Blur kernel estimation with algebraic tomography technique and intensity profiles of object boundaries

Science.gov (United States)

Ingacheva, Anastasia; Chukalina, Marina; Khanipov, Timur; Nikolaev, Dmitry

2018-04-01

Motion blur caused by camera vibration is a common source of degradation in photographs. In this paper we study the problem of finding the point spread function (PSF) of a blurred image using the tomography technique. The PSF reconstruction result strongly depends on the particular tomography technique used. We present a tomography algorithm with regularization adapted specifically for this task. We use the algebraic reconstruction technique (ART algorithm) as the starting algorithm and introduce regularization. We use the conjugate gradient method for numerical implementation of the proposed approach. The algorithm is tested using a dataset which contains 9 kernels extracted from real photographs by the Adobe corporation where the point spread function is known. We also investigate influence of noise on the quality of image reconstruction and investigate how the number of projections influence the magnitude change of the reconstruction error.

Comparative Analysis of Kernel Methods for Statistical Shape Learning

National Research Council Canada - National Science Library

Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen

2006-01-01

.... In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding...

DBKGrad: An R Package for Mortality Rates Graduation by Discrete Beta Kernel Techniques

Directory of Open Access Journals (Sweden)

Angelo Mazza

2014-04-01

Full Text Available We introduce the R package DBKGrad, conceived to facilitate the use of kernel smoothing in graduating mortality rates. The package implements univariate and bivariate adaptive discrete beta kernel estimators. Discrete kernels have been preferred because, in this context, variables such as age, calendar year and duration, are pragmatically considered as discrete and the use of beta kernels is motivated since it reduces boundary bias. Furthermore, when data on exposures to the risk of death are available, the use of adaptive bandwidth, that may be selected by cross-validation, can provide additional benefits. To exemplify the use of the package, an application to Italian mortality rates, for different ages and calendar years, is presented.

Adaptive Metric Kernel Regression

DEFF Research Database (Denmark)

Goutte, Cyril; Larsen, Jan

1998-01-01

Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...

Biasing anisotropic scattering kernels for deep-penetration Monte Carlo calculations

International Nuclear Information System (INIS)

Carter, L.L.; Hendricks, J.S.

1983-01-01

The exponential transform is often used to improve the efficiency of deep-penetration Monte Carlo calculations. This technique is usually implemented by biasing the distance-to-collision kernel of the transport equation, but leaving the scattering kernel unchanged. Dwivedi obtained significant improvements in efficiency by biasing an isotropic scattering kernel as well as the distance-to-collision kernel. This idea is extended to anisotropic scattering, particularly the highly forward Klein-Nishina scattering of gamma rays

Bivariate discrete beta Kernel graduation of mortality data.

Science.gov (United States)

Mazza, Angelo; Punzo, Antonio

2015-07-01

Various parametric/nonparametric techniques have been proposed in literature to graduate mortality data as a function of age. Nonparametric approaches, as for example kernel smoothing regression, are often preferred because they do not assume any particular mortality law. Among the existing kernel smoothing approaches, the recently proposed (univariate) discrete beta kernel smoother has been shown to provide some benefits. Bivariate graduation, over age and calendar years or durations, is common practice in demography and actuarial sciences. In this paper, we generalize the discrete beta kernel smoother to the bivariate case, and we introduce an adaptive bandwidth variant that may provide additional benefits when data on exposures to the risk of death are available; furthermore, we outline a cross-validation procedure for bandwidths selection. Using simulations studies, we compare the bivariate approach proposed here with its corresponding univariate formulation and with two popular nonparametric bivariate graduation techniques, based on Epanechnikov kernels and on P-splines. To make simulations realistic, a bivariate dataset, based on probabilities of dying recorded for the US males, is used. Simulations have confirmed the gain in performance of the new bivariate approach with respect to both the univariate and the bivariate competitors.

Insights from Classifying Visual Concepts with Multiple Kernel Learning

Science.gov (United States)

Binder, Alexander; Nakajima, Shinichi; Kloft, Marius; Müller, Christina; Samek, Wojciech; Brefeld, Ulf; Müller, Klaus-Robert; Kawanabe, Motoaki

2012-01-01

Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, 1-norm regularized MKL variants are often observed to be outperformed by an unweighted sum kernel. The main contributions of this paper are the following: we apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks from the application domain of computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum-kernel SVM and sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. Data sets (kernel matrices) as well as further information are available at http://doc.ml.tu-berlin.de/image_mkl/(Accessed 2012 Jun 25). PMID:22936970

Reduced multiple empirical kernel learning machine.

Science.gov (United States)

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

Wilson Dslash Kernel From Lattice QCD Optimization

Energy Technology Data Exchange (ETDEWEB)

Joo, Balint [Jefferson Lab, Newport News, VA; Smelyanskiy, Mikhail [Parallel Computing Lab, Intel Corporation, California, USA; Kalamkar, Dhiraj D. [Parallel Computing Lab, Intel Corporation, India; Vaidyanathan, Karthikeyan [Parallel Computing Lab, Intel Corporation, India

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.

Locally linear approximation for Kernel methods : the Railway Kernel

OpenAIRE

Muñoz, Alberto; González, Javier

2008-01-01

In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capab...

Ultrashort time-to-echo MRI of the cartilaginous endplate: technique and association with intervertebral disc degeneration

International Nuclear Information System (INIS)

Law, Travis; Anthony, Marina-Portia; Kim, Mina; Khong, Pek-Lan; Chan, Queenie; Samartzis, Dino

2013-01-01

The purpose of this study was to report the feasibility of the ultrashort time-to-echo (UTE) MRI technique to assess cartilaginous endplate (CEP) defects in humans in vivo and to assess their relationship with intervertebral disc (IVD) degeneration. Nine volunteer subjects (mean age=43.9 years; range=22–61 years) were recruited, representing 54 IVDs and 108 CEPs. The subjects underwent T2-weighted and UTE MRI to assess for the presence and severity of IVD degeneration, and for the presence of CEP defects, respectively, from T12 to S1. IVD degeneration was graded according to the Schneiderman et al. classification on T2-weighted MRI. CEP defects were defined on UTE MRI as discontinuity of high signal over four consecutive images and were independently assessed by two observers. Thirty-seven out of 108 (34.3%) CEPs had defects, which mainly occurred at T12/L1, L1/L2 and L4/L5 (P=0.008). Multivariate logistic regression revealed that lower body mass index (P=0.009) and younger (P=0.034) individuals had a decreased likelihood of having CEP defects. A statistically significant association was found to exist between the presence of CEP defects and IVD degeneration (P=0.036). A higher prevalence of degenerated IVDs with CEP defects was found at L4/5 and L5/S1, while degenerated IVDs with no CEP defects were found throughout the whole lumbar region. Mean IVD degeneration scores of the L4/5 and L5/S1 levels with CEP defects were higher in comparison with those with no CEP defects. Our study demonstrates the feasibility of using UTE MRI in humans in vivo to assess the integrity of the CEP. A statistically significant association was found to exist between the presence of CEP defects and IVD degeneration. In the lower lumbar region, more severe degeneration was found to occur in the IVDs with CEP defects than in those without defects.

On Degenerate Partial Differential Equations

OpenAIRE

Chen, Gui-Qiang G.

2010-01-01

Some of recent developments, including recent results, ideas, techniques, and approaches, in the study of degenerate partial differential equations are surveyed and analyzed. Several examples of nonlinear degenerate, even mixed, partial differential equations, are presented, which arise naturally in some longstanding, fundamental problems in fluid mechanics and differential geometry. The solution to these fundamental problems greatly requires a deep understanding of nonlinear degenerate parti...

Kernel-based noise filtering of neutron detector signals

International Nuclear Information System (INIS)

Park, Moon Ghu; Shin, Ho Cheol; Lee, Eun Ki

2007-01-01

This paper describes recently developed techniques for effective filtering of neutron detector signal noise. In this paper, three kinds of noise filters are proposed and their performance is demonstrated for the estimation of reactivity. The tested filters are based on the unilateral kernel filter, unilateral kernel filter with adaptive bandwidth and bilateral filter to show their effectiveness in edge preservation. Filtering performance is compared with conventional low-pass and wavelet filters. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters. The effectiveness and simplicity of the unilateral kernel filter with adaptive bandwidth is also demonstrated by applying it to the reactivity measurement performed during reactor start-up physics tests

An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel

Directory of Open Access Journals (Sweden)

Senyue Zhang

2016-01-01

Full Text Available According to the characteristics that the kernel function of extreme learning machine (ELM and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this paper. First, the generalized triangle Hermitian kernel function was constructed by using the product of triangular kernel and generalized Hermite Dirichlet kernel, and the proposed kernel function was proved as a valid kernel function of extreme learning machine. Then, the learning methodology of the extreme learning machine based on the proposed kernel function was presented. The biggest advantage of the proposed kernel is its kernel parameter values only chosen in the natural numbers, which thus can greatly shorten the computational time of parameter optimization and retain more of its sample data structure information. Experiments were performed on a number of binary classification, multiclassification, and regression datasets from the UCI benchmark repository. The experiment results demonstrated that the robustness and generalization performance of the proposed method are outperformed compared to other extreme learning machines with different kernels. Furthermore, the learning speed of proposed method is faster than support vector machine (SVM methods.

Combined multi-kernel head computed tomography images optimized for depicting both brain parenchyma and bone.

Science.gov (United States)

Takagi, Satoshi; Nagase, Hiroyuki; Hayashi, Tatsuya; Kita, Tamotsu; Hayashi, Katsumi; Sanada, Shigeru; Koike, Masayuki

2014-01-01

The hybrid convolution kernel technique for computed tomography (CT) is known to enable the depiction of an image set using different window settings. Our purpose was to decrease the number of artifacts in the hybrid convolution kernel technique for head CT and to determine whether our improved combined multi-kernel head CT images enabled diagnosis as a substitute for both brain (low-pass kernel-reconstructed) and bone (high-pass kernel-reconstructed) images. Forty-four patients with nondisplaced skull fractures were included. Our improved multi-kernel images were generated so that pixels of >100 Hounsfield unit in both brain and bone images were composed of CT values of bone images and other pixels were composed of CT values of brain images. Three radiologists compared the improved multi-kernel images with bone images. The improved multi-kernel images and brain images were identically displayed on the brain window settings. All three radiologists agreed that the improved multi-kernel images on the bone window settings were sufficient for diagnosing skull fractures in all patients. This improved multi-kernel technique has a simple algorithm and is practical for clinical use. Thus, simplified head CT examinations and fewer images that need to be stored can be expected.

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels.

Science.gov (United States)

Fang, Leyuan; Wang, Chong; Li, Shutao; Yan, Jun; Chen, Xiangdong; Rabbani, Hossein

2017-11-01

We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

Science.gov (United States)

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

2013-01-01

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 since 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 versus using the best candidate kernel. PMID:23471868

A framework for optimal kernel-based manifold embedding of medical image data.

Science.gov (United States)

Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

2015-04-01

Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

Local coding based matching kernel method for image classification.

Directory of Open Access Journals (Sweden)

Yan Song

Full Text Available This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.

Graphical analyses of connected-kernel scattering equations

International Nuclear Information System (INIS)

Picklesimer, A.

1982-10-01

Simple graphical techniques are employed to obtain a new (simultaneous) derivation of a large class of connected-kernel scattering equations. This class includes the Rosenberg, Bencze-Redish-Sloan, and connected-kernel multiple scattering equations as well as a host of generalizations of these and other equations. The graphical method also leads to a new, simplified form for some members of the class and elucidates the general structural features of the entire class

Resummed memory kernels in generalized system-bath master equations

International Nuclear Information System (INIS)

Mavros, Michael G.; Van Voorhis, Troy

2014-01-01

Generalized master equations provide a concise formalism for studying reduced population dynamics. Usually, these master equations require a perturbative expansion of the memory kernels governing the dynamics; in order to prevent divergences, these expansions must be resummed. Resummation techniques of perturbation series are ubiquitous in physics, but they have not been readily studied for the time-dependent memory kernels used in generalized master equations. In this paper, we present a comparison of different resummation techniques for such memory kernels up to fourth order. We study specifically the spin-boson Hamiltonian as a model system bath Hamiltonian, treating the diabatic coupling between the two states as a perturbation. A novel derivation of the fourth-order memory kernel for the spin-boson problem is presented; then, the second- and fourth-order kernels are evaluated numerically for a variety of spin-boson parameter regimes. We find that resumming the kernels through fourth order using a Padé approximant results in divergent populations in the strong electronic coupling regime due to a singularity introduced by the nature of the resummation, and thus recommend a non-divergent exponential resummation (the “Landau-Zener resummation” of previous work). The inclusion of fourth-order effects in a Landau-Zener-resummed kernel is shown to improve both the dephasing rate and the obedience of detailed balance over simpler prescriptions like the non-interacting blip approximation, showing a relatively quick convergence on the exact answer. The results suggest that including higher-order contributions to the memory kernel of a generalized master equation and performing an appropriate resummation can provide a numerically-exact solution to system-bath dynamics for a general spectral density, opening the way to a new class of methods for treating system-bath dynamics

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

Science.gov (United States)

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

RKRD: Runtime Kernel Rootkit Detection

Science.gov (United States)

Grover, Satyajit; Khosravi, Hormuzd; Kolar, Divya; Moffat, Samuel; Kounavis, Michael E.

In this paper we address the problem of protecting computer systems against stealth malware. The problem is important because the number of known types of stealth malware increases exponentially. Existing approaches have some advantages for ensuring system integrity but sophisticated techniques utilized by stealthy malware can thwart them. We propose Runtime Kernel Rootkit Detection (RKRD), a hardware-based, event-driven, secure and inclusionary approach to kernel integrity that addresses some of the limitations of the state of the art. Our solution is based on the principles of using virtualization hardware for isolation, verifying signatures coming from trusted code as opposed to malware for scalability and performing system checks driven by events. Our RKRD implementation is guided by our goals of strong isolation, no modifications to target guest OS kernels, easy deployment, minimal infra-structure impact, and minimal performance overhead. We developed a system prototype and conducted a number of experiments which show that the per-formance impact of our solution is negligible.

A trace ratio maximization approach to multiple kernel-based dimensionality reduction.

Science.gov (United States)

Jiang, Wenhao; Chung, Fu-lai

2014-01-01

Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings. Copyright © 2013 Elsevier Ltd. All rights reserved.

Data-variant kernel analysis

CERN Document Server

Motai, Yuichi

2015-01-01

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

Directory of Open Access Journals (Sweden)

Chunmei Liu

2016-01-01

Full Text Available This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour.

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

Science.gov (United States)

2016-01-01

This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour. PMID:27379165

Kernel Methods for Mining Instance Data in Ontologies

Science.gov (United States)

Bloehdorn, Stephan; Sure, York

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

Graphical analyses of connected-kernel scattering equations

International Nuclear Information System (INIS)

Picklesimer, A.

1983-01-01

Simple graphical techniques are employed to obtain a new (simultaneous) derivation of a large class of connected-kernel scattering equations. This class includes the Rosenberg, Bencze-Redish-Sloan, and connected-kernel multiple scattering equations as well as a host of generalizations of these and other equations. The basic result is the application of graphical methods to the derivation of interaction-set equations. This yields a new, simplified form for some members of the class and elucidates the general structural features of the entire class

A Fourier-series-based kernel-independent fast multipole method

International Nuclear Information System (INIS)

Zhang Bo; Huang Jingfang; Pitsianis, Nikos P.; Sun Xiaobai

2011-01-01

We present in this paper a new kernel-independent fast multipole method (FMM), named as FKI-FMM, for pairwise particle interactions with translation-invariant kernel functions. FKI-FMM creates, using numerical techniques, sufficiently accurate and compressive representations of a given kernel function over multi-scale interaction regions in the form of a truncated Fourier series. It provides also economic operators for the multipole-to-multipole, multipole-to-local, and local-to-local translations that are typical and essential in the FMM algorithms. The multipole-to-local translation operator, in particular, is readily diagonal and does not dominate in arithmetic operations. FKI-FMM provides an alternative and competitive option, among other kernel-independent FMM algorithms, for an efficient application of the FMM, especially for applications where the kernel function consists of multi-physics and multi-scale components as those arising in recent studies of biological systems. We present the complexity analysis and demonstrate with experimental results the FKI-FMM performance in accuracy and efficiency.

Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

DEFF Research Database (Denmark)

Rasmussen, Peter Mondrup; Hansen, Lars Kai; Madsen, Kristoffer Hougaard

There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus...... on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification methods. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We...

Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion

KAUST Repository

Komatitsch, Dimitri; Xie, Zhinan; Bozdağ, Ebru; de Andrade, Elliott Sales; Peter, Daniel; Liu, Qinya; Tromp, Jeroen

2016-01-01

We introduce a technique to compute exact anelastic sensitivity kernels in the time domain using parsimonious disk storage. The method is based on a reordering of the time loop of time-domain forward/adjoint wave propagation solvers combined with the use of a memory buffer. It avoids instabilities that occur when time-reversing dissipative wave propagation simulations. The total number of required time steps is unchanged compared to usual acoustic or elastic approaches. The cost is reduced by a factor of 4/3 compared to the case in which anelasticity is partially accounted for by accommodating the effects of physical dispersion. We validate our technique by performing a test in which we compare the Kα sensitivity kernel to the exact kernel obtained by saving the entire forward calculation. This benchmark confirms that our approach is also exact. We illustrate the importance of including full attenuation in the calculation of sensitivity kernels by showing significant differences with physical-dispersion-only kernels.

Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion

KAUST Repository

Komatitsch, Dimitri

2016-06-13

We introduce a technique to compute exact anelastic sensitivity kernels in the time domain using parsimonious disk storage. The method is based on a reordering of the time loop of time-domain forward/adjoint wave propagation solvers combined with the use of a memory buffer. It avoids instabilities that occur when time-reversing dissipative wave propagation simulations. The total number of required time steps is unchanged compared to usual acoustic or elastic approaches. The cost is reduced by a factor of 4/3 compared to the case in which anelasticity is partially accounted for by accommodating the effects of physical dispersion. We validate our technique by performing a test in which we compare the Kα sensitivity kernel to the exact kernel obtained by saving the entire forward calculation. This benchmark confirms that our approach is also exact. We illustrate the importance of including full attenuation in the calculation of sensitivity kernels by showing significant differences with physical-dispersion-only kernels.

Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion

Science.gov (United States)

Komatitsch, Dimitri; Xie, Zhinan; Bozdaǧ, Ebru; Sales de Andrade, Elliott; Peter, Daniel; Liu, Qinya; Tromp, Jeroen

2016-09-01

We introduce a technique to compute exact anelastic sensitivity kernels in the time domain using parsimonious disk storage. The method is based on a reordering of the time loop of time-domain forward/adjoint wave propagation solvers combined with the use of a memory buffer. It avoids instabilities that occur when time-reversing dissipative wave propagation simulations. The total number of required time steps is unchanged compared to usual acoustic or elastic approaches. The cost is reduced by a factor of 4/3 compared to the case in which anelasticity is partially accounted for by accommodating the effects of physical dispersion. We validate our technique by performing a test in which we compare the Kα sensitivity kernel to the exact kernel obtained by saving the entire forward calculation. This benchmark confirms that our approach is also exact. We illustrate the importance of including full attenuation in the calculation of sensitivity kernels by showing significant differences with physical-dispersion-only kernels.

Degenerate nonlinear diffusion equations

CERN Document Server

Favini, Angelo

2012-01-01

The aim of these notes is to include in a uniform presentation style several topics related to the theory of degenerate nonlinear diffusion equations, treated in the mathematical framework of evolution equations with multivalued m-accretive operators in Hilbert spaces. The problems concern nonlinear parabolic equations involving two cases of degeneracy. More precisely, one case is due to the vanishing of the time derivative coefficient and the other is provided by the vanishing of the diffusion coefficient on subsets of positive measure of the domain. From the mathematical point of view the results presented in these notes can be considered as general results in the theory of degenerate nonlinear diffusion equations. However, this work does not seek to present an exhaustive study of degenerate diffusion equations, but rather to emphasize some rigorous and efficient techniques for approaching various problems involving degenerate nonlinear diffusion equations, such as well-posedness, periodic solutions, asympt...

Approximate kernel competitive learning.

Science.gov (United States)

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. Copyright © 2014 Elsevier Ltd. All rights reserved.

Employment of kernel methods on wind turbine power performance assessment

DEFF Research Database (Denmark)

Skrimpas, Georgios Alexandros; Sweeney, Christian Walsted; Marhadi, Kun S.

2015-01-01

A power performance assessment technique is developed for the detection of power production discrepancies in wind turbines. The method employs a widely used nonparametric pattern recognition technique, the kernel methods. The evaluation is based on the trending of an extracted feature from...... the kernel matrix, called similarity index, which is introduced by the authors for the first time. The operation of the turbine and consequently the computation of the similarity indexes is classified into five power bins offering better resolution and thus more consistent root cause analysis. The accurate...

Classification With Truncated Distance Kernel.

Science.gov (United States)

Huang, Xiaolin; Suykens, Johan A K; Wang, Shuning; Hornegger, Joachim; Maier, Andreas

2018-05-01

This brief proposes a truncated distance (TL1) kernel, which results in a classifier that is nonlinear in the global region but is linear in each subregion. With this kernel, the subregion structure can be trained using all the training data and local linear classifiers can be established simultaneously. The TL1 kernel has good adaptiveness to nonlinearity and is suitable for problems which require different nonlinearities in different areas. Though the TL1 kernel is not positive semidefinite, some classical kernel learning methods are still applicable which means that the TL1 kernel can be directly used in standard toolboxes by replacing the kernel evaluation. In numerical experiments, the TL1 kernel with a pregiven parameter achieves similar or better performance than the radial basis function kernel with the parameter tuned by cross validation, implying the TL1 kernel a promising nonlinear kernel for classification tasks.

Exact Heat Kernel on a Hypersphere and Its Applications in Kernel SVM

Directory of Open Access Journals (Sweden)

Chenchao Zhao

2018-01-01

Full Text Available Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to other competing similarity measures. Specifically, the idea of using heat diffusion on a hypersphere to measure similarity has been previously proposed and tested by Lafferty and Lebanon [1], demonstrating promising results based on a heuristic heat kernel obtained from the zeroth order parametrix expansion; however, how well this heuristic kernel agrees with the exact hyperspherical heat kernel remains unknown. This paper presents a higher order parametrix expansion of the heat kernel on a unit hypersphere and discusses several problems associated with this expansion method. We then compare the heuristic kernel with an exact form of the heat kernel expressed in terms of a uniformly and absolutely convergent series in high-dimensional angular momentum eigenmodes. Being a natural measure of similarity between sample points dwelling on a hypersphere, the exact kernel often shows superior performance in kernel SVM classifications applied to text mining, tumor somatic mutation imputation, and stock market analysis.

Subsampling Realised Kernels

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger

2011-01-01

In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our...... that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled...

Optimal kernel shape and bandwidth for atomistic support of continuum stress

International Nuclear Information System (INIS)

Ulz, Manfred H; Moran, Sean J

2013-01-01

The treatment of atomistic scale interactions via molecular dynamics simulations has recently found favour for multiscale modelling within engineering. The estimation of stress at a continuum point on the atomistic scale requires a pre-defined kernel function. This kernel function derives the stress at a continuum point by averaging the contribution from atoms within a region surrounding the continuum point. This averaging volume, and therefore the associated stress at a continuum point, is highly dependent on the bandwidth and shape of the kernel. In this paper we propose an effective and entirely data-driven strategy for simultaneously computing the optimal shape and bandwidth for the kernel. We thoroughly evaluate our proposed approach on copper using three classical elasticity problems. Our evaluation yields three key findings: firstly, our technique can provide a physically meaningful estimation of kernel bandwidth; secondly, we show that a uniform kernel is preferred, thereby justifying the default selection of this kernel shape in future work; and thirdly, we can reliably estimate both of these attributes in a data-driven manner, obtaining values that lead to an accurate estimation of the stress at a continuum point. (paper)

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

International Nuclear Information System (INIS)

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

1986-01-01

This study was designed to compare the uptake and distribution of 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 0 C were transferred to [ 14 C]sucrose media 10 days after pollination. Kernels cultured at 35 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 [ 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 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 0 C compared to kernels cultured at 30 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 0 C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30 0 C (89%). Kernels cultured at 35 0 C had a correspondingly higher proportion of 14 C in endosperm fructose, glucose, and sucrose

Optimized Kernel Entropy Components.

Science.gov (United States)

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

2017-06-01

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.

Identification of Age-Related Macular Degeneration Using OCT Images

Science.gov (United States)

Arabi, Punal M., Dr; Krishna, Nanditha; Ashwini, V.; Prathibha, H. M.

2018-02-01

Age-related Macular Degeneration is the most leading retinal disease in the recent years. Macular degeneration occurs when the central portion of the retina, called macula deteriorates. As the deterioration occurs with the age, it is commonly referred as Age-related Macular Degeneration. This disease can be visualized by several imaging modalities such as Fundus imaging technique, Optical Coherence Tomography (OCT) technique and many other. Optical Coherence Tomography is the widely used technique for screening the Age-related Macular Degeneration disease, because it has an ability to detect the very minute changes in the retina. The Healthy and AMD affected OCT images are classified by extracting the Retinal Pigmented Epithelium (RPE) layer of the images using the image processing technique. The extracted layer is sampled, the no. of white pixels in each of the sample is counted and the mean value of the no. of pixels is calculated. The average mean value is calculated for both the Healthy and the AMD affected images and a threshold value is fixed and a decision rule is framed to classify the images of interest. The proposed method showed an accuracy of 75%.

A novel adaptive kernel method with kernel centers determined by a support vector regression approach

NARCIS (Netherlands)

Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Mulder, J.A.

2012-01-01

The optimality of the kernel number and kernel centers plays a significant role in determining the approximation power of nearly all kernel methods. However, the process of choosing optimal kernels is always formulated as a global optimization task, which is hard to accomplish. Recently, an

The development of the production process for the thorium/uranium dicarbide fuel kernels for the first charge of the Dragon Reactor

International Nuclear Information System (INIS)

Burnett, R.C.; Hankart, L.J.; Horsley, G.W.

1965-05-01

The development of methods of producing spheroidal sintered porous kernels of hyperstoichiometric thorium/uranium dicarbide solid solution from thorium/uranium monocarbide/carbon and thoria/urania/carbon powder mixes is described. The work has involved study of (i) Methods of preparing green kernels from UC/Th/C powder mixes using the rotary sieve technique. (ii) Methods of producing green kernels from UO2/Th02/C powder mixes using the planetary mill technique. (iii) The conversion by appropriate heat treatment of green kernels produced by both routes to sintered porous kernels of thorium/uranium carbide. (iv) The efficiency of the processes. (author)

Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

Science.gov (United States)

Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

2017-12-15

Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

7 CFR 981.7 - Edible kernel.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle of almond kernel that is not inedible. [41 FR 26852, June 30, 1976] ...

Suitability of point kernel dose calculation techniques in brachytherapy treatment planning

Directory of Open Access Journals (Sweden)

Lakshminarayanan Thilagam

2010-01-01

Full Text Available Brachytherapy treatment planning system (TPS is necessary to estimate the dose to target volume and organ at risk (OAR. TPS is always recommended to account for the effect of tissue, applicator and shielding material heterogeneities exist in applicators. However, most brachytherapy TPS software packages estimate the absorbed dose at a point, taking care of only the contributions of individual sources and the source distribution, neglecting the dose perturbations arising from the applicator design and construction. There are some degrees of uncertainties in dose rate estimations under realistic clinical conditions. In this regard, an attempt is made to explore the suitability of point kernels for brachytherapy dose rate calculations and develop new interactive brachytherapy package, named as BrachyTPS, to suit the clinical conditions. BrachyTPS is an interactive point kernel code package developed to perform independent dose rate calculations by taking into account the effect of these heterogeneities, using two regions build up factors, proposed by Kalos. The primary aim of this study is to validate the developed point kernel code package integrated with treatment planning computational systems against the Monte Carlo (MC results. In the present work, three brachytherapy applicators commonly used in the treatment of uterine cervical carcinoma, namely (i Board of Radiation Isotope and Technology (BRIT low dose rate (LDR applicator and (ii Fletcher Green type LDR applicator (iii Fletcher Williamson high dose rate (HDR applicator, are studied to test the accuracy of the software. Dose rates computed using the developed code are compared with the relevant results of the MC simulations. Further, attempts are also made to study the dose rate distribution around the commercially available shielded vaginal applicator set (Nucletron. The percentage deviations of BrachyTPS computed dose rate values from the MC results are observed to be within plus/minus 5

Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

Energy Technology Data Exchange (ETDEWEB)

Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

2016-02-25

Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

International Nuclear Information System (INIS)

Hao, Ming; Wang, Yanli; Bryant, Stephen H.

2016-01-01

Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

Flexible Scheduling in Multimedia Kernels: An Overview

NARCIS (Netherlands)

Jansen, P.G.; Scholten, Johan; Laan, Rene; Chow, W.S.

1999-01-01

Current Hard Real-Time (HRT) kernels have their timely behaviour guaranteed on the cost of a rather restrictive use of the available resources. This makes current HRT scheduling techniques inadequate for use in a multimedia environment where we can make a considerable profit by a better and more

Kernel versions of some orthogonal transformations

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

Kernel versions of orthogonal transformations such as principal components are based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced...... by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution also known as the kernel trick these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of this kernel...... function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component analysis (PCA) and kernel minimum noise fraction (MNF) analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function...

Model Selection in Kernel Ridge Regression

DEFF Research Database (Denmark)

Exterkate, Peter

Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...

Penetuan Bilangan Iodin pada Hydrogenated Palm Kernel Oil (HPKO) dan Refined Bleached Deodorized Palm Kernel Oil (RBDPKO)

OpenAIRE

Sitompul, Monica Angelina

2015-01-01

Have been conducted Determination of Iodin Value by method titration to some Hydrogenated Palm Kernel Oil (HPKO) and Refined Bleached Deodorized Palm Kernel Oil (RBDPKO). The result of analysis obtained the Iodin Value in Hydrogenated Palm Kernel Oil (A) = 0,16 gr I2/100gr, Hydrogenated Palm Kernel Oil (B) = 0,20 gr I2/100gr, Hydrogenated Palm Kernel Oil (C) = 0,24 gr I2/100gr. And in Refined Bleached Deodorized Palm Kernel Oil (A) = 17,51 gr I2/100gr, Refined Bleached Deodorized Palm Kernel ...

7 CFR 981.8 - Inedible kernel.

Science.gov (United States)

2010-01-01

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

7 CFR 981.408 - Inedible kernel.

Science.gov (United States)

2010-01-01

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

Bit Error-Rate Minimizing Detector for Amplify-and-Forward Relaying Systems Using Generalized Gaussian Kernel

KAUST Repository

Ahmed, Qasim Zeeshan

2013-01-01

In this letter, a new detector is proposed for amplifyand- forward (AF) relaying system when communicating with the assistance of relays. The major goal of this detector is to improve the bit error rate (BER) performance of the receiver. The probability density function is estimated with the help of kernel density technique. A generalized Gaussian kernel is proposed. This new kernel provides more flexibility and encompasses Gaussian and uniform kernels as special cases. The optimal window width of the kernel is calculated. Simulations results show that a gain of more than 1 dB can be achieved in terms of BER performance as compared to the minimum mean square error (MMSE) receiver when communicating over Rayleigh fading channels.

Viscosity kernel of molecular fluids

DEFF Research Database (Denmark)

Puscasu, Ruslan; Todd, Billy; Daivis, Peter

2010-01-01

, temperature, and chain length dependencies of the reciprocal and real-space viscosity kernels are presented. We find that the density has a major effect on the shape of the kernel. The temperature range and chain lengths considered here have by contrast less impact on the overall normalized shape. Functional...... forms that fit the wave-vector-dependent kernel data over a large density and wave-vector range have also been tested. Finally, a structural normalization of the kernels in physical space is considered. Overall, the real-space viscosity kernel has a width of roughly 3–6 atomic diameters, which means...

Disc degeneration: current surgical options

Directory of Open Access Journals (Sweden)

C Schizas

2010-10-01

Full Text Available Chronic low back pain attributed to lumbar disc degeneration poses a serious challenge to physicians. Surgery may be indicated in selected cases following failure of appropriate conservative treatment. For decades, the only surgical option has been spinal fusion, but its results have been inconsistent. Some prospective trials show superiority over usual conservative measures while others fail to demonstrate its advantages. In an effort to improve results of fusion and to decrease the incidence of adjacent segment degeneration, total disc replacement techniques have been introduced and studied extensively. Short-term results have shown superiority over some fusion techniques. Mid-term results however tend to show that this approach yields results equivalent to those of spinal fusion. Nucleus replacement has gained some popularity initially, but evidence on its efficacy is scarce. Dynamic stabilisation, a technique involving less rigid implants than in spinal fusion and performed without the need for bone grafting, represents another surgical option. Evidence again is lacking on its superiority over other surgical strategies and conservative measures. Insertion of interspinous devices posteriorly, aiming at redistributing loads and relieving pain, has been used as an adjunct to disc removal surgery for disc herniation. To date however, there is no clear evidence on their efficacy. Minimally invasive intradiscal thermocoagulation techniques have also been tried, but evidence of their effectiveness is questioned. Surgery using novel biological solutions may be the future of discogenic pain treatment. Collaboration between clinicians and basic scientists in this multidisciplinary field will undoubtedly shape the future of treating symptomatic disc degeneration.

Kernel learning algorithms for face recognition

CERN Document Server

Li, Jun-Bao; Pan, Jeng-Shyang

2013-01-01

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new

Corruption clubs: empirical evidence from kernel density estimates

NARCIS (Netherlands)

Herzfeld, T.; Weiss, Ch.

2007-01-01

A common finding of many analytical models is the existence of multiple equilibria of corruption. Countries characterized by the same economic, social and cultural background do not necessarily experience the same levels of corruption. In this article, we use Kernel Density Estimation techniques to

Radiation transport simulation in gamma irradiator systems using E G S 4 Monte Carlo code and dose mapping calculations based on point kernel technique

International Nuclear Information System (INIS)

Raisali, G.R.

