Calculates Thermal Neutron Scattering Kernel.
Energy Science and Technology Software Center (ESTSC)
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)
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.
Tedgren, Åsa Carlsson; 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. PMID:26108232
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.
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.
SCAP. Point Kernel Single or Albedo Scatter
Disney, R.K.; Bevan, S.E.
1982-08-05
SCAP solves for radiation transport in complex geometries using the single or albedo-scatter point kernel method. The program is designed to calculate the neutron or gamma-ray radiation level at detector points located within or outside a complex radiation scatter source geometry or a user-specified discrete scattering volume. The geometry is described by zones bounded by intersecting quadratic surfaces with an arbitrary maximum number of boundary surfaces per zone. The anisotropic point sources are described as point-wise energy dependent distributions of polar angles on a meridian; isotropic point sources may be specified also. The attenuation function for gamma rays is an exponential function on the primary source leg and the scatter leg with a buildup factor approximation to account for multiple scatter on the scatter leg. The neutron attenuation function is an exponential function using neutron removal cross sections on the primary source leg and scatter leg. Line or volumetric sources can be represented as distributions of isotropic point sources, with uncollided line-of-sight attenuation and buildup calculated between each source point and the detector point.
A locally adaptive kernel regression method for facies delineation
NASA Astrophysics Data System (ADS)
Fernàndez-Garcia, D.; Barahona-Palomo, M.; Henri, C. V.; Sanchez-Vila, X.
2015-12-01
Facies delineation is defined as the separation of geological units with distinct intrinsic characteristics (grain size, hydraulic conductivity, mineralogical composition). A major challenge in this area stems from the fact that only a few scattered pieces of hydrogeological information are available to delineate geological facies. Several methods to delineate facies are available in the literature, ranging from those based only on existing hard data, to those including secondary data or external knowledge about sedimentological patterns. This paper describes a methodology to use kernel regression methods as an effective tool for facies delineation. The method uses both the spatial and the actual sampled values to produce, for each individual hard data point, a locally adaptive steering kernel function, self-adjusting the principal directions of the local anisotropic kernels to the direction of highest local spatial correlation. The method is shown to outperform the nearest neighbor classification method in a number of synthetic aquifers whenever the available number of hard data is small and randomly distributed in space. In the case of exhaustive sampling, the steering kernel regression method converges to the true solution. Simulations ran in a suite of synthetic examples are used to explore the selection of kernel parameters in typical field settings. It is shown that, in practice, a rule of thumb can be used to obtain suboptimal results. The performance of the method is demonstrated to significantly improve when external information regarding facies proportions is incorporated. Remarkably, the method allows for a reasonable reconstruction of the facies connectivity patterns, shown in terms of breakthrough curves performance.
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
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.
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.
Analytical scatter kernels for portal imaging at 6 MV.
Spies, L; Bortfeld, T
2001-04-01
X-ray photon scatter kernels for 6 MV electronic portal imaging are investigated using an analytical and a semi-analytical model. The models are tested on homogeneous phantoms for a range of uniform circular fields and scatterer-to-detector air gaps relevant for clinical use. It is found that a fully analytical model based on an exact treatment of photons undergoing a single Compton scatter event and an approximate treatment of second and higher order scatter events, assuming a multiple-scatter source at the center of the scatter volume, is accurate within 1% (i.e., the residual scatter signal is less than 1% of the primary signal) for field sizes up to 100 cm2 and air gaps over 30 cm, but shows significant discrepancies for larger field sizes. Monte Carlo results are presented showing that the effective multiple-scatter source is located toward the exit surface of the scatterer, rather than at its center. A second model is therefore investigated where second and higher-order scattering is instead modeled by fitting an analytical function describing a nonstationary isotropic point-scatter source to Monte Carlo generated data. This second model is shown to be accurate to within 1% for air gaps down to 20 cm, for field sizes up to 900 cm2 and phantom thicknesses up to 50 cm. PMID:11339752
The Kernel Adaptive Autoregressive-Moving-Average Algorithm.
Li, Kan; Príncipe, José C
2016-02-01
In this paper, we present a novel kernel adaptive recurrent filtering algorithm based on the autoregressive-moving-average (ARMA) model, which is trained with recurrent stochastic gradient descent in the reproducing kernel Hilbert spaces. This kernelized recurrent system, the kernel adaptive ARMA (KAARMA) algorithm, brings together the theories of adaptive signal processing and recurrent neural networks (RNNs), extending the current theory of kernel adaptive filtering (KAF) using the representer theorem to include feedback. Compared with classical feedforward KAF methods, the KAARMA algorithm provides general nonlinear solutions for complex dynamical systems in a state-space representation, with a deferred teacher signal, by propagating forward the hidden states. We demonstrate its capabilities to provide exact solutions with compact structures by solving a set of benchmark nondeterministic polynomial-complete problems involving grammatical inference. Simulation results show that the KAARMA algorithm outperforms equivalent input-space recurrent architectures using first- and second-order RNNs, demonstrating its potential as an effective learning solution for the identification and synthesis of deterministic finite automata. PMID:25935049
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
Automated endmember determination and adaptive spectral mixture analysis using kernel methods
NASA Astrophysics Data System (ADS)
Rand, Robert S.; Banerjee, Amit; Broadwater, Joshua
2013-09-01
Various phenomena occur in geographic regions that cause pixels of a scene to contain spectrally mixed pixels. The mixtures may be linear or nonlinear. It could simply be that the pixel size of a sensor is too large so many pixels contain patches of different materials within them (linear), or there could be microscopic mixtures and multiple scattering occurring within pixels (non-linear). Often enough, scenes may contain cases of both linear and non-linear mixing on a pixel-by-pixel basis. Furthermore, appropriate endmembers in a scene are not always easy to determine. A reference spectral library of materials may or may not be available, yet, even if a library is available, using it directly for spectral unmixing may not always be fruitful. This study investigates a generalized kernel-based method for spectral unmixing that attempts to determine if each pixel in a scene is linear or non-linear, and adapts to compute a mixture model at each pixel accordingly. The effort also investigates a kernel-based support vector method for determining spectral endmembers in a scene. Two scenes of hyperspectral imagery calibrated to reflectance are used to validate the methods. We test the approaches using a HyMAP scene collected over the Waimanalo Bay region in Oahu, Hawaii, as well as an AVIRIS scene collected over the oil spill region in the Gulf of Mexico during the Deepwater Horizon oil incident.
Data consistency-driven scatter kernel optimization for x-ray cone-beam CT.
Kim, Changhwan; Park, Miran; Sung, Younghun; Lee, Jaehak; Choi, Jiyoung; Cho, Seungryong
2015-08-01
Accurate and efficient scatter correction is essential for acquisition of high-quality x-ray cone-beam CT (CBCT) images for various applications. This study was conducted to demonstrate the feasibility of using the data consistency condition (DCC) as a criterion for scatter kernel optimization in scatter deconvolution methods in CBCT. As in CBCT, data consistency in the mid-plane is primarily challenged by scatter, we utilized data consistency to confirm the degree of scatter correction and to steer the update in iterative kernel optimization. By means of the parallel-beam DCC via fan-parallel rebinning, we iteratively optimized the scatter kernel parameters, using a particle swarm optimization algorithm for its computational efficiency and excellent convergence. The proposed method was validated by a simulation study using the XCAT numerical phantom and also by experimental studies using the ACS head phantom and the pelvic part of the Rando phantom. The results showed that the proposed method can effectively improve the accuracy of deconvolution-based scatter correction. Quantitative assessments of image quality parameters such as contrast and structure similarity (SSIM) revealed that the optimally selected scatter kernel improves the contrast of scatter-free images by up to 99.5%, 94.4%, and 84.4%, and of the SSIM in an XCAT study, an ACS head phantom study, and a pelvis phantom study by up to 96.7%, 90.5%, and 87.8%, respectively. The proposed method can achieve accurate and efficient scatter correction from a single cone-beam scan without need of any auxiliary hardware or additional experimentation. PMID:26183058
Data consistency-driven scatter kernel optimization for x-ray cone-beam CT
NASA Astrophysics Data System (ADS)
Kim, Changhwan; Park, Miran; Sung, Younghun; Lee, Jaehak; Choi, Jiyoung; Cho, Seungryong
2015-08-01
Accurate and efficient scatter correction is essential for acquisition of high-quality x-ray cone-beam CT (CBCT) images for various applications. This study was conducted to demonstrate the feasibility of using the data consistency condition (DCC) as a criterion for scatter kernel optimization in scatter deconvolution methods in CBCT. As in CBCT, data consistency in the mid-plane is primarily challenged by scatter, we utilized data consistency to confirm the degree of scatter correction and to steer the update in iterative kernel optimization. By means of the parallel-beam DCC via fan-parallel rebinning, we iteratively optimized the scatter kernel parameters, using a particle swarm optimization algorithm for its computational efficiency and excellent convergence. The proposed method was validated by a simulation study using the XCAT numerical phantom and also by experimental studies using the ACS head phantom and the pelvic part of the Rando phantom. The results showed that the proposed method can effectively improve the accuracy of deconvolution-based scatter correction. Quantitative assessments of image quality parameters such as contrast and structure similarity (SSIM) revealed that the optimally selected scatter kernel improves the contrast of scatter-free images by up to 99.5%, 94.4%, and 84.4%, and of the SSIM in an XCAT study, an ACS head phantom study, and a pelvis phantom study by up to 96.7%, 90.5%, and 87.8%, respectively. The proposed method can achieve accurate and efficient scatter correction from a single cone-beam scan without need of any auxiliary hardware or additional experimentation.
Kernel Integration Code System--Multigroup Gamma-Ray Scattering.
Energy Science and Technology Software Center (ESTSC)
1988-02-15
GGG (G3) is the generic designation for a series of computer programs that enable the user to estimate gamma-ray scattering from a point source to a series of point detectors. Program output includes detector response due to each source energy, as well as a grouping by scattered energy in addition to a simple, unscattered beam result. Although G3 is basically a single-scatter program, it also includes a correction for multiple scattering by applying a buildupmore » factor for the path segment between the point of scatter and the detector point. Results are recorded with and without the buildup factor. Surfaces, defined by quadratic equations, are used to provide for a full three-dimensional description of the physical geometry. G3 evaluates scattering effects in those situations where more exact techniques are not economical. G3 was revised by Bettis and the name was changed to indicate that it was no longer identical to the G3 program. The name S3 was chosen since the scattering calculation has three steps: calculation of the flux arriving at the scatterer from the point source, calculation of the differential scattering cross section, and calculation of the scattered flux arriving at the detector.« less
NASA Astrophysics Data System (ADS)
Fomin, Fedor V.
Preprocessing (data reduction or kernelization) as a strategy of coping with hard problems is universally used in almost every implementation. The history of preprocessing, like applying reduction rules simplifying truth functions, can be traced back to the 1950's [6]. A natural question in this regard is how to measure the quality of preprocessing rules proposed for a specific problem. For a long time the mathematical analysis of polynomial time preprocessing algorithms was neglected. The basic reason for this anomaly was that if we start with an instance I of an NP-hard problem and can show that in polynomial time we can replace this with an equivalent instance I' with |I'| < |I| then that would imply P=NP in classical complexity.
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
Interpolations of nuclide-specific scattering kernels generated with Serpent
Scopatz, A.; Schneider, E.
2012-07-01
The neutron group-to-group scattering cross section is an essential input parameter for any multi-energy group physics model. However, if the analyst prefers to use Monte Carlo transport to generate group constants this data is difficult to obtain for a single species of a material. Here, the Monte Carlo code Serpent was modified to return the group transfer probabilities on a per-nuclide basis. This ability is demonstrated in conjunction with an essential physics reactor model where cross section perturbations are used to dynamically generate reactor state dependent group constants via interpolation from pre-computed libraries. The modified version of Serpent was therefore verified with three interpolation cases designed to test the resilience of the interpolation scheme to changes in intra-group fluxes. For most species, interpolation resulted in errors of less than 5% of transport-computed values. For important scatterers, such as {sup 1}H, errors less than 2% were observed. For nuclides with high errors ( > 10%), the scattering channel typically only had a small probability of occurring. (authors)
A simple method for computing the relativistic Compton scattering kernel for radiative transfer
NASA Technical Reports Server (NTRS)
Prasad, M. K.; Kershaw, D. S.; Beason, J. D.
1986-01-01
Correct computation of the Compton scattering kernel (CSK), defined to be the Klein-Nishina differential cross section averaged over a relativistic Maxwellian electron distribution, is reported. The CSK is analytically reduced to a single integral, which can then be rapidly evaluated using a power series expansion, asymptotic series, and rational approximation for sigma(s). The CSK calculation has application to production codes that aim at understanding certain astrophysical, laser fusion, and nuclear weapons effects phenomena.
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
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
Two-pion exchange contributions to the relativistic NN kernel: Peripheral scattering
Cozma, M. D.; Scholten, O.; Timmermans, R. G. E.; Tjon, J. A.
2007-01-15
The relativistic one-boson-exchange model for NN scattering is extended by including two-pion exchange (TPE) contributions in the kernel. We develop the formalism for the evaluation of the TPE diagrams within the relativistic quasipotential approach. The peripheral partial waves in elastic NN scattering are studied within this model. The TPE interactions contain a strongly attractive isoscalar-scalar component which requires a low value of the cutoff parameter: {lambda}=650-800 MeV. With this prescription, the peripheral waves can be reasonably described.
NASA Astrophysics Data System (ADS)
Li, Heng; Mohan, Radhe; Zhu, X. Ronald
2008-12-01
The clinical applications of kilovoltage x-ray cone-beam computed tomography (CBCT) have been compromised by the limited quality of CBCT images, which typically is due to a substantial scatter component in the projection data. In this paper, we describe an experimental method of deriving the scatter kernel of a CBCT imaging system. The estimated scatter kernel can be used to remove the scatter component from the CBCT projection images, thus improving the quality of the reconstructed image. The scattered radiation was approximated as depth-dependent, pencil-beam kernels, which were derived using an edge-spread function (ESF) method. The ESF geometry was achieved with a half-beam block created by a 3 mm thick lead sheet placed on a stack of slab solid-water phantoms. Measurements for ten water-equivalent thicknesses (WET) ranging from 0 cm to 41 cm were taken with (half-blocked) and without (unblocked) the lead sheet, and corresponding pencil-beam scatter kernels or point-spread functions (PSFs) were then derived without assuming any empirical trial function. The derived scatter kernels were verified with phantom studies. Scatter correction was then incorporated into the reconstruction process to improve image quality. For a 32 cm diameter cylinder phantom, the flatness of the reconstructed image was improved from 22% to 5%. When the method was applied to CBCT images for patients undergoing image-guided therapy of the pelvis and lung, the variation in selected regions of interest (ROIs) was reduced from >300 HU to <100 HU. We conclude that the scatter reduction technique utilizing the scatter kernel effectively suppresses the artifact caused by scatter in CBCT.
