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.
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%.
Adaptive wiener image restoration kernel
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.
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.
Adaptive kernels for multi-fiber reconstruction.
Barmpoutis, Angelos; Jian, Bing; Vemuri, Baba C
2009-01-01
In this paper we present a novel method for multi-fiber reconstruction given a diffusion-weighted MRI dataset. There are several existing methods that employ various spherical deconvolution kernels for achieving this task. However the kernels in all of the existing methods rely on certain assumptions regarding the properties of the underlying fibers, which introduce inaccuracies and unnatural limitations in them. Our model is a non trivial generalization of the spherical deconvolution model, which unlike the existing methods does not make use of a fix-shaped kernel. Instead, the shape of the kernel is estimated simultaneously with the rest of the unknown parameters by employing a general adaptive model that can theoretically approximate any spherical deconvolution kernel. The performance of our model is demonstrated using simulated and real diffusion-weighed MR datasets and compared quantitatively with several existing techniques in literature. The results obtained indicate that our model has superior performance that is close to the theoretic limit of the best possible achievable result.
Kernel Manifold Alignment for Domain Adaptation.
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
Kernel Manifold Alignment for Domain Adaptation
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
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.
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.
Identification of nonlinear optical systems using adaptive kernel methods
NASA Astrophysics Data System (ADS)
Wang, Xiaodong; Zhang, Changjiang; Zhang, Haoran; Feng, Genliang; Xu, Xiuling
2005-12-01
An identification approach of nonlinear optical dynamic systems, based on adaptive kernel methods which are modified version of least squares support vector machine (LS-SVM), is presented in order to obtain the reference dynamic model for solving real time applications such as adaptive signal processing of the optical systems. The feasibility of this approach is demonstrated with the computer simulation through identifying a Bragg acoustic-optical bistable system. Unlike artificial neural networks, the adaptive kernel methods possess prominent advantages: over fitting is unlikely to occur by employing structural risk minimization criterion, the global optimal solution can be uniquely obtained owing to that its training is performed through the solution of a set of linear equations. Also, the adaptive kernel methods are still effective for the nonlinear optical systems with a variation of the system parameter. This method is robust with respect to noise, and it constitutes another powerful tool for the identification of nonlinear optical systems.
A kernel adaptive algorithm for quaternion-valued inputs.
Paul, Thomas K; Ogunfunmi, Tokunbo
2015-10-01
The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations. PMID:25594982
An information theoretic approach of designing sparse kernel adaptive filters.
Liu, Weifeng; Park, Il; Principe, José C
2009-12-01
This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented. PMID:19923047
An information theoretic approach of designing sparse kernel adaptive filters.
Liu, Weifeng; Park, Il; Principe, José C
2009-12-01
This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented.
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...
Open-cluster density profiles derived using a kernel estimator
NASA Astrophysics Data System (ADS)
Seleznev, Anton F.
2016-03-01
Surface and spatial radial density profiles in open clusters are derived using a kernel estimator method. Formulae are obtained for the contribution of every star into the spatial density profile. The evaluation of spatial density profiles is tested against open-cluster models from N-body experiments with N = 500. Surface density profiles are derived for seven open clusters (NGC 1502, 1960, 2287, 2516, 2682, 6819 and 6939) using Two-Micron All-Sky Survey data and for different limiting magnitudes. The selection of an optimal kernel half-width is discussed. It is shown that open-cluster radius estimates hardly depend on the kernel half-width. Hints of stellar mass segregation and structural features indicating cluster non-stationarity in the regular force field are found. A comparison with other investigations shows that the data on open-cluster sizes are often underestimated. The existence of an extended corona around the open cluster NGC 6939 was confirmed. A combined function composed of the King density profile for the cluster core and the uniform sphere for the cluster corona is shown to be a better approximation of the surface radial density profile.The King function alone does not reproduce surface density profiles of sample clusters properly. The number of stars, the cluster masses and the tidal radii in the Galactic gravitational field for the sample clusters are estimated. It is shown that NGC 6819 and 6939 are extended beyond their tidal surfaces.
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.
Efficient particle filtering via sparse kernel density estimation.
Banerjee, Amit; Burlina, Philippe
2010-09-01
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to appropriately sample from the posterior distribution, maintain multiple hypotheses, and alleviate computational costs while preserving tracking accuracy. To address these issues, a novel utilization of the support vector data description (SVDD) density estimation method within the particle filtering framework is presented. The SVDD density estimate can be integrated into a wide range of PFs to realize several benefits. It yields a sparse representation of the posterior density that reduces the computational complexity of the PF. The proposed approach also provides an analytical expression for the posterior distribution that can be used to identify its modes for maintaining multiple hypotheses and computing the MAP estimate, and to directly sample from the posterior. We present several experiments that demonstrate the advantages of incorporating a sparse kernel density estimate in a particle filter.
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 ...
Kernel polynomial approximations for densities of states and spectral functions
Silver, R.N.; Voter, A.F.; Kress, J.D.; Roeder, H.
1996-03-01
Chebyshev polynomial approximations are an efficient and numerically stable way to calculate properties of the very large Hamiltonians important in computational condensed matter physics. The present paper derives an optimal kernal polynomial which enforces positivity of density of states and spectral estimates, achieves the best energy resolution, and preserves normalization. This kernel polynomial method (KPM) is demonstrated for electronic structure and dynamic magnetic susceptibility calculations. For tight binding Hamiltonians of Si, we show how to achieve high precision and rapid convergence of the cohesive energy and vacancy formation energy by careful attention to the order of approximation. For disordered XXZ-magnets, we show that the KPM provides a simpler and more reliable procedure for calculating spectral functions than Lanczos recursion methods. Polynomial approximations to Fermi projection operators are also proposed. 26 refs., 10 figs.
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.
NASA Astrophysics Data System (ADS)
Tian, Yuexin; Liu, Yinghui; Gao, Kun; Shu, Yuwen; Ni, Guoqiang
2014-11-01
A temporal-spatial filtering algorithm based on kernel density estimation structure is presented for background suppression in this paper. The algorithm can be divided into spatial filtering and temporal filtering. Smoothing process is applied to the background of an infrared image sequence by using the kernel density estimation algorithm in spatial filtering. The probability density of the image gray values after spatial filtering is calculated with the kernel density estimation algorithm in temporal filtering. The background residual and blind pixels are picked out based on their gray values, and are further filtered. The algorithm is validated with a real infrared image sequence. The image sequence is processed by using Fuller kernel filter, Uniform kernel filter and high-pass filter. Quantitatively analysis shows that the temporal-spatial filtering algorithm based on the nonparametric method is a satisfactory way to suppress background clutter in infrared images. The SNR is significantly improved as well.
On the equivalence between kernel self-organising maps and self-organising mixture density networks.
Yin, Hujun
2006-01-01
The kernel method has become a useful trick and has been widely applied to various learning models to extend their nonlinear approximation and classification capabilities. Such extensions have also recently occurred to the Self-Organising Map (SOM). In this paper, two recently proposed kernel SOMs are reviewed, together with their link to an energy function. The Self-Organising Mixture Network is an extension of the SOM for mixture density modelling. This paper shows that with an isotropic, density-type kernel function, the kernel SOM is equivalent to a homoscedastic Self-Organising Mixture Network, an entropy-based density estimator. This revelation on the one hand explains that kernelising SOM can improve classification performance by acquiring better probability models of the data; but on the other hand it also explains that the SOM already naturally approximates the kernel method.
Hensen, Ulf; Grubmüller, Helmut; Lange, Oliver F
2009-07-01
The quasiharmonic approximation is the most widely used estimate for the configurational entropy of macromolecules from configurational ensembles generated from atomistic simulations. This method, however, rests on two assumptions that severely limit its applicability, (i) that a principal component analysis yields sufficiently uncorrelated modes and (ii) that configurational densities can be well approximated by Gaussian functions. In this paper we introduce a nonparametric density estimation method which rests on adaptive anisotropic kernels. It is shown that this method provides accurate configurational entropies for up to 45 dimensions thus improving on the quasiharmonic approximation. When embedded in the minimally coupled subspace framework, large macromolecules of biological interest become accessible, as demonstrated for the 67-residue coldshock protein. PMID:19658735
Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System.
Liu, Chunmei; Wang, Yirui; Gao, Shangce
2016-01-01
This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour. PMID:27379165
Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System
2016-01-01
This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour. PMID:27379165
Validation tests of an improved kernel density estimation method for identifying disease clusters
NASA Astrophysics Data System (ADS)
Cai, Qiang; Rushton, Gerard; Bhaduri, Budhendra
2012-07-01
The spatial filter method, which belongs to the class of kernel density estimation methods, has been used to make morbidity and mortality maps in several recent studies. We propose improvements in the method to include spatially adaptive filters to achieve constant standard error of the relative risk estimates; a staircase weight method for weighting observations to reduce estimation bias; and a parameter selection tool to enhance disease cluster detection performance, measured by sensitivity, specificity, and false discovery rate. We test the performance of the method using Monte Carlo simulations of hypothetical disease clusters over a test area of four counties in Iowa. The simulations include different types of spatial disease patterns and high-resolution population distribution data. Results confirm that the new features of the spatial filter method do substantially improve its performance in realistic situations comparable to those where the method is likely to be used.
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.
NASA Astrophysics Data System (ADS)
Rojas-Lima, J. E.; Domínguez-Pacheco, A.; Hernández-Aguilar, C.; Cruz-Orea, A.
2016-09-01
Considering the necessity of photothermal alternative approaches for characterizing nonhomogeneous materials like maize seeds, the objective of this research work was to analyze statistically the amplitude variations of photopyroelectric signals, by means of nonparametric techniques such as the histogram and the kernel density estimator, and the probability density function of the amplitude variations of two genotypes of maize seeds with different pigmentations and structural components: crystalline and floury. To determine if the probability density function had a known parametric form, the histogram was determined which did not present a known parametric form, so the kernel density estimator using the Gaussian kernel, with an efficiency of 95 % in density estimation, was used to obtain the probability density function. The results obtained indicated that maize seeds could be differentiated in terms of the statistical values for floury and crystalline seeds such as the mean (93.11, 159.21), variance (1.64× 103, 1.48× 103), and standard deviation (40.54, 38.47) obtained from the amplitude variations of photopyroelectric signals in the case of the histogram approach. For the case of the kernel density estimator, seeds can be differentiated in terms of kernel bandwidth or smoothing constant h of 9.85 and 6.09 for floury and crystalline seeds, respectively.
Excitons in solids with time-dependent density-functional theory: the bootstrap kernel and beyond
NASA Astrophysics Data System (ADS)
Byun, Young-Moo; Yang, Zeng-Hui; Ullrich, Carsten
Time-dependent density-functional theory (TDDFT) is an efficient method to describe the optical properties of solids. Lately, a series of bootstrap-type exchange-correlation (xc) kernels have been reported to produce accurate excitons in solids, but different bootstrap-type kernels exist in the literature, with mixed results. In this presentation, we reveal the origin of the confusion and show a new empirical TDDFT xc kernel to compute excitonic properties of semiconductors and insulators efficiently and accurately. Our method can be used for high-throughput screening calculations and large unit cell calculations. Work supported by NSF Grant DMR-1408904.
Sequential kernel density approximation and its application to real-time visual tracking.
Han, Bohyung; Comaniciu, Dorin; Zhu, Ying; Davis, Larry S
2008-07-01
Visual features are commonly modeled with probability density functions in computer vision problems, but current methods such as a mixture of Gaussians and kernel density estimation suffer from either the lack of flexibility, by fixing or limiting the number of Gaussian components in the mixture, or large memory requirement, by maintaining a non-parametric representation of the density. These problems are aggravated in real-time computer vision applications since density functions are required to be updated as new data becomes available. We present a novel kernel density approximation technique based on the mean-shift mode finding algorithm, and describe an efficient method to sequentially propagate the density modes over time. While the proposed density representation is memory efficient, which is typical for mixture densities, it inherits the flexibility of non-parametric methods by allowing the number of components to be variable. The accuracy and compactness of the sequential kernel density approximation technique is illustrated by both simulations and experiments. Sequential kernel density approximation is applied to on-line target appearance modeling for visual tracking, and its performance is demonstrated on a variety of videos.
Characterization of a maximum-likelihood nonparametric density estimator of kernel type
NASA Technical Reports Server (NTRS)
Geman, S.; Mcclure, D. E.
1982-01-01
Kernel type density estimators calculated by the method of sieves. Proofs are presented for the characterization theorem: Let x(1), x(2),...x(n) be a random sample from a population with density f(0). Let sigma 0 and consider estimators f of f(0) defined by (1).
MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods
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
Risk Classification with an Adaptive Naive Bayes Kernel Machine Model
Minnier, Jessica; Yuan, Ming; Liu, Jun S.; Cai, Tianxi
2014-01-01
Genetic studies of complex traits have uncovered only a small number of risk markers explaining a small fraction of heritability and adding little improvement to disease risk prediction. Standard single marker methods may lack power in selecting informative markers or estimating effects. Most existing methods also typically do not account for non-linearity. Identifying markers with weak signals and estimating their joint effects among many non-informative markers remains challenging. One potential approach is to group markers based on biological knowledge such as gene structure. If markers in a group tend to have similar effects, proper usage of the group structure could improve power and efficiency in estimation. We propose a two-stage method relating markers to disease risk by taking advantage of known gene-set structures. Imposing a naive bayes kernel machine (KM) model, we estimate gene-set specific risk models that relate each gene-set to the outcome in stage I. The KM framework efficiently models potentially non-linear effects of predictors without requiring explicit specification of functional forms. In stage II, we aggregate information across gene-sets via a regularization procedure. Estimation and computational efficiency is further improved with kernel principle component analysis. Asymptotic results for model estimation and gene set selection are derived and numerical studies suggest that the proposed procedure could outperform existing procedures for constructing genetic risk models. PMID:26236061
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.
NASA Astrophysics Data System (ADS)
Xie, Shi-Peng; Luo, Li-Min
2012-06-01
The authors propose a combined scatter reduction and correction method to improve image quality in cone beam computed tomography (CBCT). The scatter kernel superposition (SKS) method has been used occasionally in previous studies. However, this method differs in that a scatter detecting blocker (SDB) was used between the X-ray source and the tested object to model the self-adaptive scatter kernel. This study first evaluates the scatter kernel parameters using the SDB, and then isolates the scatter distribution based on the SKS. The quality of image can be improved by removing the scatter distribution. The results show that the method can effectively reduce the scatter artifacts, and increase the image quality. Our approach increases the image contrast and reduces the magnitude of cupping. The accuracy of the SKS technique can be significantly improved in our method by using a self-adaptive scatter kernel. This method is computationally efficient, easy to implement, and provides scatter correction using a single scan acquisition.
Support vector machine with adaptive composite kernel for hyperspectral image classification
NASA Astrophysics Data System (ADS)
Li, Wei; Du, Qian
2015-05-01
With the improvement of spatial resolution of hyperspectral imagery, it is more reasonable to include spatial information in classification. The resulting spectral-spatial classification outperforms the traditional hyperspectral image classification with spectral information only. Among many spectral-spatial classifiers, support vector machine with composite kernel (SVM-CK) can provide superior performance, with one kernel for spectral information and the other for spatial information. In the original SVM-CK, the spatial information is retrieved by spatial averaging of pixels in a local neighborhood, and used in classifying the central pixel. Obviously, not all the pixels in such a local neighborhood may belong to the same class. Thus, we investigate the performance of Gaussian lowpass filter and an adaptive filter with weights being assigned based on the similarity to the central pixel. The adaptive filter can significantly improve classification accuracy while the Gaussian lowpass filter is less time-consuming and less sensitive to the window size.
Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation
Huang, Hao; Yoo, Shinjae; Yu, Dantong; Qin, Hong
2015-06-01
Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\\ models the heat diffusion traces along the entire time scale, somore » it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.« less
Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation
Huang, Hao; Yoo, Shinjae; Yu, Dantong; Qin, Hong
2015-06-01
Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\\ models the heat diffusion traces along the entire time scale, so it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.
Improvement of SVM-Based Speech/Music Classification Using Adaptive Kernel Technique
NASA Astrophysics Data System (ADS)
Lim, Chungsoo; Chang, Joon-Hyuk
In this paper, we propose a way to improve the classification performance of support vector machines (SVMs), especially for speech and music frames within a selectable mode vocoder (SMV) framework. A myriad of techniques have been proposed for SVMs, and most of them are employed during the training phase of SVMs. Instead, the proposed algorithm is applied during the test phase and works with existing schemes. The proposed algorithm modifies a kernel parameter in the decision function of SVMs to alter SVM decisions for better classification accuracy based on the previous outputs of SVMs. Since speech and music frames exhibit strong inter-frame correlation, the outputs of SVMs can guide the kernel parameter modification. Our experimental results show that the proposed algorithm has the potential for adaptively tuning classifications of support vector machines for better performance.
Chen, Rongda; Wang, Ze
2013-01-01
Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody's. However, it has a fatal defect that it can't fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody's new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management. PMID:23874558
Hardness and softness reactivity kernels within the spin-polarized density-functional theory
Chamorro, Eduardo; De Proft, Frank; Geerlings, Paul
2005-10-15
Generalized hardness and softness reactivity kernels are defined within a spin-polarized density-functional theory (SP-DFT) conceptual framework. These quantities constitute the basis for the global, local (i.e., r-position dependent), and nonlocal (i.e., r and r{sup '}-position dependents) indices devoted to the treatment of both charge-transfer and spin-polarization processes in such a reactivity framework. The exact relationships between these descriptors within a SP-DFT framework are derived and the implications for chemical reactivity in such context are outlined.
Efficient 3D movement-based kernel density estimator and application to wildlife ecology
Tracey-PR, Jeff; Sheppard, James K.; Lockwood, Glenn K.; Chourasia, Amit; Tatineni, Mahidhar; Fisher, Robert N.; Sinkovits, Robert S.
2014-01-01
We describe an efficient implementation of a 3D movement-based kernel density estimator for determining animal space use from discrete GPS measurements. This new method provides more accurate results, particularly for species that make large excursions in the vertical dimension. The downside of this approach is that it is much more computationally expensive than simpler, lower-dimensional models. Through a combination of code restructuring, parallelization and performance optimization, we were able to reduce the time to solution by up to a factor of 1000x, thereby greatly improving the applicability of the method.
Automated voxelization of 3D atom probe data through kernel density estimation.
Srinivasan, Srikant; Kaluskar, Kaustubh; Dumpala, Santoshrupa; Broderick, Scott; Rajan, Krishna
2015-12-01
Identifying nanoscale chemical features from atom probe tomography (APT) data routinely involves adjustment of voxel size as an input parameter, through visual supervision, making the final outcome user dependent, reliant on heuristic knowledge and potentially prone to error. This work utilizes Kernel density estimators to select an optimal voxel size in an unsupervised manner to perform feature selection, in particular targeting resolution of interfacial features and chemistries. The capability of this approach is demonstrated through analysis of the γ / γ' interface in a Ni-Al-Cr superalloy. PMID:25825028
Burke, TImothy P.; Kiedrowski, Brian C.; Martin, William R.; Brown, Forrest B.
2015-11-19
Kernel Density Estimators (KDEs) are a non-parametric density estimation technique that has recently been applied to Monte Carlo radiation transport simulations. Kernel density estimators are an alternative to histogram tallies for obtaining global solutions in Monte Carlo tallies. With KDEs, a single event, either a collision or particle track, can contribute to the score at multiple tally points with the uncertainty at those points being independent of the desired resolution of the solution. Thus, KDEs show potential for obtaining estimates of a global solution with reduced variance when compared to a histogram. Previously, KDEs have been applied to neutronics for one-group reactor physics problems and fixed source shielding applications. However, little work was done to obtain reaction rates using KDEs. This paper introduces a new form of the MFP KDE that is capable of handling general geometries. Furthermore, extending the MFP KDE to 2-D problems in continuous energy introduces inaccuracies to the solution. An ad-hoc solution to these inaccuracies is introduced that produces errors smaller than 4% at material interfaces.
Multi-source adaptation joint kernel sparse representation for visual classification.
Tao, JianWen; Hu, Wenjun; Wen, Shiting
2016-04-01
Most of the existing domain adaptation learning (DAL) methods relies on a single source domain to learn a classifier with well-generalized performance for the target domain of interest, which may lead to the so-called negative transfer problem. To this end, many multi-source adaptation methods have been proposed. While the advantages of using multi-source domains of information for establishing an adaptation model have been widely recognized, how to boost the robustness of the computational model for multi-source adaptation learning has only recently received attention. To address this issue for achieving enhanced performance, we propose in this paper a novel algorithm called multi-source Adaptation Regularization Joint Kernel Sparse Representation (ARJKSR) for robust visual classification problems. Specifically, ARJKSR jointly represents target dataset by a sparse linear combination of training data of each source domain in some optimal Reproduced Kernel Hilbert Space (RKHS), recovered by simultaneously minimizing the inter-domain distribution discrepancy and maximizing the local consistency, whilst constraining the observations from both target and source domains to share their sparse representations. The optimization problem of ARJKSR can be solved using an efficient alternative direction method. Under the framework ARJKSR, we further learn a robust label prediction matrix for the unlabeled instances of target domain based on the classical graph-based semi-supervised learning (GSSL) diagram, into which multiple Laplacian graphs constructed with the ARJKSR are incorporated. The validity of our method is examined by several visual classification problems. Results demonstrate the superiority of our method in comparison to several state-of-the-arts. PMID:26894961
Gonzalez, Ruben; Huang, Biao; Lau, Eric
2015-09-01
Principal component analysis has been widely used in the process industries for the purpose of monitoring abnormal behaviour. The process of reducing dimension is obtained through PCA, while T-tests are used to test for abnormality. Some of the main contributions to the success of PCA is its ability to not only detect problems, but to also give some indication as to where these problems are located. However, PCA and the T-test make use of Gaussian assumptions which may not be suitable in process fault detection. A previous modification of this method is the use of independent component analysis (ICA) for dimension reduction combined with kernel density estimation for detecting abnormality; like PCA, this method points out location of the problems based on linear data-driven methods, but without the Gaussian assumptions. Both ICA and PCA, however, suffer from challenges in interpreting results, which can make it difficult to quickly act once a fault has been detected online. This paper proposes the use of Bayesian networks for dimension reduction which allows the use of process knowledge enabling more intelligent dimension reduction and easier interpretation of results. The dimension reduction technique is combined with multivariate kernel density estimation, making this technique effective for non-linear relationships with non-Gaussian variables. The performance of PCA, ICA and Bayesian networks are compared on data from an industrial scale plant. PMID:25930233
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.
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
Using kernel density estimation to understand the influence of neighbourhood destinations on BMI
King, Tania L; Bentley, Rebecca J; Thornton, Lukar E; Kavanagh, Anne M
2016-01-01
Objectives Little is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated whether individuals living near destinations (shops and service facilities) that are more intensely distributed rather than dispersed, have lower BMIs. Study design and setting A cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Methods Destinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 400, 800 and 1200 m. Using multilevel linear regression, the association between destination intensity (classified in quintiles Q1(least)–Q5(most)) and BMI was estimated in models that adjusted for the following confounders: age, sex, country of birth, education, dominant household occupation, household type, disability/injury and area disadvantage. Separate models included a physical activity variable. Results For kernels of 800 and 1200 m, there was an inverse relationship between BMI and more intensely distributed destinations (compared to areas with least destination intensity). Effects were significant at 1200 m: Q4, β −0.86, 95% CI −1.58 to −0.13, p=0.022; Q5, β −1.03 95% CI −1.65 to −0.41, p=0.001. Inclusion of physical activity in the models attenuated effects, although effects remained marginally significant for Q5 at 1200 m: β −0.77 95% CI −1.52, −0.02, p=0.045. Conclusions This study conducted within urban Melbourne, Australia, found that participants living in areas of greater destination intensity within 1200 m of home had lower BMIs. Effects were partly explained by physical activity. The results suggest that increasing the intensity of destination distribution could reduce BMI levels by encouraging higher levels of physical activity
Kernel density estimation-based solution of the nuclear Schrödinger equation
NASA Astrophysics Data System (ADS)
Unke, Oliver Thorsten; Meuwly, Markus
2015-10-01
Solving the time-dependent Schrödinger equation for nuclear motion remains a challenge. Despite novel approaches based on Bohmian mechanics, the long-time stability and generalization to multiple dimensions remains an open question. In the present work a method based on an ensemble of classical particles instead of a wave function is employed to evolve the system. Quantum effects are introduced through forces derived from the quantum potential Q and the necessary derivatives are obtained from a density estimate using kernel density estimation. Application of the procedure to typical 1- and 2-dimensional problems yields good agreement with numerically exact solutions and favourable scaling with the number of particles is found.
NASA Astrophysics Data System (ADS)
Chen, W.; Shao, Z.; Tiong, L. K.
2015-11-01
Drought caused the most widespread damage in China, making up over 50 % of the total affected area nationwide in recent decades. In the paper, a Standardized Precipitation Index-based (SPI-based) drought risk study is conducted using historical rainfall data of 19 weather stations in Shandong province, China. Kernel density based method is adopted to carry out the risk analysis. Comparison between the bivariate Gaussian kernel density estimation (GKDE) and diffusion kernel density estimation (DKDE) are carried out to analyze the effect of drought intensity and drought duration. The results show that DKDE is relatively more accurate without boundary-leakage. Combined with the GIS technique, the drought risk is presented which reveals the spatial and temporal variation of agricultural droughts for corn in Shandong. The estimation provides a different way to study the occurrence frequency and severity of drought risk from multiple perspectives.
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.
Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels
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
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
Validation tests of an improved kernel density estimation method for identifying disease clusters
Cai, Qiang; Rushton, Gerald; Bhaduri, Budhendra L
2011-01-01
The spatial filter method, which belongs to the class of kernel density estimation methods, has been used to make morbidity and mortality maps in several recent studies. We propose improvements in the method that include a spatial basis of support designed to give a constant standard error for the standardized mortality/morbidity rate; a stair-case weight method for weighting observations to reduce estimation bias; and a method for selecting parameters to control three measures of performance of the method: sensitivity, specificity and false discovery rate. We test the performance of the method using Monte Carlo simulations of hypothetical disease clusters over a test area of four counties in Iowa. The simulations include different types of spatial disease patterns and high resolution population distribution data. Results confirm that the new features of the spatial filter method do substantially improve its performance in realistic situations comparable to those where the method is likely to be used.
Monforti, F; Vitali, L; Bellasio, R; Bianconi, R
2006-02-21
In this paper a new approach to photochemical modeling is investigated and a lagrangian particle model named Photochemical Lagrangian Particle Model (PLPM) is described. Lagrangian particle models are a consolidated tool to deal with the dispersion of pollutants in the atmosphere. Good results have been obtained dealing with inert pollutants. In recent years, a number of pioneering works have shown as Lagrangian models can be of great interest when dealing with photochemistry, provided that special care is given in the reconstruction of chemicals concentration in the atmosphere. Density reconstruction can be performed through the so called ''box counting'' method: an Eulerian grid for chemistry is introduced and density is computed counting particles in each box. In this way one of the main advantages of the Lagrangian approach, the grid independence, is lost. Photochemical reactions are treated in PLPM by means of the complex chemical mechanism SAPRC90 and four density reconstruction methods have been developed, based on the kernel density estimator approach, in order to obtain grid-free accurate concentrations. These methods are all fully grid-free but they differ each other in considering local or global features of the particles distribution, in treating the Cartesian directions separately or together and in being based on receptors or particles positions in space.
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.
Non-Gaussian probabilistic MEG source localisation based on kernel density estimation.
Mohseni, Hamid R; Kringelbach, Morten L; Woolrich, Mark W; Baker, Adam; Aziz, Tipu Z; Probert-Smith, Penny
2014-02-15
There is strong evidence to suggest that data recorded from magnetoencephalography (MEG) follows a non-Gaussian distribution. However, existing standard methods for source localisation model the data using only second order statistics, and therefore use the inherent assumption of a Gaussian distribution. In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. In the case of Gaussian data, the solution of the method is equivalent to that of widely used linearly constrained minimum variance (LCMV) beamformer. The method is also extended to handle data with highly correlated sources using the marginal distribution of the estimated joint distribution, which, in the case of Gaussian measurements, corresponds to the null-beamformer. The proposed non-Gaussian source localisation approach is shown to give better spatial estimates than the LCMV beamformer, both in simulations incorporating non-Gaussian signals, and in real MEG measurements of auditory and visual evoked responses, where the highly correlated sources are known to be difficult to estimate.
Non-Gaussian probabilistic MEG source localisation based on kernel density estimation☆
Mohseni, Hamid R.; Kringelbach, Morten L.; Woolrich, Mark W.; Baker, Adam; Aziz, Tipu Z.; Probert-Smith, Penny
2014-01-01
There is strong evidence to suggest that data recorded from magnetoencephalography (MEG) follows a non-Gaussian distribution. However, existing standard methods for source localisation model the data using only second order statistics, and therefore use the inherent assumption of a Gaussian distribution. In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. In the case of Gaussian data, the solution of the method is equivalent to that of widely used linearly constrained minimum variance (LCMV) beamformer. The method is also extended to handle data with highly correlated sources using the marginal distribution of the estimated joint distribution, which, in the case of Gaussian measurements, corresponds to the null-beamformer. The proposed non-Gaussian source localisation approach is shown to give better spatial estimates than the LCMV beamformer, both in simulations incorporating non-Gaussian signals, and in real MEG measurements of auditory and visual evoked responses, where the highly correlated sources are known to be difficult to estimate. PMID:24055702
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.
Wang, Ying; Wu, Fengchang; Giesy, John P; Feng, Chenglian; Liu, Yuedan; Qin, Ning; Zhao, Yujie
2015-09-01
Due to use of different parametric models for establishing species sensitivity distributions (SSDs), comparison of water quality criteria (WQC) for metals of the same group or period in the periodic table is uncertain and results can be biased. To address this inadequacy, a new probabilistic model, based on non-parametric kernel density estimation was developed and optimal bandwidths and testing methods are proposed. Zinc (Zn), cadmium (Cd), and mercury (Hg) of group IIB of the periodic table are widespread in aquatic environments, mostly at small concentrations, but can exert detrimental effects on aquatic life and human health. With these metals as target compounds, the non-parametric kernel density estimation method and several conventional parametric density estimation methods were used to derive acute WQC of metals for protection of aquatic species in China that were compared and contrasted with WQC for other jurisdictions. HC5 values for protection of different types of species were derived for three metals by use of non-parametric kernel density estimation. The newly developed probabilistic model was superior to conventional parametric density estimations for constructing SSDs and for deriving WQC for these metals. HC5 values for the three metals were inversely proportional to atomic number, which means that the heavier atoms were more potent toxicants. The proposed method provides a novel alternative approach for developing SSDs that could have wide application prospects in deriving WQC and use in assessment of risks to ecosystems. PMID:25953609
Wang, Ying; Wu, Fengchang; Giesy, John P; Feng, Chenglian; Liu, Yuedan; Qin, Ning; Zhao, Yujie
2015-09-01
Due to use of different parametric models for establishing species sensitivity distributions (SSDs), comparison of water quality criteria (WQC) for metals of the same group or period in the periodic table is uncertain and results can be biased. To address this inadequacy, a new probabilistic model, based on non-parametric kernel density estimation was developed and optimal bandwidths and testing methods are proposed. Zinc (Zn), cadmium (Cd), and mercury (Hg) of group IIB of the periodic table are widespread in aquatic environments, mostly at small concentrations, but can exert detrimental effects on aquatic life and human health. With these metals as target compounds, the non-parametric kernel density estimation method and several conventional parametric density estimation methods were used to derive acute WQC of metals for protection of aquatic species in China that were compared and contrasted with WQC for other jurisdictions. HC5 values for protection of different types of species were derived for three metals by use of non-parametric kernel density estimation. The newly developed probabilistic model was superior to conventional parametric density estimations for constructing SSDs and for deriving WQC for these metals. HC5 values for the three metals were inversely proportional to atomic number, which means that the heavier atoms were more potent toxicants. The proposed method provides a novel alternative approach for developing SSDs that could have wide application prospects in deriving WQC and use in assessment of risks to ecosystems.
Novelty detection by multivariate kernel density estimation and growing neural gas algorithm
NASA Astrophysics Data System (ADS)
Fink, Olga; Zio, Enrico; Weidmann, Ulrich
2015-01-01
One of the underlying assumptions when using data-based methods for pattern recognition in diagnostics or prognostics is that the selected data sample used to train and test the algorithm is representative of the entire dataset and covers all combinations of parameters and conditions, and resulting system states. However in practice, operating and environmental conditions may change, unexpected and previously unanticipated events may occur and corresponding new anomalous patterns develop. Therefore for practical applications, techniques are required to detect novelties in patterns and give confidence to the user on the validity of the performed diagnosis and predictions. In this paper, the application of two types of novelty detection approaches is compared: a statistical approach based on multivariate kernel density estimation and an approach based on a type of unsupervised artificial neural network, called the growing neural gas (GNG). The comparison is performed on a case study in the field of railway turnout systems. Both approaches demonstrate their suitability for detecting novel patterns. Furthermore, GNG proves to be more flexible, especially with respect to dimensionality of the input data and suitability for online learning.
Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator.
Fleming, C H; Fagan, W F; Mueller, T; Olson, K A; Leimgruber, P; Calabrese, J M
2015-05-01
Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology. PMID:26236833
2012-01-01
Background Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome in electrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophic disease. To this end, we propose a new method, which employs wavelets and simple feature selection. Methods For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in 90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method based on the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used for differentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalized and signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point. We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiers to those features. Results We evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods. Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemic ST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively. The SVM classifier detects 355 ischemic ST episodes. Conclusions We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removing baseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and feature extraction from morphology of ECG waveforms explicitly. It was shown that the number of selected features were sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposed KDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require any numerical values of the parameters
Kenchington, Ellen; Murillo, Francisco Javier; Lirette, Camille; Sacau, Mar; Koen-Alonso, Mariano; Kenny, Andrew; Ollerhead, Neil; Wareham, Vonda; Beazley, Lindsay
2014-01-01
The United Nations General Assembly Resolution 61/105, concerning sustainable fisheries in the marine ecosystem, calls for the protection of vulnerable marine ecosystems (VME) from destructive fishing practices. Subsequently, the Food and Agriculture Organization (FAO) produced guidelines for identification of VME indicator species/taxa to assist in the implementation of the resolution, but recommended the development of case-specific operational definitions for their application. We applied kernel density estimation (KDE) to research vessel trawl survey data from inside the fishing footprint of the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area in the high seas of the northwest Atlantic to create biomass density surfaces for four VME indicator taxa: large-sized sponges, sea pens, small and large gorgonian corals. These VME indicator taxa were identified previously by NAFO using the fragility, life history characteristics and structural complexity criteria presented by FAO, along with an evaluation of their recovery trajectories. KDE, a non-parametric neighbour-based smoothing function, has been used previously in ecology to identify hotspots, that is, areas of relatively high biomass/abundance. We present a novel approach of examining relative changes in area under polygons created from encircling successive biomass categories on the KDE surface to identify "significant concentrations" of biomass, which we equate to VMEs. This allows identification of the VMEs from the broader distribution of the species in the study area. We provide independent assessments of the VMEs so identified using underwater images, benthic sampling with other gear types (dredges, cores), and/or published species distribution models of probability of occurrence, as available. For each VME indicator taxon we provide a brief review of their ecological function which will be important in future assessments of significant adverse impact on these habitats here and elsewhere.
Kenchington, Ellen; Murillo, Francisco Javier; Lirette, Camille; Sacau, Mar; Koen-Alonso, Mariano; Kenny, Andrew; Ollerhead, Neil; Wareham, Vonda; Beazley, Lindsay
2014-01-01
The United Nations General Assembly Resolution 61/105, concerning sustainable fisheries in the marine ecosystem, calls for the protection of vulnerable marine ecosystems (VME) from destructive fishing practices. Subsequently, the Food and Agriculture Organization (FAO) produced guidelines for identification of VME indicator species/taxa to assist in the implementation of the resolution, but recommended the development of case-specific operational definitions for their application. We applied kernel density estimation (KDE) to research vessel trawl survey data from inside the fishing footprint of the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area in the high seas of the northwest Atlantic to create biomass density surfaces for four VME indicator taxa: large-sized sponges, sea pens, small and large gorgonian corals. These VME indicator taxa were identified previously by NAFO using the fragility, life history characteristics and structural complexity criteria presented by FAO, along with an evaluation of their recovery trajectories. KDE, a non-parametric neighbour-based smoothing function, has been used previously in ecology to identify hotspots, that is, areas of relatively high biomass/abundance. We present a novel approach of examining relative changes in area under polygons created from encircling successive biomass categories on the KDE surface to identify “significant concentrations” of biomass, which we equate to VMEs. This allows identification of the VMEs from the broader distribution of the species in the study area. We provide independent assessments of the VMEs so identified using underwater images, benthic sampling with other gear types (dredges, cores), and/or published species distribution models of probability of occurrence, as available. For each VME indicator taxon we provide a brief review of their ecological function which will be important in future assessments of significant adverse impact on these habitats here and
Kenchington, Ellen; Murillo, Francisco Javier; Lirette, Camille; Sacau, Mar; Koen-Alonso, Mariano; Kenny, Andrew; Ollerhead, Neil; Wareham, Vonda; Beazley, Lindsay
2014-01-01
The United Nations General Assembly Resolution 61/105, concerning sustainable fisheries in the marine ecosystem, calls for the protection of vulnerable marine ecosystems (VME) from destructive fishing practices. Subsequently, the Food and Agriculture Organization (FAO) produced guidelines for identification of VME indicator species/taxa to assist in the implementation of the resolution, but recommended the development of case-specific operational definitions for their application. We applied kernel density estimation (KDE) to research vessel trawl survey data from inside the fishing footprint of the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area in the high seas of the northwest Atlantic to create biomass density surfaces for four VME indicator taxa: large-sized sponges, sea pens, small and large gorgonian corals. These VME indicator taxa were identified previously by NAFO using the fragility, life history characteristics and structural complexity criteria presented by FAO, along with an evaluation of their recovery trajectories. KDE, a non-parametric neighbour-based smoothing function, has been used previously in ecology to identify hotspots, that is, areas of relatively high biomass/abundance. We present a novel approach of examining relative changes in area under polygons created from encircling successive biomass categories on the KDE surface to identify "significant concentrations" of biomass, which we equate to VMEs. This allows identification of the VMEs from the broader distribution of the species in the study area. We provide independent assessments of the VMEs so identified using underwater images, benthic sampling with other gear types (dredges, cores), and/or published species distribution models of probability of occurrence, as available. For each VME indicator taxon we provide a brief review of their ecological function which will be important in future assessments of significant adverse impact on these habitats here and elsewhere
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. PMID:26793269
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.
