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Sample records for kernel fisher discriminant

  1. Context quantization by kernel Fisher discriminant.

    PubMed

    Xu, Mantao; Wu, Xiaolin; Fränti, Pasi

    2006-01-01

    Optimal context quantizers for minimum conditional entropy can be constructed by dynamic programming in the probability simplex space. The main difficulty, operationally, is the resulting complex quantizer mapping function in the context space, in which the conditional entropy coding is conducted. To overcome this difficulty, we propose new algorithms for designing context quantizers in the context space based on the multiclass Fisher discriminant and the kernel Fisher discriminant (KFD). In particular, the KFD can describe linearly nonseparable quantizer cells by projecting input context vectors onto a high-dimensional curve, in which these cells become better separable. The new algorithms outperform the previous linear Fisher discriminant method for context quantization. They approach the minimum empirical conditional entropy context quantizer designed in the probability simplex space, but with a practical implementation that employs a simple scalar quantizer mapping function rather than a large lookup table.

  2. The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer

    NASA Astrophysics Data System (ADS)

    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.

  3. Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China

    NASA Astrophysics Data System (ADS)

    He, Sanwei; Pan, Peng; Dai, Lan; Wang, Haijun; Liu, Jiping

    2012-10-01

    Kernel machines are widely applied in classification because of many typical advantages, such as a good capacity to deal with high-dimensional data, good generation performance, few parameters to adjust, explainable results, etc. The kernel-based Fisher discriminant analysis (KFDA) is a typical kernel-based method based on the statistical discriminant analysis and it includes both the training and testing process. The model is trained by a dataset of environmental factors that cause landslide occurrence and target output values. Furthermore, the trained model is tested by a separate set of testing samples. This approach utilizes a kernel function to map data from the original feature space to a high-dimensional space, through which a nonlinear problem is converted into a linear one. A typical landslide study area, namely Qinggan River delta, situated in Three Gorges, China, is selected for this study and the following environmental factors are determined as independent variables of the model-lithology, elevation, normalized difference vegetation index (NDVI), slope, aspect, distance to rivers, plan curvature, and profile curvature. Judging from the accuracies of the training and testing samples, the sigmoid kernel performed better than the radial basis function kernel and the polynomial kernel. Using different ratios of landslide to non-landslide samples, the performance of KFDA is compared with the linear Fisher discriminant analysis (LFDA) and the logistic regression using a ROC/AUC validation. The results reveal that the average performance of KFDA for all ratios of samples is the most optimal with the mean AUC value as high as 0.911, while the mean AUC values of the logistic regression and LFDA are 0.867 and 0.089 respectively. Although the logistic regression performed slightly better than KFDA when the ratio of landslide to non-landslide samples was 2:1 and 3:1, its AUC values for other ratios of samples are much lower than the AUC values of KFDA. KFDA is

  4. Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach

    NASA Astrophysics Data System (ADS)

    Shi, Huaitao; Liu, Jianchang; Wu, Yuhou; Zhang, Ke; Zhang, Lixiu; Xue, Peng

    2016-04-01

    It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.

  5. Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis.

    PubMed

    Van Gestel, T; Suykens, J A K; Lanckriet, G; Lambrechts, A; De Moor, B; Vandewalle, J

    2002-05-01

    The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like the nonconvex optimization problem and the choice of the number of hidden units. In support vector machines (SVMs) for classification, as introduced by Vapnik, a nonlinear decision boundary is obtained by mapping the input vector first in a nonlinear way to a high-dimensional kernel-induced feature space in which a linear large margin classifier is constructed. Practical expressions are formulated in the dual space in terms of the related kernel function, and the solution follows from a (convex) quadratic programming (QP) problem. In least-squares SVMs (LS-SVMs), the SVM problem formulation is modified by introducing a least-squares cost function and equality instead of inequality constraints, and the solution follows from a linear system in the dual space. Implicitly, the least-squares formulation corresponds to a regression formulation and is also related to kernel Fisher discriminant analysis. The least-squares regression formulation has advantages for deriving analytic expressions in a Bayesian evidence framework, in contrast to the classification formulations used, for example, in gaussian processes (GPs). The LS-SVM formulation has clear primal-dual interpretations, and without the bias term, one explicitly constructs a model that yields the same expressions as have been obtained with GPs for regression. In this article, the Bayesian evidence framework is combined with the LS-SVM classifier formulation. Starting from the feature space formulation, analytic expressions are obtained in the dual space on the different levels of Bayesian inference, while posterior class probabilities are obtained by marginalizing over the model parameters. Empirical results obtained on 10 public domain data sets show that the LS-SVM classifier designed within the Bayesian evidence

  6. A one-class kernel fisher criterion for outlier detection.

    PubMed

    Dufrenois, Franck

    2015-05-01

    Recently, Dufrenois and Noyer proposed a one class Fisher's linear discriminant to isolate normal data from outliers. In this paper, a kernelized version of their criterion is presented. Originally on the basis of an iterative optimization process, alternating between subspace selection and clustering, I show here that their criterion has an upper bound making these two problems independent. In particular, the estimation of the label vector is formulated as an unconstrained binary linear problem (UBLP) which can be solved using an iterative perturbation method. Once the label vector is estimated, an optimal projection subspace is obtained by solving a generalized eigenvalue problem. Like many other kernel methods, the performance of the proposed approach depends on the choice of the kernel. Constructed with a Gaussian kernel, I show that the proposed contrast measure is an efficient indicator for selecting an optimal kernel width. This property simplifies the model selection problem which is typically solved by costly (generalized) cross-validation procedures. Initialization, convergence analysis, and computational complexity are also discussed. Lastly, the proposed algorithm is compared with recent novelty detectors on synthetic and real data sets.

  7. Kernel Optimization in Discriminant Analysis

    PubMed Central

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

    2011-01-01

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

  8. The MDF discrimination measure: Fisher in disguise.

    PubMed

    Loog, Marco; Duin, Robert P W; Viergever, Max A

    2004-05-01

    Recently, a discrimination measure for feature extraction for two-class data, called the maximum discriminating (MDF) measure (Talukder and Casasent [Neural Networks 14 (2001) 1201-1218]), was introduced. In the present paper, it is shown that the MDF discrimination measure produces exactly the same results as the classical Fisher criterion, on the condition that the two prior probabilities are chosen to be equal. The effect of unequal priors on the efficiency of the measures is also discussed.

  9. Semisupervised kernel marginal Fisher analysis for face recognition.

    PubMed

    Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

    2013-01-01

    Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.

  10. Semisupervised Kernel Marginal Fisher Analysis for Face Recognition

    PubMed Central

    Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

    2013-01-01

    Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm. PMID:24163638

  11. Volcano Clustering Determination: Bivariate Gaussain vs. Fisher Kernels

    NASA Astrophysics Data System (ADS)

    Canon-Tapia, E.; Mendoza-Borunda, R.

    2012-12-01

    There are several forms in which volcano clustering can be estimated quantitatively, all of which are related to some extent with kernel estimation techniques. Although these methods differ on the definition of the kernel function, the exact form of that function seems to have a relatively minor impact on the estimated spatial probability, especially if compared with the effect of a different parameter known variously as the bandwidth, kernel width, or smoothing factor. This is the case because the kernel function used to study spatial distribution of point-like features is usually symmetric around the evaluation point and independent of the topology of the distribution. While it is reasonable to accept that the exact definition of the kernel function may not be extremely influential if the topology of the data set is not changed drastically, it is unclear to what extent different topologies of the spatial distribution can introduce significant changes in the obtained density functions. An important change in topology in this context is related to the distortion that is introduced when attempting to represent features found on the surface of a sphere that are being projected into a plane. Until now, underlying all studies of volcano distribution is the implicit assumption that the map used for the estimation of the density function does not introduce a significant distortion on the topology of the data base, and that the distribution can be studied by using kernels originally devised for distributions in plane surfaces. In this work, the density distributions obtained by using two types of kernel, one devised for planar and the other for spherical surfaces, are mutually compared. The influence of the smoothing factor in these two kernels is also explored with some detail. The results show that despite their apparent differences, the bivariate Gaussian and Fisher kernels might yield identical results if an appropriate value of the smoothing parameter is selected in

  12. Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.

    PubMed

    Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

    2014-07-24

    Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

  13. Emotion Recognition from Single-Trial EEG Based on Kernel Fisher's Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

    PubMed Central

    Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

    2014-01-01

    Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. PMID:25061837

  14. Kernel PLS-SVC for Linear and Nonlinear Discrimination

    NASA Technical Reports Server (NTRS)

    Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan

    2003-01-01

    A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

  15. Discriminant Kernel Assignment for Image Coding.

    PubMed

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

    2017-06-01

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

  16. On Extensions to Fishers Linear Discriminant Function,

    DTIC Science & Technology

    1985-11-01

    AD-A163 661 ON EXTENSIONS TO FISHERS LINER DISCRININNT FUNCTION 1/1 (U) ROYAL SIGNALS AND ADAR ESTASLISHNENT NALYERN ( ENGLAND ) I D LONOSTAFF NOV 85...34o. ° 6 CONCLUSIONS If data is standardised with a linear transform to give a unit joint AW covariance matrix the Foley-Sammon axis becomes

  17. Uncorrelated regularized local Fisher discriminant analysis for face recognition

    NASA Astrophysics Data System (ADS)

    Wang, Zhan; Ruan, Qiuqi; An, Gaoyun

    2014-07-01

    A local Fisher discriminant analysis can work well for a multimodal problem. However, it often suffers from the undersampled problem, which makes the local within-class scatter matrix singular. We develop a supervised discriminant analysis technique called uncorrelated regularized local Fisher discriminant analysis for image feature extraction. In this technique, the local within-class scatter matrix is approximated by a full-rank matrix that not only solves the undersampled problem but also eliminates the poor impact of small and zero eigenvalues. Statistically uncorrelated features are obtained to remove redundancy. A trace ratio criterion and the corresponding iterative algorithm are employed to globally solve the objective function. Experimental results on four famous face databases indicate that our proposed method is effective and outperforms the conventional dimensionality reduction methods.

  18. Sparsifying the Fisher Linear Discriminant by Rotation.

    PubMed

    Hao, Ning; Dong, Bin; Fan, Jianqing

    2015-09-01

    Many high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis (LDA). To efficiently use them, sparsity of linear classifiers is a prerequisite. However, this might not be readily available in many applications, and rotations of data are required to create the needed sparsity. In this paper, we propose a family of rotations to create the required sparsity. The basic idea is to use the principal components of the sample covariance matrix of the pooled samples and its variants to rotate the data first and to then apply an existing high dimensional classifier. This rotate-and-solve procedure can be combined with any existing classifiers, and is robust against the sparsity level of the true model. We show that these rotations do create the sparsity needed for high dimensional classifications and provide theoretical understanding why such a rotation works empirically. The effectiveness of the proposed method is demonstrated by a number of simulated and real data examples, and the improvements of our method over some popular high dimensional classification rules are clearly shown.

  19. Kernel Partial Least Squares for Nonlinear Regression and Discrimination

    NASA Technical Reports Server (NTRS)

    Rosipal, Roman; Clancy, Daniel (Technical Monitor)

    2002-01-01

    This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.

  20. Selection of principal components based on Fisher discriminant ratio

    NASA Astrophysics Data System (ADS)

    Zeng, Xiangyan; Naghedolfeizi, Masoud; Arora, Sanjeev; Yousif, Nabil; Aberra, Dawit

    2016-05-01

    Principal component analysis transforms a set of possibly correlated variables into uncorrelated variables, and is widely used as a technique of dimensionality reduction and feature extraction. In some applications of dimensionality reduction, the objective is to use a small number of principal components to represent most variation in the data. On the other hand, the main purpose of feature extraction is to facilitate subsequent pattern recognition and machine learning tasks, such as classification. Selecting principal components for classification tasks aims for more than dimensionality reduction. The capability of distinguishing different classes is another major concern. Components that have larger eigenvalues do not necessarily have better distinguishing capabilities. In this paper, we investigate a strategy of selecting principal components based on the Fisher discriminant ratio. The ratio of between class variance to within class variance is calculated for each component, based on which the principal components are selected. The number of relevant components is determined by the classification accuracy. To alleviate overfitting which is common when there are few training data available, we use a cross-validation procedure to determine the number of principal components. The main objective is to select the components that have large Fisher discriminant ratios so that adequate class separability is obtained. The number of selected components is determined by the classification accuracy of the validation data. The selection method is evaluated by face recognition experiments.

  1. Kernel generalized neighbor discriminant embedding for SAR automatic target recognition

    NASA Astrophysics Data System (ADS)

    Huang, Yulin; Pei, Jifang; Yang, Jianyu; Wang, Tao; Yang, Haiguang; Wang, Bing

    2014-12-01

    In this paper, we propose a new supervised feature extraction algorithm in synthetic aperture radar automatic target recognition (SAR ATR), called generalized neighbor discriminant embedding (GNDE). Based on manifold learning, GNDE integrates class and neighborhood information to enhance discriminative power of extracted feature. Besides, the kernelized counterpart of this algorithm is also proposed, called kernel-GNDE (KGNDE). The experiment in this paper shows that the proposed algorithms have better recognition performance than PCA and KPCA.

  2. Plethysmographic arterial waveform strain discrimination by Fisher's method.

    PubMed

    Kucewicz, John C; Huang, Lingyun; Beach, Kirk W

    2004-06-01

    Plethysmography has been used for over 50 years to measure gross change in tissue blood volume. Over the cardiac cycle, perfused tissue initially expands as the blood flow into the arterioles exceeds the flow through the capillary bed. Later in the cardiac cycle, the accumulated blood drains into the venous vasculature, allowing the tissue to return to its presystolic blood volume. Specific features in the plethysmographic waveform can be used to identify normal and abnormal perfusion. We are developing a Doppler strain-imaging technique to measure the local pulsatile expansion and relaxation of tissue analogous to the gross measurement of tissue volume change with conventional plethysmography. A phantom has been built to generate plethysmographic-style strains with amplitudes of less than 0.1% in a tissue-mimicking material. With Fisher's discriminant analysis, it is shown that normal and abnormal plethysmographic-style strains can be differentiated with high sensitivities using the Fourier components of the strain waveforms normalized to compensate for the variance in the strain amplitude estimate.

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

    PubMed

    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. Learning Discriminative Stein Kernel for SPD Matrices and Its Applications.

    PubMed

    Zhang, Jianjia; Wang, Lei; Zhou, Luping; Li, Wanqing

    2016-05-01

    Stein kernel (SK) has recently shown promising performance on classifying images represented by symmetric positive definite (SPD) matrices. It evaluates the similarity between two SPD matrices through their eigenvalues. In this paper, we argue that directly using the original eigenvalues may be problematic because: 1) eigenvalue estimation becomes biased when the number of samples is inadequate, which may lead to unreliable kernel evaluation, and 2) more importantly, eigenvalues reflect only the property of an individual SPD matrix. They are not necessarily optimal for computing SK when the goal is to discriminate different classes of SPD matrices. To address the two issues, we propose a discriminative SK (DSK), in which an extra parameter vector is defined to adjust the eigenvalues of input SPD matrices. The optimal parameter values are sought by optimizing a proxy of classification performance. To show the generality of the proposed method, three kernel learning criteria that are commonly used in the literature are employed as a proxy. A comprehensive experimental study is conducted on a variety of image classification tasks to compare the proposed DSK with the original SK and other methods for evaluating the similarity between SPD matrices. The results demonstrate that the DSK can attain greater discrimination and better align with classification tasks by altering the eigenvalues. This makes it produce higher classification performance than the original SK and other commonly used methods.

  5. Multilevel image recognition using discriminative patches and kernel covariance descriptor

    NASA Astrophysics Data System (ADS)

    Lu, Le; Yao, Jianhua; Turkbey, Evrim; Summers, Ronald M.

    2014-03-01

    Computer-aided diagnosis of medical images has emerged as an important tool to objectively improve the performance, accuracy and consistency for clinical workflow. To computerize the medical image diagnostic recognition problem, there are three fundamental problems: where to look (i.e., where is the region of interest from the whole image/volume), image feature description/encoding, and similarity metrics for classification or matching. In this paper, we exploit the motivation, implementation and performance evaluation of task-driven iterative, discriminative image patch mining; covariance matrix based descriptor via intensity, gradient and spatial layout; and log-Euclidean distance kernel for support vector machine, to address these three aspects respectively. To cope with often visually ambiguous image patterns for the region of interest in medical diagnosis, discovery of multilabel selective discriminative patches is desired. Covariance of several image statistics summarizes their second order interactions within an image patch and is proved as an effective image descriptor, with low dimensionality compared with joint statistics and fast computation regardless of the patch size. We extensively evaluate two extended Gaussian kernels using affine-invariant Riemannian metric or log-Euclidean metric with support vector machines (SVM), on two medical image classification problems of degenerative disc disease (DDD) detection on cortical shell unwrapped CT maps and colitis detection on CT key images. The proposed approach is validated with promising quantitative results on these challenging tasks. Our experimental findings and discussion also unveil some interesting insights on the covariance feature composition with or without spatial layout for classification and retrieval, and different kernel constructions for SVM. This will also shed some light on future work using covariance feature and kernel classification for medical image analysis.

  6. A Gabor-Block-Based Kernel Discriminative Common Vector Approach Using Cosine Kernels for Human Face Recognition

    PubMed Central

    Kar, Arindam; Bhattacharjee, Debotosh; Basu, Dipak Kumar; Nasipuri, Mita; Kundu, Mahantapas

    2012-01-01

    In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low-energized discriminating feature vectors to obtain the real component of a complex quantity for face recognition. In order to derive positive kernel discriminative vectors, we apply only those kernel discriminative eigenvectors that are associated with nonzero eigenvalues. The feasibility of the low-energized Gabor-block-based generalized KDCV method with cosine kernel function models has been successfully tested for classification using the L 1, L2 distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach. PMID:23365559

  7. A Gabor-block-based kernel discriminative common vector approach using cosine kernels for human face recognition.

    PubMed

    Kar, Arindam; Bhattacharjee, Debotosh; Basu, Dipak Kumar; Nasipuri, Mita; Kundu, Mahantapas

    2012-01-01

    In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low-energized discriminating feature vectors to obtain the real component of a complex quantity for face recognition. In order to derive positive kernel discriminative vectors, we apply only those kernel discriminative eigenvectors that are associated with nonzero eigenvalues. The feasibility of the low-energized Gabor-block-based generalized KDCV method with cosine kernel function models has been successfully tested for classification using the L(1), L(2) distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach.

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

    PubMed

    Zhang, Lei; Tian, Feng-Chun

    2014-03-13

    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. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. An Improvement of Incremental Recursive Fisher Linear Discriminant for Online Feature Extraction

    NASA Astrophysics Data System (ADS)

    Ohta, Ryohei; Ozawa, Seiichi

    This paper proposes a new online feature extraction method called Incremental Recursive Fisher Linear Discriminant (IRFLD) whose batch learning algorithm called RFLD has been proposed by Xiang et al. In the conventional Linear Discriminant Analysis (LDA), the number of discriminant vectors is limited to the number of classes minus one due to the rank of the between-class covariance matrix. However, RFLD and the proposed IRFLD can break this limit; that is, an arbitrary number of discriminant vectors can be obtained. In the proposed IRFLD, the Pang et al.'s Incremental Linear Discriminant Analysis (ILDA) is extended such that effective discriminant vectors are recursively searched for the complementary space of a conventional discriminant subspace. In addition, to estimate a suitable number of effective discriminant vectors, the classification accuracy is evaluated with a cross-validation method in an online manner. For this purpose, validation data are obtained by performing the k-means clustering against incoming training data and previous validation data. The performance of IRFLD is evaluated for 16 benchmark data sets. The experimental results show that the final classification accuracies of IRFLD are always better than those of ILDA. We also reveal that this performance improvement is attained by adding discriminant vectors in a complementary LDA subspace.

  10. Radial basis function neural networks for nonlinear Fisher discrimination and Neyman-Pearson classification.

    PubMed

    Casasent, David; Chen, Xue-wen

    2003-01-01

    We propose a novel technique for the design of radial basis function (RBF) neural networks (NNs). To select various RBF parameters, the class membership information of training samples is utilized to produce new cluster classes. This allows emphasis of classification performance for certain class data rather than best overall classification. This allows us to control performance as desired and to approximate Neyman-Pearson classification. We also show that by properly choosing the desired output neuron levels, then the RBF hidden to output layer performs Fisher discrimination analysis, and that the full system performs a nonlinear Fisher analysis. Data on an agricultural product inspection problem and on synthetic data confirm the effectiveness of these methods.

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

    NASA Astrophysics Data System (ADS)

    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.

  12. A Feature Selection Method Based on Fisher's Discriminant Ratio for Text Sentiment Classification

    NASA Astrophysics Data System (ADS)

    Wang, Suge; Li, Deyu; Wei, Yingjie; Li, Hongxia

    With the rapid growth of e-commerce, product reviews on the Web have become an important information source for customers' decision making when they intend to buy some product. As the reviews are often too many for customers to go through, how to automatically classify them into different sentiment orientation categories (i.e. positive/negative) has become a research problem. In this paper, based on Fisher's discriminant ratio, an effective feature selection method is proposed for product review text sentiment classification. In order to validate the validity of the proposed method, we compared it with other methods respectively based on information gain and mutual information while support vector machine is adopted as the classifier. In this paper, 6 subexperiments are conducted by combining different feature selection methods with 2 kinds of candidate feature sets. Under 1006 review documents of cars, the experimental results indicate that the Fisher's discriminant ratio based on word frequency estimation has the best performance with F value 83.3% while the candidate features are the words which appear in both positive and negative texts.

  13. Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Jiang, Li; Xuan, Jianping; Shi, Tielin

    2013-12-01

    Generally, the vibration signals of faulty machinery are non-stationary and nonlinear under complicated operating conditions. Therefore, it is a big challenge for machinery fault diagnosis to extract optimal features for improving classification accuracy. This paper proposes semi-supervised kernel Marginal Fisher analysis (SSKMFA) for feature extraction, which can discover the intrinsic manifold structure of dataset, and simultaneously consider the intra-class compactness and the inter-class separability. Based on SSKMFA, a novel approach to fault diagnosis is put forward and applied to fault recognition of rolling bearings. SSKMFA directly extracts the low-dimensional characteristics from the raw high-dimensional vibration signals, by exploiting the inherent manifold structure of both labeled and unlabeled samples. Subsequently, the optimal low-dimensional features are fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories and severities of bearings. The experimental results demonstrate that the proposed approach improves the fault recognition performance and outperforms the other four feature extraction methods.

  14. Fisher's linear discriminant ratio based threshold for moving human detection in thermal video

    NASA Astrophysics Data System (ADS)

    Sharma, Lavanya; Yadav, Dileep Kumar; Singh, Annapurna

    2016-09-01

    In video surveillance, the moving human detection in thermal video is a critical phase that filters out redundant information to extract relevant information. The moving object detection is applied on thermal video because it penetrate challenging problems such as dynamic issues of background and illumination variation. In this work, we have proposed a new background subtraction method using Fisher's linear discriminant ratio based threshold. This threshold is investigated automatically during run-time for each pixel of every sequential frame. Automatically means to avoid the involvement of external source such as programmer or user for threshold selection. This threshold provides better pixel classification at run-time. This method handles problems generated due to multiple behavior of background more accurately using Fisher's ratio. It maximizes the separation between object pixel and the background pixel. To check the efficacy, the performance of this work is observed in terms of various parameters depicted in analysis. The experimental results and their analysis demonstrated better performance of proposed method against considered peer methods.

  15. Multiple Kernel Sparse Representation based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension.

    PubMed

    Zhang, Guoqing; Sun, Huaijiang; Xia, Guiyu; Sun, Quansen

    2016-07-07

    Sparse representation based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. [10] devised a SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) [22] has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier (MKSRC), and then we use it as a criterion to design a multiple kernel sparse representation based orthogonal discriminative projection method (MK-SR-ODP). The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method [33]. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.

  16. Color model and method for video fire flame and smoke detection using Fisher linear discriminant

    NASA Astrophysics Data System (ADS)

    Wei, Yuan; Jie, Li; Jun, Fang; Yongming, Zhang

    2013-02-01

    Video fire detection is playing an increasingly important role in our life. But recent research is often based on a traditional RGB color model used to analyze the flame, which may be not the optimal color space for fire recognition. It is worse when we research smoke simply using gray images instead of color ones. We clarify the importance of color information for fire detection. We present a fire discriminant color (FDC) model for flame or smoke recognition based on color images. The FDC models aim to unify fire color image representation and fire recognition task into one framework. With the definition of between-class scatter matrices and within-class scatter matrices of Fisher linear discriminant, the proposed models seek to obtain one color-space-transform matrix and a discriminate projection basis vector by maximizing the ratio of these two scatter matrices. First, an iterative basic algorithm is designed to get one-component color space transformed from RGB. Then, a general algorithm is extended to generate three-component color space for further improvement. Moreover, we propose a method for video fire detection based on the models using the kNN classifier. To evaluate the recognition performance, we create a database including flame, smoke, and nonfire images for training and testing. The test experiments show that the proposed model achieves a flame verification rate receiver operating characteristic (ROC I) of 97.5% at a false alarm rate (FAR) of 1.06% and a smoke verification rate (ROC II) of 91.5% at a FAR of 1.2%, and lots of fire video experiments demonstrate that our method reaches a high accuracy for fire recognition.

  17. Early discriminant method of infected kernel based on the erosion effects of laser ultrasonics

    NASA Astrophysics Data System (ADS)

    Fan, Chao

    2015-07-01

    To discriminate the infected kernel of the wheat as early as possible, a new kind of detection method of hidden insects, especially in their egg and larvae stage, was put forward based on the erosion effect of the laser ultrasonic in this paper. The surface of the grain is exposured by the pulsed laser, the energy of which is absorbed and the ultrasonic is excited, and the infected kernel can be recognized by appropriate signal analyzing. Firstly, the detection principle was given based on the classical wave equation and the platform was established. Then, the detected ultrasonic signal was processed both in the time domain and the frequency domain by using FFT and DCT , and six significant features were selected as the characteristic parameters of the signal by the method of stepwise discriminant analysis. Finally, a BP neural network was designed by using these six parameters as the input to classify the infected kernels from the normal ones. Numerous experiments were performed by using twenty wheat varieties, the results shown that the the infected kernels can be recognized effectively, and the false negative error and the false positive error was 12% and 9% respectively, the discriminant method of the infected kernels based on the erosion effect of laser ultrasonics is feasible.

  18. Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

    NASA Astrophysics Data System (ADS)

    Li, Quanbao; Wei, Fajie; Zhou, Shenghan

    2017-05-01

    The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

  19. Discriminating oat and groat kernels from other grains using near infrared spectroscopy

    USDA-ARS?s Scientific Manuscript database

    Oat and groats can be discriminated from other grains such as barley, wheat, rye, and triticale (non-oats) using near infrared spectroscopy. The two instruments tested were the manual version of the ARS-USDA Single Kernel Near Infrared (SKNIR) and the automated QualySense QSorter Explorer high-speed...

  20. Kernel-Based Discriminant Techniques for Educational Placement

    ERIC Educational Resources Information Center

    Lin, Miao-hsiang; Huang, Su-yun; Chang, Yuan-chin

    2004-01-01

    This article considers the problem of educational placement. Several discriminant techniques are applied to a data set from a survey project of science ability. A profile vector for each student consists of five science-educational indicators. The students are intended to be placed into three reference groups: advanced, regular, and remedial.…

  1. Discrimination of Mine Seismic Events and Blasts Using the Fisher Classifier, Naive Bayesian Classifier and Logistic Regression

    NASA Astrophysics Data System (ADS)

    Dong, Longjun; Wesseloo, Johan; Potvin, Yves; Li, Xibing

    2016-01-01

    Seismic events and blasts generate seismic waveforms that have different characteristics. The challenge to confidently differentiate these two signatures is complex and requires the integration of physical and statistical techniques. In this paper, the different characteristics of blasts and seismic events were investigated by comparing probability density distributions of different parameters. Five typical parameters of blasts and events and the probability density functions of blast time, as well as probability density functions of origin time difference for neighbouring blasts were extracted as discriminant indicators. The Fisher classifier, naive Bayesian classifier and logistic regression were used to establish discriminators. Databases from three Australian and Canadian mines were established for training, calibrating and testing the discriminant models. The classification performances and discriminant precision of the three statistical techniques were discussed and compared. The proposed discriminators have explicit and simple functions which can be easily used by workers in mines or researchers. Back-test, applied results, cross-validated results and analysis of receiver operating characteristic curves in different mines have shown that the discriminator for one of the mines has a reasonably good discriminating performance.

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

    PubMed

    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.

  3. Margin-Maximizing Feature Elimination Methods for Linear and Nonlinear Kernel-Based Discriminant Functions

    PubMed Central

    Aksu, Yaman; Miller, David J.; Kesidis, George; Yang, Qing X.

    2012-01-01

    Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature “markers.” For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom—the hyperplane’s intercept and its squared 2-norm—with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer’s disease brain image data, MFE methods give promising results. PMID:20194055

  4. A discriminative kernel-based approach to rank images from text queries.

    PubMed

    Grangier, David; Bengio, Samy

    2008-08-01

    This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance. The proposed model hence addresses the retrieval problem directly and does not rely on an intermediate image annotation task, which contrasts with previous research. Moreover, our learning procedure builds upon recent work on the online learning of kernel-based classifiers. This yields an efficient, scalable algorithm, which can benefit from recent kernels developed for image comparison. The experiments performed over stock photography data show the advantage of our discriminative ranking approach over state-of-the-art alternatives (e.g. our model yields 26.3% average precision over the Corel dataset, which should be compared to 22.0%, for the best alternative model evaluated). Further analysis of the results shows that our model is especially advantageous over difficult queries such as queries with few relevant pictures or multiple-word queries.

  5. Bilinear analysis for kernel selection and nonlinear feature extraction.

    PubMed

    Yang, Shu; Yan, Shuicheng; Zhang, Chao; Tang, Xiaoou

    2007-09-01

    This paper presents a unified criterion, Fisher + kernel criterion (FKC), for feature extraction and recognition. This new criterion is intended to extract the most discriminant features in different nonlinear spaces, and then, fuse these features under a unified measurement. Thus, FKC can simultaneously achieve nonlinear discriminant analysis and kernel selection. In addition, we present an efficient algorithm Fisher + kernel analysis (FKA), which utilizes the bilinear analysis, to optimize the new criterion. This FKA algorithm can alleviate the ill-posed problem existed in traditional kernel discriminant analysis (KDA), and usually, has no singularity problem. The effectiveness of our proposed algorithm is validated by a series of face-recognition experiments on several different databases.

  6. Identification of wheat varieties with a parallel-plate capacitance sensor using fisher linear discriminant analysis

    USDA-ARS?s Scientific Manuscript database

    Fisher’s linear discriminant (FLD) models for wheat variety classification were developed and validated. The inputs to the FLD models were the capacitance (C), impedance (Z), and phase angle ('), measured at two frequencies. Classification of wheat varieties was obtained as output of the FLD mod...

  7. Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector

    NASA Astrophysics Data System (ADS)

    Lei, Baiying; Yao, Yuan; Chen, Siping; Li, Shengli; Li, Wanjun; Ni, Dong; Wang, Tianfu

    2015-07-01

    Currently, placental maturity is performed using subjective evaluation, which can be unreliable as it is highly dependent on the observations and experiences of clinicians. To address this problem, this paper proposes a method to automatically stage placenta maturity from B-mode ultrasound (US) images based on dense sampling and novel feature descriptors. Specifically, our proposed method first densely extracts features with a regular grid based on dense sampling instead of a few unreliable interest points. Followed by, these features are clustered using generative Gaussian mixture model (GMM) to obtain high order statistics of the features. The clustering representatives (i.e., cluster means) are encoded by Fisher vector (FV) for staging accuracy enhancement. Differing from the previous studies, a multi-layer FV is investigated to exploit the spatial information rather than the single layer FV. Experimental results show that the proposed method with the dense FV has achieved an area under the receiver of characteristics (AUC) of 96.77%, sensitivity and specificity of 98.04% and 93.75% for the placental maturity staging, respectively. Our experimental results also demonstrate that the dense feature outperforms the traditional sparse feature for placental maturity staging.

  8. Joint L2,1 Norm and Fisher Discrimination Constrained Feature Selection for Rational Synthesis of Microporous Aluminophosphates.

    PubMed

    Qi, Miao; Wang, Ting; Yi, Yugen; Gao, Na; Kong, Jun; Wang, Jianzhong

    2017-04-01

    Feature selection has been regarded as an effective tool to help researchers understand the generating process of data. For mining the synthesis mechanism of microporous AlPOs, this paper proposes a novel feature selection method by joint l2,1 norm and Fisher discrimination constraints (JNFDC). In order to obtain more effective feature subset, the proposed method can be achieved in two steps. The first step is to rank the features according to sparse and discriminative constraints. The second step is to establish predictive model with the ranked features, and select the most significant features in the light of the contribution of improving the predictive accuracy. To the best of our knowledge, JNFDC is the first work which employs the sparse representation theory to explore the synthesis mechanism of six kinds of pore rings. Numerical simulations demonstrate that our proposed method can select significant features affecting the specified structural property and improve the predictive accuracy. Moreover, comparison results show that JNFDC can obtain better predictive performances than some other state-of-the-art feature selection methods.

  9. Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

    NASA Astrophysics Data System (ADS)

    Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad

    2015-01-01

    Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.

  10. Discriminating between HuR and TTP binding sites using the k-spectrum kernel method

    PubMed Central

    Goldberg, Debra S.; Dowell, Robin

    2017-01-01

    Background The RNA binding proteins (RBPs) human antigen R (HuR) and Tristetraprolin (TTP) are known to exhibit competitive binding but have opposing effects on the bound messenger RNA (mRNA). How cells discriminate between the two proteins is an interesting problem. Machine learning approaches, such as support vector machines (SVMs), may be useful in the identification of discriminative features. However, this method has yet to be applied to studies of RNA binding protein motifs. Results Applying the k-spectrum kernel to a support vector machine (SVM), we first verified the published binding sites of both HuR and TTP. Additional feature engineering highlighted the U-rich binding preference of HuR and AU-rich binding preference for TTP. Domain adaptation along with multi-task learning was used to predict the common binding sites. Conclusion The distinction between HuR and TTP binding appears to be subtle content features. HuR prefers strongly U-rich sequences whereas TTP prefers AU-rich as with increasing A content, the sequences are more likely to be bound only by TTP. Our model is consistent with competitive binding of the two proteins, particularly at intermediate AU-balanced sequences. This suggests that fine changes in the A/U balance within a untranslated region (UTR) can alter the binding and subsequent stability of the message. Both feature engineering and domain adaptation emphasized the extent to which these proteins recognize similar general sequence features. This work suggests that the k-spectrum kernel method could be useful when studying RNA binding proteins and domain adaptation techniques such as feature augmentation could be employed particularly when examining RBPs with similar binding preferences. PMID:28333956

  11. Unsupervised Wishart Classfication of Wetlands in Newfoundland, Canada Using Polsar Data Based on Fisher Linear Discriminant Analysis

    NASA Astrophysics Data System (ADS)

    Mohammadimanesh, F.; Salehi, B.; Mahdianpari, M.; Homayouni, S.

    2016-06-01

    Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.

  12. Detection of corpus callosum malformations in pediatric population using the discriminative direction in multiple kernel learning.

    PubMed

    Peruzzo, Denis; Arrigoni, Filippo; Triulzi, Fabio; Parazzini, Cecilia; Castellani, Umberto

    2014-01-01

    In this paper we propose a Multiple Kernel Learning (MKL) classifier to detect malformations of the Corpus Callosum (CC) and apply it in a pediatric population. Furthermore, we extend the concept of discriminative direction to the linear MKL methods, implementing it in a single subject analysis framework. The CC is characterized using different measures derived from Magnetic Resonance Imaging (MRI) data and the MKL approach is used to efficiently combine them. The discriminative direction analysis highlights those features that lead the classification for each given subject. In the case of a CC with malformation this means highlighting the abnormal characteristics of the CC that guide the diagnosis. Experiments show that the method correctly identifies the malformative aspects of the CC. Moreover, it is able to identify dishomogeneus, localized or widespread abnormalities among the different features. The proposed method is therefore suitable for supporting neuroradiologists in the decision-making process, providing them not only with a suggested diagnosis, but also with a description of the pathology.

  13. Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

    NASA Astrophysics Data System (ADS)

    Fiorini, Rodolfo A.; Dacquino, Gianfranco

    2005-03-01

    GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous

  14. Gabor filter based optical image recognition using Fractional Power Polynomial model based common discriminant locality preserving projection with kernels

    NASA Astrophysics Data System (ADS)

    Li, Jun-Bao

    2012-09-01

    This paper presents Gabor filter based optical image recognition using Fractional Power Polynomial model based Common Kernel Discriminant Locality Preserving Projection. This method tends to solve the nonlinear classification problem endured by optical image recognition owing to the complex illumination condition in practical applications, such as face recognition. The first step is to apply Gabor filter to extract desirable textural features characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations in illumination. In the second step we propose Class-wise Locality Preserving Projection through creating the nearest neighbor graph guided by the class labels for the textural features reduction. Finally we present Common Kernel Discriminant Vector with Fractional Power Polynomial model to reduce the dimensions of the textural features for recognition. For the performance evaluation on optical image recognition, we test the proposed method on a challenging optical image recognition problem, face recognition.

  15. A Method for Selecting between Fisher's Linear Classification Functions and Least Absolute Deviation in Predictive Discriminant Analysis.