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.
Adaptive Optimal Kernel Smooth-Windowed Wigner-Ville Distribution for Digital Communication Signal
NASA Astrophysics Data System (ADS)
Tan, Jo Lynn; Sha'ameri, Ahmad Zuribin
2009-12-01
Time-frequency distributions (TFDs) are powerful tools to represent the energy content of time-varying signal in both time and frequency domains simultaneously but they suffer from interference due to cross-terms. Various methods have been described to remove these cross-terms and they are typically signal-dependent. Thus, there is no single TFD with a fixed window or kernel that can produce accurate time-frequency representation (TFR) for all types of signals. In this paper, a globally adaptive optimal kernel smooth-windowed Wigner-Ville distribution (AOK-SWWVD) is designed for digital modulation signals such as ASK, FSK, and M-ary FSK, where its separable kernel is determined automatically from the input signal, without prior knowledge of the signal. This optimum kernel is capable of removing the cross-terms and maintaining accurate time-frequency representation at SNR as low as 0 dB. It is shown that this system is comparable to the system with prior knowledge of the signal.
Monte Carlo-based adaptive EPID dose kernel accounting for different field size responses of imagers
Wang, Song; Gardner, Joseph K.; Gordon, John J.; Li, Weidong; Clews, Luke; Greer, Peter B.; Siebers, Jeffrey V.
2009-01-01
independent and are able to predict fields with varied incident energy spectra and a H&N IMRT patient field. The proposed adaptive EPID dose kernel method provides the necessary infrastructure to build reliable and accurate portal dosimetry systems. PMID:19746793
Scene sketch generation using mixture of gradient kernels and adaptive thresholding
NASA Astrophysics Data System (ADS)
Paheding, Sidike; Essa, Almabrok; Asari, Vijayan
2016-04-01
This paper presents a simple but eﬀective algorithm for scene sketch generation from input images. The proposed algorithm combines the edge magnitudes of directional Prewitt diﬀerential gradient kernels with Kirsch kernels at each pixel position, and then encodes them into an eight bit binary code which encompasses local edge and texture information. In this binary encoding step, relative variance is employed to determine the object shape in each local region. Using relative variance enables object sketch extraction totally adaptive to any shape structure. On the other hand, the proposed technique does not require any parameter to adjust output and it is robust to edge density and noise. Two standard databases are used to show the eﬀectiveness of the proposed framework.
Structured adaptive focusing through scattering media
NASA Astrophysics Data System (ADS)
Di Battista, Diego; Ancora, Daniele; Zhang, Haisu; Lemonaki, Krystalia; Avtzi, Stella; Tzortzakis, Stelios; Leonetti, Marco; Zacharakis, Giannis
2016-03-01
The combined use of a wavefront modulator and a scattering medium forms an "opaque lens" which forces the light to focus tightly. The adaptive focus has the same shape as the correlation function of the original speckle pattern and it can be generated at defined positions with resolution up to hundreds of nanometers. We have demonstrated that manipulating the speckle pattern spatial components can structure the shape of the focus. Exploiting selectively spatial-frequencies from the speckle components we realized opaque lenses able to produce sub-correlation foci and Bessel beams.
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
NASA Astrophysics Data System (ADS)
Margerin, Ludovic; Planès, Thomas; Mayor, Jessie; Calvet, Marie
2016-01-01
Coda-wave interferometry is a technique which exploits tiny waveform changes in the coda to detect temporal variations of seismic properties in evolving media. Observed waveform changes are of two kinds: traveltime perturbations and distortion of seismograms. In the last 10 yr, various theories have been published to relate either background velocity changes to traveltime perturbations, or changes in the scattering properties of the medium to waveform decorrelation. These theories have been limited by assumptions pertaining to the scattering process itself-in particular isotropic scattering, or to the propagation regime-single-scattering and/or diffusion. In this manuscript, we unify and extend previous results from the literature using a radiative transfer approach. This theory allows us to incorporate the effect of anisotropic scattering and to cover a broad range of propagation regimes, including the contribution of coherent, singly scattered and multiply scattered waves. Using basic physical reasoning, we show that two different sensitivity kernels are required to describe traveltime perturbations and waveform decorrelation, respectively, a distinction which has not been well appreciated so far. Previous results from the literature are recovered as limiting cases of our general approach. To evaluate numerically the sensitivity functions, we introduce an improved version of a spectral technique known as the method of `rotated coordinate frames', which allows global evaluation of the Green's function of the radiative transfer equation in a finite domain. The method is validated through direct pointwise comparison with Green's functions obtained by the Monte Carlo method. To illustrate the theory, we consider a series of scattering media displaying increasing levels of scattering anisotropy and discuss the impact on the traveltime and decorrelation kernels. We also consider the related problem of imaging variations of scattering properties based on intensity
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
NASA Astrophysics Data System (ADS)
Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad
2015-01-01
Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.
Kernel based model parametrization and adaptation with applications to battery management systems
NASA Astrophysics Data System (ADS)
Weng, Caihao
With the wide spread use of energy storage systems, battery state of health (SOH) monitoring has become one of the most crucial challenges in power and energy research, as SOH significantly affects the performance and life cycle of batteries as well as the systems they are interacting with. Identifying the SOH and adapting of the battery energy/power management system accordingly are thus two important challenges for applications such as electric vehicles, smart buildings and hybrid power systems. This dissertation focuses on the identification of lithium ion battery capacity fading, and proposes an on-board implementable model parametrization and adaptation framework for SOH monitoring. Both parametric and non-parametric approaches that are based on kernel functions are explored for the modeling of battery charging data and aging signature extraction. A unified parametric open circuit voltage model is first developed to improve the accuracy of battery state estimation. Several analytical and numerical methods are then investigated for the non-parametric modeling of battery data, among which the support vector regression (SVR) algorithm is shown to be the most robust and consistent approach with respect to data sizes and ranges. For data collected on LiFePO 4 cells, it is shown that the model developed with the SVR approach is able to predict the battery capacity fading with less than 2% error. Moreover, motivated by the initial success of applying kernel based modeling methods for battery SOH monitoring, this dissertation further exploits the parametric SVR representation for real-time battery characterization supported by test data. Through the study of the invariant properties of the support vectors, a kernel based model parametrization and adaptation framework is developed. The high dimensional optimization problem in the learning algorithm could be reformulated as a parameter estimation problem, that can be solved by standard estimation algorithms such as the
Yan, Shengye; Xu, Xinxing; Xu, Dong; Lin, Stephen; Li, Xuelong
2015-03-01
We present a framework for image classification that extends beyond the window sampling of fixed spatial pyramids and is supported by a new learning algorithm. Based on the observation that fixed spatial pyramids sample a rather limited subset of the possible image windows, we propose a method that accounts for a comprehensive set of windows densely sampled over location, size, and aspect ratio. A concise high-level image feature is derived to effectively deal with this large set of windows, and this higher level of abstraction offers both efficient handling of the dense samples and reduced sensitivity to misalignment. In addition to dense window sampling, we introduce generalized adaptive l(p)-norm multiple kernel learning (GA-MKL) to learn a robust classifier based on multiple base kernels constructed from the new image features and multiple sets of prelearned classifiers from other classes. With GA-MKL, multiple levels of image features are effectively fused, and information is shared among different classifiers. Extensive evaluation on benchmark datasets for object recognition (Caltech256 and Caltech101) and scene recognition (15Scenes) demonstrate that the proposed method outperforms the state-of-the-art under a broad range of settings. PMID:24968365
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
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
Azimi-Sadjadi, Mahmood R; Salazar, Jaime; Srinivasan, Saravanakumar
2009-07-01
This paper presents an adaptable content-based image retrieval (CBIR) system developed using regularization theory, kernel-based machines, and Fisher information measure. The system consists of a retrieval subsystem that carries out similarity matching using image-dependant information, multiple mapping subsystems that adaptively modify the similarity measures, and a relevance feedback mechanism that incorporates user information. The adaptation process drives the retrieval error to zero in order to exactly meet either an existing multiclass classification model or the user high-level concepts using reference-model or relevance feedback learning, respectively. To facilitate the selection of the most informative query images during relevance feedback learning a new method based upon the Fisher information is introduced. Model-reference and relevance feedback learning mechanisms are thoroughly tested on a domain-specific image database that encompasses a wide range of underwater objects captured using an electro-optical sensor. Benchmarking results with two other relevance feedback learning methods are also provided. PMID:19447718
Zuo, K; Bellanger, J J; Yang, C; Shu, H; Le Bouquin Jeannés, R
2013-01-01
This paper aims at estimating causal relationships between signals to detect flow propagation in autoregressive and physiological models. The main challenge of the ongoing work is to discover whether neural activity in a given structure of the brain influences activity in another area during epileptic seizures. This question refers to the concept of effective connectivity in neuroscience, i.e. to the identification of information flows and oriented propagation graphs. Past efforts to determine effective connectivity rooted to Wiener causality definition adapted in a practical form by Granger with autoregressive models. A number of studies argue against such a linear approach when nonlinear dynamics are suspected in the relationship between signals. Consequently, nonlinear nonparametric approaches, such as transfer entropy (TE), have been introduced to overcome linear methods limitations and promoted in many studies dealing with electrophysiological signals. Until now, even though many TE estimators have been developed, further improvement can be expected. In this paper, we investigate a new strategy by introducing an adaptive kernel density estimator to improve TE estimation. PMID:24110694
NASA Astrophysics Data System (ADS)
Zheng, Zhihui; Gao, Lei; Xiao, Liping; Zhou, Bin; Gao, Shibo
2015-12-01
Our purpose is to develop a detection algorithm capable of searching for generic interest objects in real time without large training sets and long-time training stages. Instead of the classical sliding window object detection paradigm, we employ an objectness measure to produce a small set of candidate windows efficiently using Binarized Normed Gradients and a Laplacian of Gaussian-like filter. We then extract Locally Adaptive Regression Kernels (LARKs) as descriptors both from a model image and the candidate windows which measure the likeness of a pixel to its surroundings. Using a matrix cosine similarity measure, the algorithm yields a scalar resemblance map, indicating the likelihood of similarity between the model and the candidate windows. By employing nonparametric significance tests and non-maxima suppression, we detect the presence of objects similar to the given model. Experiments show that the proposed detection paradigm can automatically detect the presence, the number, as well as location of similar objects to the given model. The high quality and efficiency of our method make it suitable for real time multi-category object detection applications.
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
Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin
2015-01-01
An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity. PMID:26793269
NASA Astrophysics Data System (ADS)
Nakahara, Hisashi; Emoto, Kentaro
2016-08-01
Recently, coda-wave interferometry has been used to monitor temporal changes in subsurface structures. Seismic velocity changes have been detected by coda-wave interferometry in association with large earthquakes and volcanic eruptions. To constrain the spatial extent of the velocity changes, spatial homogeneity is often assumed. However, it is important to locate the region of the velocity changes correctly to understand physical mechanisms causing them. In this paper, we are concerned with the sensitivity kernels relating travel times of coda waves to velocity changes. In previous studies, sensitivity kernels have been formulated for two-dimensional single scattering and multiple scattering, three-dimensional multiple scattering, and diffusion. In this paper, we formulate and derive analytical expressions of the sensitivity kernels for three-dimensional single-scattering case. These sensitivity kernels show two peaks at both source and receiver locations, which is similar to the previous studies using different scattering models. The two peaks are more pronounced for later lapse time. We validate our formulation by comparing it with finite-difference simulations of acoustic wave propagation. Our formulation enables us to evaluate the sensitivity kernels analytically, which is particularly useful for the analysis of body waves from deeper earthquakes.
NASA Astrophysics Data System (ADS)
Rand, Robert S.; Resmini, Ronald G.
2014-09-01
Spectral mixing can occur in a number of different ways, which may be linear or non-linear. Perhaps the pixel size of a sensor is just too large so many pixels contain patches of different materials within them resulting in linear mixing of the materials. However, there are more complex situations, such as scattering that occurs in mixtures of vegetation and soil, or intimate mixing of granular materials like soils. Such multiple scattering and microscopic mixtures within pixels have varying degrees of non-linearity. Often enough, scenes may contain cases of both linear and non-linear mixing on a pixel-by-pixel basis. This study compares two approaches for use as generalized methods for un-mixing pixels in a scene that may be linear or non-linear. The first is a kernel-based fully-constrained method for spectral unmixing, which uses a kernel that seeks to capture the linear behavior of albedo in non-linear mixtures of materials. The second method directly converts reflectance to single-scattering albedo (SSA) according to Hapke theory assuming bidirectional scattering at nadir look angles and uses a constrained linear model on the computed albedo values. Multiple scenes of hyperspectral imagery calibrated to reflectance are used to validate the methods. We test the approaches using a HyMAP scene collected over the Waimanalo Bay region in Oahu, Hawaii, as well as an AVIRIS scene collected over the oil spill region in the Gulf of Mexico during the Deepwater Horizon oil incident.
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
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
A 3D point-kernel multiple scatter model for parallel-beam SPECT based on a gamma-ray buildup factor
NASA Astrophysics Data System (ADS)
Marinkovic, Predrag; Ilic, Radovan; Spaic, Rajko
2007-09-01
A three-dimensional (3D) point-kernel multiple scatter model for point spread function (PSF) determination in parallel-beam single-photon emission computed tomography (SPECT), based on a dose gamma-ray buildup factor, is proposed. This model embraces nonuniform attenuation in a voxelized object of imaging (patient body) and multiple scattering that is treated as in the point-kernel integration gamma-ray shielding problems. First-order Compton scattering is done by means of the Klein-Nishina formula, but the multiple scattering is accounted for by making use of a dose buildup factor. An asset of the present model is the possibility of generating a complete two-dimensional (2D) PSF that can be used for 3D SPECT reconstruction by means of iterative algorithms. The proposed model is convenient in those situations where more exact techniques are not economical. For the proposed model's testing purpose calculations (for the point source in a nonuniform scattering object for parallel beam collimator geometry), the multiple-order scatter PSF generated by means of the proposed model matched well with those using Monte Carlo (MC) simulations. Discrepancies are observed only at the exponential tails mostly due to the high statistic uncertainty of MC simulations in this area, but not because of the inappropriateness of the model.