Electron density measurements for plasma adaptive optics
NASA Astrophysics Data System (ADS)
Neiswander, Brian W.
Over the past 40 years, there has been growing interest in both laser communications and directed energy weapons that operate from moving aircraft. As a laser beam propagates from an aircraft in flight, it passes through boundary layers, turbulence, and shear layers in the near-region of the aircraft. These fluid instabilities cause strong density gradients which adversely affect the transmission of laser energy to a target. Adaptive optics provides corrective measures for this problem but current technology cannot respond quickly enough to be useful for high speed flight conditions. This research investigated the use of plasma as a medium for adaptive optics for aero-optics applications. When a laser beam passes through plasma, its phase is shifted proportionally to the electron density and gas heating within the plasma. As a result, plasma can be utilized as a dynamically controllable optical medium. Experiments were carried out using a cylindrical dielectric barrier discharge plasma chamber which generated a sub-atmospheric pressure, low-temperature plasma. An electrostatic model of this design was developed and revealed an important design constraint relating to the geometry of the chamber. Optical diagnostic techniques were used to characterize the plasma discharge. Single-wavelength interferometric experiments were performed and demonstrated up to 1.5 microns of optical path difference (OPD) in a 633 nm laser beam. Dual-wavelength interferometry was used to obtain time-resolved profiles of the plasma electron density and gas heating inside the plasma chamber. Furthermore, a new multi-wavelength infrared diagnostic technique was developed and proof-of-concept simulations were conducted to demonstrate the system's capabilities.
Using a kernel density estimation based classifier to predict species-specific microRNA precursors
Chang, Darby Tien-Hao; Wang, Chih-Ching; Chen, Jian-Wei
2008-01-01
Background MicroRNAs (miRNAs) are short non-coding RNA molecules participating in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, ab initio approaches obtain more attention because that they can discover species-specific pre-miRNAs. Most ab initio approaches proposed novel features to characterize RNA molecules. However, there were fewer discussions on the associated classification mechanism in a miRNA predictor. Results This study focuses on the classification algorithm for miRNA prediction. We develop a novel ab initio method, miR-KDE, in which most of the features are collected from previous works. The classification mechanism in miR-KDE is the relaxed variable kernel density estimator (RVKDE) that we have recently proposed. When compared to the famous support vector machine (SVM), RVKDE exploits more local information of the training dataset. MiR-KDE is evaluated using a training set consisted of only human pre-miRNAs to predict a benchmark collected from 40 species. The experimental results show that miR-KDE delivers favorable performance in predicting human pre-miRNAs and has advantages for pre-miRNAs from the genera taxonomically distant to humans. Conclusion We use a novel classifier of which the characteristic of exploiting local information is particularly suitable to predict species-specific pre-miRNAs. This study also provides a comprehensive analysis from the view of classification mechanism. The good performance of miR-KDE encourages more efforts on the classification methodology as well as the feature extraction in miRNA prediction. PMID:19091019
Probability Distribution Extraction from TEC Estimates based on Kernel Density Estimation
NASA Astrophysics Data System (ADS)
Demir, Uygar; Toker, Cenk; Çenet, Duygu
2016-07-01
Statistical analysis of the ionosphere, specifically the Total Electron Content (TEC), may reveal important information about its temporal and spatial characteristics. One of the core metrics that express the statistical properties of a stochastic process is its Probability Density Function (pdf). Furthermore, statistical parameters such as mean, variance and kurtosis, which can be derived from the pdf, may provide information about the spatial uniformity or clustering of the electron content. For example, the variance differentiates between a quiet ionosphere and a disturbed one, whereas kurtosis differentiates between a geomagnetic storm and an earthquake. Therefore, valuable information about the state of the ionosphere (and the natural phenomena that cause the disturbance) can be obtained by looking at the statistical parameters. In the literature, there are publications which try to fit the histogram of TEC estimates to some well-known pdf.s such as Gaussian, Exponential, etc. However, constraining a histogram to fit to a function with a fixed shape will increase estimation error, and all the information extracted from such pdf will continue to contain this error. In such techniques, it is highly likely to observe some artificial characteristics in the estimated pdf which is not present in the original data. In the present study, we use the Kernel Density Estimation (KDE) technique to estimate the pdf of the TEC. KDE is a non-parametric approach which does not impose a specific form on the TEC. As a result, better pdf estimates that almost perfectly fit to the observed TEC values can be obtained as compared to the techniques mentioned above. KDE is particularly good at representing the tail probabilities, and outliers. We also calculate the mean, variance and kurtosis of the measured TEC values. The technique is applied to the ionosphere over Turkey where the TEC values are estimated from the GNSS measurement from the TNPGN-Active (Turkish National Permanent
Oyang, Yen-Jen; Hwang, Shien-Ching; Ou, Yu-Yen; Chen, Chien-Yu; Chen, Zhi-Wei
2005-01-01
This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is
King, Tania L.; Thornton, Lukar E.; Bentley, Rebecca J.; Kavanagh, Anne M.
2015-01-01
Background Local destinations have previously been shown to be associated with higher levels of both physical activity and walking, but little is known about how the distribution of destinations is related to activity. Kernel density estimation is a spatial analysis technique that accounts for the location of features relative to each other. Using kernel density estimation, this study sought to investigate whether individuals who live near destinations (shops and service facilities) that are more intensely distributed rather than dispersed: 1) have higher odds of being sufficiently active; 2) engage in more frequent walking for transport and recreation. Methods The sample consisted of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Destinations within these areas were geocoded and kernel density estimates of destination intensity were created using kernels of 400m (meters), 800m and 1200m. Using multilevel logistic regression, the association between destination intensity (classified in quintiles Q1(least)—Q5(most)) and likelihood of: 1) being sufficiently active (compared to insufficiently active); 2) walking≥4/week (at least 4 times per week, compared to walking less), was estimated in models that were adjusted for potential confounders. Results For all kernel distances, there was a significantly greater likelihood of walking≥4/week, among respondents living in areas of greatest destinations intensity compared to areas with least destination intensity: 400m (Q4 OR 1.41 95%CI 1.02–1.96; Q5 OR 1.49 95%CI 1.06–2.09), 800m (Q4 OR 1.55, 95%CI 1.09–2.21; Q5, OR 1.71, 95%CI 1.18–2.48) and 1200m (Q4, OR 1.7, 95%CI 1.18–2.45; Q5, OR 1.86 95%CI 1.28–2.71). There was also evidence of associations between destination intensity and sufficient physical activity, however these associations were markedly attenuated when walking was included in the models. Conclusions This study, conducted within urban Melbourne, found that those who lived
Kavallieratos, Nickolas G; Athanassiou, Christos G; Mpakou, Flora D; Mpassoukou, Argyro E
2007-10-01
Laboratory bioassays were carried out to evaluate the effect of insect density (10, 30, 60, and 100 adults), wheat quantity (10, 30, 60, and 100 g), and cracked kernel containment (5, 15, 30, and 50%) on the efficacy of diatomaceous earth (DE). Three beetle species, Sitophilus oryzae (L.), Rhyzopertha dominica (F.), and Tribolium confusum Jacquelin du Val, as well as two DE formulations, Insecto and SilicoSec, and one DE enhanced with pyrethrum, PyriSec (all commercially available) were tested. In the first two series of bioassays, the three DE formulations were applied at three dose rates, 500, 1000 and 1,500 ppm. In the third series, the dose rates used were 500 and 1,000 ppm. Dead adults were counted 14 d later. For insect density, wheat quantity, and cracked kernel containment, significant differences were noted in mortality levels of the tested species among the three DE formulations and among doses. No significant differences were noted in the mortality levels among the four adult densities of any of the insects tested. The increase of wheat quantity used in the bioassays increased significantly adult mortality of T. confusum. The increase of cracked wheat containment decreased significantly adult mortality of S. oryzae.
PeaKDEck: a kernel density estimator-based peak calling program for DNaseI-seq data.
McCarthy, Michael T; O'Callaghan, Christopher A
2014-05-01
Hypersensitivity to DNaseI digestion is a hallmark of open chromatin, and DNaseI-seq allows the genome-wide identification of regions of open chromatin. Interpreting these data is challenging, largely because of inherent variation in signal-to-noise ratio between datasets. We have developed PeaKDEck, a peak calling program that distinguishes signal from noise by randomly sampling read densities and using kernel density estimation to generate a dataset-specific probability distribution of random background signal. PeaKDEck uses this probability distribution to select an appropriate read density threshold for peak calling in each dataset. We benchmark PeaKDEck using published ENCODE DNaseI-seq data and other peak calling programs, and demonstrate superior performance in low signal-to-noise ratio datasets. PMID:24407222
NASA Astrophysics Data System (ADS)
Siirila-Woodburn, Erica R.; Fernández-Garcia, Daniel; Sanchez-Vila, Xavier
2015-06-01
While particle tracking techniques are often used in risk frameworks, the number of particles needed to properly derive risk metrics such as average concentration for a given exposure duration is often unknown. If too few particles are used, error may propagate into the risk estimate. In this work, we provide a less error-prone methodology for the direct reconstruction of exposure duration averaged concentration versus time breakthrough curves from particle arrival times at a compliance surface. The approach is based on obtaining a suboptimal kernel density estimator that is applied to the sampled particle arrival times. The corresponding estimates of risk metrics obtained with this method largely outperform those by means of traditional methods (reconstruction of the breakthrough curve followed by the integration of concentration in time over the exposure duration). This is particularly true when the number of particles used in the numerical simulation is small (<105), and for small exposure times. Percent error in the peak of averaged breakthrough curves is approximately zero for all scenarios and all methods tested when the number of particles is ≥105. Our results illustrate that obtaining a representative average exposure concentration is reliant on the information contained in each individual tracked particle, more so when the number of particles is small. They further illustrate the usefulness of defining problem-specific kernel density estimators to properly reconstruct the observables of interest in a particle tracking framework without relying on the use of an extremely large number of particles.
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
Subramanian, Sundarraman
2006-01-01
This article concerns asymptotic theory for a new estimator of a survival function in the missing censoring indicator model of random censorship. Specifically, the large sample results for an inverse probability-of-non-missingness weighted estimator of the cumulative hazard function, so far not available, are derived, including an almost sure representation with rate for a remainder term, and uniform strong consistency with rate of convergence. The estimator is based on a kernel estimate for the conditional probability of non-missingness of the censoring indicator. Expressions for its bias and variance, in turn leading to an expression for the mean squared error as a function of the bandwidth, are also obtained. The corresponding estimator of the survival function, whose weak convergence is derived, is asymptotically efficient. A numerical study, comparing the performances of the proposed and two other currently existing efficient estimators, is presented. PMID:18953423
NASA Astrophysics Data System (ADS)
Priyatikanto, R.; Arifyanto, M. I.
2015-01-01
Stellar membership determination of an open cluster is an important process to do before further analysis. Basically, there are two classes of membership determination method: parametric and non-parametric. In this study, an alternative of non-parametric method based on Binned Kernel Density Estimation that accounts measurements errors (simply called BKDE- e) is proposed. This method is applied upon proper motions data to determine cluster's membership kinematically and estimate the average proper motions of the cluster. Monte Carlo simulations show that the average proper motions determination using this proposed method is statistically more accurate than ordinary Kernel Density Estimator (KDE). By including measurement errors in the calculation, the mode location from the resulting density estimate is less sensitive to non-physical or stochastic fluctuation as compared to ordinary KDE that excludes measurement errors. For the typical mean measurement error of 7 mas/yr, BKDE- e suppresses the potential of miscalculation by a factor of two compared to KDE. With median accuracy of about 93 %, BKDE- e method has comparable accuracy with respect to parametric method (modified Sanders algorithm). Application to real data from The Fourth USNO CCD Astrograph Catalog (UCAC4), especially to NGC 2682 is also performed. The mode of member stars distribution on Vector Point Diagram is located at μ α cos δ=-9.94±0.85 mas/yr and μ δ =-4.92±0.88 mas/yr. Although the BKDE- e performance does not overtake parametric approach, it serves a new view of doing membership analysis, expandable to astrometric and photometric data or even in binary cluster search.
Chen, Maoqi
2016-01-01
Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average, 10.6 ± 4.3 common motor units were identified from each trial, which showed a very high matching rate of 97.85 ± 1.85% in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield.
Chen, Maoqi; Holobar, Ales; Zhang, Xu; Zhou, Ping
2016-01-01
Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average, 10.6 ± 4.3 common motor units were identified from each trial, which showed a very high matching rate of 97.85 ± 1.85% in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield.
Chen, Maoqi
2016-01-01
Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average, 10.6 ± 4.3 common motor units were identified from each trial, which showed a very high matching rate of 97.85 ± 1.85% in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield. PMID:27642525
Chen, Maoqi; Holobar, Ales; Zhang, Xu; Zhou, Ping
2016-01-01
Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average, 10.6 ± 4.3 common motor units were identified from each trial, which showed a very high matching rate of 97.85 ± 1.85% in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield. PMID:27642525
Larsson, Joel; Båth, Magnus; Ledenius, Kerstin; Caisander, Håkan; Thilander-Klang, Anne
2016-06-01
The purpose of this study was to investigate the effect of different combinations of convolution kernel and the level of Adaptive Statistical iterative Reconstruction (ASiR™) on diagnostic image quality as well as visualisation of anatomical structures in paediatric abdominal computed tomography (CT) examinations. Thirty-five paediatric patients with abdominal pain with non-specified pathology undergoing abdominal CT were included in the study. Transaxial stacks of 5-mm-thick images were retrospectively reconstructed at various ASiR levels, in combination with three convolution kernels. Four paediatric radiologists rated the diagnostic image quality and the delineation of six anatomical structures in a blinded randomised visual grading study. Image quality at a given ASiR level was found to be dependent on the kernel, and a more edge-enhancing kernel benefitted from a higher ASiR level. An ASiR level of 70 % together with the Soft™ or Standard™ kernel was suggested to be the optimal combination for paediatric abdominal CT examinations.
Galindo, I; Romero, M C; Sánchez, N; Morales, J M
2016-01-01
Risk management stakeholders in high-populated volcanic islands should be provided with the latest high-quality volcanic information. We present here the first volcanic susceptibility map of Lanzarote and Chinijo Islands and their submarine flanks based on updated chronostratigraphical and volcano structural data, as well as on the geomorphological analysis of the bathymetric data of the submarine flanks. The role of the structural elements in the volcanic susceptibility analysis has been reviewed: vents have been considered since they indicate where previous eruptions took place; eruptive fissures provide information about the stress field as they are the superficial expression of the dyke conduit; eroded dykes have been discarded since they are single non-feeder dykes intruded in deep parts of Miocene-Pliocene volcanic edifices; main faults have been taken into account only in those cases where they could modified the superficial movement of magma. The application of kernel density estimation via a linear diffusion process for the volcanic susceptibility assessment has been applied successfully to Lanzarote and could be applied to other fissure volcanic fields worldwide since the results provide information about the probable area where an eruption could take place but also about the main direction of the probable volcanic fissures. PMID:27265878
NASA Astrophysics Data System (ADS)
Galindo, I.; Romero, M. C.; Sánchez, N.; Morales, J. M.
2016-06-01
Risk management stakeholders in high-populated volcanic islands should be provided with the latest high-quality volcanic information. We present here the first volcanic susceptibility map of Lanzarote and Chinijo Islands and their submarine flanks based on updated chronostratigraphical and volcano structural data, as well as on the geomorphological analysis of the bathymetric data of the submarine flanks. The role of the structural elements in the volcanic susceptibility analysis has been reviewed: vents have been considered since they indicate where previous eruptions took place; eruptive fissures provide information about the stress field as they are the superficial expression of the dyke conduit; eroded dykes have been discarded since they are single non-feeder dykes intruded in deep parts of Miocene-Pliocene volcanic edifices; main faults have been taken into account only in those cases where they could modified the superficial movement of magma. The application of kernel density estimation via a linear diffusion process for the volcanic susceptibility assessment has been applied successfully to Lanzarote and could be applied to other fissure volcanic fields worldwide since the results provide information about the probable area where an eruption could take place but also about the main direction of the probable volcanic fissures.
Galindo, I.; Romero, M. C.; Sánchez, N.; Morales, J. M.
2016-01-01
Risk management stakeholders in high-populated volcanic islands should be provided with the latest high-quality volcanic information. We present here the first volcanic susceptibility map of Lanzarote and Chinijo Islands and their submarine flanks based on updated chronostratigraphical and volcano structural data, as well as on the geomorphological analysis of the bathymetric data of the submarine flanks. The role of the structural elements in the volcanic susceptibility analysis has been reviewed: vents have been considered since they indicate where previous eruptions took place; eruptive fissures provide information about the stress field as they are the superficial expression of the dyke conduit; eroded dykes have been discarded since they are single non-feeder dykes intruded in deep parts of Miocene-Pliocene volcanic edifices; main faults have been taken into account only in those cases where they could modified the superficial movement of magma. The application of kernel density estimation via a linear diffusion process for the volcanic susceptibility assessment has been applied successfully to Lanzarote and could be applied to other fissure volcanic fields worldwide since the results provide information about the probable area where an eruption could take place but also about the main direction of the probable volcanic fissures. PMID:27265878
Galindo, I; Romero, M C; Sánchez, N; Morales, J M
2016-06-06
Risk management stakeholders in high-populated volcanic islands should be provided with the latest high-quality volcanic information. We present here the first volcanic susceptibility map of Lanzarote and Chinijo Islands and their submarine flanks based on updated chronostratigraphical and volcano structural data, as well as on the geomorphological analysis of the bathymetric data of the submarine flanks. The role of the structural elements in the volcanic susceptibility analysis has been reviewed: vents have been considered since they indicate where previous eruptions took place; eruptive fissures provide information about the stress field as they are the superficial expression of the dyke conduit; eroded dykes have been discarded since they are single non-feeder dykes intruded in deep parts of Miocene-Pliocene volcanic edifices; main faults have been taken into account only in those cases where they could modified the superficial movement of magma. The application of kernel density estimation via a linear diffusion process for the volcanic susceptibility assessment has been applied successfully to Lanzarote and could be applied to other fissure volcanic fields worldwide since the results provide information about the probable area where an eruption could take place but also about the main direction of the probable volcanic fissures.
NASA Astrophysics Data System (ADS)
Chen, Y.; Ho, C.; Chang, L.
2011-12-01
In previous decades, the climate change caused by global warming increases the occurrence frequency of extreme hydrological events. Water supply shortages caused by extreme events create great challenges for water resource management. To evaluate future climate variations, general circulation models (GCMs) are the most wildly known tools which shows possible weather conditions under pre-defined CO2 emission scenarios announced by IPCC. Because the study area of GCMs is the entire earth, the grid sizes of GCMs are much larger than the basin scale. To overcome the gap, a statistic downscaling technique can transform the regional scale weather factors into basin scale precipitations. The statistic downscaling technique can be divided into three categories include transfer function, weather generator and weather type. The first two categories describe the relationships between the weather factors and precipitations respectively based on deterministic algorithms, such as linear or nonlinear regression and ANN, and stochastic approaches, such as Markov chain theory and statistical distributions. In the weather type, the method has ability to cluster weather factors, which are high dimensional and continuous variables, into weather types, which are limited number of discrete states. In this study, the proposed downscaling model integrates the weather type, using the K-means clustering algorithm, and the weather generator, using the kernel density estimation. The study area is Shihmen basin in northern of Taiwan. In this study, the research process contains two steps, a calibration step and a synthesis step. Three sub-steps were used in the calibration step. First, weather factors, such as pressures, humidities and wind speeds, obtained from NCEP and the precipitations observed from rainfall stations were collected for downscaling. Second, the K-means clustering grouped the weather factors into four weather types. Third, the Markov chain transition matrixes and the
NASA Astrophysics Data System (ADS)
Duguet, T.; Bender, M.; Ebran, J.-P.; Lesinski, T.; Somà, V.
2015-12-01
This programmatic paper lays down the possibility to reconcile the necessity to resum many-body correlations into the energy kernel with the fact that safe multi-reference energy density functional (EDF) calculations cannot be achieved whenever the Pauli principle is not enforced, as is for example the case when many-body correlations are parametrized under the form of empirical density dependencies. Our proposal is to exploit a newly developed ab initio many-body formalism to guide the construction of safe, explicitly correlated and systematically improvable parametrizations of the off-diagonal energy and norm kernels that lie at the heart of the nuclear EDF method. The many-body formalism of interest relies on the concepts of symmetry breaking and restoration that have made the fortune of the nuclear EDF method and is, as such, amenable to this guidance. After elaborating on our proposal, we briefly outline the project we plan to execute in the years to come.
Wang, Guoqing; Hou, Zhenyu; Peng, Yang; Wang, Yanjun; Sun, Xiaoli; Sun, Yu-an
2011-11-01
By determination of the number of absorptive chemical components (ACCs) in mixtures using median absolute deviation (MAD) analysis and extraction of spectral profiles of ACCs using kernel independent component analysis (KICA), an adaptive KICA (AKICA) algorithm was proposed. The proposed AKICA algorithm was used to characterize the procedure for processing prepared rhubarb roots by resolution of the measured mixed raw UV spectra of the rhubarb samples that were collected at different steaming intervals. The results show that the spectral features of ACCs in the mixtures can be directly estimated without chemical and physical pre-separation and other prior information. The estimated three independent components (ICs) represent different chemical components in the mixtures, which are mainly polysaccharides (IC1), tannin (IC2), and anthraquinone glycosides (IC3). The variations of the relative concentrations of the ICs can account for the chemical and physical changes during the processing procedure: IC1 increases significantly before the first 5 h, and is nearly invariant after 6 h; IC2 has no significant changes or is slightly decreased during the processing procedure; IC3 decreases significantly before the first 5 h and decreases slightly after 6 h. The changes of IC1 can explain why the colour became black and darkened during the processing procedure, and the changes of IC3 can explain why the processing procedure can reduce the bitter and dry taste of the rhubarb roots. The endpoint of the processing procedure can be determined as 5-6 h, when the increasing or decreasing trends of the estimated ICs are insignificant. The AKICA-UV method provides an alternative approach for the characterization of the processing procedure of rhubarb roots preparation, and provides a novel way for determination of the endpoint of the traditional Chinese medicine (TCM) processing procedure by inspection of the change trends of the ICs.
NASA Astrophysics Data System (ADS)
Rahbaralam, Maryam; Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier
2015-12-01
Random walk particle tracking methods are a computationally efficient family of methods to solve reactive transport problems. While the number of particles in most realistic applications is in the order of 106-109, the number of reactive molecules even in diluted systems might be in the order of fractions of the Avogadro number. Thus, each particle actually represents a group of potentially reactive molecules. The use of a low number of particles may result not only in loss of accuracy, but also may lead to an improper reproduction of the mixing process, limited by diffusion. Recent works have used this effect as a proxy to model incomplete mixing in porous media. In this work, we propose using a Kernel Density Estimation (KDE) of the concentrations that allows getting the expected results for a well-mixed solution with a limited number of particles. The idea consists of treating each particle as a sample drawn from the pool of molecules that it represents; this way, the actual location of a tracked particle is seen as a sample drawn from the density function of the location of molecules represented by that given particle, rigorously represented by a kernel density function. The probability of reaction can be obtained by combining the kernels associated to two potentially reactive particles. We demonstrate that the observed deviation in the reaction vs time curves in numerical experiments reported in the literature could be attributed to the statistical method used to reconstruct concentrations (fixed particle support) from discrete particle distributions, and not to the occurrence of true incomplete mixing. We further explore the evolution of the kernel size with time, linking it to the diffusion process. Our results show that KDEs are powerful tools to improve computational efficiency and robustness in reactive transport simulations, and indicates that incomplete mixing in diluted systems should be modeled based on alternative mechanistic models and not on a
Adaptive density partitioning technique in the auxiliary plane wave method
NASA Astrophysics Data System (ADS)
Kurashige, Yuki; Nakajima, Takahito; Hirao, Kimihiko
2006-01-01
We have developed the adaptive density partitioning technique (ADPT) in the auxiliary plane wave method, in which a part of the density is expanded to plane waves, for the fast evaluation of Coulomb matrix. Our partitioning is based on the error estimations and allows us to control the accuracy and efficiency. Moreover, we can drastically reduce the core Gaussian products that are left in Gaussian representation (its analytical integrals is the bottleneck in this method). For the taxol molecule with 6-31G** basis, the core Gaussian products accounted only for 5% in submicrohartree error.
Bayesian inference with an adaptive proposal density for GARCH models
NASA Astrophysics Data System (ADS)
Takaishi, Tetsuya
2010-04-01
We perform the Bayesian inference of a GARCH model by the Metropolis-Hastings algorithm with an adaptive proposal density. The adaptive proposal density is assumed to be the Student's t-distribution and the distribution parameters are evaluated by using the data sampled during the simulation. We apply the method for the QGARCH model which is one of asymmetric GARCH models and make empirical studies for Nikkei 225, DAX and Hang indexes. We find that autocorrelation times from our method are very small, thus the method is very efficient for generating uncorrelated Monte Carlo data. The results from the QGARCH model show that all the three indexes show the leverage effect, i.e. the volatility is high after negative observations.
MC Kernel: Broadband Waveform Sensitivity Kernels for Seismic Tomography
NASA Astrophysics Data System (ADS)
Stähler, Simon C.; van Driel, Martin; Auer, Ludwig; Hosseini, Kasra; Sigloch, Karin; Nissen-Meyer, Tarje
2016-04-01
We present MC Kernel, a software implementation to calculate seismic sensitivity kernels on arbitrary tetrahedral or hexahedral grids across the whole observable seismic frequency band. Seismic sensitivity kernels are the basis for seismic tomography, since they map measurements to model perturbations. Their calculation over the whole frequency range was so far only possible with approximative methods (Dahlen et al. 2000). Fully numerical methods were restricted to the lower frequency range (usually below 0.05 Hz, Tromp et al. 2005). With our implementation, it's possible to compute accurate sensitivity kernels for global tomography across the observable seismic frequency band. These kernels rely on wavefield databases computed via AxiSEM (www.axisem.info), and thus on spherically symmetric models. The advantage is that frequencies up to 0.2 Hz and higher can be accessed. Since the usage of irregular, adapted grids is an integral part of regularisation in seismic tomography, MC Kernel works in a inversion-grid-centred fashion: A Monte-Carlo integration method is used to project the kernel onto each basis function, which allows to control the desired precision of the kernel estimation. Also, it means that the code concentrates calculation effort on regions of interest without prior assumptions on the kernel shape. The code makes extensive use of redundancies in calculating kernels for different receivers or frequency-pass-bands for one earthquake, to facilitate its usage in large-scale global seismic tomography.
Kernel Phase and Kernel Amplitude in Fizeau Imaging
NASA Astrophysics Data System (ADS)
Pope, Benjamin J. S.
2016-09-01
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 fhistory 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.
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.
Collins, John; Rogers, Ted
2015-04-01
There is considerable controversy about the size and importance of non-perturbative contributions to the evolution of transverse momentum dependent (TMD) parton distribution functions. Standard fits to relatively high-energy Drell-Yan data give evolution that when taken to lower Q is too rapid to be consistent with recent data in semi-inclusive deeply inelastic scattering. Some authors provide very different forms for TMD evolution, even arguing that non-perturbative contributions at large transverse distance bT are not needed or are irrelevant. Here, we systematically analyze the issues, both perturbative and non-perturbative. We make a motivated proposal for the parameterization of the non-perturbative part of the TMD evolution kernel that could give consistency: with the variety of apparently conflicting data, with theoretical perturbative calculations where they are applicable, and with general theoretical non-perturbative constraints on correlation functions at large distances. We propose and use a scheme- and scale-independent function A(bT) that gives a tool to compare and diagnose different proposals for TMD evolution. We also advocate for phenomenological studies of A(bT) as a probe of TMD evolution. The results are important generally for applications of TMD factorization. In particular, they are important to making predictions for proposed polarized Drell- Yan experiments to measure the Sivers function.
Collins, John; Rogers, Ted
2015-04-01
There is considerable controversy about the size and importance of non-perturbative contributions to the evolution of transverse momentum dependent (TMD) parton distribution functions. Standard fits to relatively high-energy Drell-Yan data give evolution that when taken to lower Q is too rapid to be consistent with recent data in semi-inclusive deeply inelastic scattering. Some authors provide very different forms for TMD evolution, even arguing that non-perturbative contributions at large transverse distance bT are not needed or are irrelevant. Here, we systematically analyze the issues, both perturbative and non-perturbative. We make a motivated proposal for the parameterization of the non-perturbative part ofmore » the TMD evolution kernel that could give consistency: with the variety of apparently conflicting data, with theoretical perturbative calculations where they are applicable, and with general theoretical non-perturbative constraints on correlation functions at large distances. We propose and use a scheme- and scale-independent function A(bT) that gives a tool to compare and diagnose different proposals for TMD evolution. We also advocate for phenomenological studies of A(bT) as a probe of TMD evolution. The results are important generally for applications of TMD factorization. In particular, they are important to making predictions for proposed polarized Drell- Yan experiments to measure the Sivers function.« less
Maize canopy architecture and adaptation to high plant density in long term selection programs
Technology Transfer Automated Retrieval System (TEKTRAN)
Grain yield since the 1930s has increased more than five-fold in large part due to improvements in adaptation to high plant density. Changes to plant architecture that associated with improved light interception have made a major contribution to improved adaptation to high plant density. Improved ...
NASA Astrophysics Data System (ADS)
Cho, K.; Kim, B.; Lee, D.; Choi, N.; Park, C.
2011-12-01
Adaptation to environment is a natural phenomena that takes place in many animals, plants and microorganisms. These adapted organisms achieve stronger applicability than unadapted organisms after habitation in a specific environment for a long time. In the biohydrometallurgical industry, adaptation to special environment conditions by selective culturing is the most popular method for improving bioleaching activity of strains-although that is time consuming. This study investigated the influence of the bioleaching efficiency of mine waste under batch experimental conditions (adaptation and pulp density) using the indigenous acidophilic bacteria collected from acid mine drainage in Go-seong and Yeon-hwa, Korea. We conducted the batch experiments at the influences of parameters, such as the adaptation of bacteria and pulp density of the mine waste. In the adaptation case, the value of pH in 1'st adaptation bacteria sample exhibited lower than in 2'nd adaptation bacteria sample. And the content of both Cu and Zn at 1'st adaptation bacteria sample appeared lower than at 2'nd adaptation bacteria sample. In the SEM analysis, the rod-shaped bacteria with 1μm in length were observed on the filter paper (pore size - 0.45μm). The results of pulp density experiments revealed that the content of both Cu and Zn increased with increasing pulp density, since the increment of pulp density resulted in the enhancement of bioleaching capacity.
Technology Transfer Automated Retrieval System (TEKTRAN)
Wheat kernel shape and size has been under selection since early domestication. Kernel morphology is a major consideration in wheat breeding, as it impacts grain yield and quality. A population of 160 recombinant inbred lines (RIL), developed using an elite (ND 705) and a nonadapted genotype (PI 414...
Visual Space: Adaptation to Texture Density Reduces Perceived Object Size.
Kingdom, Frederick A A
2016-07-25
A recent study shows that visual adaptation to dense textures, while causing an increase in the perceived sparseness of a subsequently viewed less-dense texture, paradoxically reduces the perceived size of an object, revealing a dissociation between the internal spatial representations of textures and objects.
Adapting molar data (without density) for molal models
NASA Astrophysics Data System (ADS)
Marion, Giles M.
2007-06-01
Theoretical geochemical models for electrolyte solutions based on classical thermodynamic principles rely largely upon molal concentrations as input because molality (wt/wt) is independent of temperature and pressure. On the other hand, there are countless studies in the literature where concentrations are expressed as molarity (wt/vol) because these units are more easily measured. To convert from molarity to molality requires an estimate of solution density. Unfortunately, in many, if not most, cases where molarity is the concentration of choice, solution densities are not measured. For concentrated brines such as seawater or even more dense brines, the difference between molarity and molality is significant. Without knowledge of density, these brinish, molar-based studies are closed to theoretical electrolyte solution models. The objective of this paper is to present an algorithm that can accurately calculate the density of molar-based solutions, and, as a consequence, molality. The algorithm consist of molar inputs into a molal-based model that can calculate density (FREZCHEM). The algorithm uses an iterative process for calculating absolute salinity (SA), density (ρ), and the conversion factor (CF) for molarity to molality. Three cases were examined ranging in density from 1.023 to 1.203 kg(soln.)/l. In all three cases, the SA, ρ, and CF values converged to within 1ppm by nine iterations. In all three cases, the calculated densities agreed with experimental measurements to within ±0.1%. This algorithm opens a large literature based on molar concentrations to exploration with theoretical models based on molal concentrations and classical thermodynamic principles.
NASA Astrophysics Data System (ADS)
Ngan, Henry Y. T.; Yung, Nelson H. C.; Yeh, Anthony G. O.
2015-02-01
This paper aims at presenting a comparative study of outlier detection (OD) for large-scale traffic data. The traffic data nowadays are massive in scale and collected in every second throughout any modern city. In this research, the traffic flow dynamic is collected from one of the busiest 4-armed junction in Hong Kong in a 31-day sampling period (with 764,027 vehicles in total). The traffic flow dynamic is expressed in a high dimension spatial-temporal (ST) signal format (i.e. 80 cycles) which has a high degree of similarities among the same signal and across different signals in one direction. A total of 19 traffic directions are identified in this junction and lots of ST signals are collected in the 31-day period (i.e. 874 signals). In order to reduce its dimension, the ST signals are firstly undergone a principal component analysis (PCA) to represent as (x,y)-coordinates. Then, these PCA (x,y)-coordinates are assumed to be conformed as Gaussian distributed. With this assumption, the data points are further to be evaluated by (a) a correlation study with three variant coefficients, (b) one-class support vector machine (SVM) and (c) kernel density estimation (KDE). The correlation study could not give any explicit OD result while the one-class SVM and KDE provide average 59.61% and 95.20% DSRs, respectively.
NASA Astrophysics Data System (ADS)
Sarp, Gulcan; Duzgun, Sebnem
2015-11-01
A morphometric analysis of river network, basins and relief using geomorphic indices and geostatistical analyses of Digital Elevation Model (DEM) are useful tools for discussing the morphometric evolution of the basin area. In this study, three different indices including valley floor width to height ratio (Vf), stream gradient (SL), and stream sinuosity were applied to Afşin-Elbistan lignite basin to test the imprints of tectonic activity. Perturbations of these indices are usually indicative of differences in the resistance of outcropping lithological units to erosion and active faulting. To map the clusters of high and low indices values, the Kernel density estimation (K) and the Getis-Ord Gi∗ statistics were applied to the DEM-derived indices. The K method and Gi∗ statistic highlighting hot spots and cold spots of the SL index, the stream sinuosity and the Vf index values helped to identify the relative tectonic activity of the basin area. The results indicated that the estimation by the K and Gi∗ including three conceptualization of spatial relationships (CSR) for hot spots (percent volume contours 50 and 95 categorized as high and low respectively) yielded almost similar results in regions of high tectonic activity and low tectonic activity. According to the K and Getis-Ord Gi∗ statistics, the northern, northwestern and southern parts of the basin indicates a high tectonic activity. On the other hand, low elevation plain in the central part of the basin area shows a relatively low tectonic activity.
Chen, Tai-Been; Chen, Jyh-Cheng; Lu, Henry Horng-Shing
2012-01-01
Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method. PMID:22948355
Application of adaptive cluster sampling to low-density populations of freshwater mussels
Smith, D.R.; Villella, R.F.; Lemarie, D.P.
2003-01-01
Freshwater mussels appear to be promising candidates for adaptive cluster sampling because they are benthic macroinvertebrates that cluster spatially and are frequently found at low densities. We applied adaptive cluster sampling to estimate density of freshwater mussels at 24 sites along the Cacapon River, WV, where a preliminary timed search indicated that mussels were present at low density. Adaptive cluster sampling increased yield of individual mussels and detection of uncommon species; however, it did not improve precision of density estimates. Because finding uncommon species, collecting individuals of those species, and estimating their densities are important conservation activities, additional research is warranted on application of adaptive cluster sampling to freshwater mussels. However, at this time we do not recommend routine application of adaptive cluster sampling to freshwater mussel populations. The ultimate, and currently unanswered, question is how to tell when adaptive cluster sampling should be used, i.e., when is a population sufficiently rare and clustered for adaptive cluster sampling to be efficient and practical? A cost-effective procedure needs to be developed to identify biological populations for which adaptive cluster sampling is appropriate.
