorithm

    Indian Academy of Sciences (India)

    2015-09-28

    Sep 28, 2015 ... We performed canonical correlation analysis as an unsupervised statistical tool to describe related views of the same semantic object for identifying patterns. A pattern recognition technique based on canonical correlation analysis. (CCA) was proposed for finding required genetic code in the DNA ...

  16. Generalized Canonical Time Warping.

    Science.gov (United States)

    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.

  17. Van der Vyver’s analysis of rights: a case study drawn from thirteenth-century canon law

    Directory of Open Access Journals (Sweden)

    Charles J. Reid, Jr.

    1999-03-01

    Full Text Available In an important article published in 1988, Johan Van der Vyver challenged the prevailing reliance on Wesley Hohfeld’s taxonomy of rights. Hohfeld's division of rights into claims, powers, privileges and immunities, Van der Vyver stresses, is excessively concerned with "inter-individual legal relations” at the expense of the right-holder's relationship to the object of the right. Van der Vyver proposes instead that an assertion of right involves three distinct juridic aspects:• legal capacity, which is "the competence to occupy the offices of legal subject;• legal claim, which "comprises claims of a legal subject as against other persons to a legal object";• legal entitlement, which specifies the boundaries of the right-holder's ability to use, enjoy, consume, destroy or alienate the right in question.This article applies Van der Vyver’s taxonomy to the operations of thirteenthcentury canon law, and demonstrates that Van der Vyver’s analysis provides greater depth than Hohfeld's, in that it considers both the relationship of the person claiming a particular right and the object of that right.

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

    Science.gov (United States)

    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.

  19. Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

    Science.gov (United States)

    Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

    2017-10-12

    Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

  20. Evaluation of the Relationship between Epiphytic Diatoms and Environmental Parameters with the Canonical Correspondence Analysis

    International Nuclear Information System (INIS)

    Yuce, A. M.; Gonulol, A.

    2016-01-01

    This study aimed to determine the relationship between environmental parameters and epiphytic diatoms. Four sampling sites were selected in the littoral region of Lake Egirdir. Macrophytes were taken seasonally from July 2012 to April 2013. Submerged samples (Myriophyllum spicatum L., Potamogeton perfoliatus L., Ceratophylum sp. and Valisneria sp.) were collected for the analysis of epiphytic algae. Twenty-four diatom species were identified in this study. Cocconeis pediculus Ehrenberg was identified as the most abundant epiphytic diatom on the Myriophyllum spicatum L., Potamogeton perfoliatus L., Ceratophylum sp. and Valisneria sp. Besides, physical and chemical parameters of lake water were determined. Water temperature, pH and conductivity varied from 6.9 - 26.3 degree C, 8.9-9.1, 276.1, 388.1 micro S/cm, dissolved oxygen values as 11.8 - 9.7 mg -1, respectively. Concentration of calcium, magnesium, silicon, nitrate and phosphate ranged 41.3-32.1, 36.5- 41.3, 5.7 -6.1, 1.1- 3.4 and 0.02- 0.43 mg -1, respectively. It was concluded that wave motions in aquatic environments and water quality parameters are primarily effective in the distribution of epiphytic diatoms, seasons are important in the development of some species, and macrophytes provide support for the species to attach to the surfaces according to their morphological differences, despite not being very determinative. (author)

  1. [Development of Tianma HPLC fingerprint and discriminant analysis].

    Science.gov (United States)

    Xiao, Jia-Jia; Huang, Hong; Lei, You-Cheng; Lin, Ting-Wen; Ma, Yue; Zhang, Jing; Zhang, Xing-Guo; Zhang, Da-Quan; Lv, Guang-Hua

    2017-07-01

    Tianma(the tuber of Gastrodia eleta) is a widely used and pricy Chinese herb. Its counterfeits are often found in herbal markets, which are the plant materials with similar macroscopic characteristics of Tianma. Moreover, the prices of Winter Tianma(cultivated Tianma) and Spring Tianma(mostly wild Tianma) have significant difference. However, it is difficult to identify the true or false, good or bad quality of Tianma samples. Thus, a total of 48 Tianma samples with different characteristics(including Winter Tianma, Spring Tianma, slice, powder, etc.) and 9 plant species 10 samples of Tianma counterfeits were collected and analyzed by HPLC-DAD-MS techniques. After optimizing the procedure of sample preparation, chromatographic and mass-spectral conditions, the HPLC chromatograms of all those samples were collected and compared. The similarities and Fisher discriminant analysis were further conducted between the HPLC chromatograms of Tianma and counterfeit, Winter Tianma and Spring Tianma. The results showed the HPLC chromatograms of 48 Tianma samples were similar at the correlation coefficient more than 0.848(n=48). Their mean chromatogram was simulated and used as Tianma HPLC fingerprint. There were 11 common peaks on the HPLC chromatograms of Tianma, in which 6 main peaks were chosen as characteristic peaks and identified as gastrodin, p-hydroxybenzyl alcohol, parishin A, parishin B, parishin C, parishin E, respectively by comparison of the retention time, UV and MS data with those of standard chemical compounds. All the six chemical compounds are bioactive in Tianma. However, the HPLC chromatograms of the 10 counterfeit samples were significantly different from Tianma fingerprint. The correlation coefficients between HPLC fingerprints of Tianma with the HPLC chromatograms of counterfeits were less than 0.042 and the characteristic peaks were not observed on the HPLC chromatograms of these counterfeit samples. It indicated the true or false Tianma can be

  2. Linear discriminant analysis of character sequences using occurrences of words

    KAUST Repository

    Dutta, Subhajit

    2014-02-01

    Classification of character sequences, where the characters come from a finite set, arises in disciplines such as molecular biology and computer science. For discriminant analysis of such character sequences, the Bayes classifier based on Markov models turns out to have class boundaries defined by linear functions of occurrences of words in the sequences. It is shown that for such classifiers based on Markov models with unknown orders, if the orders are estimated from the data using cross-validation, the resulting classifier has Bayes risk consistency under suitable conditions. Even when Markov models are not valid for the data, we develop methods for constructing classifiers based on linear functions of occurrences of words, where the word length is chosen by cross-validation. Such linear classifiers are constructed using ideas of support vector machines, regression depth, and distance weighted discrimination. We show that classifiers with linear class boundaries have certain optimal properties in terms of their asymptotic misclassification probabilities. The performance of these classifiers is demonstrated in various simulated and benchmark data sets.

  3. Semi-supervised learning for ordinal Kernel Discriminant Analysis.

    Science.gov (United States)

    Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

    2016-12-01

    Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Discrimination against Latina/os: A Meta-Analysis of Individual-Level Resources and Outcomes

    Science.gov (United States)

    Lee, Debbiesiu L.; Ahn, Soyeon

    2012-01-01

    This meta-analysis synthesizes the findings of 60 independent samples from 51 studies examining racial/ethnic discrimination against Latina/os in the United States. The purpose was to identify individual-level resources and outcomes that most strongly relate to discrimination. Discrimination against Latina/os significantly results in outcomes…

  5. Characteristic cortical thickness patterns in adolescents with autism spectrum disorders: interactions with age and intellectual ability revealed by canonical correlation analysis.

    Science.gov (United States)

    Misaki, Masaya; Wallace, Gregory L; Dankner, Nathan; Martin, Alex; Bandettini, Peter A

    2012-04-15

    To investigate patterns and correlates of cortical thickness in adolescent males with autism spectrum disorders (ASD) versus matched typically developing controls, we applied kernel canonical correlation analysis to whole brain cortical thickness with the explaining variables of diagnosis, age, full-scale IQ, and their interactions. The analysis found that canonical variates (patterns of cortical thickness) correlated with each of these variables. The diagnosis- and age-by-diagnosis-related canonical variates showed thinner cortex for participants with ASD, which is consistent with previous studies using a univariate analysis. In addition, the multivariate statistics found larger affected regions with higher sensitivity than those found using univariate analysis. An IQ-related effect was also found with the multivariate analysis. The effects of IQ and age-by-IQ interaction on cortical thickness differed between the diagnostic groups. For typically developing adolescents, IQ was positively correlated with cortical thickness in orbitofrontal, postcentral and superior temporal regions, and greater thinning with age was seen in dorsal frontal areas in the superior IQ (>120) group. These associations between IQ and cortical thickness were not seen in the ASD group. Differing relationships between IQ and cortical thickness implies independent associations between measures of intelligence and brain structure in ASD versus typically developing controls. We discuss these findings vis-à-vis prior results obtained utilizing univariate methods. Published by Elsevier Inc.

  6. Anti-discrimination Analysis Using Privacy Attack Strategies

    KAUST Repository

    Ruggieri, Salvatore

    2014-09-15

    Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Frèchet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.

  7. Discriminative Non-Linear Stationary Subspace Analysis for Video Classification.

    Science.gov (United States)

    Baktashmotlagh, Mahsa; Harandi, Mehrtash; Lovell, Brian C; Salzmann, Mathieu

    2014-12-01

    Low-dimensional representations are key to the success of many video classification algorithms. However, the commonly-used dimensionality reduction techniques fail to account for the fact that only part of the signal is shared across all the videos in one class. As a consequence, the resulting representations contain instance-specific information, which introduces noise in the classification process. In this paper, we introduce non-linear stationary subspace analysis: a method that overcomes this issue by explicitly separating the stationary parts of the video signal (i.e., the parts shared across all videos in one class), from its non-stationary parts (i.e., the parts specific to individual videos). Our method also encourages the new representation to be discriminative, thus accounting for the underlying classification problem. We demonstrate the effectiveness of our approach on dynamic texture recognition, scene classification and action recognition.

  8. Asymptotic performance of regularized quadratic discriminant analysis based classifiers

    KAUST Repository

    Elkhalil, Khalil

    2017-12-13

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

  9. Use of linear discriminant analysis to characterise three dairy cattle breeds on the basis of several milk characteristics

    Directory of Open Access Journals (Sweden)

    Roberto Leotta

    2010-01-01

    Full Text Available To characterise individuals of differents breeds on the basis of milk composition and to identify the best set of variablesa linear discriminant analysis (LDA, on 14 milk production traits, was performed on milk samples from 199 cows of differentbreeds (respectively, 127 subjects were Italian Friesians (IF, 62 were German Friesians (GF, and 10 were Jerseys(J and all came from the same breeding farm in Tuscany. The variables were: test day milk yield (kg milk, % Fat, %Protein,% Lactose, % solid non fat (SNF, % total solid (TS, pH and titratable acidity (TA; five rheological variables: r,k20, a30, a45, and somatic cell counts /ml (SCC; and one hygiene-related variable: total bacterial count (TBC. The analysisperformed on the 14 variables, with regard to the three breeds, allowed us to identify 10 of these as variables usefulfor discrimination (leaving out kg milk, pH, a45, and TBC. The most important variables were the percentage of Fat andTS for the first canonical variate and SNF, Lactose and Protein for the second. Fat and TS play an important role sincethey present significant values (even if opposite sign in the two variates. The resulting classification of subjects was satisfactory:79% of the Italian Friesians, 73% of German Friesians and 100% of the Jersey cows were classified correctly.

  10. An Analysis of Discrimination by Real Estate Brokers.

    Science.gov (United States)

    Yinger, John

    This paper focuses on designing policies to eliminate discrimination in the sale of single-family houses by analyzing the behavior of the agents who actually do most of the discriminating, namely real estate agents. Discriminatory practices are said to be supported by policies of house builders, lending institutions, and government, and by the…

  11. El canon literario peruano

    Directory of Open Access Journals (Sweden)

    Carlos García-Bedoya Maguiña

    2011-05-01

    Full Text Available Canon es un concepto clave en la historia literaria. En el presente artículo,se revisa la evolución histórica del canon literario peruano. Es solo con la llamada República Aristocrática, en las primeras décadas del siglo XX, que cabe hablar en el caso peruano de la formación de un auténtico canon nacional. El autor denomina a esta primera versión del canon literario peruano como canon oligárquico y destaca la importancia de la obra de Riva Agüero y de Ventura García Calderón en su configuración. Es solo más tarde, desde los años 20 y de modo definitivo desde los años 50, que puede hablarse de la emergencia de un nuevo canon literarioal que el autor propone determinar canon posoligárquico.

  12. Discrimination of Xihulongjing tea grade using an electronic tongue

    African Journals Online (AJOL)

    STORAGESEVER

    2009-12-15

    Dec 15, 2009 ... Five grades of Xihulongjing tea (grade: AAA, AA, A, B and C, from the same region and processed with the same processing method) were discriminated using α-Astree II electronic tongue (e-tongue) coupled with pattern recognition methods including principal component analysis (PCA), canonical.

  13. Discriminant analysis of maintaining a vertical position in the water

    Directory of Open Access Journals (Sweden)

    Bratuša Zoran

    2015-01-01

    Full Text Available Water polo is the only sports game that takes place in the water. During the outplay, a vertical body position with the two basic mechanisms of the leg work - a breaststroke leg kick and an eggbeater leg kick, prevails. Starting from the significance of a vertical position during the game play, the methods of assessing physical preparedness of the athletes of all the categories also include the evaluation of maintaining a vertical position and consequently the load of the leg muscles. The measurements are performed during the maintenance of a vertical position (swimming in place through one of the specified mechanisms of leg work, i.e. a vertical position technique. The aim of this paper was to determine the application of different mechanisms of the leg kicks in maintaining a vertical position with young water polo players in relation to their position. The study included 29 selected junior water polo players (age_15.8 ± 0.8 years; BH_185.2 ± 5.3cm and BW_81.7 ± 7.7kg. The measurements were performed during the tests of swimming in place at the maximum intensity lasting 10 seconds, by the breaststroke and eggbeater leg kicks. The isometric tensiometry tests were used for the measurements. The results were analysed by the application of descriptive statistics, and the kinetic selection characteristic was defined by the application of discriminant analysis. Higher average values were achieved with the breaststroke leg kick technique Fmax, ImpF and RFD (avgFmaxLEGGBK =157.46±19.93N; avgImpF_LEGGBK =45.43±10.64Ns; avgRFD_LEGGBK=337.85±80.73N/s; avgFmaxLBKICK=227.18±49.17N; avgImpF_LBKICK=55.99±14.59Ns; avgRFD_LBKICK=545.47±159.15N/s. After discriminant analysis, the results have shown that the eggbeater leg kick is a selection technique, whereas the force - Fmax is a kinetic selection variable. Based on the obtained results and the analyses performed it may be concluded that a training factor dominant for maintaining a vertical position by

  14. Discrimination of red and white rice bran from Indonesia using HPLC fingerprint analysis combined with chemometrics.

    Science.gov (United States)

    Sabir, Aryani; Rafi, Mohamad; Darusman, Latifah K

    2017-04-15

    HPLC fingerprint analysis combined with chemometrics was developed to discriminate between the red and the white rice bran grown in Indonesia. The major component in rice bran is γ-oryzanol which consisted of 4 main compounds, namely cycloartenol ferulate, cyclobranol ferulate, campesterol ferulate and β-sitosterol ferulate. Separation of these four compounds along with other compounds was performed using C18 and methanol-acetonitrile with gradient elution system. By using these intensity variations, principal component and discriminant analysis were performed to discriminate the two samples. Discriminant analysis was successfully discriminated the red from the white rice bran with predictive ability of the model showed a satisfactory classification for the test samples. The results of this study indicated that the developed method was suitable as quality control method for rice bran in terms of identification and discrimination of the red and the white rice bran. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Discrimination of ginseng cultivation regions using light stable isotope analysis.

    Science.gov (United States)

    Kim, Kiwook; Song, Joo-Hyun; Heo, Sang-Cheol; Lee, Jin-Hee; Jung, In-Woo; Min, Ji-Sook

    2015-10-01

    Korean ginseng is considered to be a precious health food in Asia. Today, thieves frequently compromise ginseng farms by pervasive theft. Thus, studies regarding the characteristics of ginseng according to growth region are required in order to deter ginseng thieves and prevent theft. In this study, 6 regions were selected on the basis of Korea regional criteria (si, gun, gu), and two ginseng-farms were randomly selected from each of the 6 regions. Then 4-6 samples of ginseng were acquired from each ginseng farm. The stable isotopic compositions of H, O, C, and N of the collected ginseng samples were analyzed. As a result, differences in the hydrogen isotope ratios could be used to distinguish regional differences, and differences in the nitrogen isotope ratios yielded characteristic information regarding the farms from which the samples were obtained. Thus, stable isotope values could be used to differentiate samples according to regional differences. Therefore, stable isotope analysis serves as a powerful tool to discriminate the regional origin of Korean ginseng samples from across Korea. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  16. Unbiased bootstrap error estimation for linear discriminant analysis.

    Science.gov (United States)

    Vu, Thang; Sima, Chao; Braga-Neto, Ulisses M; Dougherty, Edward R

    2014-12-01

    Convex bootstrap error estimation is a popular tool for classifier error estimation in gene expression studies. A basic question is how to determine the weight for the convex combination between the basic bootstrap estimator and the resubstitution estimator such that the resulting estimator is unbiased at finite sample sizes. The well-known 0.632 bootstrap error estimator uses asymptotic arguments to propose a fixed 0.632 weight, whereas the more recent 0.632+ bootstrap error estimator attempts to set the weight adaptively. In this paper, we study the finite sample problem in the case of linear discriminant analysis under Gaussian populations. We derive exact expressions for the weight that guarantee unbiasedness of the convex bootstrap error estimator in the univariate and multivariate cases, without making asymptotic simplifications. Using exact computation in the univariate case and an accurate approximation in the multivariate case, we obtain the required weight and show that it can deviate significantly from the constant 0.632 weight, depending on the sample size and Bayes error for the problem. The methodology is illustrated by application on data from a well-known cancer classification study.

  17. Robust Discriminant Analysis Based on Nonparametric Maximum Entropy

    Science.gov (United States)

    He, Ran; Hu, Bao-Gang; Yuan, Xiao-Tong

    In this paper, we propose a Robust Discriminant Analysis based on maximum entropy (MaxEnt) criterion (MaxEnt-RDA), which is derived from a nonparametric estimate of Renyi’s quadratic entropy. MaxEnt-RDA uses entropy as both objective and constraints; thus the structural information of classes is preserved while information loss is minimized. It is a natural extension of LDA from Gaussian assumption to any distribution assumption. Like LDA, the optimal solution of MaxEnt-RDA can also be solved by an eigen-decomposition method, where feature extraction is achieved by designing two Parzen probability matrices that characterize the within-class variation and the between-class variation respectively. Furthermore, MaxEnt-RDA makes use of high order statistics (entropy) to estimate the probability matrix so that it is robust to outliers. Experiments on toy problem , UCI datasets and face datasets demonstrate the effectiveness of the proposed method with comparison to other state-of-the-art methods.

  18. Classifying Linear Canonical Relations

    OpenAIRE

    Lorand, Jonathan

    2015-01-01

    In this Master's thesis, we consider the problem of classifying, up to conjugation by linear symplectomorphisms, linear canonical relations (lagrangian correspondences) from a finite-dimensional symplectic vector space to itself. We give an elementary introduction to the theory of linear canonical relations and present partial results toward the classification problem. This exposition should be accessible to undergraduate students with a basic familiarity with linear algebra.

  19. ANALYSIS ON WOMEN DISCRIMINATION IN THE LABOUR MARKET IN ROMANIA

    OpenAIRE

    Victoria-Mihaela Brînzea

    2011-01-01

    Eliminating gender-based discrimination is one of the important prerequisite for building a fair society; this can be achieved only through the active involvement of the authorities and of each person. Although during recent years there have been positive changes in the relationships between men and women, improving women's situation to some extent, it can be said that discrimination based on social gender was reduced but not eliminated entirely, equality of chances having not been achieved e...

  20. Fan fiction, early Greece, and the historicity of canon

    Directory of Open Access Journals (Sweden)

    Ahuvia Kahane

    2016-03-01

    Full Text Available The historicity of canon is considered with an emphasis on contemporary fan fiction and early Greek oral epic traditions. The essay explores the idea of canon by highlighting historical variance, exposing wider conceptual isomorphisms, and formulating a revised notion of canonicity. Based on an analysis of canon in early Greece, the discussion moves away from the idea of canon as a set of valued works and toward canon as a practice of containment in response to inherent states of surplus. This view of canon is applied to the practice of fan fiction, reestablishing the idea of canonicity in fluid production environments within a revised, historically specific understanding in early oral traditions on the one hand and in digital cultures and fan fiction on the other. Several examples of early epigraphic Greek texts embedded in oral environments are analyzed and assessed in terms of their implications for an understanding of fan fiction and its modern contexts.

  1. Relations between canonical and non-canonical inflation

    Energy Technology Data Exchange (ETDEWEB)

    Gwyn, Rhiannon [Max-Planck-Institut fuer Gravitationsphysik (Albert-Einstein-Institut), Potsdam (Germany); Rummel, Markus [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Westphal, Alexander [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany). Theory Group

    2012-12-15

    We look for potential observational degeneracies between canonical and non-canonical models of inflation of a single field {phi}. Non-canonical inflationary models are characterized by higher than linear powers of the standard kinetic term X in the effective Lagrangian p(X,{phi}) 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.

  2. Identification of genome-wide non-canonical spliced regions and analysis of biological functions for spliced sequences using Read-Split-Fly.

    Science.gov (United States)

    Bai, Yongsheng; Kinne, Jeff; Ding, Lizhong; Rath, Ethan C; Cox, Aaron; Naidu, Siva Dharman

    2017-10-03

    It is generally thought that most canonical or non-canonical splicing events involving U2- and U12 spliceosomes occur within nuclear pre-mRNAs. However, the question of whether at least some U12-type splicing occurs in the cytoplasm is still unclear. In recent years next-generation sequencing technologies have revolutionized the field. The "Read-Split-Walk" (RSW) and "Read-Split-Run" (RSR) methods were developed to identify genome-wide non-canonical spliced regions including special events occurring in cytoplasm. As the significant amount of genome/transcriptome data such as, Encyclopedia of DNA Elements (ENCODE) project, have been generated, we have advanced a newer more memory-efficient version of the algorithm, "Read-Split-Fly" (RSF), which can detect non-canonical spliced regions with higher sensitivity and improved speed. The RSF algorithm also outputs the spliced sequences for further downstream biological function analysis. We used open access ENCODE project RNA-Seq data to search spliced intron sequences against the U12-type spliced intron sequence database to examine whether some events could occur as potential signatures of U12-type splicing. The check was performed by searching spliced sequences against 5'ss and 3'ss sequences from the well-known orthologous U12-type spliceosomal intron database U12DB. Preliminary results of searching 70 ENCODE samples indicated that the presence of 5'ss with U12-type signature is more frequent than U2-type and prevalent in non-canonical junctions reported by RSF. The selected spliced sequences have also been further studied using miRBase to elucidate their functionality. Preliminary results from 70 samples of ENCODE datasets show that several miRNAs are prevalent in studied ENCODE samples. Two of these are associated with many diseases as suggested in the literature. Specifically, hsa-miR-1273 and hsa-miR-548 are associated with many diseases and cancers. Our RSF pipeline is able to detect many possible junctions

  3. Multispectral magnetic resonance image analysis using principal component and linear discriminant analysis.

    NARCIS (Netherlands)

    Witjes, H.; Rijpkema, M.J.P.; Graaf, M. van der; Melssen, W.J.; Heerschap, A.; Buydens, L.M.C.

    2003-01-01

    PURPOSE: To explore the possibilities of combining multispectral magnetic resonance (MR) images of different patients within one data matrix. MATERIALS AND METHODS: Principal component and linear discriminant analysis was applied to multispectral MR images of 12 patients with different brain tumors.

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

    Science.gov (United States)

    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.

  5. Links between patterns of racial socialization and discrimination experiences and psychological adjustment: a cluster analysis.

    Science.gov (United States)

    Ajayi, Alex A; Syed, Moin

    2014-10-01

    This study used a person-oriented analytic approach to identify meaningful patterns of barriers-focused racial socialization and perceived racial discrimination experiences in a sample of 295 late adolescents. Using cluster analysis, three distinct groups were identified: Low Barrier Socialization-Low Discrimination, High Barrier Socialization-Low Discrimination, and High Barrier Socialization-High Discrimination clusters. These groups were substantively unique in terms of the frequency of racial socialization messages about bias preparation and out-group mistrust its members received and their actual perceived discrimination experiences. Further, individuals in the High Barrier Socialization-High Discrimination cluster reported significantly higher depressive symptoms than those in the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. However, no differences in adjustment were observed between the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. Overall, the findings highlight important individual differences in how young people of color experience their race and how these differences have significant implications on psychological adjustment. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  6. Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

    DEFF Research Database (Denmark)

    Han, Xixuan; Clemmensen, Line Katrine Harder

    2015-01-01

    , and it is formulated as a generalized eigenvalue problem. Thus RGED can be applied to effectively extract sparse features and calculate sparse discriminant directions for all variants of Fisher discriminant criterion based models. Particularly, RGED can be applied to matrix-based and even tensor-based discriminant...... techniques, for instance, 2D-Linear Discriminant Analysis (2D-LDA). Furthermore, an iterative algorithm based on the alternating direction method of multipliers is developed. The algorithm approximately solves RGED with monotonically decreasing convergence and at an acceptable speed for results of modest...

  7. Comparison of linear discriminant analysis methods for the classification of cancer based on gene expression data.

    Science.gov (United States)

    Huang, Desheng; Quan, Yu; He, Miao; Zhou, Baosen

    2009-12-10

    More studies based on gene expression data have been reported in great detail, however, one major challenge for the methodologists is the choice of classification methods. The main purpose of this research was to compare the performance of linear discriminant analysis (LDA) and its modification methods for the classification of cancer based on gene expression data. The classification performance of linear discriminant analysis (LDA) and its modification methods was evaluated by applying these methods to six public cancer gene expression datasets. These methods included linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), shrinkage centroid regularized discriminant analysis (SCRDA), shrinkage linear discriminant analysis (SLDA) and shrinkage diagonal discriminant analysis (SDDA). The procedures were performed by software R 2.80. PAM picked out fewer feature genes than other methods from most datasets except from Brain dataset. For the two methods of shrinkage discriminant analysis, SLDA selected more genes than SDDA from most datasets except from 2-class lung cancer dataset. When comparing SLDA with SCRDA, SLDA selected more genes than SCRDA from 2-class lung cancer, SRBCT and Brain dataset, the result was opposite for the rest datasets. The average test error of LDA modification methods was lower than LDA method. The classification performance of LDA modification methods was superior to that of traditional LDA with respect to the average error and there was no significant difference between theses modification methods.

  8. WOMEN RESISTANCE TOWARD DISCRIMINATIONS: A MODERN LITERARY WORK ANALYSIS ON FEMINISM REVIEW IN BEKISAR MERAH

    Directory of Open Access Journals (Sweden)

    Mujiono .

    2016-02-01

    Full Text Available This study was conducted to discover the discriminations against women in the Bekisar Merah novel and how they formulate resistance to those discriminations. To address the above objective, this study used descriptive qualitative research design with a feminism approach. Source of the data in this study was the second edition of Bekisar Merah novel written by Ahmad Tohari. The data included were words, phrases, sentences, and paragraphs on Bekisar Merah which portray womens discrimination toward Lasi, the women figure in the novel, and power types formulated by her who resisted the discrimination. To analyze the data, content analysis was applied. Triangulation was used to ensure the trustworthiness of the data. The result of the study showed eight forms of discriminations and three resistances. The discriminations were domestic abuse, molestation, gender harassment, seduction behavior, imposition, coercion, bribery, and subordination. The resistances were physically, mentally, and verbally.

  9. [The properties of simple medicines according to Avicenna (980-1037): analysis of some sections of the Canon].

    Science.gov (United States)

    Ayari-Lassueur, Sylvie

    2012-01-01

    Avicenna spoke on pharmacology in several works, and this article considers his discussions in the Canon, a vast synthesis of the greco-arabian medicine of his time. More precisely, it focuses on book II, which treats simple medicines. This text makes evident that the Persian physician's central preoccupation was the efficacy of the treatment, since it concentrates on the properties of medicines. In this context, the article examines their different classifications and related topics, such as the notion of temperament, central to Avicenna's thought, and the concrete effects medicines have on the body. Yet, these theoretical notions only have sense in practical application. For Avicenna, medicine is both a theoretical and a practical science. For this reason, the second book of the Canon ends with an imposing pharmacopoeia, where the properties described theoretically at the beginning of the book appear in the list of simple medicines, so that the physician can select them according to the intended treatment's goals. The article analyzes a plant from this pharmacopoeia as an example of this practical application, making evident the logic Avicenna uses in detailing the different properties of each simple medicine.

  10. Modern Canonical Quantum General Relativity

    Science.gov (United States)

    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.

  11. Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis

    Science.gov (United States)

    Jarrell, Stephen B.; Stanley, T. D.

    2004-01-01

    The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.

  12. Statistical analysis of agarwood oil compounds in discriminating the ...

    African Journals Online (AJOL)

    Enhancing and improving the discrimination technique is the main aim to determine or grade the good quality of agarwood oil. In this paper, all statistical works were performed via SPSS software. Two parameters involved are abundance of compound (%) and quality of t agarwood oil either low or high quality. The result ...

  13. Logistic discriminant analysis of breast cancer using ultrasound measurement

    International Nuclear Information System (INIS)

    Abdolmaleki, P.; Mokhtari Dizaji, M.; Vahead, M.R.; Gity, M.

    2004-01-01

    Background: Logistic discriminant method was applied to differentiate malignant from benign in a group of patients with proved breast lesions of the basis of ultrasonic parameters. Materials and methods: Our database include 273 patients' ultrasonographic pictures consisting of 14 quantitative variables. The measured variables were ultrasound propagation velocity, acoustic impedance and attenuation coefficient at 10 MHz in breast lesions at 20, 25, 30 and 35 d ig c temperature, physical density and age. This database was randomly divided into the estimation of 201 and validation of 72 samples. The estimation samples were used to build the logistic discriminant model, and validation samples were used to validate the performance. Finally important criteria such as sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were evaluated. Results: Our results showed that the logistic discriminant method was able to classify correctly 67 out of 72 cases presented in the validation sample. The results indicate a remarkable diagnostic accuracy of 93%. Conclusion: A logistic discriminator approach is capable of predicting the probability of malignancy of breast cancer. Features from ultrasonic measurement on ultrasound imaging is used in this approach

  14. Classification of astrocyto-mas and meningiomas using statistical discriminant analysis on MRI data

    International Nuclear Information System (INIS)

    Siromoney, Anna; Prasad, G.N.S.; Raghuram, Lakshminarayan; Korah, Ipeson; Siromoney, Arul; Chandrasekaran, R.

    2001-01-01

    The objective of this study was to investigate the usefulness of Multivariate Discriminant Analysis for classifying two groups of primary brain tumours, astrocytomas and meningiomas, from Magnetic Resonance Images. Discriminant analysis is a multivariate technique concerned with separating distinct sets of objects and with allocating new objects to previously defined groups. Allocation or classification rules are usually developed from learning examples in a supervised learning environment. Data from signal intensity measurements in the multiple scan performed on each patient in routine clinical scanning was analysed using Fisher's Classification, which is one method of discriminant analysis

  15. A canonical correlation analysis-based dynamic bayesian network prior to infer gene regulatory networks from multiple types of biological data.

    Science.gov (United States)

    Baur, Brittany; Bozdag, Serdar

    2015-04-01

    One of the challenging and important computational problems in systems biology is to infer gene regulatory networks (GRNs) of biological systems. Several methods that exploit gene expression data have been developed to tackle this problem. In this study, we propose the use of copy number and DNA methylation data to infer GRNs. We developed an algorithm that scores regulatory interactions between genes based on canonical correlation analysis. In this algorithm, copy number or DNA methylation variables are treated as potential regulator variables, and expression variables are treated as potential target variables. We first validated that the canonical correlation analysis method is able to infer true interactions in high accuracy. We showed that the use of DNA methylation or copy number datasets leads to improved inference over steady-state expression. Our results also showed that epigenetic and structural information could be used to infer directionality of regulatory interactions. Additional improvements in GRN inference can be gleaned from incorporating the result in an informative prior in a dynamic Bayesian algorithm. This is the first study that incorporates copy number and DNA methylation into an informative prior in dynamic Bayesian framework. By closely examining top-scoring interactions with different sources of epigenetic or structural information, we also identified potential novel regulatory interactions.

  16. The estimation of genetic distance and discriminant variables on breed of duck (Alabio, Bali, Khaki Campbell, Mojosari and Pegagan by morphological analysis

    Directory of Open Access Journals (Sweden)

    B Brahmantiyo

    2003-03-01

    Full Text Available A study on morphological body conformation of Alabio, Bali, Khaki Campbell, Mojosari and Pegagan ducks was carried out to determine the genetic distance and discriminant variables. This research was held in Research Institute for Animal Production, Ciawi, Bogor using 65 Alabio ducks, 40 Bali ducks, 36 Khaki Campbell ducks, 60 Mojosari ducks and 30 Pegagan ducks. Seven different body parts were measured, they were the length of femur, tibia, tarsometatarsus, the circumference of tarsometatarsus, the length of third digits, wing and maxilla. General Linear Models and simple discriminant analysis were used in this observation (SAS package program. Male and female Pegagan ducks had morphological size bigger than Alabio, Bali, Khaki Campbell and Mojosari ducks. Khaki Campbell ducks were mixed with Bali ducks (47.22% and Pegagan ducks from isolated location in South Sumatera were lightly mixed with Alabio and Bali. Mahalanobis genetic distance showed that Bali and Khaki Campbell ducks, also, Alabio and Mojosari ducks had similarity, with genetic distance of 1.420 and 1.548, respectively. Results from canonical analysis showed that the most discriminant variables were obtained from the length of femur, tibia and third digits.

  17. Retinopathy risk factors in type II diabetic patients using factor analysis and discriminant analysis.

    Science.gov (United States)

    Tazhibi, Mahdi; Sarrafzade, Sheida; Amini, Masoud

    2014-01-01

    Diabetes is one of the most common chronic diseases in the world. Incidence and prevalence of diabetes are increasing in developing countries as well as in Iran. Retinopathy is the most common chronic disorder in diabetic patients. In this study, we used the information of diabetic patients' reports that refer to endocrine and metabolism research center of Isfahan University of Medical Sciences to determine diabetic retinopathy risk factors. We used factor analysis to extract retinopathy's factors. Factor analysis is using to analyze multivariate data, in which a large number of dependent variables summarize into the fewer independent factors. Factor analysis is applied, in both diabetic and nondiabetic patients, separately. To investigate the efficacy of factor analysis, we used discriminant analysis. We investigated 3535 diabetic patients whose prevalence of retinopathy was 53.4%. Six factors were extracted in each group (i.e. diabetic and nondiabetic groups). These six factors were explained 69.5% and 69.6% of total variance in diabetic and nondiabetic groups, respectively. Using original variables such as sex, weight, blood sugar control method, and some laboratory variables, the correct classification rate of discriminant analysis was identified as 67.4%. However, it decreased to 49.5% by using extracted factors. Retinopathy is one of the important disorders in diabetic patients that involves a large number of variables and can affect its incidence. By the method of factor analysis, we summarize diabetic retinopathy risk factors. Factor analysis is applied separately, in two diabetic and nondiabetic group. In this way, 10 variables were summarized into the six factors. Discriminant analysis was used to investigate the efficacy of factor analysis. Although factor analysis is a powerful way to reduce the number of variables, in this study did not worked very well.

  18. Tumor classification based on orthogonal linear discriminant analysis.

    Science.gov (United States)

    Wang, Huiya; Zhang, Shanwen

    2014-01-01

    Gene expression profiles have great potential for accurate tumor diagnosis. It is expected to enable us to diagnose tumors precisely and systematically, and also bring the researchers of machine learning two challenges, the curse of dimensionality and the small sample size problems. We propose a manifold learning based dimensional reduction algorithm named orthogonal local discriminant embedding (O-LDE) and apply it to tumor classification. Comparing with the classical local discriminant embedding (LDE), O-LDE aims to obtain an orthogonal linear projection matrix by solving an optimization problem. After being projected into a low-dimensional subspace by O-LDE, the data points of the same class maintain their intrinsic neighbor relations, whereas the neighboring points of the different classes are far from each other. Experimental results on a public tumor dataset validate the effectiveness and feasibility of the proposed algorithm.