NASA Astrophysics Data System (ADS)
Liu, Jiaqi; Han, Jing; Zhang, Yi; Bai, Lianfa
2015-10-01
Locally adaptive regression kernels model can describe the edge shape of images accurately and graphic trend of images integrally, but it did not consider images' color information while the color is an important element of an image. Therefore, we present a novel method of target recognition based on 3-D-color-space locally adaptive regression kernels model. Different from the general additional color information, this method directly calculate the local similarity features of 3-D data from the color image. The proposed method uses a few examples of an object as a query to detect generic objects with incompact, complex and changeable shapes. Our method involves three phases: First, calculating the novel color-space descriptors from the RGB color space of query image which measure the likeness of a voxel to its surroundings. Salient features which include spatial- dimensional and color -dimensional information are extracted from said descriptors, and simplifying them to construct a non-similar local structure feature set of the object class by principal components analysis (PCA). Second, we compare the salient features with analogous features from the target image. This comparison is done using a matrix generalization of the cosine similarity measure. Then the similar structures in the target image are obtained using local similarity structure statistical matching. Finally, we use the method of non-maxima suppression in the similarity image to extract the object position and mark the object in the test image. Experimental results demonstrate that our approach is effective and accurate in improving the ability to identify targets.
Liu, Yi-Hung; Wu, Chien-Te; Kao, Yung-Hwa; Chen, Ya-Ting
2013-01-01
Single-trial electroencephalography (EEG)-based emotion recognition enables us to perform fast and direct assessments of human emotional states. However, previous works suggest that a great improvement on the classification accuracy of valence and arousal levels is still needed. To address this, we propose a novel emotional EEG feature extraction method: kernel Eigen-emotion pattern (KEEP). An adaptive SVM is also proposed to deal with the problem of learning from imbalanced emotional EEG data sets. In this study, a set of pictures from IAPS are used for emotion induction. Results based on seven participants show that KEEP gives much better classification results than the widely-used EEG frequency band power features. Also, the adaptive SVM greatly improves classification performance of commonly-adopted SVM classifier. Combined use of KEEP and adaptive SVM can achieve high average valence and arousal classification rates of 73.42% and 73.57%. The highest classification rates for valence and arousal are 80% and 79%, respectively. The results are very promising. PMID:24110685
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.
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. PMID:26922785
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.
Bermejo, Guillermo A; Clore, G Marius; Schwieters, Charles D
2012-01-01
Statistical potentials that embody torsion angle probability densities in databases of high-quality X-ray protein structures supplement the incomplete structural information of experimental nuclear magnetic resonance (NMR) datasets. By biasing the conformational search during the course of structure calculation toward highly populated regions in the database, the resulting protein structures display better validation criteria and accuracy. Here, a new statistical torsion angle potential is developed using adaptive kernel density estimation to extract probability densities from a large database of more than 106 quality-filtered amino acid residues. Incorporated into the Xplor-NIH software package, the new implementation clearly outperforms an older potential, widely used in NMR structure elucidation, in that it exhibits simultaneously smoother and sharper energy surfaces, and results in protein structures with improved conformation, nonbonded atomic interactions, and accuracy. PMID:23011872
Ma, Cheng; Xu, Xiao; Liu, Yan; Wang, Lihong V.
2014-01-01
The ability to steer and focus light inside scattering media has long been sought for a multitude of applications. To form optical foci inside scattering media, the only feasible strategy at present is to guide photons by using either implanted1 or virtual2–4 guide stars, which can be inconvenient and limits potential applications. Here, we report a scheme for focusing light inside scattering media by employing intrinsic dynamics as guide stars. By time-reversing the perturbed component of the scattered light adaptively, we show that it is possible to focus light to the origin of the perturbation. Using the approach, we demonstrate non-invasive dynamic light focusing onto moving targets and imaging of a time-variant object obscured by highly scattering media. Anticipated applications include imaging and photoablation of angiogenic vessels in tumours as well as other biomedical uses. PMID:25530797
NASA Astrophysics Data System (ADS)
Shen, Yujie; Voronine, Dmitri V.; Sokolov, Alexei V.; Scully, Marlan O.
2015-08-01
We report a versatile setup based on the femtosecond adaptive spectroscopic techniques for coherent anti-Stokes Raman scattering. The setup uses a femtosecond Ti:Sapphire oscillator source and a folded 4f pulse shaper, in which the pulse shaping is carried out through conventional optical elements and does not require a spatial light modulator. Our setup is simple in alignment, and can be easily switched between the collinear single-beam and the noncollinear two-beam configurations. We demonstrate the capability for investigating both transparent and highly scattering samples by detecting transmitted and reflected signals, respectively.
Shen, Yujie; Voronine, Dmitri V.; Sokolov, Alexei V.; Scully, Marlan O.
2015-08-15
We report a versatile setup based on the femtosecond adaptive spectroscopic techniques for coherent anti-Stokes Raman scattering. The setup uses a femtosecond Ti:Sapphire oscillator source and a folded 4f pulse shaper, in which the pulse shaping is carried out through conventional optical elements and does not require a spatial light modulator. Our setup is simple in alignment, and can be easily switched between the collinear single-beam and the noncollinear two-beam configurations. We demonstrate the capability for investigating both transparent and highly scattering samples by detecting transmitted and reflected signals, respectively.
Dixon, Robert L.; Boone, John M.
2011-07-15
Purpose: Knowledge of the complete axial dose profile f(z), including its long scatter tails, provides the most complete (and flexible) description of the accumulated dose in CT scanning. The CTDI paradigm (including CTDI{sub vol}) requires shift-invariance along z (identical dose profiles spaced at equal intervals), and is therefore inapplicable to many of the new and complex shift-variant scan protocols, e.g., high dose perfusion studies using variable (or zero) pitch. In this work, a convolution-based beam model developed by Dixon et al.[Med. Phys. 32, 3712-3728, (2005)] updated with a scatter LSF kernel (or DSF) derived from a Monte Carlo simulation by Boone [Med. Phys. 36, 4547-4554 (2009)] is used to create an analytical equation for the axial dose profile f(z) in a cylindrical phantom. Using f(z), equations are derived which provide the analytical description of conventional (axial and helical) dose, demonstrating its physical underpinnings; and likewise for the peak axial dose f(0) appropriate to stationary phantom cone beam CT, (SCBCT). The methodology can also be applied to dose calculations in shift-variant scan protocols. This paper is an extension of our recent work Dixon and Boone [Med. Phys. 37, 2703-2718 (2010)], which dealt only with the properties of the peak dose f(0), its relationship to CTDI, and its appropriateness to SCBCT. Methods: The experimental beam profile data f(z) of Mori et al.[Med. Phys. 32, 1061-1069 (2005)] from a 256 channel prototype cone beam scanner for beam widths (apertures) ranging from a = 28 to 138 mm are used to corroborate the theoretical axial profiles in a 32 cm PMMA body phantom. Results: The theoretical functions f(z) closely-matched the central axis experimental profile data{sup 11} for all apertures (a = 28 -138 mm). Integration of f(z) likewise yields analytical equations for all the (CTDI-based) dosimetric quantities of conventional CT (including CTDI{sub L} itself) in addition to the peak dose f(0) relevant to
Park, Yang-Kyun; Sharp, Gregory C.; Phillips, Justin; Winey, Brian A.
2015-01-01
Purpose: To demonstrate the feasibility of proton dose calculation on scatter-corrected cone-beam computed tomographic (CBCT) images for the purpose of adaptive proton therapy. Methods: CBCT projection images were acquired from anthropomorphic phantoms and a prostate patient using an on-board imaging system of an Elekta infinity linear accelerator. Two previously introduced techniques were used to correct the scattered x-rays in the raw projection images: uniform scatter correction (CBCTus) and a priori CT-based scatter correction (CBCTap). CBCT images were reconstructed using a standard FDK algorithm and GPU-based reconstruction toolkit. Soft tissue ROI-based HU shifting was used to improve HU accuracy of the uncorrected CBCT images and CBCTus, while no HU change was applied to the CBCTap. The degree of equivalence of the corrected CBCT images with respect to the reference CT image (CTref) was evaluated by using angular profiles of water equivalent path length (WEPL) and passively scattered proton treatment plans. The CBCTap was further evaluated in more realistic scenarios such as rectal filling and weight loss to assess the effect of mismatched prior information on the corrected images. Results: The uncorrected CBCT and CBCTus images demonstrated substantial WEPL discrepancies (7.3 ± 5.3 mm and 11.1 ± 6.6 mm, respectively) with respect to the CTref, while the CBCTap images showed substantially reduced WEPL errors (2.4 ± 2.0 mm). Similarly, the CBCTap-based treatment plans demonstrated a high pass rate (96.0% ± 2.5% in 2 mm/2% criteria) in a 3D gamma analysis. Conclusions: A priori CT-based scatter correction technique was shown to be promising for adaptive proton therapy, as it achieved equivalent proton dose distributions and water equivalent path lengths compared to those of a reference CT in a selection of anthropomorphic phantoms. PMID:26233175
NASA Astrophysics Data System (ADS)
Ying, Ying-Zi; Ma, Li; Guo, Sheng-Ming
2011-05-01
In active sonar operation, the presence of background reverberation and the low signal-to-noise ratio hinder the detection of targets. This paper investigates the application of single-channel monostatic iterative time reversal to mitigate the difficulties by exploiting the resonances of the target. Theoretical analysis indicates that the iterative process will adaptively lead echoes to converge to a narrowband signal corresponding to a scattering object's dominant resonance mode, thus optimising the return level. The experiments in detection of targets in free field and near a planar interface have been performed. The results illustrate the feasibility of the method.
NASA Astrophysics Data System (ADS)
Altaç, Zekeriya; Tekkalmaz, Mesut
2013-11-01
In this study, a nodal method based on the synthetic kernel (SKN) approximation is developed for solving the radiative transfer equation (RTE) in one- and two-dimensional cartesian geometries. The RTE for a two-dimensional node is transformed to one-dimensional RTE, based on face-averaged radiation intensity. At the node interfaces, double P1 expansion is employed to the surface angular intensities with the isotropic transverse leakage assumption. The one-dimensional radiative integral transfer equation (RITE) is obtained in terms of the node-face-averaged incoming/outgoing incident energy and partial heat fluxes. The synthetic kernel approximation is employed to the transfer kernels and nodal-face contributions. The resulting SKN equations are solved analytically. One-dimensional interface-coupling nodal SK1 and SK2 equations (incoming/outgoing incident energy and net partial heat flux) are derived for the small nodal-mesh limit. These equations have simple algebraic and recursive forms which impose burden on neither the memory nor the computational time. The method was applied to one- and two-dimensional benchmark problems including hot/cold medium with transparent/emitting walls. The 2D results are free of ray effect and the results, for geometries of a few mean-free-paths or more, are in excellent agreement with the exact solutions.
Domain transfer multiple kernel learning.
Duan, Lixin; Tsang, Ivor W; Xu, Dong
2012-03-01
Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning framework into which many existing kernel methods can be readily incorporated. Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Under the DTMKL framework, we also propose two novel methods by using SVM and prelearned classifiers, respectively. Comprehensive experiments on three domain adaptation data sets (i.e., TRECVID, 20 Newsgroups, and email spam data sets) demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods. PMID:21646679
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.
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
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%.
Fully automatic hp-adaptivity for acoustic and electromagnetic scattering in three dimensions
NASA Astrophysics Data System (ADS)
Kurtz, Jason Patrick
We present an algorithm for fully automatic hp-adaptivity for finite element approximations of elliptic and Maxwell boundary value problems in three dimensions. The algorithm automatically generates a sequence of coarse grids, and a corresponding sequence of fine grids, such that the energy norm of the error decreases exponentially with respect to the number of degrees of freedom in either sequence. At each step, we employ a discrete optimization algorithm to determine the refinements for the current coarse grid such that the projection-based interpolation error for the current fine grid solution decreases with an optimal rate with respect to the number of degrees of freedom added by the refinement. The refinements are restricted only by the requirement that the resulting mesh is at most 1-irregular, but they may be anisotropic in both element size h and order of approximation p. While we cannot prove that our method converges at all, we present numerical evidence of exponential convergence for a diverse suite of model problems from acoustic and electromagnetic scattering. In particular we show that our method is well suited to the automatic resolution of exterior problems truncated by the introduction of a perfectly matched layer. To enable and accelerate the solution of these problems on commodity hardware, we include a detailed account of three critical aspects of our implementation, namely an efficient implementation of sum factorization, several efficient interfaces to the direct multi-frontal solver MUMPS, and some fast direct solvers for the computation of a sequence of nested projections.
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.
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.
Melacci, Stefano; Gori, Marco
2013-11-01
Supervised examples and prior knowledge on regions of the input space have been profitably integrated in kernel machines to improve the performance of classifiers in different real-world contexts. The proposed solutions, which rely on the unified supervision of points and sets, have been mostly based on specific optimization schemes in which, as usual, the kernel function operates on points only. In this paper, arguments from variational calculus are used to support the choice of a special class of kernels, referred to as box kernels, which emerges directly from the choice of the kernel function associated with a regularization operator. It is proven that there is no need to search for kernels to incorporate the structure deriving from the supervision of regions of the input space, because the optimal kernel arises as a consequence of the chosen regularization operator. Although most of the given results hold for sets, we focus attention on boxes, whose labeling is associated with their propositional description. Based on different assumptions, some representer theorems are given that dictate the structure of the solution in terms of box kernel expansion. Successful results are given for problems of medical diagnosis, image, and text categorization. PMID:24051728
Melacci, Stefano; Gori, Marco
2013-04-12
Supervised examples and prior knowledge on regions of the input space have been profitably integrated in kernel machines to improve the performance of classifiers in different real-world contexts. The proposed solutions, which rely on the unified supervision of points and sets, have been mostly based on specific optimization schemes in which, as usual, the kernel function operates on points only. In this paper, arguments from variational calculus are used to support the choice of a special class of kernels, referred to as box kernels, which emerges directly from the choice of the kernel function associated with a regularization operator. It is proven that there is no need to search for kernels to incorporate the structure deriving from the supervision of regions of the input space, since the optimal kernel arises as a consequence of the chosen regularization operator. Although most of the given results hold for sets, we focus attention on boxes, whose labeling is associated with their propositional description. Based on different assumptions, some representer theorems are given which dictate the structure of the solution in terms of box kernel expansion. Successful results are given for problems of medical diagnosis, image, and text categorization. PMID:23589591
Sparse representation with kernels.
