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Sample records for adaptive scatter kernel

  1. Improved scatter correction using adaptive scatter kernel superposition

    NASA Astrophysics Data System (ADS)

    Sun, M.; Star-Lack, J. M.

    2010-11-01

    Accurate scatter correction is required to produce high-quality reconstructions of x-ray cone-beam computed tomography (CBCT) scans. This paper describes new scatter kernel superposition (SKS) algorithms for deconvolving scatter from projection data. The algorithms are designed to improve upon the conventional approach whose accuracy is limited by the use of symmetric kernels that characterize the scatter properties of uniform slabs. To model scatter transport in more realistic objects, nonstationary kernels, whose shapes adapt to local thickness variations in the projection data, are proposed. Two methods are introduced: (1) adaptive scatter kernel superposition (ASKS) requiring spatial domain convolutions and (2) fast adaptive scatter kernel superposition (fASKS) where, through a linearity approximation, convolution is efficiently performed in Fourier space. The conventional SKS algorithm, ASKS, and fASKS, were tested with Monte Carlo simulations and with phantom data acquired on a table-top CBCT system matching the Varian On-Board Imager (OBI). All three models accounted for scatter point-spread broadening due to object thickening, object edge effects, detector scatter properties and an anti-scatter grid. Hounsfield unit (HU) errors in reconstructions of a large pelvis phantom with a measured maximum scatter-to-primary ratio over 200% were reduced from -90 ± 58 HU (mean ± standard deviation) with no scatter correction to 53 ± 82 HU with SKS, to 19 ± 25 HU with fASKS and to 13 ± 21 HU with ASKS. HU accuracies and measured contrast were similarly improved in reconstructions of a body-sized elliptical Catphan phantom. The results show that the adaptive SKS methods offer significant advantages over the conventional scatter deconvolution technique.

  2. The collapsed cone algorithm for 192Ir dosimetry using phantom-size adaptive multiple-scatter point kernels

    NASA Astrophysics Data System (ADS)

    Carlsson Tedgren, Åsa; Plamondon, Mathieu; Beaulieu, Luc

    2015-07-01

    /phantom for which low doses at phantom edges can be overestimated by 2-5 %. It would be possible to improve the situation by using a point kernel for multiple-scatter dose adapted to the patient/phantom dimensions at hand.

  3. The collapsed cone algorithm for (192)Ir dosimetry using phantom-size adaptive multiple-scatter point kernels.

    PubMed

    Tedgren, Åsa Carlsson; Plamondon, Mathieu; Beaulieu, Luc

    2015-07-07

    /phantom for which low doses at phantom edges can be overestimated by 2-5 %. It would be possible to improve the situation by using a point kernel for multiple-scatter dose adapted to the patient/phantom dimensions at hand.

  4. The Adaptive Kernel Neural Network

    DTIC Science & Technology

    1989-10-01

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

  5. Adaptive wiener image restoration kernel

    SciTech Connect

    Yuan, Ding

    2007-06-05

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

  6. Neutron scattering kernel for solid deuterium

    NASA Astrophysics Data System (ADS)

    Granada, J. R.

    2009-06-01

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

  7. Cold-moderator scattering kernel methods

    SciTech Connect

    MacFarlane, R. E.

    1998-01-01

    An accurate representation of the scattering of neutrons by the materials used to build cold sources at neutron scattering facilities is important for the initial design and optimization of a cold source, and for the analysis of experimental results obtained using the cold source. In practice, this requires a good representation of the physics of scattering from the material, a method to convert this into observable quantities (such as scattering cross sections), and a method to use the results in a neutron transport code (such as the MCNP Monte Carlo code). At Los Alamos, the authors have been developing these capabilities over the last ten years. The final set of cold-moderator evaluations, together with evaluations for conventional moderator materials, was released in 1994. These materials have been processed into MCNP data files using the NJOY Nuclear Data Processing System. Over the course of this work, they were able to develop a new module for NJOY called LEAPR based on the LEAP + ADDELT code from the UK as modified by D.J. Picton for cold-moderator calculations. Much of the physics for methane came from Picton`s work. The liquid hydrogen work was originally based on a code using the Young-Koppel approach that went through a number of hands in Europe (including Rolf Neef and Guy Robert). It was generalized and extended for LEAPR, and depends strongly on work by Keinert and Sax of the University of Stuttgart. Thus, their collection of cold-moderator scattering kernels is truly an international effort, and they are glad to be able to return the enhanced evaluations and processing techniques to the international community. In this paper, they give sections on the major cold moderator materials (namely, solid methane, liquid methane, and liquid hydrogen) using each section to introduce the relevant physics for that material and to show typical results.

  8. A locally adaptive kernel regression method for facies delineation

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  9. Kernel Manifold Alignment for Domain Adaptation.

    PubMed

    Tuia, Devis; Camps-Valls, Gustau

    2016-01-01

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

  10. Kernel Manifold Alignment for Domain Adaptation

    PubMed Central

    Tuia, Devis; Camps-Valls, Gustau

    2016-01-01

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

  11. Analog forecasting with dynamics-adapted kernels

    NASA Astrophysics Data System (ADS)

    Zhao, Zhizhen; Giannakis, Dimitrios

    2016-09-01

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

  12. DBRC and WCM scattering kernels for TRIPOLI-4, version 9

    NASA Astrophysics Data System (ADS)

    Zoia, Andrea; Brun, Emeric; Jouanne, Cédric; Malvagi, Fausto

    2014-06-01

    Recent works have pointed out some relevant shortcomings of the so called `Sampling of the Velocity of the Target nucleus' (SVT) algorithm, which is currently used in most Monte Carlo codes for the Doppler broadening of the elastic scattering kernel. To overcome these limitations, which affect resonant nuclei such as 238U, and whose consequences in pincell criticality calculations can amount to a drop of a few hundred pcm, the Doppler Broadening Rejection Correction (DBRC) and the Weight Correction Method (WCM) have been proposed. In this work, we illustrate the implementation of DBRC and WCM in the TRIPOLI-4 Monte Carlo code. Several validation tests are discussed, and the impact of using DBRC or WCM to replace SVT is analyzed in detail in static as well as burnup calculations for UOX and MOX pincells, and in a PWR full-core simulation.

  13. Robust visual tracking via adaptive kernelized correlation filter

    NASA Astrophysics Data System (ADS)

    Wang, Bo; Wang, Desheng; Liao, Qingmin

    2016-10-01

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

  14. An Adaptive Genetic Association Test Using Double Kernel Machines.

    PubMed

    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.

  15. Data consistency-driven scatter kernel optimization for x-ray cone-beam CT

    NASA Astrophysics Data System (ADS)

    Kim, Changhwan; Park, Miran; Sung, Younghun; Lee, Jaehak; Choi, Jiyoung; Cho, Seungryong

    2015-08-01

    Accurate and efficient scatter correction is essential for acquisition of high-quality x-ray cone-beam CT (CBCT) images for various applications. This study was conducted to demonstrate the feasibility of using the data consistency condition (DCC) as a criterion for scatter kernel optimization in scatter deconvolution methods in CBCT. As in CBCT, data consistency in the mid-plane is primarily challenged by scatter, we utilized data consistency to confirm the degree of scatter correction and to steer the update in iterative kernel optimization. By means of the parallel-beam DCC via fan-parallel rebinning, we iteratively optimized the scatter kernel parameters, using a particle swarm optimization algorithm for its computational efficiency and excellent convergence. The proposed method was validated by a simulation study using the XCAT numerical phantom and also by experimental studies using the ACS head phantom and the pelvic part of the Rando phantom. The results showed that the proposed method can effectively improve the accuracy of deconvolution-based scatter correction. Quantitative assessments of image quality parameters such as contrast and structure similarity (SSIM) revealed that the optimally selected scatter kernel improves the contrast of scatter-free images by up to 99.5%, 94.4%, and 84.4%, and of the SSIM in an XCAT study, an ACS head phantom study, and a pelvis phantom study by up to 96.7%, 90.5%, and 87.8%, respectively. The proposed method can achieve accurate and efficient scatter correction from a single cone-beam scan without need of any auxiliary hardware or additional experimentation.

  16. A simple and fast method for computing the relativistic Compton Scattering Kernel for radiative transfer

    NASA Technical Reports Server (NTRS)

    Kershaw, David S.; Prasad, Manoj K.; Beason, J. Douglas

    1986-01-01

    The Klein-Nishina differential cross section averaged over a relativistic Maxwellian electron distribution is analytically reduced to a single integral, which can then be rapidly evaluated in a variety of ways. A particularly fast method for numerically computing this single integral is presented. This is, to the authors' knowledge, the first correct computation of the Compton scattering kernel.

  17. Super-resolution reconstruction algorithm based on adaptive convolution kernel size selection

    NASA Astrophysics Data System (ADS)

    Gao, Hang; Chen, Qian; Sui, Xiubao; Zeng, Junjie; Zhao, Yao

    2016-09-01

    Restricted by the detector technology and optical diffraction limit, the spatial resolution of infrared imaging system is difficult to achieve significant improvement. Super-Resolution (SR) reconstruction algorithm is an effective way to solve this problem. Among them, the SR algorithm based on multichannel blind deconvolution (MBD) estimates the convolution kernel only by low resolution observation images, according to the appropriate regularization constraints introduced by a priori assumption, to realize the high resolution image restoration. The algorithm has been shown effective when each channel is prime. In this paper, we use the significant edges to estimate the convolution kernel and introduce an adaptive convolution kernel size selection mechanism, according to the uncertainty of the convolution kernel size in MBD processing. To reduce the interference of noise, we amend the convolution kernel in an iterative process, and finally restore a clear image. Experimental results show that the algorithm can meet the convergence requirement of the convolution kernel estimation.

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

    PubMed

    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.

  19. A simple method for computing the relativistic Compton scattering kernel for radiative transfer

    NASA Technical Reports Server (NTRS)

    Prasad, M. K.; Kershaw, D. S.; Beason, J. D.

    1986-01-01

    Correct computation of the Compton scattering kernel (CSK), defined to be the Klein-Nishina differential cross section averaged over a relativistic Maxwellian electron distribution, is reported. The CSK is analytically reduced to a single integral, which can then be rapidly evaluated using a power series expansion, asymptotic series, and rational approximation for sigma(s). The CSK calculation has application to production codes that aim at understanding certain astrophysical, laser fusion, and nuclear weapons effects phenomena.

  20. Two-pion exchange contributions to the relativistic NN kernel: Peripheral scattering

    SciTech Connect

    Cozma, M. D.; Scholten, O.; Timmermans, R. G. E.; Tjon, J. A.

    2007-01-15

    The relativistic one-boson-exchange model for NN scattering is extended by including two-pion exchange (TPE) contributions in the kernel. We develop the formalism for the evaluation of the TPE diagrams within the relativistic quasipotential approach. The peripheral partial waves in elastic NN scattering are studied within this model. The TPE interactions contain a strongly attractive isoscalar-scalar component which requires a low value of the cutoff parameter: {lambda}=650-800 MeV. With this prescription, the peripheral waves can be reasonably described.

  1. Non-stationary convolution subtraction scatter correction with a dual-exponential scatter kernel for the Hamamatsu SHR-7700 animal PET scanner

    NASA Astrophysics Data System (ADS)

    Lubberink, Mark; Kosugi, Tsuyoshi; Schneider, Harald; Ohba, Hiroyuki; Bergström, Mats

    2004-03-01

    A spatially variant convolution subtraction scatter correction was developed for a Hamamatsu SHR-7700 animal PET scanner. This scanner, with retractable septa and a gantry that can be tilted 90°, was designed for studies of conscious monkeys. The implemented dual-exponential scatter kernel takes into account both radiation scattered inside the object and radiation scattered in gantry and detectors. This is necessary because of the relatively large contribution of gantry and detector scatter in this scanner. The correction is used for scatter correction of emission as well as transmission data. Transmission scatter correction using the dual-exponential kernel leads to a measured attenuation coefficient of 0.096 cm-1 in water, compared to 0.089 cm-1 without scatter correction. Scatter correction on both emission and transmission data resulted in a residual correction error of 2.1% in water, as well as improved image contrast and hot spot quantification.

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

    PubMed

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

    2016-12-07

    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.

  3. MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods

    PubMed Central

    Schmidt, Johannes F. M.; Santelli, Claudio; Kozerke, Sebastian

    2016-01-01

    An approach to Magnetic Resonance (MR) image reconstruction from undersampled data is proposed. Undersampling artifacts are removed using an iterative thresholding algorithm applied to nonlinearly transformed image block arrays. Each block array is transformed using kernel principal component analysis where the contribution of each image block to the transform depends in a nonlinear fashion on the distance to other image blocks. Elimination of undersampling artifacts is achieved by conventional principal component analysis in the nonlinear transform domain, projection onto the main components and back-mapping into the image domain. Iterative image reconstruction is performed by interleaving the proposed undersampling artifact removal step and gradient updates enforcing consistency with acquired k-space data. The algorithm is evaluated using retrospectively undersampled MR cardiac cine data and compared to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT reconstruction. Evaluation of image quality and root-mean-squared-error (RMSE) reveal improved image reconstruction for up to 8-fold undersampled data with the proposed approach relative to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT. In conclusion, block matching and kernel methods can be used for effective removal of undersampling artifacts in MR image reconstruction and outperform methods using standard compressed sensing and ℓ1-regularized parallel imaging methods. PMID:27116675

  4. A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy

    NASA Astrophysics Data System (ADS)

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Ji, Jin-Chao

    2016-04-01

    In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.

  5. Sensitivity kernels for coda-wave interferometry and scattering tomography: theory and numerical evaluation in two-dimensional anisotropically scattering media

    NASA Astrophysics Data System (ADS)

    Margerin, Ludovic; Planès, Thomas; Mayor, Jessie; Calvet, Marie

    2016-01-01

    Coda-wave interferometry is a technique which exploits tiny waveform changes in the coda to detect temporal variations of seismic properties in evolving media. Observed waveform changes are of two kinds: traveltime perturbations and distortion of seismograms. In the last 10 yr, various theories have been published to relate either background velocity changes to traveltime perturbations, or changes in the scattering properties of the medium to waveform decorrelation. These theories have been limited by assumptions pertaining to the scattering process itself-in particular isotropic scattering, or to the propagation regime-single-scattering and/or diffusion. In this manuscript, we unify and extend previous results from the literature using a radiative transfer approach. This theory allows us to incorporate the effect of anisotropic scattering and to cover a broad range of propagation regimes, including the contribution of coherent, singly scattered and multiply scattered waves. Using basic physical reasoning, we show that two different sensitivity kernels are required to describe traveltime perturbations and waveform decorrelation, respectively, a distinction which has not been well appreciated so far. Previous results from the literature are recovered as limiting cases of our general approach. To evaluate numerically the sensitivity functions, we introduce an improved version of a spectral technique known as the method of `rotated coordinate frames', which allows global evaluation of the Green's function of the radiative transfer equation in a finite domain. The method is validated through direct pointwise comparison with Green's functions obtained by the Monte Carlo method. To illustrate the theory, we consider a series of scattering media displaying increasing levels of scattering anisotropy and discuss the impact on the traveltime and decorrelation kernels. We also consider the related problem of imaging variations of scattering properties based on intensity

  6. On radiative transfer using synthetic kernel and simplified spherical harmonics methods in linearly anisotropically scattering media

    NASA Astrophysics Data System (ADS)

    Altaç, Zekeriya

    2014-11-01

    The Synthetic Kernel (SKN) method is employed to a 3D absorbing, emitting and linearly anisotropically scattering inhomogeneous medium. Standard SKN approximation is applied only to the diffusive components of the radiative transfer equations. An alternative SKN (S KN*) method is also derived in full 3-D generality by extending the approximation to the direct wall contributions. Complete sets of boundary conditions for both SKN approaches are rigorously obtained. The simplified spherical harmonics (P2N-1 or SP2N-1) and simplified double spherical harmonics (DPN-1 or SDPN-1) equations for linearly anisotropically scattering homogeneous medium are also derived. Resulting full P2N-1 and DPN-1 (or SP2N-1 and SDPN-1) equations are cast as diagonalized second order coupled diffusion-like equations. By this analysis, it is shown that the SKN method is a high-order approximation, and simply by the selection of full or half range Gauss-Legendre quadratures, S KN* equations become identical to P2N-1 or DPN-1 (or SP2N-1 or SDPN-1) equations. Numerical verification of all methods presented is carried out using a 1D participating isotropic slab medium. The SKN method proves to be more accurate than S KN* approximation, but it is analytically more involved. It is shown that the S KN* with proposed BCs converges with increasing order of approximation, and the BCs are applicable to SPN or SDPN methods.

  7. Photon dose calculation based on electron multiple-scattering theory: primary dose deposition kernels.

    PubMed

    Wang, L; Jette, D

    1999-08-01

    The transport of the secondary electrons resulting from high-energy photon interactions is essential to energy redistribution and deposition. In order to develop an accurate dose-calculation algorithm for high-energy photons, which can predict the dose distribution in inhomogeneous media and at the beam edges, we have investigated the feasibility of applying electron transport theory [Jette, Med. Phys. 15, 123 (1988)] to photon dose calculation. In particular, the transport of and energy deposition by Compton electron and electrons and positrons resulting from pair production were studied. The primary photons are treated as the source of the secondary electrons and positrons, which are transported through the irradiated medium using Gaussian multiple-scattering theory [Jette, Med. Phys. 15, 123 (1988)]. The initial angular and kinetic energy distribution(s) of the secondary electrons (and positrons) emanating from the photon interactions are incorporated into the transport. Due to different mechanisms of creation and cross-section functions, the transport of and the energy deposition by the electrons released in these two processes are studied and modeled separately based on first principles. In this article, we focus on determining the dose distribution for an individual interaction site. We define the Compton dose deposition kernel (CDK) or the pair-production dose deposition kernel (PDK) as the dose distribution relative to the point of interaction, per unit interaction density, for a monoenergetic photon beam in an infinite homogeneous medium of unit density. The validity of this analytic modeling of dose deposition was evaluated through EGS4 Monte Carlo simulation. Quantitative agreement between these two calculations of the dose distribution and the average energy deposited per interaction was achieved. Our results demonstrate the applicability of the electron dose-calculation method to photon dose calculation.

  8. Scene sketch generation using mixture of gradient kernels and adaptive thresholding

    NASA Astrophysics Data System (ADS)

    Paheding, Sidike; Essa, Almabrok; Asari, Vijayan

    2016-04-01

    This paper presents a simple but effective algorithm for scene sketch generation from input images. The proposed algorithm combines the edge magnitudes of directional Prewitt differential gradient kernels with Kirsch kernels at each pixel position, and then encodes them into an eight bit binary code which encompasses local edge and texture information. In this binary encoding step, relative variance is employed to determine the object shape in each local region. Using relative variance enables object sketch extraction totally adaptive to any shape structure. On the other hand, the proposed technique does not require any parameter to adjust output and it is robust to edge density and noise. Two standard databases are used to show the effectiveness of the proposed framework.

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

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

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

  10. Adaptive inverse control of neural spatiotemporal spike patterns with a reproducing kernel Hilbert space (RKHS) framework.

    PubMed

    Li, Lin; Park, Il Memming; Brockmeier, Austin; Chen, Badong; Seth, Sohan; Francis, Joseph T; Sanchez, Justin C; Príncipe, José C

    2013-07-01

    The precise control of spiking in a population of neurons via applied electrical stimulation is a challenge due to the sparseness of spiking responses and neural system plasticity. We pose neural stimulation as a system control problem where the system input is a multidimensional time-varying signal representing the stimulation, and the output is a set of spike trains; the goal is to drive the output such that the elicited population spiking activity is as close as possible to some desired activity, where closeness is defined by a cost function. If the neural system can be described by a time-invariant (homogeneous) model, then offline procedures can be used to derive the control procedure; however, for arbitrary neural systems this is not tractable. Furthermore, standard control methodologies are not suited to directly operate on spike trains that represent both the target and elicited system response. In this paper, we propose a multiple-input multiple-output (MIMO) adaptive inverse control scheme that operates on spike trains in a reproducing kernel Hilbert space (RKHS). The control scheme uses an inverse controller to approximate the inverse of the neural circuit. The proposed control system takes advantage of the precise timing of the neural events by using a Schoenberg kernel defined directly in the space of spike trains. The Schoenberg kernel maps the spike train to an RKHS and allows linear algorithm to control the nonlinear neural system without the danger of converging to local minima. During operation, the adaptation of the controller minimizes a difference defined in the spike train RKHS between the system and the target response and keeps the inverse controller close to the inverse of the current neural circuit, which enables adapting to neural perturbations. The results on a realistic synthetic neural circuit show that the inverse controller based on the Schoenberg kernel outperforms the decoding accuracy of other models based on the conventional rate

  11. An adaptive kernel smoothing method for classifying Austrosimulium tillyardianum (Diptera: Simuliidae) larval instars.

    PubMed

    Cen, Guanjun; Yu, Yonghao; Zeng, Xianru; Long, Xiuzhen; Wei, Dewei; Gao, Xuyuan; Zeng, Tao

    2015-01-01

    In insects, the frequency distribution of the measurements of sclerotized body parts is generally used to classify larval instars and is characterized by a multimodal overlap between instar stages. Nonparametric methods with fixed bandwidths, such as histograms, have significant limitations when used to fit this type of distribution, making it difficult to identify divisions between instars. Fixed bandwidths have also been chosen somewhat subjectively in the past, which is another problem. In this study, we describe an adaptive kernel smoothing method to differentiate instars based on discontinuities in the growth rates of sclerotized insect body parts. From Brooks' rule, we derived a new standard for assessing the quality of instar classification and a bandwidth selector that more accurately reflects the distributed character of specific variables. We used this method to classify the larvae of Austrosimulium tillyardianum (Diptera: Simuliidae) based on five different measurements. Based on head capsule width and head capsule length, the larvae were separated into nine instars. Based on head capsule postoccipital width and mandible length, the larvae were separated into 8 instars and 10 instars, respectively. No reasonable solution was found for antennal segment 3 length. Separation of the larvae into nine instars using head capsule width or head capsule length was most robust and agreed with Crosby's growth rule. By strengthening the distributed character of the separation variable through the use of variable bandwidths, the adaptive kernel smoothing method could identify divisions between instars more effectively and accurately than previous methods.

  12. An Adaptive Kernel Smoothing Method for Classifying Austrosimulium tillyardianum (Diptera: Simuliidae) Larval Instars

    PubMed Central

    Cen, Guanjun; Zeng, Xianru; Long, Xiuzhen; Wei, Dewei; Gao, Xuyuan; Zeng, Tao

    2015-01-01

    In insects, the frequency distribution of the measurements of sclerotized body parts is generally used to classify larval instars and is characterized by a multimodal overlap between instar stages. Nonparametric methods with fixed bandwidths, such as histograms, have significant limitations when used to fit this type of distribution, making it difficult to identify divisions between instars. Fixed bandwidths have also been chosen somewhat subjectively in the past, which is another problem. In this study, we describe an adaptive kernel smoothing method to differentiate instars based on discontinuities in the growth rates of sclerotized insect body parts. From Brooks’ rule, we derived a new standard for assessing the quality of instar classification and a bandwidth selector that more accurately reflects the distributed character of specific variables. We used this method to classify the larvae of Austrosimulium tillyardianum (Diptera: Simuliidae) based on five different measurements. Based on head capsule width and head capsule length, the larvae were separated into nine instars. Based on head capsule postoccipital width and mandible length, the larvae were separated into 8 instars and 10 instars, respectively. No reasonable solution was found for antennal segment 3 length. Separation of the larvae into nine instars using head capsule width or head capsule length was most robust and agreed with Crosby’s growth rule. By strengthening the distributed character of the separation variable through the use of variable bandwidths, the adaptive kernel smoothing method could identify divisions between instars more effectively and accurately than previous methods. PMID:26546689

  13. Mapping Spatial Variations of Absorption and Scattering in the Crust: Sensitivity Kernels and Preliminary Application to the Alps

    NASA Astrophysics Data System (ADS)

    Margerin, L.; Mayor, J.; Calvet, M.

    2015-12-01

    Among the physical processes that affect the amplitude of seismic waves, attenuation is one of the most poorly understood and undetermined factor. Two basic mechanisms control seismic attenuation in the crust: scattering by small-scale heterogeneities, and absorption of seismic energy by inelastic and irreversible processes. A number of techniques have been devised to retrieve attenuation information from the modeling of direct seismic waves emitted by earthquakes. However, a major issue with the use of ballistic signals lies in the fact that their amplitude is affected by multiple factors that are difficult to disentangle in practice: radiation pattern, focussing/defocussing or site effects. Moreover, since both scattering and absorption manifest themselves as an approximately exponential decay of direct wave amplitude with distance, it is not possible to separate their effects from attenuation measurements based on ballistic waves only. In this work, we propose a multiple scattering approach to map independently scattering and absorption properties of the crust using seismic coda waves. To this end, we introduce a model of energy transport of seismic energy known as radiative transfer and use perturbation theory to derive sensitivity kernels for the intensity detected in the coda. Numerical evaluation of these kernels demonstrates that coda waves possess distinct spatial sensitivities to absorption and scattering. These results pave the way for the development of a genuine tomographic approach to the mapping of absorption and scattering in the crust. Preliminary results on the absorption structure of the Alps in the 1-32 Hz frequency reveal some interesting correlations with the geology at spatial scales ranging from a few tens to a few thousand kilometers. Regions of high absorption delineate sedimentary structures such as basins, grabens and alluvial valleys while localized zones of weak absorption correlate with mantellic or plutonic intrusions such as the

  14. Kernel based model parametrization and adaptation with applications to battery management systems

    NASA Astrophysics Data System (ADS)

    Weng, Caihao

    With the wide spread use of energy storage systems, battery state of health (SOH) monitoring has become one of the most crucial challenges in power and energy research, as SOH significantly affects the performance and life cycle of batteries as well as the systems they are interacting with. Identifying the SOH and adapting of the battery energy/power management system accordingly are thus two important challenges for applications such as electric vehicles, smart buildings and hybrid power systems. This dissertation focuses on the identification of lithium ion battery capacity fading, and proposes an on-board implementable model parametrization and adaptation framework for SOH monitoring. Both parametric and non-parametric approaches that are based on kernel functions are explored for the modeling of battery charging data and aging signature extraction. A unified parametric open circuit voltage model is first developed to improve the accuracy of battery state estimation. Several analytical and numerical methods are then investigated for the non-parametric modeling of battery data, among which the support vector regression (SVR) algorithm is shown to be the most robust and consistent approach with respect to data sizes and ranges. For data collected on LiFePO 4 cells, it is shown that the model developed with the SVR approach is able to predict the battery capacity fading with less than 2% error. Moreover, motivated by the initial success of applying kernel based modeling methods for battery SOH monitoring, this dissertation further exploits the parametric SVR representation for real-time battery characterization supported by test data. Through the study of the invariant properties of the support vectors, a kernel based model parametrization and adaptation framework is developed. The high dimensional optimization problem in the learning algorithm could be reformulated as a parameter estimation problem, that can be solved by standard estimation algorithms such as the

  15. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

    PubMed Central

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

  16. Image classification with densely sampled image windows and generalized adaptive multiple kernel learning.

    PubMed

    Yan, Shengye; Xu, Xinxing; Xu, Dong; Lin, Stephen; Li, Xuelong

    2015-03-01

    We present a framework for image classification that extends beyond the window sampling of fixed spatial pyramids and is supported by a new learning algorithm. Based on the observation that fixed spatial pyramids sample a rather limited subset of the possible image windows, we propose a method that accounts for a comprehensive set of windows densely sampled over location, size, and aspect ratio. A concise high-level image feature is derived to effectively deal with this large set of windows, and this higher level of abstraction offers both efficient handling of the dense samples and reduced sensitivity to misalignment. In addition to dense window sampling, we introduce generalized adaptive l(p)-norm multiple kernel learning (GA-MKL) to learn a robust classifier based on multiple base kernels constructed from the new image features and multiple sets of prelearned classifiers from other classes. With GA-MKL, multiple levels of image features are effectively fused, and information is shared among different classifiers. Extensive evaluation on benchmark datasets for object recognition (Caltech256 and Caltech101) and scene recognition (15Scenes) demonstrate that the proposed method outperforms the state-of-the-art under a broad range of settings.

  17. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    PubMed

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

  18. Deriving Sensitivity Kernels of Coda-Wave Travel Times to Velocity Changes Based on the Three-Dimensional Single Isotropic Scattering Model

    NASA Astrophysics Data System (ADS)

    Nakahara, Hisashi; Emoto, Kentaro

    2017-01-01

    Recently, coda-wave interferometry has been used to monitor temporal changes in subsurface structures. Seismic velocity changes have been detected by coda-wave interferometry in association with large earthquakes and volcanic eruptions. To constrain the spatial extent of the velocity changes, spatial homogeneity is often assumed. However, it is important to locate the region of the velocity changes correctly to understand physical mechanisms causing them. In this paper, we are concerned with the sensitivity kernels relating travel times of coda waves to velocity changes. In previous studies, sensitivity kernels have been formulated for two-dimensional single scattering and multiple scattering, three-dimensional multiple scattering, and diffusion. In this paper, we formulate and derive analytical expressions of the sensitivity kernels for three-dimensional single-scattering case. These sensitivity kernels show two peaks at both source and receiver locations, which is similar to the previous studies using different scattering models. The two peaks are more pronounced for later lapse time. We validate our formulation by comparing it with finite-difference simulations of acoustic wave propagation. Our formulation enables us to evaluate the sensitivity kernels analytically, which is particularly useful for the analysis of body waves from deeper earthquakes.

  19. Quasi-conformal shape of the BFKL kernel and impact factors for scattering of colourless particles

    NASA Astrophysics Data System (ADS)

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

    2011-07-01

    The NLO BFKL kernel in the Möbius representation is transformed to the quasi-conformal shape in theories containing fermions and scalars in arbitrary representations of the colour group. Corresponding transformations of impact factors of colourless particles are discussed.

  20. Adaptive diffusion kernel learning from biological networks for protein function prediction

    PubMed Central

    Sun, Liang; Ji, Shuiwang; Ye, Jieping

    2008-01-01

    Background Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods are suitable for learning from graph-based data such as biological networks, as they only require the abstraction of the similarities between objects into the kernel matrix. One key issue in kernel methods is the selection of a good kernel function. Diffusion kernels, the discretization of the familiar Gaussian kernel of Euclidean space, are commonly used for graph-based data. Results In this paper, we address the issue of learning an optimal diffusion kernel, in the form of a convex combination of a set of pre-specified kernels constructed from biological networks, for protein function prediction. Most prior work on this kernel learning task focus on variants of the loss function based on Support Vector Machines (SVM). Their extensions to other loss functions such as the one based on Kullback-Leibler (KL) divergence, which is more suitable for mining biological networks, lead to expensive optimization problems. By exploiting the special structure of the diffusion kernel, we show that this KL divergence based kernel learning problem can be formulated as a simple optimization problem, which can then be solved efficiently. It is further extended to the multi-task case where we predict multiple functions of a protein simultaneously. We evaluate the efficiency and effectiveness of the proposed algorithms using two benchmark data sets. Conclusion Results show that the performance of linearly combined diffusion kernel is better than every single candidate diffusion kernel. When the number of tasks is large, the algorithms based on multiple tasks are favored due to their competitive recognition performance and small computational costs. PMID:18366736

  1. Adaptive reproducing kernel particle method for extraction of the cortical surface.

    PubMed

    Xu, Meihe; Thompson, Paul M; Toga, Arthur W

    2006-06-01

    We propose a novel adaptive approach based on the Reproducing Kernel Particle Method (RKPM) to extract the cortical surfaces of the brain from three-dimensional (3-D) magnetic resonance images (MRIs). To formulate the discrete equations of the deformable model, a flexible particle shape function is employed in the Galerkin approximation of the weak form of the equilibrium equations. The proposed support generation method ensures that support of all particles cover the entire computational domains. The deformable model is adaptively adjusted by dilating the shape function and by inserting or merging particles in the high curvature regions or regions stopped by the target boundary. The shape function of the particle with a dilation parameter is adaptively constructed in response to particle insertion or merging. The proposed method offers flexibility in representing highly convolved structures and in refining the deformable models. Self-intersection of the surface, during evolution, is prevented by tracing backward along gradient descent direction from the crest interface of the distance field, which is computed by fast marching. These operations involve a significant computational cost. The initial model for the deformable surface is simple and requires no prior knowledge of the segmented structure. No specific template is required, e.g., an average cortical surface obtained from many subjects. The extracted cortical surface efficiently localizes the depths of the cerebral sulci, unlike some other active surface approaches that penalize regions of high curvature. Comparisons with manually segmented landmark data are provided to demonstrate the high accuracy of the proposed method. We also compare the proposed method to the finite element method, and to a commonly used cortical surface extraction approach, the CRUISE method. We also show that the independence of the shape functions of the RKPM from the underlying mesh enhances the convergence speed of the deformable

  2. Real-time detection of generic objects using objectness estimation and locally adaptive regression kernels matching

    NASA Astrophysics Data System (ADS)

    Zheng, Zhihui; Gao, Lei; Xiao, Liping; Zhou, Bin; Gao, Shibo

    2015-12-01

    Our purpose is to develop a detection algorithm capable of searching for generic interest objects in real time without large training sets and long-time training stages. Instead of the classical sliding window object detection paradigm, we employ an objectness measure to produce a small set of candidate windows efficiently using Binarized Normed Gradients and a Laplacian of Gaussian-like filter. We then extract Locally Adaptive Regression Kernels (LARKs) as descriptors both from a model image and the candidate windows which measure the likeness of a pixel to its surroundings. Using a matrix cosine similarity measure, the algorithm yields a scalar resemblance map, indicating the likelihood of similarity between the model and the candidate windows. By employing nonparametric significance tests and non-maxima suppression, we detect the presence of objects similar to the given model. Experiments show that the proposed detection paradigm can automatically detect the presence, the number, as well as location of similar objects to the given model. The high quality and efficiency of our method make it suitable for real time multi-category object detection applications.

  3. Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering.