    ERIC Educational Resources Information Center

    Meshbane, Alice; Morris, John D.

    A method for comparing the cross-validated classification accuracy of Fisher's linear classification functions (FLCFs) and the least absolute deviation is presented under varying data conditions for the two-group classification problem. With this method, separate-group as well as total-sample proportions of current classifications can be compared…

  16. Discrimination of pulp oil and kernel oil from pequi (Caryocar brasiliense) by fatty acid methyl esters fingerprinting, using GC-FID and multivariate analysis.

    PubMed

    Faria-Machado, Adelia F; Tres, Alba; van Ruth, Saskia M; Antoniassi, Rosemar; Junqueira, Nilton T V; Lopes, Paulo Sergio N; Bizzo, Humberto R

    2015-11-18

    Pequi is an oleaginous fruit whose edible oil is composed mainly by saturated and monounsaturated fatty acids. The biological and nutritional properties of pequi oil are dependent on its composition, which can change according to the oil source (pulp or kernel). There is little data in the scientific literature concerning the differences between the compositions of pequi kernel and pulp oils. Therefore, in this study, different pequi genotypes were evaluated to determine the fatty acid composition of pulp and kernel oils. PCA and PLS-DA were applied to develop a model to distinguish these oils. For all evaluated genotypes, the major fatty acids of both pulp and kernel oils were oleic and palmitic acids. Despite the apparent similarity between the analyzed samples, it was possible to discriminate pulp and kernel oils by means of their fatty acid composition using chemometrics, as well as the unique pequi genotype without endocarp spines (CPAC-PQ-SE-06).

  17. Choosing parameters of kernel subspace LDA for recognition of face images under pose and illumination variations.

    PubMed

    Huang, Jian; Yuen, Pong C; Chen, Wen-Sheng; Lai, Jian Huang

    2007-08-01

    This paper addresses the problem of automatically tuning multiple kernel parameters for the kernel-based linear discriminant analysis (LDA) method. The kernel approach has been proposed to solve face recognition problems under complex distribution by mapping the input space to a high-dimensional feature space. Some recognition algorithms such as the kernel principal components analysis, kernel Fisher discriminant, generalized discriminant analysis, and kernel direct LDA have been developed in the last five years. The experimental results show that the kernel-based method is a good and feasible approach to tackle the pose and illumination variations. One of the crucial factors in the kernel approach is the selection of kernel parameters, which highly affects the generalization capability and stability of the kernel-based learning methods. In view of this, we propose an eigenvalue-stability-bounded margin maximization (ESBMM) algorithm to automatically tune the multiple parameters of the Gaussian radial basis function kernel for the kernel subspace LDA (KSLDA) method, which is developed based on our previously developed subspace LDA method. The ESBMM algorithm improves the generalization capability of the kernel-based LDA method by maximizing the margin maximization criterion while maintaining the eigenvalue stability of the kernel-based LDA method. An in-depth investigation on the generalization performance on pose and illumination dimensions is performed using the YaleB and CMU PIE databases. The FERET database is also used for benchmark evaluation. Compared with the existing PCA-based and LDA-based methods, our proposed KSLDA method, with the ESBMM kernel parameter estimation algorithm, gives superior performance.

  18. Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins.

    PubMed

    Zhang, Guangya; Ge, Huihua

    2013-10-01

    Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins.

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

    PubMed

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

    2013-12-01

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

  20. Discriminative clustering via extreme learning machine.

    PubMed

    Huang, Gao; Liu, Tianchi; Yang, Yan; Lin, Zhiping; Song, Shiji; Wu, Cheng

    2015-10-01

    Discriminative clustering is an unsupervised learning framework which introduces the discriminative learning rule of supervised classification into clustering. The underlying assumption is that a good partition (clustering) of the data should yield high discrimination, namely, the partitioned data can be easily classified by some classification algorithms. In this paper, we propose three discriminative clustering approaches based on Extreme Learning Machine (ELM). The first algorithm iteratively trains weighted ELM (W-ELM) classifier to gradually maximize the data discrimination. The second and third methods are both built on Fisher's Linear Discriminant Analysis (LDA); but one approach adopts alternative optimization, while the other leverages kernel k-means. We show that the proposed algorithms can be easily implemented, and yield competitive clustering accuracy on real world data sets compared to state-of-the-art clustering methods.

  1. [Selection of Characteristic Wavelengths Using SPA and Qualitative Discrimination of Mildew Degree of Corn Kernels Based on SVM].

    PubMed

    Yuan, Ying; Wang, Wei; Chu, Xuan; Xi, Ming-jie

    2016-01-01

    The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy with spectral range between 833 and 2 500 nm to detect the moldy corn kernels with different levels of mildew was verified in this paper. Firstly, to avoid the influence of noise, moving average smoothing was used for spectral data preprocessing after four common pretreatment methods were compared. Then to improve the prediction performance of the model, SPXY (sample set partitioning based on joint x-y distance) was selected and used for sample set partition. Furthermore, in order to reduce the dimensions of the original spectral data, successive projection algorithm (SPA) was adopted and ultimately 7 characteristic wavelengths were extracted, the characteristic wave-lengths were 833, 927, 1 208, 1 337, 1 454, 1 861, 2 280 nm. The experimental results showed when the spectrum data of the 7 characteristic wavelengths were taken as the input of SVM, the radial basic function (RBF) used as the kernel function, and kernel parameter C = 7 760 469, γ = 0.017 003, the classification accuracies of the established SVM model were 97.78% and 93.33% for the training and testing sets respectively. In addition, the independent validation set was selected in the same standard, and used to verify the model. At last, the classification accuracy of 91.11% for the independent validation set was achieved. The result indicated that it is feasible to identify and classify different degree of moldy corn grain kernels using SPA and SVM, and characteristic wavelengths selected by SPA in this paper also lay a foundation for the online NIR detection of mildew corn kernels.

  2. A conditional entropy minimization criterion for dimensionality reduction and multiple kernel learning.

    PubMed

    Hino, Hideitsu; Murata, Noboru

    2010-11-01

    Reducing the dimensionality of high-dimensional data without losing its essential information is an important task in information processing. When class labels of training data are available, Fisher discriminant analysis (FDA) has been widely used. However, the optimality of FDA is guaranteed only in a very restricted ideal circumstance, and it is often observed that FDA does not provide a good classification surface for many real problems. This letter treats the problem of supervised dimensionality reduction from the viewpoint of information theory and proposes a framework of dimensionality reduction based on class-conditional entropy minimization. The proposed linear dimensionality-reduction technique is validated both theoretically and experimentally. Then, through kernel Fisher discriminant analysis (KFDA), the multiple kernel learning problem is treated in the proposed framework, and a novel algorithm, which iteratively optimizes the parameters of the classification function and kernel combination coefficients, is proposed. The algorithm is experimentally shown to be comparable to or outperforms KFDA for large-scale benchmark data sets, and comparable to other multiple kernel learning techniques on the yeast protein function annotation task.

  3. Miller Fisher Syndrome

    MedlinePlus

    ... new ways to diagnose, treat, and, ultimately, cure neuropathies such as Miller Fisher syndrome. Information from the ... new ways to diagnose, treat, and, ultimately, cure neuropathies such as Miller Fisher syndrome. Information from the ...

  4. Chapter 3: Fisher

    Treesearch

    Roger A. Powell; William J. Zielinski

    1994-01-01

    The fisher (Martes pennanti) is a medium-size mammalian carnivore and the largest member of the genus Martes (Anderson 1970) of the family Mustelidae in the order Carnivora. The genus Martes includes five or six other extant species. The fisher has the general body build of a stocky weasel and is long, thin, and...

  5. A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

    NASA Astrophysics Data System (ADS)

    Tien Bui, Dieu; Hoang, Nhat-Duc

    2017-09-01

    In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

  6. Comparison of species classification models of mass spectrometry data: Kernel Discriminant Analysis vs Random Forest; A case study of Afrormosia (Pericopsis elata (Harms) Meeuwen).

    PubMed

    Deklerck, V; Finch, K; Gasson, P; Van den Bulcke, J; Van Acker, J; Beeckman, H; Espinoza, E

    2017-10-15

    The genus Pericopsis includes four tree species of which only Pericopsis elata (Harms) Meeuwen is of commercial interest. Enforcement officers might have difficulties discerning this CITES-listed species from some other tropical African timber species. Therefore, we tested several methods to separate and identify these species rapidly in order to enable customs officials to uncover illegal trade. In this study, two classification methods using Direct Analysis in Real Time (DART™) ionization coupled with Time-of-Flight Mass Spectrometry (DART-TOFMS) data to discern between several species are presented. Metabolome profiles were collected using DART™ ionization coupled with TOFMS analysis of heartwood specimens of all four Pericopsis species and Haplormosia monophylla (Harms) Harms, Dalbergia melanoxylon Guill. & Perr. Harms, and Milicia excelsa (Welw.) C.C. Berg. In total, 95 specimens were analysed and the spectra evaluated. Kernel Discriminant Analysis (KDA) and Random Forest classification were used to discern the species. DART-TOFMS spectra obtained from wood slivers and post-processing analysis using KDA and Random Forest classification separated Pericopsis elata from the other Pericopsis taxa and its lookalike timbers Haplormosia monophylla, Milicia excelsa, and Dalbergia melanoxylon. Only 50 ions were needed to achieve the highest accuracy. DART-TOFMS spectra of the taxa were reproducible and the results of the chemometric analysis provided comparable accuracy. Haplormosia monophylla was visually distinguished based on the heatmap and was excluded from further analysis. Both classification methods, KDA and Random Forest, were capable of distinguishing Pericopsis elata from the other Pericopsis taxa, Milicia excelsa, and Dalbergia melanoxylon, timbers that are commonly traded. Copyright © 2017 John Wiley & Sons, Ltd.

  7. Clustering-Based Construction of Hidden Markov Models for Generative Kernels

    NASA Astrophysics Data System (ADS)

    Bicego, Manuele; Cristani, Marco; Murino, Vittorio; Pękalska, Elżbieta; Duin, Robert P. W.

    Generative kernels represent theoretically grounded tools able to increase the capabilities of generative classification through a discriminative setting. Fisher Kernel is the first and mostly-used representative, which lies on a widely investigated mathematical background. The manufacture of a generative kernel flows down through a two-step serial pipeline. In the first, “generative” step, a generative model is trained, considering one model for class or a whole model for all the data; then, features or scores are extracted, which encode the contribution of each data point in the generative process. In the second, “discriminative” part, the scores are evaluated by a discriminative machine via a kernel, exploiting the data separability. In this paper we contribute to the first aspect, proposing a novel way to fit the class-data with the generative models, in specific, focusing on Hidden Markov Models (HMM). The idea is to perform model clustering on the unlabeled data in order to discover at best the structure of the entire sample set. Then, the label information is retrieved and generative scores are computed. Experimental, comparative test provides a preliminary idea on the goodness of the novel approach, pushing forward for further developments.

  8. Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression

    PubMed Central

    Eekhoff, Alexander; Newell, Karl M.; Schöllhorn, Wolfgang I.

    2017-01-01

    Objective Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). Methods Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. Results and discussion Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. Conclusion Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over

  9. Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.

    PubMed

    Horst, Fabian; Eekhoff, Alexander; Newell, Karl M; Schöllhorn, Wolfgang I

    2017-01-01

    Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of

  10. Generalized Fisher matrices

    NASA Astrophysics Data System (ADS)

    Heavens, A. F.; Seikel, M.; Nord, B. D.; Aich, M.; Bouffanais, Y.; Bassett, B. A.; Hobson, M. P.

    2014-12-01

    The Fisher Information Matrix formalism (Fisher 1935) is extended to cases where the data are divided into two parts (X, Y), where the expectation value of Y depends on X according to some theoretical model, and X and Y both have errors with arbitrary covariance. In the simplest case, (X, Y) represent data pairs of abscissa and ordinate, in which case the analysis deals with the case of data pairs with errors in both coordinates, but X can be any measured quantities on which Y depends. The analysis applies for arbitrary covariance, provided all errors are Gaussian, and provided the errors in X are small, both in comparison with the scale over which the expected signal Y changes, and with the width of the prior distribution. This generalizes the Fisher Matrix approach, which normally only considers errors in the `ordinate' Y. In this work, we include errors in X by marginalizing over latent variables, effectively employing a Bayesian hierarchical model, and deriving the Fisher Matrix for this more general case. The methods here also extend to likelihood surfaces which are not Gaussian in the parameter space, and so techniques such as DALI (Derivative Approximation for Likelihoods) can be generalized straightforwardly to include arbitrary Gaussian data error covariances. For simple mock data and theoretical models, we compare to Markov Chain Monte Carlo experiments, illustrating the method with cosmological supernova data. We also include the new method in the FISHER4CAST software.

  11. Fisher in Adelaide.

    PubMed

    Mayo, Oliver

    2014-06-01

    R. A. Fisher spent much of his final 3 years of life in Adelaide. It was a congenial place to live and work, and he was much in demand as a speaker, in Australia and overseas. It was, however, a difficult time for him because of the sustained criticism of fiducial inference from the early 1950s onwards. The article discusses some of Fisher's work on inference from an Adelaide perspective. It also considers some of the successes arising from this time, in the statistics of field experimentation and in evolutionary genetics. A few personal recollections of Fisher as houseguest are provided. This article is the text of a article presented on August 31, 2012 at the 26th International Biometric Conference, Kobe, Japan.

  12. Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data.

    PubMed

    Zhou, Luping; Wang, Lei; Liu, Lingqiao; Ogunbona, Philip; Shen, Dinggang

    2016-11-01

    Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.

  13. Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data.

    PubMed

    Zhou, Luping; Wang, Lei; Liu, Lingqiao; Ogunbona, Philip; Shen, Dinggang

    2015-12-23

    Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.

  14. Fisher and marten

    Treesearch

    Roger A. Powell; Steven W. Buskirk; William J. Zielinski

    2003-01-01

    The genus Martes is circumboreal in distribution, with extensions into southern (M. gwatkinsii) and southeast Asia as far as 7°S latitude (M. flavigula; Anderson 1970). The fisher (subgenus Pekania) is endemic to the New World and restricted to mesic coniferous forest of the boreal zone and its...

  15. Analysis of the Fisher solution

    SciTech Connect

    Abdolrahimi, Shohreh; Shoom, Andrey A.

    2010-01-15

    We study the d-dimensional Fisher solution which represents a static, spherically symmetric, asymptotically flat spacetime with a massless scalar field. The solution has two parameters, the mass M and the 'scalar charge' {Sigma}. The Fisher solution has a naked curvature singularity which divides the spacetime manifold into two disconnected parts. The part which is asymptotically flat we call the Fisher spacetime, and another part we call the Fisher universe. The d-dimensional Schwarzschild-Tangherlini solution and the Fisher solution belong to the same theory and are dual to each other. The duality transformation acting in the parameter space (M,{Sigma}) maps the exterior region of the Schwarzschild-Tangherlini black hole into the Fisher spacetime which has a naked timelike singularity, and interior region of the black hole into the Fisher universe, which is an anisotropic expanding-contracting universe and which has two spacelike singularities representing its 'big bang' and 'big crunch'. The big bang singularity and the singularity of the Fisher spacetime are radially weak in the sense that a 1-dimensional object moving along a timelike radial geodesic can arrive to the singularities intact. At the vicinity of the singularity the Fisher spacetime of nonzero mass has a region where its Misner-Sharp energy is negative. The Fisher universe has a marginally trapped surface corresponding to the state of its maximal expansion in the angular directions. These results and derived relations between geometric quantities of the Fisher spacetime, the Fisher universe, and the Schwarzschild-Tangherlini black hole may suggest that the massless scalar field transforms the black hole event horizon into the naked radially weak disjoint singularities of the Fisher spacetime and the Fisher universe which are 'dual to the horizon'.

  16. Image texture analysis of crushed wheat kernels

    NASA Astrophysics Data System (ADS)

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

    1992-03-01

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

  17. phase_space_cosmo_fisher: Fisher matrix 2D contours

    NASA Astrophysics Data System (ADS)

    Stark, Alejo

    2016-11-01

    phase_space_cosmo_fisher produces Fisher matrix 2D contours from which the constraints on cosmological parameters can be derived. Given a specified redshift array and cosmological case, 2D marginalized contours of cosmological parameters are generated; the code can also plot the derivatives used in the Fisher matrix. In addition, this package can generate 3D plots of qH^2 and other cosmological quantities as a function of redshift and cosmology.

  18. Natural selection maximizes Fisher information.

    PubMed

    Frank, S A

    2009-02-01

    In biology, information flows from the environment to the genome by the process of natural selection. However, it has not been clear precisely what sort of information metric properly describes natural selection. Here, I show that Fisher information arises as the intrinsic metric of natural selection and evolutionary dynamics. Maximizing the amount of Fisher information about the environment captured by the population leads to Fisher's fundamental theorem of natural selection, the most profound statement about how natural selection influences evolutionary dynamics. I also show a relation between Fisher information and Shannon information (entropy) that may help to unify the correspondence between information and dynamics. Finally, I discuss possible connections between the fundamental role of Fisher information in statistics, biology and other fields of science.

  19. Weighted Bergman kernels and virtual Bergman kernels

    NASA Astrophysics Data System (ADS)

    Roos, Guy

    2005-12-01

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

  20. Band-Reweighed Gabor Kernel Embedding for Face Image Representation and Recognition.

    PubMed

    Ren, Chuan-Xian; Dai, Dao-Qing; Li, Xiao-Xin; Lai, Zhao-Rong

    2014-02-01

    Face recognition with illumination or pose variation is a challenging problem in image processing and pattern recognition. A novel algorithm using band-reweighed Gabor kernel embedding to deal with the problem is proposed in this paper. For a given image, it is first transformed by a group of Gabor filters, which output Gabor features using different orientation and scale parameters. Fisher scoring function is used to measure the importance of features in each band, and then, the features with the largest scores are preserved for saving memory requirements. The reduced bands are combined by a vector, which is determined by a weighted kernel discriminant criterion and solved by a constrained quadratic programming method, and then, the weighted sum of these nonlinear bands is defined as the similarity between two images. Compared with existing concatenation-based Gabor feature representation and the uniformly weighted similarity calculation approaches, our method provides a new way to use Gabor features for face recognition and presents a reasonable interpretation for highlighting discriminant orientations and scales. The minimum Mahalanobis distance considering the spatial correlations within the data is exploited for feature matching, and the graphical lasso is used therein for directly estimating the sparse inverse covariance matrix. Experiments using benchmark databases show that our new algorithm improves the recognition results and obtains competitive performance.

  1. Flooded area cartography with kernel-based classifiers and Landsat TM imagery

    NASA Astrophysics Data System (ADS)

    Volpi, M.; Petropoulos, G. P.; Kanevski, M.

    2012-04-01

    Timely and accurate flooding extent maps for both emergency and recovery phases are required by scientists, local authorities and decision makers. In particular, the issue of reducing exposure by quantifying vulnerability to inundation has recently began to be considered by European policies. Remote sensing can provide valuable information to this task, particularly over inaccessible regions. Provided that cloud-free conditions exist, multi-temporal optical images can be exploited for automatic cartography of the inundation. Image processing techniques based on kernels are promising tools in many remote sensing problems, ranging from biophysical parameter estimation to multi-temporal classification and change detection. The success of such methods is largely due to the explicit non-linear nature of the discriminant function and to their robustness to high-dimensional input spaces, such as those generated from remote sensing spectral bands. In our study, we examined the application of two supervised kernel-based classifiers for flooded area extraction from Landsat TM imagery. As a case study, we analyzed a region of the Missouri River in South Dakota, United States, in which images before and after a flood that took place in 2011 were available. In our approach, the mapping issue is recast as a change detection problem, whereby only the amount of water in excess to the permanent standing one was considered. Support Vector Machine (SVM) and Fisher's Linear Discriminant Analysis (LDA) classifications were applied successfully. Both classifiers were utilized in their linear and non-linear (kernel) versions. Evaluation of the ability of the two methods in delineating the flooding extent was conducted on the basis of classification accuracy assessment metrics as well as the McNemar statistical significance testing. Our findings showed the suitability of the non-linear kernel extensions to accurately map the flood extent. Possible future developments of the methodology

  2. The Influence of Fractional Diffusion in Fisher-KPP Equations

    NASA Astrophysics Data System (ADS)

    Cabré, Xavier; Roquejoffre, Jean-Michel

    2013-06-01

    We study the Fisher-KPP equation where the Laplacian is replaced by the generator of a Feller semigroup with power decaying kernel, an important example being the fractional Laplacian. In contrast with the case of the standard Laplacian where the stable state invades the unstable one at constant speed, we prove that with fractional diffusion, generated for instance by a stable Lévy process, the front position is exponential in time. Our results provide a mathematically rigorous justification of numerous heuristics about this model.

  3. Fisher waves: An individual-based stochastic model

    NASA Astrophysics Data System (ADS)

    Houchmandzadeh, B.; Vallade, M.

    2017-07-01

    The propagation of a beneficial mutation in a spatially extended population is usually studied using the phenomenological stochastic Fisher-Kolmogorov-Petrovsky-Piscounov (SFKPP) equation. We derive here an individual-based, stochastic model founded on the spatial Moran process where fluctuations are treated exactly. The mean-field approximation of this model leads to an equation that is different from the phenomenological FKPP equation. At small selection pressure, the front behavior can be mapped into a Brownian motion with drift, the properties of which can be derived from the microscopic parameters of the Moran model. Finally, we generalize the model to take into account dispersal kernels beyond migration to nearest neighbors. We show how the effective population size (which controls the noise amplitude) and the diffusion coefficient can both be computed from the dispersal kernel.

  4. Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels

    USDA-ARS?s Scientific Manuscript database

    The current US corn grading system accounts for the portion of damaged kernels, which is measured by time-consuming and inaccurate visual inspection. Near infrared spectroscopy (NIRS), a non-destructive and fast analytical method, was tested as a tool for discriminating corn kernels with heat and f...

  5. FISHER GULCH ROADLESS AREA, CALIFORNIA.

    USGS Publications Warehouse

    Huber, Donald F.; Cather, Eric E.

    1984-01-01

    The Fisher Gulch Roadless Area occupies an area of about 5. 2 sq mi near the Trinity Alps in the Klamath Mountains, about 10 mi northwest of Weaverville, California. On the basis of a study, the Fisher Gulch Roadless Area has a probable potential for small amounts of placer gold resources in a narrow elongate area along the northeast boundary. There is little promise for the occurrence of other metallic, or nonmetallic resources and the geologic terrane precludes the occurrence of fossil fuel resources.

  6. Quantum criticality from Fisher information

    NASA Astrophysics Data System (ADS)

    Song, Hongting; Luo, Shunlong; Fu, Shuangshuang

    2017-04-01

    Quantum phase transition is primarily characterized by a qualitative sudden change in the ground state of a quantum system when an external or internal parameter of the Hamiltonian is continuously varied. Investigating quantum criticality using information-theoretic methods has generated fruitful results. Quantum correlations and fidelity have been exploited to characterize the quantum critical phenomena. In this work, we employ quantum Fisher information to study quantum criticality. The singular or extremal point of the quantum Fisher information is adopted as the estimated thermal critical point. By a significant model constructed in Quan et al. (Phys Rev Lett 96: 140604, 2006), the effectiveness of this method is illustrated explicitly.

  7. On Fisher Information and Thermodynamics

    EPA Science Inventory

    Fisher information is a measure of the information obtainable by an observer from the observation of reality. However, information is obtainable only when there are patterns or features to observe, and these only exist when there is order. For example, a system in perfect disor...

  8. "Fisher v. Texas": Strictly Disappointing

    ERIC Educational Resources Information Center

    Nieli, Russell K.

    2013-01-01

    Russell K. Nieli writes in this opinion paper that as far as the ability of state colleges and universities to use race as a criteria for admission goes, "Fisher v. Texas" was a big disappointment, and failed in the most basic way. Nieli states that although some affirmative action opponents have tried to put a more positive spin on the…

  9. On Fisher Information and Thermodynamics

    EPA Science Inventory

    Fisher information is a measure of the information obtainable by an observer from the observation of reality. However, information is obtainable only when there are patterns or features to observe, and these only exist when there is order. For example, a system in perfect disor...

  10. "Fisher v. Texas": Strictly Disappointing

    ERIC Educational Resources Information Center

    Nieli, Russell K.

    2013-01-01

    Russell K. Nieli writes in this opinion paper that as far as the ability of state colleges and universities to use race as a criteria for admission goes, "Fisher v. Texas" was a big disappointment, and failed in the most basic way. Nieli states that although some affirmative action opponents have tried to put a more positive spin on the…

  11. Fisher population and landscape genetics

    Treesearch

    Michael Schwartz; Joel Saunder; Kristine L. Pilgrim; Ray Vinkey; Michael K. Lucid; Sean Parks; Nathan Albrecht

    2013-01-01

    This talk provides a population and landscape genetic overview of fishers in Idaho and Montana. We start by discussing some of our initial findings using mitochondrial DNA (Vinkey et al. 2006, Schwartz 2007, Knaus et al. 2011). On balance these results demonstrate the uniqueness of a native haplotype that persisted in the Bitterroot-Selway Ecosystem. They also show the...

  12. Semisupervised kernel matrix learning by kernel propagation.

    PubMed

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

    2010-11-01

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

  13. Approximate kernel competitive learning.

    PubMed

    Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

    2015-03-01

    Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches.

  14. In Memoriam: Albert Kenrick Fisher

    USGS Publications Warehouse

    Uhler, F.M.

    1951-01-01

    Dr. Albert Kenrick Fisher, a Founder and Past President of the American Ornithologists' Union and one of its best known Fellows for nearly 65 years, died in Washington, D. C. on June 12, 1948, after a brief illness from circulatory complications that developed as a result of advanced age. With his passing, the American Ornithologists' Union has lost one of its last links with that eminent group of bird students who founded this organization in the autumn of 1883.

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

    PubMed

    Kwak, Nojun

    2016-05-20

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

  16. Optimized Kernel Entropy Components.

    PubMed

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

    2016-02-25

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

  17. A novel fuzzy Fisher classifier for signal peptide prediction.

    PubMed

    Gao, Cui-Fang; Qiu, Zi-Xue; Wu, Xiao-Jun; Tian, Feng-Wei; Zhang, Hao; Chen, Wei

    2011-08-01

    Signal peptides recognition by bioinformatics approaches is particularly important for the efficient secretion and production of specific proteins. We concentrate on developing an integrated fuzzy Fisher clustering (IFFC) and designing a novel classifier based on IFFC for predicting secretory proteins. IFFC provides a powerful optimal discriminant vector calculated by fuzzy intra-cluster scatter matrix and fuzzy inter-cluster scatter matrix. Because the training samples and test samples are processed together in IFFC, it is convenient for users to employ their own specific samples of high reliability as training data if necessary. The cross-validation results on some existing datasets indicate that the fuzzy Fisher classifier is quite promising for signal peptide prediction.

  18. Iterative software kernels

    SciTech Connect

    Duff, I.

    1994-12-31

    This workshop focuses on kernels for iterative software packages. Specifically, the three speakers discuss various aspects of sparse BLAS kernels. Their topics are: `Current status of user lever sparse BLAS`; Current status of the sparse BLAS toolkit`; and `Adding matrix-matrix and matrix-matrix-matrix multiply to the sparse BLAS toolkit`.

  19. Learning with Box Kernels.

    PubMed

    Melacci, Stefano; Gori, Marco

    2013-04-12

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

  20. Learning with box kernels.

    PubMed

    Melacci, Stefano; Gori, Marco

    2013-11-01

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

  1. The arms race between fishers

    NASA Astrophysics Data System (ADS)

    Rijnsdorp, Adriaan D.; Poos, Jan Jaap; Quirijns, Floor J.; HilleRisLambers, Reinier; De Wilde, Jan W.; Den Heijer, Willem M.

    An analysis of the changes in the Dutch demersal fishing fleet since the 1950s revealed that competitive interactions among vessels and gear types within the constraints imposed by biological, economic and fisheries management factors are the dominant processes governing the dynamics of fishing fleets. Double beam trawling, introduced in the early 1960s, proved a successful fishing method to catch deep burying flatfish, in particular sole. In less than 10 years, the otter trawl fleet was replaced by a highly specialised beam trawling fleet, despite an initial doubling of the loss rate of vessels due to stability problems. Engine power, size of the beam trawl, number of tickler chains and fishing speed rapidly increased and fishing activities expanded into previously lightly fished grounds and seasons. Following the ban on flatfish trawling within the 12 nautical mile zone for vessels of more than 300 hp in 1975 and with the restriction of engine power to 2000 hp in 1987, the beam trawl fleet bifurcated. Changes in the fleet capacity were related to the economic results and showed a cyclic pattern with a period of 6-7 years. The arms race between fishers was fuelled by competitive interactions among fishers: while the catchability of the fleet more than doubled in the ten years following the introduction of the beam trawl, a decline in catchability was observed in reference beam trawlers that remained the same. Vessel performance was not only affected by the technological characteristics but also by the number and characteristics of competing vessels.

  2. Kernel Affine Projection Algorithms

    NASA Astrophysics Data System (ADS)

    Liu, Weifeng; Príncipe, José C.

    2008-12-01

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

  3. Intelligent classification methods of grain kernels using computer vision analysis

    NASA Astrophysics Data System (ADS)

    Lee, Choon Young; Yan, Lei; Wang, Tianfeng; Lee, Sang Ryong; Park, Cheol Woo

    2011-06-01

    In this paper, a digital image analysis method was developed to classify seven kinds of individual grain kernels (common rice, glutinous rice, rough rice, brown rice, buckwheat, common barley and glutinous barley) widely planted in Korea. A total of 2800 color images of individual grain kernels were acquired as a data set. Seven color and ten morphological features were extracted and processed by linear discriminant analysis to improve the efficiency of the identification process. The output features from linear discriminant analysis were used as input to the four-layer back-propagation network to classify different grain kernel varieties. The data set was divided into three groups: 70% for training, 20% for validation, and 10% for testing the network. The classification experimental results show that the proposed method is able to classify the grain kernel varieties efficiently.

  4. Seeing the Fisher Z-Transformation

    ERIC Educational Resources Information Center

    Bond, Charles F., Jr.; Richardson, Ken

    2004-01-01

    Since 1915, statisticians have been applying Fisher's Z-transformation to Pearson product-moment correlation coefficients. We offer new geometric interpretations of this transformation. (Contains 9 figures.)

  5. Multiple collaborative kernel tracking.

    PubMed

    Fan, Zhimin; Yang, Ming; Wu, Ying

    2007-07-01

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

  6. Multivariate acoustic detection of small explosions using Fisher's combined probability test.

    PubMed

    Arrowsmith, Stephen J; Taylor, Steven R

    2013-03-01

    A methodology for the combined acoustic detection and discrimination of explosions, which uses three discriminants, is developed for the purpose of identifying weak explosion signals embedded in complex background noise. By utilizing physical models for simple explosions that are formulated as statistical hypothesis tests, the detection/discrimination approach does not require a model for the background noise, which can be highly complex and variable in practice. Fisher's Combined Probability Test is used to combine the p-values from all multivariate discriminants. This framework is applied to acoustic data from a 400 g explosion conducted at Los Alamos National Laboratory.

  7. Robotic Intelligence Kernel: Communications

    SciTech Connect

    Walton, Mike C.

    2009-09-16

    The INL Robotic Intelligence Kernel-Comms is the communication server that transmits information between one or more robots using the RIK and one or more user interfaces. It supports event handling and multiple hardware communication protocols.

  8. Robotic Intelligence Kernel: Driver

    SciTech Connect

    2009-09-16

    The INL Robotic Intelligence Kernel-Driver is built on top of the RIK-A and implements a dynamic autonomy structure. The RIK-D is used to orchestrate hardware for sensing and action as well as software components for perception, communication, behavior and world modeling into a single cognitive behavior kernel that provides intrinsic intelligence for a wide variety of unmanned ground vehicle systems.

  9. Home range characteristics of fishers in California

    Treesearch

    W. J. Zielinski; R. L. Truex; G. A. Schmidt; F. V. Schlexer; K. N. Schmidt; R. H. Barrett

    2004-01-01

    The fisher (Martes pennanti) is a forest mustelid that historically occurred in California from the mixed conifer forests of the north coast, east to the southern Cascades, and south throughout the Sierra Nevada. Today fishers in California occur only in 2 disjunct populations in the northwestern mountains and the...

  10. Chapter 4: Fishers and American martens

    Treesearch

    K.L. Purcell; C.M. Thompson; W.J. Zielinski

    2012-01-01

    Fishers (Martes pennanti) and American martens (M. americana) are carnivorous mustelids associated with late-successional forests. The distributions of both species have decreased in the Sierra Nevada and southern Cascade region (Zielinski et al. 2005). Fishers occur primarily in lower elevation (3,500 to 7,000 ft) (1067 to...

  11. Fisher Information, Sustainability, Development and Political Instability

    EPA Science Inventory

    Fisher information is a measure of order inherent in the timer series data for any dynamic system. We have computed the Fisher Information for nation-states using the data from 1960 to 1997 from the State Instability Task Force. We find that nation-states fall into two categories...

  12. Fisher Information, Sustainability, Development and Political Instability

    EPA Science Inventory

    Fisher information is a measure of order inherent in the timer series data for any dynamic system. We have computed the Fisher Information for nation-states using the data from 1960 to 1997 from the State Instability Task Force. We find that nation-states fall into two categories...

  13. On the performance of Fisher Information Measure and Shannon entropy estimators

    NASA Astrophysics Data System (ADS)

    Telesca, Luciano; Lovallo, Michele

    2017-10-01

    The performance of two estimators of Fisher Information Measure (FIM) and Shannon entropy (SE), one based on the discretization of the FIM and SE formulae (discrete-based approach) and the other based on the kernel-based estimation of the probability density function (pdf) (kernel-based approach) is investigated. The two approaches are employed to estimate the FIM and SE of Gaussian processes (with different values of σ and size N), whose theoretic FIM and SE depend on the standard deviation σ. The FIM (SE) estimated by using the discrete-based approach is approximately constant with σ, but decreases (increases) with the bin number L; in particular, the discrete-based approach furnishes a rather correct estimation of FIM (SE) for L ∝ σ. Furthermore, for small values of σ, the larger the size N of the series, the smaller the mean relative error; while for large values of σ, the larger the size N of the series, the larger the mean relative error. The FIM (SE) estimated by using the kernel-based approach is very close to the theoretic value for any σ , and the mean relative error decreases with the increase of the length of the series. Comparing the results obtained using the discrete-based and kernel-based approaches, the estimates of FIM and SE by using the kernel-based approach are much closer to the theoretic values for any σ and any N and have to be preferred to the discrete-based estimates.

  14. Fisher Information in Ecological Systems

    NASA Astrophysics Data System (ADS)

    Frieden, B. Roy; Gatenby, Robert A.

    Fisher information is being increasingly used as a tool of research into ecological systems. For example the information was shown in Chapter 7 to provide a useful diagnostic of the health of an ecology. In other applications to ecology, extreme physical information (EPI) has been used to derive the population-rate (or Lotka-Volterra) equations of ecological systems, both directly [1] and indirectly (Chapter 5) via the quantum Schrodinger wave equation (SWE). We next build on these results, to derive (i) an uncertainty principle (8.3) of biology, (ii) a simple decision rule (8.18) for predicting whether a given ecology is susceptible to a sudden drop in population (Section 8.1), (iii) the probability law (8.57) or (8.59) on the worldwide occurrence of the masses of living creatures from mice to elephants and beyond (Section 8.2), and (iv) the famous quarter-power laws for the attributes of biological and other systems. The latter approach uses EPI to derive the simultaneous quarter-power behavior of all attributes obeyed by the law, such as metabolism rate, brain size, grazing range, etc. (Section 8.3). This maximal breadth of scope is allowed by its basis in information, which of course applies to all types of quantitative data (Section 1.4.3, Chapter 1).

  15. Gene Selection for Multiclass Prediction by Weighted Fisher Criterion

    PubMed Central

    2007-01-01

    Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes. In the first step, individually discriminatory genes (IDGs) are identified by using one-dimensional weighted Fisher criterion (wFC). In the second step, jointly discriminatory genes (JDGs) are selected by sequential search methods, based on their joint class separability measured by multidimensional weighted Fisher criterion (wFC). The performance of the selected gene subsets for multiclass prediction is evaluated by artificial neural networks (ANNs) and/or support vector machines (SVMs). By applying the proposed IDG/JDG approach to two microarray studies, that is, small round blue cell tumors (SRBCTs) and muscular dystrophies (MDs), we successfully identified a much smaller yet efficient set of JDGs for diagnosing SRBCTs and MDs with high prediction accuracies (96.9% for SRBCTs and 92.3% for MDs, resp.). These experimental results demonstrated that the two-step gene selection method is able to identify a subset of highly discriminative genes for improved multiclass prediction. PMID:17713593

  16. General perspective view of the Fisher School Covered Bridge, view ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    General perspective view of the Fisher School Covered Bridge, view looking southwest from Five Rivers Road. - Fisher School Covered Bridge, Crab Creek Road at Fiver Rivers Road, Fisher, Lincoln County, OR

  17. Interior of the Fisher School Covered Bridge, view to north ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    Interior of the Fisher School Covered Bridge, view to north showing road deck, guardrail, and howe truss. - Fisher School Covered Bridge, Crab Creek Road at Fiver Rivers Road, Fisher, Lincoln County, OR

  18. General perspective view of the Fisher School Covered Bridge, view ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    General perspective view of the Fisher School Covered Bridge, view looking east along Five Rivers Road. - Fisher School Covered Bridge, Crab Creek Road at Fiver Rivers Road, Fisher, Lincoln County, OR

  19. General topographic view of the Fisher School Covered Bridge, view ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    General topographic view of the Fisher School Covered Bridge, view looking northwest from Crab Creek Road. - Fisher School Covered Bridge, Crab Creek Road at Fiver Rivers Road, Fisher, Lincoln County, OR

  20. LETTER: Fisher renormalization for logarithmic corrections

    NASA Astrophysics Data System (ADS)

    Kenna, Ralph; Hsu, Hsiao-Ping; von Ferber, Christian

    2008-10-01

    For continuous phase transitions characterized by power-law divergences, Fisher renormalization prescribes how to obtain the critical exponents for a system under constraint from their ideal counterparts. In statistical mechanics, such ideal behaviour at phase transitions is frequently modified by multiplicative logarithmic corrections. Here, Fisher renormalization for the exponents of these logarithms is developed in a general manner. As for the leading exponents, Fisher renormalization at the logarithmic level is seen to be involutory and the renormalized exponents obey the same scaling relations as their ideal analogues. The scheme is tested in lattice animals and the Yang-Lee problem at their upper critical dimensions, where predictions for logarithmic corrections are made.