  19. Unified correspondence and canonicity

    NARCIS (Netherlands)

    Zhao, Z.

    2018-01-01

    Correspondence theory originally arises as the study of the relation between modal formulas and first-order formulas interpreted over Kripke frames. We say that a modal formula and a first-order formula correspond to each other if they are valid on the same class of Kripke frames. Canonicity theory

  20. Discrimination based on HIV/AIDS status: A comparative analysis of ...

    African Journals Online (AJOL)

    Discrimination based on HIV/AIDS status: A comparative analysis of the Nigerian court's decision in Festus Odaife & Ors v Attorney General of the Federation & Ors with other Commonwealth jurisdictions.

  1. Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome

    Directory of Open Access Journals (Sweden)

    Gaora Peadar Ó

    2010-10-01

    Full Text Available Abstract Background Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Results Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p Conclusion Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of

  2. Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

    DEFF Research Database (Denmark)

    Garcia, Emanuel; Klaas, Ilka Christine; Amigo Rubio, Jose Manuel

    2014-01-01

    ). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3...... it was about half (16%), which makes it more suitable for practical application; the model error rates were, 23 and 19%, respectively. Based on data registered automatically from one AMS farm, we were able to discriminate nonlame and lame cows, where partial least squares discriminant analysis achieved similar...

  3. Analysis of Financial Ratio to Distinguish Indonesia Joint Venture General Insurance Company Performance using Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Subiakto Soekarno

    2012-01-01

    Full Text Available Insurance industry stands as a service business that plays a significant role in Indonesiaeconomical condition. The development of insurance industry in Indonesia, both of generalinsurance and life insurance, has increased very fast. The general insurance industry itselfdivided into two major players which are local private company and Joint Venture Company.Lately, the use of statistical techniques and financial ratios models to asses financial institutionsuch as insurance company have been used as one of the appropriate combination inpredicting the performance of an industry. This research aims to distinguish between JointVenture General Insurance Companies that have a good performance and those who are lessperforming well using Discriminant Analysis. Further, the findings led that DiscriminantAnalysis is able to distinguish Joint Venture General Insurance Companies that have a goodperformance and those who are not performing well. There are also six ratios which are RBC,Technical Reserve to Investment Ratio, Debt Ratio, Return on Equity, Loss Ratio, and ExpenseRatio that stand as the most influential ratios to distinguish the performance of joint venturegeneral insurance companies. In addition, the result suggest business people to be concernedtoward those six ratios, to increase their companies’ performance.Key words: general insurance, financial ratio, discriminant analysis

  4. Research on n-γ discrimination method based on spectrum gradient analysis of signals

    International Nuclear Information System (INIS)

    Luo Xiaoliang; Liu Guofu; Yang Jun; Wang Yueke

    2013-01-01

    Having discovered that there are distinct differences between the spectrum gradient of the output neutron and γ-ray signal from liquid scintillator detectors, this paper presented a n-γ discrimination method called spectrum gradient analysis (SGA) based on frequency-domain features of the pulse signals. The basic principle and feasibility of SGA method were discussed and the validity of n-γ discrimination results of SGA was verified by the associated particle neutron flight experiment. The discrimination performance of SGA was evaluated under different conditions of sampling rates ranging from 5 G/s to 250 M/s. The results show that SGA method exhibits insensitivity to noise, strong anti-interference ability, stable discrimination performance and lower amount of calculation in contrast with time-domain n-γ discrimination methods. (authors)

  5. The canonical and grand canonical models for nuclear ...

    Indian Academy of Sciences (India)

    Many observables seen in intermediate energy heavy-ion collisions can be explained on the basis of statistical equilibrium. Calculations based on statistical equilibrium can be implemented in microcanonical ensemble, canonical ensemble or grand canonical ensemble. This paper deals with calculations with canonical ...

  6. Application of discriminant analysis and generalized distance measures to uranium exploration

    International Nuclear Information System (INIS)

    Beauchamp, J.J.; Begovich, C.L.; Kane, V.E.; Wolf, D.A.

    1980-01-01

    The National Uranium Resource Evaluation (NURE) Program has as its goal the estimation of the nation's uranium resources. It is possile to use discriminant analysis methods on hydrogeochemical data collected in the NURE Program to aid in fomulating geochemical models that can be used to identify the anomalous areas used in resource estimation. Discriminant' analysis methods have been applied to data from the Plainview, Texas Quadrangle which has approximately 850 groundwater samples with more than 40 quantitative measurements per sample. Discriminant analysis topics involving estimation of misclassification probabilities, variable selection, and robust discrimination are applied. A method using generalized distance measures is given which enables the assignment of samples to a background population or a mineralized population whose parameters were estimated from separate studies. Each topic is related to its relevance in identifying areas of possible interest to uranium exploration. However, the methodology presented here is applicable to the identification of regions associated with other types of resources. 8 figures, 3 tables

  7. Realizations of the canonical representation

    Indian Academy of Sciences (India)

    A characterisation of the maximal abelian subalgebras of the bounded operators on Hilbert space that are normalised by the canonical representation of the Heisenberg group is given. This is used to classify the perfect realizations of the canonical representation.

  8. PIXE analysis of fish otoliths. Application to fish stock discrimination

    International Nuclear Information System (INIS)

    Arai, Nobuaki; Sakamoto, Wataru; Tateno, Koji; Yoshida, Koji.

    1996-01-01

    PIXE was adopted to analyze trace elements in otoliths of Japanese flounder to discriminate among several local fish stocks. The otoliths were removed from samples caught at five different sea areas along with the coast of the Sea of Japan: Akita, Ishikawa, Kyoto (2 stations), and Fukuoka. Besides calcium as main component, strontium, manganese, and zinc were detected. Especially Sr concentrations were different among 4 areas except between 2 stations in Kyoto. It suggested that the fish in the 2 stations in Kyoto were the same stock differed to the others. (author)

  9. Canonical quantization of macroscopic electromagnetism

    OpenAIRE

    Philbin, Thomas Gerard

    2010-01-01

    Application of the standard canonical quantization rules of quantum field theory to macroscopic electromagnetism has encountered obstacles due to material dispersion and absorption. This has led to a phenomenological approach to macroscopic quantum electrodynamics where no canonical formulation is attempted. In this paper macroscopic electromagnetism is canonically quantized. The results apply to any linear, inhomogeneous, magnetodielectric medium with dielectric functions that obey the Krame...

  10. Determination of TATP, DNT, and TNT in air by FTIR and PLS-discriminant analysis

    Science.gov (United States)

    Pacheco-Londono, Leonardo; Primera-Pedrozo, Oliva M.; de la Torre, Luis F.; Hernandez-Rivera, Samuel P.

    2005-03-01

    A new processing of spectra for pattern recognition was created in order to detect explosives. Partial Least Squares (PLS) was used to create vector for recognition and those were using for discriminant analysis. PLS was adjusted with a discriminant function. IR spectra of TATP, DNT and TNT traces in air were recorded. Spectra of free air of those explosives were measured. NIR and MIR regions were studied and were used for PLS vector. NIR region is statistically significant. Two PLS were necessary for good discrimination for those explosives.

  11. Discrimination of handlebar grip samples by fourier transform infrared microspectroscopy analysis and statistics

    Directory of Open Access Journals (Sweden)

    Zeyu Lin

    2017-01-01

    Full Text Available In this paper, the authors presented a study on the discrimination of handlebar grip samples, to provide effective forensic science service for hit and run traffic cases. 50 bicycle handlebar grip samples, 49 electric bike handlebar grip samples, and 96 motorcycle handlebar grip samples have been randomly collected by the local police in Beijing (China. Fourier transform infrared microspectroscopy (FTIR was utilized as analytical technology. Then, target absorption selection, data pretreatment, and discrimination of linked samples and unlinked samples were chosen as three steps to improve the discrimination of FTIR spectrums collected from different handlebar grip samples. Principal component analysis and receiver operating characteristic curve were utilized to evaluate different data selection methods and different data pretreatment methods, respectively. It is possible to explore the evidential value of handlebar grip residue evidence through instrumental analysis and statistical treatments. It will provide a universal discrimination method for other forensic science samples as well.

  12. Classification of viable and non-viable spinach (Spinacia oleracea L.) seeds by single seed near infrared spectroscopy and extended canonical variates analysis

    DEFF Research Database (Denmark)

    Olesen, Merete Halkjær; Shetty, Nisha; Gislum, René

    2011-01-01

    studies. Spinach (Spinacia oleracea L.) is the major crop in vegetable seed production in Denmark and two seed lots with viability percentages of 90% and 97% were chosen for examination by single seed NIR spectroscopy. Lipids play a major role in both ageing and germination. During accelerated ageing......, lipid peroxidation leads to deterioration of cell membranes and contributes in that way to reducing seed viability of the seed sample. These biochemical changes may be the reason for a clear grouping between aged and non-aged seeds when performing the extended canonical variates analysis (ECVA......). Assigning the difference of scatter corrected absorbance spectra from aged and non-aged seeds also lead to CH2, CH3 and HC=CH structures, which are some of the functional groups in lipids. In the ECVA plot, there was a clear difference between seeds with and without a pericarp. Evaluating the spectra...

  13. Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis

    Science.gov (United States)

    Zhang, Xue-Xi; Yin, Jian-Hua; Mao, Zhi-Hua; Xia, Yang

    2015-06-01

    Fourier transform infrared imaging (FTIRI) combined with chemometrics algorithm has strong potential to obtain complex chemical information from biology tissues. FTIRI and partial least squares-discriminant analysis (PLS-DA) were used to differentiate healthy and osteoarthritic (OA) cartilages for the first time. A PLS model was built on the calibration matrix of spectra that was randomly selected from the FTIRI spectral datasets of healthy and lesioned cartilage. Leave-one-out cross-validation was performed in the PLS model, and the fitting coefficient between actual and predicted categorical values of the calibration matrix reached 0.95. In the calibration and prediction matrices, the successful identifying percentages of healthy and lesioned cartilage spectra were 100% and 90.24%, respectively. These results demonstrated that FTIRI combined with PLS-DA could provide a promising approach for the categorical identification of healthy and OA cartilage specimens.

  14. Meta-analysis of field experiments shows no change in racial discrimination in hiring over time

    OpenAIRE

    Quillian, Lincoln; Pager, Devah; Hexel, Ole; Midtbøen, Arnfinn Haagensen

    2017-01-01

    This study investigates change over time in the level of hiring discrimination in US labor markets. We perform a meta-analysis of every available field experiment of hiring discrimination against African Americans or Latinos (n = 28). Together, these studies represent 55,842 applications submitted for 26,326 positions. We focus on trends since 1989 (n = 24 studies), when field experiments became more common and improved methodologically. Since 1989, whites receive on average 36% more callback...

  15. Women ministers' experiences of gender discrimination in the Lutheran Church : a discourse analysis

    OpenAIRE

    2011-01-01

    M.A. The aim of this psychological study was to uncover women minister’s experiences of gender discrimination in the Lutheran Church by using a discourse analysis. Three female participants, who are involved in ministry in the Lutheran Church, were interviewed about their experiences and perceptions of gender discrimination. The resultant texts were analysed using Parker’s (2005) steps to discourse analytic reading. The discourses that were discovered indicate that power struggles are prev...

  16. Fast and robust discrimination of almonds (Prunus amygdalus) with respect to their bitterness by using near infrared and partial least squares-discriminant analysis.

    Science.gov (United States)

    Borràs, Eva; Amigo, José Manuel; van den Berg, Frans; Boqué, Ricard; Busto, Olga

    2014-06-15

    In this study, near-infrared spectroscopy (NIR) coupled to chemometrics is used to develop a fast, simple, non-destructive and robust method for discriminating sweet and bitter almonds (Prunus amygdalus) by the in situ measurement of the kernel surface without any sample pre-treatment. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) models were built to discriminate both types of almonds, obtaining high levels of sensitivity and specificity for both classes, with more than 95% of the samples correctly classified and discriminated. Moreover, the almonds were also analysed by Raman spectroscopy, the reference technique for this type of analysis, to validate and confirm the results obtained by NIR. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Comparison of discriminant analysis methods: Application to occupational exposure to particulate matter

    Science.gov (United States)

    Ramos, M. Rosário; Carolino, E.; Viegas, Carla; Viegas, Sandra

    2016-06-01

    Health effects associated with occupational exposure to particulate matter have been studied by several authors. In this study were selected six industries of five different areas: Cork company 1, Cork company 2, poultry, slaughterhouse for cattle, riding arena and production of animal feed. The measurements tool was a portable device for direct reading. This tool provides information on the particle number concentration for six different diameters, namely 0.3 µm, 0.5 µm, 1 µm, 2.5 µm, 5 µm and 10 µm. The focus on these features is because they might be more closely related with adverse health effects. The aim is to identify the particles that better discriminate the industries, with the ultimate goal of classifying industries regarding potential negative effects on workers' health. Several methods of discriminant analysis were applied to data of occupational exposure to particulate matter and compared with respect to classification accuracy. The selected methods were linear discriminant analyses (LDA); linear quadratic discriminant analysis (QDA), robust linear discriminant analysis with selected estimators (MLE (Maximum Likelihood Estimators), MVE (Minimum Volume Elipsoid), "t", MCD (Minimum Covariance Determinant), MCD-A, MCD-B), multinomial logistic regression and artificial neural networks (ANN). The predictive accuracy of the methods was accessed through a simulation study. ANN yielded the highest rate of classification accuracy in the data set under study. Results indicate that the particle number concentration of diameter size 0.5 µm is the parameter that better discriminates industries.

  18. Theoretical remarks on the statistics of three discriminants in Piety's automated signature analysis of PSD [Power Spectral Density] data

    International Nuclear Information System (INIS)

    Behringer, K.; Spiekerman, G.

    1984-01-01

    Piety (1977) proposed an automated signature analysis of power spectral density data. Eight statistical decision discriminants are introduced. For nearly all the discriminants, improved confidence statements can be made. The statistical characteristics of the last three discriminants, which are applications of non-parametric tests, are considered. (author)

  19. Global classification of human facial healthy skin using PLS discriminant analysis and clustering analysis.

    Science.gov (United States)

    Guinot, C; Latreille, J; Tenenhaus, M; Malvy, D J

    2001-04-01

    Today's classifications of healthy skin are predominantly based on a very limited number of skin characteristics, such as skin oiliness or susceptibility to sun exposure. The aim of the present analysis was to set up a global classification of healthy facial skin, using mathematical models. This classification is based on clinical, biophysical skin characteristics and self-reported information related to the skin, as well as the results of a theoretical skin classification assessed separately for the frontal and the malar zones of the face. In order to maximize the predictive power of the models with a minimum of variables, the Partial Least Square (PLS) discriminant analysis method was used. The resulting PLS components were subjected to clustering analyses to identify the plausible number of clusters and to group the individuals according to their proximities. Using this approach, four PLS components could be constructed and six clusters were found relevant. So, from the 36 hypothetical combinations of the theoretical skin types classification, we tended to a strengthened six classes proposal. Our data suggest that the association of the PLS discriminant analysis and the clustering methods leads to a valid and simple way to classify healthy human skin and represents a potentially useful tool for cosmetic and dermatological research.

  20. [Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].

    Science.gov (United States)

    Kleinhans, Sonja; Herrmann, Eva; Kohnen, Thomas; Bühren, Jens

    2017-08-15

    Background Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. Patients and Methods The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Results Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. Conclusion On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

  1. Don't ask for fair treatment? A gender analysis of ethnic discrimination, response to discrimination, and self-rated health among marriage migrants in South Korea.

    Science.gov (United States)

    Kim, Yugyun; Son, Inseo; Wie, Dainn; Muntaner, Carles; Kim, Hyunwoo; Kim, Seung-Sup

    2016-07-19

    Ethnic discrimination is increasingly common nowadays in South Korea with the influx of migrants. Despite the growing body of evidences suggests that ethnic discrimination negatively impacts health, only few researches have been conducted on the association between ethnic discrimination and health outcomes among marriage migrants in Korea. This study sought to examine how ethnic discrimination and response to the discrimination are related to self-rated health and whether the association differs by victim's gender. We conducted two-step analysis using cross-sectional dataset from the 'National Survey of Multicultural Families 2012'. First, we examined the association between perceived ethnic discrimination and self-rated health among 14,406 marriage migrants in Korea. Second, among the marriage migrants who experienced ethnic discrimination (n=5,880), we examined how response to discrimination (i.e., whether or not asking for fair treatment) is related to poor self-rated health. All analyses were conducted after being stratified by the migrant's gender. This research found the significant association between ethnic discrimination and poor self-rated health among female marriage migrants (OR: 1.53, 95 % CI: 1.32, 1.76), but not among male marriage migrants (OR: 1.16, 95 % CI: 0.81, 1.66). In the restricted analysis with marriage migrants who experienced ethnic discrimination, compared to the group who did not ask for fair treatment, female marriage migrants who asked for fair treatment were more likely to report poor self-rated health (OR: 1.21, 95 % CI: 0.98, 1.50); however, male marriage migrants who asked for fair treatment were less likely to report poor self-rated health (OR: 0.65, 95 % CI: 0.36, 1.04) although both were not statistically significant. This is the first study to investigate gender difference in the association between response to ethnic discrimination and self-rated health in South Korea. We discussed that gender may play an important role

  2. 5'-Terminal AUGs in Escherichia coli mRNAs with Shine-Dalgarno Sequences: Identification and Analysis of Their Roles in Non-Canonical Translation Initiation.

    Directory of Open Access Journals (Sweden)

    Heather J Beck

    Full Text Available Analysis of the Escherichia coli transcriptome identified a unique subset of messenger RNAs (mRNAs that contain a conventional untranslated leader and Shine-Dalgarno (SD sequence upstream of the gene's start codon while also containing an AUG triplet at the mRNA's 5'- terminus (5'-uAUG. Fusion of the coding sequence specified by the 5'-terminal putative AUG start codon to a lacZ reporter gene, as well as primer extension inhibition assays, reveal that the majority of the 5'-terminal upstream open reading frames (5'-uORFs tested support some level of lacZ translation, indicating that these mRNAs can function both as leaderless and canonical SD-leadered mRNAs. Although some of the uORFs were expressed at low levels, others were expressed at levels close to that of the respective downstream genes and as high as the naturally leaderless cI mRNA of bacteriophage λ. These 5'-terminal uORFs potentially encode peptides of varying lengths, but their functions, if any, are unknown. In an effort to determine whether expression from the 5'-terminal uORFs impact expression of the immediately downstream cistron, we examined expression from the downstream coding sequence after mutations were introduced that inhibit efficient 5'-uORF translation. These mutations were found to affect expression from the downstream cistrons to varying degrees, suggesting that some 5'-uORFs may play roles in downstream regulation. Since the 5'-uAUGs found on these conventionally leadered mRNAs can function to bind ribosomes and initiate translation, this indicates that canonical mRNAs containing 5'-uAUGs should be examined for their potential to function also as leaderless mRNAs.

  3. Optical selection of trace elements for discriminant analysis

    International Nuclear Information System (INIS)

    Rasmussen, S.E.; Erasmus, C.S.; Watterson, J.I.W.; Sellschop, J.P.F.

    This report describes different methods of element selection; a combination of stepwise multivariate analysis of variance for primary element selection, and principle component analysis regression for the element interrelationship analysis. These offer a satisfactory solution to the problem of element selection

  4. Statistics of canonical RNA pseudoknot structures.

    Science.gov (United States)

    Huang, Fenix W D; Reidys, Christian M

    2008-08-07

    In this paper we study canonical RNA pseudoknot structures. We prove central limit theorems for the distributions of the arc-numbers of k-noncrossing RNA structures with given minimum stack-size tau over n nucleotides. Furthermore we compare the space of all canonical structures with canonical minimum free energy pseudoknot structures. Our results generalize the analysis of Schuster et al. obtained for RNA secondary structures [Hofacker, I.L., Schuster, P., Stadler, P.F., 1998. Combinatorics of RNA secondary structures. Discrete Appl. Math. 88, 207-237; Jin, E.Y., Reidys, C.M., 2007b. Central and local limit theorems for RNA structures. J. Theor. Biol. 250 (2008), 547-559; 2007a. Asymptotic enumeration of RNA structures with pseudoknots. Bull. Math. Biol., 70 (4), 951-970] to k-noncrossing RNA structures. Here k2 and tau are arbitrary natural numbers. We compare canonical pseudoknot structures to arbitrary structures and show that canonical pseudoknot structures exhibit significantly smaller exponential growth rates. We then compute the asymptotic distribution of their arc-numbers. Finally, we analyze how the minimum stack-size and crossing number factor into the distributions.

  5. Meta-analysis of field experiments shows no change in racial discrimination in hiring over time.

    Science.gov (United States)

    Quillian, Lincoln; Pager, Devah; Hexel, Ole; Midtbøen, Arnfinn H

    2017-10-10

    This study investigates change over time in the level of hiring discrimination in US labor markets. We perform a meta-analysis of every available field experiment of hiring discrimination against African Americans or Latinos ( n = 28). Together, these studies represent 55,842 applications submitted for 26,326 positions. We focus on trends since 1989 ( n = 24 studies), when field experiments became more common and improved methodologically. Since 1989, whites receive on average 36% more callbacks than African Americans, and 24% more callbacks than Latinos. We observe no change in the level of hiring discrimination against African Americans over the past 25 years, although we find modest evidence of a decline in discrimination against Latinos. Accounting for applicant education, applicant gender, study method, occupational groups, and local labor market conditions does little to alter this result. Contrary to claims of declining discrimination in American society, our estimates suggest that levels of discrimination remain largely unchanged, at least at the point of hire.

  6. Discriminant analysis of milk adulteration based on near-infrared spectroscopy and pattern recognition

    Science.gov (United States)

    Liu, Rong; Lv, Guorong; He, Bin; Xu, Kexin

    2011-03-01

    Since the beginning of the 21st century, the issue of food safety is becoming a global concern. It is very important to develop a rapid, cost-effective, and widely available method for food adulteration detection. In this paper, near-infrared spectroscopy techniques and pattern recognition were applied to study the qualitative discriminant analysis method. The samples were prepared and adulterated with one of the three adulterants, urea, glucose and melamine with different concentrations. First, the spectral characteristics of milk and adulterant samples were analyzed. Then, pattern recognition methods were used for qualitative discriminant analysis of milk adulteration. Soft independent modeling of class analogy and partial least squares discriminant analysis (PLSDA) were used to construct discriminant models, respectively. Furthermore, the optimization method of the model was studied. The best spectral pretreatment methods and the optimal band were determined. In the optimal conditions, PLSDA models were constructed respectively for each type of adulterated sample sets (urea, melamine and glucose) and all the three types of adulterated sample sets. Results showed that, the discrimination accuracy of model achieved 93.2% in the classification of different adulterated and unadulterated milk samples. Thus, it can be concluded that near-infrared spectroscopy and PLSDA can be used to identify whether the milk has been adulterated or not and the type of adulterant used.

  7. Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

    Science.gov (United States)

    Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

    2016-01-01

    As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

  8. Canonical problems in scattering and potential theory

    CERN Document Server

    Vinogradov, SS; Vinogradova, ED

    2001-01-01

    Although the analysis of scattering for closed bodies of simple geometric shape is well developed, structures with edges, cavities, or inclusions have seemed, until now, intractable to analytical methods. This two-volume set describes a breakthrough in analytical techniques for accurately determining diffraction from classes of canonical scatterers with comprising edges and other complex cavity features. It is an authoritative account of mathematical developments over the last two decades that provides benchmarks against which solutions obtained by numerical methods can be verified.The first volume, Canonical Structures in Potential Theory, develops the mathematics, solving mixed boundary potential problems for structures with cavities and edges. The second volume, Acoustic and Electromagnetic Diffraction by Canonical Structures, examines the diffraction of acoustic and electromagnetic waves from several classes of open structures with edges or cavities. Together these volumes present an authoritative and uni...

  9. Canonical problems in scattering and potential theory

    CERN Document Server

    Vinogradov, SS; Vinogradova, ED

    2002-01-01

    Although the analysis of scattering for closed bodies of simple geometric shape is well developed, structures with edges, cavities, or inclusions have seemed, until now, intractable to analytical methods. This two-volume set describes a breakthrough in analytical techniques for accurately determining diffraction from classes of canonical scatterers with comprising edges and other complex cavity features. It is an authoritative account of mathematical developments over the last two decades that provides benchmarks against which solutions obtained by numerical methods can be verified.The first volume, Canonical Structures in Potential Theory, develops the mathematics, solving mixed boundary potential problems for structures with cavities and edges. The second volume, Acoustic and Electromagnetic Diffraction by Canonical Structures, examines the diffraction of acoustic and electromagnetic waves from several classes of open structures with edges or cavities. Together these volumes present an authoritative and uni...

  10. Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis.

    Science.gov (United States)

    Martins, Angélica Rocha; Talhavini, Márcio; Vieira, Maurício Leite; Zacca, Jorge Jardim; Braga, Jez Willian Batista

    2017-08-15

    The discrimination of whisky brands and counterfeit identification were performed by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA). In the proposed method all spectra were obtained with no sample preparation. The discrimination models were built with the employment of seven whisky brands: Red Label, Black Label, White Horse, Chivas Regal (12years), Ballantine's Finest, Old Parr and Natu Nobilis. The method was validated with an independent test set of authentic samples belonging to the seven selected brands and another eleven brands not included in the training samples. Furthermore, seventy-three counterfeit samples were also used to validate the method. Results showed correct classification rates for genuine and false samples over 98.6% and 93.1%, respectively, indicating that the method can be helpful for the forensic analysis of whisky samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Chemometric analysis for discrimination of extra virgin olive oils from whole and stoned olive pastes.

    Science.gov (United States)

    De Luca, Michele; Restuccia, Donatella; Clodoveo, Maria Lisa; Puoci, Francesco; Ragno, Gaetano

    2016-07-01

    Chemometric discrimination of extra virgin olive oils (EVOO) from whole and stoned olive pastes was carried out by using Fourier transform infrared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach. Four Italian commercial EVOO brands, all in both whole and stoned version, were considered in this study. The adopted chemometric methodologies were able to describe the different chemical features in phenolic and volatile compounds contained in the two types of oil by using unspecific IR spectral information. Principal component analysis (PCA) was employed in cluster analysis to capture data patterns and to highlight differences between technological processes and EVOO brands. The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extraction procedures. Discriminant analysis was extended to the evaluation of possible adulteration by addition of aliquots of oil from whole paste to the most valuable oil from stoned olives. The statistical parameters from external validation of all the PLS models were very satisfactory, with low root mean square error of prediction (RMSEP) and relative error (RE%). Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Sex determination by discriminant function analysis of palatal rugae from a population of coastal Andhra.

    Science.gov (United States)

    Bharath, Sreenivasa T; Kumar, Govind Raj; Dhanapal, Raghu; Saraswathi, Tr

    2011-07-01

    The aim of the study was to investigate differences in the palatal rugae patterns in males and females of a cross-sectional hospital-based coastal Andhra population and application of discriminant function analysis in sex identification. One hundred pre-orthodontic plaster casts, equally distributed between males and females belonging to an age range of 15-30 years, were examined for different rugae patterns. Thomas classification was adopted for analysis. Association between rugae patterns and sexual dimorphism were tested using Unpaired t test, Chi square test and discriminant function analysis developed using SAS package. Difference in unification pattern among males and females was found to be statistically significant. The total number of the rugae was not statistically significant between the sexes. Association between rugae length and shape with sex determination was computed using discriminant analysis which enabled sex differentiation in this population with an accuracy of 78%. Palatal rugae revealed a specific pattern in unification among males and females of the coastal Andhra population. Discriminant function analysis enabled sex determination of individuals. However, these interpretations were precluded by the small sample size and further research work on larger samples and use of different classification systems is required to validate its use in forensic science.

  13. Comparison of Three Criteria for Discriminant Analysis Procedure ...

    African Journals Online (AJOL)

    2012-08-01

    Aug 1, 2012 ... µ1 and covariance ∑, we can easily calculate these rates. When the parameters are not known a number of error rates may be defined. The function T(R,F) defines the error rates (APER). The first argument is the presumed distribution of the observation that will be classified. 4.0 Data Analysis. Consider to ...

  14. Time-Motion Analysis: Discriminating between winning and losing ...

    African Journals Online (AJOL)

    The current trend in video analysis is the development of performance profiles to describe individual or team patterns created from combinations of key performance indicators. The aim of this study was to quantify distance covered, high-intensity distance covered and percentage work rate at high intensity of various playing ...

  15. linear discriminant analysis of structure within african eggplant 'shum'

    African Journals Online (AJOL)

    ACSS

    observed clusters include petiole length, sepal length (or seed color), fruit calyx length, seeds per fruit, leaf fresh .... obtain means. A table of means per trait for each accession was then imported into R statistical software for UPGMA reordered hierarchical cluster analysis. ..... Mwale, S.E., Ssemakula, M.O., Sadik, K.,.

  16. Use of linear discriminant function analysis in seed morphotype ...

    African Journals Online (AJOL)

    Variation in seed morphology of the Lima bean in 31 accessions was studied. Data were collected on 100-seed weight, seed length and seed width. The differences among the accessions were significant, based on the three seed characteristics. K-means cluster analysis grouped the 31 accessions into four distinct groups, ...

  17. Use of Linear Discriminant Function Analysis in Five Yield Sub ...

    African Journals Online (AJOL)

    K-means cluster analysis grouped the 134 accessions into four distinct groups. Pairwise Mahalanobis 2 distance (D) among some of the groups was highly significant. From the study the yield sub-characters pod length, pod width, peduncle length and 100-seed weight contributed most to group separation in the cowpea ...

  18. Small visible energy scalar top iterative discriminant analysis

    Indian Academy of Sciences (India)

    criminant analysis method to optimize the expected selection efficiency at the international ... Table 1. Generated events, events used for the IDA training, events after the preselection,. √ s = 260 GeV cross-section, scaling factor, and expected number of events. Total. 50%. After σ. Factor. Expected. Process. ×1000 training.

  19. A new method for fingerprinting sediments source contributions using distances from discriminant function analysis

    Science.gov (United States)

    Mixing models have been used to predict sediment source contributions. The inherent problem of the mixing models limited the number of sediment sources. The objective of this study is to develop and evaluate a new method using Discriminant Function Analysis (DFA) to fingerprint sediment source contr...

  20. Prediction Model of Collapse Risk Based on Information Entropy and Distance Discriminant Analysis Method

    Directory of Open Access Journals (Sweden)

    Hujun He

    2017-01-01

    Full Text Available The prediction and risk classification of collapse is an important issue in the process of highway construction in mountainous regions. Based on the principles of information entropy and Mahalanobis distance discriminant analysis, we have produced a collapse hazard prediction model. We used the entropy measure method to reduce the influence indexes of the collapse activity and extracted the nine main indexes affecting collapse activity as the discriminant factors of the distance discriminant analysis model (i.e., slope shape, aspect, gradient, and height, along with exposure of the structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate, and degree of rock weathering. We employ postearthquake collapse data in relation to construction of the Yingxiu-Wolong highway, Hanchuan County, China, as training samples for analysis. The results were analyzed using the back substitution estimation method, showing high accuracy and no errors, and were the same as the prediction result of uncertainty measure. Results show that the classification model based on information entropy and distance discriminant analysis achieves the purpose of index optimization and has excellent performance, high prediction accuracy, and a zero false-positive rate. The model can be used as a tool for future evaluation of collapse risk.

  1. Multivariate Analysis of Laser-Induced Breakdown Spectroscopy for Discrimination between Explosives and Plastics

    International Nuclear Information System (INIS)

    Wang Qian-Qian; Liu Kai; Zhao Hua

    2012-01-01

    A method to distinguish explosives from plastics using laser-induced breakdown spectroscopy is discussed. A model for classification with cross-validation theory is built based on the partial least-square discriminant analysis method. Seven types of plastics and one explosive are used as samples to test the model. The experimental results demonstrate that laser-induced breakdown spectroscopy has the capacity to discriminate explosives from plastics combined with chemometrics methods. The results could be useful for prospective research of laser-induced breakdown spectroscopy on the differentiation of explosives and other materials. (fundamental areas of phenomenology(including applications))

  2. A Comparative Analysis of the Evolution of Gender Wage Discrimination: Spain Versus Galicia

    OpenAIRE

    Pena-Boquete, Yolanda

    2006-01-01

    The aim of this paper is to analyze the degree of female wage discrimination in the Spanish region of Galicia relative to the rest of Spain. The analysis starts from an established fact: women's average earnings are lower than men's. First, we try to show the causes behind this wage differential. Next, we discuss the evolution of the wage gap between 1995 and 2002, in order to bring some light on the factors potentially accounting for wage discrimination persistence in Galicia and Spain. We w...

  3. A Comparative Analysis of the Evolution of Gender Wage Discrimination: Spain Versus Galicia.

    OpenAIRE

    Yolanda Pena-Boquete

    2006-01-01

    The aim of this paper is to analyze the degree of female wage discrimination in the Spanish region of Galicia relative to the rest of Spain. The analysis starts from an established fact: women’s average earnings are lower than men’s. First, we try to show the causes behind this wage differential. Next, we discuss the evolution of the wage gap between 1995 and 2002, in order to bring some light on the factors potentially accounting for wage discrimination persistence in Galicia and Spain. We w...

  4. Race, Sex, and Discrimination in School Settings: A Multilevel Analysis of Associations With Delinquency.

    Science.gov (United States)

    Chambers, Brittany D; Erausquin, Jennifer Toller

    2018-02-01

    Adolescence is a critical phase of development and experimentation with delinquent behaviors. There is a growing body of literature exploring individual and structural impacts of discrimination on health outcomes and delinquent behaviors. However, there is limited research assessing how school diversity and discrimination impact students' delinquent behaviors. In response, the purpose of this study was to assess if individual- and school-level indicators of discrimination and diversity were associated with student delinquent behaviors among African American and White students. We analyzed Wave I (1994-1995) data from the National Longitudinal Study of Adolescent Health. Our analysis was limited to 8947 African American and White students (73% White, 48% male, and 88% parent ≥ high school education). We used multilevel zero-inflated negative binomial regression to test the association of individual- and school characteristics and discrimination with the number of self-reported delinquent behaviors. Race, sex, perceived peer inclusion, and teacher discrimination were predictors of students' delinquent behaviors. The average school perceived peer inclusion and percentage of African Americans in teaching roles were associated with delinquent behaviors. Findings from this study highlight the potential for intervention at the interpersonal and school levels to reduce delinquency among African American and White students. © 2018, American School Health Association.

  5. Baseline drift effect on the performance of neutron and γ ray discrimination using frequency gradient analysis

    International Nuclear Information System (INIS)

    Liu Guofu; Luo Xiaoliang; Yang Jun; Lin Cunbao; Hu Qingqing; Peng Jinxian

    2013-01-01

    Frequency gradient analysis (FGA) effectively discriminates neutrons and γ rays by examining the frequency-domain features of the photomultiplier tube anode signal. This approach is insensitive to noise but is inevitably affected by the baseline drift similar to other pulse shape discrimination methods. The baseline drift effect is attributed to factors such as power line fluctuation, dark current, noise disturbances, hum, and pulse tail in front-end electronics. This effect needs to be elucidated and quantified before the baseline shift can be estimated and removed from the captured signal. Therefore, the effect of baseline shift on the discrimination performance of neutrons and γ rays with organic scintillation detectors using FGA is investigated in this paper. The relationship between the baseline shift and discrimination parameters of FGA is derived and verified by an experimental system consisting of an americium—beryllium source, a BC501A liquid scintillator detector, and a 5 GSample/s 8-bit oscilloscope. The theoretical and experimental results both show that the estimation of the baseline shift is necessary, and the removal of baseline drift from the pulse shapes can improve the discrimination performance of FGA. (authors)

  6. Influence of variable selection on partial least squares discriminant analysis models for explosive residue classification

    Energy Technology Data Exchange (ETDEWEB)

    De Lucia, Frank C., E-mail: frank.delucia@us.army.mil; Gottfried, Jennifer L.