    PubMed

    Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao

    2015-01-01

    An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

  4. Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering

    PubMed Central

    Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin

    2015-01-01

    An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity. PMID:26793269

  5. Adaptive learning in complex reproducing kernel Hilbert spaces employing Wirtinger's subgradients.

    PubMed

    Bouboulis, Pantelis; Slavakis, Konstantinos; Theodoridis, Sergios

    2012-03-01

    This paper presents a wide framework for non-linear online supervised learning tasks in the context of complex valued signal processing. The (complex) input data are mapped into a complex reproducing kernel Hilbert space (RKHS), where the learning phase is taking place. Both pure complex kernels and real kernels (via the complexification trick) can be employed. Moreover, any convex, continuous and not necessarily differentiable function can be used to measure the loss between the output of the specific system and the desired response. The only requirement is the subgradient of the adopted loss function to be available in an analytic form. In order to derive analytically the subgradients, the principles of the (recently developed) Wirtinger's calculus in complex RKHS are exploited. Furthermore, both linear and widely linear (in RKHS) estimation filters are considered. To cope with the problem of increasing memory requirements, which is present in almost all online schemes in RKHS, the sparsification scheme, based on projection onto closed balls, has been adopted. We demonstrate the effectiveness of the proposed framework in a non-linear channel identification task, a non-linear channel equalization problem and a quadrature phase shift keying equalization scheme, using both circular and non circular synthetic signal sources.

  6. Osteoarthritis classification using self organizing map based on gabor kernel and contrast-limited adaptive histogram equalization.

    PubMed

    Anifah, Lilik; Purnama, I Ketut Eddy; Hariadi, Mochamad; Purnomo, Mauridhi Hery

    2013-01-01

    Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4.

  7. Osteoarthritis Classification Using Self Organizing Map Based on Gabor Kernel and Contrast-Limited Adaptive Histogram Equalization

    PubMed Central

    Anifah, Lilik; Purnama, I Ketut Eddy; Hariadi, Mochamad; Purnomo, Mauridhi Hery

    2013-01-01

    Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4. PMID:23525188

  8. Active impulsive noise control using maximum correntropy with adaptive kernel size

    NASA Astrophysics Data System (ADS)

    Lu, Lu; Zhao, Haiquan

    2017-03-01

    The active noise control (ANC) based on the principle of superposition is an attractive method to attenuate the noise signals. However, the impulsive noise in the ANC systems will degrade the performance of the controller. In this paper, a filtered-x recursive maximum correntropy (FxRMC) algorithm is proposed based on the maximum correntropy criterion (MCC) to reduce the effect of outliers. The proposed FxRMC algorithm does not requires any priori information of the noise characteristics and outperforms the filtered-x least mean square (FxLMS) algorithm for impulsive noise. Meanwhile, in order to adjust the kernel size of FxRMC algorithm online, a recursive approach is proposed through taking into account the past estimates of error signals over a sliding window. Simulation and experimental results in the context of active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

  9. Single-trial EEG-based emotion recognition using kernel Eigen-emotion pattern and adaptive support vector machine.

    PubMed

    Liu, Yi-Hung; Wu, Chien-Te; Kao, Yung-Hwa; Chen, Ya-Ting

    2013-01-01

    Single-trial electroencephalography (EEG)-based emotion recognition enables us to perform fast and direct assessments of human emotional states. However, previous works suggest that a great improvement on the classification accuracy of valence and arousal levels is still needed. To address this, we propose a novel emotional EEG feature extraction method: kernel Eigen-emotion pattern (KEEP). An adaptive SVM is also proposed to deal with the problem of learning from imbalanced emotional EEG data sets. In this study, a set of pictures from IAPS are used for emotion induction. Results based on seven participants show that KEEP gives much better classification results than the widely-used EEG frequency band power features. Also, the adaptive SVM greatly improves classification performance of commonly-adopted SVM classifier. Combined use of KEEP and adaptive SVM can achieve high average valence and arousal classification rates of 73.42% and 73.57%. The highest classification rates for valence and arousal are 80% and 79%, respectively. The results are very promising.

  10. Adaptive kernel-based image denoising employing semi-parametric regularization.

    PubMed

    Bouboulis, Pantelis; Slavakis, Konstantinos; Theodoridis, Sergios

    2010-06-01

    The main contribution of this paper is the development of a novel approach, based on the theory of Reproducing Kernel Hilbert Spaces (RKHS), for the problem of noise removal in the spatial domain. The proposed methodology has the advantage that it is able to remove any kind of additive noise (impulse, gaussian, uniform, etc.) from any digital image, in contrast to the most commonly used denoising techniques, which are noise dependent. The problem is cast as an optimization task in a RKHS, by taking advantage of the celebrated Representer Theorem in its semi-parametric formulation. The semi-parametric formulation, although known in theory, has so far found limited, to our knowledge, application. However, in the image denoising problem, its use is dictated by the nature of the problem itself. The need for edge preservation naturally leads to such a modeling. Examples verify that in the presence of gaussian noise the proposed methodology performs well compared to wavelet based technics and outperforms them significantly in the presence of impulse or mixed noise.

  11. Equalizing resolution in smoothed-particle hydrodynamics calculations using self-adaptive sinc kernels

    NASA Astrophysics Data System (ADS)

    García-Senz, Domingo; Cabezón, Rubén M.; Escartín, José A.; Ebinger, Kevin

    2014-10-01

    Context. The smoothed-particle hydrodynamics (SPH) technique is a numerical method for solving gas-dynamical problems. It has been applied to simulate the evolution of a wide variety of astrophysical systems. The method has a second-order accuracy, with a resolution that is usually much higher in the compressed regions than in the diluted zones of the fluid. Aims: We propose and check a method to balance and equalize the resolution of SPH between high- and low-density regions. This method relies on the versatility of a family of interpolators called sinc kernels, which allows increasing the interpolation quality by varying only a single parameter (the exponent of the sinc function). Methods: The proposed method was checked and validated through a number of numerical tests, from standard one-dimensional Riemann problems in shock tubes, to multidimensional simulations of explosions, hydrodynamic instabilities, and the collapse of a Sun-like polytrope. Results: The analysis of the hydrodynamical simulations suggests that the scheme devised to equalize the accuracy improves the treatment of the post-shock regions and, in general, of the rarefacted zones of fluids while causing no harm to the growth of hydrodynamic instabilities. The method is robust and easy to implement with a low computational overload. It conserves mass, energy, and momentum and reduces to the standard SPH scheme in regions of the fluid that have smooth density gradients.

  12. Analysis and Implementation of Particle-to-Particle (P2P) Graphics Processor Unit (GPU) Kernel for Black-Box Adaptive Fast Multipole Method

    DTIC Science & Technology

    2015-06-01

    ARL-TR-7315 ● JUNE 2015 US Army Research Laboratory Analysis and Implementation of Particle-to- Particle (P2P) Graphics Processor ...Particle-to- Particle (P2P) Graphics Processor Unit (GPU) Kernel for Black-Box Adaptive Fast Multipole Method by Richard H Haney and Dale Shires...reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information

  13. A Programmable Liquid Collimator for Both Coded Aperture Adaptive Imaging and Multiplexed Compton Scatter Tomography

    DTIC Science & Technology

    2012-03-01

    Assessment of COMSCAN, A Compton Backscatter Imaging Camera , for the One-Sided Non-Destructive Inspection of Aerospace Compo- nents. Technical report...A PROGRAMMABLE LIQUID COLLIMATOR FOR BOTH CODED APERTURE ADAPTIVE IMAGING AND MULTIPLEXED COMPTON SCATTER TOMOGRAPHY THESIS Jack G. M. FitzGerald, 2d...LIQUID COLLIMATOR FOR BOTH CODED APERTURE ADAPTIVE IMAGING AND MULTIPLEXED COMPTON SCATTER TOMOGRAPHY THESIS Presented to the Faculty Department of

  14. Effect of adaptive threshold filtering on ultrasonic nakagami parameter to detect variation in scatterer concentration.

    PubMed

    Tsui, Po-Hsiang; Wan, Yung-Liang; Huang, Chih-Chung; Wang, Ming-Chen

    2010-10-01

    The Nakagami parameter is associated with the Nakagami distribution estimated from ultrasonic backscattered signals and closely reflects the scatterer concentrations in tissues. There is an interest in exploring the possibility of enhancing the ability of the Nakagami parameter to characterize tissues. In this paper, we explore the effect of adaptive thresholdfiltering based on the noise-assisted empirical mode decomposition of the ultrasonic backscattered signals on the Nakagami parameter as a function of scatterer concentration for improving the Nakagami parameter performance. We carried out phantom experiments using 5 MHz focused and nonfocused transducers. Before filtering, the dynamic ranges of the Nakagami parameter, estimated using focused and nonfocused transducers between the scatterer concentrations of 2 and 32 scatterers/mm3, were 0.44 and 0.1, respectively. After filtering, the dynamic ranges of the Nakagami parameter, using the focused and nonfocused transducers, were 0.71 and 0.79, respectively. The experimental results showed that the adaptive threshold filter makes the Nakagami parameter measured by a focused transducer more sensitive to the variation in the scatterer concentration. The proposed method also endows the Nakagami parameter measured by a nonfocused transducer with the ability to differentiate various scatterer concentrations. However, the Nakagami parameters estimated by focused and nonfocused transducers after adaptive threshold filtering have different physical meanings: the former represents the statistics of signals backscattered from unresolvable scatterers while the latter is associated with stronger resolvable scatterers or local inhomogeneity due to scatterer aggregation.

  15. A texture-based rolling bearing fault diagnosis scheme using adaptive optimal kernel time frequency representation and uniform local binary patterns

    NASA Astrophysics Data System (ADS)

    Chen, Haizhou; Wang, Jiaxu; Li, Junyang; Tang, Baoping

    2017-03-01

    This paper presents a new scheme for rolling bearing fault diagnosis using texture features extracted from the time-frequency representations (TFRs) of the signal. To derive the proposed texture features, firstly adaptive optimal kernel time frequency representation (AOK-TFR) is applied to extract TFRs of the signal which essentially describe the energy distribution characteristics of the signal over time and frequency domain. Since the AOK-TFR uses the signal-dependent radially Gaussian kernel that adapts over time, it can exactly track the minor variations in the signal and provide an excellent time-frequency concentration in noisy environment. Simulation experiments are furthermore performed in comparison with common time-frequency analysis methods under different noisy conditions. Secondly, the uniform local binary pattern (uLBP), which is a computationally simple and noise-resistant texture analysis method, is used to calculate the histograms from the TFRs to characterize rolling bearing fault information. Finally, the obtained histogram feature vectors are input into the multi-SVM classifier for pattern recognition. We validate the effectiveness of the proposed scheme by several experiments, and comparative results demonstrate that the new fault diagnosis technique performs better than most state-of-the-art techniques, and yet we find that the proposed algorithm possess the adaptivity and noise resistance qualities that could be very useful in real industrial applications.

  16. Time-reversed adapted-perturbation (TRAP) optical focusing onto dynamic objects inside scattering media

    PubMed Central

    Ma, Cheng; Xu, Xiao; Liu, Yan; Wang, Lihong V.

    2014-01-01

    The ability to steer and focus light inside scattering media has long been sought for a multitude of applications. To form optical foci inside scattering media, the only feasible strategy at present is to guide photons by using either implanted1 or virtual2–4 guide stars, which can be inconvenient and limits potential applications. Here, we report a scheme for focusing light inside scattering media by employing intrinsic dynamics as guide stars. By time-reversing the perturbed component of the scattered light adaptively, we show that it is possible to focus light to the origin of the perturbation. Using the approach, we demonstrate non-invasive dynamic light focusing onto moving targets and imaging of a time-variant object obscured by highly scattering media. Anticipated applications include imaging and photoablation of angiogenic vessels in tumours as well as other biomedical uses. PMID:25530797

  17. Kernel phase and kernel amplitude in Fizeau imaging

    NASA Astrophysics Data System (ADS)

    Pope, Benjamin J. S.

    2016-12-01

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

  18. Adaptive eigenspace method for inverse scattering problems in the frequency domain

    NASA Astrophysics Data System (ADS)

    Grote, Marcus J.; Kray, Marie; Nahum, Uri

    2017-02-01

    A nonlinear optimization method is proposed for the solution of inverse scattering problems in the frequency domain, when the scattered field is governed by the Helmholtz equation. The time-harmonic inverse medium problem is formulated as a PDE-constrained optimization problem and solved by an inexact truncated Newton-type iteration. Instead of a grid-based discrete representation, the unknown wave speed is projected to a particular finite-dimensional basis of eigenfunctions, which is iteratively adapted during the optimization. Truncating the adaptive eigenspace (AE) basis at a (small and slowly increasing) finite number of eigenfunctions effectively introduces regularization into the inversion and thus avoids the need for standard Tikhonov-type regularization. Both analytical and numerical evidence underpins the accuracy of the AE representation. Numerical experiments demonstrate the efficiency and robustness to missing or noisy data of the resulting adaptive eigenspace inversion method.

  19. Adaptive phase matching probe-injection technique for enhancement of Brillouin scattering signal

    NASA Astrophysics Data System (ADS)

    Li, Hongwei; Shi, Guangyao; Lv, Yuelan; Zhang, Hongying; Gao, Wei

    2017-08-01

    We report on a simple and efficient method for enhancing Brillouin scattering signal, i.e., adaptive phase matching (APM) probe-injection technique. In this technique, a low-polarization broad-spectrum probe wave is injected opposite to the pump, which can enhance any stokes signal in its APM range instantly by selective stimulated Brillouin amplification. With advantages of simple scheme, real-time multi-signal enhancement and sweep-free measurement, this technique has a great potential for improving the signal-to-noise ratio of Brillouin gain spectrum in the Brillouin scattering application systems.

  20. A versatile setup using femtosecond adaptive spectroscopic techniques for coherent anti-Stokes Raman scattering

    SciTech Connect

    Shen, Yujie; Voronine, Dmitri V.; Sokolov, Alexei V.; Scully, Marlan O.

    2015-08-15

    We report a versatile setup based on the femtosecond adaptive spectroscopic techniques for coherent anti-Stokes Raman scattering. The setup uses a femtosecond Ti:Sapphire oscillator source and a folded 4f pulse shaper, in which the pulse shaping is carried out through conventional optical elements and does not require a spatial light modulator. Our setup is simple in alignment, and can be easily switched between the collinear single-beam and the noncollinear two-beam configurations. We demonstrate the capability for investigating both transparent and highly scattering samples by detecting transmitted and reflected signals, respectively.

  1. A versatile setup using femtosecond adaptive spectroscopic techniques for coherent anti-Stokes Raman scattering.

    PubMed

    Shen, Yujie; Voronine, Dmitri V; Sokolov, Alexei V; Scully, Marlan O

    2015-08-01

    We report a versatile setup based on the femtosecond adaptive spectroscopic techniques for coherent anti-Stokes Raman scattering. The setup uses a femtosecond Ti:Sapphire oscillator source and a folded 4f pulse shaper, in which the pulse shaping is carried out through conventional optical elements and does not require a spatial light modulator. Our setup is simple in alignment, and can be easily switched between the collinear single-beam and the noncollinear two-beam configurations. We demonstrate the capability for investigating both transparent and highly scattering samples by detecting transmitted and reflected signals, respectively.

  2. Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression.

    PubMed

    Serag, Ahmed; Aljabar, Paul; Ball, Gareth; Counsell, Serena J; Boardman, James P; Rutherford, Mary A; Edwards, A David; Hajnal, Joseph V; Rueckert, Daniel

    2012-02-01

    Medical imaging has shown that, during early development, the brain undergoes more changes in size, shape and appearance than at any other time in life. A better understanding of brain development requires a spatio-temporal atlas that characterizes the dynamic changes during this period. In this paper we present an approach for constructing a 4D atlas of the developing brain, between 28 and 44 weeks post-menstrual age at time of scan, using T1 and T2 weighted MR images from 204 premature neonates. The method used for the creation of the average 4D atlas utilizes non-rigid registration between all pairs of images to eliminate bias in the atlas toward any of the original images. In addition, kernel regression is used to produce age-dependent anatomical templates. A novelty in our approach is the use of a time-varying kernel width, to overcome the variations in the distribution of subjects at different ages. This leads to an atlas that retains a consistent level of detail at every time-point. Comparisons between the resulting atlas and atlases constructed using affine and non-rigid registration are presented. The resulting 4D atlas has greater anatomic definition than currently available 4D atlases created using various affine and non-rigid registration approaches, an important factor in improving registrations between the atlas and individual subjects. Also, the resulting 4D atlas can serve as a good representative of the population of interest as it reflects both global and local changes. The atlas is publicly available at www.brain-development.org.

  3. Scattering Kernel of Polyatomic Gases

    DTIC Science & Technology

    2005-07-13

    Energétique - IUSTI 5, rue Enrico Fermi 13453 Marseille cedex 13 France †Gilbert.Meolans@polytech.univ-mrs.fr Abstract. This paper is devoted to the...Mécanique Energétique - IUSTI 5, rue Enrico Fermi 13453 Marseille cedex 13 France 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING

  4. Proton dose calculation on scatter-corrected CBCT image: Feasibility study for adaptive proton therapy

    PubMed Central

    Park, Yang-Kyun; Sharp, Gregory C.; Phillips, Justin; Winey, Brian A.

    2015-01-01

    Purpose: To demonstrate the feasibility of proton dose calculation on scatter-corrected cone-beam computed tomographic (CBCT) images for the purpose of adaptive proton therapy. Methods: CBCT projection images were acquired from anthropomorphic phantoms and a prostate patient using an on-board imaging system of an Elekta infinity linear accelerator. Two previously introduced techniques were used to correct the scattered x-rays in the raw projection images: uniform scatter correction (CBCTus) and a priori CT-based scatter correction (CBCTap). CBCT images were reconstructed using a standard FDK algorithm and GPU-based reconstruction toolkit. Soft tissue ROI-based HU shifting was used to improve HU accuracy of the uncorrected CBCT images and CBCTus, while no HU change was applied to the CBCTap. The degree of equivalence of the corrected CBCT images with respect to the reference CT image (CTref) was evaluated by using angular profiles of water equivalent path length (WEPL) and passively scattered proton treatment plans. The CBCTap was further evaluated in more realistic scenarios such as rectal filling and weight loss to assess the effect of mismatched prior information on the corrected images. Results: The uncorrected CBCT and CBCTus images demonstrated substantial WEPL discrepancies (7.3 ± 5.3 mm and 11.1 ± 6.6 mm, respectively) with respect to the CTref, while the CBCTap images showed substantially reduced WEPL errors (2.4 ± 2.0 mm). Similarly, the CBCTap-based treatment plans demonstrated a high pass rate (96.0% ± 2.5% in 2 mm/2% criteria) in a 3D gamma analysis. Conclusions: A priori CT-based scatter correction technique was shown to be promising for adaptive proton therapy, as it achieved equivalent proton dose distributions and water equivalent path lengths compared to those of a reference CT in a selection of anthropomorphic phantoms. PMID:26233175

  5. Experimental validation of a multi-energy x-ray adapted scatter separation method

    NASA Astrophysics Data System (ADS)

    Sossin, A.; Rebuffel, V.; Tabary, J.; Létang, J. M.; Freud, N.; Verger, L.

    2016-12-01

    Both in radiography and computed tomography (CT), recently emerged energy-resolved x-ray photon counting detectors enable the identification and quantification of individual materials comprising the inspected object. However, the approaches used for these operations require highly accurate x-ray images. The accuracy of the images is severely compromised by the presence of scattered radiation, which leads to a loss of spatial contrast and, more importantly, a bias in radiographic material imaging and artefacts in CT. The aim of the present study was to experimentally evaluate a recently introduced partial attenuation spectral scatter separation approach (PASSSA) adapted for multi-energy imaging. For this purpose, a prototype x-ray system was used. Several radiographic acquisitions of an anthropomorphic thorax phantom were performed. Reference primary images were obtained via the beam-stop (BS) approach. The attenuation images acquired from PASSSA-corrected data showed a substantial increase in local contrast and internal structure contour visibility when compared to uncorrected images. A substantial reduction of scatter induced bias was also achieved. Quantitatively, the developed method proved to be in relatively good agreement with the BS data. The application of the proposed scatter correction technique lowered the initial normalized root-mean-square error (NRMSE) of 45% between the uncorrected total and the reference primary spectral images by a factor of 9, thus reducing it to around 5%.

  6. Proton dose calculation on scatter-corrected CBCT image: Feasibility study for adaptive proton therapy

    SciTech Connect

    Park, Yang-Kyun Sharp, Gregory C.; Phillips, Justin; Winey, Brian A.

    2015-08-15

    Purpose: To demonstrate the feasibility of proton dose calculation on scatter-corrected cone-beam computed tomographic (CBCT) images for the purpose of adaptive proton therapy. Methods: CBCT projection images were acquired from anthropomorphic phantoms and a prostate patient using an on-board imaging system of an Elekta infinity linear accelerator. Two previously introduced techniques were used to correct the scattered x-rays in the raw projection images: uniform scatter correction (CBCT{sub us}) and a priori CT-based scatter correction (CBCT{sub ap}). CBCT images were reconstructed using a standard FDK algorithm and GPU-based reconstruction toolkit. Soft tissue ROI-based HU shifting was used to improve HU accuracy of the uncorrected CBCT images and CBCT{sub us}, while no HU change was applied to the CBCT{sub ap}. The degree of equivalence of the corrected CBCT images with respect to the reference CT image (CT{sub ref}) was evaluated by using angular profiles of water equivalent path length (WEPL) and passively scattered proton treatment plans. The CBCT{sub ap} was further evaluated in more realistic scenarios such as rectal filling and weight loss to assess the effect of mismatched prior information on the corrected images. Results: The uncorrected CBCT and CBCT{sub us} images demonstrated substantial WEPL discrepancies (7.3 ± 5.3 mm and 11.1 ± 6.6 mm, respectively) with respect to the CT{sub ref}, while the CBCT{sub ap} images showed substantially reduced WEPL errors (2.4 ± 2.0 mm). Similarly, the CBCT{sub ap}-based treatment plans demonstrated a high pass rate (96.0% ± 2.5% in 2 mm/2% criteria) in a 3D gamma analysis. Conclusions: A priori CT-based scatter correction technique was shown to be promising for adaptive proton therapy, as it achieved equivalent proton dose distributions and water equivalent path lengths compared to those of a reference CT in a selection of anthropomorphic phantoms.

  7. Weighted Bergman kernels and virtual Bergman kernels

    NASA Astrophysics Data System (ADS)

    Roos, Guy

    2005-12-01

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

  8. The context-tree kernel for strings.

    PubMed

    Cuturi, Marco; Vert, Jean-Philippe

    2005-10-01

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

  9. Modern industrial simulation tools: Kernel-level integration of high performance parallel processing, object-oriented numerics, and adaptive finite element analysis. Final report, July 16, 1993--September 30, 1997

    SciTech Connect

    Deb, M.K.; Kennon, S.R.

    1998-04-01

    A cooperative R&D effort between industry and the US government, this project, under the HPPP (High Performance Parallel Processing) initiative of the Dept. of Energy, started the investigations into parallel object-oriented (OO) numerics. The basic goal was to research and utilize the emerging technologies to create a physics-independent computational kernel for applications using adaptive finite element method. The industrial team included Computational Mechanics Co., Inc. (COMCO) of Austin, TX (as the primary contractor), Scientific Computing Associates, Inc. (SCA) of New Haven, CT, Texaco and CONVEX. Sandia National Laboratory (Albq., NM) was the technology partner from the government side. COMCO had the responsibility of the main kernel design and development, SCA had the lead in parallel solver technology and guidance on OO technologies was Sandia`s main expertise in this venture. CONVEX and Texaco supported the partnership by hardware resource and application knowledge, respectively. As such, a minimum of fifty-percent cost-sharing was provided by the industry partnership during this project. This report describes the R&D activities and provides some details about the prototype kernel and example applications.

  10. Robotic intelligence kernel

    DOEpatents

    Bruemmer, David J.

    2009-11-17

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

  11. Fully automatic hp-adaptivity for acoustic and electromagnetic scattering in three dimensions

    NASA Astrophysics Data System (ADS)

    Kurtz, Jason Patrick

    We present an algorithm for fully automatic hp-adaptivity for finite element approximations of elliptic and Maxwell boundary value problems in three dimensions. The algorithm automatically generates a sequence of coarse grids, and a corresponding sequence of fine grids, such that the energy norm of the error decreases exponentially with respect to the number of degrees of freedom in either sequence. At each step, we employ a discrete optimization algorithm to determine the refinements for the current coarse grid such that the projection-based interpolation error for the current fine grid solution decreases with an optimal rate with respect to the number of degrees of freedom added by the refinement. The refinements are restricted only by the requirement that the resulting mesh is at most 1-irregular, but they may be anisotropic in both element size h and order of approximation p. While we cannot prove that our method converges at all, we present numerical evidence of exponential convergence for a diverse suite of model problems from acoustic and electromagnetic scattering. In particular we show that our method is well suited to the automatic resolution of exterior problems truncated by the introduction of a perfectly matched layer. To enable and accelerate the solution of these problems on commodity hardware, we include a detailed account of three critical aspects of our implementation, namely an efficient implementation of sum factorization, several efficient interfaces to the direct multi-frontal solver MUMPS, and some fast direct solvers for the computation of a sequence of nested projections.

  12. Semisupervised kernel matrix learning by kernel propagation.

    PubMed

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

    2010-11-01

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

  13. Approximate kernel competitive learning.

    PubMed

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

    2015-03-01

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

  14. SU-E-J-175: Proton Dose Calculation On Scatter-Corrected CBCT Image: Feasibility Study for Adaptive Proton Therapy

    SciTech Connect

    Park, Y; Winey, B; Sharp, G

    2014-06-01

    Purpose: To demonstrate feasibility of proton dose calculation on scattercorrected CBCT images for the purpose of adaptive proton therapy. Methods: Two CBCT image sets were acquired from a prostate cancer patient and a thorax phantom using an on-board imaging system of an Elekta infinity linear accelerator. 2-D scatter maps were estimated using a previously introduced CT-based technique, and were subtracted from each raw projection image. A CBCT image set was then reconstructed with an open source reconstruction toolkit (RTK). Conversion from the CBCT number to HU was performed by soft tissue-based shifting with reference to the plan CT. Passively scattered proton plans were simulated on the plan CT and corrected/uncorrected CBCT images using the XiO treatment planning system. For quantitative evaluation, water equivalent path length (WEPL) was compared in those treatment plans. Results: The scatter correction method significantly improved image quality and HU accuracy in the prostate case where large scatter artifacts were obvious. However, the correction technique showed limited effects on the thorax case that was associated with fewer scatter artifacts. Mean absolute WEPL errors from the plans with the uncorrected and corrected images were 1.3 mm and 5.1 mm in the thorax case and 13.5 mm and 3.1 mm in the prostate case. The prostate plan dose distribution of the corrected image demonstrated better agreement with the reference one than that of the uncorrected image. Conclusion: A priori CT-based CBCT scatter correction can reduce the proton dose calculation error when large scatter artifacts are involved. If scatter artifacts are low, an uncorrected CBCT image is also promising for proton dose calculation when it is calibrated with the soft-tissue based shifting.

  15. Putting Priors in Mixture Density Mercer Kernels

    NASA Technical Reports Server (NTRS)

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

    2004-01-01

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

  16. Kernel-Based Equiprobabilistic Topographic Map Formation.

    PubMed

    Van Hulle MM

    1998-09-15

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

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

    NASA Technical Reports Server (NTRS)

    Duvall, T. L., Jr.

    2006-01-01

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

  18. Adaptation of the University of Wisconsin High Spectral Resolution Lidar for Polarization and Multiple Scattering Measurements

    NASA Technical Reports Server (NTRS)

    Eloranta, E. W.; Piironen, P. K.

    1996-01-01

    Quantitative lidar measurements of aerosol scattering are hampered by the need for calibrations and the problem of correcting observed backscatter profiles for the effects of attenuation. The University of Wisconsin High Spectral Resolution Lidar (HSRL) addresses these problems by separating molecular scattering contributions from the aerosol scattering; the molecular scattering is then used as a calibration target that is available at each point in the observed profiles. While the HSRl approach has intrinsic advantages over competing techniques, realization of these advantages requires implementation of a technically demanding system which is potentially very sensitive to changes in temperature and mechanical alignments. This paper describes a new implementation of the HSRL in an instrumented van which allows measurements during field experiments. The HSRL was modified to measure depolarization. In addition, both the signal amplitude and depolarization variations with receiver field of view are simultaneously measured. This allows for discrimination of ice clouds from water clouds and observation of multiple scattering contributions to the lidar return.

  19. Optimized Kernel Entropy Components.

    PubMed

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

    2016-02-25

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

  20. Correction for patient table-induced scattered radiation in cone-beam computed tomography (CBCT)

    SciTech Connect

    Sun Mingshan; Nagy, Tamas; Virshup, Gary; Partain, Larry; Oelhafen, Markus; Star-Lack, Josh

    2011-04-15

    Purpose: In image-guided radiotherapy, an artifact typically seen in axial slices of x-ray cone-beam computed tomography (CBCT) reconstructions is a dark region or ''black hole'' situated below the scan isocenter. The authors trace the cause of the artifact to scattered radiation produced by radiotherapy patient tabletops and show it is linked to the use of the offset-detector acquisition mode to enlarge the imaging field-of-view. The authors present a hybrid scatter kernel superposition (SKS) algorithm to correct for scatter from both the object-of-interest and the tabletop. Methods: Monte Carlo simulations and phantom experiments were first performed to identify the source of the black hole artifact. For correction, a SKS algorithm was developed that uses separate kernels to estimate scatter from the patient tabletop and the object-of-interest. Each projection is divided into two regions, one defined by the shadow cast by the tabletop on the imager and one defined by the unshadowed region. The region not shadowed by the tabletop is processed using the recently developed fast adaptive scatter kernel superposition (fASKS) method which employs asymmetric kernels that best model scatter transport through bodylike objects. The shadowed region is convolved with a combination of slab-derived symmetric SKS kernels and asymmetric fASKS kernels. The composition of the hybrid kernels is projection-angle-dependent. To test the algorithm, pelvis phantom and in vivo data were acquired using a CBCT test stand, a Varian Acuity simulator, and a Varian On-Board Imager, all of which have similar geometries and components. Artifact intensities and Hounsfield unit (HU) accuracies in the reconstructions were assessed before and after the correction. Results: The hybrid kernel algorithm provided effective correction and produced substantially better scatter estimates than the symmetric SKS or asymmetric fASKS methods alone. HU nonuniformities in the reconstructed pelvis phantom were

  1. A self-adaptive method for creating high efficiency communication channels through random scattering media

    PubMed Central

    Hao, Xiang; Martin-Rouault, Laure; Cui, Meng

    2014-01-01

    Controlling the propagation of electromagnetic waves is important to a broad range of applications. Recent advances in controlling wave propagation in random scattering media have enabled optical focusing and imaging inside random scattering media. In this work, we propose and demonstrate a new method to deliver optical power more efficiently through scattering media. Drastically different from the random matrix characterization approach, our method can rapidly establish high efficiency communication channels using just a few measurements, regardless of the number of optical modes, and provides a practical and robust solution to boost the signal levels in optical or short wave communications. We experimentally demonstrated analog and digital signal transmission through highly scattering media with greatly improved performance. Besides scattering, our method can also reduce the loss of signal due to absorption. Experimentally, we observed that our method forced light to go around absorbers, leading to even higher signal improvement than in the case of purely scattering media. Interestingly, the resulting signal improvement is highly directional, which provides a new means against eavesdropping. PMID:25070592

  2. Iterative software kernels

    SciTech Connect

    Duff, I.

    1994-12-31

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

  3. Learning with Box Kernels.

    PubMed

    Melacci, Stefano; Gori, Marco

    2013-04-12

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

  4. Learning with box kernels.

    PubMed

    Melacci, Stefano; Gori, Marco

    2013-11-01

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

  5. Kernel Affine Projection Algorithms

    NASA Astrophysics Data System (ADS)

    Liu, Weifeng; Príncipe, José C.

    2008-12-01

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

  6. Fractal Weyl law for Linux Kernel architecture

    NASA Astrophysics Data System (ADS)

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

    2011-01-01

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

  7. Online Sequential Extreme Learning Machine With Kernels.

    PubMed

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

    2015-09-01

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

  8. Mie Light-Scattering Granulometer with an Adaptive Numerical Filtering Method. II. Experiments.

    PubMed

    Hespel, L; Delfour, A; Guillame, B

    2001-02-20

    A nephelometer is presented that theoretically requires no absolute calibration. This instrument is used for determining the particle-size distribution of various scattering media (aerosols, fogs, rocket exhausts, engine plumes, and the like) from angular static light-scattering measurements. An inverse procedure is used, which consists of a least-squares method and a regularization scheme based on numerical filtering. To retrieve the distribution function one matches the experimental data with theoretical patterns derived from Mie theory. The main principles of the inverse method are briefly presented, and the nephelometer is then described with the associated partial calibration procedure. Finally, the whole granulometer system (inverse method and nephelometer) is validated by comparison of measurements of scattering media with calibrated monodisperse or known size distribution functions.

  9. Multiple collaborative kernel tracking.

    PubMed

    Fan, Zhimin; Yang, Ming; Wu, Ying

    2007-07-01

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

  10. Fully adaptive algorithms for multivariate integral equations using the non-standard form and multiwavelets with applications to the Poisson and bound-state Helmholtz kernels in three dimensions

    NASA Astrophysics Data System (ADS)

    Frediani, Luca; Fossgaard, Eirik; Flå, Tor; Ruud, Kenneth

    2013-07-01

    We have developed and implemented a general formalism for fast numerical solution of time-independent linear partial differential equations as well as integral equations through the application of numerically separable integral operators in d ≥ 1 dimensions using the non-standard (NS) form. The proposed formalism is universal, compact and oriented towards the practical implementation into a working code using multiwavelets. The formalism is applied to the case of Poisson and bound-state Helmholtz operators in d = 3. Our algorithms are fully adaptive in the sense that the grid supporting each function is obtained on the fly while the function is being computed. In particular, when the function g = O f is obtained by applying an integral operator O, the corresponding grid is not obtained by transferring the grid from the input function f. This aspect has significant implications that will be discussed in the numerical section. The operator kernels are represented in a separated form with finite but arbitrary precision using Gaussian functions. Such a representation combined with the NS form allows us to build a sparse, banded representation of Green's operator kernel. We have implemented a code for the application of such operators in a separated NS form to a multivariate function in a finite but, in principle, arbitrary number of dimensions. The error of the method is controlled, while the low complexity of the numerical algorithm is kept. The implemented code explicitly computes all the 22d components of the d-dimensional operator. Our algorithms are described in detail in the paper through pseudo-code examples. The final goal of our work is to be able to apply this method to build a fast and accurate Kohn-Sham solver for density functional theory.