Dieudonné, Arnaud; Hobbs, Robert F.; Lebtahi, Rachida; Maurel, Fabien; Baechler, Sébastien; Wahl, Richard L.; Boubaker, Ariane; Le Guludec, Dominique; Sgouros, Georges; Gardin, Isabelle
2014-01-01
Dose kernel convolution (DK) methods have been proposed to speed up absorbed dose calculations in molecular radionuclide therapy. Our aim was to evaluate the impact of tissue density heterogeneities (TDH) on dosimetry when using a DK method and to propose a simple density-correction method. Methods This study has been conducted on 3 clinical cases: case 1, non-Hodgkin lymphoma treated with 131I-tositumomab; case 2, a neuroendocrine tumor treatment simulated with 177Lu-peptides; and case 3, hepatocellular carcinoma treated with 90Y-microspheres. Absorbed dose calculations were performed using a direct Monte Carlo approach accounting for TDH (3D-RD), and a DK approach (VoxelDose, or VD). For each individual voxel, the VD absorbed dose, DVD, calculated assuming uniform density, was corrected for density, giving DVDd. The average 3D-RD absorbed dose values, D3DRD, were compared with DVD and DVDd, using the relative difference ΔVD/3DRD. At the voxel level, density-binned ΔVD/3DRD and ΔVDd/3DRD were plotted against ρ and fitted with a linear regression. Results The DVD calculations showed a good agreement with D3DRD. ΔVD/3DRD was less than 3.5%, except for the tumor of case 1 (5.9%) and the renal cortex of case 2 (5.6%). At the voxel level, the ΔVD/3DRD range was 0%–14% for cases 1 and 2, and −3% to 7% for case 3. All 3 cases showed a linear relationship between voxel bin-averaged ΔVD/3DRD and density, ρ: case 1 (Δ = −0.56ρ + 0.62, R2 = 0.93), case 2 (Δ = −0.91ρ + 0.96, R2 = 0.99), and case 3 (Δ = −0.69ρ + 0.72, R2 = 0.91). The density correction improved the agreement of the DK method with the Monte Carlo approach (ΔVDd/3DRD < 1.1%), but with a lesser extent for the tumor of case 1 (3.1%). At the voxel level, the ΔVDd/3DRD range decreased for the 3 clinical cases (case 1, −1% to 4%; case 2, −0.5% to 1.5%, and −1.5% to 2%). No more linear regression existed for cases 2 and 3, contrary to case 1 (Δ = 0.41ρ − 0.38, R2 = 0.88) although
Density-dependent adaptive resistance allows swimming bacteria to colonize an antibiotic gradient.
Hol, Felix J H; Hubert, Bert; Dekker, Cees; Keymer, Juan E
2016-01-01
During antibiotic treatment, antibiotic concentration gradients develop. Little is know regarding the effects of antibiotic gradients on populations of nonresistant bacteria. Using a microfluidic device, we show that high-density motile Escherichia coli populations composed of nonresistant bacteria can, unexpectedly, colonize environments where a lethal concentration of the antibiotic kanamycin is present. Colonizing bacteria establish an adaptively resistant population, which remains viable for over 24 h while exposed to the antibiotic. Quantitative analysis of multiple colonization events shows that collectively swimming bacteria need to exceed a critical population density in order to successfully colonize the antibiotic landscape. After colonization, bacteria are not dormant but show both growth and swimming motility under antibiotic stress. Our results highlight the importance of motility and population density in facilitating adaptive resistance, and indicate that adaptive resistance may be a first step to the emergence of genetically encoded resistance in landscapes of antibiotic gradients.
The context-tree kernel for strings.
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.
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.
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.
A density-based adaptive quantum mechanical/molecular mechanical method.
Waller, Mark P; Kumbhar, Sadhana; Yang, Jack
2014-10-20
We present a density-based adaptive quantum mechanical/molecular mechanical (DBA-QM/MM) method, whereby molecules can switch layers from the QM to the MM region and vice versa. The adaptive partitioning of the molecular system ensures that the layer assignment can change during the optimization procedure, that is, on the fly. The switch from a QM molecule to a MM molecule is determined if there is an absence of noncovalent interactions to any atom of the QM core region. The presence/absence of noncovalent interactions is determined by analysis of the reduced density gradient. Therefore, the location of the QM/MM boundary is based on physical arguments, and this neatly removes some empiricism inherent in previous adaptive QM/MM partitioning schemes. The DBA-QM/MM method is validated by using a water-in-water setup and an explicitly solvated L-alanyl-L-alanine dipeptide. PMID:24954803
A density-based adaptive quantum mechanical/molecular mechanical method.
Waller, Mark P; Kumbhar, Sadhana; Yang, Jack
2014-10-20
We present a density-based adaptive quantum mechanical/molecular mechanical (DBA-QM/MM) method, whereby molecules can switch layers from the QM to the MM region and vice versa. The adaptive partitioning of the molecular system ensures that the layer assignment can change during the optimization procedure, that is, on the fly. The switch from a QM molecule to a MM molecule is determined if there is an absence of noncovalent interactions to any atom of the QM core region. The presence/absence of noncovalent interactions is determined by analysis of the reduced density gradient. Therefore, the location of the QM/MM boundary is based on physical arguments, and this neatly removes some empiricism inherent in previous adaptive QM/MM partitioning schemes. The DBA-QM/MM method is validated by using a water-in-water setup and an explicitly solvated L-alanyl-L-alanine dipeptide.
Approximate kernel competitive learning.
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.
Approximate kernel competitive learning.
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. PMID:25528318
Spin-adapted density matrix renormalization group algorithms for quantum chemistry
NASA Astrophysics Data System (ADS)
Sharma, Sandeep; Chan, Garnet Kin-Lic
2012-03-01
We extend the spin-adapted density matrix renormalization group (DMRG) algorithm of McCulloch and Gulacsi [Europhys. Lett. 57, 852 (2002)], 10.1209/epl/i2002-00393-0 to quantum chemical Hamiltonians. This involves using a quasi-density matrix, to ensure that the renormalized DMRG states are eigenfunctions of hat{S}^2, and the Wigner-Eckart theorem, to reduce overall storage and computational costs. We argue that the spin-adapted DMRG algorithm is most advantageous for low spin states. Consequently, we also implement a singlet-embedding strategy due to Tatsuaki [Phys. Rev. E 61, 3199 (2000)], 10.1103/PhysRevE.61.3199 where we target high spin states as a component of a larger fictitious singlet system. Finally, we present an efficient algorithm to calculate one- and two-body reduced density matrices from the spin-adapted wavefunctions. We evaluate our developments with benchmark calculations on transition metal system active space models. These include the Fe2S2, [Fe2S2(SCH3)4]2-, and Cr2 systems. In the case of Fe2S2, the spin-ladder spacing is on the microHartree scale, and here we show that we can target such very closely spaced states. In [Fe2S2(SCH3)4]2-, we calculate particle and spin correlation functions, to examine the role of sulfur bridging orbitals in the electronic structure. In Cr2 we demonstrate that spin-adaptation with the Wigner-Eckart theorem and using singlet embedding can yield up to an order of magnitude increase in computational efficiency. Overall, these calculations demonstrate the potential of using spin-adaptation to extend the range of DMRG calculations in complex transition metal problems.
Lombardo, Marco; Serrao, Sebastiano; Lombardo, Giuseppe
2014-01-01
Purpose To investigate the influence of various technical factors on the variation of cone packing density estimates in adaptive optics flood illuminated retinal images. Methods Adaptive optics images of the photoreceptor mosaic were obtained in fifteen healthy subjects. The cone density and Voronoi diagrams were assessed in sampling windows of 320×320 µm, 160×160 µm and 64×64 µm at 1.5 degree temporal and superior eccentricity from the preferred locus of fixation (PRL). The technical factors that have been analyzed included the sampling window size, the corrected retinal magnification factor (RMFcorr), the conversion from radial to linear distance from the PRL, the displacement between the PRL and foveal center and the manual checking of cone identification algorithm. Bland-Altman analysis was used to assess the agreement between cone density estimated within the different sampling window conditions. Results The cone density declined with decreasing sampling area and data between areas of different size showed low agreement. A high agreement was found between sampling areas of the same size when comparing density calculated with or without using individual RMFcorr. The agreement between cone density measured at radial and linear distances from the PRL and between data referred to the PRL or the foveal center was moderate. The percentage of Voronoi tiles with hexagonal packing arrangement was comparable between sampling areas of different size. The boundary effect, presence of any retinal vessels, and the manual selection of cones missed by the automated identification algorithm were identified as the factors influencing variation of cone packing arrangements in Voronoi diagrams. Conclusions The sampling window size is the main technical factor that influences variation of cone density. Clear identification of each cone in the image and the use of a large buffer zone are necessary to minimize factors influencing variation of Voronoi diagrams of the cone
Foitzik, Susanne; Achenbach, Alexandra; Brandt, Miriam
2009-05-01
Selection and adaptation are important processes in the coevolution between parasites and their hosts. The slave-making ant Protomognathus americanus, an obligate ant social parasite, has previously been shown to evolve morphological, behavioral, and chemical adaptations in the coevolutionary arms race with its Temnothorax hosts. Yet empirical studies have given variable results on the strength of the selection pressure this parasite exerts on its host populations. In this study, we directly investigated the pressure exerted by P. americanus and the reactions of the main host species, T. longispinosus, in two ant communities by manipulating parasite density in the field over several years. In addition, a cross-fostering design with the exchange of parasites between host populations allowed us to investigate local adaptation of parasite or host. We demonstrate a severe impact of the social parasite on the two host populations in West Virginia and New York, but also variation in host reactions between sites, as expected by the geographic mosaic theory of coevolution. Host density decreased at the West Virginia site with the presence of local slave-makers, whereas at the ecologically favorable New York site, density was unaffected. Nevertheless, social organization, colony size, and investment patterns of these host colonies at this site changed in response to our parasite manipulation. The release of P. americanus colonies led to a reduction in the number of resident queens and workers, an increase in intranest relatedness, and lower productivity, but also a higher investment in reproductives. In West Virginia, colony demography did not change, but raiding activity by New York slave-makers caused different investment patterns of host colonies. In addition, the cross-fostering element revealed local adaptation of the parasite P. americanus: slave-making colonies fared better in their sympatric host population, as they contained more slave-making ant workers and slaves
Mapping the dimensionality, density and topology of data: the growing adaptive neural gas.
Cselényi, Zsolt
2005-05-01
Self-organized maps are commonly applied for tasks of cluster analysis, vector quantization or interpolation. The artificial neural network model introduced in this paper is a hybrid model of the growing neural gas model introduced by Fritzke (Fritzke, in Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995) and the adaptive resolution clustering modification for self-organized maps proposed by Firenze (Firenze et al., in International Conference on Artificial Neural Networks, Springer-Verlag, London, 1994). The hybrid model is capable of mapping the distribution, dimensionality and topology of the input data. It has a local performance measure that enables the network to terminate growing in areas of the input space that is mapped by units reaching a performance goal. Therefore the network can accurately map clusters of data appearing on different scales of density. The capabilities of the algorithm are tested using simulated datasets with similar spatial spread but different local density distributions, and a simulated multivariate MR dataset of an anatomical human brain phantom with mild multiple sclerosis lesions. These tests demonstrate the advantages of the model compared to the growing neural gas algorithm when adaptive mapping of areas with low sample density is desirable. PMID:15848269
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`.
Metastability, Adaptability and Memory in Charge Density Waves. I. Resetting Property
NASA Astrophysics Data System (ADS)
Ito, Hiroyuki
1989-06-01
We give a possible interpretation of the adaptation of the charge density waves (CDW) to the pulse fields, which is observed to be accompanied with the memory of the width of the applied pulses (Ido step memory effect). When the identical pulse fields are repeatedly applied, successive state transitions are induced among metastable states. By the numerical simulations with the use of the Fukuyama-Lee-Rice model, we have found that only the state can be a fixed point in the transitions where the sliding motion under the pulse field satisfies a certain condition. Selecting the adequate state for a fixed point, the system adapts itself to the applied pulse width so that the current response has a common regularity regardless of the pulse width.
An Efficient Adaptive Weighted Switching Median Filter for Removing High Density Impulse Noise
NASA Astrophysics Data System (ADS)
Nair, Madhu S.; Ameera Mol, P. M.
2014-09-01
Restoration of images corrupted by impulse noise is a very active research area in image processing. In this paper, an Efficient Adaptive Weighted Switching Median filter for restoration of images that are corrupted by high density impulse noise is proposed. The filtering is performed as a two phase process—a detection phase followed by a filtering phase. In the proposed method, noise detection is done by HEIND algorithm proposed by Duan et al. The filtering algorithm is then applied to the pixels which are detected as noisy by the detection algorithm. All uncorrupted pixels in the image are left unchanged. The filtering window size is chosen adaptively depending on the local noise distribution around each corrupted pixels. Noisy pixels are replaced by a weighted median value of uncorrupted pixels in the filtering window. The weight value assigned to each uncorrupted pixels depends on its closeness to the central pixel.
Kernel bandwidth estimation for nonparametric modeling.
Bors, Adrian G; Nasios, Nikolaos
2009-12-01
Kernel density estimation is a nonparametric procedure for probability density modeling, which has found several applications in various fields. The smoothness and modeling ability of the functional approximation are controlled by the kernel bandwidth. In this paper, we describe a Bayesian estimation method for finding the bandwidth from a given data set. The proposed bandwidth estimation method is applied in three different computational-intelligence methods that rely on kernel density estimation: 1) scale space; 2) mean shift; and 3) quantum clustering. The third method is a novel approach that relies on the principles of quantum mechanics. This method is based on the analogy between data samples and quantum particles and uses the SchrOdinger potential as a cost function. The proposed methodology is used for blind-source separation of modulated signals and for terrain segmentation based on topography information.
NASA Astrophysics Data System (ADS)
He, Wenda; Juette, Arne; Denton, Erica R. E.; Zwiggelaar, Reyer
2015-03-01
Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fully incorporated with non-image based risk prediction models, (e.g. the Gail and the Tyrer-Cuzick model), because of unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographic density segmentation, a concept of stack representation is proposed, and 3D texture features were extracted by adapting techniques based on classic 2D first-order statistics. An unsupervised clustering technique was employed to achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation by incorporating an optimal centroids initialisation step, and 2) significantly reduced the number of missegmentation by using an adaptive cluster merging technique. A set of full field digital mammograms was used in the evaluation. Visual assessment indicated substantial improvement on segmented anatomical structures and tissue specific areas, especially in low mammographic density categories. The developed method demonstrated an ability to improve the quality of mammographic segmentation via clustering, and results indicated an improvement of 26% in segmented image with good quality when compared with the standard clustering approach. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.
Density of muscle spindles in prosimian shoulder muscles reflects locomotor adaptation.
Higurashi, Yasuo; Taniguchi, Yuki; Kumakura, Hiroo
2006-01-01
We examined the correlation between the density of muscle spindles in shoulder muscles and the locomotor mode in three species of prosimian primates: the slow loris (Nycticebus coucang), Garnett's galago (Otolemur garnettii), and the ring-tailed lemur (Lemur catta). The shoulder muscles (supraspinatus, infraspinatus, teres major, teres minor, and subscapularis) were embedded in celloidin and cut into transverse serial thin sections (40 microm); then, every tenth section was stained using the Azan staining technique. The relative muscle weights and the density of the muscle spindles were determined. The slow loris muscles were heavier and had sparser muscle spindles, as compared to Garnett's galago. These features suggest that the shoulder muscles of the slow loris are more adapted to generating propulsive force and stabilizing the shoulder joint during locomotion and play a less controlling role in forelimb movements. In contrast, Garnett's galago possessed smaller shoulder muscles with denser spindles that are suitable for the control of more rapid locomotor movements. The mean relative weight and the mean spindle density in the shoulder muscles of the ring-tailed lemur were between those of the other primates, suggesting that the spindle density is not simply a consequence of taxonomic status. PMID:17361082
Density of muscle spindles in prosimian shoulder muscles reflects locomotor adaptation.
Higurashi, Yasuo; Taniguchi, Yuki; Kumakura, Hiroo
2006-01-01
We examined the correlation between the density of muscle spindles in shoulder muscles and the locomotor mode in three species of prosimian primates: the slow loris (Nycticebus coucang), Garnett's galago (Otolemur garnettii), and the ring-tailed lemur (Lemur catta). The shoulder muscles (supraspinatus, infraspinatus, teres major, teres minor, and subscapularis) were embedded in celloidin and cut into transverse serial thin sections (40 microm); then, every tenth section was stained using the Azan staining technique. The relative muscle weights and the density of the muscle spindles were determined. The slow loris muscles were heavier and had sparser muscle spindles, as compared to Garnett's galago. These features suggest that the shoulder muscles of the slow loris are more adapted to generating propulsive force and stabilizing the shoulder joint during locomotion and play a less controlling role in forelimb movements. In contrast, Garnett's galago possessed smaller shoulder muscles with denser spindles that are suitable for the control of more rapid locomotor movements. The mean relative weight and the mean spindle density in the shoulder muscles of the ring-tailed lemur were between those of the other primates, suggesting that the spindle density is not simply a consequence of taxonomic status.
NASA Astrophysics Data System (ADS)
Kuboyama, Tetsuji; Hirata, Kouichi; Kashima, Hisashi; F. Aoki-Kinoshita, Kiyoko; Yasuda, Hiroshi
Learning from tree-structured data has received increasing interest with the rapid growth of tree-encodable data in the World Wide Web, in biology, and in other areas. Our kernel function measures the similarity between two trees by counting the number of shared sub-patterns called tree q-grams, and runs, in effect, in linear time with respect to the number of tree nodes. We apply our kernel function with a support vector machine (SVM) to classify biological data, the glycans of several blood components. The experimental results show that our kernel function performs as well as one exclusively tailored to glycan properties.
Robotic Intelligence Kernel: Communications
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.
Robotic Intelligence Kernel: Driver
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.
Linearized Kernel Dictionary Learning
NASA Astrophysics Data System (ADS)
Golts, Alona; Elad, Michael
2016-06-01
In this paper we present a new approach of incorporating kernels into dictionary learning. The kernel K-SVD algorithm (KKSVD), which has been introduced recently, shows an improvement in classification performance, with relation to its linear counterpart K-SVD. However, this algorithm requires the storage and handling of a very large kernel matrix, which leads to high computational cost, while also limiting its use to setups with small number of training examples. We address these problems by combining two ideas: first we approximate the kernel matrix using a cleverly sampled subset of its columns using the Nystr\\"{o}m method; secondly, as we wish to avoid using this matrix altogether, we decompose it by SVD to form new "virtual samples," on which any linear dictionary learning can be employed. Our method, termed "Linearized Kernel Dictionary Learning" (LKDL) can be seamlessly applied as a pre-processing stage on top of any efficient off-the-shelf dictionary learning scheme, effectively "kernelizing" it. We demonstrate the effectiveness of our method on several tasks of both supervised and unsupervised classification and show the efficiency of the proposed scheme, its easy integration and performance boosting properties.
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.
Alkyl ammonium cation stabilized biocidal polyiodides with adaptable high density and low pressure.
He, Chunlin; Parrish, Damon A; Shreeve, Jean'ne M
2014-05-26
The effective application of biocidal species requires building the active moiety into a molecular back bone that can be delivered and decomposed on demand under conditions of low pressure and prolonged high-temperature detonation. The goal is to destroy storage facilities and their contents while utilizing the biocidal products arising from the released energy to destroy any remaining harmful airborne agents. Decomposition of carefully selected iodine-rich compounds can produce large amounts of the very active biocides, hydroiodic acid (HI) and iodine (I2). Polyiodide anions, namely, I3(-), I5(-), which are excellent sources of such biocides, can be stabilized through interactions with large, symmetric cations, such as alkyl ammonium salts. We have designed and synthesized suitable compounds of adaptable high density up to 3.33 g cm(-3) that are low-pressure polyiodides with various alkyl ammonium cations, deliverable iodine contents of which range between 58.0-90.9%.
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.
Higher-order adaptive finite-element methods for Kohn–Sham density functional theory
Motamarri, P.; Nowak, M.R.; Leiter, K.; Knap, J.; Gavini, V.
2013-11-15
We present an efficient computational approach to perform real-space electronic structure calculations using an adaptive higher-order finite-element discretization of Kohn–Sham density-functional theory (DFT). To this end, we develop an a priori mesh-adaption technique to construct a close to optimal finite-element discretization of the problem. We further propose an efficient solution strategy for solving the discrete eigenvalue problem by using spectral finite-elements in conjunction with Gauss–Lobatto quadrature, and a Chebyshev acceleration technique for computing the occupied eigenspace. The proposed approach has been observed to provide a staggering 100–200-fold computational advantage over the solution of a generalized eigenvalue problem. Using the proposed solution procedure, we investigate the computational efficiency afforded by higher-order finite-element discretizations of the Kohn–Sham DFT problem. Our studies suggest that staggering computational savings—of the order of 1000-fold—relative to linear finite-elements can be realized, for both all-electron and local pseudopotential calculations, by using higher-order finite-element discretizations. On all the benchmark systems studied, we observe diminishing returns in computational savings beyond the sixth-order for accuracies commensurate with chemical accuracy, suggesting that the hexic spectral-element may be an optimal choice for the finite-element discretization of the Kohn–Sham DFT problem. A comparative study of the computational efficiency of the proposed higher-order finite-element discretizations suggests that the performance of finite-element basis is competing with the plane-wave discretization for non-periodic local pseudopotential calculations, and compares to the Gaussian basis for all-electron calculations to within an order of magnitude. Further, we demonstrate the capability of the proposed approach to compute the electronic structure of a metallic system containing 1688
Petrova, Olga; Gorshkov, Vladimir; Daminova, Amina; Ageeva, Marina; Moleleki, Lucy N; Gogolev, Yuri
2014-01-01
The adaptive reactions of plant pathogenic bacterium Pectobacterium atrosepticum SCRI1043 under starvation conditions were studied. The main emphasis was given to the peculiarities of stress responses depending on the bacterial population densities. When bacteria were subjected to starvation at high population densities (10(7)-10(9) CFU ml(-1)), their adaptive reactions conformed to the conventional conception of bacterial adaptation related to autolysis of part of the population, specific modification of cell ultrastructure, activation of expression of stress responsive genes and acquiring cross protection against other stress factors. In contrast, at low initial population densities (10(3)-10(5) CFU ml(-1)), as described in our recent work, the cell density increased due to multiple cell division despite the absence of exogenous growth substrate. Here we present data that demonstrate that such unconventional behavior is part of a stress response, which provides increased stress tolerance while retaining virulence. Cell morphology and gene expression in high- and low-cell-density starving Pba cultures were compared. Our investigation demonstrates the existence of alternative adaptive strategies enabling pathogenic bacteria to cope with a variety of stress factors, including starvation, especially necessary when residing outside of their host.
Calculates Thermal Neutron Scattering Kernel.
1989-11-10
Version 00 THRUSH computes the thermal neutron scattering kernel by the phonon expansion method for both coherent and incoherent scattering processes. The calculation of the coherent part is suitable only for calculating the scattering kernel for heavy water.
NASA Astrophysics Data System (ADS)
Oehlert, A. M.; Hill, C. A.; Piggot, A. M.; Fouke, B. W.
2008-12-01
As one of the core reservoirs of primary production in the world's oceans, tropical coral reefs support a complex ecosystem that directly impacts over ninety percent of marine organisms at some point in their life cycle. Corals themselves are highly complex organisms and exhibit a range of growth forms that range from branching to massive, foliaceous, columnar, encrusting, free living and laminar coralla. Fierce competition over scarce resources available to each individual coral species creates niche specialization. Throughout the Phanerozic geological record, this has driven speciation events and created distinct skeletal growth morphologies that have differential abilities in feeding strategy. In turn, this has presumably led to the development of niche specialization that can be quantitatively measured through hierarchical morphological differences from the micrometer to the meter scale. Porter (1976) observed significant differences in skeletal morphology between Caribbean coral species that reflects an adaptive geometry based on feeding strategy. Within the Montastraea species complex there are four major morphologies; columnar, bouldering, irregular mounding, and skirted. Each morphotype can be found forming high abundance along the bathymetric gradient of coral reefs that grow along the leeward coast of Curacao, Netherlands Antilles. We have undertaken a study to determine the relative relationships amongst coral morphology, skeletal density and feeding strategy by comparing the morphometric measurements of individual polyps as well as the entire colony along spatial and bathymetric gradients. Polyp diameter, mouth size, interpolyp area, and interpolyp distance were measured from high-resolution images taken on a stereoscope, and evaluated with AxioVision image analysis software. These high-resolution optical analyses have also revealed new observations regarding folded tissue structures of the outer margin of polyps in the Montastrea complex. Skeletal
Robotic Intelligence Kernel: Visualization
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.
Robotic Intelligence Kernel: Architecture
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.
Einum, Sigurd; Robertsen, Grethe; Fleming, Ian A
2008-01-01
Theory suggests an important role for population density in shaping adaptive landscapes through density-dependent selection. Here, we identify five methodological approaches for studying such selection, review the existing empirical evidence for it, and ask whether current declines in abundance can be expected to trigger evolutionary responses in salmonid fishes. Across taxa we find substantial amounts of evidence for population density influencing the location of adaptive peaks for a range of traits, and, in the presence of frequency dependence, changing the shape of selection (stabilizing versus disruptive). For salmonids, biological and theoretical considerations suggest that the optimal value of a number of traits associated with juvenile competitive ability (e.g. egg size, timing of emergence from nests, dominance ability), may depend on population density. For adults, more direct experimental and comparative evidence suggest that secondary sexual traits can be subject to density-dependent selection. There is also evidence that density affects the frequency-dependent selection likely responsible for the expression of alternative male reproductive phenotypes in salmon. Less is known however about the role of density in maintaining genetic variation among juveniles. Further efforts are required to elucidate the indirect evolutionary effects of declining population abundances, both in salmonids and in other anthropogenically challenged organisms. PMID:25567629
Feng, Shu; Gale, Michael J.; Fay, Jonathan D.; Faridi, Ambar; Titus, Hope E.; Garg, Anupam K.; Michaels, Keith V.; Erker, Laura R.; Peters, Dawn; Smith, Travis B.; Pennesi, Mark E.
2015-01-01
Purpose To describe a standardized flood-illuminated adaptive optics (AO) imaging protocol suitable for the clinical setting and to assess sampling methods for measuring cone density. Methods Cone density was calculated following three measurement protocols: 50 × 50-μm sampling window values every 0.5° along the horizontal and vertical meridians (fixed-interval method), the mean density of expanding 0.5°-wide arcuate areas in the nasal, temporal, superior, and inferior quadrants (arcuate mean method), and the peak cone density of a 50 × 50-μm sampling window within expanding arcuate areas near the meridian (peak density method). Repeated imaging was performed in nine subjects to determine intersession repeatability of cone density. Results Cone density montages could be created for 67 of the 74 subjects. Image quality was determined to be adequate for automated cone counting for 35 (52%) of the 67 subjects. We found that cone density varied with different sampling methods and regions tested. In the nasal and temporal quadrants, peak density most closely resembled histological data, whereas the arcuate mean and fixed-interval methods tended to underestimate the density compared with histological data. However, in the inferior and superior quadrants, arcuate mean and fixed-interval methods most closely matched histological data, whereas the peak density method overestimated cone density compared with histological data. Intersession repeatability testing showed that repeatability was greatest when sampling by arcuate mean and lowest when sampling by fixed interval. Conclusions We show that different methods of sampling can significantly affect cone density measurements. Therefore, care must be taken when interpreting cone density results, even in a normal population. PMID:26325414
Guo, Yi; Gao, Junbin; Kwan, Paul W
2008-08-01
In most existing dimensionality reduction algorithms, the main objective is to preserve relational structure among objects of the input space in a low dimensional embedding space. This is achieved by minimizing the inconsistency between two similarity/dissimilarity measures, one for the input data and the other for the embedded data, via a separate matching objective function. Based on this idea, a new dimensionality reduction method called Twin Kernel Embedding (TKE) is proposed. TKE addresses the problem of visualizing non-vectorial data that is difficult for conventional methods in practice due to the lack of efficient vectorial representation. TKE solves this problem by minimizing the inconsistency between the similarity measures captured respectively by their kernel Gram matrices in the two spaces. In the implementation, by optimizing a nonlinear objective function using the gradient descent algorithm, a local minimum can be reached. The results obtained include both the optimal similarity preserving embedding and the appropriate values for the hyperparameters of the kernel. Experimental evaluation on real non-vectorial datasets confirmed the effectiveness of TKE. TKE can be applied to other types of data beyond those mentioned in this paper whenever suitable measures of similarity/dissimilarity can be defined on the input data. PMID:18566501
Effects of sample size on KERNEL home range estimates
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.
Barbieri, Giancarlo; Vallone, Simona; Orsini, Francesco; Paradiso, Roberta; De Pascale, Stefania; Negre-Zakharov, Florence; Maggio, Albino
2012-11-15
Increasing salinity tolerance and water-use efficiency in crop plants are two major challenges that agriculture must face in the next decades. Many physiological mechanisms and molecular components mediating crop response to environmental stresses have been identified. However, the functional inter-links between stress adaptation responses have not been completely understood. Using two basil cultivars (Napoletano and Genovese) with contrasting ability to respond to salt stress, here we demonstrate that reduced stomatal density, high ascorbate level and polyphenol oxidase (PPO) activity coordinately contribute to improve basil adaptation and water use efficiency (WUE) in saline environment. The constitutively reduced stomatal density was associated with a "delayed" accumulation of stress molecules (and growth inhibiting signals) such as abscisic acid (ABA) and proline, in the more tolerant Genovese. Leaf volatile profiling also revealed cultivar-specific patterns, which may suggest a role for the volatile phenylpropanoid eugenol and monoterpenes in conferring stress tolerance via antioxidant and signalling functions.
Barbieri, Giancarlo; Vallone, Simona; Orsini, Francesco; Paradiso, Roberta; De Pascale, Stefania; Negre-Zakharov, Florence; Maggio, Albino
2012-11-15
Increasing salinity tolerance and water-use efficiency in crop plants are two major challenges that agriculture must face in the next decades. Many physiological mechanisms and molecular components mediating crop response to environmental stresses have been identified. However, the functional inter-links between stress adaptation responses have not been completely understood. Using two basil cultivars (Napoletano and Genovese) with contrasting ability to respond to salt stress, here we demonstrate that reduced stomatal density, high ascorbate level and polyphenol oxidase (PPO) activity coordinately contribute to improve basil adaptation and water use efficiency (WUE) in saline environment. The constitutively reduced stomatal density was associated with a "delayed" accumulation of stress molecules (and growth inhibiting signals) such as abscisic acid (ABA) and proline, in the more tolerant Genovese. Leaf volatile profiling also revealed cultivar-specific patterns, which may suggest a role for the volatile phenylpropanoid eugenol and monoterpenes in conferring stress tolerance via antioxidant and signalling functions. PMID:22840325
Kercher, Andrew K; Hunn, John D
2005-08-01
This document is a compilation of characterization data obtained on the nominal 350 {micro}m low enrichment uranium oxide/uranium carbide kernels (LEUCO) produced by BWXT for the Advanced Gas Reactor Fuel Development and Qualification Program. A 4502 g composite of LEUCO kernels was produced at BWXT by combining kernels from 8 forming runs sintered in 6 separate lots. 2150 grams were shipped to ORNL. ORNL has performed size, shape, density, and microstructural analysis on riffled samples from the kernel composite.
Bradley, Claire; Joyce, Niamh; Garcia-Larrea, Luis
2016-01-01
Adaptation in sensory cortices has been seen as a mechanism allowing the creation of transient memory representations. Here we tested the adapting properties of early responses in human somatosensory areas SI and SII by analysing somatosensory-evoked potentials over the very first repetitions of a stimulus. SI and SII generators were identified by well-defined scalp potentials and source localisation from high-density 128-channel EEG. Earliest responses (~20 ms) from area 3b in the depth of the post-central gyrus did not show significant adaptation to stimuli repeated at 300 ms intervals. In contrast, responses around 45 ms from the crown of the gyrus (areas 1 and 2) rapidly lessened to a plateau and abated at the 20th stimulation, and activities from SII in the parietal operculum at ~100 ms displayed strong adaptation with a steady amplitude decrease from the first repetition. Although responses in both SI (1-2) and SII areas showed adapting properties and hence sensory memory capacities, evidence of sensory mismatch detection has been demonstrated only for responses reflecting SII activation. This may index the passage from an early form of sensory storage in SI to more operational memory codes in SII, allowing the prediction of forthcoming input and the triggering of a specific signal when such input differs from the previous sequence. This is consistent with a model whereby the length of temporal receptive windows increases with progression in the cortical hierarchy, in parallel with the complexity and abstraction of neural representations.
NASA Astrophysics Data System (ADS)
Kajiwara, Yoshiyuki; Shiraishi, Junya; Kobayashi, Shoei; Yamagami, Tamotsu
2009-03-01
A digital phase-locked loop (PLL) with a linearly constrained adaptive filter (LCAF) has been studied for higher-linear-density optical discs. LCAF has been implemented before an interpolated timing recovery (ITR) PLL unit in order to improve the quality of phase error calculation by using an adaptively equalized partial response (PR) signal. Coefficient update of an asynchronous sampled adaptive FIR filter with a least-mean-square (LMS) algorithm has been constrained by a projection matrix in order to suppress the phase shift of the tap coefficients of the adaptive filter. We have developed projection matrices that are suitable for Blu-ray disc (BD) drive systems by numerical simulation. Results have shown the properties of the projection matrices. Then, we have designed the read channel system of the ITR PLL with an LCAF model on the FPGA board for experiments. Results have shown that the LCAF improves the tilt margins of 30 gigabytes (GB) recordable BD (BD-R) and 33 GB BD read-only memory (BD-ROM) with a sufficient LMS adaptation stability.
Deurenberg, P; Weststrate, J A; van der Kooy, K
1989-08-01
Using data from the literature on changes in the mineral content, muscle mass and the amount of water in the body during aging, the age-related changes in the chemical composition of the fat-free mass have been calculated. In men the decrease in minerals (bone loss) during aging equals the decrease in protein and water (muscle) in the fat-free mass. As a consequence the chemical composition of the fat-free mass is hardly affected by aging in men. In women, however, the loss of minerals during aging is considerably higher than the decrease in protein and water in the fat-free mass. As a consequence the change in the chemical composition of the fat-free mass in females is remarkable, and therefore in females, the density (kg/l) of the fat-free mass decreases with age. Consequently the body fat percentage calculated from body density with Siri's equation overestimates the real body fat percentage by 2-3 per cent, depending on age. Based on the calculated chemical composition of the fat-free mass at several ages, and its calculated theoretical density, Siri's equation has been adapted. In females but not in men the adapted formulas give a more valid estimate of the body fat percentage calculated from body density compared to Siri's formula.
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.
Adaptive nest clustering and density-dependent nest survival in dabbling ducks
Ringelman, Kevin M.; Eadie, John M.; Ackerman, Joshua T.
2014-01-01
Density-dependent population regulation is observed in many taxa, and understanding the mechanisms that generate density dependence is especially important for the conservation of heavily-managed species. In one such system, North American waterfowl, density dependence is often observed at continental scales, and nest predation has long been implicated as a key factor driving this pattern. However, despite extensive research on this topic, it remains unclear if and how nest density influences predation rates. Part of this confusion may have arisen because previous studies have studied density-dependent predation at relatively large spatial and temporal scales. Because the spatial distribution of nests changes throughout the season, which potentially influences predator behavior, nest survival may vary through time at relatively small spatial scales. As such, density-dependent nest predation might be more detectable at a spatially- and temporally-refined scale and this may provide new insights into nest site selection and predator foraging behavior. Here, we used three years of data on nest survival of two species of waterfowl, mallards and gadwall, to more fully explore the relationship between local nest clustering and nest survival. Throughout the season, we found that the distribution of nests was consistently clustered at small spatial scales (˜50–400 m), especially for mallard nests, and that this pattern was robust to yearly variation in nest density and the intensity of predation. We demonstrated further that local nest clustering had positive fitness consequences – nests with closer nearest neighbors were more likely to be successful, a result that is counter to the general assumption that nest predation rates increase with nest density.
Olazábal, Daniel E
2014-01-01
Parental behavior is commonly displayed by progenitors. However, other individuals, genetically related (e.g. siblings, aunts, uncles) or not with the newborns, also display parental behavior (commonly called alloparental, or adoptive behavior). I hypothesize that species that live in family or social groups where other non-reproductive members (males and females) take care of infants, have brain adaptations to promote or facilitate that behavioral response. The present work revises the evidence supporting the hypothesis that high density of oxytocin receptors (OXTR) in the nucleus accumbens (NA) is one of those adaptations. All species known to have high NA OXTR show not only female, but also male alloparental care. Therefore, I predict that high NA OXTR could be present in all species in which juvenile and adult male alloparental behavior have been observed. Strategies to test this and other alternative working hypothesis and its predictions are presented. PMID:25446893
Nowicki, Dimitri; Siegelmann, Hava
2010-06-11
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.