    2011-02-15

    Using a series of thirteen organic materials that includes novel high-nitrogen energetic materials, conventional organic military explosives, and benign organic materials, we have demonstrated the importance of variable selection for maximizing residue discrimination with partial least squares discriminant analysis (PLS-DA). We built several PLS-DA models using different variable sets based on laser induced breakdown spectroscopy (LIBS) spectra of the organic residues on an aluminum substrate under an argon atmosphere. The model classification results for each sample are presented and the influence of the variables on these results is discussed. We found that using the whole spectra as the data input for the PLS-DA model gave the best results. However, variables due to the surrounding atmosphere and the substrate contribute to discrimination when the whole spectra are used, indicating this may not be the most robust model. Further iterative testing with additional validation data sets is necessary to determine the most robust model.

  7. Sexual dimorphism in the Turkmenian population in two types of dermatoglyphic traits: discriminant analysis.

    Science.gov (United States)

    Karmakar, Bibha; Kobyliansky, Eugene

    2009-12-01

    The aim of this study is to compare the pattern of sex differences between two different sets of dermatoglyphic traits (22 quantitative and 42 indices of diversity and asymmetry). Finger and palmar prints of Turkmenian population (547 individuals) were used for Multivariate analyses includes Cluster, Discriminant and Mantel test of matrix correlations. All variables (two groups) scattered into a number of small clusters those are markedly similar between males and females. These results were confirmed by Discriminant analysis--the two groups of variables are almost similar, the percentages of correctly classified individuals are 64.14% (22 traits) and 65.45% (42 traits); and Mantel statistics--the Z values are within the level of non-significance, very good similarities in 22 (0.95) and good similarities in 42 (0.87) traits. Sex dimorphism is similar between two categories of dermatoglyphic variables may be used for sex-discrimination in different populations.

  8. Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia.

    Science.gov (United States)

    Beck, Rebecca B; McGovern, Eavan M; Butler, John S; Birsanu, Dorina; Quinlivan, Brendan; Beiser, Ines; Narasimham, Shruti; O'Riordan, Sean; Hutchinson, Michael; Reilly, Richard B

    2018-01-27

    The temporal discrimination threshold (TDT) is the shortest time interval at which an observer can discriminate two sequential stimuli as being asynchronous (typically 30-50 ms). It has been shown to be abnormal (prolonged) in neurological disorders, including cervical dystonia, a phenotype of adult onset idiopathic isolated focal dystonia. The TDT is a quantitative measure of the ability to perceive rapid changes in the environment and is considered indicative of the behavior of the visual neurons in the superior colliculus, a key node in covert attentional orienting. This article sets out methods for measuring the TDT (including two hardware options and two modes of stimuli presentation). We also explore two approaches of data analysis and TDT calculation. The application of the assessment of temporal discrimination to the understanding of the pathogenesis of cervical dystonia and adult onset idiopathic isolated focal dystonia is also discussed.

  9. Application of otolith shape analysis in identifying different ecotypes of Coilia ectenes in the Yangtze Basin, China

    Digital Repository Service at National Institute of Oceanography (India)

    Radhakrishnan, K.V.; Li, Y.; Jayalakshmy, K.V.; Liu, M.; Murphy, B.R.; Xie, S.

    of the distributional range of the species in the Yangtze Basin, were digitized and analyzed. Canonical Discriminant Analysis (CDA) of the shape indices and Fourier descriptors pooled together showed three distinct clusters of individuals representing anadromous, land...

  10. Applicability of supervised discriminant analysis models to analyze astigmatism clinical trial data.

    Science.gov (United States)

    Sedghipour, Mohammad Reza; Sadeghi-Bazargani, Homayoun

    2012-01-01

    In astigmatism clinical trials where more complex measurements are common, especially in nonrandomized small sized clinical trials, there is a demand for the development and application of newer statistical methods. The source data belonged to a project on astigmatism treatment. Data were used regarding a total of 296 eyes undergoing different astigmatism treatment modalities: wavefront-guided photorefractive keratectomy, cross-cylinder photorefractive keratectomy, and monotoric (single) photorefractive keratectomy. Astigmatism analysis was primarily done using the Alpins method. Prior to fitting partial least squares regression discriminant analysis, a preliminary principal component analysis was done for data overview. Through fitting the partial least squares regression discriminant analysis statistical method, various model validity and predictability measures were assessed. The model found the patients treated by the wavefront method to be different from the two other treatments both in baseline and outcome measures. Also, the model found that patients treated with the cross-cylinder method versus the single method didn't appear to be different from each other. This analysis provided an opportunity to compare the three methods while including a substantial number of baseline and outcome variables. Partial least squares regression discriminant analysis had applicability for the statistical analysis of astigmatism clinical trials and it may be used as an adjunct or alternative analysis method in small sized clinical trials.

  11. Multi spectral imaging analysis for meat spoilage discrimination

    DEFF Research Database (Denmark)

    Christiansen, Asger Nyman; Carstensen, Jens Michael; Papadopoulou, Olga

    In the present study, fresh beef fillets were purchased from a local butcher shop and stored aerobically and in modified atmosphere packaging (MAP, CO2 40%/O2 30%/N2 30%) at six different temperatures (0, 4, 8, 12, 16 and 20°C). Microbiological analysis in terms of total viable counts (TVC...... microbiological and (bio)chemical methods are employed to assess meat spoilage, the majority of which are slow, time-consuming and expensive procedures and thus, it would be most preferable to be replaced by faster and directly applicable methods. Therefore developing a procedure by associating image data...... with corresponding sensory data would be of great interest. The purpose of this research was to produce a method capable of quantifying and/or predicting the spoilage status (e.g. express in TVC counts as well as on sensory evaluation) using a multi spectral image of a meat sample and thereby avoid any time-consuming...

  12. Classification of Fusarium-Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis.

    Science.gov (United States)

    Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Oh, Kyoungmin; Yoo, Hyeonchae; Ham, Hyeonheui; Kim, Moon S

    2017-09-30

    The purpose of this study is to use near-infrared reflectance (NIR) spectroscopy equipment to nondestructively and rapidly discriminate Fusarium -infected hulled barley. Both normal hulled barley and Fusarium -infected hulled barley were scanned by using a NIR spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra obtained for Fusarium discrimination and the multivariate analysis method of partial least squares discriminant analysis (PLS-DA) was used for discriminant prediction. The PLS-DA prediction model developed by applying the second-order derivative pretreatment to the reflectance spectra obtained from the side of hulled barley without crease achieved 100% accuracy in discriminating the normal hulled barley and the Fusarium -infected hulled barley. These results demonstrated the feasibility of rapid discrimination of the Fusarium -infected hulled barley by combining multivariate analysis with the NIR spectroscopic technique, which is utilized as a nondestructive detection method.

  13. Palatal rugae in population differentiation between South and North Indians: A discriminant function analysis.

    Science.gov (United States)

    Shanmugam, Shankar; Anuthama, Krishnamurthy; Shaikh, Hidayathulla; Murali, Kruthika; Suresan, Vinay; Nisharudeen, Khaja; Brinda Devi, Sulur Pechimuthu; Rajasundaram, Prakash

    2012-07-01

    The present study is aimed at delineation of different types of rugae in two different populations and developing a discriminant function for the same. A total of 940 subjects were included in the present study. The sample consisted of 466 subjects from South Indian population and 474 from North Indian population in the age group of 18-23 years. Neo colloid Easy flow((™)) alginate impressions of maxillary arch were made and casts were immediately poured with Type IV dental stone. A sharp graphite pencil was used to delineate the rugae and patterns were recorded according to the classification given by Kapali et al. The association between different population and different sexes was analyzed with chi-square test and a stepwise discriminant function analysis was also performed to develop a discriminant formula. Wavy, curved and straight rugae were the most common forms in both groups. Chi-square analysis for association between rugae shape and population groups showed significant differences among all the rugae patterns at the P rugae shapes showed significant difference in straight, unification and circular type. Five rugae shapes - curved, wavy, nonspecific, unification and circular - were selected for discriminant function. The discriminant function equation obtained from the different rugae shapes in the present study was highly accurate enough to distinguish the Southern and Northern Indian population with the classification accuracy of 87.8%. Thus to identify a specific population, separate discriminant function formulae have to be developed. Hence, the study of palatal rugae is one of the simple and reliable tools for population identification in forensic science.

  14. Canonical correlations between chemical and energetic characteristics of lignocellulosic wastes

    Directory of Open Access Journals (Sweden)

    Thiago de Paula Protásio

    2012-09-01

    Full Text Available Canonical correlation analysis is a statistical multivariate procedure that allows analyzing linear correlation that may exist between two groups or sets of variables (X and Y. This paper aimed to provide canonical correlation analysis between a group comprised of lignin and total extractives contents and higher heating value (HHV with a group of elemental components (carbon, hydrogen, nitrogen and sulfur for lignocellulosic wastes. The following wastes were used: eucalyptus shavings; pine shavings; red cedar shavings; sugar cane bagasse; residual bamboo cellulose pulp; coffee husk and parchment; maize harvesting wastes; and rice husk. Only the first canonical function was significant, but it presented a low canonical R². High carbon, hydrogen and sulfur contents and low nitrogen contents seem to be related to high total extractives contents of the lignocellulosic wastes. The preliminary results found in this paper indicate that the canonical correlations were not efficient to explain the correlations between the chemical elemental components and lignin contents and higher heating values.

  15. [Near infrared spectroscopy analysis method of maize hybrid seed purity discrimination].

    Science.gov (United States)

    Huang, Hua-Jun; Yan, Yan-Lu; Shen, Bing-Hui; Liu, Zhe; Gu, Jian-Cheng; Li, Shao-Ming; Zhu, De-Hai; Zhang, Xiao-Dong; Ma, Qin; Li, Lin; An, Dong

    2014-05-01

    Near infrared spectroscopy analysis method of discrimination of maize hybrid seed purity was studied with the sample of Nong Hua 101 (NH101) from different origins and years. Spectral acquisition time lasted for 10 months. Using Fourier transform (FT) near infrared spectroscopy instruments, including 23 days in different seasons (divided into five time periods), a total of 920 near infrared diffuse reflectance spectra of single corn grain of those samples were collected. Moving window average, first derivative and vector normalization were used to pretreat all original spectra, principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to reduce data dimensionality, and the discrimination model was established based on biomimetic pattern recognition (BPR) method. Spectral distortion was calibrated by spectra pretreatment, which makes characteristics spatial distribution range of sample spectra set contract. The relative distance between hybrid and female parent increased by nearly 70-fold, and the discrimination model achieved the identification of hybrid and female parent seeds. Through the choice of representative samples, the model's response capacity to the changes in spectral acquisition time, place and environment, etc. was improved. Besides, the model's response capacity to the changes in time and site of seed production was also improved, and the robustness of the model was enhanced. The average correct acceptance rate (CAR) of the test set reached more than 95% while the average correct rejection rate (CRR) of the test set also reached 85%.

  16. Discriminant analysis of resting-state functional connectivity patterns on the Grassmann manifold

    Science.gov (United States)

    Fan, Yong; Liu, Yong; Jiang, Tianzi; Liu, Zhening; Hao, Yihui; Liu, Haihong

    2010-03-01

    The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional networks that are informative for schizophrenia diagnosis.

  17. Applicability of supervised discriminant analysis models to analyze astigmatism clinical trial data

    Directory of Open Access Journals (Sweden)

    Sedghipour MR

    2012-09-01

    Full Text Available Mohammad Reza Sedghipour,1 Homayoun Sadeghi-Bazargani2,31Nikoukari Ophthalmology University Hospital, Tabriz, Iran; 2Department of Statistics and Epidemiology, Neuroscience Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; 3Department of Public Health Sciences, Karolinska Institute, Stockholm, SwedenBackground: In astigmatism clinical trials where more complex measurements are common, especially in nonrandomized small sized clinical trials, there is a demand for the development and application of newer statistical methods.Methods: The source data belonged to a project on astigmatism treatment. Data were used regarding a total of 296 eyes undergoing different astigmatism treatment modalities: wavefront-guided photorefractive keratectomy, cross-cylinder photorefractive keratectomy, and monotoric (single photorefractive keratectomy. Astigmatism analysis was primarily done using the Alpins method. Prior to fitting partial least squares regression discriminant analysis, a preliminary principal component analysis was done for data overview. Through fitting the partial least squares regression discriminant analysis statistical method, various model validity and predictability measures were assessed.Results: The model found the patients treated by the wavefront method to be different from the two other treatments both in baseline and outcome measures. Also, the model found that patients treated with the cross-cylinder method versus the single method didn't appear to be different from each other. This analysis provided an opportunity to compare the three methods while including a substantial number of baseline and outcome variables.Conclusion: Partial least squares regression discriminant analysis had applicability for the statistical analysis of astigmatism clinical trials and it may be used as an adjunct or alternative analysis method in small sized clinical trials.Keywords: astigmatism, regression, partial least squares regression

  18. Proteogenomics analysis reveals specific genomic orientations of distal regulatory regions composed by non-canonical histone variants.

    Science.gov (United States)

    Won, Kyoung-Jae; Choi, Inchan; LeRoy, Gary; Zee, Barry M; Sidoli, Simone; Gonzales-Cope, Michelle; Garcia, Benjamin A

    2015-01-01

    Histone variants play further important roles in DNA packaging and controlling gene expression. However, our understanding about their composition and their functions is limited. Integrating proteomic and genomic approaches, we performed a comprehensive analysis of the epigenetic landscapes containing the four histone variants H3.1, H3.3, H2A.Z, and macroH2A. These histones were FLAG-tagged in HeLa cells and purified using chromatin immunoprecipitation (ChIP). By adopting ChIP followed by mass spectrometry (ChIP-MS), we quantified histone post-translational modifications (PTMs) and histone variant nucleosomal ratios in highly purified mononucleosomes. Subsequent ChIP followed by next-generation sequencing (ChIP-seq) was used to map the genome-wide localization of the analyzed histone variants and define their chromatin domains. Finally, we included in our study large datasets contained in the ENCODE database. We newly identified a group of regulatory regions enriched in H3.1 and the histone variant associated with repressive marks macroH2A. Systematic analysis identified both symmetric and asymmetric patterns of histone variant occupancies at intergenic regulatory regions. Strikingly, these directional patterns were associated with RNA polymerase II (PolII). These asymmetric patterns correlated with the enhancer activities measured using global run-on sequencing (GRO-seq) data. Our studies show that H2A.Z and H3.3 delineate the orientation of transcription at enhancers as observed at promoters. We also showed that enhancers with skewed histone variant patterns well facilitate enhancer activity. Collectively, our study indicates that histone variants are deposited at regulatory regions to assist gene regulation.

  19. Variations in students' perceived reasons for, sources of, and forms of in-school discrimination: A latent class analysis.

    Science.gov (United States)

    Byrd, Christy M; Carter Andrews, Dorinda J

    2016-08-01

    Although there exists a healthy body of literature related to discrimination in schools, this research has primarily focused on racial or ethnic discrimination as perceived and experienced by students of color. Few studies examine students' perceptions of discrimination from a variety of sources, such as adults and peers, their descriptions of the discrimination, or the frequency of discrimination in the learning environment. Middle and high school students in a Midwestern school district (N=1468) completed surveys identifying whether they experienced discrimination from seven sources (e.g., peers, teachers, administrators), for seven reasons (e.g., gender, race/ethnicity, religion), and in eight forms (e.g., punished more frequently, called names, excluded from social groups). The sample was 52% White, 15% Black/African American, 14% Multiracial, and 17% Other. Latent class analysis was used to cluster individuals based on reported sources of, reasons for, and forms of discrimination. Four clusters were found, and ANOVAs were used to test for differences between clusters on perceptions of school climate, relationships with teachers, perceptions that the school was a "good school," and engagement. The Low Discrimination cluster experienced the best outcomes, whereas an intersectional cluster experienced the most discrimination and the worst outcomes. The results confirm existing research on the negative effects of discrimination. Additionally, the paper adds to the literature by highlighting the importance of an intersectional approach to examining students' perceptions of in-school discrimination. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

  20. Discrimination in the labour market : an analysis of wage differences between women and men

    OpenAIRE

    Löfström, Åsa

    1989-01-01

    The aim of this study is to examine whether, and to what extent, the differences in wages between women and men can be explained by discrimination against women in the labour market.The first part of the analysis is a cross-sectional analysis. Firstly, a model is estimated with wages as the dependent variable and with sex, individuals' qualifications, personal characteristics, occupation and the branch of industry in which they are employed as the independent variables. The results of this re...

  1. Discourse analysis of gender equality and non-discrimination laws and strategies

    OpenAIRE

    Antonijević Zorana; Beker Kosana

    2017-01-01

    Based on the contemporary research on gender and language, using the method of discourse analysis applied to the laws and policies, this article explains how certain linguistic practice, in the context of the administrative discourse, produces meaning that may or may not contribute to its better understanding and more efficient implementation. Through discourse analysis of gender equality and non-discrimination laws and strategies in Serbia, it has been sho...

  2. Gait characterization in golden retriever muscular dystrophy dogs using linear discriminant analysis

    OpenAIRE

    Fraysse, Bodva?l; Barth?l?my, In?s; Qannari, El Mostafa; Rouger, Karl; Thorin, Chantal; Blot, St?phane; Le Guiner, Caroline; Ch?rel, Yan; Hogrel, Jean-Yves

    2017-01-01

    Background Accelerometric analysis of gait abnormalities in golden retriever muscular dystrophy (GRMD) dogs is of limited sensitivity, and produces highly complex data. The use of discriminant analysis may enable simpler and more sensitive evaluation of treatment benefits in this important preclinical model. Methods Accelerometry was performed twice monthly between the ages of 2 and 12?months on 8 healthy and 20 GRMD dogs. Seven accelerometric parameters were analysed using linear discriminan...

  3. Synthesis and analysis of discriminators under influence of broadband non-Gaussian noise

    Science.gov (United States)

    Artyushenko, V. M.; Volovach, V. I.

    2018-01-01

    We considered the problems of the synthesis and analysis of discriminators, when the useful signal is exposed to non-Gaussian additive broadband noise. It is shown that in this case, the discriminator of the tracking meter should contain the nonlinear transformation unit, the characteristics of which are determined by the Fisher information relative to the probability density function of the mixture of non-Gaussian broadband noise and mismatch errors. The parameters of the discriminatory and phase characteristics of the discriminators working under the above conditions are obtained. It is shown that the efficiency of non-linear processing depends on the ratio of power of FM noise to the power of Gaussian noise. The analysis of the information loss of signal transformation caused by the linear section of discriminatory characteristics of the unit of nonlinear transformations of the discriminator is carried out. It is shown that the average slope of the nonlinear transformation characteristic is determined by the Fisher information relative to the probability density function of the mixture of non-Gaussian noise and mismatch errors.

  4. Analysis of pulse-shape discrimination techniques for BC501A using GHz digital signal processing

    International Nuclear Information System (INIS)

    Rooney, B.D.; Dinwiddie, D.R.; Nelson, M.A.; Rawool-Sullivan, Mohini W.

    2001-01-01

    A comparison study of pulse-shape analysis techniques was conducted for a BC501A scintillator using digital signal processing (DSP). In this study, output signals from a preamplifier were input directly into a 1 GHz analog-to-digital converter. The digitized data obtained with this method was post-processed for both pulse-height and pulse-shape information. Several different analysis techniques were evaluated for neutron and gamma-ray pulse-shape discrimination. It was surprising that one of the simplest and fastest techniques resulted in some of the best pulse-shape discrimination results. This technique, referred to here as the Integral Ratio technique, was able to effectively process several thousand detector pulses per second. This paper presents the results and findings of this study for various pulse-shape analysis techniques with digitized detector signals.

  5. Extensive impact of saturated fatty acids on metabolic and cardiovascular profile in rats with diet-induced obesity: a canonical analysis.

    Science.gov (United States)

    Oliveira Junior, Silvio A; Padovani, Carlos R; Rodrigues, Sergio A; Silva, Nilza R; Martinez, Paula F; Campos, Dijon Hs; Okoshi, Marina P; Okoshi, Katashi; Dal-Pai, Maeli; Cicogna, Antonio C

    2013-04-15

    Although hypercaloric interventions are associated with nutritional, endocrine, metabolic, and cardiovascular disorders in obesity experiments, a rational distinction between the effects of excess adiposity and the individual roles of dietary macronutrients in relation to these disturbances has not previously been studied. This investigation analyzed the correlation between ingested macronutrients (including sucrose and saturated and unsaturated fatty acids) plus body adiposity and metabolic, hormonal, and cardiovascular effects in rats with diet-induced obesity. Normotensive Wistar-Kyoto rats were submitted to Control (CD; 3.2 Kcal/g) and Hypercaloric (HD; 4.6 Kcal/g) diets for 20 weeks followed by nutritional evaluation involving body weight and adiposity measurement. Metabolic and hormonal parameters included glycemia, insulin, insulin resistance, and leptin. Cardiovascular analysis included systolic blood pressure profile, echocardiography, morphometric study of myocardial morphology, and myosin heavy chain (MHC) protein expression. Canonical correlation analysis was used to evaluate the relationships between dietary macronutrients plus adiposity and metabolic, hormonal, and cardiovascular parameters. Although final group body weights did not differ, HD presented higher adiposity than CD. Diet induced hyperglycemia while insulin and leptin levels remained unchanged. In a cardiovascular context, systolic blood pressure increased with time only in HD. Additionally, in vivo echocardiography revealed cardiac hypertrophy and improved systolic performance in HD compared to CD; and while cardiomyocyte size was unchanged by diet, nuclear volume and collagen interstitial fraction both increased in HD. Also HD exhibited higher relative β-MHC content and β/α-MHC ratio than their Control counterparts. Importantly, body adiposity was weakly associated with cardiovascular effects, as saturated fatty acid intake was directly associated with most cardiac remodeling

  6. A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

    Science.gov (United States)

    Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

    2008-01-01

    As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

  7. Financial consumer protection and customer satisfaction. A relationship study by using factor analysis and discriminant analysis

    Directory of Open Access Journals (Sweden)

    Marimuthu SELVAKUMAR

    2015-11-01

    Full Text Available This paper tries to make an attempt to study the relationship between the financial consumer protection and customer satisfaction by using factor analysis and discriminant analysis. The main objectives of the study are to analyze the financial consumer protection in commercial banks, to examine the customer satisfaction of commercial banks and to identify the factors of financial consumer protection lead customer satisfaction. There are many research work carried out on financial consumer protection in financial literacy, but the identification of factors which lead the financial consumer protection and the relationship between financial consumer protection and the customer satisfaction is very important, Particularly for banks to improve its quality and increase the customer satisfaction. Therefore this study is carried out with the aim of identifying the factors of financial consumer protection and its influence on customer satisfaction. This study is both descriptive and analytical in nature. It covers both primary and secondary data. The primary data has been collected from the customers of commercial banks using pre-tested interview schedule and the secondary data has been collected from standard books, journals, magazines, websites and so on.

  8. Ship Discrimination Using Polarimetric SAR Data and Coherent Time-Frequency Analysis

    Directory of Open Access Journals (Sweden)

    Canbin Hu

    2013-12-01

    Full Text Available This paper presents a new approach for the discrimination of ship responses using polarimetric SAR (PolSAR data. The PolSAR multidimensional information is analyzed using a linear Time-Frequency (TF decomposition approach, which permits to describe the polarimetric behavior of a ship and its background area for different azimuthal angles of observation and frequencies of illumination. This paper proposes to discriminate ships from their background by using characteristics of their polarimetric TF responses, which may be associated with the intrinsic nature of the observed natural or artificial scattering structures. A statistical descriptor related to polarimetric coherence of the signal in the TF domain is proposed for detecting ships in different complex backgrounds, including SAR azimuth ambiguities, artifacts, and small natural islands, which may induce numerous false alarms. Choices of the TF analysis direction, i.e., along separate azimuth or range axis, or simultaneously in both directions, are investigated and evaluated. TF decomposition modes including range direction perform better in terms of discriminating ships from range focusing artifacts. In comparison with original full-resolution polarimetric indicators, the proposed TF polarimetric coherence descriptor is shown to qualitatively enhance the ship/background contrast and improve discrimination capabilities. Using polarimetric RADARSAT-2 data acquired over complex scenes, experimental results demonstrate the efficiency of this approach in terms of ship location retrieval and response characterization.

  9. Statistics that learn: can logistic discriminant analysis improve diagnosis in brain SPECT?

    International Nuclear Information System (INIS)

    Behin-Ain, S.; Barnden, L.; Kwiatek, R.; Del Fante, P.; Casse, R.; Burnet, R.; Chew, G.; Kitchener, M.; Boundy, K.; Unger, S.

    2002-01-01

    Full text: Logistic discriminant analysis (LDA) is a statistical technique capable of discriminating individuals within a diseased group against normals. It also enables classification of various diseases within a group of patients. This technique provides a quantitative, automated and non-subjective clinical diagnostic tool. Based on a population known to have the disease and a normal control group, an algorithm was developed and trained to identify regions in the human brain responsible for the disease in question. The algorithm outputs a statistical map representing diseased or normal probability on a voxel or cluster basis from which an index is generated for each subject. The algorithm also generates a set of coefficients which is used to generate an index for the purpose of classification of new subjects. The results are comparable and complement those of Statistical Parametric Mapping (SPM) which employs a more common linear discriminant technique. The results are presented for brain SPECT studies of two diseases: chronic fatigue syndrome (CFS) and fibromyalgia (FM). A 100% specificity and 94% sensitivity is achieved for the CFS study (similar to SPM results) and for the FM study 82% specificity and 94% sensitivity is achieved with corresponding SPM results showing 90% specificity and 82% sensitivity. The results encourages application of LDA for discrimination of new single subjects as well as of diseased and normal groups. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc

  10. Optical spectroscopic analysis for the discrimination of extra-virgin olive-oil (Conference Presentation)

    Science.gov (United States)

    McReynolds, Naomi; Auñón Garcia, Juan M.; Guengerich, Zoe; Smith, Terry K.; Dholakia, Kishan

    2017-02-01

    We present an optical spectroscopic technique, making use of both Raman signals and fluorescence spectroscopy, for the identification of five brands of commercially available extra-virgin olive-oil (EVOO). We demonstrate our technique on both a `bulk-optics' free-space system and a compact device. Using the compact device, which is capable of recording both Raman and fluorescence signals, we achieved an average sensitivity and specificity of 98.4% and 99.6% for discrimination, respectively. Our approach demonstrates that both Raman and fluorescence spectroscopy can be used for portable discrimination of EVOOs which obviates the need to use centralised laboratories and opens up the prospect of in-field testing. This technique may enable detection of EVOO that has undergone counterfeiting or adulteration. One of the main challenges facing Raman spectroscopy for use in quality control of EVOOs is that the oxidation of EVOO, which naturally occurs due to aging, causes shifts in Raman spectra with time, which implies regular retraining would be necessary. We present a potential method of analysis to minimize the effect that aging has on discrimination efficiency; we show that by discarding the first principal component, which contains information on the variations due to oxidation, we can improve discrimination efficiency thus improving the robustness of our technique.

  11. 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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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 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 mathematics (STEM) subjects at the elementary and middle school levels. While the curriculum can vary from combined STEM subjects to separated mathematics or science courses, both professional learning communities (PLC) for teachers and problem-based learning (PBL) for learners can be

  13. Perceived Emotional Intelligence and Learning Strategies in Spanish University Students: A New Perspective from a Canonical Non-symmetrical Correspondence Analysis

    Directory of Open Access Journals (Sweden)

    María C. Vega-Hernández

    2017-10-01

    Full Text Available Recent studies have revealed that emotional competences are relevant to the student’s learning process and, more specifically, in the use of learning strategies (LSs. The aim of this study is twofold. First, we aim to analyze the relationship between perceived emotional intelligence (PEI and LSs applying the scales TMMS-24 and Abridged ACRA to a sample of 2334 Spanish university students, whilst also exploring possible gender differences. Second, we aim to propose a methodological alternative based on the Canonical non-symmetrical correspondence analysis (CNCA, as an alternative to the methods traditionally used in Psychology and Education. Our results show that PEI has an impact on the LS of the students. Male participants with high scores on learning support strategies are positively related to high attention, clarity, and emotional repair. However, the use of cognitive and control LS is related to low values on the PEI dimensions. For women, high scores on cognitive, control, and learning support LS are related to high emotional attention, whereas dimensions such as study habits and learning support are related to adequate emotional repair. Participants in the 18–19 and 22–23 years age groups showed similar behavior. High scores on learning support strategies are related to high values on three dimensions of the PEI, and high values of study habits show high values for clarity and low values for attention and repair. The 20–21 and older than 24 years age groups behaved similarly. High scores on learning support strategies are related to low values on clarity, and study habits show high values for clarity and repair. This article presents the relationship between PEI and LS in university students, the differences by gender and age, and CNCA as an alternative method to techniques used in this field to study this association.

  14. Perceived Emotional Intelligence and Learning Strategies in Spanish University Students: A New Perspective from a Canonical Non-symmetrical Correspondence Analysis.

    Science.gov (United States)

    Vega-Hernández, María C; Patino-Alonso, María C; Cabello, Rosario; Galindo-Villardón, María P; Fernández-Berrocal, Pablo

    2017-01-01

    Recent studies have revealed that emotional competences are relevant to the student's learning process and, more specifically, in the use of learning strategies (LSs). The aim of this study is twofold. First, we aim to analyze the relationship between perceived emotional intelligence (PEI) and LSs applying the scales TMMS-24 and Abridged ACRA to a sample of 2334 Spanish university students, whilst also exploring possible gender differences. Second, we aim to propose a methodological alternative based on the Canonical non-symmetrical correspondence analysis (CNCA), as an alternative to the methods traditionally used in Psychology and Education. Our results show that PEI has an impact on the LS of the students. Male participants with high scores on learning support strategies are positively related to high attention, clarity, and emotional repair. However, the use of cognitive and control LS is related to low values on the PEI dimensions. For women, high scores on cognitive, control, and learning support LS are related to high emotional attention, whereas dimensions such as study habits and learning support are related to adequate emotional repair. Participants in the 18-19 and 22-23 years age groups showed similar behavior. High scores on learning support strategies are related to high values on three dimensions of the PEI, and high values of study habits show high values for clarity and low values for attention and repair. The 20-21 and older than 24 years age groups behaved similarly. High scores on learning support strategies are related to low values on clarity, and study habits show high values for clarity and repair. This article presents the relationship between PEI and LS in university students, the differences by gender and age, and CNCA as an alternative method to techniques used in this field to study this association.

  15. Canonical Labelling of Site Graphs

    Directory of Open Access Journals (Sweden)

    Nicolas Oury

    2013-06-01

    Full Text Available We investigate algorithms for canonical labelling of site graphs, i.e. graphs in which edges bind vertices on sites with locally unique names. We first show that the problem of canonical labelling of site graphs reduces to the problem of canonical labelling of graphs with edge colourings. We then present two canonical labelling algorithms based on edge enumeration, and a third based on an extension of Hopcroft's partition refinement algorithm. All run in quadratic worst case time individually. However, one of the edge enumeration algorithms runs in sub-quadratic time for graphs with "many" automorphisms, and the partition refinement algorithm runs in sub-quadratic time for graphs with "few" bisimulation equivalences. This suite of algorithms was chosen based on the expectation that graphs fall in one of those two categories. If that is the case, a combined algorithm runs in sub-quadratic worst case time. Whether this expectation is reasonable remains an interesting open problem.

  16. Canonical Chern-Simons gravity

    Science.gov (United States)

    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.

  17. Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis.

    Science.gov (United States)

    Suhail, Zobia; Denton, Erika R E; Zwiggelaar, Reyer

    2018-01-25

    Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system. We have developed a novel method for the classification of benign and malignant micro-calcification using an improved Fisher Linear Discriminant Analysis (LDA) approach for the linear transformation of segmented micro-calcification data in combination with a Support Vector Machine (SVM) variant to classify between the two classes. The results indicate an average accuracy equal to 96% which is comparable to state-of-the art methods in the literature. Graphical Abstract Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis.

  18. Application of Discriminant Analysis and Cross-Validation on Proteomics Data.

    Science.gov (United States)

    Kuligowski, Julia; Pérez-Guaita, David; Quintás, Guillermo

    2016-01-01

    High-throughput proteomic experiments have raised the importance and complexity of bioinformatic analysis to extract useful information from raw data. Discriminant analysis is frequently used to identify differences among test groups of individuals or to describe combinations of discriminant variables. However, even in relatively large studies, the number of detected variables typically largely exceeds the number of samples and the classifiers should be thoroughly validated to assess their performance for new samples. Cross-validation is a widely approach when an external validation set is not available. In this chapter, different approaches for cross-validation are presented including relevant aspects that should be taken into account to avoid overly optimistic results and the assessment of the statistical significance of cross-validated figures of merit.

  19. Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

    Directory of Open Access Journals (Sweden)

    Fubiao Feng

    2017-03-01

    Full Text Available Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA.

  20. Sparse dimensionality reduction of hyperspectral image based on semi-supervised local Fisher discriminant analysis

    Science.gov (United States)

    Shao, Zhenfeng; Zhang, Lei

    2014-09-01

    This paper presents a novel sparse dimensionality reduction method of hyperspectral image based on semi-supervised local Fisher discriminant analysis (SELF). The proposed method is designed to be especially effective for dealing with the out-of-sample extrapolation to realize advantageous complementarities between SELF and sparsity preserving projections (SPP). Compared to SELF and SPP, the method proposed herein offers highly discriminative ability and produces an explicit nonlinear feature mapping for the out-of-sample extrapolation. This is due to the fact that the proposed method can get an explicit feature mapping for dimensionality reduction and improve the classification performance of classifiers by performing dimensionality reduction. Experimental analysis on the sparsity and efficacy of low dimensional outputs shows that, sparse dimensionality reduction based on SELF can yield good classification results and interpretability in the field of hyperspectral remote sensing.

  1. Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

    Directory of Open Access Journals (Sweden)

    Khattab M.Ali Alheeti

    2017-08-01

    Full Text Available Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoc networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DoS and black hole attacks on vehicular ad hoc networks (VANETs. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA and Quadratic Discriminant Analysis (QDA which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.

  2. Z-score linear discriminant analysis for EEG based brain-computer interfaces.

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    Full Text Available Linear discriminant analysis (LDA is one of the most popular classification algorithms for brain-computer interfaces (BCI. LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA, which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

  3. Improving Implementation of Linear Discriminant Analysis for the High Dimension/Small Sample Size Problem

    Czech Academy of Sciences Publication Activity Database

    Duintjer Tebbens, Jurjen; Schlesinger, P.

    2007-01-01

    Roč. 52, č. 1 (2007), s. 423-437 ISSN 0167-9473 R&D Projects: GA AV ČR 1ET400300415; GA MŠk LC536 Institutional research plan: CEZ:AV0Z10300504 Keywords : linear discriminant analysis * numerical aspects of FLDA * small sample size problem * dimension reduction * sparsity Subject RIV: BA - General Mathematics Impact factor: 1.029, year: 2007

  4. A Retrospective Video Analysis of Canonical Babbling and Volubility in Infants with Fragile X Syndrome at 9-12 Months of Age

    Science.gov (United States)

    Belardi, Katie; Watson, Linda R.; Faldowski, Richard A.; Hazlett, Heather; Crais, Elizabeth; Baranek, Grace T.; McComish, Cara; Patten, Elena; Oller, D. Kimbrough

    2017-01-01

    An infant's vocal capacity develops significantly during the first year of life. Research suggests early measures of pre-speech development, such as canonical babbling and volubility, can differentiate typical versus disordered development. This study offers a new contribution by comparing early vocal development in 10 infants with Fragile X…

  5. Discriminant analysis on the treatment results of interstitial radium tongue implants

    International Nuclear Information System (INIS)

    Hoshina, Masao; Shibuya, Hitoshi; Horiuchi, Jun-Ichi; Matsubara, Sho; Suzuki, Soji; Takeda, Masamune

    1989-01-01

    Discriminant analysis was carried out for 48 tongue cancer patients who were treated with radium single-plane implantation. The 48 patients were grouped into 32 successfully cured without complications, five successfully cured with complications, six successfully cured but requiring additional boost therapy and five with local recurrence. To evaluate the relation between the dose distribution and the local treatment results, the analysis was based on a volume-dose relationship. The functions introduced by this discriminant analysis were linear, and the parameters used were modal dose, average dose and shape factors of histograms. Each group of treatment results had a correction rate of >80%, except for the successfully cured group with ulcers. The discriminant functions were useful as an index to obtain a final clinical treatment result at the early time of implantation, and these functions could be used as a criterion for the optimal treatment of tongue carcinoma. We were also able to recognize the limitation of the actual arrangement of sources in the single-plane implant. (author)

  6. Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

    Science.gov (United States)

    Phinyomark, A.; Hu, H.; Phukpattaranont, P.; Limsakul, C.