  11. KERNEL PHASE IN FIZEAU INTERFEROMETRY

    SciTech Connect

    Martinache, Frantz

    2010-11-20

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

  12. Acoustic Scattering Kernels from Arctic Sea Ice.

    DTIC Science & Technology

    1986-10-01

    84-C-0180 S. PErfORMING ORGANIZATION NAME AND AOMRSSS 10. PROGRAM ELEMENT. PROJECT . TASK Science Applications International Corp. AREA & WORK UNIT...al. (1986) described an implementation of SISM/ICE for the ASTRAL and PE models. The concepts are also relevant to other models, including FACT, FFP...Even if the ice field contains keels of only a single size, the projected keel width intercepted by any particular track will take on a range of

  13. Robotic Intelligence Kernel: Communications

    SciTech Connect

    Walton, Mike C.

    2009-09-16

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

  15. Robotic Intelligence Kernel: Driver

    SciTech Connect

    2009-09-16

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

  16. Kernel bandwidth optimization in spike rate estimation.

    PubMed

    Shimazaki, Hideaki; Shinomoto, Shigeru

    2010-08-01

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

  17. SEEDS ADAPTIVE OPTICS IMAGING OF THE ASYMMETRIC TRANSITION DISK OPH IRS 48 IN SCATTERED LIGHT

    SciTech Connect

    Follette, Katherine B.; Close, Laird M.; Grady, Carol A.; Swearingen, Jeremy R.; Sitko, Michael L.; Champney, Elizabeth H.; Van der Marel, Nienke; Maaskant, Koen; Min, Michiel; Takami, Michihiro; Kuchner, Marc J; McElwain, Michael W.; Muto, Takayuki; Mayama, Satoshi; Fukagawa, Misato; Russell, Ray W.; Kudo, Tomoyuki; Kusakabe, Nobuhiko; Hashimoto, Jun; Abe, Lyu; and others

    2015-01-10

    We present the first resolved near-infrared imagery of the transition disk Oph IRS 48 (WLY 2-48), which was recently observed with ALMA to have a strongly asymmetric submillimeter flux distribution. H-band polarized intensity images show a ∼60 AU radius scattered light cavity with two pronounced arcs of emission, one from northeast to southeast and one smaller, fainter, and more distant arc in the northwest. K-band scattered light imagery reveals a similar morphology, but with a clear third arc along the southwestern rim of the disk cavity. This arc meets the northwestern arc at nearly a right angle, revealing the presence of a spiral arm or local surface brightness deficit in the disk, and explaining the east-west brightness asymmetry in the H-band data. We also present 0.8-5.4 μm IRTF SpeX spectra of this object, which allow us to constrain the spectral class to A0 ± 1 and measure a low mass accretion rate of 10{sup –8.5} M {sub ☉} yr{sup –1}, both consistent with previous estimates. We investigate a variety of reddening laws in order to fit the multiwavelength spectral energy distribution of Oph IRS 48 and find a best fit consistent with a younger, higher luminosity star than previous estimates.

  18. Scatter-plot-based method for noise characteristics evaluation in remote sensing images using adaptive image clustering procedure

    NASA Astrophysics Data System (ADS)

    Abramova, Victoriya V.; Abramov, Sergey K.; Lukin, Vladimir V.; Vozel, Benoit; Chehdi, Kacem

    2016-10-01

    Several modifications of scatter-plot-based method for mixed noise parameters estimation are proposed. The modifications relate to the stage of image segmentation and they are intended to adaptively separate image blocks into clusters taking into account image peculiarities and to choose a required number of clusters. Comparative performance analysis of the proposed modifications for images from TID2008 database is performed. It is shown that the best estimation accuracy is provided by a method with automatic determination of a required number of clusters followed by block separation into clusters using k-means method. This modification allows improving the accuracy of noise characteristics estimation by up to 5% for both signal-independent and signal-dependent noise components in comparison to the basic method. The results for real-life data are presented.

  19. UNICOS Kernel Internals Application Development

    NASA Technical Reports Server (NTRS)

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

    1995-01-01

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

  20. Kernel mucking in top

    SciTech Connect

    LeFebvre, W.

    1994-08-01

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

  1. Full Waveform Inversion Using Waveform Sensitivity Kernels

    NASA Astrophysics Data System (ADS)

    Schumacher, Florian; Friederich, Wolfgang

    2013-04-01

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

  2. Robotic Intelligence Kernel: Architecture

    SciTech Connect

    2009-09-16

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

  3. Robotic Intelligence Kernel: Visualization

    SciTech Connect

    2009-09-16

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

  4. Multiple Kernel Point Set Registration.

    PubMed

    Nguyen, Thanh Minh; Wu, Q M Jonathan

    2015-12-22

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

  5. Multiple Kernel Point Set Registration.

    PubMed

    Nguyen, Thanh Minh; Wu, Q M Jonathan

    2016-06-01

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

  6. Kernel Optimization in Discriminant Analysis

    PubMed Central

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

    2011-01-01

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

  7. Adaptive multi-objective archive-based hybrid scatter search for segmentation in lung computed tomography imaging

    NASA Astrophysics Data System (ADS)

    Bong, Chin Wei; Yoong Lam, Hong; Tajudin Khader, Ahamad; Kamarulzaman, Hamzah

    2012-03-01

    This article proposes a multi-objective clustering ensemble method for medical image segmentation. The proposed method is called adaptive multi-objective archive-based hybrid scatter search (AMAHSS). It utilizes fuzzy clustering with optimization of three fitness functions: global fuzzy compactness of the clusters, fuzzy separation and symmetry distance-based cluster validity index. The AMAHSS enables the search strategy to explore intensively the search space with high-quality solutions and to move to unexplored search space when necessary. The best single solution is processed using the metaclustering algorithm. The proposed framework is designed to segment lung computed tomography images for candidate nodule detection. This candidate nodule will then be classified as cancerous or non-cancerous. The authors validate the method with standard k-means, fuzzy c-means and the multi-objective genetic algorithm with different postprocessing methods for the final solution. The results obtained from the benchmark experiment indicate that the method achieves up to 90% of the positive predictive rate.

  8. Adaptation of an evaporative light-scattering detector to micro and capillary liquid chromatography and response assessment.

    PubMed

    Gaudin, Karen; Baillet, Arlette; Chaminade, Pierre

    2004-10-08

    A commercially available evaporative light-scattering detection (ELSD) system was adapted for micro and capillary LC. Therefore the various parameters involved in the droplet formation during the nebulization step in the ELSD system were studied. It was shown that the velocity term in the Nukiyama Tanasawa equation remains constant, leading to droplets of the same order of magnitude for narrow bore and capillary columns. Consequently, the ELSD modification was performed by decreasing the internal diameter of the effluent capillary tube in the nebulizer nozzle and by keeping its external diameter constant. Next, response curves for a conventional and the developed micro and capillary LC were compared as to investigate why a linear ELSD response is often obtained when used in micro or capillary LC. By splitting the flow rate post column, we showed that the nebulization process was not at the origin of the phenomenon. For ceramide III and tripalmitin, the response curves were found to be non-linear. However the curvature was less significant when the columns internal diameter decreased. Calculated particle size profiles for micro or capillary LC suggest that the particle entering the detection chamber are bigger than under conventional LC conditions. Last, triethylamine and formic acid were used to increase the response of the detector. The response enhancement, expected from previous studies, was established for the two lipids involved in this study.

  9. Effects of sample size on KERNEL home range estimates

    USGS Publications Warehouse

    Seaman, D.E.; Millspaugh, J.J.; Kernohan, Brian J.; Brundige, Gary C.; Raedeke, Kenneth J.; Gitzen, Robert A.

    1999-01-01

    Kernel methods for estimating home range are being used increasingly in wildlife research, but the effect of sample size on their accuracy is not known. We used computer simulations of 10-200 points/home range and compared accuracy of home range estimates produced by fixed and adaptive kernels with the reference (REF) and least-squares cross-validation (LSCV) methods for determining the amount of smoothing. Simulated home ranges varied from simple to complex shapes created by mixing bivariate normal distributions. We used the size of the 95% home range area and the relative mean squared error of the surface fit to assess the accuracy of the kernel home range estimates. For both measures, the bias and variance approached an asymptote at about 50 observations/home range. The fixed kernel with smoothing selected by LSCV provided the least-biased estimates of the 95% home range area. All kernel methods produced similar surface fit for most simulations, but the fixed kernel with LSCV had the lowest frequency and magnitude of very poor estimates. We reviewed 101 papers published in The Journal of Wildlife Management (JWM) between 1980 and 1997 that estimated animal home ranges. A minority of these papers used nonparametric utilization distribution (UD) estimators, and most did not adequately report sample sizes. We recommend that home range studies using kernel estimates use LSCV to determine the amount of smoothing, obtain a minimum of 30 observations per animal (but preferably a?Y50), and report sample sizes in published results.

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

    PubMed

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

    2013-04-01

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

  11. A convolution/superposition method using primary and scatter dose kernels formed for energy bins of X-ray spectra reconstructed as a function of off-axis distance: a theoretical study on 10-MV X-ray dose calculations in thorax-like phantoms.

    PubMed

    Iwasaki, Akira; Kimura, Shigenobu; Sutoh, Kohji; Kamimura, Kazuo; Sasamori, Makoto; Komai, Fumio; Seino, Morio; Terashima, Singo; Kubota, Mamoru; Hirota, Junichi; Hosokawa, Yoichiro

    2011-07-01

    A convolution/superposition method is proposed for use with primary and scatter dose kernels formed for energy bins of X-ray spectra reconstructed as a function of off-axis distance. It should be noted that the number of energy bins is usually about ten, and that the reconstructed X-ray spectra can reasonably be applied to media with a wide range of effective Z numbers, ranging from water to lead. The study was carried out for 10-MV X-ray doses in water and thorax-like phantoms with the use of open-jaw-collimated fields. The dose calculations were made separately for primary, scatter, and electron contamination dose components, for which we used two extended radiation sources: one was on the X-ray target and the other on the flattening filter. To calculate the in-air beam intensities at points on the isocenter plane for a given jaw-collimated field, we introduced an in-air output factor (OPF(in-air)) expressed as the product of the off-center jaw-collimator scatter factor (off-center S (c)), the source off-center ratio factor (OCR(source)), and the jaw-collimator radiation reflection factor (RRF(c)). For more accurate dose calculations, we introduce an electron spread fluctuation factor (F (fwd)) to take into account the angular and spatial spread fluctuation for electrons traveling through different media.

  12. 7 CFR 51.1415 - Inedible kernels.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  13. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  14. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  15. Adaptive Kernel Based Machine Learning Methods

    DTIC Science & Technology

    2012-10-15

    multiscale collocation method with a matrix compression strategy to discretize the system of integral equations and then use the multilevel...augmentation method to solve the resulting discrete system. A priori and a posteriori 1 parameter choice strategies are developed for thesemethods. The...performance of the proximity algo- rithms for the L1/TV denoising model. This leads us to a new characterization of all solutions to the L1/TV model via fixed

  16. Sliding Window Generalized Kernel Affine Projection Algorithm Using Projection Mappings

    NASA Astrophysics Data System (ADS)

    Slavakis, Konstantinos; Theodoridis, Sergios

    2008-12-01

    Very recently, a solution to the kernel-based online classification problem has been given by the adaptive projected subgradient method (APSM). The developed algorithm can be considered as a generalization of a kernel affine projection algorithm (APA) and the kernel normalized least mean squares (NLMS). Furthermore, sparsification of the resulting kernel series expansion was achieved by imposing a closed ball (convex set) constraint on the norm of the classifiers. This paper presents another sparsification method for the APSM approach to the online classification task by generating a sequence of linear subspaces in a reproducing kernel Hilbert space (RKHS). To cope with the inherent memory limitations of online systems and to embed tracking capabilities to the design, an upper bound on the dimension of the linear subspaces is imposed. The underlying principle of the design is the notion of projection mappings. Classification is performed by metric projection mappings, sparsification is achieved by orthogonal projections, while the online system's memory requirements and tracking are attained by oblique projections. The resulting sparsification scheme shows strong similarities with the classical sliding window adaptive schemes. The proposed design is validated by the adaptive equalization problem of a nonlinear communication channel, and is compared with classical and recent stochastic gradient descent techniques, as well as with the APSM's solution where sparsification is performed by a closed ball constraint on the norm of the classifiers.

  17. A feature extraction method of the particle swarm optimization algorithm based on adaptive inertia weight and chaos optimization for Brillouin scattering spectra

    NASA Astrophysics Data System (ADS)

    Zhang, Yanjun; Zhao, Yu; Fu, Xinghu; Xu, Jinrui

    2016-10-01

    A novel particle swarm optimization algorithm based on adaptive inertia weight and chaos optimization is proposed for extracting the features of Brillouin scattering spectra. Firstly, the adaptive inertia weight parameter of the velocity is introduced to the basic particle swarm algorithm. Based on the current iteration number of particles and the adaptation value, the algorithm can change the weight coefficient and adjust the iteration speed of searching space for particles, so the local optimization ability can be enhanced. Secondly, the logical self-mapping chaotic search is carried out by using the chaos optimization in particle swarm optimization algorithm, which makes the particle swarm optimization algorithm jump out of local optimum. The novel algorithm is compared with finite element analysis-Levenberg Marquardt algorithm, particle swarm optimization-Levenberg Marquardt algorithm and particle swarm optimization algorithm by changing the linewidth, the signal-to-noise ratio and the linear weight ratio of Brillouin scattering spectra. Then the algorithm is applied to the feature extraction of Brillouin scattering spectra in different temperatures. The simulation analysis and experimental results show that this algorithm has a high fitting degree and small Brillouin frequency shift error for different linewidth, SNR and linear weight ratio. Therefore, this algorithm can be applied to the distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can effectively improve the accuracy of Brillouin frequency shift extraction.

  18. Bayesian Inversion of Seabed Scattering Data

    DTIC Science & Technology

    2014-09-30

    Bayesian Inversion of Seabed Scattering Data (Special Research Award in Ocean Acoustics ) Gavin A.M.W. Steininger School of Earth & Ocean...Figure 1: Schematic diagram of the environmental parameterizations for the monostatic- scattering kernel and reflection- coefficient forward and inverse...frequencies. Left two columns: scattering data; right two columns: reflection- coefficient data. 3 layers, hence accounting for the uncertainty of

  19. Adaptation.

    PubMed

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  20. Dynamic experiment design regularization approach to adaptive imaging with array radar/SAR sensor systems.

    PubMed

    Shkvarko, Yuriy; Tuxpan, José; Santos, Stewart

    2011-01-01

    We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the "model-free" variational analysis (VA)-based image enhancement approach and the "model-based" descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations.

  1. An Ensemble Approach to Building Mercer Kernels with Prior Information

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  2. Flexible Kernel Memory

    PubMed Central

    Nowicki, Dimitri; Siegelmann, Hava

    2010-01-01

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

  3. Adapt

    NASA Astrophysics Data System (ADS)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  4. 7 CFR 51.2295 - Half kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  5. 7 CFR 981.9 - Kernel weight.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  6. 7 CFR 981.7 - Edible kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  7. Semisupervised kernel marginal Fisher analysis for face recognition.

    PubMed

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

    2013-01-01

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

  8. Semisupervised Kernel Marginal Fisher Analysis for Face Recognition

    PubMed Central

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

    2013-01-01

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

  9. On the convergence of the inverse diffraction transform kernel using Cesàro summability

    NASA Astrophysics Data System (ADS)

    Pallotta, M.

    1995-12-01

    In diffraction tomography, optical information processing, and, more generally, Fourier optics, the diffraction transform solves both the direct and the inverse boundary value propagation problem for the Helmholtz equation. Its kernel is itself an integral. It is the representation of the evolution operator associated with translations of a constrained Cartesian coordinate. This fact threatens the inverse scattering problem with a divergence if the transform kernel is understood as a Cauchy integral. The kernel is, however, everywhere convergent if its integral representation is interpreted as a summable integral.

  10. An Approximate Approach to Automatic Kernel Selection.

    PubMed

    Ding, Lizhong; Liao, Shizhong

    2016-02-02

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

  11. Identification of Damaged Wheat Kernels and Cracked-Shell Hazelnuts with Impact Acoustics Time-Frequency Patterns

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A new adaptive time-frequency (t-f) analysis and classification procedure is applied to impact acoustic signals for detecting hazelnuts with cracked shells and three types of damaged wheat kernels. Kernels were dropped onto a steel plate, and the resulting impact acoustic signals were recorded with ...

  12. RTOS kernel in portable electrocardiograph

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  13. Density Estimation with Mercer Kernels

    NASA Technical Reports Server (NTRS)

    Macready, William G.

    2003-01-01

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

  14. Prioritizing individual genetic variants after kernel machine testing using variable selection.

    PubMed

    He, Qianchuan; Cai, Tianxi; Liu, Yang; Zhao, Ni; Harmon, Quaker E; Almli, Lynn M; Binder, Elisabeth B; Engel, Stephanie M; Ressler, Kerry J; Conneely, Karen N; Lin, Xihong; Wu, Michael C

    2016-12-01

    Kernel machine learning methods, such as the SNP-set kernel association test (SKAT), have been widely used to test associations between traits and genetic polymorphisms. In contrast to traditional single-SNP analysis methods, these methods are designed to examine the joint effect of a set of related SNPs (such as a group of SNPs within a gene or a pathway) and are able to identify sets of SNPs that are associated with the trait of interest. However, as with many multi-SNP testing approaches, kernel machine testing can draw conclusion only at the SNP-set level, and does not directly inform on which one(s) of the identified SNP set is actually driving the associations. A recently proposed procedure, KerNel Iterative Feature Extraction (KNIFE), provides a general framework for incorporating variable selection into kernel machine methods. In this article, we focus on quantitative traits and relatively common SNPs, and adapt the KNIFE procedure to genetic association studies and propose an approach to identify driver SNPs after the application of SKAT to gene set analysis. Our approach accommodates several kernels that are widely used in SNP analysis, such as the linear kernel and the Identity by State (IBS) kernel. The proposed approach provides practically useful utilities to prioritize SNPs, and fills the gap between SNP set analysis and biological functional studies. Both simulation studies and real data application are used to demonstrate the proposed approach.

  15. Water equivalent path length calculations using scatter-corrected head and neck CBCT images to evaluate patients for adaptive proton therapy

    NASA Astrophysics Data System (ADS)

    Kim, Jihun; Park, Yang-Kyun; Sharp, Gregory; Busse, Paul; Winey, Brian

    2017-01-01

    Proton therapy has dosimetric advantages due to the well-defined range of the proton beam over photon radiotherapy. When the proton beams, however, are delivered to the patient in fractionated radiation treatment, the treatment outcome is affected by delivery uncertainties such as anatomic change in the patient and daily patient setup error. This study aims at establishing a method to evaluate the dosimetric impact of the anatomic change and patient setup error during head and neck proton therapy. Range variations due to the delivery uncertainties were assessed by calculating water equivalent path length (WEPL) to the distal edge of tumor volume using planning CT and weekly treatment cone-beam CT (CBCT) images. Specifically, mean difference and root mean squared deviation (RMSD) of the distal WEPLs were calculated as the weekly range variations. To accurately calculate the distal WEPLs, an existing CBCT scatter correction algorithm was used. An automatic rigid registration was used to align the planning CT and treatment CBCT images, simulating a six degree-of-freedom couch correction at treatments. The authors conclude that the dosimetric impact of the anatomic change and patient setup error was reasonably captured in the differences of the distal WEPL variation with a range calculation uncertainty of 2%. The proposed method to calculate the distal WEPL using the scatter-corrected CBCT images can be an essential tool to decide the necessity of re-planning in adaptive proton therapy.

  16. Local Observed-Score Kernel Equating

    ERIC Educational Resources Information Center

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

    2014-01-01

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

  17. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

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

  18. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

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

  19. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

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

  20. 7 CFR 981.408 - Inedible kernel.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

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

  1. Travel-Time and Amplitude Sensitivity Kernels

    DTIC Science & Technology

    2011-09-01

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

  2. The NAS kernel benchmark program

    NASA Technical Reports Server (NTRS)

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

    1985-01-01

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

  3. Four-particle decay of the Bethe-Salpeter kernel in the high-temperature Ising model

    NASA Astrophysics Data System (ADS)

    Auil, F.

    2002-12-01

    In this article we study the four-particle decay of the Bethe-Salpeter (B-S) kernel for the high-temperature Ising model. We use the hyperplane decoupling method [T. Spencer, Commun. Math. Phys. 44, 143 (1975); R. S. Schor, Nucl. Phys. B 222, 71 (1983)] to prove exponential decay in a set of variables particularly adapted to the methods of Spencer and Zirilli [Commun. Math. Phys. 49, 1 (1976)] for the analysis of scattering and bound states in QFT, transcribed to lattice theories by Auil and Barata [Ann. Henri Poincare 2, 1065 (2001)]. We study arbitrary derivatives of the general n-point correlation functions with respect to the interpolating variables, and we are able to obtain, in some cases, information about the third derivatives of the B-S kernel. As a later consequence, we have two-body asymptotic completeness for the (massive) Euclidean lattice field theory implemented by this model. This allows us to analyze the Ornstein-Zernike behavior of four-point functions, related to the specific heat of the model.

  4. An adaptive-in-temperature method for on-the-fly sampling of thermal neutron scattering data in continuous-energy Monte Carlo codes

    NASA Astrophysics Data System (ADS)

    Pavlou, Andrew Theodore

    The Monte Carlo simulation of full-core neutron transport requires high fidelity data to represent not only the various types of possible interactions that can occur, but also the temperature and energy regimes for which these data are relevant. For isothermal conditions, nuclear cross section data are processed in advance of running a simulation. In reality, the temperatures in a neutronics simulation are not fixed, but change with respect to the temperatures computed from an associated heat transfer or thermal hydraulic (TH) code. To account for the temperature change, a code user must either 1) compute new data at the problem temperature inline during the Monte Carlo simulation or 2) pre-compute data at a variety of temperatures over the range of possible values. Inline data processing is computationally inefficient while pre-computing data at many temperatures can be memory expensive. An alternative on-the-fly approach to handle the temperature component of nuclear data is desired. By on-the-fly we mean a procedure that adjusts cross section data to the correct temperature adaptively during the Monte Carlo random walk instead of before the running of a simulation. The on-the-fly procedure should also preserve simulation runtime efficiency. While on-the-fly methods have recently been developed for higher energy regimes, the double differential scattering of thermal neutrons has not been examined in detail until now. In this dissertation, an on-the-fly sampling method is developed by investigating the temperature dependence of the thermal double differential scattering distributions. The temperature dependence is analyzed with a linear least squares regression test to develop fit coefficients that are used to sample thermal scattering data at any temperature. The amount of pre-stored thermal scattering data has been drastically reduced from around 25 megabytes per temperature per nuclide to only a few megabytes per nuclide by eliminating the need to compute data

  5. Multigroup computation of the temperature-dependent Resonance Scattering Model (RSM) and its implementation

    SciTech Connect

    Ghrayeb, S. Z.; Ouisloumen, M.; Ougouag, A. M.; Ivanov, K. N.

    2012-07-01

    A multi-group formulation for the exact neutron elastic scattering kernel is developed. This formulation is intended for implementation into a lattice physics code. The correct accounting for the crystal lattice effects influences the estimated values for the probability of neutron absorption and scattering, which in turn affect the estimation of core reactivity and burnup characteristics. A computer program has been written to test the formulation for various nuclides. Results of the multi-group code have been verified against the correct analytic scattering kernel. In both cases neutrons were started at various energies and temperatures and the corresponding scattering kernels were tallied. (authors)

  6. Adaptive density estimator for galaxy surveys

    NASA Astrophysics Data System (ADS)

    Saar, Enn

    2016-10-01

    Galaxy number or luminosity density serves as a basis for many structure classification algorithms. Several methods are used to estimate this density. Among them kernel methods have probably the best statistical properties and allow also to estimate the local sample errors of the estimate. We introduce a kernel density estimator with an adaptive data-driven anisotropic kernel, describe its properties and demonstrate the wealth of additional information it gives us about the local properties of the galaxy distribution.

  7. Spatially varying scatter compensation for chest radiographs using a hybrid Madaline artificial neural network

    NASA Astrophysics Data System (ADS)

    Lo, Joseph Y.; Baydush, Alan H.; Floyd, Carey E., Jr.

    1994-05-01

    We developed a hybrid artificial neural network for scatter compensation in digital portable chest radiographs. The network inputs an image region of interest (ROI), and outputs the scatter estimate at the ROI's center. We segmented each image into four regions by relative detected exposure, then trained a separate Adaline (adaptive linear element) or adaptive filter for each region. We produced a spatially varying hybrid Madaline (mulitple Adaline) by combining outputs from weight matrices of different sizes trained for different durations. The network was trained with 20 patient or 1280 examples, then evaluated with another 5 patients or 320 examples. Scatter estimation errors were not very different, ranging from the Adaline's 6.9 percent to the hybrid Madaline's 5.5 percent. Primary errors (more relevant to quantitative radiography techniques like dual energy imaging) were 43 percent for the Adaline, reduced to 27 percent for the Madaline, and further reduced to 19 percent for the hybrid Madaline. The trained weight matrices, which act like convolution filters, resembled the shape and magnitude of scatter point spread functions. All networks outperformed conventional convolution-subraction techniques using analytical kernels. With its spatially varying neural network model, the hybrid Madaline provided the most accurate and robust estimation of scatter and primary exposures.

  8. Nonlinear Deep Kernel Learning for Image Annotation.

    PubMed

    Jiu, Mingyuan; Sahbi, Hichem

    2017-02-08

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

  9. Fuzzy C-means clustering with local information and kernel metric for image segmentation.

    PubMed

    Gong, Maoguo; Liang, Yan; Shi, Jiao; Ma, Wenping; Ma, Jingjing

    2013-02-01

    In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.

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

    PubMed

    Kwak, Nojun

    2013-12-01

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

  11. Combustion Control of Diesel Engine using Feedback Error Learning with Kernel Online Learning Approach

    NASA Astrophysics Data System (ADS)

    Widayaka, Elfady Satya; Ohmori, Hiromitsu

    2016-09-01

    This paper shows how to design Multivariable Model Reference Adaptive Control System (MRACS) for “Tokyo University discrete-time engine model” proposed by Yasuda et al (2014). This controller configuration has the structure of “Feedback error learning (FEL)” and adaptive law is based on kernel method. Simulation results indicate that “kernelized” adaptive controllers can improve the tracking performance, the speed of convergence and the robustness to disturbances.

  12. A new implementation of scattering kernels in MCNPX

    NASA Astrophysics Data System (ADS)

    Muhrer, G.; Stupnik, A.; Schachinger, E.

    2004-08-01

    With the increased demand for cold neutrons the designers of cold sources are challenged to improve the quality of theoretical predictions of the behavior of such sources. This challenge can be met by improving codes like MCNPX or by improving the physics behind the codes. This paper discusses the impact improving the code has on the results of calculations in comparison with experiment.

  13. Diffusion Map Kernel Analysis for Target Classification

    DTIC Science & Technology

    2010-06-01

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

  14. Molecular Hydrodynamics from Memory Kernels

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  15. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  16. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

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

  17. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

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

  18. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

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

  19. 7 CFR 51.1441 - Half-kernel.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

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

  20. Cross-person activity recognition using reduced kernel extreme learning machine.

    PubMed

    Deng, Wan-Yu; Zheng, Qing-Hua; Wang, Zhong-Min

    2014-05-01

    Activity recognition based on mobile embedded accelerometer is very important for developing human-centric pervasive applications such as healthcare, personalized recommendation and so on. However, the distribution of accelerometer data is heavily affected by varying users. The performance will degrade when the model trained on one person is used to others. To solve this problem, we propose a fast and accurate cross-person activity recognition model, known as TransRKELM (Transfer learning Reduced Kernel Extreme Learning Machine) which uses RKELM (Reduced Kernel Extreme Learning Machine) to realize initial activity recognition model. In the online phase OS-RKELM (Online Sequential Reduced Kernel Extreme Learning Machine) is applied to update the initial model and adapt the recognition model to new device users based on recognition results with high confidence level efficiently. Experimental results show that, the proposed model can adapt the classifier to new device users quickly and obtain good recognition performance.

  1. Bergman Kernel from Path Integral

    NASA Astrophysics Data System (ADS)

    Douglas, Michael R.; Klevtsov, Semyon

    2010-01-01

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

  2. Kernel current source density method.

    PubMed

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

    2012-02-01

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

  3. Ranking Support Vector Machine with Kernel Approximation

    PubMed Central

    Dou, Yong

    2017-01-01

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

  4. Ranking Support Vector Machine with Kernel Approximation.

    PubMed

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

    2017-01-01

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

  5. Improving the Bandwidth Selection in Kernel Equating

    ERIC Educational Resources Information Center

    Andersson, Björn; von Davier, Alina A.

    2014-01-01

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

  6. Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.

    PubMed

    Poon, Art F Y

    2015-09-01

    The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this "kernel-ABC" method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods.

  7. Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology

    PubMed Central

    Poon, Art F.Y.

    2015-01-01

    The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this “kernel-ABC” method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods. PMID:26006189

  8. Kernel method for corrections to scaling.

    PubMed

    Harada, Kenji

    2015-07-01

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

  9. LoCoH: nonparameteric kernel methods for constructing home ranges and utilization distributions.

    PubMed

    Getz, Wayne M; Fortmann-Roe, Scott; Cross, Paul C; Lyons, Andrew J; Ryan, Sadie J; Wilmers, Christopher C

    2007-02-14

    Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: "fixed sphere-of-influence," or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an "adaptive sphere-of-influence," or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original "fixed-number-of-points," or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).

  10. LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions

    PubMed Central

    Getz, Wayne M.; Fortmann-Roe, Scott; Wilmers, Christopher C.

    2007-01-01

    Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: “fixed sphere-of-influence,” or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an “adaptive sphere-of-influence,” or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original “fixed-number-of-points,” or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu). PMID:17299587

  11. LoCoH: Non-parameteric kernel methods for constructing home ranges and utilization distributions

    USGS Publications Warehouse

    Getz, Wayne M.; Fortmann-Roe, Scott; Cross, Paul C.; Lyons, Andrew J.; Ryan, Sadie J.; Wilmers, Christopher C.

    2007-01-01

    Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: ‘‘fixed sphere-of-influence,’’ or r -LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an ‘‘adaptive sphere-of-influence,’’ or a -LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a ), and compare them to the original ‘‘fixed-number-of-points,’’ or k -LoCoH (all kernels constructed from k -1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a -LoCoH is generally superior to k - and r -LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).

  12. Cross-domain question classification in community question answering via kernel mapping

    NASA Astrophysics Data System (ADS)

    Su, Lei; Hu, Zuoliang; Yang, Bin; Li, Yiyang; Chen, Jun

    2015-10-01

    An increasingly popular method for retrieving information is via the community question answering (CQA) systems such as Yahoo! Answers and Baidu Knows. In CQA, question classification plays an important role to find the answers. However, the labeled training examples for statistical question classifier are fairly expensive to obtain, as they require the experienced human efforts. Meanwhile, unlabeled data are readily available. This paper employs the method of domain adaptation via kernel mapping to solve this problem. In detail, the kernel approach is utilized to map the target-domain data and the source-domain data into a common space, where the question classifiers are trained under the closer conditional probabilities. The kernel mapping function is constructed by domain knowledge. Therefore, domain knowledge could be transferred from the labeled examples in the source domain to the unlabeled ones in the targeted domain. The statistical training model can be improved by using a large number of unlabeled data. Meanwhile, the Hadoop Platform is used to construct the mapping mechanism to reduce the time complexity. Map/Reduce enable kernel mapping for domain adaptation in parallel in the Hadoop Platform. Experimental results show that the accuracy of question classification could be improved by the method of kernel mapping. Furthermore, the parallel method in the Hadoop Platform could effective schedule the computing resources to reduce the running time.

  13. Bayesian Kernel Mixtures for Counts.

    PubMed

    Canale, Antonio; Dunson, David B

    2011-12-01

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

  14. MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES

    PubMed Central

    Dunson, David B.

    2013-01-01

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

  15. Perturbed kernel approximation on homogeneous manifolds

    NASA Astrophysics Data System (ADS)

    Levesley, J.; Sun, X.

    2007-02-01

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

  16. Rayleigh Scattering.

    ERIC Educational Resources Information Center

    Young, Andrew T.

    1982-01-01

    The correct usage of such terminology as "Rayleigh scattering,""Rayleigh lines,""Raman lines," and "Tyndall scattering" is resolved during an historical excursion through the physics of light-scattering by gas molecules. (Author/JN)

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

    PubMed

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

    2002-03-27

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

  18. 7 CFR 51.1403 - Kernel color classification.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

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

  19. 7 CFR 51.1403 - Kernel color classification.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

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

  20. 7 CFR 51.1403 - Kernel color classification.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

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

  1. 7 CFR 51.1403 - Kernel color classification.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

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

  2. 7 CFR 51.1403 - Kernel color classification.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  3. 7 CFR 51.2296 - Three-fourths half kernel.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  4. 7 CFR 51.2125 - Split or broken kernels.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  5. Xyloglucans from flaxseed kernel cell wall: Structural and conformational characterisation.

    PubMed

    Ding, Huihuang H; Cui, Steve W; Goff, H Douglas; Chen, Jie; Guo, Qingbin; Wang, Qi

    2016-10-20

    The structure of ethanol precipitated fraction from 1M KOH extracted flaxseed kernel polysaccharides (KPI-EPF) was studied for better understanding the molecular structures of flaxseed kernel cell wall polysaccharides. Based on methylation/GC-MS, NMR spectroscopy, and MALDI-TOF-MS analysis, the dominate sugar residues of KPI-EPF fraction comprised of (1,4,6)-linked-β-d-glucopyranose (24.1mol%), terminal α-d-xylopyranose (16.2mol%), (1,2)-α-d-linked-xylopyranose (10.7mol%), (1,4)-β-d-linked-glucopyranose (10.7mol%), and terminal β-d-galactopyranose (8.5mol%). KPI-EPF was proposed as xyloglucans: The substitution rate of the backbone is 69.3%; R1 could be T-α-d-Xylp-(1→, or none; R2 could be T-α-d-Xylp-(1→, T-β-d-Galp-(1→2)-α-d-Xylp-(1→, or T-α-l-Araf-(1→2)-α-d-Xylp-(1→; R3 could be T-α-d-Xylp-(1→, T-β-d-Galp-(1→2)-α-d-Xylp-(1→, T-α-l-Fucp-(1→2)-β-d-Galp-(1→2)-α-d-Xylp-(1→, or none. The Mw of KPI-EPF was calculated to be 1506kDa by static light scattering (SLS). The structure-sensitive parameter (ρ) of KPI-EPF was calculated as 1.44, which confirmed the highly branched structure of extracted xyloglucans. This new findings on flaxseed kernel xyloglucans will be helpful for understanding its fermentation properties and potential applications.