  1. Fishers' knowledge on the coast of Brazil.

    PubMed

    Begossi, Alpina; Salivonchyk, Svetlana; Lopes, Priscila F M; Silvano, Renato A M

    2016-06-01

    Although fishers' knowledge has been recently considered into management programmes, there is still the need to establish a better understanding of fishers' perceptions and cognition. Fishers can provide novel information on the biology and ecology of species, which can potentially be used in the management of fisheries. The knowledge fishers have and how they classify nature is empirically based. It is common, for example, to observe that fishers' taxonomy is often represented by the generic level, one of the hierarchical categories of folk classification that is somewhat analogous to the Linnean genus, as it groups organisms of a higher rank than the folk species.In this study we compiled the knowledge fishers have on local fish, such as their folk names, diet and habitat. Five coastal communities widely distributed along the Brazilian coast were studied: two from the northeast (Porto Sauípe and Itacimirim, in Bahia State, n of interviewees = 34), two from the southeast (Itaipu at Niterói and Copacabana at Rio de Janeiro, Rio de Janeiro State, n = 35) and one from the south coast (Pântano do Sul, in Santa Catarina State, n = 23). Fish pictures were randomly ordered and the same order was presented to all interviewees (n = 92), when they were then asked about the species name and classification and its habitat and diet preferences. Fishers make clusters of fish species, usually hierarchically; fishers of the coast of Brazil use mostly primary lexemes (generic names) to name fish; and fishers did not differentiate between scientific species, since the same folk generic name included two different scientific species. Fishers provide information on species to which there is scarce or no information on diet and habitat, such as Rhinobatos percellens (chola guitarfish, arraia viola or cação viola), Sphoeroides dorsalis (marbled puffer, baiacu), Mycteroperca acutirostris (comb grouper, badejo) and Dasyatis guttata (longnose stingray, arraia, arraia

  2. UNICOS Kernel Internals Application Development

    NASA Technical Reports Server (NTRS)

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

    1995-01-01

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

  3. Kernel mucking in top

    SciTech Connect

    LeFebvre, W.

    1994-08-01

    For many years, the popular program top has aided system administrations in examination of process resource usage on their machines. Yet few are familiar with the techniques involved in obtaining this information. Most of what is displayed by top is available only in the dark recesses of kernel memory. Extracting this information requires familiarity not only with how bytes are read from the kernel, but also what data needs to be read. The wide variety of systems and variants of the Unix operating system in today`s marketplace makes writing such a program very challenging. This paper explores the tremendous diversity in kernel information across the many platforms and the solutions employed by top to achieve and maintain ease of portability in the presence of such divergent systems.

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

    PubMed

    Wang, Liang; Li, Chuan

    2014-06-01

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

  5. [Pathophysiology of Ataxia in Fisher Syndrome].

    PubMed

    Kuwabara, Satoshi

    2016-12-01

    Fisher syndrome is regarded as a peculiar inflammatory neuropathy associated with ophthalmoplegia, ataxia, and areflexia. The disorder is associated with preceding infection, cerebrospinal fluid albumino-cytological dissociation, and spontaneous recovery, and regarded as a variant of Guillain-Barré syndrome. The discovery of anti-GQ1b IgG antibodies led to dramatic advances in understanding the pathophysiology of Fisher syndrome. The lesions in Fisher syndrome are determined by expression of ganglioside GQ1b in the human nervous system. This review article focuses on the pathophysiology of ataxia in Fisher syndrome. Current evidence suggests that antibody attack on Group Ia neurons in the dorsal root ganglia is mainly responsible for the sensory ataxia. Involvement of the muscle spindles might also contribute to the development of ataxia.

  6. Fisher Center, Simon's Rock College, Mass.

    ERIC Educational Resources Information Center

    Dillon, David

    2000-01-01

    Describes the design of the Fisher Science And Academic Center at Simon's Rock College (Massachusetts), that allows the buildings to seamlessly blend in with their rustic surroundings. Photos and diagrams detailing design features are provided. (GR)

  7. Fishers' knowledge and seahorse conservation in Brazil

    PubMed Central

    Rosa, Ierecê ML; Alves, Rômulo RN; Bonifácio, Kallyne M; Mourão, José S; Osório, Frederico M; Oliveira, Tacyana PR; Nottingham, Mara C

    2005-01-01

    From a conservationist perspective, seahorses are threatened fishes. Concomitantly, from a socioeconomic perspective, they represent a source of income to many fishing communities in developing countries. An integration between these two views requires, among other things, the recognition that seahorse fishers have knowledge and abilities that can assist the implementation of conservation strategies and of management plans for seahorses and their habitats. This paper documents the knowledge held by Brazilian fishers on the biology and ecology of the longsnout seahorse Hippocampus reidi. Its aims were to explore collaborative approaches to seahorse conservation and management in Brazil; to assess fishers' perception of seahorse biology and ecology, in the context evaluating potential management options; to increase fishers' involvement with seahorse conservation in Brazil. Data were obtained through questionnaires and interviews made during field surveys conducted in fishing villages located in the States of Piauí, Ceará, Paraíba, Maranhão, Pernambuco and Pará. We consider the following aspects as positive for the conservation of seahorses and their habitats in Brazil: fishers were willing to dialogue with researchers; although captures and/or trade of brooding seahorses occurred, most interviewees recognized the importance of reproduction to the maintenance of seahorses in the wild (and therefore of their source of income), and expressed concern over population declines; fishers associated the presence of a ventral pouch with reproduction in seahorses (regardless of them knowing which sex bears the pouch), and this may facilitate the construction of collaborative management options designed to eliminate captures of brooding specimens; fishers recognized microhabitats of importance to the maintenance of seahorse wild populations; fishers who kept seahorses in captivity tended to recognize the condtions as poor, and as being a cause of seahorse mortality. PMID

  8. An evaluation of parturition indices in fishers

    USGS Publications Warehouse

    Frost, H.C.; York, E.C.; Krohn, W.B.; Elowe, K.D.; Decker, T.A.; Powell, S.M.; Fuller, T.K.

    1999-01-01

    Fishers (Martes pennanti) are important forest carnivores and furbearers that are susceptible to overharvest. Traditional indices used to monitor fisher populations typically overestimate litter size and proportion of females that give birth. We evaluated the usefulness of 2 indices of reproduction to determine proportion of female fishers that gave birth in a particular year. We used female fishers of known age and reproductive histories to compare appearance of placental scars with incidence of pregnancy and litter size. Microscopic observation of freshly removed reproductive tracts correctly identified pregnant fishers and correctly estimated litter size in 3 of 4 instances, but gross observation of placental scars failed to correctly identify pregnant fishers and litter size. Microscopic observations of reproductive tracts in carcasses that were not fresh also failed to identify pregnant animals and litter size. We evaluated mean sizes of anterior nipples to see if different reproductive classes could be distinguished. Mean anterior nipple size of captive and wild fishers correctly identified current-year breeders from nonbreeders. Former breeders were misclassified in 4 of 13 instances. Presence of placental scars accurately predicted parturition in a small sample size of fishers, but absence of placental scars did not signify that a female did not give birth. In addition to enabling the estimation of parturition rates in live animals more accurately than traditional indices, mean anterior nipple size also provided an estimate of the percentage of adult females that successfully raised young. Though using mean anterior nipple size to index reproductive success looks promising, additional data are needed to evaluate effects of using dried, stretched pelts on nipple size for management purposes.

  9. Fishers' knowledge and seahorse conservation in Brazil.

    PubMed

    Rosa, Ierecê Ml; Alves, Rômulo Rn; Bonifácio, Kallyne M; Mourão, José S; Osório, Frederico M; Oliveira, Tacyana Pr; Nottingham, Mara C

    2005-12-08

    From a conservationist perspective, seahorses are threatened fishes. Concomitantly, from a socioeconomic perspective, they represent a source of income to many fishing communities in developing countries. An integration between these two views requires, among other things, the recognition that seahorse fishers have knowledge and abilities that can assist the implementation of conservation strategies and of management plans for seahorses and their habitats. This paper documents the knowledge held by Brazilian fishers on the biology and ecology of the longsnout seahorse Hippocampus reidi. Its aims were to explore collaborative approaches to seahorse conservation and management in Brazil; to assess fishers' perception of seahorse biology and ecology, in the context evaluating potential management options; to increase fishers' involvement with seahorse conservation in Brazil. Data were obtained through questionnaires and interviews made during field surveys conducted in fishing villages located in the States of Piauí, Ceará, Paraíba, Maranhão, Pernambuco and Pará. We consider the following aspects as positive for the conservation of seahorses and their habitats in Brazil: fishers were willing to dialogue with researchers; although captures and/or trade of brooding seahorses occurred, most interviewees recognized the importance of reproduction to the maintenance of seahorses in the wild (and therefore of their source of income), and expressed concern over population declines; fishers associated the presence of a ventral pouch with reproduction in seahorses (regardless of them knowing which sex bears the pouch), and this may facilitate the construction of collaborative management options designed to eliminate captures of brooding specimens; fishers recognized microhabitats of importance to the maintenance of seahorse wild populations; fishers who kept seahorses in captivity tended to recognize the condtions as poor, and as being a cause of seahorse mortality.

  10. Generative and discriminant feature extraction with supervised learning

    NASA Astrophysics Data System (ADS)

    Dhir, Chandra S.; Lee, Soo-Young

    2011-06-01

    Standard unsupervised feature extraction methods such as PCA and ICA provide representative features and latent variables which minimizes the data reconstruction error. These generative features may be common to all data, and may not be optimal for classification tasks. The discriminate ICA (dICA) and discriminant NMF (dNMF) had recently been proposed which jointly maximizes Fisher linear discriminant and Negentropy of the extracted features. Motivated by independence among features and modified Fisher linear discriminant, the new algorithm extracts features with both generative and discriminant powers. Then, the features are further fine-tuned by supervised learning. Experimental results show excellent recognition performance with these features.

  11. Robotic Intelligence Kernel: Visualization

    SciTech Connect

    2009-09-16

    The INL Robotic Intelligence Kernel-Visualization is the software that supports the user interface. It uses the RIK-C software to communicate information to and from the robot. The RIK-V illustrates the data in a 3D display and provides an operating picture wherein the user can task the robot.

  12. Robotic Intelligence Kernel: Architecture

    SciTech Connect

    2009-09-16

    The INL Robotic Intelligence Kernel Architecture (RIK-A) is a multi-level architecture that supports a dynamic autonomy structure. The RIK-A is used to coalesce hardware for sensing and action as well as software components for perception, communication, behavior and world modeling into a framework that can be used to create behaviors for humans to interact with the robot.

  13. Nonparametric estimation of Fisher information from real data

    NASA Astrophysics Data System (ADS)

    Har-Shemesh, Omri; Quax, Rick; Miñano, Borja; Hoekstra, Alfons G.; Sloot, Peter M. A.

    2016-02-01

    The Fisher information matrix (FIM) is a widely used measure for applications including statistical inference, information geometry, experiment design, and the study of criticality in biological systems. The FIM is defined for a parametric family of probability distributions and its estimation from data follows one of two paths: either the distribution is assumed to be known and the parameters are estimated from the data or the parameters are known and the distribution is estimated from the data. We consider the latter case which is applicable, for example, to experiments where the parameters are controlled by the experimenter and a complicated relation exists between the input parameters and the resulting distribution of the data. Since we assume that the distribution is unknown, we use a nonparametric density estimation on the data and then compute the FIM directly from that estimate using a finite-difference approximation to estimate the derivatives in its definition. The accuracy of the estimate depends on both the method of nonparametric estimation and the difference Δ θ between the densities used in the finite-difference formula. We develop an approach for choosing the optimal parameter difference Δ θ based on large deviations theory and compare two nonparametric density estimation methods, the Gaussian kernel density estimator and a novel density estimation using field theory method. We also compare these two methods to a recently published approach that circumvents the need for density estimation by estimating a nonparametric f divergence and using it to approximate the FIM. We use the Fisher information of the normal distribution to validate our method and as a more involved example we compute the temperature component of the FIM in the two-dimensional Ising model and show that it obeys the expected relation to the heat capacity and therefore peaks at the phase transition at the correct critical temperature.

  14. Minimum classification error-based weighted support vector machine kernels for speaker verification.

    PubMed

    Suh, Youngjoo; Kim, Hoirin

    2013-04-01

    Support vector machines (SVMs) have been proved to be an effective approach to speaker verification. An appropriate selection of the kernel function is a key issue in SVM-based classification. In this letter, a new SVM-based speaker verification method utilizing weighted kernels in the Gaussian mixture model supervector space is proposed. The weighted kernels are derived by using the discriminative training approach, which minimizes speaker verification errors. Experiments performed on the NIST 2008 speaker recognition evaluation task showed that the proposed approach provides substantially improved performance over the baseline kernel-based method.

  15. A fisher vector representation of GPR data for detecting buried objects

    NASA Astrophysics Data System (ADS)

    Karem, Andrew; Khalifa, Amine B.; Frigui, Hichem

    2016-05-01

    We present a new method, based on the Fisher Vector (FV), for detecting buried explosive objects using ground- penetrating radar (GPR) data. First, low-level dense SIFT features are extracted from a grid covering a region of interest (ROIs). ROIs are identified as regions with high-energy along the (down-track, depth) dimensions of the 3-D GPR cube, or with high-energy along the (cross-track, depth) dimensions. Next, we model the training data (in the SIFT feature space) by a mixture of Gaussian components. Then, we construct FV descriptors based on the Fisher Kernel. The Fisher Kernel characterizes low-level features from an ROI by their deviation from a generative model. The deviation is the gradient of the ROI log-likelihood with respect to the generative model parameters. The vectorial representation of all the deviations is called the Fisher Vector. FV is a generalization of the standard Bag of Words (BoW) method, which provides a framework to map a set of local descriptors to a global feature vector. It is more efficient to compute than the BoW since it relies on a significantly smaller codebook. In addition, mapping a GPR signature into one global feature vector using this technique makes it more efficient to classify using simple and fast linear classifiers such as Support Vector Machines. The proposed approach is applied to detect buried explosive objects using GPR data. The selected data were accumulated across multiple dates and multiple test sites by a vehicle mounted mine detector (VMMD) using GPR sensor. This data consist of a diverse set of conventional landmines and other buried explosive objects consisting of varying shapes, metal content, and burial depths. The performance of the proposed approach is analyzed using receiver operating characteristics (ROC) and is compared to other state-of-the-art feature representation methods.

  16. Application of Fisher Information to Complex Dynamic Systems

    EPA Science Inventory

    Fisher information was developed by the statistician Ronald Fisher as a measure of the information obtainable from data being used to fit a related parameter. Starting from the work of Ronald Fisher1 and B. Roy Frieden2, we have developed Fisher information as a measure of order ...

  17. Application of Fisher Information to Complex Dynamic Systems (Tucson)

    EPA Science Inventory

    Fisher information was developed by the statistician Ronald Fisher as a measure of the information obtainable from data being used to fit a related parameter. Starting from the work of Ronald Fisher1 and B. Roy Frieden2, we have developed Fisher information as a measure of order ...

  18. Application of Fisher Information to Complex Dynamic Systems

    EPA Science Inventory

    Fisher information was developed by the statistician Ronald Fisher as a measure of the information obtainable from data being used to fit a related parameter. Starting from the work of Ronald Fisher1 and B. Roy Frieden2, we have developed Fisher information as a measure of order ...

  19. Application of Fisher Information to Complex Dynamic Systems (Tucson)

    EPA Science Inventory

    Fisher information was developed by the statistician Ronald Fisher as a measure of the information obtainable from data being used to fit a related parameter. Starting from the work of Ronald Fisher1 and B. Roy Frieden2, we have developed Fisher information as a measure of order ...

  20. Historical harvest and incidental capture of fishers in California

    Treesearch

    Jeffrey C. Lewis; William J. Zielinski

    1996-01-01

    Recent petitions to list the fisher (Martes pennanti) under the Endangered Species Act have brought attention to fisher conservation. Although commercial trapping of fishers in California ended in 1946, summarizing the commercial harvest data can provide a historical perspective to fisher conservation and may indicate the prevalence of incidental...

  1. Discriminative components of data.

    PubMed

    Peltonen, Jaakko; Kaski, Samuel

    2005-01-01

    A simple probabilistic model is introduced to generalize classical linear discriminant analysis (LDA) in finding components that are informative of or relevant for data classes. The components maximize the predictability of the class distribution which is asymptotically equivalent to 1) maximizing mutual information with the classes, and 2) finding principal components in the so-called learning or Fisher metrics. The Fisher metric measures only distances that are relevant to the classes, that is, distances that cause changes in the class distribution. The components have applications in data exploration, visualization, and dimensionality reduction. In empirical experiments, the method outperformed, in addition to more classical methods, a Renyi entropy-based alternative while having essentially equivalent computational cost.

  2. Multiple Kernel Point Set Registration.

    PubMed

    Nguyen, Thanh Minh; Wu, Q M Jonathan

    2015-12-22

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

  3. Multiple Kernel Point Set Registration.

    PubMed

    Nguyen, Thanh Minh; Wu, Q M Jonathan

    2016-06-01

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

  4. Anytime query-tuned kernel machine classifiers via Cholesky factorization

    NASA Technical Reports Server (NTRS)

    DeCoste, D.

    2002-01-01

    We recently demonstrated 2 to 64-fold query-time speedups of Support Vector Machine and Kernel Fisher classifiers via a new computational geometry method for anytime output bounds (DeCoste,2002). This new paper refines our approach in two key ways. First, we introduce a simple linear algebra formulation based on Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on benchmark datasets.

  5. Anytime query-tuned kernel machine classifiers via Cholesky factorization

    NASA Technical Reports Server (NTRS)

    DeCoste, D.

    2002-01-01

    We recently demonstrated 2 to 64-fold query-time speedups of Support Vector Machine and Kernel Fisher classifiers via a new computational geometry method for anytime output bounds (DeCoste,2002). This new paper refines our approach in two key ways. First, we introduce a simple linear algebra formulation based on Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on benchmark datasets.

  6. Optimizing spatial filters with kernel methods for BCI applications

    NASA Astrophysics Data System (ADS)

    Zhang, Jiacai; Tang, Jianjun; Yao, Li

    2007-11-01

    Brain Computer Interface (BCI) is a communication or control system in which the user's messages or commands do not depend on the brain's normal output channels. The key step of BCI technology is to find a reliable method to detect the particular brain signals, such as the alpha, beta and mu components in EEG/ECOG trials, and then translate it into usable control signals. In this paper, our objective is to introduce a novel approach that is able to extract the discriminative pattern from the non-stationary EEG signals based on the common spatial patterns(CSP) analysis combined with kernel methods. The basic idea of our Kernel CSP method is performing a nonlinear form of CSP by the use of kernel methods that can efficiently compute the common and distinct components in high dimensional feature spaces related to input space by some nonlinear map. The algorithm described here is tested off-line with dataset I from the BCI Competition 2005. Our experiments show that the spatial filters employed with kernel CSP can effectively extract discriminatory information from single-trial EGOG recorded during imagined movements. The high recognition of linear discriminative rates and computational simplicity of "Kernel Trick" make it a promising method for BCI systems.

  7. Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

    NASA Astrophysics Data System (ADS)

    Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

    2015-10-01

    The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

  8. Kernel-aligned multi-view canonical correlation analysis for image recognition

    NASA Astrophysics Data System (ADS)

    Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

    2016-09-01

    Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

  9. Kernel methods for phenotyping complex plant architecture.

    PubMed

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

    2014-02-07

    The Quantitative Trait Loci (QTL) mapping of plant architecture is a critical step for understanding the genetic determinism of plant architecture. Previous studies adopted simple measurements, such as plant-height, stem-diameter and branching-intensity for QTL mapping of plant architecture. Many of these quantitative traits were generally correlated to each other, which give rise to statistical problem in the detection of QTL. We aim to test the applicability of kernel methods to phenotyping inflorescence architecture and its QTL mapping. We first test Kernel Principal Component Analysis (KPCA) and Support Vector Machines (SVM) over an artificial dataset of simulated inflorescences with different types of flower distribution, which is coded as a sequence of flower-number per node along a shoot. The ability of discriminating the different inflorescence types by SVM and KPCA is illustrated. We then apply the KPCA representation to the real dataset of rose inflorescence shoots (n=1460) obtained from a 98 F1 hybrid mapping population. We find kernel principal components with high heritability (>0.7), and the QTL analysis identifies a new QTL, which was not detected by a trait-by-trait analysis of simple architectural measurements. The main tools developed in this paper could be use to tackle the general problem of QTL mapping of complex (sequences, 3D structure, graphs) phenotypic traits.

  10. Sepsis mortality prediction with the Quotient Basis Kernel.

    PubMed

    Ribas Ripoll, Vicent J; Vellido, Alfredo; Romero, Enrique; Ruiz-Rodríguez, Juan Carlos

    2014-05-01

    This paper presents an algorithm to assess the risk of death in patients with sepsis. Sepsis is a common clinical syndrome in the intensive care unit (ICU) that can lead to severe sepsis, a severe state of septic shock or multi-organ failure. The proposed algorithm may be implemented as part of a clinical decision support system that can be used in combination with the scores deployed in the ICU to improve the accuracy, sensitivity and specificity of mortality prediction for patients with sepsis. In this paper, we used the Simplified Acute Physiology Score (SAPS) for ICU patients and the Sequential Organ Failure Assessment (SOFA) to build our kernels and algorithms. In the proposed method, we embed the available data in a suitable feature space and use algorithms based on linear algebra, geometry and statistics for inference. We present a simplified version of the Fisher kernel (practical Fisher kernel for multinomial distributions), as well as a novel kernel that we named the Quotient Basis Kernel (QBK). These kernels are used as the basis for mortality prediction using soft-margin support vector machines. The two new kernels presented are compared against other generative kernels based on the Jensen-Shannon metric (centred, exponential and inverse) and other widely used kernels (linear, polynomial and Gaussian). Clinical relevance is also evaluated by comparing these results with logistic regression and the standard clinical prediction method based on the initial SAPS score. As described in this paper, we tested the new methods via cross-validation with a cohort of 400 test patients. The results obtained using our methods compare favourably with those obtained using alternative kernels (80.18% accuracy for the QBK) and the standard clinical prediction method, which are based on the basal SAPS score or logistic regression (71.32% and 71.55%, respectively). The QBK presented a sensitivity and specificity of 79.34% and 83.24%, which outperformed the other kernels

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

    PubMed

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

    2013-04-01

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

  12. H. A. L. Fisher: Scholar and Minister

    ERIC Educational Resources Information Center

    Judge, Harry

    2006-01-01

    H. A. L. Fisher came from an influential family, studied at Oxford and in France and Germany, and became an Oxford academic with a strong interest in public affairs. In 1912 he became Vice-Chancellor of Sheffield University and four years later was recruited by the new British Prime Minister to become his Minister of Education. In that office he…

  13. Michael Fisher at King's College London

    NASA Astrophysics Data System (ADS)

    Domb, Cyril

    Michael Fisher spent the first 16 years of his academic life in the Physics Department of King's College, London, starting as an undergraduate and ending as a full professor. A survey is undertaken of his activities and achievements during the various periods of this phase of his career.

  14. The forest carnivores: marten and fisher

    Treesearch

    William J. Zielinski

    2014-01-01

    Martens and fishers, as predators, perform important functions that help sustain the integrity of ecosystems. Both species occur primarily in mature forest environments that are characterized by dense canopy, large-diameter trees, a diverse understory community, and abundant standing and downed dead trees. Martens occur in the upper montane forests, where the threat of...

  15. FISHER INFORMATION AND ECOSYSTEM REGIME CHANGES

    EPA Science Inventory

    Following Fisher’s work, we propose two different expressions for the Fisher Information along with Shannon Information as a means of detecting and assessing shifts between alternative ecosystem regimes. Regime shifts are a consequence of bifurcations in the dynamics of an ecosys...

  16. H. A. L. Fisher: Scholar and Minister

    ERIC Educational Resources Information Center

    Judge, Harry

    2006-01-01

    H. A. L. Fisher came from an influential family, studied at Oxford and in France and Germany, and became an Oxford academic with a strong interest in public affairs. In 1912 he became Vice-Chancellor of Sheffield University and four years later was recruited by the new British Prime Minister to become his Minister of Education. In that office he…

  17. EXERGY AND FISHER INFORMATION AS ECOLOGICAL INDEXES

    EPA Science Inventory

    Ecological indices are used to provide summary information about a particular aspect of ecosystem behavior. Many such indices have been proposed and here we investigate two: exergy and Fisher Information. Exergy, a thermodynamically based index, is a measure of maximum amount o...

  18. Resting habitat selection by fishers in California

    Treesearch

    William J. Zielinski; Richard L. Truex; Gregory A. Schmidt; Fredrick V. Schlexer; Kristin N. Schmidt; Reginald H. Barrett

    2004-01-01

    We studied the resting habitat ecology of fishers (Martes pennanti) in 2 disjunct populations in California, USA: the northwestern coastal mountains (hereafter, Coastal) and the southern Sierra Nevada (hereafter, Sierra). We described resting structures and compared features surrounding resting structures (the resting site) with those at randomly...

  19. FISHER INFORMATION AND ECOSYSTEM REGIME CHANGES

    EPA Science Inventory

    Following Fisher’s work, we propose two different expressions for the Fisher Information along with Shannon Information as a means of detecting and assessing shifts between alternative ecosystem regimes. Regime shifts are a consequence of bifurcations in the dynamics of an ecosys...

  20. 7 CFR 51.1415 - Inedible kernels.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  1. Fisher information and Rényi dimensions: A thermodynamical formalism

    SciTech Connect

    Godó, B.; Nagy, Á.

    2016-08-15

    The relation between the Fisher information and Rényi dimensions is established: the Fisher information can be expressed as a linear combination of the first and second derivatives of the Rényi dimensions with respect to the Rényi parameter β. The Rényi parameter β is the parameter of the Fisher information. A thermodynamical description based on the Fisher information with β being the inverse temperature is introduced for chaotic systems. The link between the Fisher information and the heat capacity is emphasized, and the Fisher heat capacity is introduced.

  2. Kernel phase and kernel amplitude in Fizeau imaging

    NASA Astrophysics Data System (ADS)

    Pope, Benjamin J. S.

    2016-12-01

    Kernel phase interferometry is an approach to high angular resolution imaging which enhances the performance of speckle imaging with adaptive optics. Kernel phases are self-calibrating observables that generalize the idea of closure phases from non-redundant arrays to telescopes with arbitrarily shaped pupils, by considering a matrix-based approximation to the diffraction problem. In this paper I discuss the recent history of kernel phase, in particular in the matrix-based study of sparse arrays, and propose an analogous generalization of the closure amplitude to kernel amplitudes. This new approach can self-calibrate throughput and scintillation errors in optical imaging, which extends the power of kernel phase-like methods to symmetric targets where amplitude and not phase calibration can be a significant limitation, and will enable further developments in high angular resolution astronomy.

  3. HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher's exact test.

    PubMed

    Sun, Shuying; Yu, Xiaoqing

    2016-03-01

    DNA methylation is an epigenetic event that plays an important role in regulating gene expression. It is important to study DNA methylation, especially differential methylation patterns between two groups of samples (e.g. patients vs. normal individuals). With next generation sequencing technologies, it is now possible to identify differential methylation patterns by considering methylation at the single CG site level in an entire genome. However, it is challenging to analyze large and complex NGS data. In order to address this difficult question, we have developed a new statistical method using a hidden Markov model and Fisher's exact test (HMM-Fisher) to identify differentially methylated cytosines and regions. We first use a hidden Markov chain to model the methylation signals to infer the methylation state as Not methylated (N), Partly methylated (P), and Fully methylated (F) for each individual sample. We then use Fisher's exact test to identify differentially methylated CG sites. We show the HMM-Fisher method and compare it with commonly cited methods using both simulated data and real sequencing data. The results show that HMM-Fisher outperforms the current available methods to which we have compared. HMM-Fisher is efficient and robust in identifying heterogeneous DM regions.

  4. 7 CFR 981.9 - Kernel weight.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  5. The Adaptive Kernel Neural Network

    DTIC Science & Technology

    1989-10-01

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

  6. Redshift evolution of Tully-Fisher relation

    NASA Astrophysics Data System (ADS)

    Ferrero, Ismael; Abadi, Mario G.

    2017-03-01

    Using the eagle cosmological simulation of galaxy formation we test the ability of the ΛCDM cosmological model to reproduce the Tully-Fisher relation (TFR) and its redshift evolution. We find that our simulated galaxies follow a TFR that is in good agreement with observed results up to z = 1, indicating no evolution in the slope and a weak decrease in the zero-point.

  7. Olympic Fisher Reintroduction Project: 2010 Progress Report

    USGS Publications Warehouse

    Lewis, Jeffrey C.; Happe, Patti J.; Jenkins, Kurt J.; Manson, David J.

    2010-01-01

    The 2010 progress report is a summary of the reintroduction, monitoring, and research efforts undertaken during the third year of the Olympic fisher reintroduction project. Jeffrey C. Lewis of Washington Department of Fish and Wildlife, Patti J. Happe of Olympic National Park, and Kurt J. Jenkins of U. S. Geological Survey are the principal investigators of the monitoring and research program associated with the reintroduction. David J. Manson of Olympic National Park is the lead biological technician.

  8. Olympic Fisher Reintroduction Project- 2009 Progress Report

    USGS Publications Warehouse

    Lewis, Jeffrey C.; Happe, Patti J.; Jenkins, Kurt J.; Manson, David J.

    2009-01-01

    The 2009 progress report is a summary of the reintroduction, monitoring, and research efforts undertaken during the first two years of the Olympic fisher reintroduction project. Jeffrey C. Lewis of Washington Department of Fish and Wildlife, Patti J. Happe of Olympic National Park, and Kurt J. Jenkins of U. S. Geological Survey are the principal investigators of the monitoring and research program associated with the reintroduction. David J. Manson of Olympic National Park is the lead biological

  9. Fisher equation for a decaying brane

    NASA Astrophysics Data System (ADS)

    Ghoshal, Debashis

    2011-12-01

    We consider the inhomogeneous decay of an unstable D-brane. The dynamical equation that describes this process (in light-cone time) is a variant of the non-linear reaction-diffusion equation that first made its appearance in the pioneering work of (Luther and) Fisher and appears in a variety of natural phenomena. We analyze its travelling front solution using singular perturbation theory.

  10. Effects of snow on fisher and marten distributions in Idaho

    Treesearch

    Nathan Albrecht; C. Heusser; M. Schwartz; J. Sauder; R. Vinkey

    2013-01-01

    Studies have suggested that deep snow may limit fisher (Martes pennanti) distribution, and that fisher populations may in turn limit marten (Martes americana) distribution. We tested these hypotheses in the Northern Rocky Mountains of Idaho, a region which differs from previous study areas in its climate and relative fisher and marten abundance, but in which very...

  11. Current distribution of the fisher, Martes pennanti, in California

    Treesearch

    William J. Zielinski; Thomas E. Kucera; Reginald H. Barrett

    1995-01-01

    We describe the 1989-1994 distribution of the fisher, Martes pennanti, in California based on results of detection surveys that used either sooted track-plates or cameras. Fishers were detected in two regions of the state: the northwest and the southern Sierra Nevada. Despite considerable survey effort, neither fisher tracks nor photographs were...

  12. Fisher information and Rényi entropies in dynamical systems

    NASA Astrophysics Data System (ADS)

    Godó, B.; Nagy, Á.

    2017-07-01

    The link between the Fisher information and Rényi entropies is explored. The relationship is based on a thermodynamical formalism based on Fisher information with a parameter, β, which is interpreted as the inverse temperature. The Fisher heat capacity is defined and found to be sensitive to changes of higher order than the analogous quantity in the conventional formulation.

  13. Fisher classifier and its probability of error estimation

    NASA Technical Reports Server (NTRS)

    Chittineni, C. B.

    1979-01-01

    Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.

  14. Canonical energy is quantum Fisher information

    NASA Astrophysics Data System (ADS)

    Lashkari, Nima; Van Raamsdonk, Mark

    2016-04-01

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

  15. A theory of Fisher's reproductive value.

    PubMed

    Grafen, Alan

    2006-07-01

    The formal Darwinism project aims to provide a mathematically rigorous basis for optimisation thinking in relation to natural selection. This paper deals with the situation in which individuals in a population belong to classes, such as sexes, or size and/or age classes. Fisher introduced the concept of reproductive value into biology to help analyse evolutionary processes of populations divided into classes. Here a rigorously defined and very general structure justifies, and shows the unity of concept behind, Fisher's uses of reproductive value as measuring the significance for evolutionary processes of (i) an individual and (ii) a class; (iii) recursively, as calculable for a parent as a sum of its shares in the reproductive values of its offspring; and (iv) as an evolutionary maximand under natural selection. The maximand is the same for all parental classes, and is a weighted sum of offspring numbers, which implies that a tradeoff in one aspect of the phenotype can legitimately be studied separately from other aspects. The Price equation, measure theory, Markov theory and positive operators contribute to the framework, which is then applied to a number of examples, including a new and fully rigorous version of Fisher's sex ratio argument. Classes may be discrete (e.g. sex), continuous (e.g. weight at fledging) or multidimensional with discrete and continuous components (e.g. sex and weight at fledging and adult tarsus length).

  16. Cell Development obeys Maximum Fisher Information

    PubMed Central

    Frieden, B. Roy; Gatenby, Robert A.

    2014-01-01

    Eukaryotic cell development has been optimized by natural selection to obey maximal intracellular flux of messenger proteins. This, in turn, implies maximum Fisher information on angular position about a target nuclear pore complex (NPR). The cell is simply modeled as spherical, with cell membrane (CM) diameter 10μm and concentric nuclear membrane (NM) diameter 6μm. The NM contains ≈ 3000 nuclear pore complexes (NPCs). Development requires messenger ligands to travel from the CM-NPC-DNA target binding sites. Ligands acquire negative charge by phosphorylation, passing through the cytoplasm over Newtonian trajectories toward positively charged NPCs (utilizing positive nuclear localization sequences). The CM-NPC channel obeys maximized mean protein flux F and Fisher information I at the NPC, with first-order δI = 0 and approximate 2nd-order δ2I ≈ 0 stability to environmental perturbations. Many of its predictions are confirmed, including the dominance of protein pathways of from 1–4 proteins, a 4nm size for the EGFR protein and the flux value F ≈1016 proteins/m2-s. After entering the nucleus, each protein ultimately delivers its ligand information to a DNA target site with maximum probability, i.e. maximum Kullback-Liebler entropy HKL. In a smoothness limit HKL → IDNA/2, so that the total CM-NPC-DNA channel obeys maximum Fisher I. Thus maximum information → non-equilibrium, one condition for life. PMID:23747917

  17. Studying Cerebral Vasculature Using Structure Proximity and Graph Kernels

    PubMed Central

    Kwitt, Roland; Pace, Danielle; Niethammer, Marc; Aylward, Stephen

    2014-01-01

    An approach to study population differences in cerebral vasculature is proposed. This is done by 1) extending the concept of encoding cerebral blood vessel networks as spatial graphs and 2) quantifying graph similarity in a kernel-based discriminant classifier setup. We argue that augmenting graph vertices with information about their proximity to selected brain structures adds discriminative information and consequently leads to a more expressive encoding. Using graph-kernels then allows us to quantify graph similarity in a principled way. To demonstrate our approach, we assess the hypothesis that gender differences manifest as variations in the architecture of cerebral blood vessels, an observation that previously had only been tested and confirmed for the Circle of Willis. Our results strongly support this hypothesis, i.e, we can demonstrate non-trivial, statistically significant deviations from random gender classification in a cross-validation setup on 40 healthy patients. PMID:24579182

  18. Manifold Kernel Sparse Representation of Symmetric Positive-Definite Matrices and Its Applications.

    PubMed

    Wu, Yuwei; Jia, Yunde; Li, Peihua; Zhang, Jian; Yuan, Junsong

    2015-11-01

    The symmetric positive-definite (SPD) matrix, as a connected Riemannian manifold, has become increasingly popular for encoding image information. Most existing sparse models are still primarily developed in the Euclidean space. They do not consider the non-linear geometrical structure of the data space, and thus are not directly applicable to the Riemannian manifold. In this paper, we propose a novel sparse representation method of SPD matrices in the data-dependent manifold kernel space. The graph Laplacian is incorporated into the kernel space to better reflect the underlying geometry of SPD matrices. Under the proposed framework, we design two different positive definite kernel functions that can be readily transformed to the corresponding manifold kernels. The sparse representation obtained has more discriminating power. Extensive experimental results demonstrate good performance of manifold kernel sparse codes in image classification, face recognition, and visual tracking.