1992-01-01

A series of computer codes based on point kernel technique and also Monte Carlo method have been developed. These codes perform radiation transport calculations for irradiator systems having cartesian, cylindrical and mixed geometries. The monte Carlo calculations, the computer code 'EGS4' has been applied to a radiation processing type problem. This code has been acompanied by a specific user code. The set of codes developed include: GCELLS, DOSMAPM, DOSMAPC2 which simulate the radiation transport in gamma irradiator systems having cylinderical, cartesian, and mixed geometries, respectively. The program 'DOSMAP3' based on point kernel technique, has been also developed for dose rate mapping calculations in carrier type gamma irradiators. Another computer program 'CYLDETM' as a user code for EGS4 has been also developed to simulate dose variations near the interface of heterogeneous media in gamma irradiator systems. In addition a system of computer codes 'PRODMIX' has been developed which calculates the absorbed dose in the products with different densities. validation studies of the calculated results versus experimental dosimetry has been performed and good agreement has been obtained

Partial Deconvolution with Inaccurate Blur Kernel.

Science.gov (United States)

Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

2017-10-17

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning

Kernel methods for deep learning

OpenAIRE

Cho, Youngmin

2012-01-01

We introduce a new family of positive-definite kernels that mimic the computation in large neural networks. We derive the different members of this family by considering neural networks with different activation functions. Using these kernels as building blocks, we also show how to construct other positive-definite kernels by operations such as composition, multiplication, and averaging. We explore the use of these kernels in standard models of supervised learning, such as support vector mach...

Improved coronary in-stent visualization using a combined high-resolution kernel and a hybrid iterative reconstruction technique at 256-slice cardiac CT—Pilot study

International Nuclear Information System (INIS)

Oda, Seitaro; Utsunomiya, Daisuke; Funama, Yoshinori; Takaoka, Hiroko; Katahira, Kazuhiro; Honda, Keiichi; Noda, Katsuo; Oshima, Shuichi; Yamashita, Yasuyuki

2013-01-01

Objectives: To investigate the diagnostic performance of 256-slice cardiac CT for the evaluation of the in-stent lumen by using a hybrid iterative reconstruction (HIR) algorithm combined with a high-resolution kernel. Methods: This study included 28 patients with 28 stents who underwent cardiac CT. Three different reconstruction images were obtained with: (1) a standard filtered back projection (FBP) algorithm with a standard cardiac kernel (CB), (2) an FBP algorithm with a high-resolution cardiac kernel (CD), and (3) an HIR algorithm with the CD kernel. We measured image noise and kurtosis and used receiver operating characteristics analysis to evaluate observer performance in the detection of in-stent stenosis. Results: Image noise with FBP plus the CD kernel (80.2 ± 15.5 HU) was significantly higher than with FBP plus the CB kernel (28.8 ± 4.6 HU) and HIR plus the CD kernel (36.1 ± 6.4 HU). There was no significant difference in the image noise between FBP plus the CB kernel and HIR plus the CD kernel. Kurtosis was significantly better with the CD- than the CB kernel. The kurtosis values obtained with the CD kernel were not significantly different between the FBP- and HIR reconstruction algorithms. The areas under the receiver operating characteristics curves with HIR plus the CD kernel were significantly higher than with FBP plus the CB- or the CD kernel. The difference between FBP plus the CB- or the CD kernel was not significant. The average sensitivity, specificity, and positive and negative predictive value for the detection of in-stent stenosis were 83.3, 50.0, 33.3, and 91.6% for FBP plus the CB kernel, 100, 29.6, 40.0, and 100% for FBP plus the CD kernel, and 100, 54.5, 40.0, and 100% for HIR plus the CD kernel. Conclusions: The HIR algorithm combined with the high-resolution kernel significantly improved diagnostic performance in the detection of in-stent stenosis

The gravitational potential of axially symmetric bodies from a regularized green kernel

Science.gov (United States)

Trova, A.; Huré, J.-M.; Hersant, F.

2011-12-01

The determination of the gravitational potential inside celestial bodies (rotating stars, discs, planets, asteroids) is a common challenge in numerical Astrophysics. Under axial symmetry, the potential is classically found from a two-dimensional integral over the body's meridional cross-section. Because it involves an improper integral, high accuracy is generally difficult to reach. We have discovered that, for homogeneous bodies, the singular Green kernel can be converted into a regular kernel by direct analytical integration. This new kernel, easily managed with standard techniques, opens interesting horizons, not only for numerical calculus but also to generate approximations, in particular for geometrically thin discs and rings.

Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.

Directory of Open Access Journals (Sweden)

Ujjwal Maulik

Full Text Available Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request.sarkar@labri.fr.

7 CFR 981.9 - Kernel weight.

Science.gov (United States)

2010-01-01

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

Veto-Consensus Multiple Kernel Learning

NARCIS (Netherlands)

Zhou, Y.; Hu, N.; Spanos, C.J.

2016-01-01

We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. The

7 CFR 51.2295 - Half kernel.

Science.gov (United States)

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. ...

Single corn kernel wide-line NMR oil analysis for breeding purpose

Energy Technology Data Exchange (ETDEWEB)

Wilmers, M C.C.; Rettori, C; Vargas, H; Barberis, G E [Universidade Estadual de Campinas (Brazil). Inst. de Fisica; da Silva, W J [Universidade Estadual de Campinas (Brazil). Inst. de Biologia

1978-12-01

The Wide-Line NMR technique was used to determine the oil content in single corn seeds. Using distinct radio frequency (RF) power, a systematic work was done in kernels with about 10% of moisture, and also in artificially dried seeds with approximated 5% of moisture. For nondried seeds NMR spectra showed clearly the presence of three resonances with different RF saturation factor. For dried seeds, the oil concentration determined by NMR was highly correlated (r = 0,997) with that determined by a gravimetric method. The highest discrepancy between the two methods was found to be about 1,3%. When relative measurements are required as in the case of single kernel for recurrent selection program, precision in the individual selected kernel will be about 2,5%. Applying this technique, a first cycle of recurrent selection using S/sub 1/ lines for low and high oil content was performed in an open pollinated variety. Gain from selection was 12.0 and 14.1% in the populations for high and low oil contents, respectively.

An Approximate Approach to Automatic Kernel Selection.

Science.gov (United States)

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.

Iterative software kernels

Energy Technology Data Exchange (ETDEWEB)

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`.

Viscozyme L pretreatment on palm kernels improved the aroma of palm kernel oil after kernel roasting.

Science.gov (United States)

Zhang, Wencan; Leong, Siew Mun; Zhao, Feifei; Zhao, Fangju; Yang, Tiankui; Liu, Shaoquan

2018-05-01

With an interest to enhance the aroma of palm kernel oil (PKO), Viscozyme L, an enzyme complex containing a wide range of carbohydrases, was applied to alter the carbohydrates in palm kernels (PK) to modulate the formation of volatiles upon kernel roasting. After Viscozyme treatment, the content of simple sugars and free amino acids in PK increased by 4.4-fold and 4.5-fold, respectively. After kernel roasting and oil extraction, significantly more 2,5-dimethylfuran, 2-[(methylthio)methyl]-furan, 1-(2-furanyl)-ethanone, 1-(2-furyl)-2-propanone, 5-methyl-2-furancarboxaldehyde and 2-acetyl-5-methylfuran but less 2-furanmethanol and 2-furanmethanol acetate were found in treated PKO; the correlation between their formation and simple sugar profile was estimated by using partial least square regression (PLS1). Obvious differences in pyrroles and Strecker aldehydes were also found between the control and treated PKOs. Principal component analysis (PCA) clearly discriminated the treated PKOs from that of control PKOs on the basis of all volatile compounds. Such changes in volatiles translated into distinct sensory attributes, whereby treated PKO was more caramelic and burnt after aqueous extraction and more nutty, roasty, caramelic and smoky after solvent extraction. Copyright © 2018 Elsevier Ltd. All rights reserved.

A kernel version of spatial factor analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

. Schölkopf et al. introduce kernel PCA. Shawe-Taylor and Cristianini is an excellent reference for kernel methods in general. Bishop and Press et al. describe kernel methods among many other subjects. Nielsen and Canty use kernel PCA to detect change in univariate airborne digital camera images. The kernel...... version of PCA handles 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. In this paper we shall apply kernel versions of PCA, maximum autocorrelation factor (MAF) analysis...

7 CFR 51.1441 - Half-kernel.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half-kernel. 51.1441 Section 51.1441 Agriculture... 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 missing...

An iterative kernel based method for fourth order nonlinear equation with nonlinear boundary condition

Science.gov (United States)

Azarnavid, Babak; Parand, Kourosh; Abbasbandy, Saeid

2018-06-01

This article discusses an iterative reproducing kernel method with respect to its effectiveness and capability of solving a fourth-order boundary value problem with nonlinear boundary conditions modeling beams on elastic foundations. Since there is no method of obtaining reproducing kernel which satisfies nonlinear boundary conditions, the standard reproducing kernel methods cannot be used directly to solve boundary value problems with nonlinear boundary conditions as there is no knowledge about the existence and uniqueness of the solution. The aim of this paper is, therefore, to construct an iterative method by the use of a combination of reproducing kernel Hilbert space method and a shooting-like technique to solve the mentioned problems. Error estimation for reproducing kernel Hilbert space methods for nonlinear boundary value problems have yet to be discussed in the literature. In this paper, we present error estimation for the reproducing kernel method to solve nonlinear boundary value problems probably for the first time. Some numerical results are given out to demonstrate the applicability of the method.

Local Observed-Score Kernel Equating

Science.gov (United States)

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…

Credit scoring analysis using kernel discriminant

Science.gov (United States)

Widiharih, T.; Mukid, M. A.; Mustafid

2018-05-01

Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.

Kernel parameter dependence in spatial factor analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2010-01-01

kernel PCA. Shawe-Taylor and Cristianini [4] is an excellent reference for kernel methods in general. Bishop [5] and Press et al. [6] describe kernel methods among many other subjects. The kernel version of PCA handles 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. In this paper we shall apply a kernel version of maximum autocorrelation factor (MAF) [7, 8] analysis to irregularly sampled stream sediment geochemistry data from South Greenland and illustrate the dependence...... of the kernel width. The 2,097 samples each covering on average 5 km2 are analyzed chemically for the content of 41 elements....

Fixed kernel regression for voltammogram feature extraction

International Nuclear Information System (INIS)

Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N

2009-01-01

Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals

Multiple Kernel Learning with Data Augmentation

Science.gov (United States)

2016-11-22

JMLR: Workshop and Conference Proceedings 63:49–64, 2016 ACML 2016 Multiple Kernel Learning with Data Augmentation Khanh Nguyen nkhanh@deakin.edu.au...University, Australia Editors: Robert J. Durrant and Kee-Eung Kim Abstract The motivations of multiple kernel learning (MKL) approach are to increase... kernel expres- siveness capacity and to avoid the expensive grid search over a wide spectrum of kernels . A large amount of work has been proposed to

OS X and iOS Kernel Programming

CERN Document Server

Halvorsen, Ole Henry

2011-01-01

OS X and iOS Kernel Programming combines essential operating system and kernel architecture knowledge with a highly practical approach that will help you write effective kernel-level code. You'll learn fundamental concepts such as memory management and thread synchronization, as well as the I/O Kit framework. You'll also learn how to write your own kernel-level extensions, such as device drivers for USB and Thunderbolt devices, including networking, storage and audio drivers. OS X and iOS Kernel Programming provides an incisive and complete introduction to the XNU kernel, which runs iPhones, i

Model selection for Gaussian kernel PCA denoising

DEFF Research Database (Denmark)

Jørgensen, Kasper Winther; Hansen, Lars Kai

2012-01-01

We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

Paramecium: An Extensible Object-Based Kernel

NARCIS (Netherlands)

van Doorn, L.; Homburg, P.; Tanenbaum, A.S.

1995-01-01

In this paper we describe the design of an extensible kernel, called Paramecium. This kernel uses an object-based software architecture which together with instance naming, late binding and explicit overrides enables easy reconfiguration. Determining which components reside in the kernel protection

Theory of reproducing kernels and applications

CERN Document Server

Saitoh, Saburou

2016-01-01

This book provides a large extension of the general theory of reproducing kernels published by N. Aronszajn in 1950, with many concrete applications. In Chapter 1, many concrete reproducing kernels are first introduced with detailed information. Chapter 2 presents a general and global theory of reproducing kernels with basic applications in a self-contained way. Many fundamental operations among reproducing kernel Hilbert spaces are dealt with. Chapter 2 is the heart of this book. Chapter 3 is devoted to the Tikhonov regularization using the theory of reproducing kernels with applications to numerical and practical solutions of bounded linear operator equations. In Chapter 4, the numerical real inversion formulas of the Laplace transform are presented by applying the Tikhonov regularization, where the reproducing kernels play a key role in the results. Chapter 5 deals with ordinary differential equations; Chapter 6 includes many concrete results for various fundamental partial differential equations. In Chapt...

Mycological deterioration of stored palm kernels recovered from oil ...

African Journals Online (AJOL)

Palm kernels obtained from Pioneer Oil Mill Ltd. were stored for eight (8) weeks and examined for their microbiological quality and proximate composition. Seven (7) different fungal species were isolated by serial dilution plate technique. The fungal species included Aspergillus flavus Link; A nidulans Eidem; A niger ...

Kernels for structured data

CERN Document Server

Gärtner, Thomas

2009-01-01

This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by

7 CFR 981.401 - Adjusted kernel weight.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel weight... kernels in excess of five percent; less shells, if applicable; less processing loss of one percent for...

Testing Infrastructure for Operating System Kernel Development

DEFF Research Database (Denmark)

Walter, Maxwell; Karlsson, Sven

2014-01-01

Testing is an important part of system development, and to test effectively we require knowledge of the internal state of the system under test. Testing an operating system kernel is a challenge as it is the operating system that typically provides access to this internal state information. Multi......-core kernels pose an even greater challenge due to concurrency and their shared kernel state. In this paper, we present a testing framework that addresses these challenges by running the operating system in a virtual machine, and using virtual machine introspection to both communicate with the kernel...... and obtain information about the system. We have also developed an in-kernel testing API that we can use to develop a suite of unit tests in the kernel. We are using our framework for for the development of our own multi-core research kernel....

7 CFR 51.1403 - Kernel color classification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...

The definition of kernel Oz

OpenAIRE

Smolka, Gert

1994-01-01

Oz is a concurrent language providing for functional, object-oriented, and constraint programming. This paper defines Kernel Oz, a semantically complete sublanguage of Oz. It was an important design requirement that Oz be definable by reduction to a lean kernel language. The definition of Kernel Oz introduces three essential abstractions: the Oz universe, the Oz calculus, and the actor model. The Oz universe is a first-order structure defining the values and constraints Oz computes with. The ...

Fabrication of Uranium Oxycarbide Kernels for HTR Fuel

International Nuclear Information System (INIS)

Barnes, Charles; Richardson, Clay; Nagley, Scott; Hunn, John; Shaber, Eric

2010-01-01

Babcock and Wilcox (B and 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-(micro)m, 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 and W produced 425-(micro)m, 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 and W also produced 500-(micro)m, 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 and 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.

A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs

Directory of Open Access Journals (Sweden)

Chunhui Zhao

2017-02-01

Full Text Available The kernel RX (KRX detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in available KRX detectors. Accordingly, this paper presents a novel weighted spatial-spectral kernel RX (WSSKRX detector and its parallel implementation on graphics processing units (GPUs. The WSSKRX utilizes the spatial neighborhood resources to reconstruct the testing pixels by introducing a spectral factor and a spatial window, thereby effectively reducing the interference of background noise. Then, the kernel function is redesigned as a mapping trick in a KRX detector to implement the anomaly detection. In addition, a powerful architecture based on the GPU technique is designed to accelerate WSSKRX. To substantiate the performance of the proposed algorithm, both synthetic and real data are conducted for experiments.

Anisotropic hydrodynamics with a scalar collisional kernel

Science.gov (United States)

Almaalol, Dekrayat; Strickland, Michael

2018-04-01

Prior studies of nonequilibrium dynamics using anisotropic hydrodynamics have used the relativistic Anderson-Witting scattering kernel or some variant thereof. In this paper, we make the first study of the impact of using a more realistic scattering kernel. For this purpose, we consider a conformal system undergoing transversally homogenous and boost-invariant Bjorken expansion and take the collisional kernel to be given by the leading order 2 ↔2 scattering kernel in scalar λ ϕ4 . We consider both classical and quantum statistics to assess the impact of Bose enhancement on the dynamics. We also determine the anisotropic nonequilibrium attractor of a system subject to this collisional kernel. We find that, when the near-equilibrium relaxation-times in the Anderson-Witting and scalar collisional kernels are matched, the scalar kernel results in a higher degree of momentum-space anisotropy during the system's evolution, given the same initial conditions. Additionally, we find that taking into account Bose enhancement further increases the dynamically generated momentum-space anisotropy.

Magnetization transfer and spin lock MR imaging of patellar cartilage degeneration at 0.1 T

International Nuclear Information System (INIS)

Koskinen, S.K.; Ylae-Outinen, H.; Komu, M.E.S.; Aho, H.J.

1997-01-01

Purpose: To investigate magnetization transfer (MT) parameters and rotating frame relaxation time T1ρ in patellar cartilage at different levels of degeneration. Material and Methods: Thirty cadaveric patellae were examined at 0.1 T using the time-dependent saturation-transfer MT technique and the spin lock (SL) technique. In an SL experiment, nuclear spins are locked with a radiofrequency (RF) field, and the locked nuclear magnetization relaxes along the magnetic component of the locking RF field. The specimens were divided into three groups according to the level of cartilage degeneration. MT parameters and T1ρ were measured. Results: The MT effect was greater in degenerated cartilage than in normal cartilage. T1ρ was longer in advanced cartilage degeneration than in intermediate cartilage degeneration. Conculsion: The results suggest that more studies are needed to fully establish the value of SL imaging in cartilage degeneration. (orig.)

Object classification and detection with context kernel descriptors

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2014-01-01

Context information is important in object representation. By embedding context cue of image attributes into kernel descriptors, we propose a set of novel kernel descriptors called Context Kernel Descriptors (CKD) for object classification and detection. The motivation of CKD is to use spatial...... consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature selection, Kernel Entropy Component Analysis (KECA) is exploited to learn a subset of discriminative CKD. Different from Kernel Principal Component...

Ranking Support Vector Machine with Kernel Approximation.

Science.gov (United States)

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.

Ranking Support Vector Machine with Kernel Approximation

Directory of Open Access Journals (Sweden)

Kai Chen

2017-01-01

Full Text Available 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.

Dose point kernels for beta-emitting radioisotopes

International Nuclear Information System (INIS)

Prestwich, W.V.; Chan, L.B.; Kwok, C.S.; Wilson, B.

1986-01-01

Knowledge of the dose point kernel corresponding to a specific radionuclide is required to calculate the spatial dose distribution produced in a homogeneous medium by a distributed source. Dose point kernels for commonly used radionuclides have been calculated previously using as a basis monoenergetic dose point kernels derived by numerical integration of a model transport equation. The treatment neglects fluctuations in energy deposition, an effect which has been later incorporated in dose point kernels calculated using Monte Carlo methods. This work describes new calculations of dose point kernels using the Monte Carlo results as a basis. An analytic representation of the monoenergetic dose point kernels has been developed. This provides a convenient method both for calculating the dose point kernel associated with a given beta spectrum and for incorporating the effect of internal conversion. An algebraic expression for allowed beta spectra has been accomplished through an extension of the Bethe-Bacher approximation, and tested against the exact expression. Simplified expression for first-forbidden shape factors have also been developed. A comparison of the calculated dose point kernel for 32 P with experimental data indicates good agreement with a significant improvement over the earlier results in this respect. An analytic representation of the dose point kernel associated with the spectrum of a single beta group has been formulated. 9 references, 16 figures, 3 tables

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

Science.gov (United States)

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

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

Wigner functions defined with Laplace transform kernels.

Science.gov (United States)

Oh, Se Baek; Petruccelli, Jonathan C; Tian, Lei; Barbastathis, George

2011-10-24

We propose a new Wigner-type phase-space function using Laplace transform kernels--Laplace kernel Wigner function. Whereas momentum variables are real in the traditional Wigner function, the Laplace kernel Wigner function may have complex momentum variables. Due to the property of the Laplace transform, a broader range of signals can be represented in complex phase-space. We show that the Laplace kernel Wigner function exhibits similar properties in the marginals as the traditional Wigner function. As an example, we use the Laplace kernel Wigner function to analyze evanescent waves supported by surface plasmon polariton. © 2011 Optical Society of America

Metabolic network prediction through pairwise rational kernels.

Science.gov (United States)

Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

2014-09-26

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy

Study of degenerate four-quark states with SU(2) lattice Monte Carlo techniques

International Nuclear Information System (INIS)

Green, A.M.; Lukkarinen, J.; Pennanen, P.; Michael, C.

1996-01-01

The energies of four-quark states are calculated for geometries in which the quarks are situated on the corners of a series of tetrahedra and also for geometries that correspond to gradually distorting these tetrahedra into a plane. The interest in tetrahedra arises because they are composed of three degenerate partitions of the four quarks into two two-quark color singlets. This is an extension of earlier work showing that geometries with two degenerate partitions (e.g., squares) experience a large binding energy. It is now found that even larger binding energies do not result, but that for the tetrahedra the ground and first excited states become degenerate in energy. The calculation is carried out using SU(2) for static quarks in the quenched approximation with Β=2.4 on a 16 3 x32 lattice. The results are analyzed using the correlation matrix between different Euclidean times and the implications of these results are discussed for a model based on two-quark potentials. copyright 1995 The American Physical Society

Influence Function and Robust Variant of Kernel Canonical Correlation Analysis

OpenAIRE

Alam, Md. Ashad; Fukumizu, Kenji; Wang, Yu-Ping

2017-01-01

Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well-founded robust kernel methods for statistical unsupervised learning. In addition, while the influence function (IF) of an estimator can characterize its robustness, asymptotic ...

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

Science.gov (United States)

Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

2014-09-01

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Linux kernel as flexible product-line architecture

NARCIS (Netherlands)

M. de Jonge (Merijn)

2002-01-01

textabstractThe Linux kernel source tree is huge ($>$ 125 MB) and inflexible (because it is difficult to add new kernel components). We propose to make this architecture more flexible by assembling kernel source trees dynamically from individual kernel components. Users then, can select what

Flexible Scheduling by Deadline Inheritance in Soft Real Time Kernels

NARCIS (Netherlands)

Jansen, P.G.; Wygerink, Emiel

1996-01-01

Current Hard Real Time (HRT) kernels have their timely behaviour guaranteed on the cost of a rather restrictive use of the available resources. This makes HRT scheduling techniques inadequate for use in Soft Real Time (SRT) environment where we can make a considerable profit by a better and more

Exploiting graph kernels for high performance biomedical relation extraction.

Science.gov (United States)

Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri

2018-01-30

Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM

Optimal Bandwidth Selection for Kernel Density Functionals Estimation

Directory of Open Access Journals (Sweden)

Su Chen

2015-01-01

Full Text Available The choice of bandwidth is crucial to the kernel density estimation (KDE and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade. It has been known that scale and location parameters are proportional to density functionals ∫γ(xf2(xdx with appropriate choice of γ(x and furthermore equality of scale and location tests can be transformed to comparisons of the density functionals among populations. ∫γ(xf2(xdx can be estimated nonparametrically via kernel density functionals estimation (KDFE. However, the optimal bandwidth selection for KDFE of ∫γ(xf2(xdx has not been examined. We propose a method to select the optimal bandwidth for the KDFE. The idea underlying this method is to search for the optimal bandwidth by minimizing the mean square error (MSE of the KDFE. Two main practical bandwidth selection techniques for the KDFE of ∫γ(xf2(xdx are provided: Normal scale bandwidth selection (namely, “Rule of Thumb” and direct plug-in bandwidth selection. Simulation studies display that our proposed bandwidth selection methods are superior to existing density estimation bandwidth selection methods in estimating density functionals.

External radiotherapy in macular degeneration: technique and preliminary subjective response

International Nuclear Information System (INIS)

Freire, Jorge; Longton, Wallace A.; Miyamoto, Curtis T.; Brady, Luther W.; Augsburger, James; Brown, Gary; Micaily, Bizhan; Unda, Ricardo

1996-01-01

Purpose: This study attempted to assess the toxicity and possible preliminary benefits from the administration of low-dose external beam irradiation for age-related macular degeneration (ARMD). The premise of the treatment is that radiation induces regression and/or promotes inactivation of the subretinal neovasculature which would result in reabsorption of fluid and blood. This would reduce the risk for further leakage or bleeding, as well as subretinal fibrosis. Consequently, the beneficial effect could be translated into stabilization of visual acuity and prevention of progression of the wet ARMD with the possibility for slight improvement. Methods and Materials: Allegheny University Department of Radiation Oncology treated 41 patients prospectively from January through October 1995 with low-dose irradiation for wet-type macular degeneration. A total of 39 patients were treated with a total dose of 14.4 Gy in eight fractions of 1.8 Gy/fraction over 10-13 elapsed days. The first two patients were treated with a total dose of 10 Gy in fivefractions of 2 Gy. Patients were evaluated at 2-3 weeks and 2-3 months. Some of the patients (36.7%) had laser treatments in the study eye: 21.9% (9) once, 5% (2) twice, 9.7% (4) thrice or more. Subjective visual acuity and toxicity data were collected on all patients. Results: At 2-3 weeks after treatment 29 patients (70%) retained their visual acuity without change, 10 (24.5%) stated they had improved vision, and 2 (4.8%) stated their vision continued to decrease. At 2-3 months after treatment, 27 patients (65.8%) had no change in their vision, 11 (27%) had an improvement in their vision, and 3 (7.2%) had a decrease in visual acuity. Six patients of 41 in the treated group had acute transient side effects. Conclusion: Our observations in this group of 41 patients support the conclusion that many patients will have improved or stable vision after treatment with low-dose irradiation for age-related wet-type macular degeneration

Improved Multiscale Entropy Technique with Nearest-Neighbor Moving-Average Kernel for Nonlinear and Nonstationary Short-Time Biomedical Signal Analysis

Directory of Open Access Journals (Sweden)

S. P. Arunachalam

2018-01-01

Full Text Available Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robustness with respect to noise analysis using simulated sinusoidal and ECG waveforms. Feasibility of MSE to discriminate between normal sinus rhythm (NSR and atrial fibrillation (AF was tested on a single-lead ECG. In addition, the MSE algorithm was applied to identify pivot points of rotors that were induced in ex vivo isolated rabbit hearts. The improved MSE technique robustly estimated the complexity of the signal compared to that of SE with various noises, discriminated NSR and AF on single-lead ECG, and precisely identified the pivot points of ex vivo rotors by providing better contrast between the rotor core and the peripheral region. The improved MSE technique can provide efficient complexity analysis of variety of nonlinear and nonstationary short-time biomedical signals.

GRIM : Leveraging GPUs for Kernel integrity monitoring

NARCIS (Netherlands)

Koromilas, Lazaros; Vasiliadis, Giorgos; Athanasopoulos, Ilias; Ioannidis, Sotiris

2016-01-01

Kernel rootkits can exploit an operating system and enable future accessibility and control, despite all recent advances in software protection. A promising defense mechanism against rootkits is Kernel Integrity Monitor (KIM) systems, which inspect the kernel text and data to discover any malicious

7 CFR 51.2296 - Three-fourths half kernel.

Science.gov (United States)

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 than...

Mechanical deformation and glycosaminoglycan content changes in a rabbit annular puncture disc degeneration model.

Science.gov (United States)

Chan, Deva D; Khan, Safdar N; Ye, Xiaojing; Curtiss, Shane B; Gupta, Munish C; Klineberg, Eric O; Neu, Corey P

2011-08-15

Evaluation of degenerated intervertebral discs from a rabbit annular puncture model by using specialized magnetic resonance imaging (MRI) techniques, including displacement encoding with stimulated echoes and a fast-spin echo (DENSE-FSE) acquisition and delayed gadolinium-enhanced MRI of cartilage (dGEMRIC). To evaluate a rabbit disc degeneration model by using various MRI techniques. To determine the displacements and strains, spin-lattice relaxation time (T1), and glycosaminoglycan (GAG) distribution of degenerated discs as compared to normal and adjacent level discs. Annular puncture of the intervertebral disc produces disc degeneration in rabbits. DENSE-FSE has been previously demonstrated in articular cartilage for the measurement of soft tissue displacements and strains. MRI also can measure the T1 of tissue, and dGEMRIC can quantify GAG concentration in cartilage. METHODS.: In eight New Zealand white rabbits, the annulus fibrosis of a lumbar disc was punctured. After 4 weeks, the punctured and cranially adjacent motion segments were isolated for MRI and histology. MRI was used to estimate the disc volume and map T1. DENSE-FSE was used to determine displacements for the estimation of strains. dGEMRIC was then used to determine GAG distributions. Histology and standard MRI indicated degeneration in punctured discs. Disc volume increased significantly at 4 weeks after the puncture. Displacement of the nucleus pulposus was distinct from that of the annulus fibrosis in most untreated discs but not in punctured discs. T1 was significantly higher and GAG concentration significantly lower in punctured discs compared with untreated adjacent level discs. Noninvasive and quantitative MRI techniques can be used to evaluate the mechanical and biochemical changes that occur with animal models of disc degeneration. DENSE-FSE, dGEMRIC, and similar techniques have potential for evaluating the progression of disc degeneration and the efficacy of treatments.

Examining Potential Boundary Bias Effects in Kernel Smoothing on Equating: An Introduction for the Adaptive and Epanechnikov Kernels.

Science.gov (United States)

Cid, Jaime A; von Davier, Alina A

2015-05-01

Test equating is a method of making the test scores from different test forms of the same assessment comparable. In the equating process, an important step involves continuizing the discrete score distributions. In traditional observed-score equating, this step is achieved using linear interpolation (or an unscaled uniform kernel). In the kernel equating (KE) process, this continuization process involves Gaussian kernel smoothing. It has been suggested that the choice of bandwidth in kernel smoothing controls the trade-off between variance and bias. In the literature on estimating density functions using kernels, it has also been suggested that the weight of the kernel depends on the sample size, and therefore, the resulting continuous distribution exhibits bias at the endpoints, where the samples are usually smaller. The purpose of this article is (a) to explore the potential effects of atypical scores (spikes) at the extreme ends (high and low) on the KE method in distributions with different degrees of asymmetry using the randomly equivalent groups equating design (Study I), and (b) to introduce the Epanechnikov and adaptive kernels as potential alternative approaches to reducing boundary bias in smoothing (Study II). The beta-binomial model is used to simulate observed scores reflecting a range of different skewed shapes.

Adaptive Kernel in Meshsize Boosting Algorithm in KDE ...

African Journals Online (AJOL)

This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...

A kernel version of multivariate alteration detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2013-01-01

Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....

Parametric output-only identification of time-varying structures using a kernel recursive extended least squares TARMA approach

Science.gov (United States)

Ma, Zhi-Sai; Liu, Li; Zhou, Si-Da; Yu, Lei; Naets, Frank; Heylen, Ward; Desmet, Wim

2018-01-01

The problem of parametric output-only identification of time-varying structures in a recursive manner is considered. A kernelized time-dependent autoregressive moving average (TARMA) model is proposed by expanding the time-varying model parameters onto the basis set of kernel functions in a reproducing kernel Hilbert space. An exponentially weighted kernel recursive extended least squares TARMA identification scheme is proposed, and a sliding-window technique is subsequently applied to fix the computational complexity for each consecutive update, allowing the method to operate online in time-varying environments. The proposed sliding-window exponentially weighted kernel recursive extended least squares TARMA method is employed for the identification of a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudo-linear regression TARMA method via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics. Furthermore, the comparisons demonstrate the superior achievable accuracy, lower computational complexity and enhanced online identification capability of the proposed kernel recursive extended least squares TARMA approach.

Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

Science.gov (United States)

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.

Uranium kernel formation via internal gelation

International Nuclear Information System (INIS)

Hunt, R.D.; Collins, J.L.

2004-01-01

In the 1970s and 1980s, U.S. Department of Energy (DOE) conducted numerous studies on the fabrication of nuclear fuel particles using the internal gelation process. These amorphous kernels were prone to flaking or breaking when gases tried to escape from the kernels during calcination and sintering. These earlier kernels would not meet today's proposed specifications for reactor fuel. In the interim, the internal gelation process has been used to create hydrous metal oxide microspheres for the treatment of nuclear waste. With the renewed interest in advanced nuclear fuel by the DOE, the lessons learned from the nuclear waste studies were recently applied to the fabrication of uranium kernels, which will become tri-isotropic (TRISO) fuel particles. These process improvements included equipment modifications, small changes to the feed formulations, and a new temperature profile for the calcination and sintering. The modifications to the laboratory-scale equipment and its operation as well as small changes to the feed composition increased the product yield from 60% to 80%-99%. The new kernels were substantially less glassy, and no evidence of flaking was found. Finally, key process parameters were identified, and their effects on the uranium microspheres and kernels are discussed. (orig.)

Flame kernel generation and propagation in turbulent partially premixed hydrocarbon jet

KAUST Repository

Mansour, Mohy S.

2014-04-23

Flame development, propagation, stability, combustion efficiency, pollution formation, and overall system efficiency are affected by the early stage of flame generation defined as flame kernel. Studying the effects of turbulence and chemistry on the flame kernel propagation is the main aim of this work for natural gas (NG) and liquid petroleum gas (LPG). In addition the minimum ignition laser energy (MILE) has been investigated for both fuels. Moreover, the flame stability maps for both fuels are also investigated and analyzed. The flame kernels are generated using Nd:YAG pulsed laser and propagated in a partially premixed turbulent jet. The flow field is measured using 2-D PIV technique. Five cases have been selected for each fuel covering different values of Reynolds number within a range of 6100-14400, at a mean equivalence ratio of 2 and a certain level of partial premixing. The MILE increases by increasing the equivalence ratio. Near stoichiometric the energy density is independent on the jet velocity while in rich conditions it increases by increasing the jet velocity. The stability curves show four distinct regions as lifted, attached, blowout, and a fourth region either an attached flame if ignition occurs near the nozzle or lifted if ignition occurs downstream. LPG flames are more stable than NG flames. This is consistent with the higher values of the laminar flame speed of LPG. The flame kernel propagation speed is affected by both turbulence and chemistry. However, at low turbulence level chemistry effects are more pronounced while at high turbulence level the turbulence becomes dominant. LPG flame kernels propagate faster than those for NG flame. In addition, flame kernel extinguished faster in LPG fuel as compared to NG fuel. The propagation speed is likely to be consistent with the local mean equivalence ratio and its corresponding laminar flame speed. Copyright © Taylor & Francis Group, LLC.