  6. Bergman kernel from the lowest Landau level

    NASA Astrophysics Data System (ADS)

    Klevtsov, S.

    2009-07-01

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

  7. Quantum kernel applications in medicinal chemistry.

    PubMed

    Huang, Lulu; Massa, Lou

    2012-07-01

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

  8. KITTEN Lightweight Kernel 0.1 Beta

    SciTech Connect

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

    2007-12-12

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

  9. TICK: Transparent Incremental Checkpointing at Kernel Level

    SciTech Connect

    Petrini, Fabrizio; Gioiosa, Roberto

    2004-10-25

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

  10. Evaluating the Gradient of the Thin Wire Kernel

    NASA Technical Reports Server (NTRS)

    Wilton, Donald R.; Champagne, Nathan J.

    2008-01-01

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

  11. Weighted Bergman Kernels and Quantization}

    NASA Astrophysics Data System (ADS)

    Engliš, Miroslav

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

  12. RKF-PCA: robust kernel fuzzy PCA.

    PubMed

    Heo, Gyeongyong; Gader, Paul; Frigui, Hichem

    2009-01-01

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

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

    PubMed

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

    2012-11-01

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

  14. Kernels and point processes associated with Whittaker functions

    NASA Astrophysics Data System (ADS)

    Blower, Gordon; Chen, Yang

    2016-09-01

    This article considers Whittaker's confluent hypergeometric function Wκ,μ where κ is real and μ is real or purely imaginary. Then φ(x) = x-μ-1/2Wκ,μ(x) arises as the scattering function of a continuous time linear system with state space L2(1/2, ∞) and input and output spaces C. The Hankel operator Γφ on L2(0, ∞) is expressed as a matrix with respect to the Laguerre basis and gives the Hankel matrix of moments of a Jacobi weight w0(x) = xb(1 - x)a. The operation of translating φ is equivalent to deforming w0 to give wt(x) = e-t/xxb(1 - x)a. The determinant of the Hankel matrix of moments of wɛ satisfies the σ form of Painlevé's transcendental differential equation PV. It is shown that Γφ gives rise to the Whittaker kernel from random matrix theory, as studied by Borodin and Olshanski [Commun. Math. Phys. 211, 335-358 (2000)]. Whittaker kernels are closely related to systems of orthogonal polynomials for a Pollaczek-Jacobi type weight lying outside the usual Szegö class.

  15. Linear acoustic sensitivity kernels and their applications in shallow water environments

    NASA Astrophysics Data System (ADS)

    Sarkar, Bikramjit

    presence of more complicated ocean sound-speed structure, with phase being the more linear observable. Additionally, the phase-derived sensitivity kernel is shown to be a better estimator of travel-time than the TSK, for which a possible explanation is suggested. The Born approximation has also been used to derive the acoustic sensitivity to perturbations at the boundary of an environment, in contrast to the volume perturbations discussed before. A sensitivity kernel for surface scattering is presented here, along with a numerical and experimental investigation of the acoustic response to surface displacements in an ultrasonic scale waveguide. The results are presented in both point-to-point and beam-to-beam formats, and suggest the potential use of sensitivity analysis in inverting for sea-surface structure.

  16. A generalized pyramid matching kernel for human action recognition in realistic videos.

    PubMed

    Zhu, Jun; Zhou, Quan; Zou, Weijia; Zhang, Rui; Zhang, Wenjun

    2013-10-24

    Human action recognition is an increasingly important research topic in the fields of video sensing, analysis and understanding. Caused by unconstrained sensing conditions, there exist large intra-class variations and inter-class ambiguities in realistic videos, which hinder the improvement of recognition performance for recent vision-based action recognition systems. In this paper, we propose a generalized pyramid matching kernel (GPMK) for recognizing human actions in realistic videos, based on a multi-channel "bag of words" representation constructed from local spatial-temporal features of video clips. As an extension to the spatial-temporal pyramid matching (STPM) kernel, the GPMK leverages heterogeneous visual cues in multiple feature descriptor types and spatial-temporal grid granularity levels, to build a valid similarity metric between two video clips for kernel-based classification. Instead of the predefined and fixed weights used in STPM, we present a simple, yet effective, method to compute adaptive channel weights of GPMK based on the kernel target alignment from training data. It incorporates prior knowledge and the data-driven information of different channels in a principled way. The experimental results on three challenging video datasets (i.e., Hollywood2, Youtube and HMDB51) validate the superiority of our GPMK w.r.t. the traditional STPM kernel for realistic human action recognition and outperform the state-of-the-art results in the literature.

  17. Jacobian transformed and detailed balance approximations for photon induced scattering

    NASA Astrophysics Data System (ADS)

    Wienke, B. R.; Budge, K. G.; Chang, J. H.; Dahl, J. A.; Hungerford, A. L.

    2012-01-01

    Photon emission and scattering are enhanced by the number of photons in the final state, and the photon transport equation reflects this in scattering-emission kernels and source terms. This is often a complication in both theoretical and numerical analyzes, requiring approximations and assumptions about background and material temperatures, incident and exiting photon energies, local thermodynamic equilibrium, plus other related aspects of photon scattering and emission. We review earlier schemes parameterizing photon scattering-emission processes, and suggest two alternative schemes. One links the product of photon and electron distributions in the final state to the product in the initial state by Jacobian transformation of kinematical variables (energy and angle), and the other links integrands of scattering kernels in a detailed balance requirement for overall (integrated) induced effects. Compton and inverse Compton differential scattering cross sections are detailed in appropriate limits, numerical integrations are performed over the induced scattering kernel, and for tabulation induced scattering terms are incorporated into effective cross sections for comparisons and numerical estimates. Relativistic electron distributions are assumed for calculations. Both Wien and Planckian distributions are contrasted for impact on induced scattering as LTE limit points. We find that both transformed and balanced approximations suggest larger induced scattering effects at high photon energies and low electron temperatures, and smaller effects in the opposite limits, compared to previous analyzes, with 10-20% increases in effective cross sections. We also note that both approximations can be simply implemented within existing transport modules or opacity processors as an additional term in the effective scattering cross section. Applications and comparisons include effective cross sections, kernel approximations, and impacts on radiative transport solutions in 1D

  18. Kernel-Based Reconstruction of Graph Signals

    NASA Astrophysics Data System (ADS)

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

    2017-02-01

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

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

    PubMed

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

    2017-03-03

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

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

    PubMed

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

    2014-01-01

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

  1. Hierarchical Adaptive Regression Kernels for Regression with Functional Predictors

    PubMed Central

    Woodard, Dawn B.; Crainiceanu, Ciprian; Ruppert, David

    2013-01-01

    We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies stochastically across subjects. We propose Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for regression with functional predictors, and show that our method is more effective and efficient for data that include features occurring at varying locations. We apply our methodology to a large and complex dataset from the Sleep Heart Health Study, to quantify the association between sleep characteristics and health outcomes. Software and technical appendices are provided in online supplemental materials. PMID:24293988

  2. A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

    PubMed Central

    Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

    2014-01-01

    Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

  3. Mathematical Modeling of Space-time Variations in Acoustic Transmission and Scattering from Schools of Swim Bladder Fish (FY14 Annual Report)

    DTIC Science & Technology

    2014-09-30

    Mathematical modeling of space-time variations in acoustic transmission and scattering from schools of swim bladder fish (FY14 Annual Report...domain theory of acoustic scattering from, and propagation through, schools of swim bladder fish at and near the swim bladder resonance frequency...coupled differential equations. It incorporates a verified swim bladder scattering kernel for the individual fish, includes multiple scattering

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

    PubMed

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

    1998-06-01

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

  5. On Bayesian adaptive video super resolution.

    PubMed

    Liu, Ce; Sun, Deqing

    2014-02-01

    Although multiframe super resolution has been extensively studied in past decades, super resolving real-world video sequences still remains challenging. In existing systems, either the motion models are oversimplified or important factors such as blur kernel and noise level are assumed to be known. Such models cannot capture the intrinsic characteristics that may differ from one sequence to another. In this paper, we propose a Bayesian approach to adaptive video super resolution via simultaneously estimating underlying motion, blur kernel, and noise level while reconstructing the original high-resolution frames. As a result, our system not only produces very promising super resolution results outperforming the state of the art, but also adapts to a variety of noise levels and blur kernels. To further analyze the effect of noise and blur kernel, we perform a two-step analysis using the Cramer-Rao bounds. We study how blur kernel and noise influence motion estimation with aliasing signals, how noise affects super resolution with perfect motion, and finally how blur kernel and noise influence super resolution with unknown motion. Our analysis results confirm empirical observations, in particular that an intermediate size blur kernel achieves the optimal image reconstruction results.

  6. On Bayesian Adaptive Video Super Resolution.

    PubMed

    Liu, Ce; Sun, Deqing

    2013-06-26

    Although multi-frame super resolution has been extensively studied in past decades, super resolving real-world video sequences still remains challenging. In existing systems, either the motion models are oversimplified, or important factors such as blur kernel and noise level are assumed to be known. Such models cannot capture the intrinsic characteristics that may differ from one sequence to another. In this paper, we propose a Bayesian approach to adaptive video super resolution via simultaneously estimating underlying motion, blur kernel and noise level while reconstructing the original high-res frames. As a result, our system not only produces very promising super resolution results outperforming the state of the art, but also adapts to a variety of noise levels and blur kernels. To further analyze the effect of noise and blur kernel, we perform a two-step analysis using the Cramer-Rao bounds. We study how blur kernel and noise influence motion estimation with aliasing signals, how noise affects super resolution with perfect motion, and finally how blur kernel and noise influence super resolution with unknown motion. Our analysis results confirm empirical observations, in particular that an intermediate size blur kernel achieves the optimal image reconstruction results.

  7. Nonparametric entropy estimation using kernel densities.

    PubMed

    Lake, Douglas E

    2009-01-01

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

  8. Fast generation of sparse random kernel graphs

    SciTech Connect

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

    2015-09-10

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

  9. Fast generation of sparse random kernel graphs

    DOE PAGES

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

    2015-09-10

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

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

    PubMed

    Maranz, Steven; Wiesman, Zeev; Garti, Nissim

    2003-10-08

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

  11. Tile-Compressed FITS Kernel for IRAF

    NASA Astrophysics Data System (ADS)

    Seaman, R.

    2011-07-01

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

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

    PubMed

    Patra, Rakesh; Saha, Sujan Kumar

    2013-01-01

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

  13. Experimental study of turbulent flame kernel propagation

    SciTech Connect

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

    2008-07-15

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

  14. A dynamic kernel modifier for linux

    SciTech Connect

    Minnich, R. G.

    2002-09-03

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

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

    SciTech Connect

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

    1986-06-01

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

  16. Reduced multiple empirical kernel learning machine.

    PubMed

    Wang, Zhe; Lu, MingZhe; Gao, Daqi

    2015-02-01

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

  17. Lifting kernel-based sprite codec

    NASA Astrophysics Data System (ADS)

    Dasu, Aravind R.; Panchanathan, Sethuraman

    2000-12-01

    The International Standards Organization (ISO) has proposed a family of standards for compression of image and video sequences, including the JPEG, MPEG-1 and MPEG-2. The latest MPEG-4 standard has many new dimensions to coding and manipulation of visual content. A video sequence usually contains a background object and many foreground objects. Portions of this background may not be visible in certain frames due to the occlusion of the foreground objects or camera motion. MPEG-4 introduces the novel concepts of Video Object Planes (VOPs) and Sprites. A VOP is a visual representation of real world objects with shapes that need not be rectangular. Sprite is a large image composed of pixels belonging to a video object visible throughout a video segment. Since a sprite contains all parts of the background that were at least visible once, it can be used for direct reconstruction of the background Video Object Plane (VOP). Sprite reconstruction is dependent on the mode in which it is transmitted. In the Static sprite mode, the entire sprite is decoded as an Intra VOP before decoding the individual VOPs. Since sprites consist of the information needed to display multiple frames of a video sequence, they are typically much larger than a single frame of video. Therefore a static sprite can be considered as a large static image. In this paper, a novel solution to address the problem of spatial scalability has been proposed, where the sprite is encoded in Discrete Wavelet Transform (DWT). A lifting kernel method of DWT implementation has been used for encoding and decoding sprites. Modifying the existing lifting scheme while maintaining it to be shape adaptive results in a reduced complexity. The proposed scheme has the advantages of (1) avoiding the need for any extensions to image or tile border pixels and is hence superior to the DCT based low latency scheme (used in the current MPEG-4 verification model), (2) mapping the in place computed wavelet coefficients into a zero

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

    NASA Astrophysics Data System (ADS)

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

    2012-09-01

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

  19. Accuracy of Reduced and Extended Thin-Wire Kernels

    SciTech Connect

    Burke, G J

    2008-11-24

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

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

    PubMed

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

    2013-11-20

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

  1. Unitarization and low-energy scattering data

    NASA Astrophysics Data System (ADS)

    Magalhães, P. C.; Robilotta, M. R.

    2014-07-01

    A procedure based on the well-known K-matrix formalism is presented, which makes patterns in inelastic regions of low-energy scattering data considerably more transparent. It relies on the use of an empirical kernel, obtained by eliminating elastic loops from the experimental amplitude. This allows structures associated with resonances, such as locations, widths, and heights, to become visible with the naked eye. The method is illustrated with a study of the P-wave Kπ amplitude.

  2. Fabrication of Uranium Oxycarbide Kernels for HTR Fuel

    SciTech Connect

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

    2010-10-01

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

  3. Kernel Partial Least Squares for Nonlinear Regression and Discrimination

    NASA Technical Reports Server (NTRS)

    Rosipal, Roman; Clancy, Daniel (Technical Monitor)

    2002-01-01

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

  4. 7 CFR 868.254 - Broken kernels determination.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  5. 7 CFR 868.304 - Broken kernels determination.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  6. 7 CFR 981.60 - Determination of kernel weight.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

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

    PubMed

    Reyhani, Nima

    2013-07-01

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

  8. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

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

  9. 7 CFR 981.61 - Redetermination of kernel weight.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  10. Thermomechanical property of rice kernels studied by DMA

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  12. 7 CFR 981.401 - Adjusted kernel weight.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

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

  13. 7 CFR 981.401 - Adjusted kernel weight.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

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

  14. 7 CFR 981.401 - Adjusted kernel weight.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

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

  15. 7 CFR 981.401 - Adjusted kernel weight.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

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

  16. 7 CFR 981.401 - Adjusted kernel weight.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

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

  17. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

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

  18. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

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

  19. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

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

  20. 7 CFR 981.8 - Inedible kernel.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

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

  1. Protein Structure Prediction Using String Kernels

    DTIC Science & Technology

    2006-03-03

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

  2. Kernel Temporal Differences for Neural Decoding

    PubMed Central

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

    2015-01-01

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

  3. Convolution kernels for multi-wavelength imaging

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  4. Kernel regression based feature extraction for 3D MR image denoising.

    PubMed

    López-Rubio, Ezequiel; Florentín-Núñez, María Nieves

    2011-08-01

    Kernel regression is a non-parametric estimation technique which has been successfully applied to image denoising and enhancement in recent times. Magnetic resonance 3D image denoising has two features that distinguish it from other typical image denoising applications, namely the tridimensional structure of the images and the nature of the noise, which is Rician rather than Gaussian or impulsive. Here we propose a principled way to adapt the general kernel regression framework to this particular problem. Our noise removal system is rooted on a zeroth order 3D kernel regression, which computes a weighted average of the pixels over a regression window. We propose to obtain the weights from the similarities among small sized feature vectors associated to each pixel. In turn, these features come from a second order 3D kernel regression estimation of the original image values and gradient vectors. By considering directional information in the weight computation, this approach substantially enhances the performance of the filter. Moreover, Rician noise level is automatically estimated without any need of human intervention, i.e. our method is fully automated. Experimental results over synthetic and real images demonstrate that our proposal achieves good performance with respect to the other MRI denoising filters being compared.

  5. Kernel weights optimization for error diffusion halftoning method

    NASA Astrophysics Data System (ADS)

    Fedoseev, Victor

    2015-02-01

    This paper describes a study to find the best error diffusion kernel for digital halftoning under various restrictions on the number of non-zero kernel coefficients and their set of values. As an objective measure of quality, WSNR was used. The problem of multidimensional optimization was solved numerically using several well-known algorithms: Nelder- Mead, BFGS, and others. The study found a kernel function that provides a quality gain of about 5% in comparison with the best of the commonly used kernel introduced by Floyd and Steinberg. Other kernels obtained allow to significantly reduce the computational complexity of the halftoning process without reducing its quality.

  6. Generalization Performance of Regularized Ranking With Multiscale Kernels.

    PubMed

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

    2016-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  8. Zero energy scattering calculation in Euclidean space

    NASA Astrophysics Data System (ADS)

    Carbonell, J.; Karmanov, V. A.

    2016-03-01

    We show that the Bethe-Salpeter equation for the scattering amplitude in the limit of zero incident energy can be transformed into a purely Euclidean form, as it is the case for the bound states. The decoupling between Euclidean and Minkowski amplitudes is only possible for zero energy scattering observables and allows determining the scattering length from the Euclidean Bethe-Salpeter amplitude. Such a possibility strongly simplifies the numerical solution of the Bethe-Salpeter equation and suggests an alternative way to compute the scattering length in Lattice Euclidean calculations without using the Luscher formalism. The derivations contained in this work were performed for scalar particles and one-boson exchange kernel. They can be generalized to the fermion case and more involved interactions.

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

    PubMed

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

    2016-11-01

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

  10. Tracking temporal evolution of nonlinear dynamics in hippocampus using time-varying volterra kernels.

    PubMed

    Chan, Rosa H M; Song, Dong; Berger, Theodore W

    2008-01-01

    Hippocampus and other parts of the cortex are not stationary, but change as a function of time and experience. The goal of this study is to apply adaptive modeling techniques to the tracking of multiple-input, multiple-output (MIMO) nonlinear dynamics underlying spike train transformations across brain subregions, e.g. CA3 and CA1 of the hippocampus. A stochastic state point process adaptive filter will be used to track the temporal evolutions of both feedforward and feedback kernels in the natural flow of multiple behavioral events.

  11. SU-E-J-135: Feasibility of Using Quantitative Cone Beam CT for Proton Adaptive Planning

    SciTech Connect

    Jingqian, W; Wang, Q; Zhang, X; Wen, Z; Zhu, X; Frank, S; Li, H; Tsui, T; Zhu, L; Wei, J

    2015-06-15

    Purpose: To investigate the feasibility of using scatter corrected cone beam CT (CBCT) for proton adaptive planning. Methods: Phantom study was used to evaluate the CT number difference between the planning CT (pCT), quantitative CBCT (qCBCT) with scatter correction and calibrated Hounsfield units using adaptive scatter kernel superposition (ASKS) technique, and raw CBCT (rCBCT). After confirming the CT number accuracy, prostate patients, each with a pCT and several sets of weekly CBCT, were investigated for this study. Spot scanning proton treatment plans were independently generated on pCT, qCBCT and rCBCT. The treatment plans were then recalculated on all images. Dose-volume-histogram (DVH) parameters and gamma analysis were used to compare between dose distributions. Results: Phantom study suggested that Hounsfield unit accuracy for different materials are within 20 HU for qCBCT and over 250 HU for rCBCT. For prostate patients, proton dose could be calculated accurately on qCBCT but not on rCBCT. When the original plan was recalculated on qCBCT, tumor coverage was maintained when anatomy was consistent with pCT. However, large dose variance was observed when patient anatomy change. Adaptive plan using qCBCT was able to recover tumor coverage and reduce dose to normal tissue. Conclusion: It is feasible to use qu antitative CBCT (qCBCT) with scatter correction and calibrated Hounsfield units for proton dose calculation and adaptive planning in proton therapy. Partly supported by Varian Medical Systems.

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

    PubMed

    Pan, Binbin; Chen, Wen-Sheng; Xu, Chen; Chen, Bo

    2016-05-01

    The data from real world usually have nonlinear geometric structure, which are often assumed to lie on or close to a low-dimensional manifold in a high-dimensional space. How to detect this nonlinear geometric structure of the data is important for the learning algorithms. Recently, there has been a surge of interest in utilizing kernels to exploit the manifold structure of the data. Such kernels are called geometry-aware kernels and are widely used in the machine learning algorithms. The performance of these algorithms critically relies on the choice of the geometry-aware kernels. Intuitively, a good geometry-aware kernel should utilize additional information other than the geometric information. In many applications, it is required to compute the out-of-sample data directly. However, most of the geometry-aware kernel methods are restricted to the available data given beforehand, with no straightforward extension for out-of-sample data. In this paper, we propose a framework for more general geometry-aware kernel learning. The proposed framework integrates multiple sources of information and enables us to develop flexible and effective kernel matrices. Then, we theoretically show how the learned kernel matrices are extended to the corresponding kernel functions, in which the out-of-sample data can be computed directly. Under our framework, a novel family of geometry-aware kernels is developed. Especially, some existing geometry-aware kernels can be viewed as instances of our framework. The performance of the kernels is evaluated on dimensionality reduction, classification, and clustering tasks. The empirical results show that our kernels significantly improve the performance.

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

    PubMed

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

    2014-01-01

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

  14. Wilson Dslash Kernel From Lattice QCD Optimization

    SciTech Connect

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

    2015-07-01

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

  15. Bergman kernel and complex singularity exponent

    NASA Astrophysics Data System (ADS)

    Chen, Boyong; Lee, Hanjin

    2009-12-01

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

  16. Advanced Development of Certified OS Kernels

    DTIC Science & Technology

    2015-06-01

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

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

    NASA Astrophysics Data System (ADS)

    Tuma, Matthias; Igel, Christian; Mialle, Pierrick

    2014-05-01

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

  18. A Fast Reduced Kernel Extreme Learning Machine.

    PubMed

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

    2016-04-01

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

  19. Kernel Non-Rigid Structure from Motion

    PubMed Central

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

    2013-01-01

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

  20. Balancing continuous covariates based on Kernel densities.

    PubMed

    Ma, Zhenjun; Hu, Feifang

    2013-03-01

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

  1. Kernel methods for phenotyping complex plant architecture.

    PubMed

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

    2014-02-07

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

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

    PubMed

    Kwak, Nojun

    2016-05-20

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

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

    ERIC Educational Resources Information Center

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

    2008-01-01

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

  4. A kernel machine-based fMRI physiological noise removal method.

    PubMed

    Song, Xiaomu; Chen, Nan-kuei; Gaur, Pooja

    2014-02-01

    Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal. In addition, changes of heartbeat and respiration patterns also generate physiological fluctuations that have similar frequencies with BOLD activation. Most existing physiological noise-removal methods either place restrictive limitations on image acquisition or utilize filtering or regression based post-processing algorithms, which cannot distinguish the frequency-overlapping BOLD activation and the physiological noise. In this work, we address the challenge of physiological noise removal via the kernel machine technique, where a nonlinear kernel machine technique, kernel principal component analysis, is used with a specifically identified kernel function to differentiate BOLD signal from the physiological noise of the frequency. The proposed method was evaluated in human fMRI data acquired from multiple task-related and resting state fMRI experiments. A comparison study was also performed with an existing adaptive filtering method. The results indicate that the proposed method can effectively identify and reduce the physiological noise in fMRI data. The comparison study shows that the proposed method can provide comparable or better noise removal performance than the adaptive filtering approach.

  5. A Kernel Machine-based fMRI Physiological Noise Removal Method

    PubMed Central

    Song, Xiaomu; Chen, Nan-kuei; Gaur, Pooja

    2013-01-01

    Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal. In addition, changes of heartbeat and respiration patterns also generate physiological fluctuations that have similar frequencies with BOLD activation. Most existing physiological noise-removal methods either place restrictive limitations on image acquisition or utilize filtering or regression based post-processing algorithms, which cannot distinguish the frequency-overlapping BOLD activation and the physiological noise. In this work, we address the challenge of physiological noise removal via the kernel machine technique, where a nonlinear kernel machine technique, kernel principal component analysis, is used with a specifically identified kernel function to differentiate BOLD signal from the physiological noise of the frequency. The proposed method was evaluated in human fMRI data acquired from multiple task-related and resting state fMRI experiments. A comparison study was also performed with an existing adaptive filtering method. The results indicate that the proposed method can effectively identify and reduce the physiological noise in fMRI data. The comparison study shows that the proposed method can provide comparable or better noise removal performance than the adaptive filtering approach. PMID:24321306

  6. Small convolution kernels for high-fidelity image restoration

    NASA Technical Reports Server (NTRS)

    Reichenbach, Stephen E.; Park, Stephen K.

    1991-01-01

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

  7. Calibrating Phase Delay Measurements and Comparison of 3-D Waveform Kernels with and without Near-field Terms

    NASA Astrophysics Data System (ADS)

    Bremner, P. M.; Panning, M. P.

    2012-12-01

    We present the calibration of an automated scheme to properly window the fundamental surface wave mode of an event record. Multi-taper fundamental mode phase delay measurements were made on a synthetic dataset. Measurement errors are reduced when minimal over tone energy is included in the window. The time window is calibrated by simply varying the minimum and maximum surface wave velocities used to determine the beginning and ending window times with source-receiver distance, as opposed to constant velocities. We compare phase delay measurements with and without calibration against measurements made manually. Manual window setting of a small representative subset of event seismograms are used to adjust these minimum and maximum surface wave velocities. The orthogonal 2.5π-prolate spheroidal wave function eigentapers (Slepian tapers) used in multi-taper methods reduce noise biasing, and can provide error estimates in phase delay measurements. Additionally, we examine the effects of excluding near-field terms in the calculation of 3-D finite-frequency waveform kernels for Rayleigh and Love waves on a synthetic dataset. Two methods of kernel calculation based on the single scatterer Born approximation are compared, that of Panning and Nolet (2008) and Zhao and Chevrot (2011). The Panning and Nolet (2008) method calculates the strain Green's tensors for the source-scatterer and scatterer-receiver paths by the summation of asymptotic surface wave modes, which is an inherently far-field approximation. Waveform kernels are then found by convolution (in the time domain) of these strain Green's tensors. The kernels are formulated based on a hexagonal symmetry with an arbitrary orientation. The Zhao and Chevrot (2011) method creates a database of the set of strain Green's tensors for the source-scatterer (two-sided strain Green's tensor) and scatterer-receiver (one-sided strain Green's tensor) paths, and is calculated by normal mode summation. The full-wave waveform

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

    PubMed

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

    2014-10-01

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

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

    PubMed

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

    2013-01-01

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

  10. A Kernel-based Account of Bibliometric Measures

    NASA Astrophysics Data System (ADS)

    Ito, Takahiko; Shimbo, Masashi; Kudo, Taku; Matsumoto, Yuji

    The application of kernel methods to citation analysis is explored. We show that a family of kernels on graphs provides a unified perspective on the three bibliometric measures that have been discussed independently: relatedness between documents, global importance of individual documents, and importance of documents relative to one or more (root) documents (relative importance). The framework provided by the kernels establishes relative importance as an intermediate between relatedness and global importance, in which the degree of `relativity,' or the bias between relatedness and importance, is naturally controlled by a parameter characterizing individual kernels in the family.

  11. Model-based online learning with kernels.

    PubMed

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

    2013-03-01

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

  12. Robust kernel collaborative representation for face recognition

    NASA Astrophysics Data System (ADS)

    Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong

    2015-05-01

    One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

  13. Prediction of kernel density of corn using single-kernel near infrared spectroscopy

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Corn hardness as is an important property for dry and wet-millers, food processors and corn breeders developing hybrids for specific markets. Of the several methods used to measure hardness, kernel density measurements are one of the more repeatable methods to quantify hardness. Near infrared spec...

  14. Oil point pressure of Indian almond kernels

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

  15. Verification of Chare-kernel programs

    SciTech Connect

    Bhansali, S.; Kale, L.V. )

    1989-01-01

    Experience with concurrent programming has shown that concurrent programs can conceal bugs even after extensive testing. Thus, there is a need for practical techniques which can establish the correctness of parallel programs. This paper proposes a method for showing how to prove the partial correctness of programs written in the Chare-kernel language, which is a language designed to support the parallel execution of computation with irregular structures. The proof is based on the lattice proof technique and is divided into two parts. The first part is concerned with the program behavior within a single chare instance, whereas the second part captures the inter-chare interaction.

  16. Kernel learning at the first level of inference.

    PubMed

    Cawley, Gavin C; Talbot, Nicola L C

    2014-05-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  18. Delimiting Areas of Endemism through Kernel Interpolation

    PubMed Central

    Oliveira, Ubirajara; Brescovit, Antonio D.; Santos, Adalberto J.

    2015-01-01

    We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE), based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units. PMID:25611971

  19. Bergman kernel, balanced metrics and black holes

    NASA Astrophysics Data System (ADS)

    Klevtsov, Semyon

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

  20. Scientific Computing Kernels on the Cell Processor

    SciTech Connect

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

    2007-04-04

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

  1. Generalized Langevin equation with tempered memory kernel

    NASA Astrophysics Data System (ADS)

    Liemert, André; Sandev, Trifce; Kantz, Holger

    2017-01-01

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

  2. Transcriptome analysis of Ginkgo biloba kernels

    PubMed Central

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

    2015-01-01

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

  3. Aligning Biomolecular Networks Using Modular Graph Kernels

    NASA Astrophysics Data System (ADS)

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

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

  4. Wilson loops and QCD/string scattering amplitudes

    SciTech Connect

    Makeenko, Yuri; Olesen, Poul

    2009-07-15

    We generalize modern ideas about the duality between Wilson loops and scattering amplitudes in N=4 super Yang-Mills theory to large N QCD by deriving a general relation between QCD meson scattering amplitudes and Wilson loops. We then investigate properties of the open-string disk amplitude integrated over reparametrizations. When the Wilson-loop is approximated by the area behavior, we find that the QCD scattering amplitude is a convolution of the standard Koba-Nielsen integrand and a kernel. As usual poles originate from the first factor, whereas no (momentum-dependent) poles can arise from the kernel. We show that the kernel becomes a constant when the number of external particles becomes large. The usual Veneziano amplitude then emerges in the kinematical regime, where the Wilson loop can be reliably approximated by the area behavior. In this case, we obtain a direct duality between Wilson loops and scattering amplitudes when spatial variables and momenta are interchanged, in analogy with the N=4 super Yang-Mills theory case.

  5. Spatiotemporal Domain Decomposition for Massive Parallel Computation of Space-Time Kernel Density

    NASA Astrophysics Data System (ADS)

    Hohl, A.; Delmelle, E. M.; Tang, W.

    2015-07-01

    Accelerated processing capabilities are deemed critical when conducting analysis on spatiotemporal datasets of increasing size, diversity and availability. High-performance parallel computing offers the capacity to solve computationally demanding problems in a limited timeframe, but likewise poses the challenge of preventing processing inefficiency due to workload imbalance between computing resources. Therefore, when designing new algorithms capable of implementing parallel strategies, careful spatiotemporal domain decomposition is necessary to account for heterogeneity in the data. In this study, we perform octtree-based adaptive decomposition of the spatiotemporal domain for parallel computation of space-time kernel density. In order to avoid edge effects near subdomain boundaries, we establish spatiotemporal buffers to include adjacent data-points that are within the spatial and temporal kernel bandwidths. Then, we quantify computational intensity of each subdomain to balance workloads among processors. We illustrate the benefits of our methodology using a space-time epidemiological dataset of Dengue fever, an infectious vector-borne disease that poses a severe threat to communities in tropical climates. Our parallel implementation of kernel density reaches substantial speedup compared to sequential processing, and achieves high levels of workload balance among processors due to great accuracy in quantifying computational intensity. Our approach is portable of other space-time analytical tests.

  6. Scale Space Graph Representation and Kernel Matching for Non Rigid and Textured 3D Shape Retrieval.

    PubMed

    Garro, Valeria; Giachetti, Andrea

    2016-06-01

    In this paper we introduce a novel framework for 3D object retrieval that relies on tree-based shape representations (TreeSha) derived from the analysis of the scale-space of the Auto Diffusion Function (ADF) and on specialized graph kernels designed for their comparison. By coupling maxima of the Auto Diffusion Function with the related basins of attraction, we can link the information at different scales encoding spatial relationships in a graph description that is isometry invariant and can easily incorporate texture and additional geometrical information as node and edge features. Using custom graph kernels it is then possible to estimate shape dissimilarities adapted to different specific tasks and on different categories of models, making the procedure a powerful and flexible tool for shape recognition and retrieval. Experimental results demonstrate that the method can provide retrieval scores similar or better than state-of-the-art on textured and non textured shape retrieval benchmarks and give interesting insights on effectiveness of different shape descriptors and graph kernels.

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

    SciTech Connect

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

    1990-05-01

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

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

    NASA Astrophysics Data System (ADS)

    Orlandini, Giuseppina; Turro, Francesco

    2017-03-01

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

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

    ERIC Educational Resources Information Center

    Embry, Dennis D.; Biglan, Anthony

    2008-01-01

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

  10. Comparison of Kernel Equating and Item Response Theory Equating Methods

    ERIC Educational Resources Information Center

    Meng, Yu

    2012-01-01

    The kernel method of test equating is a unified approach to test equating with some advantages over traditional equating methods. Therefore, it is important to evaluate in a comprehensive way the usefulness and appropriateness of the Kernel equating (KE) method, as well as its advantages and disadvantages compared with several popular item…

  11. Integrating the Gradient of the Thin Wire Kernel

    NASA Technical Reports Server (NTRS)

    Champagne, Nathan J.; Wilton, Donald R.