  19. Robotic intelligence kernel

    DOEpatents

    Bruemmer, David J [Idaho Falls, ID

    2009-11-17

    A robot platform includes perceptors, locomotors, and a system controller. The system controller executes a robot intelligence kernel (RIK) that includes a multi-level architecture and a dynamic autonomy structure. The multi-level architecture includes a robot behavior level for defining robot behaviors, that incorporate robot attributes and a cognitive level for defining conduct modules that blend an adaptive interaction between predefined decision functions and the robot behaviors. The dynamic autonomy structure is configured for modifying a transaction capacity between an operator intervention and a robot initiative and may include multiple levels with at least a teleoperation mode configured to maximize the operator intervention and minimize the robot initiative and an autonomous mode configured to minimize the operator intervention and maximize the robot initiative. Within the RIK at least the cognitive level includes the dynamic autonomy structure.

  20. Flexible Kernel Memory

    PubMed Central

    Nowicki, Dimitri; Siegelmann, Hava

    2010-01-01

    This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces. PMID:20552013

  1. Sir Ronald A. Fisher and the International Biometric Society.

    PubMed

    Billard, Lynne

    2014-06-01

    The year 2012 marks the 50th anniversary of the death of Sir Ronald A. Fisher, one of the two Fathers of Statistics and a Founder of the International Biometric Society (the "Society"). To celebrate the extraordinary genius of Fisher and the far-sighted vision of Fisher and Chester Bliss in organizing and promoting the formation of the Society, this article looks at the origins and growth of the Society, some of the key players and events, and especially the roles played by Fisher himself as the First President. A fresh look at Fisher, the man rather than the scientific genius is also presented.

  2. [Rapid Identification of Cistanche via Fluorescence Spectrum Imaging Technology Combined with Principal Components Analysis and Fisher Distinction].

    PubMed

    Li, Yuan-peng; Huang, Fu-rong; Dong, Jia; Xiao, Chi; Xian, Rui-yi; Ma, Zhi-guo; Zhao, Jing

    2015-03-01

    In order to explore rapid reliable Hebra cistanche detection methods, identification of 3 different sources of Hebra cistanche: cistanche deserticola, cistanche tubulosa, sand rossia is studied via fluorescent spectral imaging technology combined with pattern recognition. It is found in experiment that cistanche samples have obvious fluorescence properties. Forty fluorescence spectral images of 3 different sources of Hebra cistanche samples are collected through fluorescent spectral imaging system. After carrying on denoising and binarization processing to these images, the spectral curves of each sample was drawn according to the spectral cube. The obtained spectra data in the 450 - 680 nm wavelength range is regarded as the study object of discriminant analysis. Then, principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of the three kinds of cistanche and fisher distinction is used in combination to classify them; During the experiment were compared the effects of three methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction (SNV) and first-order differential (FD) and then according to the cumulative contribution rate of the principal component and the effect of number of factors on the discriminant model to optimize the number of principal components factor. The results showed that. identification of the best after the first derivative pretreatment then the first four principal components is extracted to carry on fisher discriminant, discriminant model of 3 different sources of Hebra cistanche is set up through PCA combined with fisher discriminant the precision of original discrimination is 100%, recognition rate of the cross validation is 95%. It was thus shown that the fluorescent spectral imaging technology combined with principal components analysis and fisher distinction can be used for the identification study of 3 different sources of Hebra cistanche

  3. A Global Estimate of the Number of Coral Reef Fishers.

    PubMed

    Teh, Louise S L; Teh, Lydia C L; Sumaila, U Rashid

    2013-01-01

    Overfishing threatens coral reefs worldwide, yet there is no reliable estimate on the number of reef fishers globally. We address this data gap by quantifying the number of reef fishers on a global scale, using two approaches - the first estimates reef fishers as a proportion of the total number of marine fishers in a country, based on the ratio of reef-related to total marine fish landed values. The second estimates reef fishers as a function of coral reef area, rural coastal population, and fishing pressure. In total, we find that there are 6 million reef fishers in 99 reef countries and territories worldwide, of which at least 25% are reef gleaners. Our estimates are an improvement over most existing fisher population statistics, which tend to omit accounting for gleaners and reef fishers. Our results suggest that slightly over a quarter of the world's small-scale fishers fish on coral reefs, and half of all coral reef fishers are in Southeast Asia. Coral reefs evidently support the socio-economic well-being of numerous coastal communities. By quantifying the number of people who are employed as reef fishers, we provide decision-makers with an important input into planning for sustainable coral reef fisheries at the appropriate scale.

  4. A Global Estimate of the Number of Coral Reef Fishers

    PubMed Central

    Teh, Louise S. L.; Teh, Lydia C. L.; Sumaila, U. Rashid

    2013-01-01

    Overfishing threatens coral reefs worldwide, yet there is no reliable estimate on the number of reef fishers globally. We address this data gap by quantifying the number of reef fishers on a global scale, using two approaches - the first estimates reef fishers as a proportion of the total number of marine fishers in a country, based on the ratio of reef-related to total marine fish landed values. The second estimates reef fishers as a function of coral reef area, rural coastal population, and fishing pressure. In total, we find that there are 6 million reef fishers in 99 reef countries and territories worldwide, of which at least 25% are reef gleaners. Our estimates are an improvement over most existing fisher population statistics, which tend to omit accounting for gleaners and reef fishers. Our results suggest that slightly over a quarter of the world’s small-scale fishers fish on coral reefs, and half of all coral reef fishers are in Southeast Asia. Coral reefs evidently support the socio-economic well-being of numerous coastal communities. By quantifying the number of people who are employed as reef fishers, we provide decision-makers with an important input into planning for sustainable coral reef fisheries at the appropriate scale. PMID:23840327

  5. Hierarchy of measurement-induced Fisher information for composite states

    NASA Astrophysics Data System (ADS)

    Lu, Xiao-Ming; Luo, Shunlong; Oh, C. H.

    2012-08-01

    Quantum Fisher information, as an intrinsic quantity for quantum states, is a central concept in quantum detection and estimation. When quantum measurements are performed on quantum states, classical probability distributions arise, which in turn lead to classical Fisher information. In this paper, we exploit the classical Fisher information induced by quantum measurements and reveal a rich hierarchical structure of such measurement-induced Fisher information. We establish a general framework for the distribution and transfer of the Fisher information. In particular, we illustrate three extremal distribution types of the Fisher information: the locally owned type, the locally inaccessible type, and the fully shared type. Furthermore, we indicate the significant role played by the distribution and flow of the Fisher information in some physical problems, e.g., the non-Markovianity of open quantum processes, the environment-assisted metrology, the cloning and broadcasting, etc.

  6. Fisher-Mendel controversy in genetics: scientific argument, intellectual integrity, a fair society, Western falls and bioethical evaluation.

    PubMed

    Tang, Bing H

    2009-10-01

    This review article aims to discuss and analyze the background and findings regarding Fisher-Mendel Controversy in Genetics and to elucidate the scientific argument and intellectual integrity involved, as well as their importance in a fair society, and the lesson of Western falls as learned. At the onset of this review, the kernel of Mendel-Fisher Controversy is dissected and then identified. The fact of an organizational restructuring that had never gone towards a happy synchronization for the ensuing years since 1933 is demonstrated. It was at that time after Fisher succeeded Karl Pearson not only as the Francis Galton Professor of Eugenics but also as the chief of the Galton Laboratory at University College, London. The academic style of eugenics in the late 19th and early 20th centuries in the UK is then introduced. Fisher's ideology at that time, with its effects on the human value system and policy-making at that juncture are portrayed. Bioethical assessment is provided. Lessons in history, the emergence of the Eastern phenomenon and the decline of the Western power are outlined.

  7. 7 CFR 981.7 - Edible kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  8. 7 CFR 51.2295 - Half kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  9. Labeled Graph Kernel for Behavior Analysis

    PubMed Central

    Zhao, Ruiqi; Martinez, Aleix M.

    2016-01-01

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

  10. Labeled Graph Kernel for Behavior Analysis.

    PubMed

    Zhao, Ruiqi; Martinez, Aleix M

    2016-08-01

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

  11. Fisher, Neyman, and Bayes at FDA.

    PubMed

    Rubin, Donald B

    2016-01-01

    The wise use of statistical ideas in practice essentially requires some Bayesian thinking, in contrast to the classical rigid frequentist dogma. This dogma too often has seemed to influence the applications of statistics, even at agencies like the FDA. Greg Campbell was one of the most important advocates there for more nuanced modes of thought, especially Bayesian statistics. Because two brilliant statisticians, Ronald Fisher and Jerzy Neyman, are often credited with instilling the traditional frequentist approach in current practice, I argue that both men were actually seeking very Bayesian answers, and neither would have endorsed the rigid application of their ideas.

  12. Fisher's information function and Rasch measurement.

    PubMed

    Stone, Mark H

    2008-01-01

    Fisher's information function is reviewed with respect to an example he used for explication. A contemporary example continues the discussion with application to a rating scale instrument. The relationship of information to precision and measurement error is presented and discussed with respect to the analysis of fit. Targeting the instrument and the best test design for measuring a person with respect to information and item-person fit is discussed. The idealization of information and precision for making measures appears most effectively realized when computerized assisted testing can be employed to implement a best test design.

  13. "Antwone Fisher": how dangerous is "Dr Wonderful"?

    PubMed

    Macfarlane, Stephen

    2004-06-01

    To describe the style of psychotherapy portrayed in the film "Antwone Fisher". The rationale for this examination is that prospective patients often have little idea about what the process of psychotherapy may involve; depictions of therapy in popular films and on television serve to "prime" our patients' expectations in this regard. The film in question shows a psychiatrist who might be readily perceived as unambiguously good by a lay audience, despite a number of clear therapeutic boundary violations occurring throughout the film. Although such positive depictions might enhance the public image of psychiatry, they have the potential to create unreal expectations within patients and to promote the acceptability of boundary violations.

  14. FISHER'S GEOMETRIC MODEL WITH A MOVING OPTIMUM

    PubMed Central

    Matuszewski, Sebastian; Hermisson, Joachim; Kopp, Michael

    2014-01-01

    Fisher's geometric model has been widely used to study the effects of pleiotropy and organismic complexity on phenotypic adaptation. Here, we study a version of Fisher's model in which a population adapts to a gradually moving optimum. Key parameters are the rate of environmental change, the dimensionality of phenotype space, and the patterns of mutational and selectional correlations. We focus on the distribution of adaptive substitutions, that is, the multivariate distribution of the phenotypic effects of fixed beneficial mutations. Our main results are based on an “adaptive-walk approximation,” which is checked against individual-based simulations. We find that (1) the distribution of adaptive substitutions is strongly affected by the ecological dynamics and largely depends on a single composite parameter γ, which scales the rate of environmental change by the “adaptive potential” of the population; (2) the distribution of adaptive substitution reflects the shape of the fitness landscape if the environment changes slowly, whereas it mirrors the distribution of new mutations if the environment changes fast; (3) in contrast to classical models of adaptation assuming a constant optimum, with a moving optimum, more complex organisms evolve via larger adaptive steps. PMID:24898080

  15. The Baryonic Tully-Fisher Relation

    NASA Astrophysics Data System (ADS)

    Gurovich, Sebastián; McGaugh, Stacy S.; Freeman, Ken C.; Jerjen, Helmut; Staveley-Smith, Lister; De Blok, W. J. G.

    We validate the baryonic Tully-Fisher (TF) relation by exploring the Tully-Fisher (TF) and BTF properties of optically and HI-selected disk galaxies. The data includes galaxies from Sakai et al. (2000) calibrator sample, McGaugh et al. (2000: M2000) I-band sample, and 18 newly acquired HI-selected field dwarf galaxies observed with the ANU 2.3-m telescope and the ATNF Parkes telescope (Gurovich 2005a). As in M2000, we re-cast the TF and BTF relations as relationships between baryon mass and W20. First we report some numerical errors in M2000. Then, we calculate weighted bi-variate linear fits to the data, and finally we compare the fits of the intrinsically fainter dwarfs with the brighter galaxies of Sakai et al. (2000). With regards to the local calibrator disk galaxies of Sakai et al. (2000), our results suggest that the BTF relation is indeed tighter than the TF relation and that the slopes of the BTF relations are statistically flatter than the equivalent TF relations. Further, for the fainter galaxies which include the I-band M2000 and HI-selected galaxies of Gurovich's sample, we calculate a break from a simple power law model because of what appears to be real cosmic scatter. Not withstanding this point, the BTF models are marginally better models than the equivalent TF ones with slightly smaller χred2 values.

  16. Fisher information framework for time series modeling

    NASA Astrophysics Data System (ADS)

    Venkatesan, R. C.; Plastino, A.

    2017-08-01

    A robust prediction model invoking the Takens embedding theorem, whose working hypothesis is obtained via an inference procedure based on the minimum Fisher information principle, is presented. The coefficients of the ansatz, central to the working hypothesis satisfy a time independent Schrödinger-like equation in a vector setting. The inference of (i) the probability density function of the coefficients of the working hypothesis and (ii) the establishing of constraint driven pseudo-inverse condition for the modeling phase of the prediction scheme, is made, for the case of normal distributions, with the aid of the quantum mechanical virial theorem. The well-known reciprocity relations and the associated Legendre transform structure for the Fisher information measure (FIM, hereafter)-based model in a vector setting (with least square constraints) are self-consistently derived. These relations are demonstrated to yield an intriguing form of the FIM for the modeling phase, which defines the working hypothesis, solely in terms of the observed data. Cases for prediction employing time series' obtained from the: (i) the Mackey-Glass delay-differential equation, (ii) one ECG signal from the MIT-Beth Israel Deaconess Hospital (MIT-BIH) cardiac arrhythmia database, and (iii) one ECG signal from the Creighton University ventricular tachyarrhythmia database. The ECG samples were obtained from the Physionet online repository. These examples demonstrate the efficiency of the prediction model. Numerical examples for exemplary cases are provided.

  17. Quantum Fisher information in noninertial frames

    NASA Astrophysics Data System (ADS)

    Yao, Yao; Xiao, Xing; Ge, Li; Wang, Xiao-guang; Sun, Chang-pu

    2014-04-01

    We investigate the performance of quantum Fisher information (QFI) under the Unruh-Hawking effect, where one of the observers (e.g., Rob) is uniformly accelerated with respect to other partners. In the context of relativistic quantum information theory, we demonstrate that quantum Fisher information, as an important measure of the information content of quantum states, has a rich and subtle physical structure compared with entanglement or Bell nonlocality. In this work, we mainly focus on the parametrized (and arbitrary) pure two-qubit states, where the weight parameter θ and phase parameter ϕ are naturally introduced. Intriguingly, we prove that QFI with respect to θ (Fθ) remains unchanged for both scalar and Dirac fields. Meanwhile, we observe that QFI with respect to ϕ (Fϕ) decreases with the increase of acceleration r but remains finite in the limit of infinite acceleration. More importantly, our results show that the symmetry of Fϕ (with respect to θ =π/4) has been broken by the influence of the Unruh effect for both cases.

  18. Modulated traveling fronts for a nonlocal Fisher-KPP equation: A dynamical systems approach

    NASA Astrophysics Data System (ADS)

    Faye, Grégory; Holzer, Matt

    2015-04-01

    We consider a nonlocal generalization of the Fisher-KPP equation in one spatial dimension. As a parameter is varied, the system undergoes a Turing bifurcation. We study the dynamics near this Turing bifurcation. Our results are two-fold. First, we prove the existence of a two-parameter family of bifurcating stationary periodic solutions and derive a rigorous asymptotic approximation of these solutions. We also study the spectral stability of the bifurcating stationary periodic solutions with respect to almost co-periodic perturbations. Second, we restrict to a specific class of exponential kernels for which the nonlocal problem is transformed into a higher order partial differential equation. In this context, we prove the existence of modulated traveling fronts near the Turing bifurcation that describe the invasion of the Turing unstable homogeneous state by the periodic pattern established in the first part. Both results rely on a center manifold reduction to a finite dimensional ordinary differential equation.

  19. GeneFisher-P: variations of GeneFisher as processes in Bio-jETI

    PubMed Central

    Lamprecht, Anna-Lena; Margaria, Tiziana; Steffen, Bernhard; Sczyrba, Alexander; Hartmeier, Sven; Giegerich, Robert

    2008-01-01

    Background PCR primer design is an everyday, but not trivial task requiring state-of-the-art software. We describe the popular tool GeneFisher and explain its recent restructuring using workflow techniques. We apply a service-oriented approach to model and implement GeneFisher-P, a process-based version of the GeneFisher web application, as a part of the Bio-jETI platform for service modeling and execution. We show how to introduce a flexible process layer to meet the growing demand for improved user-friendliness and flexibility. Results Within Bio-jETI, we model the process using the jABC framework, a mature model-driven, service-oriented process definition platform. We encapsulate remote legacy tools and integrate web services using jETI, an extension of the jABC for seamless integration of remote resources as basic services, ready to be used in the process. Some of the basic services used by GeneFisher are in fact already provided as individual web services at BiBiServ and can be directly accessed. Others are legacy programs, and are made available to Bio-jETI via the jETI technology. The full power of service-based process orientation is required when more bioinformatics tools, available as web services or via jETI, lead to easy extensions or variations of the basic process. This concerns for instance variations of data retrieval or alignment tools as provided by the European Bioinformatics Institute (EBI). Conclusions The resulting service- and process-oriented GeneFisher-P demonstrates how basic services from heterogeneous sources can be easily orchestrated in the Bio-jETI platform and lead to a flexible family of specialized processes tailored to specific tasks. PMID:18460174

  20. Fisher information-based evaluation of image quality for time-of-flight PET.

    PubMed

    Vunckx, Kathleen; Zhou, Lin; Matej, Samuel; Defrise, Michel; Nuyts, Johan

    2010-02-01

    The use of time-of-flight (TOF) information during positron emission tomography (PET) reconstruction has been found to improve the image quality. In this work we quantified this improvement using two existing methods: 1) a very simple analytical expression only valid for a central point in a large uniform disk source and 2) efficient analytical approximations for postfiltered maximum likelihood expectation maximization (MLEM) reconstruction with a fixed target resolution, predicting the image quality in a pixel or in a small region of interest based on the Fisher information matrix. Using this latter method the weighting function for filtered backprojection reconstruction of TOF PET data proposed by C. Watson can be derived. The image quality was investigated at different locations in various software phantoms. Simplified as well as realistic phantoms, measured both with TOF PET systems and with a conventional PET system, were simulated. Since the time resolution of the system is not always accurately known, the effect on the image quality of using an inaccurate kernel during reconstruction was also examined with the Fisher information-based method. First, we confirmed with this method that the variance improvement in the center of a large uniform disk source is proportional to the disk diameter and inversely proportional to the time resolution. Next, image quality improvement was observed in all pixels, but in eccentric and high-count regions the contrast-to-noise ratio (CNR) increased less than in central and low- or medium-count regions. Finally, the CNR was seen to decrease when the time resolution was inaccurately modeled (too narrow or too wide) during reconstruction. Although the maximum CNR is not very sensitive to the time resolution error, using an inaccurate TOF kernel tends to introduce artifacts in the reconstructed image.

  1. An Approximate Approach to Automatic Kernel Selection.

    PubMed

    Ding, Lizhong; Liao, Shizhong

    2016-02-02

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

  2. Olympic Fisher Reintroduction Project: Progress report 2008-2011

    USGS Publications Warehouse

    Jeffrey C. Lewis,; Patti J. Happe,; Jenkins, Kurt J.; Manson, David J.

    2012-01-01

    This progress report summarizes the final year of activities of Phase I of the Olympic fisher restoration project. The intent of the Olympic fisher reintroduction project is to reestablish a self-sustaining population of fishers on the Olympic Peninsula. To achieve this goal, the Olympic fisher reintroduction project released 90 fishers within Olympic National Park from 2008 to 2010. The reintroduction of fishers to the Olympic Peninsula was designed as an adaptive management project, including the monitoring of released fishers as a means to (1) evaluate reintroduction success, (2) investigate key biological and ecological traits of fishers, and (3) inform future reintroduction, monitoring, and research efforts. This report summarizes reintroduction activities and preliminary research and monitoring results completed through December 2011. The report is non-interpretational in nature. Although we report the status of movement, survival, and home range components of the research, we have not completed final analyses and interpretation of research results. Much of the data collected during the monitoring and research project will be analyzed and interpreted in the doctoral dissertation being developed by Jeff Lewis; the completion of this dissertation is anticipated prior to April 2013. We anticipate that this work, and analyses of other data collected during the project, will result in several peer-reviewed scientific publications in ecological and conservation journals, which collectively will comprise the final reporting of work summarized here. These publications will include papers addressing post-release movements, survival, resource selection, food habits, and age determination of fishers.

  3. Kernel-based whole-genome prediction of complex traits: a review

    PubMed Central

    Morota, Gota; Gianola, Daniel

    2014-01-01

    Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics. PMID:25360145

  4. Trajectory Synthesis for Fisher Information Maximization

    PubMed Central

    Wilson, Andrew D.; Schultz, Jarvis A.; Murphey, Todd D.

    2015-01-01

    Estimation of model parameters in a dynamic system can be significantly improved with the choice of experimental trajectory. For general nonlinear dynamic systems, finding globally “best” trajectories is typically not feasible; however, given an initial estimate of the model parameters and an initial trajectory, we present a continuous-time optimization method that produces a locally optimal trajectory for parameter estimation in the presence of measurement noise. The optimization algorithm is formulated to find system trajectories that improve a norm on the Fisher information matrix (FIM). A double-pendulum cart apparatus is used to numerically and experimentally validate this technique. In simulation, the optimized trajectory increases the minimum eigenvalue of the FIM by three orders of magnitude, compared with the initial trajectory. Experimental results show that this optimized trajectory translates to an order-of-magnitude improvement in the parameter estimate error in practice. PMID:25598763

  5. Prenatal development in fishers (Martes pennanti)

    USGS Publications Warehouse

    Frost, H.C.; Krohn, W.B.; Bezembluk, E.A.; Lott, R.; Wallace, C.R.

    2005-01-01

    We evaluated and quantified prenatal growth of fishers (Martes pennanti) using ultrasonography. Seven females gave birth to 21 kits. The first identifiable embryonic structures were seen 42 d prepartum; these appeared to be unimplanted blastocysts or gestational sacs, which subsequently implanted in the uterine horns. Maternal and fetal heart rates were monitored from first detection to birth. Maternal heart rates did not differ among sampling periods, while fetal hearts rates increased from first detection to birth. Head and body differentiation, visible limbs and skeletal ossification were visible by 30, 23 and 21 d prepartum, respectively. Mean diameter of gestational sacs and crown-rump lengths were linearly related to gestational age (P < 0.001). Biparietal and body diameters were also linearly related to gestational age (P < 0.001) and correctly predicted parturition dates within 1-2 d. ?? 2004 Elsevier Inc. All rights reserved.

  6. Global Wilson-Fisher fixed points

    NASA Astrophysics Data System (ADS)

    Jüttner, Andreas; Litim, Daniel F.; Marchais, Edouard

    2017-08-01

    The Wilson-Fisher fixed point with O (N) universality in three dimensions is studied using the renormalisation group. It is shown how a combination of analytical and numerical techniques determine global fixed points to leading order in the derivative expansion for real or purely imaginary fields with moderate numerical effort. Universal and non-universal quantities such as scaling exponents and mass ratios are computed, for all N, together with local fixed point coordinates, radii of convergence, and parameters which control the asymptotic behaviour of the effective action. We also explain when and why finite-N results do not converge pointwise towards the exact infinite-N limit. In the regime of purely imaginary fields, a new link between singularities of fixed point effective actions and singularities of their counterparts by Polchinski are established. Implications for other theories are indicated.

  7. On the realization of quantum Fisher information

    NASA Astrophysics Data System (ADS)

    Saha, Aparna; Talukdar, B.; Chatterjee, Supriya

    2017-03-01

    With special attention to the role of information theory in physical sciences we present analytical results for the coordinate- and momentum-space Fisher information of some important one-dimensional quantum systems which differ in spacing of their energy levels. The studies envisaged allow us to relate the coordinate-space information ({I}ρ ) with the familiar energy levels of the quantum system. The corresponding momentum-space information ({I}γ ) does not obey such a simple relationship with the energy spectrum. Our results for the product ({I}ρ {I}γ ) depend quadratically on the principal quantum number n and satisfy an appropriate uncertainty relation derived by Dehesa et al (2007 J. Phys. A: Math. Theor. 40 1845)

  8. RTOS kernel in portable electrocardiograph

    NASA Astrophysics Data System (ADS)

    Centeno, C. A.; Voos, J. A.; Riva, G. G.; Zerbini, C.; Gonzalez, E. A.

    2011-12-01

    This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.

  9. Density Estimation with Mercer Kernels

    NASA Technical Reports Server (NTRS)

    Macready, William G.

    2003-01-01

    We present a new method for density estimation based on Mercer kernels. The density estimate can be understood as the density induced on a data manifold by a mixture of Gaussians fit in a feature space. As is usual, the feature space and data manifold are defined with any suitable positive-definite kernel function. We modify the standard EM algorithm for mixtures of Gaussians to infer the parameters of the density. One benefit of the approach is it's conceptual simplicity, and uniform applicability over many different types of data. Preliminary results are presented for a number of simple problems.

  10. A kernel Gabor-based weighted region covariance matrix for face recognition.

    PubMed

    Qin, Huafeng; Qin, Lan; Xue, Lian; Li, Yantao

    2012-01-01

    This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM).

  11. Sparse kernel entropy component analysis for dimensionality reduction of neuroimaging data.

    PubMed

    Jiang, Qikun; Shi, Jun

    2014-01-01

    The neuroimaging data typically has extremely high dimensions. Therefore, dimensionality reduction is commonly used to extract discriminative features. Kernel entropy component analysis (KECA) is a newly developed data transformation method, where the key idea is to preserve the most estimated Renyi entropy of the input space data set via a kernel-based estimator. Despite its good performance, KECA still suffers from the problem of low computational efficiency for large-scale data. In this paper, we proposed a sparse KECA (SKECA) algorithm with the recursive divide-and-conquer based solution, and then applied it to perform dimensionality reduction of neuroimaging data for classification of the Alzheimer's disease (AD). We compared the SKECA with KECA, principal component analysis (PCA), kernel PCA (KPCA) and sparse KPCA. The experimental results indicate that the proposed SKECA has most superior performance to all other algorithms when extracting discriminative features from neuroimaging data for AD classification.

  12. "The Streets of Harlem": The Short Stories of Rudolph Fisher.

    ERIC Educational Resources Information Center

    Deutsch, Leonard J.

    1979-01-01

    It is argued in this review of Fisher's stories that no other writer captured the manner and morals of Harlem in the 1920s and that these stories establish Fisher as the principal historian and social critic of the Harlem Renaissance period. (Author/EB)

  13. Job Satisfaction among Fishers in the Dominican Republic

    ERIC Educational Resources Information Center

    Ruiz, Victor

    2012-01-01

    This paper reflects on the results of a job satisfaction study of small-scale fishers in the Dominican Republic. The survey results suggest that, although fishers are generally satisfied with their occupations, they also have serious concerns. These concerns include anxieties about the level of earnings, the condition of marine resources and the…

  14. Anne Fisher and 18th-Century Literacy Training.

    ERIC Educational Resources Information Center

    Mitchell, Linda C.

    Anne Fisher, a pioneer in British education, was one of the few females in the 18th century to publish a significant grammatical work, one that was used widely in classrooms. This paper highlights Anne Fisher's historic achievement and argues from the discipline of the history of rhetoric that the two verbal disciplines of rhetoric and grammar are…

  15. Job Satisfaction among Fishers in the Dominican Republic

    ERIC Educational Resources Information Center

    Ruiz, Victor

    2012-01-01

    This paper reflects on the results of a job satisfaction study of small-scale fishers in the Dominican Republic. The survey results suggest that, although fishers are generally satisfied with their occupations, they also have serious concerns. These concerns include anxieties about the level of earnings, the condition of marine resources and the…

  16. Fisher research in the US Rocky Mountains: A critical overview

    Treesearch

    Michael Schwartz; J. Sauder

    2013-01-01

    In this talk we review the recent fisher research and monitoring efforts that have occurred throughout Idaho and Montana in past 2 decades. We begin this talk with a summary of the habitat relationship work that has examined fisher habitat use at multiple scales. These have largely been conducted using radio and satellite telemetry, although a new, joint effort to use...

  17. R. A. Fisher and his advocacy of randomization.

    PubMed

    Hall, Nancy S

    2007-01-01

    The requirement of randomization in experimental design was first stated by R. A. Fisher, statistician and geneticist, in 1925 in his book Statistical Methods for Research Workers. Earlier designs were systematic and involved the judgment of the experimenter; this led to possible bias and inaccurate interpretation of the data. Fisher's dictum was that randomization eliminates bias and permits a valid test of significance. Randomization in experimenting had been used by Charles Sanders Peirce in 1885 but the practice was not continued. Fisher developed his concepts of randomizing as he considered the mathematics of small samples, in discussions with "Student," William Sealy Gosset. Fisher published extensively. His principles of experimental design were spread worldwide by the many "voluntary workers" who came from other institutions to Rothamsted Agricultural Station in England to learn Fisher's methods.

  18. Travel-Time and Amplitude Sensitivity Kernels

    DTIC Science & Technology

    2011-09-01

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

  19. Local Observed-Score Kernel Equating

    ERIC Educational Resources Information Center

    Wiberg, Marie; van der Linden, Wim J.; von Davier, Alina A.

    2014-01-01

    Three local observed-score kernel equating methods that integrate methods from the local equating and kernel equating frameworks are proposed. The new methods were compared with their earlier counterparts with respect to such measures as bias--as defined by Lord's criterion of equity--and percent relative error. The local kernel item response…

  20. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

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

  1. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

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

  2. Characterizing contrast adaptation in a population of cat primary visual cortical neurons using Fisher information.

    PubMed

    Durant, Szonya; Clifford, Colin W G; Crowder, Nathan A; Price, Nicholas S C; Ibbotson, Michael R

    2007-06-01

    When cat V1/V2 cells are adapted to contrast at their optimal orientation, a reduction in gain and/or a shift in the contrast response function is found. We investigated how these factors combine at the population level to affect the accuracy for detecting variations in contrast. Using the contrast response function parameters from a physiologically measured population, we model the population accuracy (using Fisher information) for contrast discrimination. Adaptation at 16%, 32%, and 100% contrast causes a shift in peak accuracy. Despite an overall drop in firing rate over the whole population, accuracy is enhanced around the adapted contrast and at higher contrasts, leading to greater efficiency of contrast coding at these levels. The estimated contrast discrimination threshold curve becomes elevated and shifted toward higher contrasts after adaptation, as has been found previously in human psychophysical experiments.

  3. The NAS kernel benchmark program

    NASA Technical Reports Server (NTRS)

    Bailey, D. H.; Barton, J. T.

    1985-01-01

    A collection of benchmark test kernels that measure supercomputer performance has been developed for the use of the NAS (Numerical Aerodynamic Simulation) program at the NASA Ames Research Center. This benchmark program is described in detail and the specific ground rules are given for running the program as a performance test.

  4. Polar lipids from oat kernels

    USDA-ARS?s Scientific Manuscript database

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

  5. Adaptive wiener image restoration kernel

    DOEpatents

    Yuan, Ding

    2007-06-05

    A method and device for restoration of electro-optical image data using an adaptive Wiener filter begins with constructing imaging system Optical Transfer Function, and the Fourier Transformations of the noise and the image. A spatial representation of the imaged object is restored by spatial convolution of the image using a Wiener restoration kernel.

  6. Diffusion Kernels on Statistical Manifolds

    DTIC Science & Technology

    2004-01-16

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

  7. Increasing Fisher information by Potential Isobaric Reconstruction

    NASA Astrophysics Data System (ADS)

    Pan, Qiaoyin; Pen, Ue-Li; Inman, Derek; Yu, Hao-Ran

    2017-08-01

    Reconstruction techniques are commonly used in cosmology to reduce complicated non-linear behaviours to a more tractable linearized system. We study a new reconstruction technique that uses the moving-mesh algorithm to estimate the displacement field from non-linear matter distribution. We show the performance of this new technique by quantifying its ability to reconstruct linear modes. We study the cumulative Fisher information I(< kn) about the initial matter power spectrum in the matter power spectra in 130 N-body simulations before and after reconstruction and find that the non-linear plateau of I(< kn) is increased by a factor of ∼50 after reconstruction, from I ≃ 2.5 × 10-5/(Mpc h-1)3 to I ≃ 1.3 × 10-3/(Mpc h-1)3 at large k. This result includes the decorrelation between initial and final fields, which has been neglected in some previous studies. We expect this technique to be beneficial to problems such as baryonic acoustic oscillations, redshift space distortions and cosmic neutrinos that rely on accurately disentangling non-linear evolution from underlying linear effects.

  8. Zone of Avoidance Tully-Fisher Survey

    NASA Astrophysics Data System (ADS)

    Williams, Wendy; Woudt, Patrick; Kraan-Korteweg, Renee

    2009-10-01

    We propose to use the Parkes telescope to obtain narrowband HI spectra of a sample of galaxies in the Galactic Zone of Avoidance (ZOA). These observations, combined with high-quality near infrared photometry, will provide both the uniform coverage and accurate distance determinations (via the Tully-Fisher relation) required to map the peculiar velocity flow fields in the ZOA. The mass distribution in this region has a significant effect on the motion of the Local Group. Dynamically important structures, including the Great Attractor and the Local Void, are partially hidden behind our Galaxy. Even the most recent systematic all-sky surveys, such as the 2MASS Redshift Survey (2MRS; Huchra et al. 2005), undersample the ZOA due to stellar crowding and high dust extinction. While statistical reconstruction methods have been used to extrapolate the density field in the ZOA, they are unlikely to truely re?ect the velocity field (Loeb & Narayan 2008). Our project aims for the ?rst time to directly determine the velocity flow fields in this part of the sky. Our sample is taken from the Parkes HIZOA survey (Henning et al. 2005) and is unbiased with respect to extinction and star density.

  9. [Charles Miller Fisher: a giant of neurology].

    PubMed

    Tapia, Jorge

    2013-08-01

    C. Miller Fisher MD, one of the great neurologists in the 20th century, died in April 2012. Born in Canada, he studied medicine at the University of Toronto. As a Canadian Navy medical doctor he participated in World War II and was a war prisoner from 1941 to 1944. He did a residency in neurology at the Montreal Neurological Institute between 1946 and 1948, and later on was a Fellow in Neurology and Neuropathology at the Boston City Hospital. In 1954 he entered the Massachusetts General Hospital as a neurologist and neuropathologist, where he remained until his retirement, in 2005. His academic career ended as Professor Emeritus at Harvard University. His area of special interest in neurology was cerebrovascular disease (CVD). In 1954 he created the first Vascular Neurology service in the world and trained many leading neurologists on this field. His scientific contributions are present in more than 250 publications, as journal articles and book chapters. Many of his articles, certainly not restricted to CVD, were seminal in neurology. Several concepts and terms that he coined are currently used in daily clinical practice. The chapters on CVD, in seven consecutive editions of Harrison's Internal Medicine textbook, are among his highlights. His death was deeply felt by the neurological community.

  10. Statistical classification methods applied to seismic discrimination

    SciTech Connect

    Ryan, F.M.; Anderson, D.N.; Anderson, K.K.; Hagedorn, D.N.; Higbee, K.T.; Miller, N.E.; Redgate, T.; Rohay, A.C.

    1996-06-11

    To verify compliance with a Comprehensive Test Ban Treaty (CTBT), low energy seismic activity must be detected and discriminated. Monitoring small-scale activity will require regional (within {approx}2000 km) monitoring capabilities. This report provides background information on various statistical classification methods and discusses the relevance of each method in the CTBT seismic discrimination setting. Criteria for classification method selection are explained and examples are given to illustrate several key issues. This report describes in more detail the issues and analyses that were initially outlined in a poster presentation at a recent American Geophysical Union (AGU) meeting. Section 2 of this report describes both the CTBT seismic discrimination setting and the general statistical classification approach to this setting. Seismic data examples illustrate the importance of synergistically using multivariate data as well as the difficulties due to missing observations. Classification method selection criteria are presented and discussed in Section 3. These criteria are grouped into the broad classes of simplicity, robustness, applicability, and performance. Section 4 follows with a description of several statistical classification methods: linear discriminant analysis, quadratic discriminant analysis, variably regularized discriminant analysis, flexible discriminant analysis, logistic discriminant analysis, K-th Nearest Neighbor discrimination, kernel discrimination, and classification and regression tree discrimination. The advantages and disadvantages of these methods are summarized in Section 5.

  11. Co-existence of Fisheries and Marine Renewable Energy: The Spotlight on Fishers and Fishers' Knowledge (FK)

    NASA Astrophysics Data System (ADS)

    Campbell, M. S.; Ashley, M.; De Groot, J.; Rodwell, L.