Kernel learning at the first level of inference.

Science.gov (United States)

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Global Polynomial Kernel Hazard Estimation

DEFF Research Database (Denmark)

Hiabu, Munir; Miranda, Maria Dolores Martínez; Nielsen, Jens Perch

2015-01-01

This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it asymptotically redu...

Quantum tomography, phase-space observables and generalized Markov kernels

International Nuclear Information System (INIS)

Pellonpaeae, Juha-Pekka

2009-01-01

We construct a generalized Markov kernel which transforms the observable associated with the homodyne tomography into a covariant phase-space observable with a regular kernel state. Illustrative examples are given in the cases of a 'Schroedinger cat' kernel state and the Cahill-Glauber s-parametrized distributions. Also we consider an example of a kernel state when the generalized Markov kernel cannot be constructed.

Single pass kernel k-means clustering method

Indian Academy of Sciences (India)

paper proposes a simple and faster version of the kernel k-means clustering ... It has been considered as an important tool ... On the other hand, kernel-based clustering methods, like kernel k-means clus- ..... able at the UCI machine learning repository (Murphy 1994). ... All the data sets have only numeric valued features.

Relationship between attenuation coefficients and dose-spread kernels

International Nuclear Information System (INIS)

Boyer, A.L.

1988-01-01

Dose-spread kernels can be used to calculate the dose distribution in a photon beam by convolving the kernel with the primary fluence distribution. The theoretical relationships between various types and components of dose-spread kernels relative to photon attenuation coefficients are explored. These relations can be valuable as checks on the conservation of energy by dose-spread kernels calculated by analytic or Monte Carlo methods

Mixture Density Mercer Kernels: A Method to Learn Kernels

Data.gov (United States)

National Aeronautics and Space Administration — This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian...

Integral equations with contrasting kernels

Directory of Open Access Journals (Sweden)

Theodore Burton

2008-01-01

Full Text Available In this paper we study integral equations of the form $x(t=a(t-\\int^t_0 C(t,sx(sds$ with sharply contrasting kernels typified by $C^*(t,s=\\ln (e+(t-s$ and $D^*(t,s=[1+(t-s]^{-1}$. The kernel assigns a weight to $x(s$ and these kernels have exactly opposite effects of weighting. Each type is well represented in the literature. Our first project is to show that for $a\\in L^2[0,\\infty$, then solutions are largely indistinguishable regardless of which kernel is used. This is a surprise and it leads us to study the essential differences. In fact, those differences become large as the magnitude of $a(t$ increases. The form of the kernel alone projects necessary conditions concerning the magnitude of $a(t$ which could result in bounded solutions. Thus, the next project is to determine how close we can come to proving that the necessary conditions are also sufficient. The third project is to show that solutions will be bounded for given conditions on $C$ regardless of whether $a$ is chosen large or small; this is important in real-world problems since we would like to have $a(t$ as the sum of a bounded, but badly behaved function, and a large well behaved function.

Kernel methods in orthogonalization of multi- and hypervariate data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

A kernel version of maximum autocorrelation factor (MAF) analysis is described very briefly and applied to change detection in remotely sensed hyperspectral image (HyMap) data. The kernel version is based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis...... via inner products in the Gram matrix only. In the kernel version the inner products are replaced by inner products between nonlinear mappings into higher dimensional feature space of the original data. 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 PCA and MAF analysis handle nonlinearities by implicitly transforming data into high (even infinite...

Kernel based subspace projection of near infrared hyperspectral images of maize kernels

DEFF Research Database (Denmark)

Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben

2009-01-01

In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods ......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data.......In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...... including principal component analysis and maximum autocorrelation factor analysis. The latter utilizes the fact that interesting phenomena in images exhibit spatial autocorrelation. However, linear projections often fail to grasp the underlying variability on the data. Therefore we propose to use so...

A class of kernel based real-time elastography algorithms.

Science.gov (United States)

Kibria, Md Golam; Hasan, Md Kamrul

2015-08-01

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

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

Science.gov (United States)

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function

The Classification of Diabetes Mellitus Using Kernel k-means

Science.gov (United States)

Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.

2018-01-01

Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.

Evaluating the Application of Tissue-Specific Dose Kernels Instead of Water Dose Kernels in Internal Dosimetry : A Monte Carlo Study

NARCIS (Netherlands)

Moghadam, Maryam Khazaee; Asl, Alireza Kamali; Geramifar, Parham; Zaidi, Habib

2016-01-01

Purpose: The aim of this work is to evaluate the application of tissue-specific dose kernels instead of water dose kernels to improve the accuracy of patient-specific dosimetry by taking tissue heterogeneities into consideration. Materials and Methods: Tissue-specific dose point kernels (DPKs) and

Parsimonious Wavelet Kernel Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Wang Qin

2015-11-01

Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

KAUST Repository

Wang, Jim Jing-Yan

2014-09-20

Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.

Difference between standard and quasi-conformal BFKL kernels

International Nuclear Information System (INIS)

Fadin, V.S.; Fiore, R.; Papa, A.

2012-01-01

As it was recently shown, the colour singlet BFKL kernel, taken in Möbius representation in the space of impact parameters, can be written in quasi-conformal shape, which is unbelievably simple compared with the conventional form of the BFKL kernel in momentum space. It was also proved that the total kernel is completely defined by its Möbius representation. In this paper we calculated the difference between standard and quasi-conformal BFKL kernels in momentum space and discovered that it is rather simple. Therefore we come to the conclusion that the simplicity of the quasi-conformal kernel is caused mainly by using the impact parameter space.

A laser optical method for detecting corn kernel defects

Energy Technology Data Exchange (ETDEWEB)

Gunasekaran, S.; Paulsen, M. R.; Shove, G. C.

1984-01-01

An opto-electronic instrument was developed to examine individual corn kernels and detect various kernel defects according to reflectance differences. A low power helium-neon (He-Ne) laser (632.8 nm, red light) was used as the light source in the instrument. Reflectance from good and defective parts of corn kernel surfaces differed by approximately 40%. Broken, chipped, and starch-cracked kernels were detected with nearly 100% accuracy; while surface-split kernels were detected with about 80% accuracy. (author)

Intelligent Control of a Sensor-Actuator System via Kernelized Least-Squares Policy Iteration

Directory of Open Access Journals (Sweden)

Bo Liu

2012-02-01

Full Text Available In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL, for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Projections and nonparametric Kernelized Least-squares Policy Iteration (KLSPI. Random Projections are a fast, non-adaptive dimensionality reduction framework in which high-dimensionality data is projected onto a random lower-dimension subspace via spherically random rotation and coordination sampling. KLSPI introduce kernel trick into the LSPI framework for Reinforcement Learning, often achieving faster convergence and providing automatic feature selection via various kernel sparsification approaches. In this approach, policies are computed in a low-dimensional subspace generated by projecting the high-dimensional features onto a set of random basis. We first show how Random Projections constitute an efficient sparsification technique and how our method often converges faster than regular LSPI, while at lower computational costs. Theoretical foundation underlying this approach is a fast approximation of Singular Value Decomposition (SVD. Finally, simulation results are exhibited on benchmark MDP domains, which confirm gains both in computation time and in performance in large feature spaces.

Kernel maximum autocorrelation factor and minimum noise fraction transformations

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2010-01-01

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...

Identification of Fusarium damaged wheat kernels using image analysis

Directory of Open Access Journals (Sweden)

Ondřej Jirsa

2011-01-01

Full Text Available Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (DON content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue, very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance. The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.

Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema

Science.gov (United States)

Jin, Hyeongmin; Heo, Changyong; Kim, Jong Hyo

2018-02-01

Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 +/- 7.28% for B50f data set, 10.82 +/- 6.71% for the B30f data set, and 8.87 +/- 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a

Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images.

Science.gov (United States)

Chung, Moo K; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K

2015-05-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template. Copyright © 2015 Elsevier B.V. All rights reserved.

Digital signal processing with kernel methods

CERN Document Server

Rojo-Alvarez, José Luis; Muñoz-Marí, Jordi; Camps-Valls, Gustavo

2018-01-01

A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. * Presents the necess...

Higher-Order Hybrid Gaussian Kernel in Meshsize Boosting Algorithm

African Journals Online (AJOL)

In this paper, we shall use higher-order hybrid Gaussian kernel in a meshsize boosting algorithm in kernel density estimation. Bias reduction is guaranteed in this scheme like other existing schemes but uses the higher-order hybrid Gaussian kernel instead of the regular fixed kernels. A numerical verification of this scheme ...

Adaptive Kernel In The Bootstrap Boosting Algorithm In KDE ...

African Journals Online (AJOL)

This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...

Windows Vista Kernel-Mode: Functions, Security Enhancements and Flaws

Directory of Open Access Journals (Sweden)

Mohammed D. ABDULMALIK

2008-06-01

Full Text Available Microsoft has made substantial enhancements to the kernel of the Microsoft Windows Vista operating system. Kernel improvements are significant because the kernel provides low-level operating system functions, including thread scheduling, interrupt and exception dispatching, multiprocessor synchronization, and a set of routines and basic objects.This paper describes some of the kernel security enhancements for 64-bit edition of Windows Vista. We also point out some weakness areas (flaws that can be attacked by malicious leading to compromising the kernel.

Generalization Performance of Regularized Ranking With Multiscale Kernels.

Science.gov (United States)

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.

Three dimensional electrostatic solitary waves in a dense magnetoplasma with relativistically degenerate electrons

Energy Technology Data Exchange (ETDEWEB)

Ata-ur-Rahman,; Qamar, A. [Institute of Physics and Electronics, University of Peshawar, Peshawar 25000 (Pakistan); National Centre for Physics, QAU Campus, Shahdrah Valley Road, Islamabad 44000 (Pakistan); Masood, W. [National Centre for Physics, QAU Campus, Shahdrah Valley Road, Islamabad 44000 (Pakistan); COMSATS, Institute of Information Technology, Park Road, Chak Shahzad, Islamabad 44000 (Pakistan); Eliasson, B. [Physics Department, University of Strathclyde, Glasgow G4 0NG, Scotland (United Kingdom)

2013-09-15

In this paper, small but finite amplitude electrostatic solitary waves in a relativistic degenerate magnetoplasma, consisting of relativistically degenerate electrons and non-degenerate cold ions, are investigated. The Zakharov-Kuznetsov equation is derived employing the reductive perturbation technique and its solitary wave solution is analyzed. It is shown that only compressive electrostatic solitary structures can propagate in such a degenerate plasma system. The effects of plasma number density, ion cyclotron frequency, and direction cosines on the profiles of ion acoustic solitary waves are investigated and discussed at length. The relevance of the present investigation vis-a-vis pulsating white dwarfs is also pointed out.

Multineuron spike train analysis with R-convolution linear combination kernel.

Science.gov (United States)

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

Directory of Open Access Journals (Sweden)

Jinlu Sheng

2016-07-01

Full Text Available To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

An analysis of 1-D smoothed particle hydrodynamics kernels

International Nuclear Information System (INIS)

Fulk, D.A.; Quinn, D.W.

1996-01-01

In this paper, the smoothed particle hydrodynamics (SPH) kernel is analyzed, resulting in measures of merit for one-dimensional SPH. Various methods of obtaining an objective measure of the quality and accuracy of the SPH kernel are addressed. Since the kernel is the key element in the SPH methodology, this should be of primary concern to any user of SPH. The results of this work are two measures of merit, one for smooth data and one near shocks. The measure of merit for smooth data is shown to be quite accurate and a useful delineator of better and poorer kernels. The measure of merit for non-smooth data is not quite as accurate, but results indicate the kernel is much less important for these types of problems. In addition to the theory, 20 kernels are analyzed using the measure of merit demonstrating the general usefulness of the measure of merit and the individual kernels. In general, it was decided that bell-shaped kernels perform better than other shapes. 12 refs., 16 figs., 7 tabs

Putting Priors in Mixture Density Mercer Kernels

Science.gov (United States)

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%.

NLO corrections to the Kernel of the BKP-equations

Energy Technology Data Exchange (ETDEWEB)

Bartels, J. [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Fadin, V.S. [Budker Institute of Nuclear Physics, Novosibirsk (Russian Federation); Novosibirskij Gosudarstvennyj Univ., Novosibirsk (Russian Federation); Lipatov, L.N. [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Petersburg Nuclear Physics Institute, Gatchina, St. Petersburg (Russian Federation); Vacca, G.P. [INFN, Sezione di Bologna (Italy)

2012-10-02

We present results for the NLO kernel of the BKP equations for composite states of three reggeized gluons in the Odderon channel, both in QCD and in N=4 SYM. The NLO kernel consists of the NLO BFKL kernel in the color octet representation and the connected 3{yields}3 kernel, computed in the tree approximation.

A Fast and Simple Graph Kernel for RDF

NARCIS (Netherlands)

de Vries, G.K.D.; de Rooij, S.

2013-01-01

In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster

An SVM model with hybrid kernels for hydrological time series

Science.gov (United States)

Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

Kernel based eigenvalue-decomposition methods for analysing ham

DEFF Research Database (Denmark)

Christiansen, Asger Nyman; Nielsen, Allan Aasbjerg; Møller, Flemming

2010-01-01

methods, such as PCA, MAF or MNF. We therefore investigated the applicability of kernel based versions of these transformation. This meant implementing the kernel based methods and developing new theory, since kernel based MAF and MNF is not described in the literature yet. The traditional methods only...... have two factors that are useful for segmentation and none of them can be used to segment the two types of meat. The kernel based methods have a lot of useful factors and they are able to capture the subtle differences in the images. This is illustrated in Figure 1. You can see a comparison of the most...... useful factor of PCA and kernel based PCA respectively in Figure 2. The factor of the kernel based PCA turned out to be able to segment the two types of meat and in general that factor is much more distinct, compared to the traditional factor. After the orthogonal transformation a simple thresholding...

Kernel principal component analysis for change detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Morton, J.C.

2008-01-01

region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA...... with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially....

PKI, Gamma Radiation Reactor Shielding Calculation by Point-Kernel Method

International Nuclear Information System (INIS)

Li Chunhuai; Zhang Liwu; Zhang Yuqin; Zhang Chuanxu; Niu Xihua

1990-01-01

1 - Description of program or function: This code calculates radiation shielding problem of gamma-ray in geometric space. 2 - Method of solution: PKI uses a point kernel integration technique, describes radiation shielding geometric space by using geometric space configuration method and coordinate conversion, and makes use of calculation result of reactor primary shielding and flow regularity in loop system for coolant

Enhanced gluten properties in soft kernel durum wheat

Science.gov (United States)

Soft kernel durum wheat is a relatively recent development (Morris et al. 2011 Crop Sci. 51:114). The soft kernel trait exerts profound effects on kernel texture, flour milling including break flour yield, milling energy, and starch damage, and dough water absorption (DWA). With the caveat of reduce...

7 CFR 981.61 - Redetermination of kernel weight.

Science.gov (United States)

2010-01-01

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

Optimizing The Performance of Streaming Numerical Kernels On The IBM Blue Gene/P PowerPC 450

KAUST Repository

Malas, Tareq

2011-07-01

Several emerging petascale architectures use energy-efficient processors with vectorized computational units and in-order thread processing. On these architectures the sustained performance of streaming numerical kernels, ubiquitous in the solution of partial differential equations, represents a formidable challenge despite the regularity of memory access. Sophisticated optimization techniques beyond the capabilities of modern compilers are required to fully utilize the Central Processing Unit (CPU). The aim of the work presented here is to improve the performance of streaming numerical kernels on high performance architectures by developing efficient algorithms to utilize the vectorized floating point units. The importance of the development time demands the creation of tools to enable simple yet direct development in assembly to utilize the power-efficient cores featuring in-order execution and multiple-issue units. We implement several stencil kernels for a variety of cached memory scenarios using our Python instruction simulation and generation tool. Our technique simplifies the development of efficient assembly code for the IBM Blue Gene/P supercomputer\\'s PowerPC 450. This enables us to perform high-level design, construction, verification, and simulation on a subset of the CPU\\'s instruction set. Our framework has the capability to implement streaming numerical kernels on current and future high performance architectures. Finally, we present several automatically generated implementations, including a 27-point stencil achieving a 1.7x speedup over the best previously published results.

Consistent Estimation of Pricing Kernels from Noisy Price Data

OpenAIRE

Vladislav Kargin

2003-01-01

If pricing kernels are assumed non-negative then the inverse problem of finding the pricing kernel is well-posed. The constrained least squares method provides a consistent estimate of the pricing kernel. When the data are limited, a new method is suggested: relaxed maximization of the relative entropy. This estimator is also consistent. Keywords: $\\epsilon$-entropy, non-parametric estimation, pricing kernel, inverse problems.

Application of Reproducing Kernel Method for Solving Nonlinear Fredholm-Volterra Integrodifferential Equations

Directory of Open Access Journals (Sweden)

Omar Abu Arqub

2012-01-01

Full Text Available This paper investigates the numerical solution of nonlinear Fredholm-Volterra integro-differential equations using reproducing kernel Hilbert space method. The solution ( is represented in the form of series in the reproducing kernel space. In the mean time, the n-term approximate solution ( is obtained and it is proved to converge to the exact solution (. Furthermore, the proposed method has an advantage that it is possible to pick any point in the interval of integration and as well the approximate solution and its derivative will be applicable. Numerical examples are included to demonstrate the accuracy and applicability of the presented technique. The results reveal that the method is very effective and simple.

Stable Kernel Representations as Nonlinear Left Coprime Factorizations

NARCIS (Netherlands)

Paice, A.D.B.; Schaft, A.J. van der

1994-01-01

A representation of nonlinear systems based on the idea of representing the input-output pairs of the system as elements of the kernel of a stable operator has been recently introduced. This has been denoted the kernel representation of the system. In this paper it is demonstrated that the kernel

7 CFR 981.60 - Determination of kernel weight.

Science.gov (United States)

2010-01-01

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

End-use quality of soft kernel durum wheat

Science.gov (United States)

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...

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Ling-Yu Duan

2010-01-01

Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Tian Yonghong

2010-01-01

Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

Discrete non-parametric kernel estimation for global sensitivity analysis

International Nuclear Information System (INIS)

Senga Kiessé, Tristan; Ventura, Anne

2016-01-01

This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output. - Highlights: • We study a discrete kernel estimation for sensitivity analysis of a model. • A discrete kernel estimator of ANOVA decomposition of the model is presented. • Sensitivity indices are calculated for discrete input parameters. • An estimator of sensitivity indices is also presented with its convergence rate. • An application is realized for improving the reliability of environmental models.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

Science.gov (United States)

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Improved modeling of clinical data with kernel methods.

Science.gov (United States)

Daemen, Anneleen; Timmerman, Dirk; Van den Bosch, Thierry; Bottomley, Cecilia; Kirk, Emma; Van Holsbeke, Caroline; Valentin, Lil; Bourne, Tom; De Moor, Bart

2012-02-01

Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems

Linear and kernel methods for multi- and hypervariate change detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Canty, Morton J.

2010-01-01

. Principal component analysis (PCA) as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (which are nonlinear), may further enhance change signals relative to no-change background. The kernel versions are based on a dual...... formulation, also termed Q-mode analysis, in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution......, also known as the kernel trick, these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of the kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component...

Kernel based orthogonalization for change detection in hyperspectral images

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

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 PCA and MNF analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via...... analysis all 126 spectral bands of the HyMap are included. Changes on the ground are most likely due to harvest having taken place between the two acquisitions and solar effects (both solar elevation and azimuth have changed). Both types of kernel analysis emphasize change and unlike kernel PCA, kernel MNF...

Mitigation of artifacts in rtm with migration kernel decomposition

KAUST Repository

Zhan, Ge; Schuster, Gerard T.

2012-01-01

The migration kernel for reverse-time migration (RTM) can be decomposed into four component kernels using Born scattering and migration theory. Each component kernel has a unique physical interpretation and can be interpreted differently

Semi-Supervised Kernel PCA

DEFF Research Database (Denmark)

Walder, Christian; Henao, Ricardo; Mørup, Morten

We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least...... squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets....

Fast Interrupt Priority Management in Operating System Kernels

Science.gov (United States)

1993-05-01

We present results for the Mach 3.0 microkernel operating system, although the technique is applicable to other kernel architectures, both micro and...protection in the Mach 3.0 microkernel for several different processor architectures. For example, on the Omron Luna88k, we observed a 50% reduction in...general interrupt mask raise/lower pair within the Mach 3.0 microkernel on a variety of architectures. DTIC QUALM i.N1’R%.*1IMD 5 k81tltC Avail andl

21 CFR 176.350 - Tamarind seed kernel powder.

Science.gov (United States)

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...

Dense Medium Machine Processing Method for Palm Kernel/ Shell ...

African Journals Online (AJOL)

ADOWIE PERE

Cracked palm kernel is a mixture of kernels, broken shells, dusts and other impurities. In ... machine processing method using dense medium, a separator, a shell collector and a kernel .... efficiency, ease of maintenance and uniformity of.

Multivariate and semiparametric kernel regression

OpenAIRE

Härdle, Wolfgang; Müller, Marlene

1997-01-01

The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...

Notes on the gamma kernel

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole E.

The density function of the gamma distribution is used as shift kernel in Brownian semistationary processes modelling the timewise behaviour of the velocity in turbulent regimes. This report presents exact and asymptotic properties of the second order structure function under such a model......, and relates these to results of von Karmann and Horwath. But first it is shown that the gamma kernel is interpretable as a Green’s function....

Calculation of the thermal neutron scattering kernel using the synthetic model. Pt. 2. Zero-order energy transfer kernel

International Nuclear Information System (INIS)

Drozdowicz, K.

1995-01-01

A comprehensive unified description of the application of Granada's Synthetic Model to the slow-neutron scattering by the molecular systems is continued. Detailed formulae for the zero-order energy transfer kernel are presented basing on the general formalism of the model. An explicit analytical formula for the total scattering cross section as a function of the incident neutron energy is also obtained. Expressions of the free gas model for the zero-order scattering kernel and for total scattering kernel are considered as a sub-case of the Synthetic Model. (author). 10 refs

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří

2016-02-12

We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří ; Barton, Michael

2016-01-01

We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

Science.gov (United States)

Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

2017-12-21

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

Aflatoxin contamination of developing corn kernels.

Science.gov (United States)

Amer, M A

2005-01-01

Preharvest of corn and its contamination with aflatoxin is a serious problem. Some environmental and cultural factors responsible for infection and subsequent aflatoxin production were investigated in this study. Stage of growth and location of kernels on corn ears were found to be one of the important factors in the process of kernel infection with A. flavus & A. parasiticus. The results showed positive correlation between the stage of growth and kernel infection. Treatment of corn with aflatoxin reduced germination, protein and total nitrogen contents. Total and reducing soluble sugar was increase in corn kernels as response to infection. Sucrose and protein content were reduced in case of both pathogens. Shoot system length, seeding fresh weigh and seedling dry weigh was also affected. Both pathogens induced reduction of starch content. Healthy corn seedlings treated with aflatoxin solution were badly affected. Their leaves became yellow then, turned brown with further incubation. Moreover, their total chlorophyll and protein contents showed pronounced decrease. On the other hand, total phenolic compounds were increased. Histopathological studies indicated that A. flavus & A. parasiticus could colonize corn silks and invade developing kernels. Germination of A. flavus spores was occurred and hyphae spread rapidly across the silk, producing extensive growth and lateral branching. Conidiophores and conidia had formed in and on the corn silk. Temperature and relative humidity greatly influenced the growth of A. flavus & A. parasiticus and aflatoxin production.

Evaluation of the Single-precision Floatingpoint Vector Add Kernel Using the Intel FPGA SDK for OpenCL

Energy Technology Data Exchange (ETDEWEB)

Jin, Zheming [Argonne National Lab. (ANL), Argonne, IL (United States); Yoshii, Kazutomo [Argonne National Lab. (ANL), Argonne, IL (United States); Finkel, Hal [Argonne National Lab. (ANL), Argonne, IL (United States); Cappello, Franck [Argonne National Lab. (ANL), Argonne, IL (United States)

2017-04-20

Open Computing Language (OpenCL) is a high-level language that enables software programmers to explore Field Programmable Gate Arrays (FPGAs) for application acceleration. The Intel FPGA software development kit (SDK) for OpenCL allows a user to specify applications at a high level and explore the performance of low-level hardware acceleration. In this report, we present the FPGA performance and power consumption results of the single-precision floating-point vector add OpenCL kernel using the Intel FPGA SDK for OpenCL on the Nallatech 385A FPGA board. The board features an Arria 10 FPGA. We evaluate the FPGA implementations using the compute unit duplication and kernel vectorization optimization techniques. On the Nallatech 385A FPGA board, the maximum compute kernel bandwidth we achieve is 25.8 GB/s, approximately 76% of the peak memory bandwidth. The power consumption of the FPGA device when running the kernels ranges from 29W to 42W.

A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

Science.gov (United States)

Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying

2015-09-01

Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level. © 2015 WILEY PERIODICALS, INC.

Kernel Korner : The Linux keyboard driver

NARCIS (Netherlands)

Brouwer, A.E.

1995-01-01

Our Kernel Korner series continues with an article describing the Linux keyboard driver. This article is not for "Kernel Hackers" only--in fact, it will be most useful to those who wish to use their own keyboard to its fullest potential, and those who want to write programs to take advantage of the

The heating of UO_2 kernels in argon gas medium on the physical properties of sintered UO_2 kernels

International Nuclear Information System (INIS)

Damunir; Sri Rinanti Susilowati; Ariyani Kusuma Dewi

2015-01-01

The heating of UO_2 kernels in argon gas medium on the physical properties of sinter UO_2 kernels was conducted. The heated of the UO_2 kernels was conducted in a sinter reactor of a bed type. The sample used was the UO_2 kernels resulted from the reduction results at 800 °C temperature for 3 hours that had the density of 8.13 g/cm"3; porosity of 0.26; O/U ratio of 2.05; diameter of 1146 μm and sphericity of 1.05. The sample was put into a sinter reactor, then it was vacuumed by flowing the argon gas at 180 mmHg pressure to drain the air from the reactor. After that, the cooling water and argon gas were continuously flowed with the pressure of 5 mPa with 1.5 liter/minutes velocity. The reactor temperature was increased and variated at 1200-1500 °C temperature and for 1-4 hours. The sinters UO_2 kernels resulted from the study were analyzed in term of their physical properties including the density, porosity, diameter, sphericity, and specific surface area. The density was analyzed using pycnometer with CCl_4 solution. The porosity was determined using Haynes equation. The diameters and sphericity were showed using the Dino-lite microscope. The specific surface area was determined using surface area meter Nova-1000. The obtained products showed the the heating of UO_2 kernel in argon gas medium were influenced on the physical properties of sinters UO_2 kernel. The condition of best relatively at 1400 °C temperature and 2 hours time. The product resulted from the study was relatively at its best when heating was conducted at 1400 °C temperature and 2 hours time, produced sinters UO_2 kernel with density of 10.14 gr/ml; porosity of 7 %; diameters of 893 μm; sphericity of 1.07 and specific surface area of 4.68 m"2/g with solidify shrinkage of 22 %. (author)

Mitigation of artifacts in rtm with migration kernel decomposition

KAUST Repository

Zhan, Ge

2012-01-01

The migration kernel for reverse-time migration (RTM) can be decomposed into four component kernels using Born scattering and migration theory. Each component kernel has a unique physical interpretation and can be interpreted differently. In this paper, we present a generalized diffraction-stack migration approach for reducing RTM artifacts via decomposition of migration kernel. The decomposition leads to an improved understanding of migration artifacts and, therefore, presents us with opportunities for improving the quality of RTM images.

Realized kernels in practice

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Hansen, P. Reinhard; Lunde, Asger

2009-01-01

and find a remarkable level of agreement. We identify some features of the high-frequency data, which are challenging for realized kernels. They are when there are local trends in the data, over periods of around 10 minutes, where the prices and quotes are driven up or down. These can be associated......Realized kernels use high-frequency data to estimate daily volatility of individual stock prices. They can be applied to either trade or quote data. Here we provide the details of how we suggest implementing them in practice. We compare the estimates based on trade and quote data for the same stock...

Anatomically-aided PET reconstruction using the kernel method.

Science.gov (United States)

Hutchcroft, Will; Wang, Guobao; Chen, Kevin T; Catana, Ciprian; Qi, Jinyi

2016-09-21

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.

Spectral Kernel Approach to Study Radiative Response of Climate Variables and Interannual Variability of Reflected Solar Spectrum

Science.gov (United States)

Jin, Zhonghai; Wielicki, Bruce A.; Loukachine, Constantin; Charlock, Thomas P.; Young, David; Noeel, Stefan

2011-01-01

The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel ]derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.

Embedded real-time operating system micro kernel design

Science.gov (United States)

Cheng, Xiao-hui; Li, Ming-qiang; Wang, Xin-zheng

2005-12-01

Embedded systems usually require a real-time character. Base on an 8051 microcontroller, an embedded real-time operating system micro kernel is proposed consisting of six parts, including a critical section process, task scheduling, interruption handle, semaphore and message mailbox communication, clock managent and memory managent. Distributed CPU and other resources are among tasks rationally according to the importance and urgency. The design proposed here provides the position, definition, function and principle of micro kernel. The kernel runs on the platform of an ATMEL AT89C51 microcontroller. Simulation results prove that the designed micro kernel is stable and reliable and has quick response while operating in an application system.

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

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

Collision kernels in the eikonal approximation for Lennard-Jones interaction potential

International Nuclear Information System (INIS)

Zielinska, S.

1985-03-01

The velocity changing collisions are conveniently described by collisional kernels. These kernels depend on an interaction potential and there is a necessity for evaluating them for realistic interatomic potentials. Using the collision kernels, we are able to investigate the redistribution of atomic population's caused by the laser light and velocity changing collisions. In this paper we present the method of evaluating the collision kernels in the eikonal approximation. We discuss the influence of the potential parameters Rsub(o)sup(i), epsilonsub(o)sup(i) on kernel width for a given atomic state. It turns out that unlike the collision kernel for the hard sphere model of scattering the Lennard-Jones kernel is not so sensitive to changes of Rsub(o)sup(i) as the previous one. Contrary to the general tendency of approximating collisional kernels by the Gaussian curve, kernels for the Lennard-Jones potential do not exhibit such a behaviour. (author)

Classification of maize kernels using NIR hyperspectral imaging

DEFF Research Database (Denmark)

Williams, Paul; Kucheryavskiy, Sergey V.

2016-01-01

NIR hyperspectral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and soft. Two approaches, pixel-wise and object-wise, were investigated to group kernels according to hardness. The pixel-wise classification assigned a class to every pixel from individual...... and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale....

Influence of wheat kernel physical properties on the pulverizing process.

Science.gov (United States)

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

2014-10-01

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

Macular degeneration (image)

Science.gov (United States)

... macula in the back of the eye. The macula is important for clear central vision, allowing an individual to see fine details. There are two types of macular degeneration, dry and wet. Dry macular degeneration is more ...

Linked-cluster formulation of electron-hole interaction kernel in real-space representation without using unoccupied states.

Science.gov (United States)

Bayne, Michael G; Scher, Jeremy A; Ellis, Benjamin H; Chakraborty, Arindam

2018-05-21

Electron-hole or quasiparticle representation plays a central role in describing electronic excitations in many-electron systems. For charge-neutral excitation, the electron-hole interaction kernel is the quantity of interest for calculating important excitation properties such as optical gap, optical spectra, electron-hole recombination and electron-hole binding energies. The electron-hole interaction kernel can be formally derived from the density-density correlation function using both Green's function and TDDFT formalism. The accurate determination of the electron-hole interaction kernel remains a significant challenge for precise calculations of optical properties in the GW+BSE formalism. From the TDDFT perspective, the electron-hole interaction kernel has been viewed as a path to systematic development of frequency-dependent exchange-correlation functionals. Traditional approaches, such as MBPT formalism, use unoccupied states (which are defined with respect to Fermi vacuum) to construct the electron-hole interaction kernel. However, the inclusion of unoccupied states has long been recognized as the leading computational bottleneck that limits the application of this approach for larger finite systems. In this work, an alternative derivation that avoids using unoccupied states to construct the electron-hole interaction kernel is presented. The central idea of this approach is to use explicitly correlated geminal functions for treating electron-electron correlation for both ground and excited state wave functions. Using this ansatz, it is derived using both diagrammatic and algebraic techniques that the electron-hole interaction kernel can be expressed only in terms of linked closed-loop diagrams. It is proved that the cancellation of unlinked diagrams is a consequence of linked-cluster theorem in real-space representation. The electron-hole interaction kernel derived in this work was used to calculate excitation energies in many-electron systems and results

An iterative method for selecting degenerate multiplex PCR primers.

Science.gov (United States)

Souvenir, Richard; Buhler, Jeremy; Stormo, Gary; Zhang, Weixiong

2007-01-01

Single-nucleotide polymorphism (SNP) genotyping is an important molecular genetics process, which can produce results that will be useful in the medical field. Because of inherent complexities in DNA manipulation and analysis, many different methods have been proposed for a standard assay. One of the proposed techniques for performing SNP genotyping requires amplifying regions of DNA surrounding a large number of SNP loci. To automate a portion of this particular method, it is necessary to select a set of primers for the experiment. Selecting these primers can be formulated as the Multiple Degenerate Primer Design (MDPD) problem. The Multiple, Iterative Primer Selector (MIPS) is an iterative beam-search algorithm for MDPD. Theoretical and experimental analyses show that this algorithm performs well compared with the limits of degenerate primer design. Furthermore, MIPS outperforms an existing algorithm that was designed for a related degenerate primer selection problem.

Evolution kernel for the Dirac field

International Nuclear Information System (INIS)

Baaquie, B.E.