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
Amoeboid migration mode adaption in quasi-3D spatial density gradients of varying lattice geometry
NASA Astrophysics Data System (ADS)
Gorelashvili, Mari; Emmert, Martin; Hodeck, Kai F.; Heinrich, Doris
2014-07-01
Cell migration processes are controlled by sensitive interaction with external cues such as topographic structures of the cell’s environment. Here, we present systematically controlled assays to investigate the specific effects of spatial density and local geometry of topographic structure on amoeboid migration of Dictyostelium discoideum cells. This is realized by well-controlled fabrication of quasi-3D pillar fields exhibiting a systematic variation of inter-pillar distance and pillar lattice geometry. By time-resolved local mean-squared displacement analysis of amoeboid migration, we can extract motility parameters in order to elucidate the details of amoeboid migration mechanisms and consolidate them in a two-state contact-controlled motility model, distinguishing directed and random phases. Specifically, we find that directed pillar-to-pillar runs are found preferably in high pillar density regions, and cells in directed motion states sense pillars as attractive topographic stimuli. In contrast, cell motion in random probing states is inhibited by high pillar density, where pillars act as obstacles for cell motion. In a gradient spatial density, these mechanisms lead to topographic guidance of cells, with a general trend towards a regime of inter-pillar spacing close to the cell diameter. In locally anisotropic pillar environments, cell migration is often found to be damped due to competing attraction by different pillars in close proximity and due to lack of other potential stimuli in the vicinity of the cell. Further, we demonstrate topographic cell guidance reflecting the lattice geometry of the quasi-3D environment by distinct preferences in migration direction. Our findings allow to specifically control amoeboid cell migration by purely topographic effects and thus, to induce active cell guidance. These tools hold prospects for medical applications like improved wound treatment, or invasion assays for immune cells.
Resummed memory kernels in generalized system-bath master equations
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.
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels.
Jayasumana, Sadeep; Hartley, Richard; Salzmann, Mathieu; Li, Hongdong; Harandi, Mehrtash
2015-12-01
In this paper, we develop an approach to exploiting kernel methods with manifold-valued data. In many computer vision problems, the data can be naturally represented as points on a Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, usual Euclidean computer vision and machine learning algorithms yield inferior results on such data. In this paper, we define Gaussian radial basis function (RBF)-based positive definite kernels on manifolds that permit us to embed a given manifold with a corresponding metric in a high dimensional reproducing kernel Hilbert space. These kernels make it possible to utilize algorithms developed for linear spaces on nonlinear manifold-valued data. Since the Gaussian RBF defined with any given metric is not always positive definite, we present a unified framework for analyzing the positive definiteness of the Gaussian RBF on a generic metric space. We then use the proposed framework to identify positive definite kernels on two specific manifolds commonly encountered in computer vision: the Riemannian manifold of symmetric positive definite matrices and the Grassmann manifold, i.e., the Riemannian manifold of linear subspaces of a Euclidean space. We show that many popular algorithms designed for Euclidean spaces, such as support vector machines, discriminant analysis and principal component analysis can be generalized to Riemannian manifolds with the help of such positive definite Gaussian kernels. PMID:26539851
Potter, Kelsey A; Jorfi, Mehdi; Householder, Kyle T; Foster, E Johan; Weder, Christoph; Capadona, Jeffrey R
2014-05-01
The cellular and molecular mechanisms by which neuroinflammatory pathways respond to and propagate the reactive tissue response to intracortical microelectrodes remain active areas of research. We previously demonstrated that both the mechanical mismatch between rigid implants and the much softer brain tissue, as well as oxidative stress, contribute to the neurodegenerative reactive tissue response to intracortical implants. In this study, we utilize physiologically responsive, mechanically adaptive polymer implants based on poly(vinyl alcohol) (PVA), with the capability to also locally administer the antioxidant curcumin. The goal of this study is to investigate if the combination of two independently effective mechanisms - softening of the implant and antioxidant release - leads to synergistic effects in vivo. Over the first 4weeks of the implantation, curcumin-releasing, mechanically adaptive implants were associated with higher neuron survival and a more stable blood-brain barrier at the implant-tissue interface than the neat PVA controls. 12weeks post-implantation, the benefits of the curcumin release were lost, and both sets of compliant materials (with and without curcumin) had no statistically significant differences in neuronal density distribution profiles. Overall, however, the curcumin-releasing softening polymer implants cause minimal implant-mediated neuroinflammation, and embody the new concept of localized drug delivery from mechanically adaptive intracortical implants.
DFT calculations of molecular excited states using an orbital-dependent nonadiabatic exchange kernel
Ipatov, A. N.
2010-02-15
A density functional method for computing molecular excitation spectra is presented that uses a frequency-dependent kernel and takes into account the nonlocality of exchange interaction. Owing to its high numerical stability and the use of a nonadiabatic (frequency-dependent) exchange kernel, the proposed approach provides a qualitatively correct description of the asymptotic behavior of charge-transfer excitation energies.
Higher-order adaptive finite-element methods for orbital-free density functional theory
Motamarri, Phani; Iyer, Mrinal; Knap, Jaroslaw; Gavini, Vikram
2012-08-15
In the present work, we study various numerical aspects of higher-order finite-element discretizations of the non-linear saddle-point formulation of orbital-free density-functional theory. We first investigate the robustness of viable solution schemes by analyzing the solvability conditions of the discrete problem. We find that a staggered solution procedure where the potential fields are computed consistently for every trial electron-density is a robust solution procedure for higher-order finite-element discretizations. We next study the convergence properties of higher-order finite-element discretizations of orbital-free density functional theory by considering benchmark problems that include calculations involving both pseudopotential as well as Coulomb singular potential fields. Our numerical studies suggest close to optimal rates of convergence on all benchmark problems for various orders of finite-element approximations considered in the present study. We finally investigate the computational efficiency afforded by various higher-order finite-element discretizations, which constitutes the main aspect of the present work, by measuring the CPU time for the solution of discrete equations on benchmark problems that include large Aluminum clusters. In these studies, we use mesh coarse-graining rates that are derived from error estimates and an a priori knowledge of the asymptotic solution of the far-field electronic fields. Our studies reveal a significant 100-1000 fold computational savings afforded by the use of higher-order finite-element discretization, alongside providing the desired chemical accuracy. We consider this study as a step towards developing a robust and computationally efficient discretization of electronic structure calculations using the finite-element basis.
Volcano clustering determination: Bivariate Gauss vs. Fisher kernels
NASA Astrophysics Data System (ADS)
Cañón-Tapia, Edgardo
2013-05-01
Underlying many studies of volcano clustering is the implicit assumption that vent distribution can be studied by using kernels originally devised for distribution in plane surfaces. Nevertheless, an important change in topology in the volcanic context is related to the distortion that is introduced when attempting to represent features found on the surface of a sphere that are being projected into a plane. This work explores the extent to which different topologies of the kernel used to study the spatial distribution of vents can introduce significant changes in the obtained density functions. To this end, a planar (Gauss) and a spherical (Fisher) kernels are mutually compared. The role of the smoothing factor in these two kernels is also explored with some detail. The results indicate that the topology of the kernel is not extremely influential, and that either type of kernel can be used to characterize a plane or a spherical distribution with exactly the same detail (provided that a suitable smoothing factor is selected in each case). It is also shown that there is a limitation on the resolution of the Fisher kernel relative to the typical separation between data that can be accurately described, because data sets with separations lower than 500 km are considered as a single cluster using this method. In contrast, the Gauss kernel can provide adequate resolutions for vent distributions at a wider range of separations. In addition, this study also shows that the numerical value of the smoothing factor (or bandwidth) of both the Gauss and Fisher kernels has no unique nor direct relationship with the relevant separation among data. In order to establish the relevant distance, it is necessary to take into consideration the value of the respective smoothing factor together with a level of statistical significance at which the contributions to the probability density function will be analyzed. Based on such reference level, it is possible to create a hierarchy of
Uppal, Neha; Foxe, John J; Butler, John S; Acluche, Frantzy; Molholm, Sophie
2016-03-01
Young children are often hyperreactive to somatosensory inputs hardly noticed by adults, as exemplified by irritation to seams or labels in clothing. The neurodevelopmental mechanisms underlying changes in sensory reactivity are not well understood. Based on the idea that neurodevelopmental changes in somatosensory processing and/or changes in sensory adaptation might underlie developmental differences in somatosensory reactivity, high-density electroencephalography was used to examine how the nervous system responds and adapts to repeated vibrotactile stimulation over childhood. Participants aged 6-18 yr old were presented with 50-ms vibrotactile stimuli to the right wrist over the median nerve at 5 blocked interstimulus intervals (ranging from ∼7 to ∼1 stimulus per second). Somatosensory evoked potentials (SEPs) revealed three major phases of activation within the first 200 ms, with scalp topographies suggestive of neural generators in contralateral somatosensory cortex. Although overall SEPs were highly similar for younger, middle, and older age groups (6.1-9.8, 10.0-12.9, and 13.0-17.8 yr old), there were significant age-related amplitude differences in initial and later phases of the SEP. In contrast, robust adaptation effects for fast vs. slow presentation rates were observed that did not differ as a function of age. A greater amplitude response in the later portion of the SEP was observed for the youngest group and may be related to developmental changes in responsivity to somatosensory stimuli. These data suggest the protracted development of the somatosensory system over childhood, whereas adaptation, as assayed in this study, is largely in place by ∼7 yr of age. PMID:26763781
NASA Astrophysics Data System (ADS)
Hirthe, Eugenia M.; Graf, Thomas
2012-12-01
The automatic non-iterative second-order time-stepping scheme based on the temporal truncation error proposed by Kavetski et al. [Kavetski D, Binning P, Sloan SW. Non-iterative time-stepping schemes with adaptive truncation error control for the solution of Richards equation. Water Resour Res 2002;38(10):1211, http://dx.doi.org/10.1029/2001WR000720.] is implemented into the code of the HydroGeoSphere model. This time-stepping scheme is applied for the first time to the low-Rayleigh-number thermal Elder problem of free convection in porous media [van Reeuwijk M, Mathias SA, Simmons CT, Ward JD. Insights from a pseudospectral approach to the Elder problem. Water Resour Res 2009;45:W04416, http://dx.doi.org/10.1029/2008WR007421.], and to the solutal [Shikaze SG, Sudicky EA, Schwartz FW. Density-dependent solute transport in discretely-fractured geological media: is prediction possible? J Contam Hydrol 1998;34:273-91] problem of free convection in fractured-porous media. Numerical simulations demonstrate that the proposed scheme efficiently limits the temporal truncation error to a user-defined tolerance by controlling the time-step size. The non-iterative second-order time-stepping scheme can be applied to (i) thermal and solutal variable-density flow problems, (ii) linear and non-linear density functions, and (iii) problems including porous and fractured-porous media.
Removing blur kernel noise via a hybrid ℓp norm
NASA Astrophysics Data System (ADS)
Yu, Xin; Zhang, Shunli; Zhao, Xiaolin; Zhang, Li
2015-01-01
When estimating a sharp image from a blurred one, blur kernel noise often leads to inaccurate recovery. We develop an effective method to estimate a blur kernel which is able to remove kernel noise and prevent the production of an overly sparse kernel. Our method is based on an iterative framework which alternatingly recovers the sharp image and estimates the blur kernel. In the image recovery step, we utilize the total variation (TV) regularization to recover latent images. In solving TV regularization, we propose a new criterion which adaptively terminates the iterations before convergence. While improving the efficiency, the quality of the final results is not degraded. In the kernel estimation step, we develop a metric to measure the usefulness of image edges, by which we can reduce the ambiguity of kernel estimation caused by small-scale edges. We also propose a hybrid ℓp norm, which is composed of ℓ2 norm and ℓp norm with 0.7≤p<1, to construct a sparsity constraint. Using the hybrid ℓp norm, we reduce a wider range of kernel noise and recover a more accurate blur kernel. The experiments show that the proposed method achieves promising results on both synthetic and real images.
Gergs, André; Preuss, Thomas G; Palmqvist, Annemette
2014-01-01
Population size is often regulated by negative feedback between population density and individual fitness. At high population densities, animals run into double trouble: they might concurrently suffer from overexploitation of resources and also from negative interference among individuals regardless of resource availability, referred to as crowding. Animals are able to adapt to resource shortages by exhibiting a repertoire of life history and physiological plasticities. In addition to resource-related plasticity, crowding might lead to reduced fitness, with consequences for individual life history. We explored how different mechanisms behind resource-related plasticity and crowding-related fitness act independently or together, using the water flea Daphnia magna as a case study. For testing hypotheses related to mechanisms of plasticity and crowding stress across different biological levels, we used an individual-based population model that is based on dynamic energy budget theory. Each of the hypotheses, represented by a sub-model, is based on specific assumptions on how the uptake and allocation of energy are altered under conditions of resource shortage or crowding. For cross-level testing of different hypotheses, we explored how well the sub-models fit individual level data and also how well they predict population dynamics under different conditions of resource availability. Only operating resource-related and crowding-related hypotheses together enabled accurate model predictions of D. magna population dynamics and size structure. Whereas this study showed that various mechanisms might play a role in the negative feedback between population density and individual life history, it also indicated that different density levels might instigate the onset of the different mechanisms. This study provides an example of how the integration of dynamic energy budget theory and individual-based modelling can facilitate the exploration of mechanisms behind the regulation
NASA Astrophysics Data System (ADS)
Ardisana, R. N.; Miller, C. A.; Sivaguru, M.; Fouke, B. W.
2013-12-01
Corals are a key reservoir of biodiversity in coastal, shallow water tropical marine environments, and density banding in their aragonite skeletons is used as a sensitive record of paleoclimate. Therefore, the cellular response of corals to environmental change and its expression in skeletal structure is of significant importance. Chromatophores, pigment-bearing cells within the ectoderm of hermatypic corals, serve to both enhance the photosynthetic activity of zooxanthellae symbionts, as well as protect the coral animal from harmful UV radiation. Yet connections have not previously been drawn between chromatophore tissue density and the development of skeletal density bands. A histological analysis of the coral Montastrea faveolata has therefore been conducted across a bathymetric gradient of 1-20 m on the southern Caribbean island of Curaçao. A combination of field and laboratory photography, serial block face imaging (SBFI), two-photon laser scanning microscopy (TPLSM), and 3D image analysis has been applied to test whether M. faveolata adapts to increasing water depth and decreasing photosynthetically active radiation by shifting toward a more heterotrophic lifestyle (decreasing zooxanthellae tissue density, increasing mucocyte tissue density, and decreasing chromatophores density). This study is among the first to collect and evaluate histological data in the spatial context of an entire unprocessed coral polyp. TPLSM was used to optically thin section unprocessed tissue biopsies with quantitative image analysis to yield a nanometer-scale three-dimensional map of the quantity and distribution of the symbionts (zooxanthellae) and a host fluorescent pigments (chromatophores), which is thought to have photoprotective properties, within the context of an entire coral polyp. Preliminary results have offered new insight regarding the three-dimensional distribution and abundance of chromatophores and have identified: (1) M. faveolata tissue collected from 8M SWD do
Dechaine, Jennifer M; Johnston, Jill A; Brock, Marcus T; Weinig, Cynthia
2007-01-01
Phenotypic plasticity, the ability of a genotype to express different phenotypes across environments, is an adaptive strategy expected to evolve in heterogeneous environments. One widely held hypothesis is that the evolutionary benefits of plasticity are reduced by its costs, but when compared with the number of traits tested, the evidence for costs is limited. Selection gradients were calculated for traits and trait plasticities to test for costs of plasticity to density in a field study using recombinant inbred lines (RILs) of Brassica rapa. Significant costs of putatively adaptive plasticity were found in three out of six measured traits. For one trait, petiole length, a cost of plasticity was detected in both environments tested; such global costs are expected to more strongly constrain the evolution of plasticity than local costs expressed in a single environment. These results, in combination with evidence from studies in segregating progenies of Arabidopsis thaliana, suggest that the potential for genetic costs of plasticity exists in natural populations. Detection of costs in previous studies may have been limited because historical selection has purged genotypes with costly plasticity, and experimental conditions often lack environmental stresses.
Iris Image Blur Detection with Multiple Kernel Learning
NASA Astrophysics Data System (ADS)
Pan, Lili; Xie, Mei; Mao, Ling
In this letter, we analyze the influence of motion and out-of-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets.
Learning With Jensen-Tsallis Kernels.
Ghoshdastidar, Debarghya; Adsul, Ajay P; Dukkipati, Ambedkar
2016-10-01
Jensen-type [Jensen-Shannon (JS) and Jensen-Tsallis] kernels were first proposed by Martins et al. (2009). These kernels are based on JS divergences that originated in the information theory. In this paper, we extend the Jensen-type kernels on probability measures to define positive-definite kernels on Euclidean space. We show that the special cases of these kernels include dot-product kernels. Since Jensen-type divergences are multidistribution divergences, we propose their multipoint variants, and study spectral clustering and kernel methods based on these. We also provide experimental studies on benchmark image database and gene expression database that show the benefits of the proposed kernels compared with the existing kernels. The experiments on clustering also demonstrate the use of constructing multipoint similarities.
Huang, Jessie Y.; Howell, Rebecca M.; Mirkovic, Dragan; Followill, David S.; Kry, Stephen F.; Eklund, David; Childress, Nathan L.
2013-12-15
Purpose: Several simplifications used in clinical implementations of the convolution/superposition (C/S) method, specifically, density scaling of water kernels for heterogeneous media and use of a single polyenergetic kernel, lead to dose calculation inaccuracies. Although these weaknesses of the C/S method are known, it is not well known which of these simplifications has the largest effect on dose calculation accuracy in clinical situations. The purpose of this study was to generate and characterize high-resolution, polyenergetic, and material-specific energy deposition kernels (EDKs), as well as to investigate the dosimetric impact of implementing spatially variant polyenergetic and material-specific kernels in a collapsed cone C/S algorithm.Methods: High-resolution, monoenergetic water EDKs and various material-specific EDKs were simulated using the EGSnrc Monte Carlo code. Polyenergetic kernels, reflecting the primary spectrum of a clinical 6 MV photon beam at different locations in a water phantom, were calculated for different depths, field sizes, and off-axis distances. To investigate the dosimetric impact of implementing spatially variant polyenergetic kernels, depth dose curves in water were calculated using two different implementations of the collapsed cone C/S method. The first method uses a single polyenergetic kernel, while the second method fully takes into account spectral changes in the convolution calculation. To investigate the dosimetric impact of implementing material-specific kernels, depth dose curves were calculated for a simplified titanium implant geometry using both a traditional C/S implementation that performs density scaling of water kernels and a novel implementation using material-specific kernels.Results: For our high-resolution kernels, we found good agreement with the Mackie et al. kernels, with some differences near the interaction site for low photon energies (<500 keV). For our spatially variant polyenergetic kernels, we found
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.
Silva, P R B; Lobeck-Luchterhand, K M; Cerri, R L A; Haines, D M; Ballou, M A; Endres, M I; Chebel, R C
2016-01-01
Objectives were to evaluate the effects of prepartum stocking density on innate and adaptive leukocyte responses, serum cortisol and haptoglobin concentrations and hair cortisol concentration of Jersey cows. The cows (254 ± 3d of gestation) were balanced for parity (nulliparous vs. parous) and previous lactation projected 305-d mature equivalent milk yield and assigned to one of two treatments: 80SD=80% stocking density (38 animals/48 headlocks) and 100SD=100% stocking density (48 animals/48 headlocks). Pens (n=4) were identical in size and design and each pen received each treatment a total of 2 times (4 replicates; 80SD: n=338; 100SD: n=418). A sub-group of cows (n=48/treatment per parity) was randomly selected on week 1 of each replicate from which blood was sampled weekly from d -14 to 14 (d 0=calving) to determine polymorphonuclear leukocyte (PMNL) phagocytosis, oxidative burst, and expression of CD18 and L-selectin, and hemogram. The same sub-group of cows was treated with chicken egg ovalbumin on d -21, -7, and 7 and had blood sampled weekly from d -21 to 21 for determination of serum IgG anti-ovalbumin concentration. Blood was sampled weekly from d -21 to 21 to determine glucose, cortisol, and haptoglobin concentrations in serum. Hair samples collected at enrollment and within 24h of calving were analyzed for cortisol concentration. The percentage of leukocytes classified as granulocyte and the granulocyte to the lymphocyte ratio were not affected by treatment. Treatment did not affect the percentage of PMNL positive for phagocytosis and oxidative burst or the intensity of phagocytosis and oxidative burst. Similarly, treatment did not affect the percentage of PMNL expressing CD18 and L-selectin or the intensity of expression of CD18 and L-selectin. Concentration of IgG anti-ovalbumin was not affected by treatment. Serum concentrations of haptoglobin and cortisol were not affected by treatment. Similarly, hair cortisol concentration at calving was not
Boundary conditions for gas flow problems from anisotropic scattering kernels
NASA Astrophysics Data System (ADS)
To, Quy-Dong; Vu, Van-Huyen; Lauriat, Guy; Léonard, Céline
2015-10-01
The paper presents an interface model for gas flowing through a channel constituted of anisotropic wall surfaces. Using anisotropic scattering kernels and Chapman Enskog phase density, the boundary conditions (BCs) for velocity, temperature, and discontinuities including velocity slip and temperature jump at the wall are obtained. Two scattering kernels, Dadzie and Méolans (DM) kernel, and generalized anisotropic Cercignani-Lampis (ACL) are examined in the present paper, yielding simple BCs at the wall fluid interface. With these two kernels, we rigorously recover the analytical expression for orientation dependent slip shown in our previous works [Pham et al., Phys. Rev. E 86, 051201 (2012) and To et al., J. Heat Transfer 137, 091002 (2015)] which is in good agreement with molecular dynamics simulation results. More important, our models include both thermal transpiration effect and new equations for the temperature jump. While the same expression depending on the two tangential accommodation coefficients is obtained for slip velocity, the DM and ACL temperature equations are significantly different. The derived BC equations associated with these two kernels are of interest for the gas simulations since they are able to capture the direction dependent slip behavior of anisotropic interfaces.
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.
Probability-confidence-kernel-based localized multiple kernel learning with lp norm.
Han, Yina; Liu, Guizhong
2012-06-01
Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features in terms of their discriminative power for each individual sample. However, models excessively fitting to a specific sample would obstacle the extension to unseen data, while a more general form is often insufficient for diverse locality characterization. Hence, both learning sample-specific local models for each training datum and extending the learned models to unseen test data should be equally addressed in designing LMKL algorithm. In this paper, for an integrative solution, we propose a probability confidence kernel (PCK), which measures per-sample similarity with respect to probabilistic-prediction-based class attribute: The class attribute similarity complements the spatial-similarity-based base kernels for more reasonable locality characterization, and the predefined form of involved class probability density function facilitates the extension to the whole input space and ensures its statistical meaning. Incorporating PCK into support-vectormachine-based LMKL framework, we propose a new PCK-LMKL with arbitrary l(p)-norm constraint implied in the definition of PCKs, where both the parameters in PCK and the final classifier can be efficiently optimized in a joint manner. Evaluations of PCK-LMKL on both benchmark machine learning data sets (ten University of California Irvine (UCI) data sets) and challenging computer vision data sets (15-scene data set and Caltech-101 data set) have shown to achieve state-of-the-art performances.
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 ...
Point-Kernel Shielding Code System.
1982-02-17
Version 00 QAD-BSA is a three-dimensional, point-kernel shielding code system based upon the CCC-48/QAD series. It is designed to calculate photon dose rates and heating rates using exponential attenuation and infinite medium buildup factors. Calculational provisions include estimates of fast neutron penetration using data computed by the moments method. Included geometry routines can describe complicated source and shield geometries. An internal library contains data for many frequently used structural and shielding materials, enabling the codemore » to solve most problems with only source strengths and problem geometry required as input. This code system adapts especially well to problems requiring multiple sources and sources with asymmetrical geometry. In addition to being edited separately, the total interaction rates from many sources may be edited at each detector point. Calculated photon interaction rates agree closely with those obtained using QAD-P5A.« less
Broadband Waveform Sensitivity Kernels for Large-Scale Seismic Tomography
NASA Astrophysics Data System (ADS)
Nissen-Meyer, T.; Stähler, S. C.; van Driel, M.; Hosseini, K.; Auer, L.; Sigloch, K.
2015-12-01
Seismic sensitivity kernels, i.e. the basis for mapping misfit functionals to structural parameters in seismic inversions, have received much attention in recent years. Their computation has been conducted via ray-theory based approaches (Dahlen et al., 2000) or fully numerical solutions based on the adjoint-state formulation (e.g. Tromp et al., 2005). The core problem is the exuberant computational cost due to the large number of source-receiver pairs, each of which require solutions to the forward problem. This is exacerbated in the high-frequency regime where numerical solutions become prohibitively expensive. We present a methodology to compute accurate sensitivity kernels for global tomography across the observable seismic frequency band. These kernels rely on wavefield databases computed via AxiSEM (abstract ID# 77891, www.axisem.info), and thus on spherically symmetric models. As a consequence of this method's numerical efficiency even in high-frequency regimes, kernels can be computed in a time- and frequency-dependent manner, thus providing the full generic mapping from perturbed waveform to perturbed structure. Such waveform kernels can then be used for a variety of misfit functions, structural parameters and refiltered into bandpasses without recomputing any wavefields. A core component of the kernel method presented here is the mapping from numerical wavefields to inversion meshes. This is achieved by a Monte-Carlo approach, allowing for convergent and controllable accuracy on arbitrarily shaped tetrahedral and hexahedral meshes. We test and validate this accuracy by comparing to reference traveltimes, show the projection onto various locally adaptive inversion meshes and discuss computational efficiency for ongoing tomographic applications in the range of millions of observed body-wave data between periods of 2-30s.
Hill, Michael R H; Fried, Itzhak; Koch, Christof
2015-02-15
Peristimulus time histograms are a widespread form of visualizing neuronal responses. Kernel convolution methods transform these histograms into a smooth, continuous probability density function. This provides an improved estimate of a neuron's actual response envelope. We here develop a classifier, called the h-coefficient, to determine whether time-locked fluctuations in the firing rate of a neuron should be classified as a response or as random noise. Unlike previous approaches, the h-coefficient takes advantage of the more precise response envelope estimation provided by the kernel convolution method. The h-coefficient quantizes the smoothed response envelope and calculates the probability of a response of a given shape to occur by chance. We tested the efficacy of the h-coefficient in a large data set of Monte Carlo simulated smoothed peristimulus time histograms with varying response amplitudes, response durations, trial numbers, and baseline firing rates. Across all these conditions, the h-coefficient significantly outperformed more classical classifiers, with a mean false alarm rate of 0.004 and a mean hit rate of 0.494. We also tested the h-coefficient's performance in a set of neuronal responses recorded in humans. The algorithm behind the h-coefficient provides various opportunities for further adaptation and the flexibility to target specific parameters in a given data set. Our findings confirm that the h-coefficient can provide a conservative and powerful tool for the analysis of peristimulus time histograms with great potential for future development. PMID:25475352
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.
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…
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...
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2013 CFR
2013-01-01
... AND ORDERS; FRUITS, VEGETABLES, NUTS), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... Standards for Shelled Almonds, or which has embedded dirt or other foreign material not easily removed...
7 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...
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...
7 CFR 981.408 - Inedible kernel.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 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...
Wu, Wei; Wan, Xuejie; Shah, Farooq; Fahad, Shah; Huang, Jianliang
2014-01-01
Sheath blight of rice, caused by Rhizoctonia solani, is one of the most devastating rice diseases worldwide. No rice cultivar has been found to be completely resistant to this fungus. Identifying antioxidant enzymes activities (activity of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT)) and malondialdehyde content (MDA) responding to sheath blight infestation is imperative to understand the defensive mechanism systems of rice. In the present study, two inoculation methods (toothpick and agar block method) were tested in double-season rice. Toothpick method had greater lesion length than agar block method in late season. A higher MDA content was found under toothpick method compared with agar block method, which led to greater POD and SOD activities. Dense planting caused higher lesion length resulting in a higher MDA content, which also subsequently stimulated higher POD and SOD activity. Sheath blight severity was significantly related to the activity of antioxidant enzyme during both seasons. The present study implies that rice plants possess a system of antioxidant protective enzymes which helps them in adaptation to sheath blight infection stresses. Several agronomic practices, such as rational use of fertilizers and optimum planting density, involved in regulating antioxidant protective enzyme systems can be regarded as promising strategy to suppress the sheath blight development.
Wigner functions defined with Laplace transform kernels.
Oh, Se Baek; Petruccelli, Jonathan C; Tian, Lei; Barbastathis, George
2011-10-24
We propose a new Wigner-type phase-space function using Laplace transform kernels--Laplace kernel Wigner function. Whereas momentum variables are real in the traditional Wigner function, the Laplace kernel Wigner function may have complex momentum variables. Due to the property of the Laplace transform, a broader range of signals can be represented in complex phase-space. We show that the Laplace kernel Wigner function exhibits similar properties in the marginals as the traditional Wigner function. As an example, we use the Laplace kernel Wigner function to analyze evanescent waves supported by surface plasmon polariton.
NASA Astrophysics Data System (ADS)
Schaeck, S.; Karspeck, T.; Ott, C.; Weckler, M.; Stoermer, A. O.
2011-03-01
In March 2007 the BMW Group has launched the micro-hybrid functions brake energy regeneration (BER) and automatic start and stop function (ASSF). Valve-regulated lead-acid (VRLA) batteries in absorbent glass mat (AGM) technology are applied in vehicles with micro-hybrid power system (MHPS). In both part I and part II of this publication vehicles with MHPS and AGM batteries are subject to a field operational test (FOT). Test vehicles with conventional power system (CPS) and flooded batteries were used as a reference. In the FOT sample batteries were mounted several times and electrically tested in the laboratory intermediately. Vehicle- and battery-related diagnosis data were read out for each test run and were matched with laboratory data in a data base. The FOT data were analyzed by the use of two-dimensional, nonparametric kernel estimation for clear data presentation. The data show that capacity loss in the MHPS is comparable to the CPS. However, the influence of mileage performance, which cannot be separated, suggests that battery stress is enhanced in the MHPS although a battery refresh function is applied. Anyway, the FOT demonstrates the unsuitability of flooded batteries for the MHPS because of high early capacity loss due to acid stratification and because of vanishing cranking performance due to increasing internal resistance. Furthermore, the lack of dynamic charge acceptance for high energy regeneration efficiency is illustrated. Under the presented FOT conditions charge acceptance of lead-acid (LA) batteries decreases to less than one third for about half of the sample batteries compared to new battery condition. In part II of this publication FOT data are presented by multiple regression analysis (Schaeck et al., submitted for publication [1]).
Zhang, Zhanhui; Wu, Xiangyuan; Shi, Chaonan; Wang, Rongna; Li, Shengfei; Wang, Zhaohui; Liu, Zonghua; Xue, Yadong; Tang, Guiliang; Tang, Jihua
2016-02-01
Kernel development is an important dynamic trait that determines the final grain yield in maize. To dissect the genetic basis of maize kernel development process, a conditional quantitative trait locus (QTL) analysis was conducted using an immortalized F2 (IF2) population comprising 243 single crosses at two locations over 2 years. Volume (KV) and density (KD) of dried developing kernels, together with kernel weight (KW) at different developmental stages, were used to describe dynamic changes during kernel development. Phenotypic analysis revealed that final KW and KD were determined at DAP22 and KV at DAP29. Unconditional QTL mapping for KW, KV and KD uncovered 97 QTLs at different kernel development stages, of which qKW6b, qKW7a, qKW7b, qKW10b, qKW10c, qKV10a, qKV10b and qKV7 were identified under multiple kernel developmental stages and environments. Among the 26 QTLs detected by conditional QTL mapping, conqKW7a, conqKV7a, conqKV10a, conqKD2, conqKD7 and conqKD8a were conserved between the two mapping methodologies. Furthermore, most of these QTLs were consistent with QTLs and genes for kernel development/grain filling reported in previous studies. These QTLs probably contain major genes associated with the kernel development process, and can be used to improve grain yield and quality through marker-assisted selection.
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.
Kernel Near Principal Component Analysis
MARTIN, SHAWN B.
2002-07-01
We propose a novel algorithm based on Principal Component Analysis (PCA). First, we present an interesting approximation of PCA using Gram-Schmidt orthonormalization. Next, we combine our approximation with the kernel functions from Support Vector Machines (SVMs) to provide a nonlinear generalization of PCA. After benchmarking our algorithm in the linear case, we explore its use in both the linear and nonlinear cases. We include applications to face data analysis, handwritten digit recognition, and fluid flow.
Smith, D.R.; Rogala, J.T.; Gray, B.R.; Zigler, S.J.; Newton, T.J.
2011-01-01
Reliable estimates of abundance are needed to assess consequences of proposed habitat restoration and enhancement projects on freshwater mussels in the Upper Mississippi River (UMR). Although there is general guidance on sampling techniques for population assessment of freshwater mussels, the actual performance of sampling designs can depend critically on the population density and spatial distribution at the project site. To evaluate various sampling designs, we simulated sampling of populations, which varied in density and degree of spatial clustering. Because of logistics and costs of large river sampling and spatial clustering of freshwater mussels, we focused on adaptive and non-adaptive versions of single and two-stage sampling. The candidate designs performed similarly in terms of precision (CV) and probability of species detection for fixed sample size. Both CV and species detection were determined largely by density, spatial distribution and sample size. However, designs did differ in the rate that occupied quadrats were encountered. Occupied units had a higher probability of selection using adaptive designs than conventional designs. We used two measures of cost: sample size (i.e. number of quadrats) and distance travelled between the quadrats. Adaptive and two-stage designs tended to reduce distance between sampling units, and thus performed better when distance travelled was considered. Based on the comparisons, we provide general recommendations on the sampling designs for the freshwater mussels in the UMR, and presumably other large rivers.
Kernel-based least squares policy iteration for reinforcement learning.
Xu, Xin; Hu, Dewen; Lu, Xicheng
2007-07-01
In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating
NASA Astrophysics Data System (ADS)
Pan, Xiaolong; Liu, Bo; Zheng, Jianglong; Tian, Qinghua
2016-08-01
We propose and demonstrate a low complexity Reed-Solomon-based low-density parity-check (RS-LDPC) code with adaptive puncturing decoding algorithm for elastic optical transmission system. Partial received codes and the relevant column in parity-check matrix can be punctured to reduce the calculation complexity by adaptive parity-check matrix during decoding process. The results show that the complexity of the proposed decoding algorithm is reduced by 30% compared with the regular RS-LDPC system. The optimized code rate of the RS-LDPC code can be obtained after five times iteration.
Results from ORNL Characterization of Nominal 350 ?m NUCO Kernels from the BWXT 69300 Composite
Hunn, John D
2004-06-01
This document is a compilation of characterization data obtained on the nominal 350 {micro}m natural enrichment uranium oxide/uranium carbide kernels (NUCO) produced by BWXT for the Advanced Gas Reactor Fuel dEvelopment and Qualification Program. 5 kg of kernels were produced. G73B-NU-69300R was a 4.9 kg composite. G73B-NU-69301 was a 100 g composite. Size, shape, density, and microstructural analysis were performed on samples riffled from a 100 g sublot (69300R-38) riffled by BWXT from the 69300 composite. Measurements were made using optical microscopy to determine the size and shape of the kernels. Hg porosimetry was performed to measure density. The results are summarized in Table 1-1. Values in the table are for the composite and are calculated at 95% confidence from the measured values of a random sample taken from the 69300R-38 sublot. The NUCO kernel composite met all the specifications in Table 1-1 except the aspect ratio specification. This failure was due in part to broken kernels and in part to very irregularly shaped (bumpy) kernels which apparently came from one batch used for the composite. This abnormally shaped batch made up about 1/4 of the composite. The average open porosity of the kernels was fairly low (0.34 {+-} 0.14%). There appeared to be some closed porosity throughout the kernels but a quantitative measure was not obtained. A brief study of the microstructure of the kernels in the composite showed an oxide outer layer of varying thickness related to the process batch surrounding a center region of carbide and oxide zones. X-ray diffraction showed a phase distribution of around 69-74 wt% oxide versus 26-31 wt% carbide. Most of the carbide was in the form of uranium monocarbide (UC).
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.
Nonlinear projection trick in kernel methods: an alternative to the kernel trick.
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.
Nonlinear projection trick in kernel methods: an alternative to the kernel trick.
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. PMID:24805227
In-Shell Bulk Density as an Estimator of Farmers Stock Grade Factors
Technology Transfer Automated Retrieval System (TEKTRAN)
The objective of this research was to determine whether or not bulk density can be used to accurately estimate farmer stock grade factors such as total sound mature kernels and other kernels. Physical properties including bulk density, pod size and kernel size distributions are measured as part of t...
Stem kernels for RNA sequence analyses.
Sakakibara, Yasubumi; Popendorf, Kris; Ogawa, Nana; Asai, Kiyoshi; Sato, Kengo
2007-10-01
Several computational methods based on stochastic context-free grammars have been developed for modeling and analyzing functional RNA sequences. These grammatical methods have succeeded in modeling typical secondary structures of RNA, and are used for structural alignment of RNA sequences. However, such stochastic models cannot sufficiently discriminate member sequences of an RNA family from nonmembers and hence detect noncoding RNA regions from genome sequences. A novel kernel function, stem kernel, for the discrimination and detection of functional RNA sequences using support vector machines (SVMs) is proposed. The stem kernel is a natural extension of the string kernel, specifically the all-subsequences kernel, and is tailored to measure the similarity of two RNA sequences from the viewpoint of secondary structures. The stem kernel examines all possible common base pairs and stem structures of arbitrary lengths, including pseudoknots between two RNA sequences, and calculates the inner product of common stem structure counts. An efficient algorithm is developed to calculate the stem kernels based on dynamic programming. The stem kernels are then applied to discriminate members of an RNA family from nonmembers using SVMs. The study indicates that the discrimination ability of the stem kernel is strong compared with conventional methods. Furthermore, the potential application of the stem kernel is demonstrated by the detection of remotely homologous RNA families in terms of secondary structures. This is because the string kernel is proven to work for the remote homology detection of protein sequences. These experimental results have convinced us to apply the stem kernel in order to find novel RNA families from genome sequences. PMID:17933013
Predicting Protein Function Using Multiple Kernels.