    2016-02-01

    As an emerging industry, Marine Renewable Energy (MRE) is expected to play a major contributory role if the UK is to successfully reach it's desired target of renewable energy production by 2020. However, due to the competing objectives and priorities of MRE and other industries, for example fisheries, and in the delivering of conservation measures, the demand for space within our marine landscape is increasing, and interactions are inevitable. A semi structured interview was conducted with forty fishers across the UK to elicit further information on the challenges, barriers to progress and priority issues these fishers face in relation to MRE development. The questionnaire also included a fisher assessment of the mitigation agenda developed by de Groot et al. (2014) under the Natural Environment Research Council Marine Renewable Energy Knowledge Exchange Programme ( NERC MREKEP). Qualitative data were extracted and analysed using the text analysis software NVivo8. Fishers identified barriers to progress, and in order of the most important themes included; policy, consultation, trust, lack of knowledge, true representation of all fishers, science vs. fisher observation mismatch and timescales. Priority issues identified in order of importance were; displacement or loss of access, cable disturbance, timings of installation/repairs, effects on the seabed and specifically offshore windfarm (OWF) sitting. The consultation process caused discontent among all fishers interviewed. In relation to working towards a collaborative mitigation agenda, fishers highlighted issues of trust in relation to; trans-boundary management, data management and the consultation process. At all stages of the research, the response rate of the importance of gathering fishers' knowledge (FK) was high. Fishers underlined the importance of this data source in assessing the impacts of MRE on the sectors of the UK fleet. Thus, although at an early stage of development, an initial framework for the

  12. Uneven adaptive capacity among fishers in a sea of change

    PubMed Central

    Fuller, Emma; Crona, Beatrice I.

    2017-01-01

    Fishers worldwide operate in an environment of uncertainty and constant change. Their ability to manage risk associated with such uncertainty and subsequently adapt to change is largely a function of individual circumstances, including their access to different fisheries. However, explicit attention to the heterogeneity of fishers’ connections to fisheries at the level of the individual has been largely ignored. We illustrate the ubiquitous nature of these connections by constructing a typology of commercial fishers in the state of Maine based on the different fisheries that fishers rely on to sustain their livelihoods and find that there are over 600 combinations. We evaluate the adaptive potential of each strategy, using a set of attributes identified by fisheries experts in the state, and find that only 12% of fishers can be classified as being well positioned to adapt in the face of changing socioeconomic and ecological conditions. Sensitivity to the uneven and heterogeneous capacity of fishers to manage risk and adapt to change is critical to devising effective management strategies that broadly support fishers. This will require greater attention to the social-ecological connectivity of fishers across different jurisdictions. PMID:28604775

  13. 76 FR 63355 - Proposed Information Collection (Regulation on Application for Fisher Houses and Other Temporary...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-10-12

    ... Information Collection (Regulation on Application for Fisher Houses and Other Temporary Lodging and VHA Fisher House Application); Comment Request AGENCY: Veterans Health Administration, Department of Veterans... use of other forms of information technology. Title: Regulation on Application for Fisher Houses...

  14. The Fisher-Shannon information plane, an electron correlation tool.

    PubMed

    Romera, E; Dehesa, J S

    2004-05-15

    A new correlation measure, the product of the Shannon entropy power and the Fisher information of the electron density, is introduced by analyzing the Fisher-Shannon information plane of some two-electron systems (He-like ions, Hooke's atoms). The uncertainty and scaling properties of this information product are pointed out. In addition, the Fisher and Shannon measures of a finite many-electron system are shown to be bounded by the corresponding single-electron measures and the number of electrons of the system.

  15. Entropy, Fisher Information and Variance with Frost-Musulin Potenial

    NASA Astrophysics Data System (ADS)

    Idiodi, J. O. A.; Onate, C. A.

    2016-09-01

    This study presents the Shannon and Renyi information entropy for both position and momentum space and the Fisher information for the position-dependent mass Schrödinger equation with the Frost-Musulin potential. The analysis of the quantum mechanical probability has been obtained via the Fisher information. The variance information of this potential is equally computed. This controls both the chemical properties and physical properties of some of the molecular systems. We have observed the behaviour of the Shannon entropy. Renyi entropy, Fisher information and variance with the quantum number n respectively.

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

    PubMed

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

    2016-12-20

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

  17. Hyperspectral-imaging-based techniques applied to wheat kernels characterization

    NASA Astrophysics Data System (ADS)

    Serranti, Silvia; Cesare, Daniela; Bonifazi, Giuseppe

    2012-05-01

    Single kernels of durum wheat have been analyzed by hyperspectral imaging (HSI). Such an approach is based on the utilization of an integrated hardware and software architecture able to digitally capture and handle spectra as an image sequence, as they results along a pre-defined alignment on a surface sample properly energized. The study was addressed to investigate the possibility to apply HSI techniques for classification of different types of wheat kernels: vitreous, yellow berry and fusarium-damaged. Reflectance spectra of selected wheat kernels of the three typologies have been acquired by a laboratory device equipped with an HSI system working in near infrared field (1000-1700 nm). The hypercubes were analyzed applying principal component analysis (PCA) to reduce the high dimensionality of data and for selecting some effective wavelengths. Partial least squares discriminant analysis (PLS-DA) was applied for classification of the three wheat typologies. The study demonstrated that good classification results were obtained not only considering the entire investigated wavelength range, but also selecting only four optimal wavelengths (1104, 1384, 1454 and 1650 nm) out of 121. The developed procedures based on HSI can be utilized for quality control purposes or for the definition of innovative sorting logics of wheat.

  18. Local Kernel for Brains Classification in Schizophrenia

    NASA Astrophysics Data System (ADS)

    Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.

    In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.

  19. Kernel-based identification of regulatory modules.

    PubMed

    Schultheiss, Sebastian J

    2010-01-01

    The challenge of identifying cis-regulatory modules (CRMs) is an important milestone for the ultimate goal of understanding transcriptional regulation in eukaryotic cells. It has been approached, among others, by motif-finding algorithms that identify overrepresented motifs in regulatory sequences. These methods succeed in finding single, well-conserved motifs, but fail to identify combinations of degenerate binding sites, like the ones often found in CRMs. We have developed a method that combines the abilities of existing motif finding with the discriminative power of a machine learning technique to model the regulation of genes (Schultheiss et al. (2009) Bioinformatics 25, 2126-2133). Our software is called KIRMES: , which stands for kernel-based identification of regulatory modules in eukaryotic sequences. Starting from a set of genes thought to be co-regulated, KIRMES: can identify the key CRMs responsible for this behavior and can be used to determine for any other gene not included on that list if it is also regulated by the same mechanism. Such gene sets can be derived from microarrays, chromatin immunoprecipitation experiments combined with next-generation sequencing or promoter/whole genome microarrays. The use of an established machine learning method makes the approach fast to use and robust with respect to noise. By providing easily understood visualizations for the results returned, they become interpretable and serve as a starting point for further analysis. Even for complex regulatory relationships, KIRMES: can be a helpful tool in directing the design of biological experiments.

  20. Hyperspectral sensing data analysis based on quasiconformal mapping-based multiple kernels learning machine

    NASA Astrophysics Data System (ADS)

    Li, Jun-Bao; Xie, Xiaodan; Zhai, Jia; Pan, Jeng-Shyang

    2017-06-01

    Hyperspectral remote sensing has a strong ability of object information expression, so it provides better support for object classification. Many methods are proposed for the hyperspectral data classification. The spectrum classification is a classical nonlinear problem, and a kernel-based machine is feasible to classify the spectrum data. In the nonlinear kernel-based space, the spectrum data are more discriminative. The kernel functions determine the data distribution in the feature space. In this paper, we propose the quasiconformal multiple kernels-based machine learning for the hyperspectral data classification. In the framework, the structure of hyperspectral data is adaptively adjusted for classification. The multiple kernels extract the multiple features of hyperspectral data for classification. Multiple features-based machine learning exhibits a great potential on the classification of hyperspectral data. Two public datasets, India Pines dataset and Pavia University dataset, are used to test the proposed algorithm. Experimental results demonstrate that the proposed quasiconformal multiple kernels-based hyperspectral data classification method can show competitive performance.

  1. Extremal properties of the variance and the quantum Fisher information

    NASA Astrophysics Data System (ADS)

    Tóth, Géza; Petz, Dénes

    2013-03-01

    We show that the variance is its own concave roof. For rank-2 density matrices and operators with zero diagonal elements in the eigenbasis of the density matrix, we prove analytically that the quantum Fisher information is four times the convex roof of the variance. Strong numerical evidence suggests that this statement is true even for operators with nonzero diagonal elements or density matrices with a rank larger than 2. We also find that within the different types of generalized quantum Fisher information considered in Petz [J. Phys. A1361-644710.1088/0305-4470/35/4/305 35, 929 (2002)] and Gibilisco, Hiai, and Petz [IEEE Trans. Inf. TheoryIETTAW0018-944810.1109/TIT.2008.2008142 55, 439 (2009)], after appropriate normalization, the quantum Fisher information is the largest. Hence, we conjecture that the quantum Fisher information is four times the convex roof of the variance even for the general case.

  2. Using Fisher information to track stability in multivariate systems

    EPA Science Inventory

    With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI...

  3. Fisher-Shannon analysis of ionization processes and isoelectronic series

    SciTech Connect

    Sen, K. D.; Antolin, J.; Angulo, J. C.

    2007-09-15

    The Fisher-Shannon plane which embodies the Fisher information measure in conjunction with the Shannon entropy is tested in its ability to quantify and compare the informational behavior of the process of atomic ionization. We report the variation of such an information measure and its constituents for a comprehensive set of neutral atoms, and their isoelectronic series including the mononegative ions, using the numerical data generated on 320 atomic systems in position, momentum, and product spaces at the Hartree-Fock level. It is found that the Fisher-Shannon plane clearly reveals shell-filling patterns across the periodic table. Compared to position space, a significantly higher resolution is exhibited in momentum space. Characteristic features in the Fisher-Shannon plane accompanying the ionization process are identified, and the physical reasons for the observed patterns are described.

  4. Using Fisher information to track stability in multivariate systems

    EPA Science Inventory

    With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI) ...

  5. Nonlinear Deep Kernel Learning for Image Annotation.

    PubMed

    Jiu, Mingyuan; Sahbi, Hichem

    2017-02-08

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

  6. Constrained Fisher Scoring for a Mixture of Factor Analyzers

    DTIC Science & Technology

    2016-09-01

    ARL-TR-7836• SEP 2016 US Army Research Laboratory Constrained Fisher Scoring for a Mixture of Factor Analyzers by Gene T Whipps, Emre Ertin, and...TR-7836• SEP 2016 US Army Research Laboratory Constrained Fisher Scoring for a Mixture of Factor Analyzers by Gene T Whipps Sensors and Electron... Research Laboratory Sensors and Electron Devices Directorate ATTN: RDRL-SES-A Adelphi, MD 20783 primary author’s email: <gene.t.whipps.civ@mail.mil>. This

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

    PubMed

    Kwak, Nojun

    2013-12-01

    In kernel methods such as kernel principal component analysis (PCA) and support vector machines, the so called kernel trick is used to avoid direct calculations in a high (virtually infinite) dimensional kernel space. In this brief, based on the fact that the effective dimensionality of a kernel space is less than the number of training samples, we propose an alternative to the kernel trick that explicitly maps the input data into a reduced dimensional kernel space. This is easily obtained by the eigenvalue decomposition of the kernel matrix. The proposed method is named as the nonlinear projection trick in contrast to the kernel trick. With this technique, the applicability of the kernel methods is widened to arbitrary algorithms that do not use the dot product. The equivalence between the kernel trick and the nonlinear projection trick is shown for several conventional kernel methods. In addition, we extend PCA-L1, which uses L1-norm instead of L2-norm (or dot product), into a kernel version and show the effectiveness of the proposed approach.

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

    PubMed

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

    2010-01-01

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

  9. Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

    PubMed Central

    Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

    2016-01-01

    Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

  10. Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

    PubMed

    Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

    2016-08-16

    Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.

  11. Filters, reproducing kernel, and adaptive meshfree method

    NASA Astrophysics Data System (ADS)

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

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

  12. Several new kernel estimators for population abundance

    NASA Astrophysics Data System (ADS)

    Albadareen, Baker; Ismail, Noriszura

    2017-04-01

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

  13. Diffusion Map Kernel Analysis for Target Classification

    DTIC Science & Technology

    2010-06-01

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

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

    PubMed

    Daliri, Mohammad Reza

    2013-07-01

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

  15. Modeling an Operating System Kernel

    NASA Astrophysics Data System (ADS)

    Börger, Egon; Craig, Iain

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

  16. Molecular Hydrodynamics from Memory Kernels

    NASA Astrophysics Data System (ADS)

    Lesnicki, Dominika; Vuilleumier, Rodolphe; Carof, Antoine; Rotenberg, Benjamin

    2016-04-01

    The memory kernel for a tagged particle in a fluid, computed from molecular dynamics simulations, decays algebraically as t-3 /2 . We show how the hydrodynamic Basset-Boussinesq force naturally emerges from this long-time tail and generalize the concept of hydrodynamic added mass. This mass term is negative in the present case of a molecular solute, which is at odds with incompressible hydrodynamics predictions. Lastly, we discuss the various contributions to the friction, the associated time scales, and the crossover between the molecular and hydrodynamic regimes upon increasing the solute radius.

  17. 76 FR 78739 - Agency Information Collection (Regulation on Application for Fisher Houses and Other Temporary...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-12-19

    ... AFFAIRS Agency Information Collection (Regulation on Application for Fisher Houses and Other Temporary Lodging and VHA Fisher House Application) Activity Under OMB Review AGENCY: Veterans Health Administration... Application for Fisher Houses and Other Temporary Lodging and VHA Fisher House Application, VA Forms...

  18. Kernel Learning of Histogram of Local Gabor Phase Patterns for Face Recognition

    NASA Astrophysics Data System (ADS)

    Zhang, Baochang; Wang, Zongli; Zhong, Bineng

    2008-12-01

    This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP), which is based on Daugman's method for iris recognition and the local XOR pattern (LXP) operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC) method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP) using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.

  19. On the validity of cosmological Fisher matrix forecasts

    SciTech Connect

    Wolz, Laura; Kilbinger, Martin; Weller, Jochen; Giannantonio, Tommaso E-mail: martin.kilbinger@cea.fr E-mail: tommaso@usm.lmu.de

    2012-09-01

    We present a comparison of Fisher matrix forecasts for cosmological probes with Monte Carlo Markov Chain (MCMC) posterior likelihood estimation methods. We analyse the performance of future Dark Energy Task Force (DETF) stage-III and stage-IV dark-energy surveys using supernovae, baryon acoustic oscillations and weak lensing as probes. We concentrate in particular on the dark-energy equation of state parameters w{sub 0} and w{sub a}. For purely geometrical probes, and especially when marginalising over w{sub a}, we find considerable disagreement between the two methods, since in this case the Fisher matrix can not reproduce the highly non-elliptical shape of the likelihood function. More quantitatively, the Fisher method underestimates the marginalized errors for purely geometrical probes between 30%-70%. For cases including structure formation such as weak lensing, we find that the posterior probability contours from the Fisher matrix estimation are in good agreement with the MCMC contours and the forecasted errors only changing on the 5% level. We then explore non-linear transformations resulting in physically-motivated parameters and investigate whether these parameterisations exhibit a Gaussian behaviour. We conclude that for the purely geometrical probes and, more generally, in cases where it is not known whether the likelihood is close to Gaussian, the Fisher matrix is not the appropriate tool to produce reliable forecasts.

  20. R.A. Fisher's contributions to genetical statistics.

    PubMed

    Thompson, E A

    1990-12-01

    R. A. Fisher (1890-1962) was a professor of genetics, and many of his statistical innovations found expression in the development of methodology in statistical genetics. However, whereas his contributions in mathematical statistics are easily identified, in population genetics he shares his preeminence with Sewall Wright (1889-1988) and J. B. S. Haldane (1892-1965). This paper traces some of Fisher's major contributions to the foundations of statistical genetics, and his interactions with Wright and with Haldane which contributed to the development of the subject. With modern technology, both statistical methodology and genetic data are changing. Nonetheless much of Fisher's work remains relevant, and may even serve as a foundation for future research in the statistical analysis of DNA data. For Fisher's work reflects his view of the role of statistics in scientific inference, expressed in 1949: There is no wide or urgent demand for people who will define methods of proof in set theory in the name of improving mathematical statistics. There is a widespread and urgent demand for mathematicians who understand that branch of mathematics known as theoretical statistics, but who are capable also of recognising situations in the real world to which such mathematics is applicable. In recognising features of the real world to which his models and analyses should be applicable, Fisher laid a lasting foundation for statistical inference in genetic analyses.

  1. Fisher statistics for analysis of diffusion tensor directional information.

    PubMed

    Hutchinson, Elizabeth B; Rutecki, Paul A; Alexander, Andrew L; Sutula, Thomas P

    2012-04-30

    A statistical approach is presented for the quantitative analysis of diffusion tensor imaging (DTI) directional information using Fisher statistics, which were originally developed for the analysis of vectors in the field of paleomagnetism. In this framework, descriptive and inferential statistics have been formulated based on the Fisher probability density function, a spherical analogue of the normal distribution. The Fisher approach was evaluated for investigation of rat brain DTI maps to characterize tissue orientation in the corpus callosum, fornix, and hilus of the dorsal hippocampal dentate gyrus, and to compare directional properties in these regions following status epilepticus (SE) or traumatic brain injury (TBI) with values in healthy brains. Direction vectors were determined for each region of interest (ROI) for each brain sample and Fisher statistics were applied to calculate the mean direction vector and variance parameters in the corpus callosum, fornix, and dentate gyrus of normal rats and rats that experienced TBI or SE. Hypothesis testing was performed by calculation of Watson's F-statistic and associated p-value giving the likelihood that grouped observations were from the same directional distribution. In the fornix and midline corpus callosum, no directional differences were detected between groups, however in the hilus, significant (p<0.0005) differences were found that robustly confirmed observations that were suggested by visual inspection of directionally encoded color DTI maps. The Fisher approach is a potentially useful analysis tool that may extend the current capabilities of DTI investigation by providing a means of statistical comparison of tissue structural orientation.

  2. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  3. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

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

  4. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

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

  5. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

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

  6. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

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

  7. Kernel current source density method.

    PubMed

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

    2012-02-01

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

  8. KERNEL PHASE IN FIZEAU INTERFEROMETRY

    SciTech Connect

    Martinache, Frantz

    2010-11-20

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

  9. Bergman Kernel from Path Integral

    NASA Astrophysics Data System (ADS)

    Douglas, Michael R.; Klevtsov, Semyon

    2010-01-01

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

  10. Data-Driven Hierarchical Structure Kernel for Multiscale Part-Based Object Recognition

    PubMed Central

    Wang, Botao; Xiong, Hongkai; Jiang, Xiaoqian; Zheng, Yuan F.

    2017-01-01

    Detecting generic object categories in images and videos are a fundamental issue in computer vision. However, it faces the challenges from inter and intraclass diversity, as well as distortions caused by viewpoints, poses, deformations, and so on. To solve object variations, this paper constructs a structure kernel and proposes a multiscale part-based model incorporating the discriminative power of kernels. The structure kernel would measure the resemblance of part-based objects in three aspects: 1) the global similarity term to measure the resemblance of the global visual appearance of relevant objects; 2) the part similarity term to measure the resemblance of the visual appearance of distinctive parts; and 3) the spatial similarity term to measure the resemblance of the spatial layout of parts. In essence, the deformation of parts in the structure kernel is penalized in a multiscale space with respect to horizontal displacement, vertical displacement, and scale difference. Part similarities are combined with different weights, which are optimized efficiently to maximize the intraclass similarities and minimize the interclass similarities by the normalized stochastic gradient ascent algorithm. In addition, the parameters of the structure kernel are learned during the training process with regard to the distribution of the data in a more discriminative way. With flexible part sizes on scale and displacement, it can be more robust to the intraclass variations, poses, and viewpoints. Theoretical analysis and experimental evaluations demonstrate that the proposed multiscale part-based representation model with structure kernel exhibits accurate and robust performance, and outperforms state-of-the-art object classification approaches. PMID:24808345

  11. Enhancement of Fusarium head blight detection in free-falling wheat kernels using a bichromatic pulsed LED design

    NASA Astrophysics Data System (ADS)

    Yang, I.-Chang; Delwiche, Stephen R.; Chen, Suming; Lo, Y. Martin

    2009-02-01

    Fusarium head blight is a worldwide fungal disease of small cereal grains such as wheat that affects the yield, quality, and safety of food and feed products. The current study was implemented to develop more efficient methods for optically detecting Fusarium-damaged (scabby) kernels from normal (sound) wheat kernels. Through development of a high-power pulsed LED (green and red) inspection system, it was found that Fusarium-damaged and normal wheat kernels have different reflected energy responses. Two parameters (slope and r2) from a regression analysis of the green and red responses were used as input parameters in linear discriminant analysis models. The examined factors affecting accuracy were the orientation of the optical probe, the color contrast between normal and Fusarium-damaged kernels, and the manner in which one LED's response is time-matched to the other LED. Whereas commercial high-speed optical sorters are, on average, 50% efficient at removing mold-damaged kernels, this efficiency can rise to 95% or better under more carefully controlled, kernel-at-rest conditions in the laboratory. The current research on free-falling kernels has demonstrated accuracies (>90% for wheat samples of high visual contrast) that approach those of controlled conditions, which will lead to improvements in high-speed optical sorters.

  12. Improving the Bandwidth Selection in Kernel Equating

    ERIC Educational Resources Information Center

    Andersson, Björn; von Davier, Alina A.

    2014-01-01

    We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners…

  13. Ranking Support Vector Machine with Kernel Approximation

    PubMed Central

    Dou, Yong

    2017-01-01

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

  14. Ranking Support Vector Machine with Kernel Approximation.

    PubMed

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

    2017-01-01

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

  15. Improving the Bandwidth Selection in Kernel Equating

    ERIC Educational Resources Information Center

    Andersson, Björn; von Davier, Alina A.

    2014-01-01

    We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners…

  16. Kernel method for corrections to scaling.

    PubMed

    Harada, Kenji

    2015-07-01

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

  17. The context-tree kernel for strings.

    PubMed

    Cuturi, Marco; Vert, Jean-Philippe

    2005-10-01

    We propose a new kernel for strings which borrows ideas and techniques from information theory and data compression. This kernel can be used in combination with any kernel method, in particular Support Vector Machines for string classification, with notable applications in proteomics. By using a Bayesian averaging framework with conjugate priors on a class of Markovian models known as probabilistic suffix trees or context-trees, we compute the value of this kernel in linear time and space while only using the information contained in the spectrum of the considered strings. This is ensured through an adaptation of a compression method known as the context-tree weighting algorithm. Encouraging classification results are reported on a standard protein homology detection experiment, showing that the context-tree kernel performs well with respect to other state-of-the-art methods while using no biological prior knowledge.

  18. Phase space view of quantum mechanical systems and Fisher information

    NASA Astrophysics Data System (ADS)

    Nagy, Á.

    2016-06-01

    Pennini and Plastino showed that the form of the Fisher information generated by the canonical distribution function reflects the intrinsic structure of classical mechanics. Now, a quantum mechanical generalization of the Pennini-Plastino theory is presented based on the thermodynamical transcription of the density functional theory. Comparing to the classical case, the phase-space Fisher information contains an extra term due to the position dependence of the temperature. However, for the special case of constant temperature, the expression derived bears resemblance to the classical one. A complete analogy to the classical case is demonstrated for the linear harmonic oscillator.

  19. Fisher information as a gamma-ray detector design tool

    NASA Astrophysics Data System (ADS)

    Salçin, Esen; Furenlid, Lars R.

    2014-09-01

    The extraction of gamma-ray event information from detectors is an estimation problem as the signals are governed by multiple random effects such as information carrier (eg; scintillation-light photon, electron-hole pair) generation, propagation/transport and detection. A quantitative measure of how well the measured signals can be used to produce an estimate of the parameters is given by Fisher Information. In this work, we demonstrate several applications of Fisher Information as a powerful practical tool to quantify the information content in detector signals and help guide design decisions in scintillation and semiconductor detector development.

  20. COMPARISON OF SPARSE CODING AND KERNEL METHODS FOR HISTOPATHOLOGICAL CLASSIFICATION OF GLIOBASTOMA MULTIFORME

    PubMed Central

    Han, Ju; Chang, Hang; Loss, Leandro; Zhang, Kai; Baehner, Fredrick L.; Gray, Joe W.; Spellman, Paul; Parvin, Bahram

    2012-01-01

    This paper compares performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven to be an effective technique for restoration, and has recently been extended to classification. The main issue with classification of histology sections is inherent heterogeneity as a result of technical and biological variations. Technical variations originate from sample preparation, fixation, and staining from multiple laboratories, where biological variations originate from tissue content. Image patches are represented with invariant features at local and global scales, where local refers to responses measured with Laplacian of Gaussians, and global refers to measurements in the color space. Experiments are designed to learn dictionaries, through sparse coding, and to train classifiers through kernel methods with normal, necorotic, apoptotic, and tumor with with characteristics of high cellularity. Two different kernel methods of support vector machine (SVM) and kernel discriminant analysis (KDA) are used for comparative analysis. Preliminary investigation on histological samples of Glioblastoma multiforme (GBM) indicates that kernel methods perform as good if not better than sparse coding with redundant representation. PMID:23243485

  1. Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

    PubMed

    Williams, Paul; Geladi, Paul; Fox, Glen; Manley, Marena

    2009-10-27

    The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images, PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

  2. Discrimination of Maize Haploid Seeds from Hybrid Seeds Using Vis Spectroscopy and Support Vector Machine Method.

    PubMed

    Liu, Jin; Guo, Ting-ting; Li, Hao-chuan; Jia, Shi-qiang; Yan, Yan-lu; An, Dong; Zhang, Yao; Chen, Shao-jiang

    2015-11-01

    Doubled haploid (DH) lines are routinely applied in the hybrid maize breeding programs of many institutes and companies for their advantages of complete homozygosity and short breeding cycle length. A key issue in this approach is an efficient screening system to identify haploid kernels from the hybrid kernels crossed with the inducer. At present, haploid kernel selection is carried out manually using the"red-crown" kernel trait (the haploid kernel has a non-pigmented embryo and pigmented endosperm) controlled by the R1-nj gene. Manual selection is time-consuming and unreliable. Furthermore, the color of the kernel embryo is concealed by the pericarp. Here, we establish a novel approach for identifying maize haploid kernels based on visible (Vis) spectroscopy and support vector machine (SVM) pattern recognition technology. The diffuse transmittance spectra of individual kernels (141 haploid kernels and 141 hybrid kernels from 9 genotypes) were collected using a portable UV-Vis spectrometer and integrating sphere. The raw spectral data were preprocessed using smoothing and vector normalization methods. The desired feature wavelengths were selected based on the results of the Kolmogorov-Smirnov test. The wavelengths with p values above 0. 05 were eliminated because the distributions of absorbance data in these wavelengths show no significant difference between haploid and hybrid kernels. Principal component analysis was then performed to reduce the number of variables. The SVM model was evaluated by 9-fold cross-validation. In each round, samples of one genotype were used as the testing set, while those of other genotypes were used as the training set. The mean rate of correct discrimination was 92.06%. This result demonstrates the feasibility of using Vis spectroscopy to identify haploid maize kernels. The method would help develop a rapid and accurate automated screening-system for haploid kernels.

  3. Discriminant analysis of Chinese patent medicines based on near-infrared spectroscopy and principal component discriminant transformation.

    PubMed

    Xu, Zhihong; Liu, Yan; Li, Xiaoyong; Cai, Wensheng; Shao, Xueguang

    2015-01-01

    Principal component discriminant transformation was applied for discrimination of different Chinese patent medicines based on near-infrared (NIR) spectroscopy. In the method, an optimal set of orthogonal discriminant vectors, which highlight the differences between the NIR spectra of different classes, is designed by maximizing Fisher's discriminant function. Therefore, a model for discriminating a class and the others can be obtained with the tiny differences between the NIR spectra of different classes. Furthermore, because NIR spectra contain a large amount of redundant information, principal component analysis (PCA) is employed to reduce the dimension. On the other hand, continuous wavelet transform (CWT) is taken as the pretreatment method to remove the variant background. For identifying the method, different medicines and the same medicine from different manufactures were studied. The results show that all the models can provide 100% discrimination. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Bayesian Kernel Mixtures for Counts.

    PubMed

    Canale, Antonio; Dunson, David B

    2011-12-01

    Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.

  5. MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES

    PubMed Central

    Dunson, David B.

    2013-01-01

    Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitates sparse nonparametric modeling of multivariate random effects distributions, this article proposes a kernel partition process (KPP) in which the cluster allocation varies for different parameters. The KPP is shown to be the driving measure for a multivariate ordered Chinese restaurant process that induces a highly-flexible dependence structure in local clustering. This structure allows the relative locations of the random effects to inform the clustering process, with spatially-proximal random effects likely to be assigned the same cluster index. An exact block Gibbs sampler is developed for posterior computation, avoiding truncation of the infinite measure. The methods are applied to hormone curve data, and a dependent KPP is proposed for classification from functional predictors. PMID:24478563

  6. Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.

    PubMed

    Echinaka, Yuki; Ozeki, Yukiyasu

    2016-10-01

    The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.

  7. FISHER INFORMATION AS A METRIC FOR SUSTAINABLE REGIMES

    EPA Science Inventory

    The important question in sustainability is not whether the world is sustainable, but whether a humanly acceptable regime of the world is sustainable. We propose Fisher Information as a metric for the sustainability of dynamic regimes in complex systems. The quantity now known ...

  8. All about Community: Jane Fisher--New York Public Library

    ERIC Educational Resources Information Center

    Library Journal, 2004

    2004-01-01

    This brief article focuses on the career and accomplishments of Coordinator of Information Services, New York Public Library (NYPL), Jane Fisher. Her professional and academic career has spanned the fields of librarianship, health care, and public administration. Based on her most recent experiences and advancements, she has learned how libraries…

  9. Martens, sables, and fishers: new synthesis informs management and conservation

    Treesearch

    Keith B. Aubry; Martin G. Raphael; Marie. Oliver

    2014-01-01

    Martens, sables, and fishers are midsized carnivores belonging to the genus Martes. Their silky coats have been valued in the fur trade for centuries, which has contributed to a marked decline in their numbers. Pacific Northwest Martes species depend on structurally complex forested ecosystems and specific climatic conditions...

  10. FISHER INFORMATION AS A METRIC FOR SUSTAINABLE SYSTEM REGIMES

    EPA Science Inventory

    The important question in sustainability is not whether the world is sustainable, but whether a humanly acceptable regime of the world is sustainable. We propose Fisher Information as a metric for the sustainability of dynamic regimes in complex systems. The quantity now known ...

  11. FISHER INFORMATION AS A METRIC FOR SUSTAINABLE REGIMES

    EPA Science Inventory

    The important question in sustainability is not whether the world is sustainable, but whether a humanly acceptable regime of the world is sustainable. We propose Fisher Information as a metric for the sustainability of dynamic regimes in complex systems. The quantity now known ...

  12. Confidence intervals that match Fisher's exact or Blaker's exact tests

    PubMed Central

    Fay, Michael P.

    2010-01-01

    When analyzing a 2 × 2 table, the two-sided Fisher's exact test and the usual exact confidence interval (CI) for the odds ratio may give conflicting inferences; for example, the test rejects but the associated CI contains an odds ratio of 1. The problem is that the usual exact CI is the inversion of the test that rejects if either of the one-sided Fisher's exact tests rejects at half the nominal significance level. Further, the confidence set that is the inversion of the usual two-sided Fisher's exact test may not be an interval, so following Blaker (2000, Confidence curves and improved exact confidence intervals for discrete distributions. Canadian Journal of Statistics 28, 783–798), we define the “matching” interval as the smallest interval that contains the confidence set. We explore these 2 versions of Fisher's exact test as well as an exact test suggested by Blaker (2000) and provide the R package exact2 ×2 which automatically assigns the appropriate matching interval to each of the 3 exact tests. PMID:19948745

  13. FISHER INFORMATION AND DYNAMIC REGIME CHANGES IN ECOLOGICAL SYSTEMS

    EPA Science Inventory

    Fisher Information and Dynamic Regime Changes in Ecological Systems
    Abstract for the 3rd Conference of the International Society for Ecological Informatics
    Audrey L. Mayer, Christopher W. Pawlowski, and Heriberto Cabezas

    The sustainable nature of particular dynamic...

  14. 77 FR 15650 - Fisher House and Other Temporary Lodging

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-03-16

    ... rule is ambiguous in this regard. The capacity for self-care is required because neither Fisher House..., of $100 million or more (adjusted annually for inflation) in any year. This proposed rule would have... at a VA health care facility (generally referred to as a ``hoptel''); (2) A hotel or motel; (3)...

  15. Detection and Assessment of Ecosystem Regime Shifts from Fisher Information

    EPA Science Inventory

    Ecosystem regime shifts, which are long-term system reorganizations, have profound implications for sustainability. There is a great need for indicators of regime shifts, particularly methods that are applicable to data from real systems. We have developed a form of Fisher info...

  16. Postcolonial Appalachia: Bhabha, Bakhtin, and Diane Gilliam Fisher's "Kettle Bottom"

    ERIC Educational Resources Information Center

    Stevenson, Sheryl

    2006-01-01

    Diane Gilliam Fisher's 2004 award-winning book of poems, "Kettle Bottom," offers students a revealing vantage point for seeing Appalachian regional culture in a postcolonial context. An artful and accessible poetic sequence that was selected as the 2005 summer reading for entering students at Smith College, "Kettle Bottom"…

  17. FISHER INFORMATION AS A METRIC FOR SUSTAINABLE SYSTEM REGIMES

    EPA Science Inventory

    The important question in sustainability is not whether the world is sustainable, but whether a humanly acceptable regime of the world is sustainable. We propose Fisher Information as a metric for the sustainability of dynamic regimes in complex systems. The quantity now known ...

  18. Mortality risks and limits to population growth of fishers

    Treesearch

    Rick A. Sweitzer; Viorel D. Popescu; Craig M. Thompson; Kathryn L. Purcell; Reginald H. Barrett; Greta M. Wengert; Mourad W. Gabriel; Leslie W. Woods

    2015-01-01

    Fishers (Pekania pennanti) in the west coast states of Washington, Oregon, and California, USA have not recovered from population declines and the United States Fish and Wildlife Service has proposed options for listing them as threatened. Our objectives were to evaluate differences in survival and mortality risk from natural (e.g., predation, disease, injuries,...

  19. Fisher, Wall and Wilson on "Punishment": A Critique

    ERIC Educational Resources Information Center

    Wilson, P. S.

    1973-01-01

    Discussion based on Wilson on the justification of punishment,'' by M. Fisher and G. Wall, Journal of Moral Education, v1 n3; and The justification of punishment,'' by J. Wilson, British Journal of Educational Studies, v19 pt2. (CB)

  20. Detection and Assessment of Ecosystem Regime Shifts from Fisher Information

    EPA Science Inventory

    Ecosystem regime shifts, which are long-term system reorganizations, have profound implications for sustainability. There is a great need for indicators of regime shifts, particularly methods that are applicable to data from real systems. We have developed a form of Fisher info...

  1. All about Community: Jane Fisher--New York Public Library

    ERIC Educational Resources Information Center

    Library Journal, 2004

    2004-01-01

    This brief article focuses on the career and accomplishments of Coordinator of Information Services, New York Public Library (NYPL), Jane Fisher. Her professional and academic career has spanned the fields of librarianship, health care, and public administration. Based on her most recent experiences and advancements, she has learned how libraries…

  2. Appendix B: Fisher, lynx, wolverine summary of distribution information

    Treesearch

    Mary Maj

    1994-01-01

    We present maps depicting distributions of fisher, lynx, and wolverine in the western United States since 1961. Comparison of past and current distributions of species can shed light on population persistence, periods of population isolation, meta-population structure, and important connecting landscapes. Information on the distribution of the American marten is not...

  3. Fisher information and quantum potential well model for finance

    NASA Astrophysics Data System (ADS)

    Nastasiuk, V. A.

    2015-09-01

    The probability distribution function (PDF) for prices on financial markets is derived by extremization of Fisher information. It is shown how on that basis the quantum-like description for financial markets arises and different financial market models are mapped by quantum mechanical ones.

  4. Fisher conservation in the Pacific States: field data meet genetics.

    Treesearch

    Jonathan. Thompson

    2005-01-01

    Overtrapping of fishers in the early 1900s, combined with widespread habitat loss from clearcut logging, has resulted in the extirpation of this forest-dwelling carnivore throughout much of its former range in the Western United States. Poor dispersal abilities, low-density populations, and low reproductive rates all hinder the recovery of this little-known relative of...

  5. FISHER INFORMATION AND DYNAMIC REGIME CHANGES IN ECOLOGICAL SYSTEMS

    EPA Science Inventory

    Fisher Information and Dynamic Regime Changes in Ecological Systems
    Abstract for the 3rd Conference of the International Society for Ecological Informatics
    Audrey L. Mayer, Christopher W. Pawlowski, and Heriberto Cabezas

    The sustainable nature of particular dynamic...

  6. Perturbed kernel approximation on homogeneous manifolds

    NASA Astrophysics Data System (ADS)

    Levesley, J.; Sun, X.

    2007-02-01

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

  7. Putting Priors in Mixture Density Mercer Kernels

    NASA Technical Reports Server (NTRS)

    Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

    2004-01-01

    This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

  8. The Tully-Fisher relations for Hickson compact group galaxies

    NASA Astrophysics Data System (ADS)

    Torres-Flores, S.; Mendes de Oliveira, C.; Plana, H.; Amram, P.; Epinat, B.