Gao, Shenghua; Tsang, Ivor Wai-Hung; Chia, Liang-Tien
2013-02-01
Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks. PMID:23014744
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`.
Park, Y; Winey, B; Sharp, G
2014-06-01
Purpose: To demonstrate feasibility of proton dose calculation on scattercorrected CBCT images for the purpose of adaptive proton therapy. Methods: Two CBCT image sets were acquired from a prostate cancer patient and a thorax phantom using an on-board imaging system of an Elekta infinity linear accelerator. 2-D scatter maps were estimated using a previously introduced CT-based technique, and were subtracted from each raw projection image. A CBCT image set was then reconstructed with an open source reconstruction toolkit (RTK). Conversion from the CBCT number to HU was performed by soft tissue-based shifting with reference to the plan CT. Passively scattered proton plans were simulated on the plan CT and corrected/uncorrected CBCT images using the XiO treatment planning system. For quantitative evaluation, water equivalent path length (WEPL) was compared in those treatment plans. Results: The scatter correction method significantly improved image quality and HU accuracy in the prostate case where large scatter artifacts were obvious. However, the correction technique showed limited effects on the thorax case that was associated with fewer scatter artifacts. Mean absolute WEPL errors from the plans with the uncorrected and corrected images were 1.3 mm and 5.1 mm in the thorax case and 13.5 mm and 3.1 mm in the prostate case. The prostate plan dose distribution of the corrected image demonstrated better agreement with the reference one than that of the uncorrected image. Conclusion: A priori CT-based CBCT scatter correction can reduce the proton dose calculation error when large scatter artifacts are involved. If scatter artifacts are low, an uncorrected CBCT image is also promising for proton dose calculation when it is calibrated with the soft-tissue based shifting.
Online Sequential Extreme Learning Machine With Kernels.
Scardapane, Simone; Comminiello, Danilo; Scarpiniti, Michele; Uncini, Aurelio
2015-09-01
The extreme learning machine (ELM) was recently proposed as a unifying framework for different families of learning algorithms. The classical ELM model consists of a linear combination of a fixed number of nonlinear expansions of the input vector. Learning in ELM is hence equivalent to finding the optimal weights that minimize the error on a dataset. The update works in batch mode, either with explicit feature mappings or with implicit mappings defined by kernels. Although an online version has been proposed for the former, no work has been done up to this point for the latter, and whether an efficient learning algorithm for online kernel-based ELM exists remains an open problem. By explicating some connections between nonlinear adaptive filtering and ELM theory, in this brief, we present an algorithm for this task. In particular, we propose a straightforward extension of the well-known kernel recursive least-squares, belonging to the kernel adaptive filtering (KAF) family, to the ELM framework. We call the resulting algorithm the kernel online sequential ELM (KOS-ELM). Moreover, we consider two different criteria used in the KAF field to obtain sparse filters and extend them to our context. We show that KOS-ELM, with their integration, can result in a highly efficient algorithm, both in terms of obtained generalization error and training time. Empirical evaluations demonstrate interesting results on some benchmarking datasets. PMID:25561597
NASA Technical Reports Server (NTRS)
Eloranta, E. W.; Piironen, P. K.
1996-01-01
Quantitative lidar measurements of aerosol scattering are hampered by the need for calibrations and the problem of correcting observed backscatter profiles for the effects of attenuation. The University of Wisconsin High Spectral Resolution Lidar (HSRL) addresses these problems by separating molecular scattering contributions from the aerosol scattering; the molecular scattering is then used as a calibration target that is available at each point in the observed profiles. While the HSRl approach has intrinsic advantages over competing techniques, realization of these advantages requires implementation of a technically demanding system which is potentially very sensitive to changes in temperature and mechanical alignments. This paper describes a new implementation of the HSRL in an instrumented van which allows measurements during field experiments. The HSRL was modified to measure depolarization. In addition, both the signal amplitude and depolarization variations with receiver field of view are simultaneously measured. This allows for discrimination of ice clouds from water clouds and observation of multiple scattering contributions to the lidar return.
KERNEL PHASE IN FIZEAU INTERFEROMETRY
Martinache, Frantz
2010-11-20
The detection of high contrast companions at small angular separation appears feasible in conventional direct images using the self-calibration properties of interferometric observable quantities. The friendly notion of closure phase, which is key to the recent observational successes of non-redundant aperture masking interferometry used with adaptive optics, appears to be one example of a wide family of observable quantities that are not contaminated by phase noise. In the high-Strehl regime, soon to be available thanks to the coming generation of extreme adaptive optics systems on ground-based telescopes, and already available from space, closure phase like information can be extracted from any direct image, even taken with a redundant aperture. These new phase-noise immune observable quantities, called kernel phases, are determined a priori from the knowledge of the geometry of the pupil only. Re-analysis of archive data acquired with the Hubble Space Telescope NICMOS instrument using this new kernel-phase algorithm demonstrates the power of the method as it clearly detects and locates with milliarcsecond precision a known companion to a star at angular separation less than the diffraction limit.
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.
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.
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.
Mie Light-Scattering Granulometer with an Adaptive Numerical Filtering Method. II. Experiments.
Hespel, L; Delfour, A; Guillame, B
2001-02-20
A nephelometer is presented that theoretically requires no absolute calibration. This instrument is used for determining the particle-size distribution of various scattering media (aerosols, fogs, rocket exhausts, engine plumes, and the like) from angular static light-scattering measurements. An inverse procedure is used, which consists of a least-squares method and a regularization scheme based on numerical filtering. To retrieve the distribution function one matches the experimental data with theoretical patterns derived from Mie theory. The main principles of the inverse method are briefly presented, and the nephelometer is then described with the associated partial calibration procedure. Finally, the whole granulometer system (inverse method and nephelometer) is validated by comparison of measurements of scattering media with calibrated monodisperse or known size distribution functions. PMID:18357082
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.
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.
Zhang, Yan-jun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong
2015-10-01
According to the high precision extracting characteristics of scattering spectrum in Brillouin optical time domain reflection optical fiber sensing system, this paper proposes a new algorithm based on flies optimization algorithm with adaptive mutation and generalized regression neural network. The method takes advantages of the generalized regression neural network which has the ability of the approximation ability, learning speed and generalization of the model. Moreover, by using the strong search ability of flies optimization algorithm with adaptive mutation, it can enhance the learning ability of the neural network. Thus the fitting degree of Brillouin scattering spectrum and the extraction accuracy of frequency shift is improved. Model of actual Brillouin spectrum are constructed by Gaussian white noise on theoretical spectrum, whose center frequency is 11.213 GHz and the linewidths are 40-50, 30-60 and 20-70 MHz, respectively. Comparing the algorithm with the Levenberg-Marquardt fitting method based on finite element analysis, hybrid algorithm particle swarm optimization, Levenberg-Marquardt and the least square method, the maximum frequency shift error of the new algorithm is 0.4 MHz, the fitting degree is 0.991 2 and the root mean square error is 0.024 1. The simulation results show that the proposed algorithm has good fitting degree and minimum absolute error. Therefore, the algorithm can be used on distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can improve the fitting of Brillouin scattering spectrum and the precision of frequency shift extraction effectively. PMID:26904844
NASA Technical Reports Server (NTRS)
Eloranta, E. W.; Piironen, P. K.
1992-01-01
A new implementation of the High Spectral Resolution Lidar (HSRL) in an instrument van which allows measurements during field experiments is described. The instrument was modified to provide measurements of depolarization. In addition, both the signal amplitude and depolarization variations with receiver field of view are simultaneously measured. These modifications allow discrimination of ice clouds from water clouds and observation of multiple scattering contributions to the lidar return.
Full Waveform Inversion Using Waveform Sensitivity Kernels
NASA Astrophysics Data System (ADS)
Schumacher, Florian; Friederich, Wolfgang
2013-04-01
We present a full waveform inversion concept for applications ranging from seismological to enineering contexts, in which the steps of forward simulation, computation of sensitivity kernels, and the actual inversion are kept separate of each other. We derive waveform sensitivity kernels from Born scattering theory, which for unit material perturbations are identical to the Born integrand for the considered path between source and receiver. The evaluation of such a kernel requires the calculation of Green functions and their strains for single forces at the receiver position, as well as displacement fields and strains originating at the seismic source. We compute these quantities in the frequency domain using the 3D spectral element code SPECFEM3D (Tromp, Komatitsch and Liu, 2008) and the 1D semi-analytical code GEMINI (Friederich and Dalkolmo, 1995) in both, Cartesian and spherical framework. We developed and implemented the modularized software package ASKI (Analysis of Sensitivity and Kernel Inversion) to compute waveform sensitivity kernels from wavefields generated by any of the above methods (support for more methods is planned), where some examples will be shown. As the kernels can be computed independently from any data values, this approach allows to do a sensitivity and resolution analysis first without inverting any data. In the context of active seismic experiments, this property may be used to investigate optimal acquisition geometry and expectable resolution before actually collecting any data, assuming the background model is known sufficiently well. The actual inversion step then, can be repeated at relatively low costs with different (sub)sets of data, adding different smoothing conditions. Using the sensitivity kernels, we expect the waveform inversion to have better convergence properties compared with strategies that use gradients of a misfit function. Also the propagation of the forward wavefield and the backward propagation from the receiver
Robotic Intelligence Kernel: Architecture
Energy Science and Technology Software Center (ESTSC)
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.
NASA Technical Reports Server (NTRS)
Spafford, Eugene H.; Mckendry, Martin S.
1986-01-01
An overview of the internal structure of the Clouds kernel was presented. An indication of how these structures will interact in the prototype Clouds implementation is given. Many specific details have yet to be determined and await experimentation with an actual working system.
Robotic Intelligence Kernel: Visualization
Energy Science and Technology Software Center (ESTSC)
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.
SEEDS ADAPTIVE OPTICS IMAGING OF THE ASYMMETRIC TRANSITION DISK OPH IRS 48 IN SCATTERED LIGHT
Follette, Katherine B.; Close, Laird M.; Grady, Carol A.; Swearingen, Jeremy R.; Sitko, Michael L.; Champney, Elizabeth H.; Van der Marel, Nienke; Maaskant, Koen; Min, Michiel; Takami, Michihiro; Kuchner, Marc J; McElwain, Michael W.; Muto, Takayuki; Mayama, Satoshi; Fukagawa, Misato; Russell, Ray W.; Kudo, Tomoyuki; Kusakabe, Nobuhiko; Hashimoto, Jun; Abe, Lyu; and others
2015-01-10
We present the first resolved near-infrared imagery of the transition disk Oph IRS 48 (WLY 2-48), which was recently observed with ALMA to have a strongly asymmetric submillimeter flux distribution. H-band polarized intensity images show a ∼60 AU radius scattered light cavity with two pronounced arcs of emission, one from northeast to southeast and one smaller, fainter, and more distant arc in the northwest. K-band scattered light imagery reveals a similar morphology, but with a clear third arc along the southwestern rim of the disk cavity. This arc meets the northwestern arc at nearly a right angle, revealing the presence of a spiral arm or local surface brightness deficit in the disk, and explaining the east-west brightness asymmetry in the H-band data. We also present 0.8-5.4 μm IRTF SpeX spectra of this object, which allow us to constrain the spectral class to A0 ± 1 and measure a low mass accretion rate of 10{sup –8.5} M {sub ☉} yr{sup –1}, both consistent with previous estimates. We investigate a variety of reddening laws in order to fit the multiwavelength spectral energy distribution of Oph IRS 48 and find a best fit consistent with a younger, higher luminosity star than previous estimates.
SEEDS Adaptive Optics Imaging of the Asymmetric Transition Disk Oph IRS 48 in Scattered Light
NASA Astrophysics Data System (ADS)
Follette, Katherine B.; Grady, Carol A.; Swearingen, Jeremy R.; Sitko, Michael L.; Champney, Elizabeth H.; van der Marel, Nienke; Takami, Michihiro; Kuchner, Marc J.; Close, Laird M.; Muto, Takayuki; Mayama, Satoshi; McElwain, Michael W.; Fukagawa, Misato; Maaskant, Koen; Min, Michiel; Russell, Ray W.; Kudo, Tomoyuki; Kusakabe, Nobuhiko; Hashimoto, Jun; Abe, Lyu; Akiyama, Eiji; Brandner, Wolfgang; Brandt, Timothy D.; Carson, Joseph; Currie, Thayne; Egner, Sebastian E.; Feldt, Markus; Goto, Miwa; Guyon, Olivier; Hayano, Yutaka; Hayashi, Masahiko; Hayashi, Saeko; Henning, Thomas; Hodapp, Klaus; Ishii, Miki; Iye, Masanori; Janson, Markus; Kandori, Ryo; Knapp, Gillian R.; Kuzuhara, Masayuki; Kwon, Jungmi; Matsuo, Taro; Miyama, Shoken; Morino, Jun-Ichi; Moro-Martin, Amaya; Nishimura, Tetsuo; Pyo, Tae-Soo; Serabyn, Eugene; Suenaga, Takuya; Suto, Hiroshi; Suzuki, Ryuji; Takahashi, Yasuhiro; Takato, Naruhisa; Terada, Hiroshi; Thalmann, Christian; Tomono, Daigo; Turner, Edwin L.; Watanabe, Makoto; Wisniewski, John P.; Yamada, Toru; Takami, Hideki; Usuda, Tomonori; Tamura, Motohide
2015-01-01
We present the first resolved near-infrared imagery of the transition disk Oph IRS 48 (WLY 2-48), which was recently observed with ALMA to have a strongly asymmetric submillimeter flux distribution. H-band polarized intensity images show a ~60 AU radius scattered light cavity with two pronounced arcs of emission, one from northeast to southeast and one smaller, fainter, and more distant arc in the northwest. K-band scattered light imagery reveals a similar morphology, but with a clear third arc along the southwestern rim of the disk cavity. This arc meets the northwestern arc at nearly a right angle, revealing the presence of a spiral arm or local surface brightness deficit in the disk, and explaining the east-west brightness asymmetry in the H-band data. We also present 0.8-5.4 μm IRTF SpeX spectra of this object, which allow us to constrain the spectral class to A0 ± 1 and measure a low mass accretion rate of 10-8.5 M ⊙ yr-1, both consistent with previous estimates. We investigate a variety of reddening laws in order to fit the multiwavelength spectral energy distribution of Oph IRS 48 and find a best fit consistent with a younger, higher luminosity star than previous estimates. Based on data collected at Subaru Telescope, which is operated by the National Astronomical Observatory of Japan.