Yu, Guoxian; Rangwala, Huzefa; Domeniconi, Carlotta; Zhang, Guoji; Zhang, Zili
2015-01-01
High-throughput experimental techniques provide a wide variety of heterogeneous proteomic data sources. To exploit the information spread across multiple sources for protein function prediction, these data sources are transformed into kernels and then integrated into a composite kernel. Several methods first optimize the weights on these kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these approaches result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some approaches optimize the loss of binary classifiers and learn weights for the different kernels iteratively. For multi-class or multi-label data, these methods have to solve the problem of optimizing weights on these kernels for each of the labels, which are computationally expensive and ignore the correlation among labels. In this paper, we propose a method called Predicting Protein Function using Multiple Kernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reduces the empirical loss of multi-label classifier for each of the labels simultaneously. ProMK can integrate kernels selectively and downgrade the weights on noisy kernels. We investigate the performance of ProMK on several publicly available protein function prediction benchmarks and synthetic datasets. We show that the proposed approach performs better than previously proposed protein function prediction approaches that integrate multiple data sources and multi-label multiple kernel learning methods. The codes of our proposed method are available at https://sites.google.com/site/guoxian85/promk.
Kernel earth mover's distance for EEG classification.
Daliri, Mohammad Reza
2013-07-01
Here, we propose a new kernel approach based on the earth mover's distance (EMD) for electroencephalography (EEG) signal classification. The EEG time series are first transformed into histograms in this approach. The distance between these histograms is then computed using the EMD in a pair-wise manner. We bring the distances into a kernel form called kernel EMD. The support vector classifier can then be used for the classification of EEG signals. The experimental results on the real EEG data show that the new kernel method is very effective, and can classify the data with higher accuracy than traditional methods.
Density estimation with non-parametric methods
NASA Astrophysics Data System (ADS)
Fadda, D.; Slezak, E.; Bijaoui, A.
1998-01-01
One key issue in several astrophysical problems is the evaluation of the density probability function underlying an observational discrete data set. We here review two non-parametric density estimators which recently appeared in the astrophysical literature, namely the adaptive kernel density estimator and the Maximum Penalized Likelihood technique, and describe another method based on the wavelet transform. The efficiency of these estimators is tested by using extensive numerical simulations in the one-dimensional case. The results are in good agreement with theoretical functions and the three methods appear to yield consistent estimates. However, the Maximum Penalized Likelihood suffers from a lack of resolution and high computational cost due to its dependency on a minimization algorithm. The small differences between kernel and wavelet estimates are mainly explained by the ability of the wavelet method to take into account local gaps in the data distribution. This new approach is very promising, since smaller structures superimposed onto a larger one are detected only by this technique, especially when small samples are investigated. Thus, wavelet solutions appear to be better suited for subclustering studies. Nevertheless, kernel estimates seem more robust and are reliable solutions although some small-scale details can be missed. In order to check these estimators with respect to previous studies, two galaxy redshift samples, related to the galaxy cluster A3526 and to the Corona Borealis region, have been analyzed. In both these cases claims for bimodality are confirmed at a high confidence level. The complete version of this paper with the whole set of figures can be accessed from the electronic version of the A\\&A Suppl. Ser. managed by Editions de Physique as well as from the SISSA database (astro-ph/9704096).
NASA Astrophysics Data System (ADS)
van Dijk, A. I. J. M.; Bruijnzeel, L. A.
2001-07-01
To improve the description of rainfall partitioning by a vegetation canopy that changes in time a number of adaptations to the revised analytical model for rainfall interception by sparse canopies [J. Hydrol., 170 (1995) 79] was proposed in the first of two papers. The current paper presents an application of this adapted analytical model to simulate throughfall, stemflow and interception as measured in a mixed agricultural cropping system involving cassava, maize and rice during two seasons of growth and serial harvesting in upland West Java, Indonesia. Measured interception losses were 18 and 8% during the two measuring periods, while stemflow fractions were estimated at 2 and 4%, respectively. The main reasons for these discrepancies were differences in vegetation density and composition, as well as differences in the exposure of the two sites used in the two respective years. Functions describing the development of the leaf area index of each of the component crops in time were developed. Leaf area index (ranging between 0.7 and 3.8) was related to canopy cover fraction (0.41-0.94). Using average values and time series of the respective parameters, interception losses were modelled using both the revised analytical model and the presently adapted version. The results indicate that the proposed model adaptations substantially improve the performance of the analytical model and provide a more solid base for parameterisation of the analytical model in vegetation of variable density.
Molecular Hydrodynamics from Memory Kernels.
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. PMID:27104730
Cross-person activity recognition using reduced kernel extreme learning machine.
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.
Cross-person activity recognition using reduced kernel extreme learning machine.
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. PMID:24513850
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…
Sparse Density Estimation on the Multinomial Manifold.
Hong, Xia; Gao, Junbin; Chen, Sheng; Zia, Tanveer
2015-11-01
A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first- and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with an accuracy competitive with those of existing kernel density estimators. PMID:25647665
Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology
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
Bayesian Kernel Mixtures for Counts
Canale, Antonio; Dunson, David B.
2011-01-01
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. PMID:22523437
Bayesian Kernel Mixtures for Counts.
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. PMID:22523437
MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES
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
LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions
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
LoCoH: Non-parameteric kernel methods for constructing home ranges and utilization distributions
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).
LoCoH: nonparameteric kernel methods for constructing home ranges and utilization distributions.
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).
Modeling non-stationarity of kernel weights for k-space reconstruction in partially parallel imaging
Miao, Jun; Wong, Wilbur C. K.; Narayan, Sreenath; Huo, Donglai; Wilson, David L.
2011-01-01
Purpose: In partially parallel imaging, most k-space-based reconstruction algorithms such as GRAPPA adopt a single finite-size kernel to approximate the true relationship between sampled and nonsampled signals. However, the estimation of this kernel based on k-space signals is imperfect, and the authors are investigating methods dealing with local variation of k-space signals. Methods: To model nonstationarity of kernel weights, similar to performing a spatially adaptive regularization, the authors fit a set of linear functions using concepts from geographically weighted regression, a methodology used in geophysical analysis. Instead of a reconstruction with a single set of kernel weights, the authors use multiple sets. A missing signal is reconstructed with its kernel weights set determined by k-space clustering. Simulated and acquired MR data with several different image content and acquisition schemes, including MR tagging, were tested. A perceptual difference model (Case-PDM) was used to quantitatively evaluate the quality of over 1000 test images, and to optimize the parameters of our algorithm. Results: A MOdeling Non-stationarity of KErnel wEightS (“MONKEES”) reconstruction with two sets of kernel weights gave reconstructions with significantly better image quality than the original GRAPPA in all test images. Using more sets produced improved image quality but with diminishing returns. As a rule of thumb, at least two sets of kernel weights, one from low- and the other from high frequency k-space, should be used. Conclusions: The authors conclude that the MONKEES can significantly and robustly improve the image quality in parallel MR imaging, particularly, cardiac imaging. PMID:21928649
Asymmetric scatter kernels for software-based scatter correction of gridless mammography
NASA Astrophysics Data System (ADS)
Wang, Adam; Shapiro, Edward; Yoon, Sungwon; Ganguly, Arundhuti; Proano, Cesar; Colbeth, Rick; Lehto, Erkki; Star-Lack, Josh
2015-03-01
Scattered radiation remains one of the primary challenges for digital mammography, resulting in decreased image contrast and visualization of key features. While anti-scatter grids are commonly used to reduce scattered radiation in digital mammography, they are an incomplete solution that can add radiation dose, cost, and complexity. Instead, a software-based scatter correction method utilizing asymmetric scatter kernels is developed and evaluated in this work, which improves upon conventional symmetric kernels by adapting to local variations in object thickness and attenuation that result from the heterogeneous nature of breast tissue. This fast adaptive scatter kernel superposition (fASKS) method was applied to mammography by generating scatter kernels specific to the object size, x-ray energy, and system geometry of the projection data. The method was first validated with Monte Carlo simulation of a statistically-defined digital breast phantom, which was followed by initial validation on phantom studies conducted on a clinical mammography system. Results from the Monte Carlo simulation demonstrate excellent agreement between the estimated and true scatter signal, resulting in accurate scatter correction and recovery of 87% of the image contrast originally lost to scatter. Additionally, the asymmetric kernel provided more accurate scatter correction than the conventional symmetric kernel, especially at the edge of the breast. Results from the phantom studies on a clinical system further validate the ability of the asymmetric kernel correction method to accurately subtract the scatter signal and improve image quality. In conclusion, software-based scatter correction for mammography is a promising alternative to hardware-based approaches such as anti-scatter grids.
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.
Protoribosome by quantum kernel energy method.
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.
Ideal regularization for learning kernels from labels.
Pan, Binbin; Lai, Jianhuang; Shen, Lixin
2014-08-01
In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. The proposed regularization, referred to as ideal regularization, is a linear function of the kernel matrix to be learned. The ideal regularization allows us to develop efficient algorithms to exploit labels. Three applications of the ideal regularization are considered. Firstly, we use the ideal regularization to incorporate the labels into a standard kernel, making the resulting kernel more appropriate for learning tasks. Next, we employ the ideal regularization to learn a data-dependent kernel matrix from an initial kernel matrix (which contains prior similarity information, geometric structures, and labels of the data). Finally, we incorporate the ideal regularization to some state-of-the-art kernel learning problems. With this regularization, these learning problems can be formulated as simpler ones which permit more efficient solvers. Empirical results show that the ideal regularization exploits the labels effectively and efficiently.
Kernel score statistic for dependent data.
Malzahn, Dörthe; Friedrichs, Stefanie; Rosenberger, Albert; Bickeböller, Heike
2014-01-01
The kernel score statistic is a global covariance component test over a set of genetic markers. It provides a flexible modeling framework and does not collapse marker information. We generalize the kernel score statistic to allow for familial dependencies and to adjust for random confounder effects. With this extension, we adjust our analysis of real and simulated baseline systolic blood pressure for polygenic familial background. We find that the kernel score test gains appreciably in power through the use of sequencing compared to tag-single-nucleotide polymorphisms for very rare single nucleotide polymorphisms with <1% minor allele frequency.
Correa, Claudia C; Ballard, J William O
2012-11-01
In symbiotic associations such as those between Wolbachia and insects, the within-host symbiont density plays an important role in the maintenance of the infection in natural populations, as it relates to transmission fidelity and pathogenicity of the symbiont. Within-host density is speculated to be the result of complex interactions between the bacterial genotype, the host genotype and the environment, which may account for the substantial variation in Wolbachia titres among wild collected individuals compared to laboratory lines. Using quantitative PCR, we screened the Wolbachia gonadal density of individuals from 50 isofemale Drosophila simulans lines raised in standard conditions for at least two generations after collection from the wild. Although these newly collected lines displayed significant variation of ovarian Wolbachia titres, such variation was lost by F(19). Assaying these flies at different ages and under different environmental conditions indicated that symbiont titres in female gonads were not affected by the conditions tested. However, Wolbachia density in male gonads was consistently affected by these treatments in a line-specific way. We propose that the differences in Wolbachia densities among ovaries of F(4) flies are the consequence of large differences in the field-collected females caused by the variable environment, and carried over for at least four generations. In addition, we provide evidence of sex-specific dynamics of Wolbachia in gonads of females and males. In combination, our results support the view of sex-specific Wolbachia evolutionary interactions for males and females, which has been predicted by theory and observed experimentally.
Nonlocal energy-optimized kernel: Recovering second-order exchange in the homogeneous electron gas
NASA Astrophysics Data System (ADS)
Bates, Jefferson E.; Laricchia, Savio; Ruzsinszky, Adrienn
2016-01-01
In order to remedy some of the shortcomings of the random phase approximation (RPA) within adiabatic connection fluctuation-dissipation (ACFD) density functional theory, we introduce a short-ranged, exchange-like kernel that is one-electron self-correlation free and exact for two-electron systems in the high-density limit. By tuning a free parameter in our model to recover an exact limit of the homogeneous electron gas correlation energy, we obtain a nonlocal, energy-optimized kernel that reduces the errors of RPA for both homogeneous and inhomogeneous solids. Using wave-vector symmetrization for the kernel, we also implement RPA renormalized perturbation theory for extended systems, and demonstrate its capability to describe the dominant correlation effects with a low-order expansion in both metallic and nonmetallic systems. The comparison of ACFD structural properties with experiment is also shown to be limited by the choice of norm-conserving pseudopotential.
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2014 CFR
2014-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2014-01-01 2014-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2012 CFR
2012-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2012-01-01 2012-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2013 CFR
2013-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2013-01-01 2013-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2011 CFR
2011-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2011-01-01 2011-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2010 CFR
2010-01-01
... weight of delivery 10,000 10,000 2. Percent of edible kernel weight 53.0 84.0 3. Less weight loss in... 7 Agriculture 8 2010-01-01 2010-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel...
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... STANDARDS) United States Standards for Grades of Shelled Almonds Definitions § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 7 Agriculture 2 2011-01-01 2011-01-01 false Split or broken kernels. 51.2125 Section 51.2125... STANDARDS) United States Standards for Grades of Shelled Almonds Definitions § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 7 Agriculture 2 2012-01-01 2012-01-01 false Split or broken kernels. 51.2125 Section 51.2125... STANDARDS) United States Standards for Grades of Shelled Almonds Definitions § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will...
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...
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...
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...
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...
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...
Gocha, Timothy P; Agnew, Amanda M
2016-05-01
Intracortical remodeling, and the osteons it produces, is one aspect of the bone microstructure that is influenced by and, in turn, can influence its mechanical properties. Previous research examining the spatial distribution of intracortical remodeling density across the femoral midshaft has been limited to either considering only small regions of the cortex or, when looking at the entirety of the cortex, considering only a single individual. This study examined the spatial distribution of all remodeling events (intact osteons, fragmentary osteons, and resorptive bays) across the entirety of the femoral midshaft in a sample of 30 modern cadaveric donors. The sample consisted of 15 males and 15 females, aged 21-97 years at time of death. Using geographic information systems software, the femoral cortex was subdivided radially into thirds and circumferentially into octants, and the spatial location of all remodeling events was marked. Density maps and calculation of osteon population density in cortical regions of interest revealed that remodeling density is typically highest in the periosteal third of the bone, particularly in the lateral and anterolateral regions of the cortex. Due to modeling drift, this area of the midshaft femur has some of the youngest primary tissue, which consequently reveals that the lateral and anterolateral regions of the femoral midshaft have higher remodeling rates than elsewhere in the cortex. This is likely the result of tension/shear forces and/or greater strain magnitudes acting upon the anterolateral femur, which results in a greater amount of microdamage in need of repair than is seen in the medial and posterior regions of the femoral midshaft, which are more subject to compressive forces and/or lesser strain magnitudes.
KITTEN Lightweight Kernel 0.1 Beta
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 providesmore » 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.« less
Quantum kernel applications in medicinal chemistry.
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. PMID:22857535
Variational Dirichlet Blur Kernel Estimation.
Zhou, Xu; Mateos, Javier; Zhou, Fugen; Molina, Rafael; Katsaggelos, Aggelos K
2015-12-01
Blind image deconvolution involves two key objectives: 1) latent image and 2) blur estimation. For latent image estimation, we propose a fast deconvolution algorithm, which uses an image prior of nondimensional Gaussianity measure to enforce sparsity and an undetermined boundary condition methodology to reduce boundary artifacts. For blur estimation, a linear inverse problem with normalization and nonnegative constraints must be solved. However, the normalization constraint is ignored in many blind image deblurring methods, mainly because it makes the problem less tractable. In this paper, we show that the normalization constraint can be very naturally incorporated into the estimation process by using a Dirichlet distribution to approximate the posterior distribution of the blur. Making use of variational Dirichlet approximation, we provide a blur posterior approximation that considers the uncertainty of the estimate and removes noise in the estimated kernel. Experiments with synthetic and real data demonstrate that the proposed method is very competitive to the state-of-the-art blind image restoration methods. PMID:26390458
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
TICK: Transparent Incremental Checkpointing at Kernel Level
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
Misquitta, Alston J; Szalewicz, Krzysztof
2005-06-01
A symmetry-adapted perturbation theory based on Kohn-Sham determinants [SAPT(KS)] and utilizing asymptotically corrected exchange-correlation potentials has been applied to the He2, Ne2, (H2O)2, and (CO2)2 dimers. It is shown that SAPT(KS) is able to recover the electrostatic, first-order exchange, second-order induction, and exchange-induction energies with an accuracy approaching and occasionally surpassing that of regular SAPT at the currently programmed theory level. The use of the asymptotic corrections is critical to achieve this accuracy. The SAPT(KS) results can be obtained at a small fraction of the time needed for regular SAPT calculations. The robustness of the SAPT(KS) method with respect to the basis set size is also demonstrated. A theoretical justification for high accuracy of SAPT(KS) predictions for the electrostatic, first-order exchange, and second-order induction energies has been provided.
A kernel autoassociator approach to pattern classification.
Zhang, Haihong; Huang, Weimin; Huang, Zhiyong; Zhang, Bailing
2005-06-01
Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains. PMID:15971928
A kernel autoassociator approach to pattern classification.
Zhang, Haihong; Huang, Weimin; Huang, Zhiyong; Zhang, Bailing
2005-06-01
Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.
PET Image Reconstruction Using Kernel Method
Wang, Guobao; Qi, Jinyi
2014-01-01
Image reconstruction from low-count PET projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization (EM) algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4D dynamic PET patient dataset showed promising results. PMID:25095249
Density estimators in particle hydrodynamics. DTFE versus regular SPH
NASA Astrophysics Data System (ADS)
Pelupessy, F. I.; Schaap, W. E.; van de Weygaert, R.
2003-05-01
We present the results of a study comparing density maps reconstructed by the Delaunay Tessellation Field Estimator (DTFE) and by regular SPH kernel-based techniques. The density maps are constructed from the outcome of an SPH particle hydrodynamics simulation of a multiphase interstellar medium. The comparison between the two methods clearly demonstrates the superior performance of the DTFE with respect to conventional SPH methods, in particular at locations where SPH appears to fail. Filamentary and sheetlike structures form telling examples. The DTFE is a fully self-adaptive technique for reconstructing continuous density fields from discrete particle distributions, and is based upon the corresponding Delaunay tessellation. Its principal asset is its complete independence of arbitrary smoothing functions and parameters specifying the properties of these. As a result it manages to faithfully reproduce the anisotropies of the local particle distribution and through its adaptive and local nature proves to be optimally suited for uncovering the full structural richness in the density distribution. Through the improvement in local density estimates, calculations invoking the DTFE will yield a much better representation of physical processes which depend on density. This will be crucial in the case of feedback processes, which play a major role in galaxy and star formation. The presented results form an encouraging step towards the application and insertion of the DTFE in astrophysical hydrocodes. We describe an outline for the construction of a particle hydrodynamics code in which the DTFE replaces kernel-based methods. Further discussion addresses the issue and possibilities for a moving grid-based hydrocode invoking the DTFE, and Delaunay tessellations, in an attempt to combine the virtues of the Eulerian and Lagrangian approaches.
NASA Astrophysics Data System (ADS)
Tsai, T. C.; Chen, J. P.; Dearden, C.
2014-12-01
The wide variety of ice crystal shapes and growth habits makes it a complicated issue in cloud models. This study developed the bulk ice adaptive habit parameterization based on the theoretical approach of Chen and Lamb (1994) and introduced a 6-class hydrometeors double-moment (mass and number) bulk microphysics scheme with gamma-type size distribution function. Both the proposed schemes have been implemented into the Weather Research and Forecasting model (WRF) model forming a new multi-moment bulk microphysics scheme. Two new moments of ice crystal shape and volume are included for tracking pristine ice's adaptive habit and apparent density. A closure technique is developed to solve the time evolution of the bulk moments. For the verification of the bulk ice habit parameterization, some parcel-type (zero-dimension) calculations were conducted and compared with binned numerical calculations. The results showed that: a flexible size spectrum is important in numerical accuracy, the ice shape can significantly enhance the diffusional growth, and it is important to consider the memory of growth habit (adaptive growth) under varying environmental conditions. Also, the derived results with the 3-moment method were much closer to the binned calculations. A field campaign of DIAMET was selected to simulate in the WRF model for real-case studies. The simulations were performed with the traditional spherical ice and the new adaptive shape schemes to evaluate the effect of crystal habits. Some main features of narrow rain band, as well as the embedded precipitation cells, in the cold front case were well captured by the model. Furthermore, the simulations produced a good agreement in the microphysics against the aircraft observations in ice particle number concentration, ice crystal aspect ratio, and deposition heating rate especially within the temperature region of ice secondary multiplication production.
Dowling, Jason A.; Lambert, Jonathan; Parker, Joel; Salvado, Olivier; Fripp, Jurgen; Capp, Anne; Wratten, Chris; Denham, James W.; Greer, Peter B.
2012-05-01
Purpose: Prostate radiation therapy dose planning directly on magnetic resonance imaging (MRI) scans would reduce costs and uncertainties due to multimodality image registration. Adaptive planning using a combined MRI-linear accelerator approach will also require dose calculations to be performed using MRI data. The aim of this work was to develop an atlas-based method to map realistic electron densities to MRI scans for dose calculations and digitally reconstructed radiograph (DRR) generation. Methods and Materials: Whole-pelvis MRI and CT scan data were collected from 39 prostate patients. Scans from 2 patients showed significantly different anatomy from that of the remaining patient population, and these patients were excluded. A whole-pelvis MRI atlas was generated based on the manually delineated MRI scans. In addition, a conjugate electron-density atlas was generated from the coregistered computed tomography (CT)-MRI scans. Pseudo-CT scans for each patient were automatically generated by global and nonrigid registration of the MRI atlas to the patient MRI scan, followed by application of the same transformations to the electron-density atlas. Comparisons were made between organ segmentations by using the Dice similarity coefficient (DSC) and point dose calculations for 26 patients on planning CT and pseudo-CT scans. Results: The agreement between pseudo-CT and planning CT was quantified by differences in the point dose at isocenter and distance to agreement in corresponding voxels. Dose differences were found to be less than 2%. Chi-squared values indicated that the planning CT and pseudo-CT dose distributions were equivalent. No significant differences (p > 0.9) were found between CT and pseudo-CT Hounsfield units for organs of interest. Mean {+-} standard deviation DSC scores for the atlas-based segmentation of the pelvic bones were 0.79 {+-} 0.12, 0.70 {+-} 0.14 for the prostate, 0.64 {+-} 0.16 for the bladder, and 0.63 {+-} 0.16 for the rectum
Condensed-to-atoms hardness kernel from the response of molecular fragment approach
NASA Astrophysics Data System (ADS)
Miranda-Quintana, Ramón Alain
2016-08-01
Condensed reactivity descriptors obtained from the response of molecular fragment (RMF) approach are analyzed within the variational formulation of conceptual density functional theory. It is shown that this approach can serve as the basis of a coherent formulation of the hardness kernel.
ERIC Educational Resources Information Center
Holland, Paul W.; Thayer, Dorothy T.
A new and unified approach to test equating is described that is based on log-linear models for smoothing score distributions and on the kernel method of nonparametric density estimation. The new method contains both linear and standard equipercentile methods as special cases and can handle several important equating data collection designs. An…
Chakrabartty, Shantanu; Gore, Amit; Oweiss, Karim G
2006-01-01
On chip signal compression is one of the key technologies driving development of energy efficient biotelemetry devices. In this paper, we describe a novel architecture for analog-to-digital (A/D) conversion that combines sigma delta conversion with the spatial data compression in a single module. The architecture called multiple-input multiple-output (MIMO) sigma-delta is based on a min-max gradient descent optimization of a regularized cost function that naturally leads to an A/D formulation. Experimental results with simulated and recorded multichannel data demonstrate the effectiveness of the proposed architecture to eliminate cross-channel redundancy in high density microelectrode data, thus superceding the performance of parallel independent data converters in terms of its energy efficiency.
Estimating stellar mean density through seismic inversions
NASA Astrophysics Data System (ADS)
Reese, D. R.; Marques, J. P.; Goupil, M. J.; Thompson, M. J.; Deheuvels, S.
2012-03-01
improvement to the mean density estimates because of their poorly adapted kernels.
NASA Astrophysics Data System (ADS)
Valdivia, Valeska; Hennebelle, Patrick
2014-11-01
Context. Ultraviolet radiation plays a crucial role in molecular clouds. Radiation and matter are tightly coupled and their interplay influences the physical and chemical properties of gas. In particular, modeling the radiation propagation requires calculating column densities, which can be numerically expensive in high-resolution multidimensional simulations. Aims: Developing fast methods for estimating column densities is mandatory if we are interested in the dynamical influence of the radiative transfer. In particular, we focus on the effect of the UV screening on the dynamics and on the statistical properties of molecular clouds. Methods: We have developed a tree-based method for a fast estimate of column densities, implemented in the adaptive mesh refinement code RAMSES. We performed numerical simulations using this method in order to analyze the influence of the screening on the clump formation. Results: We find that the accuracy for the extinction of the tree-based method is better than 10%, while the relative error for the column density can be much more. We describe the implementation of a method based on precalculating the geometrical terms that noticeably reduces the calculation time. To study the influence of the screening on the statistical properties of molecular clouds we present the probability distribution function of gas and the associated temperature per density bin and the mass spectra for different density thresholds. Conclusions: The tree-based method is fast and accurate enough to be used during numerical simulations since no communication is needed between CPUs when using a fully threaded tree. It is then suitable to parallel computing. We show that the screening for far UV radiation mainly affects the dense gas, thereby favoring low temperatures and affecting the fragmentation. We show that when we include the screening, more structures are formed with higher densities in comparison to the case that does not include this effect. We
Keller, Brad M.; Nathan, Diane L.; Wang Yan; Zheng Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina
2012-08-15
Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., 'FOR PROCESSING') and vendor postprocessed (i.e., 'FOR PRESENTATION'), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then
Direct Density Derivative Estimation.
Sasaki, Hiroaki; Noh, Yung-Kyun; Niu, Gang; Sugiyama, Masashi
2016-06-01
Estimating the derivatives of probability density functions is an essential step in statistical data analysis. A naive approach to estimate the derivatives is to first perform density estimation and then compute its derivatives. However, this approach can be unreliable because a good density estimator does not necessarily mean a good density derivative estimator. To cope with this problem, in this letter, we propose a novel method that directly estimates density derivatives without going through density estimation. The proposed method provides computationally efficient estimation for the derivatives of any order on multidimensional data with a hyperparameter tuning method and achieves the optimal parametric convergence rate. We further discuss an extension of the proposed method by applying regularized multitask learning and a general framework for density derivative estimation based on Bregman divergences. Applications of the proposed method to nonparametric Kullback-Leibler divergence approximation and bandwidth matrix selection in kernel density estimation are also explored. PMID:27140943
Pennesi, Mark E; Garg, Anupam K; Feng, Shu; Michaels, Keith V; Smith, Travis B; Fay, Jonathan D; Weiss, Alison R; Renner, Laurie M; Hurst, Sawan; McGill, Trevor J; Cornea, Anda; Rittenhouse, Kay D; Sperling, Marvin; Fruebis, Joachim; Neuringer, Martha
2014-01-01
The aim of this study was to assess the feasibility of using a commercially available high-resolution adaptive optics (AO) camera to image the cone mosaic in Japanese macaques (Macaca fuscata) with dominantly inherited drusen. The macaques examined develop drusen closely resembling those seen in humans with age-related macular degeneration (AMD). For each animal, we acquired and processed images from the AO camera, montaged the results into a composite image, applied custom cone-counting software to detect individual cone photoreceptors, and created a cone density map of the macular region. We conclude that flood-illuminated AO provides a promising method of visualizing the cone mosaic in nonhuman primates. Future studies will quantify the longitudinal change in the cone mosaic and its relationship to the severity of drusen in these animals.
Canters, R A M; Franckena, M; van der Zee, J; Van Rhoon, G C
2008-12-01
For an efficient clinical use of HTP (hyperthermia treatment planning), optimization methods are needed. In this study, a complaint-adaptive PD (power density) optimization as a tool for HTP-guided steering in deep hyperthermia of pelvic tumors is developed and tested. PD distribution in patients is predicted using FE-models. Two goal functions, Opt1 and Opt2, are applied to optimize PD distributions. Optimization consists of three steps: initial optimization, adaptive optimization after a first complaint and increasing the weight of a region after recurring complaints. Opt1 initially considers only target PD whereas Opt2 also takes into account hot spots. After patient complaints though, both limit PD in a region. Opt1 and Opt2 are evaluated in a phantom test, using patient models and during hyperthermia treatment. The phantom test and a sensitivity study in ten patient models, show that HTP-guided steering is most effective in peripheral complaint regions. Clinical evaluation in two groups of five patients shows that time between complaints is longer using Opt2 (p = 0.007). However, this does not lead to significantly different temperatures (T50s of 40.3 (Opt1) versus 40.1 degrees C (Opt2) (p = 0.898)). HTP-guided steering is feasible in terms of PD reduction in complaint regions and in time consumption. Opt2 is preferable in future use, because of better complaint reduction and control.
A tensor-product-kernel framework for multiscale neural activity decoding and control.
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
Overestimation and Underestimation Biases in Photon Mapping with Non-Constant Kernels.
Garcia Hernandez, Ruben Jesus; Ureña, Carlos; Poch, Jordi; Sbert, Mateu
2014-10-01
This paper presents an analysis of the overestimation bias in common used filtering kernels in the context of photon mapping density estimation. We use the joint distribution of order statistics to calculate the expected value of the estimators of irradiance, and show that the estimator provided by the cone filter is not consistent unless the slope is one (yielding the triangular kernel), and that the Epanechnikov and Silverman kernels are consistent. The Gaussian filter has two different estimation biases: the original normalization constant α underestimates radiance by 46.9 percent, and the use of the kth nearest photon reduces this underestimation slightly. We also show that a new normalization constant for the Gaussian filter together with discarding the contribution of the kth nearest photon in the Gaussian and cone filter estimators produces new, consistent estimators. The specialized differential filter also benefits from the new estimate. PMID:26357390
Fast generation of sparse random kernel graphs
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.
Fast generation of sparse random kernel graphs
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
Evaluation of sintering effects on SiC incorporated UO2 kernels under Ar and Ar-4%H2 environments
Silva, Chinthaka M; Lindemer, Terrence; Hunt, Rodney Dale; Collins, Jack Lee; Terrani, Kurt A; Snead, Lance Lewis
2013-01-01
Silicon carbide (SiC) is suggested as an oxygen getter in UO2 kernels used for TRISO particle fuels to lower oxygen potential and prevent kernel migration during irradiation. Scanning electron microscopy and X-ray diffractometry analyses performed on sintered kernels verified that internal gelation process can be used to incorporate SiC in urania fuel kernels. Sintering in either Ar or Ar-4%H2 at 1500 C lowered the SiC content in the UO2 kernels to some extent. Formation of UC was observed as the major chemical phase in the process, while other minor phases such as U3Si2C2, USi2, U3Si2, and UC2 were also identified. UC formation was presumed to be occurred by two reactions. The first was the SiC reaction with its protective SiO2 oxide layer on SiC grains to produce volatile SiO and free carbon that subsequently reacted with UO2 to form UC. The second process was direct UO2 reaction with SiC grains to form SiO, CO, and UC, especially in Ar-4%H2. A slightly higher density and UC content was observed in the sample sintered in Ar-4%H2, but the use of both atmospheres produced kernels with ~95% of theoretical density. It is suggested that incorporating CO in the sintering gas would prevent UC formation and preserve the initial SiC content.
Experimental study of turbulent flame kernel propagation
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)
Sogi, Dalbir Singh; Siddiq, Muhammad; Greiby, Ibrahim; Dolan, Kirk D
2013-12-01
Mango processing produces significant amount of waste (peels and kernels) that can be utilized for the production of value-added ingredients for various food applications. Mango peel and kernel were dried using different techniques, such as freeze drying, hot air, vacuum and infrared. Freeze dried mango waste had higher antioxidant properties than those from other techniques. The ORAC values of peel and kernel varied from 418-776 and 1547-1819 μmol TE/g db. The solubility of freeze dried peel and kernel powder was the highest. The water and oil absorption index of mango waste powders ranged between 1.83-6.05 and 1.66-3.10, respectively. Freeze dried powders had the lowest bulk density values among different techniques tried. The cabinet dried waste powders can be potentially used in food products to enhance their nutritional and antioxidant properties. PMID:23871007
On Bayesian adaptive video super resolution.
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.
Volatile compound formation during argan kernel roasting.
El Monfalouti, Hanae; Charrouf, Zoubida; Giordano, Manuela; Guillaume, Dominique; Kartah, Badreddine; Harhar, Hicham; Gharby, Saïd; Denhez, Clément; Zeppa, Giuseppe
2013-01-01
Virgin edible argan oil is prepared by cold-pressing argan kernels previously roasted at 110 degrees C for up to 25 minutes. The concentration of 40 volatile compounds in virgin edible argan oil was determined as a function of argan kernel roasting time. Most of the volatile compounds begin to be formed after 15 to 25 minutes of roasting. This suggests that a strictly controlled roasting time should allow the modulation of argan oil taste and thus satisfy different types of consumers. This could be of major importance considering the present booming use of edible argan oil.
Reduced multiple empirical kernel learning machine.
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
Utilizing Kernelized Advection Schemes in Ocean Models
NASA Astrophysics Data System (ADS)
Zadeh, N.; Balaji, V.
2008-12-01
There has been a recent effort in the ocean model community to use a set of generic FORTRAN library routines for advection of scalar tracers in the ocean. In a collaborative project called Hybrid Ocean Model Environement (HOME), vastly different advection schemes (space-differencing schemes for advection equation) become available to modelers in the form of subroutine calls (kernels). In this talk we explore the possibility of utilizing ESMF data structures in wrapping these kernels so that they can be readily used in ESMF gridded components.
Kernel abortion in maize. II. Distribution of /sup 14/C among kernel carboydrates
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.
Accuracy of Reduced and Extended Thin-Wire Kernels
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.
Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image.
Xie, Xudong; Lam, Kin-Man
2006-09-01
In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable. However, this method does not consider the structural characteristics of the face images, and it is difficult to determine which nonlinear mapping is more effective for face recognition. In this paper, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical property of the input features, but also adopts an eigenmask to emphasize those important facial feature points. Therefore, after this mapping, the transformed features have a higher discriminating power, and the relative importance of the features adapts to the spatial importance of the face images. This new nonlinear mapping is combined with the conventional kernel PCA to be called "doubly" nonlinear mapping kernel PCA. The proposed algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with fractional power polynomial models. Experiments show that consistent and promising results are obtained.
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.
Fabrication of Uranium Oxycarbide Kernels for HTR Fuel
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.
NASA Astrophysics Data System (ADS)
Hatton, Joan; McCurdy, Boyd; Greer, Peter B.
2009-08-01
The availability of cone beam computerized tomography (CBCT) images at the time of treatment has opened possibilities for dose calculations representing the delivered dose for adaptive radiation therapy. A significant component in the accuracy of dose calculation is the calibration of the Hounsfield unit (HU) number to electron density (ED). The aim of this work is to assess the impact of HU to ED calibration phantom insert composition and phantom volume on dose calculation accuracy for CBCT. CBCT HU to ED calibration curves for different commercial phantoms were measured and compared. The effect of the scattering volume of the phantom on the HU to ED calibration was examined as a function of phantom length and radial diameter. The resulting calibration curves were used at the treatment planning system to calculate doses for geometrically simple phantoms and a pelvic anatomical phantom to compare against measured doses. Three-dimensional dose distributions for the pelvis phantom were calculated using the HU to ED curves and compared using Chi comparisons. The HU to ED calibration curves for the commercial phantoms diverge at densities greater than that of water, depending on the elemental composition of the phantom insert. The effect of adding scatter material longitudinally, increasing the phantom length from 5 cm to 26 cm, was found to be up to 260 HU numbers for the high-density insert. The change in the HU value, by increasing the diameter of the phantom from 18 to 40 cm, was found to be up to 1200 HU for the high-density insert. The effect of phantom diameter on the HU to ED curve can lead to dose differences for 6 MV and 18 MV x-rays under bone inhomogeneities of up to 20% in extreme cases. These results show significant dosimetric differences when using a calibration phantom with materials which are not tissue equivalent. More importantly, the amount of scattering material used with the HU to ED calibration phantom has a significant effect on the dosimetric
Code of Federal Regulations, 2010 CFR
2010-01-01
... Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards, Inspections, Marketing Practices), DEPARTMENT OF AGRICULTURE REGULATIONS AND STANDARDS UNDER THE AGRICULTURAL MARKETING ACT OF 1946... the separated half of a kernel with not more than one-eighth broken off....
Kernel Temporal Differences for Neural Decoding
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
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...
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...
7 CFR 981.8 - Inedible kernel.
Code of Federal Regulations, 2010 CFR
2010-01-01
... and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE ALMONDS GROWN IN CALIFORNIA Order... of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or brown spot, as defined in the United States Standards for Shelled Almonds, or which has embedded...
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...
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...