    2012-01-01

    The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.

  7. Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW.

    Science.gov (United States)

    Stewart, C M; Newlands, S D; Perachio, A A

    2004-12-01

    Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.

  8. Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

    Science.gov (United States)

    Yu, Jianbo

    2016-11-01

    The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

  9. Using Dynamic Fourier Analysis to Discriminate Between Seismic Signals from Natural Earthquakes and Mining Explosions

    Directory of Open Access Journals (Sweden)

    Maria C. Mariani

    2017-08-01

    Full Text Available A sequence of intraplate earthquakes occurred in Arizona at the same location where miningexplosions were carried out in previous years. The explosions and some of the earthquakes generatedvery similar seismic signals. In this study Dynamic Fourier Analysis is used for discriminating signalsoriginating from natural earthquakes and mining explosions. Frequency analysis of seismogramsrecorded at regional distances shows that compared with the mining explosions the earthquake signalshave larger amplitudes in the frequency interval ~ 6 to 8 Hz and significantly smaller amplitudes inthe frequency interval ~ 2 to 4 Hz. This type of analysis permits identifying characteristics in theseismograms frequency yielding to detect potentially risky seismic events.

  10. DNA pattern recognition using canonical correlation algorithm.

    Science.gov (United States)

    Sarkar, B K; Chakraborty, Chiranjib

    2015-10-01

    We performed canonical correlation analysis as an unsupervised statistical tool to describe related views of the same semantic object for identifying patterns. A pattern recognition technique based on canonical correlation analysis (CCA) was proposed for finding required genetic code in the DNA sequence. Two related but different objects were considered: one was a particular pattern, and other was test DNA sequence. CCA found correlations between two observations of the same semantic pattern and test sequence. It is concluded that the relationship possesses maximum value in the position where the pattern exists. As a case study, the potential of CCA was demonstrated on the sequence found from HIV-1 preferred integration sites. The subsequences on the left and right flanking from the integration site were considered as the two views, and statistically significant relationships were established between these two views to elucidate the viral preference as an important factor for the correlation.

  11. [Etiological analysis and establishment of a discriminant model for lower respiratory tract infections in hospitalized patients].

    Science.gov (United States)

    Chen, Y S; Lin, X H; Li, H R; Hua, Z D; Lin, M Q; Huang, W S; Yu, T; Lyu, H Y; Mao, W P; Liang, Y Q; Peng, X R; Chen, S J; Zheng, H; Lian, S Q; Hu, X L; Yao, X Q

    2017-12-12

    Objective: To analyze the pathogens of lower respiratory tract infection(LRTI) including bacterial, viral and mixed infection, and to establish a discriminant model based on clinical features in order to predict the pathogens. Methods: A total of 243 hospitalized patients with lower respiratory tract infections were enrolled in Fujian Provincial Hospital from April 2012 to September 2015. The clinical data and airway (sputum and/or bronchoalveolar lavage) samples were collected. Microbes were identified by traditional culture (for bacteria), loop-mediated isothermal amplification(LAMP) and gene sequencing (for bacteria and atypical pathogen), or Real-time quantitative polymerase chain reaction (Real-time PCR)for viruses. Finally, a discriminant model was established by using the discriminant analysis methods to help to predict bacterial, viral and mixed infections. Results: Pathogens were detected in 53.9% (131/243) of the 243 cases.Bacteria accounted for 23.5%(57/243, of which 17 cases with the virus, 1 case with Mycoplasma pneumoniae and virus), mainly Pseudomonas Aeruginosa and Klebsiella Pneumonia. Atypical pathogens for 4.9% (12/243, of which 3 cases with the virus, 1 case of bacteria and viruses), all were mycoplasma pneumonia. Viruses for 34.6% (84/243, of which 17 cases of bacteria, 3 cases with Mycoplasma pneumoniae, 1 case with Mycoplasma pneumoniae and bacteria) of the cases, mainly Influenza A virus and Human Cytomegalovirus, and other virus like adenovirus, human parainfluenza virus, respiratory syncytial virus, human metapneumovirus, human boca virus were also detected fewly. Seven parameters including mental status, using antibiotics prior to admission, complications, abnormal breath sounds, neutrophil alkaline phosphatase (NAP) score, pneumonia severity index (PSI) score and CRUB-65 score were enrolled after univariate analysis, and discriminant analysis was used to establish the discriminant model by applying the identified pathogens as the

  12. Gait characterization in golden retriever muscular dystrophy dogs using linear discriminant analysis.

    Science.gov (United States)

    Fraysse, Bodvaël; Barthélémy, Inès; Qannari, El Mostafa; Rouger, Karl; Thorin, Chantal; Blot, Stéphane; Le Guiner, Caroline; Chérel, Yan; Hogrel, Jean-Yves

    2017-04-12

    Accelerometric analysis of gait abnormalities in golden retriever muscular dystrophy (GRMD) dogs is of limited sensitivity, and produces highly complex data. The use of discriminant analysis may enable simpler and more sensitive evaluation of treatment benefits in this important preclinical model. Accelerometry was performed twice monthly between the ages of 2 and 12 months on 8 healthy and 20 GRMD dogs. Seven accelerometric parameters were analysed using linear discriminant analysis (LDA). Manipulation of the dependent and independent variables produced three distinct models. The ability of each model to detect gait alterations and their pattern change with age was tested using a leave-one-out cross-validation approach. Selecting genotype (healthy or GRMD) as the dependent variable resulted in a model (Model 1) allowing a good discrimination between the gait phenotype of GRMD and healthy dogs. However, this model was not sufficiently representative of the disease progression. In Model 2, age in months was added as a supplementary dependent variable (GRMD_2 to GRMD_12 and Healthy_2 to Healthy_9.5), resulting in a high overall misclassification rate (83.2%). To improve accuracy, a third model (Model 3) was created in which age was also included as an explanatory variable. This resulted in an overall misclassification rate lower than 12%. Model 3 was evaluated using blinded data pertaining to 81 healthy and GRMD dogs. In all but one case, the model correctly matched gait phenotype to the actual genotype. Finally, we used Model 3 to reanalyse data from a previous study regarding the effects of immunosuppressive treatments on muscular dystrophy in GRMD dogs. Our model identified significant effect of immunosuppressive treatments on gait quality, corroborating the original findings, with the added advantages of direct statistical analysis with greater sensitivity and more comprehensible data representation. Gait analysis using LDA allows for improved analysis of

  13. Is it really organic? – Multi-isotopic analysis as a tool to discriminate between organic and conventional plants

    DEFF Research Database (Denmark)

    Laursen, K.H.; Mihailova, A.; Kelly, S.D.

    2013-01-01

    for discrimination of organically and conventionally grown plants. The study was based on wheat, barley, faba bean and potato produced in rigorously controlled long-term field trials comprising 144 experimental plots. Nitrogen isotope analysis revealed the use of animal manure, but was unable to discriminate between...... plants that were fertilised with synthetic nitrogen fertilisers or green manures from atmospheric nitrogen fixing legumes. This limitation was bypassed using oxygen isotope analysis of nitrate in potato tubers, while hydrogen isotope analysis allowed complete discrimination of organic and conventional...

  14. Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Lei Pan

    2017-05-01

    Full Text Available Recently, sparse and low-rank graph-based discriminant analysis (SLGDA has yielded satisfactory results in hyperspectral image (HSI dimensionality reduction (DR, for which sparsity and low-rankness are simultaneously imposed to capture both local and global structure of hyperspectral data. However, SLGDA fails to exploit the spatial information. To address this problem, a tensor sparse and low-rank graph-based discriminant analysis (TSLGDA is proposed in this paper. By regarding the hyperspectral data cube as a third-order tensor, small local patches centered at the training samples are extracted for the TSLGDA framework to maintain the structural information, resulting in a more discriminative graph. Subsequently, dimensionality reduction is performed on the tensorial training and testing samples to reduce data redundancy. Experimental results of three real-world hyperspectral datasets demonstrate that the proposed TSLGDA algorithm greatly improves the classification performance in the low-dimensional space when compared to state-of-the-art DR methods.

  15. Discrimination analysis of human lung cancer cells associated with histological type and malignancy using Raman spectroscopy

    Science.gov (United States)

    Oshima, Yusuke; Shinzawa, Hideyuki; Takenaka, Tatsuji; Furihata, Chie; Sato, Hidetoshi

    2010-01-01

    The Raman spectroscopic technique enables the observation of intracellular molecules without fixation or labeling procedures in situ. Raman spectroscopy is a promising technology for diagnosing cancers-especially lung cancer, one of the most common cancers in humans-and other diseases. The purpose of this study was to find an effective marker for the identification of cancer cells and their malignancy using Raman spectroscopy. We demonstrate a classification of cultured human lung cancer cells using Raman spectroscopy, principal component analysis (PCA), and linear discrimination analysis (LDA). Raman spectra of single, normal lung cells, along with four cancer cells with different pathological types, were successfully obtained with an excitation laser at 532 nm. The strong appearance of bands due to cytochrome c (cyt-c) indicates that spectra are resonant and enhanced via the Q-band near 550 nm with excitation light. The PCA loading plot suggests a large contribution of cyt-c in discriminating normal cells from cancer cells. The PCA results reflect the nature of the original cancer, such as its histological type and malignancy. The five cells were successfully discriminated by the LDA.

  16. BAYESIAN WAVELET-BASED CURVE CLASSIFICATION VIA DISCRIMINANT ANALYSIS WITH MARKOV RANDOM TREE PRIORS

    Science.gov (United States)

    Stingo, Francesco C.; Vannucci, Marina; Downey, Gerard

    2014-01-01

    Discriminant analysis is an effective tool for the classification of experimental units into groups. When the number of variables is much larger than the number of observations it is necessary to include a dimension reduction procedure into the inferential process. Here we present a typical example from chemometrics that deals with the classification of different types of food into species via near infrared spectroscopy. We take a nonparametric approach by modeling the functional predictors via wavelet transforms and then apply discriminant analysis in the wavelet domain. We consider a Bayesian conjugate normal discriminant model, either linear or quadratic, that avoids independence assumptions among the wavelet coefficients. We introduce latent binary indicators for the selection of the discriminatory wavelet coefficients and propose prior formulations that use Markov random tree (MRT) priors to map scale-location connections among wavelets coefficients. We conduct posterior inference via MCMC methods, we show performances on our case study on food authenticity and compare results to several other procedures.. PMID:24761126

  17. Two-dimensional linear discriminant analysis for classification of three-way chemical data.

    Science.gov (United States)

    Silva, Adenilton C da; Soares, Sófacles F C; Insausti, Matías; Galvão, Roberto K H; Band, Beatriz S F; Araújo, Mário César U de

    2016-09-28

    The two-dimensional linear discriminant analysis (2D-LDA) algorithm was originally proposed in the context of face image processing for the extraction of features with maximal discriminant power. However, despite its promising performance in image processing tasks, the 2D-LDA algorithm has not yet been used in applications involving chemical data. The present paper bridges this gap by investigating the use of 2D-LDA in classification problems involving three-way spectral data. The investigation was concerned with simulated data, as well as real-life data sets involving the classification of dry-cured Parma ham according to ageing by surface autofluorescence spectrometry and the classification of edible vegetable oils according to feedstock using total synchronous fluorescence spectrometry. The results were compared with those obtained by using the spectral data with no feature extraction, U-PLS-DA (Partial Least Squares Discriminant Analysis applied to the unfolded data), and LDA employing TUCKER-3 or PARAFAC scores. In the simulated data set, all methods yielded a correct classification rate of 100%. However, in the Parma ham and vegetable oil data sets, better classification rates were obtained by using 2D-LDA (86% and 100%), compared with no feature extraction (76% and 77%), U-PLS-DA (81% and 92%), PARAFAC-LDA (76% and 86%) and TUCKER3-LDA (86% and 93%). Published by Elsevier B.V.

  18. INCOME INEQUALITY IN SOME MAJOR EUROPEAN UNION ECONOMIES A DISCRIMINANT ANALYSIS

    Directory of Open Access Journals (Sweden)

    JYOTIRMAYEE KAR

    2012-12-01

    Full Text Available This exercise is an attempt to assess the importance of some social, economic, demographic and infrastructural factors which account for the prevailing income inequality across some of the EU countries. Using discriminant analysis the study suggests that crime recorded by police is the most important predictor in discriminating between the group of countries with relatively more equitable distribution of income from those with less. This variable is followed by number of students in the country. Reduction in the level of crime and improvement in the student strength could help in reducing income inequality. Quite intuitively, improvement in all the economic factors like GDP per capita and agricultural index will help to reduce income inequality. Identical is the case of the demographic factors. This calls for implementation of developmental policies towards improvement in these areas.

  19. A multivariate discriminate analysis of behavioral measures in genetically nervous dogs.

    Science.gov (United States)

    Walls, R C; Murphree, O D; Angel, C; Newton, J E

    1976-01-01

    For some years we have studied a strain of genetically nervous dogs in the Neuropsychiatric Research Laboratory, Veterans Administration Hospital, North Little Rock, Arkansas. In the manner of Pavlov and Gantt and later Scott and Fuller we have characterized these dogs in such descriptive terms as timid, human aversive, and catatonic-like. Behavioral tests have been administered on nearly all dogs in this longitudinal study, and we are using these data to try to develop statistical procedures to maximize the discriminatory power of the behavioral assay and to more accurately characterize the behavioral deficit. A multivariate discriminate analysis of 13 variables on 91 healthy and 63 nervous dogs assayed at 3 months of age shows: (1) that much of our present behavioral testing procedures is redundant, and (2) that simple "friendliness to humans" in the dog is as effective for discriminating between the two groups as any of the 13 measures, taken either singly or collectively.

  20. Principal component analysis for neural electron/jet discrimination in highly segmented calorimeters

    International Nuclear Information System (INIS)

    Vassali, M.R.; Seixas, J.M.

    2001-01-01

    A neural electron/jet discriminator based on calorimetry is developed for the second-level trigger system of the ATLAS detector. As preprocessing of the calorimeter information, a principal component analysis is performed on each segment of the two sections (electromagnetic and hadronic) of the calorimeter system, in order to reduce significantly the dimension of the input data space and fully explore the detailed energy deposition profile, which is provided by the highly-segmented calorimeter system. It is shown that projecting calorimeter data onto 33 segmented principal components, the discrimination efficiency of the neural classifier reaches 98.9% for electrons (with only 1% of false alarm probability). Furthermore, restricting data projection onto only 9 components, an electron efficiency of 99.1% is achieved (with 3% of false alarm), which confirms that a fast triggering system may be designed using few components

  1. Sex assessment from carpals bones: discriminant function analysis in a contemporary Mexican sample.

    Science.gov (United States)

    Mastrangelo, Paola; De Luca, Stefano; Sánchez-Mejorada, Gabriela

    2011-06-15

    Sex assessment is one of the first essential steps in human identification, in both medico-legal cases and bio-archaeological contexts. Fragmentary human remains compromised by different types of burial or physical insults may frustrate the use of the traditional sex estimation methods, such as the analysis of the skull and pelvis. Currently, the application of discriminant functions to sex unidentified skeletal remains is steadily increasing. However, several studies have demonstrated that, due to variation in size and patterns of sexual dimorphism, discriminant functions are population-specific. In this study, in order to improve sex assessment from skeletal remains and to establish population-specific discriminant functions, the diagnostic values of the carpal bones were considered. A sample of 136 individuals (78 males, 58 females) of known sex and age was analyzed. They belong to a contemporary identified collection from the Laboratory of Physical Anthropology, Faculty of Medicine, UNAM (Universidad Nacional Autónoma de México, Mexico City). The age of the individuals ranged between 25 and 85 years. Between four and nine measurements of each carpal bone were taken. Independent t-tests confirm that all carpals are sexually dimorphic. Univariate measurements produce accuracy levels that range from 61.8% to 90.8%. Classification accuracies ranged between 81.3% and 92.3% in the multivariate stepwise discriminant analysis. In addition, intra- and inter-observer error tests were performed. These indicated that replication of measurements was satisfactory for the same observer over time and between observers. These results suggest that carpal bones can be used for assessing sex in both forensic and bio-archaeological identification procedures and that bone dimensions are population specific. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  2. Study on discriminant analysis by military mental disorder prediction scale for mental disorder of new recruits

    Directory of Open Access Journals (Sweden)

    Li-yi ZHANG

    2011-11-01

    Full Text Available Objective To examine the predictive role of the Military Mental Disorder Prediction Scale on the mental disorder of new recruits.Methods The present study examined 115 new recruits diagnosed with mental disorder and 115 healthy new recruits.The recruits were tested using the Military Mental Disorder Prediction Scale.The discriminant function was built by discriminant analysis method.The current study analyzed the predictive value of 11 factors(family medical record and past medical record(X1,growth experience(X2,introversion(X3,stressor(X4,poor mental defense(X5,social support(X6,psychosis(X7,depression(X8,mania(X9,neurosis(X10,and personality disorder(X11 aside from lie factor on the mental disorder of new recruits.Results The mental disorder group has higher total score and factor score in family medical record and past medical record,introversion,stressor,poor mental defense,social support,psychosis,depression,mania,neurosis,personality disorder,and lie than those of the contrast group(P < 0.01.For the score of growth experience factor,that of the mental disorder group is higher than the score of the contrast group(P < 0.05.All 11 factors except the lie factor in the Mental Disorder Prediction Scale are taken as independent variables by enforced introduction to obtain the Fisher linear discriminant function as follows: The mental disorder group=-7.014-0.278X1+1.556X2+1.563X3+0.878X4+0.183X5-0.845X6-0.562X7-0.353X8+1.246X9-0.505X10+1.029X11.The contrast group=-2.971+0.056X1+2.194X2+0.707X3+0.592X4-0.086X5-0.888X6-0.133X7-0.360X8+0.654X9-0.467X10+0.308X11.The discriminant function has an accuracy rate of 76.5% on the new recruits with mental disorders and 100% on the healthy new recruits.The total accurate discrimination rate is 88.3% and the total inaccurate discrimination rate is 11.7%.Conclusion The Military Mental Disorder Prediction Scale has a high accuracy rate on the prediction of mental disorder of new recruits and is worthy of

  3. Fluorescence spectral analysis for the discrimination of complex, similar mixtures with the aid of chemometrics.

    Science.gov (United States)

    Ni, Yongnian; Lai, Yanhua; Kokot, Serge

    2012-07-01

    An analytical method for the classification of complex real-world samples was researched and developed with the use of excitation-emission fluorescence matrix (EEFM) spectroscopy, using the medicinal herbs, Rhizoma corydalis decumbentis (RCD) and Rhizoma corydalis (RC) as example samples. The data set was obtained from various authentic RCD-A and RC-A, adulterated AD, and commercial RCD-C and RC-C samples. The spectra (range: λ(ex) = 215∼395 nm and λ(em) = 290∼560 nm), arranged in two- and three-way data matrix formats, were processed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) to produce two-dimensional component-by-component plots for qualitative data classification. The RCD-A and RC-A object groups were clearly discriminated, but the AD and the RCD-C as well as RC-C samples were less well separated. PARAFAC analysis produced somewhat better discrimination, and loadings plots revealed the presence of the marker compound Protopine-a strongly fluorescing substance-as well as at least two other unidentified fluorescent components. Classification performance of the common K-nearest neighbors (KNN) and linear discrimination analysis (LDA) methods was relatively poor when compared with that of the back propagation- and radial basis function-artificial neural networks (BP-ANN and RBF-ANN) models on the basis of two- and three-way formatted data. The best results were obtained with the three-way fingerprints and the RBF-ANN model. Subsequently, the quality of the commercial samples (RCD-C and RC-C) was classified on the best optimized RBF-ANN model. Thus, EEFM spectroscopy, which provides three-way measured data, is potentially a powerful analytical technique for the analysis of complex real-world substances provided the classification is performed by the RBF-ANN or similar ANN methods.

  4. Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

    Science.gov (United States)

    Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

    2015-11-03

    Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

  5. Statistical analysis of magnetic parameters in order to discriminate hydrocarbon-related conditions

    Science.gov (United States)

    Aldana, M.; Ñuflo, J.; Costanzo-Alvarez, V.; Guzmán, O.; Guerrero-Suárez, S.; Martín-Hernández, F.; Osete, M. L.

    2012-04-01

    In this work we try to discriminate near surface magnetic anomalies related to hydrocarbon microseepage using just magnetic parameters. We present preliminary results for two oil fields located at Eastern Venezuela and characterized by different geochemical conditions. Cross-plots that combine hysteresis data (Mrs/Ms and Hcr/Hc), Magnetic Susceptibility (MS) and S-ratio were analysed searching for patterns associated with different type of MS anomalies, i.e. related (A type) and not related (B type) to hydrocarbon migration, with different reducing conditions (associated or not with the presence of organic matter) and/or with distinct chief magnetic mineralogies at these MS anomalous levels (i.e. Fe-oxides or Fe-sulphides). K-means and Hierarchical Cluster Analyses were applied and the possibility of pattern recognition, combining more than two magnetic variables, was examined. The results obtained seem to indicate that it is possible to discriminate between anomalies associated with different chief magnetic mineralogies. Nevertheless, the statistical analysis of the parameters applied here does not discriminate between anomalies related to hydrocarbon microseepage and those reflecting just lithological contrasts, or between anomalies associated with different reducing conditions.

  6. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA).

    Science.gov (United States)

    Kassouf, Amine; Maalouly, Jacqueline; Rutledge, Douglas N; Chebib, Hanna; Ducruet, Violette

    2014-11-01

    Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of the proposed approach was also tested using two spectrometers with considerable differences in their sensitivities. Discrimination rates were not affected proving that the developed approach could be extrapolated to different spectrometers. MIR combined with ICA is a promising tool for plastic waste separation that can help improve performance in this field; however further technological improvements and developments are required before it can be applied

  7. Applying linear discriminant analysis to predict groundwater redox conditions conducive to denitrification

    Science.gov (United States)

    Wilson, S. R.; Close, M. E.; Abraham, P.

    2018-01-01

    Diffuse nitrate losses from agricultural land pollute groundwater resources worldwide, but can be attenuated under reducing subsurface conditions. In New Zealand, the ability to predict where groundwater denitrification occurs is important for understanding the linkage between land use and discharges of nitrate-bearing groundwater to streams. This study assesses the application of linear discriminant analysis (LDA) for predicting groundwater redox status for Southland, a major dairy farming region in New Zealand. Data cases were developed by assigning a redox status to samples derived from a regional groundwater quality database. Pre-existing regional-scale geospatial databases were used as training variables for the discriminant functions. The predictive accuracy of the discriminant functions was slightly improved by optimising the thresholds between sample depth classes. The models predict 23% of the region as being reducing at shallow depths (<15 m), and 37% at medium depths (15-75 m). Predictions were made at a sub-regional level to determine whether improvements could be made with discriminant functions trained by local data. The results indicated that any gains in predictive success were offset by loss of confidence in the predictions due to the reduction in the number of samples used. The regional scale model predictions indicate that subsurface reducing conditions predominate at low elevations on the coastal plains where poorly drained soils are widespread. Additional indicators for subsurface denitrification are a high carbon content of the soil, a shallow water table, and low-permeability clastic sediments. The coastal plains are an area of widespread groundwater discharge, and the soil and hydrology characteristics require the land to be artificially drained to render the land suitable for farming. For the improvement of water quality in coastal areas, it is therefore important that land and water management efforts focus on understanding hydrological

  8. Periodicity, the Canon and Sport

    Directory of Open Access Journals (Sweden)

    Thomas F. Scanlon

    2015-10-01

    Full Text Available The topic according to this title is admittedly a broad one, embracing two very general concepts of time and of the cultural valuation of artistic products. Both phenomena are, in the present view, largely constructed by their contemporary cultures, and given authority to a great extent from the prestige of the past. The antiquity of tradition brings with it a certain cachet. Even though there may be peripheral debates in any given society which question the specifics of periodization or canonicity, individuals generally accept the consensus designation of a sequence of historical periods and they accept a list of highly valued artistic works as canonical or authoritative. We will first examine some of the processes of periodization and of canon-formation, after which we will discuss some specific examples of how these processes have worked in the sport of two ancient cultures, namely Greece and Mesoamerica.

  9. Positive geometries and canonical forms

    Science.gov (United States)

    Arkani-Hamed, Nima; Bai, Yuntao; Lam, Thomas

    2017-11-01

    Recent years have seen a surprising connection between the physics of scattering amplitudes and a class of mathematical objects — the positive Grassmannian, positive loop Grassmannians, tree and loop Amplituhedra — which have been loosely referred to as "positive geometries". The connection between the geometry and physics is provided by a unique differential form canonically determined by the property of having logarithmic singularities (only) on all the boundaries of the space, with residues on each boundary given by the canonical form on that boundary. The structures seen in the physical setting of the Amplituhedron are both rigid and rich enough to motivate an investigation of the notions of "positive geometries" and their associated "canonical forms" as objects of study in their own right, in a more general mathematical setting. In this paper we take the first steps in this direction. We begin by giving a precise definition of positive geometries and canonical forms, and introduce two general methods for finding forms for more complicated positive geometries from simpler ones — via "triangulation" on the one hand, and "push-forward" maps between geometries on the other. We present numerous examples of positive geometries in projective spaces, Grassmannians, and toric, cluster and flag varieties, both for the simplest "simplex-like" geometries and the richer "polytope-like" ones. We also illustrate a number of strategies for computing canonical forms for large classes of positive geometries, ranging from a direct determination exploiting knowledge of zeros and poles, to the use of the general triangulation and push-forward methods, to the representation of the form as volume integrals over dual geometries and contour integrals over auxiliary spaces. These methods yield interesting representations for the canonical forms of wide classes of positive geometries, ranging from the simplest Amplituhedra to new expressions for the volume of arbitrary convex

  10. Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

    International Nuclear Information System (INIS)

    Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng

    2014-01-01

    The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy

  11. Alternative for the evaluation of coffee seedlings using Fisher's discriminant analysis

    Directory of Open Access Journals (Sweden)

    Katia Alves Campos

    2016-06-01

    Full Text Available ABSTRACT One of the applications of Fisher's linear discriminant function (FDF is its use in transforming multivariate data into a new univariate variable. This then makes possible a new option for the variance analysis of multivariate data, in addition to the multivariate analysis of variance (MANOVA. The aim of this work was to select groups of seven characteristics of quality in coffee seedlings using six criteria for selection, to use the FDF to transform such groupings of characteristics into a new variable, and then to compare interpretation of the results obtained from the univariate and multivariate analyses of variance of the characteristics and this new variable, with a view to its use in evaluating coffee seedlings. A randomised block design was used to assess the effect of organic fertiliser on the formation of seedlings in coffee cv. Catuaí Vermelho IAC-44, evaluating the following characteristics: seedling height, diameter, root length, dry weight of shoots and roots, leaf area, number of leaves and total dry weight. According to the selection criteria used, different subsets of the selected characteristics are possible. The use of the FDF is shown to be viable in discriminating between treatments. Univariate analysis of the new variable obtained with the FDF and multivariate analysis (MANOVA was able to detect differences between the treatments, however, it is simpler to apply FDF methodology.

  12. Application of phylogenetic microarray analysis to discriminate sources of fecal pollution.

    Science.gov (United States)

    Dubinsky, Eric A; Esmaili, Laleh; Hulls, John R; Cao, Yiping; Griffith, John F; Andersen, Gary L

    2012-04-17

    Conventional methods for fecal source tracking typically use single biomarkers to systematically identify or exclude sources. High-throughput DNA sequence analysis can potentially identify all sources of microbial contaminants in a single test by measuring the total diversity of fecal microbial communities. In this study, we used phylogenetic microarray analysis to determine the comprehensive suite of bacteria that define major sources of fecal contamination in coastal California. Fecal wastes were collected from 42 different populations of humans, birds, cows, horses, elk, and pinnipeds. We characterized bacterial community composition using a DNA microarray that probes for 16S rRNA genes of 59,316 different bacterial taxa. Cluster analysis revealed strong differences in community composition among fecal wastes from human, birds, pinnipeds, and grazers. Actinobacteria, Bacilli, and many Gammaproteobacteria taxa discriminated birds from mammalian sources. Diverse families within the Clostridia and Bacteroidetes taxa discriminated human wastes, grazers, and pinnipeds from each other. We found 1058 different bacterial taxa that were unique to either human, grazing mammal, or bird fecal wastes. These OTUs can serve as specific identifier taxa for these sources in environmental waters. Two field tests in marine waters demonstrate the capacity of phylogenetic microarray analysis to track multiple sources with one test.

  13. Discriminant analysis of mandibular measurements for the estimation of sex in a modern Brazilian sample.

    Science.gov (United States)

    Lopez-Capp, Thais Torralbo; Rynn, Christopher; Wilkinson, Caroline; de Paiva, Luiz Airton Saavedra; Michel-Crosato, Edgard; Biazevic, Maria Gabriela Haye

    2017-09-26

    The present study aimed to evaluate the accuracy of mandibular measurements for sex determination in a Brazilian population. The sample was composed of 100 mandibles, of which 53 were female and 47 were male, and the average age was 57.03 years. The mandible measurement protocol was composed of 15 measurements, of which six were bilateral and nine were unique. Mandibles were directly measured using a digital caliper and a protractor. The descriptive analysis of the present study revealed higher mean values for male mandibles compared to those for female mandibles with the exception of the left mandibular angle. Among the 21 measures analyzed in this group, 15 were statistically significant (p BGB; area under the ROC curve (AUC) = 0.764) followed by the right maximum ramus height (MRHr; AUC = 0.763). A reference table for estimating sex in a Brazilian population using mandible measurements was developed based on the ROC curve analysis. Mandibular measures provide a simple and reliable method for sex discrimination in Brazilian adults due to the sexual dimorphism revealed by analysis of the metric variables and the satisfactory results demonstrated by discriminant formulas, ROC curve analysis, and the reference table.

  14. An intercomparison of different topography effects on discrimination performance of fuzzy change vector analysis algorithm

    Science.gov (United States)

    Singh, Sartajvir; Talwar, Rajneesh

    2018-02-01

    Detection of snow cover changes is vital for avalanche hazard analysis and flood flashes that arise due to variation in temperature. Hence, multitemporal change detection is one of the practical mean to estimate the snow cover changes over larger area using remotely sensed data. There have been some previous studies that examined how accuracy of change detection analysis is affected by different topography effects over Northwestern Indian Himalayas. The present work emphases on the intercomparison of different topography effects on discrimination performance of fuzzy based change vector analysis (FCVA) as change detection algorithm that includes extraction of change-magnitude and change-direction from a specific pixel belongs multiple or partial membership. The qualitative and quantitative analysis of the proposed FCVA algorithm is performed under topographic conditions and topographic correction conditions. The experimental outcomes confirmed that in change category discrimination procedure, FCVA with topographic correction achieved 86.8% overall accuracy and 4.8% decay (82% of overall accuracy) is found in FCVA without topographic correction. This study suggests that by incorporating the topographic correction model over mountainous region satellite imagery, performance of FCVA algorithm can be significantly improved up to great extent in terms of determining actual change categories.

  15. The Dutch Central Sensitization Inventory (CSI): Factor Analysis, Discriminative Power, and Test-Retest Reliability.

    Science.gov (United States)

    Kregel, Jeroen; Vuijk, Pieter J; Descheemaeker, Filip; Keizer, Doeke; van der Noord, Robert; Nijs, Jo; Cagnie, Barbara; Meeus, Mira; van Wilgen, Paul

    2016-07-01

    A standardized assessment of central sensitization can be performed with the Central Sensitization Inventory (CSI), an English questionnaire consisting of 25 items relating to current health symptoms. The aim of this study was to translate the CSI into Dutch, to perform a factor analysis to reveal the underlying structure, examine its discriminative power, and test-retest reliability. The CSI was first translated into Dutch. A factor analysis was conducted on CSI data of a large group of chronic pain patients (n=368). The ability to discriminate between chronic pain patients (n=188) and pain-free controls (n=49) was determined and the test-retest reliability for chronic pain patients (n=36) and controls (n=45) with a time interval of 3 weeks was evaluated. The exploratory factor analysis resulted in a 4-factor model based on 20 items, representing the domains "General disability and physical symptoms" (Cronbach α=0.80), "Higher central sensitivity"(Cronbach α=0.78), "Urological and dermatological symptoms"(Cronbach α=0.60), and "Emotional distress"(Cronbach α=0.80). Furthermore, a parsimonious second-order factor model was found, where the factor "General central sensitization" was underlying the 4 first-order factors. Chronic pain patients scored significantly worse on all 4 factors. The test-retest reliability was excellent values in both chronic pain patients (ICC=0.88) and controls (ICC=0.91). The original CSI was translated into Dutch and did not reveal any problems during data acquisition. The domains represented by the 4 factors may be useful in setting up specific patient profiles and treatment targets. To conclude, the Dutch CSI revealed 4 distinguishable domains, showed good internal consistency for the total score and 3 out of 4 domains, good discriminative power, and excellent test-retest reliability.

  16. Discourse analysis of gender equality and non-discrimination laws and strategies

    Directory of Open Access Journals (Sweden)

    Antonijević Zorana

    2017-01-01

    Full Text Available Based on the contemporary research on gender and language, using the method of discourse analysis applied to the laws and policies, this article explains how certain linguistic practice, in the context of the administrative discourse, produces meaning that may or may not contribute to its better understanding and more efficient implementation. Through discourse analysis of gender equality and non-discrimination laws and strategies in Serbia, it has been shown how and with what consequences the socio-political and academic elites affect defining and promoting certain concepts (gender, sex, gender equality, discrimination in one social and historical moment. The paper is placed in the theoretical framework of three visions of gender equality: perspective of equal treatment, women‘s perspectives and gender perspective (Booth, Bennett 2002, that are corresponding to the three strategies for achieving gender equality: equal treatment, specific policy of gender equality and gender mainstreaming (Verloo, 2001. The discourse analysis of the Law on Gender Equality (2009, the National Strategy for the Improvement of the Position of Women and Advancement of Gender Equality (2009, the Law on Prohibition of Discrimination (2009 and the Strategy for Prevention and Protection against Discrimination (2013, has shown the context of use and meaning of terms gender and sex, as well as implications it has on their potential to change the existing paradigms and understanding of gender equality, and the implementation of policies in Serbia. Analysis of the use of terms sex and gender in the most important legal and strategic documents for achieving gender equality, showed that the choice of certain categories and terms is always a political choice. The authors show how these documents are written in the key of two gender perspectives and strategies: equal treatment and the specific policy of gender equality, while the third - introduction of a gender perspective

  17. Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhigao Zeng

    2016-01-01

    Full Text Available This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.

  18. Discrimination of bromodeoxyuridine labelled and unlabelled mitotic cells in flow cytometric bromodeoxyuridine/DNA analysis

    DEFF Research Database (Denmark)

    Jensen, P O; Larsen, J K; Christensen, I J

    1994-01-01

    Bromodeoxyuridine (BrdUrd) labelled and unlabelled mitotic cells, respectively, can be discriminated from interphase cells using a new method, based on immunocytochemical staining of BrdUrd and flow cytometric four-parameter analysis of DNA content, BrdUrd incorporation, and forward and orthogonal...... light scatter. The method was optimized using the human leukemia cell lines HL-60 and K-562. Samples of 10(5) ethanol-fixed cells were treated with pepsin/HCl and stained as a nuclear suspension with anti-BrdUrd antibody, FITC-conjugated secondary antibody, and propidium iodide. Labelled mitoses could...