Kernel optimization in discriminant analysis.
You, Di; Hamsici, Onur C; Martinez, Aleix M
2011-03-01
Kernel mapping is one of the most used approaches to intrinsically derive nonlinear classifiers. The idea is to use a kernel function which maps the original nonlinearly separable problem to a space of intrinsically larger dimensionality where the classes are linearly separable. A major problem in the design of kernel methods is to find the kernel parameters that make the problem linear in the mapped representation. This paper derives the first criterion that specifically aims to find a kernel representation where the Bayes classifier becomes linear. We illustrate how this result can be successfully applied in several kernel discriminant analysis algorithms. Experimental results, using a large number of databases and classifiers, demonstrate the utility of the proposed approach. The paper also shows (theoretically and experimentally) that a kernel version of Subclass Discriminant Analysis yields the highest recognition rates. PMID:20820072
Adaptive Spatial Filtering of Interferometric Data Stack Oriented to Distributed Scatterers
NASA Astrophysics Data System (ADS)
Zhang, Y.; Xie, C.; Shao, Y.; Yuan, M.
2013-07-01
Standard interferometry poses a challenge in non-urban areas due to temporal and spatial decorrelation of the radar signal, where there is high signal noise. Techniques such as Small Baseline Subset Algorithm (SBAS) have been proposed to make use of multiple interferometric combinations to alleviate the problem. However, the interferograms used in SBAS are multilooked with a boxcar (rectangle) filter to reduce phase noise, resulting in a loss of resolution and signal superstition from different objects. In this paper, we proposed a modified adaptive spatial filtering algorithm for accurate estimation of interferogram and coherence without resolution loss even in rural areas, to better support the deformation monitoring with time series interferometric synthetic aperture radar (InSAR) technique. The implemented method identifies the statistically homogenous pixels in a neighbourhood based on the goodness-of-fit test, and then applies an adaptive spatial filtering of interferograms. Three statistical tests for the identification of distributed targets will be presented, applied to real data. PALSAR data of the yellow river delta in China is used for demonstrating the effectiveness of this algorithm in rural areas.
Lee, Myung Hee; Liu, Yufeng
2013-12-01
The continuum regression technique provides an appealing regression framework connecting ordinary least squares, partial least squares and principal component regression in one family. It offers some insight on the underlying regression model for a given application. Moreover, it helps to provide deep understanding of various regression techniques. Despite the useful framework, however, the current development on continuum regression is only for linear regression. In many applications, nonlinear regression is necessary. The extension of continuum regression from linear models to nonlinear models using kernel learning is considered. The proposed kernel continuum regression technique is quite general and can handle very flexible regression model estimation. An efficient algorithm is developed for fast implementation. Numerical examples have demonstrated the usefulness of the proposed technique. PMID:24058224
Broom, Donald M
2006-01-01
The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and
NASA Astrophysics Data System (ADS)
Afeyan, Bedros; Hüller, Stefan
2012-10-01
We will show the results of changing STUD pulse configurations in order to maintain strict control of parametric instabilities in high energy density plasmas (HEDP). Nonlinear optical processes (NLOP) in HEDP respond to changing plasma conditions which are unknown and not easily knowable by standard experimental procedures. Adapting to changing and unknown plasma conditions is one feature of STUD pulses which is absent in other beam conditioning techniques. We demonstrate this by simulating long enough that plasma conditions change, instability gains are altered and new STUD pulse configurations become necessary. Two such configurations are spliced together or run independently and compared. All available methods of changing STUD pulse characteristics are explored, such as duty cycle (20% vs 50%) and modulation period (cutting hot spots in half and into quarters) as well as phase scrambling and number of spikes before the spatial distribution of hot spots is randomized (1, 2, 3 and infinity).
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.
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.
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
Gaudin, Karen; Baillet, Arlette; Chaminade, Pierre
2004-10-01
A commercially available evaporative light-scattering detection (ELSD) system was adapted for micro and capillary LC. Therefore the various parameters involved in the droplet formation during the nebulization step in the ELSD system were studied. It was shown that the velocity term in the Nukiyama Tanasawa equation remains constant, leading to droplets of the same order of magnitude for narrow bore and capillary columns. Consequently, the ELSD modification was performed by decreasing the internal diameter of the effluent capillary tube in the nebulizer nozzle and by keeping its external diameter constant. Next, response curves for a conventional and the developed micro and capillary LC were compared as to investigate why a linear ELSD response is often obtained when used in micro or capillary LC. By splitting the flow rate post column, we showed that the nebulization process was not at the origin of the phenomenon. For ceramide III and tripalmitin, the response curves were found to be non-linear. However the curvature was less significant when the columns internal diameter decreased. Calculated particle size profiles for micro or capillary LC suggest that the particle entering the detection chamber are bigger than under conventional LC conditions. Last, triethylamine and formic acid were used to increase the response of the detector. The response enhancement, expected from previous studies, was established for the two lipids involved in this study. PMID:15532554
Shkvarko, Yuriy; Tuxpan, José; Santos, Stewart
2011-01-01
We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the "model-free" variational analysis (VA)-based image enhancement approach and the "model-based" descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations. PMID:22163859
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.
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
Code of Federal Regulations, 2011 CFR
2011-01-01
... 7 Agriculture 8 2011-01-01 2011-01-01 false Kernel weight. 981.9 Section 981.9 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels,...
Code of Federal Regulations, 2010 CFR
2010-01-01
... 7 Agriculture 8 2010-01-01 2010-01-01 false Kernel weight. 981.9 Section 981.9 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels,...
Cusp Kernels for Velocity-Changing Collisions
NASA Astrophysics Data System (ADS)
McGuyer, B. H.; Marsland, R., III; Olsen, B. A.; Happer, W.
2012-05-01
We introduce an analytical kernel, the “cusp” kernel, to model the effects of velocity-changing collisions on optically pumped atoms in low-pressure buffer gases. Like the widely used Keilson-Storer kernel [J. Keilson and J. E. Storer, Q. Appl. Math. 10, 243 (1952)QAMAAY0033-569X], cusp kernels are characterized by a single parameter and preserve a Maxwellian velocity distribution. Cusp kernels and their superpositions are more useful than Keilson-Storer kernels, because they are more similar to real kernels inferred from measurements or theory and are easier to invert to find steady-state velocity distributions.
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.
Bivariate discrete beta Kernel graduation of mortality data.
Mazza, Angelo; Punzo, Antonio
2015-07-01
Various parametric/nonparametric techniques have been proposed in literature to graduate mortality data as a function of age. Nonparametric approaches, as for example kernel smoothing regression, are often preferred because they do not assume any particular mortality law. Among the existing kernel smoothing approaches, the recently proposed (univariate) discrete beta kernel smoother has been shown to provide some benefits. Bivariate graduation, over age and calendar years or durations, is common practice in demography and actuarial sciences. In this paper, we generalize the discrete beta kernel smoother to the bivariate case, and we introduce an adaptive bandwidth variant that may provide additional benefits when data on exposures to the risk of death are available; furthermore, we outline a cross-validation procedure for bandwidths selection. Using simulations studies, we compare the bivariate approach proposed here with its corresponding univariate formulation and with two popular nonparametric bivariate graduation techniques, based on Epanechnikov kernels and on P-splines. To make simulations realistic, a bivariate dataset, based on probabilities of dying recorded for the US males, is used. Simulations have confirmed the gain in performance of the new bivariate approach with respect to both the univariate and the bivariate competitors. PMID:25084764
Modified kernel-based nonlinear feature extraction.
Ma, J.; Perkins, S. J.; Theiler, J. P.; Ahalt, S.
2002-01-01
Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high performance. However, a serious limitation that is inherent in these algorithms -- the maximal number of features extracted by them is limited by the number of classes involved -- dramatically degrades their flexibility. Here we propose a modified version of those KFE algorithms (MKFE), This algorithm is developed from a special form of scatter-matrix, whose rank is not determined by the number of classes involved, and thus breaks the inherent limitation in those KFE algorithms. Experimental results suggest that MKFE algorithm is .especially useful when the training set is small.
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.
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 ...
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.
Point-Kernel Shielding Code System.
Energy Science and Technology Software Center (ESTSC)
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.
Technology Transfer Automated Retrieval System (TEKTRAN)
Oat (Avena sativa L.) kernels appear to contain much higher polar lipid concentrations than other plant tissues. We have extracted, identified, and quantified polar lipids from 18 oat genotypes grown in replicated plots in three environments in order to determine genotypic or environmental variation...
Accelerating the Original Profile Kernel
Hamp, Tobias; Goldberg, Tatyana; Rost, Burkhard
2013-01-01
One of the most accurate multi-class protein classification systems continues to be the profile-based SVM kernel introduced by the Leslie group. Unfortunately, its CPU requirements render it too slow for practical applications of large-scale classification tasks. Here, we introduce several software improvements that enable significant acceleration. Using various non-redundant data sets, we demonstrate that our new implementation reaches a maximal speed-up as high as 14-fold for calculating the same kernel matrix. Some predictions are over 200 times faster and render the kernel as possibly the top contender in a low ratio of speed/performance. Additionally, we explain how to parallelize various computations and provide an integrative program that reduces creating a production-quality classifier to a single program call. The new implementation is available as a Debian package under a free academic license and does not depend on commercial software. For non-Debian based distributions, the source package ships with a traditional Makefile-based installer. Download and installation instructions can be found at https://rostlab.org/owiki/index.php/Fast_Profile_Kernel. Bugs and other issues may be reported at https://rostlab.org/bugzilla3/enter_bug.cgi?product=fastprofkernel. PMID:23825697
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, 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, 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...
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, 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...
Four-particle decay of the Bethe-Salpeter kernel in the high-temperature Ising model
NASA Astrophysics Data System (ADS)
Auil, F.
2002-12-01
In this article we study the four-particle decay of the Bethe-Salpeter (B-S) kernel for the high-temperature Ising model. We use the hyperplane decoupling method [T. Spencer, Commun. Math. Phys. 44, 143 (1975); R. S. Schor, Nucl. Phys. B 222, 71 (1983)] to prove exponential decay in a set of variables particularly adapted to the methods of Spencer and Zirilli [Commun. Math. Phys. 49, 1 (1976)] for the analysis of scattering and bound states in QFT, transcribed to lattice theories by Auil and Barata [Ann. Henri Poincare 2, 1065 (2001)]. We study arbitrary derivatives of the general n-point correlation functions with respect to the interpolating variables, and we are able to obtain, in some cases, information about the third derivatives of the B-S kernel. As a later consequence, we have two-body asymptotic completeness for the (massive) Euclidean lattice field theory implemented by this model. This allows us to analyze the Ornstein-Zernike behavior of four-point functions, related to the specific heat of the model.
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.
Derivation of aerodynamic kernel functions
NASA Technical Reports Server (NTRS)
Dowell, E. H.; Ventres, C. S.
1973-01-01
The method of Fourier transforms is used to determine the kernel function which relates the pressure on a lifting surface to the prescribed downwash within the framework of Dowell's (1971) shear flow model. This model is intended to improve upon the potential flow aerodynamic model by allowing for the aerodynamic boundary layer effects neglected in the potential flow model. For simplicity, incompressible, steady flow is considered. The proposed method is illustrated by deriving known results from potential flow theory.
Kernel CMAC with improved capability.
Horváth, Gábor; Szabó, Tamás
2007-02-01
The cerebellar model articulation controller (CMAC) has some attractive features, namely fast learning capability and the possibility of efficient digital hardware implementation. Although CMAC was proposed many years ago, several open questions have been left even for today. The most important ones are about its modeling and generalization capabilities. The limits of its modeling capability were addressed in the literature, and recently, certain questions of its generalization property were also investigated. This paper deals with both the modeling and the generalization properties of CMAC. First, a new interpolation model is introduced. Then, a detailed analysis of the generalization error is given, and an analytical expression of this error for some special cases is presented. It is shown that this generalization error can be rather significant, and a simple regularized training algorithm to reduce this error is proposed. The results related to the modeling capability show that there are differences between the one-dimensional (1-D) and the multidimensional versions of CMAC. This paper discusses the reasons of this difference and suggests a new kernel-based interpretation of CMAC. The kernel interpretation gives a unified framework. Applying this approach, both the 1-D and the multidimensional CMACs can be constructed with similar modeling capability. Finally, this paper shows that the regularized training algorithm can be applied for the kernel interpretations too, which results in a network with significantly improved approximation capabilities. PMID:17278566
RKRD: Runtime Kernel Rootkit Detection
NASA Astrophysics Data System (ADS)
Grover, Satyajit; Khosravi, Hormuzd; Kolar, Divya; Moffat, Samuel; Kounavis, Michael E.
In this paper we address the problem of protecting computer systems against stealth malware. The problem is important because the number of known types of stealth malware increases exponentially. Existing approaches have some advantages for ensuring system integrity but sophisticated techniques utilized by stealthy malware can thwart them. We propose Runtime Kernel Rootkit Detection (RKRD), a hardware-based, event-driven, secure and inclusionary approach to kernel integrity that addresses some of the limitations of the state of the art. Our solution is based on the principles of using virtualization hardware for isolation, verifying signatures coming from trusted code as opposed to malware for scalability and performing system checks driven by events. Our RKRD implementation is guided by our goals of strong isolation, no modifications to target guest OS kernels, easy deployment, minimal infra-structure impact, and minimal performance overhead. We developed a system prototype and conducted a number of experiments which show that the per-formance impact of our solution is negligible.