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...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2011 CFR
2011-04-01
... 21 Food and Drugs 3 2011-04-01 2011-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2012 CFR
2012-04-01
... 21 Food and Drugs 3 2012-04-01 2012-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2013 CFR
2013-04-01
... 21 Food and Drugs 3 2013-04-01 2013-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2014 CFR
2014-04-01
... 21 Food and Drugs 3 2014-04-01 2014-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a..., packaging, transporting, or holding food, subject to the provisions of this section. (a) Tamarind...
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...
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... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
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... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
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... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
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... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
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... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
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... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 7 Agriculture 2 2014-01-01 2014-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of...
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... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the...
7 CFR 51.2125 - Split or broken kernels.
Code of Federal Regulations, 2013 CFR
2013-01-01
... 7 Agriculture 2 2013-01-01 2013-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... § 51.2125 Split or broken kernels. Split or broken kernels means seven-eighths or less of...
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... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall...
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...
Carbothermic Synthesis of ~820- m UN Kernels. Investigation of Process Variables
Lindemer, Terrence; Silva, Chinthaka M; Henry, Jr, John James; McMurray, Jake W; Jolly, Brian C; Hunt, Rodney Dale; Terrani, Kurt A
2015-06-01
This report details the continued investigation of process variables involved in converting sol-gel-derived, urainia-carbon microspheres to ~820-μm-dia. UN fuel kernels in flow-through, vertical refractory-metal crucibles at temperatures up to 2123 K. Experiments included calcining of air-dried UO_{3}-H_{2}O-C microspheres in Ar and H_{2}-containing gases, conversion of the resulting UO_{2}-C kernels to dense UO_{2}:2UC in the same gases and vacuum, and its conversion in N_{2} to in UC_{1-x}N_{x}. The thermodynamics of the relevant reactions were applied extensively to interpret and control the process variables. Producing the precursor UO_{2}:2UC kernel of ~96% theoretical density was required, but its subsequent conversion to UC_{1-x}N_{x} at 2123 K was not accompanied by sintering and resulted in ~83-86% of theoretical density. Decreasing the UC_{1-x}N_{x} kernel carbide component via HCN evolution was shown to be quantitatively consistent with present and past experiments and the only useful application of H2 in the entire process.
Du, Guo-dong; Lü, De-guo; Zhao, Ling; Wang, Su-su; Cai, Qian
2011-03-01
In order to explore the photosynthetic adaption mechanisms of kernel-used apricot under high temperature stress, gas exchange technique and chlorophyll fluorescence transient technique (JIP-test) were adopted to study the leaf photosynthetic characteristics and photosystem II (PS II) photochemical activity of 4 year-old 'Chaoren' (Armeniaca vulgaris x sibirica) growing on Horqin sandy land at 25 degrees C, 30 degrees C, 40 degrees C, and 50 degrees C. Within a definite temperature range, and as the temperature increased, the 'Chaoren' could enhance its leaf photosynthetic pigments content and ratio to maintain the light absorption, transfer, and conversion, and thereby, to ensure the function of photosynthetic apparatus. However, when the temperature exceeded the physiological adjustment threshold of leaves, the chlorophyll began to be decomposed, net photosynthetic rate (Pn) declined obviously, and intercellular CO2 concentration (Ci) increased, indicating that the decline in photosynthesis was limited by mesophyll factor. At 40 degrees C, the density of PS II reaction centers per excited cross-section (RC/CS0) dropped distinctly; and at 50 degrees C, the K phase (Wk) and J phase (Vj) in the O-J-I-P chlorophyll fluorescence transients increased distinctly, indicating that high temperature damaged the oxygen-evolving complex (OEC), donor sides, and PS II reaction centers. In addition, the minimum chlorophyll fluorescence (F0) at 50 degrees C increased significantly by 1.26 times, compared with the control, and the maximum photochemical efficiency (Fv/Fm) and performance index (PI(ABS)) reduced to 37.9% and 10.3% of the control, respectively. High temperature injured the function of the donor and acceptor sides in the PS II of photosynthetic apparatus, leading to the decrease of photosynthetic efficiency, and being one of the main mechanisms for the damage of photosynthetic apparatus in kernel-used apricot leaves under high temperature stress.
Hohenstein, Edward G; Parrish, Robert M; Sherrill, C David; Turney, Justin M; Schaefer, Henry F
2011-11-01
Symmetry-adapted perturbation theory (SAPT) provides a means of probing the fundamental nature of intermolecular interactions. Low-orders of SAPT (here, SAPT0) are especially attractive since they provide qualitative (sometimes quantitative) results while remaining tractable for large systems. The application of density fitting and Laplace transformation techniques to SAPT0 can significantly reduce the expense associated with these computations and make even larger systems accessible. We present new factorizations of the SAPT0 equations with density-fitted two-electron integrals and the first application of Laplace transformations of energy denominators to SAPT. The improved scalability of the DF-SAPT0 implementation allows it to be applied to systems with more than 200 atoms and 2800 basis functions. The Laplace-transformed energy denominators are compared to analogous partial Cholesky decompositions of the energy denominator tensor. Application of our new DF-SAPT0 program to the intercalation of DNA by proflavine has allowed us to determine the nature of the proflavine-DNA interaction. Overall, the proflavine-DNA interaction contains important contributions from both electrostatics and dispersion. The energetics of the intercalator interaction are are dominated by the stacking interactions (two-thirds of the total), but contain important contributions from the intercalator-backbone interactions. It is hypothesized that the geometry of the complex will be determined by the interactions of the intercalator with the backbone, because by shifting toward one side of the backbone, the intercalator can form two long hydrogen-bonding type interactions. The long-range interactions between the intercalator and the next-nearest base pairs appear to be negligible, justifying the use of truncated DNA models in computational studies of intercalation interaction energies.
NASA Astrophysics Data System (ADS)
Gonthier, Jérôme F.; Sherrill, C. David
2016-10-01
Symmetry-Adapted Perturbation Theory (SAPT) is one of the most popular approaches to energy component analysis of non-covalent interactions between closed-shell systems, yielding both accurate interaction energies and meaningful interaction energy components. In recent years, the full open-shell equations for SAPT up to second-order in the intermolecular interaction and zeroth-order in the intramolecular correlation (SAPT0) were published [P. S. Zuchowski et al., J. Chem. Phys. 129, 084101 (2008); M. Hapka et al., ibid. 137, 164104 (2012)]. Here, we utilize density-fitted electron repulsion integrals to produce an efficient computational implementation. This approach is used to examine the effect of ionization on π-π interactions. For the benzene dimer radical cation, comparison against reference values indicates a good performance for open-shell SAPT0, except in cases with substantial charge transfer. For π stacking between hydrogen-bonded pairs of nucleobases, dispersion interactions still dominate binding, in spite of the creation of a positive charge.
Lao, Ka Un; Herbert, John M.
2014-01-28
The performance of second-order symmetry-adapted perturbation theory (SAPT) calculations using Kohn-Sham (KS) orbitals is evaluated against benchmark results for intermolecular interactions. Unlike previous studies of this “SAPT(KS)” methodology, the present study uses non-empirically tuned long-range corrected (LRC) functionals for the monomers. The proper v{sub xc} (r)→0 asymptotic limit is achieved by tuning the range separation parameter in order to satisfy the condition that the highest occupied KS energy level equals minus the molecule's ionization energy, for each monomer unit. Tests for He{sub 2}, Ne{sub 2}, and the S22 and S66 data sets reveal that this condition is important for accurate prediction of the non-dispersion components of the energy, although errors in SAPT(KS) dispersion energies remain unacceptably large. In conjunction with an empirical dispersion potential, however, the SAPT(KS) method affords good results for S22 and S66, and also accurately predicts the whole potential energy curve for the sandwich isomer of the benzene dimer. Tuned LRC functionals represent an attractive alternative to other asymptotic corrections that have been employed in density-functional-based SAPT calculations, and we recommend the use of tuned LRC functionals in both coupled-perturbed SAPT(DFT) calculations and dispersion-corrected SAPT(KS) calculations.
Chare kernel; A runtime support system for parallel computations
Shu, W. ); Kale, L.V. )
1991-03-01
This paper presents the chare kernel system, which supports parallel computations with irregular structure. The chare kernel is a collection of primitive functions that manage chares, manipulative messages, invoke atomic computations, and coordinate concurrent activities. Programs written in the chare kernel language can be executed on different parallel machines without change. Users writing such programs concern themselves with the creation of parallel actions but not with assigning them to specific processors. The authors describe the design and implementation of the chare kernel. Performance of chare kernel programs on two hypercube machines, the Intel iPSC/2 and the NCUBE, is also given.
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.
NASA Astrophysics Data System (ADS)
Disher, Brandon; Hajdok, George; Wang, An; Craig, Jeff; Gaede, Stewart; Battista, Jerry J.
2013-06-01
Cone-beam computed tomography (CBCT) has rapidly become a clinically useful imaging modality for image-guided radiation therapy. Unfortunately, CBCT images of the thorax are susceptible to artefacts due to scattered photons, beam hardening, lag in data acquisition, and respiratory motion during a slow scan. These limitations cause dose errors when CBCT image data are used directly in dose computations for on-line, dose adaptive radiation therapy (DART). The purpose of this work is to assess the magnitude of errors in CBCT numbers (HU), and determine the resultant effects on derived tissue density and computed dose accuracy for stereotactic body radiation therapy (SBRT) of lung cancer. Planning CT (PCT) images of three lung patients were acquired using a Philips multi-slice helical CT simulator, while CBCT images were obtained with a Varian On-Board Imaging system. To account for erroneous CBCT data, three practical correction techniques were tested: (1) conversion of CBCT numbers to electron density using phantoms, (2) replacement of individual CBCT pixel values with bulk CT numbers, averaged from PCT images for tissue regions, and (3) limited replacement of CBCT lung pixels values (LCT) likely to produce artificial lateral electron disequilibrium. For each corrected CBCT data set, lung SBRT dose distributions were computed for a 6 MV volume modulated arc therapy (VMAT) technique within the Philips Pinnacle treatment planning system. The reference prescription dose was set such that 95% of the planning target volume (PTV) received at least 54 Gy (i.e. D95). Further, we used the relative depth dose factor as an a priori index to predict the effects of incorrect low tissue density on computed lung dose in regions of severe electron disequilibrium. CT number profiles from co-registered CBCT and PCT patient lung images revealed many reduced lung pixel values in CBCT data, with some pixels corresponding to vacuum (-1000 HU). Similarly, CBCT data in a plastic lung
Online kernel principal component analysis: a reduced-order model.
Honeine, Paul
2012-09-01
Kernel principal component analysis (kernel-PCA) is an elegant nonlinear extension of one of the most used data analysis and dimensionality reduction techniques, the principal component analysis. In this paper, we propose an online algorithm for kernel-PCA. To this end, we examine a kernel-based version of Oja's rule, initially put forward to extract a linear principal axe. As with most kernel-based machines, the model order equals the number of available observations. To provide an online scheme, we propose to control the model order. We discuss theoretical results, such as an upper bound on the error of approximating the principal functions with the reduced-order model. We derive a recursive algorithm to discover the first principal axis, and extend it to multiple axes. Experimental results demonstrate the effectiveness of the proposed approach, both on synthetic data set and on images of handwritten digits, with comparison to classical kernel-PCA and iterative kernel-PCA.
A Novel Framework for Learning Geometry-Aware Kernels.
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.
Quark-hadron duality: Pinched kernel approach
NASA Astrophysics Data System (ADS)
Dominguez, C. A.; Hernandez, L. A.; Schilcher, K.; Spiesberger, H.
2016-08-01
Hadronic spectral functions measured by the ALEPH collaboration in the vector and axial-vector channels are used to study potential quark-hadron duality violations (DV). This is done entirely in the framework of pinched kernel finite energy sum rules (FESR), i.e. in a model independent fashion. The kinematical range of the ALEPH data is effectively extended up to s = 10 GeV2 by using an appropriate kernel, and assuming that in this region the spectral functions are given by perturbative QCD. Support for this assumption is obtained by using e+ e‑ annihilation data in the vector channel. Results in both channels show a good saturation of the pinched FESR, without further need of explicit models of DV.
Wilson Dslash Kernel From Lattice QCD Optimization
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.
NASA Astrophysics Data System (ADS)
Bologna, Mauro
2010-09-01
This paper addresses the problem of finding an asymptotic solution for first- and second-order integro-differential equations containing an arbitrary kernel, by evaluating the corresponding inverse Laplace and Fourier transforms. The aim of the paper is to go beyond the Tauberian theorem in the case of integral-differential equations which are widely used by the scientific community. The results are applied to the convolute form of the Lindblad equation setting generic conditions on the kernel in such a way as to generate a positive definite density matrix, and show that the structure of the eigenvalues of the correspondent Liouvillian operator plays a crucial role in determining the positivity of the density matrix.
Searching and Indexing Genomic Databases via Kernelization
Gagie, Travis; Puglisi, Simon J.
2015-01-01
The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper, we survey the 20-year history of this idea and discuss its relation to kernelization in parameterized complexity. PMID:25710001
A Fast Reduced Kernel Extreme Learning Machine.
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.
Semi-Supervised Kernel Mean Shift Clustering.
Anand, Saket; Mittal, Sushil; Tuzel, Oncel; Meer, Peter
2014-06-01
Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original distance metric fails to capture the underlying cluster structure. Despite recent advances in semi-supervised clustering methods, there has been little effort towards incorporating supervision into mean shift. We propose a semi-supervised framework for kernel mean shift clustering (SKMS) that uses only pairwise constraints to guide the clustering procedure. The points are first mapped to a high-dimensional kernel space where the constraints are imposed by a linear transformation of the mapped points. This is achieved by modifying the initial kernel matrix by minimizing a log det divergence-based objective function. We show the advantages of SKMS by evaluating its performance on various synthetic and real datasets while comparing with state-of-the-art semi-supervised clustering algorithms. PMID:26353281
Kernel methods for phenotyping complex plant architecture.
Kawamura, Koji; Hibrand-Saint Oyant, Laurence; Foucher, Fabrice; Thouroude, Tatiana; Loustau, Sébastien
2014-02-01
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.
A Fast Reduced Kernel Extreme Learning Machine.
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. PMID:26829605
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
Selection and properties of alternative forming fluids for TRISO fuel kernel production
Baker, M. P.; King, J. C.; Gorman, B. P.; Marshall, Doug W.
2013-01-01
Current Very High Temperature Reactor (VHTR) designs incorporate TRi-structural ISOtropic (TRISO) fuel, which consists of a spherical fissile fuel kernel surrounded by layers of pyrolytic carbon and silicon carbide. An internal sol-gel process forms the fuel kernel using wet chemistry to produce uranium oxyhydroxide gel spheres by dropping a cold precursor solution into a hot column of trichloroethylene (TCE). Over time, gelation byproducts inhibit complete gelation, and the TCE must be purified or discarded. The resulting TCE waste stream contains both radioactive and hazardous materials and is thus considered a mixed hazardous waste. Changing the forming fluid to a non-hazardous alternative could greatly improve the economics of TRISO fuel kernel production. Selection criteria for a replacement forming fluid narrowed a list of ~10,800 chemicals to yield ten potential replacement forming fluids: 1-bromododecane, 1- bromotetradecane, 1-bromoundecane, 1-chlorooctadecane, 1-chlorotetradecane, 1-iododecane, 1-iodododecane, 1-iodohexadecane, 1-iodooctadecane, and squalane. The density, viscosity, and surface tension for each potential replacement forming fluid were measured as a function of temperature between 25 °C and 80 °C. Calculated settling velocities and heat transfer rates give an overall column height approximation. 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane show the greatest promise as replacements, and future tests will verify their ability to form satisfactory fuel kernels.
Lindström, Tom; Håkansson, Nina; Wennergren, Uno
2011-05-22
Ecological and epidemiological invasions occur in a spatial context. We investigated how these processes correlate to the distance dependence of spread or dispersal between spatial entities such as habitat patches or epidemiological units. Distance dependence is described by a spatial kernel, characterized by its shape (kurtosis) and width (variance). We also developed a novel method to analyse and generate point-pattern landscapes based on spectral representation. This involves two measures: continuity, which is related to autocorrelation and contrast, which refers to variation in patch density. We also analysed some empirical data where our results are expected to have implications, namely distributions of trees (Quercus and Ulmus) and farms in Sweden. Through a simulation study, we found that kernel shape was not important for predicting the invasion speed in randomly distributed patches. However, the shape may be essential when the distribution of patches deviates from randomness, particularly when the contrast is high. We conclude that the speed of invasions depends on the spatial context and the effect of the spatial kernel is intertwined with the spatial structure. This implies substantial demands on the empirical data, because it requires knowledge of shape and width of the spatial kernel, and spatial structure.
A substitute for the singular Green kernel in the Newtonian potential of celestial bodies
NASA Astrophysics Data System (ADS)
Huré, J.-M.; Dieckmann, A.
2012-05-01
The "point mass singularity" inherent in Newton's law for gravitation represents a major difficulty in accurately determining the potential and forces inside continuous bodies. Here we report a simple and efficient analytical method to bypass the singular Green kernel 1/|r - r'| inside the source without altering the nature of the interaction. We build an equivalent kernel made up of a "cool kernel", which is fully regular (and contains the long-range - GM/r asymptotic behavior), and the gradient of a "hyperkernel", which is also regular. Compared to the initial kernel, these two components are easily integrated over the source volume using standard numerical techniques. The demonstration is presented for three-dimensional distributions in cylindrical coordinates, which are well-suited to describing rotating bodies (stars, discs, asteroids, etc.) as commonly found in the Universe. An example of implementation is given. The case of axial symmetry is treated in detail, and the accuracy is checked by considering an exact potential/surface density pair corresponding to a flat circular disc. This framework provides new tools to keep or even improve the physical realism of models and simulations of self-gravitating systems, and represents, for some of them, a conclusive alternative to softened gravity.
The shape of the spatial kernel and its implications for biological invasions in patchy environments
Lindström, Tom; Håkansson, Nina; Wennergren, Uno
2011-01-01
Ecological and epidemiological invasions occur in a spatial context. We investigated how these processes correlate to the distance dependence of spread or dispersal between spatial entities such as habitat patches or epidemiological units. Distance dependence is described by a spatial kernel, characterized by its shape (kurtosis) and width (variance). We also developed a novel method to analyse and generate point-pattern landscapes based on spectral representation. This involves two measures: continuity, which is related to autocorrelation and contrast, which refers to variation in patch density. We also analysed some empirical data where our results are expected to have implications, namely distributions of trees (Quercus and Ulmus) and farms in Sweden. Through a simulation study, we found that kernel shape was not important for predicting the invasion speed in randomly distributed patches. However, the shape may be essential when the distribution of patches deviates from randomness, particularly when the contrast is high. We conclude that the speed of invasions depends on the spatial context and the effect of the spatial kernel is intertwined with the spatial structure. This implies substantial demands on the empirical data, because it requires knowledge of shape and width of the spatial kernel, and spatial structure. PMID:21047854
Selection and properties of alternative forming fluids for TRISO fuel kernel production
NASA Astrophysics Data System (ADS)
Baker, M. P.; King, J. C.; Gorman, B. P.; Marshall, D. W.
2013-01-01
Current Very High Temperature Reactor (VHTR) designs incorporate TRi-structural ISOtropic (TRISO) fuel, which consists of a spherical fissile fuel kernel surrounded by layers of pyrolytic carbon and silicon carbide. An internal sol-gel process forms the fuel kernel using wet chemistry to produce uranium oxyhydroxide gel spheres by dropping a cold precursor solution into a hot column of trichloroethylene (TCE). Over time, gelation byproducts inhibit complete gelation, and the TCE must be purified or discarded. The resulting TCE waste stream contains both radioactive and hazardous materials and is thus considered a mixed hazardous waste. Changing the forming fluid to a non-hazardous alternative could greatly improve the economics of TRISO fuel kernel production. Selection criteria for a replacement forming fluid narrowed a list of ˜10,800 chemicals to yield ten potential replacement forming fluids: 1-bromododecane, 1-bromotetradecane, 1-bromoundecane, 1-chlorooctadecane, 1-chlorotetradecane, 1-iododecane, 1-iodododecane, 1-iodohexadecane, 1-iodooctadecane, and squalane. The density, viscosity, and surface tension for each potential replacement forming fluid were measured as a function of temperature between 25 °C and 80 °C. Calculated settling velocities and heat transfer rates give an overall column height approximation. 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane show the greatest promise as replacements, and future tests will verify their ability to form satisfactory fuel kernels.
NASA Astrophysics Data System (ADS)
Wouters, Sebastian; Poelmans, Ward; Ayers, Paul W.; Van Neck, Dimitri
2014-06-01
The density matrix renormalization group (DMRG) has become an indispensable numerical tool to find exact eigenstates of finite-size quantum systems with strong correlation. In the fields of condensed matter, nuclear structure and molecular electronic structure, it has significantly extended the system sizes that can be handled compared to full configuration interaction, without losing numerical accuracy. For quantum chemistry (QC), the most efficient implementations of DMRG require the incorporation of particle number, spin and point group symmetries in the underlying matrix product state (MPS) ansatz, as well as the use of so-called complementary operators. The symmetries introduce a sparse block structure in the MPS ansatz and in the intermediary contracted tensors. If a symmetry is non-abelian, the Wigner-Eckart theorem allows to factorize a tensor into a Clebsch-Gordan coefficient and a reduced tensor. In addition, the fermion signs have to be carefully tracked. Because of these challenges, implementing DMRG efficiently for QC is not straightforward. Efficient and freely available implementations are therefore highly desired. In this work we present CheMPS2, our free open-source spin-adapted implementation of DMRG for ab initio QC. Around CheMPS2, we have implemented the augmented Hessian Newton-Raphson complete active space self-consistent field method, with exact Hessian. The bond dissociation curves of the 12 lowest states of the carbon dimer were obtained at the DMRG(28 orbitals, 12 electrons, DSU(2) = 2500)/cc-pVDZ level of theory. The contribution of 1 s core correlation to the X1Σg+ bond dissociation curve of the carbon dimer was estimated by comparing energies at the DMRG(36o, 12e, DSU(2) = 2500)/cc-pCVDZ and DMRG-SCF(34o, 8e, DSU(2) = 2500)/cc-pCVDZ levels of theory.
FABRICATION OF URANIUM OXYCARBIDE KERNELS AND COMPACTS FOR HTR FUEL
Dr. Jeffrey A. Phillips; Eric L. Shaber; Scott G. Nagley
2012-10-01
fabrication process was improved and changed. Changes to the kernel fabrication process included replacing the carbon black powder feed with a surface-modified carbon slurry and shortening the sintering schedule. AGR-2 TRISO particles were produced in a 6-inch diameter coater using a charge size about 21-times that of the 2-inch diameter coater used to coat AGR-1 particles. The compacting process was changed to increase matrix density and throughput by increasing the temperature and pressure of pressing and using a different type of press. AGR-2 fuel began irradiation in the ATR in late spring 2010.
Nonlinear feature extraction using kernel principal component analysis with non-negative pre-image.
Kallas, Maya; Honeine, Paul; Richard, Cedric; Amoud, Hassan; Francis, Clovis
2010-01-01
The inherent physical characteristics of many real-life phenomena, including biological and physiological aspects, require adapted nonlinear tools. Moreover, the additive nature in some situations involve solutions expressed as positive combinations of data. In this paper, we propose a nonlinear feature extraction method, with a non-negativity constraint. To this end, the kernel principal component analysis is considered to define the most relevant features in the reproducing kernel Hilbert space. These features are the nonlinear principal components with high-order correlations between input variables. A pre-image technique is required to get back to the input space. With a non-negative constraint, we show that one can solve the pre-image problem efficiently, using a simple iterative scheme. Furthermore, the constrained solution contributes to the stability of the algorithm. Experimental results on event-related potentials (ERP) illustrate the efficiency of the proposed method.
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.
Multiple kernel learning for sparse representation-based classification.
Shrivastava, Ashish; Patel, Vishal M; Chellappa, Rama
2014-07-01
In this paper, we propose a multiple kernel learning (MKL) algorithm that is based on the sparse representation-based classification (SRC) method. Taking advantage of the nonlinear kernel SRC in efficiently representing the nonlinearities in the high-dimensional feature space, we propose an MKL method based on the kernel alignment criteria. Our method uses a two step training method to learn the kernel weights and sparse codes. At each iteration, the sparse codes are updated first while fixing the kernel mixing coefficients, and then the kernel mixing coefficients are updated while fixing the sparse codes. These two steps are repeated until a stopping criteria is met. The effectiveness of the proposed method is demonstrated using several publicly available image classification databases and it is shown that this method can perform significantly better than many competitive image classification algorithms. PMID:24835226
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps.
Rasmussen, Peter Mondrup; Madsen, Kristoffer Hougaard; Lund, Torben Ellegaard; Hansen, Lars Kai
2011-04-01
There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification models. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher discriminant, and the SVM, and conclude that the sensitivity map is a versatile and computationally efficient tool for visualization of nonlinear kernel models in neuroimaging.
NASA Astrophysics Data System (ADS)
Pedretti, Daniele; Fernàndez-Garcia, Daniel
2013-09-01
Particle tracking methods to simulate solute transport deal with the issue of having to reconstruct smooth concentrations from a limited number of particles. This is an error-prone process that typically leads to large fluctuations in the determined late-time behavior of breakthrough curves (BTCs). Kernel density estimators (KDE) can be used to automatically reconstruct smooth BTCs from a small number of particles. The kernel approach incorporates the uncertainty associated with subsampling a large population by equipping each particle with a probability density function. Two broad classes of KDE methods can be distinguished depending on the parametrization of this function: global and adaptive methods. This paper shows that each method is likely to estimate a specific portion of the BTCs. Although global methods offer a valid approach to estimate early-time behavior and peak of BTCs, they exhibit important fluctuations at the tails where fewer particles exist. In contrast, locally adaptive methods improve tail estimation while oversmoothing both early-time and peak concentrations. Therefore a new method is proposed combining the strength of both KDE approaches. The proposed approach is universal and only needs one parameter (α) which slightly depends on the shape of the BTCs. Results show that, for the tested cases, heavily-tailed BTCs are properly reconstructed with α ≈ 0.5 .
Monte Carlo Code System for Electron (Positron) Dose Kernel Calculations.
CHIBANI, OMAR
1999-05-12
Version 00 KERNEL performs dose kernel calculations for an electron (positron) isotropic point source in an infinite homogeneous medium. First, the auxiliary code PRELIM is used to prepare cross section data for the considered medium. Then the KERNEL code simulates the transport of electrons and bremsstrahlung photons through the medium until all particles reach their cutoff energies. The deposited energy is scored in concentric spherical shells at a radial distance ranging from zero to twice the source particle range.
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.
Embedded real-time operating system micro kernel design
NASA Astrophysics Data System (ADS)
Cheng, Xiao-hui; Li, Ming-qiang; Wang, Xin-zheng
2005-12-01
Embedded systems usually require a real-time character. Base on an 8051 microcontroller, an embedded real-time operating system micro kernel is proposed consisting of six parts, including a critical section process, task scheduling, interruption handle, semaphore and message mailbox communication, clock managent and memory managent. Distributed CPU and other resources are among tasks rationally according to the importance and urgency. The design proposed here provides the position, definition, function and principle of micro kernel. The kernel runs on the platform of an ATMEL AT89C51 microcontroller. Simulation results prove that the designed micro kernel is stable and reliable and has quick response while operating in an application system.
Robust visual tracking via speedup multiple kernel ridge regression
NASA Astrophysics Data System (ADS)
Qian, Cheng; Breckon, Toby P.; Li, Hui
2015-09-01
Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize the nonlinear structure well. We propose an appearance model based on kernel ridge regression for visual tracking. Dense sampling is fulfilled around the target image patches to collect the training samples. In order to obtain a kernel space in favor of describing the target appearance, multiple kernel learning is introduced into the selection of kernels. Under the framework, instead of a single kernel, a linear combination of kernels is learned from the training samples to create a kernel space. Resorting to the circulant property of a kernel matrix, a fast interpolate iterative algorithm is developed to seek coefficients that are assigned to these kernels so as to give an optimal combination. After the regression function is learned, all candidate image patches gathered are taken as the input of the function, and the candidate with the maximal response is regarded as the object image patch. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art tracking methods.
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.
LFK. Livermore FORTRAN Kernel Computer Test
McMahon, F.H.
1990-05-01
LFK, the Livermore FORTRAN Kernels, is a computer performance test that measures a realistic floating-point performance range for FORTRAN applications. Informally known as the Livermore Loops test, the LFK test may be used as a computer performance test, as a test of compiler accuracy (via checksums) and efficiency, or as a hardware endurance test. The LFK test, which focuses on FORTRAN as used in computational physics, measures the joint performance of the computer CPU, the compiler, and the computational structures in units of Megaflops/sec or Mflops. A C language version of subroutine KERNEL is also included which executes 24 samples of C numerical computation. The 24 kernels are a hydrodynamics code fragment, a fragment from an incomplete Cholesky conjugate gradient code, the standard inner product function of linear algebra, a fragment from a banded linear equations routine, a segment of a tridiagonal elimination routine, an example of a general linear recurrence equation, an equation of state fragment, part of an alternating direction implicit integration code, an integrate predictor code, a difference predictor code, a first sum, a first difference, a fragment from a two-dimensional particle-in-cell code, a part of a one-dimensional particle-in-cell code, an example of how casually FORTRAN can be written, a Monte Carlo search loop, an example of an implicit conditional computation, a fragment of a two-dimensional explicit hydrodynamics code, a general linear recurrence equation, part of a discrete ordinates transport program, a simple matrix calculation, a segment of a Planckian distribution procedure, a two-dimensional implicit hydrodynamics fragment, and determination of the location of the first minimum in an array.
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.
Verification of Chare-kernel programs
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.
A Kernel-Free Particle-Finite Element Method for Hypervelocity Impact Simulation. Chapter 4
NASA Technical Reports Server (NTRS)
Park, Young-Keun; Fahrenthold, Eric P.
2004-01-01
An improved hybrid particle-finite element method has been developed for the simulation of hypervelocity impact problems. Unlike alternative methods, the revised formulation computes the density without reference to any kernel or interpolation functions, for either the density or the rate of dilatation. This simplifies the state space model and leads to a significant reduction in computational cost. The improved method introduces internal energy variables as generalized coordinates in a new formulation of the thermomechanical Lagrange equations. Example problems show good agreement with exact solutions in one dimension and good agreement with experimental data in a three dimensional simulation.
NASA Astrophysics Data System (ADS)
Lindemer, T. B.; Voit, S. L.; Silva, C. M.; Besmann, T. M.; Hunt, R. D.
2014-05-01
The US Department of Energy is developing a new nuclear fuel that would be less susceptible to ruptures during a loss-of-coolant accident. The fuel would consist of tristructural isotropic coated particles with uranium nitride (UN) kernels with diameters near 825 μm. This effort explores factors involved in the conversion of uranium oxide-carbon microspheres into UN kernels. An analysis of previous studies with sufficient experimental details is provided. Thermodynamic calculations were made to predict pressures of carbon monoxide and other relevant gases for several reactions that can be involved in the conversion of uranium oxides and carbides into UN. Uranium oxide-carbon microspheres were heated in a microbalance with an attached mass spectrometer to determine details of calcining and carbothermic conversion in argon, nitrogen, and vacuum. A model was derived from experiments on the vacuum conversion to uranium oxide-carbide kernels. UN-containing kernels were fabricated using this vacuum conversion as part of the overall process. Carbonitride kernels of ∼89% of theoretical density were produced along with several observations concerning the different stages of the process.
Linear and kernel methods for multi- and hypervariate change detection
NASA Astrophysics Data System (ADS)
Nielsen, Allan A.; Canty, Morton J.
2010-10-01
The iteratively re-weighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsuper- vised change detection in multi- and hyperspectral remote sensing imagery as well as for automatic radiometric normalization of multi- or hypervariate multitemporal image sequences. Principal component analysis (PCA) as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (which are nonlinear), may further enhance change signals relative to no-change background. The kernel versions are based on a dual formulation, also termed Q-mode analysis, in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution, also known as the kernel trick, these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of the kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component analysis (PCA), kernel MAF and kernel MNF analyses handle nonlinearities by implicitly transforming data into high (even innite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In image analysis the Gram matrix is often prohibitively large (its size is the number of pixels in the image squared). In this case we may sub-sample the image and carry out the kernel eigenvalue analysis on a set of training data samples only. To obtain a transformed version of the entire image we then project all pixels, which we call the test data, mapped nonlinearly onto the primal eigenvectors. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric normalization and kernel PCA/MAF/MNF transformations have been written
Fructan metabolism in developing wheat (Triticum aestivum L.) kernels.
Verspreet, Joran; Cimini, Sara; Vergauwen, Rudy; Dornez, Emmie; Locato, Vittoria; Le Roy, Katrien; De Gara, Laura; Van den Ende, Wim; Delcour, Jan A; Courtin, Christophe M
2013-12-01
Although fructans play a crucial role in wheat kernel development, their metabolism during kernel maturation is far from being understood. In this study, all major fructan-metabolizing enzymes together with fructan content, fructan degree of polymerization and the presence of fructan oligosaccharides were examined in developing wheat kernels (Triticum aestivum L. var. Homeros) from anthesis until maturity. Fructan accumulation occurred mainly in the first 2 weeks after anthesis, and a maximal fructan concentration of 2.5 ± 0.3 mg fructan per kernel was reached at 16 days after anthesis (DAA). Fructan synthesis was catalyzed by 1-SST (sucrose:sucrose 1-fructosyltransferase) and 6-SFT (sucrose:fructan 6-fructosyltransferase), and to a lesser extent by 1-FFT (fructan:fructan 1-fructosyltransferase). Despite the presence of 6G-kestotriose in wheat kernel extracts, the measured 6G-FFT (fructan:fructan 6G-fructosyltransferase) activity levels were low. During kernel filling, which lasted from 2 to 6 weeks after anthesis, kernel fructan content decreased from 2.5 ± 0.3 to 1.31 ± 0.12 mg fructan per kernel (42 DAA) and the average fructan degree of polymerization decreased from 7.3 ± 0.4 (14 DAA) to 4.4 ± 0.1 (42 DAA). FEH (fructan exohydrolase) reached maximal activity between 20 and 28 DAA. No fructan-metabolizing enzyme activities were registered during the final phase of kernel maturation, and fructan content and structure remained unchanged. This study provides insight into the complex metabolism of fructans during wheat kernel development and relates fructan turnover to the general phases of kernel development.
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.
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.
Delimiting Areas of Endemism through Kernel Interpolation
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
Pareto-path multitask multiple kernel learning.
Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2015-01-01
A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches. PMID:25532155
Scientific Computing Kernels on the Cell Processor
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.
Pareto-path multitask multiple kernel learning.
Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2015-01-01
A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.
Stable Local Volatility Calibration Using Kernel Splines
NASA Astrophysics Data System (ADS)
Coleman, Thomas F.; Li, Yuying; Wang, Cheng
2010-09-01
We propose an optimization formulation using L1 norm to ensure accuracy and stability in calibrating a local volatility function for option pricing. Using a regularization parameter, the proposed objective function balances the calibration accuracy with the model complexity. Motivated by the support vector machine learning, the unknown local volatility function is represented by a kernel function generating splines and the model complexity is controlled by minimizing the 1-norm of the kernel coefficient vector. In the context of the support vector regression for function estimation based on a finite set of observations, this corresponds to minimizing the number of support vectors for predictability. We illustrate the ability of the proposed approach to reconstruct the local volatility function in a synthetic market. In addition, based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is simple and resembles the observed implied volatility surface in shape. Stability is illustrated by calibrating local volatility functions using market option data from different dates.
Transcriptome analysis of Ginkgo biloba kernels
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
Collins, J.L.
2004-12-02
The main objective of the Depleted UO{sub 2} Kernels Production Task at Oak Ridge National Laboratory (ORNL) was to conduct two small-scale production campaigns to produce 2 kg of UO{sub 2} kernels with diameters of 500 {+-} 20 {micro}m and 3.5 kg of UO{sub 2} kernels with diameters of 350 {+-} 10 {micro}m for the U.S. Department of Energy Advanced Fuel Cycle Initiative Program. The final acceptance requirements for the UO{sub 2} kernels are provided in the first section of this report. The kernels were prepared for use by the ORNL Metals and Ceramics Division in a development study to perfect the triisotropic (TRISO) coating process. It was important that the kernels be strong and near theoretical density, with excellent sphericity, minimal surface roughness, and no cracking. This report gives a detailed description of the production efforts and results as well as an in-depth description of the internal gelation process and its chemistry. It describes the laboratory-scale gel-forming apparatus, optimum broth formulation and operating conditions, preparation of the acid-deficient uranyl nitrate stock solution, the system used to provide uniform broth droplet formation and control, and the process of calcining and sintering UO{sub 3} {center_dot} 2H{sub 2}O microspheres to form dense UO{sub 2} kernels. The report also describes improvements and best past practices for uranium kernel formation via the internal gelation process, which utilizes hexamethylenetetramine and urea. Improvements were made in broth formulation and broth droplet formation and control that made it possible in many of the runs in the campaign to produce the desired 350 {+-} 10-{micro}m-diameter kernels, and to obtain very high yields.