  19. Fault Diagnosis Method on Polyvinyl Chloride Polymerization Process Based on Dynamic Kernel Principal Component and Fisher Discriminant Analysis Method

    Directory of Open Access Journals (Sweden)

    Shu-zhi Gao

    2016-01-01

    Full Text Available In view of the fact that the production process of Polyvinyl chloride (PVC polymerization has more fault types and its type is complex, a fault diagnosis algorithm based on the hybrid Dynamic Kernel Principal Component Analysis-Fisher Discriminant Analysis (DKPCA-FDA method is proposed in this paper. Kernel principal component analysis and Dynamic Kernel Principal Component Analysis are used for fault diagnosis of Polyvinyl chloride (PVC polymerization process, while Fisher Discriminant Analysis (FDA method was adopted to make failure data for further separation. The simulation results show that the Dynamic Kernel Principal Component Analyses to fault diagnosis of Polyvinyl chloride (PVC polymerization process have better diagnostic accuracy, the Fisher Discriminant Analysis (FDA can further realize the fault isolation, and the actual fault in the process of Polyvinyl chloride (PVC polymerization production can be monitored by Dynamic Kernel Principal Component Analysis.

  20. Automatic untargeted metabolic profiling analysis coupled with Chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability.

    Science.gov (United States)

    Han, Lu; Zhang, Yue-Ming; Song, Jing-Jing; Fan, Mei-Juan; Yu, Yong-Jie; Liu, Ping-Ping; Zheng, Qing-Xia; Chen, Qian-Si; Bai, Chang-Cai; Sun, Tao; She, Yuan-Bin

    2018-03-16

    Untargeted metabolic profiling analysis is employed to screen metabolites for specific purposes, such as geographical origin discrimination. However, the data analysis remains a challenging task. In this work, a new automatic untargeted metabolic profiling analysis coupled with a chemometric strategy was developed to improve the metabolite identification results and to enhance the geographical origin discrimination capability. Automatic untargeted metabolic profiling analysis with chemometrics (AuMPAC) was used to screen the total ion chromatographic (TIC) peaks that showed significant differences among the various geographical regions. Then, a chemometric peak resolution strategy is employed for the screened TIC peaks. The retrieved components were further analyzed using ANOVA, and those that showed significant differences were used to build a geographical origin discrimination model by using two-way encoding partial least squares. To demonstrate its performance, a geographical origin discrimination of flaxseed samples from six geographical regions in China was conducted, and 18 TIC peaks were screened. A total of 19 significant different metabolites were obtained after the peak resolution. The accuracy of the geographical origin discrimination was up to 98%. A comparison of the AuMPAC, AMDIS, and XCMS indicated that AuMPACobtained the best geographical origin discrimination results. In conclusion, AuMPAC provided another method for data analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

  1. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA)

    Energy Technology Data Exchange (ETDEWEB)

    Kassouf, Amine, E-mail: amine.kassouf@agroparistech.fr [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France); Maalouly, Jacqueline, E-mail: j_maalouly@hotmail.com [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); Rutledge, Douglas N., E-mail: douglas.rutledge@agroparistech.fr [INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France); Chebib, Hanna, E-mail: hchebib@hotmail.com [ER004 “Lebanese Food Packaging”, Faculty of Sciences II, Lebanese University, 90656 Jdeideth El Matn, Fanar (Lebanon); Ducruet, Violette, E-mail: violette.ducruet@agroparistech.fr [INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy (France); AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris (France)

    2014-11-15

    Highlights: • An innovative technique, MIR-ICA, was applied to plastic packaging separation. • This study was carried out on PE, PP, PS, PET and PLA plastic packaging materials. • ICA was applied to discriminate plastics and 100% separation rates were obtained. • Analyses performed on two spectrometers proved the reproducibility of the method. • MIR-ICA is a simple and fast technique allowing plastic identification/classification. - Abstract: Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of

  2. Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA)

    International Nuclear Information System (INIS)

    Kassouf, Amine; Maalouly, Jacqueline; Rutledge, Douglas N.; Chebib, Hanna; Ducruet, Violette

    2014-01-01

    Highlights: • An innovative technique, MIR-ICA, was applied to plastic packaging separation. • This study was carried out on PE, PP, PS, PET and PLA plastic packaging materials. • ICA was applied to discriminate plastics and 100% separation rates were obtained. • Analyses performed on two spectrometers proved the reproducibility of the method. • MIR-ICA is a simple and fast technique allowing plastic identification/classification. - Abstract: Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of

  3. The application of sparse estimation of covariance matrix to quadratic discriminant analysis.

    Science.gov (United States)

    Sun, Jiehuan; Zhao, Hongyu

    2015-02-18

    Although Linear Discriminant Analysis (LDA) is commonly used for classification, it may not be directly applied in genomics studies due to the large p, small n problem in these studies. Different versions of sparse LDA have been proposed to address this significant challenge. One implicit assumption of various LDA-based methods is that the covariance matrices are the same across different classes. However, rewiring of genetic networks (therefore different covariance matrices) across different diseases has been observed in many genomics studies, which suggests that LDA and its variations may be suboptimal for disease classifications. However, it is not clear whether considering differing genetic networks across diseases can improve classification in genomics studies. We propose a sparse version of Quadratic Discriminant Analysis (SQDA) to explicitly consider the differences of the genetic networks across diseases. Both simulation and real data analysis are performed to compare the performance of SQDA with six commonly used classification methods. SQDA provides more accurate classification results than other methods for both simulated and real data. Our method should prove useful for classification in genomics studies and other research settings, where covariances differ among classes.

  4. Discriminant analysis of Social Work’s performance in licensure examination

    Directory of Open Access Journals (Sweden)

    Jonel R. Alonzo

    2017-12-01

    Full Text Available Many research studies have examined academic factors as predictors of success in licensure examination. The purpose of this descriptive discriminant analysis was to explore possible factors in passing social work licensure examination. Data were examined from academic records of 69 (37 passed and 32 failed Social Work graduates of the University of Mindanao who took Social Work Licensure Examination 2014. This can be used as a basis of Social Work program in planning and administering strategies to improve its national passing rates. Discriminant analysis was employed along five academic factors which are Human Behavior and Social Environment (HBSE, Social Work Programs and Policies (SWPP, Social Work Methods (SWM, Field Practice (FP and Grade Point Average (GPA. The analysis generated three significant predictors accounting for 76.22% of between group variability. The function had a hit ratio of 100%. Structure matrix revealed that three cluster subjects were identified as good factors of passing the social work licensure examination: HBSE, SWPP and SWM had a correlation value of 0.713, 0.768 and 0.840, respectively.

  5. NBLDA: negative binomial linear discriminant analysis for RNA-Seq data.

    Science.gov (United States)

    Dong, Kai; Zhao, Hongyu; Tong, Tiejun; Wan, Xiang

    2016-09-13

    RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statistical methods that have been developed for microarray data can be applied to RNA-Seq data, they are not ideal due to the discrete nature of RNA-Seq data. The Poisson distribution and negative binomial distribution are commonly used to model count data. Recently, Witten (Annals Appl Stat 5:2493-2518, 2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson assumption may not be as appropriate as the negative binomial distribution when biological replicates are available and in the presence of overdispersion (i.e., when the variance is larger than or equal to the mean). However, it is more complicated to model negative binomial variables because they involve a dispersion parameter that needs to be estimated. In this paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data. By Bayes' rule, we construct the classifier by fitting a negative binomial model, and propose some plug-in rules to estimate the unknown parameters in the classifier. The relationship between the negative binomial classifier and the Poisson classifier is explored, with a numerical investigation of the impact of dispersion on the discriminant score. Simulation results show the superiority of our proposed method. We also analyze two real RNA-Seq data sets to demonstrate the advantages of our method in real-world applications. We have developed a new classifier using the negative binomial model for RNA-seq data classification. Our simulation results show that our proposed classifier has a better performance than existing works. The proposed classifier can serve as an effective tool for classifying RNA-seq data. Based on the comparison results, we have provided some guidelines for scientists to decide which method should be used in the discriminant analysis of RNA-Seq data

  6. Discriminant analysis to predict the occurrence of ELMs in H-mode discharges

    International Nuclear Information System (INIS)

    Kardaun, O.J.W.F.; Itoh, S.; Itoh, K.; Kardaun, J.W.P.F.

    1993-08-01

    After an exposition of its theoretical background, discriminant analysis is applied to the H-mode confinement database to find the region in plasma parameter space in which H-mode with small ELMs (Edge Localized Modes) is likely to occur. The boundary of this region is determined by the condition that the probability of appearance of such a type of H-mode, as a function of the plasma parameters, should be (1) larger than some threshold value and (2) larger than the corresponding probability for other types of H-mode (i.e., H-mode without ELMs or with giant ELMs). In practice, the discrimination has been performed for the ASDEX, JET and JFT-2M tokamaks (a) using four instantaneous plasma parameters (injected power P inj , magnetic field B t , plasma current I p and line averaged electron density (n-bar e ) and (b) taking also memory effects of the plasma and the distance between the plasma and the wall into account, while using variables that are normalised with respect to machine size. Generally speaking, it is found that there is a substantial overlap between the region of H-mode with small ELMs and the region of the two other types of H-mode. However, the ELM-free and the giant ELM H-modes relatively rarely appear in the region, that, according to the analysis, is allocated to small ELMs. A reliable production of H-mode with only small ELMs seems well possible by choosing this regime in parameter space. In the present study, it was not attempted to arrive at a unified discrimination across the machines. So, projection from one machine to another remains difficult, and a reliable determination of the region where small ELMs occur still requires a training sample from the device under consideration. (author) 53 refs

  7. Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).

    Science.gov (United States)

    Bevilacqua, Marta; Marini, Federico

    2014-08-01

    The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks). Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Rapid Discrimination of the Geographical Origins of an Oolong Tea (Anxi-Tieguanyin by Near-Infrared Spectroscopy and Partial Least Squares Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Si-Min Yan

    2014-01-01

    Full Text Available This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV transformation, taking second-order derivatives (D2 spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

  9. CANONICAL BACKWARD DIFFERENTIATION SCHEMES FOR ...

    African Journals Online (AJOL)

    The schemes are based on rational interpolation obtained from canonical polynomials. They are A-stable. The test problems show that they give better results than Euler backward method and trapezoidal method near a singular point. KEY WORDS: backward differentiation scheme, collocation, initial value problems.

  10. Romanticism, Sexuality, and the Canon.

    Science.gov (United States)

    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)

  11. Canonical Authors in Consumption Theory

    DEFF Research Database (Denmark)

    Canonical Authors in Consumption Theory is the first work to compile the contributions of the greatest social thinkers in the global conversation about consumption and consumer culture. A prestigious reference work, it offers original chapters by the world's most prominent thought leaders and sur...

  12. Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy

    Science.gov (United States)

    Zhu, Ying; Tan, Tuck Lee

    2016-04-01

    An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

  13. Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy.

    Science.gov (United States)

    Zhu, Ying; Tan, Tuck Lee

    2016-04-15

    An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects. Copyright © 2016 Elsevier B.V. All rights reserved.

  14. Classification of hand preshaping in persons with stroke using Linear Discriminant Analysis.

    Science.gov (United States)

    Puthenveettil, Saumya; Fluet, Gerard; Qiu, Qinyin; Adamovich, Sergei

    2012-01-01

    This study describes the analysis of hand preshaping using Linear Discriminant Analysis (LDA) to predict hand formation during reaching and grasping tasks of the hemiparetic hand, following a series of upper extremity motor intervention treatments. The purpose of this study is to use classification of hand posture as an additional tool for evaluating the effectiveness of therapies for upper extremity rehabilitation such as virtual reality (VR) therapy and conventional physical therapy. Classification error for discriminating between two objects during hand preshaping is obtained for the hemiparetic and unimpaired hands pre and post training. Eight subjects post stroke participated in a two-week training session consisting of upper extremity motor training. Four subjects trained with interactive VR computer games and four subjects trained with clinical physical therapy procedures of similar intensity. Subjects' finger joint angles were measured during a kinematic reach to grasp test using CyberGlove® and arm joint angles were measured using the trackSTAR™ system prior to training and after training. The unimpaired hand of subjects preshape into the target object with greater accuracy than the hemiparetic hand as indicated by lower classification errors. Hemiparetic hand improved in preshaping accuracy and time to reach minimum error. Classification of hand preshaping may provide insight into improvements in motor performance elicited by robotically facilitated virtually simulated training sessions or conventional physical therapy.

  15. Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies.

    Science.gov (United States)

    Oswald, Frederick L; Mitchell, Gregory; Blanton, Hart; Jaccard, James; Tetlock, Philip E

    2013-08-01

    This article reports a meta-analysis of studies examining the predictive validity of the Implicit Association Test (IAT) and explicit measures of bias for a wide range of criterion measures of discrimination. The meta-analysis estimates the heterogeneity of effects within and across 2 domains of intergroup bias (interracial and interethnic), 6 criterion categories (interpersonal behavior, person perception, policy preference, microbehavior, response time, and brain activity), 2 versions of the IAT (stereotype and attitude IATs), 3 strategies for measuring explicit bias (feeling thermometers, multi-item explicit measures such as the Modern Racism Scale, and ad hoc measures of intergroup attitudes and stereotypes), and 4 criterion-scoring methods (computed majority-minority difference scores, relative majority-minority ratings, minority-only ratings, and majority-only ratings). IATs were poor predictors of every criterion category other than brain activity, and the IATs performed no better than simple explicit measures. These results have important implications for the construct validity of IATs, for competing theories of prejudice and attitude-behavior relations, and for measuring and modeling prejudice and discrimination.

  16. Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals.

    Science.gov (United States)

    Umapathy, Karthikeyan; Krishnan, Sridhar

    2006-03-01

    Knee joint disorders are common in the elderly population, athletes, and outdoor sports enthusiasts. These disorders are often painful and incapacitating. Vibration signals [vibroarthrographic (VAG)] are emitted at the knee joint during the swinging movement of the knee. These VAG signals contain information that can be used to characterize certain pathological aspects of the knee joint. In this paper, we present a noninvasive method for screening knee joint disorders using the VAG signals. The proposed approach uses wavelet packet decompositions and a modified local discriminant bases algorithm to analyze the VAG signals and to identify the highly discriminatory basis functions. We demonstrate the effectiveness of using a combination of multiple dissimilarity measures to arrive at the optimal set of discriminatory basis functions, thereby maximizing the classification accuracy. A database of 89 VAG signals containing 51 normal and 38 abnormal samples were used in this study. The features extracted from the coefficients of the selected basis functions were analyzed and classified using a linear-discriminant-analysis-based classifier. A classification accuracy as high as 80% was achieved using this true nonstationary signal analysis approach.

  17. Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

    Science.gov (United States)

    Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

    2015-01-01

    Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

  18. Kernel Fisher Discriminant Analysis Based on a Regularized Method for Multiclassification and Application in Lithological Identification

    Directory of Open Access Journals (Sweden)

    Dejiang Luo

    2015-01-01

    Full Text Available This study aimed to construct a kernel Fisher discriminant analysis (KFDA method from well logs for lithology identification purposes. KFDA, via the use of a kernel trick, greatly improves the multiclassification accuracy compared with Fisher discriminant analysis (FDA. The optimal kernel Fisher projection of KFDA can be expressed as a generalized characteristic equation. However, it is difficult to solve the characteristic equation; therefore, a regularized method is used for it. In the absence of a method to determine the value of the regularized parameter, it is often determined based on expert human experience or is specified by tests. In this paper, it is proposed to use an improved KFDA (IKFDA to obtain the optimal regularized parameter by means of a numerical method. The approach exploits the optimal regularized parameter selection ability of KFDA to obtain improved classification results. The method is simple and not computationally complex. The IKFDA was applied to the Iris data sets for training and testing purposes and subsequently to lithology data sets. The experimental results illustrated that it is possible to successfully separate data that is nonlinearly separable, thereby confirming that the method is effective.

  19. A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

    2015-12-01

    This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

  20. Study of age-related changes in postural control during quiet standing through Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Andrade Adriano O

    2009-11-01

    Full Text Available Abstract Background The human body adopts a number of strategies to maintain an upright position. The analysis of the human balance allows for the understanding and identification of such strategies. The displacement of the centre of pressure (COP is a measure that has been successfully employed in studies regarding the postural control. Most of these investigations are related to the analysis of individuals suffering from neuromuscular disorders. Recent studies have shown that the elderly population is growing very fast in many countries all over the world, and therefore, researches that try to understand changes in this group are required. In this context, this study proposes the analysis of the postural control, measured by the displacement of the COP, in groups of young and elderly adults. Methods In total 59 subjects participated of this study. They were divided into seven groups according to their age. The displacement of the COP was collected for each subject standing on a force plate. Two experimental conditions, of 30 seconds each, were investigated: opened eyes and closed eyes. Traditional and recent digital signal processing tools were employed for feature computation from the displacement of the COP. Statistical analyses were carried out in order to identify significant differences between the features computed from the distinct groups that could allow for their discrimination. Results Our results showed that Linear Discrimination Analysis (LDA, which is one of the most popular feature extraction and classifier design techniques, could be successfully employed as a linear transformation, based on the linear combination of standard features for COP analysis, capable of estimating a unique feature, so-called LDA-value, from which it was possible to discriminate the investigated groups and show a high correlation between this feature and age. Conclusion These results show that the analysis of features computed from the displacement of

  1. Classification of Fusarium-Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis

    OpenAIRE

    Jongguk Lim; Giyoung Kim; Changyeun Mo; Kyoungmin Oh; Hyeonchae Yoo; Hyeonheui Ham; Moon S. Kim

    2017-01-01

    The purpose of this study is to use near-infrared reflectance (NIR) spectroscopy equipment to nondestructively and rapidly discriminate Fusarium-infected hulled barley. Both normal hulled barley and Fusarium-infected hulled barley were scanned by using a NIR spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra obtained for Fusarium discrimination and the multivariate analysis method of partial least squares discri...

  2. Improving discrimination of savanna tree species through a multiple endmember spectral-angle-mapper (SAM) approach: canopy level analysis

    CSIR Research Space (South Africa)

    Cho, Moses A

    2010-11-01

    Full Text Available that the lowest performance for discriminating rainforest species compared to linear discriminant analysis and maximum likelihood classifiers [10]. Our research hypothesis therefore centres on the fact that a multiple-endmember approach, involving many..., and R. F. Hughes, "Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR," Remote Sensing of Environment, vol. 112, pp. 1942-1955, 2008. [21] P. J. Curran, "Remote sensing of foliar chemistry," Remote Sensing...

  3. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-11-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the

  4. Differential involvement of Atg16L1 in Crohn disease and canonical autophagy: analysis of the organization of the Atg16L1 complex in fibroblasts.

    Science.gov (United States)

    Fujita, Naonobu; Saitoh, Tatsuya; Kageyama, Shun; Akira, Shizuo; Noda, Takeshi; Yoshimori, Tamotsu

    2009-11-20

    A single nucleotide polymorphism in Atg16L1, an autophagy-related gene (ATG), is a risk factor for Crohn disease, a major form of chronic inflammatory bowel disease. However, it is still unknown how the Atg16L1 variant contributes to disease development. The Atg16L1 protein possesses a C-terminal WD repeat domain whose function is entirely unknown, and the Crohn disease-associated mutation (T300A) is within this domain. To elucidate the function of the WD repeat domain, we established an experimental system in which a WD repeat domain mutant of Atg16L1 is stably expressed in Atg16L1-deficient mouse embryonic fibroblasts. Using the system, we show that the Atg16L1 complex forms a dimeric complex and that the total Atg16L1 protein level is strictly maintained, possibly by the ubiquitin proteasome system. Furthermore, we show that an Atg16L1 WD repeat domain deletion and the T300A mutant have little impact on canonical autophagy and autophagy against Salmonella enterica serovar Typhimurium. Therefore, we propose that Atg16L1 T300A is differentially involved in Crohn disease and canonical autophagy.

  5. Color quantization method based on principal component analysis and linear discriminant analysis for palette-based image generation

    Science.gov (United States)

    Ueda, Yoshiaki; Koga, Takanori; Suetake, Noriaki; Uchino, Eiji

    2017-12-01

    High performance of color quantization processing is very important for obtaining limited-color images with good quality. The median cut algorithm (MCA) is a typical color quantization method. Its computational cost is low owing to its simple algorithm, but the quality of output images is mediocre at best. In this paper, we describe a modification of MCA. In our method, we use a combination of principal component analysis (PCA) and linear discriminant analysis (LDA) to accomplish effective partitioning of color space. Concretely, PCA and LDA are used to calculate partitioning planes and their positions, respectively. We verify the effectiveness of our method through experiments using 24-bit full-color natural images.

  6. Combining pharmacophore fingerprints and PLS-discriminant analysis for virtual screening and SAR elucidation

    DEFF Research Database (Denmark)

    Askjær, Sune; Langgård, Morten

    2008-01-01

    the lead optimization toward a final drug candidate. This paper presents a combined approach to solving these two problems of ligand-based virtual screening and elucidation of SAR based on interplay between pharmacophore fingerprints and interpretation of PLS-discriminant analysis (PLS-DA) models....... The virtual screening capability of the PLS-DA method is compared to group fusion maximum similarity searching in a test using four graph-based pharmacophore fingerprints over a range of 10 diverse targets. The PLS-DA method was generally found to do better than the Smax method. The GpiDAPH3 and PCH...... fingerprints proved superior to the TGT and TGD fingerprints. Examples of SAR elucidation based on PLS-DA model interpretation of model coefficients using a reversible pharmacophore fingerprint are given. In addition, we tested the hypothesis that feature combinations coming from the analysis of two...

  7. Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shima, Keisuke; Shibanoki, Taro; Kurita, Yuichi; Tsuji, Toshio

    2013-01-01

    This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

  8. Colored inks analysis and differentiation: A first step in artistic contemporary prints discrimination

    Energy Technology Data Exchange (ETDEWEB)

    Vila, Anna [Department de Pintura, Conservacio-Restauracio, Facultat de Belles Arts, Universitat de Barcelona, C/Pau Gargallo 4, 08028 Barcelona (Spain)]. E-mail: avila@sct.ub.es; Ferrer, Nuria [Serveis Cientificotecnics, Universitat de Barcelona, C/Lluis Sole i Sabaris 1, 08028 Barcelona (Spain)]. E-mail: nferrer@sctub.es; Garcia, Jose F. [Department de Pintura, Conservacio-Restauracio, Facultat de Belles Arts, Universitat de Barcelona, C/Pau Gargallo 4, 08028 Barcelona (Spain)]. E-mail: ifgarcia@ub.edu

    2007-04-04

    Prints are the most popular artistic technique. Due to their manufacturing procedure, they are also one of the most frequently falsified types of artwork. In terms of their economic and historic value, the chemical analysis and characterisation of coloured inks and their principal constituent materials (pigments), together with the historical and aesthetic information available in the Catalogues Raisonees, are important tools in distinguishing originals from non-original prints. The chemical characterisation and discrimination of coloured inks has test in this study. Analysis using Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM) and X-ray diffraction (XRD) has been done on blue pigments and inks, due to this colour is one of the most representative for the presence of organic and inorganic materials in their composition. Conclusion obtained for this colour would demonstrate the capability of the approach when it is applied to any other coloured set of inks.

  9. Constructing canonical bases of quantized enveloping algebras

    OpenAIRE

    Graaf, W.A. de

    2001-01-01

    An algorithm for computing the elements of a given weight of the canonical basis of a quantized enveloping algebra is described. Subsequently, a similar algorithm is presented for computing the canonical basis of a finite-dimensional module.

  10. Mean field canonical treatments at finite temperature

    International Nuclear Information System (INIS)

    Rossignoli, R.

    1990-01-01

    A method is proposed to make mean field and higher order canonical treatments at finite temperature. Definite improvements are made over the usual Hartree-Fock thermal (great canonical) treatment. (Author). 10 refs., 3 figs

  11. Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis.

    Science.gov (United States)

    Feng, Xuping; Zhao, Yiying; Zhang, Chu; Cheng, Peng; He, Yong

    2017-08-17

    There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS-DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.

  12. Discrimination of Aurantii Fructus Immaturus and Fructus Poniciri Trifoliatae Immaturus by Flow Injection UV Spectroscopy (FIUV) and 1H NMR using Partial Least-squares Discriminant Analysis (PLS-DA)

    Science.gov (United States)

    Two simple fingerprinting methods, flow-injection UV spectroscopy (FIUV) and 1H nuclear magnetic resonance (NMR), for discrimination of Aurantii FructusImmaturus and Fructus Poniciri TrifoliataeImmaturususing were described. Both methods were combined with partial least-squares discriminant analysis...

  13. Titchmarsh-Weyl theory for canonical systems

    Directory of Open Access Journals (Sweden)

    Keshav Raj Acharya

    2014-11-01

    Full Text Available The main purpose of this paper is to develop Titchmarsh- Weyl theory of canonical systems. To this end, we first observe the fact that Schrodinger and Jacobi equations can be written into canonical systems. We then discuss the theory of Weyl m-function for canonical systems and establish the relation between the Weyl m-functions of Schrodinger equations and that of canonical systems which involve Schrodinger equations.

  14. Fast and Simple Discriminative Analysis of Anthocyanins-Containing Berries Using LC/MS Spectral Data.

    Science.gov (United States)

    Yang, Heejung; Kim, Hyun Woo; Kwon, Yong Soo; Kim, Ho Kyong; Sung, Sang Hyun

    2017-09-01

    Anthocyanins are potent antioxidant agents that protect against many degenerative diseases; however, they are unstable because they are vulnerable to external stimuli including temperature, pH and light. This vulnerability hinders the quality control of anthocyanin-containing berries using classical high-performance liquid chromatography (HPLC) analytical methodologies based on UV or MS chromatograms. To develop an alternative approach for the quality assessment and discrimination of anthocyanin-containing berries, we used MS spectral data acquired in a short analytical time rather than UV or MS chromatograms. Mixtures of anthocyanins were separated from other components in a short gradient time (5 min) due to their higher polarity, and the representative MS spectrum was acquired from the MS chromatogram corresponding to the mixture of anthocyanins. The chemometric data from the representative MS spectra contained reliable information for the identification and relative quantification of anthocyanins in berries with good precision and accuracy. This fast and simple methodology, which consists of a simple sample preparation method and short gradient analysis, could be applied to reliably discriminate the species and geographical origins of different anthocyanin-containing berries. These features make the technique useful for the food industry. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

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

    Science.gov (United States)

    Zhao, Jianhua; Shi, Lei; Zhu, Ji

    2015-08-01

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

  16. Bearing Performance Degradation Assessment Using Linear Discriminant Analysis and Coupled HMM

    International Nuclear Information System (INIS)

    Liu, T; Chen, J; Zhou, X N; Xiao, W B

    2012-01-01

    Bearing is one of the most important units in rotary machinery, its performance may vary significantly under different working stages. Thus it is critical to choose the most effective features for bearing performance degradation prediction. Linear Discriminant Analysis (LDA) is a useful method in finding few feature's dimensions that best discriminate a set of features extracted from original vibration signals. Another challenge in bearing performance degradation is how to build a model to recognize the different conditions with the data coming from different monitoring channels. In this paper, coupled hidden Markov models (CHMM) is presented to model interacting processes which can overcome the defections of the HMM. Because the input data in CHMM are collected by several sensors, and the interacting information can be fused by coupled modalities, it is more effective than HMM which used only one state chain. The model can be used in estimating the bearing performance degradation states according to several observation data. When becoming degradation pattern recognition, the new observation features should be input into the pre-trained CHMM and calculate the performance index (PI) of the outputs, the changing of PI could be used to describe the different degradation level of the bearings. The results show that PI will decline with the increase of the bearing degradation. Assessment results of the whole life time experimental bearing signals validate the feasibility and effectiveness of this method.

  17. A New Method for Improving the Discrimination Power and Weights Dispersion in the Data Envelopment Analysis

    Directory of Open Access Journals (Sweden)

    S. Kordrostami

    2013-06-01

    Full Text Available The appropriate choice of input-output weights is necessary to have a successful DEA model. Generally, if the number of DMUs i.e., n, is less than number of inputs and outputs i.e., m+s, then many of DMUs are introduced as efficient then the discrimination between DMUs is not possible. Besides, DEA models are free to choose the best weights. For resolving the problems that are resulted from freedom of weights, some constraints are set on the input-output weights. Symmetric weight constraints are a kind of weight constrains. In this paper, we represent a new model based on a multi-criterion data envelopment analysis (MCDEA are developed to moderate the homogeneity of weights distribution by using symmetric weight constrains.Consequently, we show that the improvement of the dispersal of unrealistic input-output weights and the increasing discrimination power for our suggested models. Finally, as an application of the new model, we use this model to evaluate and ranking guilan selected hospitals.

  18. Promises of silent salesman to the FMCG industry: an investigation using linear discriminant analysis approach

    Directory of Open Access Journals (Sweden)

    Shekhar Suraj Kushe

    2015-12-01

    Full Text Available Packaging which is often called as the ‘silent salesman’ is an important component of marketing. Today the importance of packaging has risen to such an extent that product packaging is rightly called as the fifth ‘P’ of marketing mix. FMCG are products which are utilized by large number of people. The present study examined the discriminating power of five selected FMCG packaging variables namely ‘picture’, ‘colour’, ‘size’, ‘shape’ and ‘material’ amidst those who purchased FMCG based on these packaging variables and for those who purchased FMCG not based on these packaging variables. Descriptive research was carried out in the study. Respondents (students were asked to rate four packaging variable on a five point Likert’s scale. Discriminant analysis showed that only two variables namely ‘Colour’ (.706 and ‘Shape’ (–.527 were good predictors. Variables ‘Picture’, ‘size’ and ‘material’ were considered as poor predictors as far as the student communities were considered. The cross validated classification showed that out of the 240 samples drawn, 91.8% of the cases were correctly classified.

  19. A general soft label based linear discriminant analysis for semi-supervised dimensionality reduction.

    Science.gov (United States)

    Zhao, Mingbo; Zhang, Zhao; Chow, Tommy W S; Li, Bing

    2014-07-01

    Dealing with high-dimensional data has always been a major problem in research of pattern recognition and machine learning, and Linear Discriminant Analysis (LDA) is one of the most popular methods for dimension reduction. However, it only uses labeled samples while neglecting unlabeled samples, which are abundant and can be easily obtained in the real world. In this paper, we propose a new dimension reduction method, called "SL-LDA", by using unlabeled samples to enhance the performance of LDA. The new method first propagates label information from the labeled set to the unlabeled set via a label propagation process, where the predicted labels of unlabeled samples, called "soft labels", can be obtained. It then incorporates the soft labels into the construction of scatter matrixes to find a transformed matrix for dimension reduction. In this way, the proposed method can preserve more discriminative information, which is preferable when solving the classification problem. We further propose an efficient approach for solving SL-LDA under a least squares framework, and a flexible method of SL-LDA (FSL-LDA) to better cope with datasets sampled from a nonlinear manifold. Extensive simulations are carried out on several datasets, and the results show the effectiveness of the proposed method. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. Discriminant function analysis for sex assessment in pelvic girdle bones: sample from the contemporary Mexican population.

    Science.gov (United States)

    Gómez-Valdés, Jorge Alfredo; Torres Ramírez, Guillermo; Báez Molgado, Socorro; Herrera Sain-Leu, Patricia; Castrejón Caballero, José Luis; Sánchez-Mejorada, Gabriela

    2011-03-01

    Sex assessment of skeletal remains plays an important role in forensic anthropology. The pelvic bones are the most studied part of the postcranial skeleton for the assessment of sex. It is evident that a population-specific approach improves rates of accuracy within the group. The present study proposes a discriminant function method for the sex assessment of skeletal remains from a contemporary Mexican population. A total of 146 adult human pelvic bones (61 females and 85 males) from the skeletal series pertaining to the National Autonomous University of Mexico were evaluated. Twenty-four direct metrical parameters of coxal and sacral bones were measured and subsequently, sides and sex differences were evaluated, applying a stepwise discriminant function analysis. Coxal and sacra functions achieved accuracies of 99% and 87%, respectively. These analyses follow a population-specific approach; nevertheless, we consider that our results are applicable to any other Hispanic samples for purposes of forensic human identification. © 2011 American Academy of Forensic Sciences.

  1. Sex determination from the talus in a contemporary Greek population using discriminant function analysis.

    Science.gov (United States)

    Peckmann, Tanya R; Orr, Kayla; Meek, Susan; Manolis, Sotiris K

    2015-07-01

    The determination of sex is an important part of building the biological profile for unknown human remains. Many of the bones traditionally used for the determination of sex are often found fragmented or incomplete in forensic and archaeological cases. The goal of the present research was to derive discriminant function equations from the talus, a preservationally favoured bone, for sexing skeletons from a contemporary Greek population. Nine parameters were measured on 182 individuals (96 males and 86 females) from the University of Athens Human Skeletal Reference Collection. The individuals ranged in age from 20 to 99 years old. The statistical analyses showed that all measured parameters were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The average accuracy of sex classification ranged from 65.2% to 93.4% for the univariate analysis, 90%-96.5% for the direct method and 86.7% for the stepwise method. Comparisons to other populations were made. Overall, the cross-validated accuracies ranged from 65.5% to 83.2% and males were most often correctly identified. The talus was shown to be useful for sex determination in the modern Greek population. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  2. Sex determination from the calcaneus in a 20th century Greek population using discriminant function analysis.

    Science.gov (United States)

    Peckmann, Tanya R; Orr, Kayla; Meek, Susan; Manolis, Sotiris K

    2015-12-01

    The skull and post-cranium have been used for the determination of sex for unknown human remains. However, in forensic cases where skeletal remains often exhibit postmortem damage and taphonomic changes the calcaneus may be used for the determination of sex as it is a preservationally favored bone. The goal of the present research was to derive discriminant function equations from the calcaneus for estimation of sex from a contemporary Greek population. Nine parameters were measured on 198 individuals (103 males and 95 females), ranging in age from 20 to 99 years old, from the University of Athens Human Skeletal Reference Collection. The statistical analyses showed that all variables were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The average accuracy of sex classification ranged from 70% to 90% for the univariate analysis, 82.9% to 87.5% for the direct method, and 86.2% for the stepwise method. Comparisons to other populations were made. Overall, the cross-validated accuracies ranged from 48.6% to 56.1% with males most often identified correctly and females most often misidentified. The calcaneus was shown to be useful for sex determination in the twentieth century Greek population. Copyright © 2015 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

  3. Discrimination of Semen cassiae from two related species based on the multivariate analysis of high-performance liquid chromatography fingerprints.

    Science.gov (United States)

    Tang, Liying; Wu, Hongwei; Zhou, Xidan; Xu, Yilong; Zhou, Guohong; Wang, Ting; Kou, Zhenzhen; Wang, Zhuju

    2015-07-01

    A simple and efficient high-performance liquid chromatography fingerprint method was developed to discriminate Semen cassiae from two related species: Cassia obtusifolia L. (CO) and Cassia tora L. (CT), the seeds of which are abbreviated as COS and CTS, respectively. 22 major bioactive ingredients in 42 samples (20 COS and 22 CTS) collected from different provinces of China were identified. The statistical methods included similarity analysis and partial least-squares discriminant analysis. The pattern analysis method was specific and could be readily used for the comprehensive evaluation of Semen cassiae samples. Therefore, high-performance liquid chromatography fingerprint in combination with pattern analysis provided a simple and reliable method for discriminating between COS and CTS. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Canonical quantum gravity and consistent discretizations

    Indian Academy of Sciences (India)

    Canonical quantum gravity is an attempt to apply the rules of quantum mechanics in their most traditional canonical form to general relativity. The first question that arises concerning this field of endeavor is: why? After all, attempts to canonically quantize general relativity were done in the 1960s, notably by DeWitt following ...