    2008-01-01

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

  12. 7 CFR 981.60 - Determination of kernel weight.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

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

  13. 7 CFR 981.60 - Determination of kernel weight.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

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

  14. 7 CFR 981.60 - Determination of kernel weight.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

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

  15. 7 CFR 981.60 - Determination of kernel weight.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

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

  16. High speed sorting of Fusarium-damaged wheat kernels

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  17. End-use quality of soft kernel durum wheat

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  18. Optimal Bandwidth Selection in Observed-Score Kernel Equating

    ERIC Educational Resources Information Center

    Häggström, Jenny; Wiberg, Marie

    2014-01-01

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

  19. Parametric kernel-driven active contours for image segmentation

    NASA Astrophysics Data System (ADS)

    Wu, Qiongzhi; Fang, Jiangxiong

    2012-10-01

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

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

    PubMed Central

    Biglan, Anthony

    2008-01-01

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

  1. Computing the roots of complex orthogonal and kernel polynomials

    SciTech Connect

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

    1988-01-01

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

  2. Scatter correction in digital mammography based on image deconvolution.

    PubMed

    Ducote, J L; Molloi, S

    2010-03-07

    X-ray scatter is a major cause of nonlinearity in densitometry measurements using digital mammography. Previous scatter correction techniques have primarily used a single scatter point spread function to estimate x-ray scatter. In this study, a new algorithm to correct x-ray scatter based on image convolution was implemented using a spatially variant scatter point spread function which is energy and thickness dependent. The scatter kernel was characterized in terms of its scattering fraction (SF) and scatter radial extent (k) on uniform Lucite phantoms with thickness of 0.8-8.0 cm. The algorithm operates on a pixel-by-pixel basis by grouping pixels of similar thicknesses into a series of mask images that are individually deconvolved using Fourier image analysis with a distinct kernel for each image. The algorithm was evaluated with three Lucite step phantoms and one anthropomorphic breast phantom using a full-field digital mammography system at energies of 24, 28, 31 and 49 kVp. The true primary signal was measured with a multi-hole collimator. The effect on image quality was also evaluated. For all 16 studies, the average mean percentage error in estimating the true primary signal was found to be -2.13% and the average rms percentage error was 2.60%. The image quality was seen to improve at every energy up to 25% at 49 kVp. The results indicate that a technique based on a spatially variant scatter point spread function can accurately estimate x-ray scatter.

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

    SciTech Connect

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

    2005-07-19

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

  4. Anatomically-aided PET reconstruction using the kernel method

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  5. A novel extended kernel recursive least squares algorithm.

    PubMed

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

    2012-08-01

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

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

    PubMed

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

    2016-11-01

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

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

    PubMed

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

    2016-11-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

  9. Anisotropic Elastic Resonance Scattering model for the Neutron Transport equation

    SciTech Connect

    Mohamed Ouisloumen; Abderrafi M. Ougouag; Shadi Z. Ghrayeb

    2014-11-24

    The resonance scattering transfer cross-section has been reformulated to account for anisotropic scattering in the center-of-mass of the neutron-nucleus system. The main innovation over previous implementations is the relaxation of the ubiquitous assumption of isotropic scattering in the center-of-mass and the actual effective use of scattering angle distributions from evaluated nuclear data files in the computation of the angular moments of the resonant scattering kernels. The formulas for the high order anisotropic moments in the laboratory system are also derived. A multi-group numerical formulation is derived and implemented into a module incorporated within the NJOY nuclear data processing code. An ultra-fine energy mesh cross section library was generated using these new theoretical models and then was used for fuel assembly calculations with the PARAGON lattice physics code. The results obtained indicate a strong effect of this new model on reactivity, multi-group fluxes and isotopic inventory during depletion.

  10. Scatter of X-rays on polished surfaces

    NASA Technical Reports Server (NTRS)

    Hasinger, G.

    1981-01-01

    In investigating the dispersion properties of telescope mirrors used in X-ray astronomy, the slight scattering characteristics of X-ray radiation by statistically rough surfaces were examined. The mathematics and geometry of scattering theory are described. The measurement test assembly is described and results of measurements on samples of plane mirrors are given. Measurement results are evaluated. The direct beam, the convolution of the direct beam and the scattering halo, curve fitting by the method of least squares, various autocorrelation functions, results of the fitting procedure for small scattering, and deviations in the kernel of the scattering distribution are presented. A procedure for quality testing of mirror systems through diagnosis of rough surfaces is described.

  11. A new orientation-adaptive interpolation method.

    PubMed

    Wang, Qing; Ward, Rabab Kreidieh

    2007-04-01

    We propose an isophote-oriented, orientation-adaptive interpolation method. The proposed method employs an interpolation kernel that adapts to the local orientation of isophotes, and the pixel values are obtained through an oriented, bilinear interpolation. We show that, by doing so, the curvature of the interpolated isophotes is reduced, and, thus, zigzagging artifacts are largely suppressed. Analysis and experiments show that images interpolated using the proposed method are visually pleasing and almost artifact free.

  12. On the Kernelization Complexity of Colorful Motifs

    NASA Astrophysics Data System (ADS)

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

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

  13. Kernel density estimation using graphical processing unit

    NASA Astrophysics Data System (ADS)

    Sunarko, Su'ud, Zaki

    2015-09-01

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

  14. Privacy preserving RBF kernel support vector machine.

    PubMed

    Li, Haoran; Xiong, Li; Ohno-Machado, Lucila; Jiang, Xiaoqian

    2014-01-01

    Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data.

  15. Learning molecular energies using localized graph kernels

    NASA Astrophysics Data System (ADS)

    Ferré, Grégoire; Haut, Terry; Barros, Kipton

    2017-03-01

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

  16. The flare kernel in the impulsive phase

    NASA Technical Reports Server (NTRS)

    Dejager, C.

    1986-01-01

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

  17. Labeled Graph Kernel for Behavior Analysis.

    PubMed

    Zhao, Ruiqi; Martinez, Aleix M

    2016-08-01

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

  18. Labeled Graph Kernel for Behavior Analysis

    PubMed Central

    Zhao, Ruiqi; Martinez, Aleix M.

    2016-01-01

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

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

    PubMed

    Wang, Liang; Li, Chuan

    2014-06-01

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

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

    PubMed

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

    2013-10-01

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

  1. Gaussian kernel width optimization for sparse Bayesian learning.

    PubMed

    Mohsenzadeh, Yalda; Sheikhzadeh, Hamid

    2015-04-01

    Sparse kernel methods have been widely used in regression and classification applications. The performance and the sparsity of these methods are dependent on the appropriate choice of the corresponding kernel functions and their parameters. Typically, the kernel parameters are selected using a cross-validation approach. In this paper, a learning method that is an extension of the relevance vector machine (RVM) is presented. The proposed method can find the optimal values of the kernel parameters during the training procedure. This algorithm uses an expectation-maximization approach for updating kernel parameters as well as other model parameters; therefore, the speed of convergence and computational complexity of the proposed method are the same as the standard RVM. To control the convergence of this fully parameterized model, the optimization with respect to the kernel parameters is performed using a constraint on these parameters. The proposed method is compared with the typical RVM and other competing methods to analyze the performance. The experimental results on the commonly used synthetic data, as well as benchmark data sets, demonstrate the effectiveness of the proposed method in reducing the performance dependency on the initial choice of the kernel parameters.

  2. Relaxation and diffusion models with non-singular kernels

    NASA Astrophysics Data System (ADS)

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

    2017-02-01

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

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

    PubMed

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

    2014-01-01

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

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

    PubMed

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

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

  5. Experimental and numerical investigation of the 3D SPECT photon detection kernel for non-uniform attenuating media

    NASA Astrophysics Data System (ADS)

    Riauka, Terence A.; Hooper, H. Richard; Gortel, Zbigniew W.

    1996-07-01

    Experimental tests for non-uniform attenuating media are performed to validate theoretical expressions for the photon detection kernel, obtained from a recently proposed analytical theory of photon propagation and detection for SPECT. The theoretical multi-dimensional integral expressions for the photon detection kernel, which are computed numerically, describe the probability that a photon emitted from a given source voxel will trigger detection of a photon at a particular projection pixel. The experiments were performed using a cylindrical water-filled phantom with large cylindrical air-filled inserts to simulate inhomogeneity of the medium. A point-like, a short thin cylindrical and a large cylindrical radiation source of were placed at various positions within the phantom. The values numerically calculated from the theoretical kernel expressions are in very good agreement with the experimentally measured data. The significance of Compton-scattered photons in planar image formation is discussed and highlighted by these results. Using both experimental measurements and the calculated values obtained from the theory, the kernel's size is investigated. This is done by determining the square pixel neighbourhood of the gamma camera that must be connected to a particular radiation source voxel to account for a specific fraction of all counts recorded at all camera pixels. It is shown that the kernel's size is primarily dependent upon the source position and the properties of the attenuating medium through Compton scattering events, with 3D depth-dependent collimator resolution playing an important but secondary role, at least for imaging situations involving parallel hole collimation. By considering small point-like sources within a non-uniform elliptical phantom, approximating the human thorax, it is demonstrated

  6. Unified connected theory of few-body reaction mechanisms in N-body scattering theory

    NASA Technical Reports Server (NTRS)

    Polyzou, W. N.; Redish, E. F.

    1978-01-01

    A unified treatment of different reaction mechanisms in nonrelativistic N-body scattering is presented. The theory is based on connected kernel integral equations that are expected to become compact for reasonable constraints on the potentials. The operators T/sub +-//sup ab/(A) are approximate transition operators that describe the scattering proceeding through an arbitrary reaction mechanism A. These operators are uniquely determined by a connected kernel equation and satisfy an optical theorem consistent with the choice of reaction mechanism. Connected kernel equations relating T/sub +-//sup ab/(A) to the full T/sub +-//sup ab/ allow correction of the approximate solutions for any ignored process to any order. This theory gives a unified treatment of all few-body reaction mechanisms with the same dynamic simplicity of a model calculation, but can include complicated reaction mechanisms involving overlapping configurations where it is difficult to formulate models.

  7. Stimulated Brillouin Scattering Microscopic Imaging

    PubMed Central

    Ballmann, Charles W.; Thompson, Jonathan V.; Traverso, Andrew J.; Meng, Zhaokai; Scully, Marlan O.; Yakovlev, Vladislav V.

    2015-01-01

    Two-dimensional stimulated Brillouin scattering microscopy is demonstrated for the first time using low power continuous-wave lasers tunable around 780 nm. Spontaneous Brillouin spectroscopy has much potential for probing viscoelastic properties remotely and non-invasively on a microscopic scale. Nonlinear Brillouin scattering spectroscopy and microscopy may provide a way to tremendously accelerate the data aquisition and improve spatial resolution. This general imaging setup can be easily adapted for specific applications in biology and material science. The low power and optical wavelengths in the water transparency window used in this setup provide a powerful bioimaging technique for probing the mechanical properties of hard and soft tissue. PMID:26691398

  8. Organizing for ontological change: The kernel of an AIDS research infrastructure.

    PubMed

    Ribes, David; Polk, Jessica Beth

    2015-04-01

    Is it possible to prepare and plan for emergent and changing objects of research? Members of the Multicenter AIDS Cohort Study have been investigating AIDS for over 30 years, and in that time, the disease has been repeatedly transformed. Over the years and across many changes, members have continued to study HIV disease while in the process regenerating an adaptable research organization. The key to sustaining this technoscientific flexibility has been what we call the kernel of a research infrastructure: ongoing efforts to maintain the availability of resources and services that may be brought to bear in the investigation of new objects. In the case of the Multicenter AIDS Cohort Study, these resources are as follows: specimens and data, calibrated instruments, heterogeneous experts, and participating cohorts of gay and bisexual men. We track three ontological transformations, examining how members prepared for and responded to changes: the discovery of a novel retroviral agent (HIV), the ability to test for that agent, and the transition of the disease from fatal to chronic through pharmaceutical intervention. Respectively, we call the work, 'technologies', and techniques of adapting to these changes, 'repurposing', 'elaborating', and 'extending the kernel'.

  9. Estimation of scattered radiation in digital breast tomosynthesis

    NASA Astrophysics Data System (ADS)

    Diaz, O.; Dance, D. R.; Young, K. C.; Elangovan, P.; Bakic, P. R.; Wells, K.

    2014-08-01

    Digital breast tomosynthesis (DBT) is a promising technique to overcome the tissue superposition limitations found in planar 2D x-ray mammography. However, as most DBT systems do not employ an anti-scatter grid, the levels of scattered radiation recorded within the image receptor are significantly higher than that observed in planar 2D x-ray mammography. Knowledge of this field is necessary as part of any correction scheme and for computer modelling and optimisation of this examination. Monte Carlo (MC) simulations are often used for this purpose, however they are computationally expensive and a more rapid method of calculation is desirable. This issue is addressed in this work by the development of a fast kernel-based methodology for scatter field estimation using a detailed realistic DBT geometry. Thickness-dependent scatter kernels, which were validated against the literature with a maximum discrepancy of 4% for an idealised geometry, have been calculated and a new physical parameter (air gap distance) was used to estimate more accurately the distribution of scattered radiation for a series of anthropomorphic breast phantom models. The proposed methodology considers, for the first time, the effects of scattered radiation from the compression paddle and breast support plate, which can represent more than 30% of the total scattered radiation recorded within the image receptor. The results show that the scatter field estimator can calculate scattered radiation images in an average of 80 min for projection angles up to 25° with equal to or less than a 10% error across most of the breast area when compared with direct MC simulations.

  10. Fast discontinuous Galerkin lattice-Boltzmann simulations on GPUs via maximal kernel fusion

    NASA Astrophysics Data System (ADS)

    Mazzeo, Marco D.

    2013-03-01

    A GPU implementation of the discontinuous Galerkin lattice-Boltzmann method with square spectral elements, and highly optimised for speed and precision of calculations is presented. An extensive analysis of the numerous variants of the fluid solver unveils that best performance is obtained by maximising CUDA kernel fusion and by arranging the resulting kernel tasks so as to trigger memory coherent and scattered loads in a specific manner, albeit at the cost of introducing cross-thread load unbalancing. Surprisingly, any attempt to vanish this, to maximise thread occupancy and to adopt conventional work tiling or distinct custom kernels highly tuned via ad hoc data and computation layouts invariably deteriorate performance. As such, this work sheds light into the possibility to hide fetch latencies of workloads involving heterogeneous loads in a way that is more effective than what is achieved with frequently suggested techniques. When simulating the lid-driven cavity on a NVIDIA GeForce GTX 480 via a 5-stage 4th-order Runge-Kutta (RK) scheme, the first four digits of the obtained centreline velocity values, or more, converge to those of the state-of-the-art literature data at a simulation speed of 7.0G primitive variable updates per second during the collision stage and 4.4G ones during each RK step of the advection by employing double-precision arithmetic (DPA) and a computational grid of 642 4×4-point elements only. The new programming engine leads to about 2× performance w.r.t. the best programming guidelines in the field. The new fluid solver on the above GPU is also 20-30 times faster than a highly optimised version running on a single core of a Intel Xeon X5650 2.66 GHz.

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

    PubMed

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

    2007-09-01

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

  12. Inheritance of Kernel Color in Corn: Explanations and Investigations.

    ERIC Educational Resources Information Center

    Ford, Rosemary H.

    2000-01-01

    Offers a new perspective on traditional problems in genetics on kernel color in corn, including information about genetic regulation, metabolic pathways, and evolution of genes. (Contains 15 references.) (ASK)

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

    NASA Astrophysics Data System (ADS)

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

    2011-06-01

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

  14. Nonlinear hyperspectral unmixing based on constrained multiple kernel NMF

    NASA Astrophysics Data System (ADS)

    Cui, Jiantao; Li, Xiaorun; Zhao, Liaoying

    2014-05-01

    Nonlinear spectral unmixing constitutes an important field of research for hyperspectral imagery. An unsupervised nonlinear spectral unmixing algorithm, namely multiple kernel constrained nonnegative matrix factorization (MKCNMF) is proposed by coupling multiple-kernel selection with kernel NMF. Additionally, a minimum endmemberwise distance constraint and an abundance smoothness constraint are introduced to alleviate the uniqueness problem of NMF in the algorithm. In the MKCNMF, two problems of optimizing matrices and selecting the proper kernel are jointly solved. The performance of the proposed unmixing algorithm is evaluated via experiments based on synthetic and real hyperspectral data sets. The experimental results demonstrate that the proposed method outperforms some existing unmixing algorithms in terms of spectral angle distance (SAD) and abundance fractions.

  15. Hash subgraph pairwise kernel for protein-protein interaction extraction.

    PubMed

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng

    2012-01-01

    Extracting protein-protein interaction (PPI) from biomedical literature is an important task in biomedical text mining (BioTM). In this paper, we propose a hash subgraph pairwise (HSP) kernel-based approach for this task. The key to the novel kernel is to use the hierarchical hash labels to express the structural information of subgraphs in a linear time. We apply the graph kernel to compute dependency graphs representing the sentence structure for protein-protein interaction extraction task, which can efficiently make use of full graph structural information, and particularly capture the contiguous topological and label information ignored before. We evaluate the proposed approach on five publicly available PPI corpora. The experimental results show that our approach significantly outperforms all-path kernel approach on all five corpora and achieves state-of-the-art performance.

  16. On the asymptotic expansion of the Bergman kernel

    NASA Astrophysics Data System (ADS)

    Seto, Shoo

    Let (L, h) → (M, o) be a polarized Kahler manifold. We define the Bergman kernel for H0(M, Lk), holomorphic sections of the high tensor powers of the line bundle L. In this thesis, we will study the asymptotic expansion of the Bergman kernel. We will consider the on-diagonal, near-diagonal and far off-diagonal, using L2 estimates to show the existence of the asymptotic expansion and computation of the coefficients for the on and near-diagonal case, and a heat kernel approach to show the exponential decay of the off-diagonal of the Bergman kernel for noncompact manifolds assuming only a lower bound on Ricci curvature and C2 regularity of the metric.

  17. Kernel-based Linux emulation for Plan 9.

    SciTech Connect

    Minnich, Ronald G.

    2010-09-01

    CNKemu is a kernel-based system for the 9k variant of the Plan 9 kernel. It is designed to provide transparent binary support for programs compiled for IBM's Compute Node Kernel (CNK) on the Blue Gene series of supercomputers. This support allows users to build applications with the standard Blue Gene toolchain, including C++ and Fortran compilers. While the CNK is not Linux, IBM designed the CNK so that the user interface has much in common with the Linux 2.0 system call interface. The Plan 9 CNK emulator hence provides the foundation of kernel-based Linux system call support on Plan 9. In this paper we discuss cnkemu's implementation and some of its more interesting features, such as the ability to easily intermix Plan 9 and Linux system calls.

  18. Scattering, Adsorption, and Langmuir-Hinshelwood Desorption Models for Physisorptive and Chemisorptive Gas-Surface Systems

    DTIC Science & Technology

    2013-09-01

    Gas -Surface Interaction Models in DSMC Analysis of Rarefied Hypersonic Flow ,” 39th AIAA Thermophysics Conference. Miami: AIAA, June 2007. 128. Payne...collides with the surface. θ is measured in monolayers (ML). 2. The gas molecule scatters using a scattering method or kernel. Accommoda- tion coefficients...Simulation of Gas Flows . New York: Oxford University Press, 1994. 21. Bitensky, I., E. Parilis, S. Della-Negra, and Y. Beyec. “Emission of hydrogen ions

  19. Resummed memory kernels in generalized system-bath master equations.

    PubMed

    Mavros, Michael G; Van Voorhis, Troy

    2014-08-07

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

  20. Landslide: Systematic Dynamic Race Detection in Kernel Space

    DTIC Science & Technology

    2012-05-01

    the general challenges of kernel-level concurrency, and we evaluate its effectiveness and usability as a debugging aid. We show that our techniques make...effectiveness and usability as a de- bugging aid. We show that our techniques make systematic testing in kernel-space feasible and that Landslide is a useful...Binary Instrumentation and Applications, WBIA ’09, pages 62–71, New York, NY, USA, 2009. ACM. [SKM+11] Eunsoo Seo , Mohammad Maifi Hasan Khan, Prasant

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

    PubMed

    Chen, Yi; Hunter, Ian W

    2013-04-01

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

  2. The Weighted Super Bergman Kernels Over the Supermatrix Spaces

    NASA Astrophysics Data System (ADS)

    Feng, Zhiming

    2015-12-01

    The purpose of this paper is threefold. Firstly, using Howe duality for , we obtain integral formulas of the super Schur functions with respect to the super standard Gaussian distributions. Secondly, we give explicit expressions of the super Szegö kernels and the weighted super Bergman kernels for the Cartan superdomains of type I. Thirdly, combining these results, we obtain duality relations of integrals over the unitary groups and the Cartan superdomains, and the marginal distributions of the weighted measure.

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  4. Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

    DTIC Science & Technology

    2016-01-05

    events (and subsequently, their likelihood of occurrence) based on historical evidence of the counts of previous event occurrences. The novel Bayesian...Aug-2014 22-May-2015 Approved for Public Release; Distribution Unlimited Final Report: Sparse Event Modeling with Hierarchical Bayesian Kernel Methods...Sparse Event Modeling with Hierarchical Bayesian Kernel Methods Report Title The research objective of this proposal was to develop a predictive Bayesian

  5. Resummed memory kernels in generalized system-bath master equations

    NASA Astrophysics Data System (ADS)

    Mavros, Michael G.; Van Voorhis, Troy

    2014-08-01

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

  6. Resummed memory kernels in generalized system-bath master equations

    SciTech Connect

    Mavros, Michael G.; Van Voorhis, Troy

    2014-08-07

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

  7. Modeling reactive transport with particle tracking and kernel estimators

    NASA Astrophysics Data System (ADS)

    Rahbaralam, Maryam; Fernandez-Garcia, Daniel; Sanchez-Vila, Xavier

    2015-04-01

    Groundwater reactive transport models are useful to assess and quantify the fate and transport of contaminants in subsurface media and are an essential tool for the analysis of coupled physical, chemical, and biological processes in Earth Systems. Particle Tracking Method (PTM) provides a computationally efficient and adaptable approach to solve the solute transport partial differential equation. On a molecular level, chemical reactions are the result of collisions, combinations, and/or decay of different species. For a well-mixed system, the chem- ical reactions are controlled by the classical thermodynamic rate coefficient. Each of these actions occurs with some probability that is a function of solute concentrations. PTM is based on considering that each particle actually represents a group of molecules. To properly simulate this system, an infinite number of particles is required, which is computationally unfeasible. On the other hand, a finite number of particles lead to a poor-mixed system which is limited by diffusion. Recent works have used this effect to actually model incomplete mix- ing in naturally occurring porous media. In this work, we demonstrate that this effect in most cases should be attributed to a defficient estimation of the concentrations and not to the occurrence of true incomplete mixing processes in porous media. To illustrate this, we show that a Kernel Density Estimation (KDE) of the concentrations can approach the well-mixed solution with a limited number of particles. KDEs provide weighting functions of each particle mass that expands its region of influence, hence providing a wider region for chemical reactions with time. Simulation results show that KDEs are powerful tools to improve state-of-the-art simulations of chemical reactions and indicates that incomplete mixing in diluted systems should be modeled based on alternative conceptual models and not on a limited number of particles.

  8. Abundance estimation of solid and liquid mixtures in hyperspectral imagery with albedo-based and kernel-based methods

    NASA Astrophysics Data System (ADS)

    Rand, Robert S.; Resmini, Ronald G.; Allen, David W.

    2016-09-01

    This study investigates methods for characterizing materials that are mixtures of granular solids, or mixtures of liquids, which may be linear or non-linear. Linear mixtures of materials in a scene are often the result of areal mixing, where the pixel size of a sensor is relatively large so they contain patches of different materials within them. Non-linear mixtures are likely to occur with microscopic mixtures of solids, such as mixtures of powders, or mixtures of liquids, or wherever complex scattering of light occurs. This study considers two approaches for use as generalized methods for un-mixing pixels in a scene that may be linear or non-linear. One method is based on earlier studies that indicate non-linear mixtures in reflectance space are approximately linear in albedo space. This method converts reflectance to single-scattering albedo (SSA) according to Hapke theory assuming bidirectional scattering at nadir look angles and uses a constrained linear model on the computed albedo values. The other method is motivated by the same idea, but uses a kernel that seeks to capture the linear behavior of albedo in non-linear mixtures of materials. The behavior of the kernel method can be highly dependent on the value of a parameter, gamma, which provides flexibility for the kernel method to respond to both linear and non-linear phenomena. Our study pays particular attention to this parameter for responding to linear and non-linear mixtures. Laboratory experiments on both granular solids and liquid solutions are performed with scenes of hyperspectral data.

  9. Protoribosome by quantum kernel energy method.

    PubMed

    Huang, Lulu; Krupkin, Miri; Bashan, Anat; Yonath, Ada; Massa, Lou

    2013-09-10

    Experimental evidence suggests the existence of an RNA molecular prebiotic entity, called by us the "protoribosome," which may have evolved in the RNA world before evolution of the genetic code and proteins. This vestige of the RNA world, which possesses all of the capabilities required for peptide bond formation, seems to be still functioning in the heart of all of the contemporary ribosome. Within the modern ribosome this remnant includes the peptidyl transferase center. Its highly conserved nucleotide sequence is suggestive of its robustness under diverse environmental conditions, and hence on its prebiotic origin. Its twofold pseudosymmetry suggests that this entity could have been a dimer of self-folding RNA units that formed a pocket within which two activated amino acids might be accommodated, similar to the binding mode of modern tRNA molecules that carry amino acids or peptidyl moieties. Using quantum mechanics and crystal coordinates, this work studies the question of whether the putative protoribosome has properties necessary to function as an evolutionary precursor to the modern ribosome. The quantum model used in the calculations is density functional theory--B3LYP/3-21G*, implemented using the kernel energy method to make the computations practical and efficient. It occurs that the necessary conditions that would characterize a practicable protoribosome--namely (i) energetic structural stability and (ii) energetically stable attachment to substrates--are both well satisfied.

  10. Enhanced FMAM based on empirical kernel map.

    PubMed

    Wang, Min; Chen, Songcan

    2005-05-01

    The existing morphological auto-associative memory models based on the morphological operations, typically including morphological auto-associative memories (auto-MAM) proposed by Ritter et al. and our fuzzy morphological auto-associative memories (auto-FMAM), have many attractive advantages such as unlimited storage capacity, one-shot recall speed and good noise-tolerance to single erosive or dilative noise. However, they suffer from the extreme vulnerability to noise of mixing erosion and dilation, resulting in great degradation on recall performance. To overcome this shortcoming, we focus on FMAM and propose an enhanced FMAM (EFMAM) based on the empirical kernel map. Although it is simple, EFMAM can significantly improve the auto-FMAM with respect to the recognition accuracy under hybrid-noise and computational effort. Experiments conducted on the thumbnail-sized faces (28 x 23 and 14 x 11) scaled from the ORL database show the average accuracies of 92%, 90%, and 88% with 40 classes under 10%, 20%, and 30% randomly generated hybrid-noises, respectively, which are far higher than the auto-FMAM (67%, 46%, 31%) under the same noise levels.

  11. Generalized Bergman kernels and geometric quantization

    NASA Astrophysics Data System (ADS)

    Tuynman, G. M.

    1987-03-01

    In geometric quantization it is well known that, if f is an observable and F a polarization on a symplectic manifold (M,ω), then the condition ``Xf leaves F invariant'' (where Xf denotes the Hamiltonian vector field associated to f ) is sufficient to guarantee that one does not have to compute the BKS kernel explicitly in order to know the corresponding quantum operator. It is shown in this paper that this condition on f can be weakened to ``Xf leaves F+F° invariant''and the corresponding quantum operator is then given implicitly by formula (4.8); in particular when F is a (positive) Kähler polarization, all observables can be quantized ``directly'' and moreover, an ``explicit'' formula for the corresponding quantum operator is derived (Theorem 5.8). Applying this to the phase space R2n one obtains a quantization prescription which ressembles the normal ordering of operators in quantum field theory. When we translate this prescription to the usual position representation of quantum mechanics, the result is (a.o) that the operator associated to a classical potential is multiplication by a function which is essentially the convolution of the potential function with a Gaussian function of width ℏ, instead of multiplication by the potential itself.

  12. Local Kernel for Brains Classification in Schizophrenia

    NASA Astrophysics Data System (ADS)

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

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

  13. The Dynamic Kernel Scheduler-Part 1

    NASA Astrophysics Data System (ADS)

    Adelmann, Andreas; Locans, Uldis; Suter, Andreas

    2016-10-01

    Emerging processor architectures such as GPUs and Intel MICs provide a huge performance potential for high performance computing. However developing software that uses these hardware accelerators introduces additional challenges for the developer. These challenges may include exposing increased parallelism, handling different hardware designs, and using multiple development frameworks in order to utilise devices from different vendors. The Dynamic Kernel Scheduler (DKS) is being developed in order to provide a software layer between the host application and different hardware accelerators. DKS handles the communication between the host and the device, schedules task execution, and provides a library of built-in algorithms. Algorithms available in the DKS library will be written in CUDA, OpenCL, and OpenMP. Depending on the available hardware, the DKS can select the appropriate implementation of the algorithm. The first DKS version was created using CUDA for the Nvidia GPUs and OpenMP for Intel MIC. DKS was further integrated into OPAL (Object-oriented Parallel Accelerator Library) in order to speed up a parallel FFT based Poisson solver and Monte Carlo simulations for particle-matter interaction used for proton therapy degrader modelling. DKS was also used together with Minuit2 for parameter fitting, where χ2 and max-log-likelihood functions were offloaded to the hardware accelerator. The concepts of the DKS, first results, and plans for the future will be shown in this paper.

  14. Kernel MAD Algorithm for Relative Radiometric Normalization

    NASA Astrophysics Data System (ADS)

    Bai, Yang; Tang, Ping; Hu, Changmiao

    2016-06-01

    The multivariate alteration detection (MAD) algorithm is commonly used in relative radiometric normalization. This algorithm is based on linear canonical correlation analysis (CCA) which can analyze only linear relationships among bands. Therefore, we first introduce a new version of MAD in this study based on the established method known as kernel canonical correlation analysis (KCCA). The proposed method effectively extracts the non-linear and complex relationships among variables. We then conduct relative radiometric normalization experiments on both the linear CCA and KCCA version of the MAD algorithm with the use of Landsat-8 data of Beijing, China, and Gaofen-1(GF-1) data derived from South China. Finally, we analyze the difference between the two methods. Results show that the KCCA-based MAD can be satisfactorily applied to relative radiometric normalization, this algorithm can well describe the nonlinear relationship between multi-temporal images. This work is the first attempt to apply a KCCA-based MAD algorithm to relative radiometric normalization.

  15. Searching for efficient Markov chain Monte Carlo proposal kernels.

    PubMed

    Yang, Ziheng; Rodríguez, Carlos E

    2013-11-26

    Markov chain Monte Carlo (MCMC) or the Metropolis-Hastings algorithm is a simulation algorithm that has made modern Bayesian statistical inference possible. Nevertheless, the efficiency of different Metropolis-Hastings proposal kernels has rarely been studied except for the Gaussian proposal. Here we propose a unique class of Bactrian kernels, which avoid proposing values that are very close to the current value, and compare their efficiency with a number of proposals for simulating different target distributions, with efficiency measured by the asymptotic variance of a parameter estimate. The uniform kernel is found to be more efficient than the Gaussian kernel, whereas the Bactrian kernel is even better. When optimal scales are used for both, the Bactrian kernel is at least 50% more efficient than the Gaussian. Implementation in a Bayesian program for molecular clock dating confirms the general applicability of our results to generic MCMC algorithms. Our results refute a previous claim that all proposals had nearly identical performance and will prompt further research into efficient MCMC proposals.

  16. Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery.

    PubMed

    Feng, Yunlong; Lv, Shao-Gao; Hang, Hanyuan; Suykens, Johan A K

    2016-03-01

    Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic net regularization (Zou & Hastie, 2005). The kernel in KENReg is not required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens (2014) showed that KENReg has some nice properties including stability, sparseness, and generalization. In this letter, we continue our study on KENReg by conducting a refined learning theory analysis. This letter makes the following three main contributions. First, we present refined error analysis on the generalization performance of KENReg. The main difficulty of analyzing the generalization error of KENReg lies in characterizing the population version of its empirical target function. We overcome this by introducing a weighted Banach space associated with the elastic net regularization. We are then able to conduct elaborated learning theory analysis and obtain fast convergence rates under proper complexity and regularity assumptions. Second, we study the sparse recovery problem in KENReg with fixed design and show that the kernelization may improve the sparse recovery ability compared to the classical elastic net regularization. Finally, we discuss the interplay among different properties of KENReg that include sparseness, stability, and generalization. We show that the stability of KENReg leads to generalization, and its sparseness confidence can be derived from generalization. Moreover, KENReg is stable and can be simultaneously sparse, which makes it attractive theoretically and practically.

  17. Multiple Kernel Learning for Visual Object Recognition: A Review.

    PubMed

    Bucak, Serhat S; Rong Jin; Jain, Anil K

    2014-07-01

    Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels for a given recognition task. A number of studies have shown that MKL is a useful tool for object recognition, where each image is represented by multiple sets of features and MKL is applied to combine different feature sets. We review the state-of-the-art for MKL, including different formulations and algorithms for solving the related optimization problems, with the focus on their applications to object recognition. One dilemma faced by practitioners interested in using MKL for object recognition is that different studies often provide conflicting results about the effectiveness and efficiency of MKL. To resolve this, we conduct extensive experiments on standard datasets to evaluate various approaches to MKL for object recognition. We argue that the seemingly contradictory conclusions offered by studies are due to different experimental setups. The conclusions of our study are: (i) given a sufficient number of training examples and feature/kernel types, MKL is more effective for object recognition than simple kernel combination (e.g., choosing the best performing kernel or average of kernels); and (ii) among the various approaches proposed for MKL, the sequential minimal optimization, semi-infinite programming, and level method based ones are computationally most efficient.

  18. Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification

    NASA Astrophysics Data System (ADS)

    Liu, Yongbin; He, Bing; Liu, Fang; Lu, Siliang; Zhao, Yilei

    2016-12-01

    Fault pattern identification is a crucial step for the intelligent fault diagnosis of real-time health conditions in monitoring a mechanical system. However, many challenges exist in extracting the effective feature from vibration signals for fault recognition. A new feature fusion method is proposed in this study to extract new features using kernel joint approximate diagonalization of eigen-matrices (KJADE). In the method, the input space that is composed of original features is mapped into a high-dimensional feature space by nonlinear mapping. Then, the new features can be estimated through the eigen-decomposition of the fourth-order cumulative kernel matrix obtained from the feature space. Therefore, the proposed method could be used to reduce data redundancy because it extracts the inherent pattern structure of different fault classes as it is nonlinear by nature. The integration evaluation factor of between-class and within-class scatters (SS) is employed to depict the clustering performance quantitatively, and the new feature subset extracted by the proposed method is fed into a multi-class support vector machine for fault pattern identification. Finally, the effectiveness of the proposed method is verified by experimental vibration signals with different bearing fault types and severities. Results of several cases show that the KJADE algorithm is efficient in feature fusion for bearing fault identification.