1982-06-01

The evolution kernel for the free Dirac field is calculated using the Wilson lattice fermions. We discuss the difficulties due to which this calculation has not been previously performed in the continuum theory. The continuum limit is taken, and the complete energy eigenfunctions as well as the propagator are then evaluated in a new manner using the kernel. (author)

Gradient-based adaptation of general gaussian kernels.

Science.gov (United States)

Glasmachers, Tobias; Igel, Christian

2005-10-01

Gradient-based optimizing of gaussian kernel functions is considered. The gradient for the adaptation of scaling and rotation of the input space is computed to achieve invariance against linear transformations. This is done by using the exponential map as a parameterization of the kernel parameter manifold. By restricting the optimization to a constant trace subspace, the kernel size can be controlled. This is, for example, useful to prevent overfitting when minimizing radius-margin generalization performance measures. The concepts are demonstrated by training hard margin support vector machines on toy data.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

Science.gov (United States)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

Science.gov (United States)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga-Koontz Transform

Science.gov (United States)

Binol, Hamidullah; Ochilov, Shuhrat; Alam, Mohammad S.; Bal, Abdullah

2017-02-01

Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga-Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.

Differential charge-transfer cross sections for systems with energetically degenerate or near-degenerate channels

International Nuclear Information System (INIS)

Nguyen, H.; Bredy, R.; Camp, H.A.; DePaola, B.D.; Awata, T.

2004-01-01

Resolution plays a vital role in spectroscopic studies. In the usual recoil-ion momentum spectroscopy (RIMS), Q-value resolution is relied upon to distinguish between different collision channels: The better the Q-value resolution, the better one is able to resolve energetically similar channels. Although traditional COLTRIMS greatly improves Q-value resolution by cooling the target and thus greatly reducing the initial target momentum spread, the resolution of the technique is still limited by target temperature. However, with the recent development in RIMS, namely, magneto-optical trap recoil ion momentum spectroscopy (MOTRIMS) superior recoil ion momentum resolution as well as charge transfer measurements with laser excited targets have become possible. Through MOTRIMS, methods for the measurements of target excited state fraction and kinematically complete relative charge transfer cross sections have been developed, even for some systems having energetically degenerate or nearly degenerate channels. In the present work, the systems of interest having energy degeneracies or near degeneracies are Rb + , K + , and Li + colliding with trapped Rb(5l), where l=s and p

Open Problem: Kernel methods on manifolds and metric spaces

DEFF Research Database (Denmark)

Feragen, Aasa; Hauberg, Søren

2016-01-01

Radial kernels are well-suited for machine learning over general geodesic metric spaces, where pairwise distances are often the only computable quantity available. We have recently shown that geodesic exponential kernels are only positive definite for all bandwidths when the input space has strong...... linear properties. This negative result hints that radial kernel are perhaps not suitable over geodesic metric spaces after all. Here, however, we present evidence that large intervals of bandwidths exist where geodesic exponential kernels have high probability of being positive definite over finite...... datasets, while still having significant predictive power. From this we formulate conjectures on the probability of a positive definite kernel matrix for a finite random sample, depending on the geometry of the data space and the spread of the sample....

MR imaging of central nervous system white matter tract degeneration (Wallerian degeneration)

International Nuclear Information System (INIS)

Kuhn, M.J.; Johnson, K.A.; Davis, K.R.

1987-01-01

Wallerian degeneration is readily demonstrated by MR imaging. Twenty-one patients with MR signal abnormalities in various central nervous system (CNS) white matter tracts were evaluated with regard to (1) nature of signal abnormality, (2) MR anatomy of the involved tract, and (3) primary pathology (e.g., infarct, tumor, hemorrhage). Most examples of wallerian degeneration result in a thin, continuous band of long T1, long T2 signal abnormality conforming to the known anatomic pathway of a CNS axonal tract. Old, large cortical infarcts have the greatest propensity to show subsequent white-matter tract degeneration. Corticospinal tract degeneration is the type most readily visualized, often seen extending completely from the cerebral cortex through the medulla

Fully-Automated High-Throughput NMR System for Screening of Haploid Kernels of Maize (Corn by Measurement of Oil Content.

Directory of Open Access Journals (Sweden)

Hongzhi Wang

Full Text Available One of the modern crop breeding techniques uses doubled haploid plants that contain an identical pair of chromosomes in order to accelerate the breeding process. Rapid haploid identification method is critical for large-scale selections of double haploids. The conventional methods based on the color of the endosperm and embryo seeds are slow, manual and prone to error. On the other hand, there exists a significant difference between diploid and haploid seeds generated by high oil inducer, which makes it possible to use oil content to identify the haploid. This paper describes a fully-automated high-throughput NMR screening system for maize haploid kernel identification. The system is comprised of a sampler unit to select a single kernel to feed for measurement of NMR and weight, and a kernel sorter to distribute the kernel according to the measurement result. Tests of the system show a consistent accuracy of 94% with an average screening time of 4 seconds per kernel. Field test result is described and the directions for future improvement are discussed.

Fully-Automated High-Throughput NMR System for Screening of Haploid Kernels of Maize (Corn) by Measurement of Oil Content

Science.gov (United States)

Xu, Xiaoping; Huang, Qingming; Chen, Shanshan; Yang, Peiqiang; Chen, Shaojiang; Song, Yiqiao

2016-01-01

One of the modern crop breeding techniques uses doubled haploid plants that contain an identical pair of chromosomes in order to accelerate the breeding process. Rapid haploid identification method is critical for large-scale selections of double haploids. The conventional methods based on the color of the endosperm and embryo seeds are slow, manual and prone to error. On the other hand, there exists a significant difference between diploid and haploid seeds generated by high oil inducer, which makes it possible to use oil content to identify the haploid. This paper describes a fully-automated high-throughput NMR screening system for maize haploid kernel identification. The system is comprised of a sampler unit to select a single kernel to feed for measurement of NMR and weight, and a kernel sorter to distribute the kernel according to the measurement result. Tests of the system show a consistent accuracy of 94% with an average screening time of 4 seconds per kernel. Field test result is described and the directions for future improvement are discussed. PMID:27454427

Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population.

Science.gov (United States)

Zhang, Zhanhui; Wu, Xiangyuan; Shi, Chaonan; Wang, Rongna; Li, Shengfei; Wang, Zhaohui; Liu, Zonghua; Xue, Yadong; Tang, Guiliang; Tang, Jihua

2016-02-01

Kernel development is an important dynamic trait that determines the final grain yield in maize. To dissect the genetic basis of maize kernel development process, a conditional quantitative trait locus (QTL) analysis was conducted using an immortalized F2 (IF2) population comprising 243 single crosses at two locations over 2 years. Volume (KV) and density (KD) of dried developing kernels, together with kernel weight (KW) at different developmental stages, were used to describe dynamic changes during kernel development. Phenotypic analysis revealed that final KW and KD were determined at DAP22 and KV at DAP29. Unconditional QTL mapping for KW, KV and KD uncovered 97 QTLs at different kernel development stages, of which qKW6b, qKW7a, qKW7b, qKW10b, qKW10c, qKV10a, qKV10b and qKV7 were identified under multiple kernel developmental stages and environments. Among the 26 QTLs detected by conditional QTL mapping, conqKW7a, conqKV7a, conqKV10a, conqKD2, conqKD7 and conqKD8a were conserved between the two mapping methodologies. Furthermore, most of these QTLs were consistent with QTLs and genes for kernel development/grain filling reported in previous studies. These QTLs probably contain major genes associated with the kernel development process, and can be used to improve grain yield and quality through marker-assisted selection.

A point kernel shielding code, PKN-HP, for high energy proton incident

Energy Technology Data Exchange (ETDEWEB)

Kotegawa, Hiroshi [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

1996-06-01

A point kernel integral technique code PKN-HP, and the related thick target neutron yield data have been developed to calculate neutron and secondary gamma-ray dose equivalents in ordinary concrete and iron shields for fully stopping length C, Cu and U-238 target neutrons produced by 100 MeV-10 GeV proton incident in a 3-dimensional geometry. The comparisons among calculation results of the present code and other calculation techniques, and measured values showed the usefulness of the code. (author)

Predictive Model Equations for Palm Kernel (Elaeis guneensis J ...

African Journals Online (AJOL)

Estimated error of ± 0.18 and ± 0.2 are envisaged while applying the models for predicting palm kernel and sesame oil colours respectively. Keywords: Palm kernel, Sesame, Palm kernel, Oil Colour, Process Parameters, Model. Journal of Applied Science, Engineering and Technology Vol. 6 (1) 2006 pp. 34-38 ...

Heat kernel analysis for Bessel operators on symmetric cones

DEFF Research Database (Denmark)

Möllers, Jan

2014-01-01

. The heat kernel is explicitly given in terms of a multivariable $I$-Bessel function on $Ω$. Its corresponding heat kernel transform defines a continuous linear operator between $L^p$-spaces. The unitary image of the $L^2$-space under the heat kernel transform is characterized as a weighted Bergmann space...

A multi-scale kernel bundle for LDDMM

DEFF Research Database (Denmark)

Sommer, Stefan Horst; Nielsen, Mads; Lauze, Francois Bernard

2011-01-01

The Large Deformation Diffeomorphic Metric Mapping framework constitutes a widely used and mathematically well-founded setup for registration in medical imaging. At its heart lies the notion of the regularization kernel, and the choice of kernel greatly affects the results of registrations...

Azadirachtin derivatives from seed kernels of Azadirachta excelsa.

Science.gov (United States)

Kanokmedhakul, Somdej; Kanokmedhakul, Kwanjai; Prajuabsuk, Thirada; Panichajakul, Sanha; Panyamee, Piyanan; Prabpai, Samran; Kongsaeree, Palangpon

2005-07-01

Three new azadirachtin derivatives, named azadirachtins O-Q (1-3), along with the known azadirachtin B (4), azadirachtin L (5), azadirachtin M (6) 11alpha-azadirachtin H (7), 11beta-azadirachtin H (8), and azadirachtol (9) were isolated from seed kernels of Azadirachta excelsa. Their structures were established by spectroscopic techniques, and the structure of 3 was confirmed by X-ray analysis. Compounds 1-7 and 9 exhibited toxicity to the diamondback moth (Plutella xylostella) with an LD50 of 0.75-1.92 microg/g body weight, in 92 h.

Analysis of total hydrogen content in palm oil and palm kernel oil ...

African Journals Online (AJOL)

A fast and non-destructive technique based on thermal neutron moderation has been used for determining the total hydrogen content in two types of red palm oil (dzomi and amidze) and palm kernel oil produced by traditio-nal methods in Ghana. An equipment consisting of an 241Am-Be neutron source and 3He neutron ...

Training Lp norm multiple kernel learning in the primal.

Science.gov (United States)

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

2013-10-01

Some multiple kernel learning (MKL) models are usually solved by utilizing the alternating optimization method where one alternately solves SVMs in the dual and updates kernel weights. Since the dual and primal optimization can achieve the same aim, it is valuable in exploring how to perform Lp norm MKL in the primal. In this paper, we propose an Lp norm multiple kernel learning algorithm in the primal where we resort to the alternating optimization method: one cycle for solving SVMs in the primal by using the preconditioned conjugate gradient method and other cycle for learning the kernel weights. It is interesting to note that the kernel weights in our method can obtain analytical solutions. Most importantly, the proposed method is well suited for the manifold regularization framework in the primal since solving LapSVMs in the primal is much more effective than solving LapSVMs in the dual. In addition, we also carry out theoretical analysis for multiple kernel learning in the primal in terms of the empirical Rademacher complexity. It is found that optimizing the empirical Rademacher complexity may obtain a type of kernel weights. The experiments on some datasets are carried out to demonstrate the feasibility and effectiveness of the proposed method. Copyright © 2013 Elsevier Ltd. All rights reserved.

Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.

Science.gov (United States)

Ma, Yuntao; Chen, Youjia; Zhu, Jinyu; Meng, Lei; Guo, Yan; Li, Baoguo; Hoogenboom, Gerrit

2018-02-13

Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source- and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

RTOS kernel in portable electrocardiograph

Science.gov (United States)

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.

RTOS kernel in portable electrocardiograph

International Nuclear Information System (INIS)

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

2011-01-01

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.

Denoising by semi-supervised kernel PCA preimaging

DEFF Research Database (Denmark)

Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai

2014-01-01

Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality reduction, prior to the so-called pre-imag...

Sentiment classification with interpolated information diffusion kernels

NARCIS (Netherlands)

Raaijmakers, S.

2007-01-01

Information diffusion kernels - similarity metrics in non-Euclidean information spaces - have been found to produce state of the art results for document classification. In this paper, we present a novel approach to global sentiment classification using these kernels. We carry out a large array of

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

Science.gov (United States)

de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino

2018-05-01

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.

Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray

Directory of Open Access Journals (Sweden)

Lan Shu

2008-07-01

Full Text Available Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples. However, a simple microarray experiment often leads to very high-dimensional data and a huge amount of information, the vast amount of data challenges researchers into extracting the important features and reducing the high dimensionality. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding(LLE is proposed, and fuzzy K-nearest neighbors algorithm which denoises datasets will be introduced as a replacement to the classical LLE’s KNN algorithm. In addition, kernel method based support vector machine (SVM will be used to classify genomic microarray data sets in this paper. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results confirm the superiority and high success rates of the presented method.

Linear and kernel methods for multivariate change detection

DEFF Research Database (Denmark)

Canty, Morton J.; Nielsen, Allan Aasbjerg

2012-01-01

), as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (nonlinear), may further enhance change signals relative to no-change background. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric...... normalization, and kernel PCA/MAF/MNF transformations are presented that function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. The train/test approach to kernel PCA is evaluated against a Hebbian learning procedure. Matlab code is also available...... that allows fast data exploration and experimentation with smaller datasets. New, multiresolution versions of IR-MAD that accelerate convergence and that further reduce no-change background noise are introduced. Computationally expensive matrix diagonalization and kernel image projections are programmed...

Panel data specifications in nonparametric kernel regression

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...

Scuba: scalable kernel-based gene prioritization.

Science.gov (United States)

Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio

2018-01-25

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

Scatter kernel estimation with an edge-spread function method for cone-beam computed tomography imaging

International Nuclear Information System (INIS)

Li Heng; Mohan, Radhe; Zhu, X Ronald

2008-01-01

The clinical applications of kilovoltage x-ray cone-beam computed tomography (CBCT) have been compromised by the limited quality of CBCT images, which typically is due to a substantial scatter component in the projection data. In this paper, we describe an experimental method of deriving the scatter kernel of a CBCT imaging system. The estimated scatter kernel can be used to remove the scatter component from the CBCT projection images, thus improving the quality of the reconstructed image. The scattered radiation was approximated as depth-dependent, pencil-beam kernels, which were derived using an edge-spread function (ESF) method. The ESF geometry was achieved with a half-beam block created by a 3 mm thick lead sheet placed on a stack of slab solid-water phantoms. Measurements for ten water-equivalent thicknesses (WET) ranging from 0 cm to 41 cm were taken with (half-blocked) and without (unblocked) the lead sheet, and corresponding pencil-beam scatter kernels or point-spread functions (PSFs) were then derived without assuming any empirical trial function. The derived scatter kernels were verified with phantom studies. Scatter correction was then incorporated into the reconstruction process to improve image quality. For a 32 cm diameter cylinder phantom, the flatness of the reconstructed image was improved from 22% to 5%. When the method was applied to CBCT images for patients undergoing image-guided therapy of the pelvis and lung, the variation in selected regions of interest (ROIs) was reduced from >300 HU to <100 HU. We conclude that the scatter reduction technique utilizing the scatter kernel effectively suppresses the artifact caused by scatter in CBCT.

MULTITASKER, Multitasking Kernel for C and FORTRAN Under UNIX

International Nuclear Information System (INIS)

Brooks, E.D. III

1988-01-01

1 - Description of program or function: MULTITASKER implements a multitasking kernel for the C and FORTRAN programming languages that runs under UNIX. The kernel provides a multitasking environment which serves two purposes. The first is to provide an efficient portable environment for the development, debugging, and execution of production multiprocessor programs. The second is to provide a means of evaluating the performance of a multitasking program on model multiprocessor hardware. The performance evaluation features require no changes in the application program source and are implemented as a set of compile- and run-time options in the kernel. 2 - Method of solution: The FORTRAN interface to the kernel is identical in function to the CRI multitasking package provided for the Cray XMP. This provides a migration path to high speed (but small N) multiprocessors once the application has been coded and debugged. With use of the UNIX m4 macro preprocessor, source compatibility can be achieved between the UNIX code development system and the target Cray multiprocessor. The kernel also provides a means of evaluating a program's performance on model multiprocessors. Execution traces may be obtained which allow the user to determine kernel overhead, memory conflicts between various tasks, and the average concurrency being exploited. The kernel may also be made to switch tasks every cpu instruction with a random execution ordering. This allows the user to look for unprotected critical regions in the program. These features, implemented as a set of compile- and run-time options, cause extra execution overhead which is not present in the standard production version of the kernel

Multiple kernel boosting framework based on information measure for classification

International Nuclear Information System (INIS)

Qi, Chengming; Wang, Yuping; Tian, Wenjie; Wang, Qun

2016-01-01

The performance of kernel-based method, such as support vector machine (SVM), is greatly affected by the choice of kernel function. Multiple kernel learning (MKL) is a promising family of machine learning algorithms and has attracted many attentions in recent years. MKL combines multiple sub-kernels to seek better results compared to single kernel learning. In order to improve the efficiency of SVM and MKL, in this paper, the Kullback–Leibler kernel function is derived to develop SVM. The proposed method employs an improved ensemble learning framework, named KLMKB, which applies Adaboost to learning multiple kernel-based classifier. In the experiment for hyperspectral remote sensing image classification, we employ feature selected through Optional Index Factor (OIF) to classify the satellite image. We extensively examine the performance of our approach in comparison to some relevant and state-of-the-art algorithms on a number of benchmark classification data sets and hyperspectral remote sensing image data set. Experimental results show that our method has a stable behavior and a noticeable accuracy for different data set.

Formulation and Optimization of Lansoprazole Pellets Using Factorial Design Prepared by Extrusion-Spheronization Technique Using Carboxymethyl Tamarind Kernel Powder.

Science.gov (United States)

Muley, Sagar Sopanrao; Nandgude, Tanaji; Poddar, Sushilkumar

2017-01-01

In the present study, Lansoprazole pellets were prepared employing a novel excipient Carboxymethyl tamarind kernel powder (CMTKP) using extrusion-spheronization technique. Various research studies including patents have been carried out on this polymer. Pellet formulation was optimized for formulation parameters (concentration of microcrystalline cellulose, CMTKP, croscarmellose sodium and isopropyl alcohol). Process parameters (speed and duration of spheronization) were optimized using factorial design. The pellets were evaluated for yield, bulk and tapped density, particle size, hardness, drug content, disintegration time and drug release. The optimized batch showed 93.53% yield, 0.307 kg/cm2 hardness, 2.15 mm average particle size, 292 sec disintegration time and 90.46% drug content. Drug release of the optimized batch (2F7) and marketed formulation (LANZOL cap) was found to be 82.33% and 80.07%, respectively. An accelerated study indicated that optimized formulation was stable. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

What Is Age-Related Macular Degeneration?

Science.gov (United States)

... Eye Health / Eye Health A-Z Age-Related Macular Degeneration Sections What Is Macular Degeneration? How is AMD ... What Does Macular Degeneration Look Like? What Is Macular Degeneration? Leer en Español: ¿Qué es la degeneración macular ...

Optimizing the performance of streaming numerical kernels on the IBM Blue Gene/P PowerPC 450 processor

KAUST Repository

Malas, Tareq Majed Yasin

2012-05-21

Several emerging petascale architectures use energy-efficient processors with vectorized computational units and in-order thread processing. On these architectures the sustained performance of streaming numerical kernels, ubiquitous in the solution of partial differential equations, represents a challenge despite the regularity of memory access. Sophisticated optimization techniques are required to fully utilize the CPU. We propose a new method for constructing streaming numerical kernels using a high-level assembly synthesis and optimization framework. We describe an implementation of this method in Python targeting the IBM® Blue Gene®/P supercomputer\\'s PowerPC® 450 core. This paper details the high-level design, construction, simulation, verification, and analysis of these kernels utilizing a subset of the CPU\\'s instruction set. We demonstrate the effectiveness of our approach by implementing several three-dimensional stencil kernels over a variety of cached memory scenarios and analyzing the mechanically scheduled variants, including a 27-point stencil achieving a 1.7× speedup over the best previously published results. © The Author(s) 2012.

Diagnosis of non-exudative (DRY) age related macular degeneration by non-invasive photon-correlation spectroscopy.

Science.gov (United States)

Fankhauser, Franz Ii; Ott, Maria; Munteanu, Mihnea

2016-01-01

Photon-correlation spectroscopy (PCS) (quasi-elastic light scattering spectroscopy, dynamic light scattering spectroscopy) allows the non-invasively reveal of local dynamics and local heterogeneities of macromolecular systems. The capability of this technique to diagnose the retinal pathologies by in-vivo investigations of spatial anomalies of retinas displaying non-exudative senile macular degeneration was evaluated. Further, the potential use of the technique for the diagnosis of the macular degeneration was analyzed and displayed by the Receiver Operating Curve (ROC). The maculae and the peripheral retina of 73 normal eyes and of 26 eyes afflicted by an early stage of non-exudative senile macular degeneration were characterized by time-correlation functions and analyzed in terms of characteristic decay times and apparent size distributions. The characteristics of the obtained time-correlation functions of the eyes afflicted with nonexudative macular degeneration and of normal eyes differed significantly, which could be referred to a significant change of the nano- and microstructure of the investigated pathologic maculas. Photon-correlation spectroscopy is able to assess the macromolecular and microstructural aberrations in the macula afflicted by non-exudative, senile macular degeneration. It has been demonstrated that macromolecules of this disease show a characteristic abnormal behavior in the macula.

Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt Seed Viability with Multivariate Data Analysis

Directory of Open Access Journals (Sweden)

Guangjun Qiu

2018-03-01

Full Text Available The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.

Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis.

Science.gov (United States)

Qiu, Guangjun; Lü, Enli; Lu, Huazhong; Xu, Sai; Zeng, Fanguo; Shui, Qin

2018-03-28

The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000-2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.

Splitting deformations of degenerations of complex curves towards the classification of atoms of degenerations

CERN Document Server

2006-01-01

The author develops a deformation theory for degenerations of complex curves; specifically, he treats deformations which induce splittings of the singular fiber of a degeneration. He constructs a deformation of the degeneration in such a way that a subdivisor is "barked" (peeled) off from the singular fiber. These "barking deformations" are related to deformations of surface singularities (in particular, cyclic quotient singularities) as well as the mapping class groups of Riemann surfaces (complex curves) via monodromies. Important applications, such as the classification of atomic degenerations, are also explained.

The integral first collision kernel method for gamma-ray skyshine analysis[Skyshine; Gamma-ray; First collision kernel; Monte Carlo calculation

Energy Technology Data Exchange (ETDEWEB)

Sheu, R.-D.; Chui, C.-S.; Jiang, S.-H. E-mail: shjiang@mx.nthu.edu.tw

2003-12-01

A simplified method, based on the integral of the first collision kernel, is presented for performing gamma-ray skyshine calculations for the collimated sources. The first collision kernels were calculated in air for a reference air density by use of the EGS4 Monte Carlo code. These kernels can be applied to other air densities by applying density corrections. The integral first collision kernel (IFCK) method has been used to calculate two of the ANSI/ANS skyshine benchmark problems and the results were compared with a number of other commonly used codes. Our results were generally in good agreement with others but only spend a small fraction of the computation time required by the Monte Carlo calculations. The scheme of the IFCK method for dealing with lots of source collimation geometry is also presented in this study.

Degenerate pressure driven modified nucleus-acoustic waves in degenerate plasmas

Science.gov (United States)

Mamun, A. A.

2018-02-01

The existence of degenerate pressure driven modified nucleus-acoustic (DPDMNA) waves propagating in a cold degenerate quantum plasma (DQP) system [containing cold inertialess degenerate electron species (DES), cold inertial non-degenerate light nucleus species (LNS), and stationary heavy nucleus species (HNS)] is predicted for the first time. The DPDMNA waves (in which the mass density of the cold LNS provides the inertia and the cold inertialess DES gives rise to the restoring force) are new since they completely disappear if the degenerate pressure of the cold DES is neglected. It is found that the phase speed (Vp) of the DPDMNA waves decreases with the rise of the charge number density of the stationary HNS for both non-relativistic and ultra-relativistic DES, and that the ultra-relativistic DES does not have any effect on Vp when β = 1, where β = Λc/Λe with Λ e = ne 0 - 1 / 3 being the average inter-electron distance in the DQP system and Λc being the constant (˜10-10 cm) for the DES. However, the ultra-relativistic DES does have quite a significant effect on Vp for β ≫ 1 and β ≪ 1, and the ultra-relativistic effect significantly enhances (reduces) Vp for β ≫ 1 (β ≪ 1). The DPDMNA waves and their dispersion properties are expected to be useful in understanding the basic features of the electrostatic perturbation mode in space and laboratory DQP systems.

Calculation of degenerated Eigenmodes with modified power method

Energy Technology Data Exchange (ETDEWEB)

Zhang, Peng; Lee, Hyun Suk; Lee, Deok Jung [School of Mechanical and Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan (Korea, Republic of)

2017-02-15

The modified power method has been studied by many researchers to calculate the higher Eigenmodes and accelerate the convergence of the fundamental mode. Its application to multidimensional problems may be unstable due to degenerated or near-degenerated Eigenmodes. Complex Eigenmode solutions are occasionally encountered in such cases, and the shapes of the corresponding eigenvectors may change during the simulation. These issues must be addressed for the successful implementation of the modified power method. Complex components are examined and an approximation method to eliminate the usage of the complex numbers is provided. A technique to fix the eigenvector shapes is also provided. The performance of the methods for dealing with those aforementioned problems is demonstrated with two dimensional one group and three dimensional one group homogeneous diffusion problems.

A kernel adaptive algorithm for quaternion-valued inputs.

Science.gov (United States)

Paul, Thomas K; Ogunfunmi, Tokunbo

2015-10-01

The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations.

Improving the Bandwidth Selection in Kernel Equating

Science.gov (United States)

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…

Point kernels and superposition methods for scatter dose calculations in brachytherapy

International Nuclear Information System (INIS)

Carlsson, A.K.

2000-01-01

Point kernels have been generated and applied for calculation of scatter dose distributions around monoenergetic point sources for photon energies ranging from 28 to 662 keV. Three different approaches for dose calculations have been compared: a single-kernel superposition method, a single-kernel superposition method where the point kernels are approximated as isotropic and a novel 'successive-scattering' superposition method for improved modelling of the dose from multiply scattered photons. An extended version of the EGS4 Monte Carlo code was used for generating the kernels and for benchmarking the absorbed dose distributions calculated with the superposition methods. It is shown that dose calculation by superposition at and below 100 keV can be simplified by using isotropic point kernels. Compared to the assumption of full in-scattering made by algorithms currently in clinical use, the single-kernel superposition method improves dose calculations in a half-phantom consisting of air and water. Further improvements are obtained using the successive-scattering superposition method, which reduces the overestimates of dose close to the phantom surface usually associated with kernel superposition methods at brachytherapy photon energies. It is also shown that scatter dose point kernels can be parametrized to biexponential functions, making them suitable for use with an effective implementation of the collapsed cone superposition algorithm. (author)

Online learning control using adaptive critic designs with sparse kernel machines.

Science.gov (United States)

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

Wheat kernel dimensions: how do they contribute to kernel weight at ...

Indian Academy of Sciences (India)

2011-12-02

Dec 2, 2011 ... yield components, is greatly influenced by kernel dimensions. (KD), such as ..... six linkage gaps, and it covered 3010.70 cM of the whole genome with an ...... Ersoz E. et al. 2009 The Genetic architecture of maize flowering.

A multi-label learning based kernel automatic recommendation method for support vector machine.

Science.gov (United States)

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

Using the Intel Math Kernel Library on Peregrine | High-Performance

Science.gov (United States)

Computing | NREL the Intel Math Kernel Library on Peregrine Using the Intel Math Kernel Library on Peregrine Learn how to use the Intel Math Kernel Library (MKL) with Peregrine system software. MKL architectures. Core math functions in MKL include BLAS, LAPACK, ScaLAPACK, sparse solvers, fast Fourier

Unsupervised multiple kernel learning for heterogeneous data integration.

Science.gov (United States)

Mariette, Jérôme; Villa-Vialaneix, Nathalie

2018-03-15

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

Kernel bundle EPDiff

DEFF Research Database (Denmark)

Sommer, Stefan Horst; Lauze, Francois Bernard; Nielsen, Mads

2011-01-01

In the LDDMM framework, optimal warps for image registration are found as end-points of critical paths for an energy functional, and the EPDiff equations describe the evolution along such paths. The Large Deformation Diffeomorphic Kernel Bundle Mapping (LDDKBM) extension of LDDMM allows scale space...

Proteome analysis of the almond kernel (Prunus dulcis).

Science.gov (United States)

Li, Shugang; Geng, Fang; Wang, Ping; Lu, Jiankang; Ma, Meihu

2016-08-01

Almond (Prunus dulcis) is a popular tree nut worldwide and offers many benefits to human health. However, the importance of almond kernel proteins in the nutrition and function in human health requires further evaluation. The present study presents a systematic evaluation of the proteins in the almond kernel using proteomic analysis. The nutrient and amino acid content in almond kernels from Xinjiang is similar to that of American varieties; however, Xinjiang varieties have a higher protein content. Two-dimensional electrophoresis analysis demonstrated a wide distribution of molecular weights and isoelectric points of almond kernel proteins. A total of 434 proteins were identified by LC-MS/MS, and most were proteins that were experimentally confirmed for the first time. Gene ontology (GO) analysis of the 434 proteins indicated that proteins involved in primary biological processes including metabolic processes (67.5%), cellular processes (54.1%), and single-organism processes (43.4%), the main molecular function of almond kernel proteins are in catalytic activity (48.0%), binding (45.4%) and structural molecule activity (11.9%), and proteins are primarily distributed in cell (59.9%), organelle (44.9%), and membrane (22.8%). Almond kernel is a source of a wide variety of proteins. This study provides important information contributing to the screening and identification of almond proteins, the understanding of almond protein function, and the development of almond protein products. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

Science.gov (United States)

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

2014-01-01

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

Control Transfer in Operating System Kernels

Science.gov (United States)

1994-05-13

microkernel system that runs less code in the kernel address space. To realize the performance benefit of allocating stacks in unmapped kseg0 memory, the...review how I modified the Mach 3.0 kernel to use continuations. Because of Mach’s message-passing microkernel structure, interprocess communication was...critical control transfer paths, deeply- nested call chains are undesirable in any case because of the function call overhead. 4.1.3 Microkernel Operating

Self-reported optometric practise patterns in age-related macular degeneration.

Science.gov (United States)

Ly, Angelica; Nivison-Smith, Lisa; Zangerl, Barbara; Assaad, Nagi; Kalloniatis, Michael

2017-11-01

The use of advanced imaging in clinical practice is emerging and the use of this technology by optometrists in assessing patients with age-related macular degeneration is of interest. Therefore, this study explored contemporary, self-reported patterns of practice regarding age-related macular degeneration diagnosis and management using a cross-sectional survey of optometrists in Australia and New Zealand. Practising optometrists were surveyed on four key areas, namely, demographics, clinical skills and experience, assessment and management of age-related macular degeneration. Questions pertaining to self-rated competency, knowledge and attitudes used a five-point Likert scale. Completed responses were received from 127 and 87 practising optometrists in Australia and New Zealand, respectively. Advanced imaging showed greater variation in service delivery than traditional techniques (such as slitlamp funduscopy) and trended toward optical coherence tomography, which was routinely performed in age-related macular degeneration by 49 per cent of respondents. Optical coherence tomography was also associated with higher self-rated competency, knowledge and perceived relevance to practice than other modalities. Most respondents (93 per cent) indicated that they regularly applied patient symptoms, case history, visual function results and signs from traditional testing, when queried about their management of patients with age-related macular degeneration. Over half (63 per cent) also considered advanced imaging, while 31 per cent additionally considered all of these as well as the disease stage and clinical guidelines. Contrary to the evidence base, 68 and 34 per cent rated nutritional supplements as highly relevant or relevant in early age-related macular degeneration and normal aging changes, respectively. These results highlight the emergence of multimodal and advanced imaging (especially optical coherence tomography) in the assessment of age-related macular degeneration

Measurement of Weight of Kernels in a Simulated Cylindrical Fuel Compact for HTGR

International Nuclear Information System (INIS)

Kim, Woong Ki; Lee, Young Woo; Kim, Young Min; Kim, Yeon Ku; Eom, Sung Ho; Jeong, Kyung Chai; Cho, Moon Sung; Cho, Hyo Jin; Kim, Joo Hee

2011-01-01

The TRISO-coated fuel particle for the high temperature gas-cooled reactor (HTGR) is composed of a nuclear fuel kernel and outer coating layers. The coated particles are mixed with graphite matrix to make HTGR fuel element. The weight of fuel kernels in an element is generally measured by the chemical analysis or a gamma-ray spectrometer. Although it is accurate to measure the weight of kernels by the chemical analysis, the samples used in the analysis cannot be put again in the fabrication process. Furthermore, radioactive wastes are generated during the inspection procedure. The gamma-ray spectrometer requires an elaborate reference sample to reduce measurement errors induced from the different geometric shape of test sample from that of reference sample. X-ray computed tomography (CT) is an alternative to measure the weight of kernels in a compact nondestructively. In this study, X-ray CT is applied to measure the weight of kernels in a cylindrical compact containing simulated TRISO-coated particles with ZrO 2 kernels. The volume of kernels as well as the number of kernels in the simulated compact is measured from the 3-D density information. The weight of kernels was calculated from the volume of kernels or the number of kernels. Also, the weight of kernels was measured by extracting the kernels from a compact to review the result of the X-ray CT application

3-D waveform tomography sensitivity kernels for anisotropic media

KAUST Repository

Djebbi, Ramzi

2014-01-01

The complications in anisotropic multi-parameter inversion lie in the trade-off between the different anisotropy parameters. We compute the tomographic waveform sensitivity kernels for a VTI acoustic medium perturbation as a tool to investigate this ambiguity between the different parameters. We use dynamic ray tracing to efficiently handle the expensive computational cost for 3-D anisotropic models. Ray tracing provides also the ray direction information necessary for conditioning the sensitivity kernels to handle anisotropy. The NMO velocity and η parameter kernels showed a maximum sensitivity for diving waves which results in a relevant choice of those parameters in wave equation tomography. The δ parameter kernel showed zero sensitivity; therefore it can serve as a secondary parameter to fit the amplitude in the acoustic anisotropic inversion. Considering the limited penetration depth of diving waves, migration velocity analysis based kernels are introduced to fix the depth ambiguity with reflections and compute sensitivity maps in the deeper parts of the model.