  5. Extending canonical Monte Carlo methods

    International Nuclear Information System (INIS)

    Velazquez, L; Curilef, S

    2010-01-01

    In this paper, we discuss the implications of a recently obtained equilibrium fluctuation-dissipation relation for the extension of the available Monte Carlo methods on the basis of the consideration of the Gibbs canonical ensemble to account for the existence of an anomalous regime with negative heat capacities C α with α≈0.2 for the particular case of the 2D ten-state Potts model

  6. A Canonical Password Strength Measure

    OpenAIRE

    Panferov, Eugene

    2015-01-01

    We notice that the "password security" discourse is missing the most fundamental notion of the "password strength" -- it was never properly defined. We propose a canonical definition of the "password strength", based on the assessment of the efficiency of a set of possible guessing attack. Unlike naive password strength assessments our metric takes into account the attacker's strategy, and we demonstrate the necessity of that feature. This paper does NOT advise you to include "at least three ...

  7. On Kolmogorov asymptotics of estimators of the misclassification error rate in linear discriminant analysis

    KAUST Repository

    Zollanvari, Amin

    2013-05-24

    We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

  8. Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

    Science.gov (United States)

    Santoso, Noviyanti; Wibowo, Wahyu

    2018-03-01

    A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

  9. Discrimination of intact and injured Listeria monocytogenes by Fourier transform infrared spectroscopy and principal component analysis.

    Science.gov (United States)

    Lin, Mengshi; Al-Holy, Murad; Al-Qadiri, Hamzah; Kang, Dong-Hyun; Cavinato, Anna G; Huang, Yiqun; Rasco, Barbara A

    2004-09-22

    Fourier transform infrared spectroscopy (FT-IR, 4000-600 cm(-)(1)) was used to discriminate between intact and sonication-injured Listeria monocytogenes ATCC 19114 and to distinguish this strain from other selected Listeria strains (L. innocua ATCC 51742, L. innocua ATCC 33090, and L. monocytogenes ATCC 7644). FT-IR vibrational overtone and combination bands from mid-IR active components of intact and injured bacterial cells produced distinctive "fingerprints" at wavenumbers between 1500 and 800 cm(-)(1). Spectral data were analyzed by principal component analysis. Clear segregations of different intact and injured strains of Listeria were observed, suggesting that FT-IR can detect biochemical differences between intact and injured bacterial cells. This technique may provide a tool for the rapid assessment of cell viability and thereby the control of foodborne pathogens.

  10. Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data.

    Science.gov (United States)

    Qu, Yinsheng; Adam, Bao-Ling; Thornquist, Mark; Potter, John D; Thompson, Mary Lou; Yasui, Yutaka; Davis, John; Schellhammer, Paul F; Cazares, Lisa; Clements, MaryAnn; Wright, George L; Feng, Ziding

    2003-03-01

    We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97% and specificity of 100%.

  11. Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

    Science.gov (United States)

    Portillo-Portillo, Jose; Leyva, Roberto; Sanchez, Victor; Sanchez-Perez, Gabriel; Perez-Meana, Hector; Olivares-Mercado, Jesus; Toscano-Medina, Karina; Nakano-Miyatake, Mariko

    2016-01-01

    This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework’s computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy. PMID:28025484

  12. Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Jose Portillo-Portillo

    2016-12-01

    Full Text Available This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA. The framework, which employs gait energy images (GEIs, creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework’s computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.

  13. On Kolmogorov Asymptotics of Estimators of the Misclassification Error Rate in Linear Discriminant Analysis.

    Science.gov (United States)

    Zollanvari, Amin; Genton, Marc G

    2013-08-01

    We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

  14. Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications*

    Science.gov (United States)

    Murphy, Thomas Brendan; Dean, Nema; Raftery, Adrian E.

    2010-01-01

    Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins. PMID:20936055

  15. Derivation of Mayer Series from Canonical Ensemble

    International Nuclear Information System (INIS)

    Wang Xian-Zhi

    2016-01-01

    Mayer derived the Mayer series from both the canonical ensemble and the grand canonical ensemble by use of the cluster expansion method. In 2002, we conjectured a recursion formula of the canonical partition function of a fluid (X.Z. Wang, Phys. Rev. E 66 (2002) 056102). In this paper we give a proof for this formula by developing an appropriate expansion of the integrand of the canonical partition function. We further derive the Mayer series solely from the canonical ensemble by use of this recursion formula. (paper)

  16. Derivation of Mayer Series from Canonical Ensemble

    Science.gov (United States)

    Wang, Xian-Zhi

    2016-02-01

    Mayer derived the Mayer series from both the canonical ensemble and the grand canonical ensemble by use of the cluster expansion method. In 2002, we conjectured a recursion formula of the canonical partition function of a fluid (X.Z. Wang, Phys. Rev. E 66 (2002) 056102). In this paper we give a proof for this formula by developing an appropriate expansion of the integrand of the canonical partition function. We further derive the Mayer series solely from the canonical ensemble by use of this recursion formula.

  17. From Protein Sequence to Protein Function via Multi-Label Linear Discriminant Analysis.

    Science.gov (United States)

    Wang, Hua; Yan, Lin; Huang, Heng; Ding, Chris

    2017-01-01

    Sequence describes the primary structure of a protein, which contains important structural, characteristic, and genetic information and thereby motivates many sequence-based computational approaches to infer protein function. Among them, feature-base approaches attract increased attention because they make prediction from a set of transformed and more biologically meaningful sequence features. However, original features extracted from sequence are usually of high dimensionality and often compromised by irrelevant patterns, therefore dimension reduction is necessary prior to classification for efficient and effective protein function prediction. A protein usually performs several different functions within an organism, which makes protein function prediction a multi-label classification problem. In machine learning, multi-label classification deals with problems where each object may belong to more than one class. As a well-known feature reduction method, linear discriminant analysis (LDA) has been successfully applied in many practical applications. It, however, by nature is designed for single-label classification, in which each object can belong to exactly one class. Because directly applying LDA in multi-label classification causes ambiguity when computing scatters matrices, we apply a new Multi-label Linear Discriminant Analysis (MLDA) approach to address this problem and meanwhile preserve powerful classification capability inherited from classical LDA. We further extend MLDA by l 1 -normalization to overcome the problem of over-counting data points with multiple labels. In addition, we incorporate biological network data using Laplacian embedding into our method, and assess the reliability of predicted putative functions. Extensive empirical evaluations demonstrate promising results of our methods.

  18. Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis.

    Science.gov (United States)

    Acquah, Gifty E; Via, Brian K; Billor, Nedret; Fasina, Oladiran O; Eckhardt, Lori G

    2016-08-27

    As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest

  19. A longitudinal analysis of Hispanic youth acculturation and cigarette smoking: the roles of gender, culture, family, and discrimination.

    Science.gov (United States)

    Lorenzo-Blanco, Elma I; Unger, Jennifer B; Ritt-Olson, Anamara; Soto, Daniel; Baezconde-Garbanati, Lourdes

    2013-05-01

    Risk for smoking initiation increases as Hispanic youth acculturate to U.S. society, and this association seems to be stronger for Hispanic girls than boys. To better understand the influence of culture, family, and everyday discrimination on cigarette smoking, we tested a process-oriented model of acculturation and cigarette smoking. Data came from Project RED (Reteniendo y Entendiendo Diversidad para Salud), which included 1,436 Hispanic students (54% girls) from Southern California. We used data from 9th to 11th grade (85% were 14 years old, and 86% were U.S. born) to test the influence of acculturation-related experiences on smoking over time. Multigroup structural equation analysis suggested that acculturation was associated with increased familismo and lower traditional gender roles, and enculturation was linked more with familismo and respeto. Familismo, respeto, and traditional gender roles were linked with lower family conflict and increased family cohesion, and these links were stronger for girls. Familismo and respeto were further associated with lower discrimination. Conversely, fatalismo was linked with worse family functioning (especially for boys) and increased discrimination in both the groups. Discrimination was the only predictor of smoking for boys and girls. In all, the results of the current study indicate that reducing discrimination and helping youth cope with discrimination may prevent or reduce smoking in Hispanic boys and girls. This may be achieved by promoting familismo and respeto and by discouraging fatalistic beliefs.

  20. CANONICAL CORRELATION OF MORPHOLOGIC CHARACTERISTIC AND MOTORIC ABILITIES OF YOUNG JUDO ATHLETES

    Directory of Open Access Journals (Sweden)

    Lulzim Ibri

    2013-07-01

    Full Text Available In sample from 80 young judo athletes aged from 16-17 year, was applied the system a total of 18 variables, of which 10 are morphologic characteristic and 8 motoric abilities variables, with a purpose to determinate mutual report between each other, while the information were analyzed by using canonical correlation analysis. With case of authentication statistically important relation was achieve one pair of canonical correlations statistically important. In morphologic variables field the canonical factor is interpreted in first canonical structure is the consists of variables: adipose tissue under skin of stomach (ATST, adipose tissue under skin of triceps (ATTR, adipose tissue under skin of biceps (ATBI, adipose tissue under skin of sub scapulars (ATSS, adipose tissue under skin of sub iliac a (ATSI and adipose tissue under skin of list (ATSL, so that is interpreted as a canonical factor of adipose tissue: And second structure of canonical factors of anthropometric characteristics is the consists of variables: body length: body length (LEBO, length of the leg (LELE and length of the arm (LEAR, so that is interpreted as a canonical factor of longitudinal dimensionality. The first structure of canonical factors in motoric variables is can not be interpreted because of low values of motor variables, while second structure of canonical factors of motoric abilities is the consists of variables: squeeze palm (SQPA, so that is interpreted as a canonical factor of strong factor in palm. Based on structure analysis of matrix results of canonical factors results were shown that to young judo athletes of this age exist statistically valid correlations between canonical factor of anthropometric variables and canonical factor of variables to motoric abilities which is (Rc=77 that is statistically valid in level (P=00.

  1. Discrimination among populations of sockeye salmon fry with Fourier analysis of otolith banding patterns formed during incubation

    Science.gov (United States)

    Finn, James E.; Burger, Carl V.; Holland-Bartels, Leslie E.

    1997-01-01

    We used otolith banding patterns formed during incubation to discriminate among hatchery- and wild-incubated fry of sockeye salmon Oncorhynchus nerka from Tustumena Lake, Alaska. Fourier analysis of otolith luminance profiles was used to describe banding patterns: the amplitudes of individual Fourier harmonics were discriminant variables. Correct classification of otoliths to either hatchery or wild origin was 83.1% (cross-validation) and 72.7% (test data) with the use of quadratic discriminant function analysts on 10 Fourier amplitudes. Overall classification rates among the six test groups (one hatchery and five wild groups) were 46.5% (cross-validation) and 39.3% (test data) with the use of linear discriminant function analysis on 16 Fourier amplitudes. Although classification rates for wild-incubated fry from any one site never exceeded 67% (cross-validation) or 60% (test data), location-specific information was evident for all groups because the probability of classifying an individual to its true incubation location was significantly greater than chance. Results indicate phenotypic differences in otolith microstructure among incubation sites separated by less than 10 km. Analysis of otolith luminance profiles is a potentially useful technique for discriminating among and between various populations of hatchery and wild fish.

  2. Phonological experience modulates voice discrimination: Evidence from functional brain networks analysis.

    Science.gov (United States)

    Hu, Xueping; Wang, Xiangpeng; Gu, Yan; Luo, Pei; Yin, Shouhang; Wang, Lijun; Fu, Chao; Qiao, Lei; Du, Yi; Chen, Antao

    2017-10-01

    Numerous behavioral studies have found a modulation effect of phonological experience on voice discrimination. However, the neural substrates underpinning this phenomenon are poorly understood. Here we manipulated language familiarity to test the hypothesis that phonological experience affects voice discrimination via mediating the engagement of multiple perceptual and cognitive resources. The results showed that during voice discrimination, the activation of several prefrontal regions was modulated by language familiarity. More importantly, the same effect was observed concerning the functional connectivity from the fronto-parietal network to the voice-identity network (VIN), and from the default mode network to the VIN. Our findings indicate that phonological experience could bias the recruitment of cognitive control and information retrieval/comparison processes during voice discrimination. Therefore, the study unravels the neural substrates subserving the modulation effect of phonological experience on voice discrimination, and provides new insights into studying voice discrimination from the perspective of network interactions. Copyright © 2017. Published by Elsevier Inc.

  3. Demographic Consequences of Gender Discrimination in China: Simulation Analysis of Policy Options.

    Science.gov (United States)

    Quanbao, Jiang; Shuzhuo, Li; Marcus W, Feldman

    2011-08-01

    The large number of missing females in China, a consequence of gender discrimination, is having and will continue to have a profound effect on the country's population development. In this paper, we analyze the causes of this gender discrimination in terms of institutions, culture and, economy, and suggest public policies that might help eliminate gender discrimination. Using a population simulation model, we study the effect of public policies on the sex ratio at birth and excess female child mortality, and the effect of gender discrimination on China's population development. We find that gender discrimination will decrease China's population size, number of births, and working age population, accelerate population aging and exacerbate the male marriage squeeze. These results provide theoretical support for suggesting that the government enact and implement public policies aimed at eliminating gender discrimination.

  4. The Analysis of the Ethnical Discrimination on the Manpower’s Market under the Economical Crisis

    Directory of Open Access Journals (Sweden)

    Mihaela Hrisanta DOBRE

    2012-06-01

    Full Text Available Discrimination means any difference, exclusion, restriction, preference or different treatment that brings forth disadvantages for a person or a group as compared to other ones that are in similar situations. The reasons on which discrimination is based can be various, such as race, nationality, ethnics, religion, gender, sexual orientation, language, age, disabilities etc. and in this case we talk about multiple discrimination. In Romania the main forms of discrimination are linked to ethnics and to sexual appurtenance. Within this column we analysed the discrimination amongst the Romany ethnics people, according to a statistical investigation (Access onto the Labour Market – A Chance for You, the research goal being to identify the answer to the following questions: Is there any discrimination inside the Romany ethnic group? What is the correlation between their level of education and their income? What is the correlation between the level of education of the parents and the respondent’s?

  5. Discrimination of land-use types in a catchment by energy dispersive X-ray fluorescence and principal component analysis.

    Science.gov (United States)

    Melquiades, F L; Andreoni, L F S; Thomaz, E L

    2013-07-01

    Differences in composition and chemical elemental concentration are important information for soil samples classification. The objective of this study is to present a direct methodology, that is non-destructive and without complex sample preparation, in order to discriminate different land-use types and soil degradation, employing energy dispersive X-ray fluorescence and multivariate analysis. Sample classification results from principal component analysis, utilizing spectral data and elemental concentration values demonstrate that the methodology is efficient to discriminate different land-use types. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. Demographic Consequences of Gender Discrimination in China: Simulation Analysis of Policy Options

    OpenAIRE

    Quanbao, Jiang; Shuzhuo, Li; Marcus W., Feldman

    2011-01-01

    The large number of missing females in China, a consequence of gender discrimination, is having and will continue to have a profound effect on the country's population development. In this paper, we analyze the causes of this gender discrimination in terms of institutions, culture and, economy, and suggest public policies that might help eliminate gender discrimination. Using a population simulation model, we study the effect of public policies on the sex ratio at birth and excess female chil...

  7. Laws' masks descriptors applied to bone texture analysis: an innovative and discriminant tool in osteoporosis

    International Nuclear Information System (INIS)

    Rachidi, M.; Marchadier, A.; Gadois, C.; Lespessailles, E.; Chappard, C.; Benhamou, C.L.

    2008-01-01

    The objective of this study was to explore Laws' masks analysis to describe structural variations of trabecular bone due to osteoporosis on high-resolution digital radiographs and to check its dependence on the spatial resolution. Laws' masks are well established as one of the best methods for texture analysis in image processing and are used in various applications, but not in bone tissue characterisation. This method is based on masks that aim to filter the images. From each mask, five classical statistical parameters can be calculated. The study was performed on 182 healthy postmenopausal women with no fractures and 114 age-matched women with fractures [26 hip fractures (HFs), 29 vertebrae fractures (VFs), 29 wrist fractures (WFs) and 30 other fractures (OFs)]. For all subjects radiographs were obtained of the calcaneus with a new high-resolution X-ray device with direct digitisation (BMA, D3A, France). The lumbar spine, femoral neck, and total hip bone mineral density (BMD) were assessed by dual-energy X-ray absorptiometry. In terms of reproducibility, the best results were obtained with the TR E5E5 mask, especially for three parameters: ''mean'', ''standard deviation'' and ''entropy'' with, respectively, in vivo mid-term root mean square average coefficient of variation (RMSCV)%=1.79, 4.24 and 2.05. The ''mean'' and ''entropy'' parameters had a better reproducibility but ''standard deviation'' showed a better discriminant power. Thus, for univariate analysis, the difference between subjects with fractures and controls was significant (P -3 ) and significant for each fracture group independently (P -4 for HF, P=0.025 for VF and P -3 for OF). After multivariate analysis with adjustment for age and total hip BMD, the difference concerning the ''standard deviation'' parameter remained statistically significant between the control group and the HF and VF groups (P -5 , and P=0.04, respectively). No significant correlation between these Laws' masks parameters and

  8. Discrimination of cultivation ages and cultivars of ginseng leaves using Fourier transform infrared spectroscopy combined with multivariate analysis.

    Science.gov (United States)

    Kwon, Yong-Kook; Ahn, Myung Suk; Park, Jong Suk; Liu, Jang Ryol; In, Dong Su; Min, Byung Whan; Kim, Suk Weon

    2014-01-01

    To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng.

  9. A Comparative Study of Feature Selection Methods for the Discriminative Analysis of Temporal Lobe Epilepsy

    Directory of Open Access Journals (Sweden)

    Chunren Lai

    2017-12-01

    Full Text Available It is crucial to differentiate patients with temporal lobe epilepsy (TLE from the healthy population and determine abnormal brain regions in TLE. The cortical features and changes can reveal the unique anatomical patterns of brain regions from structural magnetic resonance (MR images. In this study, structural MR images from 41 patients with left TLE, 34 patients with right TLE, and 58 normal controls (NC were acquired, and four kinds of cortical measures, namely cortical thickness, cortical surface area, gray matter volume (GMV, and mean curvature, were explored for discriminative analysis. Three feature selection methods including the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM, and the support vector machine-recursive feature elimination (SVM-RFE were investigated to extract dominant features among the compared groups for classification using the support vector machine (SVM classifier. The results showed that the SVM-RFE achieved the highest performance (most classifications with more than 84% accuracy, followed by the SCDRM, and the t-test. Especially, the surface area and GMV exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical measures were combined. Additionally, the dominant regions with higher classification weights were mainly located in the temporal and the frontal lobe, including the entorhinal cortex, rostral middle frontal, parahippocampal cortex, superior frontal, insula, and cuneus. This study concluded that the cortical features provided effective information for the recognition of abnormal anatomical patterns and the proposed methods had the potential to improve the clinical diagnosis of TLE.

  10. Classification of root canal microorganisms using electronic-nose and discriminant analysis

    Directory of Open Access Journals (Sweden)

    Özbilge Hatice

    2010-11-01

    Full Text Available Abstract Background Root canal treatment is a debridement process which disrupts and removes entire microorganisms from the root canal system. Identification of microorganisms may help clinicians decide on treatment alternatives such as using different irrigants, intracanal medicaments and antibiotics. However, the difficulty in cultivation and the complexity in isolation of predominant anaerobic microorganisms make clinicians resort to empirical medical treatments. For this reason, identification of microorganisms is not a routinely used procedure in root canal treatment. In this study, we aimed at classifying 7 different standard microorganism strains which are frequently seen in root canal infections, using odor data collected using an electronic nose instrument. Method Our microorganism odor data set consisted of 5 repeated samples from 7 different classes at 4 concentration levels. For each concentration, 35 samples were classified using 3 different discriminant analysis methods. In order to determine an optimal setting for using electronic-nose in such an application, we have tried 3 different approaches in evaluating sensor responses. Moreover, we have used 3 different sensor baseline values in normalizing sensor responses. Since the number of sensors is relatively large compared to sample size, we have also investigated the influence of two different dimension reduction methods on classification performance. Results We have found that quadratic type dicriminant analysis outperforms other varieties of this method. We have also observed that classification performance decreases as the concentration decreases. Among different baseline values used for pre-processing the sensor responses, the model where the minimum values of sensor readings in the sample were accepted as the baseline yields better classification performance. Corresponding to this optimal choice of baseline value, we have noted that among different sensor response model and

  11. Harold Bloom : canon e influencia

    OpenAIRE

    Pérez Vázquez, Ángel

    1998-01-01

    This essay is intended as an introduction to some of the most important aspects of the critical of the North American literary critic and theorist. Harold Bloom. The point of view from which these pages are written is mainly descriptive, and, to a very limited extent, polemical as well. The essay starts by outlining Bloom's visión of the Anglo- American literary canon, as well as his main theoretical tenets and intellectual sources. The main point of focus, though, is Bloom's theory...

  12. Perceived Discrimination among African American Adolescents and Allostatic Load: A Longitudinal Analysis with Buffering Effects

    Science.gov (United States)

    Brody, Gene H.; Lei, Man-Kit; Chae, David H.; Yu, Tianyi; Kogan, Steven M.; Beach, Steven R. H.

    2014-01-01

    This study was designed to examine the prospective relations of perceived racial discrimination with allostatic load (AL), along with a possible buffer of the association. A sample of 331 African Americans in the rural South provided assessments of perceived discrimination from ages 16 to 18 years. When youth were 18 years, caregivers reported…

  13. Race, Sex, and Discrimination in School Settings: A Multilevel Analysis of Associations with Delinquency

    Science.gov (United States)

    Chambers, Brittany D.; Erausquin, Jennifer Toller

    2018-01-01

    Background: Adolescence is a critical phase of development and experimentation with delinquent behaviors. There is a growing body of literature exploring individual and structural impacts of discrimination on health outcomes and delinquent behaviors. However, there is limited research assessing how school diversity and discrimination impact…

  14. Classification of the long-QT syndrome based on discriminant analysis of T-wave morphology

    DEFF Research Database (Denmark)

    Struijk, Johannes J.; Kanters, Jørgen K.; Andersen, M P

    2006-01-01

    been shown to be useful discriminators, but no single ECG parameter has been sufficient to solve the diagnostic problem. In this study we present a method for discrimination among persons with a normal genotype and those with mutations in the KCNQ1 (KvLQT1 or LQT1) and KCNH2 (HERG or LQT2) genes...

  15. Ultrasonic analysis to discriminate bread dough of different types of flour

    International Nuclear Information System (INIS)

    García-Álvarez, J; García-Hernández, M J; Chávez, J A; Turó, A; Salazar, J; Rosell, C M

    2012-01-01

    Many varieties of bread are prepared using flour coming from wheat. However, there are other types of flours milled from rice, legumes and some fruits and vegetables that are also suitable for baking purposes, used alone or in combination with wheat flour. The type of flour employed strongly influences the dough consistency, which is a relevant property for determining the dough potential for breadmaking purposes. Traditional methods for dough testing are relatively expensive, time-consuming, off-line and often require skilled operators. In this work, ultrasonic analysis are performed in order to obtain acoustic properties of bread dough samples prepared using two different types of flour, wheat flour and rice flour. The dough acoustic properties can be related to its viscoelastic characteristics, which in turn determine the dough feasibility for baking. The main advantages of the ultrasonic dough testing can be, among others, its low cost, fast, hygienic and on-line performance. The obtained results point out the potential of the ultrasonic analysis to discriminate doughs of different types of flour.

  16. Simultaneous analysis of multiple PCR amplicons enhances capillary SSCP discrimination of MHC alleles.

    Science.gov (United States)

    Alcaide, Miguel; López, Lidia; Tanferna, Alessandro; Blas, Julio; Sergio, Fabrizio; Hiraldo, Fernando

    2010-04-01

    Major histocompatibility complex (MHC) genotyping still remains one of the most challenging issues for evolutionary ecologists. To date, none of the proposed methods have proven to be perfect, and all provide both important pros and cons. Although denaturing capillary electrophoresis has become a popular alternative, allele identification commonly relies upon conformational polymorphisms of two single-stranded DNA molecules at the most. Using the MHC class II (beta chain, exon 2) of the black kite (Aves: Accipitridae) as our model system, we show that the simultaneous analysis of overlapping PCR amplicons from the same target region substantially enhances allele discrimination. To cover this aim, we designed a multiplex PCR capable to generate four differentially sized and labeled amplicons from the same allele. Informative peaks to assist allele calling then fourfold those generated by the analysis of single PCR amplicons. Our approach proved successful to differentiate all the alleles (N=13) isolated from eight unrelated birds at a single optimal run temperature and electrophoretic conditions. In particular, we emphasize that this approach may constitute a straightforward and cost-effective alternative for the genotyping of single or duplicated MHC genes displaying low to moderate sets of divergent alleles.

  17. Using stable isotope analysis to discriminate gasoline on the basis of its origin.

    Science.gov (United States)

    Heo, Su-Young; Shin, Woo-Jin; Lee, Sin-Woo; Bong, Yeon-Sik; Lee, Kwang-Sik

    2012-03-15

    Leakage of gasoline and diesel from underground tanks has led to a severe environmental problem in many countries. Tracing the production origin of gasoline and diesel is required to enable the development of dispute resolution and appropriate remediation strategies for the oil-contaminated sites. We investigated the bulk and compound-specific isotopic compositions of gasoline produced by four oil companies in South Korea: S-Oil, SK, GS and Hyundai. The relative abundance of several compounds in gasoline was determined by the peak height of the major ion (m/z 44). The δ(13)C(Bulk) and δD(Bulk) values of gasoline produced by S-Oil were significantly different from those of SK, GS and Hyundai. In particular, the compound-specific isotopic value (δ(13)C(CSIA)) of methyl tert-butyl ether (MTBE) in S-Oil gasoline was significantly lower than that of gasoline produced by other oil companies. The abundance of several compounds in gasoline, such as n-pentane, MTBE, n-hexane, toluene, ethylbenzene and o-xylene, differed widely among gasoline from different oil companies. This study shows that gasoline can be forensically discriminated according to the oil company responsible for its manufacture using stable isotope analysis combined with multivariate statistical analysis. Copyright © 2012 John Wiley & Sons, Ltd.

  18. Discrimination of cardiac subcellular creatine kinase fluxes by NMR spectroscopy: a new method of analysis.

    Science.gov (United States)

    Joubert, F; Hoerter, J A; Mazet, J L

    2001-12-01

    A challenge in the understanding of creatine kinase (CK) fluxes reflected by NMR magnetization transfer in the perfused rat heart is the choice of a kinetic model of analysis. The complexity of the energetic pathways, due to the presence of adenosine triphosphate (ATP)-inorganic phosphate (Pi) exchange, of metabolite compartmentation and of subcellular localization of CK isozymes cannot be resolve from the sole information obtained from a single NMR protocol. To analyze multicompartment exchanges, we propose a new strategy based on the simultaneous analysis of four inversion transfer protocols. The time course of ATP and Phosphocreatine (PCr) magnetizations computed from the McConnell equations were adjusted to their experimental value for exchange networks of increasing complexity (up to six metabolite pools). Exchange schemes were selected by the quality of their fit and their consistency with data from other sources: the size of mitochondrial pools and the ATP synthesis flux. The consideration of ATP-Pi exchange and of ATP compartmentation were insufficient to describe the data. The most appropriate exchange scheme in our normoxic heart involved the discrimination of three specific CK activities (cytosolic, mitochondrial, and close to ATPases). At the present level of heart contractility, the energy is transferred from mitochondria to myofibrils mainly by PCr.

  19. Analysis of phonocardiogram signals through proactive denoising using novel self-discriminant learner.

    Science.gov (United States)

    Puri, Chetanya; Singh, Rituraj; Bandyopadhyay, Soma; Ukil, Arijit; Mukherjee, Ayan

    2017-07-01

    Phonocardiogram (PCG) records heart sound and murmurs, which contains significant information of cardiac health. Analysis of PCG signal has the potential to detect abnormal cardiac condition. However, the presence of noise and motion artifacts in PCG hinders the accuracy of clinical event detection. Thus, noise detection and elimination are crucial to ensure accurate clinical analysis. In this paper, we present a robust denoising technique, Proclean that precisely detects the noisy PCG signal through pattern recognition, and statistical learning. We propose a novel self-discriminant learner that ensures to obtain distinct feature set to distinguish clean and noisy PCG signals without human-in-loop. We demonstrate that our proposed denoising leads to higher accuracy in subsequent clinical analytics for medical investigation. Our extensive experimentations with publicly available MIT-Physionet datasets show that we achieve more than 85% accuracy for noisy PCG signal detection. Further, we establish that physiological abnormality detection improves by more than 20%, when our proposed denoising mechanism is applied.

  20. Rapid discrimination of different Apiaceae species based on HPTLC fingerprints and targeted flavonoids determination using multivariate image analysis.

    Science.gov (United States)

    Shawky, Eman; Abou El Kheir, Rasha M

    2018-02-11

    Species of Apiaceae are used in folk medicine as spices and in officinal medicinal preparations of drugs. They are an excellent source of phenolics exhibiting antioxidant activity, which are of great benefit to human health. Discrimination among Apiaceae medicinal herbs remains an intricate challenge due to their morphological similarity. In this study, a combined "untargeted" and "targeted" approach to investigate different Apiaceae plants species was proposed by using the merging of high-performance thin layer chromatography (HPTLC)-image analysis and pattern recognition methods which were used for fingerprinting and classification of 42 different Apiaceae samples collected from Egypt. Software for image processing was applied for fingerprinting and data acquisition. HPTLC fingerprint assisted by principal component analysis (PCA) and hierarchical cluster analysis (HCA)-heat maps resulted in a reliable untargeted approach for discrimination and classification of different samples. The "targeted" approach was performed by developing and validating an HPTLC method allowing the quantification of eight flavonoids. The combination of quantitative data with PCA and HCA-heat-maps allowed the different samples to be discriminated from each other. The use of chemometrics tools for evaluation of fingerprints reduced expense and analysis time. The proposed method can be adopted for routine discrimination and evaluation of the phytochemical variability in different Apiaceae species extracts. Copyright © 2018 John Wiley & Sons, Ltd.

  1. The Contribution of Regression Analysis to the Elimination of Gender Based Wage Discrimination in Academia: A Simulation.

    Science.gov (United States)

    Raymond, Richard D.; And Others

    1990-01-01

    Describes the use of regression analysis in eliminating sex discrimination in a university's salary structure and examines regression models usually accepted by courts. Estimates salary regressions for a large, midwestern university for 1983-84 in a simulating exercise exploring alternative elimination methods. Includes 23 references and 11 court…

  2. Canonical trivialization of gravitational gradients

    International Nuclear Information System (INIS)

    Niedermaier, Max

    2017-01-01

    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. (paper)

  3. Canonical trivialization of gravitational gradients

    Science.gov (United States)

    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.

  4. Diagnosing basal cell carcinoma in vivo by near-infrared Raman spectroscopy: a Principal Components Analysis discrimination algorithm

    Science.gov (United States)

    Silveira, Landulfo, Jr.; Silveira, Fabrício L.; Bodanese, Benito; Pacheco, Marcos Tadeu T.; Zângaro, Renato A.

    2012-02-01

    This work demonstrated the discrimination among basal cell carcinoma (BCC) and normal human skin in vivo using near-infrared Raman spectroscopy. Spectra were obtained in the suspected lesion prior resectional surgery. After tissue withdrawn, biopsy fragments were submitted to histopathology. Spectra were also obtained in the adjacent, clinically normal skin. Raman spectra were measured using a Raman spectrometer (830 nm) with a fiber Raman probe. By comparing the mean spectra of BCC with the normal skin, it has been found important differences in the 800-1000 cm-1 and 1250-1350 cm-1 (vibrations of C-C and amide III, respectively, from lipids and proteins). A discrimination algorithm based on Principal Components Analysis and Mahalanobis distance (PCA/MD) could discriminate the spectra of both tissues with high sensitivity and specificity.

  5. Diagnosis of Periodontal Disease from Saliva Samples Using Fourier Transform Infrared Microscopy Coupled with Partial Least Squares Discriminant Analysis.

    Science.gov (United States)

    Fujii, Satoshi; Sato, Shinobu; Fukuda, Keisuke; Okinaga, Toshinori; Ariyoshi, Wataru; Usui, Michihiko; Nakashima, Keisuke; Nishihara, Tatsuji; Takenaka, Shigeori

    2016-01-01

    Diagnosis of periodontal disease by Fourier transform infrared (FT-IR) microscopic technique was achieved for saliva samples. Twenty-two saliva samples, collected from 10 patients with periodontal disease and 12 normal volunteers, were pre-processed and analyzed by FT-IR microscopy. We found that the periodontal samples showed a larger raw IR spectrum than the control samples. In addition, the shape of the second derivative spectrum was clearly different between the periodontal and control samples. Furthermore, the amount of saliva content and the mixture ratio were different between the two samples. Partial least squares discriminant analysis was used for the discrimination of periodontal samples based on the second derivative spectrum. The leave-one-out cross-validation discrimination accuracy was 94.3%. Thus, these results show that periodontal disease may be diagnosed by analyzing saliva samples with FT-IR microscopy.

  6. A canonical approach to forces in molecules

    Science.gov (United States)

    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.

  7. Canonical duality theory unified methodology for multidisciplinary study

    CERN Document Server

    Latorre, Vittorio; Ruan, Ning

    2017-01-01

    This book on canonical duality theory provides a comprehensive review of its philosophical origin, physics foundation, and mathematical statements in both finite- and infinite-dimensional spaces. A ground-breaking methodological theory, canonical duality theory can be used for modeling complex systems within a unified framework and for solving a large class of challenging problems in multidisciplinary fields in engineering, mathematics, and the sciences. This volume places a particular emphasis on canonical duality theory’s role in bridging the gap between non-convex analysis/mechanics and global optimization.  With 18 total chapters written by experts in their fields, this volume provides a nonconventional theory for unified understanding of the fundamental difficulties in large deformation mechanics, bifurcation/chaos in nonlinear science, and the NP-hard problems in global optimization. Additionally, readers will find a unified methodology and powerful algorithms for solving challenging problems in comp...

  8. Multivariate analysis of microarray data by principal component discriminant analysis: Prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

    NARCIS (Netherlands)

    Werf, M.J. van der; Pieterse, B.; Luijk, N. van; Schuren, F.; Werff van der - Vat, B. van der; Overkamp, K.; Jellema, R.H.

    2006-01-01

    The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four

  9. Canonical ensembles and nonzero density quantum chromodynamics

    International Nuclear Information System (INIS)

    Hasenfratz, A.; Toussaint, D.

    1992-01-01

    We study QCD with nonzero chemical potential on 4 4 lattices by averaging over the canonical partition functions, or sectors with fixed quark number. We derive a condensed matrix of size 2x3xL 3 whose eigenvalues can be used to find the canonical partition functions. We also experiment with a weight for configuration generation which respects the Z(3) symmetry which forces the canonical partition function to be zero for quark numbers that are not multiples of three. (orig.)

  10. Análise discriminante paramétrica para reconhecimento de defeitos em tábuas de eucalipto utilizando imagens digitais Parametric discriminant analysis for recognition of defects in eucalyptus lumber using digital images

    Directory of Open Access Journals (Sweden)

    Joseph Kalil Khoury Junior

    2005-04-01

    work was to evaluate, using multivariate analysis, the discriminating power of color images percents. In this work, linear and quadratic discriminant analysis were accomplished for classification of defects and clear wood in digital images of eucalyptus lumber. The percent features of the histogram for the red, green and blue bands, from two sizes of image blocks were used for developing and testing the discriminant functions. 492 blocks were used, containing the 12 studied defects and clear wood, derived from images of 40 lumbers randomly sampled. The features were analyzed with their original values, scores of the principal components and scores of the canonical variables. The smallest global misclassification errors were 19% and 24% for linear discriminant function with the canonical variable scores using block sizes of 64x64 and 32x32 pixels, respectively. The percent features were considered appropriate to discriminate defects and clear wood in digital images.