  19. A Distributed Learning Method for ℓ1-Regularized Kernel Machine over Wireless Sensor Networks

    PubMed Central

    Ji, Xinrong; Hou, Cuiqin; Hou, Yibin; Gao, Fang; Wang, Shulong

    2016-01-01

    In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ1 norm regularization (ℓ1-regularized) is investigated, and a novel distributed learning algorithm for the ℓ1-regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost. PMID:27376298

  20. A Distributed Learning Method for ℓ 1 -Regularized Kernel Machine over Wireless Sensor Networks.

    PubMed

    Ji, Xinrong; Hou, Cuiqin; Hou, Yibin; Gao, Fang; Wang, Shulong

    2016-07-01

    In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ 1 norm regularization ( ℓ 1 -regularized) is investigated, and a novel distributed learning algorithm for the ℓ 1 -regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost.

  1. Out-of-Sample Extensions for Non-Parametric Kernel Methods.

    PubMed

    Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang

    2017-02-01

    Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.

  2. Fast computation of synthetic seismograms within a medium containing remote localized perturbations: A numerical solution to the scattering problem

    NASA Astrophysics Data System (ADS)

    Masson, Yder; Romanowicz, Barbara

    2016-11-01

    We derive a fast discrete solution to the scattering problem. This solution allows us to compute accurate synthetic seismograms or waveforms for arbitrary locations of sources and receivers within a medium containing localized perturbations. The key to efficiency is that wave propagation modeling does not need to be carried out in the entire volume that encompasses the sources and the receivers but only within the sub-volume containing the perturbations or scatterers. The proposed solution has important applications, for example, it permits the imaging of remote targets located in regions where no sources or receivers are present. Our solution relies on domain decomposition: within a small volume that contains the scatterers, wave propagation is modeled numerically, while in the surrounding volume, where the medium isn't perturbed, the response is obtained through wavefield extrapolation. The originality of this work is the derivation of discrete formulas for representation theorems and Kirchhoff-Helmholtz integrals that naturally adapt to the numerical scheme employed for modeling wave propagation. Our solution applies, for example, to finite difference methods or finite/spectral elements methods. The synthetic seismograms obtained with our solution can be considered "exact" as the total numerical error is comparable to that of the method employed for modeling wave propagation. We detail a basic implementation of our solution in the acoustic case using the finite difference method and present numerical examples that demonstrate the accuracy of the method. We show that ignoring some terms accounting for higher order scattering effects in our solution has a limited effect on the computed seismograms and significantly reduces the computational effort. Finally, we show that our solution can be used to compute localised sensitivity kernels and we discuss applications to target oriented imaging. Extension to the elastic case is straightforward and summarised in a

  3. Variable aggregation rates in colloidal gold: Kernel homogeneity dependence on aggregant concentration

    NASA Astrophysics Data System (ADS)

    Olivier, B. J.; Sorensen, C. M.

    1990-02-01

    Dynamic light scattering is used to study the dependence of the aggregation kernel homogeneity λ on the aggregant concentration [HCl] for aqueous gold sols. We find the cluster growth kinetics are well described by a powerlaw, Rapp~tz/D, where Rapp is the measured apparent radius, D the cluster fractal dimension, and z=1/(1-λ) for all aggregant concentrations. The values for the dynamic exponent z, and hence the homogeneity λ, are functions of HCl concentration. We find the larger HCl concentrations yield a fast-aggregation regime characterized by λ~=-0.6. Smaller HCl concentrations yield a continuum of aggregation regimes characterized by homogeneities evolving from λ~=-0.6 towards 1.0. Our results do not support the view that aggregation in gold colloids is based on two limiting regimes, diffusion-limited and reaction-limited aggregation.

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

    PubMed

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

    2012-01-01

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

  5. Kernel machine testing for risk prediction with stratified case cohort studies.

    PubMed

    Payne, Rebecca; Neykov, Matey; Jensen, Majken Karoline; Cai, Tianxi

    2016-06-01

    Large assembled cohorts with banked biospecimens offer valuable opportunities to identify novel markers for risk prediction. When the outcome of interest is rare, an effective strategy to conserve limited biological resources while maintaining reasonable statistical power is the case cohort (CCH) sampling design, in which expensive markers are measured on a subset of cases and controls. However, the CCH design introduces significant analytical complexity due to outcome-dependent, finite-population sampling. Current methods for analyzing CCH studies focus primarily on the estimation of simple survival models with linear effects; testing and estimation procedures that can efficiently capture complex non-linear marker effects for CCH data remain elusive. In this article, we propose inverse probability weighted (IPW) variance component type tests for identifying important marker sets through a Cox proportional hazards kernel machine (CoxKM) regression framework previously considered for full cohort studies (Cai et al., 2011). The optimal choice of kernel, while vitally important to attain high power, is typically unknown for a given dataset. Thus, we also develop robust testing procedures that adaptively combine information from multiple kernels. The proposed IPW test statistics have complex null distributions that cannot easily be approximated explicitly. Furthermore, due to the correlation induced by CCH sampling, standard resampling methods such as the bootstrap fail to approximate the distribution correctly. We, therefore, propose a novel perturbation resampling scheme that can effectively recover the induced correlation structure. Results from extensive simulation studies suggest that the proposed IPW CoxKM testing procedures work well in finite samples. The proposed methods are further illustrated by application to a Danish CCH study of Apolipoprotein C-III markers on the risk of coronary heart disease.

  6. Anthraquinones isolated from the browned Chinese chestnut kernels (Castanea mollissima blume)

    NASA Astrophysics Data System (ADS)

    Zhang, Y. L.; Qi, J. H.; Qin, L.; Wang, F.; Pang, M. X.

    2016-08-01

    Anthraquinones (AQS) represent a group of secondary metallic products in plants. AQS are often naturally occurring in plants and microorganisms. In a previous study, we found that AQS were produced by enzymatic browning reaction in Chinese chestnut kernels. To find out whether non-enzymatic browning reaction in the kernels could produce AQS too, AQS were extracted from three groups of chestnut kernels: fresh kernels, non-enzymatic browned kernels, and browned kernels, and the contents of AQS were determined. High performance liquid chromatography (HPLC) and nuclear magnetic resonance (NMR) methods were used to identify two compounds of AQS, rehein(1) and emodin(2). AQS were barely exists in the fresh kernels, while both browned kernel groups sample contained a high amount of AQS. Thus, we comfirmed that AQS could be produced during both enzymatic and non-enzymatic browning process. Rhein and emodin were the main components of AQS in the browned kernels.

  7. Modularized seismic full waveform inversion based on waveform sensitivity kernels - The software package ASKI

    NASA Astrophysics Data System (ADS)

    Schumacher, Florian; Friederich, Wolfgang; Lamara, Samir; Gutt, Phillip; Paffrath, Marcel

    2015-04-01

    We present a seismic full waveform inversion concept for applications ranging from seismological to enineering contexts, based on sensitivity kernels for full waveforms. The kernels are derived from Born scattering theory as the Fréchet derivatives of linearized frequency-domain full waveform data functionals, quantifying the influence of elastic earth model parameters and density on the data values. For a specific source-receiver combination, the kernel is computed from the displacement and strain field spectrum originating from the source evaluated throughout the inversion domain, as well as the Green function spectrum and its strains originating from the receiver. By storing the wavefield spectra of specific sources/receivers, they can be re-used for kernel computation for different specific source-receiver combinations, optimizing the total number of required forward simulations. In the iterative inversion procedure, the solution of the forward problem, the computation of sensitivity kernels and the derivation of a model update is held completely separate. In particular, the model description for the forward problem and the description of the inverted model update are kept independent. Hence, the resolution of the inverted model as well as the complexity of solving the forward problem can be iteratively increased (with increasing frequency content of the inverted data subset). This may regularize the overall inverse problem and optimizes the computational effort of both, solving the forward problem and computing the model update. The required interconnection of arbitrary unstructured volume and point grids is realized by generalized high-order integration rules and 3D-unstructured interpolation methods. The model update is inferred solving a minimization problem in a least-squares sense, resulting in Gauss-Newton convergence of the overall inversion process. The inversion method was implemented in the modularized software package ASKI (Analysis of Sensitivity

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

    PubMed Central

    Goldberg, Debra S.; Dowell, Robin

    2017-01-01

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

  9. Rayleigh scattering. [molecular scattering terminology redefined

    NASA Technical Reports Server (NTRS)

    Young, A. T.

    1981-01-01

    The physical phenomena of molecular scattering are examined with the objective of redefining the confusing terminology currently used. The following definitions are proposed: molecular scattering consists of Rayleigh and vibrational Raman scattering; the Rayleigh scattering consists of rotational Raman lines and the central Cabannes line; the Cabannes line is composed of the Brillouin doublet and the central Gross or Landau-Placzek line. The term 'Rayleigh line' should never be used.

  10. Mean kernels to improve gravimetric geoid determination based on modified Stokes's integration

    NASA Astrophysics Data System (ADS)

    Hirt, C.

    2011-11-01

    Gravimetric geoid computation is often based on modified Stokes's integration, where Stokes's integral is evaluated with some stochastic or deterministic kernel modification. Accurate numerical evaluation of Stokes's integral requires the modified kernel to be integrated across the area of each discretised grid cell (mean kernel). Evaluating the modified kernel at the center of the cell (point kernel) is an approximation, which may result in larger numerical integration errors near the computation point, where the modified kernel exhibits a strongly nonlinear behavior. The present study deals with the computation of whole-of-the-cell mean values of modified kernels, exemplified here with the Featherstone-Evans-Olliver (1998) kernel modification [Featherstone, W.E., Evans, J.D., Olliver, J.G., 1998. A Meissl-modified Vaníček and Kleusberg kernel to reduce the truncation error in gravimetric geoid computations. Journal of Geodesy 72(3), 154-160]. We investigate two approaches (analytical and numerical integration), which are capable of providing accurate mean kernels. The analytical integration approach is based on kernel weighting factors which are used for the conversion of point to mean kernels. For the efficient numerical integration, Gauss-Legendre quadrature is applied. The comparison of mean kernels from both approaches shows a satisfactory mutual agreement at the level of 10 -4 and better, which is considered to be sufficient for practical geoid computation requirements. Closed-loop tests based on the EGM2008 geopotential model demonstrate that using mean instead of point kernels reduces numerical integration errors by ˜65%. The use of mean kernels is recommended in remove-compute-restore geoid determination with the Featherstone-Evans-Olliver (1998) kernel or any other kernel modification under the condition that the kernel changes rapidly across the cells in the neighborhood of the computation point.

  11. The Modularized Software Package ASKI - Full Waveform Inversion Based on Waveform Sensitivity Kernels Utilizing External Seismic Wave Propagation Codes

    NASA Astrophysics Data System (ADS)

    Schumacher, F.; Friederich, W.

    2015-12-01

    We present the modularized software package ASKI which is a flexible and extendable toolbox for seismic full waveform inversion (FWI) as well as sensitivity or resolution analysis operating on the sensitivity matrix. It utilizes established wave propagation codes for solving the forward problem and offers an alternative to the monolithic, unflexible and hard-to-modify codes that have typically been written for solving inverse problems. It is available under the GPL at www.rub.de/aski. The Gauss-Newton FWI method for 3D-heterogeneous elastic earth models is based on waveform sensitivity kernels and can be applied to inverse problems at various spatial scales in both Cartesian and spherical geometries. The kernels are derived in the frequency domain from Born scattering theory as the Fréchet derivatives of linearized full waveform data functionals, quantifying the influence of elastic earth model parameters on the particular waveform data values. As an important innovation, we keep two independent spatial descriptions of the earth model - one for solving the forward problem and one representing the inverted model updates. Thereby we account for the independent needs of spatial model resolution of forward and inverse problem, respectively. Due to pre-integration of the kernels over the (in general much coarser) inversion grid, storage requirements for the sensitivity kernels are dramatically reduced.ASKI can be flexibly extended to other forward codes by providing it with specific interface routines that contain knowledge about forward code-specific file formats and auxiliary information provided by the new forward code. In order to sustain flexibility, the ASKI tools must communicate via file output/input, thus large storage capacities need to be accessible in a convenient way. Storing the complete sensitivity matrix to file, however, permits the scientist full manual control over each step in a customized procedure of sensitivity/resolution analysis and full

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

    PubMed

    Dufrenois, Franck

    2015-05-01

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

  13. On flame kernel formation and propagation in premixed gases

    SciTech Connect

    Eisazadeh-Far, Kian; Metghalchi, Hameed; Parsinejad, Farzan; Keck, James C.

    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)

  14. CRKSPH - A Conservative Reproducing Kernel Smoothed Particle Hydrodynamics Scheme

    NASA Astrophysics Data System (ADS)

    Frontiere, Nicholas; Raskin, Cody D.; Owen, J. Michael

    2017-03-01

    We present a formulation of smoothed particle hydrodynamics (SPH) that utilizes a first-order consistent reproducing kernel, a smoothing function that exactly interpolates linear fields with particle tracers. Previous formulations using reproducing kernel (RK) interpolation have had difficulties maintaining conservation of momentum due to the fact the RK kernels are not, in general, spatially symmetric. Here, we utilize a reformulation of the fluid equations such that mass, linear momentum, and energy are all rigorously conserved without any assumption about kernel symmetries, while additionally maintaining approximate angular momentum conservation. Our approach starts from a rigorously consistent interpolation theory, where we derive the evolution equations to enforce the appropriate conservation properties, at the sacrifice of full consistency in the momentum equation. Additionally, by exploiting the increased accuracy of the RK method's gradient, we formulate a simple limiter for the artificial viscosity that reduces the excess diffusion normally incurred by the ordinary SPH artificial viscosity. Collectively, we call our suite of modifications to the traditional SPH scheme Conservative Reproducing Kernel SPH, or CRKSPH. CRKSPH retains many benefits of traditional SPH methods (such as preserving Galilean invariance and manifest conservation of mass, momentum, and energy) while improving on many of the shortcomings of SPH, particularly the overly aggressive artificial viscosity and zeroth-order inaccuracy. We compare CRKSPH to two different modern SPH formulations (pressure based SPH and compatibly differenced SPH), demonstrating the advantages of our new formulation when modeling fluid mixing, strong shock, and adiabatic phenomena.

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

    PubMed

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

    2016-12-01

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

  16. Coupled kernel embedding for low resolution face image recognition.

    PubMed

    Ren, Chuan-Xian; Dai, Dao-Qing; Yan, Hong

    2012-08-01

    Practical video scene and face recognition systems are sometimes confronted with low-resolution (LR) images. The faces may be very small even if the video is clear, thus it is difficult to directly measure the similarity between the faces and the high-resolution (HR) training samples. Traditional super-resolution (SR) methods based face recognition usually have limited performance because the target of SR may not be consistent with that of classification, and time-consuming SR algorithms are not suitable for real-time applications. In this paper, a new feature extraction method called Coupled Kernel Embedding (CKE) is proposed for LR face recognition without any SR preprocessing. In this method, the final kernel matrix is constructed by concatenating two individual kernel matrices in the diagonal direction, and the (semi-)positively definite properties are preserved for optimization. CKE addresses the problem of comparing multi-modal data that are difficult for conventional methods in practice due to the lack of an efficient similarity measure. Particularly, different kernel types (e.g., linear, Gaussian, polynomial) can be integrated into an uniformed optimization objective, which cannot be achieved by simple linear methods. CKE solves this problem by minimizing the dissimilarities captured by their kernel Gram matrices in the low- and high-resolution spaces. In the implementation, the nonlinear objective function is minimized by a generalized eigenvalue decomposition. Experiments on benchmark and real databases show that our CKE method indeed improves the recognition performance.

  17. Optimizing spatial filters with kernel methods for BCI applications

    NASA Astrophysics Data System (ADS)

    Zhang, Jiacai; Tang, Jianjun; Yao, Li

    2007-11-01

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

  18. Travel-time sensitivity kernels in long-range propagation.

    PubMed

    Skarsoulis, E K; Cornuelle, B D; Dzieciuch, M A

    2009-11-01

    Wave-theoretic travel-time sensitivity kernels (TSKs) are calculated in two-dimensional (2D) and three-dimensional (3D) environments and their behavior with increasing propagation range is studied and compared to that of ray-theoretic TSKs and corresponding Fresnel-volumes. The differences between the 2D and 3D TSKs average out when horizontal or cross-range marginals are considered, which indicates that they are not important in the case of range-independent sound-speed perturbations or perturbations of large scale compared to the lateral TSK extent. With increasing range, the wave-theoretic TSKs expand in the horizontal cross-range direction, their cross-range extent being comparable to that of the corresponding free-space Fresnel zone, whereas they remain bounded in the vertical. Vertical travel-time sensitivity kernels (VTSKs)-one-dimensional kernels describing the effect of horizontally uniform sound-speed changes on travel-times-are calculated analytically using a perturbation approach, and also numerically, as horizontal marginals of the corresponding TSKs. Good agreement between analytical and numerical VTSKs, as well as between 2D and 3D VTSKs, is found. As an alternative method to obtain wave-theoretic sensitivity kernels, the parabolic approximation is used; the resulting TSKs and VTSKs are in good agreement with normal-mode results. With increasing range, the wave-theoretic VTSKs approach the corresponding ray-theoretic sensitivity kernels.

  19. Face detection based on multiple kernel learning algorithm

    NASA Astrophysics Data System (ADS)

    Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun

    2016-09-01

    Face detection is important for face localization in face or facial expression recognition, etc. The basic idea is to determine whether there is a face in an image or not, and also its location, size. It can be seen as a binary classification problem, which can be well solved by support vector machine (SVM). Though SVM has strong model generalization ability, it has some limitations, which will be deeply analyzed in the paper. To access them, we study the principle and characteristics of the Multiple Kernel Learning (MKL) and propose a MKL-based face detection algorithm. In the paper, we describe the proposed algorithm in the interdisciplinary research perspective of machine learning and image processing. After analyzing the limitation of describing a face with a single feature, we apply several ones. To fuse them well, we try different kernel functions on different feature. By MKL method, the weight of each single function is determined. Thus, we obtain the face detection model, which is the kernel of the proposed method. Experiments on the public data set and real life face images are performed. We compare the performance of the proposed algorithm with the single kernel-single feature based algorithm and multiple kernels-single feature based algorithm. The effectiveness of the proposed algorithm is illustrated. Keywords: face detection, feature fusion, SVM, MKL

  20. Spine labeling in axial magnetic resonance imaging via integral kernels.

    PubMed

    Miles, Brandon; Ben Ayed, Ismail; Hojjat, Seyed-Parsa; Wang, Michael H; Li, Shuo; Fenster, Aaron; Garvin, Gregory J

    2016-12-01

    This study investigates a fast integral-kernel algorithm for classifying (labeling) the vertebra and disc structures in axial magnetic resonance images (MRI). The method is based on a hierarchy of feature levels, where pixel classifications via non-linear probability product kernels (PPKs) are followed by classifications of 2D slices, individual 3D structures and groups of 3D structures. The algorithm further embeds geometric priors based on anatomical measurements of the spine. Our classifier requires evaluations of computationally expensive integrals at each pixel, and direct evaluations of such integrals would be prohibitively time consuming. We propose an efficient computation of kernel density estimates and PPK evaluations for large images and arbitrary local window sizes via integral kernels. Our method requires a single user click for a whole 3D MRI volume, runs nearly in real-time, and does not require an intensive external training. Comprehensive evaluations over T1-weighted axial lumbar spine data sets from 32 patients demonstrate a competitive structure classification accuracy of 99%, along with a 2D slice classification accuracy of 88%. To the best of our knowledge, such a structure classification accuracy has not been reached by the existing spine labeling algorithms. Furthermore, we believe our work is the first to use integral kernels in the context of medical images.

  1. Evolutionary Metabolomics Reveals Domestication-Associated Changes in Tetraploid Wheat Kernels

    PubMed Central

    Beleggia, Romina; Rau, Domenico; Laidò, Giovanni; Platani, Cristiano; Nigro, Franca; Fragasso, Mariagiovanna; De Vita, Pasquale; Scossa, Federico; Fernie, Alisdair R.; Nikoloski, Zoran; Papa, Roberto

    2016-01-01

    Domestication and breeding have influenced the genetic structure of plant populations due to selection for adaptation from natural habitats to agro-ecosystems. Here, we investigate the effects of selection on the contents of 51 primary kernel metabolites and their relationships in three Triticum turgidum L. subspecies (i.e., wild emmer, emmer, durum wheat) that represent the major steps of tetraploid wheat domestication. We present a methodological pipeline to identify the signature of selection for molecular phenotypic traits (e.g., metabolites and transcripts). Following the approach, we show that a reduction in unsaturated fatty acids was associated with selection during domestication of emmer (primary domestication). We also show that changes in the amino acid content due to selection mark the domestication of durum wheat (secondary domestication). These effects were found to be partially independent of the associations that unsaturated fatty acids and amino acids have with other domestication-related kernel traits. Changes in contents of metabolites were also highlighted by alterations in the metabolic correlation networks, indicating wide metabolic restructuring due to domestication. Finally, evidence is provided that wild and exotic germplasm can have a relevant role for improvement of wheat quality and nutritional traits. PMID:27189559

  2. Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction.

    PubMed

    Singh, Ritambhara; Lanchantin, Jack; Robins, Gabriel; Qi, Yanjun

    2016-09-15

    Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related methods in the literature fail to perform such predictions accurately, since they do not consider sample distribution shift of sequence segments from an annotated (source) context to an unannotated (target) context. We, therefore, propose a method called "Transfer String Kernel" (TSK) that achieves improved prediction of transcription factor binding site (TFBS) using knowledge transfer via cross-context sample adaptation. TSK maps sequence segments to a high-dimensional feature space using a discriminative mismatch string kernel framework. In this high-dimensional space, labeled examples of the source context are re-weighted so that the revised sample distribution matches the target context more closely. We have experimentally verified TSK for TFBS identifications on fourteen different TFs under a cross-organism setting. We find that TSK consistently outperforms the state-of-the-art TFBS tools, especially when working with TFs whose binding sequences are not conserved across contexts. We also demonstrate the generalizability of TSK by showing its cutting-edge performance on a different set of cross-context tasks for the MHC peptide binding predictions.

  3. nd Scattering Observables Derived from the Quark-Model Baryon-Baryon Interaction

    SciTech Connect

    Fujiwara, Y.; Fukukawa, K.

    2010-05-12

    We solve the nd scattering in the Faddeev formalism, employing the NN sector of the quark-model baryon-baryon interaction fss2. The energy-dependence of the NN interaction, inherent to the (3q)-(3q) resonating-group formulation, is eliminated by the standard off-shell transformation utilizing the 1/sq root(N) factor, where N is the normalization kernel for the (3q)-(3q) system. This procedure yields an extra nonlocality, whose effect is very important to reproduce all the scattering observables below E{sub n}<=65 MeV. The different off-shell properties from the standard meson-exchange potentials, related to the non-locality of the quark-exchange kernel, yields appreciable effects to the differential cross sections and polarization observables of the nd elastic scattering, which are usually attributed to the specific properties of three-body forces.

  4. A method of smoothed particle hydrodynamics using spheroidal kernels

    NASA Technical Reports Server (NTRS)

    Fulbright, Michael S.; Benz, Willy; Davies, Melvyn B.

    1995-01-01

    We present a new method of three-dimensional smoothed particle hydrodynamics (SPH) designed to model systems dominated by deformation along a preferential axis. These systems cause severe problems for SPH codes using spherical kernels, which are best suited for modeling systems which retain rough spherical symmetry. Our method allows the smoothing length in the direction of the deformation to evolve independently of the smoothing length in the perpendicular plane, resulting in a kernel with a spheroidal shape. As a result the spatial resolution in the direction of deformation is significantly improved. As a test case we present the one-dimensional homologous collapse of a zero-temperature, uniform-density cloud, which serves to demonstrate the advantages of spheroidal kernels. We also present new results on the problem of the tidal disruption of a star by a massive black hole.

  5. Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.

    PubMed

    Li, Shuang; Liu, Bing; Zhang, Chen

    2016-01-01

    Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.

  6. Weighted Feature Gaussian Kernel SVM for Emotion Recognition

    PubMed Central

    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. PMID:27807443

  7. Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing

    PubMed Central

    Li, Shuang; Liu, Bing; Zhang, Chen

    2016-01-01

    Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios. PMID:27247562

  8. Compression loading behaviour of sunflower seeds and kernels

    NASA Astrophysics Data System (ADS)

    Selvam, Thasaiya A.; Manikantan, Musuvadi R.; Chand, Tarsem; Sharma, Rajiv; Seerangurayar, Thirupathi

    2014-10-01

    The present study was carried out to investigate the compression loading behaviour of five Indian sunflower varieties (NIRMAL-196, NIRMAL-303, CO-2, KBSH-41, and PSH- 996) under four different moisture levels (6-18% d.b). The initial cracking force, mean rupture force, and rupture energy were measured as a function of moisture content. The observed results showed that the initial cracking force decreased linearly with an increase in moisture content for all varieties. The mean rupture force also decreased linearly with an increase in moisture content. However, the rupture energy was found to be increasing linearly for seed and kernel with moisture content. NIRMAL-196 and PSH-996 had maximum and minimum values of all the attributes studied for both seed and kernel, respectively. The values of all the studied attributes were higher for seed than kernel of all the varieties at all moisture levels. There was a significant effect of moisture and variety on compression loading behaviour.

  9. Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

    PubMed

    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.

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

    PubMed

    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.

  11. A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

    PubMed Central

    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. PMID:25893896

  12. The Effects of Kernel Feeding by Halyomorpha halys (Hemiptera: Pentatomidae) on Commercial Hazelnuts.

    PubMed

    Hedstrom, C S; Shearer, P W; Miller, J C; Walton, V M

    2014-10-01

    Halyomorpha halys Stål, the brown marmorated stink bug (Hemiptera: Pentatomidae), is an invasive pest with established populations in Oregon. The generalist feeding habits of H. halys suggest it has the potential to be a pest of many specialty crops grown in Oregon, including hazelnuts, Corylus avellana L. The objectives of this study were to: 1) characterize the damage to developing hazelnut kernels resulting from feeding by H. halys adults, 2) determine how the timing of feeding during kernel development influences damage to kernels, and 3) determine if hazelnut shell thickness has an effect on feeding frequency on kernels. Adult brown marmorated stink bugs were allowed to feed on developing nuts for 1-wk periods from initial kernel development (spring) until harvest (fall). Developing nuts not exposed to feeding by H. halys served as a control treatment. The degree of damage and diagnostic symptoms corresponded with the hazelnut kernels' physiological development. Our results demonstrated that when H. halys fed on hazelnuts before kernel expansion, development of the kernels could cease, resulting in empty shells. When stink bugs fed during kernel expansion, kernels appeared malformed. When stink bugs fed on mature nuts the kernels exhibited corky, necrotic areas. Although significant differences in shell thickness were observed among the cultivars, no significant differences occurred in the proportions of damaged kernels based on field tests and laboratory choice tests. The results of these studies demonstrated that commercial hazelnuts are susceptible to damage caused by the feeding of H. halys throughout the entire period of kernel development.

  13. Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image

    NASA Astrophysics Data System (ADS)

    Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Ononye, Ambrose; Brown, Robert L.; Cleveland, Thomas E.

    2010-04-01

    Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.

  14. Aflatoxin detection in whole corn kernels using hyperspectral methods

    NASA Astrophysics Data System (ADS)

    Casasent, David; Chen, Xue-Wen

    2004-03-01

    Hyperspectral (HS) data for the inspection of whole corn kernels for aflatoxin is considered. The high-dimensionality of HS data requires feature extraction or selection for good classifier generalization. For fast and inexpensive data collection, only several features (λ responses) can be used. These are obtained by feature selection from the full HS response. A new high dimensionality branch and bound (HDBB) feature selection algorithm is used; it is found to be optimum, fast and very efficient. Initial results indicate that HS data is very promising for aflatoxin detection in whole kernel corn.

  15. Characterizations of linear Volterra integral equations with nonnegative kernels

    NASA Astrophysics Data System (ADS)

    Naito, Toshiki; Shin, Jong Son; Murakami, Satoru; Ngoc, Pham Huu Anh

    2007-11-01

    We first introduce the notion of positive linear Volterra integral equations. Then, we offer a criterion for positive equations in terms of the resolvent. In particular, equations with nonnegative kernels are positive. Next, we obtain a variant of the Paley-Wiener theorem for equations of this class and its extension to perturbed equations. Furthermore, we get a Perron-Frobenius type theorem for linear Volterra integral equations with nonnegative kernels. Finally, we give a criterion for positivity of the initial function semigroup of linear Volterra integral equations and provide a necessary and sufficient condition for exponential stability of the semigroups.

  16. Source identity and kernel functions for Inozemtsev-type systems

    NASA Astrophysics Data System (ADS)

    Langmann, Edwin; Takemura, Kouichi

    2012-08-01

    The Inozemtsev Hamiltonian is an elliptic generalization of the differential operator defining the BCN trigonometric quantum Calogero-Sutherland model, and its eigenvalue equation is a natural many-variable generalization of the Heun differential equation. We present kernel functions for Inozemtsev Hamiltonians and Chalykh-Feigin-Veselov-Sergeev-type deformations thereof. Our main result is a solution of a heat-type equation for a generalized Inozemtsev Hamiltonian which is the source of all these kernel functions. Applications are given, including a derivation of simple exact eigenfunctions and eigenvalues of the Inozemtsev Hamiltonian.

  17. FUV Continuum in Flare Kernels Observed by IRIS

    NASA Astrophysics Data System (ADS)

    Daw, Adrian N.; Kowalski, Adam; Allred, Joel C.; Cauzzi, Gianna

    2016-05-01

    Fits to Interface Region Imaging Spectrograph (IRIS) spectra observed from bright kernels during the impulsive phase of solar flares are providing long-sought constraints on the UV/white-light continuum emission. Results of fits of continua plus numerous atomic and molecular emission lines to IRIS far ultraviolet (FUV) spectra of bright kernels are presented. Constraints on beam energy and cross sectional area are provided by cotemporaneous RHESSI, FERMI, ROSA/DST, IRIS slit-jaw and SDO/AIA observations, allowing for comparison of the observed IRIS continuum to calculations of non-thermal electron beam heating using the RADYN radiative-hydrodynamic loop model.

  18. Q-branch Raman scattering and modern kinetic thoery

    SciTech Connect

    Monchick, L.

    1993-12-01

    The program is an extension of previous APL work whose general aim was to calculate line shapes of nearly resonant isolated line transitions with solutions of a popular quantum kinetic equation-the Waldmann-Snider equation-using well known advanced solution techniques developed for the classical Boltzmann equation. The advanced techniques explored have been a BGK type approximation, which is termed the Generalized Hess Method (GHM), and conversion of the collision operator to a block diagonal matrix of symmetric collision kernels which then can be approximated by discrete ordinate methods. The latter method, which is termed the Collision Kernel method (CC), is capable of the highest accuracy and has been used quite successfully for Q-branch Raman scattering. The GHM method, not quite as accurate, is applicable over a wider range of pressures and has proven quite useful.

  19. Research on classifying performance of SVMs with basic kernel in HCCR

    NASA Astrophysics Data System (ADS)

    Sun, Limin; Gai, Zhaoxin

    2006-02-01

    It still is a difficult task for handwritten chinese character recognition (HCCR) to put into practical use. An efficient classifier occupies very important position for increasing offline HCCR rate. SVMs offer a theoretically well-founded approach to automated learning of pattern classifiers for mining labeled data sets. As we know, the performance of SVM largely depends on the kernel function. In this paper, we investigated the classification performance of SVMs with various common kernels in HCCR. We found that except for sigmoid kernel, SVMs with polynomial kernel, linear kernel, RBF kernel and multi-quadratic kernel are all efficient classifier for HCCR, their behavior has a little difference, taking one with another, SVM with multi-quadratic kernel is the best.

  20. A multiple-kernel fuzzy C-means algorithm for image segmentation.

    PubMed

    Chen, Long; Chen, C L Philip; Lu, Mingzhu

    2011-10-01

    In this paper, a generalized multiple-kernel fuzzy C-means (FCM) (MKFCM) methodology is introduced as a framework for image-segmentation problems. In the framework, aside from the fact that the composite kernels are used in the kernel FCM (KFCM), a linear combination of multiple kernels is proposed and the updating rules for the linear coefficients of the composite kernel are derived as well. The proposed MKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in image-segmentation problems. That is, different pixel information represented by different kernels is combined in the kernel space to produce a new kernel. It is shown that two successful enhanced KFCM-based image-segmentation algorithms are special cases of MKFCM. Several new segmentation algorithms are also derived from the proposed MKFCM framework. Simulations on the segmentation of synthetic and medical images demonstrate the flexibility and advantages of MKFCM-based approaches.

  1. Object tracking via kernel-based forward-backward keypoint matching

    NASA Astrophysics Data System (ADS)

    Zhao, Qi; Du, Zhiying; Zhang, Hong; Yuan, Ding; Sun, Mingui

    2017-02-01

    Object tracking is a challenging research task due to target appearance variation caused by deformation and occlusion. Keypoint matching based tracker can handle partial occlusion problem, but it's vulnerable to matching faults and inflexible to target deformation. In this paper, we propose an innovative keypoint matching procedure to address above issues. Firstly, the scale and orientation of corresponding keypoints are applied to estimate the target's status. Secondly, a kernel function is employed in order to discard the mismatched keypoints, so as to improve the estimation accuracy. Thirdly, the model updating mechanism is applied to adapt to target deformation. Moreover, in order to avoid bad updating, backward matching is used to determine whether or not to update target model. Extensive experiments on challenging image sequences show that our method performs favorably against state-of-the-art methods.