MRI T2* mapping correlates with biochemistry and histology in intervertebral disc degeneration in a large animal model

NARCIS (Netherlands)

Detiger, Suzanne E. L.; Holewijn, Roderick M.; Hoogendoorn, Roel J. W.; van Royen, Barend J.; Helder, Marco N.; Berger, Ferco H.; Kuijer, Joost P. A.; Smit, Theo H.

2015-01-01

To evaluate intervertebral disc (IVD) degeneration and treatments, an objective diagnostic tool is needed. Recently, T2* relaxation time mapping was proposed as a technique to assess early IVD degeneration, yet the correlation with biochemical content and histological features has not been

MRI T2* mapping correlates with biochemistry and histology in intervertebral disc degeneration in a large animal model

NARCIS (Netherlands)

Detiger, S.E.L.; Holewijn, R.M.; Hoogendoorn, R.J.W.; van Royen, B.J.; Helder, M.N.; Berger, F.H.; Kuijer, J.P.A.; Smit, T.H.

2015-01-01

Purpose: To evaluate intervertebral disc (IVD) degeneration and treatments, an objective diagnostic tool is needed. Recently, T2* relaxation time mapping was proposed as a technique to assess early IVD degeneration, yet the correlation with biochemical content and histological features has not been

The dipole form of the gluon part of the BFKL kernel

International Nuclear Information System (INIS)

Fadin, V.S.; Fiore, R.; Grabovsky, A.V.; Papa, A.

2007-01-01

The dipole form of the gluon part of the color singlet BFKL kernel in the next-to-leading order (NLO) is obtained in the coordinate representation by direct transfer from the momentum representation, where the kernel was calculated before. With this paper the transformation of the NLO BFKL kernel to the dipole form, started a few months ago with the quark part of the kernel, is completed

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

Science.gov (United States)

Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

An hp-adaptive strategy for the solution of the exact kernel curved wire Pocklington equation

NARCIS (Netherlands)

D.J.P. Lahaye (Domenico); P.W. Hemker (Piet)

2007-01-01

textabstractIn this paper we introduce an adaptive method for the numerical solution of the Pocklington integro-differential equation with exact kernel for the current induced in a smoothly curved thin wire antenna. The hp-adaptive technique is based on the representation of the discrete solution,

Kinetic study of Chromium VI adsorption onto palm kernel shell activated carbon

Science.gov (United States)

Mohammad, Masita; Sadeghi Louyeh, Shiva; Yaakob, Zahira

2018-04-01

Heavy metal contamination of industrial effluent is one of the significant environmental problems due to their toxicity and its accumulation throughout the food chain. Adsorption is one of the promising methods for removal of heavy metals from aqua solution because of its simple technique, efficient, reliable and low-cost due to the utilization of residue from the agricultural industry. In this study, activated carbon from palm kernel shells has been produced through chemical activation process using zinc chloride as an activating agent and carbonized at 800 °C. Palm kernel shell activated carbon, PAC was assessed for its efficiency to remove Chromium (VI) ions from aqueous solutions through a batch adsorption process. The kinetic mechanisms have been analysed using Lagergren first-order kinetics model, second-order kinetics model and intra-particle diffusion model. The characterizations such as BET surface area, surface morphology, SEM-EDX have been done. The result shows that the activation process by ZnCl2 was successfully improved the porosity and modified the functional group of palm kernel shell. The result shows that the maximum adsorption capacity of Cr is 11.40mg/g at 30ppm initial metal ion concentration and 0.1g/50mL of adsorbent concentration. The adsorption process followed the pseudo second orders kinetic model.

Low-pressure degenerate four-wave mixing spectroscopy with flam atomization

International Nuclear Information System (INIS)

Nolan, T.G.; Koutny, L.B.; Blazewicz, P.R.; Whitten, W.B.; Ramsey, J.M.

1988-01-01

A combination of degenerate four-wave mixing spectroscopy and a low-pressure sampling technique has been studied for isotopic analysis in an air-acetylene flame. Hyperfine spectra of D lines of sodium and several mixtures of lithium isotopes obtained in this way are presented

A new discrete dipole kernel for quantitative susceptibility mapping.

Science.gov (United States)

Milovic, Carlos; Acosta-Cabronero, Julio; Pinto, José Miguel; Mattern, Hendrik; Andia, Marcelo; Uribe, Sergio; Tejos, Cristian

2018-09-01

Most approaches for quantitative susceptibility mapping (QSM) are based on a forward model approximation that employs a continuous Fourier transform operator to solve a differential equation system. Such formulation, however, is prone to high-frequency aliasing. The aim of this study was to reduce such errors using an alternative dipole kernel formulation based on the discrete Fourier transform and discrete operators. The impact of such an approach on forward model calculation and susceptibility inversion was evaluated in contrast to the continuous formulation both with synthetic phantoms and in vivo MRI data. The discrete kernel demonstrated systematically better fits to analytic field solutions, and showed less over-oscillations and aliasing artifacts while preserving low- and medium-frequency responses relative to those obtained with the continuous kernel. In the context of QSM estimation, the use of the proposed discrete kernel resulted in error reduction and increased sharpness. This proof-of-concept study demonstrated that discretizing the dipole kernel is advantageous for QSM. The impact on small or narrow structures such as the venous vasculature might by particularly relevant to high-resolution QSM applications with ultra-high field MRI - a topic for future investigations. The proposed dipole kernel has a straightforward implementation to existing QSM routines. Copyright © 2018 Elsevier Inc. All rights reserved.

Comparison of correlation analysis techniques for irregularly sampled time series

Directory of Open Access Journals (Sweden)

K. Rehfeld

2011-06-01

Full Text Available Geoscientific measurements often provide time series with irregular time sampling, requiring either data reconstruction (interpolation or sophisticated methods to handle irregular sampling. We compare the linear interpolation technique and different approaches for analyzing the correlation functions and persistence of irregularly sampled time series, as Lomb-Scargle Fourier transformation and kernel-based methods. In a thorough benchmark test we investigate the performance of these techniques.

All methods have comparable root mean square errors (RMSEs for low skewness of the inter-observation time distribution. For high skewness, very irregular data, interpolation bias and RMSE increase strongly. We find a 40 % lower RMSE for the lag-1 autocorrelation function (ACF for the Gaussian kernel method vs. the linear interpolation scheme,in the analysis of highly irregular time series. For the cross correlation function (CCF the RMSE is then lower by 60 %. The application of the Lomb-Scargle technique gave results comparable to the kernel methods for the univariate, but poorer results in the bivariate case. Especially the high-frequency components of the signal, where classical methods show a strong bias in ACF and CCF magnitude, are preserved when using the kernel methods.

We illustrate the performances of interpolation vs. Gaussian kernel method by applying both to paleo-data from four locations, reflecting late Holocene Asian monsoon variability as derived from speleothem δ¹⁸O measurements. Cross correlation results are similar for both methods, which we attribute to the long time scales of the common variability. The persistence time (memory is strongly overestimated when using the standard, interpolation-based, approach. Hence, the Gaussian kernel is a reliable and more robust estimator with significant advantages compared to other techniques and suitable for large scale application to paleo-data.

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations

Directory of Open Access Journals (Sweden)

Zhengbin Liu

2016-08-01

Full Text Available Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis. In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits.

SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

Energy Technology Data Exchange (ETDEWEB)

Khazaee, M [shahid beheshti university, Tehran, Tehran (Iran, Islamic Republic of); Asl, A Kamali [Shahid Beheshti University, Tehran, Iran., Tehran, Tehran (Iran, Islamic Republic of); Geramifar, P [Shariati Hospital, Tehran, Iran., Tehran, Tehran (Iran, Islamic Republic of)

2015-06-15

Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine.

SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

International Nuclear Information System (INIS)

Khazaee, M; Asl, A Kamali; Geramifar, P

2015-01-01

Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine

Accelerating Scientific Applications using High Performance Dense and Sparse Linear Algebra Kernels on GPUs

KAUST Repository

Abdelfattah, Ahmad

2015-01-15

High performance computing (HPC) platforms are evolving to more heterogeneous configurations to support the workloads of various applications. The current hardware landscape is composed of traditional multicore CPUs equipped with hardware accelerators that can handle high levels of parallelism. Graphical Processing Units (GPUs) are popular high performance hardware accelerators in modern supercomputers. GPU programming has a different model than that for CPUs, which means that many numerical kernels have to be redesigned and optimized specifically for this architecture. GPUs usually outperform multicore CPUs in some compute intensive and massively parallel applications that have regular processing patterns. However, most scientific applications rely on crucial memory-bound kernels and may witness bottlenecks due to the overhead of the memory bus latency. They can still take advantage of the GPU compute power capabilities, provided that an efficient architecture-aware design is achieved. This dissertation presents a uniform design strategy for optimizing critical memory-bound kernels on GPUs. Based on hierarchical register blocking, double buffering and latency hiding techniques, this strategy leverages the performance of a wide range of standard numerical kernels found in dense and sparse linear algebra libraries. The work presented here focuses on matrix-vector multiplication kernels (MVM) as repre- sentative and most important memory-bound operations in this context. Each kernel inherits the benefits of the proposed strategies. By exposing a proper set of tuning parameters, the strategy is flexible enough to suit different types of matrices, ranging from large dense matrices, to sparse matrices with dense block structures, while high performance is maintained. Furthermore, the tuning parameters are used to maintain the relative performance across different GPU architectures. Multi-GPU acceleration is proposed to scale the performance on several devices. The

Scientific opinion on the acute health risks related to the presence of cyanogenic glycosides in raw apricot kernels and products derived from raw apricot kernels

DEFF Research Database (Denmark)

Petersen, Annette

of kernels promoted (10 and 60 kernels/day for the general population and cancer patients, respectively), exposures exceeded the ARfD 17–413 and 3–71 times in toddlers and adults, respectively. The estimated maximum quantity of apricot kernels (or raw apricot material) that can be consumed without exceeding...

Solutions to quasilinear equations of $N$-biharmonic type with degenerate coercivity

Directory of Open Access Journals (Sweden)

Sami Aouaoui

2014-10-01

Full Text Available In this article we show the existence of multiple solutions for quasilinear equations in divergence form with degenerate coercivity. Our strategy is to combine a variational method and an iterative technique to obtain the solutions.

Kernel Function Tuning for Single-Layer Neural Networks

Czech Academy of Sciences Publication Activity Database

Vidnerová, Petra; Neruda, Roman

-, accepted 28.11. 2017 (2018) ISSN 2278-0149 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : single-layer neural networks * kernel methods * kernel function * optimisation Subject RIV: IN - Informatics, Computer Science http://www.ijmerr.com/

Broken rice kernels and the kinetics of rice hydration and texture during cooking.

Science.gov (United States)

Saleh, Mohammed; Meullenet, Jean-Francois

2013-05-01

During rice milling and processing, broken kernels are inevitably present, although to date it has been unclear as to how the presence of broken kernels affects rice hydration and cooked rice texture. Therefore, this work intended to study the effect of broken kernels in a rice sample on rice hydration and texture during cooking. Two medium-grain and two long-grain rice cultivars were harvested, dried and milled, and the broken kernels were separated from unbroken kernels. Broken rice kernels were subsequently combined with unbroken rice kernels forming treatments of 0, 40, 150, 350 or 1000 g kg(-1) broken kernels ratio. Rice samples were then cooked and the moisture content of the cooked rice, the moisture uptake rate, and rice hardness and stickiness were measured. As the amount of broken rice kernels increased, rice sample texture became increasingly softer (P hardness was negatively correlated to the percentage of broken kernels in rice samples. Differences in the proportions of broken rice in a milled rice sample play a major role in determining the texture properties of cooked rice. Variations in the moisture migration kinetics between broken and unbroken kernels caused faster hydration of the cores of broken rice kernels, with greater starch leach-out during cooking affecting the texture of the cooked rice. The texture of cooked rice can be controlled, to some extent, by varying the proportion of broken kernels in milled rice. © 2012 Society of Chemical Industry.

Multivariate realised kernels

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...

Process for producing metal oxide kernels and kernels so obtained

International Nuclear Information System (INIS)

Lelievre, Bernard; Feugier, Andre.

1974-01-01

The process desbribed is for producing fissile or fertile metal oxide kernels used in the fabrication of fuels for high temperature nuclear reactors. This process consists in adding to an aqueous solution of at least one metallic salt, particularly actinide nitrates, at least one chemical compound capable of releasing ammonia, in dispersing drop by drop the solution thus obtained into a hot organic phase to gel the drops and transform them into solid particles. These particles are then washed, dried and treated to turn them into oxide kernels. The organic phase used for the gel reaction is formed of a mixture composed of two organic liquids, one acting as solvent and the other being a product capable of extracting the anions from the metallic salt of the drop at the time of gelling. Preferably an amine is used as product capable of extracting the anions. Additionally, an alcohol that causes a part dehydration of the drops can be employed as solvent, thus helping to increase the resistance of the particles [fr

Ideal Gas Resonance Scattering Kernel Routine for the NJOY Code

International Nuclear Information System (INIS)

Rothenstein, W.

1999-01-01

In a recent publication an expression for the temperature-dependent double-differential ideal gas scattering kernel is derived for the case of scattering cross sections that are energy dependent. Some tabulations and graphical representations of the characteristics of these kernels are presented in Ref. 2. They demonstrate the increased probability that neutron scattering by a heavy nuclide near one of its pronounced resonances will bring the neutron energy nearer to the resonance peak. This enhances upscattering, when a neutron with energy just below that of the resonance peak collides with such a nuclide. A routine for using the new kernel has now been introduced into the NJOY code. Here, its principal features are described, followed by comparisons between scattering data obtained by the new kernel, and the standard ideal gas kernel, when such comparisons are meaningful (i.e., for constant values of the scattering cross section a 0 K). The new ideal gas kernel for variable σ s 0 (E) at 0 K leads to the correct Doppler-broadened σ s T (E) at temperature T

Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

Directory of Open Access Journals (Sweden)

Xin Zhao

2017-01-01

Full Text Available Fungi infection in maize kernels is a major concern worldwide due to its toxic metabolites such as mycotoxins, thus it is necessary to develop appropriate techniques for early detection of fungi infection in maize kernels. Thirty-six sterilised maize kernels were inoculated each day with Aspergillus parasiticus from one to seven days, and then seven groups (D1, D2, D3, D4, D5, D6, D7 were determined based on the incubated time. Another 36 sterilised kernels without inoculation with fungi were taken as control (DC. Hyperspectral images of all kernels were acquired within spectral range of 921–2529 nm. Background, labels and bad pixels were removed using principal component analysis (PCA and masking. Separability computation for discrimination of fungal contamination levels indicated that the model based on the data of the germ region of individual kernels performed more effectively than on that of the whole kernels. Moreover, samples with a two-day interval were separable. Thus, four groups, DC, D1–2 (the group consisted of D1 and D2, D3–4 (D3 and D4, and D5–7 (D5, D6, and D7, were defined for subsequent classification. Two separate sample sets were prepared to verify the influence on a classification model caused by germ orientation, that is, germ up and the mixture of germ up and down with 1:1. Two smooth preprocessing methods (Savitzky-Golay smoothing, moving average smoothing and three scatter-correction methods (normalization, standard normal variate, and multiple scatter correction were compared, according to the performance of the classification model built by support vector machines (SVM. The best model for kernels with germ up showed the promising results with accuracies of 97.92% and 91.67% for calibration and validation data set, respectively, while accuracies of the best model for samples of the mixed kernels were 95.83% and 84.38%. Moreover, five wavelengths (1145, 1408, 1935, 2103, and 2383 nm were selected as the key

Geodesic exponential kernels: When Curvature and Linearity Conflict

DEFF Research Database (Denmark)

Feragen, Aase; Lauze, François; Hauberg, Søren

2015-01-01

manifold, the geodesic Gaussian kernel is only positive definite if the Riemannian manifold is Euclidean. This implies that any attempt to design geodesic Gaussian kernels on curved Riemannian manifolds is futile. However, we show that for spaces with conditionally negative definite distances the geodesic...

Real time kernel performance monitoring with SystemTap

CERN Multimedia

CERN. Geneva

2018-01-01

SystemTap is a dynamic method of monitoring and tracing the operation of a running Linux kernel. In this talk I will present a few practical use cases where SystemTap allowed me to turn otherwise complex userland monitoring tasks in simple kernel probes.

Vis-NIR hyperspectral imaging and multivariate analysis for prediction of the moisture content and hardness of Pistachio kernels roasted in different conditions

Directory of Open Access Journals (Sweden)

T Mohammadi Moghaddam

2015-09-01

Full Text Available Introduction: Pistachio nut is one of the most delicious and nutritious nuts in the world and it is being used as a salted and roasted product or as an ingredient in snacks, ice cream, desserts, etc. (Maghsudi, 2010; Kashaninejad et al. 2006. Roasting is one of the most important food processes which provides useful attributes to the product. One of the objectives of nut roasting is to alter and significantly enhance the flavor, texture, color and appearance of the product (Ozdemir, 2001. In recent years, spectral imaging techniques (i.e. hyperspectral and multispectral imaging have emerged as powerful tools for safequality inspection of various agricultural commodities (Gowen et al., 2007. The objectives of this study were to apply reflectance hyperspectral imaging for non-destructive determination of moisture content and hardness of pistachio kernels roasted in different conditions. Materials and methods: Dried O’hadi pistachio nuts were supplied from a local market in Mashhad. Pistachio nuts were soaked in 5L of 20% salt solution for 20min (Goktas Seyhan, 2003. For roasting process, three temperatures (90, 120 and 150°C, three times (20, 35 and 50 min and three air velocities (0.5, 1.5 and 2.5 m s-1 were applied. The moisture content of pistachio kernels was measured in triplicate using oven drying (3 gr samples at 105 °C for 12 hours. Uniaxial compression test by a 35mm diameter plastic cylinder, was made on the pistachio kernels, which were mounted on a platform. Samples were compressed at a depth of 2mm and speed of 30 mm min-1. A hyperspectral imaging system in the Vis-NIR range (400-1000 nm was employed. The spectral pre-processing techniques: first derivative and second derivative, median filter, Savitzkye-Golay, wavelet, multiplicative scatter correction (MSC and standard normal variate transformation (SNV were used. To make models at PLSR and ANN methods, ParLeS software and Matlab R2009a were used, respectively. The coefficient

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

Science.gov (United States)

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

2016-12-01

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

MRI histogram analysis enables objective and continuous classification of intervertebral disc degeneration.

Science.gov (United States)

Waldenberg, Christian; Hebelka, Hanna; Brisby, Helena; Lagerstrand, Kerstin Magdalena

2018-05-01

Magnetic resonance imaging (MRI) is the best diagnostic imaging method for low back pain. However, the technique is currently not utilized in its full capacity, often failing to depict painful intervertebral discs (IVDs), potentially due to the rough degeneration classification system used clinically today. MR image histograms, which reflect the IVD heterogeneity, may offer sensitive imaging biomarkers for IVD degeneration classification. This study investigates the feasibility of using histogram analysis as means of objective and continuous grading of IVD degeneration. Forty-nine IVDs in ten low back pain patients (six males, 25-69 years) were examined with MRI (T2-weighted images and T2-maps). Each IVD was semi-automatically segmented on three mid-sagittal slices. Histogram features of the IVD were extracted from the defined regions of interest and correlated to Pfirrmann grade. Both T2-weighted images and T2-maps displayed similar histogram features. Histograms of well-hydrated IVDs displayed two separate peaks, representing annulus fibrosus and nucleus pulposus. Degenerated IVDs displayed decreased peak separation, where the separation was shown to correlate strongly with Pfirrmann grade (P histogram appearances. Histogram features correlated well with IVD degeneration, suggesting that IVD histogram analysis is a suitable tool for objective and continuous IVD degeneration classification. As histogram analysis revealed IVD heterogeneity, it may be a clinical tool for characterization of regional IVD degeneration effects. To elucidate the usefulness of histogram analysis in patient management, IVD histogram features between asymptomatic and symptomatic individuals needs to be compared.

Ideal gas scattering kernel for energy dependent cross-sections

International Nuclear Information System (INIS)

Rothenstein, W.; Dagan, R.

1998-01-01

A third, and final, paper on the calculation of the joint kernel for neutron scattering by an ideal gas in thermal agitation is presented, when the scattering cross-section is energy dependent. The kernel is a function of the neutron energy after scattering, and of the cosine of the scattering angle, as in the case of the ideal gas kernel for a constant bound atom scattering cross-section. The final expression is suitable for numerical calculations

Parameter optimization in the regularized kernel minimum noise fraction transformation

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2012-01-01

Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF...... analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given....

Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares

Directory of Open Access Journals (Sweden)

Jianjun Liu

2017-11-01

Full Text Available As a widely used classifier, sparse representation classification (SRC has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other norms (e.g., ℓ 2 -norm can be used to regularize the coding coefficients, except for the sparsity ℓ 1 -norm. In this paper, we show that in the kernel space the nonnegative constraint can also play the same role, and thus suggest the investigation of kernel fully constrained least squares (KFCLS for hyperspectral image classification. Furthermore, in order to improve the classification performance of KFCLS by incorporating spatial-spectral information, we investigate two kinds of spatial-spectral methods using two regularization strategies: (1 the coefficient-level regularization strategy, and (2 the class-level regularization strategy. Experimental results conducted on four real hyperspectral images demonstrate the effectiveness of the proposed KFCLS, and show which way to incorporate spatial-spectral information efficiently in the regularization framework.

On flame kernel formation and propagation in premixed gases

Energy Technology Data Exchange (ETDEWEB)

Eisazadeh-Far, Kian; Metghalchi, Hameed [Northeastern University, Mechanical and Industrial Engineering Department, Boston, MA 02115 (United States); Parsinejad, Farzan [Chevron Oronite Company LLC, Richmond, CA 94801 (United States); Keck, James C. [Massachusetts Institute of Technology, Cambridge, MA 02139 (United States)

2010-12-15

Flame kernel formation and propagation in premixed gases have been studied experimentally and theoretically. The experiments have been carried out at constant pressure and temperature in a constant volume vessel located in a high speed shadowgraph system. The formation and propagation of the hot plasma kernel has been simulated for inert gas mixtures using a thermodynamic model. The effects of various parameters including the discharge energy, radiation losses, initial temperature and initial volume of the plasma have been studied in detail. The experiments have been extended to flame kernel formation and propagation of methane/air mixtures. The effect of energy terms including spark energy, chemical energy and energy losses on flame kernel formation and propagation have been investigated. The inputs for this model are the initial conditions of the mixture and experimental data for flame radii. It is concluded that these are the most important parameters effecting plasma kernel growth. The results of laminar burning speeds have been compared with previously published results and are in good agreement. (author)

A scatter model for fast neutron beams using convolution of diffusion kernels

International Nuclear Information System (INIS)

Moyers, M.F.; Horton, J.L.; Boyer, A.L.

1988-01-01

A new model is proposed to calculate dose distributions in materials irradiated with fast neutron beams. Scattered neutrons are transported away from the point of production within the irradiated material in the forward, lateral and backward directions, while recoil protons are transported in the forward and lateral directions. The calculation of dose distributions, such as for radiotherapy planning, is accomplished by convolving a primary attenuation distribution with a diffusion kernel. The primary attenuation distribution may be quickly calculated for any given set of beam and material conditions as it describes only the magnitude and distribution of first interaction sites. The calculation of energy diffusion kernels is very time consuming but must be calculated only once for a given energy. Energy diffusion distributions shown in this paper have been calculated using a Monte Carlo type of program. To decrease beam calculation time, convolutions are performed using a Fast Fourier Transform technique. (author)

A simple technique for treating age-related macular degeneration with external beam radiotherapy

International Nuclear Information System (INIS)

Roos, Daniel E.; Francis, J. Winston; Newnham, W. John

1999-01-01

Purpose: To develop a simple external beam photon radiotherapy technique to treat age-related macular degeneration without the need for simulation, planning computed tomography (CT) or computer dosimetry. Methods and Materials: The goal was to enable the treatment to be set up reliably on the treatment machine on Day 1 with the patient supine in a head cast without any prior planning. Using measurements of ocular globe topography from Karlsson et al. (Int J Radiat Oncol Biol Phys 1996; 33: 705-712), we chose a point 1.5 cm behind the anterior surface of the upper eyelid (ASUE) as the isocentre of a half-beam, blocked, 5.0 x 3.0-cm, angled lateral field to treat the involved eye. This would position the isocentre about 0.5 cm behind the posterior surface of the lens, and a little over 1 cm in front of the macula, according to Karlsson et al. The setup requires initial adjustment of the gantry from horizontal (to account for any asymmetry of position of the eyes), then angling 15 deg. posteriorly to avoid the contralateral eye. Finally, the couch is raised to position the isocentre 1.5 cm behind the ASUE. Results: To verify the applicability of the technique, we performed CT and computer dosimetry on the first 11 eyes so treated. Our CT measurements were in good agreement with Karlsson et al. The lens dose was < 5% and the macula was within the 95% isodose curve in each case (6-MV linac). Treatment setup time is approximately 10 min each day. The 11 patients were treated with 5 x 2.00 Gy (2 patients) or 5 x 3.00 Gy (9 patients), and subjective response on follow-up over 1 to 12 months (median 4 months) was comparable to previously reported results, with no significant acute side effects. Conclusion: Our technique is easy to set up and reliably treats the macula, with sparing of the lens and contralateral eye. It enables treatment to commence rapidly and cost-effectively without the need for simulation or CT computer planning

Large Time Asymptotics for a Continuous Coagulation-Fragmentation Model with Degenerate Size-Dependent Diffusion

KAUST Repository

Desvillettes, Laurent

2010-01-01

We study a continuous coagulation-fragmentation model with constant kernels for reacting polymers (see [M. Aizenman and T. Bak, Comm. Math. Phys., 65 (1979), pp. 203-230]). The polymers are set to diffuse within a smooth bounded one-dimensional domain with no-flux boundary conditions. In particular, we consider size-dependent diffusion coefficients, which may degenerate for small and large cluster-sizes. We prove that the entropy-entropy dissipation method applies directly in this inhomogeneous setting. We first show the necessary basic a priori estimates in dimension one, and second we show faster-than-polynomial convergence toward global equilibria for diffusion coefficients which vanish not faster than linearly for large sizes. This extends the previous results of [J.A. Carrillo, L. Desvillettes, and K. Fellner, Comm. Math. Phys., 278 (2008), pp. 433-451], which assumes that the diffusion coefficients are bounded below. © 2009 Society for Industrial and Applied Mathematics.

A method for manufacturing kernels of metallic oxides and the thus obtained kernels

International Nuclear Information System (INIS)

Lelievre Bernard; Feugier, Andre.

1973-01-01

A method is described for manufacturing fissile or fertile metal oxide kernels, consisting in adding at least a chemical compound capable of releasing ammonia to an aqueous solution of actinide nitrates dispersing the thus obtained solution dropwise in a hot organic phase so as to gelify the drops and transform them into solid particles, washing drying and treating said particles so as to transform them into oxide kernels. Such a method is characterized in that the organic phase used in the gel-forming reactions comprises a mixture of two organic liquids, one of which acts as a solvent, whereas the other is a product capable of extracting the metal-salt anions from the drops while the gel forming reaction is taking place. This can be applied to the so-called high temperature nuclear reactors [fr

New Fukui, dual and hyper-dual kernels as bond reactivity descriptors.

Science.gov (United States)

Franco-Pérez, Marco; Polanco-Ramírez, Carlos-A; Ayers, Paul W; Gázquez, José L; Vela, Alberto

2017-06-21

We define three new linear response indices with promising applications for bond reactivity using the mathematical framework of τ-CRT (finite temperature chemical reactivity theory). The τ-Fukui kernel is defined as the ratio between the fluctuations of the average electron density at two different points in the space and the fluctuations in the average electron number and is designed to integrate to the finite-temperature definition of the electronic Fukui function. When this kernel is condensed, it can be interpreted as a site-reactivity descriptor of the boundary region between two atoms. The τ-dual kernel corresponds to the first order response of the Fukui kernel and is designed to integrate to the finite temperature definition of the dual descriptor; it indicates the ambiphilic reactivity of a specific bond and enriches the traditional dual descriptor by allowing one to distinguish between the electron-accepting and electron-donating processes. Finally, the τ-hyper dual kernel is defined as the second-order derivative of the Fukui kernel and is proposed as a measure of the strength of ambiphilic bonding interactions. Although these quantities have never been proposed, our results for the τ-Fukui kernel and for τ-dual kernel can be derived in zero-temperature formulation of the chemical reactivity theory with, among other things, the widely-used parabolic interpolation model.

Quasi-Dual-Packed-Kerneled Au49 (2,4-DMBT)27 Nanoclusters and the Influence of Kernel Packing on the Electrochemical Gap.

Science.gov (United States)

Liao, Lingwen; Zhuang, Shengli; Wang, Pu; Xu, Yanan; Yan, Nan; Dong, Hongwei; Wang, Chengming; Zhao, Yan; Xia, Nan; Li, Jin; Deng, Haiteng; Pei, Yong; Tian, Shi-Kai; Wu, Zhikun

2017-10-02

Although face-centered cubic (fcc), body-centered cubic (bcc), hexagonal close-packed (hcp), and other structured gold nanoclusters have been reported, it was unclear whether gold nanoclusters with mix-packed (fcc and non-fcc) kernels exist, and the correlation between kernel packing and the properties of gold nanoclusters is unknown. A Au 49 (2,4-DMBT) 27 nanocluster with a shell electron count of 22 has now been been synthesized and structurally resolved by single-crystal X-ray crystallography, which revealed that Au 49 (2,4-DMBT) 27 contains a unique Au 34 kernel consisting of one quasi-fcc-structured Au 21 and one non-fcc-structured Au 13 unit (where 2,4-DMBTH=2,4-dimethylbenzenethiol). Further experiments revealed that the kernel packing greatly influences the electrochemical gap (EG) and the fcc structure has a larger EG than the investigated non-fcc structure. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimal Control for the Degenerate Elliptic Logistic Equation

International Nuclear Information System (INIS)

Delgado, M.; Montero, J.A.; Suarez, A.

2002-01-01

We consider the optimal control of harvesting the diffusive degenerate elliptic logistic equation. Under certain assumptions, we prove the existence and uniqueness of an optimal control. Moreover, the optimality system and a characterization of the optimal control are also derived. The sub-supersolution method, the singular eigenvalue problem and differentiability with respect to the positive cone are the techniques used to obtain our results

Multivariable Christoffel-Darboux Kernels and Characteristic Polynomials of Random Hermitian Matrices

Directory of Open Access Journals (Sweden)

Hjalmar Rosengren

2006-12-01

Full Text Available We study multivariable Christoffel-Darboux kernels, which may be viewed as reproducing kernels for antisymmetric orthogonal polynomials, and also as correlation functions for products of characteristic polynomials of random Hermitian matrices. Using their interpretation as reproducing kernels, we obtain simple proofs of Pfaffian and determinant formulas, as well as Schur polynomial expansions, for such kernels. In subsequent work, these results are applied in combinatorics (enumeration of marked shifted tableaux and number theory (representation of integers as sums of squares.

A multi-resolution approach to heat kernels on discrete surfaces

KAUST Repository

Vaxman, Amir

2010-07-26

Studying the behavior of the heat diffusion process on a manifold is emerging as an important tool for analyzing the geometry of the manifold. Unfortunately, the high complexity of the computation of the heat kernel - the key to the diffusion process - limits this type of analysis to 3D models of modest resolution. We show how to use the unique properties of the heat kernel of a discrete two dimensional manifold to overcome these limitations. Combining a multi-resolution approach with a novel approximation method for the heat kernel at short times results in an efficient and robust algorithm for computing the heat kernels of detailed models. We show experimentally that our method can achieve good approximations in a fraction of the time required by traditional algorithms. Finally, we demonstrate how these heat kernels can be used to improve a diffusion-based feature extraction algorithm. © 2010 ACM.

Compactly Supported Basis Functions as Support Vector Kernels for Classification.

Science.gov (United States)

Wittek, Peter; Tan, Chew Lim

2011-10-01

Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.

Genome-wide Association Analysis of Kernel Weight in Hard Winter Wheat

Science.gov (United States)

Wheat kernel weight is an important and heritable component of wheat grain yield and a key predictor of flour extraction. Genome-wide association analysis was conducted to identify genomic regions associated with kernel weight and kernel weight environmental response in 8 trials of 299 hard winter ...

A Heterogeneous Multi-core Architecture with a Hardware Kernel for Control Systems

DEFF Research Database (Denmark)

Li, Gang; Guan, Wei; Sierszecki, Krzysztof

2012-01-01

Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints. This pa......Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints......). Second, a heterogeneous multi-core architecture is investigated, focusing on its performance in relation to hard real-time constraints and predictable behavior. Third, the hardware implementation of HARTEX is designated to support the heterogeneous multi-core architecture. This hardware kernel has...... several advantages over a similar kernel implemented in software: higher-speed processing capability, parallel computation, and separation between the kernel itself and the applications being run. A microbenchmark has been used to compare the hardware kernel with the software kernel, and compare...

Generalized synthetic kernel approximation for elastic moderation of fast neutrons

International Nuclear Information System (INIS)

Yamamoto, Koji; Sekiya, Tamotsu; Yamamura, Yasunori.