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
Visualizing and Interacting with Kernelized Data.
Barbosa, A; Paulovich, F V; Paiva, A; Goldenstein, S; Petronetto, F; Nonato, L G
2016-03-01
Kernel-based methods have experienced a substantial progress in the last years, tuning out an essential mechanism for data classification, clustering and pattern recognition. The effectiveness of kernel-based techniques, though, depends largely on the capability of the underlying kernel to properly embed data in the feature space associated to the kernel. However, visualizing how a kernel embeds the data in a feature space is not so straightforward, as the embedding map and the feature space are implicitly defined by the kernel. In this work, we present a novel technique to visualize the action of a kernel, that is, how the kernel embeds data into a high-dimensional feature space. The proposed methodology relies on a solid mathematical formulation to map kernelized data onto a visual space. Our approach is faster and more accurate than most existing methods while still allowing interactive manipulation of the projection layout, a game-changing trait that other kernel-based projection techniques do not have. PMID:26829242
On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo.
Filippi, Sarah; Barnes, Chris P; Cornebise, Julien; Stumpf, Michael P H
2013-03-01
Approximate Bayesian computation (ABC) has gained popularity over the past few years for the analysis of complex models arising in population genetics, epidemiology and system biology. Sequential Monte Carlo (SMC) approaches have become work-horses in ABC. Here we discuss how to construct the perturbation kernels that are required in ABC SMC approaches, in order to construct a sequence of distributions that start out from a suitably defined prior and converge towards the unknown posterior. We derive optimality criteria for different kernels, which are based on the Kullback-Leibler divergence between a distribution and the distribution of the perturbed particles. We will show that for many complicated posterior distributions, locally adapted kernels tend to show the best performance. We find that the added moderate cost of adapting kernel functions is easily regained in terms of the higher acceptance rate. We demonstrate the computational efficiency gains in a range of toy examples which illustrate some of the challenges faced in real-world applications of ABC, before turning to two demanding parameter inference problems in molecular biology, which highlight the huge increases in efficiency that can be gained from choice of optimal kernels. We conclude with a general discussion of the rational choice of perturbation kernels in ABC SMC settings. PMID:23502346
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
Image texture analysis of crushed wheat kernels
NASA Astrophysics Data System (ADS)
Zayas, Inna Y.; Martin, C. R.; Steele, James L.; Dempster, Richard E.
1992-03-01
The development of new approaches for wheat hardness assessment may impact the grain industry in marketing, milling, and breeding. This study used image texture features for wheat hardness evaluation. Application of digital imaging to grain for grading purposes is principally based on morphometrical (shape and size) characteristics of the kernels. A composite sample of 320 kernels for 17 wheat varieties were collected after testing and crushing with a single kernel hardness characterization meter. Six wheat classes where represented: HRW, HRS, SRW, SWW, Durum, and Club. In this study, parameters which characterize texture or spatial distribution of gray levels of an image were determined and used to classify images of crushed wheat kernels. The texture parameters of crushed wheat kernel images were different depending on class, hardness and variety of the wheat. Image texture analysis of crushed wheat kernels showed promise for use in class, hardness, milling quality, and variety discrimination.
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. PMID:24513850
Code of Federal Regulations, 2011 CFR
2011-01-01
... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume...
Code of Federal Regulations, 2010 CFR
2010-01-01
... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume...
Code of Federal Regulations, 2013 CFR
2013-01-01
..., CERTIFICATION, AND STANDARDS) United States Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than...
Code of Federal Regulations, 2012 CFR
2012-01-01
... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume...
Code of Federal Regulations, 2014 CFR
2014-01-01
..., CERTIFICATION, AND STANDARDS) United States Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than...
Corn kernel oil and corn fiber oil
Technology Transfer Automated Retrieval System (TEKTRAN)
Unlike most edible plant oils that are obtained directly from oil-rich seeds by either pressing or solvent extraction, corn seeds (kernels) have low levels of oil (4%) and commercial corn oil is obtained from the corn germ (embryo) which is an oil-rich portion of the kernel. Commercial corn oil cou...
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
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; 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
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.
Huang, Lulu; Massa, Lou
2010-01-01
The Kernel Energy Method (KEM) provides a way to calculate the ab-initio energy of very large biological molecules. The results are accurate, and the computational time reduced. However, by use of a list of double kernel interactions a significant additional reduction of computational effort may be achieved, still retaining ab-initio accuracy. A numerical comparison of the indices that name the known double interactions in question, allow one to list higher order interactions having the property of topological continuity within the full molecule of interest. When, that list of interactions is unpacked, as a kernel expansion, which weights the relative importance of each kernel in an expression for the total molecular energy, high accuracy, and a further significant reduction in computational effort results. A KEM molecular energy calculation based upon the HF/STO3G chemical model, is applied to the protein insulin, as an illustration. PMID:21243065
Kernel map compression for speeding the execution of kernel-based methods.
Arif, Omar; Vela, Patricio A
2011-06-01
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machines. Unfortunately, after learning, the computational complexity of execution through a kernel is of the order of the size of the training set, which is quite large for many applications. This paper proposes a two-step procedure for arriving at a compact and computationally efficient execution procedure. After learning in the kernel space, the proposed extension exploits the universal approximation capabilities of generalized radial basis function neural networks to efficiently approximate and replace the projections onto the empirical kernel map used during execution. Sample applications demonstrate significant compression of the kernel representation with graceful performance loss. PMID:21550884
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 51.2296 - Three-fourths half kernel.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 7 Agriculture 2 2010-01-01 2010-01-01 false Three-fourths half kernel. 51.2296 Section 51.2296... STANDARDS) United States Standards for Shelled English Walnuts (Juglans Regia) Definitions § 51.2296 Three-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more...
UPDATE OF GRAY KERNEL DISEASE OF MACADAMIA - 2006
Technology Transfer Automated Retrieval System (TEKTRAN)
Gray kernel is an important disease of macadamia that affects the quality of kernels with gray discoloration and a permeating, foul odor that can render entire batches of nuts unmarketable. We report on the successful production of gray kernel in raw macadamia kernels artificially inoculated with s...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2011 CFR
2011-01-01
... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams;...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2010 CFR
2010-01-01
... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams;...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2014 CFR
2014-01-01
... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams;...
7 CFR 981.401 - Adjusted kernel weight.
Code of Federal Regulations, 2012 CFR
2012-01-01
... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams;...
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...
KITTEN Lightweight Kernel 0.1 Beta
Energy Science and Technology Software Center (ESTSC)
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
Biological sequence classification with multivariate string kernels.
Kuksa, Pavel P
2013-01-01
String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequence classification, remote homology detection, or protein superfamily and fold prediction. However, typical string kernel methods rely on the analysis of discrete 1D string data (e.g., DNA or amino acid sequences). In this paper, we address the multiclass biological sequence classification problems using multivariate representations in the form of sequences of features vectors (as in biological sequence profiles, or sequences of individual amino acid physicochemical descriptors) and a class of multivariate string kernels that exploit these representations. On three protein sequence classification tasks, the proposed multivariate representations and kernels show significant 15-20 percent improvements compared to existing state-of-the-art sequence classification methods. PMID:24384708
Biological Sequence Analysis with Multivariate String Kernels.
Kuksa, Pavel P
2013-03-01
String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequence classification, remote homology detection, or protein superfamily and fold prediction. However, typical string kernel methods rely on analysis of discrete one-dimensional (1D) string data (e.g., DNA or amino acid sequences). In this work we address the multi-class biological sequence classification problems using multivariate representations in the form of sequences of features vectors (as in biological sequence profiles, or sequences of individual amino acid physico-chemical descriptors) and a class of multivariate string kernels that exploit these representations. On a number of protein sequence classification tasks proposed multivariate representations and kernels show significant 15-20\\% improvements compared to existing state-of-the-art sequence classification methods. PMID:23509193
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
Energy Science and Technology Software Center (ESTSC)
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
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
PET image reconstruction using kernel method.
Wang, Guobao; Qi, Jinyi
2015-01-01
Image reconstruction from low-count positron emission tomography (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 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 4-D dynamic PET patient dataset showed promising results. PMID:25095249
Xyloglucans from flaxseed kernel cell wall: Structural and conformational characterisation.
Ding, Huihuang H; Cui, Steve W; Goff, H Douglas; Chen, Jie; Guo, Qingbin; Wang, Qi
2016-10-20
The structure of ethanol precipitated fraction from 1M KOH extracted flaxseed kernel polysaccharides (KPI-EPF) was studied for better understanding the molecular structures of flaxseed kernel cell wall polysaccharides. Based on methylation/GC-MS, NMR spectroscopy, and MALDI-TOF-MS analysis, the dominate sugar residues of KPI-EPF fraction comprised of (1,4,6)-linked-β-d-glucopyranose (24.1mol%), terminal α-d-xylopyranose (16.2mol%), (1,2)-α-d-linked-xylopyranose (10.7mol%), (1,4)-β-d-linked-glucopyranose (10.7mol%), and terminal β-d-galactopyranose (8.5mol%). KPI-EPF was proposed as xyloglucans: The substitution rate of the backbone is 69.3%; R1 could be T-α-d-Xylp-(1→, or none; R2 could be T-α-d-Xylp-(1→, T-β-d-Galp-(1→2)-α-d-Xylp-(1→, or T-α-l-Araf-(1→2)-α-d-Xylp-(1→; R3 could be T-α-d-Xylp-(1→, T-β-d-Galp-(1→2)-α-d-Xylp-(1→, T-α-l-Fucp-(1→2)-β-d-Galp-(1→2)-α-d-Xylp-(1→, or none. The Mw of KPI-EPF was calculated to be 1506kDa by static light scattering (SLS). The structure-sensitive parameter (ρ) of KPI-EPF was calculated as 1.44, which confirmed the highly branched structure of extracted xyloglucans. This new findings on flaxseed kernel xyloglucans will be helpful for understanding its fermentation properties and potential applications. PMID:27474598
Evaluating the Gradient of the Thin Wire Kernel
NASA Technical Reports Server (NTRS)
Wilton, Donald R.; Champagne, Nathan J.
2008-01-01
Recently, a formulation for evaluating the thin wire kernel was developed that employed a change of variable to smooth the kernel integrand, canceling the singularity in the integrand. Hence, the typical expansion of the wire kernel in a series for use in the potential integrals is avoided. The new expression for the kernel is exact and may be used directly to determine the gradient of the wire kernel, which consists of components that are parallel and radial to the wire axis.
Collision kernels from velocity-selective optical pumping with magnetic depolarization
NASA Astrophysics Data System (ADS)
Bhamre, T.; Marsland, R., III; Kominis, I. K.; McGuyer, B. H.; Happer, W.
2013-04-01
We experimentally demonstrate how magnetic depolarization of velocity-selective optical pumping can be used to single out the collisional cusp kernel best describing spin- and velocity-relaxing collisions between potassium atoms and low-pressure helium. The range of pressures and transverse fields used simulate the optical pumping regime pertinent to sodium guidestars employed in adaptive optics. We measure the precession of spin-velocity modes under the application of transverse magnetic fields, simulating the natural configuration of mesospheric sodium optical pumping in the geomagnetic field. We also provide a full theoretical account of the experimental data using the recently developed cusp kernels, which realistically quantify velocity damping collisions in this optical pumping regime. A single cusp kernel with a sharpness s=13±2 provides a global fit to the K-He data.
Hierarchical Adaptive Regression Kernels for Regression with Functional Predictors
Woodard, Dawn B.; Crainiceanu, Ciprian; Ruppert, David
2013-01-01
We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies stochastically across subjects. We propose Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for regression with functional predictors, and show that our method is more effective and efficient for data that include features occurring at varying locations. We apply our methodology to a large and complex dataset from the Sleep Heart Health Study, to quantify the association between sleep characteristics and health outcomes. Software and technical appendices are provided in online supplemental materials. PMID:24293988
A Generalized Pyramid Matching Kernel for Human Action Recognition in Realistic Videos
Zhu, Jun; Zhou, Quan; Zou, Weijia; Zhang, Rui; Zhang, Wenjun
2013-01-01
Human action recognition is an increasingly important research topic in the fields of video sensing, analysis and understanding. Caused by unconstrained sensing conditions, there exist large intra-class variations and inter-class ambiguities in realistic videos, which hinder the improvement of recognition performance for recent vision-based action recognition systems. In this paper, we propose a generalized pyramid matching kernel (GPMK) for recognizing human actions in realistic videos, based on a multi-channel “bag of words” representation constructed from local spatial-temporal features of video clips. As an extension to the spatial-temporal pyramid matching (STPM) kernel, the GPMK leverages heterogeneous visual cues in multiple feature descriptor types and spatial-temporal grid granularity levels, to build a valid similarity metric between two video clips for kernel-based classification. Instead of the predefined and fixed weights used in STPM, we present a simple, yet effective, method to compute adaptive channel weights of GPMK based on the kernel target alignment from training data. It incorporates prior knowledge and the data-driven information of different channels in a principled way. The experimental results on three challenging video datasets (i.e., Hollywood2, Youtube and HMDB51) validate the superiority of our GPMK w.r.t. the traditional STPM kernel for realistic human action recognition and outperform the state-of-the-art results in the literature. PMID:24284771
NASA Astrophysics Data System (ADS)
Woolley, J. W.; Wilson, H. B.; Woodbury, K. A.
2008-11-01
Thermocouples or other measuring devices are often imbedded into a solid to provide data for an inverse calculation. It is well-documented that such installations will result in erroneous (biased) sensor readings, unless the thermal properties of the measurement wires and surrounding insulation can be carefully matched to those of the parent domain. Since this rarely can be done, or doing so is prohibitively expensive, an alternative is to include a sensor model in the solution of the inverse problem. In this paper we consider a technique in which a thermocouple model is used to generate a correction kernel for use in the inverse solver. The technique yields a kernel function with terms in the Laplace domain. The challenge of determining the values of the correction kernel function is the focus of this paper. An adaptation of the sequential function specification method[1] as well as numerical Laplace transform inversion techniques are considered for determination of the kernel function values. Each inversion method is evaluated with analytical test functions which provide simulated "measurements". Reconstruction of the undisturbed temperature from the "measured" temperature and the correction kernel is demonstrated.
A class of kernel based real-time elastography algorithms.