  2. Adaptive classifier for steel strip surface defects

    NASA Astrophysics Data System (ADS)

    Jiang, Mingming; Li, Guangyao; Xie, Li; Xiao, Mang; Yi, Li

    2017-01-01

    Surface defects detection system has been receiving increased attention as its precision, speed and less cost. One of the most challenges is reacting to accuracy deterioration with time as aged equipment and changed processes. These variables will make a tiny change to the real world model but a big impact on the classification result. In this paper, we propose a new adaptive classifier with a Bayes kernel (BYEC) which update the model with small sample to it adaptive for accuracy deterioration. Firstly, abundant features were introduced to cover lots of information about the defects. Secondly, we constructed a series of SVMs with the random subspace of the features. Then, a Bayes classifier was trained as an evolutionary kernel to fuse the results from base SVMs. Finally, we proposed the method to update the Bayes evolutionary kernel. The proposed algorithm is experimentally compared with different algorithms, experimental results demonstrate that the proposed method can be updated with small sample and fit the changed model well. Robustness, low requirement for samples and adaptive is presented in the experiment.

  3. Collinear limits beyond the leading order from the scattering equations

    NASA Astrophysics Data System (ADS)

    Nandan, Dhritiman; Plefka, Jan; Wormsbecher, Wadim

    2017-02-01

    The structure of tree-level scattering amplitudes for collinear massless bosons is studied beyond their leading splitting function behavior. These near-collinear limits at sub-leading order are best studied using the Cachazo-He-Yuan (CHY) formulation of the S-matrix based on the scattering equations. We compute the collinear limits for gluons, gravitons and scalars. It is shown that the CHY integrand for an n-particle gluon scattering amplitude in the collinear limit at sub-leading order is expressed as a convolution of an ( n - 1)-particle gluon integrand and a collinear kernel integrand, which is universal. Our representation is shown to obey recently proposed amplitude relations in which the collinear gluons of same helicity are replaced by a single graviton. Finally, we extend our analysis to effective field theories and study the collinear limit of the non-linear sigma model, Einstein-Maxwell-Scalar and Yang-Mills-Scalar theory.

  4. Sub-second pencil beam dose calculation on GPU for adaptive proton therapy.

    PubMed

    da Silva, Joakim; Ansorge, Richard; Jena, Rajesh

    2015-06-21

    Although proton therapy delivered using scanned pencil beams has the potential to produce better dose conformity than conventional radiotherapy, the created dose distributions are more sensitive to anatomical changes and patient motion. Therefore, the introduction of adaptive treatment techniques where the dose can be monitored as it is being delivered is highly desirable. We present a GPU-based dose calculation engine relying on the widely used pencil beam algorithm, developed for on-line dose calculation. The calculation engine was implemented from scratch, with each step of the algorithm parallelized and adapted to run efficiently on the GPU architecture. To ensure fast calculation, it employs several application-specific modifications and simplifications, and a fast scatter-based implementation of the computationally expensive kernel superposition step. The calculation time for a skull base treatment plan using two beam directions was 0.22 s on an Nvidia Tesla K40 GPU, whereas a test case of a cubic target in water from the literature took 0.14 s to calculate. The accuracy of the patient dose distributions was assessed by calculating the γ-index with respect to a gold standard Monte Carlo simulation. The passing rates were 99.2% and 96.7%, respectively, for the 3%/3 mm and 2%/2 mm criteria, matching those produced by a clinical treatment planning system.

  5. Increasing accuracy of dispersal kernels in grid-based population models

    USGS Publications Warehouse

    Slone, D.H.

    2011-01-01

    Dispersal kernels in grid-based population models specify the proportion, distance and direction of movements within the model landscape. Spatial errors in dispersal kernels can have large compounding effects on model accuracy. Circular Gaussian and Laplacian dispersal kernels at a range of spatial resolutions were investigated, and methods for minimizing errors caused by the discretizing process were explored. Kernels of progressively smaller sizes relative to the landscape grid size were calculated using cell-integration and cell-center methods. These kernels were convolved repeatedly, and the final distribution was compared with a reference analytical solution. For large Gaussian kernels (σ > 10 cells), the total kernel error was <10 &sup-11; compared to analytical results. Using an invasion model that tracked the time a population took to reach a defined goal, the discrete model results were comparable to the analytical reference. With Gaussian kernels that had σ ≤ 0.12 using the cell integration method, or σ ≤ 0.22 using the cell center method, the kernel error was greater than 10%, which resulted in invasion times that were orders of magnitude different than theoretical results. A goal-seeking routine was developed to adjust the kernels to minimize overall error. With this, corrections for small kernels were found that decreased overall kernel error to <10-11 and invasion time error to <5%.

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

    PubMed

    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.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 ...

  8. An Implementation of Multiprogramming and Process Management for a Security Kernel Operating System.

    DTIC Science & Technology

    1980-06-01

    multiplexing technique for a distributed kernel and presents a virtual interrupt mechanism. Its structure is loop free to permit future expansion into more...coordinates the asynchronous interaction of system processes. This implementation describes a processor multiplexing technique for a distributed kernel...system. This implementation employs a processor multiplexing technique for a distributed kernel and presents a virtual interrupt mechanism. The

  9. Evaluating Equating Results: Percent Relative Error for Chained Kernel Equating

    ERIC Educational Resources Information Center

    Jiang, Yanlin; von Davier, Alina A.; Chen, Haiwen

    2012-01-01

    This article presents a method for evaluating equating results. Within the kernel equating framework, the percent relative error (PRE) for chained equipercentile equating was computed under the nonequivalent groups with anchor test (NEAT) design. The method was applied to two data sets to obtain the PRE, which can be used to measure equating…

  10. Predicting disease trait with genomic data: a composite kernel approach.

    PubMed

    Yang, Haitao; Li, Shaoyu; Cao, Hongyan; Zhang, Chichen; Cui, Yuehua

    2016-06-02

    With the advancement of biotechniques, a vast amount of genomic data is generated with no limit. Predicting a disease trait based on these data offers a cost-effective and time-efficient way for early disease screening. Here we proposed a composite kernel partial least squares (CKPLS) regression model for quantitative disease trait prediction focusing on genomic data. It can efficiently capture nonlinear relationships among features compared with linear learning algorithms such as Least Absolute Shrinkage and Selection Operator or ridge regression. We proposed to optimize the kernel parameters and kernel weights with the genetic algorithm (GA). In addition to improved performance for parameter optimization, the proposed GA-CKPLS approach also has better learning capacity and generalization ability compared with single kernel-based KPLS method as well as other nonlinear prediction models such as the support vector regression. Extensive simulation studies demonstrated that GA-CKPLS had better prediction performance than its counterparts under different scenarios. The utility of the method was further demonstrated through two case studies. Our method provides an efficient quantitative platform for disease trait prediction based on increasing volume of omics data.

  11. 7 CFR 981.61 - Redetermination of kernel weight.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... SERVICE (MARKETING AGREEMENTS AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS... weight. The Board, on the basis of reports by handlers, shall redetermine the kernel weight of almonds... for almonds on which the obligation has been assumed by another handler. The redetermined...

  12. 7 CFR 981.61 - Redetermination of kernel weight.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... SERVICE (MARKETING AGREEMENTS AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS... weight. The Board, on the basis of reports by handlers, shall redetermine the kernel weight of almonds... for almonds on which the obligation has been assumed by another handler. The redetermined...

  13. 7 CFR 981.61 - Redetermination of kernel weight.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... SERVICE (Marketing Agreements and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS... weight. The Board, on the basis of reports by handlers, shall redetermine the kernel weight of almonds... for almonds on which the obligation has been assumed by another handler. The redetermined...

  14. 7 CFR 981.61 - Redetermination of kernel weight.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... SERVICE (Marketing Agreements and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS... weight. The Board, on the basis of reports by handlers, shall redetermine the kernel weight of almonds... for almonds on which the obligation has been assumed by another handler. The redetermined...

  15. PERI - Auto-tuning Memory Intensive Kernels for Multicore

    SciTech Connect

    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.

  16. Notes on a storage manager for the Clouds kernel

    NASA Technical Reports Server (NTRS)

    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.

  17. Comparative Analysis of Kernel Methods for Statistical Shape Learning

    DTIC Science & Technology

    2006-01-01

    successfully used by the machine learning community for pattern recognition and image denoising [14]. A Gaussian kernel was used by Cremers et al. [8] for...matrix M, where φi ∈ RNd . Using Singular Value Decomposition ( SVD ), the covariance matrix 1nMM T is decomposed as: UΣUT = 1 n MMT (1) where U is a

  18. Classification of Microarray Data Using Kernel Fuzzy Inference System.

    PubMed

    Kumar, Mukesh; Kumar Rath, Santanu

    2014-01-01

    The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function.

  19. 7 CFR 868.254 - Broken kernels determination.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... 7 Agriculture 7 2013-01-01 2013-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD ADMINISTRATION (FEDERAL GRAIN INSPECTION SERVICE), DEPARTMENT OF AGRICULTURE GENERAL REGULATIONS AND...

  20. 7 CFR 868.254 - Broken kernels determination.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... 7 Agriculture 7 2014-01-01 2014-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD ADMINISTRATION (FEDERAL GRAIN INSPECTION SERVICE), DEPARTMENT OF AGRICULTURE GENERAL REGULATIONS AND...

  1. 7 CFR 868.304 - Broken kernels determination.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... 7 Agriculture 7 2014-01-01 2014-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD ADMINISTRATION (FEDERAL GRAIN INSPECTION SERVICE), DEPARTMENT OF AGRICULTURE GENERAL REGULATIONS AND...

  2. 7 CFR 868.254 - Broken kernels determination.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... 7 Agriculture 7 2012-01-01 2012-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD ADMINISTRATION (FEDERAL GRAIN INSPECTION SERVICE), DEPARTMENT OF AGRICULTURE GENERAL REGULATIONS AND...

  3. 7 CFR 868.304 - Broken kernels determination.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... 7 Agriculture 7 2012-01-01 2012-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD ADMINISTRATION (FEDERAL GRAIN INSPECTION SERVICE), DEPARTMENT OF AGRICULTURE GENERAL REGULATIONS AND...

  4. 7 CFR 868.304 - Broken kernels determination.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 7 Agriculture 7 2011-01-01 2011-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD ADMINISTRATION (FEDERAL GRAIN INSPECTION SERVICE), DEPARTMENT OF AGRICULTURE GENERAL REGULATIONS AND...

  5. 7 CFR 868.254 - Broken kernels determination.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 7 Agriculture 7 2011-01-01 2011-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD ADMINISTRATION (FEDERAL GRAIN INSPECTION SERVICE), DEPARTMENT OF AGRICULTURE GENERAL REGULATIONS AND...

  6. 7 CFR 868.304 - Broken kernels determination.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... 7 Agriculture 7 2013-01-01 2013-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD ADMINISTRATION (FEDERAL GRAIN INSPECTION SERVICE), DEPARTMENT OF AGRICULTURE GENERAL REGULATIONS AND...

  7. Online multiple kernel similarity learning for visual search.

    PubMed

    Xia, Hao; Hoi, Steven C H; Jin, Rong; Zhao, Peilin

    2014-03-01

    Recent years have witnessed a number of studies on distance metric learning to improve visual similarity search in content-based image retrieval (CBIR). Despite their successes, most existing methods on distance metric learning are limited in two aspects. First, they usually assume the target proximity function follows the family of Mahalanobis distances, which limits their capacity of measuring similarity of complex patterns in real applications. Second, they often cannot effectively handle the similarity measure of multimodal data that may originate from multiple resources. To overcome these limitations, this paper investigates an online kernel similarity learning framework for learning kernel-based proximity functions which goes beyond the conventional linear distance metric learning approaches. Based on the framework, we propose a novel online multiple kernel similarity (OMKS) learning method which learns a flexible nonlinear proximity function with multiple kernels to improve visual similarity search in CBIR. We evaluate the proposed technique for CBIR on a variety of image data sets in which encouraging results show that OMKS outperforms the state-of-the-art techniques significantly.

  8. Online Multiple Kernel Similarity Learning for Visual Search.

    PubMed

    Xia, Hao; Hoi, Steven C H; Jin, Rong; Zhao, Peilin

    2013-08-13

    Recent years have witnessed a number of studies on distance metric learning to improve visual similarity search in Content-Based Image Retrieval (CBIR). Despite their popularity and success, most existing methods on distance metric learning are limited in two aspects. First, they typically assume the target proximity function follows the family of Mahalanobis distances, which limits their capacity of measuring similarity of complex patterns in real applications. Second, they often cannot effectively handle the similarity measure of multi-modal data that may originate from multiple resources. To overcome these limitations, this paper investigates an online kernel ranking framework for learning kernel-based proximity functions, which goes beyond the conventional linear distance metric learning approaches. Based on the framework, we propose a novel Online Multiple Kernel Ranking (OMKR) method, which learns a flexible nonlinear proximity function with multiple kernels to improve visual similarity search in CBIR. We evaluate the proposed technique for CBIR on a variety of image data sets, in which encouraging results show that OMKR outperforms the state-of-the-art techniques significantly.

  9. Estimating Filtering Errors Using the Peano Kernel Theorem

    SciTech Connect

    Jerome Blair

    2009-02-20

    The Peano Kernel Theorem is introduced and a frequency domain derivation is given. It is demonstrated that the application of this theorem yields simple and accurate formulas for estimating the error introduced into a signal by filtering it to reduce noise.

  10. Microwave moisture meter for in-shell peanut kernels

    Technology Transfer Automated Retrieval System (TEKTRAN)

    . A microwave moisture meter built with off-the-shelf components was developed, calibrated and tested in the laboratory and in the field for nondestructive and instantaneous in-shell peanut kernel moisture content determination from dielectric measurements on unshelled peanut pod samples. The meter ...

  11. Music emotion detection using hierarchical sparse kernel machines.

    PubMed

    Chin, Yu-Hao; Lin, Chang-Hong; Siahaan, Ernestasia; Wang, Jia-Ching

    2014-01-01

    For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

  12. Matrix kernels for MEG and EEG source localization and imaging

    SciTech Connect

    Mosher, J.C.; Lewis, P.S.; Leahy, R.M.

    1994-12-31

    The most widely used model for electroencephalography (EEG) and magnetoencephalography (MEG) assumes a quasi-static approximation of Maxwell`s equations and a piecewise homogeneous conductor model. Both models contain an incremental field element that linearly relates an incremental source element (current dipole) to the field or voltage at a distant point. The explicit form of the field element is dependent on the head modeling assumptions and sensor configuration. Proper characterization of this incremental element is crucial to the inverse problem. The field element can be partitioned into the product of a vector dependent on sensor characteristics and a matrix kernel dependent only on head modeling assumptions. We present here the matrix kernels for the general boundary element model (BEM) and for MEG spherical models. We show how these kernels are easily interchanged in a linear algebraic framework that includes sensor specifics such as orientation and gradiometer configuration. We then describe how this kernel is easily applied to ``gain`` or ``transfer`` matrices used in multiple dipole and source imaging models.

  13. Quality Characteristics of Soft Kernel Durum -- A New Cereal Crop

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Production of crops is in part limited by consumer demand and utilization. In this regard, world production of durum wheat (Triticum turgidum subsp. durum is limited by its culinary uses. The leading constraint is its very hard kernels. Puroindolines, which act to soften the endosperm, are completel...

  14. 21 CFR 176.350 - Tamarind seed kernel powder.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ...) FOOD FOR HUMAN CONSUMPTION (CONTINUED) INDIRECT FOOD ADDITIVES: PAPER AND PAPERBOARD COMPONENTS Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind..., manufacturing, packing, processing, preparing, treating, packaging, transporting, or holding food, subject...

  15. Popping the Kernel Modeling the States of Matter

    ERIC Educational Resources Information Center

    Hitt, Austin; White, Orvil; Hanson, Debbie

    2005-01-01

    This article discusses how to use popcorn to engage students in model building and to teach them about the nature of matter. Popping kernels is a simple and effective method to connect the concepts of heat, motion, and volume with the different phases of matter. Before proceeding with the activity the class should discuss the nature of scientific…

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

    NASA Astrophysics Data System (ADS)

    Patnaik, Rohit

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

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

    PubMed

    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.

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

    PubMed

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

    2007-08-01

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

  19. Determining the Parameters of the Hereditary Kernels of Nonlinear Viscoelastic Isotropic Materials in Torsion

    NASA Astrophysics Data System (ADS)

    Golub, V. P.; Ragulina, V. S.; Fernati, P. V.

    2015-03-01

    A method for determining the parameters of the hereditary kernels for nonlinear viscoelastic materials is tested in conditions of pure torsion. A Rabotnov-type model is chosen. The parameters of the hereditary kernels are determined by fitting discrete values of the kernels found using a similarity condition. The discrete values of the kernels in the zone of singularity occurring in short-term tests are found using weight functions. The Abel kernel, a combination of power and exponential functions, and a fractional-exponential function are considered

  20. Effects of Amygdaline from Apricot Kernel on Transplanted Tumors in Mice.

    PubMed

    Yamshanov, V A; Kovan'ko, E G; Pustovalov, Yu I

    2016-03-01

    The effects of amygdaline from apricot kernel added to fodder on the growth of transplanted LYO-1 and Ehrlich carcinoma were studied in mice. Apricot kernels inhibited the growth of both tumors. Apricot kernels, raw and after thermal processing, given 2 days before transplantation produced a pronounced antitumor effect. Heat-processed apricot kernels given in 3 days after transplantation modified the tumor growth and prolonged animal lifespan. Thermal treatment did not considerably reduce the antitumor effect of apricot kernels. It was hypothesized that the antitumor effect of amygdaline on Ehrlich carcinoma and LYO-1 lymphosarcoma was associated with the presence of bacterial genome in the tumor.

  1. Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

    PubMed Central

    Zhao, Xin; Cheung, Leo Wang-Kit

    2007-01-01

    Background Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. Results A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences

  2. Collision statistics for random flights with anisotropic scattering and absorption.

    PubMed

    Zoia, A; Dumonteil, E; Mazzolo, A

    2011-12-01

    For a broad class of random walks with anisotropic scattering kernels and absorption, we derive explicit formulas that allow expressing the moments of the collision number n(V) performed in a volume V as a function of the particle equilibrium distribution. Our results apply to arbitrary domains V and boundary conditions, and allow assessing the hitting statistics for systems where the typical displacements are comparable to the domain size, so that the diffusion limit is possibly not attained. An example is discussed for one-dimensional random flights with exponential displacements, where analytical calculations can be carried out.

  3. Optics as Scattering

    ERIC Educational Resources Information Center

    di Francia, Giuliano Toraldo

    1973-01-01

    The art of deriving information about an object from the radiation it scatters was once limited to visible light. Now due to new techniques, much of the modern physical science research utilizes radiation scattering. (DF)

  4. Scattering from binary optics

    NASA Technical Reports Server (NTRS)

    Ricks, Douglas W.

    1993-01-01

    There are a number of sources of scattering in binary optics: etch depth errors, line edge errors, quantization errors, roughness, and the binary approximation to the ideal surface. These sources of scattering can be systematic (deterministic) or random. In this paper, scattering formulas for both systematic and random errors are derived using Fourier optics. These formulas can be used to explain the results of scattering measurements and computer simulations.

  5. Determination and verification of a 2D pencil-beam kernel for a radiosurgery system with cones

    SciTech Connect

    Vargas-Verdesoto, Milton Xavier; Álvarez-Romero, José Trinidad

    2013-07-01

    The quality and correctness of dosimetric data of small fields in stereotactic radiosurgery (SRS) depends significantly on the election of the detector employed in the measurements. This work provides an independent method of verification of these data through the determination of a polyenergetic 2-dimensional pencil-beam kernel for a BrainLAB SRS system with cones, employing the deconvolution/convolution of a reference experimental off-axis ratio (OAR) profile (cone diameter c{sub 0} = 35 mm). The kernel in real space k{sub c{sub 0}}(r,z{sub 0}) is convolved with the ideal fluence Φ for the cones 7.5 to 35 mm in diameter to obtain the OAR profiles, and the total scatter factors, S{sub t}, which are compared with experimental values of the same quantities. The experimental OARs and S{sub t} factors are measured in water with a PTW 60003 diamond detector. Additionally, the reference OAR is corrected for beam divergence and spectral fluence fluctuations defining a function of boundary correction factors (BF). The BF and Φ functions are transformed to the conjugate space with the zeroth-order Hankel transform, appropriated to the radial symmetry of the cones. Therefore, the kernel in real space k{sub c{sub 0}}(r,z{sub 0}) is the inverse Hankel transform of the ratio of the Hankel transforms of BF and Φ. Finally, an uncertainty analysis according to the Guide to the Expression of Uncertainty in Measurement is carried out for 3 different values of k{sub c{sub 0}}(r,z{sub 0}). Calculated and measured OARs agree within the dose/distance-to-agreement criteria of 2%/0.12 mm; while, S{sub t} factors agree within 2%. This procedure supplies an independent method to validate the dosimetric data necessary to feed treatment planning systems for SRS with cones.

  6. Mexican Hat Wavelet Kernel ELM for Multiclass Classification

    PubMed Central

    Wang, Jie; Ma, Tian-Lei

    2017-01-01

    Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems. Moreover, the validity of the Mexican Hat wavelet as a kernel function of ELM is rigorously proved. Experimental results on different data sets show that the performance of the proposed classifier is significantly superior to the compared classifiers. PMID:28321249

  7. Improved Rotating Kernel Transformation Based Contourlet Domain Image Denoising Framework.

    PubMed

    Guo, Qing; Dong, Fangmin; Sun, Shuifa; Ren, Xuhong; Feng, Shiyu; Gao, Bruce Zhi

    A contourlet domain image denoising framework based on a novel Improved Rotating Kernel Transformation is proposed, where the difference of subbands in contourlet domain is taken into account. In detail: (1). A novel Improved Rotating Kernel Transformation (IRKT) is proposed to calculate the direction statistic of the image; The validity of the IRKT is verified by the corresponding extracted edge information comparing with the state-of-the-art edge detection algorithm. (2). The direction statistic represents the difference between subbands and is introduced to the threshold function based contourlet domain denoising approaches in the form of weights to get the novel framework. The proposed framework is utilized to improve the contourlet soft-thresholding (CTSoft) and contourlet bivariate-thresholding (CTB) algorithms. The denoising results on the conventional testing images and the Optical Coherence Tomography (OCT) medical images show that the proposed methods improve the existing contourlet based thresholding denoising algorithm, especially for the medical images.

  8. Studying Cerebral Vasculature Using Structure Proximity and Graph Kernels

    PubMed Central

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

    2014-01-01

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

  9. Physicochemical properties of phosphate ester from palm kernel oil

    NASA Astrophysics Data System (ADS)

    Adawiyah Norzali, Nor Rabbi'atul; Badri, Khairiah Haji; Ahmad, Ishak

    2013-12-01

    The physicochemical properties of phosphate ester from palm kernel oil have been studied. The phosphate ester was synthesized via ring-opening of epoxidized palm kernel oil with phosphoric acid. The amount of phosphoric acid (H3PO4) was varied at 0, 2.5, 5.0 and 7.5 wt%. Acid values of PKO and EPKO were 1.85 and 1.87 mg KOH/g respectively. However, the acid values increased with increasing amount of H3PO4 with values of 10.62 mg KOH/g, 31.34 mg KOH/g and 110.95 mg KOH/g respectively. The hydrolysis of the EPKO has successfully converted it to PEPKO with hydroxyl value of 16.16 mg KOH/g, 26.90 and 35.33 mg KOH/g at H3PO4 of 2.5, 5.0, and 7.5wt%.

  10. Some physical properties of ginkgo nuts and kernels

    NASA Astrophysics Data System (ADS)

    Ch'ng, P. E.; Abdullah, M. H. R. O.; Mathai, E. J.; Yunus, N. A.

    2013-12-01

    Some data of the physical properties of ginkgo nuts at a moisture content of 45.53% (±2.07) (wet basis) and of their kernels at 60.13% (± 2.00) (wet basis) are presented in this paper. It consists of the estimation of the mean length, width, thickness, the geometric mean diameter, sphericity, aspect ratio, unit mass, surface area, volume, true density, bulk density, and porosity measures. The coefficient of static friction for nuts and kernels was determined by using plywood, glass, rubber, and galvanized steel sheet. The data are essential in the field of food engineering especially dealing with design and development of machines, and equipment for processing and handling agriculture products.

  11. Kernel Methods on Spike Train Space for Neuroscience: A Tutorial

    NASA Astrophysics Data System (ADS)

    Park, Il Memming; Seth, Sohan; Paiva, Antonio R. C.; Li, Lin; Principe, Jose C.

    2013-07-01

    Over the last decade several positive definite kernels have been proposed to treat spike trains as objects in Hilbert space. However, for the most part, such attempts still remain a mere curiosity for both computational neuroscientists and signal processing experts. This tutorial illustrates why kernel methods can, and have already started to, change the way spike trains are analyzed and processed. The presentation incorporates simple mathematical analogies and convincing practical examples in an attempt to show the yet unexplored potential of positive definite functions to quantify point processes. It also provides a detailed overview of the current state of the art and future challenges with the hope of engaging the readers in active participation.

  12. Generalized Rayleigh scattering. I. Basic theory.

    NASA Astrophysics Data System (ADS)

    Ivanov, V. V.

    1995-11-01

    The classsical problem of multiple molecular (in particular, Rayleigh) scattering in plane-parallel atmospheres is considered from a somewhat broader viewpoint than usual. The general approach and ideology are borrowed from non-LTE line formation theory. The main emphasis is on the depth dependence of the corresponding source matrix rather than on the emergent radiation. We study the azimuth-averaged radiation field of polarized radiation in a semi-infinite atmosphere with embedded primary sources. The corresponding 2x2 phase matrix of molecular scattering is P=(1-W) P_I_+W P_R_, where P_I_ and P_R_ are the phase matrices of the scalar isotropic scattering and of the Rayleigh scattering, respectively, and W is the depolarization parameter. Contrary to the usual assumption that W{in}[0,1], we assume W{in} [0,{infinity}) and call this generalized Rayleigh scattering (GRS). Using the factorization of P which is intimately related to its diadic expansion, we reduce the problem to an integral equation for the source matrix S(τ) with a matrix displacement kernel. In operator form this equation is S={LAMBDA}S+S^*^, where {LAMBDA} is the matrix {LAMBDA}-operator and S^*^ is the primary source term. This leads to a new concept, the matrix albedo of single scattering λ =diag(λ_I_,λ_Q_), where λ_I_ is the usual (scalar) single scattering albedo and λ_Q_=0.7Wλ_I_. Its use enables one to formulate matrix equivalents of many of the results of the scalar theory in exactly the same form as in the scalar case. Of crucial importance is the matrix equivalent of the sqrt(ɛ) law of the scalar theory. Another useful new concept is the λ-plane, i.e., the plane with the axes (λ_I_,λ_Q_). Systematic use of the matrix sqrt(ɛ) law and of the λ-plane proved to be a useful instrument in classifying various limiting and particular cases of GRS and in discussing numerical data on the matrix source functions (to be given in Paper II of the series).

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  14. Outlier detection with the kernelized spatial depth function.

    PubMed

    Chen, Yixin; Dang, Xin; Peng, Hanxiang; Bart, Henry L

    2009-02-01

    Statistical depth functions provide from the "deepest" point a "center-outward ordering" of multidimensional data. In this sense, depth functions can measure the "extremeness" or "outlyingness" of a data point with respect to a given data set. Hence, they can detect outliers--observations that appear extreme relative to the rest of the observations. Of the various statistical depths, the spatial depth is especially appealing because of its computational efficiency and mathematical tractability. In this article, we propose a novel statistical depth, the kernelized spatial depth (KSD), which generalizes the spatial depth via positive definite kernels. By choosing a proper kernel, the KSD can capture the local structure of a data set while the spatial depth fails. We demonstrate this by the half-moon data and the ring-shaped data. Based on the KSD, we propose a novel outlier detection algorithm, by which an observation with a depth value less than a threshold is declared as an outlier. The proposed algorithm is simple in structure: the threshold is the only one parameter for a given kernel. It applies to a one-class learning setting, in which "normal" observations are given as the training data, as well as to a missing label scenario, where the training set consists of a mixture of normal observations and outliers with unknown labels. We give upper bounds on the false alarm probability of a depth-based detector. These upper bounds can be used to determine the threshold. We perform extensive experiments on synthetic data and data sets from real applications. The proposed outlier detector is compared with existing methods. The KSD outlier detector demonstrates a competitive performance.

  15. Strictly Positive Definite Kernels on a Product of Spheres II

    NASA Astrophysics Data System (ADS)

    Guella, Jean C.; Menegatto, Valdir A.; Peron, Ana P.

    2016-10-01

    We present, among other things, a necessary and sufficient condition for the strict positive definiteness of an isotropic and positive definite kernel on the cartesian product of a circle and a higher dimensional sphere. The result complements similar results previously obtained for strict positive definiteness on a product of circles [Positivity, to appear, arXiv:1505.01169] and on a product of high dimensional spheres [J. Math. Anal. Appl. 435 (2016), 286-301, arXiv:1505.03695].

  16. Equilibrium studies of copper ion adsorption onto palm kernel fibre.

    PubMed

    Ofomaja, Augustine E

    2010-07-01

    The equilibrium sorption of copper ions from aqueous solution using a new adsorbent, palm kernel fibre, has been studied. Palm kernel fibre is obtained in large amounts as a waste product of palm oil production. Batch equilibrium studies were carried out and system variables such as solution pH, sorbent dose, and sorption temperature were varied. The equilibrium sorption data was then analyzed using the Langmuir, Freundlich, Dubinin-Radushkevich (D-R) and Temkin isotherms. The fit of these isotherm models to the equilibrium sorption data was determined, using the linear coefficient of determination, r(2), and the non-linear Chi-square, chi(2) error analysis. The results revealed that sorption was pH dependent and increased with increasing solution pH above the pH(PZC) of the palm kernel fibre with an optimum dose of 10g/dm(3). The equilibrium data were found to fit the Langmuir isotherm model best, with a monolayer capacity of 3.17 x 10(-4)mol/g at 339K. The sorption equilibrium constant, K(a), increased with increasing temperature, indicating that bond strength between sorbate and sorbent increased with temperature and sorption was endothermic. This was confirmed by the increase in the values of the Temkin isotherm constant, B(1), with increasing temperature. The Dubinin-Radushkevich (D-R) isotherm parameter, free energy, E, was in the range of 15.7-16.7kJ/mol suggesting that the sorption mechanism was ion exchange. Desorption studies showed that a high percentage of the copper was desorbed from the adsorbent using acid solutions (HCl, HNO(3) and CH(3)COOH) and the desorption percentage increased with acid concentration. The thermodynamics of the copper ions/palm kernel fibre system indicate that the process is spontaneous and endothermic.

  17. USB Support for the Least Privilege Separation Kernel

    DTIC Science & Technology

    2012-03-01

    Separation Kernel MBR Master Boot Record OHCI Open Host Controller Interface OO Object-Orientated OS Operating System PC Personal Computer PCI...supported hardware devices. Then the BIOS will read the Master Boot Record ( MBR ) from a bootable device determined by the boot sequence stored in...the BIOS configuration setting and copies it into system memory. The BIOS then jumps to the code in memory to begin the next stage. The MBR

  18. Kernel Extended Real-Valued Negative Selection Algorithm (KERNSA)

    DTIC Science & Technology

    2013-06-01

    comparing supervised classification learning algorithms. Neural computation, 10(7):1895–1923, 1998. [23] Thomas Dietterich. Ensemble methods in machine...2003. [70] Bernhard Schölkopf. The kernel trick for distances. Advances in neural information processing systems, pages 301–307, 2001. [71] Bernhard ...pages 371–377. IEEE, 1999. [74] Thomas Stibor and Jonathan Timmis. Comments on real-valued negative selection vs. real-valued positive selection and

  19. A Kernel for Open Source Drug Discovery in Tropical Diseases

    PubMed Central

    Ortí, Leticia; Carbajo, Rodrigo J.; Pieper, Ursula; Eswar, Narayanan; Maurer, Stephen M.; Rai, Arti K.; Taylor, Ginger; Todd, Matthew H.; Pineda-Lucena, Antonio; Sali, Andrej; Marti-Renom, Marc A.

    2009-01-01

    Background Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private–public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues—open source drug discovery—has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such “kernels”. Methodology/Principal Findings Here, we use a computational pipeline for: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. Conclusions/Significance The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases. PMID:19381286

  20. Travel-Time Sensitivity Kernels in Long-Range Propagation

    DTIC Science & Technology

    2008-09-30

    wave-theoretic TSKs and Fresnel volumes associated with particular eigenrays , seeking connections between the ray-theoretic and wave- theoretic...sensitivity areas about the eigenrays (marked by the dashed lines). For the range of 30 km the banana-doughnut stereotype is reproduced with the...kernel concentrated about the corresponding eigenray with zero sensitivity on the eigenray , negative sensitivity at a distance and positive sensitivity

  1. Benchmarking NWP Kernels on Multi- and Many-core Processors

    NASA Astrophysics Data System (ADS)

    Michalakes, J.; Vachharajani, M.

    2008-12-01

    Increased computing power for weather, climate, and atmospheric science has provided direct benefits for defense, agriculture, the economy, the environment, and public welfare and convenience. Today, very large clusters with many thousands of processors are allowing scientists to move forward with simulations of unprecedented size. But time-critical applications such as real-time forecasting or climate prediction need strong scaling: faster nodes and processors, not more of them. Moreover, the need for good cost- performance has never been greater, both in terms of performance per watt and per dollar. For these reasons, the new generations of multi- and many-core processors being mass produced for commercial IT and "graphical computing" (video games) are being scrutinized for their ability to exploit the abundant fine- grain parallelism in atmospheric models. We present results of our work to date identifying key computational kernels within the dynamics and physics of a large community NWP model, the Weather Research and Forecast (WRF) model. We benchmark and optimize these kernels on several different multi- and many-core processors. The goals are to (1) characterize and model performance of the kernels in terms of computational intensity, data parallelism, memory bandwidth pressure, memory footprint, etc. (2) enumerate and classify effective strategies for coding and optimizing for these new processors, (3) assess difficulties and opportunities for tool or higher-level language support, and (4) establish a continuing set of kernel benchmarks that can be used to measure and compare effectiveness of current and future designs of multi- and many-core processors for weather and climate applications.

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  3. Multi-label multi-kernel transfer learning for human protein subcellular localization.