1975-01-01

A method of synthetic kernel approximation is examined in some detail with a view to simplifying the treatment of the elastic moderation of fast neutrons. A sequence of unified kernel (fsub(N)) is introduced, which is then divided into two subsequences (Wsub(n)) and (Gsub(n)) according to whether N is odd (Wsub(n)=fsub(2n-1), n=1,2, ...) or even (Gsub(n)=fsub(2n), n=0,1, ...). The W 1 and G 1 kernels correspond to the usual Wigner and GG kernels, respectively, and the Wsub(n) and Gsub(n) kernels for n>=2 represent generalizations thereof. It is shown that the Wsub(n) kernel solution with a relatively small n (>=2) is superior on the whole to the Gsub(n) kernel solution for the same index n, while both converge to the exact values with increasing n. To evaluate the collision density numerically and rapidly, a simple recurrence formula is derived. In the asymptotic region (except near resonances), this recurrence formula allows calculation with a relatively coarse mesh width whenever hsub(a)<=0.05 at least. For calculations in the transient lethargy region, a mesh width of order epsilon/10 is small enough to evaluate the approximate collision density psisub(N) with an accuracy comparable to that obtained analytically. It is shown that, with the present method, an order of approximation of about n=7 should yield a practically correct solution diviating not more than 1% in collision density. (auth.)

Validation of Born Traveltime Kernels

Science.gov (United States)

Baig, A. M.; Dahlen, F. A.; Hung, S.

2001-12-01

Most inversions for Earth structure using seismic traveltimes rely on linear ray theory to translate observed traveltime anomalies into seismic velocity anomalies distributed throughout the mantle. However, ray theory is not an appropriate tool to use when velocity anomalies have scale lengths less than the width of the Fresnel zone. In the presence of these structures, we need to turn to a scattering theory in order to adequately describe all of the features observed in the waveform. By coupling the Born approximation to ray theory, the first order dependence of heterogeneity on the cross-correlated traveltimes (described by the Fréchet derivative or, more colourfully, the banana-doughnut kernel) may be determined. To determine for what range of parameters these banana-doughnut kernels outperform linear ray theory, we generate several random media specified by their statistical properties, namely the RMS slowness perturbation and the scale length of the heterogeneity. Acoustic waves are numerically generated from a point source using a 3-D pseudo-spectral wave propagation code. These waves are then recorded at a variety of propagation distances from the source introducing a third parameter to the problem: the number of wavelengths traversed by the wave. When all of the heterogeneity has scale lengths larger than the width of the Fresnel zone, ray theory does as good a job at predicting the cross-correlated traveltime as the banana-doughnut kernels do. Below this limit, wavefront healing becomes a significant effect and ray theory ceases to be effective even though the kernels remain relatively accurate provided the heterogeneity is weak. The study of wave propagation in random media is of a more general interest and we will also show our measurements of the velocity shift and the variance of traveltime compare to various theoretical predictions in a given regime.

Effect of Palm Kernel Cake Replacement and Enzyme ...

African Journals Online (AJOL)

A feeding trial which lasted for twelve weeks was conducted to study the performance of finisher pigs fed five different levels of palm kernel cake replacement for maize (0%, 40%, 40%, 60%, 60%) in a maize-palm kernel cake based ration with or without enzyme supplementation. It was a completely randomized design ...

Indian hedgehog contributes to human cartilage endplate degeneration.

Science.gov (United States)

Wang, Shaowei; Yang, Kun; Chen, Shuai; Wang, Jiying; Du, Guoqing; Fan, Shunwu; Wei, Lei

2015-08-01

To determine the role of Indian hedgehog (Ihh) signaling in human cartilage endplate (CEP) degeneration. CEP-degenerated tissues from patients with Modic I or II changes (n = 9 and 45, respectively) and normal tissues from vertebral burst fracture patients (n = 17) were collected. Specimens were either cut into slices for organ culture ex vivo or digested to isolate chondrocytes for cell culture in vitro. Ihh expression and the effect of Ihh on cartilage degeneration were determined by investigating degeneration markers in this study. Ihh expression and cartilage degeneration markers significantly increased in the Modic I and II groups. The expression of cartilage degeneration markers was positively correlated with degeneration severity. Gain-of-function for Ihh promoted expression of cartilage degeneration markers in vitro, while loss-of-function for Ihh inhibited their expression both in vitro and ex vivo. These findings demonstrated that Ihh promotes CEP degeneration. Blocking Ihh pathway has potential clinical usage for attenuating CEP degeneration.

Efficient Online Subspace Learning With an Indefinite Kernel for Visual Tracking and Recognition

NARCIS (Netherlands)

Liwicki, Stephan; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Pantic, Maja

2012-01-01

We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert space to Krein space. We then formulate an

Flour quality and kernel hardness connection in winter wheat

Directory of Open Access Journals (Sweden)

Szabó B. P.

2016-12-01

Full Text Available Kernel hardness is controlled by friabilin protein and it depends on the relation between protein matrix and starch granules. Friabilin is present in high concentration in soft grain varieties and in low concentration in hard grain varieties. The high gluten, hard wheat our generally contains about 12.0–13.0% crude protein under Mid-European conditions. The relationship between wheat protein content and kernel texture is usually positive and kernel texture influences the power consumption during milling. Hard-textured wheat grains require more grinding energy than soft-textured grains.

Deep kernel learning method for SAR image target recognition

Science.gov (United States)

Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

2017-10-01

With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

Influence of differently processed mango seed kernel meal on ...

African Journals Online (AJOL)

Influence of differently processed mango seed kernel meal on performance response of west African ... and TD( consisted spear grass and parboiled mango seed kernel meal with concentrate diet in a ratio of 35:30:35). ... HOW TO USE AJOL.

A framework for dense triangular matrix kernels on various manycore architectures

KAUST Repository

Charara, Ali

2017-06-06

We present a new high-performance framework for dense triangular Basic Linear Algebra Subroutines (BLAS) kernels, ie, triangular matrix-matrix multiplication (TRMM) and triangular solve (TRSM), on various manycore architectures. This is an extension of a previous work on a single GPU by the same authors, presented at the EuroPar\\'16 conference, in which we demonstrated the effectiveness of recursive formulations in enhancing the performance of these kernels. In this paper, the performance of triangular BLAS kernels on a single GPU is further enhanced by implementing customized in-place CUDA kernels for TRMM and TRSM, which are called at the bottom of the recursion. In addition, a multi-GPU implementation of TRMM and TRSM is proposed and we show an almost linear performance scaling, as the number of GPUs increases. Finally, the algorithmic recursive formulation of these triangular BLAS kernels is in fact oblivious to the targeted hardware architecture. We, therefore, port these recursive kernels to homogeneous x86 hardware architectures by relying on the vendor optimized BLAS implementations. Results reported on various hardware architectures highlight a significant performance improvement against state-of-the-art implementations. These new kernels are freely available in the KAUST BLAS (KBLAS) open-source library at https://github.com/ecrc/kblas.

A framework for dense triangular matrix kernels on various manycore architectures

KAUST Repository

Charara, Ali; Keyes, David E.; Ltaief, Hatem

2017-01-01

We present a new high-performance framework for dense triangular Basic Linear Algebra Subroutines (BLAS) kernels, ie, triangular matrix-matrix multiplication (TRMM) and triangular solve (TRSM), on various manycore architectures. This is an extension of a previous work on a single GPU by the same authors, presented at the EuroPar'16 conference, in which we demonstrated the effectiveness of recursive formulations in enhancing the performance of these kernels. In this paper, the performance of triangular BLAS kernels on a single GPU is further enhanced by implementing customized in-place CUDA kernels for TRMM and TRSM, which are called at the bottom of the recursion. In addition, a multi-GPU implementation of TRMM and TRSM is proposed and we show an almost linear performance scaling, as the number of GPUs increases. Finally, the algorithmic recursive formulation of these triangular BLAS kernels is in fact oblivious to the targeted hardware architecture. We, therefore, port these recursive kernels to homogeneous x86 hardware architectures by relying on the vendor optimized BLAS implementations. Results reported on various hardware architectures highlight a significant performance improvement against state-of-the-art implementations. These new kernels are freely available in the KAUST BLAS (KBLAS) open-source library at https://github.com/ecrc/kblas.

PERI - auto-tuning memory-intensive kernels for multicore

International Nuclear Information System (INIS)

Williams, S; Carter, J; Oliker, L; Shalf, J; Yelick, K; Bailey, D; Datta, K

2008-01-01

We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to sparse matrix vector multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the high-performance computing literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4x improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications

PERI - Auto-tuning Memory Intensive Kernels for Multicore

Energy Technology Data Exchange (ETDEWEB)

Bailey, David H; Williams, Samuel; Datta, Kaushik; Carter, Jonathan; Oliker, Leonid; Shalf, John; Yelick, Katherine; Bailey, David H

2008-06-24

We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications.

Kernel Bayesian ART and ARTMAP.

Science.gov (United States)

Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan

2018-02-01

Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

Directory of Open Access Journals (Sweden)

Yi-Hung Liu

2014-01-01

Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

Design and construction of palm kernel cracking and separation ...

African Journals Online (AJOL)

Design and construction of palm kernel cracking and separation machines. ... Username, Password, Remember me, or Register. DOWNLOAD FULL TEXT Open Access DOWNLOAD FULL TEXT Subscription or Fee Access. Design and construction of palm kernel cracking and separation machines. JO Nordiana, K ...

Variable kernel density estimation in high-dimensional feature spaces

CSIR Research Space (South Africa)

Van der Walt, Christiaan M

2017-02-01

Full Text Available Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high...

Intelligent Design of Metal Oxide Gas Sensor Arrays Using Reciprocal Kernel Support Vector Regression

Science.gov (United States)

Dougherty, Andrew W.

Metal oxides are a staple of the sensor industry. The combination of their sensitivity to a number of gases, and the electrical nature of their sensing mechanism, make the particularly attractive in solid state devices. The high temperature stability of the ceramic material also make them ideal for detecting combustion byproducts where exhaust temperatures can be high. However, problems do exist with metal oxide sensors. They are not very selective as they all tend to be sensitive to a number of reduction and oxidation reactions on the oxide's surface. This makes sensors with large numbers of sensors interesting to study as a method for introducing orthogonality to the system. Also, the sensors tend to suffer from long term drift for a number of reasons. In this thesis I will develop a system for intelligently modeling metal oxide sensors and determining their suitability for use in large arrays designed to analyze exhaust gas streams. It will introduce prior knowledge of the metal oxide sensors' response mechanisms in order to produce a response function for each sensor from sparse training data. The system will use the same technique to model and remove any long term drift from the sensor response. It will also provide an efficient means for determining the orthogonality of the sensor to determine whether they are useful in gas sensing arrays. The system is based on least squares support vector regression using the reciprocal kernel. The reciprocal kernel is introduced along with a method of optimizing the free parameters of the reciprocal kernel support vector machine. The reciprocal kernel is shown to be simpler and to perform better than an earlier kernel, the modified reciprocal kernel. Least squares support vector regression is chosen as it uses all of the training points and an emphasis was placed throughout this research for extracting the maximum information from very sparse data. The reciprocal kernel is shown to be effective in modeling the sensor

Heat Kernel Asymptotics of Zaremba Boundary Value Problem

Energy Technology Data Exchange (ETDEWEB)

Avramidi, Ivan G. [Department of Mathematics, New Mexico Institute of Mining and Technology (United States)], E-mail: iavramid@nmt.edu

2004-03-15

The Zaremba boundary-value problem is a boundary value problem for Laplace-type second-order partial differential operators acting on smooth sections of a vector bundle over a smooth compact Riemannian manifold with smooth boundary but with discontinuous boundary conditions, which include Dirichlet boundary conditions on one part of the boundary and Neumann boundary conditions on another part of the boundary. We study the heat kernel asymptotics of Zaremba boundary value problem. The construction of the asymptotic solution of the heat equation is described in detail and the heat kernel is computed explicitly in the leading approximation. Some of the first nontrivial coefficients of the heat kernel asymptotic expansion are computed explicitly.

An Ensemble Approach to Building Mercer Kernels with Prior Information

Science.gov (United States)

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

2005-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Exploration of Shorea robusta (Sal seeds, kernels and its oil

Directory of Open Access Journals (Sweden)

Shashi Kumar C.

2016-12-01

Full Text Available Physical, mechanical, and chemical properties of Shorea robusta seed with wing, seed without wing, and kernel were investigated in the present work. The physico-chemical composition of sal oil was also analyzed. The physico-mechanical properties and proximate composition of seed with wing, seed without wing, and kernel at three moisture contents of 9.50% (w.b, 9.54% (w.b, and 12.14% (w.b, respectively, were studied. The results show that the moisture content of the kernel was highest as compared to seed with wing and seed without wing. The sphericity of the kernel was closer to that of a sphere as compared to seed with wing and seed without wing. The hardness of the seed with wing (32.32, N/mm and seed without wing (42.49, N/mm was lower than the kernels (72.14, N/mm. The proximate composition such as moisture, protein, carbohydrates, oil, crude fiber, and ash content were also determined. The kernel (30.20%, w/w contains higher oil percentage as compared to seed with wing and seed without wing. The scientific data from this work are important for designing of equipment and processes for post-harvest value addition of sal seeds.

A survey of kernel-type estimators for copula and their applications

Science.gov (United States)

Sumarjaya, I. W.

2017-10-01

Copulas have been widely used to model nonlinear dependence structure. Main applications of copulas include areas such as finance, insurance, hydrology, rainfall to name but a few. The flexibility of copula allows researchers to model dependence structure beyond Gaussian distribution. Basically, a copula is a function that couples multivariate distribution functions to their one-dimensional marginal distribution functions. In general, there are three methods to estimate copula. These are parametric, nonparametric, and semiparametric method. In this article we survey kernel-type estimators for copula such as mirror reflection kernel, beta kernel, transformation method and local likelihood transformation method. Then, we apply these kernel methods to three stock indexes in Asia. The results of our analysis suggest that, albeit variation in information criterion values, the local likelihood transformation method performs better than the other kernel methods.

Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration

International Nuclear Information System (INIS)

Nebelung, Sven; Brill, Nicolai; Tingart, Markus; Jahr, Holger; Pufe, Thomas; Kuhl, Christiane; Truhn, Daniel

2016-01-01

To evaluate the usefulness of quantitative parameters obtained by optical coherence tomography (OCT) and magnetic resonance imaging (MRI) in the comprehensive assessment of human articular cartilage degeneration. Human osteochondral samples of variable degeneration (n = 45) were obtained from total knee replacements and assessed by MRI sequences measuring T1, T1ρ, T2 and T2* relaxivity and by OCT-based quantification of irregularity (OII, optical irregularity index), homogeneity (OHI, optical homogeneity index) and attenuation (OAI, optical attenuation index). Samples were also assessed macroscopically (Outerbridge classification) and histologically (Mankin classification) as grade-0 (Mankin scores 0-4)/grade-I (scores 5-8)/grade-II (scores 9-10)/grade-III (score 11-14). After data normalisation, differences between Mankin grades and correlations between imaging parameters were assessed using ANOVA and Tukey's post-hoc test and Spearman's correlation coefficients, respectively. Sensitivities and specificities in the detection of Mankin grade-0 were calculated. Significant degeneration-related increases were found for T2 and OII and decreases for OAI, while T1, T1ρ, T2* or OHI did not reveal significant changes in relation to degeneration. A number of significant correlations between imaging parameters and histological (sub)scores were found, in particular for T2 and OII. Sensitivities and specificities in the detection of Mankin grade-0 were highest for OHI/T1 and OII/T1ρ, respectively. Quantitative OCT and MRI techniques seem to complement each other in the comprehensive assessment of cartilage degeneration. Sufficiently large structural and compositional changes in the extracellular matrix may thus be parameterized and quantified, while the detection of early degeneration remains challenging. (orig.)

Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration

Energy Technology Data Exchange (ETDEWEB)

Nebelung, Sven [Aachen University Hospital, Department of Orthopaedics, Aachen (Germany); Institute of Anatomy and Cell Biology, RWTH, Aachen (Germany); Brill, Nicolai [Fraunhofer Institute for Production Technology, Aachen (Germany); Tingart, Markus; Jahr, Holger [Aachen University Hospital, Department of Orthopaedics, Aachen (Germany); Pufe, Thomas [Institute of Anatomy and Cell Biology, RWTH, Aachen (Germany); Kuhl, Christiane; Truhn, Daniel [Aachen University Hospital, Department of Diagnostic and Interventional Radiology, Aachen (Germany)

2016-04-15

To evaluate the usefulness of quantitative parameters obtained by optical coherence tomography (OCT) and magnetic resonance imaging (MRI) in the comprehensive assessment of human articular cartilage degeneration. Human osteochondral samples of variable degeneration (n = 45) were obtained from total knee replacements and assessed by MRI sequences measuring T1, T1ρ, T2 and T2* relaxivity and by OCT-based quantification of irregularity (OII, optical irregularity index), homogeneity (OHI, optical homogeneity index) and attenuation (OAI, optical attenuation index). Samples were also assessed macroscopically (Outerbridge classification) and histologically (Mankin classification) as grade-0 (Mankin scores 0-4)/grade-I (scores 5-8)/grade-II (scores 9-10)/grade-III (score 11-14). After data normalisation, differences between Mankin grades and correlations between imaging parameters were assessed using ANOVA and Tukey's post-hoc test and Spearman's correlation coefficients, respectively. Sensitivities and specificities in the detection of Mankin grade-0 were calculated. Significant degeneration-related increases were found for T2 and OII and decreases for OAI, while T1, T1ρ, T2* or OHI did not reveal significant changes in relation to degeneration. A number of significant correlations between imaging parameters and histological (sub)scores were found, in particular for T2 and OII. Sensitivities and specificities in the detection of Mankin grade-0 were highest for OHI/T1 and OII/T1ρ, respectively. Quantitative OCT and MRI techniques seem to complement each other in the comprehensive assessment of cartilage degeneration. Sufficiently large structural and compositional changes in the extracellular matrix may thus be parameterized and quantified, while the detection of early degeneration remains challenging. (orig.)

Calcium channel blockers inhibit retinal degeneration in the retinal-degeneration-B mutant of Drosophila.

Science.gov (United States)

Sahly, I; Bar Nachum, S; Suss-Toby, E; Rom, A; Peretz, A; Kleiman, J; Byk, T; Selinger, Z; Minke, B

1992-01-01

Light accelerates degeneration of photoreceptor cells of the retinal degeneration B (rdgB) mutant of Drosophila. During early stages of degeneration, light stimuli evoke spikes from photoreceptors of the mutant fly; no spikes can be recorded from photoreceptors of the wild-type fly. Production of spike potentials from mutant photoreceptors was blocked by diltiazem, verapamil hydrochloride, and cadmium. Little, if any, effect of the (-)-cis isomer or (+)-cis isomer of diltiazem on the light response was seen. Further, the (+)-cis isomer was approximately 50 times more effective than the (-)-cis isomer in blocking the Ca2+ spikes, indicating that diltiazem action on the rdgB eye is mediated by means of blocking voltage-sensitive Ca2+ channels, rather than by blocking the light-sensitive channels. Application of the Ca(2+)-channel blockers (+)-cis-diltiazem and verapamil hydrochloride to the eyes of rdgB flies over a 7-day period largely inhibited light-dependent degeneration of the photoreceptor cells. Pulse labeling with [32P]phosphate showed much greater incorporation into eye proteins of [32P]phosphate in rdgB flies than in wild-type flies. Retarding the light-induced photoreceptor degeneration in the mutant by Ca(2+)-channel blockers, thus, suggests that toxic increase in intracellular Ca2+ by means of voltage-gated Ca2+ channels, possibly secondary to excessive phosphorylation, leads to photoreceptor degeneration in the rdgB mutant. Images PMID:1309615

Irradiation performance of coated fuel particles with fission product retaining kernel additives

International Nuclear Information System (INIS)

Foerthmann, R.

1979-10-01

The four irradiation experiments FRJ2-P17, FRJ2-P18, FRJ2-P19, and FRJ2-P20 for testing the efficiency of fission product-retaining kernel additives in coated fuel particles are described. The evaluation of the obtained experimental data led to the following results: - zirconia and alumina kernel additives are not suitable for an effective fission product retention in oxide fuel kernels, - alumina-silica kernel additives reduce the in-pile release of Sr 90 and Ba 140 from BISO-coated particles at temperatures of about 1200 0 C by two orders of magnitude, and the Cs release from kernels by one order of magnitude, - effective transport coefficients including all parameters which contribute to kernel release are given for (Th,U)O 2 mixed oxide kernels and low enriched UO 2 kernels containing 5 wt.% alumina-silica additives: 10g sub(K)/cm 2 s -1 = - 36 028/T + 6,261 (Sr 90), 10g Dsub(K)/cm 2 c -2 = - 29 646/T + 5,826 (Cs 134/137), alumina-silica kernel additives are ineffective for retaining Ag 110 m in coated particles. However, also an intact SiC-interlayer was found not to be effective at temperatures above 1200 0 C, - the penetration of the buffer layer by fission product containing eutectic additive melt during irradiation can be avoided by using additives which consist of alumina and mullite without an excess of silica, - annealing of LASER-failed irradiated particles and the irradiation test FRJ12-P20 indicate that the efficiency of alumina-silica kernel additives is not altered if the coating becomes defect. (orig.) [de

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

Science.gov (United States)

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

Science.gov (United States)

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

Dose calculation methods in photon beam therapy using energy deposition kernels

International Nuclear Information System (INIS)

Ahnesjoe, A.

1991-01-01

The problem of calculating accurate dose distributions in treatment planning of megavoltage photon radiation therapy has been studied. New dose calculation algorithms using energy deposition kernels have been developed. The kernels describe the transfer of energy by secondary particles from a primary photon interaction site to its surroundings. Monte Carlo simulations of particle transport have been used for derivation of kernels for primary photon energies form 0.1 MeV to 50 MeV. The trade off between accuracy and calculational speed has been addressed by the development of two algorithms; one point oriented with low computional overhead for interactive use and one for fast and accurate calculation of dose distributions in a 3-dimensional lattice. The latter algorithm models secondary particle transport in heterogeneous tissue by scaling energy deposition kernels with the electron density of the tissue. The accuracy of the methods has been tested using full Monte Carlo simulations for different geometries, and found to be superior to conventional algorithms based on scaling of broad beam dose distributions. Methods have also been developed for characterization of clinical photon beams in entities appropriate for kernel based calculation models. By approximating the spectrum as laterally invariant, an effective spectrum and dose distribution for contaminating charge particles are derived form depth dose distributions measured in water, using analytical constraints. The spectrum is used to calculate kernels by superposition of monoenergetic kernels. The lateral energy fluence distribution is determined by deconvolving measured lateral dose distributions by a corresponding pencil beam kernel. Dose distributions for contaminating photons are described using two different methods, one for estimation of the dose outside of the collimated beam, and the other for calibration of output factors derived from kernel based dose calculations. (au)

Boundary singularity of Poisson and harmonic Bergman kernels

Czech Academy of Sciences Publication Activity Database

Engliš, Miroslav

2015-01-01

Roč. 429, č. 1 (2015), s. 233-272 ISSN 0022-247X R&D Projects: GA AV ČR IAA100190802 Institutional support: RVO:67985840 Keywords : harmonic Bergman kernel * Poisson kernel * pseudodifferential boundary operators Subject RIV: BA - General Mathematics Impact factor: 1.014, year: 2015 http://www.sciencedirect.com/science/article/pii/S0022247X15003170

Optimal Bandwidth Selection in Observed-Score Kernel Equating

Science.gov (United States)

Häggström, Jenny; Wiberg, Marie

2014-01-01

The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent…

Kernel structures for Clouds

Science.gov (United States)

Spafford, Eugene H.; Mckendry, Martin S.

1986-01-01

An overview of the internal structure of the Clouds kernel was presented. An indication of how these structures will interact in the prototype Clouds implementation is given. Many specific details have yet to be determined and await experimentation with an actual working system.

Commutators of Integral Operators with Variable Kernels on Hardy ...

Indian Academy of Sciences (India)

Home; Journals; Proceedings – Mathematical Sciences; Volume 115; Issue 4. Commutators of Integral Operators with Variable Kernels on Hardy Spaces. Pu Zhang Kai Zhao. Volume 115 Issue 4 November 2005 pp 399-410 ... Keywords. Singular and fractional integrals; variable kernel; commutator; Hardy space.

Oven-drying reduces ruminal starch degradation in maize kernels

NARCIS (Netherlands)

Ali, M.; Cone, J.W.; Hendriks, W.H.; Struik, P.C.

2014-01-01

The degradation of starch largely determines the feeding value of maize (Zea mays L.) for dairy cows. Normally, maize kernels are dried and ground before chemical analysis and determining degradation characteristics, whereas cows eat and digest fresh material. Drying the moist maize kernels

Perspectives for practical application of the combined fuel kernels in VVER-type reactors

International Nuclear Information System (INIS)

Baranov, V.; Ternovykh, M.; Tikhomirov, G.; Khlunov, A.; Tenishev, A.; Kurina, I.

2011-01-01

The paper considers the main physical processes that take place in fuel kernels under real operation conditions of VVER-type reactors. Main attention is given to the effects induced by combinations of layers with different physical properties inside of fuel kernels on these physical processes. Basic neutron-physical characteristics were calculated for some combined fuel kernels in fuel rods of VVER-type reactors. There are many goals in development of the combined fuel kernels, and these goals define selecting the combinations and compositions of radial layers inside of the kernels. For example, the slower formation of the rim-layer on outer surface of the kernels made of enriched uranium dioxide can be achieved by introduction of inner layer made of natural or depleted uranium dioxide. Other potential goals (lower temperature in the kernel center, better conditions for burn-up of neutron poisons, better retention of toxic materials) could be reached by other combinations of fuel compositions in central and peripheral zones of the fuel kernels. Also, the paper presents the results obtained in experimental manufacturing of the combined fuel pellets. (authors)

An obstructive sleep apnea detection approach using kernel density classification based on single-lead electrocardiogram.

Science.gov (United States)

Chen, Lili; Zhang, Xi; Wang, Hui

2015-05-01

Obstructive sleep apnea (OSA) is a common sleep disorder that often remains undiagnosed, leading to an increased risk of developing cardiovascular diseases. Polysomnogram (PSG) is currently used as a golden standard for screening OSA. However, because it is time consuming, expensive and causes discomfort, alternative techniques based on a reduced set of physiological signals are proposed to solve this problem. This study proposes a convenient non-parametric kernel density-based approach for detection of OSA using single-lead electrocardiogram (ECG) recordings. Selected physiologically interpretable features are extracted from segmented RR intervals, which are obtained from ECG signals. These features are fed into the kernel density classifier to detect apnea event and bandwidths for density of each class (normal or apnea) are automatically chosen through an iterative bandwidth selection algorithm. To validate the proposed approach, RR intervals are extracted from ECG signals of 35 subjects obtained from a sleep apnea database ( http://physionet.org/cgi-bin/atm/ATM ). The results indicate that the kernel density classifier, with two features for apnea event detection, achieves a mean accuracy of 82.07 %, with mean sensitivity of 83.23 % and mean specificity of 80.24 %. Compared with other existing methods, the proposed kernel density approach achieves a comparably good performance but by using fewer features without significantly losing discriminant power, which indicates that it could be widely used for home-based screening or diagnosis of OSA.

Occurrence of 'super soft' wheat kernel texture in hexaploid and tetraploid wheats

Science.gov (United States)

Wheat kernel texture is a key trait that governs milling performance, flour starch damage, flour particle size, flour hydration properties, and baking quality. Kernel texture is commonly measured using the Perten Single Kernel Characterization System (SKCS). The SKCS returns texture values (Hardness...

Kernel Methods for Machine Learning with Life Science Applications

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie

Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear a...

Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression

DEFF Research Database (Denmark)

Exterkate, Peter; Groenen, Patrick J.F.; Heij, Christiaan

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predi...

Mode of inheritance and combining abilities for kernel row number, kernel number per row and grain yield in maize (Zea mays L.)

NARCIS (Netherlands)

Bocanski, J.; Sreckov, Z.; Nastasic, A.; Ivanovic, M.; Djalovic, I.; Vukosavljev, M.

2010-01-01

Bocanski J., Z. Sreckov, A. Nastasic, M. Ivanovic, I.Djalovic and M. Vukosavljev (2010): Mode of inheritance and combining abilities for kernel row number, kernel number per row and grain yield in maize (Zea mays L.) - Genetika, Vol 42, No. 1, 169- 176. Utilization of heterosis requires the study of

Reproducing Kernels and Coherent States on Julia Sets

Energy Technology Data Exchange (ETDEWEB)

Thirulogasanthar, K., E-mail: santhar@cs.concordia.ca; Krzyzak, A. [Concordia University, Department of Computer Science and Software Engineering (Canada)], E-mail: krzyzak@cs.concordia.ca; Honnouvo, G. [Concordia University, Department of Mathematics and Statistics (Canada)], E-mail: g_honnouvo@yahoo.fr

2007-11-15

We construct classes of coherent states on domains arising from dynamical systems. An orthonormal family of vectors associated to the generating transformation of a Julia set is found as a family of square integrable vectors, and, thereby, reproducing kernels and reproducing kernel Hilbert spaces are associated to Julia sets. We also present analogous results on domains arising from iterated function systems.

Reproducing Kernels and Coherent States on Julia Sets

International Nuclear Information System (INIS)

Thirulogasanthar, K.; Krzyzak, A.; Honnouvo, G.

2007-01-01

We construct classes of coherent states on domains arising from dynamical systems. An orthonormal family of vectors associated to the generating transformation of a Julia set is found as a family of square integrable vectors, and, thereby, reproducing kernels and reproducing kernel Hilbert spaces are associated to Julia sets. We also present analogous results on domains arising from iterated function systems

DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding.

Science.gov (United States)

Ma, Wenxiu; Yang, Lin; Rohs, Remo; Noble, William Stafford

2017-10-01

Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of adjacent base pairs, etc. Several methods have been developed to jointly account for DNA sequence and shape properties in predicting TF binding affinity. However, a limitation of these methods is that they typically require a training set of aligned TF binding sites. We describe a sequence + shape kernel that leverages DNA sequence and shape information to better understand protein-DNA binding preference and affinity. This kernel extends an existing class of k-mer based sequence kernels, based on the recently described di-mismatch kernel. Using three in vitro benchmark datasets, derived from universal protein binding microarrays (uPBMs), genomic context PBMs (gcPBMs) and SELEX-seq data, we demonstrate that incorporating DNA shape information improves our ability to predict protein-DNA binding affinity. In particular, we observe that (i) the k-spectrum + shape model performs better than the classical k-spectrum kernel, particularly for small k values; (ii) the di-mismatch kernel performs better than the k-mer kernel, for larger k; and (iii) the di-mismatch + shape kernel performs better than the di-mismatch kernel for intermediate k values. The software is available at https://bitbucket.org/wenxiu/sequence-shape.git. rohs@usc.edu or william-noble@uw.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

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

Science.gov (United States)

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.

Mixed kernel function support vector regression for global sensitivity analysis

Science.gov (United States)

Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng

2017-11-01

Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.

Feature Selection and Kernel Learning for Local Learning-Based Clustering.

Science.gov (United States)

Zeng, Hong; Cheung, Yiu-ming

2011-08-01

The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

Semisupervised kernel marginal Fisher analysis for face recognition.

Science.gov (United States)

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.

Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

Science.gov (United States)

Wei, Wei; Jia, Qingxuan

2016-01-01

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods.

TORCH Computational Reference Kernels - A Testbed for Computer Science Research

Energy Technology Data Exchange (ETDEWEB)

Kaiser, Alex; Williams, Samuel Webb; Madduri, Kamesh; Ibrahim, Khaled; Bailey, David H.; Demmel, James W.; Strohmaier, Erich

2010-12-02

For decades, computer scientists have sought guidance on how to evolve architectures, languages, and programming models in order to improve application performance, efficiency, and productivity. Unfortunately, without overarching advice about future directions in these areas, individual guidance is inferred from the existing software/hardware ecosystem, and each discipline often conducts their research independently assuming all other technologies remain fixed. In today's rapidly evolving world of on-chip parallelism, isolated and iterative improvements to performance may miss superior solutions in the same way gradient descent optimization techniques may get stuck in local minima. To combat this, we present TORCH: A Testbed for Optimization ResearCH. These computational reference kernels define the core problems of interest in scientific computing without mandating a specific language, algorithm, programming model, or implementation. To compliment the kernel (problem) definitions, we provide a set of algorithmically-expressed verification tests that can be used to verify a hardware/software co-designed solution produces an acceptable answer. Finally, to provide some illumination as to how researchers have implemented solutions to these problems in the past, we provide a set of reference implementations in C and MATLAB.

Fuzzy-based multi-kernel spherical support vector machine for ...

Indian Academy of Sciences (India)

In the proposed classifier, we design a new multi-kernel function based on the fuzzy triangular membership function. Finally, a newly developed multi-kernel function is incorporated into the spherical support vector machine to enhance the performance significantly. The experimental results are evaluated and performance is ...

Ultrafast Degenerate Transient Lens Spectroscopy in Semiconductor Nanosctructures

Directory of Open Access Journals (Sweden)

Leontyev A.V.

2015-01-01

Full Text Available We report the non-resonant excitation and probing of the nonlinear refractive index change in bulk semiconductors and semiconductor quantum dots through degenerate transient lens spectroscopy. The signal oscillates at the center laser field frequency, and the envelope of the former in quantum dots is distinctly different from the one in bulk sample. We discuss the applicability of this technique for polarization state probing in semiconductor media with femtosecond temporal resolution.

Modelling microwave heating of discrete samples of oil palm kernels

International Nuclear Information System (INIS)

Law, M.C.; Liew, E.L.; Chang, S.L.; Chan, Y.S.; Leo, C.P.