NASA Astrophysics Data System (ADS)
Darvish, Behnam; Mobasher, Bahram; Sobral, David; Scoville, Nicholas; Aragon-Calvo, Miguel
2015-06-01
It is well-known that a galaxy’s environment has a fundamental influence in shaping its properties. We study the environmental effects on galaxy evolution, with an emphasis on the environment defined as the local number density of galaxies. The density field is estimated with different estimators (weighted adaptive kernel smoothing, 10th and 5th nearest neighbors, Voronoi and Delaunay tessellation) for a Ks < 24 sample of ∼190,000 galaxies in the COSMOS field at 0.1 < z < 3.1. The performance of each estimator is evaluated with extensive simulations. We show that overall there is a good agreement between the estimated density fields using different methods over ∼2 dex in overdensity values. However, our simulations show that adaptive kernel and Voronoi tessellation outperform other methods. Using the Voronoi tessellation method, we assign surface densities to a mass complete sample of quiescent and star-forming galaxies out to z ∼ 3. We show that at a fixed stellar mass, the median color of quiescent galaxies does not depend on their host environment out to z ∼ 3. We find that the number and stellar mass density of massive (>1011 {{M}ȯ }) star-forming galaxies have not significantly changed since z ∼ 3, regardless of their environment. However, for massive quiescent systems at lower redshifts (z ≲ 1.3), we find a significant evolution in the number and stellar mass densities in denser environments compared to lower density regions. Our results suggest that the relation between stellar mass and local density is more fundamental than the color–density relation and that environment plays a significant role in quenching star-formation activity in galaxies at z ≲ 1.
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…
Evidence-based kernels: fundamental units of behavioral influence.
Embry, Dennis D; Biglan, Anthony
2008-09-01
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.
Evidence-based Kernels: Fundamental Units of Behavioral Influence
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
Code of Federal Regulations, 2010 CFR
2010-01-01
... Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards, Inspections, Marketing Practices), DEPARTMENT OF AGRICULTURE REGULATIONS AND STANDARDS UNDER THE AGRICULTURAL MARKETING ACT OF 1946... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of...
Code of Federal Regulations, 2011 CFR
2011-01-01
... Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards, Inspections, Marketing Practices), DEPARTMENT OF AGRICULTURE REGULATIONS AND STANDARDS UNDER THE AGRICULTURAL MARKETING ACT OF 1946... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of...
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…
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…
Sugar uptake into kernels of tunicate tassel-seed maize
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.
Introduction to Kernel Methods: Classification of Multivariate Data
NASA Astrophysics Data System (ADS)
Fauvel, M.
2016-05-01
In this chapter, kernel methods are presented for the classification of multivariate data. An introduction example is given to enlighten the main idea of kernel methods. Then emphasis is done on the Support Vector Machine. Structural risk minimization is presented, and linear and non-linear SVM are described. Finally, a full example of SVM classification is given on simulated hyperspectral data.
7 CFR 981.60 - Determination of kernel weight.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 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...
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...
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...
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...
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...
Scale Space Graph Representation and Kernel Matching for Non Rigid and Textured 3D Shape Retrieval.
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.
Accumulation of storage products in oat during kernel development.
Banaś, A; Dahlqvist, A; Debski, H; Gummeson, P O; Stymne, S
2000-12-01
Lipids, proteins and starch are the main storage products in oat seeds. As a first step in elucidating the regulatory mechanisms behind the deposition of these compounds, two different oat varieties, 'Freja' and 'Matilda', were analysed during kernel development. In both cultivars, the majority of the lipids accumulated at very early stage of development but Matilda accumulated about twice the amount of lipids compared to Freja. Accumulation of proteins and starch started also in the early stage of kernel development but, in contrast to lipids, continued over a considerably longer period. The high-oil variety Matilda also accumulated higher amounts of proteins than Freja. The starch content in Freja kernels was higher than in Matilda kernels and the difference was most pronounced during the early stage of development when oil synthesis was most active. Oleosin accumulation continued during the whole period of kernel development.
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.
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.
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels
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.
Technology Transfer Automated Retrieval System (TEKTRAN)
The current US corn grading system accounts for the portion of damaged kernels, which is measured by time-consuming and inaccurate visual inspection. Near infrared spectroscopy (NIRS), a non-destructive and fast analytical method, was tested as a tool for discriminating corn kernels with heat and f...
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.
Kernel-machine-based classification in multi-polarimetric SAR data
NASA Astrophysics Data System (ADS)
Middelmann, Wolfgang; Ebert, Alfons; Thoennessen, Ulrich
2005-05-01
The focus of this paper is the classification of military vehicles in multi-polarimetric high-resolution spotlight SAR images in an ATR framework. Kernel machines as robust classification methods are the basis of our approach. A novel kernel machine the Relevance Vector Machine with integrated Generator (RVMG) controlling the trade-off between classification quality and computational effort is used. It combines the high classification quality of the Support Vector Machine by margin maximization and the low effort of the Relevance Vector Machine caused by the special statistical approach. Moreover multi-class classification capability is given by an efficient decision heuristic, an adaptive feature extraction based on Fourier coefficients allows the module to do real time execution, and a parameterized reject criterion is proposed in this paper. Investigations with a nine class data set from QinetiQ deal with fully polarimetric SAR data. The objective is to assess polarimetric features in combination with several kernel machines. Tests approve the high potential of RVMG. Moreover it is shown that polarimetric features can improve the classification quality for hard targets. Among these the simple energy based features prove more favorable than complex ones. Especially the two coplanar polarizations embody the essential information, but a better generalizability is caused by using all four channels. An important property of a classifier used in the ATR framework is the capability to reject objects not belonging to any of the trained classes. Therefore the QinetiQ data are divided into four training classes and five classes of confusion objects. The classification module with reject criterion is controlled by the reject parameter and the kernel parameter. Both parameters are varied to determine ROC curves related to different polarimetric features.
Privacy preserving RBF kernel support vector machine.
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. PMID:25013805
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.
Labeled Graph Kernel for Behavior Analysis.
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.
Hua, Wen-Yu; Ghosh, Debashis
2015-09-01
Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression framework that models covariate effects parametrically and genetic markers non-parametrically, while KDC represents a class of methods that include distance covariance (DC) and Hilbert-Schmidt independence criterion (HSIC), which are nonparametric tests of independence. We show that the equivalence between the score test of KMR and the KDC statistic under certain conditions can lead to a novel generalization of the KDC test that incorporates covariates. Our contributions are 3-fold: (1) establishing the equivalence between KMR and KDC; (2) showing that the principles of KMR can be applied to the interpretation of KDC; (3) the development of a broader class of KDC statistics, where the class members are statistics corresponding to different kernel combinations. Finally, we perform simulation studies and an analysis of real data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The ADNI study suggest that SNPs of FLJ16124 exhibit pairwise interaction effects that are strongly correlated to the changes of brain region volumes. PMID:25939365
Hua, Wen-Yu; Ghosh, Debashis
2015-09-01
Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression framework that models covariate effects parametrically and genetic markers non-parametrically, while KDC represents a class of methods that include distance covariance (DC) and Hilbert-Schmidt independence criterion (HSIC), which are nonparametric tests of independence. We show that the equivalence between the score test of KMR and the KDC statistic under certain conditions can lead to a novel generalization of the KDC test that incorporates covariates. Our contributions are 3-fold: (1) establishing the equivalence between KMR and KDC; (2) showing that the principles of KMR can be applied to the interpretation of KDC; (3) the development of a broader class of KDC statistics, where the class members are statistics corresponding to different kernel combinations. Finally, we perform simulation studies and an analysis of real data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The ADNI study suggest that SNPs of FLJ16124 exhibit pairwise interaction effects that are strongly correlated to the changes of brain region volumes.
PROPERTIES OF A SOLAR FLARE KERNEL OBSERVED BY HINODE AND SDO
Young, P. R.; Doschek, G. A.; Warren, H. P.; Hara, H.
2013-04-01
Flare kernels are compact features located in the solar chromosphere that are the sites of rapid heating and plasma upflow during the rise phase of flares. An example is presented from a M1.1 class flare in active region AR 11158 observed on 2011 February 16 07:44 UT for which the location of the upflow region seen by EUV Imaging Spectrometer (EIS) can be precisely aligned to high spatial resolution images obtained by the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). A string of bright flare kernels is found to be aligned with a ridge of strong magnetic field, and one kernel site is highlighted for which an upflow speed of Almost-Equal-To 400 km s{sup -1} is measured in lines formed at 10-30 MK. The line-of-sight magnetic field strength at this location is Almost-Equal-To 1000 G. Emission over a continuous range of temperatures down to the chromosphere is found, and the kernels have a similar morphology at all temperatures and are spatially coincident with sizes at the resolution limit of the AIA instrument ({approx}<400 km). For temperatures of 0.3-3.0 MK the EIS emission lines show multiple velocity components, with the dominant component becoming more blueshifted with temperature from a redshift of 35 km s{sup -1} at 0.3 MK to a blueshift of 60 km s{sup -1} at 3.0 MK. Emission lines from 1.5-3.0 MK show a weak redshifted component at around 60-70 km s{sup -1} implying multi-directional flows at the kernel site. Significant non-thermal broadening corresponding to velocities of Almost-Equal-To 120 km s{sup -1} is found at 10-30 MK, and the electron density in the kernel, measured at 2 MK, is 3.4 Multiplication-Sign 10{sup 10} cm{sup -3}. Finally, the Fe XXIV {lambda}192.03/{lambda}255.11 ratio suggests that the EIS calibration has changed since launch, with the long wavelength channel less sensitive than the short wavelength channel by around a factor two.
Boisdenghien, Zino; Fias, Stijn; Van Alsenoy, Christian; De Proft, Frank; Geerlings, Paul
2014-07-28
Most of the work done on the linear response kernel χ(r,r') has focussed on its atom-atom condensed form χAB. Our previous work [Boisdenghien et al., J. Chem. Theory Comput., 2013, 9, 1007] was the first effort to truly focus on the non-condensed form of this function for closed (sub)shell atoms in a systematic fashion. In this work, we extend our method to the open shell case. To simplify the plotting of our results, we average our results to a symmetrical quantity χ(r,r'). This allows us to plot the linear response kernel for all elements up to and including argon and to investigate the periodicity throughout the first three rows in the periodic table and in the different representations of χ(r,r'). Within the context of Spin Polarized Conceptual Density Functional Theory, the first two-dimensional plots of spin polarized linear response functions are presented and commented on for some selected cases on the basis of the atomic ground state electronic configurations. Using the relation between the linear response kernel and the polarizability we compare the values of the polarizability tensor calculated using our method to high-level values. PMID:24837234
Predicting Spatial Patterns of Plant Recruitment Using Animal-Displacement Kernels
Santamaría, Luis; Rodríguez-Pérez, Javier; Larrinaga, Asier R.; Pias, Beatriz
2007-01-01
For plants dispersed by frugivores, spatial patterns of recruitment are primarily influenced by the spatial arrangement and characteristics of parent plants, the digestive characteristics, feeding behaviour and movement patterns of animal dispersers, and the structure of the habitat matrix. We used an individual-based, spatially-explicit framework to characterize seed dispersal and seedling fate in an endangered, insular plant-disperser system: the endemic shrub Daphne rodriguezii and its exclusive disperser, the endemic lizard Podarcis lilfordi. Plant recruitment kernels were chiefly determined by the disperser's patterns of space utilization (i.e. the lizard's displacement kernels), the position of the various plant individuals in relation to them, and habitat structure (vegetation cover vs. bare soil). In contrast to our expectations, seed gut-passage rate and its effects on germination, and lizard speed-of-movement, habitat choice and activity rhythm were of minor importance. Predicted plant recruitment kernels were strongly anisotropic and fine-grained, preventing their description using one-dimensional, frequency-distance curves. We found a general trade-off between recruitment probability and dispersal distance; however, optimal recruitment sites were not necessarily associated to sites of maximal adult-plant density. Conservation efforts aimed at enhancing the regeneration of endangered plant-disperser systems may gain in efficacy by manipulating the spatial distribution of dispersers (e.g. through the creation of refuges and feeding sites) to create areas favourable to plant recruitment. PMID:17925856
Visualization of High-Dimensional Point Clouds Using Their Density Distribution's Topology.
Oesterling, P; Heine, C; Janicke, H; Scheuermann, G; Heyer, G
2011-11-01
We present a novel method to visualize multidimensional point clouds. While conventional visualization techniques, like scatterplot matrices or parallel coordinates, have issues with either overplotting of entities or handling many dimensions, we abstract the data using topological methods before presenting it. We assume the input points to be samples of a random variable with a high-dimensional probability distribution which we approximate using kernel density estimates on a suitably reconstructed mesh. From the resulting scalar field we extract the join tree and present it as a topological landscape, a visualization metaphor that utilizes the human capability of understanding natural terrains. In this landscape, dense clusters of points show up as hills. The nesting of hills indicates the nesting of clusters. We augment the landscape with the data points to allow selection and inspection of single points and point sets. We also present optimizations to make our algorithm applicable to large data sets and to allow interactive adaption of our visualization to the kernel window width used in the density estimation.
Gaussian kernel width optimization for sparse Bayesian learning.
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. PMID:25794377
Yao, H; Hruska, Z; Kincaid, R; Brown, R; Cleveland, T; Bhatnagar, D
2010-05-01
The objective of this study was to examine the relationship between fluorescence emissions of corn kernels inoculated with Aspergillus flavus and aflatoxin contamination levels within the kernels. Aflatoxin contamination in corn has been a long-standing problem plaguing the grain industry with potentially devastating consequences to corn growers. In this study, aflatoxin-contaminated corn kernels were produced through artificial inoculation of corn ears in the field with toxigenic A. flavus spores. The kernel fluorescence emission data were taken with a fluorescence hyperspectral imaging system when corn kernels were excited with ultraviolet light. Raw fluorescence image data were preprocessed and regions of interest in each image were created for all kernels. The regions of interest were used to extract spectral signatures and statistical information. The aflatoxin contamination level of single corn kernels was then chemically measured using affinity column chromatography. A fluorescence peak shift phenomenon was noted among different groups of kernels with different aflatoxin contamination levels. The fluorescence peak shift was found to move more toward the longer wavelength in the blue region for the highly contaminated kernels and toward the shorter wavelengths for the clean kernels. Highly contaminated kernels were also found to have a lower fluorescence peak magnitude compared with the less contaminated kernels. It was also noted that a general negative correlation exists between measured aflatoxin and the fluorescence image bands in the blue and green regions. The correlation coefficients of determination, r(2), was 0.72 for the multiple linear regression model. The multivariate analysis of variance found that the fluorescence means of four aflatoxin groups, <1, 1-20, 20-100, and >or=100 ng g(-1) (parts per billion), were significantly different from each other at the 0.01 level of alpha. Classification accuracy under a two-class schema ranged from 0.84 to
Characterising brain network topologies: A dynamic analysis approach using heat kernels.
Chung, A W; Schirmer, M D; Krishnan, M L; Ball, G; Aljabar, P; Edwards, A D; Montana, G
2016-11-01
Network theory provides a principled abstraction of the human brain: reducing a complex system into a simpler representation from which to investigate brain organisation. Recent advancement in the neuroimaging field is towards representing brain connectivity as a dynamic process in order to gain a deeper understanding of how the brain is organised for information transport. In this paper we propose a network modelling approach based on the heat kernel to capture the process of heat diffusion in complex networks. By applying the heat kernel to structural brain networks, we define new features which quantify change in heat propagation. Identifying suitable features which can classify networks between cohorts is useful towards understanding the effect of disease on brain architecture. We demonstrate the discriminative power of heat kernel features in both synthetic and clinical preterm data. By generating an extensive range of synthetic networks with varying density and randomisation, we investigate heat diffusion in relation to changes in network topology. We demonstrate that our proposed features provide a metric of network efficiency and may be indicative of organisational principles commonly associated with, for example, small-world architecture. In addition, we show the potential of these features to characterise and classify between network topologies. We further demonstrate our methodology in a clinical setting by applying it to a large cohort of preterm babies scanned at term equivalent age from which diffusion networks were computed. We show that our heat kernel features are able to successfully predict motor function measured at two years of age (sensitivity, specificity, F-score, accuracy = 75.0, 82.5, 78.6, and 82.3%, respectively).
Characterising brain network topologies: A dynamic analysis approach using heat kernels.
Chung, A W; Schirmer, M D; Krishnan, M L; Ball, G; Aljabar, P; Edwards, A D; Montana, G
2016-11-01
Network theory provides a principled abstraction of the human brain: reducing a complex system into a simpler representation from which to investigate brain organisation. Recent advancement in the neuroimaging field is towards representing brain connectivity as a dynamic process in order to gain a deeper understanding of how the brain is organised for information transport. In this paper we propose a network modelling approach based on the heat kernel to capture the process of heat diffusion in complex networks. By applying the heat kernel to structural brain networks, we define new features which quantify change in heat propagation. Identifying suitable features which can classify networks between cohorts is useful towards understanding the effect of disease on brain architecture. We demonstrate the discriminative power of heat kernel features in both synthetic and clinical preterm data. By generating an extensive range of synthetic networks with varying density and randomisation, we investigate heat diffusion in relation to changes in network topology. We demonstrate that our proposed features provide a metric of network efficiency and may be indicative of organisational principles commonly associated with, for example, small-world architecture. In addition, we show the potential of these features to characterise and classify between network topologies. We further demonstrate our methodology in a clinical setting by applying it to a large cohort of preterm babies scanned at term equivalent age from which diffusion networks were computed. We show that our heat kernel features are able to successfully predict motor function measured at two years of age (sensitivity, specificity, F-score, accuracy = 75.0, 82.5, 78.6, and 82.3%, respectively). PMID:27421183
Bridging the gap between the KERNEL and RT-11
Hendra, R.G.
1981-06-01
A software package is proposed to allow users of the PL-11 language, and the LSI-11 KERNEL in general, to use their PL-11 programs under RT-11. Further, some general purpose extensions to the KERNEL are proposed that facilitate some number conversions and strong manipulations. A Floating Point Package of procedures to allow full use of the hardware floating point capability of the LSI-11 computers is proposed. Extensions to the KERNEL that allow a user to read, write and delete disc files in the manner of RT-11 is also proposed. A device directory listing routine is also included.
Spectrophotometric method for determination of phosphine residues in cashew kernels.
Rangaswamy, J R
1988-01-01
A spectrophotometric method reported for determination of phosphine (PH3) residues in wheat has been extended for determination of these residues in cashew kernels. Unlike the spectrum for wheat, the spectrum of PH3 residue-AgNO3 chromophore from cashew kernels does not show an absorption maximum at 400 nm; nevertheless, reading the absorbance at 400 nm afforded good recoveries of 90-98%. No interference occurred from crop materials, and crop controls showed low absorbance; the method can be applied for determinations as low as 0.01 ppm PH3 residue in cashew kernels.
Initial-state splitting kernels in cold nuclear matter
NASA Astrophysics Data System (ADS)
Ovanesyan, Grigory; Ringer, Felix; Vitev, Ivan
2016-09-01
We derive medium-induced splitting kernels for energetic partons that undergo interactions in dense QCD matter before a hard-scattering event at large momentum transfer Q2. Working in the framework of the effective theory SCETG, we compute the splitting kernels beyond the soft gluon approximation. We present numerical studies that compare our new results with previous findings. We expect the full medium-induced splitting kernels to be most relevant for the extension of initial-state cold nuclear matter energy loss phenomenology in both p+A and A+A collisions.
Kernel simplex growing algorithm for hyperspectral endmember extraction
NASA Astrophysics Data System (ADS)
Zhao, Liaoying; Zheng, Junpeng; Li, Xiaorun; Wang, Lijiao
2014-01-01
In order to effectively extract endmembers for hyperspectral imagery where linear mixing model may not be appropriate due to multiple scattering effects, this paper extends the simplex growing algorithm (SGA) to its kernel version. A new simplex volume formula without dimension reduction is used in SGA to form a new simplex growing algorithm (NSGA). The original data are nonlinearly mapped into a high-dimensional space where the scatters can be ignored. To avoid determining complex nonlinear mapping, a kernel function is used to extend the NSGA to kernel NSGA (KNSGA). Experimental results of simulated and real data prove that the proposed KNSGA approach outperforms SGA and NSGA.
Multitasking kernel for the C and Fortran programming languages
Brooks, E.D. III
1984-09-01
A multitasking kernel for the C and Fortran programming languages which runs on the Unix operating system is presented. The kernel provides a multitasking environment which serves two purposes. The first is to provide an efficient portable environment for the coding, debugging and execution of production multiprocessor programs. The second is to provide a means of evaluating the performance of a multitasking program on model multiprocessors. The performance evaluation features require no changes in the source code of the application and are implemented as a set of compile and run time options in the kernel.
Monte Carlo Code System for Electron (Positron) Dose Kernel Calculations.
1999-05-12
Version 00 KERNEL performs dose kernel calculations for an electron (positron) isotropic point source in an infinite homogeneous medium. First, the auxiliary code PRELIM is used to prepare cross section data for the considered medium. Then the KERNEL code simulates the transport of electrons and bremsstrahlung photons through the medium until all particles reach their cutoff energies. The deposited energy is scored in concentric spherical shells at a radial distance ranging from zero to twicemore » the source particle range.« less
NASA Astrophysics Data System (ADS)
Bortolot, Zachary J.
Forests have been proposed as a means of reducing atmospheric carbon dioxide levels due to their ability to store carbon as biomass. To quantify the amount of atmospheric carbon sequestered by forests, biomass and density estimates are oven needed. This study develops, implements, and tests an individual tree-based algorithm for obtaining forest density and biomass using orthophotographs and small footprint LiDAR imagery. It was designed to work with a range of forests and image types without modification, which is accomplished by using generic properties of trees found in many types of images. Multiple parameters are employed to determine how these generic properties are used. To set these parameters, training data is used in conjunction with an optimization algorithm (a modified Nelder-Mead simplex algorithm or a genetic algorithm). The training data consist of small images in which density and biomass are known. A first test of this technique was performed using 25 circular plots (radius = 15 m) placed in young pine plantations in central Virginia, together with false color orthophotograph (spatial resolution = 0.5 m) or small footprint LiDAR (interpolated to 0.5 m) imagery. The highest density prediction accuracies (r2 up to 0.88, RMSE as low as 83 trees/ha) were found for runs where photointerpreted densities were used for training and testing. For tests run using density measurements made on the ground, accuracies were consistency higher for orthophotograph-based results than for LiDAR-based results, and were higher for trees with DBH ≥10cm than for trees with DBH ≥7 cm. Biomass estimates obtained by the algorithm using LiDAR imagery had a lower RMSE (as low as 15.6 t/ha) than most comparable studies. The correlations between the actual and predicted values (r2 up to 0.64) were lower than comparable studies, but were generally highly significant (p ≤ 0.05 or 0.01). In all runs there was no obvious sensitive to which training and testing data were
Garza-Gisholt, Eduardo; Hemmi, Jan M.; Hart, Nathan S.; Collin, Shaun P.
2014-01-01
Topographic maps that illustrate variations in the density of different neuronal sub-types across the retina are valuable tools for understanding the adaptive significance of retinal specialisations in different species of vertebrates. To date, such maps have been created from raw count data that have been subjected to only limited analysis (linear interpolation) and, in many cases, have been presented as iso-density contour maps with contour lines that have been smoothed ‘by eye’. With the use of stereological approach to count neuronal distribution, a more rigorous approach to analysing the count data is warranted and potentially provides a more accurate representation of the neuron distribution pattern. Moreover, a formal spatial analysis of retinal topography permits a more robust comparison of topographic maps within and between species. In this paper, we present a new R-script for analysing the topography of retinal neurons and compare methods of interpolating and smoothing count data for the construction of topographic maps. We compare four methods for spatial analysis of cell count data: Akima interpolation, thin plate spline interpolation, thin plate spline smoothing and Gaussian kernel smoothing. The use of interpolation ‘respects’ the observed data and simply calculates the intermediate values required to create iso-density contour maps. Interpolation preserves more of the data but, consequently includes outliers, sampling errors and/or other experimental artefacts. In contrast, smoothing the data reduces the ‘noise’ caused by artefacts and permits a clearer representation of the dominant, ‘real’ distribution. This is particularly useful where cell density gradients are shallow and small variations in local density may dramatically influence the perceived spatial pattern of neuronal topography. The thin plate spline and the Gaussian kernel methods both produce similar retinal topography maps but the smoothing parameters used may affect
Kernel-based Linux emulation for Plan 9.
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.
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)
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.
Isolation and purification of D-mannose from palm kernel.
Zhang, Tao; Pan, Ziguo; Qian, Chao; Chen, Xinzhi
2009-09-01
An economically viable procedure for the isolation and purification of d-mannose from palm kernel was developed in this research. The palm kernel was catalytically hydrolyzed with sulfuric acid at 100 degrees C and then fermented by mannan-degrading enzymes. The solution after fermentation underwent filtration in a silica gel column, desalination by ion-exchange resin, and crystallization in ethanol to produce pure d-mannose in a total yield of 48.4% (based on the weight of the palm kernel). Different enzymes were investigated, and the results indicated that endo-beta-mannanase was the best enzyme to promote the hydrolysis of the oligosaccharides isolated from the palm kernel. The pure d-mannose sample was characterized by FTIR, (1)H NMR, and (13)C NMR spectra.
NASA Astrophysics Data System (ADS)
Huang, Fengzhen; Li, Jingzhen; Cao, Jun
2015-02-01
Temporally and Spatially Modulated Fourier Transform Imaging Spectrometer (TSMFTIS) is a new imaging spectrometer without moving mirrors and slits. As applied in remote sensing, TSMFTIS needs to rely on push-broom of the flying platform to obtain the interferogram of the target detected, and if the moving state of the flying platform changed during the imaging process, the target interferogram picked up from the remote sensing image sequence will deviate from the ideal interferogram, then the target spectrum recovered shall not reflect the real characteristic of the ground target object. Therefore, in order to achieve a high precision spectrum recovery of the target detected, the geometry position of the target point on the TSMFTIS image surface can be calculated in accordance with the sub-pixel image registration method, and the real point interferogram of the target can be obtained with image interpolation method. The core idea of the interpolation methods (nearest, bilinear and cubic etc) are to obtain the grey value of the point to be interpolated by weighting the grey value of the pixel around and with the kernel function constructed by the distance between the pixel around and the point to be interpolated. This paper adopts the gauss-based kernel regression mode, present a kernel function that consists of the grey information making use of the relative deviation and the distance information, then the kernel function is controlled by the deviation degree between the grey value of the pixel around and the means value so as to adjust weights self adaptively. The simulation adopts the partial spectrum data obtained by the pushbroom hyperspectral imager (PHI) as the spectrum of the target, obtains the successively push broomed motion error image in combination with the related parameter of the actual aviation platform; then obtains the interferogram of the target point with the above interpolation method; finally, recovers spectrogram with the nonuniform fast
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.
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.
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.
Kernel spectral clustering with memory effect
NASA Astrophysics Data System (ADS)
Langone, Rocco; Alzate, Carlos; Suykens, Johan A. K.
2013-05-01
Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.
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.
Kernel approximation for solving few-body integral equations
NASA Astrophysics Data System (ADS)
Christie, I.; Eyre, D.
1986-06-01
This paper investigates an approximate method for solving integral equations that arise in few-body problems. The method is to replace the kernel by a degenerate kernel defined on a finite dimensional subspace of piecewise Lagrange polynomials. Numerical accuracy of the method is tested by solving the two-body Lippmann-Schwinger equation with non-separable potentials, and the three-body Amado-Lovelace equation with separable two-body potentials.
NASA Astrophysics Data System (ADS)
Patrick, Christopher E.; Thygesen, Kristian S.
2015-09-01
We present calculations of the correlation energies of crystalline solids and isolated systems within the adiabatic-connection fluctuation-dissipation formulation of density-functional theory. We perform a quantitative comparison of a set of model exchange-correlation kernels originally derived for the homogeneous electron gas (HEG), including the recently introduced renormalized adiabatic local-density approximation (rALDA) and also kernels which (a) satisfy known exact limits of the HEG, (b) carry a frequency dependence, or (c) display a 1/k2 divergence for small wavevectors. After generalizing the kernels to inhomogeneous systems through a reciprocal-space averaging procedure, we calculate the lattice constants and bulk moduli of a test set of 10 solids consisting of tetrahedrally bonded semiconductors (C, Si, SiC), ionic compounds (MgO, LiCl, LiF), and metals (Al, Na, Cu, Pd). We also consider the atomization energy of the H2 molecule. We compare the results calculated with different kernels to those obtained from the random-phase approximation (RPA) and to experimental measurements. We demonstrate that the model kernels correct the RPA's tendency to overestimate the magnitude of the correlation energy whilst maintaining a high-accuracy description of structural properties.
Patrick, Christopher E. Thygesen, Kristian S.
2015-09-14
We present calculations of the correlation energies of crystalline solids and isolated systems within the adiabatic-connection fluctuation-dissipation formulation of density-functional theory. We perform a quantitative comparison of a set of model exchange-correlation kernels originally derived for the homogeneous electron gas (HEG), including the recently introduced renormalized adiabatic local-density approximation (rALDA) and also kernels which (a) satisfy known exact limits of the HEG, (b) carry a frequency dependence, or (c) display a 1/k{sup 2} divergence for small wavevectors. After generalizing the kernels to inhomogeneous systems through a reciprocal-space averaging procedure, we calculate the lattice constants and bulk moduli of a test set of 10 solids consisting of tetrahedrally bonded semiconductors (C, Si, SiC), ionic compounds (MgO, LiCl, LiF), and metals (Al, Na, Cu, Pd). We also consider the atomization energy of the H{sub 2} molecule. We compare the results calculated with different kernels to those obtained from the random-phase approximation (RPA) and to experimental measurements. We demonstrate that the model kernels correct the RPA’s tendency to overestimate the magnitude of the correlation energy whilst maintaining a high-accuracy description of structural properties.
Enzymatic treatment of peanut kernels to reduce allergen levels.
Yu, Jianmei; Ahmedna, Mohamed; Goktepe, Ipek; Cheng, Hsiaopo; Maleki, Soheila
2011-08-01
This study investigated the use of enzymatic treatment to reduce peanut allergens in peanut kernels as affected by processing conditions. Two major peanut allergens, Ara h 1 and Ara h 2, were used as indicators of process effectiveness. Enzymatic treatment effectively reduced Ara h 1 and Ara h 2 in roasted peanut kernels by up to 100% under optimal conditions. For instance, treatment of roasted peanut kernels with α-chymotrypsin and trypsin for 1-3h significantly increased the solubility of peanut protein while reducing Ara h 1 and Ara h 2 in peanut kernel extracts by 100% and 98%, respectively, based on ELISA readings. Ara h 1 and Ara h 2 levels in peanut protein extracts were inversely correlated with protein solubility in roasted peanut. Blanching of kernels enhanced the effectiveness of enzyme treatment in roasted peanuts but not in raw peanuts. The optimal concentration of enzyme was determined by response surface to be in the range of 0.1-0.2%. No consistent results were obtained for raw peanut kernels since Ara h 1 and Ara h 2 increased in peanut protein extracts under some treatment conditions and decreased in others. PMID:25214091
Jozan, S.; Faye, J.C.; Tournier, J.F.; Tauber, J.P.; David, J.F.; Bayard, F.
1985-11-27
The responsiveness of the human mammary carcinoma cell line MCF-7 to estradiol and tamoxifen treatment has been studied in different culture conditions. Cells from exponentially growing cultures were compared with cells in their initial cycles after replating from confluent cultures (''confluent-log'' cells). It has been observed that estradiol stimulation of tritiated thymidine incorporation decreases with cell density and that ''confluent-log'' cells are estrogen unresponsive for a period of four cell cycles in serum-free medium conditions. On the other hand, growth of cells replated from exponentially growing, as well as from confluent cultures, can be inhibited by tamoxifen or a combined treatment with tamoxifen and the progestin levonorgestrel. This growth inhibitory effect can be rescued by estradiol when cells are replated from exponentially growing cultures. The growth inhibitory effect cannot be rescued by estradiol alone (10(-10) to 10(-8) M) when cells are replated from confluent cultures. In this condition, the addition of steroid depleted serum is necessary to reverse the state of estradiol unresponsiveness. Serum can be replaced by high density lipoproteins but not by low density lipoproteins or lipoprotein deficient serum. The present data show that estradiol and HDL interact in the control of MCF-7 cell proliferation.
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 L1, L2 distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach. PMID:23365559
Gabor-based kernel PCA with fractional power polynomial models for face recognition.
Liu, Chengjun
2004-05-01
This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power
Thermal-to-visible face recognition using multiple kernel learning
NASA Astrophysics Data System (ADS)
Hu, Shuowen; Gurram, Prudhvi; Kwon, Heesung; Chan, Alex L.
2014-06-01
Recognizing faces acquired in the thermal spectrum from a gallery of visible face images is a desired capability for the military and homeland security, especially for nighttime surveillance and intelligence gathering. However, thermal-tovisible face recognition is a highly challenging problem, due to the large modality gap between thermal and visible imaging. In this paper, we propose a thermal-to-visible face recognition approach based on multiple kernel learning (MKL) with support vector machines (SVMs). We first subdivide the face into non-overlapping spatial regions or blocks using a method based on coalitional game theory. For comparison purposes, we also investigate uniform spatial subdivisions. Following this subdivision, histogram of oriented gradients (HOG) features are extracted from each block and utilized to compute a kernel for each region. We apply sparse multiple kernel learning (SMKL), which is a MKLbased approach that learns a set of sparse kernel weights, as well as the decision function of a one-vs-all SVM classifier for each of the subjects in the gallery. We also apply equal kernel weights (non-sparse) and obtain one-vs-all SVM models for the same subjects in the gallery. Only visible images of each subject are used for MKL training, while thermal images are used as probe images during testing. With subdivision generated by game theory, we achieved Rank-1 identification rate of 50.7% for SMKL and 93.6% for equal kernel weighting using a multimodal dataset of 65 subjects. With uniform subdivisions, we achieved a Rank-1 identification rate of 88.3% for SMKL, but 92.7% for equal kernel weighting.
Protein fold recognition using geometric kernel data fusion
Zakeri, Pooya; Jeuris, Ben; Vandebril, Raf; Moreau, Yves
2014-01-01
Motivation: Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. Results: We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. Availability and implementation: The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/ Contact: pooyapaydar@gmail.com or yves
NASA Astrophysics Data System (ADS)
Xu, Wen Wu; Li, Yadong; Gao, Yi; Zeng, Xiao Cheng
2016-01-01
We have analyzed the structures of two medium-sized thiolate-protected gold nanoparticles (RS-AuNPs) Au40(SR)24 and Au52(SR)32 and identified the distinct structural features in their Au kernels [Sci. Adv., 2015, 1, e1500425]. We find that both Au kernels of the Au40(SR)24 and Au52(SR)32 nanoclusters can be classified as interpenetrating cuboctahedra. Simulated X-ray diffraction patterns of the RS-AuNPs with the cuboctahedral kernel are collected and then compared with the X-ray diffraction patterns of the RS-AuNPs of two other prevailing Au-kernels identified from previous experiments, namely the Ino-decahedral kernel and icosahedral kernel. The distinct X-ray diffraction patterns of RS-AuNPs with the three different types of Au-kernels can be utilized as signature features for future studies of structures of RS-AuNPs. Moreover, the simulated UV/Vis absorption spectra and Kohn-Sham orbital energy-level diagrams are obtained for the Au40(SR)24 and Au52(SR)32, on the basis of time-dependent density functional theory computation. The extrapolated optical band-edges of Au40(SR)24 and Au52(SR)32 are 1.1 eV and 1.25 eV, respectively. The feature peaks in the UV/Vis absorption spectra of the two clusters can be attributed to the d --> sp electronic transition. Lastly, the catalytic activities of the Au40(SR)24 and Au52(SR)32 are examined using CO oxidation as a probe. Both medium-sized thiolate-protected gold clusters can serve as effective stand-alone nanocatalysts.We have analyzed the structures of two medium-sized thiolate-protected gold nanoparticles (RS-AuNPs) Au40(SR)24 and Au52(SR)32 and identified the distinct structural features in their Au kernels [Sci. Adv., 2015, 1, e1500425]. We find that both Au kernels of the Au40(SR)24 and Au52(SR)32 nanoclusters can be classified as interpenetrating cuboctahedra. Simulated X-ray diffraction patterns of the RS-AuNPs with the cuboctahedral kernel are collected and then compared with the X-ray diffraction patterns of the RS
Datta, Dipayan Gauss, Jürgen
2015-07-07
We report analytical calculations of isotropic hyperfine-coupling constants in radicals using a spin-adapted open-shell coupled-cluster theory, namely, the unitary group based combinatoric open-shell coupled-cluster (COSCC) approach within the singles and doubles approximation. A scheme for the evaluation of the one-particle spin-density matrix required in these calculations is outlined within the spin-free formulation of the COSCC approach. In this scheme, the one-particle spin-density matrix for an open-shell state with spin S and M{sub S} = + S is expressed in terms of the one- and two-particle spin-free (charge) density matrices obtained from the Lagrangian formulation that is used for calculating the analytic first derivatives of the energy. Benchmark calculations are presented for NO, NCO, CH{sub 2}CN, and two conjugated π-radicals, viz., allyl and 1-pyrrolyl in order to demonstrate the performance of the proposed scheme.
Mueller, Laurence D; Cabral, Larry G
2012-01-01
Recent studies with Drosophila have suggested that there is extensive genetic variability for phenotypic plasticity of body size versus food level. If true, we expect that the outcome of evolution at very different food levels should yield genotypes whose adult size show different patterns of phenotypic plasticity. We have tested this prediction with six independent populations of Drosophila melanogaster kept at extreme densities for 125 generations. We found that the phenotypic plasticity of body size versus food level is not affected by selection or the presence of competitors of a different genotype. However, we document increasing among population variation in phenotypic plasticity due to random genetic drift. Several reasons are explored to explain these results including the possibility that the use of highly inbred lines to make inferences about the evolution of genetically variable populations may be misleading.