    PubMed

    Mei, Suyu

    2012-01-01

    Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology (GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated. Up to the present, there are far few researches specialized for predicting the subcellular localization of human proteins that may reside in multiple cellular compartments. In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization (MLMK-TLM). MLMK-TLM proposes a multi-label confusion matrix, formally formulates three multi-labelling performance measures and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which to further extends our published work GO-TLM (gene ontology based transfer learning model for protein subcellular localization) and MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for multiplex human protein subcellular localization. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM will gain more practical applicability. The experiments on human protein benchmark dataset show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database. The software can be freely downloaded at http://soft.synu.edu.cn/upload/msy.rar.

  4. Fast metabolite identification with Input Output Kernel Regression

    PubMed Central

    Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho

    2016-01-01

    Motivation: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. Results: We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods. Availability and implementation: Contact: celine.brouard@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307628

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

    NASA Astrophysics Data System (ADS)

    Serranti, Silvia; Cesare, Daniela; Bonifazi, Giuseppe

    2012-05-01

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

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

    PubMed

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

    2016-05-01

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

  7. Kernel regression for fMRI pattern prediction

    PubMed Central

    Chu, Carlton; Ni, Yizhao; Tan, Geoffrey; Saunders, Craig J.; Ashburner, John

    2011-01-01

    This paper introduces two kernel-based regression schemes to decode or predict brain states from functional brain scans as part of the Pittsburgh Brain Activity Interpretation Competition (PBAIC) 2007, in which our team was awarded first place. Our procedure involved image realignment, spatial smoothing, detrending of low-frequency drifts, and application of multivariate linear and non-linear kernel regression methods: namely kernel ridge regression (KRR) and relevance vector regression (RVR). RVR is based on a Bayesian framework, which automatically determines a sparse solution through maximization of marginal likelihood. KRR is the dual-form formulation of ridge regression, which solves regression problems with high dimensional data in a computationally efficient way. Feature selection based on prior knowledge about human brain function was also used. Post-processing by constrained deconvolution and re-convolution was used to furnish the prediction. This paper also contains a detailed description of how prior knowledge was used to fine tune predictions of specific “feature ratings,” which we believe is one of the key factors in our prediction accuracy. The impact of pre-processing was also evaluated, demonstrating that different pre-processing may lead to significantly different accuracies. Although the original work was aimed at the PBAIC, many techniques described in this paper can be generally applied to any fMRI decoding works to increase the prediction accuracy. PMID:20348000

  8. Optimization of numerical orbitals using the Helmholtz kernel

    NASA Astrophysics Data System (ADS)

    Solala, Eelis; Losilla, Sergio A.; Sundholm, Dage; Xu, Wenhua; Parkkinen, Pauli

    2017-02-01

    We present an integration scheme for optimizing the orbitals in numerical electronic structure calculations on general molecules. The orbital optimization is performed by integrating the Helmholtz kernel in the double bubble and cube basis, where bubbles represent the steep part of the functions in the vicinity of the nuclei, whereas the remaining cube part is expanded on an equidistant three-dimensional grid. The bubbles' part is treated by using one-center expansions of the Helmholtz kernel in spherical harmonics multiplied with modified spherical Bessel functions of the first and second kinds. The angular part of the bubble functions can be integrated analytically, whereas the radial part is integrated numerically. The cube part is integrated using a similar method as we previously implemented for numerically integrating two-electron potentials. The behavior of the integrand of the auxiliary dimension introduced by the integral transformation of the Helmholtz kernel has also been investigated. The correctness of the implementation has been checked by performing Hartree-Fock self-consistent-field calculations on H2, H2O, and CO. The obtained energies are compared with reference values in the literature showing that an accuracy of 10-4 to 10-7 Eh can be obtained with our approach.

  9. Optimization of numerical orbitals using the Helmholtz kernel.

    PubMed

    Solala, Eelis; Losilla, Sergio A; Sundholm, Dage; Xu, Wenhua; Parkkinen, Pauli

    2017-02-28

    We present an integration scheme for optimizing the orbitals in numerical electronic structure calculations on general molecules. The orbital optimization is performed by integrating the Helmholtz kernel in the double bubble and cube basis, where bubbles represent the steep part of the functions in the vicinity of the nuclei, whereas the remaining cube part is expanded on an equidistant three-dimensional grid. The bubbles' part is treated by using one-center expansions of the Helmholtz kernel in spherical harmonics multiplied with modified spherical Bessel functions of the first and second kinds. The angular part of the bubble functions can be integrated analytically, whereas the radial part is integrated numerically. The cube part is integrated using a similar method as we previously implemented for numerically integrating two-electron potentials. The behavior of the integrand of the auxiliary dimension introduced by the integral transformation of the Helmholtz kernel has also been investigated. The correctness of the implementation has been checked by performing Hartree-Fock self-consistent-field calculations on H2, H2O, and CO. The obtained energies are compared with reference values in the literature showing that an accuracy of 10(-4) to 10(-7) Eh can be obtained with our approach.

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

    PubMed

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

    2016-12-20

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

  11. KNBD: A Remote Kernel Block Server for Linux

    NASA Technical Reports Server (NTRS)

    Becker, Jeff

    1999-01-01

    I am developing a prototype of a Linux remote disk block server whose purpose is to serve as a lower level component of a parallel file system. Parallel file systems are an important component of high performance supercomputers and clusters. Although supercomputer vendors such as SGI and IBM have their own custom solutions, there has been a void and hence a demand for such a system on Beowulf-type PC Clusters. Recently, the Parallel Virtual File System (PVFS) project at Clemson University has begun to address this need (1). Although their system provides much of the functionality of (and indeed was inspired by) the equivalent file systems in the commercial supercomputer market, their system is all in user-space. Migrating their 10 services to the kernel could provide a performance boost, by obviating the need for expensive system calls. Thanks to Pavel Machek, the Linux kernel has provided the network block device (2) with kernels 2.1.101 and later. You can configure this block device to redirect reads and writes to a remote machine's disk. This can be used as a building block for constructing a striped file system across several nodes.

  12. Blur kernel estimate in single noisy image deblurring

    NASA Astrophysics Data System (ADS)

    Sun, Shijie; Zhao, Huaici; Li, Bo

    2014-11-01

    Restoring blurred images is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely under constrained. Recently many single image blind deconvolution methods have been proposed, but these state-of-the-art single image deblurring techniques are still sensitive to image noise, and can degrade their performance rapidly especially when the noise level of the input blurred images increases. In this work, we estimate the blur kernel accurately by applying a series of directional low-pass filters in different orientations to the input blurred image, and effectively constructing the Radon transform of the blur kernel from each filtered image. Finally, we use a robust non-blind deconvolution method with outlier handling, which can effectively reduce ringing artifacts, to generate the final results. Our experimental results on both synthetic and real-world examples show that our method achieves comparable quality to existing approaches on blurry noisy-free images, and higher quality outputs than previous approaches on blurry and noisy images.

  13. Reproducing Kernels in Harmonic Spaces and Their Numerical Implementation

    NASA Astrophysics Data System (ADS)

    Nesvadba, Otakar

    2010-05-01

    In harmonic analysis such as the modelling of the Earth's gravity field, the importance of Hilbert's space of harmonic functions with the reproducing kernel is often discussed. Moreover, in case of an unbounded domain given by the exterior of the sphere or an ellipsoid, the reproducing kernel K(x,y) can be expressed analytically by means of closed formulas or by infinite series. Nevertheless, the straightforward numerical implementation of these formulas leads to dozen of problems, which are mostly connected with the floating-point arithmetic and a number representation. The contribution discusses numerical instabilities in K(x,y) and gradK(x,y) that can be overcome by employing elementary functions, in particular expm1 and log1p. Suggested evaluation scheme for reproducing kernels offers uniform formulas within the whole solution domain as well as superior speed and near-perfect accuracy (10-16 for IEC 60559 double-precision numbers) when compared with the straightforward formulas. The formulas can be easily implemented on the majority of computer platforms, especially when C standard library ISO/IEC 9899:1999 is available.

  14. Initial Kernel Timing Using a Simple PIM Performance Model

    NASA Technical Reports Server (NTRS)

    Katz, Daniel S.; Block, Gary L.; Springer, Paul L.; Sterling, Thomas; Brockman, Jay B.; Callahan, David

    2005-01-01

    This presentation will describe some initial results of paper-and-pencil studies of 4 or 5 application kernels applied to a processor-in-memory (PIM) system roughly similar to the Cascade Lightweight Processor (LWP). The application kernels are: * Linked list traversal * Sun of leaf nodes on a tree * Bitonic sort * Vector sum * Gaussian elimination The intent of this work is to guide and validate work on the Cascade project in the areas of compilers, simulators, and languages. We will first discuss the generic PIM structure. Then, we will explain the concepts needed to program a parallel PIM system (locality, threads, parcels). Next, we will present a simple PIM performance model that will be used in the remainder of the presentation. For each kernel, we will then present a set of codes, including codes for a single PIM node, and codes for multiple PIM nodes that move data to threads and move threads to data. These codes are written at a fairly low level, between assembly and C, but much closer to C than to assembly. For each code, we will present some hand-drafted timing forecasts, based on the simple PIM performance model. Finally, we will conclude by discussing what we have learned from this work, including what programming styles seem to work best, from the point-of-view of both expressiveness and performance.

  15. Travel-time sensitivity kernels in ocean acoustic tomography

    NASA Astrophysics Data System (ADS)

    Skarsoulis, E. K.; Cornuelle, B. D.

    2004-07-01

    Wave-theoretic ocean acoustic propagation modeling is combined with the peak arrival approach for tomographic travel-time observables to derive the sensitivity kernel of travel times with respect to sound-speed variations. This is the Born-Fréchet kernel relating the three-dimensional spatial distribution of sound-speed variations with the induced travel-time variations. The derivation is based on the first Born approximation of the Green's function. The application of the travel-time sensitivity kernel to an ocean acoustic waveguide gives a picture close to the ray-theoretic one in the case of high frequencies. However, in the low-frequency case, of interest in ocean acoustic tomography, for example, there are significant deviations. Low-frequency travel times are sensitive to sound-speed changes in Fresnel-zone-scale areas surrounding the eigenrays, but not on the eigenrays themselves, where the sensitivity is zero. Further, there are areas of positive sensitivity, where, e.g., a sound-speed increase results in an increase of arrival times, i.e., a further delay of arrivals, in contrast with the common expectation. These findings are confirmed by forward acoustic predictions from a coupled-mode code.

  16. Knowledge Driven Image Mining with Mixture Density Mercer Kernels

    NASA Technical Reports Server (NTRS)

    Srivastava, Ashok N.; Oza, Nikunj

    2004-01-01

    This paper presents a new methodology for automatic knowledge driven image 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. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

  17. Multiple scattering technique lidar

    NASA Technical Reports Server (NTRS)

    Bissonnette, Luc R.

    1992-01-01

    The Bernouilli-Ricatti equation is based on the single scattering description of the lidar backscatter return. In practice, especially in low visibility conditions, the effects of multiple scattering can be significant. Instead of considering these multiple scattering effects as a nuisance, we propose here to use them to help resolve the problems of having to assume a backscatter-to-extinction relation and specifying a boundary value for a position far remote from the lidar station. To this end, we have built a four-field-of-view lidar receiver to measure the multiple scattering contributions. The system has been described in a number of publications that also discuss preliminary results illustrating the multiple scattering effects for various environmental conditions. Reported here are recent advances made in the development of a method of inverting the multiple scattering data for the determination of the aerosol scattering coefficient.

  18. Time-lapse monitoring of localized changes within heterogeneous media with scattered waves

    NASA Astrophysics Data System (ADS)

    Chinaemerem, Kanu

    travel-time changes or decorrelation estimated from the time-lapse coda waves. The imaging algorithm is defined to invert for the location and magnitude of changes within both statistically homogeneous and statistically heterogeneous scattering media. The imaging of the localized change requires an appropriate computation of the sensitivity of the scattered waves to the monitored change. I develop a novel approach to compute the sensitivity kernel needed to image localized changes present within a scattering medium. I compute the sensitivity kernel, using an a-priori scattering model that has similar statistical properties as the actual medium, by computing the intensity of the scattered waves generated at both the source and the receiver locations. This approach of the kernel computation allows one to compute the sensitivity kernel for any heterogeneous scattering medium with a prescribed boundary condition. Generating the kernel with the a priori model prevents one from invoking a homogeneity assumption of the scattering model. I apply the imaging algorithm on both numerical and laboratory experiments. The numerical experiment provides an opportunity to evaluate the resolution of the monitored change for each coda lapse time. In the laboratory experiment, I accurately resolve the change induced within two concrete blocks due to localize stress and heat changes. I also monitored velocity changes present within the subsurface beneath the Eastern section of the Basin and Range Province, Western US. Time-lapse monitoring with coda waves, generated with repeating active sources, over a period of four months suggests of the presence of a maximum velocity change of approximately 0.2% with the Eastern section of the Basin and Range Province. This observed velocity change are likely induced by the deformation within the Basin and Range Province.

  19. Application of alternative synthetic kernel approximation to radiative transfer in regular and irregular two-dimensional media

    NASA Astrophysics Data System (ADS)

    Altaç, Zekeriya; Sert, Zerrin

    2017-01-01

    Alternative synthetic kernel (ASKN) approximation, just as the standard SKN method, is derived from the radiative integral transfer equations in full 3D generality. The direct and diffuse terms of thermal radiation appear explicitly in the radiative integral transfer equations as surface and volume integrals, respectively. In standard SKN method, the approximation is employed to the diffuse terms while direct terms are evaluated analytically. The alternative formulation differs from the standard one in that the direct radiation wall contributions are also approximated with the same spirit of the synthetic kernel approximation. This alternative formulation also yields a set of coupled partial differential-the ASKN-equations which could be solved using finite volume methods. This approximation is applied to radiative transfer calculations in regular and irregular two-dimensional absorbing, emitting and isotropically scattering media. Four benchmark problems-one rectangular and three irregular media-are considered, and the net radiative flux and/or incident energy solutions along the boundaries are compared with available exact, standard discrete ordinates S4 and S12, modified discrete ordinates S4, Monte Carlo and collocation spectral method to assess the accuracy of the method. The ASKN approximation yields ray effect free incident energy and radiative flux distributions, and low order ASKN solutions are generally better than those of the high order standard discrete ordinates method.

  20. Organizing for ontological change: The kernel of an AIDS research infrastructure

    PubMed Central

    Polk, Jessica Beth

    2015-01-01

    Is it possible to prepare and plan for emergent and changing objects of research? Members of the Multicenter AIDS Cohort Study have been investigating AIDS for over 30 years, and in that time, the disease has been repeatedly transformed. Over the years and across many changes, members have continued to study HIV disease while in the process regenerating an adaptable research organization. The key to sustaining this technoscientific flexibility has been what we call the kernel of a research infrastructure: ongoing efforts to maintain the availability of resources and services that may be brought to bear in the investigation of new objects. In the case of the Multicenter AIDS Cohort Study, these resources are as follows: specimens and data, calibrated instruments, heterogeneous experts, and participating cohorts of gay and bisexual men. We track three ontological transformations, examining how members prepared for and responded to changes: the discovery of a novel retroviral agent (HIV), the ability to test for that agent, and the transition of the disease from fatal to chronic through pharmaceutical intervention. Respectively, we call the work, ‘technologies’, and techniques of adapting to these changes, ‘repurposing’, ‘elaborating’, and ‘extending the kernel’. PMID:26477206

  1. Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis

    NASA Astrophysics Data System (ADS)

    Zhu, Fengle; Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Brown, Robert; Bhatnagar, Deepak; Cleveland, Thomas

    2015-05-01

    Aflatoxins are secondary metabolites produced by certain fungal species of the Aspergillus genus. Aflatoxin contamination remains a problem in agricultural products due to its toxic and carcinogenic properties. Conventional chemical methods for aflatoxin detection are time-consuming and destructive. This study employed fluorescence and reflectance visible near-infrared (VNIR) hyperspectral images to classify aflatoxin contaminated corn kernels rapidly and non-destructively. Corn ears were artificially inoculated in the field with toxigenic A. flavus spores at the early dough stage of kernel development. After harvest, a total of 300 kernels were collected from the inoculated ears. Fluorescence hyperspectral imagery with UV excitation and reflectance hyperspectral imagery with halogen illumination were acquired on both endosperm and germ sides of kernels. All kernels were then subjected to chemical analysis individually to determine aflatoxin concentrations. A region of interest (ROI) was created for each kernel to extract averaged spectra. Compared with healthy kernels, fluorescence spectral peaks for contaminated kernels shifted to longer wavelengths with lower intensity, and reflectance values for contaminated kernels were lower with a different spectral shape in 700-800 nm region. Principal component analysis was applied for data compression before classifying kernels into contaminated and healthy based on a 20 ppb threshold utilizing the K-nearest neighbors algorithm. The best overall accuracy achieved was 92.67% for germ side in the fluorescence data analysis. The germ side generally performed better than endosperm side. Fluorescence and reflectance image data achieved similar accuracy.

  2. General-form 3-3-3 interpolation kernel and its simplified frequency-response derivation

    NASA Astrophysics Data System (ADS)

    Deng, Tian-Bo

    2016-11-01

    An interpolation kernel is required in a wide variety of signal processing applications such as image interpolation and timing adjustment in digital communications. This article presents a general-form interpolation kernel called 3-3-3 interpolation kernel and derives its frequency response in a closed-form by using a simple derivation method. This closed-form formula is preliminary to designing various 3-3-3 interpolation kernels subject to a set of design constraints. The 3-3-3 interpolation kernel is formed through utilising the third-degree piecewise polynomials, and it is an even-symmetric function. Thus, it will suffice to consider only its right-hand side when deriving its frequency response. Since the right-hand side of the interpolation kernel contains three piecewise polynomials of the third degree, i.e. the degrees of the three piecewise polynomials are (3,3,3), we call it the 3-3-3 interpolation kernel. Once the general-form frequency-response formula is derived, we can systematically formulate the design of various 3-3-3 interpolation kernels subject to a set of design constraints, which are targeted for different interpolation applications. Therefore, the closed-form frequency-response expression is preliminary to the optimal design of various 3-3-3 interpolation kernels. We will use an example to show the optimal design of a 3-3-3 interpolation kernel based on the closed-form frequency-response expression.

  3. Kernel component analysis using an epsilon-insensitive robust loss function.

    PubMed

    Alzate, Carlos; Suykens, Johan A K

    2008-09-01

    Kernel principal component analysis (PCA) is a technique to perform feature extraction in a high-dimensional feature space, which is nonlinearly related to the original input space. The kernel PCA formulation corresponds to an eigendecomposition of the kernel matrix: eigenvectors with large eigenvalues correspond to the principal components in the feature space. Starting from the least squares support vector machine (LS-SVM) formulation to kernel PCA, we extend it to a generalized form of kernel component analysis (KCA) with a general underlying loss function made explicit. For classical kernel PCA, the underlying loss function is L(2) . In this generalized form, one can plug in also other loss functions. In the context of robust statistics, it is known that the L(2) loss function is not robust because its influence function is not bounded. Therefore, outliers can skew the solution from the desired one. Another issue with kernel PCA is the lack of sparseness: the principal components are dense expansions in terms of kernel functions. In this paper, we introduce robustness and sparseness into kernel component analysis by using an epsilon-insensitive robust loss function. We propose two different algorithms. The first method solves a set of nonlinear equations with kernel PCA as starting points. The second method uses a simplified iterative weighting procedure that leads to solving a sequence of generalized eigenvalue problems. Simulations with toy and real-life data show improvements in terms of robustness together with a sparse representation.

  4. Visual Adaptation

    PubMed Central

    Webster, Michael A.

    2015-01-01

    Sensory systems continuously mold themselves to the widely varying contexts in which they must operate. Studies of these adaptations have played a long and central role in vision science. In part this is because the specific adaptations remain a powerful tool for dissecting vision, by exposing the mechanisms that are adapting. That is, “if it adapts, it's there.” Many insights about vision have come from using adaptation in this way, as a method. A second important trend has been the realization that the processes of adaptation are themselves essential to how vision works, and thus are likely to operate at all levels. That is, “if it's there, it adapts.” This has focused interest on the mechanisms of adaptation as the target rather than the probe. Together both approaches have led to an emerging insight of adaptation as a fundamental and ubiquitous coding strategy impacting all aspects of how we see. PMID:26858985

  5. Color image projection through a strongly scattering wall.

    PubMed

    Conkey, Donald B; Piestun, Rafael

    2012-12-03

    We present multi-color image projection through highly scattering media for image formation without need of reconstruction. We overcome the fundamental limitations to the transmission of visual information imposed by multiple scattering phenomena via multi-parametric adaptive wavefront modulation that takes into account the scattering properties of the medium. In order to evaluate the wavefront modulation required for a specific image formation we implement a global optimization via a genetic algorithm. We create color images by diffraction and multiple scattering effects as well as via RGB demosaicing.

  6. Effect of the single-scattering phase function on light transmission through disordered media with large inhomogeneities

    NASA Astrophysics Data System (ADS)

    Marinyuk, V. V.; Sheberstov, S. V.

    2017-01-01

    We calculate the total transmission coefficient (transmittance) of a disordered medium with large (compared to the light wavelength) inhomogeneities. To model highly forward scattering in the medium we take advantage of the Gegenbauer kernel phase function. In a subdiffusion thickness range, the transmittance is shown to be sensitive to the specific form of the single-scattering phase function. The effect reveals itself at grazing angles of incidence and originates from small-angle multiple scattering of light. Our results are in a good agreement with numerical solutions to the radiative transfer equation.

  7. Scatter correction in CBCT with an offset detector through a deconvolution method using data consistency

    NASA Astrophysics Data System (ADS)

    Kim, Changhwan; Park, Miran; Lee, Hoyeon; Cho, Seungryong

    2016-03-01

    Our earlier work has demonstrated that the data consistency condition can be used as a criterion for scatter kernel optimization in deconvolution methods in a full-fan mode cone-beam CT [1]. However, this scheme cannot be directly applied to CBCT system with an offset detector (half-fan mode) because of transverse data truncation in projections. In this study, we proposed a modified scheme of the scatter kernel optimization method that can be used in a half-fan mode cone-beam CT, and have successfully shown its feasibility. Using the first-reconstructed volume image from half-fan projection data, we acquired full-fan projection data by forward projection synthesis. The synthesized full-fan projections were partly used to fill the truncated regions in the half-fan data. By doing so, we were able to utilize the existing data consistency-driven scatter kernel optimization method. The proposed method was validated by a simulation study using the XCAT numerical phantom and also by an experimental study using the ACS head phantom.

  8. Light scattering by adjacent red blood cells: a mathematical model

    NASA Astrophysics Data System (ADS)

    Uzunoglou, Nikolaos K.; Stamatakos, Georgios; Koutsouris, Dimitrios; Yova-Loukas, Dido M.

    1995-01-01

    Simple approximate scattering theories such as the Rayleigh-Gans theory are not generally applicable to the case of light scattering by red blood cell (RBC) aggregates, including thrombus. This is mainly due to the extremely short distance separating erythrocytes in the aggregates (of the order of 25 nm) as well as to the substantial size of the aggregates. Therefore, in this paper a new mathematical model predicting the electromagnetic field produced by the scattering of a plane electromagnetic wave by a system of two adjacent RBCs is presented. Each RBC is modeled as a homogeneous dielectric ellipsoid of complex index of refraction surrounded by transparent plasma. The relative position and orientation of the ellipsoids are arbitrary. Scattering is formulated in terms of an integral equation which, however, contains two singular kernels. The singular equation is transformed into a pair of nonsingular integral equations for the Fourier transform of the internal field of each RBC. The latter equations are solved by reducing them by quadrature into a matrix equation. The resulting solutions are used to estimate the scattering amplitude. Convergence aspects concerning the numerical calculation of the matrix elements originating from the interaction between the RBCs are also presented.

  9. Implementing dosimetry in GATE: dose-point kernel validation with GEANT4 4.8.1.

    PubMed

    Ferrer, Ludovic; Chouin, Nicolas; Bitar, Abdalkader; Lisbona, Albert; Bardiès, Manuel

    2007-02-01

    GATE is a recent Monte Carlo code, based on GEANT4, and used in nuclear medicine mainly for imaging and detector design. Our goal was to implement dosimetry within GATE (i.e., combining the excellent potential of Gate for image modeling with GEANT4 dosimetric capabilities. The latest release of GEANT4 (4.8.1) completely revised the electron multiple scattering propagation algorithm. In this work, we calculated dose point kernels (DPK) for 0.01, 0.05, 0.1, 1, and 3 MeV monoenergetic electrons. We then compared our results with data obtained with another Monte Carlo code (MCNPX) or from the reference publication from Berger and Seltzer. To facilitate comparison, all calculated dose distributions were scaled to the corresponding R(CSDA), as given by the ESTAR NIST web database. Some GEANT4 parameters (i.e., Stepmax), or the shell thickness, had to be adjusted in order to achieve good agreement for energies below 1 MeV. For all energies except 10 keV, calculated DPKs do not differ significantly from the reference, as assessed by a Kolmogorov-Smirnov test. This preliminary step allowed us to consider the integration of GEANT4 dosimetric capabilities within the Gate framework.

  10. An improved method for rebinning kernels from cylindrical to Cartesian coordinates.

    PubMed

    Rathee, S; McClean, B A; Field, C

    1993-01-01

    This paper describes the errors in rebinning photon dose point spread functions and pencil beam kernels (PBKs) from cylindrical to Cartesian coordinates. An area overlap method, which assumes that the fractional energy deposited per unit volume remains constant within cylindrical voxels, provides large deviations (up to 20%) in rebinned Cartesian voxels while conserving the total energy. A modified area overlap method is presented that allows the fractional energy deposited per unit volume within cylindrical voxels to vary according to an interpolating function. This method rebins the kernels accurately in each Cartesian voxel while conserving the total energy. The dose distributions were computed for a partially blocked beam of uniform fluence using the Cartesian coordinate kernel and the kernels rebinned by both methods. The kernel rebinned by the modified area overlap method provided errors less than 1.7%, while the kernel rebinned by the area overlap method gave errors up to 4.4%.

  11. Kernels, Degrees of Freedom, and Power Properties of Quadratic Distance Goodness-of-Fit Tests

    PubMed Central

    Lindsay, Bruce G.; Markatou, Marianthi; Ray, Surajit

    2014-01-01

    In this article, we study the power properties of quadratic-distance-based goodness-of-fit tests. First, we introduce the concept of a root kernel and discuss the considerations that enter the selection of this kernel. We derive an easy to use normal approximation to the power of quadratic distance goodness-of-fit tests and base the construction of a noncentrality index, an analogue of the traditional noncentrality parameter, on it. This leads to a method akin to the Neyman-Pearson lemma for constructing optimal kernels for specific alternatives. We then introduce a midpower analysis as a device for choosing optimal degrees of freedom for a family of alternatives of interest. Finally, we introduce a new diffusion kernel, called the Pearson-normal kernel, and study the extent to which the normal approximation to the power of tests based on this kernel is valid. Supplementary materials for this article are available online. PMID:24764609

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

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

    NASA Astrophysics Data System (ADS)

    Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

    2015-10-01

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

  14. Adaptive Management

    EPA Science Inventory

    Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive managem...

  15. Cylindrical-Wave Approach for Electromagnetic Scattering by Subsurface Targets in a Lossy Medium

    NASA Astrophysics Data System (ADS)

    Frezza, F.; Pajewski, L.; Ponti, C.; Schettini, G.; Tedeschi, N.

    2012-04-01

    The Cylindrical-Wave Approach (CWA) rigorously solves, in the spectral domain, the electromagnetic forward scattering by a finite set of buried two-dimensional perfectly-conducting or dielectric objects [1]-[3]. In this technique, the field scattered by underground objects is represented in terms of a superposition of cylindrical waves. Use is made of the plane-wave spectrum [1] to take into account the interaction of such waves with the planar interface between air and soil, and between different layers eventually present in the ground. In this work we present the progress we recently made to improve the method. In particular, we have faced the fundamental problem of losses in the ground: this is of significant importance in remote sensing applications, since real soils often have complex permittivity and conductivity, and sometimes also a complex permeability. First, a convergent closed-form representation of the cylindrical-wave angular spectrum in a generic lossy medium has been found [4]. To obtain this spectrum, the canonical Sommerfeld representation of the first-kind Hankel function of integer order has been used; its integration path has been modified to ensure the integral convergence for complex values of the wavenumber. Subsequently, the solution to the scattering problem of a plane-wave propagating in air, impinging on the interface with a dissipative medium, and interacting with a buried perfectly-conducting cylinder, has been derived. The developed method may return the field values in each point of the space, both in the near and far zones; moreover it may be applied for any polarization, and for arbitrary values of the cylinder size and of the distance between the cylinder and the air-soil interface. The theoretical solution has been implemented in a Fortran code. The numerical evaluation of the reflected and transmitted cylindrical wave functions in the presence of lossy media was a critical point: we extended the Gaussian adaptive quadrature

  16. Modeling diffuse reflectance measurements of light scattered by layered tissues

    NASA Astrophysics Data System (ADS)

    Rohde, Shelley B.

    In this dissertation, we first present a model for the diffuse reflectance due to a continuous beam incident normally on a half space composed of a uniform scattering and absorbing medium. This model is the result of an asymptotic analysis of the radiative transport equation for strong scattering, weak absorption and a defined beam width. Through comparison with the diffuse reflectance computed using the numerical solution of the radiative transport equation, we show that this diffuse reflectance model gives results that are accurate for small source-detector separation distances. We then present an explicit model for the diffuse reflectance due to a collimated beam of light incident normally on layered tissues. This model is derived using the corrected diffusion approximation applied to a layered medium, and it takes the form of a convolution with an explicit kernel and the incident beam profile. This model corrects the standard diffusion approximation over all source-detector separation distances provided the beam is sufficiently wide compared to the scattering mean-free path. We validate this model through comparison with Monte Carlo simulations. Then we use this model to estimate the optical properties of an epithelial layer from Monte Carlo simulation data. Using measurements at small source-detector separations and this model, we are able to estimate the absorption coefficient, scattering coefficient and anisotropy factor of epithelial tissues efficiently with reasonable accuracy. Finally, we present an extension of the corrected diffusion approximation for an obliquely incident beam. This model is formed through a Fourier Series representation in the azimuthal angle which allows us to exhibit the break in axisymmetry when combined with the previous analysis. We validate this model with Monte Carlo simulations. This model can also be written in the form of a convolution of an explicit kernel with the incident beam profile. Additionally, it can be used to

  17. Standardized total tract digestibility of phosphorus in copra meal, palm kernel expellers, palm kernel meal, and soybean meal fed to growing pigs.

    PubMed

    Almaguer, B L; Sulabo, R C; Liu, Y; Stein, H H

    2014-06-01

    Sixty-six barrows (initial BW: 27.4 ± 2.8 kg) were used to determine the standardized total tract digestibility (STTD) of P in copra meal (CM), palm kernel expellers from Indonesia (PKE-IN), palm kernel expellers from Costa Rica (PKE-CR), palm kernel meal from Costa Rica (PKM), and soybean meal (SBM) without or with exogenous phytase. Pigs were housed individually in metabolism cages and allotted to 11 diets with 6 replicate pigs per diet in a generalized randomized block design. Five diets were formulated by mixing cornstarch and sugar with CM, PKE-IN, PKE-CR, PKM, or SBM. Five additional diets, which were identical to the initial 5 diets but supplemented with 800 units of phytase, were also formulated. A P-free diet was used to measure basal endogenous losses of P by the pigs. Feces were collected for 5 d using the marker to marker approach after a 5-d adaptation period. Analyzed total P in CM, PKE-IN, PKE-CR, PKM, and SBM was 0.52, 0.51, 0.53, 0.54, and 0.67%, respectively. Phytate P was 0.22, 0.35, 0.38, 0.32, and 0.44% in CM, PKE-IN, PKE-CR, PKM, and SBM, respectively. Addition of phytase increased (P < 0.05) the apparent total tract digestibility (ATTD) of P from 60.6 to 80.8, 27.3 to 56.5, 32.6 to 59.9, 48.9 to 64.1, and 41.1 to 72.2% in CM, PKE-IN, PKE-CR, PKM, and SBM, respectively. The ATTD of P in CM was greater (P < 0.05) than in any of the other ingredients. The ATTD of P in SBM and PKM was greater (P < 0.05) than in PKE-IN, with PKE-CR being intermediate. The STTD of P increased (P < 0.05) from 70.6 to 90.3, 37.6 to 66.4, 43.2 to 69.9, 57.9 to 73.5, and 49.6 to 81.1% in CM, PKE-IN, PKE-CR, PKM, and SBM, respectively, when microbial phytase was added to the diets. When expressed as a percentage of total P, phytate P concentration in the ingredient negatively affected (P < 0.05) the ATTD of P (107.09 - 1.0564 × % phytate P; R(2) = 87.1) and the STTD of P (116.3 - 1.0487 × % phytate P; R(2) = 89.4). In conclusion, microbial phytase increased P

  18. Enrichment of the finite element method with reproducing kernel particle method

    SciTech Connect

    Chen, Y.; Liu, W.K.; Uras, R.A.

    1995-07-01

    Based on the reproducing kernel particle method on enrichment procedure is introduced to enhance the effectiveness of the finite element method. The basic concepts for the reproducing kernel particle method are briefly reviewed. By adopting the well-known completeness requirements, a generalized form of the reproducing kernel particle method is developed. Through a combination of these two methods their unique advantages can be utilized. An alternative approach, the multiple field method is also introduced.

  19. On the integral kernels of derivatives of the Ornstein-Uhlenbeck semigroup

    NASA Astrophysics Data System (ADS)

    Teuwen, Jonas

    2016-11-01

    This paper presents a closed-form expression for the integral kernels associated with the derivatives of the Ornstein-Uhlenbeck semigroup etL. Our approach is to expand the Mehler kernel into Hermite polynomials and apply the powers LN of the Ornstein-Uhlenbeck operator to it, where we exploit the fact that the Hermite polynomials are eigenfunctions for L. As an application we give an alternative proof of the kernel estimates by Ref. 10, making all relevant quantities explicit.

  20. An Integrated Architecture for Automatic Indication, Avoidance and Profiling of Kernel Rootkit Attacks

    DTIC Science & Technology

    2014-08-20

    major area in computer systems security research. The techniques produced by this research, such as kernel memory shadowing, kernel data mapping...enforcing a W⊕KX memory access control policy from the host virtual machine monitor (VMM or hypervisor). The W⊕KX memory access control policy guarantees...that no region of guest memory is both writable and kernel-executable. In this research, it is observed that the guest system must have a way to