2016-01-01

Highlights: • Microwave (MW) drying of oil palm kernels is experimentally determined and modelled. • MW heating of discrete samples of oil palm kernels (OPKs) is simulated. • OPK heating is due to contact effect, MW interference and heat transfer mechanisms. • Electric field vectors circulate within OPKs sample. • Loosely-packed arrangement improves temperature uniformity of OPKs. - Abstract: Recently, microwave (MW) pre-treatment of fresh palm fruits has showed to be environmentally friendly compared to the existing oil palm milling process as it eliminates the condensate production of palm oil mill effluent (POME) in the sterilization process. Moreover, MW-treated oil palm fruits (OPF) also possess better oil quality. In this work, the MW drying kinetic of the oil palm kernels (OPK) was determined experimentally. Microwave heating/drying of oil palm kernels was modelled and validated. The simulation results show that temperature of an OPK is not the same over the entire surface due to constructive and destructive interferences of MW irradiance. The volume-averaged temperature of an OPK is higher than its surface temperature by 3–7 °C, depending on the MW input power. This implies that point measurement of temperature reading is inadequate to determine the temperature history of the OPK during the microwave heating process. The simulation results also show that arrangement of OPKs in a MW cavity affects the kernel temperature profile. The heating of OPKs were identified to be affected by factors such as local electric field intensity due to MW absorption, refraction, interference, the contact effect between kernels and also heat transfer mechanisms. The thermal gradient patterns of OPKs change as the heating continues. The cracking of OPKs is expected to occur first in the core of the kernel and then it propagates to the kernel surface. The model indicates that drying of OPKs is a much slower process compared to its MW heating. The model is useful

Finite frequency traveltime sensitivity kernels for acoustic anisotropic media: Angle dependent bananas

KAUST Repository

Djebbi, Ramzi

2013-08-19

Anisotropy is an inherent character of the Earth subsurface. It should be considered for modeling and inversion. The acoustic VTI wave equation approximates the wave behavior in anisotropic media, and especially it\\'s kinematic characteristics. To analyze which parts of the model would affect the traveltime for anisotropic traveltime inversion methods, especially for wave equation tomography (WET), we drive the sensitivity kernels for anisotropic media using the VTI acoustic wave equation. A Born scattering approximation is first derived using the Fourier domain acoustic wave equation as a function of perturbations in three anisotropy parameters. Using the instantaneous traveltime, which unwraps the phase, we compute the kernels. These kernels resemble those for isotropic media, with the η kernel directionally dependent. They also have a maximum sensitivity along the geometrical ray, which is more realistic compared to the cross-correlation based kernels. Focusing on diving waves, which is used more often, especially recently in waveform inversion, we show sensitivity kernels in anisotropic media for this case.

Finite frequency traveltime sensitivity kernels for acoustic anisotropic media: Angle dependent bananas

KAUST Repository

Djebbi, Ramzi; Alkhalifah, Tariq Ali

2013-01-01

Anisotropy is an inherent character of the Earth subsurface. It should be considered for modeling and inversion. The acoustic VTI wave equation approximates the wave behavior in anisotropic media, and especially it's kinematic characteristics. To analyze which parts of the model would affect the traveltime for anisotropic traveltime inversion methods, especially for wave equation tomography (WET), we drive the sensitivity kernels for anisotropic media using the VTI acoustic wave equation. A Born scattering approximation is first derived using the Fourier domain acoustic wave equation as a function of perturbations in three anisotropy parameters. Using the instantaneous traveltime, which unwraps the phase, we compute the kernels. These kernels resemble those for isotropic media, with the η kernel directionally dependent. They also have a maximum sensitivity along the geometrical ray, which is more realistic compared to the cross-correlation based kernels. Focusing on diving waves, which is used more often, especially recently in waveform inversion, we show sensitivity kernels in anisotropic media for this case.

Striatonigral Degeneration

Science.gov (United States)

... See More About Research The NINDS supports and conducts research on disorders of the brain and nervous system such as striatonigral degeneration. This research ... Publications Definition Striatonigral ...

The non-invasive investigation of lumbar disc degeneration in patients with chronic low back pain

International Nuclear Information System (INIS)

Buirski, G.

1989-01-01

The painful degenerate disc is a recognised cause of low back pain. Magnetic Resonance Imaging (MRI) has now replaced discography in the non-invasive assessment of disk degeneration. However, the prohibitive capital expense of MRI and the small number of MR units in Australia produce limitations in clinical access. In contrast, Computed Tomography (CT) is readily available and is performed in most patients prior to MRI referral. This prospective study was undertaken to determine whether preliminary CT could offer any information about disc degeneration and so reduce the demand on a MRI scanner. 30 consecutive patients were studied all of whom had both CT and MRI examinations. Of a total 107 discs examined by both techniques, MRI was able to identify 37 degenerate discs. Conclusive evidence of degeneration (i.e. the presence of intervertebral gas) was only seen in 3 discs at CT (1 patient). Of the 29 posterior disc bulges found on CT, all were both bulging and degenerate on MRI. Indications for MRI based on the CT findings are recommended. Using these criteria, 13% (4 patients) of this study group could have avoided an expensive and unnecessary MR investigation. A useful algorithm for the investigation and assessment of patients with chronic low back pain is discussed. 8 refs., 5 figs., 1 tab

The influence of maize kernel moisture on the sterilizing effect of gamma rays

International Nuclear Information System (INIS)

Khanymova, T.; Poloni, E.

1980-01-01

The influence of 4 levels of maize kernel moisture (16, 20, 25 and 30%) on gamma-ray sterilizing effect was studied and the after-effect of radiation on the microorganisms at short term storage was followed up. Maize kernels of the hybrid Knezha-36 produced in 1975 were used. Gamma-ray treatment of the kernels was effected by GUBEh-4000 irradiator at doses of 0.2 and 0.3 Mrad and after that they were stored for a month at 12 deg and 25 deg C and controlled moisture conditions. Surface and subepidermal infection of the kernels was determined immediately post irradiation and at the end of the experiment. Non-irradiated kernels were used as controls. Results indicated that the initial kernel moisture has a considerable influence on the sterilizing effect of gamma-rays at the rates used in the experiment and affects to a considerable extent the post-irradiation recovery of organisms. The speed of recovery was highest in the treatment with 30% moisture and lowest in the treatment with 16% kernel moisture. Irradiation of the kernels causes pronounced changes on the surface and subepidermal infection. This was due to the unequal radio resistance to the microbial components and to the modifying effect of the moisture holding capacity. The useful effect of maize kernel irradiation was more prolonged at 12 deg C than at 25 deg C

Capturing option anomalies with a variance-dependent pricing kernel

NARCIS (Netherlands)

Christoffersen, P.; Heston, S.; Jacobs, K.

2013-01-01

We develop a GARCH option model with a variance premium by combining the Heston-Nandi (2000) dynamic with a new pricing kernel that nests Rubinstein (1976) and Brennan (1979). While the pricing kernel is monotonic in the stock return and in variance, its projection onto the stock return is

Resolvent kernel for the Kohn Laplacian on Heisenberg groups

Directory of Open Access Journals (Sweden)

Neur Eddine Askour

2002-07-01

Full Text Available We present a formula that relates the Kohn Laplacian on Heisenberg groups and the magnetic Laplacian. Then we obtain the resolvent kernel for the Kohn Laplacian and find its spectral density. We conclude by obtaining the Green kernel for fractional powers of the Kohn Laplacian.

Implementation of pencil kernel and depth penetration algorithms for treatment planning of proton beams

International Nuclear Information System (INIS)

Russell, K.R.; Saxner, M.; Ahnesjoe, A.; Montelius, A.; Grusell, E.; Dahlgren, C.V.

2000-01-01

The implementation of two algorithms for calculating dose distributions for radiation therapy treatment planning of intermediate energy proton beams is described. A pencil kernel algorithm and a depth penetration algorithm have been incorporated into a commercial three-dimensional treatment planning system (Helax-TMS, Helax AB, Sweden) to allow conformal planning techniques using irregularly shaped fields, proton range modulation, range modification and dose calculation for non-coplanar beams. The pencil kernel algorithm is developed from the Fermi-Eyges formalism and Moliere multiple-scattering theory with range straggling corrections applied. The depth penetration algorithm is based on the energy loss in the continuous slowing down approximation with simple correction factors applied to the beam penumbra region and has been implemented for fast, interactive treatment planning. Modelling of the effects of air gaps and range modifying device thickness and position are implicit to both algorithms. Measured and calculated dose values are compared for a therapeutic proton beam in both homogeneous and heterogeneous phantoms of varying complexity. Both algorithms model the beam penumbra as a function of depth in a homogeneous phantom with acceptable accuracy. Results show that the pencil kernel algorithm is required for modelling the dose perturbation effects from scattering in heterogeneous media. (author)

[Lattice degeneration of the retina].

Science.gov (United States)

Boĭko, E V; Suetov, A A; Mal'tsev, D S

2014-01-01

Lattice degeneration of the retina is a clinically important type of peripheral retinal dystrophies due to its participation in the pathogenesis of rhegmatogenous retinal detachment. In spite of extensive epidemiological, morphological, and clinical data, the question on causes of this particular type of retinal dystrophies currently remains debatable. Existing hypotheses on pathogenesis of retinal structural changes in lattice degeneration explain it to a certain extent. In clinical ophthalmology it is necessary to pay close attention to this kind of degenerations and distinguish between cases requiring preventive treatment and those requiring monitoring.

Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population.

Science.gov (United States)

Cheng, Ruiru; Kong, Zhongxin; Zhang, Liwei; Xie, Quan; Jia, Haiyan; Yu, Dong; Huang, Yulong; Ma, Zhengqiang

2017-07-01

Seven kernel dimension QTLs were identified in wheat, and kernel thickness was found to be the most important dimension for grain weight improvement. Kernel morphology and weight of wheat (Triticum aestivum L.) affect both yield and quality; however, the genetic basis of these traits and their interactions has not been fully understood. In this study, to investigate the genetic factors affecting kernel morphology and the association of kernel morphology traits with kernel weight, kernel length (KL), width (KW) and thickness (KT) were evaluated, together with hundred-grain weight (HGW), in a recombinant inbred line population derived from Nanda2419 × Wangshuibai, with data from five trials (two different locations over 3 years). The results showed that HGW was more closely correlated with KT and KW than with KL. A whole genome scan revealed four QTLs for KL, one for KW and two for KT, distributed on five different chromosomes. Of them, QKl.nau-2D for KL, and QKt.nau-4B and QKt.nau-5A for KT were newly identified major QTLs for the respective traits, explaining up to 32.6 and 41.5% of the phenotypic variations, respectively. Increase of KW and KT and reduction of KL/KT and KW/KT ratios always resulted in significant higher grain weight. Lines combining the Nanda 2419 alleles of the 4B and 5A intervals had wider, thicker, rounder kernels and a 14% higher grain weight in the genotype-based analysis. A strong, negative linear relationship of the KW/KT ratio with grain weight was observed. It thus appears that kernel thickness is the most important kernel dimension factor in wheat improvement for higher yield. Mapping and marker identification of the kernel dimension-related QTLs definitely help realize the breeding goals.

Using kernel density estimates to investigate lymphatic filariasis in northeast Brazil

Science.gov (United States)

Medeiros, Zulma; Bonfim, Cristine; Brandão, Eduardo; Netto, Maria José Evangelista; Vasconcellos, Lucia; Ribeiro, Liany; Portugal, José Luiz

2012-01-01

After more than 10 years of the Global Program to Eliminate Lymphatic Filariasis (GPELF) in Brazil, advances have been seen, but the endemic disease persists as a public health problem. The aim of this study was to describe the spatial distribution of lymphatic filariasis in the municipality of Jaboatão dos Guararapes, Pernambuco, Brazil. An epidemiological survey was conducted in the municipality, and positive filariasis cases identified in this survey were georeferenced in point form, using the GPS. A kernel intensity estimator was applied to identify clusters with greater intensity of cases. We examined 23 673 individuals and 323 individuals with microfilaremia were identified, representing a mean prevalence rate of 1.4%. Around 88% of the districts surveyed presented cases of filarial infection, with prevalences of 0–5.6%. The male population was more affected by the infection, with 63.8% of the cases (P<0.005). Positive cases were found in all age groups examined. The kernel intensity estimator identified the areas of greatest intensity and least intensity of filarial infection cases. The case distribution was heterogeneous across the municipality. The kernel estimator identified spatial clusters of cases, thus indicating locations with greater intensity of transmission. The main advantage of this type of analysis lies in its ability to rapidly and easily show areas with the highest concentration of cases, thereby contributing towards planning, monitoring, and surveillance of filariasis elimination actions. Incorporation of geoprocessing and spatial analysis techniques constitutes an important tool for use within the GPELF. PMID:22943547

Non-negative Feynman endash Kac kernels in Schroedinger close-quote s interpolation problem

International Nuclear Information System (INIS)

Blanchard, P.; Garbaczewski, P.; Olkiewicz, R.

1997-01-01

The local formulations of the Markovian interpolating dynamics, which is constrained by the prescribed input-output statistics data, usually utilize strictly positive Feynman endash Kac kernels. This implies that the related Markov diffusion processes admit vanishing probability densities only at the boundaries of the spatial volume confining the process. We discuss an extension of the framework to encompass singular potentials and associated non-negative Feynman endash Kac-type kernels. It allows us to deal with a class of continuous interpolations admitted by general non-negative solutions of the Schroedinger boundary data problem. The resulting nonstationary stochastic processes are capable of both developing and destroying nodes (zeros) of probability densities in the course of their evolution, also away from the spatial boundaries. This observation conforms with the general mathematical theory (due to M. Nagasawa and R. Aebi) that is based on the notion of multiplicative functionals, extending in turn the well known Doob close-quote s h-transformation technique. In view of emphasizing the role of the theory of non-negative solutions of parabolic partial differential equations and the link with open-quotes Wiener exclusionclose quotes techniques used to evaluate certain Wiener functionals, we give an alternative insight into the issue, that opens a transparent route towards applications.copyright 1997 American Institute of Physics

A Heterogeneous Multi-core Architecture with a Hardware Kernel for Control Systems

DEFF Research Database (Denmark)

Li, Gang; Guan, Wei; Sierszecki, Krzysztof

2012-01-01

Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints....... This paper presents a multi-core architecture incorporating a hardware kernel on FPGAs, intended for high performance applications in control engineering domain. First, the hardware kernel is investigated on the basis of a component-based real-time kernel HARTEX (Hard Real-Time Executive for Control Systems...

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

Science.gov (United States)

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

2009-08-01

Previous studies have reported effects of pollen source on the oil concentration of maize (Zea mays) kernels through modifications to both the embryo/kernel ratio and embryo oil concentration. The present study expands upon previous analyses by addressing pollen source effects on the growth of kernel structures (i.e. pericarp, endosperm and embryo), allocation of embryo chemical constituents (i.e. oil, protein, starch and soluble sugars), and the anatomy and histology of the embryos. Maize kernels with different oil concentration were obtained from pollinations with two parental genotypes of contrasting oil concentration. The dynamics of the growth of kernel structures and allocation of embryo chemical constituents were analysed during the post-flowering period. Mature kernels were dissected to study the anatomy (embryonic axis and scutellum) and histology [cell number and cell size of the scutellums, presence of sub-cellular structures in scutellum tissue (starch granules, oil and protein bodies)] of the embryos. Plants of all crosses exhibited a similar kernel number and kernel weight. Pollen source modified neither the growth period of kernel structures, nor pericarp growth rate. By contrast, pollen source determined a trade-off between embryo and endosperm growth rates, which impacted on the embryo/kernel ratio of mature kernels. Modifications to the embryo size were mediated by scutellum cell number. Pollen source also affected (P embryo chemical compounds. Negative correlations among embryo oil concentration and those of starch (r = 0.98, P embryos with low oil concentration had an increased (P embryo/kernel ratio and allocation of embryo chemicals seems to be related to the early established sink strength (i.e. sink size and sink activity) of the embryos.

The global kernel k-means algorithm for clustering in feature space.

Science.gov (United States)

Tzortzis, Grigorios F; Likas, Aristidis C

2009-07-01

Kernel k-means is an extension of the standard k -means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters, and, due to its incremental nature and search procedure, locates near-optimal solutions avoiding poor local minima. Furthermore, two modifications are developed to reduce the computational cost that do not significantly affect the solution quality. The proposed methods are extended to handle weighted data points, which enables their application to graph partitioning. We experiment with several data sets and the proposed approach compares favorably to kernel k -means with random restarts.

Cerebellar Degeneration

Science.gov (United States)

... FARA) National Ataxia Foundation (NAF) National Multiple Sclerosis Society See all related organizations Publications Degeneración cerebelosa Order NINDS Publications Definition Cerebellar degeneration is a process in which neurons ( ...

On methods to increase the security of the Linux kernel

International Nuclear Information System (INIS)

Matvejchikov, I.V.

2014-01-01

Methods to increase the security of the Linux kernel for the implementation of imposed protection tools have been examined. The methods of incorporation into various subsystems of the kernel on the x86 architecture have been described [ru

Relationship between processing score and kernel-fraction particle size in whole-plant corn silage.

Science.gov (United States)

Dias Junior, G S; Ferraretto, L F; Salvati, G G S; de Resende, L C; Hoffman, P C; Pereira, M N; Shaver, R D

2016-04-01

Kernel processing increases starch digestibility in whole-plant corn silage (WPCS). Corn silage processing score (CSPS), the percentage of starch passing through a 4.75-mm sieve, is widely used to assess degree of kernel breakage in WPCS. However, the geometric mean particle size (GMPS) of the kernel-fraction that passes through the 4.75-mm sieve has not been well described. Therefore, the objectives of this study were (1) to evaluate particle size distribution and digestibility of kernels cut in varied particle sizes; (2) to propose a method to measure GMPS in WPCS kernels; and (3) to evaluate the relationship between CSPS and GMPS of the kernel fraction in WPCS. Composite samples of unfermented, dried kernels from 110 corn hybrids commonly used for silage production were kept whole (WH) or manually cut in 2, 4, 8, 16, 32 or 64 pieces (2P, 4P, 8P, 16P, 32P, and 64P, respectively). Dry sieving to determine GMPS, surface area, and particle size distribution using 9 sieves with nominal square apertures of 9.50, 6.70, 4.75, 3.35, 2.36, 1.70, 1.18, and 0.59 mm and pan, as well as ruminal in situ dry matter (DM) digestibilities were performed for each kernel particle number treatment. Incubation times were 0, 3, 6, 12, and 24 h. The ruminal in situ DM disappearance of unfermented kernels increased with the reduction in particle size of corn kernels. Kernels kept whole had the lowest ruminal DM disappearance for all time points with maximum DM disappearance of 6.9% at 24 h and the greatest disappearance was observed for 64P, followed by 32P and 16P. Samples of WPCS (n=80) from 3 studies representing varied theoretical length of cut settings and processor types and settings were also evaluated. Each WPCS sample was divided in 2 and then dried at 60 °C for 48 h. The CSPS was determined in duplicate on 1 of the split samples, whereas on the other split sample the kernel and stover fractions were separated using a hydrodynamic separation procedure. After separation, the

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

Science.gov (United States)

Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao

2017-06-21

In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.

A relationship between Gel'fand-Levitan and Marchenko kernels

International Nuclear Information System (INIS)

Kirst, T.; Von Geramb, H.V.; Amos, K.A.

1989-01-01

An integral equation which relates the output kernels of the Gel'fand-Levitan and Marchenko inverse scattering equations is specified. Structural details of this integral equation are studied when the S-matrix is a rational function, and the output kernels are separable in terms of Bessel, Hankel and Jost solutions. 4 refs

Migration of ThO2 kernels under the influence of a temperature gradient

International Nuclear Information System (INIS)

Smith, C.L.

1976-11-01

BISO coated ThO 2 fertile fuel kernels will migrate up the thermal gradients imposed across coated particles during HTGR operation. Thorium dioxide kernel migration has been studied as a function of temperature (1300 to 1700 0 C) and ThO 2 kernel burnup (0.9 to 5.8 percent FIMA) in out-of-pile, postirradiation thermal gradient heating experiments. The studies were conducted to obtain descriptions of migration rates that will be used in core design studies to evaluate the impact of ThO 2 migration on fertile fuel performance in an operating HTGR and to define characteristics needed by any comprehensive model describing ThO 2 kernel migration. The kinetics data generated in these postirradiation studies are consistent with in-pile data collected by investigators at Oak Ridge National Laboratory, which supports use of the more precise postirradiation heating results in HTGR core design studies. Observations of intergranular carbon deposits on the cool side of migrating kernels support the assumption that the kinetics of kernel migration are controlled by solid state diffusion within irradiated ThO 2 kernels. The migration is characterized by a period of no migration (incubation period) followed by migration at the equilibrium rate for ThO 2 . The incubation period decreases with increasing temperature and kernel burnup. The improved understanding of the kinetics of ThO 2 kernel migration provided by this work will contribute to an optimization of HTGR core design and an increased confidence in fuel performance predictions

Nutrition quality of extraction mannan residue from palm kernel cake on brolier chicken

Science.gov (United States)

Tafsin, M.; Hanafi, N. D.; Kejora, E.; Yusraini, E.

2018-02-01

This study aims to find out the nutrient residue of palm kernel cake from mannan extraction on broiler chicken by evaluating physical quality (specific gravity, bulk density and compacted bulk density), chemical quality (proximate analysis and Van Soest Test) and biological test (metabolizable energy). Treatment composed of T0 : palm kernel cake extracted aquadest (control), T1 : palm kernel cake extracted acetic acid (CH3COOH) 1%, T2 : palm kernel cake extracted aquadest + mannanase enzyme 100 u/l and T3 : palm kernel cake extracted acetic acid (CH3COOH) 1% + enzyme mannanase 100 u/l. The results showed that mannan extraction had significant effect (P<0.05) in improving the quality of physical and numerically increase the value of crude protein and decrease the value of NDF (Neutral Detergent Fiber). Treatments had highly significant influence (P<0.01) on the metabolizable energy value of palm kernel cake residue in broiler chickens. It can be concluded that extraction with aquadest + enzyme mannanase 100 u/l yields the best nutrient quality of palm kernel cake residue for broiler chicken.

Explicit signal to noise ratio in reproducing kernel Hilbert spaces

DEFF Research Database (Denmark)

Gomez-Chova, Luis; Nielsen, Allan Aasbjerg; Camps-Valls, Gustavo

2011-01-01

This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose...... an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal features jointly. Results show that the proposed KMNF provides the most noise-free features when confronted...

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

Science.gov (United States)

Tamiminia, Haifa; Homayouni, Saeid; McNairn, Heather; Safari, Abdoreza

2017-06-01

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.

Reduction of Aflatoxins in Apricot Kernels by Electronic and Manual Color Sorting

Directory of Open Access Journals (Sweden)

Rosanna Zivoli

2016-01-01

Full Text Available The efficacy of color sorting on reducing aflatoxin levels in shelled apricot kernels was assessed. Naturally-contaminated kernels were submitted to an electronic optical sorter or blanched, peeled, and manually sorted to visually identify and sort discolored kernels (dark and spotted from healthy ones. The samples obtained from the two sorting approaches were ground, homogenized, and analysed by HPLC-FLD for their aflatoxin content. A mass balance approach was used to measure the distribution of aflatoxins in the collected fractions. Aflatoxin B1 and B2 were identified and quantitated in all collected fractions at levels ranging from 1.7 to 22,451.5 µg/kg of AFB1 + AFB2, whereas AFG1 and AFG2 were not detected. Excellent results were obtained by manual sorting of peeled kernels since the removal of discolored kernels (2.6%–19.9% of total peeled kernels removed 97.3%–99.5% of total aflatoxins. The combination of peeling and visual/manual separation of discolored kernels is a feasible strategy to remove 97%–99% of aflatoxins accumulated in naturally-contaminated samples. Electronic optical sorter gave highly variable results since the amount of AFB1 + AFB2 measured in rejected fractions (15%–18% of total kernels ranged from 13% to 59% of total aflatoxins. An improved immunoaffinity-based HPLC-FLD method having low limits of detection for the four aflatoxins (0.01–0.05 µg/kg was developed and used to monitor the occurrence of aflatoxins in 47 commercial products containing apricot kernels and/or almonds commercialized in Italy. Low aflatoxin levels were found in 38% of the tested samples and ranged from 0.06 to 1.50 μg/kg for AFB1 and from 0.06 to 1.79 μg/kg for total aflatoxins.

Patterns of cortical degeneration in an elderly cohort with cerebral small vessel disease.

NARCIS (Netherlands)

Reid, A.T.; Norden, A.G.W. van; Laat, K.F. de; Oudheusden, L.J.B. van; Zwiers, M.P.; Evans, A.C.; Leeuw, F.E. de; Kotter, R.

2010-01-01

Emerging noninvasive neuroimaging techniques allow for the morphometric analysis of patterns of gray and white matter degeneration in vivo, which may help explain and predict the occurrence of cognitive impairment and Alzheimer's disease. A single center prospective follow-up study (Radboud

Effects of de-oiled palm kernel cake based fertilizers on sole maize ...

African Journals Online (AJOL)

A study was conducted to determine the effect of de-oiled palm kernel cake based fertilizer formulations on the yield of sole maize and cassava crops. Two de-oiled palm kernel cake based fertilizer formulations A and B were compounded from different proportions of de-oiled palm kernel cake, urea, muriate of potash and ...

Gaussian processes with optimal kernel construction for neuro-degenerative clinical onset prediction

Science.gov (United States)

Canas, Liane S.; Yvernault, Benjamin; Cash, David M.; Molteni, Erika; Veale, Tom; Benzinger, Tammie; Ourselin, Sébastien; Mead, Simon; Modat, Marc

2018-02-01

Gaussian Processes (GP) are a powerful tool to capture the complex time-variations of a dataset. In the context of medical imaging analysis, they allow a robust modelling even in case of highly uncertain or incomplete datasets. Predictions from GP are dependent of the covariance kernel function selected to explain the data variance. To overcome this limitation, we propose a framework to identify the optimal covariance kernel function to model the data.The optimal kernel is defined as a composition of base kernel functions used to identify correlation patterns between data points. Our approach includes a modified version of the Compositional Kernel Learning (CKL) algorithm, in which we score the kernel families using a new energy function that depends both the Bayesian Information Criterion (BIC) and the explained variance score. We applied the proposed framework to model the progression of neurodegenerative diseases over time, in particular the progression of autosomal dominantly-inherited Alzheimer's disease, and use it to predict the time to clinical onset of subjects carrying genetic mutation.

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

Science.gov (United States)

Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2018-06-12

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

Science.gov (United States)

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Matrix Remodeling During Intervertebral Disc Growth and Degeneration Detected by Multichromatic FAST Staining

Science.gov (United States)

Leung, Victor Y.L.; Chan, Wilson C.W.; Hung, Siu-Chun; Cheung, Kenneth M.C.; Chan, Danny

2009-01-01

Various imaging techniques have been used to assess degeneration of the intervertebral disc, including many histological methods, but cartilage-oriented histological stains do not clearly show the comparatively complex structures of the disc. In addition, there is no integrated method to assess efficiently both the compartmental organization and matrix composition in disc samples. In this study, a novel histological method, termed FAST staining, has been developed to investigate disc growth and degeneration by sequential staining with fast green, Alcian blue, Safranin-O, and tartrazine to generate multichromatic histological profiles (FAST profiles). This identifies the major compartments of the vertebra-disc region, including the cartilaginous endplate and multiple zones of the annulus fibrosus, by specific FAST profile patterns. A disc degeneration model in rabbit established using a previously described puncture method showed gradual but profound alteration of the FAST profile during disc degeneration, supporting continual alteration of glycosaminoglycan. Changes of the FAST profile pattern in the nucleus pulposus and annulus fibrosus of the postnatal mouse spine suggested matrix remodeling activity during the growth of intervertebral discs. In summary, we developed an effective staining method capable of defining intervertebral disc compartments in detail and showing matrix remodeling events within the disc. The FAST staining method may be used to develop a histopathological grading system to evaluate disc degeneration or malformation. (J Histochem Cytochem 57:249–256, 2009) PMID:19001641

Fluidization calculation on nuclear fuel kernel coating

International Nuclear Information System (INIS)

Sukarsono; Wardaya; Indra-Suryawan

1996-01-01

The fluidization of nuclear fuel kernel coating was calculated. The bottom of the reactor was in the from of cone on top of the cone there was a cylinder, the diameter of the cylinder for fluidization was 2 cm and at the upper part of the cylinder was 3 cm. Fluidization took place in the cone and the first cylinder. The maximum and the minimum velocity of the gas of varied kernel diameter, the porosity and bed height of varied stream gas velocity were calculated. The calculation was done by basic program

Notes on a storage manager for the Clouds kernel

Science.gov (United States)

Pitts, David V.; Spafford, Eugene H.

1986-01-01

The Clouds project is research directed towards producing a reliable distributed computing system. The initial goal is to produce a kernel which provides a reliable environment with which a distributed operating system can be built. The Clouds kernal consists of a set of replicated subkernels, each of which runs on a machine in the Clouds system. Each subkernel is responsible for the management of resources on its machine; the subkernal components communicate to provide the cooperation necessary to meld the various machines into one kernel. The implementation of a kernel-level storage manager that supports reliability is documented. The storage manager is a part of each subkernel and maintains the secondary storage residing at each machine in the distributed system. In addition to providing the usual data transfer services, the storage manager ensures that data being stored survives machine and system crashes, and that the secondary storage of a failed machine is recovered (made consistent) automatically when the machine is restarted. Since the storage manager is part of the Clouds kernel, efficiency of operation is also a concern.

Metabolite identification through multiple kernel learning on fragmentation trees.

Science.gov (United States)

Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho

2014-06-15

Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. © The Author 2014. Published by Oxford University Press.

Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information

Directory of Open Access Journals (Sweden)

Yi Wang

2018-03-01

Full Text Available In this paper, we introduce a novel classification framework for hyperspectral images (HSIs by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, the spectral kernel is constructed through each pixel’s vector value in the original HSI, and the spatial kernel is modeled by using the extended morphological profile method due to its simplicity and effectiveness. To accurately characterize hierarchical structure features, the techniques of Fish-Markov selector (FMS, marker-based hierarchical segmentation (MHSEG and algebraic multigrid (AMG are combined. First, the FMS algorithm is used on the original HSI for feature selection to produce its spectral subset. Then, the multigrid structure of this subset is constructed using the AMG method. Subsequently, the MHSEG algorithm is exploited to obtain a hierarchy consist of a series of segmentation maps. Finally, the hierarchical structure information is represented by using these segmentation maps. The main contributions of this work is to present an effective composite kernel for HSI classification by utilizing spatial structure information in multiple scales. Experiments were conducted on two hyperspectral remote sensing images to validate that the proposed framework can achieve better classification results than several popular kernel-based classification methods in terms of both qualitative and quantitative analysis. Specifically, the proposed classification framework can achieve 13.46–15.61% in average higher than the standard SVM classifier under different training sets in the terms of overall accuracy.

Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel

International Nuclear Information System (INIS)

Zhang, Yao; Wang, Jianxue; Luo, Xu

2015-01-01

Highlights: • Quantitative information on the uncertainty of wind power generation. • Kernel density estimator provides non-Gaussian predictive distributions. • Logarithmic transformation reduces the skewness of wind power density. • Boundary kernel method eliminates the density leakage near the boundary. - Abstracts: Probabilistic wind power forecasting not only produces the expectation of wind power output, but also gives quantitative information on the associated uncertainty, which is essential for making better decisions about power system and market operations with the increasing penetration of wind power generation. This paper presents a novel kernel density estimator for probabilistic wind power forecasting, addressing two characteristics of wind power which have adverse impacts on the forecast accuracy, namely, the heavily skewed and double-bounded nature of wind power density. Logarithmic transformation is used to reduce the skewness of wind power density, which improves the effectiveness of the kernel density estimator in a transformed scale. Transformations partially relieve the boundary effect problem of the kernel density estimator caused by the double-bounded nature of wind power density. However, the case study shows that there are still some serious problems of density leakage after the transformation. In order to solve this problem in the transformed scale, a boundary kernel method is employed to eliminate the density leak at the bounds of wind power distribution. The improvement of the proposed method over the standard kernel density estimator is demonstrated by short-term probabilistic forecasting results based on the data from an actual wind farm. Then, a detailed comparison is carried out of the proposed method and some existing probabilistic forecasting methods

Kernel based pattern analysis methods using eigen-decompositions for reading Icelandic sagas

DEFF Research Database (Denmark)

Christiansen, Asger Nyman; Carstensen, Jens Michael

We want to test the applicability of kernel based eigen-decomposition methods, compared to the traditional eigen-decomposition methods. We have implemented and tested three kernel based methods methods, namely PCA, MAF and MNF, all using a Gaussian kernel. We tested the methods on a multispectral...... image of a page in the book 'hauksbok', which contains Icelandic sagas....

Intervertebral disc degeneration in dogs

NARCIS (Netherlands)

Bergknut, N.

2011-01-01

Back pain is common in both dogs and humans, and is often associated with intervertebral disc (IVD) degeneration. The IVDs are essential structures of the spine and degeneration can ultimately result in diseases such as IVD herniation or spinal instability. In order to design new treatments halting

Intervertebral disc degeneration in dogs

NARCIS (Netherlands)

Bergknut, Niklas

Back pain is common in both dogs and humans, and is often associated with intervertebral disc (IVD) degeneration. The IVDs are essential structures of the spine and degeneration can ultimately result in diseases such as IVD herniation or spinal instability. In order to design new treatments halting

Second order degenerate elliptic equations

International Nuclear Information System (INIS)

Duong Minh Duc.

1988-08-01

Using an improved Sobolev inequality we study a class of elliptic operators which is degenerate inside the domain and strongly degenerate near the boundary of the domain. Our results are applicable to the L 2 -boundary value problem and the mixed boundary problem. (author). 18 refs

Influence of Kernel Age on Fumonisin B1 Production in Maize by Fusarium moniliforme

Science.gov (United States)

Warfield, Colleen Y.; Gilchrist, David G.