  11. Analysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis

    Directory of Open Access Journals (Sweden)

    Pang Herbert

    2010-10-01

    Full Text Available Abstract Breast cancer tumours among African Americans are usually more aggressive than those found in Caucasian populations. African-American patients with breast cancer also have higher mortality rates than Caucasian women. A better understanding of the disease aetiology of these breast cancers can help to improve and develop new methods for cancer prevention, diagnosis and treatment. The main goal of this project was to identify genes that help differentiate between oestrogen receptor-positive and -negative samples among a small group of African-American patients with breast cancer. Breast cancer microarrays from one of the largest genomic consortiums were analysed using 13 African-American and 201 Caucasian samples with oestrogen receptor status. We used a shrinkage-based classification method to identify genes that were informative in discriminating between oestrogen receptor-positive and -negative samples. Subset analysis and permutation were performed to obtain a set of genes unique to the African-American population. We identified a set of 156 probe sets, which gave a misclassification rate of 0.16 in distinguishing between oestrogen receptor-positive and -negative patients. The biological relevance of our findings was explored through literature-mining techniques and pathway mapping. An independent dataset was used to validate our findings and we found that the top ten genes mapped onto this dataset gave a misclassification rate of 0.15. The described method allows us best to utilise the information available from small sample size microarray data in the context of ethnic minorities.

  12. Analysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis.

    Science.gov (United States)

    Pang, Herbert; Ebisu, Keita; Watanabe, Emi; Sue, Laura Y; Tong, Tiejun

    2010-10-01

    Breast cancer tumours among African Americans are usually more aggressive than those found in Caucasian populations. African-American patients with breast cancer also have higher mortality rates than Caucasian women. A better understanding of the disease aetiology of these breast cancers can help to improve and develop new methods for cancer prevention, diagnosis and treatment. The main goal of this project was to identify genes that help differentiate between oestrogen receptor-positive and -negative samples among a small group of African-American patients with breast cancer. Breast cancer microarrays from one of the largest genomic consortiums were analysed using 13 African-American and 201 Caucasian samples with oestrogen receptor status. We used a shrinkage-based classification method to identify genes that were informative in discriminating between oestrogen receptor-positive and -negative samples. Subset analysis and permutation were performed to obtain a set of genes unique to the African-American population. We identified a set of 156 probe sets, which gave a misclassification rate of 0.16 in distinguishing between oestrogen receptor-positive and -negative patients. The biological relevance of our findings was explored through literature-mining techniques and pathway mapping. An independent dataset was used to validate our findings and we found that the top ten genes mapped onto this dataset gave a misclassification rate of 0.15. The described method allows us best to utilise the information available from small sample size microarray data in the context of ethnic minorities.

  13. Partial Least Square Discriminant Analysis Discovered a Dietary Pattern Inversely Associated with Nasopharyngeal Carcinoma Risk.

    Science.gov (United States)

    Lo, Yen-Li; Pan, Wen-Harn; Hsu, Wan-Lun; Chien, Yin-Chu; Chen, Jen-Yang; Hsu, Mow-Ming; Lou, Pei-Jen; Chen, I-How; Hildesheim, Allan; Chen, Chien-Jen

    2016-01-01

    Evidence on the association between dietary component, dietary pattern and nasopharyngeal carcinoma (NPC) is scarce. A major challenge is the high degree of correlation among dietary constituents. We aimed to identify dietary pattern associated with NPC and to illustrate the dose-response relationship between the identified dietary pattern scores and the risk of NPC. Taking advantage of a matched NPC case-control study, data from a total of 319 incident cases and 319 matched controls were analyzed. Dietary pattern was derived employing partial least square discriminant analysis (PLS-DA) performed on energy-adjusted food frequencies derived from a 66-item food-frequency questionnaire. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated with multiple conditional logistic regression models, linking pattern scores and NPC risk. A high score of the PLS-DA derived pattern was characterized by high intakes of fruits, milk, fresh fish, vegetables, tea, and eggs ordered by loading values. We observed that one unit increase in the scores was associated with a significantly lower risk of NPC (ORadj = 0.73, 95% CI = 0.60-0.88) after controlling for potential confounders. Similar results were observed among Epstein-Barr virus seropositive subjects. An NPC protective diet is indicated with more phytonutrient-rich plant foods (fruits, vegetables), milk, other protein-rich foods (in particular fresh fish and eggs), and tea. This information may be used to design potential dietary regimen for NPC prevention.

  14. Analysis of PEG oligomers in black gel inks: Discrimination and ink dating.

    Science.gov (United States)

    Sun, Qiran; Luo, Yiwen; Xiang, Ping; Yang, Xu; Shen, Min

    2017-08-01

    Carbon-based black gel inks are common samples in forensic practice of questioned document examination in China, but there are few analytical methods for this type of ink. In this study, a liquid chromatography-.high resolution mass spectrometry (LC-HRMS) method was established for the analysis of PEG oligomers in carbon-based black gel ink entries. The coupled instruments achieve both the identification and quantification of PEG oligomers in ink entries with reproducible results. Twenty carbon-based black gel inks, whose Raman spectra appeared identical, were analyzed using the LC-HRMS method. As a result, the twenty gel inks were classified into four groups according to the distribution of PEG oligomers. Artificially aging of PEG 400 and a gel ink showed that as PEG degraded, the relative amounts of low molecular weight PEG oligomers increased, while those of high molecular weight decreased. The degradation of PEG oligomers in a naturally aged gel ink was consistent with those in the artificially aged samples, but occurred more slowly. This study not only provided a new method for discriminating carbon-based black gel ink entries, but also offered a new approach for studying the relative ink dating of carbon-based black gel ink entries. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Study and discrimination of human cervical tissue images through multifractal analysis

    Science.gov (United States)

    Jagtap, Jaidip; Singh, Pankaj; Pantola, Chayanika; Agarwal, Asha; Pandey, Kiran; Pradhan, Asima

    2013-03-01

    We report here a study of confocal microscope images to classify cervical precancers by a multifractal analysis. This study is performed using an inverted confocal microscope with laser scanning fluorescence imaging. The periodic structure of collagen present in the stromal region of cervical tissue gets disordered with progress in grade of dysplasia. This disorder is investigated through the β-exponent of a Discrete Fourier Transform (DFT) of the confocal images, enabling us to discriminate between the lowest and highest grades of dysplasia in human cervical tissue sections. The Holder exponent from 2D images further classifies various grades of dysplasia from normal tissue sections though Gd3 and Gd1 are indistinguishable. DFT however, clearly distinguishes Gd3 from Gd1. In addition to stromal images, epithelial images were also investigated for better classification. The cellular density of epithelium increases with depth for various grades of dysplasia and is not uniform. The Holder exponent, which measures multifractality, is higher for dysplastic tissue sections than for normal ones because of the above morphological differences. Extraction of subtle fluctuations from optical images through multifractal studies promise to be a powerful diagnostic technique.

  16. PENDETEKSIAN KANKER PARU–PARU DENGAN MENGGUNAKAN TRANSFORMASI WAVELET DAN METODE LINEAR DISCRIMINANT ANALYSIS

    Directory of Open Access Journals (Sweden)

    Hanung Tyas Saksono

    2010-07-01

    Full Text Available Kanker merupakan pertumbuhan dan penyebaran sel-sel abnormal yang memiliki karakteristik yang khas. Kanker yang sudah menyebar dan tidak dapat terkontrol lagi, biasanya akan menyebabkan kematian. Kanker paru-paru lebih sering menyebabkan pria meninggal dibanding kanker lain, dimana yang sering menjadi penyebab kanker paru-paru adalah merokok. Cara yang digunakan untuk mendeteksi kanker paru-paru ialah melalui pemeriksaan hasil foto rontgen dada. Penelitian ini bertujuan untuk menghasilkan suatu sistem aplikasi yang dapat mendiagnosa citra paru-parudan mengklasifikasikan paru-paruke dalam tipe kanker, normal atau efusi serta menganalisa performansi sistem yang digunakan dalam proses pengenalan citra paru-paru. Proses pendeteksian diawali dengan pemrosesan awal pada citra paru-paru, proses ekstraksi ciri menggunakan Transformasi Wavelet, dan proses klasifikasi menggunakan Linear Discriminant Analysis (LDA. Pemrosesan awal dilakukan untuk membuang informasi yang tidak dibutuhkan dalam pengolahan citra. Proses ekstraksi ciri dilakukan dengan cara mengurangi dimensi citra paru- paru yang akan menjadi masukan pada proses pengenalan menggunakan LDA. Pada penelitian ini citra latih yang digunakan adalah 60 buah citra, yang terdiri dari 20 kelas citra kondisi normal, 20 kelas citra kondisi kanker, dan 20 kelas citra kondisi efusi. Citra uji yang akan digunakan juga berjumlah 60 buah citra, yang tediri dari 20 citra untuk masing-masing kelas. Akurasi yang dihasilkan sistem pada pendeteksian kanker paru-paru ini sebesar 100% untuk citra latih dan 95% untuk citra uji.

  17. Generalized linear discriminant analysis: a unified framework and efficient model selection.

    Science.gov (United States)

    Ji, Shuiwang; Ye, Jieping

    2008-10-01

    High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the curse-of-dimensionality. Linear discriminant analysis (LDA) is a well-known method for supervised dimensionality reduction. When dealing with high-dimensional and low sample size data, classical LDA suffers from the singularity problem. Over the years, many algorithms have been developed to overcome this problem, and they have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed, which elucidates the properties of various algorithms and their relationships. Based on the proposed framework, we show that the matrix computations involved in LDA-based algorithms can be simplified so that the cross-validation procedure for model selection can be performed efficiently. We conduct extensive experiments using a collection of high-dimensional data sets, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.

  18. Semi-supervised Spatial-spectral Discriminant Analysis for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    HOU Banghuan

    2017-09-01

    Full Text Available In order to make full use of the spatial information embedded in the hyperspectral image to improve the classification accuracy, a semi-supervised spatial-spectral discriminant analysis (S3DA algorithm for hyperspectral image classification is proposed. According to the spatial consistency property of hyperspectral image, the intra-class scatter matrix infered from a little labeled samples preserves the spectral similarity of the same class pixels, while the spatial local pixel scatter matrix defined by the unlabeled spatial neighbors uncovers the spatial-domain local pixel neighborhood structures and the ground objects detailed distribution. The S3DA method not only maintains the spectral-domain separability of the data set, but also preserves the spatial-domain local pixel neighborhood structure, which promotes the compactness of the same class pixels or the spatial neighbor pixels in the projected subspace and enhances the classification performance. The overall classification accuracies respectively reach 81.50% and 71.77% on the PaviaU and Indian Pines data sets. Compared with the traditional spectral methods, the proposed method can effectively improve ground objects classification accuracy.

  19. The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis.

    Science.gov (United States)

    Treder, Matthias S; Porbadnigk, Anne K; Shahbazi Avarvand, Forooz; Müller, Klaus-Robert; Blankertz, Benjamin

    2016-04-01

    We introduce a novel beamforming approach for estimating event-related potential (ERP) source time series based on regularized linear discriminant analysis (LDA). The optimization problems in LDA and linearly-constrained minimum-variance (LCMV) beamformers are formally equivalent. The approaches differ in that, in LCMV beamformers, the spatial patterns are derived from a source model, whereas in an LDA beamformer the spatial patterns are derived directly from the data (i.e., the ERP peak). Using a formal proof and MEG simulations, we show that the LDA beamformer is robust to correlated sources and offers a higher signal-to-noise ratio than the LCMV beamformer and PCA. As an application, we use EEG data from an oddball experiment to show how the LDA beamformer can be harnessed to detect single-trial ERP latencies and estimate connectivity between ERP sources. Concluding, the LDA beamformer optimally reconstructs ERP sources by maximizing the ERP signal-to-noise ratio. Hence, it is a highly suited tool for analyzing ERP source time series, particularly in EEG/MEG studies wherein a source model is not available. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Raman spectroscopy: a noninvasive analysis tool for the discrimination of human skin cells.

    Science.gov (United States)

    Pudlas, Marieke; Koch, Steffen; Bolwien, Carsten; Thude, Sibylle; Jenne, Nele; Hirth, Thomas; Walles, Heike; Schenke-Layland, Katja

    2011-10-01

    Noninvasive monitoring of tissue-engineered (TE) constructs during their in vitro maturation or postimplantation in vivo is highly relevant for graft evaluation. However, traditional methods for studying cell and matrix components in engineered tissues such as histology, immunohistochemistry, or biochemistry require invasive tissue processing, resulting in the need to sacrifice of TE constructs. Raman spectroscopy offers the unique possibility to analyze living cells label-free in situ and in vivo solely based on their phenotype-specific biochemical fingerprint. In this study, we aimed to determine the applicability of Raman spectroscopy for the noninvasive identification and spectral separation of primary human skin fibroblasts, keratinocytes, and melanocytes, as well as immortalized keratinocytes (HaCaT cells). Multivariate analysis of cell-type-specific Raman spectra enabled the discrimination between living primary and immortalized keratinocytes. We further noninvasively distinguished between fibroblasts, keratinocytes, and melanocytes. Our findings are especially relevant for the engineering of in vitro skin models and for the production of artificial skin, where both the biopsy and the transplant consist of several cell types. To realize a reproducible quality of TE skin, the determination of the purity of the cell populations as well as the detection of potential molecular changes are important. We conclude therefore that Raman spectroscopy is a suitable tool for the noninvasive in situ quality control of cells used in skin tissue engineering applications. © Mary Ann Liebert, Inc.

  1. Discrimination of Anemonefish Species by PCR-RFLP Analysis of Mitochondrial Gene Fragments

    Directory of Open Access Journals (Sweden)

    Chuta Boonphakdee

    2008-01-01

    Full Text Available A means of discriminating among species of clown anemonefishes, based on restriction enzyme analysis of partial mitochondrial DNA sequences, was investigated. Mitochondrial 16S rRNA and cytochrome b genes from 6 species (7 strains of anemonefish (Premnas biculeatus, Amphiprion polymnus, A. sandaracinos, A. perideraion, A. ocellaris, A. ocellaris var. and A. percula were PCR-amplified. A 623-bp portion of 16S rRNA gene was obtained from different fishes using the same pair of primers. Further investigation of this 16S rRNA fragment, by restriction endonuclease digestion with BfuCI and RsaI, was not able to distinguish all fishes studied, but did yield 3 different digestion patterns. The first was specific to P. biculaetus, the sole member of the genus Premnas, while the remaining two separated the Amphiprion species into 2 groups: 1 A. polymnas, A. sandaracinos and A. perideraion, and 2 A. ocellaris, A. ocellaris var. and A. percula. In contrast to this, restriction endonuclease digestion of a 786-bp fragment of the cytochrome b gene with HinfI and RsaI, was able to differentiate different 7 anemonefishes. This utility marker is valuable for unambiguous species/strain identification of juvenile anemonefishes.

  2. Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

    Science.gov (United States)

    Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

    2017-09-01

    In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

    Directory of Open Access Journals (Sweden)

    Shengwen Guo

    2017-05-01

    Full Text Available Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI. Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI, the converted MCI (cMCI, and the normal control (NC groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM. An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and

  4. UV-visible microscope spectrophotometric polarization and dichroism with increased discrimination power in forensic analysis

    Science.gov (United States)

    Purcell, Dale Kevin

    Microanalysis of transfer (Trace) evidence is the application of a microscope and microscopical techniques for the collection, observation, documentation, examination, identification, and discrimination of micrometer sized particles or domains. Microscope spectrophotometry is the union of microscopy and spectroscopy for microanalysis. Analytical microspectroscopy is the science of studying the emission, reflection, transmission, and absorption of electromagnetic radiation to determine the structure or chemical composition of microscopic-size materials. Microscope spectrophotometry instrument designs have evolved from monochromatic illumination which transmitted through the microscope and sample and then is detected by a photometer detector (photomultiplier tube) to systems in which broad-band (white light) illumination falls incident upon a sample followed by a non-scanning grating spectrometer equipped with a solid-state multi-element detector. Most of these small modern spectrometers are configured with either silicon based charged-couple device detectors (200-950 nm) or InGaAs based diode array detectors (850-2300 nm) with computerized data acquisition and signal processing being common. A focus of this research was to evaluate the performance characteristics of various modern forensic (UV-Vis) microscope photometer systems as well as review early model instrumental designs. An important focus of this research was to efficiently measure ultraviolet-visible spectra of microscopically small specimens for classification, differentiation, and possibly individualization. The first stage of the project consisted of the preparation of microscope slides containing neutral density filter reference materials, molecular fluorescence reference materials, and dichroic reference materials. Upon completion of these standard slide preparations analysis began with measurements in order to evaluate figures of merit for comparison of the instruments investigated. The figures of

  5. The Meridians of Reference of Indian Astronomical Canons

    Science.gov (United States)

    Mercier, R.

    The canons of Sanskrit astronomy depend on mean motions which are normally postulated to refer to the central meridian of Ujjain. The present work is a statistical analysis of these mean motions designed to discover the optimum position of the meridian, by comparison with modern mean motions. This follows earlier work done by Billard in determining the optimum year.

  6. Classification Of Brain Tumor Extracts By High Resolution 1h Mrs Using Partial Least Squares Discriminant Analysis

    OpenAIRE

    Faria A.V.; Macedo Jr. F.C.; Marsaioli A.J.; Ferreira M.M.C.; Cendes F.

    2011-01-01

    High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain ...

  7. Detection of Counterfeit Durateston® Using Fourier Transform Infrared Spectroscopy and Partial Least Squares - Discriminant Analysis

    OpenAIRE

    Neves, Diana B. J.; Talhavini, Márcio; Braga, Jez Willian B.; Zacca, Jorge J.; Caldas, Eloisa D.

    2017-01-01

    Medicines containing anabolic steroids are one of the main targets for counterfeiting worldwide, including Brazil. The aim of this work was to propose a method for discriminating original and counterfeit Durateston® ampoules by Fourier transform infrared spectroscopy (FTIR) followed by chemometric analysis. Ninety-six ampoules of Durateston®, 49 originals and 47 counterfeits, were analyzed by gas chromatography with mass spectrometry (GC-MS) and by FTIR. Principal component analysis was appli...

  8. Digital discrimination of neutrons and γ-rays in liquid scintillators using pulse gradient analysis

    International Nuclear Information System (INIS)

    D'Mellow, B.; Aspinall, M.D.; Mackin, R.O.; Joyce, M.J.; Peyton, A.J.

    2007-01-01

    A method for the digital discrimination of neutrons and γ-rays in mixed radiation fields is described. Pulses in the time domain, arising from the interaction of photons and neutrons in a liquid scintillator, have been produced using an accepted empirical model and from experimental measurements with an americium-beryllium source. Neutrons and γ-rays have been successfully discriminated in both of these data sets in the digital domain. The digital discrimination method described in this paper is simple and exploits samples early in the life of the pulse. It is thus compatible with current embedded system technologies, offers a degree of immunity to pulse pile-up and heralds a real-time means for neutron/γ discrimination that is fundamental to many potential industrial applications

  9. Percolation in the canonical ensemble

    Science.gov (United States)

    Hu, Hao; Blöte, Henk W. J.; Deng, Youjin

    2012-12-01

    We study the bond percolation problem under the constraint that the total number of occupied bonds is fixed, so that the canonical ensemble applies. We show via an analytical approach that at criticality, the constraint can induce new finite-size corrections with exponent ycan = 2yt - d both in energy-like and magnetic quantities, where yt = 1/ν is the thermal renormalization exponent and d is the spatial dimension. Furthermore, we find that while most of the universal parameters remain unchanged, some universal amplitudes, like the excess cluster number, can be modified and become non-universal. We confirm these predictions by extensive Monte Carlo simulations of the two-dimensional percolation problem which has ycan = -1/2. This article is part of ‘Lattice models and integrability’, a special issue of Journal of Physics A: Mathematical and Theoretical in honour of F Y Wu's 80th birthday.

  10. Size matters! Body height and labor market discrimination : a cross-European analysis

    OpenAIRE

    Cinnirella, Francesco; Winter, Joachim

    2009-01-01

    Taller workers earn on average higher salaries. Recent research has proposed cognitive abilities and social skills as explanations for the height-wage premium. Another possible mechanism, employer discrimination, has found little support. In this paper, we provide some evidence in favor of the discrimination hypothesis. Using a cross section of 13 countries, we show that there is a consistent height-wage premium across Europe and that it is largely due to occupational sorting. We show that he...

  11. Schizophrenia in males of cognitive performance: discriminative and diagnostic values.

    Science.gov (United States)

    Camozzato, Analuiza; Chaves, Márcia L F

    2002-12-01

    To evaluate the discriminative and diagnostic values of neuropsychological tests for identifying schizophrenia patients. A cross-sectional study with 36 male schizophrenia outpatients and 72 healthy matched volunteers was carried out. Participants underwent the following neuropsychological tests: Wisconsin Card Sorting test, Verbal Fluency, Stroop test, Mini Mental State Examination, and Spatial Recognition Span. Sensitivity and specificity estimated the diagnostic value of tests with cutoffs obtained using Receiver Operating Characteristic curves. The latent class model (diagnosis of schizophrenia) was used as gold standard. Although patients presented lower scores in most tests, the highest canonical function for the discriminant analysis was 0.57 (Verbal Fluency M). The best sensitivity and specificity were obtained in the Verbal Fluency M test (75 and 65, respectively). The neuropsychological tests showed moderate diagnostic value for the identification of schizophrenia patients. These findings suggested that the cognitive impairment measured by these tests might not be homogeneous among schizophrenia patients.

  12. Schizophrenia in males of cognitive performance: discriminative and diagnostic values

    Directory of Open Access Journals (Sweden)

    Analuiza Camozzato

    2002-12-01

    Full Text Available OBJECTIVE: To evaluate the discriminative and diagnostic values of neuropsychological tests for identifying schizophrenia patients. METHODS: A cross-sectional study with 36 male schizophrenia outpatients and 72 healthy matched volunteers was carried out. Participants underwent the following neuropsychological tests: Wisconsin Card Sorting test, Verbal Fluency, Stroop test, Mini Mental State Examination, and Spatial Recognition Span. Sensitivity and specificity estimated the diagnostic value of tests with cutoffs obtained using Receiver Operating Characteristic curves. The latent class model (diagnosis of schizophrenia was used as gold standard. RESULTS: Although patients presented lower scores in most tests, the highest canonical function for the discriminant analysis was 0.57 (Verbal Fluency M. The best sensitivity and specificity were obtained in the Verbal Fluency M test (75 and 65, respectively. CONCLUSIONS: The neuropsychological tests showed moderate diagnostic value for the identification of schizophrenia patients. These findings suggested that the cognitive impairment measured by these tests might not be homogeneous among schizophrenia patients.

  13. Schizophrenia in males of cognitive performance: discriminative and diagnostic values

    Directory of Open Access Journals (Sweden)

    Camozzato Analuiza

    2002-01-01

    Full Text Available OBJECTIVE: To evaluate the discriminative and diagnostic values of neuropsychological tests for identifying schizophrenia patients. METHODS: A cross-sectional study with 36 male schizophrenia outpatients and 72 healthy matched volunteers was carried out. Participants underwent the following neuropsychological tests: Wisconsin Card Sorting test, Verbal Fluency, Stroop test, Mini Mental State Examination, and Spatial Recognition Span. Sensitivity and specificity estimated the diagnostic value of tests with cutoffs obtained using Receiver Operating Characteristic curves. The latent class model (diagnosis of schizophrenia was used as gold standard. RESULTS: Although patients presented lower scores in most tests, the highest canonical function for the discriminant analysis was 0.57 (Verbal Fluency M. The best sensitivity and specificity were obtained in the Verbal Fluency M test (75 and 65, respectively. CONCLUSIONS: The neuropsychological tests showed moderate diagnostic value for the identification of schizophrenia patients. These findings suggested that the cognitive impairment measured by these tests might not be homogeneous among schizophrenia patients.

  14. Discrimination of irradiated MOX fuel from UOX fuel by multivariate statistical analysis of simulated activities of gamma-emitting isotopes

    Science.gov (United States)

    Åberg Lindell, M.; Andersson, P.; Grape, S.; Hellesen, C.; Håkansson, A.; Thulin, M.

    2018-03-01

    This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost. A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items. The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope 134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.

  15. Discrimination of the production season of Chinese green tea by chemical analysis in combination with supervised pattern recognition.

    Science.gov (United States)

    Xu, Wenping; Song, Qiushuang; Li, Daxiang; Wan, Xiaochun

    2012-07-18

    High-performance liquid chromatography (HPLC) has been used to quantify levels of free amino acids, catechins, and caffeine in Chinese green tea. Levels of free amino acids and catechins in green tea leaves show obvious variation from spring to summer, which is useful information to identify the production season of commercial green tea. Supervised pattern recognition methods such as the K-nearest neighbor (KNN) method and Bayesian discriminant method (a type of linear discriminant analysis (LDA)) were used to discriminate between the production seasons of Chinese green tea. The optimal accuracy of the KNN method was ≤97.61 and ≤94.80% as validated by resubstitution and cross-validation tests, respectively, and that of LDA was ≤95.22 and ≤93.54%, respectively. Compared with LDA, the KNN method did not require a Gaussian distribution and was more accurate than LDA. The KNN method in combination with chemical analysis is recommended for discrimination of the production seasons of Chinese green tea.

  16. Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM.

    Science.gov (United States)

    Song, Hong; Chen, Lei; Gao, RuiQi; Bogdan, Iordachescu Ilie Mihaita; Yang, Jian; Wang, Shuliang; Dong, Wentian; Quan, Wenxiang; Dang, Weimin; Yu, Xin

    2017-12-20

    Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal people and patients, which easily lead to misdiagnosis. In recent years, functional Near-Infrared Spectroscopy (fNIRS) has been widely used in clinical diagnosis, it can get the hemoglobin concentration through the variation of optical intensity. Firstly, the prefrontal brain networks were constructed based on oxy-Hb signals from 52-channel fNIRS data of schizophrenia and healthy controls. Then, Complex Brain Network Analysis (CBNA) was used to extract features from the prefrontal brain networks. Finally, a classier based on Support Vector Machine (SVM) is designed and trained to discriminate schizophrenia from healthy controls. We recruited a sample which contains 34 healthy controls and 42 schizophrenia patients to do the one-back memory task. The hemoglobin response was measured in the prefrontal cortex during the task using a 52-channel fNIRS system. The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 85.5%, 92.8% for schizophrenia samples and 76.5% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. Our results suggested that, using the appropriate classification method, fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.

  17. High-resolution melting combines with Bayes discriminant analysis: a novel hepatitis C virus genotyping method.

    Science.gov (United States)

    Wu, Daxian; Fu, Xiaoyu; Wen, Ya; Liu, Bingjie; Deng, Zhongping; Dai, Lizhong; Tan, Deming

    2017-08-01

    Current hepatitis C virus (HCV) genotyping techniques are often highly technical, costly, or need improvements in sensitivity and specificity. These limitations indicate the need of novel methods for HCV genotyping. The present study aimed to develop a novel genotyping method combining high-resolution melting (HRM) analysis with Bayes discriminant analysis (BDA). Target gene fragment including 5'-untranslated and core region was selected. Four or five inner amplicons for every serum were amplified using nested PCR, HRM was used to determine the melting temperature of the amplicons, and HCV genotypes were then analyzed utilizing BDA. In initial genotyping (HCV genotypes were classified into 1b, 2a, 3a, 3b, and 6a), both the overall accuracy rate and the cross-validation accuracy rate were 92.6 %, external validation accuracy rate was 95.0 %. To enhance the accuracy rate of genotyping, HCV genotypes were firstly classified into 1b, 3a, 3b, and 2a-6a, followed by a supplementary genotyping for 2a-6a. Both the overall accuracy rate and the cross-validation accuracy rate reached 97.5 %, and external validation accuracy rate was 100 %. Comparing adjusted HRM genotyping with type-specific probe technique, the difference in accuracy rates was not significant. However, the limit of detection and cost were lower for HRM. Comparing with sequencing, the limit detection of HRM was the same as the former, but the cost of HRM was lower. Hence, HRM combined with BDA was a novel method that equipped with superior accuracy, high sensitivity, and lower cost and therefore could be a better technique for HCV genotyping.

  18. Evaluation of sensory panels of consumers of specialty coffee beverages using the boosting method in discriminant analysis

    Directory of Open Access Journals (Sweden)

    Gilberto Rodrigues Liska

    2015-12-01

    Full Text Available Automatic classification methods have been widely used in numerous situations and the boosting method has become known for use of a classification algorithm, which considers a set of training data and, from that set, constructs a classifier with reweighted versions of the training set. Given this characteristic, the aim of this study is to assess a sensory experiment related to acceptance tests with specialty coffees, with reference to both trained and untrained consumer groups. For the consumer group, four sensory characteristics were evaluated, such as aroma, body, sweetness, and final score, attributed to four types of specialty coffees. In order to obtain a classification rule that discriminates trained and untrained tasters, we used the conventional Fisher’s Linear Discriminant Analysis (LDA and discriminant analysis via boosting algorithm (AdaBoost. The criteria used in the comparison of the two approaches were sensitivity, specificity, false positive rate, false negative rate, and accuracy of classification methods. Additionally, to evaluate the performance of the classifiers, the success rates and error rates were obtained by Monte Carlo simulation, considering 100 replicas of a random partition of 70% for the training set, and the remaining for the test set. It was concluded that the boosting method applied to discriminant analysis yielded a higher sensitivity rate in regard to the trained panel, at a value of 80.63% and, hence, reduction in the rate of false negatives, at 19.37%. Thus, the boosting method may be used as a means of improving the LDA classifier for discrimination of trained tasters.

  19. Price Discrimination

    OpenAIRE

    Armstrong, M.

    2008-01-01

    This paper surveys recent economic research on price discrimination, both in monopoly and oligopoly markets. Topics include static and dynamic forms of price discrimination, and both final and input markets are considered. Potential antitrust aspects of price discrimination are highlighted throughout the paper. The paper argues that the informational requirements to make accurate policy are very great, and with most forms of price discrimination a laissez-faire policy may be the best availabl...

  20. Lithological discrimination using a Wavelet Based Fractal Analysis at the Teapot Dome Field, Wyoming-USA

    Science.gov (United States)

    García, Alejandro; Aldana, Milagrosa; Cabrera, Ana

    2013-04-01

    In this work, we have applied a Wavelet Based Fractal Analysis (WBFA) to well logs and seismic data at the Teapot Dome Field, Natrona Country, Wyoming-USA, trying to characterize a reservoir using fractal parameters, as intercept (b), slope (m) and fractal dimension (D), and to correlate them with the sedimentation processes and/or the lithological characteristics of the area. The WBFA was first applied to the available logs (Gamma Ray, Spontaneous Potential, Density, Neutron Porosity and Deep Resistivity) from 20 wells located at sectors 27, 28, 33 and 34 of the 3D seismic of the Teapot Dome field. Also the WBFA was applied to the calculated curve of water saturation (Sw). At a second step, the method was used to analyze a set of seismic traces close to the studied wells, extracted from the 3D seismic data. Maps of the fractal parameters were obtained. A spectral analysis of the seismic data was also performed in order to identify seismic facies and to establish a possible correlation with the fractal results. The WBFA results obtained for the wells logs indicate a correlation between fractal parameters and the lithological content in the studied interval (i.e. top-base of the Frontier Formation). Particularly, for the Gamma Ray logs the fractal dimension D can be correlated with the sand-shale content: values of D lower than 0.9 are observed for those wells with more sand content (sandy wells); values of D between 0.9 and 1.1 correspond to wells where the sand packs present numerous inter-bedded shale layers (sandy-shale wells); finally, wells with more shale content (shaly wells) have D values greater than 1.1. The analysis of the seismic traces allowed the discrimination of shaly from sandy zones. The D map generated for the seismic traces indicates that this value can be associated with the shale content in the area. The iso-frequency maps obtained from the seismic spectral analysis show trends associated to the lithology of the field. These trends are similar

  1. The Current Canon in British Romantics Studies.

    Science.gov (United States)

    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. The canonical controller and its regularity

    NARCIS (Netherlands)

    Willems, Jan C.; Belur, Madhu N.; Anak Agung Julius, A.A.J.; Trentelman, Harry L.

    2003-01-01

    This paper deals with properties of canonical controllers. We first specify the behavior that they implement. It follows that a canonical controller implements the desired controlled behavior if and only if the desired behavior is implementable. We subsequently investigate the regularity of the

  3. CANONICAL EXTENSIONS OF SYMMETRIC LINEAR RELATIONS

    NARCIS (Netherlands)

    Sandovici, Adrian; Davidson, KR; Gaspar, D; Stratila, S; Timotin, D; Vasilescu, FH

    2006-01-01

    The concept of canonical extension of Hermitian operators has been recently introduced by A. Kuzhel. This paper deals with a generalization of this notion to the case of symmetric linear relations. Namely, canonical regular extensions of symmetric linear relations in Hilbert spaces are studied. The

  4. Log canonical thresholds of smooth Fano threefolds

    International Nuclear Information System (INIS)

    Cheltsov, Ivan A; Shramov, Konstantin A

    2008-01-01

    The complex singularity exponent is a local invariant of a holomorphic function determined by the integrability of fractional powers of the function. The log canonical thresholds of effective Q-divisors on normal algebraic varieties are algebraic counterparts of complex singularity exponents. For a Fano variety, these invariants have global analogues. In the former case, it is the so-called α-invariant of Tian; in the latter case, it is the global log canonical threshold of the Fano variety, which is the infimum of log canonical thresholds of all effective Q-divisors numerically equivalent to the anticanonical divisor. An appendix to this paper contains a proof that the global log canonical threshold of a smooth Fano variety coincides with its α-invariant of Tian. The purpose of the paper is to compute the global log canonical thresholds of smooth Fano threefolds (altogether, there are 105 deformation families of such threefolds). The global log canonical thresholds are computed for every smooth threefold in 64 deformation families, and the global log canonical thresholds are computed for a general threefold in 20 deformation families. Some bounds for the global log canonical thresholds are computed for 14 deformation families. Appendix A is due to J.-P. Demailly.

  5. Modern Canonical Quantum General Relativity;

    Energy Technology Data Exchange (ETDEWEB)

    Kiefer, Claus [Institute for Theoretical Physics, Universitaet zu Koeln, Zuelpicher Strasse 77, 50937 Cologne (Germany)

    2008-06-21

    The open problem of constructing a consistent and experimentally tested quantum theory of the gravitational field has its place at the heart of fundamental physics. The main approaches can be roughly divided into two classes: either one seeks a unified quantum framework of all interactions or one starts with a direct quantization of general relativity. In the first class, string theory (M-theory) is the only known example. In the second class, one can make an additional methodological distinction: while covariant approaches such as path-integral quantization use the four-dimensional metric as an essential ingredient of their formalism, canonical approaches start with a foliation of spacetime into spacelike hypersurfaces in order to arrive at a Hamiltonian formulation. The present book is devoted to one of the canonical approaches-loop quantum gravity. It is named modern canonical quantum general relativity by the author because it uses connections and holonomies as central variables, which are analogous to the variables used in Yang-Mills theories. In fact, the canonically conjugate variables are a holonomy of a connection and the flux of a non-Abelian electric field. This has to be contrasted with the older geometrodynamical approach in which the metric of three-dimensional space and the second fundamental form are the fundamental entities, an approach which is still actively being pursued. It is the author's ambition to present loop quantum gravity in a way in which every step is formulated in a mathematically rigorous form. The formal Leitmotiv of loop quantum gravity is background independence. Non-gravitational theories are usually quantized on a given non-dynamical background. In contrast, due to the geometrical nature of gravity, no such background exists in quantum gravity. Instead, the notion of a background is supposed to emerge a posteriori as an approximate notion from quantum states of geometry. As a consequence, the standard ultraviolet

  6. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System

    Directory of Open Access Journals (Sweden)

    Zvi N Roth

    2016-03-01

    Full Text Available Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e. discriminative information may be less relevant than information regarding the relative location of two objects (i.e. relative information. For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action.We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in early visual cortex, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS, but not from EVC, where we find the spatial representation to be warped.We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model’s receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC.These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representation into relative spatial representation along the visual stream.

  7. Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System.