    2013-07-01

    We used K-band photometry, maximum rotational velocities derived from Fabry-Perot data and H I observed and predicted masses to study, for the first time, the K band, stellar and baryonic Tully-Fisher relations for galaxies in Hickson compact groups. We compared these relations with the ones defined for galaxies in less dense environments from the Gassendi HAlpha survey of Spirals and from a sample of gas-rich galaxies. We find that most of the Hickson compact group galaxies lie on the K-band Tully-Fisher relation defined by field galaxies with a few low-mass outliers, namely HCG 49b and HCG 96c, which appear to have had strong recent burst of star formation. The stellar Tully-Fisher relation for compact group galaxies presents a similar dispersion to that of the K-band relation, and it has no significant outliers when a proper computation of the stellar mass is done for the strongly star-forming galaxies. The scatter in these relations can be reduced if the gaseous component is taken into account, i.e. if a baryonic Tully-Fisher relation is considered. In order to explain the positions of the galaxies off the K-band Tully-Fisher relation, we favour a scenario in which their luminosities are brightened due to strong star formation or AGN activity. We argue that strong bursts of star formation can affect the B- and K-band luminosities of HCG 49b and HCG 96c and in the case of the latter also AGN activity may affect the K-band magnitude considerably, without affecting their total masses.

  9. Assessing Fishers' Support of Striped Bass Management Strategies.

    PubMed

    Murphy, Robert D; Scyphers, Steven B; Grabowski, Jonathan H

    2015-01-01

    Incorporating the perspectives and insights of stakeholders is an essential component of ecosystem-based fisheries management, such that policy strategies should account for the diverse interests of various groups of anglers to enhance their efficacy. Here we assessed fishing stakeholders' perceptions on the management of Atlantic striped bass (Morone saxatilis) and receptiveness to potential future regulations using an online survey of recreational and commercial fishers in Massachusetts and Connecticut (USA). Our results indicate that most fishers harbored adequate to positive perceptions of current striped bass management policies when asked to grade their state's management regime. Yet, subtle differences in perceptions existed between recreational and commercial fishers, as well as across individuals with differing levels of fishing experience, resource dependency, and tournament participation. Recreational fishers in both states were generally supportive or neutral towards potential management actions including slot limits (71%) and mandated circle hooks to reduce mortality of released fish (74%), but less supportive of reduced recreational bag limits (51%). Although commercial anglers were typically less supportive of management changes than their recreational counterparts, the majority were still supportive of slot limits (54%) and mandated use of circle hooks (56%). Our study suggests that both recreational and commercial fishers are generally supportive of additional management strategies aimed at sustaining healthy striped bass populations and agree on a variety of strategies. However, both stakeholder groups were less supportive of harvest reductions, which is the most direct measure of reducing mortality available to fisheries managers. By revealing factors that influence stakeholders' support or willingness to comply with management strategies, studies such as ours can help managers identify potential stakeholder support for or conflicts that may

  10. Assessing Fishers' Support of Striped Bass Management Strategies

    PubMed Central

    Murphy, Robert D.; Scyphers, Steven B.; Grabowski, Jonathan H.

    2015-01-01

    Incorporating the perspectives and insights of stakeholders is an essential component of ecosystem-based fisheries management, such that policy strategies should account for the diverse interests of various groups of anglers to enhance their efficacy. Here we assessed fishing stakeholders’ perceptions on the management of Atlantic striped bass (Morone saxatilis) and receptiveness to potential future regulations using an online survey of recreational and commercial fishers in Massachusetts and Connecticut (USA). Our results indicate that most fishers harbored adequate to positive perceptions of current striped bass management policies when asked to grade their state’s management regime. Yet, subtle differences in perceptions existed between recreational and commercial fishers, as well as across individuals with differing levels of fishing experience, resource dependency, and tournament participation. Recreational fishers in both states were generally supportive or neutral towards potential management actions including slot limits (71%) and mandated circle hooks to reduce mortality of released fish (74%), but less supportive of reduced recreational bag limits (51%). Although commercial anglers were typically less supportive of management changes than their recreational counterparts, the majority were still supportive of slot limits (54%) and mandated use of circle hooks (56%). Our study suggests that both recreational and commercial fishers are generally supportive of additional management strategies aimed at sustaining healthy striped bass populations and agree on a variety of strategies. However, both stakeholder groups were less supportive of harvest reductions, which is the most direct measure of reducing mortality available to fisheries managers. By revealing factors that influence stakeholders’ support or willingness to comply with management strategies, studies such as ours can help managers identify potential stakeholder support for or conflicts that

  11. Approximating W projection as a separable kernel

    NASA Astrophysics Data System (ADS)

    Merry, Bruce

    2016-02-01

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

  12. Invariance kernel of biological regulatory networks.

    PubMed

    Ahmad, Jamil; Roux, Olivier

    2010-01-01

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

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

    PubMed Central

    Huang, Lulu; Massa, Lou

    2010-01-01

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

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

    PubMed

    Arif, Omar; Vela, Patricio A

    2011-06-01

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

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

    PubMed

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

    2002-03-27

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

  16. Damage Identification with Linear Discriminant Operators

    SciTech Connect

    Farrar, C.R.; Nix, D.A.; Duffey, T.A.; Cornwell, P.J.; Pardoen, G.C.

    1999-02-08

    This paper explores the application of statistical pattern recognition and machine learning techniques to vibration-based damage detection. First, the damage detection process is described in terms of a problem in statistical pattern recognition. Next, a specific example of a statistical-pattern-recognition-based damage detection process using a linear discriminant operator, ''Fisher's Discriminant'', is applied to the problem of identifying structural damage in a physical system. Accelerometer time histories are recorded from sensors attached to the system as that system is excited using a measured input. Linear Prediction Coding (LPC) coefficients are utilized to convert the accelerometer time-series data into multi-dimensional samples representing the resonances of the system during a brief segment of the time series. Fisher's discriminant is then used to find the linear projection of the LPC data distributions that best separates data from undamaged and damaged systems. The method i s applied to data from concrete bridge columns as the columns are progressively damaged. For this case, the method captures a clear distinction between undamaged and damaged vibration profiles. Further, the method assigns a probability of damage that can be used to rank systems in order of priority for inspection.

  17. 7 CFR 51.2296 - Three-fourths half kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  18. 7 CFR 51.2125 - Split or broken kernels.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will...

  19. 7 CFR 51.1403 - Kernel color classification.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... 7 Agriculture 2 2014-01-01 2014-01-01 false Kernel color classification. 51.1403 Section 51.1403... Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color classifications provided in this section. When the color of kernels in a lot...

  20. 7 CFR 51.1403 - Kernel color classification.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 7 Agriculture 2 2011-01-01 2011-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...

  1. 7 CFR 51.1403 - Kernel color classification.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... 7 Agriculture 2 2012-01-01 2012-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...

  2. 7 CFR 51.1403 - Kernel color classification.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... 7 Agriculture 2 2013-01-01 2013-01-01 false Kernel color classification. 51.1403 Section 51.1403... Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color classifications provided in this section. When the color of kernels in a lot...

  3. Regularized discriminative direction for shape difference analysis.

    PubMed

    Zhou, Luping; Hartley, Richard; Wang, Lei; Lieby, Paulette; Barnes, Nick

    2008-01-01

    The "discriminative direction" has been proven useful to reveal the subtle difference between two anatomical shape classes. When a shape moves along this direction, its deformation will best manifest the class difference detected by a kernel classifier. However, we observe that such a direction cannot maintain a shape's "anatomical" correctness, introducing spurious difference. To overcome this drawback, we develop a regularized discriminative direction by requiring a shape to conform to its population distribution when it deforms along the discriminative direction. Instead of iterative optimization, an analytic solution is provided to directly work out this direction. Experimental study shows its superior performance in detecting and localizing the difference of hippocampal shapes for sex. The result is supported by other independent research in the same domain.

  4. Sugar Profile of Kernels as a Marker of Origin and Ripening Time of Peach (Prunus persicae L.).

    PubMed

    Stanojević, Marija; Trifković, Jelena; Akšić, Milica Fotirić; Rakonjac, Vera; Nikolić, Dragan; Šegan, Sandra; Milojković-Opsenica, Dušanka

    2015-12-01

    Large amounts of fruit seeds, especially peach, are discarded annually in juice or conserve producing industries which is a potential waste of valuable resource and serious disposal problem. Regarding the fact that peach seeds can be obtained as a byproduct from processing companies their exploitation should be greater and, consequently more information of cultivars' kernels and their composition is required. A total of 25 samples of kernels from various peach germplasm (including commercial cultivars, perspective hybrids and vineyard peach accessions) differing in origin and ripening time were characterized by evaluation of their sugar composition. Twenty characteristic carbohydrates and sugar alcohols were determined and quantified using high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC/PAD). Sucrose, glucose and fructose are the most important sugars in peach kernels similar to other representatives of the Rosaceae family. Also, high amounts of sugars in seeds of promising hybrids implies that through conventional breeding programs peach kernels with high sugar content can be obtained. In addition, by the means of several pattern recognition methods the variables that discriminate peach kernels arising from diverse germplasm and different stage of maturity were identified and successful models for further prediction were developed. Sugars such as ribose, trehalose, arabinose, galactitol, fructose, maltose, sorbitol, sucrose, iso-maltotriose were marked as most important for such discrimination.

  5. Kernel-Based Equiprobabilistic Topographic Map Formation.

    PubMed

    Van Hulle MM

    1998-09-15

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

  6. Bergman kernel from the lowest Landau level

    NASA Astrophysics Data System (ADS)

    Klevtsov, S.

    2009-07-01

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

  7. Quantum kernel applications in medicinal chemistry.

    PubMed

    Huang, Lulu; Massa, Lou

    2012-07-01

    Progress in the quantum mechanics of biological molecules is being driven by computational advances. The notion of quantum kernels can be introduced to simplify the formalism of quantum mechanics, making it especially suitable for parallel computation of very large biological molecules. The essential idea is to mathematically break large biological molecules into smaller kernels that are calculationally tractable, and then to represent the full molecule by a summation over the kernels. The accuracy of the kernel energy method (KEM) is shown by systematic application to a great variety of molecular types found in biology. These include peptides, proteins, DNA and RNA. Examples are given that explore the KEM across a variety of chemical models, and to the outer limits of energy accuracy and molecular size. KEM represents an advance in quantum biology applicable to problems in medicine and drug design.

  8. KITTEN Lightweight Kernel 0.1 Beta

    SciTech Connect

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

    2007-12-12

    The Kitten Lightweight Kernel is a simplified OS (operating system) kernel that is intended to manage a compute node's hardware resources. It provides a set of mechanisms to user-level applications for utilizing hardware resources (e.g., allocating memory, creating processes, accessing the network). Kitten is much simpler than general-purpose OS kernels, such as Linux or Windows, but includes all of the esssential functionality needed to support HPC (high-performance computing) MPI, PGAS and OpenMP applications. Kitten provides unique capabilities such as physically contiguous application memory, transparent large page support, and noise-free tick-less operation, which enable HPC applications to obtain greater efficiency and scalability than with general purpose OS kernels.

  9. TICK: Transparent Incremental Checkpointing at Kernel Level

    SciTech Connect

    Petrini, Fabrizio; Gioiosa, Roberto

    2004-10-25

    TICK is a software package implemented in Linux 2.6 that allows the save and restore of user processes, without any change to the user code or binary. With TICK a process can be suspended by the Linux kernel upon receiving an interrupt and saved in a file. This file can be later thawed in another computer running Linux (potentially the same computer). TICK is implemented as a Linux kernel module, in the Linux version 2.6.5

  10. Weighted Bergman Kernels and Quantization}

    NASA Astrophysics Data System (ADS)

    Engliš, Miroslav

    Let Ω be a bounded pseudoconvex domain in CN, φ, ψ two positive functions on Ω such that - log ψ, - log φ are plurisubharmonic, and z∈Ω a point at which - log φ is smooth and strictly plurisubharmonic. We show that as k-->∞, the Bergman kernels with respect to the weights φkψ have an asymptotic expansion for x,y near z, where φ(x,y) is an almost-analytic extension of &\\phi(x)=φ(x,x) and similarly for ψ. Further, . If in addition Ω is of finite type, φ,ψ behave reasonably at the boundary, and - log φ, - log ψ are strictly plurisubharmonic on Ω, we obtain also an analogous asymptotic expansion for the Berezin transform and give applications to the Berezin quantization. Finally, for Ω smoothly bounded and strictly pseudoconvex and φ a smooth strictly plurisubharmonic defining function for Ω, we also obtain results on the Berezin-Toeplitz quantization.

  11. Evaluating the Gradient of the Thin Wire Kernel

    NASA Technical Reports Server (NTRS)

    Wilton, Donald R.; Champagne, Nathan J.

    2008-01-01

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

  12. Discriminative Projection Selection Based Face Image Hashing

    NASA Astrophysics Data System (ADS)

    Karabat, Cagatay; Erdogan, Hakan

    Face image hashing is an emerging method used in biometric verification systems. In this paper, we propose a novel face image hashing method based on a new technique called discriminative projection selection. We apply the Fisher criterion for selecting the rows of a random projection matrix in a user-dependent fashion. Moreover, another contribution of this paper is to employ a bimodal Gaussian mixture model at the quantization step. Our simulation results on three different databases demonstrate that the proposed method has superior performance in comparison to previously proposed random projection based methods.

  13. RKF-PCA: robust kernel fuzzy PCA.

    PubMed

    Heo, Gyeongyong; Gader, Paul; Frigui, Hichem

    2009-01-01

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

  14. 77 FR 72409 - Importer of Controlled Substances; Notice of Application; Fisher Clinical Services, Inc.

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-12-05

    ... Enforcement Administration Importer of Controlled Substances; Notice of Application; Fisher Clinical Services..., 2012, Fisher Clinical Services, Inc., 7554 Schantz Road, Allentown, Pennsylvania 18106, made application to the Drug Enforcement Administration (DEA) for registration as an importer of levorphanol...

  15. 77 FR 67396 - Importer of Controlled Substances; Notice of Application, Fisher Clinical Services, Inc.

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-11-09

    ... Enforcement Administration Importer of Controlled Substances; Notice of Application, Fisher Clinical Services..., 2012, Fisher Clinical Services, Inc., 7554 Schantz Road, ] Allentown, Pennsylvania 18106, made application to the Drug Enforcement Administration (DEA) for registration as an importer of Tapentadol...

  16. 77 FR 75670 - Importer of Controlled Substances; Notice of Registration; Fisher Clinical Services,Inc.

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-12-21

    ... Enforcement Administration Importer of Controlled Substances; Notice of Registration; Fisher Clinical Services... FR 60143, Fisher Clinical Services, Inc., 7554 Schantz Road, Allentown, Pennsylvania 18106, made... listed substances for analytical research and clinical trials. No comments or objections have...

  17. Astronaut Anna Fisher practices control of the RMS in a trainer

    NASA Technical Reports Server (NTRS)

    1984-01-01

    Astronaut Anna Lee Fisher, mission specialist for 51-A, practices control of the remote manipulator system (RMS) at a special trainer at JSC. Dr. Fisher is pictured in the manipulator development facility (MDF) of JSC's Shuttle mockup and integration laboratory.

  18. Astronaut Anna Fisher practices control of the RMS in a trainer

    NASA Technical Reports Server (NTRS)

    1984-01-01

    Astronaut Anna Lee Fisher, mission specialist for 51-A, practices control of the remote manipulator system (RMS) at a special trainer at JSC. Dr. Fisher is pictured in the manipulator development facility (MDF) of JSC's Shuttle mockup and integration laboratory.

  19. Fishers' knowledge about fish trophic interactions in the southeastern Brazilian coast.

    PubMed

    Ramires, Milena; Clauzet, Mariana; Barrella, Walter; Rotundo, Matheus M; Silvano, Renato Am; Begossi, Alpina

    2015-03-05

    Data derived from studies of fishers' local ecological knowledge (LEK) can be invaluable to the proposal of new studies and more appropriate management strategies. This study analyzed the fisher's LEK about trophic relationships of fishes in the southeastern Brazilian coast, comparing fishers' LEK with scientific knowledge to provide new hypotheses. The initial contacts with fishers were made through informal visits in their residences, to explain the research goals, meet fishers and their families, check the number of resident fishers and ask for fishers' consent to participate in the research. After this initial contact, fishers were selected to be included in the interviews through the technique of snowball sampling. The fishers indicated by others who attended the criteria to be included in the research were interviewed by using a semi-structured standard questionnaire. There were interviewed 26 artisanal fishers from three communities of the Ilhabela: Jabaquara, Fome and Serraria. The interviewed fishers showed a detailed knowledge about the trophic interactions of the studied coastal fishes, as fishers mentioned 17 food items for these fishes and six fish and three mammals as fish predators. The most mentioned food items were small fish, shrimps and crabs, while the most mentioned predators were large reef fishes. Fishers also mentioned some predators, such as sea otters, that have not been reported by the biological literature and are poorly known. The LEK of the studied fishers showed a high degree of concordance with the scientific literature regarding fish diet. This study evidenced the value of fishers' LEK to improve fisheries research and management, as well as the needy to increase the collaboration among managers, biologists and fishers.

  20. Kernel Manifold Alignment for Domain Adaptation

    PubMed Central

    Tuia, Devis; Camps-Valls, Gustau

    2016-01-01

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

  1. Kernel Manifold Alignment for Domain Adaptation.

    PubMed

    Tuia, Devis; Camps-Valls, Gustau

    2016-01-01

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

  2. Optimal witnessing of the quantum Fisher information with few measurements

    NASA Astrophysics Data System (ADS)

    Apellaniz, Iagoba; Kleinmann, Matthias; Gühne, Otfried; Tóth, Géza

    2017-03-01

    We show how to verify the metrological usefulness of quantum states based on the expectation values of an arbitrarily chosen set of observables. In particular, we estimate the quantum Fisher information as a figure of merit of metrological usefulness. Our approach gives a tight lower bound on the quantum Fisher information for the given incomplete information. We apply our method to the results of various multiparticle quantum states prepared in experiments with photons and trapped ions, as well as to spin-squeezed states and Dicke states realized in cold gases. Our approach can be used for detecting and quantifying metrologically useful entanglement in very large systems, based on a few operator expectation values. We also gain new insights into the difference between metrological useful multipartite entanglement and entanglement in general.

  3. A lattice Boltzmann model for the Burgers-Fisher equation.

    PubMed

    Zhang, Jianying; Yan, Guangwu

    2010-06-01

    A lattice Boltzmann model is developed for the one- and two-dimensional Burgers-Fisher equation based on the method of the higher-order moment of equilibrium distribution functions and a series of partial differential equations in different time scales. In order to obtain the two-dimensional Burgers-Fisher equation, vector sigma(j) has been used. And in order to overcome the drawbacks of "error rebound," a new assumption of additional distribution is presented, where two additional terms, in first order and second order separately, are used. Comparisons with the results obtained by other methods reveal that the numerical solutions obtained by the proposed method converge to exact solutions. The model under new assumption gives better results than that with second order assumption.

  4. Control methods for improved Fisher information with quantum sensing

    NASA Astrophysics Data System (ADS)

    Gefen, Tuvia; Jelezko, Fedor; Retzker, Alex

    2017-09-01

    Recently new approaches for sensing the frequency of time dependent Hamiltonians have been presented, and it was shown that the optimal Fisher information scales as T4. We present here our interpretation of this new scaling, where the relative phase is accumulated quadratically with time, and show that this can be produced by a variety of simple pulse sequences. Interestingly, this scaling has a limited duration, and we show that certain pulse sequences prolong the effect. The performance of these schemes is analyzed and we examine their relevance to state-of-the-art experiments. We analyze the T3 scaling of the Fisher information which appears when multiple synchronized measurements are performed, and is the optimal scaling in the case of a finite coherence time.

  5. Scombroid fish poisoning. Underreporting and prevention among noncommercial recreational fishers.

    PubMed Central

    Gellert, G A; Ralls, J; Brown, C; Huston, J; Merryman, R

    1992-01-01

    Food-borne diseases, including those caused by seafood products, are common and greatly underreported sources of morbidity. In this article we review the epidemiology of scombroid fish poisoning and its possible relationship to the noncommercial and recreational catch and sale of fish. More than 20% of all fish sold in the United States is caught by sport fishers, and outbreaks of scombroid fish poisoning have involved improperly handled fish from private catches. We report an outbreak of scombroid fish poisoning among recreational fishers in California. The unregulated sale of recreationally caught fish for consumption and the prevention of scombrotoxism are discussed from the perspectives of public health agencies, clinicians, and the fishing public. Scientific and policy issues that require further attention are high-lighted. PMID:1475947

  6. Fisheries productivity and its effects on the consumption of animal protein and food sharing of fishers' and non-fishers' families.

    PubMed

    da Costa, Mikaelle Kaline Bezerra; de Melo, Clarissy Dinyz; Lopes, Priscila Fabiana Macedo

    2014-01-01

    This study compared the consumption of animal protein and food sharing among fishers' and non-fishers' families of the northeastern Brazilian coast. The diet of these families was registered through the 24-hour-recall method during 10 consecutive days in January (good fishing season) and June (bad fishing season) 2012. Fish consumption was not different between the fishers' and non-fishers' families, but varied according to fisheries productivity to both groups. Likewise, food sharing was not different between the two groups, but food was shared more often when fisheries were productive. Local availability of fish, more than a direct dependency on fisheries, determines local patterns of animal protein consumption, but a direct dependency on fisheries exposes families to a lower-quality diet in less-productive seasons. As such, fisheries could shape and affect the livelihoods of coastal villages, including fishers' and non-fishers' families.

  7. Surface photometry of Tully-Fisher calibrator galaxies

    NASA Astrophysics Data System (ADS)

    Macri, L. M.; Huchra, J. P.; Sakai, Shoko; Mould, J. R.; Hughes, S. M. G.

    1999-09-01

    We present BVRI surface photometry of spiral galaxies used in the absolute calibration of the Tully-Fisher relation by Sakai et al. (1999). Galaxies were observed at the Fred L. Whipple Observatory 1.2-m telescope and at the Mount Stromlo and Siding Spring Observatories 1-m telescope between 1994 and 1999. The surface photometry measurements were carried out using the SFOTO package of Han (1991).

  8. Takotsubo cardiomyopathy associated with Miller-Fisher syndrome.

    PubMed

    Gill, Dalvir; Liu, Kan

    2016-12-22

    51-year-old female who presented with progressive paresthesia, numbness of the lower extremities, double vision, and trouble walking. Physical exam was remarkable for areflexia, and ptosis. Her initial EKG showed nonspecific ST segment changes and her Troponin T was elevated to 0.41ng/mL which peaked at 0.66ng/mL. Echocardiogram showed a depressed left ventricular ejection fraction to 35% with severely hypokinetic anterior wall and left ventricular apex was severely hypokinetic. EMG nerve conduction study showed severely decreased conduction velocity and prolonged distal latency in all nerves consistent with demyelinating disease. She was treated with 5days of intravenous immunoglobulin therapy to which she showed significant improvement in strength in her lower extremities. Echocardiogram repeated 4days later showing an improved left ventricular ejection fraction of 55% and no left ventricular wall motion abnormalities. Takotsubo cardiomyopathy is a rare complication of Miller-Fisher syndrome and literature review did not reveal any cases. Miller-Fisher syndrome is an autoimmune process that affects the peripheral nervous system causing autonomic dysfunction which may involve the heart. Due to significant autonomic dysfunction in Miller-Fisher syndrome, it could lead to arrhythmias, blood pressure changes, acute coronary syndrome and myocarditis, Takotsubo cardiomyopathy can be difficult to distinguish. The treatment of Takotsubo cardiomyopathy is supportive with beta-blockers and angiotensin-converting enzyme inhibitors are recommended until left ventricle ejection fraction improvement. Takotsubo cardiomyopathy is a rare complication during the acute phase of Miller-Fisher syndrome and must be distinguished from autonomic dysfunction as both diagnoses have different approaches to treatment.

  9. Traditional botanical knowledge of artisanal fishers in southern Brazil

    PubMed Central

    2013-01-01

    Background This study characterized the botanical knowledge of artisanal fishers of the Lami community, Porto Alegre, southern Brazil based on answers to the following question: Is the local botanical knowledge of the artisanal fishers of the rural-urban district of Lami still active, even since the district’s insertion into the metropolitan region of Porto Alegre? Methods This region, which contains a mosaic of urban and rural areas, hosts the Lami Biological Reserve (LBR) and a community of 13 artisanal fisher families. Semi-structured interviews were conducted with 15 fishers, complemented by participatory observation techniques and free-lists; in these interviews, the species of plants used by the community and their indicated uses were identified. Results A total of 111 species belonging to 50 families were identified. No significant differences between the diversities of native and exotic species were found. Seven use categories were reported: medicinal (49%), human food (23.2%), fishing (12.3%), condiments (8%), firewood (5%), mystical purposes (1.45%), and animal food (0.72%). The medicinal species with the highest level of agreement regarding their main uses (AMUs) were Aloe arborescens Mill., Plectranthus barbatus Andrews, Dodonaea viscosa Jacq., Plectranthus ornatus Codd, Eugenia uniflora L., and Foeniculum vulgare Mill. For illness and diseases, most plants were used for problems with the digestive system (20 species), followed by the respiratory system (16 species). This community possesses a wide botanical knowledge, especially of medicinal plants, comparable to observations made in other studies with fishing communities in coastal areas of the Atlantic Forest of Brazil. Conclusions Ethnobotanical studies in rural-urban areas contribute to preserving local knowledge and provide information that aids in conserving the remaining ecosystems in the region. PMID:23898973

  10. Astronaut William Fisher anchored to foot restraint on Discovery

    NASA Image and Video Library

    1985-09-01

    51I-102-048 (4-5 Sept 1985) --- A 35mm frame showing astronaut William F. Fisher standing on the edge of Discovery's cargo bay (in foot restraint) during the second day of a two-day effort to capture, repair and re-release the Syncom IV-3 communications satellite. Astronaut James D. van Hoften, standing on the Discovery's RMS arm, exposed the frame.

  11. R A Fisher, design theory, and the Indian connection.

    PubMed

    Rau, A R P

    2009-09-01

    Design Theory, a branch of mathematics, was born out of the experimental statistics research of the population geneticist R A Fisher and of Indian mathematical statisticians in the 1930s. The field combines elements of combinatorics, finite projective geometries, Latin squares, and a variety of further mathematical structures, brought together in surprising ways. This essay will present these structures and ideas as well as how the field came together, in itself an interesting story.

  12. Canine Distemper in an isolated population of fishers (Martes pennanti) from California

    Treesearch

    Stefan m. Keller; Mourad Gabriel; Karen A. Terio; Edward J. Dubovi; Elizabeth Van Wormer; Rick Sweitzer; Reginald Barret; Craig Thompson; Kathryn Purcell; Linda. Munson

    2012-01-01

    Four fishers (Martes pennanti) from an insular population in the southern Sierra Nevada Mountains, California, USA died as a consequence of an infection with canine distemper virus (CDV) in 2009. Three fishers were found in close temporal and spatial relationship; the fourth fisher died 4 mo later at a 70 km distance from the initial group. Gross...

  13. When reintroductions are augmentations: the genetic legacy of the fisher (Martes pennanti) in Montana

    Treesearch

    Ray S. Vinkey; Michael K. Schwartz; Kevin S. McKelvey; Kerry R. Foresman; Kristine L. Pilgrim; Brian J. Giddings; Eric C. Lofroth

    2006-01-01

    Fishers (Martes pennanti) were purportedly extirpated from Montana by 1930 and extant populations are assumed to be descended from translocated fishers. To determine the lineage of fisher populations, we sequenced 2 regions of the mitochondrial DNA genome from 207 tissue samples from British Columbia, Minnesota, Wisconsin, and Montana. In...

  14. Ancient DNA confirms native Rocky Mountain fisher (Martes pennanti) avoided early 20th century extinction

    Treesearch

    Michael K. Schwartz

    2007-01-01

    Until recently it was assumed that fishers (Martes pennanti) in the Rocky Mountains all were descended from reintroduced stocks. However, a recent study reported that mitochondrial DNA (cytochrome-b and control region) haplotypes of fishers found only in west-central Montana are likely derived from a relic population of fishers that escaped harvests conducted in the...

  15. Stratified Fisher's Exact Test and its Sample Size Calculation

    PubMed Central

    Jung, Sin-Ho

    2013-01-01

    Summary Chi-squared test has been a popular approach to the analysis of a 2 × 2 table when the sample sizes for the four cells are large. When the large sample assumption does not hold, however, we need an exact testing method such as Fisher's test. When the study population is heterogeneous, we often partition the subjects into multiple strata, so that each stratum consists of homogeneous subjects and hence the stratified analysis has an improved testing power. While Mantel-Haenszel test has been widely used as an extension of the chi-squared test to test on stratified 2×2 tables with a large-sample approximation, we have been lacking an extension of Fisher's test for stratified exact testing. In this paper, we discuss an exact testing method for stratified 2 × 2 tables which is simplified to the standard Fisher's test in single 2 × 2 table cases, and propose its sample size calculation method that can be useful for designing a study with rare cell frequencies. PMID:24395208

  16. Stratified Fisher's exact test and its sample size calculation.

    PubMed

    Jung, Sin-Ho

    2014-01-01

    Chi-squared test has been a popular approach to the analysis of a 2 × 2 table when the sample sizes for the four cells are large. When the large sample assumption does not hold, however, we need an exact testing method such as Fisher's test. When the study population is heterogeneous, we often partition the subjects into multiple strata, so that each stratum consists of homogeneous subjects and hence the stratified analysis has an improved testing power. While Mantel-Haenszel test has been widely used as an extension of the chi-squared test to test on stratified 2 × 2 tables with a large-sample approximation, we have been lacking an extension of Fisher's test for stratified exact testing. In this paper, we discuss an exact testing method for stratified 2 × 2 tables that is simplified to the standard Fisher's test in single 2 × 2 table cases, and propose its sample size calculation method that can be useful for designing a study with rare cell frequencies. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. A Wright-Fisher model with indirect selection.

    PubMed

    Goudenège, Ludovic; Zitt, Pierre-André

    2015-12-01

    We study a generalization of the Wright-Fisher model in which some individuals adopt a behavior that is harmful to others without any direct advantage for themselves. This model is motivated by studies of spiteful behavior in nature, including several species of parasitoid hymenoptera in which sperm-depleted males continue to mate despite not being fertile. We first study a single reproductive season, then use it as a building block for a generalized Wright-Fisher model. In the large population limit, for male-skewed sex ratios, we rigorously derive the convergence of the renormalized process to a diffusion with a frequency-dependent selection and genetic drift. This allows a quantitative comparison of the indirect selective advantage with the direct one classically considered in the Wright-Fisher model. From the mathematical point of view, each season is modeled by a mix between samplings with and without replacement, and analyzed by a sort of "reverse numerical analysis", viewing a key recurrence relation as a discretization scheme for a PDE. The diffusion approximation is then obtained by classical methods.

  18. Fisher equation for anisotropic diffusion: simulating South American human dispersals.

    PubMed

    Martino, Luis A; Osella, Ana; Dorso, Claudio; Lanata, José L

    2007-09-01

    The Fisher equation is commonly used to model population dynamics. This equation allows describing reaction-diffusion processes, considering both population growth and diffusion mechanism. Some results have been reported about modeling human dispersion, always assuming isotropic diffusion. Nevertheless, it is well-known that dispersion depends not only on the characteristics of the habitats where individuals are but also on the properties of the places where they intend to move, then isotropic approaches cannot adequately reproduce the evolution of the wave of advance of populations. Solutions to a Fisher equation are difficult to obtain for complex geometries, moreover, when anisotropy has to be considered and so few studies have been conducted in this direction. With this scope in mind, we present in this paper a solution for a Fisher equation, introducing anisotropy. We apply a finite difference method using the Crank-Nicholson approximation and analyze the results as a function of the characteristic parameters. Finally, this methodology is applied to model South American human dispersal.

  19. Fishing, fish consumption and advisory awareness among Louisiana's recreational fishers.

    PubMed

    Katner, Adrienne; Ogunyinka, Ebenezer; Sun, Mei-Hung; Soileau, Shannon; Lavergne, David; Dugas, Dianne; Suffet, Mel

    2011-11-01

    This paper presents results from the first known population-based survey of recreational fishers in Louisiana (n=1774). The ultimate goal of this study was to obtain data in support of the development of regional advisories for a high exposure population with unique seafood consumption patterns. Between July and August of 2008, a survey was mailed to a random sample of licensed recreational fishers to characterize local fishing habits, sportfish consumption, and advisory awareness. Eighty-eight percent of respondents reported eating sportfish. Respondents ate an estimated mean of four fish meals per month, of which, approximately half were sportfish. Over half of all sportfish meals (54%) were caught in the Gulf of Mexico or bordering brackish areas. Sportfish consumption varied by license and gender; and was highest among Sportsman's Paradise license holders (2.8±0.2 meals per month), and males (2.2±0.1 meals per month). The most frequently consumed sportfish species were red drum, speckled trout, catfish, bass, crappie and bream. Advisory awareness rates varied by gender, ethnicity, geographic area, license type, age and education; and were lowest among women (53%), African-Americans (43%), fishers from the southeast of Louisiana (50%), holders of Senior Hunting and Fishing licenses (51%), individuals between 15 and 19 years of age (41%), and individuals with less than a high school education (43%). Results were used to identify ways to optimize monitoring, advisory development and outreach activities. Copyright © 2011 Elsevier Inc. All rights reserved.

  20. Fisher Pierce products for improving distribution system reliability

    SciTech Connect

    1994-12-31

    The challenges facing the electric power utility today in the 1990s has changed significantly from those of even 10 years ago. The proliferation of automation and the personnel computer have heightened the requirements and demands put on the electric distribution system. Today`s customers, fighting to compete in a world market, demand quality, uninterrupted power service. Privatization and the concept of unregulated competition require utilities to streamline to minimize system support costs and optimize power delivery efficiency. Fisher Pierce, serving the electric utility industry for over 50 years, offers a line of products to assist utilities in meeting these challenges. The Fisher Pierce Family of products provide tools for the electric utility to exceed customer service demands. A full line of fault indicating devices are offered to expedite system power restoration both locally and in conjunction with SCADA systems. Fisher Pierce is the largest supplier of roadway lighting controls, manufacturing on a 6 million dollar automated line assuring the highest quality in the world. The distribution system capacitor control line offers intelligent local or radio linked switching control to maintain system voltage and Var levels for quality and cost efficient power delivery under varying customer loads. Additional products, designed to authenticate revenue metering calibration and verify on sight metering service wiring, help optimize the profitability of the utility assuring continuous system service improvements for their customers.

  1. Sustainable theory of a logistic model - Fisher information approach.

    PubMed

    Al-Saffar, Avan; Kim, Eun-Jin

    2017-03-01

    Information theory provides a useful tool to understand the evolution of complex nonlinear systems and their sustainability. In particular, Fisher information has been evoked as a useful measure of sustainability and the variability of dynamical systems including self-organising systems. By utilising Fisher information, we investigate the sustainability of the logistic model for different perturbations in the positive and/or negative feedback. Specifically, we consider different oscillatory modulations in the parameters for positive and negative feedback and investigate their effect on the evolution of the system and Probability Density Functions (PDFs). Depending on the relative time scale of the perturbation to the response time of the system (the linear growth rate), we demonstrate the maintenance of the initial condition for a long time, manifested by a broad bimodal PDF. We present the analysis of Fisher information in different cases and elucidate its implications for the sustainability of population dynamics. We also show that a purely oscillatory growth rate can lead to a finite amplitude solution while self-organisation of these systems can break down with an exponentially growing solution due to the periodic fluctuations in negative feedback. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Perceived racial discrimination and hypertension: a comprehensive systematic review.

    PubMed

    Dolezsar, Cynthia M; McGrath, Jennifer J; Herzig, Alyssa J M; Miller, Sydney B

    2014-01-01

    Discrimination is posited to underlie racial disparities in hypertension. Extant literature suggests a possible association between racial discrimination and blood pressure, although inconsistent findings have been reported. The aim of this comprehensive systematic review was to quantitatively evaluate the association between perceived racial discrimination with hypertensive status and systolic, diastolic, and ambulatory blood pressure. Electronic database search of PubMed and PsycINFO (keywords: blood pressure/hypertension/diastolic/systolic, racism/discrimination/prejudice/unfair treatment) was combined with descendancy and ascendancy approaches. Forty-four articles (N = 32,651) met inclusion criteria. Articles were coded for demographics, hypertensive diagnosis, blood pressure measurement, discrimination measure and constructs, study quality, and effect sizes. Random effects meta-analytic models were tested based on Fisher's Z, the derived common effect size metric. Overall, perceived racial discrimination was associated with hypertensive status, Zhypertension = 0.048, 95% CI [.013, .087], but not with resting blood pressure, Zsystolic = 0.011, 95% CI [-.006, .031], Zdiastolic = .016, 95% CI [-.006, .034]. Moderators that strengthened the relation included sex (male), race (Black), age (older), education (lower), and hypertensive status. Perceived discrimination was most strongly associated with nighttime ambulatory blood pressure, especially among Blacks. Despite methodological limitations in the existing literature, there was a small, significant association between perceived discrimination and hypertension. Future studies should consider ambulatory nighttime blood pressure, which may more accurately capture daily variation attributable to experienced racial discrimination. Perceived discrimination may partly explain racial health disparities. 2014 APA, all rights reserved

  3. Kernel-Based Reconstruction of Graph Signals

    NASA Astrophysics Data System (ADS)

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

    2017-02-01

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

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

    PubMed

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

    2017-03-03

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

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

    PubMed

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

    2014-01-01

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

  6. Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation

    PubMed Central

    Huang, Meiyan; Huang, Wei; Jiang, Jun; Zhou, Yujia; Yang, Ru; Zhao, Jie; Feng, Yanqiu; Feng, Qianjin; Chen, Wufan

    2016-01-01

    Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset. PMID:27273091

  7. A Physical Basis of the Tully-Fisher Relation

    NASA Astrophysics Data System (ADS)

    Rhee, M.-H.