Kibria, Md Golam; Hasan, Md Kamrul
2015-08-01
In this paper, a novel real-time kernel-based and gradient-based Phase Root Seeking (PRS) algorithm for ultrasound elastography is proposed. The signal-to-noise ratio of the strain image resulting from this method is improved by minimizing the cross-correlation discrepancy between the pre- and post-compression radio frequency signals with an adaptive temporal stretching method and employing built-in smoothing through an exponentially weighted neighborhood kernel in the displacement calculation. Unlike conventional PRS algorithms, displacement due to tissue compression is estimated from the root of the weighted average of the zero-lag cross-correlation phases of the pair of corresponding analytic pre- and post-compression windows in the neighborhood kernel. In addition to the proposed one, the other time- and frequency-domain elastography algorithms (Ara et al., 2013; Hussain et al., 2012; Hasan et al., 2012) proposed by our group are also implemented in real-time using Java where the computations are serially executed or parallely executed in multiple processors with efficient memory management. Simulation results using finite element modeling simulation phantom show that the proposed method significantly improves the strain image quality in terms of elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe) and mean structural similarity (MSSIM) for strains as high as 4% as compared to other reported techniques in the literature. Strain images obtained for the experimental phantom as well as in vivo breast data of malignant or benign masses also show the efficacy of our proposed method over the other reported techniques in the literature. PMID:25929595
Azimuthal decorrelation of forward jets in deep inelastic scattering
Sabio Vera, Agustin; Schwennsen, Florian
2008-01-01
We study the azimuthal angle decorrelation of forward jets in deep inelastic scattering. We make predictions for this observable at HERA describing the high energy limit of the relevant scattering amplitudes with quasi-multi-Regge kinematics together with a collinearly-improved evolution kernel for multiparton emissions.
NASA Astrophysics Data System (ADS)
Ulmer, W.
2013-02-01
Scatter processes of photons lead to blurring of images produced by CT (computed tomography) or CBCT (cone beam computed tomography). Multiple scatter is described by, at least, one Gaussian kernel. In various tasks, this approximation is crude; we need two/three Gaussian kernels to account for long-range tails (Landau tails), which appear in Molière scatter of protons, energy straggling and electron capture of charged particles passing through matter and Compton scatter. The ideal image (source function) is subjected to Gaussian convolution to yield a blurred image. The inverse problem is to obtain the source image from a detected image. Deconvolution methods of linear combinations of two/three Gaussian kernels with different parameters s0, s1, s2 can be derived via an inhomogeneous Fredholm integral equation of second kind (IFIE2) and Liouville - Neumann series (LNS) to provide the source function ρ. Scatter functions s0, s1, s2 are best determined by Monte-Carlo. An advantage of LNS is given, if the scatter functions s0, s1, s2 depend on coordinates. The convergence criterion can always be satisfied with regard to the above mentioned cases. A generalization is given by an analysis of the Dirac equation and Fermi-Dirac statistics leading to Landau tails applied to Bethe-Bloch equation (BBE) of charged particles and electron capture.
Nonparametric entropy estimation using kernel densities.
Lake, Douglas E
2009-01-01
The entropy of experimental data from the biological and medical sciences provides additional information over summary statistics. Calculating entropy involves estimates of probability density functions, which can be effectively accomplished using kernel density methods. Kernel density estimation has been widely studied and a univariate implementation is readily available in MATLAB. The traditional definition of Shannon entropy is part of a larger family of statistics, called Renyi entropy, which are useful in applications that require a measure of the Gaussianity of data. Of particular note is the quadratic entropy which is related to the Friedman-Tukey (FT) index, a widely used measure in the statistical community. One application where quadratic entropy is very useful is the detection of abnormal cardiac rhythms, such as atrial fibrillation (AF). Asymptotic and exact small-sample results for optimal bandwidth and kernel selection to estimate the FT index are presented and lead to improved methods for entropy estimation. PMID:19897106
Tile-Compressed FITS Kernel for IRAF
NASA Astrophysics Data System (ADS)
Seaman, R.
2011-07-01
The Flexible Image Transport System (FITS) is a ubiquitously supported standard of the astronomical community. Similarly, the Image Reduction and Analysis Facility (IRAF), developed by the National Optical Astronomy Observatory, is a widely used astronomical data reduction package. IRAF supplies compatibility with FITS format data through numerous tools and interfaces. The most integrated of these is IRAF's FITS image kernel that provides access to FITS from any IRAF task that uses the basic IMIO interface. The original FITS kernel is a complex interface of purpose-built procedures that presents growing maintenance issues and lacks recent FITS innovations. A new FITS kernel is being developed at NOAO that is layered on the CFITSIO library from the NASA Goddard Space Flight Center. The simplified interface will minimize maintenance headaches as well as add important new features such as support for the FITS tile-compressed (fpack) format.
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
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
2015-01-01
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 𝒪(n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity. PMID:26356296
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)
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. PMID:23472454
Modified wavelet kernel methods for hyperspectral image classification
NASA Astrophysics Data System (ADS)
Hsu, Pai-Hui; Huang, Xiu-Man
2015-10-01
Hyperspectral images have the capability of acquiring images of earth surface with several hundred of spectral bands. Providing such abundant spectral data should increase the abilities in classifying land use/cover type. However, due to the high dimensionality of hyperspectral data, traditional classification methods are not suitable for hyperspectral data classification. The common method to solve this problem is dimensionality reduction by using feature extraction before classification. Kernel methods such as support vector machine (SVM) and multiple kernel learning (MKL) have been successfully applied to hyperspectral images classification. In kernel methods applications, the selection of kernel function plays an important role. The wavelet kernel with multidimensional wavelet functions can find the optimal approximation of data in feature space for classification. The SVM with wavelet kernels (called WSVM) have been also applied to hyperspectral data and improve classification accuracy. In this study, wavelet kernel method combined multiple kernel learning algorithm and wavelet kernels was proposed for hyperspectral image classification. After the appropriate selection of a linear combination of kernel functions, the hyperspectral data will be transformed to the wavelet feature space, which should have the optimal data distribution for kernel learning and classification. Finally, the proposed methods were compared with the existing methods. A real hyperspectral data set was used to analyze the performance of wavelet kernel method. According to the results the proposed wavelet kernel methods in this study have well performance, and would be an appropriate tool for hyperspectral image classification.
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.
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.
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.
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
Kernel method and linear recurrence system
NASA Astrophysics Data System (ADS)
Hou, Qing-Hu; Mansour, Toufik
2008-06-01
Based on the kernel method, we present systematic methods to solve equation systems on generating functions of two variables. Using these methods, we get the generating functions for the number of permutations which avoid 1234 and 12k(k-1)...3 and permutations which avoid 1243 and 12...k.
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, 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, 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...
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, 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...
INTACT OR UNIT-KERNEL SWEET CORN
This report evaluates process and product modifications in canned and frozen sweet corn manufacture with the objective of reducing the total effluent produced in processing. In particular it evaluates the proposed replacement of process steps that yield cut or whole kernel corn w...
Arbitrary-resolution global sensitivity kernels
NASA Astrophysics Data System (ADS)
Nissen-Meyer, T.; Fournier, A.; Dahlen, F.
2007-12-01
Extracting observables out of any part of a seismogram (e.g. including diffracted phases such as Pdiff) necessitates the knowledge of 3-D time-space wavefields for the Green functions that form the backbone of Fréchet sensitivity kernels. While known for a while, this idea is still computationally intractable in 3-D, facing major simulation and storage issues when high-frequency wavefields are considered at the global scale. We recently developed a new "collapsed-dimension" spectral-element method that solves the 3-D system of elastodynamic equations in a 2-D space, based on exploring symmetry considerations of the seismic-wave radiation patterns. We will present the technical background on the computation of waveform kernels, various examples of time- and frequency-dependent sensitivity kernels and subsequently extracted time-window kernels (e.g. banana- doughnuts). Given the computationally light-weighted 2-D nature, we will explore some crucial parameters such as excitation type, source time functions, frequency, azimuth, discontinuity locations, and phase type, i.e. an a priori view into how, when, and where seismograms carry 3-D Earth signature. A once-and-for-all database of 2-D waveforms for various source depths shall then serve as a complete set of global time-space sensitivity for a given spherically symmetric background model, thereby allowing for tomographic inversions with arbitrary frequencies, observables, and phases.
Application of the matrix exponential kernel
NASA Technical Reports Server (NTRS)
Rohach, A. F.
1972-01-01
A point matrix kernel for radiation transport, developed by the transmission matrix method, has been used to develop buildup factors and energy spectra through slab layers of different materials for a point isotropic source. Combinations of lead-water slabs were chosen for examples because of the extreme differences in shielding properties of these two materials.
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, 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...
Applying Single Kernel Sorting Technology to Developing Scab Resistant Lines
Technology Transfer Automated Retrieval System (TEKTRAN)
We are using automated single-kernel near-infrared (SKNIR) spectroscopy instrumentation to sort fusarium head blight (FHB) infected kernels from healthy kernels, and to sort segregating populations by hardness to enhance the development of scab resistant hard and soft wheat varieties. We sorted 3 r...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2012 CFR
2012-04-01
... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing... 21 Food and Drugs 3 2012-04-01 2012-04-01 false Tamarind seed kernel powder. 176.350 Section...
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...) INDIRECT FOOD ADDITIVES: PAPER AND PAPERBOARD COMPONENTS 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...
21 CFR 176.350 - Tamarind seed kernel powder.
Code of Federal Regulations, 2013 CFR
2013-04-01
... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing... 21 Food and Drugs 3 2013-04-01 2013-04-01 false Tamarind seed kernel powder. 176.350 Section...
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...
Thermomechanical property of rice kernels studied by DMA
Technology Transfer Automated Retrieval System (TEKTRAN)
The thermomechanical property of the rice kernels was investigated using a dynamic mechanical analyzer (DMA). The length change of rice kernel with a loaded constant force along the major axis direction was detected during temperature scanning. The thermomechanical transition occurred in rice kernel...
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 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 868.304 - 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 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, 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 868.254 - 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 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, 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...
ERIC Educational Resources Information Center
Young, Andrew T.
1982-01-01
The correct usage of such terminology as "Rayleigh scattering,""Rayleigh lines,""Raman lines," and "Tyndall scattering" is resolved during an historical excursion through the physics of light-scattering by gas molecules. (Author/JN)
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.
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.
Calculation Of The Nanbu-Trubnikov Kernel: Implications For Numerical Modeling Of Coulomb Collisions
Dimits, A; Cohen, B I; Wang, C; Caflisch, R; Huang, Y
2009-07-02
We investigate the accuracy of and assumptions underlying the numerical binary Monte-Carlo collision operator due to Nanbu [K. Nanbu, Phys. Rev. E 55 (1997)]. The numerical experiments that resulted in Nanbu's parameterized collision kernel are approximate realizations of the Coulomb-Lorentz pitch-angle scattering process, for which an analytical solution is available. It is demonstrated empirically that Nanbu's collision operator quite accurately recovers the effects of Coulomb-Lorentz pitch-angle collisions, or processes that approximate these even for very large values of the collisional time step. An investigation of the analytical solution shows that Nanbu's parameterized kernel is highly accurate for small values of the normalized collision time step, but loses some of its accuracy for larger values of the time step. Finally, a practical collision algorithm is proposed that for small-mass-ratio Coulomb collisions improves on the accuracy of Nanbu's algorithm.
Difference image analysis: automatic kernel design using information criteria
NASA Astrophysics Data System (ADS)
Bramich, D. M.; Horne, Keith; Alsubai, K. A.; Bachelet, E.; Mislis, D.; Parley, N.
2016-03-01
We present a selection of methods for automatically constructing an optimal kernel model for difference image analysis which require very few external parameters to control the kernel design. Each method consists of two components; namely, a kernel design algorithm to generate a set of candidate kernel models, and a model selection criterion to select the simplest kernel model from the candidate models that provides a sufficiently good fit to the target image. We restricted our attention to the case of solving for a spatially invariant convolution kernel composed of delta basis functions, and we considered 19 different kernel solution methods including six employing kernel regularization. We tested these kernel solution methods by performing a comprehensive set of image simulations and investigating how their performance in terms of model error, fit quality, and photometric accuracy depends on the properties of the reference and target images. We find that the irregular kernel design algorithm employing unregularized delta basis functions, combined with either the Akaike or Takeuchi information criterion, is the best kernel solution method in terms of photometric accuracy. Our results are validated by tests performed on two independent sets of real data. Finally, we provide some important recommendations for software implementations of difference image analysis.
A meshfree unification: reproducing kernel peridynamics
NASA Astrophysics Data System (ADS)
Bessa, M. A.; Foster, J. T.; Belytschko, T.; Liu, Wing Kam
2014-06-01
This paper is the first investigation establishing the link between the meshfree state-based peridynamics method and other meshfree methods, in particular with the moving least squares reproducing kernel particle method (RKPM). It is concluded that the discretization of state-based peridynamics leads directly to an approximation of the derivatives that can be obtained from RKPM. However, state-based peridynamics obtains the same result at a significantly lower computational cost which motivates its use in large-scale computations. In light of the findings of this study, an update to the method is proposed such that the limitations regarding application of boundary conditions and the use of non-uniform grids are corrected by using the reproducing kernel approximation.
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.
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
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
Multiple kernel learning for dimensionality reduction.
Lin, Yen-Yu; Liu, Tyng-Luh; Fuh, Chiou-Shann
2011-06-01
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting data representations are typically high-dimensional and assume diverse forms. Hence, finding a way of transforming them into a unified space of lower dimension generally facilitates the underlying tasks such as object recognition or clustering. To this end, the proposed approach (termed MKL-DR) generalizes the framework of multiple kernel learning for dimensionality reduction, and distinguishes itself with the following three main contributions: first, our method provides the convenience of using diverse image descriptors to describe useful characteristics of various aspects about the underlying data. Second, it extends a broad set of existing dimensionality reduction techniques to consider multiple kernel learning, and consequently improves their effectiveness. Third, by focusing on the techniques pertaining to dimensionality reduction, the formulation introduces a new class of applications with the multiple kernel learning framework to address not only the supervised learning problems but also the unsupervised and semi-supervised ones. PMID:20921580
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
A Kernel Classification Framework for Metric Learning.