    Science.gov (United States)

    Roth, Zvi N

    2016-01-01

    Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream.

  8. Racial discrimination mediates race differences in sleep problems: A longitudinal analysis.

    Science.gov (United States)

    Fuller-Rowell, Thomas E; Curtis, David S; El-Sheikh, Mona; Duke, Adrienne M; Ryff, Carol D; Zgierska, Aleksandra E

    2017-04-01

    To examine changes in sleep problems over a 1.5-year period among Black or African American (AA) and White or European American (EA) college students and to consider the role of racial discrimination as a mediator of race differences in sleep problems over time. Students attending a large, predominantly White university (N = 133, 41% AA, 57% female, mean age = 18.8, SD = .90) reported on habitual sleep characteristics and experiences of racial discrimination at baseline and follow-up assessments. A latent variable for sleep problems was assessed from reports of sleep latency, duration, efficiency, and quality. Longitudinal models were used to examine race differences in sleep problems over time and the mediating role of perceived discrimination. Covariates included age, gender, parent education, parent income, body mass index, self-rated physical health, and depressive symptoms. Each of the individual sleep measures was also examined separately, and sensitivity analyses were conducted using alternative formulations of the sleep problems measure. AAs had greater increases in sleep problems than EAs. Perceived discrimination was also associated with increases in sleep problems over time and mediated racial disparities in sleep. This pattern of findings was similar when each of the sleep indicators was considered separately and held with alternative sleep problems measures. The findings highlight the importance of racial disparities in sleep across the college years and suggest that experiences of discrimination contribute to group disparities. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  9. DNA content analysis allows discrimination between Trypanosoma cruzi and Trypanosoma rangeli.

    Science.gov (United States)

    Naves, Lucila Langoni; da Silva, Marcos Vinícius; Fajardo, Emanuella Francisco; da Silva, Raíssa Bernardes; De Vito, Fernanda Bernadelli; Rodrigues, Virmondes; Lages-Silva, Eliane; Ramírez, Luis Eduardo; Pedrosa, André Luiz

    2017-01-01

    Trypanosoma cruzi, a human protozoan parasite, is the causative agent of Chagas disease. Currently the species is divided into six taxonomic groups. The genome of the CL Brener clone has been estimated to be 106.4-110.7 Mb, and DNA content analyses revealed that it is a diploid hybrid clone. Trypanosoma rangeli is a hemoflagellate that has the same reservoirs and vectors as T. cruzi; however, it is non-pathogenic to vertebrate hosts. The haploid genome of T. rangeli was previously estimated to be 24 Mb. The parasitic strains of T. rangeli are divided into KP1(+) and KP1(-). Thus, the objective of this study was to investigate the DNA content in different strains of T. cruzi and T. rangeli by flow cytometry. All T. cruzi and T. rangeli strains yielded cell cycle profiles with clearly identifiable G1-0 (2n) and G2-M (4n) peaks. T. cruzi and T. rangeli genome sizes were estimated using the clone CL Brener and the Leishmania major CC1 as reference cell lines because their genome sequences have been previously determined. The DNA content of T. cruzi strains ranged from 87,41 to 108,16 Mb, and the DNA content of T. rangeli strains ranged from 63,25 Mb to 68,66 Mb. No differences in DNA content were observed between KP1(+) and KP1(-) T. rangeli strains. Cultures containing mixtures of the epimastigote forms of T. cruzi and T. rangeli strains resulted in cell cycle profiles with distinct G1 peaks for strains of each species. These results demonstrate that DNA content analysis by flow cytometry is a reliable technique for discrimination between T. cruzi and T. rangeli isolated from different hosts.

  10. The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer

    Science.gov (United States)

    Wen, Gezheng; Markey, Mia K.

    2015-03-01

    It is resource-intensive to conduct human studies for task-based assessment of medical image quality and system optimization. Thus, numerical model observers have been developed as a surrogate for human observers. The Hotelling observer (HO) is the optimal linear observer for signal-detection tasks, but the high dimensionality of imaging data results in a heavy computational burden. Channelization is often used to approximate the HO through a dimensionality reduction step, but how to produce channelized images without losing significant image information remains a key challenge. Kernel local Fisher discriminant analysis (KLFDA) uses kernel techniques to perform supervised dimensionality reduction, which finds an embedding transformation that maximizes betweenclass separability and preserves within-class local structure in the low-dimensional manifold. It is powerful for classification tasks, especially when the distribution of a class is multimodal. Such multimodality could be observed in many practical clinical tasks. For example, primary and metastatic lesions may both appear in medical imaging studies, but the distributions of their typical characteristics (e.g., size) may be very different. In this study, we propose to use KLFDA as a novel channelization method. The dimension of the embedded manifold (i.e., the result of KLFDA) is a counterpart to the number of channels in the state-of-art linear channelization. We present a simulation study to demonstrate the potential usefulness of KLFDA for building the channelized HOs (CHOs) and generating reliable decision statistics for clinical tasks. We show that the performance of the CHO with KLFDA channels is comparable to that of the benchmark CHOs.

  11. A decision support framework for the discrimination of children with controlled epilepsy based on EEG analysis

    Directory of Open Access Journals (Sweden)

    Fabri Simon G

    2010-06-01

    Full Text Available Abstract Background In this work we consider hidden signs (biomarkers in ongoing EEG activity expressing epileptic tendency, for otherwise normal brain operation. More specifically, this study considers children with controlled epilepsy where only a few seizures without complications were noted before starting medication and who showed no clinical or electrophysiological signs of brain dysfunction. We compare EEG recordings from controlled epileptic children with age-matched control children under two different operations, an eyes closed rest condition and a mathematical task. The aim of this study is to develop reliable techniques for the extraction of biomarkers from EEG that indicate the presence of minor neurophysiological signs in cases where no clinical or significant EEG abnormalities are observed. Methods We compare two different approaches for localizing activity differences and retrieving relevant information for classifying the two groups. The first approach focuses on power spectrum analysis whereas the second approach analyzes the functional coupling of cortical assemblies using linear synchronization techniques. Results Differences could be detected during the control (rest task, but not on the more demanding mathematical task. The spectral markers provide better diagnostic ability than their synchronization counterparts, even though a combination (or fusion of both is needed for efficient classification of subjects. Conclusions Based on these differences, the study proposes concrete biomarkers that can be used in a decision support system for clinical validation. Fusion of selected biomarkers in the Theta and Alpha bands resulted in an increase of the classification score up to 80% during the rest condition. No significant discrimination was achieved during the performance of a mathematical subtraction task.

  12. Discriminant analysis as a decision-making tool for geochemically fingerprinting sources of groundwater salinity.

    Science.gov (United States)

    Chien, Nathaniel P; Lautz, Laura K

    2018-03-15

    Concern over contamination of groundwater resources in areas impacted by anthropogenic activities has led to an increasing number of baseline groundwater quality surveys intended to provide context for interpreting water quality data. Flexible screening tools that can parse through these large, regional datasets to identify spatial or temporal changes in water quality are becoming more important to groundwater scientists. One such tool, developed from previous work by the authors, makes use of linear discriminant analysis (LDA) to identify the most probable source of chloride salinity in groundwater samples based on their geochemical fingerprints. Here, we applied the model to a dataset of shallow groundwater with known sources of contamination compiled from two studies of groundwater quality in Illinois: Panno et al. (2005) and Hwang et al. (2015). By predicting the source of salinity in groundwater samples for which the sources of contamination are known, we validated model prediction-accuracy. Results show high classification accuracy for groundwater samples impacted by basin brines (e.g. deep saline groundwater) and road salt (>80%), with diminishing success for those impacted by organic sources of chloride, such as septic effluent and animal waste. Posterior probabilities, a statistic inherent to LDA, provide a proxy for prediction confidence that enables the model to be used for assessment and accountability measures, such as identifying parties responsible for contamination. LDA is complementary to fingerprinting using halogen ratios (e.g. Cl/Br) because it implicitly relies on halogen ratios for classification decisions while providing a clearer, more quantitative classification of contamination sources. Our model is ideal for regional assessment or initial screening of salinity sources in groundwater because it makes use of commonly measured solute concentrations in publicly available water quality databases. Copyright © 2017 Elsevier B.V. All rights

  13. A New Biased Discriminant Analysis Using Composite Vectors for Eye Detection.

    Science.gov (United States)

    Chunghoon Kim; Sang-Il Choi; Turk, M; Chong-Ho Choi

    2012-08-01

    We propose a new biased discriminant analysis (BDA) using composite vectors for eye detection. A composite vector consists of several pixels inside a window on an image. The covariance of composite vectors is obtained from their inner product and can be considered as a generalization of the covariance of pixels. The proposed composite BDA (C-BDA) method is a BDA using the covariance of composite vectors. We construct a hybrid cascade detector for eye detection, using Haar-like features in the earlier stages and composite features obtained from C-BDA in the later stages. The proposed detector runs in real time; its execution time is 5.5 ms on a typical PC. The experimental results for the CMU PIE database and our own real-world data set show that the proposed detector provides robust performance to several kinds of variations such as facial pose, illumination, eyeglasses, and partial occlusion. On the whole, the detection rate per pair of eyes is 98.0% for the 3604 face images of the CMU PIE database and 95.1% for the 2331 face images of the real-world data set. In particular, it provides a 99.7% detection rate for the 2120 CMU PIE images without glasses. Face recognition performance is also investigated using the eye coordinates from the proposed detector. The recognition results for the real-world data set show that the proposed detector gives similar performance to the method using manually located eye coordinates, showing that the accuracy of the proposed eye detector is comparable with that of the ground-truth data.

  14. Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

    Directory of Open Access Journals (Sweden)

    Guan Yu

    Full Text Available Accurately identifying mild cognitive impairment (MCI individuals who will progress to Alzheimer's disease (AD is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI and fluorodeoxyglucose positron emission tomography (FDG-PET. However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI subjects and 226 stable MCI (sMCI subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images and also the single-task classification method (using only MRI or only subjects with both MRI and

  15. Discrimination of basal cell carcinoma and melanoma from normal skin biopsies in vitro through Raman spectroscopy and principal component analysis.

    Science.gov (United States)

    Bodanese, Benito; Silveira, Fabrício Luiz; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu T; Pasqualucci, Carlos Augusto; Silveira, Landulfo

    2012-07-01

    Raman spectroscopy has been employed to discriminate between malignant (basal cell carcinoma [BCC] and melanoma [MEL]) and normal (N) skin tissues in vitro, aimed at developing a method for cancer diagnosis. Raman spectroscopy is an analytical tool that could be used to diagnose skin cancer rapidly and noninvasively. Skin biopsy fragments of ≈ 2 mm(2) from excisional surgeries were scanned through a Raman spectrometer (830 nm excitation wavelength, 50 to 200 mW of power, and 20 sec exposure time) coupled to a fiber optic Raman probe. Principal component analysis (PCA) and Euclidean distance were employed to develop a discrimination model to classify samples according to histopathology. In this model, we used a set of 145 spectra from N (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues. We demonstrated that principal components (PCs) 1 to 4 accounted for 95.4% of all spectral variation. These PCs have been spectrally correlated to the biochemicals present in tissues, such as proteins, lipids, and melanin. The scores of PC2 and PC3 revealed statistically significant differences among N, BCC, and MEL (ANOVA, p<0.05) and were used in the discrimination model. A total of 28 out of 30 spectra were correctly diagnosed as N, 93 out of 96 as BCC, and 13 out of 19 as MEL, with an overall accuracy of 92.4%. This discrimination model based on PCA and Euclidean distance could differentiate N from malignant (BCC and MEL) with high sensitivity and specificity.

  16. Application of Discriminant Analysis for Studying the Source Rock Potential of Probable Formations in the Lorestan Basin, Iran

    Directory of Open Access Journals (Sweden)

    Amir Negahdari

    2014-06-01

    Full Text Available Understanding the performance and role of each formation in a petroleum play is crucial for the efficient and precise exploration and exploitation of trapped hydrocarbons in a sedimentary basin. The Lorestan basin is one of the most important hydrocarbon basins of Iran, and it includes various oil-prone potential source rocks and reservoir rocks. Previous geochemical studies of the basin were not accurate and there remain various uncertainties about the potential of the probable source rocks of the basin. In the present research, the geochemical characteristics of four probable source rocks of the Lorestan basin are studied using Rock-Eval pyrolysis and discriminant analysis. In achieving this goal, several discriminant functions are defined to evaluate the discriminant factor for the division of samples into two groups. The function with the highest discriminant factor was selected for the classification of probable source rocks into two groups: weak and strong. Among the studied formations, Garau and Pabdeh had the richest and poorest source rocks of the Lorestan basin, respectively. The comparison of the obtained results with the previous literature shows that the proposed model is more reliable for the recognition of the richness of source rock in the area.

  17. Subclassification and Detection of New Markers for the Discrimination of Primary Liver Tumors by Gene Expression Analysis Using Oligonucleotide Arrays.

    Science.gov (United States)

    Hass, Holger G; Vogel, Ulrich; Scheurlen, Michael; Jobst, Jürgen

    2017-12-26

    The failure to correctly differentiate between intrahepatic cholangiocarcinoma [CC] and hepatocellular carcinoma [HCC] is a significant clinical problem, particularly in terms of the different treatment goals for both cancers. In this study a specific gene expression profile to discriminate these two subgroups of liver cancer was established and potential diagnostic markers for clinical use were analyzed. To evaluate the gene expression profiles of HCC and intrahepatic CC, Oligonucleotide arrays ( Affymetrix U133A) were used. Overexpressed genes were checked for their potential use as new markers for discrimination and their expression values were validated by reverse transcription polymerase chain reaction and immunohistochemistry analyses. 695 genes/expressed sequence tags (ESTs) in HCC (245 up-/450 down-regulated) and 552 genes/ESTs in CC (221 up-/331 down-regulated) were significantly dysregulated (p〈0.05, fold change >2, ≥70%). Using a supervised learning method, and one-way analysis of variance a specific 270-gene expression profile that enabled rapid, reproducible differentiation between both tumors and non-malignant liver tissues was established. A panel of 12 genes (e.g. HSP90β, ERG1, GPC3, TKT, ACLY, and NME1 for HCC; SPT2, T4S3, CNX43, TTD1, HBD01 for CC) were detected and partly described for the first time as potential discrimination markers. A specific gene expression profile for discrimination of primary liver cancer was identified and potential marker genes with feasible clinical impact were described.

  18. Automated discrimination of lower and higher grade gliomas based on histopathological image analysis

    Directory of Open Access Journals (Sweden)

    Hojjat Seyed Mousavi

    2015-01-01

    Full Text Available Introduction: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images. To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG and high-grade glioma (HGG which represent a more advanced stage of the disease. Results: We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1 A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2 a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP. In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1 Successful detection rates of pseudopalisading necrosis

  19. Differential discriminator

    International Nuclear Information System (INIS)

    Dukhanov, V.I.; Mazurov, I.B.

    1981-01-01

    A principal flowsheet of a differential discriminator intended for operation in a spectrometric circuit with statistical time distribution of pulses is described. The differential discriminator includes four integrated discriminators and a channel of piled-up signal rejection. The presence of the rejection channel enables the discriminator to operate effectively at loads of 14x10 3 pulse/s. The temperature instability of the discrimination thresholds equals 250 μV/ 0 C. The discrimination level changes within 0.1-5 V, the level shift constitutes 0.5% for the filling ratio of 1:10. The rejection coefficient is not less than 90%. Alpha spectrum of the 228 Th source is presented to evaluate the discriminator operation with the rejector. The rejector provides 50 ns time resolution

  20. Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study

    International Nuclear Information System (INIS)

    Hooman, A.; Mohammadzadeh, M.

    2008-01-01

    Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curve (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions

  1. EVOLUTION OF NEUROENDOCRINE CELL POPULATION AND PEPTIDERGIC INNERVATION, ASSESSED BY DISCRIMINANT ANALYSIS, DURING POSTNATAL DEVELOPMENT OF THE RAT PROSTATE

    Directory of Open Access Journals (Sweden)

    Rosario Rodríguez

    2011-05-01

    Full Text Available Serotonin immunoreactive neuroendocrine cells and peptidergic nerves (NPY and VIP could have a role in prostate growth and function. In the present study, rats grouped by stages of postnatal development (prepubertal, pubertal, young and aged adults were employed in order to ascertain whether age causes changes in the number of serotoninergic neuroendocrine cells and the length of NPY and VIP fibres. Discriminant analysis was performed in order to ascertain the classificatory power of stereologic variables (absolute and relative measurements of cell number and fibre length on age groups. The following conclusions were drawn: a discriminant analysis confirms the androgen-dependence of both neuroendocrine cells and NPYVIP innervation during the postnatal development of the rat prostate; b periglandular innervation has more relevance than interglandular innervation in classifying the rats in age groups; and c peptidergic nerves from ventral, ampullar and periductal regions were more age-dependent than nerves from the dorso-lateral region.

  2. The difference between the Weil height and the canonical height on elliptic curves

    Science.gov (United States)

    Silverman, Joseph H.

    1990-10-01

    Estimates for the difference of the Weil height and the canonical height of points on elliptic curves are used for many purposes, both theoretical and computational. In this note we give an explicit estimate for this difference in terms of the j-invariant and discriminant of the elliptic curve. The method of proof, suggested by Serge Lang, is to use the decomposition of the canonical height into a sum of local heights. We illustrate one use for our estimate by computing generators for the Mordell-Weil group in three examples.

  3. Dimensionality reduction for microarray data using local mean based discriminant analysis.

    Science.gov (United States)

    Cui, Yan; Zheng, Chun-Hou; Yang, Jian

    2013-03-01

    A new method is proposed for finding a low dimensional subspace of high dimensional microarray data. We developed a new criterion for constructing the weight matrix by using local neighborhood information to discover the intrinsic discriminant structure in the data. Also this approach applies regularized least square technique to extract relevant features. We assess the performance of the proposed methodology by applying it to four publicly available tumor datasets. In a low dimensional subspace, the proposed method classified these tumors accurately and reliably. Also, through a comparison study, we verify the reliability of the dimensionality reduction and discrimination results.

  4. [Study on the genuineness and producing area of Panax notoginseng based on infrared spectroscopy combined with discriminant analysis].

    Science.gov (United States)

    Liu, Fei; Wang, Yuan-zhong; Yang, Chun-yan; Jin, Hang

    2015-01-01

    The genuineness and producing area of Panax notoginseng were studied based on infrared spectroscopy combined with discriminant analysis. The infrared spectra of 136 taproots of P. notoginseng from 13 planting point in 11 counties were collected and the second derivate spectra were calculated by Omnic 8. 0 software. The infrared spectra and their second derivate spectra in the range 1 800 - 700 cm-1 were used to build model by stepwise discriminant analysis, which was in order to distinguish study on the genuineness of P. notoginseng. The model built based on the second derivate spectra showed the better recognition effect for the genuineness of P. notoginseng. The correct rate of returned classification reached to 100%, and the prediction accuracy was 93. 4%. The stability of model was tested by cross validation and the method was performed extrapolation validation. The second derivate spectra combined with the same discriminant analysis method were used to distinguish the producing area of P. notoginseng. The recognition effect of models built based on different range of spectrum and different numbers of samples were compared and found that when the model was built by collecting 8 samples from each planting point as training sample and the spectrum in the range 1 500 - 1 200 cm-1 , the recognition effect was better, with the correct rate of returned classification reached to 99. 0%, and the prediction accuracy was 76. 5%. The results indicated that infrared spectroscopy combined with discriminant analysis showed good recognition effect for the genuineness of P. notoginseng. The method might be a hopeful new method for identification of genuineness of P. notoginseng in practice. The method could recognize the producing area of P. notoginseng to some extent and could be a new thought for identification of the producing area of P. natoginseng.

  5. Rotation and Noise Invariant Near-Infrared Face Recognition by means of Zernike Moments and Spectral Regression Discriminant Analysis

    Czech Academy of Sciences Publication Activity Database

    Farokhi, S.; Shamsuddin, S. M.; Flusser, Jan; Sheikh, U. U.; Khansari, M.; Jafari-Khouzani, K.

    2013-01-01

    Roč. 22, č. 1 (2013), s. 1-11 ISSN 1017-9909 R&D Projects: GA ČR GAP103/11/1552 Keywords : face recognition * infrared imaging * image moments Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.850, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/flusser-rotation and noise invariant near-infrared face recognition by means of zernike moments and spectral regression discriminant analysis.pdf

  6. A qualitative analysis of hate speech reported to the Romanian National Council for Combating Discrimination (2003‑2015)

    OpenAIRE

    Adriana Iordache

    2015-01-01

    The article analyzes the specificities of Romanian hate speech over a period of twelve years through a qualitative analysis of 384 Decisions of the National Council for Combating Discrimination. The study employs a coding methodology which allows one to separate decisions according to the group that was the victim of hate speech. The article finds that stereotypes employed are similar to those encountered in the international literature. The main target of hate speech is the Roma, who are ...

  7. Functional connectivity in tactile object discrimination: a principal component analysis of an event related fMRI-Study.

    Science.gov (United States)

    Hartmann, Susanne; Missimer, John H; Stoeckel, Cornelia; Abela, Eugenio; Shah, Jon; Seitz, Rüdiger J; Weder, Bruno J

    2008-01-01

    Tactile object discrimination is an essential human skill that relies on functional connectivity between the neural substrates of motor, somatosensory and supramodal areas. From a theoretical point of view, such distributed networks elude categorical analysis because subtraction methods are univariate. Thus, the aim of this study was to identify the neural networks involved in somatosensory object discrimination using a voxel-based principal component analysis (PCA) of event-related functional magnetic resonance images. Seven healthy, right-handed subjects aged between 22 and 44 years were required to discriminate with their dominant hand the length differences between otherwise identical parallelepipeds in a two-alternative forced-choice paradigm. Of the 34 principal components retained for analysis according to the 'bootstrapped' Kaiser-Guttman criterion, t-tests applied to the subject-condition expression coefficients showed significant mean differences between the object presentation and inter-stimulus phases in PC 1, 3, 26 and 32. Specifically, PC 1 reflected object exploration or manipulation, PC 3 somatosensory and short-term memory processes. PC 26 evinced the perception that certain parallelepipeds could not be distinguished, while PC 32 emerged in those choices when they could be. Among the cerebral regions evident in the PCs are the left posterior parietal lobe and premotor cortex in PC 1, the left superior parietal lobule (SPL) and the right cuneus in PC 3, the medial frontal and orbitofrontal cortex bilaterally in PC 26, and the right intraparietal sulcus, anterior SPL and dorsolateral prefrontal cortex in PC 32. The analysis provides evidence for the concerted action of large-scale cortico-subcortical networks mediating tactile object discrimination. Parallel to activity in nodes processing object-related impulses we found activity in key cerebral regions responsible for subjective assessment and validation.

  8. Do Consumers Pay for Being Healthy Conscious?— An Analysis of Price Discrimination on Healthier Food Product

    OpenAIRE

    Zhan, Congnan

    2010-01-01

    ‘Healthier food product’ has experienced a rapid growth rate in recent years in U.S. because of the increasing consumer demand for healthier and environmental friendlier lifestyle. This analysis is looking for price discrimination evidences by comparing price cost margins of regular food products and healthier food products. Price cost margins are computed by solving firms' profit maximization problem and relevant parameters are estimated from consumers' choice decisions. Specifically, price ...

  9. Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

    Science.gov (United States)

    Liu, Yan; Salvendy, Gavriel

    2009-05-01

    This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.

  10. A Qualitative Analysis of Multiracial Students' Experiences with Prejudice and Discrimination in College

    Science.gov (United States)

    Museus, Samuel D.; Lambe Sariñana, Susan A.; Yee, April L.; Robinson, Thomas E.

    2016-01-01

    Mixed-race persons constitute a substantial and growing population in the United States. We examined multiracial college students' experiences with prejudice and discrimination in college with conducted focus group interviews with 12 mixed-race participants and individual interviews with 22 mixed-race undergraduates to understand how they…

  11. Analysis of gene expression using gene sets discriminates cancer patients with and without late radiation toxicity

    NARCIS (Netherlands)

    Svensson, J. Peter; Stalpers, Lukas J. A.; Esveldt-van Lange, Rebecca E. E.; Franken, Nicolaas A. P.; Haveman, Jaap; Klein, Binie; Turesson, Ingela; Vrieling, Harry; Giphart-Gassler, Micheline

    2006-01-01

    BACKGROUND: Radiation is an effective anti-cancer therapy but leads to severe late radiation toxicity in 5%-10% of patients. Assuming that genetic susceptibility impacts this risk, we hypothesized that the cellular response of normal tissue to X-rays could discriminate patients with and without late

  12. Detection of Mango Infested with Fruit Fly Eggs and Larvae by Infrared Imaging and Discriminant Analysis

    Science.gov (United States)

    Fruit fly infestation causes significant loss of perishable products around the world and is an economic threat to growers, processors, and exporters. A rapid, economical, and non-destructive technique for detection of fruit fly infestation is reported based on hyperspectral imaging and discriminant...

  13. Gender-based discrimination in South Africa: A quantitative analysis of fairness of remuneration

    Directory of Open Access Journals (Sweden)

    Renier Steyn

    2015-05-01

    Full Text Available Equity is important to most individuals and its perceived absence  may impact negatively on individual and organisational performance. The concept of equity presupposes fair treatment, while discrimination implies unfair treatment. The perceptions of discrimination, or being treated unfairly, may result from psycho-social processes, or from data that justifies discrimination and is quantifiable. Objectives: To assess whether differences in post grading and remuneration for males and females are based on gender, rather than on quantifiable variables that could justify these differences. Method: Biographical information was gathered from 1740 employees representing 29 organisations. The data collected included self-reported post grading (dependent variable and 14 independent variables, which may predict the employees’ post gradings. The independent variables related primarily to education, tenure and family responsibility. Results: Males reported higher post gradings and higher salaries than those of females, but the difference was not statistically significant and the practical significance of this difference was slight. Qualification types, job specific training, and membership of professional bodies did not affect post grading along gender lines. The ways in which work experience was measured had no influence on post grading or salary for either males or females. Furthermore, family responsibility, union membership and the type of work the employees performed did not influence the employees’ post grading. The only difference found concerned the unfair treatment of males, particularly those who were well-qualified.   Conclusions: Objective evidence of unfair gender-based discrimination affecting post grading and salary is scarce, and the few differences that do occur have little statistical and practical significance. Perceptions of being discriminated against may therefore more often be seen as the result of psycho-social processes and

  14. School Literary Canon and Teaching of Literature in Middle School: A Critical Analysis of High School Programs in El Salvador Canon literario escolar y enseñanza de la literatura en la educación media: Un análisis crítico de los programas de enseñanza secundaria en El Salvador

    Directory of Open Access Journals (Sweden)

    Mauricio Aguilar Ciciliano

    2013-08-01

    Full Text Available This article analyzes the pedagogical-didactic model for the teaching of Literature in Middle School in the Salvadoran Educational system. This is part of a larger work towards a PhD project. The main goal of this project is to characterize the historical process in the construction of this model through a critical analysis of canonization sources. The findings suggest that the teaching of Literature is performed based on a historicist, pro-European, male-based approach. Among the consequences of this type of education are progressive invisibility of women writers and the marginal status of the Salvadoran literature, despite the reformist discourse that postulates gender equality and strengthening of the national identity as central policies in the current educational project.Recibido 20 de marzo de 2013 • Corregido 14 de junio de 2013 • Aceptado 19 de junio de 2013 Este artículo analiza el modelo didáctico-pedagógico para la enseñanza de la literatura en la educación media salvadoreña. Es parte de un trabajo más amplio de tesis doctoral. El objetivo es caracterizar el proceso histórico de conformación de dicho modelo mediante un análisis crítico de las fuentes de canonización. Los hallazgos sugieren que la enseñanza de la literatura se realiza con base en un enfoque historicista, europeizante y masculino; entre las consecuencias de este tipo de enseñanza se encuentran la progresiva invisibilización de la mujer escritora y el estatus marginal que ocupa la literatura salvadoreña, pese al discurso reformista que postula la equidad de género y el fortalecimiento de la identidad nacional como políticas centrales del actual proyecto educativo.Doctor en Educación de la Universidad de Costa Rica. Máster en Derechos Humanos y Educación para la Paz. Licenciado en Letras. Investigador del Consejo de Investigaciones Científicas de la Universidad de El Salvador (CIC-UES. Actualmente labora como profesor de la Universidad de El

  15. Studies in genetic discrimination. Final progress report

    Energy Technology Data Exchange (ETDEWEB)

    1994-06-01

    We have screened 1006 respondents in a study of genetic discrimination. Analysis of these responses has produced evidence of the range of institutions engaged in genetic discrimination and demonstrates the impact of this discrimination on the respondents to the study. We have found that both ignorance and policy underlie genetic discrimination and that anti-discrimination laws are being violated.

  16. Discrimination of selected species of pathogenic bacteria using near-infrared Raman spectroscopy and principal components analysis

    Science.gov (United States)

    de Siqueira e Oliveira, Fernanda SantAna; Giana, Hector Enrique; Silveira, Landulfo

    2012-10-01

    A method, based on Raman spectroscopy, for identification of different microorganisms involved in bacterial urinary tract infections has been proposed. Spectra were collected from different bacterial colonies (Gram-negative: Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa and Enterobacter cloacae, and Gram-positive: Staphylococcus aureus and Enterococcus spp.), grown on culture medium (agar), using a Raman spectrometer with a fiber Raman probe (830 nm). Colonies were scraped from the agar surface and placed on an aluminum foil for Raman measurements. After preprocessing, spectra were submitted to a principal component analysis and Mahalanobis distance (PCA/MD) discrimination algorithm. We found that the mean Raman spectra of different bacterial species show similar bands, and S. aureus was well characterized by strong bands related to carotenoids. PCA/MD could discriminate Gram-positive bacteria with sensitivity and specificity of 100% and Gram-negative bacteria with sensitivity ranging from 58 to 88% and specificity ranging from 87% to 99%.

  17. Live-dead discrimination analysis, qPCR assessment for opportunistic pathogens, and population analysis at ozone wastewater treatment plants.

    Science.gov (United States)

    Jäger, Thomas; Alexander, Johannes; Kirchen, Silke; Dötsch, Andreas; Wieland, Arne; Hiller, Christian; Schwartz, Thomas

    2018-01-01

    In respect to direct and indirect water reuse, the microbiological quality of treated wastewater is highly important. Conventional wastewater treatment plants are normally not equipped with advanced technologies for the elimination of bacteria. Molecular biology analyses were combined with live-dead discrimination analysis of wastewater population using Propidium monoazide (PMA) to study population shifts during ozonation (1 g ozone/g DOC) at a municipal wastewater treatment plant. Escherichia coli, enterococci, and Pseudomonas aeruginosa were quantified by polymerase chain reaction (qPCR) and the whole wastewater population was analyzed by metagenomic sequencing. The PMA-qPCR experiments showed that the abundances of P. aeruginosa didn't change by ozone treatment, whereas a reduction was observed for E. coli and enterococci. Results comparing conventional cultivation experiments with PMA-qPCR underlined the presence of viable but not culturable cells (VBNC) and their regrowth potential after ozone treatment. Illumina HiSeq sequencing results with and without PMA treatment demonstrated high population similarities in water samples originating from ozone inflow sampling sides. Upon using PMA treatment after ozonation, population shifts became visible and also underlined the importance of PMA treatment for the evaluation of elimination and selection processes during ozonation at WWTPs. Amongst a number of 14 most abundant genera identified in the inflow samples, 9 genera were found to be reduced, whereas 4 genera increased in relative abundance and 1 genus almost remained constant. The strongest increase in relative abundance after ozonation was detected for Oscillatoria spp., Microcoleus spp. and Nitrospira spp. Beside this, a continuous release of Pseudomonas spp. (including P. aeruginosa) to the downstream receiving body was confirmed. Regrowth experiments demonstrated a high prevalence of P. aeruginosa as part of the surviving bacterial population. Summing

  18. Optimal Threshold Determination for Discriminating Driving Anger Intensity Based on EEG Wavelet Features and ROC Curve Analysis

    Directory of Open Access Journals (Sweden)

    Ping Wan

    2016-08-01

    Full Text Available Driving anger, called “road rage”, has become increasingly common nowadays, affecting road safety. A few researches focused on how to identify driving anger, however, there is still a gap in driving anger grading, especially in real traffic environment, which is beneficial to take corresponding intervening measures according to different anger intensity. This study proposes a method for discriminating driving anger states with different intensity based on Electroencephalogram (EEG spectral features. First, thirty drivers were recruited to conduct on-road experiments on a busy route in Wuhan, China where anger could be inducted by various road events, e.g., vehicles weaving/cutting in line, jaywalking/cyclist crossing, traffic congestion and waiting red light if they want to complete the experiments ahead of basic time for extra paid. Subsequently, significance analysis was used to select relative energy spectrum of β band (β% and relative energy spectrum of θ band (θ% for discriminating the different driving anger states. Finally, according to receiver operating characteristic (ROC curve analysis, the optimal thresholds (best cut-off points of β% and θ% for identifying none anger state (i.e., neutral were determined to be 0.2183 ≤ θ% < 1, 0 < β% < 0.2586; low anger state is 0.1539 ≤ θ% < 0.2183, 0.2586 ≤ β% < 0.3269; moderate anger state is 0.1216 ≤ θ% < 0.1539, 0.3269 ≤ β% < 0.3674; high anger state is 0 < θ% < 0.1216, 0.3674 ≤ β% < 1. Moreover, the discrimination performances of verification indicate that, the overall accuracy (Acc of the optimal thresholds of β% for discriminating the four driving anger states is 80.21%, while 75.20% for that of θ%. The results can provide theoretical foundation for developing driving anger detection or warning devices based on the relevant optimal thresholds.

  19. An Investigation of Structure, Flexibility, and Function Variables that Discriminate Asymptomatic Foot Types.

    Science.gov (United States)

    Shultz, Sarah P; Song, Jinsup; Kraszewski, Andrew P; Hafer, Jocelyn F; Rao, Smita; Backus, Sherry; Hillstrom, Rajshree M; Hillstrom, Howard J

    2017-07-01

    It has been suggested that foot type considers not only foot structure (high, normal, low arch), but also function (overpronation, normal, oversupination) and flexibility (reduced, normal, excessive). Therefore, this study used canonical regression analyses to assess which variables of foot structure, function, and flexibility can accurately discriminate between clinical foot type classifications. The feet of 61 asymptomatic, healthy adults (18-77 years) were classified as cavus (N = 24), rectus (N = 54), or planus (N = 44) using standard clinical measures. Custom jigs assessed foot structure and flexibility. Foot function was assessed using an emed-x plantar pressure measuring device. Canonical regression analyses were applied separately to extract essential structure, flexibility, and function variables. A third canonical regression analysis was performed on the extracted variables to identify a combined model. The initial combined model included 30 extracted variables; however 5 terminal variables (malleolar valgus index, arch height index while sitting, first metatarsophalangeal joint laxity while standing, pressure-time integral and maximum contact area of medial arch) were able to correctly predict 80.7% of foot types. These remaining variables focused on specific foot characteristics (hindfoot alignment, arch height, midfoot mechanics, Windlass mechanism) that could be essential to discriminating foot type.

  20. Morphometric analysis to discriminate between species: The case of the Megalobulimus leucostoma complex

    Directory of Open Access Journals (Sweden)

    Victor Borda

    2014-10-01

    Full Text Available Plasticity of conchological characters had led to erroneous descriptions and the accumulation of synonyms making difficult the discrimination among species. The land snail genus Megalobulimus is an example of this problem. Megalobulimus leucostoma (Sowerby, 1835 has three subspecies which are difficult to differentiate by using the original descriptions. The aim of this paper is to discriminate among the subspecies of M. leucostoma by using morphometric and distribution analyses. Both provide substantial differences between M. l. leucostoma and M. l lacunosus that would not support the subspecies status of the former. Megalobulimus leucostoma weyrauchi fits into the great conchological variability of M. l .leucostoma; also the sympatric status between these two subspecies would not support the subspecies status of the former, and M. l. weyrauchi should be considered as part of M. l. leucostoma.