    1996-04-01

    This thesis consists of two main parts. The first part presents a series of theoretical and interpretative studies on the subject of the Tully-Fisher relation. We mainly focus on a better understanding of the physical basis of the Tully-Fisher relation. The second parts of the thesis presents the results of the new Westerbork HI observations of spiral galaxies. We study the HI properties and the Tully-Fisher relation of spiral and irregular galaxies based on these observations. In the first part, we first analyse the dependence of the luminous mass-to-light ratio of spiral galaxies on the present star formation rate, and find that galaxies with high present star formation rates have low luminous mass-to-light ratios, presumably as a result of the enhanced luminosity. On this basis we argue that variations in the stellar content of galaxies result in a major source of intrinsic scatter in the Tully-Fisher relation. We have also analysed the relation between the (maximum) luminous mass and circular velocity, and find it to have small scatter. We therefore propose that the physical basis of the Tully-Fisher relation lies in a relationship between the luminous mass and circular velocity, in combination with a `well-behaved' relation between luminous and dark matter (Chapter 2). We show that the errors in the Tully-Fisher relation are intercorrelated through inclination corrections, resulting in a lower combined scatter than the individual errors. Ignoring this effect could result in an underestimation of the intrinsic scatter in the Tully-Fisher relation. We discuss a compensation effect between luminosity enhancement due to stellar activity and luminosity dimming due to dust, which could result in a small apparent scatter in the TF relation because high star formation activity is associated with high dust content. We argue that the Tully-Fisher relation for the low surface brightness galaxies and IRAS mini-survey galaxies could also be the results of some compensation

  8. Online Sequential Extreme Learning Machine With Kernels.

    PubMed

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

    2015-09-01

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

  9. Analog forecasting with dynamics-adapted kernels

    NASA Astrophysics Data System (ADS)

    Zhao, Zhizhen; Giannakis, Dimitrios

    2016-09-01

    Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens’ delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nyström method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.

  10. Kernel bandwidth optimization in spike rate estimation.

    PubMed

    Shimazaki, Hideaki; Shinomoto, Shigeru

    2010-08-01

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

  11. Feature expectation heightens visual sensitivity during fine orientation discrimination

    PubMed Central

    Cheadle, Sam; Egner, Tobias; Wyart, Valentin; Wu, Claire; Summerfield, Christopher

    2015-01-01

    Attending to a stimulus enhances the sensitivity of perceptual decisions. However, it remains unclear how perceptual sensitivity varies according to whether a feature is expected or unexpected. Here, observers made fine discrimination judgments about the orientation of visual gratings embedded in low spatial-frequency noise, and psychophysical reverse correlation was used to estimate decision ‘kernels' that revealed how visual features influenced choices. Orthogonal cues alerted subjects to which of two spatial locations was likely to be probed (spatial attention cue) and which of two oriented gratings was likely to occur (feature expectation cue). When an expected (relative to unexpected) feature occurred, decision kernels shifted away from the category boundary, allowing observers to capitalize on more informative, “off-channel” stimulus features. By contrast, the spatial attention cue had a multiplicative influence on decision kernels, consistent with an increase in response gain. Feature expectation thus heightens sensitivity to the most informative visual features, independent of selective attention. PMID:26505967

  12. Enhanced Data Representation by Kernel Metric Learning for Dementia Diagnosis.

    PubMed

    Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

    2017-01-01

    Alzheimer's disease (AD) is the kind of dementia that affects the most people around the world. Therefore, an early identification supporting effective treatments is required to increase the life quality of a wide number of patients. Recently, computer-aided diagnosis tools for dementia using Magnetic Resonance Imaging scans have been successfully proposed to discriminate between patients with AD, mild cognitive impairment, and healthy controls. Most of the attention has been given to the clinical data, provided by initiatives as the ADNI, supporting reliable researches on intervention, prevention, and treatments of AD. Therefore, there is a need for improving the performance of classification machines. In this paper, we propose a kernel framework for learning metrics that enhances conventional machines and supports the diagnosis of dementia. Our framework aims at building discriminative spaces through the maximization of center kernel alignment function, aiming at improving the discrimination of the three considered neurological classes. The proposed metric learning performance is evaluated on the widely-known ADNI database using three supervised classification machines (k-nn, SVM and NNs) for multi-class and bi-class scenarios from structural MRIs. Specifically, from ADNI collection 286 AD patients, 379 MCI patients and 231 healthy controls are used for development and validation of our proposed metric learning framework. For the experimental validation, we split the data into two subsets: 30% of subjects used like a blindfolded assessment and 70% employed for parameter tuning. Then, in the preprocessing stage, each structural MRI scan a total of 310 morphological measurements are automatically extracted from by FreeSurfer software package and concatenated to build an input feature matrix. Obtained test performance results, show that including a supervised metric learning improves the compared baseline classifiers in both scenarios. In the multi-class scenario

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

    PubMed

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

    1998-06-01

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

  14. Fusion and kernel type selection in adaptive image retrieval

    NASA Astrophysics Data System (ADS)

    Doloc-Mihu, Anca; Raghavan, Vijay V.

    2007-04-01

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

  15. Robust C-Loss Kernel Classifiers.

    PubMed

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

    2016-12-29

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

  16. Nonparametric entropy estimation using kernel densities.

    PubMed

    Lake, Douglas E

    2009-01-01

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

  17. Fast generation of sparse random kernel graphs

    SciTech Connect

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.

  18. Fast generation of sparse random kernel graphs

    DOE PAGES

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

  19. Kernel bandwidth estimation for nonparametric modeling.

    PubMed

    Bors, Adrian G; Nasios, Nikolaos

    2009-12-01

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

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

    PubMed

    Maranz, Steven; Wiesman, Zeev; Garti, Nissim

    2003-10-08

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

  1. Fractal Weyl law for Linux Kernel architecture

    NASA Astrophysics Data System (ADS)

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

    2011-01-01

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

  2. Tile-Compressed FITS Kernel for IRAF

    NASA Astrophysics Data System (ADS)

    Seaman, R.

    2011-07-01

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

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

    PubMed

    Patra, Rakesh; Saha, Sujan Kumar

    2013-01-01

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

  4. A dynamic kernel modifier for linux

    SciTech Connect

    Minnich, R. G.

    2002-09-03

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

  5. Experimental study of turbulent flame kernel propagation

    SciTech Connect

    Mansour, Mohy; Peters, Norbert; Schrader, Lars-Uve

    2008-07-15

    Flame kernels in spark ignited combustion systems dominate the flame propagation and combustion stability and performance. They are likely controlled by the spark energy, flow field and mixing field. The aim of the present work is to experimentally investigate the structure and propagation of the flame kernel in turbulent premixed methane flow using advanced laser-based techniques. The spark is generated using pulsed Nd:YAG laser with 20 mJ pulse energy in order to avoid the effect of the electrodes on the flame kernel structure and the variation of spark energy from shot-to-shot. Four flames have been investigated at equivalence ratios, {phi}{sub j}, of 0.8 and 1.0 and jet velocities, U{sub j}, of 6 and 12 m/s. A combined two-dimensional Rayleigh and LIPF-OH technique has been applied. The flame kernel structure has been collected at several time intervals from the laser ignition between 10 {mu}s and 2 ms. The data show that the flame kernel structure starts with spherical shape and changes gradually to peanut-like, then to mushroom-like and finally disturbed by the turbulence. The mushroom-like structure lasts longer in the stoichiometric and slower jet velocity. The growth rate of the average flame kernel radius is divided into two linear relations; the first one during the first 100 {mu}s is almost three times faster than that at the later stage between 100 and 2000 {mu}s. The flame propagation is slightly faster in leaner flames. The trends of the flame propagation, flame radius, flame cross-sectional area and mean flame temperature are related to the jet velocity and equivalence ratio. The relations obtained in the present work allow the prediction of any of these parameters at different conditions. (author)

  6. Discriminating harmonicity

    NASA Astrophysics Data System (ADS)

    Kidd, Gerald; Mason, Christine R.; Brughera, Andrew; Chiu, Chung-Yiu Peter

    2003-08-01

    Simultaneous tones that are harmonically related tend to be grouped perceptually to form a unitary auditory image. A partial that is mistuned stands out from the other tones, and harmonic complexes with different fundamental frequencies can readily be perceived as separate auditory objects. These phenomena are evidence for the strong role of harmonicity in perceptual grouping and segregation of sounds. This study measured the discriminability of harmonicity directly. In a two interval, two alternative forced-choice (2I2AFC) paradigm, the listener chose which of two sounds, signal or foil, was composed of tones that more closely matched an exact harmonic relationship. In one experiment, the signal was varied from perfectly harmonic to highly inharmonic by adding frequency perturbation to each component. The foil always had 100% perturbation. Group mean performance decreased from greater than 90% correct for 0% signal perturbation to near chance for 80% signal perturbation. In the second experiment, adding a masker presented simultaneously with the signals and foils disrupted harmonicity. Both monaural and dichotic conditions were tested. Signal level was varied relative to masker level to obtain psychometric functions from which slopes and midpoints were estimated. Dichotic presentation of these audible stimuli improved performance by 3-10 dB, due primarily to a release from ``informational masking'' by the perceptual segregation of the signal from the masker.

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

    SciTech Connect

    Hanft, J.M.; Jones, R.J.

    1986-06-01

    This study was designed to compare the uptake and distribution of /sup 14/C among fructose, glucose, sucrose, and starch in the cob, pedicel, and endosperm tissues of maize (Zea mays L.) kernels induced to abort by high temperature with those that develop normally. Kernels cultured in vitro at 309 and 35/sup 0/C were transferred to (/sup 14/C)sucrose media 10 days after pollination. Kernels cultured at 35/sup 0/C aborted prior to the onset of linear dry matter accumulation. Significant uptake into the cob, pedicel, and endosperm of radioactivity associated with the soluble and starch fractions of the tissues was detected after 24 hours in culture on atlageled media. After 8 days in culture on (/sup 14/C)sucrose media, 48 and 40% of the radioactivity associated with the cob carbohydrates was found in the reducing sugars at 30 and 35/sup 0/C, respectively. Of the total carbohydrates, a higher percentage of label was associated with sucrose and lower percentage with fructose and glucose in pedicel tissue of kernels cultured at 35/sup 0/C compared to kernels cultured at 30/sup 0/C. These results indicate that sucrose was not cleaved to fructose and glucose as rapidly during the unloading process in the pedicel of kernels induced to abort by high temperature. Kernels cultured at 35/sup 0/C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30/sup 0/C (89%). Kernels cultured at 35/sup 0/C had a correspondingly higher proportion of /sup 14/C in endosperm fructose, glucose, and sucrose.

  8. Full Waveform Inversion Using Waveform Sensitivity Kernels

    NASA Astrophysics Data System (ADS)

    Schumacher, Florian; Friederich, Wolfgang

    2013-04-01

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

  9. Volatile compound formation during argan kernel roasting.

    PubMed

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

    2013-01-01

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

  10. Reduced multiple empirical kernel learning machine.

    PubMed

    Wang, Zhe; Lu, MingZhe; Gao, Daqi

    2015-02-01

    Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3

  11. Evidence for Fisher renormalization in the compressible phi4 model.

    PubMed

    Tröster, A

    2008-04-11

    We present novel Fourier Monte Carlo simulations of a compressible phi4-model on a simple-cubic lattice with linear-quadratic coupling of order parameter and strain, focusing on the detection of fluctuation-induced first-order transitions and deviations from standard critical behavior. The former is indeed observed in the constant stress ensemble and for auxetic systems at constant strain, while for regular isotropic systems at constant strain, we find strong evidence for Fisher-renormalized critical behavior and are led to predict the existence of a tricritical point.

  12. Occurrences of candidiasis in a Fisher's lovebird and a budgerigar.

    PubMed

    Sato, Y; Aoyagi, T; Kobayashi, T; Inoue, J

    2001-08-01

    Two cage birds, a two-month-old Fisher's lovebird (Agapornis fischeri) and a one-year-old budgerigar (Melopsittacus undulatus), manifested clinical symptoms with general weakness, loss of appetite and ruffled feathers, then died. Pathological findings revealed a large quantity of yellowish-white pseudomembrane on the mucosal membrane of the esophagus and crop in these two birds. Histopathologically, blastospores (5.5 microm long x 3.4 microm wide) and pseudohyphae were detected in the lesions of conspicuous parakeratosis and moderate acanthosis in the stratified squamous epithelium. These two birds were diagnosed as having had candidiasis.

  13. Fisher-Schroedinger models for statistical encryption of covert information

    NASA Astrophysics Data System (ADS)

    Venkatesan, R. C.

    2007-04-01

    The theoretical framework for a principled procedure to encrypt/decrypt covert information (code)into/from the null spaces of a hierarchy of statistical distributions possessing ill-conditioned eigenstructures, is suggested. The statistical distributions are inferred using incomplete constraints, employing the generalized nonextensive thermostatistics (NET) Fisher information as the measure of uncertainty. The hierarchy of inferred statistical distributions possess a quantum mechanical connotation for unit values of the nonextensivity parameter. A systematic strategy to encrypt/decrypt code via unitary projections into the null spaces of the ill-conditioned eigenstructures, is presented.

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

    NASA Astrophysics Data System (ADS)

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

    2012-09-01

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

  15. Accuracy of Reduced and Extended Thin-Wire Kernels

    SciTech Connect

    Burke, G J

    2008-11-24

    Some results are presented comparing the accuracy of the reduced thin-wire kernel and an extended kernel with exact integration of the 1/R term of the Green's function and results are shown for simple wire structures.

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

    PubMed

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

    2013-11-20

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

  17. A Further Evaluation of Picture Prompts during Auditory-Visual Conditional Discrimination Training

    ERIC Educational Resources Information Center

    Carp, Charlotte L.; Peterson, Sean P.; Arkel, Amber J.; Petursdottir, Anna I.; Ingvarsson, Einar T.

    2012-01-01

    This study was a systematic replication and extension of Fisher, Kodak, and Moore (2007), in which a picture prompt embedded into a least-to-most prompting sequence facilitated acquisition of auditory-visual conditional discriminations. Participants were 4 children who had been diagnosed with autism; 2 had limited prior receptive skills, and 2 had…

  18. A Further Evaluation of Picture Prompts during Auditory-Visual Conditional Discrimination Training

    ERIC Educational Resources Information Center

    Carp, Charlotte L.; Peterson, Sean P.; Arkel, Amber J.; Petursdottir, Anna I.; Ingvarsson, Einar T.

    2012-01-01

    This study was a systematic replication and extension of Fisher, Kodak, and Moore (2007), in which a picture prompt embedded into a least-to-most prompting sequence facilitated acquisition of auditory-visual conditional discriminations. Participants were 4 children who had been diagnosed with autism; 2 had limited prior receptive skills, and 2 had…

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

    PubMed

    Nielsen, Allan Aasbjerg

    2011-03-01

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

  20. Fabrication of Uranium Oxycarbide Kernels for HTR Fuel

    SciTech Connect

    Charles Barnes; CLay Richardson; Scott Nagley; John Hunn; Eric Shaber

    2010-10-01

    Babcock and Wilcox (B&W) has been producing high quality uranium oxycarbide (UCO) kernels for Advanced Gas Reactor (AGR) fuel tests at the Idaho National Laboratory. In 2005, 350-µm, 19.7% 235U-enriched UCO kernels were produced for the AGR-1 test fuel. Following coating of these kernels and forming the coated-particles into compacts, this fuel was irradiated in the Advanced Test Reactor (ATR) from December 2006 until November 2009. B&W produced 425-µm, 14% enriched UCO kernels in 2008, and these kernels were used to produce fuel for the AGR-2 experiment that was inserted in ATR in 2010. B&W also produced 500-µm, 9.6% enriched UO2 kernels for the AGR-2 experiments. Kernels of the same size and enrichment as AGR-1 were also produced for the AGR-3/4 experiment. In addition to fabricating enriched UCO and UO2 kernels, B&W has produced more than 100 kg of natural uranium UCO kernels which are being used in coating development tests. Successive lots of kernels have demonstrated consistent high quality and also allowed for fabrication process improvements. Improvements in kernel forming were made subsequent to AGR-1 kernel production. Following fabrication of AGR-2 kernels, incremental increases in sintering furnace charge size have been demonstrated. Recently small scale sintering tests using a small development furnace equipped with a residual gas analyzer (RGA) has increased understanding of how kernel sintering parameters affect sintered kernel properties. The steps taken to increase throughput and process knowledge have reduced kernel production costs. Studies have been performed of additional modifications toward the goal of increasing capacity of the current fabrication line to use for production of first core fuel for the Next Generation Nuclear Plant (NGNP) and providing a basis for the design of a full scale fuel fabrication facility.

  1. Classification of Hazelnut Kernels by Using Impact Acoustic Time-Frequency Patterns

    NASA Astrophysics Data System (ADS)

    Kalkan, Habil; Ince, Nuri Firat; Tewfik, Ahmed H.; Yardimci, Yasemin; Pearson, Tom

    2007-12-01

    Hazelnuts with damaged or cracked shells are more prone to infection with aflatoxin producing molds ( Aspergillus flavus). These molds can cause cancer. In this study, we introduce a new approach that separates damaged/cracked hazelnut kernels from good ones by using time-frequency features obtained from impact acoustic signals. The proposed technique requires no prior knowledge of the relevant time and frequency locations. In an offline step, the algorithm adaptively segments impact signals from a training data set in time using local cosine packet analysis and a Kullback-Leibler criterion to assess the discrimination power of different segmentations. In each resulting time segment, the signal is further decomposed into subbands using an undecimated wavelet transform. The most discriminative subbands are selected according to the Euclidean distance between the cumulative probability distributions of the corresponding subband coefficients. The most discriminative subbands are fed into a linear discriminant analysis classifier. In the online classification step, the algorithm simply computes the learned features from the observed signal and feeds them to the linear discriminant analysis (LDA) classifier. The algorithm achieved a throughput rate of 45 nuts/s and a classification accuracy of 96% with the 30 most discriminative features, a higher rate than those provided with prior methods.

  2. Early electrophysiological findings in Fisher-Bickerstaff syndrome.

    PubMed

    Alberti, M A; Povedano, M; Montero, J; Casasnovas, C

    2017-09-06

    The term Fisher-Bickerstaff syndrome (FBS) has been proposed to describe the clinical spectrum encompassing Miller-Fisher syndrome (MFS) and Bickerstaff brainstem encephalitis. The pathophysiology of FBS and the nature of the underlying neuropathy (demyelinating or axonal) are still subject to debate. This study describes the main findings of an early neurophysiological study on 12 patients diagnosed with FBS. Retrospective evaluation of clinical characteristics and electrophysiological findings of 12 patients with FBS seen in our neurology department within 10 days of disease onset. Follow-up electrophysiological studies were also evaluated, where available. The most frequent electrophysiological finding, present in 5 (42%) patients, was reduced sensory nerve action potential (SNAP) amplitude in one or more nerves. Abnormalities were rarely found in motor neurography, with no signs of demyelination. The cranial nerve exam revealed abnormalities in 3 patients (facial neurography and/or blink reflex test). Three patients showed resolution of SNAP amplitude reduction in serial neurophysiological studies, suggesting the presence of reversible sensory nerve conduction block. Results from cranial MRI scans were normal in all patients. An electrophysiological pattern of sensory axonal neuropathy, with no associated signs of demyelination, is an early finding of FBS. Early neurophysiological evaluation and follow-up are essential for diagnosing patients with FBS. Copyright © 2017 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

  3. The Stefan problem for the Fisher-KPP equation

    NASA Astrophysics Data System (ADS)

    Du, Yihong; Guo, Zongming

    We study the Fisher-KPP equation with a free boundary governed by a one-phase Stefan condition. Such a problem arises in the modeling of the propagation of a new or invasive species, with the free boundary representing the propagation front. In one space dimension this problem was investigated in Du and Lin (2010) [11], and the radially symmetric case in higher space dimensions was studied in Du and Guo (2011) [10]. In both cases a spreading-vanishing dichotomy was established, namely the species either successfully spreads to all the new environment and stabilizes at a positive equilibrium state, or fails to establish and dies out in the long run; moreover, in the case of spreading, the asymptotic spreading speed was determined. In this paper, we consider the non-radially symmetric case. In such a situation, similar to the classical Stefan problem, smooth solutions need not exist even if the initial data are smooth. We thus introduce and study the "weak solution" for a class of free boundary problems that include the Fisher-KPP as a special case. We establish the existence and uniqueness of the weak solution, and through suitable comparison arguments, we extend some of the results obtained earlier in Du and Lin (2010) [11] and Du and Guo (2011) [10] to this general case. We also show that the classical Aronson-Weinberger result on the spreading speed obtained through the traveling wave solution approach is a limiting case of our free boundary problem here.

  4. Benefits and risks of diversification for individual fishers.

    PubMed

    Anderson, Sean C; Ward, Eric J; Shelton, Andrew O; Adkison, Milo D; Beaudreau, Anne H; Brenner, Richard E; Haynie, Alan C; Shriver, Jennifer C; Watson, Jordan T; Williams, Benjamin C

    2017-09-18

    Individuals relying on natural resource extraction for their livelihood face high income variability driven by a mix of environmental, biological, management, and economic factors. Key to managing these industries is identifying how regulatory actions and individual behavior affect income variability, financial risk, and, by extension, the economic stability and the sustainable use of natural resources. In commercial fisheries, communities and vessels fishing a greater diversity of species have less revenue variability than those fishing fewer species. However, it is unclear whether these benefits extend to the actions of individual fishers and how year-to-year changes in diversification affect revenue and revenue variability. Here, we evaluate two axes by which fishers in Alaska can diversify fishing activities. We show that, despite increasing specialization over the last 30 years, fishing a set of permits with higher species diversity reduces individual revenue variability, and fishing an additional permit is associated with higher revenue and lower variability. However, increasing species diversity within the constraints of existing permits has a fishery-dependent effect on revenue and is usually (87% probability) associated with increased revenue uncertainty the following year. Our results demonstrate that the most effective option for individuals to decrease revenue variability is to participate in additional or more diverse fisheries. However, this option is expensive, often limited by regulations such as catch share programs, and consequently unavailable to many individuals. With increasing climatic variability, it will be particularly important that individuals relying on natural resources for their livelihood have effective strategies to reduce financial risk.

  5. Using Fisher information to track stability in multivariate ...

    EPA Pesticide Factsheets

    With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI) can be applied to sustainability science problems and used in data mining applications by analyzing patterns in data. FI was developed as a measure of information content in data, and it has been adapted to assess order in complex system behaviors. The main advantage of the approach is the ability to collapse multiple variables into an index that can be used to assess stability and track overall trends in a system, including its regimes and regime shifts. Here, we provide a brief overview of FI theory, followed by a simple step-by-step numerical example on how to compute FI. Furthermore, we introduce an open source Python library that can be freely downloaded from GitHub and we use it in a simple case study to evaluate the evolution of FI for the global-mean temperature from 1880 to 2015. Results indicate significant declines in FI starting in 1978, suggesting a possible regime shift. Demonstrate Fisher information as a useful method for assessing patterns in big data.

  6. A Case of Miller Fisher Syndrome and Literature Review

    PubMed Central

    Taboada, Javier

    2017-01-01

    Miller Fisher syndrome (MFS)  was first recognized by James Collier in 1932 as a clinical triad of ataxia, areflexia, and ophthalmoplegia. Later, it was described in 1956 by Charles Miller Fisher as a possible variant of Guillain-Barré syndrome (GBS). Here, we write a case of a patient with atypical presentation of this clinical triad as the patient presented with double vision initially due to unilateral ocular involvement that progressed to bilateral ophthalmoplegia. He developed weakness of the lower extremities and areflexia subsequently. A diagnosis of MFS was made due to the clinical presentation and the presence of albuminocytologic dissociation in the cerebrospinal fluid (CSF) along with normal results of brain imaging and blood workup. The patient received intravenous immune globulin (IVIG), and his symptoms improved. The initial diagnosis of MFS is based on the clinical presentation and is confirmed by cerebral spinal fluid analysis and clinical neurophysiology studies. This case which emphasizes the knowledge of a rare syndrome can help narrow down the differentials to act promptly and appropriately manage such patients. PMID:28367386

  7. Which Fishers are Satisfied in the Caribbean? A Comparative Analysis of Job Satisfaction Among Caribbean Lobster Fishers.

    PubMed

    Monnereau, Iris; Pollnac, Richard

    2012-10-01

    Lobster fishing (targeting the spiny lobster Panulirus argus) is an important economic activity throughout the Wider Caribbean Region both as a source of income and employment for the local population as well as foreign exchange for national governments. Due to the high unit prices of the product, international lobster trade provides a way to improve the livelihoods of fisheries-dependent populations. The specie harvested is identical throughout the region and end market prices are roughly similar. In this paper we wish to investigate to which extent lobster fishers' job satisfaction differs in three countries in the Caribbean and how these differences can be explained by looking at the national governance arrangements.

  8. End-use quality of soft kernel durum wheat

    USDA-ARS?s Scientific Manuscript database

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

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

    PubMed

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

    2011-05-31

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

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

    USDA-ARS?s Scientific Manuscript database

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

  11. Thermomechanical property of rice kernels studied by DMA

    USDA-ARS?s Scientific Manuscript database

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

  12. 7 CFR 868.254 - Broken kernels determination.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...

  13. 7 CFR 868.304 - Broken kernels determination.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...

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

    PubMed

    Reyhani, Nima

    2013-07-01

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

  15. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

  16. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

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

  17. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

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

  18. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

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

  19. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

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

  20. Convolution kernels for multi-wavelength imaging

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  1. Arbitrary-resolution global sensitivity kernels

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

  2. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... washing: Provided, That the presence of web or frass shall not be considered serious damage for the...

  3. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

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

  4. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

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

  5. Kernel Temporal Differences for Neural Decoding

    PubMed Central

    Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

    2015-01-01

    We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

  6. Kernel temporal differences for neural decoding.

    PubMed

    Bae, Jihye; Sanchez Giraldo, Luis G; Pohlmeyer, Eric A; Francis, Joseph T; Sanchez, Justin C; Príncipe, José C

    2015-01-01

    We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces.

  7. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

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

  8. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  9. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

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

  10. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

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

  11. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

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

  12. Symbol recognition with kernel density matching.

    PubMed

    Zhang, Wan; Wenyin, Liu; Zhang, Kun

    2006-12-01

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

  13. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

    PubMed Central

    Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

    2016-01-01

    Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392

  14. Spark Ignited Turbulent Flame Kernel Growth

    SciTech Connect

    Santavicca, D.A.

    1995-06-01

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

  15. Statistical algorithms for a comprehensive test ban treaty discrimination framework

    SciTech Connect

    Foote, N.D.; Anderson, D.N.; Higbee, K.T.; Miller, N.E.; Redgate, T.; Rohay, A.C.; Hagedorn, D.N.

    1996-10-01

    Seismic discrimination is the process of identifying a candidate seismic event as an earthquake or explosion using information from seismic waveform features (seismic discriminants). In the CTBT setting, low energy seismic activity must be detected and identified. A defensible CTBT discrimination decision requires an understanding of false-negative (declaring an event to be an earthquake given it is an explosion) and false-position (declaring an event to be an explosion given it is an earthquake) rates. These rates are derived from a statistical discrimination framework. A discrimination framework can be as simple as a single statistical algorithm or it can be a mathematical construct that integrates many different types of statistical algorithms and CTBT technologies. In either case, the result is the identification of an event and the numerical assessment of the accuracy of an identification, that is, false-negative and false-positive rates. In Anderson et al., eight statistical discrimination algorithms are evaluated relative to their ability to give results that effectively contribute to a decision process and to be interpretable with physical (seismic) theory. These algorithms can be discrimination frameworks individually or components of a larger framework. The eight algorithms are linear discrimination (LDA), quadratic discrimination (QDA), variably regularized discrimination (VRDA), flexible discrimination (FDA), logistic discrimination, K-th nearest neighbor (KNN), kernel discrimination, and classification and regression trees (CART). In this report, the performance of these eight algorithms, as applied to regional seismic data, is documented. Based on the findings in Anderson et al. and this analysis: CART is an appropriate algorithm for an automated CTBT setting.

  16. Identifying alternate pathways for climate change to impact inland recreational fishers

    USGS Publications Warehouse

    Hunt, Len M.; Fenichel, Eli P.; Fulton, David C.; Mendelsohn, Robert; Smith, Jordan W.; Tunney, Tyler D.; Lynch, Abigail J.; Paukert, Craig P.; Whitney, James E.

    2016-01-01

    Fisheries and human dimensions literature suggests that climate change influences inland recreational fishers in North America through three major pathways. The most widely recognized pathway suggests that climate change impacts habitat and fish populations (e.g., water temperature impacting fish survival) and cascades to impact fishers. Climate change also impacts recreational fishers by influencing environmental conditions that directly affect fishers (e.g., increased temperatures in northern climates resulting in extended open water fishing seasons and increased fishing effort). The final pathway occurs from climate change mitigation and adaptation efforts (e.g., refined energy policies result in higher fuel costs, making distant trips more expensive). To address limitations of past research (e.g., assessing climate change impacts for only one pathway at a time and not accounting for climate variability, extreme weather events, or heterogeneity among fishers), we encourage researchers to refocus their efforts to understand and document climate change impacts to inland fishers.

  17. Multiple Factors Affect Socioeconomics and Wellbeing of Artisanal Sea Cucumber Fishers.

    PubMed

    Purcell, Steven W; Ngaluafe, Poasi; Foale, Simon J; Cocks, Nicole; Cullis, Brian R; Lalavanua, Watisoni

    2016-01-01

    Small-scale fisheries are important to livelihoods and subsistence seafood consumption of millions of fishers. Sea cucumbers are fished worldwide for export to Asia, yet few studies have assessed factors affecting socioeconomics and wellbeing among fishers. We interviewed 476 men and women sea cucumber fishers at multiple villages within multiple locations in Fiji, Kiribati, Tonga and New Caledonia using structured questionnaires. Low rates of subsistence consumption confirmed a primary role of sea cucumbers in income security. Prices of sea cucumbers sold by fishers varied greatly among countries, depending on the species. Gender variation in landing prices could be due to women catching smaller sea cucumbers or because some traders take advantage of them. Dissatisfaction with fishery income was common (44% of fishers), especially for i-Kiribati fishers, male fishers, and fishers experiencing difficulty selling their catch, but was uncorrelated with sale prices. Income dissatisfaction worsened with age. The number of livelihood activities averaged 2.2-2.5 across countries, and varied significantly among locations. Sea cucumbers were often a primary source of income to fishers, especially in Tonga. Other common livelihood activities were fishing other marine resources, copra production in Kiribati, agriculture in Fiji, and salaried jobs in New Caledonia. Fishing other coastal and coral reef resources was the most common fall-back livelihood option if fishers were forced to exit the fishery. Our data highlight large disparities in subsistence consumption, gender-related price equity, and livelihood diversity among parallel artisanal fisheries. Improvement of supply chains in dispersed small-scale fisheries appears as a critical need for enhancing income and wellbeing of fishers. Strong evidence for co-dependence among small-scale fisheries, through fall-back livelihood preferences of fishers, suggests that resource managers must mitigate concomitant effects on other

  18. Multiple Factors Affect Socioeconomics and Wellbeing of Artisanal Sea Cucumber Fishers

    PubMed Central

    Ngaluafe, Poasi; Foale, Simon J.; Cocks, Nicole; Cullis, Brian R.; Lalavanua, Watisoni

    2016-01-01

    Small-scale fisheries are important to livelihoods and subsistence seafood consumption of millions of fishers. Sea cucumbers are fished worldwide for export to Asia, yet few studies have assessed factors affecting socioeconomics and wellbeing among fishers. We interviewed 476 men and women sea cucumber fishers at multiple villages within multiple locations in Fiji, Kiribati, Tonga and New Caledonia using structured questionnaires. Low rates of subsistence consumption confirmed a primary role of sea cucumbers in income security. Prices of sea cucumbers sold by fishers varied greatly among countries, depending on the species. Gender variation in landing prices could be due to women catching smaller sea cucumbers or because some traders take advantage of them. Dissatisfaction with fishery income was common (44% of fishers), especially for i-Kiribati fishers, male fishers, and fishers experiencing difficulty selling their catch, but was uncorrelated with sale prices. Income dissatisfaction worsened with age. The number of livelihood activities averaged 2.2–2.5 across countries, and varied significantly among locations. Sea cucumbers were often a primary source of income to fishers, especially in Tonga. Other common livelihood activities were fishing other marine resources, copra production in Kiribati, agriculture in Fiji, and salaried jobs in New Caledonia. Fishing other coastal and coral reef resources was the most common fall-back livelihood option if fishers were forced to exit the fishery. Our data highlight large disparities in subsistence consumption, gender-related price equity, and livelihood diversity among parallel artisanal fisheries. Improvement of supply chains in dispersed small-scale fisheries appears as a critical need for enhancing income and wellbeing of fishers. Strong evidence for co-dependence among small-scale fisheries, through fall-back livelihood preferences of fishers, suggests that resource managers must mitigate concomitant effects on

  19. Diffusion on a hypersphere: application to the Wright-Fisher model

    NASA Astrophysics Data System (ADS)

    Maruyama, Kishiko; Itoh, Yoshiaki

    2016-04-01

    The eigenfunction expansion by Gegenbauer polynomials for the diffusion on a hypersphere is transformed into the diffusion for the Wright-Fisher model with a particular mutation rate. We use the Ito calculus considering stochastic differential equations. The expansion gives a simple interpretation of the Griffiths eigenfunction expansion for the Wright-Fisher model. Our representation is useful to simulate the Wright-Fisher model as well as Brownian motion on a hypersphere.

  20. Kernel weights optimization for error diffusion halftoning method

    NASA Astrophysics Data System (ADS)

    Fedoseev, Victor

    2015-02-01

    This paper describes a study to find the best error diffusion kernel for digital halftoning under various restrictions on the number of non-zero kernel coefficients and their set of values. As an objective measure of quality, WSNR was used. The problem of multidimensional optimization was solved numerically using several well-known algorithms: Nelder- Mead, BFGS, and others. The study found a kernel function that provides a quality gain of about 5% in comparison with the best of the commonly used kernel introduced by Floyd and Steinberg. Other kernels obtained allow to significantly reduce the computational complexity of the halftoning process without reducing its quality.

  1. Generalization Performance of Regularized Ranking With Multiscale Kernels.

    PubMed

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

    2016-05-01

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

  2. The pre-image problem in kernel methods.

    PubMed

    Kwok, James Tin-yau; Tsang, Ivor Wai-hung

    2004-11-01

    In this paper, we address the problem of finding the pre-image of a feature vector in the feature space induced by a kernel. This is of central importance in some kernel applications, such as on using kernel principal component analysis (PCA) for image denoising. Unlike the traditional method which relies on nonlinear optimization, our proposed method directly finds the location of the pre-image based on distance constraints in the feature space. It is noniterative, involves only linear algebra and does not suffer from numerical instability or local minimum problems. Evaluations on performing kernel PCA and kernel clustering on the USPS data set show much improved performance.

  3. The Cosmological Origin of the Tully-Fisher Relation

    NASA Astrophysics Data System (ADS)

    Steinmetz, Matthias; Navarro, Julio F.

    1999-03-01

    We use high-resolution cosmological simulations that include the effects of gasdynamics and star formation to investigate the origin of the Tully-Fisher relation in the standard cold dark matter cosmogony. Stars are assumed to form in collapsing, Jeans-unstable gas clumps at a rate set by the local gas density and the dynamical/cooling timescale. The energetic feedback from stellar evolution is assumed to heat the gas-surrounding regions of ongoing star formation, where it is radiated away very rapidly. The star formation algorithm thus has little effect on the rate at which gas cools and collapses, and, as a result, most galaxies form their stars very early. Luminosities are computed for each model galaxy using their full star formation histories and the latest spectrophotometric models. We find that the stellar mass of model galaxies is proportional to the total baryonic mass within the virial radius of their surrounding halos. Circular velocity then correlates tightly with the total luminosity of the galaxy, which reflects the equivalence between mass and circular velocity of systems identified in a cosmological context. The slope of the relation steepens slightly from the blue to the red bandpasses and is in fairly good agreement with observations. Its scatter is small, decreasing from ~0.38 mag in the U band to ~0.24 mag in the K band. The particular cosmological model we explore here seems unable to account for the zero point of the correlation. Model galaxies are too faint at z=0 (by about 2 mag) if the circular velocity at the edge of the luminous galaxy is used as an estimator of the rotation speed. The model Tully-Fisher relation is brighter in the past by ~0.7 mag in the B band at z=1, which is at odds with recent observations of z~1 galaxies. We conclude that the slope and tightness of the Tully-Fisher relation can be naturally explained in hierarchical models, but that its normalization and evolution depend strongly on the star formation algorithm

  4. Sarcocystis neurona-associated meningoencephalitis and description of intramuscular sarcocysts in a fisher (Martes pennanti).