Kercher, Andrew K; Hunn, John D
2006-11-01
Measurements were made using optical microscopy to determine the size and shape of the LEU03 kernels. Hg porosimetry was performed to measure density. The results are summarized in Table 1-1. Values in the table are for the composite and are calculated at 95% confidence from the measured values of a random riffled sample. The LEu03 kernel composite met all the specifications in Table 1-1. The BWXT results for measuring the same kernel properties are given in Table 1-2. BWXT characterization methods were significantly different from ORNL methods, which resulted in slight differences in the reported results. BWXT performed manual microscopy measurements for mean diameter (100 particles measured along 2 axes) and aspect ratio (100 particles measured); ORNL used automated image acquisition and analysis (3847 particles measured along 180 axes). Diameter measurements were in good agreement. The narrower confidence interval in the ORNL results for average mean diameter is due to the greater number of particles measured. The critical limits for mean diameter reported at ORNL and BWXT are similar, because ORNL measured a larger standard deviation (10.46 {micro}m vs. 8.70 {micro}m). Aspect ratio satisfied the specification with greater margin in the ORNL results mostly because of the larger sample size resulting in a lower uncertainty in the binomial distribution statistical calculation. ORNL measured 11 out of 3847 kernels exceeding the control limit (1.05); BWXT measured 1 out of 100 particles exceeding the control limit. BWXT used the aspect ratio of perpendicular diameters in a random image plane, where one diameter was a maximum or a minimum. ORNL used the aspect ratio of the absolute maximum and minimum diameters in a random image plane. The ORNL technique can be expected to yield higher measured aspect ratios. Hand tabling was performed at ORNL prior to characterization by repeatedly pouring a small fraction of the kernels in a pan and tilting the pan so that rounder
Kernel Machine Testing for Risk Prediction with Stratified Case Cohort Studies
Payne, Rebecca; Neykov, Matey; Jensen, Majken Karoline; Cai, Tianxi
2015-01-01
Summary 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 paper, 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. PMID:26692376
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.
Kavallieratos, Nickolas G; Athanassiou, Christos G; Arthur, Frank H; Throne, James E
2012-01-01
Tests were conducted to determine whether the lesser grain borer, Rhyzopertha dominica (F.) (Coleoptera: Bostrychidae), selects rough rice (Oryza sativa L. (Poales: Poaceae)) kernels with cracked hulls for reproduction when these kernels are mixed with intact kernels. Differing amounts of kernels with cracked hulls (0, 5, 10, and 20%) of the varieties Francis and Wells were mixed with intact kernels, and the number of adult progeny emerging from intact kernels and from kernels with cracked hulls was determined. The Wells variety had been previously classified as tolerant to R. dominica, while the Francis variety was classified as moderately susceptible. Few F 1 progeny were produced in Wells regardless of the percentage of kernels with cracked hulls, few of the kernels with cracked hulls had emergence holes, and little firass was produced from feeding damage. At 10 and 20% kernels with cracked hulls, the progeny production, number of emergence holes in kernels with cracked hulls, and the amount of firass was greater in Francis than in Wells. The proportion of progeny emerging from kernels with cracked hulls increased as the proportion of kernels with cracked hulls increased. The results indicate that R. dominica select kernels with cracked hulls for reproduction.
Travel-time sensitivity kernels in long-range propagation.
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.
Characterization of the desiccation of wheat kernels by multivariate imaging.
Jaillais, B; Perrin, E; Mangavel, C; Bertrand, D
2011-06-01
Variations in the quality of wheat kernels can be an important problem in the cereal industry. In particular, desiccation conditions play an essential role in both the technological characteristics of the kernel and its ability to sprout. In planta desiccation constitutes a key stage in the determinism of the functional properties of seeds. The impact of desiccation on the endosperm texture of seed is presented in this work. A simple imaging system had previously been developed to acquire multivariate images to characterize the heterogeneity of food materials. A special algorithm for the use under principal component analysis (PCA) was developed to process the acquired multivariate images. Wheat grains were collected at physiological maturity, and were subjected to two types of drying conditions that induced different kinetics of water loss. A data set containing 24 images (dimensioned 702 × 524 pixels) corresponding to the different desiccation stages of wheat kernels was acquired at different wavelengths and then analyzed. A comparison of the images of kernel sections highlighted changes in kernel texture as a function of their drying conditions. Slow drying led to a floury texture, whereas fast drying caused a glassy texture. The automated imaging system thus developed is sufficiently rapid and economical to enable the characterization in large collections of grain texture as a function of time and water content.
[Utilizable value of wild economic plant resource--acron kernel].
He, R; Wang, K; Wang, Y; Xiong, T
2000-04-01
Peking whites breeding hens were selected. Using true metabolizable energy method (TME) to evaluate the available nutritive value of acorn kernel, while maize and rice were used as control. The results showed that the contents of gross energy (GE), apparent metabolizable energy (AME), true metabolizable energy (TME) and crude protein (CP) in the acorn kernel were 16.53 mg/kg-1, 11.13 mg.kg-1, 11.66 mg.kg-1 and 10.63%, respectively. The apparent availability and true availability of crude protein were 45.55% and 49.83%. The gross content of 17 amino acids, essential amino acids and semiessential amino acids were 9.23% and 4.84%. The true availability of amino acid and the content of true available amino acid were 60.85% and 6.09%. The contents of tannin and hydrocyanic acid were 4.55% and 0.98% in acorn kernel. The available nutritive value of acorn kernel is similar to maize or slightly lower, but slightly higher than that of rice. Acorn kernel is a wild economic plant resource to exploit and utilize but it contains higher tannin and hydrocyanic acid. PMID:11767593
Aleurone cell identity is suppressed following connation in maize kernels.
Geisler-Lee, Jane; Gallie, Daniel R
2005-09-01
Expression of the cytokinin-synthesizing isopentenyl transferase enzyme under the control of the Arabidopsis (Arabidopsis thaliana) SAG12 senescence-inducible promoter reverses the normal abortion of the lower floret from a maize (Zea mays) spikelet. Following pollination, the upper and lower floret pistils fuse, producing a connated kernel with two genetically distinct embryos and the endosperms fused along their abgerminal face. Therefore, ectopic synthesis of cytokinin was used to position two independent endosperms within a connated kernel to determine how the fused endosperm would affect the development of the two aleurone layers along the fusion plane. Examination of the connated kernel revealed that aleurone cells were present for only a short distance along the fusion plane whereas starchy endosperm cells were present along most of the remainder of the fusion plane, suggesting that aleurone development is suppressed when positioned between independent starchy endosperms. Sporadic aleurone cells along the fusion plane were observed and may have arisen from late or imperfect fusion of the endosperms of the connated kernel, supporting the observation that a peripheral position at the surface of the endosperm and not proximity to maternal tissues such as the testa and pericarp are important for aleurone development. Aleurone mosaicism was observed in the crown region of nonconnated SAG12-isopentenyl transferase kernels, suggesting that cytokinin can also affect aleurone development.
Kernel Methods for Mining Instance Data in Ontologies
NASA Astrophysics Data System (ADS)
Bloehdorn, Stephan; Sure, York
The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.
Insights from Classifying Visual Concepts with Multiple Kernel Learning
Binder, Alexander; Nakajima, Shinichi; Kloft, Marius; Müller, Christina; Samek, Wojciech; Brefeld, Ulf; Müller, Klaus-Robert; Kawanabe, Motoaki
2012-01-01
Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, 1-norm regularized MKL variants are often observed to be outperformed by an unweighted sum kernel. The main contributions of this paper are the following: we apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks from the application domain of computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum-kernel SVM and sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. Data sets (kernel matrices) as well as further information are available at http://doc.ml.tu-berlin.de/image_mkl/(Accessed 2012 Jun 25). PMID:22936970
NASA Astrophysics Data System (ADS)
Bates, Jefferson; Laricchia, Savio; Ruzsinszky, Adrienn
The Random Phase Approximation (RPA) is quickly becoming a standard method beyond semi-local Density Functional Theory that naturally incorporates weak interactions and eliminates self-interaction error. RPA is not perfect, however, and suffers from self-correlation error as well as an incorrect description of short-ranged correlation typically leading to underbinding. To improve upon RPA we introduce a short-ranged, exchange-like kernel that is one-electron self-correlation free for one and two electron systems in the high-density limit. By tuning the one free parameter in our model to recover an exact limit of the homogeneous electron gas correlation energy we obtain a non-local, energy-optimized kernel that reduces the errors of RPA for both homogeneous and inhomogeneous solids. To reduce the computational cost of the standard kernel-corrected RPA, we also implement RPA renormalized perturbation theory for extended systems, and demonstrate its capability to describe the dominant correlation effects with a low-order expansion in both metallic and non-metallic systems. Furthermore we stress that for norm-conserving implementations the accuracy of RPA and beyond RPA structural properties compared to experiment is inherently limited by the choice of pseudopotential. Current affiliation: King's College London.
Evolutionary Metabolomics Reveals Domestication-Associated Changes in Tetraploid Wheat Kernels.
Beleggia, Romina; Rau, Domenico; Laidò, Giovanni; Platani, Cristiano; Nigro, Franca; Fragasso, Mariagiovanna; De Vita, Pasquale; Scossa, Federico; Fernie, Alisdair R; Nikoloski, Zoran; Papa, Roberto
2016-07-01
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
Evolutionary Metabolomics Reveals Domestication-Associated Changes in Tetraploid Wheat Kernels.
Beleggia, Romina; Rau, Domenico; Laidò, Giovanni; Platani, Cristiano; Nigro, Franca; Fragasso, Mariagiovanna; De Vita, Pasquale; Scossa, Federico; Fernie, Alisdair R; Nikoloski, Zoran; Papa, Roberto
2016-07-01
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.
Evolutionary Metabolomics Reveals Domestication-Associated Changes in Tetraploid Wheat Kernels
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
Scalar heat kernel with boundary in the worldline formalism
NASA Astrophysics Data System (ADS)
Bastianelli, Fiorenzo; Corradini, Olindo; Pisani, Pablo A. G.; Schubert, Christian
2008-10-01
The worldline formalism has in recent years emerged as a powerful tool for the computation of effective actions and heat kernels. However, implementing nontrivial boundary conditions in this formalism has turned out to be a difficult problem. Recently, such a generalization was developed for the case of a scalar field on the half-space Bbb R+ × Bbb RD-1, based on an extension of the associated worldline path integral to the full Bbb RD using image charges. We present here an improved version of this formalism which allows us to write down non-recursive master formulas for the n-point contribution to the heat kernel trace of a scalar field on the half-space with Dirichlet or Neumann boundary conditions. These master formulas are suitable to computerization. We demonstrate the efficiency of the formalism by a calculation of two new heat-kernel coefficients for the half-space, a4 and a9/2.
Weighted Feature Gaussian Kernel SVM for Emotion Recognition
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
Improved Online Support Vector Machines Spam Filtering Using String Kernels
NASA Astrophysics Data System (ADS)
Amayri, Ola; Bouguila, Nizar
A major bottleneck in electronic communications is the enormous dissemination of spam emails. Developing of suitable filters that can adequately capture those emails and achieve high performance rate become a main concern. Support vector machines (SVMs) have made a large contribution to the development of spam email filtering. Based on SVMs, the crucial problems in email classification are feature mapping of input emails and the choice of the kernels. In this paper, we present thorough investigation of several distance-based kernels and propose the use of string kernels and prove its efficiency in blocking spam emails. We detail a feature mapping variants in text classification (TC) that yield improved performance for the standard SVMs in filtering task. Furthermore, to cope for realtime scenarios we propose an online active framework for spam filtering.
Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing
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
Recurrent kernel machines: computing with infinite echo state networks.
Hermans, Michiel; Schrauwen, Benjamin
2012-01-01
Echo state networks (ESNs) are large, random recurrent neural networks with a single trained linear readout layer. Despite the untrained nature of the recurrent weights, they are capable of performing universal computations on temporal input data, which makes them interesting for both theoretical research and practical applications. The key to their success lies in the fact that the network computes a broad set of nonlinear, spatiotemporal mappings of the input data, on which linear regression or classification can easily be performed. One could consider the reservoir as a spatiotemporal kernel, in which the mapping to a high-dimensional space is computed explicitly. In this letter, we build on this idea and extend the concept of ESNs to infinite-sized recurrent neural networks, which can be considered recursive kernels that subsequently can be used to create recursive support vector machines. We present the theoretical framework, provide several practical examples of recursive kernels, and apply them to typical temporal tasks.
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.
Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.
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.
Jenkins, K.W.; Klein, M.; Chakraborty, N.; Cant, R.S.
2006-04-15
Strain rate and curvature effects on the propagation of turbulent premixed flame kernels have been investigated in the thin-reaction-zones regime using three-dimensional compressible direct numerical simulations (DNS) with single-step Arrhenius chemistry. An initially spherical laminar flame kernel is allowed to interact with the surrounding turbulent fluid motion to provide a propagating turbulent flame with a strong mean spherical curvature. The statistical behavior of the local displacement speed in response to strain and curvature is investigated in detail. The results demonstrate clearly that the mean curvature inherent to the flame kernel configuration has a significant influence on the propagation of the flame. It has been found that the mean density-weighted displacement speed rS{sub d} in the case of flame kernels varies significantly over the flame brush and remains different from r{sub 0}S{sub L} (where r{sub 0} is the reactant density and S{sub L} is laminar flame speed), unlike statistically planar flames. It is also shown that the magnitude of reaction progress variable gradient ||c| is negatively correlated with curvature in the case of flame kernels, in contrast to the weak correlation between ||c| and curvature in the case of planar flames. This correlation induces a net positive correlation between the combined reaction and normal diffusion components of displacement speed (S{sub r}+S{sub n}) and curvature in flame kernels, whereas the previous studies based on statistically planar flames did not observe any appreciable correlation between (S{sub r}+S{sub n}) and curvature. (author)
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.
A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine
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
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.
Heat kernel smoothing using Laplace-Beltrami eigenfunctions.
Seo, Seongho; Chung, Moo K; Vorperian, Houri K
2010-01-01
We present a novel surface smoothing framework using the Laplace-Beltrami eigenfunctions. The Green's function of an isotropic diffusion equation on a manifold is constructed as a linear combination of the Laplace-Beltraimi operator. The Green's function is then used in constructing heat kernel smoothing. Unlike many previous approaches, diffusion is analytically represented as a series expansion avoiding numerical instability and inaccuracy issues. This proposed framework is illustrated with mandible surfaces, and is compared to a widely used iterative kernel smoothing technique in computational anatomy. The MATLAB source code is freely available at http://brainimaging.waisman.wisc.edu/ chung/lb.
Optical remote sensor for peanut kernel abortion classification.
Ozana, Nisan; Buchsbaum, Stav; Bishitz, Yael; Beiderman, Yevgeny; Schmilovitch, Zeev; Schwarz, Ariel; Shemer, Amir; Keshet, Joseph; Zalevsky, Zeev
2016-05-20
In this paper, we propose a simple, inexpensive optical device for remote measurement of various agricultural parameters. The sensor is based on temporal tracking of backreflected secondary speckle patterns generated when illuminating a plant with a laser and while applying periodic acoustic-based pressure stimulation. By analyzing different parameters using a support-vector-machine-based algorithm, peanut kernel abortion can be detected remotely. This paper presents experimental tests which are the first step toward an implementation of a noncontact device for the detection of agricultural parameters such as kernel abortion. PMID:27411126
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.
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.
The Feasibility of Palm Kernel Shell as a Replacement for Coarse Aggregate in Lightweight Concrete
NASA Astrophysics Data System (ADS)
Itam, Zarina; Beddu, Salmia; Liyana Mohd Kamal, Nur; Ashraful Alam, Md; Issa Ayash, Usama
2016-03-01
Implementing sustainable materials into the construction industry is fast becoming a trend nowadays. Palm Kernel Shell is a by-product of Malaysia’s palm oil industry, generating waste as much as 4 million tons per annum. As a means of producing a sustainable, environmental-friendly, and affordable alternative in the lightweight concrete industry, the exploration of the potential of Palm Kernel Shell to be used as an aggregate replacement was conducted which may give a positive impact to the Malaysian construction industry as well as worldwide concrete usage. This research investigates the feasibility of PKS as an aggregate replacement in lightweight concrete in terms of compressive strength, slump test, water absorption, and density. Results indicate that by using PKS for aggregate replacement, it increases the water absorption but decreases the concrete workability and strength. Results however, fall into the range acceptable for lightweight aggregates, hence it can be concluded that there is potential to use PKS as aggregate replacement for lightweight concrete.
Ignition kernel formation and lift-off behaviour of jet-in-hot-coflow flames
Oldenhof, E.; Tummers, M.J.; van Veen, E.H.; Roekaerts, D.J.E.M.
2010-06-15
The stabilisation region of turbulent non-premixed flames of natural gas mixtures burning in a hot and diluted coflow is studied by recording the flame luminescence with an intensified high-speed camera. The flame base is found to behave fundamentally differently from that of a conventional lifted jet flame in a cold air coflow. Whereas the latter flame has a sharp interface that moves up and down, ignition kernels are continuously being formed in the jet-in-hot-coflow flames, growing in size while being convected downstream. To study the lift-off height effectively given these highly variable flame structures, a new definition of lift-off height is introduced. An important parameter determining lift-off height is the mean ignition frequency density in the flame stabilisation region. An increase in coflow temperature and the addition of small quantities of higher alkanes both increase ignition frequencies, and decrease the distance between the jet exit and the location where the first ignition kernels appear. Both mechanisms lower the lift-off height. An increase in jet Reynolds number initially leads to a significant decrease of the location where ignition first occurs. Higher jet Reynolds numbers (above 5000) do not strongly alter the location of first ignition but hamper the growth of flame pockets and reduce ignition frequencies in flames with lower coflow temperatures, leading to larger lift-off heights. (author)
NASA Astrophysics Data System (ADS)
Burston, Raymond; Gizon, Laurent; Birch, Aaron C.
2015-12-01
Time-distance helioseismology uses cross-covariances of wave motions on the solar surface to determine the travel times of wave packets moving from one surface location to another. We review the methodology to interpret travel-time measurements in terms of small, localised perturbations to a horizontally homogeneous reference solar model. Using the first Born approximation, we derive and compute 3D travel-time sensitivity (Fréchet) kernels for perturbations in sound-speed, density, pressure, and vector flows. While kernels for sound speed and flows had been computed previously, here we extend the calculation to kernels for density and pressure, hence providing a complete description of the effects of solar dynamics and structure on travel times. We treat three thermodynamic quantities as independent and do not assume hydrostatic equilibrium. We present a convenient approach to computing damped Green's functions using a normal-mode summation. The Green's function must be computed on a wavenumber grid that has sufficient resolution to resolve the longest lived modes. The typical kernel calculations used in this paper are computer intensive and require on the order of 600 CPU hours per kernel. Kernels are validated by computing the travel-time perturbation that results from horizontally-invariant perturbations using two independent approaches. At fixed sound-speed, the density and pressure kernels are approximately related through a negative multiplicative factor, therefore implying that perturbations in density and pressure are difficult to disentangle. Mean travel-times are not only sensitive to sound-speed, density and pressure perturbations, but also to flows, especially vertical flows. Accurate sensitivity kernels are needed to interpret complex flow patterns such as convection.
Higher-order Lipatov kernels and the QCD Pomeron
White, A.R.
1994-08-12
Three closely related topics are covered. The derivation of O(g{sup 4}) Lipatov kernels in pure glue QCD. The significance of quarks for the physical Pomeron in QCD. The possible inter-relation of Pomeron dynamics with Electroweak symmetry breaking.
PERI - Auto-tuning Memory Intensive Kernels for Multicore
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.
Metabolite identification through multiple kernel learning on fragmentation trees
Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho
2014-01-01
Motivation: Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Results: Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. Contact: huibin.shen@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24931979
Enzymatic treatment of peanut kernels to reduce allergen levels
Technology Transfer Automated Retrieval System (TEKTRAN)
This study investigated the use of enzymatic treatment to reduce peanut allergens in peanut kernel by processing conditions, such as, pretreatment with heat and proteolysis at different enzyme concentrations and treatment times. Two major peanut allergens, Ara h 1 and Ara h 2, were used as indicator...
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…
Music emotion detection using hierarchical sparse kernel machines.
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.
High-Speed Tracking with Kernelized Correlation Filters.
Henriques, João F; Caseiro, Rui; Martins, Pedro; Batista, Jorge
2015-03-01
The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies-any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source. PMID:26353263
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.
Uniqueness Result in the Cauchy Dirichlet Problem via Mehler Kernel
NASA Astrophysics Data System (ADS)
Dhungana, Bishnu P.
2014-09-01
Using the Mehler kernel, a uniqueness theorem in the Cauchy Dirichlet problem for the Hermite heat equation with homogeneous Dirichlet boundary conditions on a class P of bounded functions U( x, t) with certain growth on U x ( x, t) is established.
High-Speed Tracking with Kernelized Correlation Filters.
Henriques, João F; Caseiro, Rui; Martins, Pedro; Batista, Jorge
2015-03-01
The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies-any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.
PERI - auto-tuning memory-intensive kernels for multicore
NASA Astrophysics Data System (ADS)
Williams, S.; Datta, K.; Carter, J.; Oliker, L.; Shalf, J.; Yelick, K.; Bailey, D.
2008-07-01
We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to sparse matrix vector multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the high-performance computing literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4× 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.
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 ...
Estimating Filtering Errors Using the Peano Kernel Theorem
Jerome Blair
2008-03-01
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.
Stereotype Measurement and the "Kernel of Truth" Hypothesis.
ERIC Educational Resources Information Center
Gordon, Randall A.
1989-01-01
Describes a stereotype measurement suitable for classroom demonstration. Illustrates C. McCauley and C. L. Stitt's diagnostic ratio measure and examines the validity of the "kernel of truth" hypothesis. Uses this as a starting point for class discussion. Reports results and gives suggestions for discussion of related concepts. (Author/NL)
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…
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...
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...
7 CFR 981.61 - Redetermination of kernel weight.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 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...
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...
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...
Estimating Filtering Errors Using the Peano Kernel Theorem
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.
Matrix kernels for MEG and EEG source localization and imaging
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.
The Stokes problem for the ellipsoid using ellipsoidal kernels
NASA Technical Reports Server (NTRS)
Zhu, Z.
1981-01-01
A brief review of Stokes' problem for the ellipsoid as a reference surface is given. Another solution of the problem using an ellipsoidal kernel, which represents an iterative form of Stokes' integral, is suggested with a relative error of the order of the flattening. On studying of Rapp's method in detail the procedures of improving its convergence are discussed.
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
Increasing accuracy of dispersal kernels in grid-based population models
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%.
Genome Mapping of Kernel Characteristics in Hard Red Spring Wheat Breeding Lines
Technology Transfer Automated Retrieval System (TEKTRAN)
Kernel characteristics, particularly kernel weight, kernel size, and grain protein content, are important components of grain yield and quality in wheat. Development of high performing wheat cultivars, with high grain yield and quality, is a major focus in wheat breeding programs worldwide. Here, we...
Low Cost Real-Time Sorting of in Shell Pistachio Nuts from Kernels
Technology Transfer Automated Retrieval System (TEKTRAN)
A high speed sorter for separating pistachio nuts with (in shell) and without (kernels) shells is reported. Testing indicates 95% accuracy in removing kernels from the in shell stream with no false positive results out of 1000 kernels tested. Testing with 1000 each of in shell, shell halves, and ker...
Chung, Moo K.; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K.
2014-01-01
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 regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface. PMID:25791435
Bláha, Jan; Pachl, Petr; Novák, Petr; Vaněk, Ondřej
2015-05-01
Lectin-like transcript 1 (LLT1, gene clec2d) was identified to be a ligand for the single human NKR-P1 receptor present on NK and NK-T lymphocytes. Naturally, LLT1 is expressed on the surface of NK cells, stimulating IFN-γ production, and is up-regulated upon activation of other immune cells, e.g. TLR-stimulated dendritic cells and B cells or T cell receptor-activated T cells. While in normal tissues LLT1:NKR-P1 interaction (representing an alternative "missing-self" recognition system) play an immunomodulatory role in regulation of crosstalk between NK and antigen presenting cells, LLT1 is upregulated in glioblastoma cells, one of the most lethal tumors, where it acts as a mediator of immune escape of glioma cells. Here we report transient expression and characterization of soluble His176Cys mutant of LLT1 ectodomain in an eukaryotic expression system of human suspension-adapted HEK293S GnTI(-) cell line with uniform N-glycans. The His176Cys mutation is critical for C-type lectin-like domain stability, leading to the reconstruction of third canonical disulfide bridge in LLT1, as shown by mass spectrometry. Purified soluble LLT1 is homogeneous, deglycosylatable and forms a non-covalent homodimer whose dimerization is not dependent on presence of its N-glycans. As a part of production of soluble LLT1, we have adapted HEK293S GnTI(-) cell line to growth in suspension in media facilitating transient transfection and optimized novel high cell density transfection protocol, greatly enhancing protein yields. This transfection protocol is generally applicable for protein production within this cell line, especially for protein crystallography. PMID:25623399
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.
Effects of Amygdaline from Apricot Kernel on Transplanted Tumors in Mice.
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.
Effects of Amygdaline from Apricot Kernel on Transplanted Tumors in Mice.
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. PMID:27021084
NASA Astrophysics Data System (ADS)
Jourde, K.; Gibert, D.; Marteau, J.
2015-08-01
This paper examines how the resolution of small-scale geological density models is improved through the fusion of information provided by gravity measurements and density muon radiographies. Muon radiography aims at determining the density of geological bodies by measuring their screening effect on the natural flux of cosmic muons. Muon radiography essentially works like a medical X-ray scan and integrates density information along elongated narrow conical volumes. Gravity measurements are linked to density by a 3-D integration encompassing the whole studied domain. We establish the mathematical expressions of these integration formulas - called acquisition kernels - and derive the resolving kernels that are spatial filters relating the true unknown density structure to the density distribution actually recovered from the available data. The resolving kernel approach allows one to quantitatively describe the improvement of the resolution of the density models achieved by merging gravity data and muon radiographies. The method developed in this paper may be used to optimally design the geometry of the field measurements to be performed in order to obtain a given spatial resolution pattern of the density model to be constructed. The resolving kernels derived in the joined muon-gravimetry case indicate that gravity data are almost useless for constraining the density structure in regions sampled by more than two muon tomography acquisitions. Interestingly, the resolution in deeper regions not sampled by muon tomography is significantly improved by joining the two techniques. The method is illustrated with examples for the La Soufrière volcano of Guadeloupe.
NASA Astrophysics Data System (ADS)
Jourde, K.; Gibert, D.; Marteau, J.
2015-04-01
This paper examines how the resolution of small-scale geological density models is improved through the fusion of information provided by gravity measurements and density muon radiographies. Muon radiography aims at determining the density of geological bodies by measuring their screening effect on the natural flux of cosmic muons. Muon radiography essentially works like medical X-ray scan and integrates density information along elongated narrow conical volumes. Gravity measurements are linked to density by a 3-D integration encompassing the whole studied domain. We establish the mathematical expressions of these integration formulas - called acquisition kernels - and derive the resolving kernels that are spatial filters relating the true unknown density structure to the density distribution actually recovered from the available data. The resolving kernels approach allows to quantitatively describe the improvement of the resolution of the density models achieved by merging gravity data and muon radiographies. The method developed in this paper may be used to optimally design the geometry of the field measurements to perform in order to obtain a given spatial resolution pattern of the density model to construct. The resolving kernels derived in the joined muon/gravimetry case indicate that gravity data are almost useless to constrain the density structure in regions sampled by more than two muon tomography acquisitions. Interestingly the resolution in deeper regions not sampled by muon tomography is significantly improved by joining the two techniques. The method is illustrated with examples for La Soufrière of Guadeloupe volcano.
Importance of Many-Body Effects in the Kernel of Hemoglobin for Ligand Binding
NASA Astrophysics Data System (ADS)
Weber, Cédric; O'Regan, David D.; Hine, Nicholas D. M.; Littlewood, Peter B.; Kotliar, Gabriel; Payne, Mike C.
2013-03-01
We propose a mechanism for binding of diatomic ligands to heme based on a dynamical orbital selection process. This scenario may be described as bonding determined by local valence fluctuations. We support this model using linear-scaling first-principles calculations, in combination with dynamical mean-field theory, applied to heme, the kernel of the hemoglobin metalloprotein central to human respiration. We find that variations in Hund’s exchange coupling induce a reduction of the iron 3d density, with a concomitant increase of valence fluctuations. We discuss the comparison between our computed optical absorption spectra and experimental data, our picture accounting for the observation of optical transitions in the infrared regime, and how the Hund’s coupling reduces, by a factor of 5, the strong imbalance in the binding energies of heme with CO and O2 ligands.
Camphor Tree Seed Kernel Oil Reduces Body Fat Deposition and Improves Blood Lipids in Rats.
Fu, Jing; Wang, Baogui; Gong, Deming; Zeng, Cheng; Jiang, Yihao; Zeng, Zheling
2015-08-01
The total and positional fatty acid composition in camphor tree (Cinnamomum camphora) seed kernel oil (CKO) were analyzed, and for the first time, the effect of CKO on body fat deposition and blood lipids in rats was studied. The major fatty acids in CKO were determined to be decanoic acid (C10:0, 51.49%) and dodecanoic acid (C12:0, 40.08%), and uniformly distributed at Sn-1, 3, and Sn-2 positions in triglyceride (TG). Rats were randomly divided into control, CKO, lard, and soybean oil groups. At the end of the experiment, levels of blood lipids and the fats of abdomen in the rats were measured. The main organ were weighted and used for the histological examination. The results showed that body weight and fat deposition in CKO group were significantly lower than the lard and soybean groups. Moderate consumption of CKO was found to improve the levels of blood TG and low density lipoprotein cholesterol.
Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
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
O'Rourke, Patrick K; Hutchison, W D
2004-06-01
Late-season infestations of European corn borer, Ostrinia nubilalis (Hübner), and corn earworm, Helicoverpa zea (Boddie), were sampled to develop binomial sequential sampling plans for larval infestations and damaged kernels in sweet corn, Zea mays L., ears, near harvest. Fields were sampled to obtain a range of larval densities likely to be encountered over a range of infestation levels and field conditions. Binomial sampling plans were developed for O. nubilalis larvae, H. zea larvae, O. nubilalis, and H. zea larvae combined, and for damaged sweet corn kernels. Observed densities ranged from 0.01 to 4.40 larvae per ear for O. nubilalis, 0.005-1.62 larvae per ear for H. zea, and 0.004-36.12 damaged kernels per ear. Results of resampling analyses, based on the proportion of ears infested with one or more larvae, or damaged kernels, indicated an average sample size of 34-37 ears was necessary to classify whether larval infestations, or the incidence of damaged kernels, exceeded 5%. Two operating characteristic curves are presented for each of the four sampling plans. Initial results, with upper bounds of 0.10, and alpha (type I) and beta (type II) error rates at 0.10 and 0.05, respectively, resulted in a 90% probability of making the correct management decision at infestation levels >10%. To improve performance of the sampling plans, we modified the binomial plans by reducing the upper bound to 0.075, while maintaining the same error rates. This plan resulted in a higher probability (>95%) of making the correct management decision to reject a sweet corn load when infestation levels are >10%. PMID:15279284
Effective face recognition using bag of features with additive kernels
NASA Astrophysics Data System (ADS)
Yang, Shicai; Bebis, George; Chu, Yongjie; Zhao, Lindu
2016-01-01
In past decades, many techniques have been used to improve face recognition performance. The most common and well-studied ways are to use the whole face image to build a subspace based on the reduction of dimensionality. Differing from methods above, we consider face recognition as an image classification problem. The face images of the same person are considered to fall into the same category. Each category and each face image could be both represented by a simple pyramid histogram. Spatial dense scale-invariant feature transform features and bag of features method are used to build categories and face representations. In an effort to make the method more efficient, a linear support vector machine solver, Pegasos, is used for the classification in the kernel space with additive kernels instead of nonlinear SVMs. Our experimental results demonstrate that the proposed method can achieve very high recognition accuracy on the ORL, YALE, and FERET databases.
Reproducing kernel particle method for free and forced vibration analysis
NASA Astrophysics Data System (ADS)
Zhou, J. X.; Zhang, H. Y.; Zhang, L.
2005-01-01
A reproducing kernel particle method (RKPM) is presented to analyze the natural frequencies of Euler-Bernoulli beams as well as Kirchhoff plates. In addition, RKPM is also used to predict the forced vibration responses of buried pipelines due to longitudinal travelling waves. Two different approaches, Lagrange multipliers as well as transformation method , are employed to enforce essential boundary conditions. Based on the reproducing kernel approximation, the domain of interest is discretized by a set of particles without the employment of a structured mesh, which constitutes an advantage over the finite element method. Meanwhile, RKPM also exhibits advantages over the classical Rayleigh-Ritz method and its counterparts. Numerical results presented here demonstrate the effectiveness of this novel approach for both free and forced vibration analysis.
Undersampled dynamic magnetic resonance imaging using kernel principal component analysis.
Wang, Yanhua; Ying, Leslie
2014-01-01
Compressed sensing (CS) is a promising approach to accelerate dynamic magnetic resonance imaging (MRI). Most existing CS methods employ linear sparsifying transforms. The recent developments in non-linear or kernel-based sparse representations have been shown to outperform the linear transforms. In this paper, we present an iterative non-linear CS dynamic MRI reconstruction framework that uses the kernel principal component analysis (KPCA) to exploit the sparseness of the dynamic image sequence in the feature space. Specifically, we apply KPCA to represent the temporal profiles of each spatial location and reconstruct the images through a modified pre-image problem. The underlying optimization algorithm is based on variable splitting and fixed-point iteration method. Simulation results show that the proposed method outperforms conventional CS method in terms of aliasing artifact reduction and kinetic information preservation. PMID:25570262
Hydroxocobalamin treatment of acute cyanide poisoning from apricot kernels.
Cigolini, Davide; Ricci, Giogio; Zannoni, Massimo; Codogni, Rosalia; De Luca, Manuela; Perfetti, Paola; Rocca, Giampaolo
2011-05-24
Clinical experience with hydroxocobalamin in acute cyanide poisoning via ingestion remains limited. This case concerns a 35-year-old mentally ill woman who consumed more than 20 apricot kernels. Published literature suggests each kernel would have contained cyanide concentrations ranging from 0.122 to 4.09 mg/g (average 2.92 mg/g). On arrival, the woman appeared asymptomatic with a raised pulse rate and slight metabolic acidosis. Forty minutes after admission (approximately 70 min postingestion), the patient experienced headache, nausea and dyspnoea, and was hypotensive, hypoxic and tachypnoeic. Following treatment with amyl nitrite and sodium thiosulphate, her methaemoglobin level was 10%. This prompted the administration of oxygen, which evoked a slight improvement in her vital signs. Hydroxocobalamin was then administered. After 24 h, she was completely asymptomatic with normalised blood pressure and other haemodynamic parameters. This case reinforces the safety and effectiveness of hydroxocobalamin in acute cyanide poisoning by ingestion.
Hydroxocobalamin treatment of acute cyanide poisoning from apricot kernels.
Cigolini, Davide; Ricci, Giogio; Zannoni, Massimo; Codogni, Rosalia; De Luca, Manuela; Perfetti, Paola; Rocca, Giampaolo
2011-09-01
Clinical experience with hydroxocobalamin in acute cyanide poisoning via ingestion remains limited. This case concerns a 35-year-old mentally ill woman who consumed more than 20 apricot kernels. Published literature suggests each kernel would have contained cyanide concentrations ranging from 0.122 to 4.09 mg/g (average 2.92 mg/g). On arrival, the woman appeared asymptomatic with a raised pulse rate and slight metabolic acidosis. Forty minutes after admission (approximately 70 min postingestion), the patient experienced headache, nausea and dyspnoea, and was hypotensive, hypoxic and tachypnoeic. Following treatment with amyl nitrite and sodium thiosulphate, her methaemoglobin level was 10%. This prompted the administration of oxygen, which evoked a slight improvement in her vital signs. Hydroxocobalamin was then administered. After 24 h, she was completely asymptomatic with normalised blood pressure and other haemodynamic parameters. This case reinforces the safety and effectiveness of hydroxocobalamin in acute cyanide poisoning by ingestion.
Realistic dispersion kernels applied to cohabitation reaction dispersion equations
NASA Astrophysics Data System (ADS)
Isern, Neus; Fort, Joaquim; Pérez-Losada, Joaquim
2008-10-01
We develop front spreading models for several jump distance probability distributions (dispersion kernels). We derive expressions for a cohabitation model (cohabitation of parents and children) and a non-cohabitation model, and apply them to the Neolithic using data from real human populations. The speeds that we obtain are consistent with observations of the Neolithic transition. The correction due to the cohabitation effect is up to 38%.
Linux Kernel Co-Scheduling For Bulk Synchronous Parallel Applications
Jones, Terry R
2011-01-01
This paper describes a kernel scheduling algorithm that is based on co-scheduling principles and that is intended for parallel applications running on 1000 cores or more where inter-node scalability is key. Experimental results for a Linux implementation on a Cray XT5 machine are presented.1 The results indicate that Linux is a suitable operating system for this new scheduling scheme, and that this design provides a dramatic improvement in scaling performance for synchronizing collective operations at scale.