1999-01-01

Production of fumonisins by Fusarium moniliforme on naturally infected maize ears is an important food safety concern due to the toxic nature of this class of mycotoxins. Assessing the potential risk of fumonisin production in developing maize ears prior to harvest requires an understanding of the regulation of toxin biosynthesis during kernel maturation. We investigated the developmental-stage-dependent relationship between maize kernels and fumonisin B1 production by using kernels collected at the blister (R2), milk (R3), dough (R4), and dent (R5) stages following inoculation in culture at their respective field moisture contents with F. moniliforme. Highly significant differences (P ≤ 0.001) in fumonisin B1 production were found among kernels at the different developmental stages. The highest levels of fumonisin B1 were produced on the dent stage kernels, and the lowest levels were produced on the blister stage kernels. The differences in fumonisin B1 production among kernels at the different developmental stages remained significant (P ≤ 0.001) when the moisture contents of the kernels were adjusted to the same level prior to inoculation. We concluded that toxin production is affected by substrate composition as well as by moisture content. Our study also demonstrated that fumonisin B1 biosynthesis on maize kernels is influenced by factors which vary with the developmental age of the tissue. The risk of fumonisin contamination may begin early in maize ear development and increases as the kernels reach physiological maturity. PMID:10388675

Differential metabolome analysis of field-grown maize kernels in response to drought stress

Science.gov (United States)

Drought stress constrains maize kernel development and can exacerbate aflatoxin contamination. In order to identify drought responsive metabolites and explore pathways involved in kernel responses, a metabolomics analysis was conducted on kernels from a drought tolerant line, Lo964, and a sensitive ...

kernel oil by lipolytic organisms

African Journals Online (AJOL)

USER

2010-08-02

Aug 2, 2010 ... Rancidity of extracted cashew oil was observed with cashew kernel stored at 70, 80 and 90% .... method of American Oil Chemist Society AOCS (1978) using glacial ..... changes occur and volatile products are formed that are.

Characterisation and final disposal behaviour of theoria-based fuel kernels in aqueous phases

International Nuclear Information System (INIS)

Titov, M.

2005-08-01

Two high-temperature reactors (AVR and THTR) operated in Germany have produced about 1 million spent fuel elements. The nuclear fuel in these reactors consists mainly of thorium-uranium mixed oxides, but also pure uranium dioxide and carbide fuels were tested. One of the possible solutions of utilising spent HTR fuel is the direct disposal in deep geological formations. Under such circumstances, the properties of fuel kernels, and especially their leaching behaviour in aqueous phases, have to be investigated for safety assessments of the final repository. In the present work, unirradiated ThO 2 , (Th 0.906 ,U 0.094 )O 2 , (Th 0.834 ,U 0.166 )O 2 and UO 2 fuel kernels were investigated. The composition, crystal structure and surface of the kernels were investigated by traditional methods. Furthermore, a new method was developed for testing the mechanical properties of ceramic kernels. The method was successfully used for the examination of mechanical properties of oxide kernels and for monitoring their evolution during contact with aqueous phases. The leaching behaviour of thoria-based oxide kernels and powders was investigated in repository-relevant salt solutions, as well as in artificial leachates. The influence of different experimental parameters on the kernel leaching stability was investigated. It was shown that thoria-based fuel kernels possess high chemical stability and are indifferent to presence of oxidative and radiolytic species in solution. The dissolution rate of thoria-based materials is typically several orders of magnitude lower than of conventional UO 2 fuel kernels. The life time of a single intact (Th,U)O 2 kernel under aggressive conditions of salt repository was estimated as about hundred thousand years. The importance of grain boundary quality on the leaching stability was demonstrated. Numerical Monte Carlo simulations were performed in order to explain the results of leaching experiments. (orig.)

Implementation of a revised numerical integration technique into QAD

International Nuclear Information System (INIS)

De Gangi, N.L.

1983-01-01

A technique for numerical integration through a uniform volume source is developed. It is applied to gamma radiation transport shielding problems. The method is based on performing a numerical angular and ray point kernel integration and is incorporated into the QAD-CG computer code (i.e. QAD-UE). Several test problems are analyzed with this technique. Convergence properties of the method are analyzed. Gamma dose rates from a large tank and post LOCA dose rates inside a containment building are evaluated. Results are consistent with data from other methods. The new technique provides several advantages. User setup requirements for large volume source problems are reduced from standard point kernel requirements. Calculational efficiencies are improved. An order of magnitude improvement is seen with a test problem

Nonlinear electromagnetic waves in a degenerate electron-positron plasma

Energy Technology Data Exchange (ETDEWEB)

El-Labany, S.K., E-mail: skellabany@hotmail.com [Department of Physics, Faculty of Science, Damietta University, New Damietta (Egypt); El-Taibany, W.F., E-mail: eltaibany@hotmail.com [Department of Physics, College of Science for Girls in Abha, King Khalid University, Abha (Saudi Arabia); El-Samahy, A.E.; Hafez, A.M.; Atteya, A., E-mail: ahmedsamahy@yahoo.com, E-mail: am.hafez@sci.alex.edu.eg, E-mail: ahmed_ateya2002@yahoo.com [Department of Physics, Faculty of Science, Alexandria University, Alexandria (Egypt)

2015-08-15

Using the reductive perturbation technique (RPT), the nonlinear propagation of magnetosonic solitary waves in an ultracold, degenerate (extremely dense) electron-positron (EP) plasma (containing ultracold, degenerate electron, and positron fluids) is investigated. The set of basic equations is reduced to a Korteweg-de Vries (KdV) equation for the lowest-order perturbed magnetic field and to a KdV type equation for the higher-order perturbed magnetic field. The solutions of these evolution equations are obtained. For better accuracy and searching on new features, the new solutions are analyzed numerically based on compact objects (white dwarf) parameters. It is found that including the higher-order corrections results as a reduction (increment) of the fast (slow) electromagnetic wave amplitude but the wave width is increased in both cases. The ranges where the RPT can describe adequately the total magnetic field including different conditions are discussed. (author)

Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification

Directory of Open Access Journals (Sweden)

Yu Feng

2017-01-01

Full Text Available This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems.

CLASS-PAIR-GUIDED MULTIPLE KERNEL LEARNING OF INTEGRATING HETEROGENEOUS FEATURES FOR CLASSIFICATION

Directory of Open Access Journals (Sweden)

Q. Wang

2017-10-01

Full Text Available In recent years, many studies on remote sensing image classification have shown that using multiple features from different data sources can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL can conveniently be embedded in a variety of characteristics. The conventional combined kernel learned by MKL can be regarded as the compromise of all basic kernels for all classes in classification. It is the best of the whole, but not optimal for each specific class. For this problem, this paper proposes a class-pair-guided MKL method to integrate the heterogeneous features (HFs from multispectral image (MSI and light detection and ranging (LiDAR data. In particular, the one-against-one strategy is adopted, which converts multiclass classification problem to a plurality of two-class classification problem. Then, we select the best kernel from pre-constructed basic kernels set for each class-pair by kernel alignment (KA in the process of classification. The advantage of the proposed method is that only the best kernel for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on two real data sets, and the experimental results show that the proposed method achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

Omnibus risk assessment via accelerated failure time kernel machine modeling.

Science.gov (United States)

Sinnott, Jennifer A; Cai, Tianxi

2013-12-01

Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. © 2013, The International Biometric Society.

A covariant technique for the calculation of the one-loop effective action

International Nuclear Information System (INIS)

Avramidi, I.G.

1991-01-01

We develop a manifestly covariant technique for a heat kernel calculation in the presence of arbitrary background fields in a curved space. The four lowest-order coefficients of the Schwinger-De Witt asymptotic expansion are explicitly computed. We also calculate the heat kernel asymptotic expansion up to terms of third order in rapidly varying background fields (curvatures). This approximate series is summed and covariant nonlocal expressions for the heat kernel, ξ-function and one-loop effective action are obtained. Other related problems are discussed. (orig.)

Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics

Directory of Open Access Journals (Sweden)

Chuang Lin

2015-01-01

Full Text Available Kernel Locality Preserving Projection (KLPP algorithm can effectively preserve the neighborhood structure of the database using the kernel trick. We have known that supervised KLPP (SKLPP can preserve within-class geometric structures by using label information. However, the conventional SKLPP algorithm endures the kernel selection which has significant impact on the performances of SKLPP. In order to overcome this limitation, a method named supervised kernel optimized LPP (SKOLPP is proposed in this paper, which can maximize the class separability in kernel learning. The proposed method maps the data from the original space to a higher dimensional kernel space using a data-dependent kernel. The adaptive parameters of the data-dependent kernel are automatically calculated through optimizing an objective function. Consequently, the nonlinear features extracted by SKOLPP have larger discriminative ability compared with SKLPP and are more adaptive to the input data. Experimental results on ORL, Yale, AR, and Palmprint databases showed the effectiveness of the proposed method.

Introducing etch kernels for efficient pattern sampling and etch bias prediction

Science.gov (United States)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka

2018-01-01

Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels, as well as the choice of calibration patterns, is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels-"internal, external, curvature, Gaussian, z_profile"-designed to represent the finest details of the resist geometry to characterize precisely the etch bias at any point along a resist contour. By evaluating the etch kernels on various structures, it is possible to map their etch signatures in a multidimensional space and analyze them to find an optimal sampling of structures. The etch kernels evaluated on these structures were combined with experimental etch bias derived from scanning electron microscope contours to train artificial neural networks to predict etch bias. The method applied to contact and line/space layers shows an improvement in etch model prediction accuracy over standard etch model. This work emphasizes the importance of the etch kernel definition to characterize and predict complex etch effects.

SCAP-82, Single Scattering, Albedo Scattering, Point-Kernel Analysis in Complex Geometry

International Nuclear Information System (INIS)

Disney, R.K.; Vogtman, S.E.

1987-01-01

1 - Description of problem or function: SCAP solves for radiation transport in complex geometries using the single or albedo scatter point kernel method. The program is designed to calculate the neutron or gamma ray radiation level at detector points located within or outside a complex radiation scatter source geometry or a user specified discrete scattering volume. Geometry is describable by zones bounded by intersecting quadratic surfaces within an arbitrary maximum number of boundary surfaces per zone. Anisotropic point sources are describable as pointwise energy dependent distributions of polar angles on a meridian; isotropic point sources may also be specified. The attenuation function for gamma rays is an exponential function on the primary source leg and the scatter leg with a build- up factor approximation to account for multiple scatter on the scat- ter leg. The neutron attenuation function is an exponential function using neutron removal cross sections on the primary source leg and scatter leg. Line or volumetric sources can be represented as a distribution of isotropic point sources, with un-collided line-of-sight attenuation and buildup calculated between each source point and the detector point. 2 - Method of solution: A point kernel method using an anisotropic or isotropic point source representation is used, line-of-sight material attenuation and inverse square spatial attenuation between the source point and scatter points and the scatter points and detector point is employed. A direct summation of individual point source results is obtained. 3 - Restrictions on the complexity of the problem: - The SCAP program is written in complete flexible dimensioning so that no restrictions are imposed on the number of energy groups or geometric zones. The geometric zone description is restricted to zones defined by boundary surfaces defined by the general quadratic equation or one of its degenerate forms. The only restriction in the program is that the total

Automatic performance tuning of parallel and accelerated seismic imaging kernels

KAUST Repository

Haberdar, Hakan

2014-01-01

With the increased complexity and diversity of mainstream high performance computing systems, significant effort is required to tune parallel applications in order to achieve the best possible performance for each particular platform. This task becomes more and more challenging and requiring a larger set of skills. Automatic performance tuning is becoming a must for optimizing applications such as Reverse Time Migration (RTM) widely used in seismic imaging for oil and gas exploration. An empirical search based auto-tuning approach is applied to the MPI communication operations of the parallel isotropic and tilted transverse isotropic kernels. The application of auto-tuning using the Abstract Data and Communication Library improved the performance of the MPI communications as well as developer productivity by providing a higher level of abstraction. Keeping productivity in mind, we opted toward pragma based programming for accelerated computation on latest accelerated architectures such as GPUs using the fairly new OpenACC standard. The same auto-tuning approach is also applied to the OpenACC accelerated seismic code for optimizing the compute intensive kernel of the Reverse Time Migration application. The application of such technique resulted in an improved performance of the original code and its ability to adapt to different execution environments.

Multiple kernel learning using single stage function approximation for binary classification problems

Science.gov (United States)

Shiju, S.; Sumitra, S.

2017-12-01

In this paper, the multiple kernel learning (MKL) is formulated as a supervised classification problem. We dealt with binary classification data and hence the data modelling problem involves the computation of two decision boundaries of which one related with that of kernel learning and the other with that of input data. In our approach, they are found with the aid of a single cost function by constructing a global reproducing kernel Hilbert space (RKHS) as the direct sum of the RKHSs corresponding to the decision boundaries of kernel learning and input data and searching that function from the global RKHS, which can be represented as the direct sum of the decision boundaries under consideration. In our experimental analysis, the proposed model had shown superior performance in comparison with that of existing two stage function approximation formulation of MKL, where the decision functions of kernel learning and input data are found separately using two different cost functions. This is due to the fact that single stage representation helps the knowledge transfer between the computation procedures for finding the decision boundaries of kernel learning and input data, which inturn boosts the generalisation capacity of the model.

Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method

Science.gov (United States)

Spencer, Benjamin; Qi, Jinyi; Badawi, Ramsey D.; Wang, Guobao

2017-03-01

Dynamic PET image reconstruction is a challenging problem because of the ill-conditioned nature of PET and the lowcounting statistics resulted from short time-frames in dynamic imaging. The kernel method for image reconstruction has been developed to improve image reconstruction of low-count PET data by incorporating prior information derived from high-count composite data. In contrast to most of the existing regularization-based methods, the kernel method embeds image prior information in the forward projection model and does not require an explicit regularization term in the reconstruction formula. Inspired by the existing highly constrained back-projection (HYPR) algorithm for dynamic PET image denoising, we propose in this work a new type of kernel that is simpler to implement and further improves the kernel-based dynamic PET image reconstruction. Our evaluation study using a physical phantom scan with synthetic FDG tracer kinetics has demonstrated that the new HYPR kernel-based reconstruction can achieve a better region-of-interest (ROI) bias versus standard deviation trade-off for dynamic PET parametric imaging than the post-reconstruction HYPR denoising method and the previously used nonlocal-means kernel.

Improved Variable Window Kernel Estimates of Probability Densities

OpenAIRE

Hall, Peter; Hu, Tien Chung; Marron, J. S.

1995-01-01

Variable window width kernel density estimators, with the width varying proportionally to the square root of the density, have been thought to have superior asymptotic properties. The rate of convergence has been claimed to be as good as those typical for higher-order kernels, which makes the variable width estimators more attractive because no adjustment is needed to handle the negativity usually entailed by the latter. However, in a recent paper, Terrell and Scott show that these results ca...

Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5

Science.gov (United States)

Pendergrass, Angeline G.; Conley, Andrew; Vitt, Francis M.

2018-02-01

Radiative kernels at the top of the atmosphere are useful for decomposing changes in atmospheric radiative fluxes due to feedbacks from atmosphere and surface temperature, water vapor, and surface albedo. Here we describe and validate radiative kernels calculated with the large-ensemble version of CAM5, CESM1.1.2, at the top of the atmosphere and the surface. Estimates of the radiative forcing from greenhouse gases and aerosols in RCP8.5 in the CESM large-ensemble simulations are also diagnosed. As an application, feedbacks are calculated for the CESM large ensemble. The kernels are freely available at https://doi.org/10.5065/D6F47MT6" target="_blank">https://doi.org/10.5065/D6F47MT6, and accompanying software can be downloaded from https://github.com/apendergrass/cam5-kernels" target="_blank">https://github.com/apendergrass/cam5-kernels.

Computing an element in the lexicographic kernel of a game

NARCIS (Netherlands)

Faigle, U.; Kern, Walter; Kuipers, Jeroen

The lexicographic kernel of a game lexicographically maximizes the surplusses $s_{ij}$ (rather than the excesses as would the nucleolus). We show that an element in the lexicographic kernel can be computed efficiently, provided we can efficiently compute the surplusses $s_{ij}(x)$ corresponding to a

Computing an element in the lexicographic kernel of a game

NARCIS (Netherlands)

Faigle, U.; Kern, Walter; Kuipers, J.

2002-01-01

The lexicographic kernel of a game lexicographically maximizes the surplusses $s_{ij}$ (rather than the excesses as would the nucleolus). We show that an element in the lexicographic kernel can be computed efficiently, provided we can efficiently compute the surplusses $s_{ij}(x)$ corresponding to a

Capturing Option Anomalies with a Variance-Dependent Pricing Kernel

DEFF Research Database (Denmark)

Christoffersen, Peter; Heston, Steven; Jacobs, Kris

2013-01-01

We develop a GARCH option model with a new pricing kernel allowing for a variance premium. While the pricing kernel is monotonic in the stock return and in variance, its projection onto the stock return is nonmonotonic. A negative variance premium makes it U shaped. We present new semiparametric...... evidence to confirm this U-shaped relationship between the risk-neutral and physical probability densities. The new pricing kernel substantially improves our ability to reconcile the time-series properties of stock returns with the cross-section of option prices. It provides a unified explanation...... for the implied volatility puzzle, the overreaction of long-term options to changes in short-term variance, and the fat tails of the risk-neutral return distribution relative to the physical distribution....

Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images

Directory of Open Access Journals (Sweden)

Fenghua Huang

2014-01-01

Full Text Available To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1 different radial basis kernel functions (RBFs are employed for spectral and textural features, and a new combined radial basis kernel function (CRBF is proposed by combining them in a weighted manner; (2 the binary decision tree-based multiclass SMO (BDT-SMO is used in the classification of hyperspectral fused images; (3 experiments are carried out, where the single radial basis function- (SRBF- based BDT-SMO classifier and the CRBF-based BDT-SMO classifier are used, respectively, to classify the land usages of hyperspectral fused images, and genetic algorithms (GA are used to optimize the kernel parameters of the classifiers. The results show that, compared with SRBF, CRBF-based BDT-SMO classifiers display greater classification accuracy and efficiency.

Steerability of Hermite Kernel

Czech Academy of Sciences Publication Activity Database

Yang, Bo; Flusser, Jan; Suk, Tomáš

2013-01-01

Roč. 27, č. 4 (2013), 1354006-1-1354006-25 ISSN 0218-0014 R&D Projects: GA ČR GAP103/11/1552 Institutional support: RVO:67985556 Keywords : Hermite polynomials * Hermite kernel * steerability * adaptive filtering Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.558, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/yang-0394387. pdf

Discriminative kernel feature extraction and learning for object recognition and detection

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2015-01-01

Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency...... even in high-dimensional space. In addition, the latent connection between Rényi quadratic entropy and the mapping data in kernel feature space further facilitates us to capture the geometric structure as well as the information about the underlying labels of the CKD using CSQMI. Thus the resulting...... codebook and reduced CKD are discriminative. We report superior performance of our algorithm for object recognition on benchmark datasets like Caltech-101 and CIFAR-10, as well as for detection on a challenging chicken feet dataset....

Overview of real-time kernels at the Superconducting Super Collider Laboratory

International Nuclear Information System (INIS)

Low, K.; Acharya, S.; Allen, M.; Faught, E.; Haenni, D.; Kalbfleisch, C.

1991-01-01

The Superconducting Super Collider Laboratory (SSCL) will have many subsystems that will require real-time microprocessor control. Examples of such Sub-systems requiring real-time controls are power supply ramp generators and quench protection monitors for the superconducting magnets. The authors plan on using a commercial multitasking real-time kernel in these systems. These kernels must perform in a consistent, reliable and efficient manner. Actual performance measurements have been conducted on four different kernels, all running on the same hardware platform. The measurements fall into two categories. Throughput measurements covering the 'non-real-time' aspects of the kernel include process creation/termination times, interprocess communication facilities involving messages, semaphores and shared memory and memory allocation/deallocation. Measurements concentrating on real-time response are context switch times, interrupt latencies and interrupt task response

Overview of real-time kernels at the Superconducting Super Collider Laboratory

International Nuclear Information System (INIS)

Low, K.; Acharya, S.; Allen, M.; Faught, E.; Haenni, D.; Kalbfleisch, C.

1991-05-01

The Superconducting Super Collider Laboratory (SSCL) will have many subsystems that will require real-time microprocessor control. Examples of such sub-systems requiring real-time controls are power supply ramp generators and quench protection monitors for the superconducting magnets. We plan on using a commercial multitasking real-time kernel in these systems. These kernels must perform in a consistent, reliable and efficient manner. Actual performance measurements have been conducted on four different kernels, all running on the same hardware platform. The measurements fall into two categories. Throughput measurements covering the ''non-real-time'' aspects of the kernel include process creation/termination times, interprocess communication facilities involving messages, semaphores and shared memory and memory allocation/deallocation. Measurements concentrating on real-time response are context switch times, interrupt latencies and interrupt task response. 6 refs., 2 tabs

Centralization of extruded medial meniscus delays cartilage degeneration in rats.

Science.gov (United States)

Ozeki, Nobutake; Muneta, Takeshi; Kawabata, Kenichi; Koga, Hideyuki; Nakagawa, Yusuke; Saito, Ryusuke; Udo, Mio; Yanagisawa, Katsuaki; Ohara, Toshiyuki; Mochizuki, Tomoyuki; Tsuji, Kunikazu; Saito, Tomoyuki; Sekiya, Ichiro

2017-05-01

Meniscus extrusion often observed in knee osteoarthritis has a strong correlation with the progression of cartilage degeneration and symptom in the patients. We recently reported a novel procedure "arthroscopic centralization" in which the capsule was sutured to the edge of the tibial plateau to reduce meniscus extrusion in the human knee. However, there is no animal model to study the efficacy of this procedure. The purposes of this study were [1] to establish a model of centralization for the extruded medial meniscus in a rat model; and [2] to investigate the chondroprotective effect of this procedure. Medial meniscus extrusion was induced by the release of the anterior synovial capsule and the transection of the meniscotibial ligament. Centralization was performed by the pulled-out suture technique. Alternatively, control rats had only the medial meniscus extrusion surgery. Medial meniscus extrusion was evaluated by micro-CT and macroscopic findings. Cartilage degeneration of the medial tibial plateau was evaluated macroscopically and histologically. By micro-CT analysis, the medial meniscus extrusion was significantly improved in the centralization group in comparison to the extrusion group throughout the study. Both macroscopically and histologically, the cartilage lesion of the medial tibial plateau was prevented in the centralization group but was apparent in the control group. We developed medial meniscus extrusion in a rat model, and centralization of the extruded medial meniscus by the pull-out suture technique improved the medial meniscus extrusion and delayed cartilage degeneration, though the effect was limited. Centralization is a promising treatment to prevent the progression of osteoarthritis. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Structured Kernel Subspace Learning for Autonomous Robot Navigation.

Science.gov (United States)

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

An Adaptive Genetic Association Test Using Double Kernel Machines.

Science.gov (United States)

Zhan, Xiang; Epstein, Michael P; Ghosh, Debashis

2015-10-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study.

Round atrophic holes in lattice degeneration--an important cause of phakic retinal detachment.

Science.gov (United States)

Tillery, W V; Lucier, A C

1976-01-01

Round atrophic holes in lattice degeneration are an important cause of phakic retinal detachment. Detachments due solely to round holes in lattice accounted for almost 2.8% of all retinal detachments treated at Wills Eye Hospital from January 1970 to August 1973. These detachments had the following important characteristics: 1. One of the patients were under the age of 30 years. 2. Over 75% of the patients had refractive errors more myopic than -3 D spherical equivalent. 3. Inferior detachments were slightly more common than superior detachments. When located inferiorly, there was a tendency for slow progression as indicated by the frequent presence of pigmented demarcation lines. 4. Surgical repair with standard scleral buckling techniques was successful in 98% of these detachments. Young, moderate to highly myopic patients with round holes in areas of lattice degeneration seem to have a greater risk of developing this type of detachment. Patients with the triad of youth, myopia, and round holes in lattice degeneration deserve close observation.

Pencil kernel correction and residual error estimation for quality-index-based dose calculations

International Nuclear Information System (INIS)

Nyholm, Tufve; Olofsson, Joergen; Ahnesjoe, Anders; Georg, Dietmar; Karlsson, Mikael

2006-01-01

Experimental data from 593 photon beams were used to quantify the errors in dose calculations using a previously published pencil kernel model. A correction of the kernel was derived in order to remove the observed systematic errors. The remaining residual error for individual beams was modelled through uncertainty associated with the kernel model. The methods were tested against an independent set of measurements. No significant systematic error was observed in the calculations using the derived correction of the kernel and the remaining random errors were found to be adequately predicted by the proposed method

A Walk-based Semantically Enriched Tree Kernel Over Distributed Word Representations

DEFF Research Database (Denmark)

Srivastava, Shashank; Hovy, Dirk

2013-01-01

We propose a walk-based graph kernel that generalizes the notion of tree-kernels to continuous spaces. Our proposed approach subsumes a general framework for word-similarity, and in particular, provides a flexible way to incorporate distributed representations. Using vector representations......, such an approach captures both distributional semantic similarities among words as well as the structural relations between them (encoded as the structure of the parse tree). We show an efficient formulation to compute this kernel using simple matrix multiplication operations. We present our results on three...

On defining and computing fuzzy kernels on L-valued simple graphs

International Nuclear Information System (INIS)

Bisdorff, R.; Roubens, M.

1996-01-01

In this paper we introduce the concept of fuzzy kernels defined on valued-finite simple graphs in a sense close to fuzzy preference modelling. First we recall the classic concept of kernel associated with a crisp binary relation defined on a finite set. In a second part, we introduce fuzzy binary relations. In a third part, we generalize the crisp kernel concept to such fuzzy binary relations and in a last part, we present an application to fuzzy choice functions on fuzzy outranking relations

Accuracy of approximations of solutions to Fredholm equations by kernel methods

Czech Academy of Sciences Publication Activity Database

Gnecco, G.; Kůrková, Věra; Sanguineti, M.

2012-01-01

Roč. 218, č. 14 (2012), s. 7481-7497 ISSN 0096-3003 R&D Projects: GA ČR GAP202/11/1368; GA MŠk OC10047 Grant - others:CNR-AV ČR(CZ-IT) Project 2010–2012 “Complexity of Neural -Network and Kernel Computational Models Institutional research plan: CEZ:AV0Z10300504 Keywords : approximate solutions to integral equations * radial and kernel-based networks * Gaussian kernels * model complexity * analysis of algorithms Subject RIV: IN - Informatics, Computer Science Impact factor: 1.349, year: 2012

Adaptive kernel regression for freehand 3D ultrasound reconstruction

Science.gov (United States)

Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

2017-03-01

Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

The rabbit as an animal model for post-natal vitreous matrix differentiation and degeneration

NARCIS (Netherlands)

Los, L. I.

2008-01-01

Purpose This study evaluates whether rabbits are a suitable animal model to study post-natal vitreous differentiation and degeneration. Methods Human and rabbit eyes of various ages were studied by complementary anatomical techniques, light microscopy, and transmission electron microscopy. Results

On weights which admit the reproducing kernel of Bergman type

Directory of Open Access Journals (Sweden)

Zbigniew Pasternak-Winiarski

1992-01-01

Full Text Available In this paper we consider (1 the weights of integration for which the reproducing kernel of the Bergman type can be defined, i.e., the admissible weights, and (2 the kernels defined by such weights. It is verified that the weighted Bergman kernel has the analogous properties as the classical one. We prove several sufficient conditions and necessary and sufficient conditions for a weight to be an admissible weight. We give also an example of a weight which is not of this class. As a positive example we consider the weight μ(z=(Imz2 defined on the unit disk in ℂ.

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

Energy Technology Data Exchange (ETDEWEB)

Bo Lijun [Xidian University, Department of Mathematics (China); Wang Yongjin [Nankai University, School of Business (China); Yang Xuewei, E-mail: xwyangnk@yahoo.com.cn [Nanjing University, School of Management and Engineering (China)

2013-08-01

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

Macular degeneration

Science.gov (United States)

The macula is the part of the retina that distinguishes fine details at the center of the field of vision. Macular degeneration results from a partial breakdown of the insulating layer between the retina and the choroid layer of ...

Extracting Feature Model Changes from the Linux Kernel Using FMDiff

NARCIS (Netherlands)

Dintzner, N.J.R.; Van Deursen, A.; Pinzger, M.

2014-01-01

The Linux kernel feature model has been studied as an example of large scale evolving feature model and yet details of its evolution are not known. We present here a classification of feature changes occurring on the Linux kernel feature model, as well as a tool, FMDiff, designed to automatically

Genetic relationship between plant growth, shoot and kernel sizes in ...

African Journals Online (AJOL)

Maize (Zea mays L.) ear vascular tissue transports nutrients that contribute to grain yield. To assess kernel heritabilities that govern ear development and plant growth, field studies were conducted to determine the combining abilities of parents that differed for kernel-size, grain-filling rates and shoot-size. Thirty two hybrids ...

Ensemble Approach to Building Mercer Kernels

Data.gov (United States)

National Aeronautics and Space Administration — 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...

Learning a peptide-protein binding affinity predictor with kernel ridge regression

Science.gov (United States)

2013-01-01

Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting

The uranium recovery from UO{sub 2} kernel production effluent

Energy Technology Data Exchange (ETDEWEB)

Chen, Xiaotong, E-mail: chenxiaotong@tsinghua.edu.cn; He, Linfeng; Liu, Bing; Tang, Yaping; Tang, Chunhe

2016-12-15

Graphical abstract: In this study, a flow sheet including evaporation, flocculation, filtration, adsorption, and reverse osmosis was established for the UO{sub 2} kernel production effluent of HTR spherical fuel elements. The uranium recovery could reach 99.9% after the treatment, with almost no secondary pollution produced. Based on the above experimental results, the treating flow process in this study would be feasible for laboratory- and engineering-scale treatment of UO{sub 2} kernel production effluent of HTR spherical fuel elements. - Highlights: • A flow sheet including evaporation, flocculation, filtration, adsorption, and reverse osmosis was established for the UO{sub 2} kernel production effluent. • The uranium recovery could reach 99.9% after the treatment, with almost no secondary pollution produced. • The treating flow process would be feasible for laboratory- and engineering-scale treatment of UO{sub 2} kernel production effluent. - Abstract: For the fabrication of coated particle fuel elements of high temperature gas cooled reactors, the ceramic UO{sub 2} kernels are prepared through chemical gelation of uranyl nitrate solution droplets, which produces radioactive effluent with components of ammonia, uranium, organic compounds and ammonium nitrate. In this study, a flow sheet including evaporation, flocculation, filtration, adsorption, and reverse osmosis was established for the effluent treating. The uranium recovery could reach 99.9% after the treatment, with almost no secondary pollution produced.

Kernel method for air quality modelling. II. Comparison with analytic solutions

Energy Technology Data Exchange (ETDEWEB)

Lorimer, G S; Ross, D G

1986-01-01

The performance of Lorimer's (1986) kernel method for solving the advection-diffusion equation is tested for instantaneous and continuous emissions into a variety of model atmospheres. Analytical solutions are available for comparison in each case. The results indicate that a modest minicomputer is quite adequate for obtaining satisfactory precision even for the most trying test performed here, which involves a diffusivity tensor and wind speed which are nonlinear functions of the height above ground. Simulations of the same cases by the particle-in-cell technique are found to provide substantially lower accuracy even when use is made of greater computer resources.

Absorbed dose kernel and self-shielding calculations for a novel radiopaque glass microsphere for transarterial radioembolization.

Science.gov (United States)

Church, Cody; Mawko, George; Archambault, John Paul; Lewandowski, Robert; Liu, David; Kehoe, Sharon; Boyd, Daniel; Abraham, Robert; Syme, Alasdair

2018-02-01

Radiopaque microspheres may provide intraprocedural and postprocedural feedback during transarterial radioembolization (TARE). Furthermore, the potential to use higher resolution x-ray imaging techniques as opposed to nuclear medicine imaging suggests that significant improvements in the accuracy and precision of radiation dosimetry calculations could be realized for this type of therapy. This study investigates the absorbed dose kernel for novel radiopaque microspheres including contributions of both short and long-lived contaminant radionuclides while concurrently quantifying the self-shielding of the glass network. Monte Carlo simulations using EGSnrc were performed to determine the dose kernels for all monoenergetic electron emissions and all beta spectra for radionuclides reported in a neutron activation study of the microspheres. Simulations were benchmarked against an accepted 90 Y dose point kernel. Self-shielding was quantified for the microspheres by simulating an isotropically emitting, uniformly distributed source, in glass and in water. The ratio of the absorbed doses was scored as a function of distance from a microsphere. The absorbed dose kernel for the microspheres was calculated for (a) two bead formulations following (b) two different durations of neutron activation, at (c) various time points following activation. Self-shielding varies with time postremoval from the reactor. At early time points, it is less pronounced due to the higher energies of the emissions. It is on the order of 0.4-2.8% at a radial distance of 5.43 mm with increased size from 10 to 50 μm in diameter during the time that the microspheres would be administered to a patient. At long time points, self-shielding is more pronounced and can reach values in excess of 20% near the end of the range of the emissions. Absorbed dose kernels for 90 Y, 90m Y, 85m Sr, 85 Sr, 87m Sr, 89 Sr, 70 Ga, 72 Ga, and 31 Si are presented and used to determine an overall kernel for the

Detoxification of Jatropha curcas kernel cake by a novel Streptomyces fimicarius strain.

Science.gov (United States)

Wang, Xing-Hong; Ou, Lingcheng; Fu, Liang-Liang; Zheng, Shui; Lou, Ji-Dong; Gomes-Laranjo, José; Li, Jiao; Zhang, Changhe

2013-09-15

A huge amount of kernel cake, which contains a variety of toxins including phorbol esters (tumor promoters), is projected to be generated yearly in the near future by the Jatropha biodiesel industry. We showed that the kernel cake strongly inhibited plant seed germination and root growth and was highly toxic to carp fingerlings, even though phorbol esters were undetectable by HPLC. Therefore it must be detoxified before disposal to the environment. A mathematic model was established to estimate the general toxicity of the kernel cake by determining the survival time of carp fingerling. A new strain (Streptomyces fimicarius YUCM 310038) capable of degrading the total toxicity by more than 97% in a 9-day solid state fermentation was screened out from 578 strains including 198 known strains and 380 strains isolated from air and soil. The kernel cake fermented by YUCM 310038 was nontoxic to plants and carp fingerlings and significantly promoted tobacco plant growth, indicating its potential to transform the toxic kernel cake to bio-safe animal feed or organic fertilizer to remove the environmental concern and to reduce the cost of the Jatropha biodiesel industry. Microbial strain profile essential for the kernel cake detoxification was discussed. Copyright © 2013 Elsevier B.V. All rights reserved.