    PubMed

    Gerhold, Richard W; Howerth, Elizabeth W; Lindsay, David S

    2005-01-01

    A free-ranging juvenile fisher (Martes pennanti) with ataxia, lethargy, stupor, and intermittent, whole-body tremors was examined postmortem. Microscopically, the fisher had protozoal meningoencephalitis caused by Sarcocystis neurona, which was confirmed by immunohistochemistry, polymerase chain reaction (PCR) and restriction fragment length polymorphism testing, and genetic sequencing. Sarcocysts found in the skeletal muscle of the fisher were negative for S. neurona by PCR, but were morphologically similar to previous light and electron microscopy descriptions of S. neurona. This is the first report of clinical neural S. neurona infection in a fisher.

  5. Quantum Fisher information of the Greenberg-Horne-Zeilinger state in decoherence channels

    NASA Astrophysics Data System (ADS)

    Ma, Jian; Huang, Yi-Xiao; Wang, Xiaoguang; Sun, C. P.

    2011-08-01

    Quantum Fisher information of a parameter characterizes the sensitivity of the state with respect to changes of the parameter. In this article, we study the quantum Fisher information of a state with respect to SU(2) rotations under three decoherence channels: the amplitude-damping, phase-damping, and depolarizing channels. The initial state is chosen to be a Greenberg-Horne-Zeilinger state of which the phase sensitivity can achieve the Heisenberg limit. By using the Kraus operator representation, the quantum Fisher information is obtained analytically. We observe the decay and sudden change of the quantum Fisher information in all three channels.

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  7. Learning bounds for kernel regression using effective data dimensionality.

    PubMed

    Zhang, Tong

    2005-09-01

    Kernel methods can embed finite-dimensional data into infinite-dimensional feature spaces. In spite of the large underlying feature dimensionality, kernel methods can achieve good generalization ability. This observation is often wrongly interpreted, and it has been used to argue that kernel learning can magically avoid the "curse-of-dimensionality" phenomenon encountered in statistical estimation problems. This letter shows that although using kernel representation, one can embed data into an infinite-dimensional feature space; the effective dimensionality of this embedding, which determines the learning complexity of the underlying kernel machine, is usually small. In particular, we introduce an algebraic definition of a scale-sensitive effective dimension associated with a kernel representation. Based on this quantity, we derive upper bounds on the generalization performance of some kernel regression methods. Moreover, we show that the resulting convergent rates are optimal under various circumstances.

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

    PubMed

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

    2016-11-01

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

  9. Enhancing teleportation of quantum Fisher information by partial measurements

    NASA Astrophysics Data System (ADS)

    Xiao, Xing; Yao, Yao; Zhong, Wo-Jun; Li, Yan-Ling; Xie, Ying-Mao

    2016-01-01

    The purport of quantum teleportation is to completely transfer information from one party to another distant partner. However, from the perspective of parameter estimation, it is the information carried by a particular parameter, not the information of total quantum state that needs to be teleported. Due to the inevitable noise in environments, we propose two schemes to enhance quantum Fisher information (QFI) teleportation under amplitude damping noise with the technique of partial measurements. We find that post-partial measurement can greatly enhance the teleported QFI, while the combination of prior partial measurement and post-partial measurement reversal could completely eliminate the effect of decoherence. We show that, somewhat consequentially, enhancing QFI teleportation is more economic than that of improving fidelity teleportation. Our work extends the ability of partial measurements as a quantum technique to battle decoherence in quantum information processing.

  10. Detailed H I kinematics of Tully-Fisher calibrator galaxies

    NASA Astrophysics Data System (ADS)

    Ponomareva, Anastasia A.; Verheijen, Marc A. W.; Bosma, Albert

    2016-12-01

    We present spatially resolved H I kinematics of 32 spiral galaxies which have Cepheid or/and tip of the red giant branch distances, and define a calibrator sample for the Tully-Fisher relation. The interferometric H I data for this sample were collected from available archives and supplemented with new Giant Metrewave Radio Telescope observations. This paper describes a uniform analysis of the H I kinematics of this inhomogeneous data set. Our main result is an atlas for our calibrator sample that presents global H I profiles, integrated H I column-density maps, H I surface-density profiles and, most importantly, detailed kinematic information in the form of high-quality rotation curves derived from highly resolved, two-dimensional velocity fields and position-velocity diagrams.

  11. Fisher information and the thermodynamics of scale-invariant systems

    NASA Astrophysics Data System (ADS)

    Hernando, A.; Vesperinas, C.; Plastino, A.

    2010-02-01

    We present a thermodynamic formulation for scale-invariant systems based on the minimization with constraints of the Fisher information measure. In such a way a clear analogy between these systems’ thermal properties and those of gases and fluids is seen to emerge in a natural fashion. We focus our attention on the non-interacting scenario, speaking thus of scale-free ideal gases (SFIGs) and present some empirical evidences regarding such disparate systems as electoral results, city populations and total citations in Physics journals, that seem to indicate that SFIGs do exist. We also illustrate the way in which Zipf’s law can be understood in a thermodynamical context as the surface of a finite system. Finally, we derive an equivalent microscopic description of our systems which totally agrees with previous numerical simulations found in the literature.

  12. Fisher symmetry and the geometry of quantum states

    NASA Astrophysics Data System (ADS)

    Gross, Jonathan A.; Barnum, Howard; Caves, Carlton M.

    The quantum Fisher information (QFI) is a valuable tool on account of the achievable lower bound it provides for single-parameter estimation. Due to the existence of incompatible quantum observables, however, the lower bound provided by the QFI cannot be saturated in the general multi-parameter case. A bound demonstrated by Gill and Massar (GM) captures some of the limitations that incompatibility imposes in the multi-parameter case. We further explore the structure of measurements allowed by quantum mechanics, identifying restrictions beyond those given by the QFI and GM bound. These additional restrictions give insight into the geometry of quantum state space and notions of measurement symmetry related to the QFI.

  13. In memoriam: Harvey I. Fisher, 1916-1994

    USGS Publications Warehouse

    Waring, G.H.; Robbins, Chandler S.

    1996-01-01

    Harvey Irvin Fisher was born on 15 June 1916 in Edgar, Nebraska, and spent his youth near Blue Springs, Missouri. Following his A.A. from Kansas City in 1935, and B.S. from Kansas State University in 1937, he married Mildred Hoch; they had three sons. He received his Ph.D. from the University of California, Berkeley, in 1942. From 1942 through 1945, while at Berkeley, he was Technical Curator, Museum of Vertebrate Zoology, Biologist with the Crocker Radiation Laboratory, and Assistant Editor of The Condor. In 1944 he joined the AOU, became a Fellow in 1950, and was Editor of The Auk from 1948 to 1952 (to which he contributed numerous book reviews). He later became a Fellow of the American Association for the Advancement of Science and of the International Academy of Science.

  14. [Fisher's syndrome. Peripheral or central origin (author's transl)].

    PubMed

    Collard, M; Mathe, J F; Guihenneuc, P; Coquillat, G; Eber, A M; Ruh, D

    1978-05-01

    The syndrome described by M. Fisher in 1956 includes ophtalmoplegia, ataxia, and generalized loss of reflexes. It is classically considered to be of peripheral origin and its relation to Guillain and Barre's syndrome in its mesencephalic form is debatable. The authors review 5 cases and discuss the question of a probable central origin. They base their opinion on the pathognomonic features of these cases and those in the literature, as well as the results of their oculographic and electromyographic studies. They stress the importance of the nature of the ataxia; the severe equilibrium disturbances noted in these patients could result, contrary to usual thinking, more from a central vestibular syndrome than from a cerebellar lesion.

  15. Noisy metrology: a saturable lower bound on quantum Fisher information

    NASA Astrophysics Data System (ADS)

    Yousefjani, R.; Salimi, S.; Khorashad, A. S.

    2017-06-01

    In order to provide a guaranteed precision and a more accurate judgement about the true value of the Cramér-Rao bound and its scaling behavior, an upper bound (equivalently a lower bound on the quantum Fisher information) for precision of estimation is introduced. Unlike the bounds previously introduced in the literature, the upper bound is saturable and yields a practical instruction to estimate the parameter through preparing the optimal initial state and optimal measurement. The bound is based on the underling dynamics, and its calculation is straightforward and requires only the matrix representation of the quantum maps responsible for encoding the parameter. This allows us to apply the bound to open quantum systems whose dynamics are described by either semigroup or non-semigroup maps. Reliability and efficiency of the method to predict the ultimate precision limit are demonstrated by three main examples.

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

    PubMed

    Pan, Binbin; Chen, Wen-Sheng; Xu, Chen; Chen, Bo

    2016-05-01

    The data from real world usually have nonlinear geometric structure, which are often assumed to lie on or close to a low-dimensional manifold in a high-dimensional space. How to detect this nonlinear geometric structure of the data is important for the learning algorithms. Recently, there has been a surge of interest in utilizing kernels to exploit the manifold structure of the data. Such kernels are called geometry-aware kernels and are widely used in the machine learning algorithms. The performance of these algorithms critically relies on the choice of the geometry-aware kernels. Intuitively, a good geometry-aware kernel should utilize additional information other than the geometric information. In many applications, it is required to compute the out-of-sample data directly. However, most of the geometry-aware kernel methods are restricted to the available data given beforehand, with no straightforward extension for out-of-sample data. In this paper, we propose a framework for more general geometry-aware kernel learning. The proposed framework integrates multiple sources of information and enables us to develop flexible and effective kernel matrices. Then, we theoretically show how the learned kernel matrices are extended to the corresponding kernel functions, in which the out-of-sample data can be computed directly. Under our framework, a novel family of geometry-aware kernels is developed. Especially, some existing geometry-aware kernels can be viewed as instances of our framework. The performance of the kernels is evaluated on dimensionality reduction, classification, and clustering tasks. The empirical results show that our kernels significantly improve the performance.

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

    PubMed

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

    2014-01-01

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

  18. Inverse of the string theory KLT kernel

    NASA Astrophysics Data System (ADS)

    Mizera, Sebastian

    2017-06-01

    The field theory Kawai-Lewellen-Tye (KLT) kernel, which relates scattering amplitudes of gravitons and gluons, turns out to be the inverse of a matrix whose components are bi-adjoint scalar partial amplitudes. In this note we propose an analogous construction for the string theory KLT kernel. We present simple diagrammatic rules for the computation of the α'-corrected bi-adjoint scalar amplitudes that are exact in α'. We find compact expressions in terms of graphs, where the standard Feynman propagators 1 /p 2 are replaced by either 1 /sin(π α' p 2 /2) or 1 /tan(π α' p 2 /2), as determined by a recursive procedure. We demonstrate how the same object can be used to conveniently expand open string partial amplitudes in a BCJ basis.

  19. Motion Blur Kernel Estimation via Deep Learning.

    PubMed

    Xu, Xiangyu; Pan, Jinshan; Zhang, Yu-Jin; Yang, Ming-Hsuan

    2017-09-18

    The success of the state-of-the-art deblurring methods mainly depends on restoration of sharp edges in a coarse-tofine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images. Motivated by the success of the existing filtering based deblurring methods, the proposed model consists of two stages: suppressing extraneous details and enhancing sharp edges. We show that the two-stage model simplifies the learning process and effectively restores sharp edges. Facilitated by the learned sharp edges, the proposed deblurring algorithm does not require any coarse-to-fine strategy or edge selection, thereby significantly simplifying kernel estimation and reducing computation load. Extensive experimental results on challenging blurry images demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of visual quality and run-time.

  20. Wilson Dslash Kernel From Lattice QCD Optimization

    SciTech Connect

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

    2015-07-01

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

  1. Classification of LV wall motion in cardiac MRI using kernel Dictionary Learning with a parametric approach.

    PubMed

    Mantilla, Juan; Paredes, Jose; Bellanger, Jean-J; Donal, Erwan; Leclercq, Christophe; Medina, Ruben; Garreau, Mireille

    2015-01-01

    In this paper, we propose a parametric approach for the assessment of wall motion in Left Ventricle (LV) function in cardiac cine-Magnetic Resonance Imaging (MRI). Time-signal intensity curves (TSICs) are identified in Spatio-temporal image profiles extracted from different anatomical segments in a cardiac MRI sequence. Different parameters are constructed from specific TSICs that present a decreasing then increasing shape reflecting dynamic information of the LV contraction. The parameters extracted from these curves are related to: 1) an average curve based on a clustering process, 2) curve skewness and 3) cross correlation values between each average clustered curve and a patient-specific reference. Several tests are performed in order to construct different vectors to train a sparse classifier based on kernel Dictionary Learning (DL). Results are compared with other classifiers like Support Vector Machine (SVM) and Discriminative Dictionary Learning. The best classification performance is obtained with information of skewness and the average curve with an accuracy about 94% using the mentioned sparse based kernel DL with a radial basis function kernel.

  2. Bergman kernel and complex singularity exponent

    NASA Astrophysics Data System (ADS)

    Chen, Boyong; Lee, Hanjin

    2009-12-01

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

  3. Control Transfer in Operating System Kernels

    DTIC Science & Technology

    1994-05-13

    the Programming Symposium, pages 181-203, 1974. [Leffler et al. 89] S. Leffler, M. McKusick, M. Karels, and J. Quarterman. The Design and...increased modularity in operating systems only increases the importance of control transfer. My thesis is that a programming language abstraction...continuations provide allows the kernel designer when necessary to choose implementation performance over convenience, without affecting the design of

  4. Interpretation and Visualization of Non-Linear Data Fusion in Kernel Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis

    PubMed Central

    Smolinska, Agnieszka; Blanchet, Lionel; Coulier, Leon; Ampt, Kirsten A. M.; Luider, Theo; Hintzen, Rogier Q.; Wijmenga, Sybren S.; Buydens, Lutgarde M. C.

    2012-01-01

    Background In the last decade data fusion has become widespread in the field of metabolomics. Linear data fusion is performed most commonly. However, many data display non-linear parameter dependences. The linear methods are bound to fail in such situations. We used proton Nuclear Magnetic Resonance and Gas Chromatography-Mass Spectrometry, two well established techniques, to generate metabolic profiles of Cerebrospinal fluid of Multiple Sclerosis (MScl) individuals. These datasets represent non-linearly separable groups. Thus, to extract relevant information and to combine them a special framework for data fusion is required. Methodology The main aim is to demonstrate a novel approach for data fusion for classification; the approach is applied to metabolomics datasets coming from patients suffering from MScl at a different stage of the disease. The approach involves data fusion in kernel space and consists of four main steps. The first one is to extract the significant information per data source using Support Vector Machine Recursive Feature Elimination. This method allows one to select a set of relevant variables. In the next step the optimized kernel matrices are merged by linear combination. In step 3 the merged datasets are analyzed with a classification technique, namely Kernel Partial Least Square Discriminant Analysis. In the final step, the variables in kernel space are visualized and their significance established. Conclusions We find that fusion in kernel space allows for efficient and reliable discrimination of classes (MScl and early stage). This data fusion approach achieves better class prediction accuracy than analysis of individual datasets and the commonly used mid-level fusion. The prediction accuracy on an independent test set (8 samples) reaches 100%. Additionally, the classification model obtained on fused kernels is simpler in terms of complexity, i.e. just one latent variable was sufficient. Finally, visualization of variables importance in

  5. Interpretation and visualization of non-linear data fusion in kernel space: study on metabolomic characterization of progression of multiple sclerosis.

    PubMed

    Smolinska, Agnieszka; Blanchet, Lionel; Coulier, Leon; Ampt, Kirsten A M; Luider, Theo; Hintzen, Rogier Q; Wijmenga, Sybren S; Buydens, Lutgarde M C

    2012-01-01

    In the last decade data fusion has become widespread in the field of metabolomics. Linear data fusion is performed most commonly. However, many data display non-linear parameter dependences. The linear methods are bound to fail in such situations. We used proton Nuclear Magnetic Resonance and Gas Chromatography-Mass Spectrometry, two well established techniques, to generate metabolic profiles of Cerebrospinal fluid of Multiple Sclerosis (MScl) individuals. These datasets represent non-linearly separable groups. Thus, to extract relevant information and to combine them a special framework for data fusion is required. The main aim is to demonstrate a novel approach for data fusion for classification; the approach is applied to metabolomics datasets coming from patients suffering from MScl at a different stage of the disease. The approach involves data fusion in kernel space and consists of four main steps. The first one is to extract the significant information per data source using Support Vector Machine Recursive Feature Elimination. This method allows one to select a set of relevant variables. In the next step the optimized kernel matrices are merged by linear combination. In step 3 the merged datasets are analyzed with a classification technique, namely Kernel Partial Least Square Discriminant Analysis. In the final step, the variables in kernel space are visualized and their significance established. We find that fusion in kernel space allows for efficient and reliable discrimination of classes (MScl and early stage). This data fusion approach achieves better class prediction accuracy than analysis of individual datasets and the commonly used mid-level fusion. The prediction accuracy on an independent test set (8 samples) reaches 100%. Additionally, the classification model obtained on fused kernels is simpler in terms of complexity, i.e. just one latent variable was sufficient. Finally, visualization of variables importance in kernel space was achieved.

  6. Boosted learned kernels for data-driven vesselness measure

    NASA Astrophysics Data System (ADS)

    Grisan, E.

    2017-03-01

    Common vessel centerline extraction methods rely on the computation of a measure providing the likeness of the local appearance of the data to a curvilinear tube-like structure. The most popular techniques rely on empirically designed (hand crafted) measurements as the widely used Hessian vesselness, the recent oriented flux tubeness or filters (e.g. the Gaussian matched filter) that are developed to respond to local features, without exploiting any context information nor the rich structural information embedded in the data. At variance with the previously proposed methods, we propose a completely data-driven approach for learning a vesselness measure from expert-annotated dataset. For each data point (voxel or pixel), we extract the intensity values in a neighborhood region, and estimate the discriminative convolutional kernel yielding a positive response for vessel data and negative response for non-vessel data. The process is iterated within a boosting framework, providing a set of linear filters, whose combined response is the learned vesselness measure. We show the results of the general-use proposed method on the DRIVE retinal images dataset, comparing its performance against the hessian-based vesselness, oriented flux antisymmetry tubeness, and vesselness learned with a probabilistic boosting tree or with a regression tree. We demonstrate the superiority of our approach that yields a vessel detection accuracy of 0.95, with respect to 0.92 (hessian), 0.90 (oriented flux) and 0.85 (boosting tree).

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

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

  8. Balancing continuous covariates based on Kernel densities.

    PubMed

    Ma, Zhenjun; Hu, Feifang

    2013-03-01

    The balance of important baseline covariates is essential for convincing treatment comparisons. Stratified permuted block design and minimization are the two most commonly used balancing strategies, both of which require the covariates to be discrete. Continuous covariates are typically discretized in order to be included in the randomization scheme. But breakdown of continuous covariates into subcategories often changes the nature of the covariates and makes distributional balance unattainable. In this article, we propose to balance continuous covariates based on Kernel density estimations, which keeps the continuity of the covariates. Simulation studies show that the proposed Kernel-Minimization can achieve distributional balance of both continuous and categorical covariates, while also keeping the group size well balanced. It is also shown that the Kernel-Minimization is less predictable than stratified permuted block design and minimization. Finally, we apply the proposed method to redesign the NINDS trial, which has been a source of controversy due to imbalance of continuous baseline covariates. Simulation shows that imbalances such as those observed in the NINDS trial can be generally avoided through the implementation of the new method. Copyright © 2012 Elsevier Inc. All rights reserved.

  9. Kernel Non-Rigid Structure from Motion

    PubMed Central

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

    2013-01-01

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

  10. A Fast Reduced Kernel Extreme Learning Machine.

    PubMed

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

    2016-04-01

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

  11. Survival of fishers in the southern Sierra Nevada region of California

    Treesearch

    Richard A. Sweitzer; Craig M. Thompson; Rebecca E. Green; Reginald H. Barrett; Kathryn L. Purcell

    2015-01-01

    Fishers in the western United States were recently proposed for listing under the U.S. Endangered Species Act because of concerns for loss of suitable habitat and evidence of a diversity of mortality risks that reduce survival. One of 2 remnant populations of fishers in California is in the southern Sierra Nevada region, where we studied them at 2 research sites in the...

  12. Determining the gender of American martens and fishers at track plate stations

    Treesearch

    Keith M. Slauson; Richard L. Truex; William J. Zielinski

    2008-01-01

    Determining the gender of American martens (Martes americana) and fishers (M. pennanti) from track plate stations would significantly augment the information currently gathered from this simple and inexpensive survey method. We used track-plate impressions collected from captured individual martens and fishers of known gender to...

  13. Fisher Information, Entropy, and the Second and Third Laws of Thermodynamics

    EPA Science Inventory

    We propose Fisher Information as a new calculable thermodynamic property that can be shown to follow the Second and the Third Laws of Thermodynamics. Fisher Information is, however, qualitatively different from entropy and potentially possessing a great deal more structure. Hence...

  14. Changes in the structural and functional characteristics of fisher (Pekania pennanti) rest structures over time

    Treesearch

    Bill Zielinski; Fredrick V. Schlexer

    2015-01-01

    Resting habitat used by fishers (Pekania pennanti) has been relatively well studied but information on the persistence of their resting structures over time is unknown. We selected for reexamination 73 of 195 resting structures used by by fishers in northwestern California and compared their condition on the date they were found with their...

  15. An evaluation of a weaning index for wild fishers (Pekania [Martes] pennanti) in California

    Treesearch

    Sean M. Matthews; J. Mark Higley; John T. Finn; Kerry M. Rennie; Craig M. Thompson; Kathryn L. Purcell; Rick A. Sweitzer; Sandra L. Haire; Paul R. Sievert; Todd K. Fuller

    2013-01-01

    Conservation concern for fishers (Pekania [Martes] pennanti) in the Pacific states has highlighted a need to develop cost-effective methods of monitoring reproduction in extant and reintroduced fisher populations. We evaluated the efficacy of nipple size as a predictive index of weaning success for females...

  16. 33 CFR 110.50a - Fishers Island Sound, Stonington, Conn.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Fishers Island Sound, Stonington, Conn. 110.50a Section 110.50a Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.50a Fishers Island...

  17. 33 CFR 110.50a - Fishers Island Sound, Stonington, Conn.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false Fishers Island Sound, Stonington, Conn. 110.50a Section 110.50a Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.50a Fishers Island...

  18. 33 CFR 110.50a - Fishers Island Sound, Stonington, Conn.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false Fishers Island Sound, Stonington, Conn. 110.50a Section 110.50a Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.50a Fishers Island...

  19. 33 CFR 110.50a - Fishers Island Sound, Stonington, Conn.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 false Fishers Island Sound, Stonington, Conn. 110.50a Section 110.50a Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.50a Fishers Island...

  20. 33 CFR 110.50a - Fishers Island Sound, Stonington, Conn.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 false Fishers Island Sound, Stonington, Conn. 110.50a Section 110.50a Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.50a Fishers Island...

  1. Resting structures and resting habitat of fishers in the southern Sierra Nevada, California

    Treesearch

    Kathryn L. Purcell; Amie K. Mazzoni; Sylvia R. Mori; Brian B. Boroski

    2009-01-01

    The fisher (Martes pennanti) is a forest mustelid endemic to North America that has experienced range reductions in Pacific states that have led to their listing under the Endangered Species Act as warranted but precluded by higher priorities. The viability of the southern Sierra Nevada fisher population is of particular concern due to its reduced...

  2. Here today, here tomorrow: Managing forests for fisher habitat in the Northern Rockies

    Treesearch

    Sue Miller; Michael Schwartz; Lucretia E. Olson

    2016-01-01

    The fisher is a unique member of the weasel family and a sensitive species in the northern Rockies. They were almost extirpated by trapping in the early twentieth century, but these animals (a mix between a native and introduced population) now inhabit a swath of mesic coniferous forests in Idaho and Montana. Forest managers need information on fisher distribution and...

  3. Meta-analyses of habitat selection by fishers at resting sites in the Pacific coastal region

    Treesearch

    Keith B. Aubry; Catherine M. Raley; Steven W. Buskirk; William J. Zielinski; Michael K. Schwartz; Richard T. Golightly; Kathryn L. Purcell; Richard D. Weir; J. Scott. Yaeger

    2013-01-01

    The fisher (Pekania pennanti) is a species of conservation concern throughout the Pacific coastal region in North America. A number of radiotelemetry studies of habitat selection by fishers at resting sites have been conducted in this region, but the applicability of observed patterns beyond the boundaries of each study area is unknown. Broadly...

  4. Historical perspective on the reintroduction of the fisher and American marten in Michigan and Wisconsin

    Treesearch

    Bronwyn W. Williams; Jonathan H. Gilbert; Patrick A. Zollner

    2007-01-01

    Management of mustelid species such as fishers and martens requires an understanding of the history of local populations. This is particularly true in areas where populations were extirpated and restored through reintroduction efforts. During the late 19th and 20th centuries, fishers (Martes pennanti) and American martens (Martes americana...

  5. Case study 6.1: DNA survey for fisher in northern Idaho

    Treesearch

    Samuel Cushman; Kevin McKelvey; Michael Schwartz

    2008-01-01

    Unique haplotypes indicating the presence of a residual native population of fisher were found in central Idaho (Vinkey et al. 2006). Fishers had been detected previously using camera sets in the Selkirk Mountains just south of the Canadian border, but their population status and genetic composition were unknown. The purpose of the study was to provide a comprehensive...

  6. Using forest inventory data to assess fisher resting habitat suitability in California.

    Treesearch

    William J. Zielinski; Richard L. Truex; Jeffrey R. Dunk; Tom Gaman

    2006-01-01

    The fisher (Martes pennanti) is a forest-dwelling carnivore whose current distribution and association with late-seral forest conditions make it vulnerable to stand-altering human activities or natural disturbances. Fishers select a variety of structures for daily resting bouts. These habitat elements, together with foraging and reproductive (denning) habitat,...

  7. Fisher Information, Entropy, and the Second and Third Laws of Thermodynamics

    EPA Science Inventory

    We propose Fisher Information as a new calculable thermodynamic property that can be shown to follow the Second and the Third Laws of Thermodynamics. Fisher Information is, however, qualitatively different from entropy and potentially possessing a great deal more structure. Hence...

  8. A Case of Miller Fisher Syndrome, Thromboembolic Disease, and Angioedema: Association or Coincidence?

    PubMed Central

    Salehi, Nooshin; Choi, Eric D.; Garrison, Roger C.

    2017-01-01

    Patient: Male, 32 Final Diagnosis: Miller Fisher syndrome Symptoms: Ataxia • headache • ophthalmoplegia Medication: — Clinical Procedure: Plasmapheresis Specialty: Neurology Objective: Rare co-existance of disease or pathology Background: Miller Fisher Syndrome is characterized by the clinical triad of ophthalmoplegia, ataxia, and areflexia, and is considered to be a variant of Guillain-Barre Syndrome. Miller Fisher Syndrome is observed in approximately 1–5% of all Guillain-Barre cases in Western countries. Patients with Miller Fisher Syndrome usually have good recovery without residual deficits. Venous thromboembolism is a common complication of Guillain-Barre Syndrome and has also been reported in Miller Fisher Syndrome, but it has generally been reported in the presence of at least one prothrombotic risk factor such as immobility. A direct correlation between venous thromboembolism and Miller Fisher Syndrome or Guillain-Barre Syndrome has not been previously described. Case Report: We report the case of a 32-year-old Hispanic male who presented with acute, severe thromboembolic disease and concurrently demonstrated characteristic clinical features of Miller Fisher Syndrome including ophthalmoplegia, ataxia, and areflexia. Past medical and family history were negative for thromboembolic disease, and subsequent hypercoagulability workup was unremarkable. During the course of hospitalization, the patient also developed angioedema. Conclusions: We describe a possible association between Miller Fisher Syndrome, thromboembolic disease, and angioedema. PMID:28090073

  9. Astronaut Anna Fisher pictured near the aft flight deck of Discovery

    NASA Image and Video Library

    1984-11-12

    51A-20-004 (12 Nov. 1984) --- Astronaut Anna L. Fisher is pictured near the aft flight deck of Discovery, where she remained very busy on Nov. 12 and 14 while fellow crew members worked to retrieve two stranded communications satellites. Fisher appears to be taking photos from the observation station. A camera floats just above her head. Photo credit: NASA

  10. Evaluating the sustainability of a regional system using Fisher information in the San Luis Basin, Colorado

    EPA Science Inventory

    This paper describes the theory, data, and methodology necessary for using Fisher information to assess the sustainability of the San Luis Basin (SLB) regional system over time. Fisher information was originally developed as a measure of the information content in data and is an ...

  11. Linear discriminant analysis with misallocation in training samples

    NASA Technical Reports Server (NTRS)

    Chhikara, R. (Principal Investigator); Mckeon, J.

    1982-01-01

    Linear discriminant analysis for a two-class case is studied in the presence of misallocation in training samples. A general appraoch to modeling of mislocation is formulated, and the mean vectors and covariance matrices of the mixture distributions are derived. The asymptotic distribution of the discriminant boundary is obtained and the asymptotic first two moments of the two types of error rate given. Certain numerical results for the error rates are presented by considering the random and two non-random misallocation models. It is shown that when the allocation procedure for training samples is objectively formulated, the effect of misallocation on the error rates of the Bayes linear discriminant rule can almost be eliminated. If, however, this is not possible, the use of Fisher rule may be preferred over the Bayes rule.

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

    ERIC Educational Resources Information Center

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

    2008-01-01

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

  13. More than Anecdotes: Fishers' Ecological Knowledge Can Fill Gaps for Ecosystem Modeling.

    PubMed

    Bevilacqua, Ana Helena V; Carvalho, Adriana R; Angelini, Ronaldo; Christensen, Villy

    2016-01-01

    Ecosystem modeling applied to fisheries remains hampered by a lack of local information. Fishers' knowledge could fill this gap, improving participation in and the management of fisheries. The same fishing area was modeled using two approaches: based on fishers' knowledge and based on scientific information. For the former, the data was collected by interviews through the Delphi methodology, and for the latter, the data was gathered from the literature. Agreement between the attributes generated by the fishers' knowledge model and scientific model is discussed and explored, aiming to improve data availability, the ecosystem model, and fisheries management. The ecosystem attributes produced from the fishers' knowledge model were consistent with the ecosystem attributes produced by the scientific model, and elaborated using only the scientific data from literature. This study provides evidence that fishers' knowledge may suitably complement scientific data, and may improve the modeling tools for the research and management of fisheries.

  14. Post-tsunami relocation of fisher settlements in South Asia: evidence from the Coromandel Coast, India.

    PubMed

    Bavinck, Maarten; de Klerk, Leo; van der Plaat, Felice; Ravesteijn, Jorik; Angel, Dominique; Arendsen, Hendrik; van Dijk, Tom; de Hoog, Iris; van Koolwijk, Ant; Tuijtel, Stijn; Zuurendonk, Benjamin

    2015-07-01

    The tsunami that struck the coasts of India on 26 December 2004 resulted in the large-scale destruction of fisher habitations. The post-tsunami rehabilitation effort in Tamil Nadu was directed towards relocating fisher settlements in the interior. This paper discusses the outcomes of a study on the social effects of relocation in a sample of nine communities along the Coromandel Coast. It concludes that, although the participation of fishing communities in house design and in allocation procedures has been limited, many fisher households are satisfied with the quality of the facilities. The distance of the new settlements to the shore, however, is regarded as an impediment to engaging in the fishing profession, and many fishers are actually moving back to their old locations. This raises questions as to the direction of coastal zone policy in India, as well as to the weight accorded to safety (and other coastal development interests) vis-à-vis the livelihood needs of fishers.

  15. Fisher's contributions to genetics and heredity, with special emphasis on the Gregor Mendel controversy.

    PubMed

    Piegorsch, W W

    1990-12-01

    R. A. Fisher is widely respected for his contributions to both statistics and genetics. For instance, his 1930 text on The Genetical Theory of Natural Selection remains a watershed contribution in that area. Fisher's subsequent research led him to study the work of (Johann) Gregor Mendel, the 19th century monk who first developed the basic principles of heredity with experiments on garden peas. In examining Mendel's original 1865 article, Fisher noted that the conformity between Mendel's reported and proposed (theoretical) ratios of segregating individuals was unusually good, "too good" perhaps. The resulting controversy as to whether Mendel "cooked" his data for presentation has continued to the current day. This review highlights Fisher's most salient points as regards Mendel's "too good" fit, within the context of Fisher's extensive contributions to the development of genetical and evolutionary theory.

  16. Management decision making for fisher populations informed by occupancy modeling

    USGS Publications Warehouse

    Fuller, Angela K.; Linden, Daniel W.; Royle, J. Andrew

    2016-01-01

    Harvest data are often used by wildlife managers when setting harvest regulations for species because the data are regularly collected and do not require implementation of logistically and financially challenging studies to obtain the data. However, when harvest data are not available because an area had not previously supported a harvest season, alternative approaches are required to help inform management decision making. When distribution or density data are required across large areas, occupancy modeling is a useful approach, and under certain conditions, can be used as a surrogate for density. We collaborated with the New York State Department of Environmental Conservation (NYSDEC) to conduct a camera trapping study across a 70,096-km2 region of southern New York in areas that were currently open to fisher (Pekania [Martes] pennanti) harvest and those that had been closed to harvest for approximately 65 years. We used detection–nondetection data at 826 sites to model occupancy as a function of site-level landscape characteristics while accounting for sampling variation. Fisher occupancy was influenced positively by the proportion of conifer and mixed-wood forest within a 15-km2 grid cell and negatively associated with road density and the proportion of agriculture. Model-averaged predictions indicated high occupancy probabilities (>0.90) when road densities were low (<1 km/km2) and coniferous and mixed forest proportions were high (>0.50). Predicted occupancy ranged 0.41–0.67 in wildlife management units (WMUs) currently open to trapping, which could be used to guide a minimum occupancy threshold for opening new areas to trapping seasons. There were 5 WMUs that had been closed to trapping but had an average predicted occupancy of 0.52 (0.07 SE), and above the threshold of 0.41. These areas are currently under consideration by NYSDEC for opening a conservative harvest season. We demonstrate the use of occupancy modeling as an aid to management

  17. The interaction between seaweed farming as an alternative occupation and fisher numbers in the central Philippines.

    PubMed

    Hill, Nicholas A O; Rowcliffe, J Marcus; Koldewey, Heather J; Milner-Gulland, E J

    2012-04-01

    Alternative occupations are frequently promoted as a means to reduce the number of people exploiting declining fisheries. However, there is little evidence that alternative occupations reduce fisher numbers. Seaweed farming is frequently promoted as a lucrative alternative occupation for artisanal fishers in Southeast Asia. We examined how the introduction of seaweed farming has affected village-level changes in the number of fishers on Danajon Bank, central Philippines, where unsustainable fishing has led to declining fishery yields. To determine how fisher numbers had changed since seaweed farming started, we interviewed the heads of household from 300 households in 10 villages to examine their perceptions of how fisher numbers had changed in their village and the reasons they associated with these changes. We then asked key informants (people with detailed knowledge of village members) to estimate fisher numbers in these villages before seaweed farming began and at the time of the survey. We compared the results of how fisher numbers had changed in each village with the wealth, education, seaweed farm sizes, and other attributes of households in these villages, which we collected through interviews, and with village-level factors such as distance to markets. We also asked people why they either continued to engage in or ceased fishing. In four villages, respondents thought seaweed farming and low fish catches had reduced fisher numbers, at least temporarily. In one of these villages, there was a recent return to fishing due to declines in the price of seaweed and increased theft of seaweed. In another four villages, fisher numbers increased as human population increased, despite the widespread uptake of seaweed farming. Seaweed farming failed for technical reasons in two other villages. Our results suggest seaweed farming has reduced fisher numbers in some villages, a result that may be correlated with socioeconomic status, but the heterogeneity of outcomes is

  18. Small convolution kernels for high-fidelity image restoration

    NASA Technical Reports Server (NTRS)

    Reichenbach, Stephen E.; Park, Stephen K.

    1991-01-01

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

  19. Small convolution kernels for high-fidelity image restoration

    NASA Technical Reports Server (NTRS)

    Reichenbach, Stephen E.; Park, Stephen K.

    1991-01-01

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

  20. Identifying anatomical shape difference by regularized discriminative direction.

    PubMed

    Zhou, Luping; Hartley, Richard; Wang, Lei; Lieby, Paulette; Barnes, Nick

    2009-06-01

    Identifying the shape difference between two groups of anatomical objects is important for medical image analysis and computer-aided diagnosis. A method called "discriminative direction" in the literature has been proposed to solve this problem. In that method, the shape difference between groups is identified by deforming a shape along the discriminative direction. This paper conducts a thorough study about inferring this discriminative direction in an efficient and accurate way. First, finding the discriminative direction is reformulated as a preimage problem in kernel-based learning. This provides a complementary but conceptually simpler solution than the previous method. More importantly, we find that a shape deforming along the original discriminative direction cannot faithfully maintain its anatomical correctness. This unnecessarily introduces spurious shape differences and leads to inaccurate analysis. To overcome this problem, this paper further proposes a regularized discriminative direction by requiring a shape to conform to its underlying distribution when it deforms. Two different approaches are developed to impose the regularization, one from the perspective of probability distributions and the other from a geometric point of view, and their relationship is discussed. After verifying their superior performance through controlled experiments, we apply the proposed methods to detecting and localizing the hippocampal shape difference between sexes. We get results consistent with other independent research, providing a more compact representation of the shape difference compared with the established discriminative direction method.