Wang, Faqiang; Zuo, Wangmeng; Zhang, Lei; Meng, Deyu; Zhang, David
2015-09-01
Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel classification framework. First, doublets and triplets are constructed from the training samples, and a family of degree-2 polynomial kernel functions is proposed for pairs of doublets or triplets. Then, a kernel classification framework is established to generalize many popular metric learning methods such as LMNN and ITML. The proposed framework can also suggest new metric learning methods, which can be efficiently implemented, interestingly, using the standard support vector machine (SVM) solvers. Two novel metric learning methods, namely, doublet-SVM and triplet-SVM, are then developed under the proposed framework. Experimental results show that doublet-SVM and triplet-SVM achieve competitive classification accuracies with state-of-the-art metric learning methods but with significantly less training time. PMID:25347887
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
SU-E-J-135: Feasibility of Using Quantitative Cone Beam CT for Proton Adaptive Planning
Jingqian, W; Wang, Q; Zhang, X; Wen, Z; Zhu, X; Frank, S; Li, H; Tsui, T; Zhu, L; Wei, J
2015-06-15
Purpose: To investigate the feasibility of using scatter corrected cone beam CT (CBCT) for proton adaptive planning. Methods: Phantom study was used to evaluate the CT number difference between the planning CT (pCT), quantitative CBCT (qCBCT) with scatter correction and calibrated Hounsfield units using adaptive scatter kernel superposition (ASKS) technique, and raw CBCT (rCBCT). After confirming the CT number accuracy, prostate patients, each with a pCT and several sets of weekly CBCT, were investigated for this study. Spot scanning proton treatment plans were independently generated on pCT, qCBCT and rCBCT. The treatment plans were then recalculated on all images. Dose-volume-histogram (DVH) parameters and gamma analysis were used to compare between dose distributions. Results: Phantom study suggested that Hounsfield unit accuracy for different materials are within 20 HU for qCBCT and over 250 HU for rCBCT. For prostate patients, proton dose could be calculated accurately on qCBCT but not on rCBCT. When the original plan was recalculated on qCBCT, tumor coverage was maintained when anatomy was consistent with pCT. However, large dose variance was observed when patient anatomy change. Adaptive plan using qCBCT was able to recover tumor coverage and reduce dose to normal tissue. Conclusion: It is feasible to use qu antitative CBCT (qCBCT) with scatter correction and calibrated Hounsfield units for proton dose calculation and adaptive planning in proton therapy. Partly supported by Varian Medical Systems.
How bandwidth selection algorithms impact exploratory data analysis using kernel density estimation.
Harpole, Jared K; Woods, Carol M; Rodebaugh, Thomas L; Levinson, Cheri A; Lenze, Eric J
2014-09-01
Exploratory data analysis (EDA) can reveal important features of underlying distributions, and these features often have an impact on inferences and conclusions drawn from data. Graphical analysis is central to EDA, and graphical representations of distributions often benefit from smoothing. A viable method of estimating and graphing the underlying density in EDA is kernel density estimation (KDE). This article provides an introduction to KDE and examines alternative methods for specifying the smoothing bandwidth in terms of their ability to recover the true density. We also illustrate the comparison and use of KDE methods with 2 empirical examples. Simulations were carried out in which we compared 8 bandwidth selection methods (Sheather-Jones plug-in [SJDP], normal rule of thumb, Silverman's rule of thumb, least squares cross-validation, biased cross-validation, and 3 adaptive kernel estimators) using 5 true density shapes (standard normal, positively skewed, bimodal, skewed bimodal, and standard lognormal) and 9 sample sizes (15, 25, 50, 75, 100, 250, 500, 1,000, 2,000). Results indicate that, overall, SJDP outperformed all methods. However, for smaller sample sizes (25 to 100) either biased cross-validation or Silverman's rule of thumb was recommended, and for larger sample sizes the adaptive kernel estimator with SJDP was recommended. Information is provided about implementing the recommendations in the R computing language. PMID:24885339
Protein interaction sentence detection using multiple semantic kernels
2011-01-01
Background Detection of sentences that describe protein-protein interactions (PPIs) in biomedical publications is a challenging and unresolved pattern recognition problem. Many state-of-the-art approaches for this task employ kernel classification methods, in particular support vector machines (SVMs). In this work we propose a novel data integration approach that utilises semantic kernels and a kernel classification method that is a probabilistic analogue to SVMs. Semantic kernels are created from statistical information gathered from large amounts of unlabelled text using lexical semantic models. Several semantic kernels are then fused into an overall composite classification space. In this initial study, we use simple features in order to examine whether the use of combinations of kernels constructed using word-based semantic models can improve PPI sentence detection. Results We show that combinations of semantic kernels lead to statistically significant improvements in recognition rates and receiver operating characteristic (ROC) scores over the plain Gaussian kernel, when applied to a well-known labelled collection of abstracts. The proposed kernel composition method also allows us to automatically infer the most discriminative kernels. Conclusions The results from this paper indicate that using semantic information from unlabelled text, and combinations of such information, can be valuable for classification of short texts such as PPI sentences. This study, however, is only a first step in evaluation of semantic kernels and probabilistic multiple kernel learning in the context of PPI detection. The method described herein is modular, and can be applied with a variety of feature types, kernels, and semantic models, in order to facilitate full extraction of interacting proteins. PMID:21569604
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
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.
A new method by steering kernel-based Richardson-Lucy algorithm for neutron imaging restoration
NASA Astrophysics Data System (ADS)
Qiao, Shuang; Wang, Qiao; Sun, Jia-ning; Huang, Ji-peng
2014-01-01
Motivated by industrial applications, neutron radiography has become a powerful tool for non-destructive investigation techniques. However, resulted from a combined effect of neutron flux, collimated beam, limited spatial resolution of detector and scattering, etc., the images made with neutrons are degraded severely by blur and noise. For dealing with it, by integrating steering kernel regression into Richardson-Lucy approach, we present a novel restoration method in this paper, which is capable of suppressing noise while restoring details of the blurred imaging result efficiently. Experimental results show that compared with the other methods, the proposed method can improve the restoration quality both visually and quantitatively.
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.
Scale-invariant Lipatov kernels from t-channel unitarity
Coriano, C.; White, A.R.
1994-11-14
The Lipatov equation can be regarded as a reggeon Bethe-Salpeter equation in which higher-order reggeon interactions give higher-order kernels. Infra-red singular contributions in a general kernel are produced by t-channel nonsense states and the allowed kinematic forms are determined by unitarity. Ward identity and infra-red finiteness gauge invariance constraints then determine the corresponding scale-invariant part of a general higher-order kernel.
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.
Influence of wheat kernel physical properties on the pulverizing process.
Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula
2014-10-01
The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p < 0.05) were found between wheat kernel physical properties and pulverizing process of wheat kernel, especially wheat kernel hardness index (obtained on the basis of Single Kernel Characterization System) and vitreousness significantly and positively correlated with the grinding energy indices and the mass fraction of coarse particles (> 0.5 mm). Among the kernel mechanical properties determined on the basis of uniaxial compression test only the rapture force was correlated with the impact grinding results. The results showed also positive and significant relationships between kernel ash content and grinding energy requirements. On the basis of wheat physical properties the multiple linear regression was proposed for predicting the average particle size of pulverized kernel. PMID:25328207
A short- time beltrami kernel for smoothing images and manifolds.
Spira, Alon; Kimmel, Ron; Sochen, Nir
2007-06-01
We introduce a short-time kernel for the Beltrami image enhancing flow. The flow is implemented by "convolving" the image with a space dependent kernel in a similar fashion to the solution of the heat equation by a convolution with a Gaussian kernel. The kernel is appropriate for smoothing regular (flat) 2-D images, for smoothing images painted on manifolds, and for simultaneously smoothing images and the manifolds they are painted on. The kernel combines the geometry of the image and that of the manifold into one metric tensor, thus enabling a natural unified approach for the manipulation of both. Additionally, the derivation of the kernel gives a better geometrical understanding of the Beltrami flow and shows that the bilateral filter is a Euclidean approximation of it. On a practical level, the use of the kernel allows arbitrarily large time steps as opposed to the existing explicit numerical schemes for the Beltrami flow. In addition, the kernel works with equal ease on regular 2-D images and on images painted on parametric or triangulated manifolds. We demonstrate the denoising properties of the kernel by applying it to various types of images and manifolds. PMID:17547140
Isolation of bacterial endophytes from germinated maize kernels.
Rijavec, Tomaz; Lapanje, Ales; Dermastia, Marina; Rupnik, Maja
2007-06-01
The germination of surface-sterilized maize kernels under aseptic conditions proved to be a suitable method for isolation of kernel-associated bacterial endophytes. Bacterial strains identified by partial 16S rRNA gene sequencing as Pantoea sp., Microbacterium sp., Frigoribacterium sp., Bacillus sp., Paenibacillus sp., and Sphingomonas sp. were isolated from kernels of 4 different maize cultivars. Genus Pantoea was associated with a specific maize cultivar. The kernels of this cultivar were often overgrown with the fungus Lecanicillium aphanocladii; however, those exhibiting Pantoea growth were never colonized with it. Furthermore, the isolated bacterium strain inhibited fungal growth in vitro. PMID:17668041
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.
Dimits, A M; Wang, C; Caflisch, R; Cohen, B I; Huang, Y
2008-08-06
We investigate the accuracy of and assumptions underlying the numerical binary Monte-Carlo collision operator due to Nanbu [K. Nanbu, Phys. Rev. E 55 (1997)]. The numerical experiments that resulted in the parameterization of the collision kernel used in Nanbu's operator are argued to be an approximate realization of the Coulomb-Lorentz pitch-angle scattering process, for which an analytical solution for the collision kernel is available. It is demonstrated empirically that Nanbu's collision operator quite accurately recovers the effects of Coulomb-Lorentz pitch-angle collisions, or processes that approximate these (such interspecies Coulomb collisions with very small mass ratio) even for very large values of the collisional time step. An investigation of the analytical solution shows that Nanbu's parameterized kernel is highly accurate for small values of the normalized collision time step, but loses some of its accuracy for larger values of the time step. Careful numerical and analytical investigations are presented, which show that the time dependence of the relaxation of a temperature anisotropy by Coulomb-Lorentz collisions has a richer structure than previously thought, and is not accurately represented by an exponential decay with a single decay rate. Finally, a practical collision algorithm is proposed that for small-mass-ratio interspecies Coulomb collisions improves on the accuracy of Nanbu's algorithm.
Optimized Derivative Kernels for Gamma Ray Spectroscopy
Vlachos, D. S.; Kosmas, O. T.; Simos, T. E.
2007-12-26
In gamma ray spectroscopy, the photon detectors measure the number of photons with energy that lies in an interval which is called a channel. This accumulation of counts produce a measuring function that its deviation from the ideal one may produce high noise in the unfolded spectrum. In order to deal with this problem, the ideal accumulation function is interpolated with the use of special designed derivative kernels. Simulation results are presented which show that this approach is very effective even in spectra with low statistics.
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.
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...
NASA Astrophysics Data System (ADS)
Bremner, P. M.; Panning, M. P.
2012-12-01
We present the calibration of an automated scheme to properly window the fundamental surface wave mode of an event record. Multi-taper fundamental mode phase delay measurements were made on a synthetic dataset. Measurement errors are reduced when minimal over tone energy is included in the window. The time window is calibrated by simply varying the minimum and maximum surface wave velocities used to determine the beginning and ending window times with source-receiver distance, as opposed to constant velocities. We compare phase delay measurements with and without calibration against measurements made manually. Manual window setting of a small representative subset of event seismograms are used to adjust these minimum and maximum surface wave velocities. The orthogonal 2.5π-prolate spheroidal wave function eigentapers (Slepian tapers) used in multi-taper methods reduce noise biasing, and can provide error estimates in phase delay measurements. Additionally, we examine the effects of excluding near-field terms in the calculation of 3-D finite-frequency waveform kernels for Rayleigh and Love waves on a synthetic dataset. Two methods of kernel calculation based on the single scatterer Born approximation are compared, that of Panning and Nolet (2008) and Zhao and Chevrot (2011). The Panning and Nolet (2008) method calculates the strain Green's tensors for the source-scatterer and scatterer-receiver paths by the summation of asymptotic surface wave modes, which is an inherently far-field approximation. Waveform kernels are then found by convolution (in the time domain) of these strain Green's tensors. The kernels are formulated based on a hexagonal symmetry with an arbitrary orientation. The Zhao and Chevrot (2011) method creates a database of the set of strain Green's tensors for the source-scatterer (two-sided strain Green's tensor) and scatterer-receiver (one-sided strain Green's tensor) paths, and is calculated by normal mode summation. The full-wave waveform
Hirayama, S; Takayanagi, T; Fujii, Y; Fujimoto, R; Fujitaka, S; Umezawa, M; Nagamine, Y; Hosaka, M; Yasui, K; Toshito, T
2014-06-15
Purpose: To present the validity of our beam modeling with double and triple Gaussian dose kernels for spot scanning proton beams in Nagoya Proton Therapy Center. This study investigates the conformance between the measurements and calculation results in absolute dose with two types of beam kernel. Methods: A dose kernel is one of the important input data required for the treatment planning software. The dose kernel is the 3D dose distribution of an infinitesimal pencil beam of protons in water and consists of integral depth doses and lateral distributions. We have adopted double and triple Gaussian model as lateral distribution in order to take account of the large angle scattering due to nuclear reaction by fitting simulated inwater lateral dose profile for needle proton beam at various depths. The fitted parameters were interpolated as a function of depth in water and were stored as a separate look-up table for the each beam energy. The process of beam modeling is based on the method of MDACC [X.R.Zhu 2013]. Results: From the comparison results between the absolute doses calculated by double Gaussian model and those measured at the center of SOBP, the difference is increased up to 3.5% in the high-energy region because the large angle scattering due to nuclear reaction is not sufficiently considered at intermediate depths in the double Gaussian model. In case of employing triple Gaussian dose kernels, the measured absolute dose at the center of SOBP agrees with calculation within ±1% regardless of the SOBP width and maximum range. Conclusion: We have demonstrated the beam modeling results of dose distribution employing double and triple Gaussian dose kernel. Treatment planning system with the triple Gaussian dose kernel has been successfully verified and applied to the patient treatment with a spot scanning technique in Nagoya Proton Therapy Center.